{ "cells": [ { "cell_type": "markdown", "id": "94a4daa2", "metadata": {}, "source": [ "# Example 1: Basic Modelling with PyTracerLab\n", "Here we show how to use the PyTracerLab package to perform the most basic standard application of lumped parameter models: calibrating parameter values (especially mean travel time of the travel time distribution) of the lumped parameter model from observations of tracer concentrations in an observation well." ] }, { "cell_type": "code", "execution_count": 1, "id": "b25444c9", "metadata": {}, "outputs": [], "source": [ "import PyTracerLab.model as ism\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime" ] }, { "cell_type": "markdown", "id": "ac5cff6c", "metadata": {}, "source": [ "## 1. Load (Synthetic) Observation Data\n", "See Example 4 on how this data is generated." ] }, { "cell_type": "code", "execution_count": 2, "id": "7456a1bb", "metadata": {}, "outputs": [], "source": [ "# load input series\n", "# this would be the tracer concentration in precipitation or recharge in a\n", "# practical problem\n", "file_name = \"example_input_series_1tracer.csv\"\n", "data = np.genfromtxt(\n", " file_name,\n", " delimiter=\",\",\n", " dtype=[\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### plot input series, output series, and observations\n", "\n", "# get observation timesteps\n", "timesteps = [t.year + t.month / 12.0 for t in timestamps]\n", "\n", "# create figure\n", "fig, ax = plt.subplots(1, 1)\n", "# plot input series\n", "ax.plot(\n", " timesteps,\n", " input_series,\n", " label=\"Tracer Input\",\n", " c=\"grey\"\n", ")\n", "# plot output series\n", "ax.plot(\n", " timesteps,\n", " output_series,\n", " label=\"Tracer Output\",\n", " c=\"green\"\n", ")\n", "# plot observations\n", "ax.scatter(\n", " timesteps,\n", " obs_series,\n", " label=\"Observations\",\n", " color=\"red\",\n", " marker=\"x\",\n", " zorder=10\n", ")\n", "ax.set_xlabel(\"Time [months]\")\n", "ax.set_ylabel(\"Tracer concentration [-]\")\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "68d49bd6", "metadata": {}, "source": [ "## 2. Calibrate Model Parameters" ] }, { "cell_type": "code", "execution_count": 4, "id": "60800290", "metadata": {}, "outputs": [], "source": [ "t_half = 12.3 * 12.0\n", "lambda_ = np.log(2.0) / t_half\n", "\n", "### define model (use the same structure / units as the true model)\n", "# time step is 1 month\n", "m = ism.Model(\n", " dt=1.0,\n", " lambda_=lambda_,\n", " input_series=input_series,\n", " target_series=obs_series,\n", " steady_state_input=1., # this is the true value\n", " n_warmup_half_lives=10\n", ")\n", "\n", "# add an exponential-piston-flow unit\n", "# define the initial model parameters for inference\n", "epm_mtt_init = 12 * 1 # 1 year\n", "epm_eta_init = 1.1\n", "m.add_unit(\n", " ism.EPMUnit(mtt=epm_mtt_init, eta=epm_eta_init),\n", " fraction=.8, # 80 percent of the overall response; is the true value\n", " bounds=[(1.0, 12.0 * 50.), (1.0, 3.0)],\n", " prefix=\"epm\"\n", ")\n", "\n", "# add a piston-flow unit\n", "# define the true model parameters\n", "pm_mtt_init = 12 * 1 # 1 year\n", "m.add_unit(\n", " ism.PMUnit(mtt=pm_mtt_init),\n", " fraction=.2, # 20 percent of the overall response, is the true value\n", " bounds=[(1.0, 12.0 * 50.)],\n", " prefix=\"pm\"\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "432a52d0", "metadata": {}, "outputs": [], "source": [ "# create a solver\n", "solver = ism.Solver(m)\n", "# run DE\n", "sol, sim = solver.differential_evolution(maxiter=10000, popsize=10)" ] }, { "cell_type": "code", "execution_count": 6, "id": "7d7366fa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EPM MTT (true): 480 EPM MTT (cal): 484.3422808609019\n", "EPM Eta (true): 1.5 EPM Eta (cal): 1.4664123142091618\n", "PM MTT (true): 60 PM MTT (cal): 60.49984389012326\n" ] } ], "source": [ "# define true parameters\n", "epm_mtt_true = 12 * 40 # 40 years\n", "epm_eta_true = 1.5\n", "pm_mtt_true = 12 * 5 # 5 years (faster than EPM)\n", "\n", "# print solution\n", "print(\"EPM MTT (true):\", epm_mtt_true, \"EPM MTT (cal):\", sol[\"epm.mtt\"])\n", "print(\"EPM Eta (true):\", epm_eta_true, \"EPM Eta (cal):\", sol[\"epm.eta\"])\n", "print(\"PM MTT (true):\", pm_mtt_true, \"PM MTT (cal):\", sol[\"pm.mtt\"])" ] }, { "cell_type": "code", "execution_count": 7, "id": "2124a4e7", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### plot input series, true output series, observations, and´\n", "# calibrated output\n", "\n", "# create figure\n", "fig, ax = plt.subplots(1, 1)\n", "# plot output series\n", "ax.plot(\n", " timesteps,\n", " output_series,\n", " label=\"Tracer Output\",\n", " c=\"green\",\n", " zorder=0\n", ")\n", "# plot calibrated results\n", "ax.plot(\n", " timesteps,\n", " sim,\n", " label=\"Calibration\",\n", " c=\"black\",\n", " alpha=1.,\n", " ls=\":\"\n", ")\n", "\n", "# plot observations\n", "ax.scatter(\n", " timesteps,\n", " obs_series,\n", " label=\"Observations\",\n", " color=\"red\",\n", " marker=\"x\",\n", " zorder=100000000\n", ")\n", "ax.set_xlabel(\"Time [months]\")\n", "ax.set_ylabel(\"Tracer concentration [-]\")\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "fd5c4fba", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "isosimpy_app", "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": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 }