{ "cells": [ { "cell_type": "markdown", "id": "72695384", "metadata": {}, "source": [ "# Uncertainty and Sensitivity Analyses \n", "\n", "*Click the badge below to try this tutorial interactively in your browser:*\n", "\n", "[![Launch Binder](../images/custom_binder_logo.svg)](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dlab%252Ftree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain)\n", "\n", "*You can also run this tutorial in [Google Colab](https://colab.research.google.com). It takes a one-time setup per session: follow the [Colab instructions](https://qsdsan.readthedocs.io/en/latest/tutorials/index.html#run-in-colab).*\n", "\n", "- **Prepared by:**\n", "\n", " - [Yalin Li](https://github.com/yalinli2)\n", "\n", "- **Learning objectives.** After this tutorial, you will be able to:\n", "\n", " - Define `Parameter` and `Metric` objects on a `Model`\n", " - Run Monte Carlo uncertainty analysis\n", " - Perform sensitivity analysis (rank correlation, Morris, FAST/Sobol)\n", "\n", "- **Prerequisites:** [7. TEA](https://qsdsan.readthedocs.io/en/latest/tutorials/7_TEA.html), [8. LCA](https://qsdsan.readthedocs.io/en/latest/tutorials/8_LCA.html)\n", "\n", "* **Covered topics:**\n", "\n", " - 1. Model, Parameter, and Metric\n", " - 2. Perform Uncertainty and Sensitivity Analyses\n", "\n", "> **Companion video.** A walkthrough of this tutorial is available on [YouTube](https://youtu.be/_pIfUEda2jc), presented by [Hannah Lohman](https://github.com/haclohman). Recorded against `QSDsan` v1.2.0. The concepts still apply, but if the code on screen differs from this notebook, follow the notebook.\n" ] }, { "cell_type": "markdown", "id": "36f9d0f3530a", "metadata": {}, "source": [ "\n", "\n", "## Setup\n", "\n", "Import `QSDsan` and confirm the installed version.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "10f22a45", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:15.973767Z", "iopub.status.busy": "2026-05-27T12:53:15.973767Z", "iopub.status.idle": "2026-05-27T12:53:31.820839Z", "shell.execute_reply": "2026-05-27T12:53:31.820839Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This tutorial was made with qsdsan v1.5.3.\n" ] } ], "source": [ "import qsdsan as qs\n", "print(f'This tutorial was made with qsdsan v{qs.__version__}.')" ] }, { "cell_type": "markdown", "id": "2aae4f13", "metadata": {}, "source": [ "## 1. `Model`, `Parameter`, and `Metric` \n", "To perform uncertainty and sensitivity analyses, we wrap a `System` in a `Model`, then attach `Parameter` objects (the uncertain inputs) and `Metric` objects (the outputs we care about)." ] }, { "cell_type": "markdown", "id": "071eb251", "metadata": {}, "source": [ "### 1.1. Build the system with TEA and LCA\n", "\n", "We reuse the **aerobic activated-sludge plant** from the [7. TEA](https://qsdsan.readthedocs.io/en/latest/tutorials/7_TEA.html) and [8. LCA](https://qsdsan.readthedocs.io/en/latest/tutorials/8_LCA.html) tutorials (4,000 m³/d, COD ≈ 430 mg/L), packaged for convenience as `qsdsan.utils.create_example_treatment_systems`. That tutorial pair walks through how the plant, its TEA, and its LCA are built; here we just recap the setup before turning the knobs.\n", "\n", "The LCA registries (indicators, items, construction, transportation) are scoped to the **active flowsheet**, so we work in a dedicated flowsheet to keep this tutorial isolated from anything you may have loaded elsewhere." ] }, { "cell_type": "code", "execution_count": 2, "id": "a277eee6", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:31.820839Z", "iopub.status.busy": "2026-05-27T12:53:31.820839Z", "iopub.status.idle": "2026-05-27T12:53:32.357409Z", "shell.execute_reply": "2026-05-27T12:53:32.357409Z" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "173819185271:c->173813688435:w\n", "\n", "\n", " aer effluent\n", "\n", "\n", "\n", "\n", "\n", "173819185271:c->173813688475:w\n", "\n", "\n", " aer sludge\n", "\n", "\n", "\n", "\n", "\n", "173813688115:e->173819185271:c\n", "\n", "\n", " ww aer\n", "\n", "\n", "\n", "\n", "\n", "173819185271\n", "\n", "\n", "aer\n", "Aerobic plant\n", "\n", "\n", "\n", "\n", "\n", "173813688115\n", "\n", "\n", "\n", "\n", "173813688435\n", "\n", "\n", "\n", "\n", "173813688475\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from qsdsan import Model\n", "from qsdsan.utils import create_example_treatment_systems\n", "\n", "# Dedicated flowsheet keeps this tutorial's LCA registries isolated\n", "qs.main_flowsheet.set_flowsheet('uncertainty_demo')\n", "\n", "# Impact indicator lives in the active flowsheet, so (re)define it here\n", "qs.ImpactIndicator('GlobalWarming', alias='GWP', unit='kg CO2-eq')\n", "\n", "# Aerobic plant from 7_TEA / 8_LCA (we ignore the anaerobic one for this tutorial)\n", "aer_sys, _ = create_example_treatment_systems()\n", "aer = aer_sys.units[0]\n", "aer_sys.simulate()\n", "aer_sys.diagram()" ] }, { "cell_type": "code", "execution_count": 3, "id": "c3be03d1", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.357409Z", "iopub.status.busy": "2026-05-27T12:53:32.357409Z", "iopub.status.idle": "2026-05-27T12:53:32.364602Z", "shell.execute_reply": "2026-05-27T12:53:32.364602Z" } }, "outputs": [], "source": [ "# TEA: revenue comes from a treatment fee on the influent.\n", "# A negative stream price means the plant is paid to take the wastewater\n", "# (so revenue = -price * mass flow); see the 7_TEA tutorial for context.\n", "ww = aer.ins[0]\n", "ww.price = -0.0006 # USD/kg (~$0.60/m3 treatment fee)\n", "qs.PowerUtility.price = 0.08 # USD/kWh\n", "tea = qs.TEA(aer_sys, discount_rate=0.05, lifetime=20)" ] }, { "cell_type": "code", "execution_count": 4, "id": "5205defc", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.364602Z", "iopub.status.busy": "2026-05-27T12:53:32.364602Z", "iopub.status.idle": "2026-05-27T12:53:32.371788Z", "shell.execute_reply": "2026-05-27T12:53:32.371788Z" } }, "outputs": [], "source": [ "# LCA: a little concrete and steel for the reactor (Construction items),\n", "# plus electricity as an \"other\" activity that tracks the simulation.\n", "Concrete = qs.ImpactItem('Concrete', functional_unit='m3', GWP=300.)\n", "Steel = qs.ImpactItem('Steel', functional_unit='kg', GWP=2.)\n", "electricity = qs.ImpactItem('electricity', functional_unit='kWh', GWP=0.6)\n", "\n", "aer.construction = [\n", " qs.Construction(linked_unit=aer, item=Concrete, quantity=300,\n", " quantity_unit='m3', lifetime=30),\n", " qs.Construction(linked_unit=aer, item=Steel, quantity=30000,\n", " quantity_unit='kg', lifetime=30),\n", "]" ] }, { "cell_type": "code", "execution_count": 5, "id": "1d199e0a", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.371788Z", "iopub.status.busy": "2026-05-27T12:53:32.371788Z", "iopub.status.idle": "2026-05-27T12:53:32.377134Z", "shell.execute_reply": "2026-05-27T12:53:32.377134Z" } }, "outputs": [], "source": [ "lifetime = 20\n", "lca = qs.LCA(system=aer_sys, lifetime=lifetime,\n", " electricity=lambda: aer.power_utility.rate * 24 * 365 * lifetime,\n", " simulate_system=False)" ] }, { "cell_type": "markdown", "id": "829199f2", "metadata": {}, "source": [ "
\n", "\n", "**Declaring default materials.** Here we attach `Construction` objects to an existing unit instance because the aerobic plant did not declare any built-in construction materials. For *new* unit classes that always need certain materials, declare them once as a class-level `_construction_specs` tuple instead; see § 4 of [8. LCA](https://qsdsan.readthedocs.io/en/latest/tutorials/8_LCA.html#s4) for the canonical pattern.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "c598ed3c", "metadata": {}, "source": [ "### 1.2. Create a system model and add uncertainty parameters and metrics\n", "\n", "The `Model` class wraps a `System` and exposes the `parameter` and `metric` decorators we use to declare uncertain inputs and tracked outputs." ] }, { "cell_type": "code", "execution_count": 6, "id": "8eaeecd2", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.377134Z", "iopub.status.busy": "2026-05-27T12:53:32.377134Z", "iopub.status.idle": "2026-05-27T12:53:32.382862Z", "shell.execute_reply": "2026-05-27T12:53:32.382862Z" } }, "outputs": [], "source": [ "model = Model(aer_sys)" ] }, { "cell_type": "markdown", "id": "571449c3", "metadata": {}, "source": [ "Uncertainty parameters can be added with `model.parameter`. For this plant we add six:\n", "\n", "- **Treatment fee** charged for influent (TEA)\n", "- **Electricity price** (TEA)\n", "- **Installed plant capital** (TEA)\n", "- **Steel GWP** characterization factor (LCA)\n", "- **Electricity GWP** characterization factor (LCA)\n", "- **Fraction of substrate oxidized**, `X_oxid` (couples to both): it changes the oxygen demand and so the electricity consumption" ] }, { "cell_type": "code", "execution_count": 7, "id": "1a28dd29", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.382862Z", "iopub.status.busy": "2026-05-27T12:53:32.382862Z", "iopub.status.idle": "2026-05-27T12:53:32.388326Z", "shell.execute_reply": "2026-05-27T12:53:32.388326Z" } }, "outputs": [], "source": [ "# We use the package `chaospy` to create the distributions\n", "from chaospy import distributions as shape" ] }, { "cell_type": "code", "execution_count": 8, "id": "6017316e", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.388326Z", "iopub.status.busy": "2026-05-27T12:53:32.388326Z", "iopub.status.idle": "2026-05-27T12:53:32.394626Z", "shell.execute_reply": "2026-05-27T12:53:32.394626Z" } }, "outputs": [], "source": [ "# There are several ways to register parameters; the decorator form below is the most common.\n", "param = model.parameter\n", "\n", "# A treatment fee that varies between $0.30 and $1.00 per m3 of influent.\n", "# `element` labels which part of the system the parameter belongs to (free-form string or object);\n", "# `kind='isolated'` means the value is read at evaluation time but does not require resimulation\n", "# (only `_cost` reads the wastewater price, not `_run`).\n", "dist = shape.Uniform(lower=0.30, upper=1.00)\n", "@param(name='Treatment fee', element='TEA', kind='isolated',\n", " units='USD/m3', baseline=0.60, distribution=dist)\n", "def set_fee(fee_per_m3):\n", " ww.price = -fee_per_m3 / 1000 # USD/kg, negative = revenue" ] }, { "cell_type": "code", "execution_count": 9, "id": "37749565", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.396490Z", "iopub.status.busy": "2026-05-27T12:53:32.396490Z", "iopub.status.idle": "2026-05-27T12:53:32.403373Z", "shell.execute_reply": "2026-05-27T12:53:32.402765Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The same `parameter` API also accepts a `setter=` callable, which is handy when the\n", "# setter is a one-liner or comes from elsewhere.\n", "baseline = qs.PowerUtility.price\n", "dist = shape.Triangle(lower=baseline*0.75, midpoint=baseline, upper=baseline*1.25)\n", "\n", "def set_e_price(p):\n", " qs.PowerUtility.price = p\n", "\n", "param(setter=set_e_price, name='Electricity price', kind='isolated', element='TEA',\n", " units='USD/kWh', baseline=baseline, distribution=dist)" ] }, { "cell_type": "code", "execution_count": 10, "id": "74d6bd47", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.403373Z", "iopub.status.busy": "2026-05-27T12:53:32.403373Z", "iopub.status.idle": "2026-05-27T12:53:32.409544Z", "shell.execute_reply": "2026-05-27T12:53:32.409544Z" } }, "outputs": [], "source": [ "# LCA characterization factors. They feed only the LCA, so `kind='isolated'`.\n", "baseline = Steel.CFs['GlobalWarming']\n", "dist = shape.Uniform(lower=baseline*0.9, upper=baseline*1.5)\n", "@param(name='Steel GWP', element='LCA', kind='isolated',\n", " units='kg CO2-eq/kg', baseline=baseline, distribution=dist)\n", "def set_steel_GWP(cf):\n", " Steel.CFs['GlobalWarming'] = cf\n", "\n", "baseline = electricity.CFs['GlobalWarming']\n", "dist = shape.Uniform(lower=baseline*0.7, upper=baseline*1.3)\n", "@param(name='Electricity GWP', element='LCA', kind='isolated',\n", " units='kg CO2-eq/kWh', baseline=baseline, distribution=dist)\n", "def set_e_GWP(cf):\n", " electricity.CFs['GlobalWarming'] = cf" ] }, { "cell_type": "code", "execution_count": 11, "id": "645c9bb0", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.409544Z", "iopub.status.busy": "2026-05-27T12:53:32.409544Z", "iopub.status.idle": "2026-05-27T12:53:32.416732Z", "shell.execute_reply": "2026-05-27T12:53:32.416732Z" } }, "outputs": [], "source": [ "# Installed plant capital. Cost only, so still `kind='isolated'`.\n", "baseline = aer.plant_capital\n", "dist = shape.Triangle(lower=baseline*0.8, midpoint=baseline, upper=baseline*1.2)\n", "@param(name='Plant capital', element=aer, kind='isolated',\n", " units='USD', baseline=baseline, distribution=dist)\n", "def set_plant_capital(c):\n", " aer.plant_capital = c\n", "\n", "# Fraction of substrate oxidized. This one feeds `_run` (it changes the O2 demand\n", "# and therefore the electricity draw), so it must be `kind='coupled'` to trigger a resimulation.\n", "baseline = aer.X_oxid\n", "dist = shape.Uniform(lower=0.85, upper=0.99)\n", "@param(name='X_oxid', element=aer, kind='coupled',\n", " units='-', baseline=baseline, distribution=dist)\n", "def set_X_oxid(x):\n", " aer.X_oxid = x" ] }, { "cell_type": "code", "execution_count": 12, "id": "bf841033", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.416732Z", "iopub.status.busy": "2026-05-27T12:53:32.416732Z", "iopub.status.idle": "2026-05-27T12:53:32.423981Z", "shell.execute_reply": "2026-05-27T12:53:32.423981Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Metrics. As with `parameter`, both decorator and functional forms work.\n", "metric = model.metric\n", "\n", "@metric(name='Net earnings', units='USD/yr', element='TEA')\n", "def get_net_earnings():\n", " return tea.net_earnings\n", "\n", "# In 8_LCA we switched to `get_total_impacts(time_frame='yr')`, which returns\n", "# annual impacts directly and updates the unit suffix on save_report tables.\n", "def get_annual_GWP():\n", " return lca.get_total_impacts(time_frame='yr')['GlobalWarming']\n", "metric(getter=get_annual_GWP, name='GWP', units='kg CO2-eq/yr', element='LCA')" ] }, { "cell_type": "code", "execution_count": 13, "id": "6f1b1935", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.423981Z", "iopub.status.busy": "2026-05-27T12:53:32.423981Z", "iopub.status.idle": "2026-05-27T12:53:32.432909Z", "shell.execute_reply": "2026-05-27T12:53:32.432909Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model:\n", "parameters: TEA - Treatment fee [USD/m3]\n", " TEA - Electricity price [USD/kWh]\n", " LCA - Steel GWP [kg CO2-eq/kg]\n", " LCA - Electricity GWP [kg CO2-eq/kWh]\n", " Aerobic plant-Aer - Plant capital [USD]\n", " Aerobic plant-Aer - X oxid [-]\n", "indicators: TEA - Net earnings [USD/yr]\n", " LCA - GWP [kg CO2-eq/yr]\n" ] } ], "source": [ "model.show()" ] }, { "cell_type": "markdown", "id": "48c14149", "metadata": {}, "source": [ "## 2. Perform uncertainty and sensitivity analyses " ] }, { "cell_type": "markdown", "id": "387ff3df", "metadata": {}, "source": [ "### 2.1. Uncertainty analysis" ] }, { "cell_type": "code", "execution_count": 14, "id": "867d766d", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.432909Z", "iopub.status.busy": "2026-05-27T12:53:32.432909Z", "iopub.status.idle": "2026-05-27T12:53:32.441806Z", "shell.execute_reply": "2026-05-27T12:53:32.441806Z" } }, "outputs": [], "source": [ "# Sample the joint parameter space with Latin hypercube (`rule='L'`).\n", "import numpy as np\n", "np.random.seed(3221) # for reproducibility\n", "\n", "samples = model.sample(N=100, rule='L')\n", "model.load_samples(samples)" ] }, { "cell_type": "code", "execution_count": 15, "id": "6df46b22", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.444350Z", "iopub.status.busy": "2026-05-27T12:53:32.441806Z", "iopub.status.idle": "2026-05-27T12:53:32.471746Z", "shell.execute_reply": "2026-05-27T12:53:32.471746Z" } }, "outputs": [], "source": [ "# Run the 100 simulations\n", "model.evaluate()" ] }, { "cell_type": "code", "execution_count": 16, "id": "eff2cc52", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.471746Z", "iopub.status.busy": "2026-05-27T12:53:32.471746Z", "iopub.status.idle": "2026-05-27T12:53:32.477469Z", "shell.execute_reply": "2026-05-27T12:53:32.477469Z" } }, "outputs": [ { "data": { "text/html": [ "
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ElementTEALCAAerobic plant-AerTEALCA
VariableTreatment fee [USD/m3]Electricity price [USD/kWh]Steel GWP [kg CO2-eq/kg]Electricity GWP [kg CO2-eq/kWh]Plant capital [USD]X oxid [-]Net earnings [USD/yr]GWP [kg CO2-eq/yr]
00.5710.0831.930.6475.16e+060.9677.95e+052.04e+05
10.7010.07372.660.4585.65e+060.8979.89e+051.38e+05
20.4520.06882.640.715.04e+060.9056.27e+052.1e+05
30.5530.07022.460.5774.31e+060.9197.73e+051.75e+05
40.5090.09032.340.7245.28e+060.9777.01e+052.3e+05
...........................
950.7570.06792.130.6944.58e+060.881.07e+061.99e+05
960.3460.08642.950.6845.1e+060.8694.68e+051.96e+05
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100 rows × 8 columns

\n", "
" ], "text/plain": [ "Element TEA LCA Aerobic plant-Aer \\\n", "Variable Treatment fee [USD/m3] Electricity price [USD/kWh] Steel GWP [kg CO2-eq/kg] Electricity GWP [kg CO2-eq/kWh] Plant capital [USD] X oxid [-] \n", "0 0.571 0.083 1.93 0.647 5.16e+06 0.967 \n", "1 0.701 0.0737 2.66 0.458 5.65e+06 0.897 \n", "2 0.452 0.0688 2.64 0.71 5.04e+06 0.905 \n", "3 0.553 0.0702 2.46 0.577 4.31e+06 0.919 \n", "4 0.509 0.0903 2.34 0.724 5.28e+06 0.977 \n", ".. ... ... ... ... ... ... \n", "95 0.757 0.0679 2.13 0.694 4.58e+06 0.88 \n", "96 0.346 0.0864 2.95 0.684 5.1e+06 0.869 \n", "97 0.966 0.0706 2.7 0.554 5.57e+06 0.903 \n", "98 0.543 0.0762 2.95 0.533 5.41e+06 0.954 \n", "99 0.694 0.0793 2.04 0.426 5.05e+06 0.974 \n", "\n", "Element TEA LCA \n", "Variable Net earnings [USD/yr] GWP [kg CO2-eq/yr] \n", "0 7.95e+05 2.04e+05 \n", "1 9.89e+05 1.38e+05 \n", "2 6.27e+05 2.1e+05 \n", "3 7.73e+05 1.75e+05 \n", "4 7.01e+05 2.3e+05 \n", ".. ... ... \n", "95 1.07e+06 1.99e+05 \n", "96 4.68e+05 1.96e+05 \n", "97 1.38e+06 1.66e+05 \n", "98 7.57e+05 1.69e+05 \n", "99 9.74e+05 1.38e+05 \n", "\n", "[100 rows x 8 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Parameter values and metric results are stored in `model.table`\n", "model.table" ] }, { "cell_type": "markdown", "id": "913e1074", "metadata": {}, "source": [ "
\n", "Optional: result organization and exporting (click to expand)\n", "\n", "You can split the results table into parameters, results, and percentiles and write them to Excel:\n", "\n", "```python\n", "import pandas as pd\n", "def organize_results(model, path):\n", " idx = len(model.parameters)\n", " parameters = model.table.iloc[:, :idx]\n", " results = model.table.iloc[:, idx:]\n", " percentiles = results.quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])\n", " with pd.ExcelWriter(path) as writer:\n", " parameters.to_excel(writer, sheet_name='Parameters')\n", " results.to_excel(writer, sheet_name='Results')\n", " percentiles.to_excel(writer, sheet_name='Percentiles')\n", "\n", "organize_results(model, 'example_model.xlsx')\n", "```\n", "\n", "For convenience, the `qsdsan.stats` analysis functions (`get_correlations`, `morris_analysis`, ...) all take a `file=` argument that writes results straight to Excel.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "stats-module-pointer", "metadata": {}, "source": [ "QSDsan ships a small `qsdsan.stats` module that wraps [SALib](https://salib.readthedocs.io/en/latest/) (sensitivity analysis) and [seaborn](https://seaborn.pydata.org/) (plotting) for the most common uncertainty and sensitivity workflows; the rest of this tutorial uses it. See the [stats module API page](https://qsdsan.readthedocs.io/en/latest/api/stats.html) for the full list of analysis and plotting functions, along with their signatures and worked examples." ] }, { "cell_type": "code", "execution_count": 17, "id": "49f93272", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.477469Z", "iopub.status.busy": "2026-05-27T12:53:32.477469Z", "iopub.status.idle": "2026-05-27T12:53:32.690063Z", "shell.execute_reply": "2026-05-27T12:53:32.690063Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Marginal distribution of the TEA metric\n", "fig, ax = qs.stats.plot_uncertainties(model, x_axis=model.metrics[0])\n", "fig" ] }, { "cell_type": "code", "execution_count": 18, "id": "plot_unc_gwp", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.690063Z", "iopub.status.busy": "2026-05-27T12:53:32.690063Z", "iopub.status.idle": "2026-05-27T12:53:32.783917Z", "shell.execute_reply": "2026-05-27T12:53:32.783917Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Marginal distribution of the LCA metric (drawn separately so it gets its own y-scale)\n", "fig, ax = qs.stats.plot_uncertainties(model, x_axis=model.metrics[1])\n", "fig" ] }, { "cell_type": "code", "execution_count": 19, "id": "fad0fede", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:32.783917Z", "iopub.status.busy": "2026-05-27T12:53:32.783917Z", "iopub.status.idle": "2026-05-27T12:53:33.194208Z", "shell.execute_reply": "2026-05-27T12:53:33.194208Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2-D KDE shows the joint distribution\n", "fig, ax = qs.stats.plot_uncertainties(model, x_axis=model.metrics[0], y_axis=model.metrics[1],\n", " kind='kde-kde', center_kws={'fill': True})\n", "fig" ] }, { "cell_type": "markdown", "id": "ec8bbece", "metadata": {}, "source": [ "### 2.2. Sensitivity analysis\n", "\n", "`qsdsan.stats` exposes four families of sensitivity methods, each paired with a `plot_*` companion:\n", "\n", "| Method | Function | Plot | Notes |\n", "|---|---|---|---|\n", "| Rank correlation (Spearman / Pearson / Kendall / KS) | `get_correlations` | `plot_correlations` | cheap; reuses the Monte Carlo table from § 2.1. |\n", "| Elementary effects (Morris) | `morris_analysis` (also `morris_till_convergence`) | `plot_morris_results` (`plot_morris_convergence`) | one-at-a-time screening with its own sampler. |\n", "| Fourier amplitude (eFAST / RBD-FAST) | `fast_analysis` | `plot_fast_results` | variance-based, single-index. |\n", "| Sobol indices | `sobol_analysis` | `plot_sobol_results` | variance-based, first-order + total. |\n", "\n", "Each function and each `plot_*` companion accepts a `file=` argument that writes results to Excel. See the [stats module API page](https://qsdsan.readthedocs.io/en/latest/api/stats.html) for full signatures and the worked FAST/Sobol examples we skip below." ] }, { "cell_type": "markdown", "id": "s221_md", "metadata": {}, "source": [ "#### 2.2.1. Rank correlation\n", "\n", "Rank correlation reuses the Monte Carlo samples we already generated, so it is the cheapest (i.e., uses the least additional computational resources) screening tool. `get_correlations` returns two multi-indexed DataFrames: the correlation coefficients and their p-values." ] }, { "cell_type": "code", "execution_count": 20, "id": "432ad373", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:33.194208Z", "iopub.status.busy": "2026-05-27T12:53:33.194208Z", "iopub.status.idle": "2026-05-27T12:53:33.210583Z", "shell.execute_reply": "2026-05-27T12:53:33.210583Z" } }, "outputs": [ { "data": { "text/html": [ "
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ElementTEALCA
Input yNet earnings [USD/yr]GWP [kg CO2-eq/yr]
ElementInput x
TEATreatment fee [USD/m3]10
Electricity price [USD/kWh]-0.06-0.01
LCASteel GWP [kg CO2-eq/kg]-0.08-0.15
Electricity GWP [kg CO2-eq/kWh]0.010.97
Aerobic plant-AerPlant capital [USD]-0.040
X oxid [-]00.16
\n", "
" ], "text/plain": [ "Element TEA LCA\n", "Input y Net earnings [USD/yr] GWP [kg CO2-eq/yr]\n", "Element Input x \n", "TEA Treatment fee [USD/m3] 1 0\n", " Electricity price [USD/kWh] -0.06 -0.01\n", "LCA Steel GWP [kg CO2-eq/kg] -0.08 -0.15\n", " Electricity GWP [kg CO2-eq/kWh] 0.01 0.97\n", "Aerobic plant-Aer Plant capital [USD] -0.04 0\n", " X oxid [-] 0 0.16" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# `r_df` and `p_df` are dataframes containing the correlation coefficients and p-values.\n", "r_df, p_df = qs.stats.get_correlations(model, kind='Spearman')\n", "r_df.round(2)" ] }, { "cell_type": "code", "execution_count": 21, "id": "p_df_round", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:33.210583Z", "iopub.status.busy": "2026-05-27T12:53:33.210583Z", "iopub.status.idle": "2026-05-27T12:53:33.219488Z", "shell.execute_reply": "2026-05-27T12:53:33.219488Z" } }, "outputs": [ { "data": { "text/html": [ "
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ElementTEALCA
Input yNet earnings [USD/yr]GWP [kg CO2-eq/yr]
ElementInput x
TEATreatment fee [USD/m3]00.985
Electricity price [USD/kWh]0.5460.954
LCASteel GWP [kg CO2-eq/kg]0.4320.132
Electricity GWP [kg CO2-eq/kWh]0.9540
Aerobic plant-AerPlant capital [USD]0.6980.967
X oxid [-]0.9850.102
\n", "
" ], "text/plain": [ "Element TEA LCA\n", "Input y Net earnings [USD/yr] GWP [kg CO2-eq/yr]\n", "Element Input x \n", "TEA Treatment fee [USD/m3] 0 0.985\n", " Electricity price [USD/kWh] 0.546 0.954\n", "LCA Steel GWP [kg CO2-eq/kg] 0.432 0.132\n", " Electricity GWP [kg CO2-eq/kWh] 0.954 0\n", "Aerobic plant-Aer Plant capital [USD] 0.698 0.967\n", " X oxid [-] 0.985 0.102" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p_df.round(3)" ] }, { "cell_type": "code", "execution_count": 22, "id": "def1f45d", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:33.219488Z", "iopub.status.busy": "2026-05-27T12:53:33.219488Z", "iopub.status.idle": "2026-05-27T12:53:33.478251Z", "shell.execute_reply": "2026-05-27T12:53:33.478251Z" } }, "outputs": [ { "data": { "image/png": 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mmBb5Sufw2rp1q7Rs2dKrcQGhjtaFEWr58uWBHkLQCqdjs3TpUhMcXXXVVfLxxx+XG3LZrm5ZtWqVCap08ksNg51/ADvTMKxdu3YlwjVPaYWZPrYzXadf89SyZcssy3369BFf0F9yTp48aarj1q1bJ99++63pl6zHS0P7/X6ecBZA6Fgzb77E7dwZ6GGEnbjMLNm6aFGghwEAAACUKql+fb8enaT6DTj6ZahWrVqJ8OjEiRMeHzPnbZKTk7067omJiZbl2rVrmwu83dW8eXNzwXVZ57+ASETQFWEOHDhgqtYeeOCBQA8l6ITTscnNzZWnnnpKrr322hLVUc7VWxpSldajWH+Ya+WjToa5b9++Mh9TK5ocrVixwuNxL1682OWknnrFjX7N29Cyb9++4k8aEmqFqlbCaQAGABmbNwvN9fwjIy3NfhUjAAAAEGxqtLSGEb5W3c/7D3V6zst5vqvMzEyP9pGTk1Pi4m8NprxRs2ZNy3LHjp53qXDexp0L24FwFxStC115/PHHfbavLl26SKTTlnJa0fPaa6+Z8KJRo0aBHlLQCLdjo5VPt99+uyxZsqTE19q0aSPnnnuuDBw4UJo0aSK1atUyV7douHT48GETJs2aNUt+/fVXSw/ijRs3yvDhw+WLL74wrfpc6dWrl+kLbJOenm7G4sl8X9pnuKyvnXPOOW7vSyfkPHLkiMcVXYMHDy73M0NfMxomasvG7du3y/z5802Fl40+7nXXXSdff/21Oc4ILs8//7x5zXvalxvwVFZWlhzfsFE8a4wBdxWuXS87t2yRZq1bc9AAAAAQdGq2bSdxVatKfgU61JQnrmo1qdnG86kYIk39+vXNuRubQ4cOSWpqqtvbHzx4sETbQnem+CiLnlfbsWNHqcGXO/R8nqOMjAyvxgSEg6A9y3fNNdcEeghhRZP9cePGBXoYQSmcjo2evHcVcjVr1sxUqp1//vkut9MT/jq31CWXXGJuaWlp8sQTT5jWhzZ79uwx+/jggw9c9g3WMmvdj60iS8MgbV+o81ZVJOjSMuydO3faA7eyQjB3qrl0f+7Mn6WBnadzi+kvFGPHjpX//ve/9nUa8j355JPy9ttve7Qv+N+QIUM4zKgU6/5YKHHb//cHDHwrLjNTtixdRtAFAACAoBQVHS11e/aSPXNm+3zful/dP8qmFx9v2rTJvrx7926PDpnz/bVtoC/G5NhqUKvGPOXcmUnPwQGRjk9E+IX+EHG8ReoYKtv48eNLhFwaNGllUWkhlyt6dctHH30kF1xwgWX9okWL5JtvvnG5jVZunXrqqRWep0uDNK2Ocmwz2KlTJ/uyfs2TX0j8NT+XK3r1zauvvir9+vWzrNdKL/okA5HraFqaRNNaz68yt2z17wMAAAAAXqjXs7ckNfDtXFq6v3o9e/l0n+Gqbdu2Jbr/eGLrVuvfG85zY1WEdltypPO/e8q5BWNFqsKAcEPQBYSJNWvWyPvvv18i5Jo0aZIkJSVV6OqQF198Ubp27WpZ/+abb5Y6J4pWQ1V0ni7nii2tEHMOpzyp6nKem8yfQZfteGnLVed2eD/88INfHxdA8DpRztyG8F723j0u53YEAAAAgoFWXTX/+6USk+CbhuYxVaqY/VHN5Z7OnTtbllevXu3R8Xa+gNv5HFlFnHbaaZZlvTjf06ounS7E15VmQKgj6ALCaN4hxwAqJSVFXnjhBYmLi6vwPnXbRx55xLJO+wgvXLjQ5f179+5tWV61alWpoZiz3377zf5/bY3Ys2dPU9VVkaDrwIEDsmvXLssEpM5j84emTZuWeJx58+b5/XEBBB+dty935/8+h+Afxdu3y16Hz3sAAAAg2CSm1JHWVw3zOuzSkKv10GvM/uCeHj16WM6L6Xkqxzm7ylJYWFjiPJTzeaqKBl3VqlWzL2dnZ8uCBQs8mgt6/fr1lnXlzTUPRAKCLiAM6HxUjvNpqX/+859Su3Ztr/et7QM1dHI0c+ZMl/ft1q2b5RcI/eHrfJWJK9pLePHixfbldu3aySmnnGJ++DtWo2nrRP1Fw9NqLv0etLViZXC+ukdDN3olBxd9PbRu3drSGhPwte0bN0rMnr0cWD+Lyzohuzds5DgDAAAgqFVt3FjaXj+ywm0Mdbu2191g9gP3JScnW+aO124Q06dPd2vb2bNny+HDhy3TfOjNW9oJyHl6kffee8/tC8WnTZsmeXl59uUWLVpIy5YtvR4XEOqsPbbgMT2JrWWvhw4dMlcE1KhRQ+rUqWNO+OuJel9dFa5t6bQvrPZg1Q9E7b3avn1709fVuVWav2l4oVUqe/fulerVq5urBjSY8CU9lvo9a/WQPl5CQoLUqlXLzAGl/XC14gf/8+WXX1oOR+PGjWXw4ME+O0S6L9vcXw0bNpS6deu6vF+VKlXM68ExdNP2heX9IrB27VrJyMgocYWMhmYasv3666+W10V5V6po8OeoMqq5bPT970h/iTpy5IipsENw0Kul9Ab406Ft2yU2P5+D7Gf628CJfQSKAAAACH5aidXu+hvlwJLF8teSxZJ/PKvcbeKqVpO6PXuZObloV1gxV1xxhcydO9e+rFN8DBo0yJzfKo2ef9XpPBwNHTpUfGXEiBEmcLOFW3oe66uvvpIrr7yyzO32798vb7zxhmXd1Vdf7bNxAaGMoKsCtDrj66+/lo8//rhEqajjfD1ajXLLLbfI2WefXaEnRwO0d99915zkL61Xq4Zp+oGtj+NY9qp2794t55xzjsvt9uzZY5mQsVGjRjJnzhz7slbXXHfddfbll19+WS666CJz1cCzzz5rwidHGrg9/PDD9nmQnCd71H6z7vjjjz/MPFP6b2lzbugPomuuuUauv/56E4CVpqwxVOTY/Pe//5W7777bvj4xMdGUMOvVIe7S7+mMM84wwYftdaLPb7169aSi9CoOHZtzMOXLAPSss86Se++91/xbXqipoZJz0FXeLwOObQtVv3797P/X15Qt6LLdt7yga9myZT4vLXdXvosT22W9TgGEp9yj/wvv4edjneFe6xEAAAAg0DSsqt+7jwmuMjZvksytW+Tk/n2Sc/iwFBUUSHRsrFSpXVuS6jeQ6i1bSc02bQm4vKTn//Rc1saN/9cJQi+0vu2220wVlauLkk+cOCH33HOP7Ny5076uQYMGctVVV5X6GBpajR492rLuo48+KjGXvU2HDh3Medbvv//evu6JJ54wF5D//e9/d7mNFlmMGjXKhHA2ej6xrHEBkYTWhR7avn27XH755ebDq7SQyxaG6Qn+22+/XW688UZLtYo7FVyPPvqoSfF/+umnMick1MDkP//5j/lwdG7X5mvark7na3IOudTmzZst5bye0u/jjjvukJEjR8r8+fPLnFheK8nGjx9vwpwtW7ZIZdGQRyvpbLQi5Oeff/ZoH/q92UIuWwDjTcil9HnX14yj8847T3xJA1V9LbtTuedcPeU8cacrGmzaxMfHS/fu3V2GXu7M03X8+HFLqKmBpC8mC/WkytORVqU5h9AAwp87V2fCR8faxe8lAAAAQLAHXrXatZdmF14s7W8cJV3/9ZB0H/2o+VeXdb1+nSouHxzrqCh5/PHHzcXmNnreSM8rTp061ZxHUrm5ueY8rBYUOJ6nUmPGjPH5Rcx67lcvrne8cPpf//qX/Pvf/zbdjGzTYGh3oylTppgATM+/On9fet4LAEGXR3TCQi0H3bBhQ4mvxcTEmLaFjh+ajifmdTsNaMqjH64ajOkHrSs6X5GrCiI9uX7zzTebcM0fdP/6AVxav1htYVjRcEX3PWzYMNP71hWdW8nVh7a2ctSqM21vWBk0gHG+quLbb7/1aB9aCehIQ1Nv2VoK2mioohV2gdK5c2fL87Vt27Yyg169UsYxDNOQS69gsdG2h47tALXS0fZLiCta7u04J5buT5+7yuI8V5q220Rw0ddDaZ/XgK/kEb5UmrwyfiYAAAAAgF4AraGQo4MHD5pznXre6PTTTzedubSSS883Orr11ltL7QrlDZ2i5cMPP7SEXXreVc8datim3Yx69OhhpvTQsTteOK90rBXtIgaEI1oXuumvv/4yZa1Hjx61VGpoSza9AkBPZutJ08LCQpO6a19VbfNnO+GuJ/vvvPNO+fzzz8s86a7t/5zDqmbNmpkPVa0oss37paGZzsv0zjvv2FulaVXP/fffb8IXDYe0+sj2Ia5Bw6uvvmrfp35NW9HZlNd+b/LkyfZwQU8Q28aiIZO2ktOKsopc2aCVW/rBrMfHUceOHU05rrb50+9F6Q+aTz75xFzFYDuuWrb7wAMPmOOqYaO7KnpshgwZYlpW2ixatMi8Nkqbs8qRlhY79gTWQOrcc88Vb6WlpVmWte1iIE/g6+tb56izVV7pD2kNiQcMGODy/tom07Hdn6s2g9q+0BYq6mtGj3tpx865stHWTrMy6Hvf+f1bmW0TUTb9jFy3dq28NXGCNGrYUFauWm2eM/38Zt4/+Fr+8RNm/ij4X2FmlvnZUNlzlgIAAAAIHbYWf+PGjSvRPctV9yo9t6bdp+666y6/jalJkyby6aefykMPPWTOdTnSCjO9OdPzr3p/LRoA8D+cEXCTlo46Jufabu7NN98sUa2hYYsm7nq75JJLTLs3W0C0bt06ef31100w44pWNDnPtXThhRfK888/XyIc03mqNIzRIOimm24ybfRs80vpB6QGYxoQ6VxWtjmpHMMcDW9sX3OHrSKnf//+Zr4uDbtstGWhq3mJ3PHZZ5+VCAa0feGDDz5YIqxp2bKljB071vS31e/dFnZphY+2Vbz44ovdftyKHpv27dubm62qT4PNH374wYy5PD/++KOZT8vxufVF2bNzSKg/JANN2xc6thjUKqvSgi7ncnDnVoW2dY7Vcxquuht0VVbQpO9z/UXDkYbh9EoODhq4rl65Urqd1tkeajVr2tQE0OvXrZOOVN7BxwpOnpQ4jmqlKD52zATZWl0OAAAAAKXRczR6nkinRNGL0UubLkbPQ2nBgl7I7W86/5dWdn333XfmAn+9WNxVRy09hzho0CAzLi2KABAiQZdWpfiChkeNGzf2ah+aqDum6tpWTefFKm++Ii0t1TDshhtusIcyGkJppZJjUGSj93WkpbP6wVtWdY7eRyuinnvuOUt4pEGXr2m4N3HixBJtBGvXrl2h/enV13ocHWlY5RwWONMP9eHDh1sqq/S4ehJ0eUPbDT7zzDP25W+++catoMu5baFWAvqCc+mylj4HmvNkm2XN06WhlePYdUJOZ85VWaXN06VBolboOO5Pg0l/0l8+NKx78sknzRx+jvTqGm/nYINvaKDdrEnjEpVbemJ8x+7d5nmkqgu+oq+n4sJCDmglKS7It7SsBQAAAIDS6AXiesG7Xiyn00/olCp6bk3P92phgYZbKSkpHp8r9HZ6Ep0uRW/79+8357a0g5RenKsX67do0cIUVdi6XgEIoaArmHzwwQeWZQ1Zygu5HE/469xVtkot/RDV+bd0Pi3n9nNr1661rHviiSfcakGn1UdvvfWWmZxQ6QeiTqroq7DQZsSIET6d4FADDu2Ha6M/ULQ3rjs0LNRATyuqlF7toB/+lXE1t/7QeeGFF+xVbFrdlZ6eLq1bty51m507d1oq15o3b+6zq0J0jitHjvNbBYpWOuoPX1s1o1bd6XPl3F5SW3A6VqRpoOUqbNCwSI+vHmfb8dy1a1eJ6jWtmnS8GkcryzwNL7SVoqvScEf6df3edOz6vGolpTP9jNBWoqGmU6dObh2zt99+27QwtdHPNp2fzd3PRQ2nHT322GMl1pVm+vTplsfSEGvgwIFlbnPmGWfI5Dcmuvxafl6+CUQ1fC/Pr7/+arl4Ql/b7v4yqz879HPdeZ2+5tyhj6XzNNro1Wf6WegObbWgF0U4uuCCC0q0PnVFP/cdA2SlrXl1glx36HN77bXXlrgQxDmkd0V7lc+bN6/Ea08rnd2hVdRaPeuorM9q5/fCjBkzLOueffZZeffdd8vdVt9Dj5wxQCpvdsDIlpedYz6PnCvEtZL67rvvdrlNaXOOAgAAAIgM+vd1ad2HAql+/frmBsAzBF3l0BPajhUnFanE0RaGji0JFyxYUCLomj9/folJElNTU93avwYbWsGlLQQ14debP9rXaWjgS87fs7aic7caSYOPG2+80QQnGhrp91xZAY+OUSd7dHxOtaqrrFDDX9Vcyhb22XjaRvKVV16RSZMmefy4esK3tBP8+rzohJm2Ock0BNm8eXOJ6irnyqyy2gxqCGYLupRWUTm3BVy2bFmJbTylz5Xz8+UpPZGu89r5ojVlZSutbL+8152ytVAtj6sgUV+37m7vXLmhJ6zL23bX7t1mjkVXnzGHDx8ywaU7J76d76NjcXfcrt6beizc3d7Vc+Dutq5CPH2uK/rYuj93ty3tteLO9q5ej7547Iq+Tj157OJoZuiqLPqJ4Op58eQ9AgAAAAAAQlfQBl2PP/64T/ZTs2ZNr7bXq9gdT05qmNKmTRuP9uFcuaNX5TtPmu7c2s3VPEVl0Xm6/EnnCHO3is1d3n7Ppc11Vhk04HEMur7//nu57777XFbC6Ilxx/mltErPl0GXzilmq+ZTwXJST6t2bEGX0ivtywu6ynoNaAjm2K5Sy8udg65Azc9loxWFWrlyyy23BEVlXUXouN2p6HKuzlPuVny6CgB1PjN3t3eudNXxlretVt998933csN11sqi3Lw82bg5ze3ny/nY6FjcHbd+j66ORUUrZfU5cHdbx583Nvo9u7O9q/vo/tx97NJeK+5s7+p58cVjV/R16sljR7lRkQ3fKIqOLjGXaXnvEf3Z7G6wDwAAAAAAglvQBl3aji8YaCs05xOVU6ZM8Xg/up0tMNPqFm115jhxoPPcPv6eV8hTderUcXkSyRuOLeuC8XsuyxlnnGGOia31orbg0+BF23E50/BF2+w5VsbpRJO+oi0CHYMu7eEbDJwrADXo0jmrHCthFi5caF/WyjzthVwaPbZ6ktlWmaLH2/mkpWPbKm0v54/KRtvJUz0Bf8opp5jWatomVIO9/v37h2QVl3O479gez126jWPFnae0pZ9zWz936XPtzmNrq7zlq9dIk4YNpHq1arJn3z7JOnFC7v3nP+W+CraZ1BaK3nzf7rZrdEVbtXnz2DNnzqzwtkOGDDG3ilqyZEmFt9V2je62bHTFm2M2evRoc3PHl/9yr7UjvFe1Xj1ZNv2rEuu1baVz60ob/V3M3cp5AAAAAAAQ3II26AoW2urKUVZWlk+qzRyDCaUTHzrydNJDf6tRo4ZP96dtwvQkUzB/z+UFHZdeeqm88847lvaFroIuXe/Il9VcSgNTxzmiduzY4dH255xzjlu9f3WizoyMDLf3qxWAWlFp28a5gk9DZMf9lVd9pYGehgrLly+3z0XnOE+Xnrz2ZH8VacmI0KbBZM9evUwwvfvAX9KgUWNp6+PPNsAmvmqylD/rG3whpkZ1n1+MAwAAAAAAQgdBVzkyMzP9cuCdAwPn0EdP6gcTX4/nxIkTfn8Mf9MwxDHo0laGjz32mOVkm87x8tNPP1naDJ5//vk+HYfOB6XzVdls3brVvJ7crcrR8Ehv5Xn77bc9Crq0xZsGf7NmzTLLO3fuNBU1GjZ42rbQ8T62oMtW1WULupzbFlZkfi6EP31dahUe4G9x1aoRdFWSuBD7/QEAAAAAAPgWE0iUw3F+Ll/Ky8uTSObOHEDBrlWrVtK1a1dLtd+cOXMs95k9e7YlLP3b3/5W4fl4StO9e/cSr9lly5ZJMLYvdKzqcgy6tEJOW/+Vxzm8cmxf6Pg96+vL+bEBoDLFVa3GAa8k8dU41gAAAAAARDIquspRvXp1y7LO9fDKK6/45XEOHz5cZsVTOB9X2/fs6xCoMqq6HOeF+vbbb02Y5bjsfH9f0yonx7mrbI975plnSqA5h1dr166Vs88+W3JycizHTSvKqrlxovK0004zVXG294djdZfj/7Vtoq1yDHD22muvmfeLvm/uueceDhD8IqFmyZ9z8I/4GhxrAAAAAAAiGRVdHs5N5Ty3lr+Cn0OHDkk4q1KlSon5NELxe9bgU78Xm/nz55vKLqVt/n777Tf717TFnnP1lS9oQHTGGWdY1mkbRed53wJB2yrWqVPHMi+XWr16taVa0t35tDSYcJwHTecj0zaN+r06zlNG20KUZcKECfLyyy+bfwF/qdWkqRTGxHCA/axYRJLqN+A4AwAAAAAQwQi63GhP52jz5s1+eSJatmxpWd60aZNH2y9YsECuu+46efTRR2Xy5MmyZMkSCXbefs/Tpk2Tm266ycyLpXNl2UKUyqTzijnOuaXhzc8//2z+r/86hjmDBw/2W8vG4cOHl2iN+cILL0gwcKzqsj1HzvNpuRt0Od+3uLhY0tPTvdofAPhD844dpKB+PQ6un+UnJkrDNqkcZwAAAAAAIhhBVzm6detmCScOHjxoTqx7QqtOrr32Whk9erRMnDhRvv76a1OF4shxrie1aNEijx5j8eLF5jZ16lRTqaDBV7Dz9nvWaim9ff755/Liiy/KmjVrJBCc2xH+9NNP5t9Zs2bZ1+lrSIMuf+nfv7906tTJsu7777+X6dOn++wxNFTyNujS949WXzkGU0lJSdKlSxePWjU6B6RaIWYTFxcnp59+eoXGCgC+rLaNa9aUA+pvzZpIY6cLZwAAAAAAQGQh6CpHrVq15NRTT7Ws++ijjzw6yO+//76psNLQQVtlPf/88yXa9jmfvF+6dKns3r3b7QBCW9U5cj7RHx0dfE+18/eswZC7c5NlZ2fLr7/+allX0XDD22PTu3dvadSokSWw07aFjlV1PXr0kMaNG4u/aJA2duzYEhVjuu6HH37wat86n5buZ+/evRU+Po40kFy5cqUlCNOWhJ5UWdarV89SZekYdGmAGmpzvQEIP/p5nNyAlnr+ltioUYnfqQAAAAAAQGQJvvQjCF1//fWWZa2acrf6aO3ateb+ji666KISJ/Y7dOhgqXAqKiqSp59+2q3H+Oabb2Tnzp32ZZ0TyTlEinGaJ6SgoEAC7ayzzpIGDicBjx8/Lq+88opb27777ruWqjgNI3U+qIrw9tjoyczLLrvMvpybm2sq9zQgKq3qyx86d+4st912m2Wdtk68//77zWtJj68nNECdOXOmXHzxxfLFF19UeFxNmzaVhg0b2pe1otE2j1lF2ww6zsG1ceNGWb9+vcuvAUAg1WjZwswhBf+p1qIFhxcAAAAAgAhH0OWGCy64wDKflIZQd955p/z+++9lbrd161b5xz/+YQlOqlSpIrfccovL+48aNcqyPHfuXHnmmWfM45VGK2OeeOIJyzqdq8s5SEtOTrYsHzt2rMz9VgYd44033mhZ9/HHH8t7771X5na//PKLvPXWW5Z1OldXRfni2DjPv6XtFB1b8znO4+VP99xzjwwaNKhEYKXH9dxzz5XXXntNtm/fXuY+/vrrL/nwww/lwgsvlHvvvVd27drlsqpKq9Qq0r5wzpw5lq85h7LucAzHtA2iVvi5+hoABFK7Pn0kj6ouv8lPrCJNurrf+hYAAAAAAIQn9/uFVbIpU6b4dH9///vfpWrVqhUOZF599VUZOnSovUpHK1I0XNHqrKuuuspUY+ncQEqDhG+//da0LHSei+vBBx+UunXrunycc845R4YMGSLTpk2ztElcvny5qdTRQEBDE7Vt2zZzvw8++MBU7di0adNGRo4cWWLf+r1rOzdbIKDfx6RJk0zopt+fhnGetI/zFZ27TAO9P/74w75OWzvq3FsagvXs2dO0JNKwRudi+uyzz+TLL7+0zBd1xhlnmFCmonxxbJo0aWLGqvOkKcfnRIMn5zDNXzRs0znatNWg4+tIHT16VN58801z0yqr9u3bS/369c1rSqvQdO4sPcblzUGn1WljxoyxvxbdbV84Y8YM8//CwkL7em1BqKGZpxzDLMf96XPpPFcZAARKSp06ktixvRTt28eT4AdRHTtI644dObYAAAAAAES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" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The bubble chart makes the result much easier to read at a glance.\n", "fig, ax = qs.stats.plot_correlations(r_df)\n", "fig" ] }, { "cell_type": "markdown", "id": "s222_md", "metadata": {}, "source": [ "#### 2.2.2. Elementary effects (Morris)\n", "\n", "Morris One-at-A-Time screening estimates each parameter's mean absolute elementary effect (μ\\*) and the standard deviation of those effects (σ). Parameters with high μ\\* are influential; parameters with high σ relative to μ\\* have nonlinear or interaction effects. Morris uses its own sampling design, so we generate fresh samples and re-evaluate before calling `morris_analysis`." ] }, { "cell_type": "code", "execution_count": 23, "id": "0e00daab", "metadata": { "execution": { "iopub.execute_input": "2026-05-27T12:53:33.478251Z", "iopub.status.busy": "2026-05-27T12:53:33.478251Z", "iopub.status.idle": "2026-05-27T12:53:34.260303Z", "shell.execute_reply": "2026-05-27T12:53:34.260303Z" } }, "outputs": [ { "data": { "image/png": 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97Q8nnnhifFLR008/bStI6DtKtS5K1jzuuONsoNFLAUItn7cSp5deX99VeVbFjNE+lI0WyAAAAAAAoDRowrhzUrQKF8U6lmjcSGNFmojupEno/fr1s+N5qSYaP/jgg3ZsT5O+/WhsVMW3Tj31VLPvvvtmvA4a09KyqnCSCm2lonE0FUTSeNARRxyRdDK9CjBpuZLReJdzzEvjho8//rjrMc4xLY09xcZIlUgwfPhwO17s/WxjNAn84osv9g0av/322/b5GgdMlnxw+umnm169emVUfVNjfJqUrvHmZF2V9DkqmSH2OQalsWLnOK4+Qy2rqPPNAw88YMcV/Yqj6T217Z155pm+3ZeSbdPpxpI1zhlkDDqZU045xXzyySeB3y+WIKF9KBkVltP2NGHCBJvklow6GB1yyCH2uw4bUHbSGLTGptUhSWOrySimoe/75JNPprIrSlIm58yNN9447Zi9zpmxmJkfve6dd97pe85UTElxDJ1T1OlMiVLpYgKKaaiooo65QfnFah577DF7LLjppptsEluQc2YsCUnHxdhno+VJ19lt7ty59tiT6pyp89Krr75qxo4daydiJUt8S3fOVAwvSBxQ1xDJll3FVpOdM3WOCxMHjMUXdd6/+uqrA50znXbZZRcbR04XB/Q7/qeKv77++uv23KPPyJsM6IwDartMdi3oZ88993Td3m+//ezn5yzwoGX1vqf2vaCxYj/OArbOhENRol6Yc+Z1111nxowZ43vOLI+iBQAAAAAAFAPvHFwVGVJMIBPe3+waS09F42nObnnNmjWz4+HpqCCTk+Zn+40Hhul4V8jd6jQ3vSzjL8nssccerhiH8ghUQMqZE6ExmTDFyhRn8Y4PMvYC5BeJdSVOgathw4YFChAkC4gHrZiswfZbb73VBnfSJUGVBwWvbr755rTLpuCKAl6qduiVi/UqS8c6BTlUZVJVBspCWe8KBI0fPz5+n6o1quKBAlretr6peLv3KbFq//33T/p4XWg6E+s0kUcXD/q+BgwYELhdrgJYzoChEskUZPQm1qWiIJdzWcpCiXVaD31+Tz31VEIL31QUJEsWHNV6pEus0wWbd59XomR58EuuLZQq7AAAAAAAoHio2NSgQYN8/00Tux9++GHfxDqNz2jMUpOJ043P6N+VkKQ/JQvoeQpWBqUxGU1c1sT0oDSepYqa+lNCgBK9WrZsafJFk8svueSSpB19nMHYs88+21xwwQWmb9++8XHAa665Ju36apzqrrvuskFgJTcG7Qqoz0ZFxUaOHGn+/ffftI/VuuhPyQODBw82ZRmv1Zje//73v5Tjwfo3VULVWLKKaimhoCzJZIVKiZNDhgxxVblNRtuRkiy1vynZ4rDDDst5TOOWW24xTzzxhF3G8iouBhTTOdOZkBOjZN7YpKBMz5k6NyixSeeEdMdsLz0+7HP8aLmVXFRW6ZLq0omdMzPhPWeqYOMVV1wRKClPyx0kgd15ztR5PFM632p7UnJiLFEuCF23KRao80uYOGCQRFN9dn6c8UN9pmWhRH/9OWNe+t7KEu91ClLAVjFUJdu1atXKdsULe87UtYs6C4YpWuAsRAsAAAAAUfbxxx+bKK+HuqkHoXFev3GkUuKdN63GH5n8PtbYh7cIVLpu9krqc/7eD9KtTrwFEFXYMSzva6iIFIzZbLPNXGM1ixcvtgWXglq0aJHrtsbxNtlkEz5aII9IrCtRGrxXEpmzAmEmVN2wYcOGgR57wgknuCr+FZpU1Sn9qKJmmG5y2aKqjkGpwmW2KCjiTKxTxQNVKi5rIEgtgFO1q1Ww9vnnn3cFtzRZ5v777w+VyKiLOVXIVNBXwR4lkjkT65QomK4qqi5GsxX4UrVUdazTpKhsUmKdgqjpEi6d9PmrsmS+xToWZnpMAQAAAAAAiI2zXH/99Sk/jAMPPNA30U0T/VN170pVNEkTiZXYtd1226V9vMZjTjrppFAFqvwmPKtLjZIS8pGgpXErdRoKk8xwzz332OCxin317t3bjrmFGfdUslWQ6qFKINBYXyYJEvoc1elHAe5044F+1MXIOU4ahLaxs846y05WT9Z1sBgpYVXjrWELzi1ZssRcdtlltgpvqk542Ypp6Bhxzjnn2ITKdMXVgKjT/pAsqS4ZJXC99NJLNvE5kzifzpk67pa3bCTVFQqdM3U8S9f5trwpsTwsXZ8deeSRNhk7X4VadY2mLouqIq44WzaVtfhpzLx588y1114buICttpEwMWXnMUJFFdLxFi3QOTrTqvwAAAAAUEzCFNuL8nowx/P/ivhk4zPV73dvHGaXXXZJ+Rw1RXHq0aNHoPfydqzLpNucCgo6MR7w/79/59ipd/tIx/v4fBbZBPB/ohPBRSgKdpc1qS7WblYdx4LwC7aps1aVKlVsAD1d8EeTRZQdf/DBB9vud1p+VdtLRQlUCoI0aNDAnHLKKbYyn4L9r7zyiskGb1VKJdop2KPJNJrgon9XVQZNQkk1SUOTKfQ8Vcxt3LixvU+TbLSckyZNcj1Wn5M6nCkpK6wmTZrYLPa6deva/yqpSV3Ykq3bzjvvHL/dtWtX+3wFVGK8SWZqC6zvRxdb+sz13er1tS6TJ0+2F3PeypJz5861F2vJqhboNb00YckZNNpzzz1t1ztVrkyV+DZhwgS7Dv3797cXnvq+Yo9XoC5dgGnatGkJ26S2L2cCo/aHbt26xW+/8cYbSV/Pm1Snz0vroWVr0aKFrUBw0UUXxf/dubzJqJOdqkOr+kEyqoTgpO+2PCb0aNv2BhS17wQ9pgAAAAAAAIi6a6xcuTLph6EEKo25OGmMSolO3mJbGiPZb7/97PiOgmDqsqIxGo0LaazO2ZVLyXJnnnmm7fyTKmCqcSyNR3mT6qpXr24OOuggW3lUz1fxIwVP//jjD7tcSmLQ/ztpPFSJZ1deeaXrfnVDca6jd0xK46oaF4vROGkq+jzPO++8eDBXn8sBBxxg9tprL7usGuvU2KLGaL3Bvttvv91OSncm1el5+lyVdKcxLo2nqfOYuqE4qauKilvtsMMOKZdPE76d3WBi45kaJ1RlVr2PPk99R/os9d05P3+tl77bsB2QNJld45nOyQMqSKblrV+/vh1j1Di4Egm8490ae9Q6e8d1tR04vzuNmTmXa6uttnJ9X/quy0Lbm8aHg75f7D6vxx9/3O57XhobViKrtrk6derYCq36vrVNepNYtS3re9J+lGlMQ+uz7777mrZt25patWrZKr0ab1a3HmdRL3XP0vixEoO8xwOgVOh8dMYZZwTqbual80+6mFyh0nFGccVkcc50XVn1uSlZXN3OdE2hWE66rnWKH+lcpGO8nq+4jPP8kQ3J4qqKM+o8qQT0bBWLdNpjjz3suUxxwHPPPTejxDFdF5x22mn2nKkOsN6uaIobO+n8rvijzoEqvqnYo64/1NnOS9c7WqYw53hdeyhRUfFHb+xO32Gq60zR9lCWJEB1/NU1pjoRJusQp/0vk0RFJ52bvZPftK0qJukthBm2aMHJJ59skxP13QIAAAAAUAq8HcYUc8iEYkze8fimTZumfM4777wT/391NUsXU4n5+uuvy9RtTuPvGgcoa3JevqiwX9DifmWlGMXbb7+dcWMc79ihX1wGQG6RWFeCdEJVkMLbvleB6ccee8y8++67gV9LwSYFw7t06RJqGfR+t912m00gUjBLFYzTUXBEky+OPfZYe1vBPwX9FRzShAUvdeDSeygo5qRAVhBaJ53wFXjRxYQCKUoEVIJYMsr6v+GGG+z/q2J1jCZzOBPSnBR4UoVjbwvl7bff3vTs2dMGimbPnu36N31XmsgSZhJFnz597GQYJdTFKNkwWUVuBYCcASkFhbQ86hSX7PXVwc6vlbE+S30eqlbo3fb0Plr/ZBdofl0BNQlCNDFDn4UCat9++22gAKEqq2p7U/BI368zgKQJPwoepepS6KRtUQE1ZxVtfSeqzO28WApCF8KjRo1yJRLqtpO2BbUGvuOOO1K+ltYjVeVlb2KdEjrzTUFPv4k3QY4FAAAAAAAAMZpUrXEh52RqFYtSIFETzjWW16lTJ5v05DRs2LCEpLpk44nSvXt3O+n53nvvNQ899JBrjENJc2PGjHGNu3k75HjH4FTBU5O3k1V1VeKPXlfLozFbpxdffNF2+3KOw2nMKNWYlDqxqLBXUJpAHptErqqYGlPzjpspUKvX1OR6Z1E1jYOOGzfO/r8SuDTJ2ltdVZ+1nqvxwkceecT1b88991zKILASurxJdRrz1XfqVxlVSZIaF9Xz9HnGCqBpvFOBbo0ta4wvTGBTz1ViprYJ7/euMWR1+tM48ZNPPun6N62rN7HOO57oHUtWklo2A79aZqdM3k9jqt6xPU3MV+dIjWF66fvXhHuN519xxRWuJAyNder71nh4JjENfd5eGu/U96N9Rd19nMkV11xzjR0XjkplaSAMHcO8xQ9Fhfp0HAyT+KV9SB20MqVzmGJUhx9+uJ2E9NFHH5nnn3/eN1HKr+igzu06fgeZoBJLqtOxW89TB70YXS8EoWO1zh/607L7xcKc1GE2dizV+2ld01Fy8PHHH2/Pqd6im2Eo3qRznr7rdMsZu3ZSzCxGn7U+12TbgxKmde7QNVCm3dgU49X1mo7h7dq1s5+XH2cc0JnYJ/ou9BreGHGyeGwsGVvJ47pu81JCm7qbOgul6vykc1i6brpl7aynDsqxz3706NG2SEEQ+vyUXK7Ef+f1sB/FH/0KW2ofVBfLo446KiHpLlacU+f2dEULdN2oOLG68+o6DwAAAACAqPM2s/Abd0tHYyuKITmdcMIJaZtvOAtFKQYSpLGGxsi8xYzCdkXTmJVzHERjBanmXJcSNRVx+uKLL0I931uIUuOYAPKLxLoc0EnLG5wuC1V2CzPpIRVVoVQg2Ukdt1QpV4EzDXSrumCyZCu/BCcFg1T9UIP3u+++u6tbV7JEMgUsYoFrVV5OFnjxBssUPNdFQyzZSu919tln20kT3ucpaJRsMks6vXr1spUcnRRM0EWEglr6jv1svPHGod9LSU4KknkT62L8kuc0EUCTMoJOqFBARJNnwlIy4dSpU23QRFR92S+xTkl+F154YdrX+/DDD33vT5WsmCrAqO0hVu0gaMBXAbG7777bJq1pew2aWKfP3FutURM00nW5C0IXtfpcvd35tDzeC6X33nsv7eulS6xTF8XyTKzTxfmll16aEPBWgFT7FwAAAAAAQFDOCfgq/KQ/dZJxVs/0dv7QWJw3mUuTppU8lmo8UUlYSmjTON5NN90Uv1/jS6oo6iy05eTtsqWOWQ888EDSpLoYLYuSgDSGooSDGI2lqiOXcxJ8rmjsWN1RnB3OvMlUV199te0646XvQZ+pN5gYowQHjcFqLMs5TuRMePDSunsnm7dp08Y89dRTdrww1fibJvFvvvnmdhwzFvjVOLK+HyV7hXHRRRfZcfRklMigMVmNIzvH2jUupnUt9kqjijE4JwkoeK7xaiVHpKLxfD1OSXaamC96He1PybrwpItppKJkAcUh9N2vWrXK3qdEEHWd8sYUgFLgndwTS1BVYpXG7L00Zq/YkjcupGPc0UcfbROnvUUWg8YXFavRuVjdLGPxRZ1Hldzj7TSmZHPvBCMlUmv50iU8eY/dOqY4zzPe7mSx87QzWUvnq7AVpp3xGBUIjRWMTEbnzIcffjh+zlQHVz3PGc/R5+5M+kpGE6u0vH5JVIq5egtaKmHKmeCteJuSrJPRpCCdz5Rglwltb7G4Y7ICmzFDhgxxJdU56fPQud+v+GqybVKFTBVz90us86s0r+0yyGfuLfCgbczbqTgIfRaKe+s71HkuSAFb8e4ffvS9emOp2u6UiK6Yul9SXey9vNe4yYoW6Bhz8cUX2+vioEmrAAAAAAAUK+988UzGAlR4zjkWprF2zdMO2q0u1hAmCMWVvMWBUsVV/HjHK9RQRWNn+L+CThpriY3BqjCRxs+CjJFobMUZBxS/ooIAcovEuhxYsmSJDQRli4LE2aLqcX/88YfrPg14xwLQmmCgE7UmJ0yYMCFQBzBZuHChrSSpP50YUk0MuO6661zVYF9//fWkj1XwSpNfYlT9UkEuZ/Vav2CWliHTpLrYhYcmyThP+LGJKplcBKSrVKhgmYIzYbz66qsJiXWxqpvei6ZMkuqc7xMLcOl7U1DIW+1RQZJ01PUvWULib7/9Fnq5FOhxthDWfpdM586dXYFSVU1Q0E+fuSqDO5fR7zMUJdA5g866KNaFUDYS6w466KCESSe6oPJOItK+6g0o+5kyZUq8amo6+j6DdnHMhILU+kz1Hatapjpiav/yq86hILF+GAAAAAAAAISlCb9+hZ806V5/TprI7xyvU1BLhZiCjieqq8rHH39sxzliVADq2GOPTRg71JiIt8qkxurCjPkqycAbUNPr5iOxTkkIyZLqYjR2qGJqKiDmpM8jWVJdjMavNHnfmRihToNKQPCrsKoEuliCVGwcWF0EgwZ/lZyhz1/dVJwd8jTOGrRrnboDpkqqc1LnG2+SicbIijmxTuvjnXw/YMCAtEl1MSoupkQ5Z3FCjbFqLFTjuGFjGunoNXVscHbYU0xCY5FKtAQKrehmrFtorqmoo+IsSmpNNWbvFwNR/ME7gScW61Kync6zQZORvPFFv+VQXFDHaOdno/Og9/we5Nitc0aQ8663S6azCngYOt8sWLAg7eO850x1IRs6dKiNVcboM1WSv/M8KDoeOuNssffzXpPo2kdxMV3DOOO/3phfqrhtjIqmbrvttiYTSnoOGmvNpNJ7jM736hLr7KSqWKI3eS4VfV7vv/9+oMcqiVv7jD5nJfQl49eB0W8bTJVYp8IL3m7AmRaa1TaVqkNesnNmsqIF2p7UdS9I7BgAAAAAgGKmJh7OZiPff/+9HctOV1QxRk02NCbm1Lt3b1vQMJWJEyfG/1/jaXvssUeg99P8aC8VLErWFMZLMbFp06a57nPO4S51mvussaRY7FBxrnHjxtnxk3T0ncaKEcbGMvUHIL9IrCsRqgynPz+nnnpqVt9LQQ5NDvDTtm1bs88++yRkVevErKQ5VYt2BsYUKFM1RiUTKdCgzni//vprPLFO1WWV+JVtqhwwffp0s+OOO7ruVxKfLn4ySS5KRRNjFDzU+gWhIIcuiFSR23kRpc/G6/zzzzeZ0qQib6VHVSH0VnJUEpiCg6ocmQl9j2Fpso5TsoQ4BVZVNVvVU51efvllO7FFFzOx5yro4xeo0nbrpe0y3cSgoJTEqr90giTVib4zBbu926+fTLvVpTqmZKJnz542cAwAAAAAABCWkrP69u0beBxK40Leiddhu2qom4gzsS7ZeKLGkJSIp7FP/Wn8ThPKw/BLwspH4oXG1Q477LC0j9PkeHWN8ybWecfjkvEr+qTxLSV9eHm7mqlgVcuWLU0YSiRR17TYBHC9lzoDBf1ewoxhderUKeE+Z3C0GHnHhlW4S2N7Yey33372e1MSZYwmEPgl1nnfT/uYs9NRECeccIIZPnx4fBxaAe3x48fbDpdAoRbdzIRfLCNVB1Bn91Uv7ddKfE4W9/Hr9Ka4lTqR6hyVrCNYJklUOnfqXOGs+h32PBjm2N28efOE+1IVd0xG5xmdb4LwO2fqHKzP0VmE0a+QZ9OmTX0LWHqT1/T96FioOK3znO2NYeqzPuaYY+x1i47Tfq+txDolSoelZfJWXdc5OBklZ6VK0EpVFKF+/fqmVatWdjKbM0FbhTeDXgepiEHQxDpVaNdnrH1L15b6/FQI0xtn1eemznmpaLlT6d+/v/0rC3XWUzKwuig7kzWVMKfiorHYabpzpl/RAl2vaSJg0KIFAAAAAAAUox122ME19qPf0CrWdMUVV6R97hdffGEL2TiL76h43ZlnnpnyeSoYpOfGaKw8XSKes2OdlxID03XIi43PDRw40Hc8BO4xPmfsUI1fNHaSqsifxo5UTNCJudRA+ahYTu+LEuUXYFdyk04KGmT3XhQowDJq1CibdKTKzqqA6awsqGCSX9Jaug5xQXz66acJ902dOjXlc5JVWNQEGi8lCqZ7Pz86yeqz0GfivSDyVlmsVq1aRhUBNIlEwR5VH1Z1USdVx/QGQhRAKwt9X2G/M283t2TVT5W4qaqZmtzjpPVTgMs7CSOT4GghUIDQyVvFPJlCaBesi0lVXgUAAAAAAMiEgo1BO8D5jSd279499HsqYcrbyc1vfE8VS6+66io7kV1jaCqG5JdIlooKQ2Wzg0uYdVTSYhDez0Ljh0ETPPySGv0KTKnomXf8c6+99jJhqbiZt7ta0LFZCVLMKkbjt94x3KDFswqVtwOfCrF5x7rT0Ti6d7/z+w40qd/bHS+T71zj5N7OPmG+c6DUxMbsk8XgJFXHLcVfnIJO7kl2vFACrzf+FvY8GObYreKW3mVWocuwdM73doPzk+qcqY68zuXyi6X5xSD1eXk/M8ULdTz0TuTxdldTQqW+/0cffdQmlbVv3z7h9ZVw50x0DDPhzC9xMRdi26H3+O9M6o7x+/wVPwtzfovF5VQYQLHUe+65xzfxL0i1cb9rv2zSuVTXprrO8xYtUBzeW5Qz3TlTRQuc25sKsqZKmAQAAAAAIArUKa5WrVqu+x555JG0xbc091sNcZzjTYqTqKO8Ct6koqQt5/jQ3nvvHXh5/TrW3XXXXWbBggVpx7hOOeWUhPiMbLbZZoHfvxT06NHDFX9aunSpLT7kLUzpjIFceOGF5pdffnHFsJxjgn70vWs8K/an7wdA2ZFYlwMKQKjzWrb+rrnmGlMIFOwYOXKkzbBXFbtMdOzYMfRzdtppJ3vgV3U8r48//tjkijdxS1R1OhPeAJ8uglRBMt37JQuwJUvg8yaFZRqsrF27tm9F6FgwRxWFndQ1MFUQNYiwz/d+XsmCTGqt61cdWtVUdYEZ+3fnhUyh87sY9W5PkydPTvs6CgiWZ2KdjimjR482N9xwg01yBAAAAAAAyIRfl6tk/MbglPwWlsZVvMlZQcf3wlC3k1deeSXh/rKOxQURphONd+K4kiCCJuX5jQv5JQ74jc2G7VaXbAJ90O9O33uQCfFO3iJlqlpbrJSM4A0AZ/odeCvZ6rXV5cdJBea8BdWy9X567XzsR0Ax8Y7ZZ3JeU3czb+KSusp6ixwGTVjS+UBxQm/yWJj9VzG1sh67M+kUGzSGmeqcqe5yDz/8sC08quOW3+P84lp+j4sVaFSCXhjJKo/7dS1MJ2yH07KIxQG9iXV+Hev8lits4VJvXM7vnCnptsXff//ddnHNBV27qqCq4v2KqyYrWhD2nKkJX97PiwR2AAAAAEDUaZ71aaed5rpPv5/VIf7yyy+3RX/0O3/58uX2N/gzzzxjE6ZUkMdZgE+FkNTZLMj4lcaIvIlcQah4lpbBbyxPHev0/t9++63tnqbl1fiJikWed9555oQTTkiafKfxkD///NN2ss9k/CxqNA45ePBg19in8kA09jR27Nh4MqXGOl9//XVb4Mw7pqTOgH75EgByL3VqM0ryoK4DugJmCqwocFSnTh0bbNH/jxs3zj5OJ06/wXidAFJ1HvN7XhC6iNAkEv2pAuLs2bNt9rzeL1kimy5O/Oi5Qah1bdjnKkjg977egJ8mT3g/C7/3C0MXJ95JGd7AX1DpurbpQmr8+PHx2woMKXNe3fRiF4erVq2yF0r600VWuoumsB3rtB1ookUsAdBb+SEmljjnDQJp+XRR6E2s86tyqqDttGnTXBOFFFh68803Tb72S1Vd136gKo9abq9mzZqZzz//3BUw1DaW7HORDh06+FYEzyYFb3WRp+OI1kEV2bfbbjv7uWuZAQAAAAAAyqpVq1aBH/vDDz8k3DdgwICkhaxS8U7MLsv4nsb2NPapqpQKbn7//fd2fMevAmi+bLrpphk/N0zBr6Cfvd93d8cdd2Q0BqrPN5PvTu8VtkCU9/Fhx0ELid/4uKrrZpJ8402ii30PzoJvft+5Kv4qoB+W9zvWeLbGwWOJJkA2im4+/vjjZX4dbatBJ8RkSjGOBg0apB2z99sHU1F8TJOF/Cppe/dBJcled911doLRpEmTErpTOjnjUTGahJIsRui3vmGO3SowqklETorNxBLSgiYFBk0807k+WUzTG2v063qqawgvv2X86quv7Pt4v9epU6emfP9ksVFn3CwIfX7qhueVquvwVltt5brO07YStPNrLA7oLTDpl+zm15Uv7PWFvkddX7Rp0ybp56aq8xMnTky4/6CDDrLbtLa7bHa21TooDqiJftrndX2mSVv6E117ej377LMJ23+Qc6bisNrGcllwAgAAAACAQnPOOefYxCjv7+AXX3zR/qWz+eab265x22+/fdrHaszAmYSl8YygHeM0ZuGcG61CTxovU2EgFW1SIR79pZoHfO6555rhw4e77h81apT9a9iwoTn55JMDLUvUqQmMkusGDRrkGjdSLFJ/mtOtcSC/WJE+41yPDQNIjsS6EqFEKFWL857UvHSgVgVY/SngoBNmujavzkkMqm6n5DudKP0SqfSYPffcM/By62SiYF7YCSRaj3TtdNNRkCzIfU4LFy60f+koAKG/MK+djt9nlK4tcDJ+1RqdVFlUF1bz58+P36fK2drG5s6daz8DbyXhXFCL4dhFhF+SlgJEsYBc3bp1E/5dwbNtt93WdntLNXlmt912cwUIDzjgAHPbbbclVLYOQkl6Ciw594///e9/NlFQQVYFNPXnTMbU9uyXTOekz15BSe23os9fwVBVM021XmU5ptx8880ZPx8AAAAAACBbwhQO8huDy1bxpKDje5rE/NZbb9kxIk26VjJdIVbyzLRol4RNPsv08/3ggw+y8toaU9N4XLoqoGESBpMp5sQ6v65I2o6dE+mz+fp+33k2O+Do/UisQ5R4E82SJekpkS627StWpeQ2/XmFTYxJFpf75JNPfGNaSu7RpB6/ZLwg6xo0sS5shWe/JD8VlAwbc0sXa3Mei8oS09R36E1S90ta/+OPP3zfR3HgoLFgp1iF7aC0LfqdR7U9JqNEvH79+sVv77333q7YZDqxOKASSBWHS8bvfON3zktHk9tiiXV+z1eBVL9zpl/F+HQUi+/WrZv9/1NOOcV3P4sVQFXcNuj+4ncsEFXTT3Ud5E0kLGtBWQAAAAAAioHGndTtrW/fvqGKEKkokgrtqDuZmlYE8dFHH7nmFWucJKhvvvkmoUDOGWecYc4888yExjF+66gCWfvtt58tfOcXz/I2Pil16kwoN954Y3xOd6oGRdoe+vTp4xoHA5B/JNaVkLImbqWj6nWqHKiDu070OpF6qVXs9OnTzQ477OC7fB9++GFC+9nyqsqcSWJdtt8vDL8KhqoSkAklxykxK9nzFZA79NBD7QVhTCwhLJ8U1I0Fhf0qkzuDnH6d22KfmZI3n3766aTv452sUZaENH12qlz73nvvxe/TxWmYCTWqrqp9w0nflZL2nJOJdKGeKrHO260PAAAAAACgFBPrsiXda3/xxRdm2LBh5uOPPw71un5jQfmQi+S4Qh7f1uurqmoqmY63RkU+Ygzl+X5AqchmgmqmNIFEneHyIWiHuWzz64yWL5l04s01FWzMt1gcUF3rUiXWqVBlkE6AQfat008/PZLnME3ey0XRAgAAAAAAohCjevzxx83zzz9vHnvssZTFbdRRXs1pevXqZefbhzFx4kTX7TCdzbyFfpQIt+OOO5onnnjC3HLLLb7jhZp/rbnSV1xxRXxZVXhnyJAhtghVjOIqPXv2DLUupZJcpzEpxQXffffdhAS7GH3G559/vuncuXPelxGAG4l1JcTZxjUXnJM71Dkr1cC7N7FO1R979+4dOIlOFxeqYDhz5kzz119/mXx9Xrn8DHP9/YStbqnvJFl7YX3mkyZNyui1jzzySFuR2y/rPiwlacYomVMXcqr2GKNqkNqmNt10U9f93okw6RLrvFVZy5JYJ7vssosrsS5dUp0CoM2bN7ctgvVcVZpQEp2X1sOZWJcqOK7ukkHaRwMAAAAAABS6MMlO5TW+98ADD5i77747UHElJQBsueWWdgxV41AqKKXKpaUun+PbKIzvgO8cQDErz2605ZVMmErXrl3L7RpRsbVUiZzewq/JCpqGiVt+9tlnptSvW4IULQAAAAAAIAo0x/foo4+2f+rirkQ2FV3SPGl1gK9Xr56N+7Rt2zajgkhKZHPOOd58881DdYnzdqxr166d/a/m4WvMRJ3otcy///67nVus3/OKUWm5nU488USbAPbJJ5/YOdl6HY35lHpRwmSaNWtm7rrrLjtOqEYp+nw1/145FvoO9VkqHyKsd955JyfLmyopFCgFJNaVEL+OXaqS/OKLL9qAgSrv6USuCsgvvfRSzpZDJ2AnVQg85ZRTfANMuoDQiaNNmzY2432LLbYwrVu3No0aNbL/rosQv8Q6rcOsWbN83//ee+819913X8L9TZo0SXuy0WeYKpFPk1xeeeWVhPt1AePM0G/fvr0ZN26cyabatWsn3Keuc5nSZ+GXeKXqgqq26HcC1felCz+try4CdVE1YMAA14Shc845xybWZcPXX39tJ1eoerYuRv2S55Tpf+yxx5p//vkn4d90ASg777xzQlKek3Pb1LaoRL1cBbu0jTmTDvVer732mmv/TTYBy5vwpw6RyQK36ppXaFXHAQAAAAAA8j1GqvGvZ599NqfvOXLkSBs486PgZIcOHeyYp8Y+NQaqMcbq1avHHzNv3rycLl+xfncKNobtnhKjMTN95vLDDz+4Pm8E/w7k1VdfDV1Zt6wxDbrfAIVJ8RMVCHzmmWfi9ylO9NRTT9mii37ximrVqpltttkmHgdU3CdI/KxFixY2zjF27Nisr8dNN91k44jz5893xXVisR0ts3eykOI2mXaJ03qoqnk6c+bM8U20V2zLG+/ZdtttEyqRq2PczTffbGOymvzkvd8ZBzzmmGN844BK2HPGOws92S8WB9QEr1TL/tNPPyXcF3RCmB4Xi8cqhjx37lz72SeLhWqZVODUSYUyvXHA2AS3GG1fivuFke6ceeaZZ2Z8LRWV5D8AAAAAALKtadOm9i+b1BzEOXddzTmC0riFd5zHO+6guJT+gtBzvc9HaopBqVMhgMJGYl0JUUcvv0pxqpj3xhtvuO5Xkl39+vVzshzeTmWXX3550uSfxo0b24Bbqha6+aT3S5VYlyxo5g3WJGvpmu3EumSJYkGoLbHay3qDcaqsnSwrXcEjtTKOfS8PP/ywKwlMAVVNEApSmTsZJU3GAjEK7qmSgqomfPzxx76PVyc6JdYpadRLGf+iYJVew9uZzo+6wpWFlv3BBx/0TYrr37+/TWhUBXNnu2bvBJJk208s6VRVDWLfv4LQuVgPAAAAAACAYuQdT9T4aC5pAvQ999yTcH/Pnj3NWWedFU/uSkVjYMj/d4dgMYZly5blPaZB9xugbIImqPbq1cvVyatly5bxeKKS36666irX47///nuz0047ue5TApFfHFATdS666CLTrVs3G/eJnTOdCV6pKIYZK8JZqBS3U7w1m6/nRxOZlHQXo89FSfvexLqg/OKAOh4rZnjYYYeZPn362ESwYhCLA+qzU9HTZEVZUyXlpaNCo0rSj9E+43ftJ0OHDrUx46uvvtp1f5A4YCax1XTnzGwWLQAAAAAAALnjbRoTJrFOzW/+/fff+G2Nk2Q78Q8AoiD/peFQbtSRza8CXyyo4DR9+vScLYczEKHAiwJ4qRLV9JgRI0bYTnB67JIlS1KuUy5l+n7eqoZlCaQpOKaWvvqvs9Kfqn5638d5MRTWokWLbFVOdYKLVVpU4FOJaskokevtt9+O31anNSdVvZRMq1mKOtQ5zZw50/5XbXL9KHCoi0pvFUcFh5xBSG+3t2SCPi7V5+pNLpWTTjrJJvd5g5F+CXCpth/v8n355ZeBHgcAAAAAAFAKvON7CxYsyGk3DRWh8ha/uvjii80tt9wSKKlOsjkpv5h5A70aJ3R2E0L5xRii8n5AqQiamJzJOdMb/1ByuDcOqAQndZrbZ5994kl1yc6ZxSxZIlymFAf0S4j0dg1VAqTf5KhYcqM3Rqfkx1RxQCVfqQiqOt0pyT0W64vJtFNfrnnjgLvuumuo56v4axDqDuikeLZfHFAqV64c6PNKdu2XrFBtMunOmRQtAAAAAACgOEycODH+/zVr1jRdunQJ/Fxvc462bdtmddkAICpIrCshO+ywQ8J9SuJRMo/X448/nvK1Jk+ebDuaTZkyxcybNy8h2SmVZs2axf//rbfeSvv4l19+2dx55512wskxxxxjTj755JTrlCkFjvRe6lqmjl9+lf/SvV/VqlV973cGBmNBy4ceesgmnimgGGZyjB5/7rnnmoMOOsgGGWOUVFe3bl3XY//55x9TFk8++aTtmBaraqnvW5+Tk3cCUCyZTgFWZ+VHBfu0zOK3vQRNtvN+L7GAbLLEOrnhhhsSEtZ23HFH1+099tgj7XtrHbzPC8uZGOpdDwWkP//8c1eAbZdddkl4rKq+JvtcvOuh7y4WEHW2FVbnQAAAAAAAgFLjHd/TeEyqwl+pvPDCC2nHE51FqERFztSpLgy/saCyFK4qVp07d064L9OOOQpCa+xNXWMWLlyYMH4Gf9tss03CGHim34H2u2effdZ29lHFXL/OjMliGpkoS0wDiJqgCaqZnDODdEk788wzTbVq1dKeM1MlIGXSvSvfslUc9NFHH7Vx2w8++CDQa+rc5nfOXLx4sW9y1ptvvpkyDqg4leJ9isMp9rfffvu5vr+g30W+r1288Ty/eFu6TnSZJNZ5u/1lcg7zu/aT5557Luk5M9n7pSpgm6xoAedMAAAAAAAKyxtvvGHHHPSnxjlVqlTJOLGuXbt2OVhCACh+lct7AZA/m222mR0gVweymPHjx5szzjjDVuxbtmxZ/P5PPvnETJ061XTt2jXhdTTJYNCgQTb47KxopwC4N4HMjzNT3rksfhSMmTRpUtJA3k477ZT0eWE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" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "inputs = qs.stats.define_inputs(model)\n", "samples = qs.stats.generate_samples(inputs, kind='Morris', N=10, seed=554)\n", "model.load_samples(samples)\n", "model.evaluate()\n", "morris_dct = qs.stats.morris_analysis(model, inputs, seed=554, nan_policy='fill_mean')\n", "\n", "# One panel per metric so each gets its own (mu*, sigma) scale\n", "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n", "for ax, m in zip(axes, model.metrics):\n", " qs.stats.plot_morris_results(morris_dct, metric=m, ax=ax, label_kind='name')\n", " ax.set_title(m.name)\n", "fig.tight_layout()\n", "fig" ] }, { "cell_type": "markdown", "id": "morris_interp", "metadata": {}, "source": [ "The **Net earnings** panel is dominated by the treatment fee (consistent with the Spearman result above), so the other five parameters collapse near the origin. The **GWP** panel is more informative: the electricity CF has the largest mean effect; `X_oxid` sits high on σ relative to μ\\*, signalling a nonlinear or interaction-driven contribution (it changes both the oxygen demand and the electricity draw it feeds into)." ] }, { "cell_type": "markdown", "id": "s223_md", "metadata": {}, "source": [ "#### 2.2.3. Variance-based methods (FAST and Sobol)\n", "\n", "`fast_analysis` and `sobol_analysis` follow the same shape as Morris: generate a method-specific sample with `generate_samples`, evaluate the model, then call the analysis function. They are more expensive (Sobol especially) but partition the output variance into first-order and (for Sobol) total-order indices that capture interactions. See the worked examples on the [stats module API page](https://qsdsan.readthedocs.io/en/latest/api/stats.html)." ] }, { "cell_type": "markdown", "id": "nav-footer-9_uncertainty_and_sensitivity_analyses", "metadata": {}, "source": [ "\n", "\n", "---\n", "\n", "↑ Back to top" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" }, "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 }