{ "cells": [ { "cell_type": "markdown", "id": "28c4658c", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Dynamic Simulation \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", " - [Joy Zhang](https://github.com/joyxyz1994/)\n", "\n", "- **Learning objectives.** After this tutorial, you will be able to:\n", "\n", " - Set up and run a dynamic simulation\n", " - Manage state variables and time-series outputs\n", " - Save and inspect dynamic results\n", "\n", "- **Prerequisites:** [10. Process](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html)\n", "\n", "- **Covered topics:**\n", "\n", " - 1. Understanding dynamic simulation with QSDsan\n", " - 2. Writing a dynamic SanUnit\n", " - 3. Other convenient features\n", "\n", "> **Companion video.** A walkthrough of this tutorial is available on [YouTube](https://youtu.be/1Rr1QxUiE5k), presented by [Yalin Li](https://github.com/yalinli2). Recorded against `QSDsan` v1.3.1. The concepts still apply, but if the code on screen differs from this notebook, follow the notebook.\n" ] }, { "cell_type": "markdown", "id": "b4f9dac9697a", "metadata": {}, "source": [ "\n", "\n", "## Setup\n", "\n", "Import `QSDsan` and confirm the installed version.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "3dc1138e", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:37:45.750819Z", "iopub.status.busy": "2026-05-30T20:37:45.749818Z", "iopub.status.idle": "2026-05-30T20:38:00.641616Z", "shell.execute_reply": "2026-05-30T20:38:00.641139Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This tutorial was made with qsdsan v1.5.3 and exposan v1.5.3\n" ] } ], "source": [ "import qsdsan as qs, exposan\n", "print(f'This tutorial was made with qsdsan v{qs.__version__} and exposan v{exposan.__version__}')" ] }, { "cell_type": "markdown", "id": "b7f9ccfc", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 1. Understanding dynamic simulation with QSDsan " ] }, { "cell_type": "markdown", "id": "2bc790e7", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "From previous tutorials, we've covered how to use QSDsan's [SanUnit](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html) and [WasteStream](https://qsdsan.readthedocs.io/en/latest/tutorials/3_WasteStream.html) classes to model the mass/energy flows throughout a system. You may have noticed, the simulation results generated by `SanUnit._run` are **static**, i.e., they don't carry time-related information. \n", "\n", "In this tutorial, we will learn about the **dynamic** simulation features in QSDsan. First we will focus on performing dynamic simulations with an existing system to understand the basics. Then we'll go over how to implement your own algorithms for dynamic simulations. " ] }, { "cell_type": "markdown", "id": "5e4d0755", "metadata": {}, "source": [ "### 1.1. An example system\n", "Let's use [Benchmark Simulation Model no.1 (BSM1)](https://iwa-mia.org/benchmarking/#BSM1) as an example. BSM1 describes an activated sludge treatment process that can be commonly found in conventional wastewater treatment facilities. It uses the same activated-sludge layout (two anoxic tanks, three aerated tanks, and a clarifier with two recycles) built and illustrated in [6. System](https://qsdsan.readthedocs.io/en/latest/tutorials/6_System.html); here we load the full implementation, with process models, from [EXPOsan](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/bsm1)." ] }, { "cell_type": "markdown", "id": "a8a07c91", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### 1.1.1. Running dynamic simulation" ] }, { "cell_type": "code", "execution_count": 2, "id": "a1c82016", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:00.644744Z", "iopub.status.busy": "2026-05-30T20:38:00.644744Z", "iopub.status.idle": "2026-05-30T20:38:02.514055Z", "shell.execute_reply": "2026-05-30T20:38:02.514055Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "176231565848:c->176231547140:c\n", "\n", "\n", "\n", " ws1\n", "\n", "\n", "\n", "\n", "\n", "176231547140:c->176231547818:c\n", "\n", "\n", "\n", " ws3\n", "\n", "\n", "\n", "\n", "\n", "176231547818:c->176231547872:c\n", "\n", "\n", "\n", " ws5\n", "\n", "\n", "\n", "\n", "\n", "176231547872:c->176231544113:c\n", "\n", "\n", "\n", " ws7\n", "\n", "\n", "\n", "\n", "\n", "176231544113:c->176231565848:c\n", "\n", "\n", "\n", " RWW\n", "\n", "\n", "\n", "\n", "\n", "176231544113:c->176231543954:c\n", "\n", "\n", "\n", " treated\n", "\n", "\n", "\n", "\n", "\n", "176231543954:c->176231565848:c\n", "\n", "\n", "\n", " RAS\n", "\n", "\n", "\n", "\n", "\n", "176231543954:c->176238912484:w\n", "\n", "\n", " effluent\n", "\n", "\n", "\n", "\n", "\n", "176231543954:c->176238912524:w\n", "\n", "\n", " WAS\n", "\n", "\n", "\n", "\n", "\n", "176238920004:e->176231565848:c\n", "\n", "\n", " wastewater\n", "\n", "\n", "\n", "\n", "\n", "176231565848\n", "\n", "\n", "A1\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "176231547140\n", "\n", "\n", "A2\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "176231547818\n", "\n", "\n", "O1\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "176231547872\n", "\n", "\n", "O2\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "176231544113\n", "\n", "\n", "O3\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "176231543954\n", "\n", "\n", "C1\n", "Flat bottom circular clarifier\n", "\n", "\n", "\n", "\n", "\n", "176238920004\n", "\n", "\n", "\n", "\n", "176238912484\n", "\n", "\n", "\n", "\n", "176238912524\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's load the BSM1 system first\n", "from exposan import bsm1\n", "bsm1.load()\n", "sys = bsm1.sys\n", "\n", "# The BSM1 system is composed of 5 CSTRs in series, \n", "# followed by a flat-bottom circular clarifier.\n", "# sys.units\n", "sys.diagram()" ] }, { "cell_type": "code", "execution_count": 3, "id": "03c7b593", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:02.514055Z", "iopub.status.busy": "2026-05-30T20:38:02.514055Z", "iopub.status.idle": "2026-05-30T20:38:03.357844Z", "shell.execute_reply": "2026-05-30T20:38:03.356159Z" }, "slideshow": { "slide_type": "slide" }, "tags": [ "raises-exception" ] }, "outputs": [ { "ename": "TypeError", "evalue": "solve_ivp() missing 1 required positional argument: 't_span'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# If we try to simulate it like we'd do for a \"static\" system\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m sys.simulate()\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3374\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3354\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msimulate\u001b[39m(\u001b[38;5;28mself\u001b[39m, update_configuration: Optional[\u001b[38;5;28mbool\u001b[39m]=\u001b[38;5;28;01mNone\u001b[39;00m, units=\u001b[38;5;28;01mNone\u001b[39;00m, \n\u001b[32m 3355\u001b[39m design_and_cost=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 3356\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 3357\u001b[39m \u001b[33;03m If system is dynamic, run the system dynamically. Otherwise, converge \u001b[39;00m\n\u001b[32m 3358\u001b[39m \u001b[33;03m the path of unit operations to steady state. After running/converging \u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 3372\u001b[39m \u001b[33;03m \u001b[39;00m\n\u001b[32m 3373\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3374\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.flowsheet:\n\u001b[32m 3375\u001b[39m specifications = \u001b[38;5;28mself\u001b[39m._specifications\n\u001b[32m 3376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m specifications \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._running_specifications:\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_flowsheet.py:120\u001b[39m, in \u001b[36mFlowsheet.__exit__\u001b[39m\u001b[34m(self, type, exception, traceback)\u001b[39m\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mtype\u001b[39m, exception, traceback):\n\u001b[32m 119\u001b[39m main_flowsheet.set_flowsheet(\u001b[38;5;28mself\u001b[39m._temporary_stack.pop())\n\u001b[32m--> \u001b[39m\u001b[32m120\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exception: \u001b[38;5;28;01mraise\u001b[39;00m exception\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3418\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3416\u001b[39m \u001b[38;5;28mself\u001b[39m._setup(update_configuration, units)\n\u001b[32m 3417\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.isdynamic: \n\u001b[32m-> \u001b[39m\u001b[32m3418\u001b[39m outputs = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdynamic_run\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3419\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m design_and_cost: \u001b[38;5;28mself\u001b[39m._summary()\n\u001b[32m 3420\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3513\u001b[39m, in \u001b[36mSystem.dynamic_run\u001b[39m\u001b[34m(self, **dynsim_kwargs)\u001b[39m\n\u001b[32m 3511\u001b[39m \u001b[38;5;66;03m# Integrate\u001b[39;00m\n\u001b[32m 3512\u001b[39m \u001b[38;5;28mself\u001b[39m.dynsim_kwargs[\u001b[33m'\u001b[39m\u001b[33mprint_t\u001b[39m\u001b[33m'\u001b[39m] = print_t \u001b[38;5;66;03m# self.dynsim_kwargs might be reset by `state_reset_hook`\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3513\u001b[39m \u001b[38;5;28mself\u001b[39m.scope.sol = sol = \u001b[30;43msolve_ivp\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfun\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mDAE\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43my0\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43my0\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mdk_cp\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3514\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m print_msg:\n\u001b[32m 3515\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m sol.status == \u001b[32m0\u001b[39m:\n", "\u001b[31mTypeError\u001b[39m: solve_ivp() missing 1 required positional argument: 't_span'" ] } ], "source": [ "# If we try to simulate it like we'd do for a \"static\" system\n", "sys.simulate()" ] }, { "cell_type": "markdown", "id": "07f91f64", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We run into this error because QSDsan (essentially biosteam in the background) considers this system dynamic, and additional arguments are required for `simulate` to work." ] }, { "cell_type": "code", "execution_count": 4, "id": "b349b9a3", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:03.359369Z", "iopub.status.busy": "2026-05-30T20:38:03.359369Z", "iopub.status.idle": "2026-05-30T20:38:03.366079Z", "shell.execute_reply": "2026-05-30T20:38:03.366079Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can verify that by\n", "sys.isdynamic" ] }, { "cell_type": "code", "execution_count": 5, "id": "985f9b58", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:03.366079Z", "iopub.status.busy": "2026-05-30T20:38:03.366079Z", "iopub.status.idle": "2026-05-30T20:38:03.374612Z", "shell.execute_reply": "2026-05-30T20:38:03.373470Z" } }, "outputs": [ { "data": { "text/plain": [ "{: True,\n", " : True,\n", " : True,\n", " : True,\n", " : True,\n", " : True}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is because the system contains at least one dynamic SanUnit\n", "{u: u.isdynamic for u in sys.units}" ] }, { "cell_type": "code", "execution_count": 6, "id": "43772dae", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:03.376619Z", "iopub.status.busy": "2026-05-30T20:38:03.376619Z", "iopub.status.idle": "2026-05-30T20:38:03.383123Z", "shell.execute_reply": "2026-05-30T20:38:03.382618Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "System: bsm1_sys\n", "Highest convergence error among components in recycle\n", "streams {C1-1, O3-0} after 1 loops:\n", "- flow rate 1.46e-11 kmol/hr (2.7e-14%)\n", "- temperature 0.00e+00 K (0%)\n", "ins...\n", "[0] wastewater \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 23.1\n", " S_S 53.4\n", " X_I 39.4\n", " X_S 155\n", " X_BH 21.7\n", " S_NH 1.73\n", " S_ND 5.34\n", " ... 4.26e+04\n", "outs...\n", "[0] effluent \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 22.6\n", " S_S 52.3\n", " X_I 38.5\n", " X_S 152\n", " X_BH 21.2\n", " S_NH 1.7\n", " S_ND 5.23\n", " ... 4.17e+04\n", "[1] WAS \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 0.481\n", " S_S 1.11\n", " X_I 0.821\n", " X_S 3.25\n", " X_BH 0.452\n", " S_NH 0.0361\n", " S_ND 0.111\n", " ... 888\n" ] } ], "source": [ "# If we disable dynamic simulation, then `simulate` would work as usual\n", "sys.isdynamic = False\n", "sys.simulate()\n", "sys.show()" ] }, { "cell_type": "markdown", "id": "cc28e85f", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "To perform a dynamic simulation of the system, we need to provide at least one additional keyword argument, i.e., `t_span`, as suggested in the error message. `t_span` is a 2-tuple indicating the simulation period.\n", "\n", "
\n", "\n", "**Note:** Whether `t_span = (0,10)` means 0-10 days or 0-10 hours/minutes/months depends entirely on units of the parameters in the system's ODEs. For BSM1, it'd mean 0-10 days because all parameters in the ODEs express time in the unit of \"day\".\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "0c111c81", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Other often-used keyword arguments include:\n", "\n", "- `t_eval`: a 1d array to specify the output time points\n", "- `method`: a string specifying the ordinary differential equation (ODE) solver\n", "- `atol` and `rtol`: the absolute and relative error tolerances the solver holds each step to; tighten them (smaller values) if a trajectory looks under-resolved or jagged, or loosen them to trade accuracy for speed\n", "- `state_reset_hook`: controls what happens to the system's dynamic state *before* integration starts. Common values:\n", " - `'reset_cache'` — clear the compiled DAE plus every unit's and stream's `_state` / `_dstate`, then re-initialize from the static-converged design. Use this when you want each `sys.simulate(...)` call to behave like a fresh first run (e.g., after changing influent characteristics, kinetic parameters, or unit sizes between simulations). It's the safe default when you're not sure.\n", " - `'clear_state'` — lighter-weight: zero out unit and stream state arrays but keep the compiled DAE. Useful when only the initial conditions changed.\n", " - `None` (default) — leave existing state intact. The next simulation **resumes** from wherever the previous one left off — useful for stitching successive time windows together, or for warm-starting from a converged state.\n", "\n", "`t_span`, `t_eval`, `method`, `atol`, and `rtol` are essentially passed to [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) as keyword arguments. See the [System.dynamic_run documentation](https://biosteam.readthedocs.io/en/latest/API/System.html#biosteam.System.dynamic_run) for a complete list of keyword arguments (it also accepts `print_msg=True`, which prints the solver's completion or failure message, useful when a run does not finish). You may notice that `scipy.integrate.solve_ivp` also requires input of `fun` (i.e., the ODEs) and `y0` (i.e., the initial condition); we'll come back to how `System.simulate` automates the compilation of these inputs in [§2.1](#2.1.-How-the-integrator-drives-a-dynamic-unit)." ] }, { "cell_type": "markdown", "id": "9c8b4556", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "
\n", "\n", "**Tip:** For systems that are expected to converge to some sort of \"steady state\", it is usually faster to simulate with implicit ODE solvers (e.g., `method = BDF` or `method = LSODA`) than with explicit ones. If one solver fails to complete integration through the entire specified simulation period, always try with alternative ones. See [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) for the full list of methods and guidance on choosing among them.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 7, "id": "45ef4032", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:03.386129Z", "iopub.status.busy": "2026-05-30T20:38:03.386129Z", "iopub.status.idle": "2026-05-30T20:38:04.963120Z", "shell.execute_reply": "2026-05-30T20:38:04.962139Z" }, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "System: bsm1_sys\n", "Highest convergence error among components in recycle\n", "streams {C1-1, O3-0} after 5 loops:\n", "- flow rate 1.46e-11 kmol/hr (4e-14%)\n", "- temperature 0.00e+00 K (0%)\n", "ins...\n", "[0] wastewater \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 23.1\n", " S_S 53.4\n", " X_I 39.4\n", " X_S 155\n", " X_BH 21.7\n", " S_NH 1.73\n", " S_ND 5.34\n", " ... 4.26e+04\n", "outs...\n", "[0] effluent \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 22.6\n", " S_S 0.67\n", " X_I 3.3\n", " X_S 0.142\n", " X_BH 7.36\n", " X_BA 0.43\n", " X_P 1.3\n", " ... 4.17e+04\n", "[1] WAS \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 0.481\n", " S_S 0.0143\n", " X_I 36\n", " X_S 1.55\n", " X_BH 80.3\n", " X_BA 4.69\n", " X_P 14.1\n", " ... 884\n" ] } ], "source": [ "# Let's try simulating the BSM1 system from day 0 to day 50 in the dynamic mode.\n", "# Use shorter time or try changing method to 'RK23' (explicit solver) if it takes a long time\n", "sys.isdynamic = True\n", "sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n", "sys.show()" ] }, { "cell_type": "markdown", "id": "972442da", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### 1.1.2. Retrieve dynamic simulation data\n", "The `show` method only displays the system's state at the end of the simulation period. How do we retrieve information on system dynamics? QSDsan uses [Scope](https://qsdsan.readthedocs.io/en/latest/api/utility_functions/scope.html) objects to keep track of values of state variables during simulation." ] }, { "cell_type": "code", "execution_count": 8, "id": "3d7a8b0d", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:04.965246Z", "iopub.status.busy": "2026-05-30T20:38:04.965246Z", "iopub.status.idle": "2026-05-30T20:38:04.969792Z", "shell.execute_reply": "2026-05-30T20:38:04.969278Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "(, )" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This shows the units/streams whose state variables are kept track of \n", "# during dynamic simulations.\n", "sys.scope.subjects" ] }, { "cell_type": "code", "execution_count": 9, "id": "5fedeb57", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:04.970797Z", "iopub.status.busy": "2026-05-30T20:38:04.970797Z", "iopub.status.idle": "2026-05-30T20:38:04.981610Z", "shell.execute_reply": "2026-05-30T20:38:04.981610Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We see that A1 and effluent are tracked, so we can retrieve their \n", "# time series data through their `scope` attribute, which stores a \n", "# `SanUnitScope` for unit operations\n", "A1 = sys.flowsheet.unit.A1\n", "A1.scope\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "26a40a92", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:04.983802Z", "iopub.status.busy": "2026-05-30T20:38:04.983802Z", "iopub.status.idle": "2026-05-30T20:38:04.987819Z", "shell.execute_reply": "2026-05-30T20:38:04.987819Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Or `WasteStreamScope` object for streams\n", "eff = sys.flowsheet.stream.effluent\n", "eff.scope" ] }, { "cell_type": "code", "execution_count": 11, "id": "a7c7fa4d", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:04.990045Z", "iopub.status.busy": "2026-05-30T20:38:04.990045Z", "iopub.status.idle": "2026-05-30T20:38:05.209402Z", "shell.execute_reply": "2026-05-30T20:38:05.209402Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# `Scope` objects include a function for convenient visualization of time-series data\n", "fig, ax = A1.scope.plot_time_series(('S_NH', 'S_S'))" ] }, { "cell_type": "markdown", "id": "e11-tracker-note", "metadata": {}, "source": [ "Every `SanUnit` and `WasteStream` has a `.scope` attribute, but only the ones in `sys.scope.subjects` (shown above) actually accumulate data during a simulation. For everything else, the scope object exists but its `record` stays empty.\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "51cc75b4", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:05.212884Z", "iopub.status.busy": "2026-05-30T20:38:05.211885Z", "iopub.status.idle": "2026-05-30T20:38:05.216330Z", "shell.execute_reply": "2026-05-30T20:38:05.216330Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Every SanUnit has a .scope attribute; here A2 is *not* in `sys.scope.subjects`\n", "A2 = sys.flowsheet.unit.A2\n", "A2.scope" ] }, { "cell_type": "code", "execution_count": 13, "id": "e11-untracked-record", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:05.218334Z", "iopub.status.busy": "2026-05-30T20:38:05.218334Z", "iopub.status.idle": "2026-05-30T20:38:05.222615Z", "shell.execute_reply": "2026-05-30T20:38:05.222615Z" } }, "outputs": [ { "data": { "text/plain": [ "array([], shape=(0, 1), dtype=float64)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ...but because A2 wasn't tracked, its record never got filled.\n", "A2.scope.record" ] }, { "cell_type": "markdown", "id": "8b8727df", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "For `A1`, it is tracked, so each row in the `record` attribute is values of `A1`'s state variables at a certain time point." ] }, { "cell_type": "code", "execution_count": 14, "id": "cab34aab", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:05.224621Z", "iopub.status.busy": "2026-05-30T20:38:05.224621Z", "iopub.status.idle": "2026-05-30T20:38:05.230748Z", "shell.execute_reply": "2026-05-30T20:38:05.229743Z" }, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "array([0.000e+00, 5.098e-10, 1.020e-09, 6.117e-09, 1.122e-08, 6.219e-08,\n", " 1.132e-07, 3.166e-07, 5.199e-07, 7.233e-07, 1.403e-06, 2.083e-06,\n", " 2.763e-06, 8.673e-06, 1.458e-05, 2.049e-05, 3.168e-05, 4.287e-05,\n", " 5.406e-05, 6.525e-05, 1.049e-04, 1.446e-04, 1.843e-04, 2.239e-04,\n", " 3.091e-04, 3.942e-04, 4.793e-04, 5.645e-04, 6.496e-04, 8.359e-04,\n", " 1.022e-03, 1.209e-03, 1.395e-03, 1.581e-03, 1.768e-03, 2.185e-03,\n", " 2.602e-03, 2.896e-03, 3.189e-03, 3.399e-03, 3.567e-03, 3.736e-03,\n", " 3.905e-03, 4.038e-03, 4.171e-03, 4.304e-03, 4.438e-03, 4.571e-03,\n", " 4.704e-03, 4.849e-03, 4.993e-03, 5.138e-03, 5.283e-03, 5.427e-03,\n", " 5.572e-03, 5.833e-03, 6.093e-03, 6.354e-03, 6.614e-03, 6.875e-03,\n", " 7.332e-03, 7.790e-03, 8.248e-03, 8.706e-03, 9.409e-03, 1.011e-02,\n", " 1.081e-02, 1.152e-02, 1.274e-02, 1.396e-02, 1.518e-02, 1.641e-02,\n", " 1.848e-02, 2.055e-02, 2.195e-02, 2.335e-02, 2.426e-02, 2.516e-02,\n", " 2.607e-02, 2.698e-02, 2.948e-02, 3.073e-02, 3.199e-02, 3.324e-02,\n", " 3.386e-02, 3.449e-02, 3.511e-02, 3.730e-02, 3.949e-02, 4.168e-02,\n", " 4.688e-02, 5.208e-02, 5.304e-02, 5.378e-02, 5.453e-02, 5.527e-02,\n", " 5.601e-02, 5.719e-02, 5.836e-02, 5.954e-02, 6.201e-02, 6.447e-02,\n", " 6.694e-02, 6.940e-02, 7.411e-02, 7.883e-02, 7.942e-02, 8.001e-02,\n", " 8.060e-02, 8.126e-02, 8.193e-02, 8.259e-02, 8.325e-02, 8.392e-02,\n", " 8.458e-02, 8.524e-02, 8.560e-02, 8.596e-02, 8.631e-02, 8.674e-02,\n", " 8.717e-02, 8.763e-02, 8.809e-02, 9.040e-02, 9.270e-02, 9.500e-02,\n", " 9.584e-02, 9.667e-02, 9.751e-02, 1.040e-01, 1.105e-01, 1.111e-01,\n", " 1.116e-01, 1.122e-01, 1.175e-01, 1.229e-01, 1.239e-01, 1.248e-01,\n", " 1.258e-01, 1.264e-01, 1.270e-01, 1.273e-01, 1.277e-01, 1.282e-01,\n", " 1.286e-01, 1.292e-01, 1.298e-01, 1.304e-01, 1.333e-01, 1.362e-01,\n", " 1.370e-01, 1.377e-01, 1.384e-01, 1.440e-01, 1.447e-01, 1.453e-01,\n", " 1.471e-01, 1.488e-01, 1.606e-01, 1.724e-01, 1.738e-01, 1.753e-01,\n", " 1.767e-01, 1.913e-01, 2.058e-01, 2.415e-01, 2.771e-01, 3.127e-01,\n", " 3.741e-01, 4.356e-01, 4.970e-01, 5.584e-01, 6.171e-01, 6.757e-01,\n", " 7.343e-01, 7.930e-01, 9.226e-01, 1.052e+00, 1.182e+00, 1.311e+00,\n", " 1.498e+00, 1.684e+00, 1.871e+00, 2.241e+00, 2.612e+00, 2.983e+00,\n", " 3.353e+00, 3.893e+00, 4.433e+00, 4.972e+00, 5.512e+00, 6.349e+00,\n", " 7.186e+00, 8.023e+00, 8.860e+00, 1.042e+01, 1.198e+01, 1.354e+01,\n", " 1.510e+01, 1.740e+01, 1.971e+01, 2.201e+01, 2.431e+01, 2.829e+01,\n", " 3.227e+01, 3.625e+01, 4.022e+01, 4.682e+01, 5.000e+01])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# `time_series` stores the time data\n", "A1.scope.time_series" ] }, { "cell_type": "markdown", "id": "c14d81b0", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The tracked time-series data can be exported to a file in two ways.\n", "```python\n", "sys.scope.export('bsm1_time_series.xlsx')\n", "```\n", "\n", "or\n", "\n", "```python\n", "import numpy as np\n", "sys.simulate(state_reset_hook='reset_cache',\n", " t_span=(0, 50),\n", " t_eval=np.arange(0, 51, 1),\n", " method='BDF',\n", " export_state_to=('bsm1_time_series.xlsx'))\n", "```" ] }, { "cell_type": "markdown", "id": "5b93411d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We can also (re-)define which unit or stream to track after the system has been constructed." ] }, { "cell_type": "code", "execution_count": 15, "id": "b818bfbf", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:05.231756Z", "iopub.status.busy": "2026-05-30T20:38:05.231756Z", "iopub.status.idle": "2026-05-30T20:38:05.237184Z", "shell.execute_reply": "2026-05-30T20:38:05.237184Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "(, )" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's say we want to track the clarifier and the waste activated sludge\n", "C1 = sys.flowsheet.unit.C1\n", "WAS = sys.flowsheet.stream.WAS\n", "sys.set_dynamic_tracker(C1, WAS)\n", "sys.scope.subjects" ] }, { "cell_type": "markdown", "id": "f5386dbd", "metadata": {}, "source": [ "However, we would need to rerun the simulation to retrieve results." ] }, { "cell_type": "code", "execution_count": 16, "id": "f6b35327", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:05.239196Z", "iopub.status.busy": "2026-05-30T20:38:05.239196Z", "iopub.status.idle": "2026-05-30T20:38:06.920099Z", "shell.execute_reply": "2026-05-30T20:38:06.920099Z" }, "scrolled": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# You can use a shorter time or try changing method to 'RK23' (explicit solver) if it takes a long time\n", "sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n", "# The clarifier are modeled as 10 layers, so we can track the TSS in each layer\n", "fig, ax = C1.scope.plot_time_series([f'TSS{i}' for i in range(1,11)])" ] }, { "cell_type": "code", "execution_count": 17, "id": "68dcbad5", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:06.924248Z", "iopub.status.busy": "2026-05-30T20:38:06.924248Z", "iopub.status.idle": "2026-05-30T20:38:07.111176Z", "shell.execute_reply": "2026-05-30T20:38:07.111176Z" }, "scrolled": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = WAS.scope.plot_time_series(('X_BH', 'X_BA'))" ] }, { "cell_type": "markdown", "id": "0eb92bf1", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "So far we've learned how to simulate any dynamic system developed with QSDsan. \n", "A complete list of existing unit operations within QSDsan is available [in the documentation](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/index.html). Unit operations labeled as \"QSDsan dynamic\" are enabled for dynamic simulations. Any system composed of the enabled units can be simulated dynamically as we learned above." ] }, { "cell_type": "markdown", "id": "3d13e036", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 1.2. When is a system \"dynamic\"?\n", "It's ultimately the user's decision whether a system should be run dynamically. This section will cover the essentials to switch to the dynamic mode for system simulation." ] }, { "cell_type": "markdown", "id": "94eab6a5", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### 1.2.1. `System.isdynamic` vs. `SanUnit.isdynamic` vs. `SanUnit.hasode` \n", "\n", "- Simply speaking, when `.isdynamic` is `True`, the program will attempt dynamic simulation. Users can directly enable/disable the dynamic mode by setting the `isdynamic` property of a `System` object.\n", "\n", "- The program will set the value of `.isdynamic` when it's not specified by users. `.isdynamic` is considered `True` in all cases except when `.isdynamic` is `False` for all units.\n", "\n", "- Setting `.isdynamic = True` does not guarantee the unit can be simulated dynamically. Just like how the `_run` method must be defined for static simulation, a series of additional methods must be defined to enable dynamic simulation.\n", "\n", "- If a unit operation has ODE algorithms, i.e., `.hasode` is `True`, it means a unit has the fundamental methods to compile ODEs. This is a **sufficient but not necessary** condition for dynamic simulation, because a unit doesn't have to be described with ODEs to be capable of dynamic simulations." ] }, { "cell_type": "code", "execution_count": 18, "id": "c130f36f", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.114062Z", "iopub.status.busy": "2026-05-30T20:38:07.114062Z", "iopub.status.idle": "2026-05-30T20:38:07.118259Z", "shell.execute_reply": "2026-05-30T20:38:07.118259Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "{: True,\n", " : True,\n", " : True,\n", " : True,\n", " : True,\n", " : True}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# All units in the BSM1 system above have ODEs\n", "{u: u.hasode for u in sys.units}" ] }, { "cell_type": "markdown", "id": "33a3d638", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 2. Writing a dynamic `SanUnit` \n", "\n", "Whether a system can be simulated dynamically ultimately boils down to whether all the units in the system have the fundamental methods required for dynamic simulations. In this section, you'll learn how to implement your own algorithms to create a `SanUnit` subclass capable of dynamic simulations." ] }, { "cell_type": "markdown", "id": "e11-sec-2-1-heading", "metadata": {}, "source": [ "### 2.1. How the integrator drives a dynamic unit \n", "\n", "Before getting into the per-method mechanics in §2.2 and §2.3, it helps to picture **how** the integrator orchestrates a dynamic system over one step. The figure below shows one update cycle.\n", "\n", "\"Per-step\n", "\"Per-step\n", "\n", "*Per step:* `solve_ivp` passes `(t, y)` down. Each unit's `_compile_AE` / `_compile_ODE` writes its `_state` and `_dstate`. `_update_state` / `_update_dstate` write the unit's state into each outlet `WasteStream`. The downstream unit reads its inlets' `.state` / `.dstate` as `y_ins` / `dy_ins`. The aggregated `dy/dt` (across the system's ODE state) is returned to `solve_ivp`, which advances time.\n", "\n", "A few things to note from this picture:\n", "\n", "- **State lives in two places.** Each `SanUnit` keeps its own `_state` / `_dstate` arrays (what the unit's algorithm is computing); each `WasteStream` keeps its `state` / `dstate` arrays (the inlets the next unit reads). The two are not the same buffer. `_update_state` and `_update_dstate` are the bridge: they're called inside the unit's compiled function so the outlet stream sees the latest values.\n", "- **Units communicate only through streams.** A downstream unit never reads its upstream's `_state` directly — it reads its inlets' `state` (and `dstate`) as `y_ins` (and `dy_ins`).\n", "- **One global state vector.** The integrator's `y` vector is the concatenation of the ODE-state of every unit. `solve_ivp` doesn't know about streams; it just sees one big state vector." ] }, { "cell_type": "markdown", "id": "e11-sec-ae-vs-ode", "metadata": {}, "source": [ "#### 2.1.1. `_compile_AE` vs. `_compile_ODE`: which one when?\n", "\n", "`_compile_AE` (for **algebraic equation** units) and `_compile_ODE` (for **ordinary differential equation** units) both fit the same data-flow diagram, but they describe different physics.\n", "\n", "- `_compile_ODE` — use when the unit has *holdup* or *inertia*: tanks, reactors, settlers, anywhere a balance reads `d(state)/dt = inflow − outflow + reactions`. The function you compile populates `_dstate`; the integrator integrates it.\n", "- `_compile_AE` — use when the unit's state is *instantaneously* determined by its inputs: mixers, splitters, pumps without holdup, hydraulic delays. There is no time derivative to integrate; the function you compile sets `_state` directly each time the integrator asks for the system's state.\n", "\n", "Mixed systems work naturally: AE units act as **pass-throughs within each integrator step**, while ODE units are what `solve_ivp` actually integrates. If you find yourself writing `dy_dt = 0` for everything in a unit, you probably wanted `_compile_AE` instead.\n", "\n", "
\n", "\n", "**Tip:** A unit declares its choice by implementing the corresponding `_compile_*` method and exposing the matching `AE` or `ODE` property (you'll see both patterns in §2.4 and §2.5). A unit can implement only one of the two: an ODE unit doesn't need an AE; an AE unit doesn't need an ODE.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "220c984a", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 2.2. Basic structure\n", "\n", "During **static** system simulations, `_run` directly defines the mass and/or energy flows of the effluent `WasteStream` objects of the unit after calculation. \n", "\n", "In comparison, during **dynamic** simulations, all information are stored as `_state` and `_dstate` attributes of the relevant `SanUnit` objects as well as `state` and `dstate` properties of `WasteStream` objects. These information won't be translated to mass or energy flows until dynamic simulation is completed.\n", "\n", "- `WasteStream.state` is a 1d `numpy.array` of length $n+1$, $n$ is the length of the components associated with the `thermo`. Each element of the array represents value of one state variable." ] }, { "cell_type": "markdown", "id": "b1529db3", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "
\n", "\n", "**Tip:** When the dynamic simulation finishes, QSDsan reconstructs each `WasteStream`'s mass flow as `state[:-1] * state[-1]` (in g/d). That product determines what the two conventions below mean.\n", "\n", "- **Liquid `WasteStream` (the default):** the first $n$ elements are component concentrations \\[mg/L = g/m³\\] and the last element is the total volumetric flow \\[m³/d\\]. Their product gives mass flow \\[g/d\\].\n", "- **Gaseous `WasteStream`:** a gas's volumetric flow at the operating $T$ and $P$ depends on composition, so working in concentrations is awkward. As the same `state[:-1] * state[-1]` algorithm still has to produce mass flow, so the convention is to store the component mass flows \\[g/d\\] directly in the first $n$ elements and fix the last element at $1$, so the reconstructed mass flow is whatever you stored.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "d997b05d", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- `WasteStream.dstate` is an array of the exact same shape as `WasteStream.state`, storing values of the time derivatives (i.e., the rates of change) of the state variables." ] }, { "cell_type": "code", "execution_count": 19, "id": "825050c1", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.121263Z", "iopub.status.busy": "2026-05-30T20:38:07.120264Z", "iopub.status.idle": "2026-05-30T20:38:07.125480Z", "shell.execute_reply": "2026-05-30T20:38:07.124471Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sf = sys.flowsheet.stream\n", "sf.effluent.dstate.shape == sf.effluent.state.shape" ] }, { "cell_type": "markdown", "id": "eb706d47", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "`SanUnit._state` is also a 1d `numpy.array`, but the length of the array is not assumed, because the state variables relevant for a `SanUnit` is entirely dependent on the unit operation itself. Therefore, there is no predefined units of measure or order for state variables of a unit operation." ] }, { "cell_type": "code", "execution_count": 20, "id": "956dbc0f", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.127255Z", "iopub.status.busy": "2026-05-30T20:38:07.127255Z", "iopub.status.idle": "2026-05-30T20:38:07.131256Z", "shell.execute_reply": "2026-05-30T20:38:07.131256Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "C1._state.shape == A1._state.shape\n", "# C1._state.shape == C1._dstate.shape" ] }, { "cell_type": "markdown", "id": "38e2a8f8", "metadata": {}, "source": [ "But similar to `WasteStream`, `SanUnit._dstate` must have the exact same shape as the `_state` array, as each element corresponds to the time derivative of a state variable." ] }, { "cell_type": "code", "execution_count": 21, "id": "561a5589", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.133425Z", "iopub.status.busy": "2026-05-30T20:38:07.133425Z", "iopub.status.idle": "2026-05-30T20:38:07.137244Z", "shell.execute_reply": "2026-05-30T20:38:07.137244Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "{'S_I': 30.0,\n", " 'S_S': 2.8098492768502354,\n", " 'X_I': 1147.9023456598527,\n", " 'X_S': 82.1496729433189,\n", " 'X_BH': 2551.149645860236,\n", " 'X_BA': 148.1855399395735,\n", " 'X_P': 447.1138645933302,\n", " 'S_O': 0.004288922586191978,\n", " 'S_NO': 5.338947439089076,\n", " 'S_NH': 7.9289428538204,\n", " 'S_ND': 1.216685860427014,\n", " 'X_ND': 5.285736721431849,\n", " 'S_ALK': 59.15831466857708,\n", " 'S_N2': 25.00787300530831,\n", " 'H2O': 997375.2641947934,\n", " 'Q': 92230.0}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Some dynamic units in QSDsan have a `state` property that formats\n", "# the data in `_state` for better readability\n", "A1.state" ] }, { "cell_type": "markdown", "id": "b6a928f2", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 2.3. Fundamental methods\n", "In addition to proper `__init__` and `_run` methods ([SanUnit advanced tutorial](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html#1.1.-Fundamental-methods)), a few more methods are required in a `SanUnit` subclass for dynamic simulation. Users typically won't interact with these methods but they will be called by `System.simulate` to manipulate the values of the arrays mentioned above (i.e., `._state`, `._dstate`, `.state`, and `.dstate`)." ] }, { "cell_type": "markdown", "id": "976dabeb", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- `_init_state`, called after `_run` to generate an initial condition for the unit, i.e., defining shape and values of the `_state` and `_dstate` arrays. For example:\n", "```python\n", "import numpy as np\n", "def _init_state(self):\n", " inf = self.ins[0]\n", " self._state = np.ones(len(inf.components)+1)\n", " self._dstate = self._state * 0.\n", "```\n", "This method (not saying it makes sense) assumes $n+1$ state variables and gives an initial value of 1 to all of them. Then it also sets the initial time derivatives to be 0. " ] }, { "cell_type": "markdown", "id": "3a3de71d", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- `_update_state`, to update effluent streams' state arrays based on current state (and maybe dstate) of the SanUnit. For example:\n", "```python\n", "def _update_state(self):\n", " arr = self._state # retrieving the current state of the SanUnit\n", " eff, = self.outs # assuming this SanUnit has one outlet only\n", " eff.state[:] = arr # assume arr has the same shape as WasteStream.state\n", "```\n", "The goal of this method is to update the values in `.state` for each `WasteStream` in `.outs`." ] }, { "cell_type": "markdown", "id": "8deec2a2", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- `_update_dstate`, to update effluent streams' `dstate` arrays based on current `_state` and `_dstate` of the SanUnit. The signature and often the algorithm are similar to `_update_state`.\n", "\n", "\n", "- `_compile_ODE` or `_compile_AE`, used to define the function that updates the `_dstate` and/or `_state` of the `SanUnit` based on its influent streams' `state`/`dstate` and potentially its own current state. The defined function will be stored as `SanUnit._ODE` or `SanUnit._AE`. These methods should follow some general forms like below:\n", "```python\n", "@property\n", "def ODE(self):\n", " if self._ODE is None:\n", " self._compile_ODE()\n", " return self._ODE \n", "```" ] }, { "cell_type": "markdown", "id": "a431142f", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "```python\n", "def _compile_ODE(self):\n", " _dstate = self._dstate\n", " _update_dstate = self._update_dstate\n", " def dy_dt(t, y_ins, y, dy_ins):\n", " _dstate[:] = some_algorithm(t, y_ins, y, dy_ins)\n", " _update_dstate()\n", " self._ODE = dy_dt\n", "```" ] }, { "cell_type": "markdown", "id": "83c50a89", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "```python\n", "@property\n", "def AE(self):\n", " if self._AE is None:\n", " self._compile_AE()\n", " return self._AE\n", "```" ] }, { "cell_type": "markdown", "id": "dd66c263", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "```python\n", "def _compile_AE(self):\n", " _state = self._state\n", " _dstate = self._dstate\n", " _update_state = self._update_state\n", " _update_dstate = self._update_dstate\n", " def y_t(t, y_ins, dy_ins):\n", " _state[:] = some_algorithm(t, y_ins, dy_ins)\n", " _dstate[:] = some_other_algorithm(t, y_ins, dy_ins)\n", " _update_state()\n", " _update_dstate()\n", " self._AE = y_t\n", "```" ] }, { "cell_type": "markdown", "id": "a144502d", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "
\n", "\n", "**Note:** When writing the `dy_dt` or `y_t` functions, use `._state[:] = ` rather than `._state = ` because it's generally faster to update values in an existing array than overwriting this array with a newly created array.\n", "\n", "
\n", "\n", "In the next subsection, we'll learn more about the `ODE` and `AE` methods." ] }, { "cell_type": "markdown", "id": "afd475f2", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 2.4. Making a simple MixerSplitter (`_compile_AE`)\n", "\n", "Let's say we want to make an ideal mixer-splitter that instantly mixes all streams at the inlets and then evenly split them across the outlets." ] }, { "cell_type": "code", "execution_count": 22, "id": "c38b235a", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.139254Z", "iopub.status.busy": "2026-05-30T20:38:07.139254Z", "iopub.status.idle": "2026-05-30T20:38:07.143713Z", "shell.execute_reply": "2026-05-30T20:38:07.143713Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# Typically if implemented as a static SanUnit, it'd be pretty simple\n", "# Let's ignore `_design` and `_cost` for now.\n", "class MixerSplitter1(qs.SanUnit):\n", " _N_outs = 3\n", " _ins_size_is_fixed = False\n", " _outs_size_is_fixed = False\n", " def __init__(self, ID='', ins=None, outs=(), thermo=None, \n", " init_with='WasteStream', **kwargs):\n", " qs.SanUnit.__init__(self, ID, ins, outs, thermo, init_with, **kwargs)\n", " self.mixed = qs.WasteStream()\n", " \n", " def _run(self):\n", " mixed = self.mixed\n", " mixed.mix_from(self.ins)\n", " n_outs = len(self.outs)\n", " flow = mixed.get_total_flow('kg/hr')/n_outs\n", " for out in self.outs:\n", " out.copy_like(mixed)\n", " out.set_total_flow(flow, 'kg/hr')\n", " \n", " def _design(self):\n", " pass\n", " \n", " def _cost(self):\n", " pass" ] }, { "cell_type": "code", "execution_count": 23, "id": "9b5ce52d", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.145719Z", "iopub.status.busy": "2026-05-30T20:38:07.145719Z", "iopub.status.idle": "2026-05-30T20:38:07.148944Z", "shell.execute_reply": "2026-05-30T20:38:07.148944Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CompiledComponents([\n", " S_I, S_S, X_I, X_S, \n", " X_BH, X_BA, X_P, S_O, \n", " S_NO, S_NH, S_ND, X_ND,\n", " S_ALK, S_N2, H2O, \n", "])\n" ] } ], "source": [ "# Let's try simulating it with the components used in BSM1\n", "cmps = qs.get_thermo().chemicals\n", "cmps.show()" ] }, { "cell_type": "code", "execution_count": 24, "id": "12aa03d9", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.150949Z", "iopub.status.busy": "2026-05-30T20:38:07.150949Z", "iopub.status.idle": "2026-05-30T20:38:07.252516Z", "shell.execute_reply": "2026-05-30T20:38:07.252516Z" }, "scrolled": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WasteStream: inf2\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow (g/hr): S_S 3e+03\n", " S_NH 2.1e+03\n", " H2O 8e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", " COD : 3708.6 mg/L\n", " BOD : 2659.0 mg/L\n", " TC : 1186.7 mg/L\n", " TOC : 1186.7 mg/L\n", " TN : 2596.0 mg/L\n", " TP : 37.1 mg/L\n", " Component concentrations (mg/L):\n", " S_S 3708.6\n", " S_NH 2596.0\n", " H2O 988949.8\n" ] } ], "source": [ "# Now let's make a couple fake influents\n", "inf1 = qs.WasteStream('inf1', H2O=1000, S_O=5)\n", "inf2 = qs.WasteStream('inf2', H2O=800, S_S=3, S_NH=2.1)\n", "inf2.show()" ] }, { "cell_type": "code", "execution_count": 25, "id": "4ff4c667", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.254858Z", "iopub.status.busy": "2026-05-30T20:38:07.254858Z", "iopub.status.idle": "2026-05-30T20:38:07.720723Z", "shell.execute_reply": "2026-05-30T20:38:07.720723Z" }, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MixerSplitter1: M1\n", "ins...\n", "[0] inf1\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow (g/hr): S_O 5e+03\n", " H2O 1e+06\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", "[1] inf2\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow (g/hr): S_S 3e+03\n", " S_NH 2.1e+03\n", " H2O 8e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", " COD : 3708.6 mg/L\n", " BOD : 2659.0 mg/L\n", " TC : 1186.7 mg/L\n", " TOC : 1186.7 mg/L\n", " TN : 2596.0 mg/L\n", " TP : 37.1 mg/L\n", "outs...\n", "[0] ws11\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow (g/hr): S_S 1e+03\n", " S_O 1.67e+03\n", " S_NH 700\n", " H2O 6e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", " COD : 1650.2 mg/L\n", " BOD : 1183.2 mg/L\n", " TC : 528.1 mg/L\n", " TOC : 528.1 mg/L\n", " TN : 1155.1 mg/L\n", " TP : 16.5 mg/L\n", "[1] ws12\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow (g/hr): S_S 1e+03\n", " S_O 1.67e+03\n", " S_NH 700\n", " H2O 6e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", " COD : 1650.2 mg/L\n", " BOD : 1183.2 mg/L\n", " TC : 528.1 mg/L\n", " TOC : 528.1 mg/L\n", " TN : 1155.1 mg/L\n", " TP : 16.5 mg/L\n", "[2] ws13\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow (g/hr): S_S 1e+03\n", " S_O 1.67e+03\n", " S_NH 700\n", " H2O 6e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", " COD : 1650.2 mg/L\n", " BOD : 1183.2 mg/L\n", " TC : 528.1 mg/L\n", " TOC : 528.1 mg/L\n", " TN : 1155.1 mg/L\n", " TP : 16.5 mg/L\n" ] } ], "source": [ "MS1 = MixerSplitter1(ins=(inf1, inf2))\n", "MS1.simulate()\n", "MS1.show()" ] }, { "cell_type": "code", "execution_count": 26, "id": "72151a1e", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.723730Z", "iopub.status.busy": "2026-05-30T20:38:07.722730Z", "iopub.status.idle": "2026-05-30T20:38:07.814146Z", "shell.execute_reply": "2026-05-30T20:38:07.814146Z" }, "scrolled": true, "slideshow": { "slide_type": "slide" }, "tags": [ "raises-exception" ] }, "outputs": [ { "ename": "AttributeError", "evalue": "'MixerSplitter1' object has no attribute '_init_state'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Obviously, it's not ready for dynamic simulation\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m# You will receive an error if you try to simulate it with `isdynamic=True`\u001b[39;00m\n\u001b[32m 3\u001b[39m MS1_dyn = MixerSplitter1(ins=(inf1.copy(), inf2.copy()), isdynamic=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 4\u001b[39m dyn_sys = qs.System(path=(MS1_dyn,))\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m dyn_sys.simulate(t_span=(\u001b[32m0\u001b[39m,\u001b[32m5\u001b[39m))\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3374\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3354\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msimulate\u001b[39m(\u001b[38;5;28mself\u001b[39m, update_configuration: Optional[\u001b[38;5;28mbool\u001b[39m]=\u001b[38;5;28;01mNone\u001b[39;00m, units=\u001b[38;5;28;01mNone\u001b[39;00m, \n\u001b[32m 3355\u001b[39m design_and_cost=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 3356\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 3357\u001b[39m \u001b[33;03m If system is dynamic, run the system dynamically. Otherwise, converge \u001b[39;00m\n\u001b[32m 3358\u001b[39m \u001b[33;03m the path of unit operations to steady state. After running/converging \u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 3372\u001b[39m \u001b[33;03m \u001b[39;00m\n\u001b[32m 3373\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3374\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.flowsheet:\n\u001b[32m 3375\u001b[39m specifications = \u001b[38;5;28mself\u001b[39m._specifications\n\u001b[32m 3376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m specifications \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._running_specifications:\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_flowsheet.py:120\u001b[39m, in \u001b[36mFlowsheet.__exit__\u001b[39m\u001b[34m(self, type, exception, traceback)\u001b[39m\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mtype\u001b[39m, exception, traceback):\n\u001b[32m 119\u001b[39m main_flowsheet.set_flowsheet(\u001b[38;5;28mself\u001b[39m._temporary_stack.pop())\n\u001b[32m--> \u001b[39m\u001b[32m120\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exception: \u001b[38;5;28;01mraise\u001b[39;00m exception\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3418\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3416\u001b[39m \u001b[38;5;28mself\u001b[39m._setup(update_configuration, units)\n\u001b[32m 3417\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.isdynamic: \n\u001b[32m-> \u001b[39m\u001b[32m3418\u001b[39m outputs = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdynamic_run\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3419\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m design_and_cost: \u001b[38;5;28mself\u001b[39m._summary()\n\u001b[32m 3420\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3509\u001b[39m, in \u001b[36mSystem.dynamic_run\u001b[39m\u001b[34m(self, **dynsim_kwargs)\u001b[39m\n\u001b[32m 3507\u001b[39m \u001b[38;5;66;03m# Load initial states\u001b[39;00m\n\u001b[32m 3508\u001b[39m \u001b[38;5;28mself\u001b[39m.converge()\n\u001b[32m-> \u001b[39m\u001b[32m3509\u001b[39m y0, idx, nr = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_load_state\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3510\u001b[39m dk[\u001b[33m'\u001b[39m\u001b[33my0\u001b[39m\u001b[33m'\u001b[39m] = y0\n\u001b[32m 3511\u001b[39m \u001b[38;5;66;03m# Integrate\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3206\u001b[39m, in \u001b[36mSystem._load_state\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 3204\u001b[39m idx = {}\n\u001b[32m 3205\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m unit \u001b[38;5;129;01min\u001b[39;00m units: \n\u001b[32m-> \u001b[39m\u001b[32m3206\u001b[39m \u001b[30;43munit\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_init_state\u001b[39;49m()\n\u001b[32m 3207\u001b[39m unit._update_state()\n\u001b[32m 3208\u001b[39m unit._update_dstate()\n", "\u001b[31mAttributeError\u001b[39m: 'MixerSplitter1' object has no attribute '_init_state'" ] } ], "source": [ "# Obviously, it's not ready for dynamic simulation\n", "# You will receive an error if you try to simulate it with `isdynamic=True`\n", "MS1_dyn = MixerSplitter1(ins=(inf1.copy(), inf2.copy()), isdynamic=True)\n", "dyn_sys = qs.System(path=(MS1_dyn,))\n", "dyn_sys.simulate(t_span=(0,5))" ] }, { "cell_type": "markdown", "id": "0c4eb0cd", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Since the mixer-splitter mixes and splits instantly, we can express this process with a set of algebraic equations (AEs). Assume its array of state variables follow the \"concentration-volumetric flow\" convention. In mathematical forms, state variables of the mixer-splitter ($C_m$, component concentrations; $Q_m$, total volumetric flow) follow:\n", "$$Q_m = \\sum_{i \\in ins} Q_i \\tag{1}$$\n", "$$Q_mC_m = \\sum_{i \\in ins} Q_iC_i$$\n", "$$\\therefore C_m = \\frac{\\sum_{i \\in ins} Q_iC_i}{Q_m} \\tag{2}$$" ] }, { "cell_type": "markdown", "id": "a37f98d9", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Therefore, the time derivatives $\\dot{Q_m}$ follow:\n", "$$\\dot{Q_m} = \\sum_{i \\in ins} \\dot{Q_i} \\tag{3}$$\n", "$$Q_m\\dot{C_m} + C_m\\dot{Q_m} = \\sum_{i \\in ins} (Q_i\\dot{C_i} + C_i\\dot{Q_i})$$\n", "$$\\therefore \\dot{C_m} = \\frac{1}{Q_m}\\cdot(\\sum_{i \\in ins}Q_i\\dot{C_i} + \\sum_{i \\in ins}C_i\\dot{Q_i} - C_m\\dot{Q_m}) \\tag{4}$$" ] }, { "cell_type": "markdown", "id": "7578a12e", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "For any effluent `WasteStream` $j$:\n", "$$Q_j = \\frac{Q_m}{n_{outs}} \\tag{5}$$\n", "$$C_j = C_m \\tag{6}$$\n", "$$\\therefore \\dot{Q_j} = \\frac{\\dot{Q_m}}{n_{outs}} \\tag{7}$$\n", "$$\\dot{C_j} = \\dot{C_m} \\tag{8}$$" ] }, { "cell_type": "markdown", "id": "58304881", "metadata": {}, "source": [ "The diagram below uses an illustrative case with $m = 3$ inlets and $n = 4$ components; the same shapes generalize to any `m` and `n`. Equation (1) is `Q_m = sum(Q_ins)`, equation (2) is the flow-weighted concentration `C_m = Q_ins · C_ins / Q_m`, and equations (5)–(8) just slice the result across the outlets.\n", "\n", "The matrix product `Q_ins · C_ins` yields an n-vector of flow-weighted sums, which divided by `Q_m` gives the mixer's component concentrations." ] }, { "cell_type": "markdown", "id": "e11-fig-mixer-arrays", "metadata": {}, "source": [ "\"y_ins\n", "\"y_ins" ] }, { "cell_type": "markdown", "id": "d023b665", "metadata": {}, "source": [ "Now, let's try to implement this algorithm in methods for dynamic simulation." ] }, { "cell_type": "code", "execution_count": 27, "id": "38abf7cb", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.814146Z", "iopub.status.busy": "2026-05-30T20:38:07.814146Z", "iopub.status.idle": "2026-05-30T20:38:07.822816Z", "shell.execute_reply": "2026-05-30T20:38:07.822816Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import numpy as np\n", "class MixerSplitter2(MixerSplitter1):\n", " def _init_state(self):\n", " mixed = self.mixed\n", " self._state = np.empty(len(cmps)+1)\n", " self._state[:-1] = mixed.conc # first n element be the component concentrations of the mixed stream\n", " self._state[-1] = mixed.F_vol * 24 # last element be the total volumetric flow, in m3/d\n", " self._dstate = self._state * 0.\n", " \n", " def _update_state(self):\n", " y = self._state\n", " n_outs = len(self.outs)\n", " for ws in self.outs:\n", " if ws.state is None: ws.state = y.copy() # initialize the state using a copy\n", " else: ws.state[:-1] = y[:-1] # equation (6)\n", " ws.state[-1] = y[-1]/n_outs # equation (5)\n", " \n", " def _update_dstate(self):\n", " dy = self._dstate\n", " n_outs = len(self.outs)\n", " for ws in self.outs:\n", " if ws.dstate is None: ws.dstate = dy.copy()\n", " else: ws.dstate[:-1] = dy[:-1] # equation (8)\n", " ws.dstate[-1] = dy[-1]/n_outs # equation (7)\n", " \n", " @property\n", " def AE(self):\n", " if self._AE is None:\n", " self._compile_AE()\n", " return self._AE\n", " \n", " def _compile_AE(self):\n", " _state = self._state\n", " _dstate = self._dstate\n", " _update_state = self._update_state\n", " _update_dstate = self._update_dstate\n", " def y_t(t, y_ins, dy_ins):\n", " Q_ins = y_ins[:,-1]\n", " C_ins = y_ins[:,:-1]\n", " dQ_ins = dy_ins[:,-1]\n", " dC_ins = dy_ins[:,:-1]\n", " _state[-1] = Q = sum(Q_ins) # equation (1)\n", " _state[:-1] = C = Q_ins @ C_ins / Q # equation (2)\n", " _dstate[-1] = dQ = sum(dQ_ins) # equation (3)\n", " _dstate[:-1] = dC = (Q_ins @ dC_ins + dQ_ins @ C_ins - C*dQ) / Q # equation (4)\n", " _update_state()\n", " _update_dstate()\n", " self._AE = y_t" ] }, { "cell_type": "markdown", "id": "da258438", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "
\n", "\n", "**Note:** 1. All `SanUnit._AE` must take exactly these three positional arguments (`t`, `y_ins`, `dy_ins`). `t` is time as a `float`. Both `y_ins` and `dy_ins` are **2d** `numpy.array` of the same shape `(m, n+1)`, where $m$ is the number of inlets, $n+1$ is the length of the `state` or `dstate` array of a `WasteStream`.\n", "\n", "2. All `SanUnit._AE` must update both `_state` and `_dstate` of the `SanUnit`, and must call `_update_state` and `_update_dstate` afterwards.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 28, "id": "ba8c9001", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.826043Z", "iopub.status.busy": "2026-05-30T20:38:07.824821Z", "iopub.status.idle": "2026-05-30T20:38:07.833308Z", "shell.execute_reply": "2026-05-30T20:38:07.833308Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# Now let's see if this works\n", "MS2 = MixerSplitter2(ins=(inf1.copy(), inf2.copy()), isdynamic=True)\n", "dyn_sys2 = qs.System(path=(MS2,))\n", "dyn_sys2.set_dynamic_tracker(MS2)\n", "dyn_sys2.simulate(t_span=(0,5))" ] }, { "cell_type": "code", "execution_count": 29, "id": "a4f65bf6", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:07.835314Z", "iopub.status.busy": "2026-05-30T20:38:07.835314Z", "iopub.status.idle": "2026-05-30T20:38:08.020788Z", "shell.execute_reply": "2026-05-30T20:38:08.020788Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# You'll see the mass flows stay constant through the simulation period, \n", "# but still it means the system was simulated dynamically.\n", "fig, ax = MS2.scope.plot_time_series(('S_S', 'S_NH', 'S_O'))" ] }, { "cell_type": "markdown", "id": "22788b98", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Many commonly used unit operations, such as [Pump](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/bst/pumping.html#qsdsan.unit_operations.Pump), [Mixer](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/bst/abstract.html#qsdsan.unit_operations.Mixer), [Splitter](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/bst/abstract.html#qsdsan.unit_operations.Splitter), and [HydraulicDelay](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/abstract.html#qsdsan.unit_operations.HydraulicDelay), have implemented the fundamental methods to be used in a dynamic system. You can always refer to the source codes of these units to learn more about how they work." ] }, { "cell_type": "markdown", "id": "a04a8ab5", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 2.5. Making an inactive CompleteMixTank (`_compile_ODE`)" ] }, { "cell_type": "markdown", "id": "21dca6ff", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "As you can see above, it's not very useful to dynamically simulate a system without any ODEs. So let's make a simple inactive complete mix tank (inactive means no reactions). Assume the reactor has a fixed liquid volume $V$, and thus the effluent volumetric flow rate changes instantly with influents. The mass balance of this type of reactor can be described as:\n", "$$Q = \\sum_{i \\in ins} Q_i \\tag{9}$$\n", "$$\\therefore \\dot{Q} = \\sum_{i \\in ins} \\dot{Q_i} \\tag{10}$$\n", "$$\\frac{d(VC)}{dt} = \\sum_{i \\in ins} Q_iC_i - QC$$\n", "$$\\therefore \\dot{C} = \\frac{1}{V}(\\sum_{i \\in ins} Q_iC_i - QC) \\tag{11}$$\n", "Equations (10) and (11) are the governing ODEs of this unit." ] }, { "cell_type": "markdown", "id": "e11-fig-cmt-arrays", "metadata": {}, "source": [ "\"A\n", "\"A\n", "\n", "A fixed-volume CSTR with instant mixing. The unit's state is what's *in* the tank: an n-vector of concentrations `C` and a scalar volumetric flow `Q`. The integrator advances both via the ODE shown.\n", "\n", "Notice the asymmetry with the MixerSplitter: there, state was algebraically determined by the inlets at each step. Here, state has inertia (the holdup `V`), so it needs a real `dy/dt`." ] }, { "cell_type": "code", "execution_count": 30, "id": "c4706ed2", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:08.024290Z", "iopub.status.busy": "2026-05-30T20:38:08.024290Z", "iopub.status.idle": "2026-05-30T20:38:08.031614Z", "shell.execute_reply": "2026-05-30T20:38:08.030608Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "class CompleteMixTank(qs.SanUnit):\n", " \n", " _N_outs = 1\n", " _ins_size_is_fixed = False\n", " \n", " def __init__(self, ID='', ins=None, outs=(), thermo=None, \n", " init_with='WasteStream', V=10, **kwargs):\n", " qs.SanUnit.__init__(self, ID, ins, outs, thermo, init_with, **kwargs)\n", " self.V = V\n", " \n", " def _run(self):\n", " out, = self.outs\n", " out.mix_from(self.ins)\n", " \n", " def set_init_conc(self,**concentrations):\n", " cmps = self.thermo.chemicals\n", " C = np.zeros(len(cmps))\n", " idx = cmps.indices(list(concentrations.keys()))\n", " C[idx] = list(concentrations.values())\n", " self._init_concs = C\n", " \n", " def _init_state(self):\n", " out, = self.outs\n", " self._state = np.empty(len(cmps)+1)\n", " self._state[:-1] = self._init_concs # first n element be the component concentrations of the mixed stream\n", " self._state[-1] = out.F_vol*24 # last element be the total volumetric flow\n", " self._dstate = self._state*0.\n", " \n", " def _update_state(self):\n", " out, = self.outs\n", " out.state = self._state\n", " \n", " def _update_dstate(self):\n", " out, = self.outs\n", " out.dstate = self._dstate\n", " \n", " @property\n", " def ODE(self):\n", " if self._ODE is None:\n", " self._compile_ODE()\n", " return self._ODE \n", " \n", " def _compile_ODE(self):\n", " _dstate = self._dstate\n", " _update_dstate = self._update_dstate\n", " V = self.V\n", " def dy_dt(t, y_ins, y, dy_ins):\n", " Q_ins = y_ins[:,-1]\n", " C_ins = y_ins[:,:-1]\n", " dQ_ins = dy_ins[:,-1]\n", " Q = sum(Q_ins) # equation (9)\n", " C = y[:-1]\n", " _dstate[-1] = sum(dQ_ins) # dQ, equation (10)\n", " _dstate[:-1] = (Q_ins @ C_ins - Q*C)/V # dC, equation (11)\n", " _update_dstate()\n", " self._ODE = dy_dt" ] }, { "cell_type": "markdown", "id": "472f1577", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "
\n", "\n", "**Note:** 1. All `SanUnit._ODE` must take exactly these four positional arguments: `t`, `y_ins`, and `dy_ins` are the same as the ones in `SanUnit._AE`. `y` is a **1d** `numpy.array`, because it is equal to the `_state` array of the unit.\n", "\n", "2. Unlike `_AE`, all `SanUnit._ODE` updates only the `_dstate` array of the `SanUnit`, and only calls `_update_dstate` afterwards.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 31, "id": "493239c1", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:08.033615Z", "iopub.status.busy": "2026-05-30T20:38:08.032615Z", "iopub.status.idle": "2026-05-30T20:38:08.040591Z", "shell.execute_reply": "2026-05-30T20:38:08.040591Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# Let's see if it works\n", "CMT = CompleteMixTank(ins=(inf1.copy(), inf2.copy()), V=50,\n", " isdynamic=True)\n", "dyn_sys3 = qs.System(path=(CMT,))\n", "dyn_sys3.set_dynamic_tracker(CMT)\n", "\n", "# We set the initial condition to be different from the steady state,\n", "# so we can see how the system evolves to the steady state.\n", "CMT.set_init_conc(S_S=500, S_NH=700, S_O=290)\n", "dyn_sys3.simulate(t_span=(0,5))" ] }, { "cell_type": "code", "execution_count": 32, "id": "c3df8f02", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:08.042604Z", "iopub.status.busy": "2026-05-30T20:38:08.042604Z", "iopub.status.idle": "2026-05-30T20:38:08.239150Z", "shell.execute_reply": "2026-05-30T20:38:08.239150Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "CMT.scope.plot_time_series(('S_NH', 'S_S', 'S_O'))" ] }, { "cell_type": "markdown", "id": "3970aeaa", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Many commonly used unit operations described by ODEs have been implemented in QSDsan, such as [CSTR](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/suspended_growth_bioreactor.html#qsdsan.unit_operations.CSTR), [BatchExperiment](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/suspended_growth_bioreactor.html#qsdsan.unit_operations.BatchExperiment), and [FlatBottomCircularClarifier](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/static/clarifier.html)." ] }, { "cell_type": "markdown", "id": "b085b491", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 3. Other convenient features \n", "### 3.1. `ExogenousDynamicVariable`\n", "The [ExogenousDynamicVariable](https://qsdsan.readthedocs.io/en/latest/api/utility_functions/dynamics.html#qsdsan.utils.ExogenousDynamicVariable) class is created to enable incorporation of exogenous dynamic variables in unit simulations. By \"dynamic\", it means the variable value changes over time. By \"exogenous\", it means the variable isn't explicitly dependent on any unit operation or stream. \"Ambient temperature\" or \"sunlight irradiance\" is a good example. They are environmental conditions that are often beyond control but have an effect on the operation or performance of the system." ] }, { "cell_type": "markdown", "id": "d0365c64", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "There are generally two ways to create an `ExogenousDynamicVariable`:\n", "\n", "**1. Define the variable as a function of time.** Let's say we want to create a variable to represent the changing reaction temperature. Assume the temperature value \\[K\\] can be expressed as $T = 298.15 + 5\\cdot \\sin(t)$, indicating that the temperature fluctuates around $25^{\\circ}C$ by $\\pm 5^{\\circ}C$. Then simply,\n", "```python\n", "T = EDV('T', function=lambda t: 298.15+5*np.sin(t))\n", "```" ] }, { "cell_type": "markdown", "id": "e2885b32", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**2. Provide time-series data to describe the dynamics of the variable.** For demonstration purpose, we'll just make up the data. In practice, this should be used if you have real data (e.g., knowing the change of temperature over time).\n", "```python\n", "t_arr = np.linspace(0, 5)\n", "y_arr = 298.15+5*np.sin(t_arr)\n", "T = EDV('T', t=t_arr, y=y_arr)\n", "```" ] }, { "cell_type": "markdown", "id": "8a5bd5b7", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Once created, these `ExogenousDynamicVariable` objects can be incorporated into any `SanUnit` upon its initialization or through the `SanUnit.exo_dynamic_vars` property setter. " ] }, { "cell_type": "code", "execution_count": 33, "id": "2639a8b7", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:08.242366Z", "iopub.status.busy": "2026-05-30T20:38:08.241365Z", "iopub.status.idle": "2026-05-30T20:38:10.078072Z", "shell.execute_reply": "2026-05-30T20:38:10.078072Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All impact items have been removed from the registry.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Yalin\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\thermosteam\\_stream.py:407: RuntimeWarning: has been replaced in registry\n", " self._register(ID)\n" ] }, { "data": { "text/plain": [ "(,)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see an example\n", "from exposan.metab import create_system\n", "sys_mt = create_system()\n", "uf_mt = sys_mt.flowsheet.unit\n", "uf_mt.R1.exo_dynamic_vars" ] }, { "cell_type": "code", "execution_count": 34, "id": "a7e11837", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:10.080079Z", "iopub.status.busy": "2026-05-30T20:38:10.080079Z", "iopub.status.idle": "2026-05-30T20:38:10.083992Z", "shell.execute_reply": "2026-05-30T20:38:10.083992Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "[295.15]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The evaluation of these variables during unit simulation is done through \n", "# the `eval_exo_dynamic_vars` method\n", "uf_mt.R1.eval_exo_dynamic_vars(t=0.1)" ] }, { "cell_type": "markdown", "id": "e11-edv-batch-intro", "metadata": {}, "source": [ "#### 3.1.1. Batch construction from a file\n", "\n", "`EDV.batch_init` is the easiest way to define several variables at once. It expects a CSV/Excel file with a `t` column and one extra column per variable." ] }, { "cell_type": "code", "execution_count": 35, "id": "e11-edv-batch-code", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:10.085996Z", "iopub.status.busy": "2026-05-30T20:38:10.085996Z", "iopub.status.idle": "2026-05-30T20:38:10.109590Z", "shell.execute_reply": "2026-05-30T20:38:10.109590Z" } }, "outputs": [ { "data": { "text/plain": [ "[, ]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from qsdsan.utils import ExogenousDynamicVariable as EDV\n", "import os, tempfile\n", "import pandas as pd\n", "\n", "# Build a small time series in memory; in practice this would be a logged\n", "# data file (e.g., ambient temperature and sunlight irradiance).\n", "tmpdir = tempfile.mkdtemp()\n", "csv_path = os.path.join(tmpdir, 'env_vars.csv')\n", "t = np.linspace(0, 5, 51)\n", "pd.DataFrame({\n", " 't': t,\n", " 'T': 293.15 + 5.0 * np.sin(2 * np.pi * t / 2.0), # 20 +/- 5 degC, 2-day period\n", " 'I': 400.0 + 200.0 * np.sin(2 * np.pi * t / 1.0), # made-up irradiance, 1-day period\n", "}).to_csv(csv_path, index=False)\n", "\n", "# `batch_init` returns one `ExogenousDynamicVariable` per non-`t` column.\n", "edvs = EDV.batch_init(csv_path)\n", "edvs\n" ] }, { "cell_type": "markdown", "id": "e11-edv-use-intro", "metadata": {}, "source": [ "#### 3.1.2. Using an EDV inside a custom dynamic `SanUnit`\n", "\n", "To plug an EDV into a `SanUnit`, pass it (or several) via `exogenous_vars=` at construction. Inside `_compile_ODE` or `_compile_AE`, call `self.eval_exo_dynamic_vars(t)` to read the current values. The example below extends the `CompleteMixTank` from §2.5 with a temperature-dependent first-order decay rate on one chosen component, using an Arrhenius form $k(T) = k_{ref}\\, \\exp\\!\\left(\\frac{E_a}{R}\\left(\\frac{1}{T_{ref}} - \\frac{1}{T}\\right)\\right)$." ] }, { "cell_type": "code", "execution_count": 36, "id": "e11-edv-class-code", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:10.112753Z", "iopub.status.busy": "2026-05-30T20:38:10.112270Z", "iopub.status.idle": "2026-05-30T20:38:10.118820Z", "shell.execute_reply": "2026-05-30T20:38:10.117810Z" } }, "outputs": [], "source": [ "class EnvAwareCMT(CompleteMixTank):\n", " \"\"\"CompleteMixTank with a temperature-dependent first-order decay on one component.\"\"\"\n", " def __init__(self, ID='', ins=None, outs=(), V=10, target='S_S',\n", " k_ref=2.0, T_ref=293.15, Ea_R=4000.0,\n", " exogenous_vars=(), **kwargs):\n", " super().__init__(ID=ID, ins=ins, outs=outs, V=V,\n", " exogenous_vars=exogenous_vars, **kwargs)\n", " self._idx = self.thermo.chemicals.index(target)\n", " self.k_ref, self.T_ref, self.Ea_R = k_ref, T_ref, Ea_R\n", "\n", " @property\n", " def ODE(self):\n", " if self._ODE is None: self._compile_ODE()\n", " return self._ODE\n", "\n", " def _compile_ODE(self):\n", " _dstate = self._dstate\n", " _update_dstate = self._update_dstate\n", " V, idx = self.V, self._idx\n", " k_ref, T_ref, Ea_R = self.k_ref, self.T_ref, self.Ea_R\n", " eval_exo = self.eval_exo_dynamic_vars\n", " def dy_dt(t, y_ins, y, dy_ins):\n", " Q_ins = y_ins[:, -1]\n", " C_ins = y_ins[:, :-1]\n", " Q = sum(Q_ins)\n", " C = y[:-1]\n", " _dstate[-1] = sum(dy_ins[:, -1])\n", " _dstate[:-1] = (Q_ins @ C_ins - Q*C) / V # same as CompleteMixTank\n", " T, = eval_exo(t) # exogenous T(t)\n", " k = k_ref * np.exp(Ea_R * (1.0/T_ref - 1.0/T)) # Arrhenius\n", " _dstate[idx] -= k * C[idx] # first-order sink\n", " _update_dstate()\n", " self._ODE = dy_dt\n" ] }, { "cell_type": "code", "execution_count": 37, "id": "e11-edv-run-code", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:10.119824Z", "iopub.status.busy": "2026-05-30T20:38:10.119824Z", "iopub.status.idle": "2026-05-30T20:38:10.401324Z", "shell.execute_reply": "2026-05-30T20:38:10.401324Z" } }, "outputs": [ { "data": { "image/png": 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ggwkJALDe+nUvCv/v9N1CPNb0+g8/Kw9X/vPdkE6nnW0AAAAAAIAOJCQAQJvYf5s+4b9Hb9/s+ofGzwl3j5vubAMAAAAAAHQgIQEA2swFh2wTRg3fvNn1P350Yhg/c7EzDgAAAAAA0EGEBABouz8q8Vj4n1N2DYN6FTe5vi6ZDhfd82ZYVFHrrAMAAAAAAHQAIQEA2lSPkvxw85f3CAWJpv/EzF5cFS752/iQSqWdeQAAAAAAgHYmJABAm9t5UI9wzfE7Nrv+ucnzwm///aEzDwAAAAAA0M6EBADYIE7fe3A4cfeBza7/9dOTw/NT5jn7AAAAAAAA7UhIAIANIhaLheu+sHMY3r9bk+vT6RD+697xYc7iKq8AAAAAAABAOxESAGCDKS5IhN99efdQVpjX5PqFFbXh4r+8GWrrU14FAAAAAACAdiAkAMAGtXXfsvCLL+7S7Po3ZywO1//rfa8CAAAAAABAOxASAGCDO3rnAeHrB27V7PrbX5wWHn1njlcCAAAAAABgAxMSAKBdfPdzw8OeQ3s1u/47f38nfPhZuVcDAAAAAABgAxISAKBd5Cfi4bdf2j30KStocn1FbTJceM8bobK23isCAAAAAACwgQgJANBu+vcoCr85bbcQjzW9fvKn5eHKf7wb0um0VwUAAAAAAGADEBIAoF0dsG2fcNmRw5pd/+D4OeGeV2a0a5sAAAAAAAC6CiEBANrdhYduGw4fvnmz63/8yMTwzqzF7domAAAAAACArkBIAID2/+MTj4X/OWVkGNSruMn1tclUuODuN8Oiitp2bxsAAAAAAMCmTEgAgA7Rs6Qg/O7Lu4eCRNN/imYvrgqX3Tc+pFLpdm8bAAAAAADApkpIAIAOs8ugnuGHx49odv2/J80Lv/vPh+3aJgAAAAAAgE2ZkMAqiouLw5AhQ9LRNHjwYJevAmxgX9p7SDhht4HNrv+fpyaHFz+c73UAAAAAAABoA0ICqzjooIOS06ZNK89ObXGSAWheLBYL152wUxjWr6zJ9dFoA9/661vhkyXVTiMAAAAAAMB62uRCAkuXLg1z586N1dXVdXRTAGihkoK8cPMZe4Sywrwm1y+oqA0X/eXNUJdMOacAAAAAAABdOSSQSqXC3XffnXfUUUcV9+jRo1uvXr26DRo0qKyoqKjbFltsUfbVr3616Jlnnkl0dDsBWLNt+paFn5+0S7Pr35i+KPzsXx84jQAAAAAAAOuh6Us227GD/+GHH85Lp9OZ+UQiEY4//vj6lu7/0UcfxU466aSSd999NxN2yB4n65NPPondfffd+dH0hS98of6OO+6o6tatW5s/DwDaxjG7DAivT98y3P7itCbX/+mFj8MeQ3uFo3ce4JQDAAAAAABsbCGBV155JX7iiScWR+NRR4499tj6loYEpk2bFjvggANK582bF8uGA7LHaSy77sEHH8wbPXp0yVNPPVVZVtb0uNcAdLzvfW6H8PbMxeHNGYubXH/F398Jw/t3C1v39V4OAAAAAACwUQ038Nhjj+U17si/9NJLa1tagSCqIPDZZ5/FsuGAaIqOs+rUeN2rr76auPzyy4s27LMCYH0U5MXDTV/ePfQuLWhyfXlNfbjg7jdDZW2LC88AAAAAAADQGUICzz//fEMlg6233jp16KGHJluy35/+9Kf8t956K964ckA8Hg9f/epX6/7v//6vcuLEiRWvvvpqxW9/+9vq4cOHp7Jhgej2j3/8Y352eAIAOqcBPYrD/zttt9BEgZiMSZ8uC1f9873VhpkBAAAAAABgzTqsszyqBvDWW28lslf6n3DCCS2+JPR///d/Gy4vjTqIEolEeOCBB6puv/326s997nPJKBiw5557pi688MK6t956qyI6drYjKXrcKGSwgZ4WAG3kwO36hMuOGNbs+n+8NTv89dWZzjcAAAAAAMDGEBL46KOPYsuWLWu4CvToo49uUUjg1Vdfjb///vuZKgLZCgHf+ta3ao8//vgm9y8oKAj33HNP1dChQ9PZfe6//34hAYCNwEWHbRsO3b5vs+uveXhCeHfWknZtEwAAAAAAwMasw0ICH374YbzxUAF77bVXi4YaeOyxxxqGKMiGAK688sqaNe1TVFQULr300ppsIOGTTz6JzZo1q5ki1gB0FvF4LPz6lF3DwJ7FTa6vTabCBfe8ERZX1rZ72wAAAAAAADZGHRYSmD59esNjb7HFFunS0tIW7ffcc881hASiygBHHHFEfe/evde630knnVSf3Scyfvz4DnvuALRcr9KC8Lsv7x4KEk2/bc9aVBUuu+/tkEotD4IBAAAAAADQvA7rKF+6dGks22nfq1evFvXspFKp8MYbbySywwZERo0a1aJhCgYOHJju27dvOrvf7NmzhQQANhIjB/cMPzhuRLPrn/3gs3Dzcx+1a5sAAAAAAAA2Rjml+9tTVVVVw/3CwsIWhQQmTpwYLy8vb6gGEDnwwANbNExBpH///ul58+Zldl62bJnhBtrhNX7hhRcS77//fnzRokWxaGiIwYMHp/bdd9/ktttu65JfoFXO2GdIeGPawvDg+DlNrr/hyUlhtyE9w/7b9HFmAQAAAAAAOltIoPHwAkuWLGlRh/24ceMSjeejTueRI0emWvqYRUVFDR3TlZWVoSu46qqrCq+77rqCdd3/jDPOqLvrrruqW7PPp59+GrvmmmsK77rrrvyKioomt9ltt91SV111Vc2JJ57YokoQAFFA7Kcn7hwmzFkapnxWvtoJiUYb+NZf3wqPfvOg0L9HkRMGAAAAAADQhA4rud+7d+9Mh31U/n/69OnxZDLZ6pDATjvtlMzPz2/xYy5evLghjFBcXNzaJtMCzzzzTGLHHXcs/f3vf99sQCDy1ltvxU866aTiM844o6impsa5BVqkpCAv3HzGHqG0IOfPQYP55bXh4r+8GeqSLc6PAQAAAAAAdCkdFhLYbrvtGnpwamtrw0svvdR0j08jTz/9dF50JWkULIhuWzPUQGTBggUNIYEePXood9/GnnvuucRxxx1X0vg8R3r27Jmp+DB06NB0IpH7Mt9zzz35p556anEqpUMPaJltNy8LP//iLs2uf336ovCLxz9wOgEAAAAAADrTcAN77rlnMi8vL2QrCNxyyy35Bx10ULOd/s8//3xixowZsSgckHXYYYe1OCQwZ86c2MKFCxt2Hjp0aJfslf7Zz35Ws+uuu7b4vA0cOLBFYYqFCxeG008/vbiqqqph2ZAhQ9K//vWvq7/whS/Ux+PL8yjRa/iTn/yk8I9//GNDCYiHHnoo71e/+lXBFVdcUdva5wN0TcfuskV4fdqicMdL05pcf+vzH4c9hvYKR+00oN3bBgAAAAAA0Jl1WEggKvd/2GGH1T/11FOZNvz1r3/NP/300+uOPvroJjuwr7766sLG8z169AhHHXVUi8ezj0IGjee33377LhkSiMIZo0aNalUFhpb42c9+Vjh37tyGEMaWW26Zfv755ysGDRqUEzKIggO33npr9eDBg1M//OEPG17Tn/70p4XnnHNObe/evdu6acAm6sqjdwhvz1oc3pqxuMn1l9//Tti+f/ewVZ/Sdm8bAAAAAABAZ9Vhww1Ezj///LroNqoOEJWbP/nkk0uuv/76guiq9KwPP/wwduKJJxZHpewbDzVw2mmn1RUUFLT4sZ588smGQETPnj3TUSd2mz+hLurTTz+N3XzzzTkvxh/+8IeqVQMCjV111VW1jYeLWLJkSfj5z3+eEwQBWJOCvHi46Uu7h96lTf8tWFZTHy64+41QVdvmuSgAAAAAAICNVoeGBE488cT6Qw45JJnt+I9K1V911VWF/fr169a/f/+yvn37lm2//fZlUTn6xvsVFRWFK6+8sqaljxMd94EHHsiPHiOa9t9/fz1Gbegvf/lLXnl5ecN81Pk/evToNZ7jaPiBq6++Ouc1/POf/5wfhUUAWmqLnsXhN6ftGhqNRJPjg0+WhasefC8TMAMAAAAAAKCDQwKR22+/vWrAgAGZ3ptspYBkMhk+++yz2IIFC2LRfDZEkN3muuuuq17TVeqruv/++/OWLl3a0El06KGHCgm0oYcffji/8fzZZ5+dqRCxNtGwB40rOkQVCV566aWcYSEA1uag7fqGS0YNa3b9A2/OCn97baYTCQAAAAAA0BlCAlEn8XPPPVcxbNiwVDYM0NSUDQt873vfq73kkkta1Akdia5M/9nPflaYDRlETjjhhBbvz5otW7YsvPjiizkd+2PGjKlvyXmLqgkcfvjhOds+8sgjOVUjAFrim4dvGw4e1rfZ9Vc/PCH8e9JnTiYAAAAAANDldXhIILLtttum33nnnYobbrihetddd82EBRpPhYWF4aijjqr/z3/+U3nttde2eJiBbCn8Dz74IJ6tIrDzzjuntt56a3Wn28i7776bqKurywl9bLHFFi0+vwcccEBOVYe33367U/ybBDYu8Xgs/O+pu4YtehQ1ub62PhXO+fPr4T4VBQAAAAAAgC6u01y1nZ+fHy699NK6aFq4cGGYNWtWfOnSpbFevXqlt9lmm1RRUdMdP2tz1FFHJT/66KPy7Hy3bt1CV1ddXR0++uij+Pz582PRee/Tp09q4MCB6dLS0lYfa+LEiTmd+sOHD2/VUA4jRozI2X7SpEmGGwDWSe/SgvC7M/YIJ//+pVCXXD2rlEylwxUPvBPmLKkK/zVqu4ZhbAAAAAAAALqSThMSaKx3797RlGqLY/Xp0yfdp0+ftjjUJuHiiy8umjZtWjwKCjSWl5cXdtttt2Q0VMDFF19c169fvxZVA5g0aVJOSGDw4MGtqtIwZMiQnO1nzJgRq6qqCsXFxa05DEDGroN7hh8cOyJc/dCEZs/I/z49JXyypDpc+4WdQl5C8RIAAAAAAKBr0TvSxURDL6waEIjU19eH1157LXHttdcWbrXVVmVXXnllYbRsbebNm5dzKe7AgQNbFe7o379/OgooZKVSqRBVOGjNMQAaO3PfoeHUPQev8aTc+9rMcO6dr4eKmrW/zwEAAAAAAGxKOmUlATpWdCX/9ddfX/Diiy8mHn300co1DdFQXl6e06Hf2iEL4vF4pmrAsmXLVj1mqyoSNOWTTz6JrRpiWJspU6YIzsBGLhpG4PoTdw49SvLDLWOnNrvdvyfNC6ffOi7c9rW9Qp+ywnZtIwAAAAAAQEcREugiHWb77LNP8nOf+1x9dDtixIjUZpttlo466KOr9t94440oDJB399135zeuMjB27NjEKaecUvzII49UNb7av7Hy8vKc+aKiolZ37kf7LFu2rKEzv3FgYH389re/LbjuuusK2uRgwEYlHo+FK4/eIQzoURR+/OjEkG7mnemdWUvCib97Kfz57L3DVn1aF3ICAAAAAADYGLlqehM3ZsyY+okTJ1a8/PLLlVdffXXtmDFjkoMHD06XlJREnfNh0KBB6c9//vP1t956a/WkSZPK99tvv2Tj/R9//PG8G2+8Mb+549fU1ORcqV9Q0Po++cLC3Ct4q6qqDDcAtImzDtgq/O5Lu4eCvOb/3M1YWBlOuvml8NaMRc46AAAAAACwyRMS2MQddNBByeHDh6dasu2QIUPSzzzzTGVUbaDx8uuvv76woqKiyX0KCwtzrs+tra1tdRtramrWuxoBQHM+t/OAcM85+4Qexc3mncLCitrM0ANPTfzUiQQAAAAAADZphhsgR3Fxcbjzzjurdtxxx7L6+vrMsnnz5sWiigInnXTS8gWNlJWV5cxXV1e3ugrAqvt069atTV6Viy++uPbUU0+ta80+U6ZMiZ900knFbdIAoNPYa8ve4YEL9gtfve21MHtxVZPbVNelwvl3vR5+/Pmdwhn7Dm33NgIAAAAAALQHIQFWM2zYsPQxxxxT/9BDDzX8+3jyySebCwnkXPXfXMWB5qRSqWh4gTUec131798/HU1tcSxg47ft5t3CPy/cP3zt9tfCxLlLm9wmlQ7hqgffC3OXVIVvj94+xGJGPwEAAAAAADYthhugSYcffnhOIGDy5MlN/lvp27dvTif87NmzW/Vv6pNPPollKxZk/kHG46sdE6CtbN69KPzt/H3DQdv1WeN2N/37o/Df978dautbNFoLAAAAAADARkNIgCYNGTIkp6M+GnKgqe223377nB60GTNmtOqy2+nTp8dWfdxoyAOADaVbUX647Wt7hRN3H7jG7f7x5uzw9T+/FpZVt2rUEgAAAAAAgE5NSIAm5efn54QE6uqa7iTbYYcdckICH3zwQaI1p/T999/P2X7YsGFJLwmwoeUn4uGGk0eGiw/bdo3bPT9lfjj1D+PCp0urvSgAAAAAAMAmQUiAJs2dOzfn30ZzQwDsvPPOyfz8/JzKALNnz25xNYEXX3wxJyQwcuRItb2BdhGLxcK3x2wfrjthpxBfw7vWxLlLw4m/eyl8+NkyrwwAAAAAALDRExKgRZ33gwYNajIk0L1793DAAQfkXP3/5JNP5rXktKZSqfDss8/mbHv88cfXe0mA9vTlfYaGW87cMxTlN/8ncfbiqnDSzS+H16YtbNe2AQAAAAAAtDUhAVazaNGi8OCDD64sDxBCOPzww5vtvD/uuONy1t122205+zbnmWeeSUybNq3h+t3NN988vd9++xluAGh3R4zoF/567r6hd2lBs9ssqaoLX/7jK+Ff785t17YBAAAAAAC0JSEBVnPZZZcVLV68uGG+oKAgHHPMMc2GBL70pS/VlZaWNsy/8MILiaeeeiqnEkFTVQR+/OMfFzZe9pWvfKUukVjjbgAbzG5DeoUHLtg/DOld0uw2tfWpcOFf3gy3vfCxVwIAAAAAANgoCQlswq699tqCV199tcWvcV1dXbjkkksK77jjjpxKAOeee27twIEDmxxuINK/f//0N77xjdrGy84777ziWbNmxdbUtihMkJ3v0aNH+O53v1vT0rYCbAhb9SnNBAV2GdSj2W3S6RB+/OjEcN3/TQypVLNvjQAAAAAAAJ2SkMAm7Mknn8zbZ599Svfbb7+SG264oeCdd96JR0GAVUVVA+6+++68Pffcs/Q3v/lNTq3trbfeOn3NNdfkBACa8r3vfa+mX79+Db1l0TACBxxwQOk///nPvKhqQNaMGTNi5557btEPf/jDnCoC3/nOd2o222yz9Xi2AG2jb7fCcO95+4bDtu+7xu1uff7j8F9/Gx9q6o2SAgAAAAAAbDzyOroBbHjjxo1LRFMIobCwsDBEVQG6d++ejkr7L1y4MDZ9+vRY4478rKjT/7HHHqvs06fPWi+VjTr4//rXv1YdffTRJdXV1Q2BgBNPPLG4Z8+eYejQoaklS5bEZs6cGUsmczvUjj322PrvfOc7aw0iALSXkoK8cOtX9gxXPfheuPe1mc1u98jbc8JnS6vDLV/ZM/QozinCAgAAAAAA0CmpJNDF1NTUhKlTp8bGjx8ff+ONN+Iff/xxkwGBMWPG1I8fP75i++23X31lMw477LDkww8/XNmrV6/0qpUK3n777XhUXWDVgMCpp55af//991fF4/4pAp1LXiIerj9x53DpEcPWuN0rHy8MJ//+pTBncVW7tQ0AAAAAAGBd6ZndhF155ZU15557bt0OO+yQiqoGrE1ZWVk48cQT65999tnKxx9/vKp///6tHmz7yCOPTE6YMKHivPPOqyspKWl2u5EjR6buu+++qnvvvbeqqKiotQ8D0C5isVj4ryO2C7/44i4hEY81u93kT8vDib97KXzwyVKvDAAAAAAA0KnF0um0Ho0uoKKiIrz33nvR1fzxOXPmxMrLyzMVBHr27Jnu3bt3esSIEamo4z4vr+1GoKisrAwvvPBCYuLEifHFixfHCgoKwqBBg1L77rtvctiwYa0OILSHd999N77LLruUZuffe++9sOOOO3Zso4BO4T+TPgsX3vNmqKzNrYjSWLfCvPCHM/cI+2/bp13bBgAAAAAAbFwmTJgQdtppp4b5d955p2LnnXducZX39SEkAI0ICQBr8u6sJeGsO14L88trmt0mPxELvzp5ZPj8rgOdTAAAAAAAoNOFBAw3AAAttPOgHuGfF+4ftu7TUHBkNXXJdPive8eH3z/3UUinO2XRFAAAAAAAoAsTEgCAVhjcuyT8/YL9w+5Deq5xu5/964NwzcMTQjIlKAAAAAAAAHQeQgIA0Eq9SwvCX87dN4we0W+N2/355enhwnveCNV1SecYAAAAAADoFIQEAGAdFOUnws1n7BG+st/QNW73xIRPw5f/+EpYVFHrPAMAAAAAAB1OSAAA1lEiHgs/On7H8J2jhq9xuzemLwon/f6lMHNhpXMNAAAAAAB0KCEBAFgPsVgsXHDoNuF/T9015CdizW43dV5FOOF3L4X3Zi9xvgEAAAAAgA4jJAAAbeALuw0Md5y1d+hWmNfsNvPLa8Kpf3g5PDd5nnMOAAAAAAB0CCEBAGgjB2zbJ9z3jf1Cv+6FzW5TUZsMZ9/xWrj/9ZnOOwAAAAAA0O6EBACgDe0woHv4x4UHhO02L2t2m2QqHS7/+zvh/z0zJaTTaecfAAAAAABoN0ICANDGBvYsDn//xv5hn616r3G7/3lqcrjyn++G+mTKawAAAAAAALQLIQEA2AB6lOSHO7++dzhmlwFr3O6vr84M5931Rqisrfc6AAAAAAAAG5yQAABsIIV5iXDjabuFcw7cao3bPfvBZ+H0W8aF+eU1XgsAAAAAAGCDEhIAgA35hzYeC1cdOyL84NgRIRZrfru3Zy0JJ938Upg6r9zrAQAAAAAAbDBCAgDQDr5+4Fbht6fvHgrymv/TO31BZfjcb54P//v05FBdl/S6AAAAAAAAbU5IAADayTG7DAh3f32f0L0or9ltaupT4X+fnhJG3fBc+Ne7c0M6nfb6AAAAAAAAbUZIAADa0d5b9Q4PXLB/GNizeI3bzV5cFS64583w5T++EiZ/uqzd2gcAAAAAAGzahAQAoJ1t169b+MeF+4cdBnRf67YvfbQgMwTBNQ9PCEuq6tqlfQAAAAAAwKZLSAAAOkC/7kXhvvP3DQdt12et2yZT6XDHS9PCYb/6T7j31RmZeQAAAAAAgHUhJAAAHaRbUX6446y9w09P2Dn0Kslf6/YLK2rDd//xbvjCTS+GN6Yvapc2AgAAAAAAmxYhAQDoQIl4LHxpnyHh398+NHx1v6EhHlv7Pu/OXhJOuvmlcNl948NnS6vbo5kAAAAAAMAmQkgAADqBniUF4Uef3yn837cOCvts1btF+/zjzdmZIQj+8NxHobY+tcHbCAAAAAAAbPyEBACgE9lhQPdw73n7ht9+abcwoEfRWrevqE2G6//1QTjqf8eG/0z6rF3aCAAAAAAAbLyEBACgk4nFYuHYXbYIz/z3IeFbh28bCvLW/ud66vyK8LXbXwvn/Pm1MG1+Rbu0EwAAAAAA2PgICQBAJ1VSkBcuG719eOayQ8LoEf1atM/T738WRv96bPjlEx+Eipr6Dd5GAAAAAABg4yIkAACd3ODeJeGWr+wZ7jx777BN39K1bl+bTIWb/v1RGHXDc+Gh8bNDOp1ul3YCAAAAAACdn5AAAGwkDh7WNzx+ycHhqmN2CGWFeWvd/pOl1eG/7h0fTvnDy2HCnCXt0kYAAAAAAKBzExIAgI1IfiIezjlo6/Dstw8JJ+8xqEX7vDZtUTjuxhfCVQ++GxZV1G7wNgIAAAAAAJ2XkAAAbIQ271YUfnnyyPDPC/cPIwf1WOv2qXQId4+bEQ791X/CXS9PC8loAQAAAAAA0OUICQDARmy3Ib3CPy88IPzii7uEPmUFa91+SVVd+MFDE8KxN74QXpm6oF3aCAAAAAAAdB5CAgCwkYvHY+GUPQeHZ799aDjnwK1CXjy21n3en7s0nHrLuPDNv74V5i6papd2AgAAAAAAHU9IAAA2Ed2L8sNVx44Ij19yUDhouz4t2ueRt+eEw3/1XLjp3x+G6rrkBm8jAAAAAADQsYQEAGATs+3m3cKdZ+8d/nDmHmFQr+K1bl9Vlwy/fGJSGP3rseGpiZ+GdDrdLu0EAAAAAADan5AAAGyCYrFYGLNj//D0ZYeEy44cFory1/4nf8bCynDuna+Hr97+Wvjws/J2aScAAAAAANC+hAQAYBNWlJ8I3xq1XXjmvw8Nx+wyoEX7jJ08Lxz1v2PDTx97PyyrrtvgbQQAAAAAANqPkAAAdAEDexaHm760e/jLufuE7ft1W+v29al0uGXs1HDYr54Lf39jVkilDEEAAAAAAACbAiEBAOhC9t+mT/i/bx0YfnT8jqF7Ud5at59fXhO+ff/b4aTfvxTenrm4XdoIAAAAAABsOEICANDF5CXi4av7bxn+/e1Dw5f2GRJisbXv89aMxeELv3sxfOfv72SCAwAAAAAAwMZJSAAAuqjNygrDT0/YOTxy8YFhj6G91rp9Oh3C316fGQ771X/CH5+fGipq6tulnQAAAAAAQNsREgCALm6ngT3C37+xX/jfU3cNm3crXOv2y6rrw7X/937Y+7qnM5UF3pi+KKSjBAEAAAAAANDprX0wYgBgkxeLxcIXdhsYjhjRL/z22Q/Dn16YGuqSa+74r6hNZioLRNO2m5eFU/ccHE7YfWDoU7b2oAEAAAAAANAxVBIAABqUFeaF735ueHjy0kPCYdv3bfGZ+fCz8nDdY++HfX/6TDj/rtfDsx98GuqTKWcWAAAAAAA6GZUEAIDVbNWnNNx+1t6Zzv4fPzIxTFtQ2aKzVJ9KhycmfJqZ+nUvDF/cY1A4Zc/BYehmpc4yAAAAAAB0AioJAADNOnx4v/DEpQeH7xw1PJQUJFp1pj5dWhNu+vdH4ZBf/iecdsvL4Z9vzQpVtUlnGwAAAAAAOpBKAgDAGhXmJcIFh24TTtx9YLh17NTwj7dmh4UVta06a+OmLsxMVxdNCMeP3CKcutfgsPPAHiEWizn7AAAAAADQjoQEAIAW6de9KFx17IhwxVHDwzPvfxr+9vrMMHbyvJBKt/wELquuD/e8MiMzDe/fLRMW+MKuA0Ov0gKvAgAAAAAAtAMhAQCgVQry4uFzOw/ITHOXVIW/vz4r3PfGzDBzYVWrjvPBJ8vCjx6ZGK5/7INw5I79wql7Dg4HbtsnxOOqCwAAAAAAwIYiJAAArLMBPYrDN0dtFy46bNswbuqCcN/rM8O/3vsk1NSnWnyM2mQq/N87czPTwJ7F4Yt7DAon7zkoDOpV4pUBAAAAAIA2JiQAAKy36Or//bftk5l+VFkXHn57dmY4gvdmL23VcWYvrgq/eWZK+H/PTslUFThlz8HhyBH9QlF+wqsEAAAAAABtQEgAAGhTPUryw5n7bZmZJsxZEu57bWZ4cPycsKSqrsXHSKdDeH7K/MzUsyQ/fGHXgZnAwIgtunu1AAAAAABgPQgJAAAbzI5b9Ag/+nyP8L2jdwhPTvw0Exh44cP5rTrG4sq6cMdL0zLTzgN7hFP2GhyOH7lF6FGcv8HaDQAAAAAAmyohAQBgg4uGC4g69qNp5sLKcP8bs8LfX58Z5iypbtVx3p29JDNd++jEcPTOA8LJew4K+261WWa4AwAAAAAAYO2EBACAdjW4d0m47Mhh4b9GbZepKnDf6zPDUxM+DbXJVIuPUVOfCv98a3ZmGtK7JJyy56DwxT0Gh/49ijZo2wEAAAAAYGMnJAAAdIhEPBYOGdY3My2sqA0PvjU7Exj44JNlrTrOjIWV4VdPTg7/89TkzLFO3WtwOHx4v1CQF99gbQcAAAAAgI2VkAAA0OF6lxaEsw/cKpx1wJbhnVlLwt9enxkeGT8nLKupb/ExUukQ/j1pXmbarLQgnLDbwExgYLt+3TZo2wEAAAAAYGMiJAAAdBqxWCyMHNwzM/3gmBHhX+/NDX97bWZ45eOFrTrOgora8McXPs5Mw/t3Cwdu2yfsv+1mYe+tNgtlhT7+AAAAAADQdfmVHADolIoLEuHE3QdlpmnzKzJDEfz9jVnhs2U1rTpONHxBNEWBgWiIg5GDeoQDtu0T9ttms7D7kF6hKD+xwZ4DAAAAAAB0NkICAECnt2Wf0nDFUcPDZUcOC89NnpcJDDzz/mehPhpjoBWSqXR4c8bizHTjsx+Gwrx42HPLXmH/bfqE/bfZLOw8sEfIS8Q32PMAAAAAAICOJiQAAGw0og78UTv0y0zzltWEf741KzMcwUfzKtbpeDX1qfDihwsyU6RbYV7YZ+veYb9t+oQDtt0sDNu8W4jHY238LAAAAAAAoOMICQAAG6W+3QrDeQdvE849aOvw5oxF4b7XZoVH3pkTKmuT63zMZTX14en3P8tMkc1KCzLDEuy/IjQwpHdJiMWEBgAAAAAA2HgJCQAAG7Wo036Pob0z09XHjQj/987c8LfXZ4Y3pi9a72MvqKgNj74zNzNFBvYszgxLsP+2y4MD/boXtcEzAAAAAACA9iMkAABsMkoL88Ipew3OTDMXVoYXP5wfXvpoQWaaX16z3sefvbgq3P/GrMwU2aZvaThg2z6Z4MC+W28WepYUtMGzAAAAAACADUdIAADYJA3uXRJO23tIZkqn02HKZ+UNoYFxUxeEZdX16/0YH82ryEx3vjw9RKMQ7LhF90yFgSg0sNeWvTOhBQAAAAAA6Ez8cg0AdIkhCYb165aZzjpgq1CfTIUJc5aGFz+aH17+aEF4bdrCUF2XWq/HSKdDeG/20sx0y9ipIS8eC7sN6Rn226ZPOGCbzcKuQ3qGwrxEmz0nAAAAAABYF0ICAECXk5eIh5GDe2amCw/dNtTUJ8NbMxYvH5rgw/lh/MzFoT6VXq/HiPZ/bdqizPT/npkSivLjmeoCUaWBA7bdLOy4RY+QiMfa7DkBAAAAAEBLCAkAAF1edIX/vltvlpkuO3JYqKipD69OW5ipMhANUTBx7tJMpYD1EVUqeH7K/MwU6V6Ul3m8vbfqHXYY0D0M798tbFZW2OVfCwAAAAAANiwhAQCAVZQW5oXDtt88M0UWVdSGcVMXLK808NH88NG8ivU+Z0ur68OTEz/NTFl9uxVmwgLZ0MDw/t3DNpuXGqYAAAAAAIA2IyQAALAWvUoLwud2HpCZIp8sqQ4vT50fXvxw+fAEc5ZUt8k5nLesJjNlqw1kPqzFY2HrvqWZwMDwAd3CDitu+3cvCrGY4QoAAAAAAGgdIQEAgFbq36MonLDboMyUTqfD9AWVDVUGoiEKFlTUttk5rU+lw+RPyzPTw2+vXN6jOD+36sCA7mFYv7JQUuDjHQAAAAAAzfMrMgDAeoiu5t+yT2lm+tI+Q0Iq6tT/bFmmysDLH80P46YuDOU19W1+jpdU1YVXPl6YmVa2JYShvUsaqg5EtzsM6BYG9yoJ8biqAwAAAAAACAkAALSpqDM+00nfv3v4+oFbhfpkKrw7e0lDpYHXpy0KNfWpDXLW0+kQpi2ozEyPT/ikYXlJQSJs339laGD7fsvv9yjJ3yDtAAAAAACg81JJAABgQ37YSsTDbkN6ZaaLDts2VNclw5szFoWXPlwQxs9cHN6fu7RNhydoSmVtMrw1Y3FmamyLHkWZYQqywxXs0L9b2KpPaabNAAAAAABsmoQEAADaUVF+Iuy/TZ/MlDVvWU344JOl4YO5y8IHn0TT0jDl0/JQm9wwFQey5iypzkzPfvBZw7KCvHjYbvOyFdUQuoVh/buFIb1LwhY9i0JhXmKDtgcAAAAAgA1PSAAAoIP17VYY+nbrGw7arm/DsrpkKkybXxHej0IDc5cuDw/MXZrp1N+QautTYcKcpZmpsVgshH7disLg3sVhUK+SMLjX8ttBvYvD4F4lYUCPIhUIAAAAAAA2AkICAACdUH4iHrbr1y0zHT9yi4blSyrrllcdWFFx4P25y8LkT5dlhhTYkNLpED5ZWp2ZXpu2aLX1iXgs9O/eOERQEgb1Kg6Dey+/7de9KLMNAAAAAAAdS0gAAGAj0qMkP+yz9WaZKSuVSoeZiyozgYGVwxYsDdMXVmY699tDMpUOsxdXZaYQFq62Pj8RC1v0LF4tPJCtShBVU4hF5QoAAAAAANighAQAADZy8XgsDN2sNDMdtVP/huUVNfWZKgPZoQqyQxcsra5v9zbWJdNh+oLKzNSUwrx4GNgoNJC5bTS0Qe/SAiECAAAAAIA2ICQAALCJKi3MC7sN6ZWZstLpdJi7pLphqIIoQDDpk6Xho3kVmWoAHaWmPhWmzqvITE0pKUjkVB5oHCLYvHth2Ky00HAGAAAAAAAtICQAANCFRCX9o7L/0XT48H4Ny6vrkuGjeeUNQxVkqg98sizMW1YTOoPK2mSY/Gl5ZmpKNFLBZqUFoU9ZYWbogr4rbrPzDcu7FYaexfmZ6gsAAAAAAF2RkAAAAKEoPxF23KJHZmqsqjYZZi2qDLMWVWVuZ2ZvFy6/XVRZ1ynOXjodwvzy2swUhRvWJC8eC5uV5QYK+jQRLIjmuxfnGeYAAAAAANikCAkAANCs4oJE2K5ft8zUlGXVdWH24qqG0EAUJpi5cMXtosqwrLq+053d+lQ6fLq0JjOtTUEiviI4ULBaRYJVqxSUFiQECgAAAACATk9IgA1uypQpsVdeeSUxc+bMeG1tbejVq1d6hx12SB144IHJ4uJirwAAbMS6FeWH4f2jqXuT65dU1TWEBnIqEixcHiKIhhHozGqTqUwIIprWpjg/Efp0K1hemaBRmKB3aUHoUZwfepYUZIY66FmSn5mPzl3CsAcAAAAAQDsTEmCDeeCBB/KuvfbawvHjx8ebWl9WVhbOPPPMuh/96Ec1ffv2TXspAGDTE3WG9xjYI+w0MHcYg0g6nc4MV9B4+IKZDUGC5fPVdamwsaiqSy4PPyxce6AgEouF0L1oeWggCg/0KFkRJmgUJIiCBctvs9ssX16Yl9jgzwcAAAAA2DQJCdDmqqurw1lnnVV077335q9pu/Ly8nDzzTfn33///Xn33Xdf1WGHHda5LyUEANpULBbLXGUfTbsM6tlkiGB+eW1DcGDVigSzF1VlrvTfWKXTyystRNP0Vu4bVS1YGSSIAgQrwwQ9VsxngwXds9uUFBgSAQAAAAAQEqB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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Reset thermo to bsm1.cmps (the METAB example switched it to ADM1).\n", "qs.set_thermo(bsm1.cmps)\n", "\n", "# Pick the T variable from the batch we just loaded, build a clean inlet,\n", "# and run the tank with V=50 m3 starting from S_S = 200 mg/L.\n", "T_var = next(v for v in edvs if v.ID == 'T')\n", "\n", "inf_env = qs.WasteStream('inf_env', H2O=1000) # near-zero S_S inlet\n", "tank = EnvAwareCMT(ID='T1', ins=inf_env, V=50,\n", " target='S_S', k_ref=2.0, T_ref=293.15, Ea_R=4000.0,\n", " exogenous_vars=(T_var,), isdynamic=True)\n", "tank.set_init_conc(S_S=200.0)\n", "sys_env = qs.System('env_sys', path=(tank,))\n", "sys_env.set_dynamic_tracker(tank)\n", "sys_env.simulate(t_span=(0, 5), method='BDF',\n", " t_eval=np.linspace(0, 5, 201),\n", " state_reset_hook='reset_cache')\n", "\n", "# Pull the tracked S_S series out of the scope record, then plot T(t) and\n", "# S_S(t) on a shared figure so the decay-rate response to T is visible.\n", "import matplotlib.pyplot as plt\n", "\n", "# SanUnitScope.header entries look like ('T1', 'S_S [mg/L]'); split off the\n", "# units suffix to match by component name.\n", "S_S_idx = next(i for i, h in enumerate(tank.scope.header)\n", " if h[1].split()[0] == 'S_S')\n", "t_rec = tank.scope.time_series\n", "S_S_rec = tank.scope.record[:, S_S_idx]\n", "\n", "t_grid = np.linspace(0, 5, 201)\n", "T_grid = np.array([T_var(ti) for ti in t_grid]) - 273.15 # degC\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(7, 4.5))\n", "ax1.plot(t_grid, T_grid, color='C3')\n", "ax1.set_ylabel('T [degC]')\n", "ax1.set_title('Exogenous temperature drives the decay rate')\n", "ax2.plot(t_rec, S_S_rec, color='C0')\n", "ax2.set_ylabel('S_S [mg/L]'); ax2.set_xlabel('time [d]')\n", "fig.tight_layout()\n" ] }, { "cell_type": "markdown", "id": "a89fa738", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "For convenience, `ExogenousDynamicVariable` also has a `classmethod` that enables batch creation of multiple variables at once. We just need to provide a file of the time-series data, including a column `t` for time points and additional columns of the variable values. See the [documentation](https://qsdsan.readthedocs.io/en/latest/api/utility_functions/dynamics.html#qsdsan.utils.ExogenousDynamicVariable.batch_init) of `ExogenousDynamicVariable.batch_init` for detailed usage." ] }, { "cell_type": "markdown", "id": "d8205f55", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 3.2. `DynamicInfluent`\n", "[DynamicInfluent](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/DynamicInfluent.html) is a `SanUnit` subclass for generating dynamic influent streams from user-defined time-series data. The use of this class is, to some extent, similar to an `ExogenousDynamicVariable`." ] }, { "cell_type": "code", "execution_count": 38, "id": "7c2c9521", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:10.401324Z", "iopub.status.busy": "2026-05-30T20:38:10.401324Z", "iopub.status.idle": "2026-05-30T20:38:10.612920Z", "shell.execute_reply": "2026-05-30T20:38:10.611914Z" }, "scrolled": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KoeAk8ldffSWbN2/26ZhQNipXrux2X7XOKI7i7l+wHUHnzp0lISGhWM+HfyEwAMCUvFUYMLoXdbCKtXquMEBLAgD+GCJ7cM5mmbvhiNf9nh3YSv7Sp7H4i3aJFeXalp7/MbbvVIZ8uznF8DEBAAAAAEopoZVI3auMq2aQ0NKYcwWp+vXrF3osKytL/JUKC6jQQMFqi0899ZRPx4Sy0bKl+89ycYMgW7ZsKVb7g4ULF+bfpx1B4KMlgR/JycmRjRs3Wnbu3Bl6/vz5kOzsbImLi3MlJCS4Onbs6GzSpIkzNLT0E5l2u11Wr15t2bZtW+jZs2dDLBaL1KpVy9WpUydHmzZt9GdRgSBi81JhgMBA2aAlAYBAkGN3yF/+u0kW7z6lu09oiMhLd1whgzolir9RVQZ+2XVSXB6KCby5OEkGtK3F7zUAAAAACDQ9HhL5bFX5n6f7Q+V/jiCXmZlZ6LEqVaqIP1NtCd566y05deqPz0L+97//yapVBny/oVypVf4Fff/99zJs2LAiPVe1p1AtBorq119/1appXDRgwIBijBT+iMCAH1i3bl3oG2+8Yf3mm2/CVSpHj5rUHz16tP2RRx7JLckvnPT0dHn++ecjpk2bFq4CCZ72UaGExx57zDZ27Fh7WYQTgECsMBBu4Xu/XFsS5OSVyfEBoLQyc/Nk3CcbZNX+s7r7hFtC5K2h7eXGNjX98oI3q1FBBratJfO3FC4bd+Rctnyx4YgM70ovSgAAAAAIKE2v+6NVwPYvy+8cbQaJNL22/I5vEuvXr3e7X6NGDbFareLPYmJi5P/+7//koYf+DIw8+eST0qtXL5+OC6Vz7bXXSkREhLb6X5k/f76kpKRI7dq1L/tc1ZrixIkTJWpHoKpstGnTpoSjhr9gVsyHVGLn0UcfjejWrVvMnDlzvIYFlGPHjoW88MIL1pYtW8b+8MMPluKca+vWraFXXHFF7EsvvWTVCwsoe/fuDR0/fnzkddddF33hwoXinAIImgoDEbQkKBOxeoGBXAIDAHwvNcsuwz9a6zUsoH4fTBvZyW/DAhc93K+pWFQZBA/eWbJXq6IAAAAAAAgwN74iUqGc/j2qjnvDy2J2Z86ckc8++0yczpIVXrbZbPLOO++4PXbddddJIJg4caIkJv5ZSXHp0qXyyy+/+HRMKJ2qVavKbbfdln9fVTG///77L/v9ffr0aTVXWaxzqeoFF1FdIDgQGPChcePGRb722mvWS39Yo6OjpVWrVk7VIqBhw4aukBD3D4BPnToVcvvtt0d///33RQoN7Nq1K/Saa66JPnjwoNuBYmNjpXXr1s7GjRs7w8PD3Z6zaNEiy/XXXx+t3lCAYGSnwoDPAgOZNofWGwsAfOVMRq7c9eEa2XxYPxwZY7XIrDGdpU+z6uLv6leNkcGd6njcdjItVz5dfcjwMQEAAAAASim6ssjwr0QiK5btpVTHU8dVxzc5VVJdlWxXq6Nnz56tTbAWp8X08OHDZceOHW6Pjxw5UgKBWon+z3/+0+0x2hIEvqefftqtwsWCBQtk1KhRWgVyT/bt26dVJjhy5IhcOhepZ+vWrXL48OH8+wQGggMtCXxk7ty5YTNmzHCbpW/evLnz5Zdfzr3++uvzCk7gnzx5MuS9994Lf+mllyJUYk1R/7/nnnui9uzZk1G5sv4vdrvdLoMGDYo6e/Zs/k96pUqVXK+99lrusGHD7BffOM6ePSuvvPJKxCuvvJIfYFi7dq1FVUB47733vJc+AIKowoBaoam3ShNl05LA4XRJjt0pUdZiFUoBgDJxPDVbhk1fK8mnC/cYvCg+KlwLC7RLLOMPZcrRA32byFcbUzz+fvtg2X65q0td3SAXAAAAAMBPJbQSued/IrPvEEk/XjaVBVRYQB0X+Xbu3CkjRoyQv/zlLzJ48GBtArVHjx5Ss2bhCg/Hjh2Tb7/9Vs2nyMGDB9223X777dK3b9+AubKjR4+Wl156SZs0RnBo2bKlFhpQt4tUGGbx4sVy9913S6dOnSQuLk4tTJYlS5bIvHnztPCLMmHCBJkyZcplz6FCCBepY/Xu3btUY05OTlaLmEv03I4dO6o5z1KdH3/gU0Mfee655yIK3u/QoYNz2bJlmWrV/6USEhJczz33nO3qq6923HjjjdF5eX+U8z5z5owKEliffvrpP1IEHnz44YfhO3bsyK8koX5wli9fnqUqCxTcr0qVKvLiiy/mtmrVyjFy5Mioi49PmzbNOmnSJHuzZs1KVpMH8FM2nQoDVguFV8pKhUj9XzGqLQGBAQBGO3Q2UwsLHD2vv2KgamyEzL63szSvESeBpFbFKBnWta7M+M39wwrlXKZNPl55QCZd08QnYwMAAAAAlIKa3L9vlciPj4tsm1fy47QZ9EcbAioL6EpLS5Pp06drt4vzJqrMe8WKFbVJ1ePHj2sTrZ506dJFZsyYIYEkLCxMnn32Wa3KAoLHk08+KYcOHcr/PlbU9+5rr72m+5wHHnhAC7wUDAyo7w9PvvvuO7cWHJdWMC+uTz/9VLuVxK+//lrqwAL+wMyYD+zbty9k586dbtf+vffey/YUFiiof//+jtGjR9sLPvbDDz/ozsjl5ubKf/7zH7dgwosvvphzaVigoBEjRuTddddd+edQ4YRnnnnG7RhAMLDrVBgIt1BdoLwrDCiZuX8EnwDAKHtPpsugKau9hgVqxUfKFxO6BlxY4KL7ezeWqHDP1Vs+XJ4sF7J0M6YAAAAAAH+mJvnvmC5y9xci9boX77lq/7vn/fF8wgJu1JxM27ZtdS+dqsy8Z88eVY1ZK8PuKSwQGhoqEydOlF9++UVbbR1ohg4dqrVkQPBQrQWmTZumVY+IispfH+yRCgX861//krfffltr0VFQfHx8of1PnDghGzZsyL9PO4LgQWDAB3bv3u123WvXru3q2rVrkVbw33HHHW6Bgf379+u+hj/++GPY0aNH82c/69Wr57r33nvdnu/Js88+m1uwV8nXX38dduGCfo9fIBDl6lUYCKNMflmJtXqvMAAARtmekiqDp66WU+n6XZbqV4mWLyZ2k4bVvAc4/Vm1ChEypkd9j9vSc/NkyrJkw8cEAAAAAChDTa/7o0XBfatFrv6bSMPeIpGXtNNT99XjarvaT+3f9FpeBg9U9YAtW7aoeRZ5/fXXtcnPopY3V+0KHnzwQe35H3zwgVSoUCEgr7EKPKgJYwQXNcf3+OOPa4GXf//739K1a1dVzVwLCKgggGpN8MQTT0hSUpI89dRT2nPOnTt32cDA999/Ly6XS/uzxWKRm266yaCvCOWNlgQ+cO7cObclzHXq1ClyuX816V/wfmpqqu5y6Pnz57u9viNHjrSpN//LadKkiUu1P1i+fLk2c2q329WbQNjw4cOZ4UPQtySICCNHVVZiIvTDFwQGABhlw8Fzcs+M9dqEuZ5mCRXk03s7S/UKkQH/woy/upF8svqQpOcU/npnrjqgBQqC4esEAAAAAFNLaCmS8M8//qwm72wZInk2kTCriDVWzRb6eoQBpWHDhvLwww9rNzUZqsq5q4nUw4cPqzkYyc7OlujoaC0UUKtWLa0qQZ06dcQfTJ48WbuVxi233JI/CVwco0eP1m5loSyPVZ4OHizcCrIo6tevX6JrXNprk5iYqLUoULfL+f33393u16tXr9A+CxYsyP/zVVddJZUrVy72mGbOnKnd4F8IDPjApamc7OzsIv/2zsrKcrtfpUoVl7cKAwXvX3fddY6inueaa67JuxgYUL7//vtwAgMIJnaH5x8dWhKUnTBLqESGh0qOvXA4g5YEAIywYu9pGf/JRsm26/8VqG2deJk1prNUjLYGxYsSHx0uE3o2lFcXJhXapt6P3/91v0we2MonYwMAAAAAlAMVDoioIEJj4TK6nCHa5K66AWayePHi/D+rxccdOnRw266CM4sWLcq/TzuC4MJSWh9o376926fWe/bsCb20N4ietWvXui3Z7dSpk8dPwI8fPx5y8uTJ/CBCRESE7r6e9OjRw23frVu38r2CoGLL8/zjYKXCQJmKjfCcS6PCAIDy9vOOEzJ25gavYYHODSrL7Hu7BE1Y4KJ7ujeQKjGev6bP1h6Wo+fdA6gAAAAAAABm8+yzz2oBkYI3s658X7FihdZe46I2bdoUarOhAgUqNHDRwIEDxd+oahuXvqazZs3y9bACApPAPlC3bl1X165d8z+9zs3NlTfeeOOyn1Tn5OTI22+/7bbf2LFj7Z723blzp9tr27BhQ6cKDRRVq1at3JYEJycnh6rWBEDwVxjgbbEsERgA4Avfbk6R+/+7SWwO/a5PvZpWk1n3dJYKkeESbGIiwuT+Po09blPX5O3Few0fEwAAAAAAAMpfcVsfpKWlybhx49weGzNmTKH9vvvuu/w/N2nSRJo1a1aKUcLfMDPmIy+++GKuKulx0XPPPRfx8ccf635iff78ebn99tujVDWCi4/deOONebfeeqvHhry7d+92e23r1KlTrHeIhIQEV2Tkn/1tbTabFhoozjEAf6Y3iUSFgbKftPKElgQAyst/1x6Sh7/YIg6n/l99bmhdQ6aN7ChRVrfCTUFlWJe6UjP+z7/LFfTVphRJPl206lYAAAAAAAAIHIcOHZJevXrJTz/9JA6H98LjqqpA9+7dVSX0/MeqVKkiI0eOLLTv1KlTtTCCuiUlFW6FicDmeSYH5a5Xr16ON998M+fBBx+MVD9ceXl5qlpA5Pvvv2+99dZb7c2bN3dGRUW5zpw5E6raEMyZMyfs/Pnz+S0G+vbt65g7d+6ftT8ucerUqfx9lTp16ugvsdNRs2ZN14EDB/KPo1ockBhCsMjN0wkMUGHAkMBARo7HrBMAlMq05fvlhf/t9rrP7R1qy8t3XCFhQf5+HxlukQf6NpH/+2ZboW0qTPHGor3yzl3tfTI2AAAAAAAAo7Vt21Z++eUXr/u0atVKgsHy5cu1W0JCglx//fXSsWNHqV27tkRHR0tqaqrs379fazGwZMmSQs+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4zLp2kLSLCGrE0QMAcGJJcS1kbK8o+XHz8RX5Ow8WyVdr98nEAbGcSgAAAAAA4HPX8FftyZVtWYW1FvTBu2hJADQB9gr/6lU7ons7jz2XUcKAhYQBj5q7rnFjjE6WegE07f3VMumN1DqLBU6JjZRPbkimWAAA4DP+OraHGAU2/Wv+dsOiSQAAAAAAAG9fw5+7fh/fBB9CwQAAj/tgZbrHnsvoYrmNhAGPUu0BfJmzyrjoRqUQfHjdYGkZavXomAAAqIuqwr8gqYPuvszcUpm9JlP7uKqqSgrLHJJbXK79qz4HAAAAAADw5jX89Zn5fAN8CC0JAD+Xl+fbE7F65qzbLxf06+CRHjWGLQlIGPAYNTFh1CLA153Rva28fuUACbaavT0UAACOc8eZ3eXr3w+Is/L4IoDp87bIvN8PyOYDBVpLnmqRwRbpExMhSbEt5IJ+McT/AQAAAADQzHnjGv6Gffna8wYYxSfCoygYAPzcE9/vFn/0+i+7PVIw4DBqSWDmj5CnFNkrak1U+Iuz+0TLvy7rJ7ZAigUAAL4poU2oXDowVj5e9UeawNFKyp2yYnfOcberv8nLduZo26uLdmlJOjeP6CIjEz3XMgoAAAAAAHiHmqQ/VGiXrVmFsj27ULZlFR632MAT1PMVlzslzMZUtS/guwD4uT05xeKPVu3J1f4QqTjdxkTCgPc5nP4XfRzXMlhevry/BNK6AgDg4/4yqpt8/uvek/57uyotV1a9n6ulPz0yvjcteAAAAAAAaCLUpHx1UYD6t7pIIK/ENxb4afM3Nm+PAgoFA4Cfaxfuv79N567fJ3dHJzbqc5QbXDy3MBHsMf6Y5pB5pFQW7zjkkRQMAADq++Zfva5xOJ31bhmVujtHZlwzSBKjI/imAAAAAADgJ8ocTtl5sOi4woAD+WXiy2gd7TsoGAD83E3DYmTe5kPij9Zn5jf6c5Aw4H0qUkj1S/a3tgSeapsBAMDJ2nKgQC57M1VrP9AQsgvsMumNVJl9YzJFAwAAAAAA+JgKZ6Wk5ZTUpAZUFwik5RRLpZ8F/ao5g1Ar7YB9hd8VDFRWVsqqVatMixcvDty6daspNzc3ID8/P0D13HjuuefKBg4cqN+wHGiigkLCxF9t2Jev9csJCGi8FejlTv1fCbZAU6M9J2pT398+MRFar2R/4qm2GQAAnIwjxeVy9XurGrwgTx3vqndXyfe3n057AgAAAAAAvEDNm+zPL5PtqijgqOKAnYeKDBdJ+pu+MZGNOjeEJlowUF5eLi+//LL1lVdesWZkZNT6CaqecDxy5IjuT9Z1110XNH/+fO1r7dixY+Uvv/xS4qlxA42t1OG/fxzUBenicqe2Ar2xOAz+eNKSwLOSYlv4XcGAp9pmAABwMh6eu0lLBGgM6riPfL1JXrqsf6McHwAAAAAA/CG3uPy4VgKqUKDQXtGkT1FSXKS3hwB/KxjYvn17wKRJk0J+//13kyoOUKqrTqo/r8ukSZMc7777rkV9nJGRYV6+fLl5yJAhDZPbCXiZ1ezfK+W1ajibeDxhwN/Pm785v18HeXXRLvE3nmibAQCAuxZszZa56/c36ombs26/XNCvA+15AAAAAABoAMX2CtlxsEgrBqguDFDpAYcKG2cxgCvahtskMTpcukeFS0RQoLz48w6PPff5STEeey40gYKBnTt3BgwfPjz08OHDWtsBVSig/j26cOBERQNjxoxxduvWrXLHjh3aDOHs2bMDKRhAU9GhRZD4M2sjtwZwGBQMWGhJ4FGJ0REyKKGVrErLFX/iibYZAAC46/VFuz1y0l7/ZTcFAwAAAAAAuLlIcs/h4v+1EiiQbVlFWnFARq73ws/DbYFa693u0eHSIyr8j4+jwqVVqLXW/VRKsCeu4Q/q1IpWwD7GpwsG7Ha7nHPOOSGHDh0KqJ6s6dixY9Vtt91mP+uss5yqvUB4eHi4KxM5F110UcUzzzyj/eT/+OOP6uv2XskO0IAigmv/QvcnkcEWCbWaG/U57AYtCUgY8LybRnSWVe/7V8GAJ9pmAADgjq1ZBR4rwFu1J1eLRVQXEgAAAAAAwJ8qK6tk75HSPwsDsv9ID9h9uEgczhOnozfWAs1u7cK0ogCtOOB/BQLtI4NcWhTnqWv4N5/RpdGfA+7x6RmQl19+2bpz505T9Q/x+eefX/HRRx+VhoSEuH2sc845RysYUCtFt2/fblKJBW3atPHO/1igAan/H1ZzgJR76Q9QffSNiWz0ldtaywMvJBvgeKMSo+T8pA6NHqHsb20zAABwx9x1nv07Onf9Prk7OtGjzwkAAAAAgK9Q84qHiuyyPatIK+L/o5VAkezILpSScu90PzcFiCS0Ca1JC6guEIhvFSKB9WjH7Ilr+Kr94cjEdo12fDTRgoHqycR+/fpVfvrpp6VW68mtpj711FOdR7cv2Lhxo2nEiBHe+Z8MNLCwIIvkFpf73XlNiots9OcwaklAwoB3PHp+b1m5J0eyC/wn5IXiEgCAL1m/N8+zz5eZ79HnAwAAAADAWwrKHFohwNasQi0t4I/0gEI5UuLw2pg6RAYd10qga7swCbKY/e4aflSETR4Z37vBj4smXDCwadMmU2ZmplYtoCb6//nPf5adbLGAEhYWJh06dKjat2+fdkyVXEDBAJoCVQRjtIre152fFNPoz0HCgG9pGWqVGdcMkklvpGpx/77OE20zAABw53Xfxn0FHj1hG/bla8/b2KlQAAAAAAB4SpnDKbsOFWlpAdXFAduzi2RfXqnXvgktQix/Jgb8r0CgW1S4do26KVzDV1+HOq46PnyPzxYM/Pbbb6ajJ/vHjh1b7zSAFi1a1BQM5OXlccULTUKRvULb/M2gTq080g/XMGGAlgRekxgdIbNvTJar3l3l80kDnmibAQCAq9RrPk8X3KnnKy53SpjNZ986AgAAAACgy1lZJek5xX8WBvwvMSAtp0Tb5w3BFrN0jwrTkgKOLg5oG27zmWvRDX0NXyULqGIBdVz4Jp+96nPw4EGtYED954iPj680m+u/wjMkJKTmf39JSUm9jwf4AofTO3/U6uvmM7p45HmMEgYs9ejjg/pTLwy+v/10eeTrTTLHw72Yfa1tBgAAvv66T3s9ZfPKUwMAAAAAcEIqGS+roEwrBtC27D+KA3ZkF4ndSwnNgaYA6dw2VCsMSPxfKwFVHBDXMkRMJt8oDPDENfwL+nXQ2hCQLODbfLZgoKysrOZjm83WIFfG8vPza/4HRkRE+OcsK3AMi9n3/7Do/YEYmdjOI89VTsKAz1IvEF66rL/28/D6L7tl1Z5caY5tMwAA8PXXfSQzAQAAAAB8RV5JeU1RwLajUgMKyryXxBzXKrimnUB1YUDnNmF+/366PtfwVcq0WjjqqbkgNNGCgbZt29ZM6Ofk5NT7f1RFRYXs27fPpHd8wJ+p/urq0rG//ECr6BlVTeYpRgkDVj8stGiqRiVGaZt6UTd3/T5Zn5mv9Us+OnJZ9TdS7QHUiv9fth/ySP9mT7XNAADAVaotgPqb6Mm2BGpFxOo9OXJG93Z+sQICAAAAAJraynnVnk4lzqkicvW+0Fdi6xtbSXmF7DxY9EcrgaMKBA4Weq/NbZswm/SIDpMeURHav6o4QG2hTbyNn7vX8NVCPK6t+xef/Qnu0KFDZfUvw4yMjIC8vDxp0aLFSR9v1apV5uLi4prPu3fv7p0MEqCBqZ6y/lIsoP5gqD41noyeIWHAf6gXEHdHJ0r17371s60KPlQVplYY878XwgPiW8o1769pMm0zAABwlfpb2CcmQpbtzPHYSauorJJp76+RhNYhcmVyvFwyIE4iQywee34AAAAAaG62ZhXI3HX7Zf3ePG3h1LGTsup9YVJsC7mgX9OYlHU4KyXtcPEfhQH/KwpQxQEZuSVS5aXJD1WY0T0qTHpER0iPqDDpHh2uJQi0Dmve/fpcvYYP/+OzBQPDhg1zBgYGitPp1H7oZs+ebbnxxhtPeinNG2+8UXNVKzIysuq0006jYABNgrd62Z5MsoAqFlB9bzzJOGHA7NFxwD3qhYV6UabXL1lVMp6f1EHmrj/5vkm+1DYDAAB3qItCniwYqJaWUyL//HaLPP/jdrmwfweZkpwgvTp49nUdAAAAADRlC7Zmy+uLdsuqNOPYd1U8oN4Tqu3VRbtkUEIruXmEf8S+V1ZWyb680pqCgOrigF2Hirw2z2E1m6RLuzBJrGkl8EeRQIfIICa/63ENH/7HZwsGVJpAcnKyc+nSpdqs3jPPPGO78sorHaGhoW4fa9myZeaPP/7YUl3ZMm7cOO81MgGaSC9bdydfVRsCTyYLKM7KKqms8t/zBmOPnt9bVu7JkewCu9+3zQAAwB3n9+ugXRTyllKHUz5elaltpyW0lCkpCXJW72i/78sIAAAAAN5ypLhcHp676aQWSKniglXv53rtGryRw0X2PwoD/pcaoNIDdmQXaivSvUFNDya0DtVSAqrTAtRqeZWmF2jm/SzgswUDyl133VW+dOnSYDXRn5aWFnD55ZcHf/7556VWq+u/8H799VfTJZdcElydVGAymeT+++8vb9SBA028l607PeBVrLu3qhuN0gUULmr7N/XCVyVWTHojtUF/9r3RNgMAAHeotCa1gqSuFSeesjrtiLa1DbfJ5YM6yhWDO0pURJC3hwUAAAAAfmPLgQK5+r1V9V4YNWfdfkndnePxlN8ie0VNUcDRBQI5xd6bhouOCNKKAXr8LzVApQd0aRsmwVZShwG/LBi48MILK4YPH+5csmSJWRUNfPvtt4GDBg0KfeaZZ8rGjh1bZxlSZmZmwL///W/ra6+9Zi0pKdFuU8e47LLLHKeccgrtCNBkeKOX7YnYAk0y99ZhXu+fVO40/q9uoWrQ76kXvrNvTJar3q3/C2pvts0AAMBdN43orK0g8RWHCu3y7/k75NWFO2Vc72iZmhKvFY7SuxAAAAAA6i4WuOzNhlsQpa6RqgVW6pppQ1/jtFc4ZdfB4j8KA44qDlAtBrxFLf7SCgP+lxqgtRVoFy6RITUdygE0hYIB5dNPPy1NTk4OzcjI0PLDf//9d9PZZ58dEhUVVdWvXz+taEAlB6iLUU8++aTthRdeqNq1a5dJbUfvU//27t278s033yzz8pcENJletkbsFZUS3zrE28OoM2FAFTXA/6kXvt/ffro88vUmrYr2ZPlaZBcAAHUZlRgl5yd1OKm4SlcN69pGu8jyw8YsqTDq8XQMdb9vNxzQNnXBZkpKvFzUP0ZCVU9DAAAAAECtNgQqWaChk4PV8dQCK3XN9GSudao2vxm5JbVTA7ILZc/hYm2fNwRZTFpSgNqqWwmorV24jUJ1oIH4/JUbVRjw/fffl1xwwQXB27dvN1VP/mdlZQX88MMPNeNXty1evNhc/XG16vsnJSVVzp07tyQkxPuTmEBT62WrJ7ugTOJbh/pswgAtCZoO9cL3pcv6a5P+r/+yW1btyfWbthkAAJysR8/vLSv35DRIyo5e6s7Ll/fX/saq13QfrcyQj1ZlaEkCrlIXlB78aqM8/d1WmTggViseUBGQAAAAAACRh+duapT3c4o6rlpgpa6ZGlHzZgcL7bJVFQZkFf7xb3ah7DhYKGUO74R0m00B0rlNqJYWUFMYEBUuca1CtH0AmnHBgNKjR4/K3377rfiOO+4Iev/99y0VFRU1VUN6MZfVt6lfeGazWa6++mrHSy+9VBYcHOzxsQOeWmXdJswqh4u81xfoWFn53i8YcNSRMEBLgqa52lJtqup17vp9sj4zXzbsy69VpatiqvrGREpSXKScnxTj9bYZAACcLDWZr1rpqLjJhlyRov5WquNWr0SJigiSO8d0l1tGdpUfNmXJrBXpsirN9eK8QnuFvL88TduGd2sjU1MSZFRiOy72AAAAAGi2FmzNbtTEOEWlsaoFVup6aX6J4482AlpiQIFszyrSPm7odAN3xLYMrt1KICpcOrcNFVugti4YgIf5RcGAopIBVDuBBx980P7vf//bqtIFNm/ebDo6TeBoXbp0qRw7dmzFXXfdVd6lSxfv5KQAHuSoYzW9N2QVeL/7BwkDzZMqArg7OlH7WP2NKC53au0pVKpEqNVMTBUAoEkVjarelCpusiFWpqhkAVUsoNfrUv0dHZ/UQdtUn82ZK9Llq7X7pNShdYlzyZIdh7UtpkWwXJHcUS47raO0oh0QAAAAgGbm9UW7PfI8d85eL8EWs1ev1bcOtWrXa1VBgFYYEB0u3dqFSXiQxWtjAuDHBQPVOnbsWPXcc8/Z1XbkyBHZsmWLOScnJyAvL08rKmjTpk1V586dK+Pi4igSQLOhJkULSivEl6j4Wm9Tk8RGSBhoHlTiTJjqm2zz9kgAAGgcanJf9aZUcZNqBcnJUitPHhnf26Uelz3bR8j0CX3lvrMT5fNf98oHqelaP0tX7csrlWe+3yb/+nmHnHdKe7kqJUGS4lqc9NgBAAAAwF9szSpwK7WtPlSCgKdSBNRCrWNbCajP24RxYRbwB35XMHC0li1bypAhQ1xf0gI0UYVlDvG1Cpms/Mbpv9RQCQO2QJNHxwIAANBY1CS/6k2pJv1f/2W3rNrj+sWnQZ1ayc1ndJGRie1Oqn3BtcM6ybQhCbJk52GZtSJN5m89KAYhcLrFnV/+tk/bkmIjtXYF557SXoIsRFACAAAAaJrm1qPQ2xdYzAHSpW3YH0UB1YUBUeFakpzJdHwLcQD+wa8LBgD84UiJ93oNGSFhAAAAwLNUb0q1bcsqlLnr98n6zHzZsC+/1ooSNcnfNyZSkuIi5fykGO0CT32pi0JndG+rbZm5JfLBynSZvTpT8tx4jbp+b7789bP18sS8LTLptDi5YnBHiW0ZUu+xAQAAAIAvWb83T/xBQIBIfKuQWq0EVHFAQptQ0nuBJoiCAaAJKLH7VjsCxZt9kao5DBIGzKYAbQMAAGiKVBHA3dGJNa2risud2mp+a6BJi4lULXsaS1yrELn/7J5y55nd5ev1+2XminStaMFVucXl8tqiXfLGL7u04oerhsTL0C5tWKkCAAAAwO+p92cb9xWIr4mKsP1ZGPC/lgLd2oVLsJX0N6C5oGAAaAJCbL73h9uXEwasZtoRAACA5kEVB4TZAkU83DZStRW4ZGCcXDwgVtZl5smsFenyze8H6mwZdbTKKpGft2RrW+c2oTIlJV4mDoiViCBLo48dAAAAABpDkb2iVgKcp4UHBdYUBVT/qzbV5g5A80bBANAEtAzx3h/0QQmtZFXa8X1yDxbYtYrJxlzBdtIFA4EUDAAAAHiCei3Yv2NLbXvg3J5aq4IPU9Nlf77rxaW7DxfLo19vlmd/2CYX9Y+RqSkJDdJKAQAAAAA8yeGs8soJf/nyfjIwoZVERwR59Xo9AN/llYKBf/zjH14vV3rsscfKvT0GoKGEB1m0nkJVVZ6PKrptdFe58p1Vx+1Tq8dUpGzrMA8vZztmDHosJAwAAAB4XJswm9wysqvceHpnmb/1oJY6sHTnYZcfX1LulA9XZmjboE6t5KqUBBnbO4rXdgAAAAD8gsXsncn6kYlRfyTPAYABr/yG+Oc//2nzdhUTBQNoStT/p3bhNskusHvuOUVkxjWD6kw3yCoo827BgEHCgI2EAQAAAK8JNJtkXO9obdt5sEg+SE2Xz3/dq8VzumrVnlxtUwWskwfFy+WD4qRdRFCjjhsAAAAA6kNN2kcGWzzalkA9X6jV91oaA/AtzS6XW0WkA03RiB7tPPp843pHSWJ0hLZSzGzSLwDKLnA9ataTEU/equQEAABAbV3bhckj5/eW1AdGy+MX9pFu7cLcOkWqYPbFn7fLkKcWyF8+Xiur03J5zwcAAADAZxf+9YmJ8Ohz9o2JpA0BAN8tGFAT997YgKZq2tAEjz7fnWN6aP+qYoG2BikCWfmeSzzQU17h1L3dSsIAAACAz620mZIcLz/eebp8fH2ynNM32rAoVU9FZZV8vX6/XPL6Cjn7pSXy8aoMKSl3PbEAAAAAADwhKbaFR090UlykR58PgH/ySkuCn3/+ucQbzws0ZWq1f/eoMNmeXdToz9UjKlx6RIfXfB4VGaS1HziW3m2eVO7Ub0lAwQAAAIDvrrhJ6dJa2w7kl8rHKzPko1WZcrjI9ULUrVmFcv+XG+TJeVvk0oFxWiFCQpvQRh03AAAAALji/H4d5NVFuzx2ss5PivHYcwHwX14pGBg1apT+sl8A9XLf2YlyzftrPPI8R4uOsMl6nftl5/tqS4Jm140FAADA77SPDJa7xvaQW0d1k+82HpCZK9Ll1/QjLj++sKxC3lm6R9tO795WrkqJ19p4uZNcAAAAAAANvfBvUEIrWZWW2+gndlCnVrUW/gGATxUM4MS2bNliWrdunSkzM9NUUlIiwcHBEh0dXdW9e3dn//79K4OCgk76NJaWlsrSpUvN6jmOHDkSYLVaJS4urjI5OdnZtWtX+jb4sVGJUXJW72j5flNWoz3H2X2iZWRiu1q3RUfo/zxmF3q3YMBeYZAwQMEAAACA31DpUBf0i9G2TfvzZdaKdPlq3T4pc+i/1tOzePshbYttGawlDqjkgZah1kYdNwAAAADouWlEZ1n1fuMXDNx8Rhe+AQBcQsGADykoKJB//etf1vfee8+alpZmuOxFTfAPHDjQOWHChIq//vWv5a4ePzs7O+CRRx6xzZo1y1JcXKx7H1WM8OCDD9rVsU/yy4CXTZ/QV1an5UpOscs/Gi5rHWqVJy/qe9ztqiWBniyvJwzQkgAAAKAp6d0hUp6aeIrcf3ZP+ezXTC11ICPX9Y53e4+UyvTvtsoLP22X8Ukd5KqUBOkbS09PAAAAAJ5d+Hd+UgeZu35/oz3HBf06HLfwDwCMkMvtI+bMmRPYrVu3sIcffthWV7GAUl5eLsuXLzc/88wzLi+JmT9/vrl3796hr7/+umGxgLJ27VrTxIkTg6+88sogu931PqHwHWql1IfXD5YwW8PWA6njqePqrcQyTBgo8G7BQDkJAwAAAE1SZIhFrhveWRb9bYS8N+00GdmjrQQEuJdE9fmve2X8K0vlwv8sk/+u3Sv2CjrnAQAAAPCMR8/vLVERtkY5tjruI+N7N8qxATRNFAz4gGeffdZ60UUXBR88eLDWJS7VdqBTp05VKk2gd+/ela1btz6pdgG//PKLefz48SE5OTm1jt+iRQtJSkqqjI+PrzKbzbUe8+GHH1omTZoUXFnpeswnfKsP0uc3p0jb8IZ5waGOo46njqsnyqBg4EiJQ8ocTt8rGAjkVx8AAEBTYDIFyMge7eS9aYO04oEbTu8skcEWt46xLjNP7py9XoZMXyDP/rBV9uWVNtp4AQAAAEBRC/NmXDPI7fcvJ6KOp45LCzYAzaIlwdatW00rV6407969OyA/Pz+gqKgoICwsrCoyMrKqc+fOVYMHD3YmJib6/Gz3G2+8YbnnnntqzeqOHTu24rbbbisfPXq0UxUNHG3v3r0B8+fPD/zqq68C16xZU3uWX0dubq5cfvnlwaWlf1706tixY9WLL75YduGFF1aYTH9MnGZkZAQ8/vjjtrfffttydOrBc889Z73nnnsaPtsejU5N7v94x+nyyNebZM66/fWKLlLViHW9wDAqGFAOFtilY+sQ8aWWBBYzBQMAAABNTXzrUHngnJ5y55nd5ev1+2XGijTZtL/A5cerll7/WbhLXlu0S87sGSVXDUmQIV1aS4A70QUAAAAA4MY1/Nk3Jstlb6ZKXomjQZIFVLGA0cI/AGgSBQNqUvvVV1+1zpw505KdnX3CqzZRUVFVU6dOddx8883lahW9+Jjt27cH3HHHHTUzrRaLRd59993SK6+8ssLoMbGxsVVXXXWVQ22qGOBEnnrqKduBAwdqzlVCQkLVkiVLitVxjr6fKiJ46623yuLi4ipVW4Tq25988knbddddV96qVauT/TLhRWqS/6XL+muT/q//sltW7Tnxz0y1QZ1ayc1ndHGpz1F0pHHBQFZBmdcKBkgYAAAAaH6CrWa59LQ4uWRgrPyWkSezVqTJtxsOiMPp2lvCyiqRHzdna1uXtqEyNSVBJpwaI+FBDbvyBwAAAADU5H73qDBZtedIvU6GKwv/AMCvCwacTqc888wz1ieeeMKmVspXVdW+0HP0io+j92VlZQWouP+XX37Z+ve//92uVsoHBvrOl3zDDTcEl5X92eN95syZpZdddplhscCxTjSJr4oqXnvttVp/Hd54443SY4sFjvbggw+W//TTT4FLly7V0gvy8/Pl6aeftj399NN2V8cF3zMqMUrbtmUVytz1+2R9Zr5s2Jcv+aWOWlFFfWMiJSkuUs5PipEe0eEuHz/MFqhtRfYK3YIBb7EbJAzQkgAAAKDpU+8TB8S31La/n9tLZq/OkA9XZsiBfNdfn+46VCwPz90kz3y/VSacGitTU+KlW5Trr5MBAAAAoC6b9ufXq1jAnYV/AGDEd2bPDdjtdpkwYULw999/H1hdDFBdIFD9+bEFBMfeRxUZPPTQQ7bFixebv/rqq9JjY/694csvvwz85ZdfaloKTJgwocKdYgFXfPTRR4FFRUU1nw8bNsw5duzYOhvKqxYF//jHP+xjx46tWRI+Y8YMy/Tp0+3V7Qvgv1QRwN3RiTX/N4rLndoqfDWBHmo11ytuVcUdFR06/kc4240Lsg3NUWFQMEBLAgAAgGalbbhNbh3VTW46o4v8vCVbZq5Il+W7clx+vHrdPCs1XdtSOrfWCgfG9IqSQF5XAgAAAKiHd5bsMdx3SmykpOeUNNjCPwDw24KBiRMnBn/33XfaONVkpprkVFunTp2qhg8fXtG7d+/KNm3aVIWGhlYVFxcHHD58OGDjxo2mpUuXBu7Zsyfg6MeplfPqeN9++22pt7+uN998s1ae5SOPPNLgK/jnzp1b6zmuueYal5rgjB492qlaF6SlpQVUJxUsX77crAoOGnqM8B71/0KlAkhNA4r6UW0J1AqsY2V7MWGgnIQBAAAAHEVN8J/Vp7227cgu1AoAvvh1r1YQ4KoVu3O0LToiSK4Y3FEuG9RRK0gAAAAAAHdk5ZfJ3PX7dfeppLQvbh7S4Av/AMDvCgbeffddy7x58wKPTgsYPHiw86mnnrKfccYZJ7yis2jRIvN9991nW7Vqlbm6aEAlFbz33nuWadOmuTR53hgyMzMDfv7555pzf8opp1T27dtXfyn0SSosLJRly5bVJBgo48aNcynBQCUJjBo1qkKd/+rbvv7660AKBlCXqAj95A5vtiRwGBQMWMy8oAIAAGjuVGuBxy7oI3eP6yH/XbtPZixP0y2ANaJe5z7/03b594Idck7f9lrqwKkdW3LxDgAAAIBLZqxIk4pK/Q7S1w/v1CgL/wBAj09nzD/55JPW6ol+5fbbby9fsWJFiSvFAsqIESOcqampJX/5y1/K1TGqj/XEE09YxYtUEYTT+eeXMGLEiAZtRaBs2LDB7HD8WROhEgM6dOig/5dHx9ChQ2ud4/Xr1/v0zwq8T62w0uPVhAHDlgS1amkAAADQjIUHWWRqSoL8fNcZ8tF1g2Vc7ygxuVFf6nBWyZx1+2XiayvkvJeXyuzVGVLqRmIBAAAAgOan2F4hH6am6+7r2CpExvSK9viYADRfPjsJvHr1atPu3bu18amJ/rPPPrvixRdfPKnY/pdeesmuHl9deLBnzx6TOr54yZo1a2rNViYlJdXMaq5Zs8Z0yy232Pr27RvasmXL8JCQkPD4+PiwUaNGhTz11FNWlU7gynNs3ry51teXmJjo1hWrXr161br/tm3bmGGF3yUMGBYMBPrsrz4AAAB4iXrfOaRrG3ljykBZeu8ouXVkV2kd6l6t+ab9BXLvFxskefp8eeLbzZKe43piAQAAAIDm4/Nf90pBmf5a0muGJojZnSpmAKgnn50127hxozZBXT3J/+STT55UsUC16sdXtzeoPr4vFAx07ty5UrUQuPrqq4NOO+200FdffdW6ceNGU15enpSWlkpGRkbAwoULzffff7+tR48eYffcc4+tvLy8zufYtm1bre9tXFycy+kCSseOHWvdX41BjQVwt2Agu8Be8//Y08qd+s9LSwIAAADUpUOLYPnbuB6y/P5R8q9J/aR/xxZunbD8Uoe8tWSPjHhukUx7b5Us3HpQKg2iRgEAAAA0L87KKnln6R7dfRFBgXLJwDiPjwlA8xYoPmr//v015VPt27evOuWUU/SXCrtIreJXkfzquKpo4Ojje1p1ckI1k8kkw4cPD3Ul9l9N2j/77LNWVXTw1VdflUREROje79ChQ7W+vpiYGLfOX3R0dFVgYKBUVPxR4VZZWSmHDx8OcLfw4FhZWVkBx47tRHbs2OGzhS34U3RkkOEq/yMlDmnl5uqshlBeoR+sYSNhAAAAAC6wBZrlwv4x2rZhb77MXJEmc9fvF7tBktWxVN3swm2HtC2+dYhcOTheLhkYKy1CvNolDwAAAIAX/bQ5WzJyS3T3TR4cL6E2n526A9BE+exvHTVZrajJ/fbt29erWODoSfDqQoHq43ua0+kUlSZwtNtvvz2ouliguv2C2tTkfHFxsfz222/mjz76yHLgwIGaiXaVODB16tTgr776SnfZf1FRUa1J+dDQULfGqYoYgoODa431f8esV8HAK6+8Yn3iiSe4OtYERRskDChZ+WVeKRhQ/WT1WMzUoAAAAMA9fWMj5dlLkuSBc3rKZ79myqzUdMnMdT2FLT2nRJ6Yt0We/2mbXJAUI1NS4qVPTCTfBgAAAKCZeXvJbt3bA00BcvWQBI+PBwB8dtYsISGhpkggPz+/QdIACgoKaj6Oj49vkCIEd+Xn5x8Xz75u3Trt+9CqVauqBQsWlHz77belt956q+OCCy6omDx5csVzzz1n37ZtW9GkSZNqNbSZM2dO4HvvvWfRe56ioqJanwcFBbk90X/sY44tdACO1ibMKkZtlbILyrxyslS6gR4rCQMAAAA4SS1DrXLD6V1k0d9GyrtXD5Qzurd16/FljkqZvSZTznt5qUx8bbnMWbfP8HUrAAAAgKZlbcYRWZN+RHff+KQOhkm+ANAsCwaGDh3qVKvt1eR6WlqaScXh1+d4KgZfHaf682HDhulnlTeywsJC3a/DbDbL119/XTpixAjdcYWHh8uHH35YOnr06Fr7p0+fblXtAo5lt9trPY/V6v7qbpvNVuvz0tJSr7VxgO8LNJukbXjtnxmvFww4KRgAAABA4zCbAmRUYpTMuGaQLPzbCLl2WCet36g7fk0/Ird/sk6GPLVAnv9xmxzIdz2xAAAAAID/eXvpHsN91w3v5NGxAIDPtySIjY2tGjlypHPBggVmFeP/yiuvWB555JHykz3eyy+/bKmoqNAi/0eNGuVUxxcvCArSrw6bNm2aY8iQIXUWMaiigtdff720R48eYdVFAjt27DCp9gTHFhLYbLZaX195ufunzm63Hzv2ep+zW2+9tXzSpEkOdx6jvsaJEycG1/e54Zm2BNkFtX9ulCwvFQw4DFZq0ZIAAAAADalTm1B56Lxe8tex3WXOuv0yc0W6bDnwZ8LdiRwussvLC3bKq4t2ydheUVq7gpTOrbX3rwAAAACahszcEvluwwHdfUO6tJbeHWhZBsA7fLZgQHnmmWfKUlJSQtVE/9NPP207/fTTnWqy393jzJ8/3/zMM8/Y1MUWtdL+ueee887s5R9JAbqT7tdff71LM/pdu3bVCinU11R926JFi44rGAgLC6v1uLKyMrevNB37GJVyUF/R0dFVaqv3geCToiJUQUy+zyQM2EkYAAAAgAeFWAPl8kEd5bLT4rT0gBkr0rULghWVrr0FclZWyXcbs7StW7swmZoSLxedGithNp9+6w4AAADABe8tSxOjtwakCwDwJp9tSaCceuqple+8806pWlmvVrufd955Ic8995xVFRC4Qt3v2WeftY4fPz5ErbBXx1HHS0pK8lqDyJCQEG0cx07EDxgwwOUxnX766bVOwK+//lr7gH8UDNT6s1NcXOzWOFWCQWlpaZ3HBPQLBo6Xle/5ggHVzsRhVDBg9ulffQAAAPBzqlh9YEIrefny/rL8vlFy55ndJSpCv32XkR0Hi+ShOZsk+cn58vCcjbLzYFGjjRcAAABA4yooc8js1Rm6+7q0DZUR3dvxLQDgNT6/TOHKK6+saN++fcm1114bnJGREXDvvffaXnrpJesll1ziUCvt+/bt62zTpk2VmogvKSmRQ4cOBWzYsMGsVt1/9tlnlv379weoicP4+PgqVSxwMgkFDa1t27ZVWVlZNav3O3fuXHlsEUFdEhMTa82Cqq9Z7zmO/nzfvn1uzZCq8R1dmGEymY47JnCs6EiDggGdNgWNTa3iqjL4ibUGUjAAAAAAz2gXESS3n9lN/m9kF/lxU7bMXJEmK/fkuvz4InuFllSgtqFdW8uU5AQ5s2c7CaQIFgAAAPAbn6zKkOJy/emp64Z3FpOJdmQAmlnBgNlsDj/ZVRpq8n/fvn0BqmjgpZdeqvP+6r7Vj1PFBmPGjAmp/ryioqJQvERN+GdlZdVUCERERLg1EX/s/fPy8o77S9KjR49aRQXq63fnOdLT02vdv2PHjlXBwcHuHALNkFHCgDdaEpRXGId2kDAAAAAAT7OYTXLuKe21bVtWoVY48N+1+6TE4KKhnmU7c7StQ2SQXJEcL5NOi5M2Ye4lFwAAAADwLJWE+/6yNN19rUOtclH/GL4lALzKK8ts1US+u1s1NdlfXThwoq36vnrP6009e/asNZOp2i24o6ysrNZkvt5E/rHPsXXrVtcjDERky5Ytte7fvXt3ryczwPdFGxQM5BaXi73Csz9CRu0Iqi/WAgAAAN7SIzpcnrior6Q+MFoeHt9LOrcJdevx+/PL5NkftsmQ6QvkztnrZG3GEa+/zwUAAACgb96GA9preD1XJsdLkMWt6RsAaHBemzU7eiLfFUYFBA39GE/o379/rZnTgwcPuvV9yM7OrnXyWrdufdwXp1o1WCyWWokBKpnB1edYtmxZrb9QSUlJxrOvwP9ERxqvbjro4bYEdSYM0JIAAAAAPiAiyCLThnaSn+86Q2ZdO0jG9IoSd5JIy52VWkrBRa8ul/NfWSafrcmUMge13gAAAICvUHNTby/ZY3idekpKvMfHBAA+0ZJg2LBhTncLBpqS8ePHV5hMJqms/GNCMy0tLSAnJ0dN/Lv0+N9+++3Y1f/HzYxGRETI0KFDnYsWLaq5748//hg4bdo0x4mOr8a1YMGCWj8b559/foVLg0OzZtSSoLotQVwrrSuIR9jrKBiwUTAAAAAAH6L6lQ7v1lbb9h4pkQ9XZsjs1ZlaUperNuzLl7s//12emLdFJg2M01YqefL1NwAAAIDjrdqTq71W1zOhfwwtxgA034KBxYsXl0gzFh0dXZWSkuI8ehX/Z599ZrnppptOOJnvcDhkzpw5tb5vI0aMqDAqTDi6YODdd9+1uFIwMH/+fLMqYqj+vF27dtp4T/Q4IDzIIqFWsxTr9GHNKtCPXGostCQAAACAP4ptGSL3npUot4/uJt/+fkBmpqbL+sw8lx+fV+KQNxbvljeX7JZRPdrJ1CEJMrxrG60oAQAAAIBnvWWQLqBcO6yTR8cCAEZo5O0lN9xwQ62lIi+88IK1rOzEE6qvv/665eiWBCpJ4Oyzz9YtGJg8ebIjNPTPXphLly41//TTT+YTpQs89thjtXLlp06d6jCb6aED10RF6qcMZBn0aGosKp7VCC0JAAAA4OtUH9OJA2Jlzi1DtW3iqbFuvY5VXfnmbz0oV727SkY9v0jeWbpH8ktPWD8OAAAAoIHsPlQk87dm6+4b0aOtdIsK51wD8AkUDHjJFVdcUdG7d++aGc0dO3aYrr/++iCn03gh//Lly80PPPBA0LGFBy1atDBMMrjppptqFSbccMMNwXv37jVcWvLPf/7TqgoLqj+PjIyU++67z7PN5+HXosKDDFsSeJKjospwn8XM6ioAAAD4j6S4FvL8pUmSev9oLX0gpkWwW49PyymRx7/ZLMlPzpf7v/xdNu8vaLSxAgAAAPjDu8v2aIW8eq4f3pnTBMBnUDDgJWrF/gsvvFAWEPDnxOUHH3xgGTNmTMiqVatqfV/y8vLkmWeesY4bNy6kqKio5vZu3bpVPvTQQ3VO5t9///32qKiomj9JqtXA0KFDQ//73/8GqjSBahkZGQGqYOHhhx+ulS5w77332lu3bl3vrxfNR7RRwkCBZ+tOyusoviFhAAAAAP6oVahVbh7RRRbfM1LemjpQhndr49bjSx1O+XhVppzz7yVyyevLZe76/VJeYZzMBQAAAODkHCkul89/3au7r2f7CBnShXkXAL4j0NsDaM7Gjh3rfPzxx+0PPvhgzST9woULzYMHDw5Vk/wxMTFVxcXFsmfPHlN5ea2gAGnVqlXVZ599VqpaEtRFTfZ//PHHpeecc05IdcsDVRwwYcKEYJVMEB8fX5mfnx+QmZkZcGy6wXnnnVdx77331n5i4ASiIgwSBjzcksBex4VPq5laKQAAAPgvsylAxvSK0jYVczorNV0+X7NXCu263ep0rU47om1tw21y+aCOcsXgjoav5QEAAAC454PUdClz6F+jvm5YJzl6MSkAeBuzZl7297//vVwlDVgsllq3Z2dnB/z222+mbdu2HVcsoJIFli9fXpKUlOTSUpCRI0c6586dW9KyZcuqY5ML1q9fb1KpA8cWC0yaNKlCFSSYTPyIwD3REbVCKmpkebolgbPKsB0BL8YAAADQVHRuGyYPj+8tqQ+Mlicu6iOJ0e71QT1UaJd/z98hQ59aILd8+Jus3J0jVUa5qQAAAABOyF7hlBkr0nX3RUXYZHxSB84iAJ/CbLAPuPPOOx3r1q0rvvjiiyuOLRw4WkJCQtXzzz9v37BhQ3GPHj3cyo0cM2aMc9OmTcU33HCDIyQkxPB+qgjh008/Lf3kk09Kg4JYXYKGbElQ5tELj0bRqqQLAAAAoCkKtQXKFYPj5bvbh8vsG5Ll3FPaS6DJ9VVLFZVV8u2GAzLpzVQ5619LtBVRxW4kFgAAAAD4w5x1++VwkX6L3quGJNAyF4DP8buWBJs3bzYtXbrUvG7dOvPhw4cD8vLyAo5dgX8ianXxwoULS8SH9OrVq1Kt6M/Pzxf19W3fvt2kWgWEhYVJdHR05YABAyp79uxZr+aS7du3r3rjjTfKXnzxxTL1HOpcqvNntVolNja2Mjk52dm9e3eWkqBejGJM1QR+fqlDWoRYPXKGHU79/y6WQOqkAAAA0HSp97uDO7fWtuyCMvloZYZ8tCpDSxJw1bbsQnnwq43y9HdbZeKAWJmSEi9d2oY16rgBAACApkAtmntnyR7dfcEWs0we1NHjYwKAJlMw8OWXXwY+/fTT1jVr1pjr+8val+PIIyMj5dxzz3WqrbGeQyUMjB071qm2xnoONF9GCQOKShnwVMEACQMAAABo7lQx751jusstI7vKD5uyZOaKNFmddsTlxxfaK+T95WnaNrxbG5makiCjEtuJ2Y3kAgAAAKA5WbLjsFaAq+fSgbEeuz4OAE2qYKCiokKuv/76oJkzZ1qOnfA/Ot5crwjgRPsBNLy2YTZR/930ug9k5ZdJYnSEdwsGSBgAAABAM6NeA6s+qWrbvL9AZqWmy1dr90mpw+nWhU+1xbQIliuSO8plp3WUVqFc7AQAAACO9taS3bonRF0znza0EycLgE/y+Wzu2267LWjGjBmWoyf/1cfVhQPVW/VtR296+wE0rkCzSdqE2XT3qUhUTyk3aElgNfv8rz0AAACg0fTqECHTJ/SV1AdGy0Pn9ZKE1iFuPX5fXqk88/02SZ4+X+76dJ2sz8xrtLECAAAA/mRbVqFWZKtnbK8oSWgT6vExAYArfHrmbMGCBebXX3/dUj3pb7Va5b777ivfvHlzcWlpaeHRBQA//PBDibpt7969Rd9//33J3XffXd62bduq6vskJiZWrlmzpriysrLQ6XTq58EAaBDREfptCbLyXe+bWl8kDAAAAADGIoMtcu2wTrLgryNkxjWDZHRiO23Vkzuvt7/8bZ9c8J9lcsErS+WLX/dKmRuJBQAAAEBT87ZBuoBy/fDOHh0LADSZgoHp06dry5TVpL/ZbJavvvqq5Mknn7SryX+b7fgVzOq2Dh06VI0dO9b59NNP29PT04vuuOOOcrVv27ZtppEjR4YuWrTI7IUvBWhWVK9UPVm+kDBASwIAAACghskUIGd0byvvXH2aLL57pNx4RmdpEaJ1BHTZ+r358tfP1suQpxbI099vlb1HSjjDAAAAaFYOFpbJnHX7dfclxbWQAfEtPT4mAPD7goGioiJRk/vV6QLXXnutY9y4cW4tV1AFBC+88IL93//+d5kqOigsLJSJEycGHzhwwI11EwDcFR3p/ZYEjgr9ggELLQkAAAAAXXGtQuT+s3tK6v2j5dmLT5G+MZFunanc4nJ5bdEuOf2ZhXLdjDWyZMchqaykNSAAAACavlkr0g0XsV0/vJM2zwUAvspnCwaWL19udjqdWrqAcvPNN2tJASfjlltucUyYMKFCfZyXlxfw4IMP6s9mAmjklgQ+kDBAwQAAAABQpyCLWS4ZGCdzbx0q//2/ITKhf4xbr6NVjcDPW7Jlyjur5MwXfpH3lu2RgjIHZx0AAABNUmm5Uz5ITdfdF9MiWM7qHe3xMQFAkygYSEtLqxlbZGSknHLKKfqzf/9jt9fdG/3BBx/U7qAKED755BNLaWlpww0WgEstCVQsk7cLBiy0JAAAAABcolZB9e/YUl6Y1E+W3z9K7h7XQzpE6r/WN7L7cLE8+vVmSX5yvvz9vxtkW1YhZx8AAABNyhe/7ZUjJfoFstOGJkggi9gA+DifLRjIzc0NqL5AERsbqzvzFxgYWPNxWVlZnXku/fr1q2zXrp0WV1BWViZLly41N/igAWiiDS4iHi4ql3KDVgENzeh5SBgAAAAA3NcmzCa3jOwqi+8ZKW9MGSDDurZx6/El5U75cGWGjPvXYrn0jRXy7e8HxGFQ5AsAAAD4C9WC692le3T3hdsCZdJpcR4fEwA0mYKBigqtg4AmNDRUt+lhWFhYVXXLguzs7BM2gImJiak5ztatW332aweaasKAJ1MGjAoGbCQMAAAAACdNrY4a1ztaPrhusPx81xly9ZAECbP9WczvilV7cuWWj36TYU8vkJd+3iEHCzyXRAYAAAA0pPlbD2qpWnouGxQn4UEWTjgAn+ezk+aRkZE1k/tFRUW6xQAtWrSo+Tg9Pf2EX0tl5Z8TiHl5eScsMADQ8AUD2R66GGi0Wsli5r8+AAAA0BC6tguTR87vLakPjJbHL+wj3dqFufX47AK7vPjzdhny1AL5y8drZXVartZGEAAAAPAXby/ZrXu72RQgVw/t5PHxAMDJcG8ZgAfFx8drVwnUxYIjR47ozvB1797dmZaWpn0Nq1evNp8osWDXrl0m1eJAsdlsjTJuACIRQYESbDFLqcN53OnIyrd7tyUBCQMAAABAg1IJA1OS4+XKwR0ldXeuzEpNkx82ZYuz0rXJ/4rKKvl6/X5tS4wOl6uGJMgF/TpIiNVnL1kAAAAAsmFvvqzck6t7Js7t215iWgRzlgD4BZ9NGOjTp0/NTOOBAwcCCgsLj7tPUlJSZXVRwfLly8179+41XDr8xRdfBBYVFdWsVujQoQPNEoFGogpzoiP1UwayPJYwoH9x0mL22V97AAAAgN+/D0jp0lpevWKALL13pNw2qqu0CXOvWH9rVqHc/+UGGfzkfHn8m82SZhDvCgAAAHjbWwbpAsp1w0kXAOA/fHbmrHPnzlVRUVE1M36rVq06LkHgoosuclRflFAJAjfddFOQw6HdVMvOnTsD7rzzzqDqdAFl2LBhxy99BtBgoiJsXm1JYCdhAAAAAPCa9pHBctfYHrL8vlHy0mX9ZEB8S7ceX1hWIe8s3SMjnlskV727SuZvcT2xAAAAAGhs+/NK5dsNB3T3DerUSk6J/bOlNgD4Op/O9xsxYkTF7NmzLerjb7/9NnD06NG1JvmTk5Mre/fuXbl582at8OG7774LPPXUU0Ovueaa8sTExMry8vKAJUuWmN9++22rSihQ6QKqaEAVCyQkJHClAWhE0REGCQP5nikYKHfSkgAAAADwNtUS7IJ+Mdq2aX++zFqRLl+t2ydlDtdD/37Zfkjb4loFy5WD4+XSgXHSMtTaqOMGAAAA6vL+8jTDgtbrh3fm5AHwKz6bMKBMmDChovrjL7/80lJZefwFhZdffrlm9lEVBGzatMn0t7/9Lei8884LmTBhQvCLL75oLSgoqLm/xWKRp556yjMzlkAzFmXQksBTCQMOo4QBWhIAAAAAXtG7Q6Q8NfEUWXn/mfLguT2lY6sQtx6fmVsq07/bKsnT58vdn63XesYCAAAAnlZkr5CPV2bo7uvUJlRGJ7bz+JgAoMkWDIwfP76iX79+lSotIDQ0tGrp0qXHtSUYMWKE89///rc2A6nSA9SmCgeqt+rb1ceqWOC1114rS0lJcX0pA4AGTRjwVMGAYcIABQMAAACAV0WGWOS64Z1l0d9GyHvTTpORPdrKUR0EXWo/9tmve2X8K0vloleXyX/X7hV7BV0HAQAA4BmzV2dKob1mvWst1wzrJCaTGy9uAcAH+HRLgqCgIPntt9+KT3S/W265xdGjR49KlSzw+++/1yqCqC4aGDx4sPPZZ5+1q3YEjThkAP8TZdSSoKCspj1IY3IYFAxYAn26TgoAAABoNtSF1JE92mlbek6xfJCaLp+u2Sv5pQ6Xj7E2I0/b/vnNFrlsUJxcMTheOrQIbtRxAwAAoPmqcFbKu0v36O5rEWKRi0+N9fiYAKBJFwy448wzz3SuW7eueNu2baYVK1aYs7KyAtSkZPv27auGDh1a0a1bN/1mMgA8WjCgepUWlFZoq4oaUzktCQAAAAC/Ed86VP5+bi+5a0wP+Xr9fpmxIk027f+zveCJ5BSXy38W7pLXFu2SMb2iZGpKggzp0rrRC5UBAADQvPywKVv25ZXq7rtycLwEW48LygYAn9dkCgaqqaQBtXl7HEBzFx2pXzBQnTLgtYIBEgYAAAAAn6UusF56WpxcMjBWfsvIk5kr0mTehgPicLq2BqCy6o+LuGrr2i5MpiTHy4RTYyQ8qHHffwAAAKDpU4tU31qy27AV7tQh8R4fEwA0BLK5ATSKduE2wz6kqmCgsZUbtCRQL9wAAAAA+DaVDDAgvqW8dFl/WX7faPnrmO4SbZBiZmTnwSJ5eO4mSX5yvjz01UbZkV3YaOMFAABA0/dr+hFZl5mnu+/8fh2kXbh7r1cBwFcwcwagUVjMJmkdatPdl53vgYIBEgYAAACAJqFtuE3+MrqbLL13pLx+5amS0rm1W48vLnfKrNR0GfPiYrn8zVT5bsMBrfcsAAAA4I63l+wx3Hfd8E6cTAB+q8m1JADgO6IjbXK4yO5bCQO0JAAAAAD8UqDZJGf1aa9t27MLZdaKdPnyt71aQYCrVuzO0bb2kUEyeVBHuWxQR60gAQAAAKhLek6x/LA5S3ff8G5tJDE6ghMIwG+RMACg0RhFhmZ7oGDAYZAwoJIPAAAAAPi37lHh8viFfST1gdHy6Pm9pUvbULcefyC/TJ7/absMeWq+3P7JWvk1PVfrSQsAAADoeW9Zmhi9XLxueGdOGgC/5pWEgbS0tOM6myckJFS5cr+Govd8ABpWlBcLBkgYAAAAAJq+8CCLXDUkQaamxMvyXTkyc0Wa/LQ5WypdfMfvcFbJnHX7ta13hwjtOOcnxUiw1dzYQwcAAICfyC9xyKdrMnX3dY8Kk9O7tfH4mADA7wsGOnfuHBYQ8GctgPq4oqKi8ET3ayhGzwfAMwUDjd2SQK0MUhf+9FjMjVaHBAAAAMBL1Pv8oV3baNu+vFL5aGW6fLIqU3KKy10+xqb9BXLvFxvkyXlb5dKBsXJlcrzEt3YvuQAAAABNz4er0qXEoA3WdcM6a69FAcCfeaVgoJqrcX/EAgJNqyVBVr7dK+kCii2QlgQAAABAUxbTIljuHpcot43uJvM2HJCZK9JlbUaey4/PL3XIW0v2yNtL98iI7m1lakqCnNG9rZhMXAgGAABobsorKmXG8jTdfW3CbHJB/w4eHxMANKmCAQBNW1SkfsFATrFdHM5KsZhNjfYizojVTLQoAAAA0BzYAs1yUf9YbduwN19rVzB3/X6x1/F+4WhqjcPCbYe0Lb51iFw5OF4uGRgrLUKsjT52AAAA+IZvft8v2QX6C+CuSonXXnMCgL/zSsHAlVde6WjI+wHwr4QBdeHtYKFdW/nTGIzaESiWQFYFAQAAAM1N39hIefaSJHngnJ5a/9kPVqZLZm6py49PzymRJ+Ztked/2iYXJMXIlJR46RMT2ahjBgAAgHep9GuVPKUnyGKSK5LjPT4mAGgyBQMzZ84sa8j7AfCvggElK7+s0QoG6k4YoCUBAAAA0Fy1DLXKjWd0keuGd5ZF2w5q7Qp+2X7I5ceXOSpl9ppMbRsQ31KmpsTL2X3ai5XWZwAAAE3Oil05suVAge6+iafGSqtQkqcANA20JADQaCKCA7VKS3VR7VjZBY1XD6TaHRhprDYIAAAAAPyH2RQgo3tGaduew8XyQWq6fLYmUwrKKlw+xq/pR7Tt8bAtcvmgOJk8uKO0j2ycomgAAAB43ltLdhvuu3ZYJ4+OBQAaEzNnABpNQECAYcpAYxYM1NWT1MbKHwAAAABH6dQmVB46r5ekPjBapk/oKz3bR7h1fg4X2eXlBTtl2NML5eYPfpXluw5r8bUAAADwXzsPFsrCbfpJVGf2bCed24Z5fEwA0OwSBrZt22b69ttva8Y3duzYij59+hjPAgLwSVERQZKWU3Lc7VmNWDBQZ0sCCgYAAAAA6AixBsrlgzrKZafFyZr0I1q7gu82HJCKStcm/52VVfLdxixt69YuTGtXcNGpsRJm89lLLwAAADDwztI9hudGtbcCgKbEZ9+1zps3z3z33XfbqlcpX3TRRQ5vjwnAyRUM6MnOpyUBAAAAAN+jrkGcltBK2w6e21M+XpUpH65Ml4OFdpePseNgkTw0Z5M8/f02mXhqjExJSZCu7ViFBgAA4A9UgtQXv+3T3dcnJkIGd2rl8TEBQLNsSVBUVBSgIvzU1r59+6pOnTqR5wf4oehI/YKBRk0YcJIwAAAAAKD+2kUEye1ndpNl942S/0w+1e2Lw0X2CpmxIl3OfOEXueLtVPl+Y5ZU1PF+BQAAAN73QWq6YYrt9cM7awWmANCU+GzCQNu2bbUCAfWLt3379rybBppawkCB66tz3GX0Yk69jgs08WIOAAAAgHssZpOce0p7bduaVSCzVqTLf9fuk5Jyp8vHWLYzR9s6RAbJFcnxMum0OGkTpgUrAgAAwEeUOZzaaz097SOD5Jy+7T0+JgBotgkDMTExNYkC+fn5zPABfiraoGAgK79MSxDxZMKAushH9ScAAACA+kiMjpAnLuorqQ+MlofH95LObULdevz+/DJ59odtMmT6Arlz9jpZm3Gk0d4bAQAAwD2qKDSnuFx339VDErRrzADQ1PhswsCQIUMqLBaLOBwOycjIMBUVFUlYGP3+AH8THam/YqbU4ZSCsgqJDLZ4LGHAxos5AAAAAA0kIsgi04Z2kqtSEmTZrsMyc0W6zN+SLZVVrhc6qwvSausbEylTU+JlfFIHCbKY+R4BAAB4QWVllbyzdI/uvlCrWS4b1NHjYwIAT/DZUqjWrVvLmWeeWaE+Li8vl08//bThZxUBeK0lgZJdUNYoz+kwShgI9NlfeQAAAAD8lMkUIMO7tZW3pg6UxfeMlJtHdJFWoVa3jrFhX77c/fnvkjx9vkyft0Uyc0sabbwAAADQ98v2Q7LzYJHuvkmndWyUxW8A4At8evbs/vvvL6+OD3/ooYds2dnZtCYA/Ey7cM8XDBglDFhJGAAAAADQiGJbhsi9ZyXK8vtGyfOXJElSXAu3Hp9X4pA3Fu+W059dKNe+v1q7aK1WugEAAKDxvbVkt+7tpgCRaUMT+BYAaLJ8umBg2LBhzscff9yuevllZWUFjBo1KmTTpk0+PWYAtVkDTdImTH91TVa+hwsGSBgAAAAA4AGqrcDEAbEy55ah2jbx1Fi33o9UVYnM33pQrnp3lYx6fpEWjZtf6mjUMQMAADRnm/bny/JdObr7zu7TXuJahXh8TADgKT4/+f7AAw+Uv/LKK2VBQUGyZcsW04ABA0KnTJkS9O2335pzcvR/eQPwj5QBj7ckMBNSAgAAAMCzVMrA85cmSer9o7X0gZgWwW49Pi2nRB7/ZrMkPzlf7v/yd9m8v6DRxgoAANBcvbNkj+G+a4d38uhYAMDTAsWHderUKaz648DAP4ZaXl4uH330kUVt6vOwsDAJDw+vslhc7x2j2hzs3r1bvxENgAYXHRkkmw8cf1Erq5EKBuyGCQPmRnk+AAAAADiRVqFWuXlEF7nh9M6yYOtBmbkiTZbsOOzyiSt1OOXjVZnadlpCS5makiDjekeTpAYAAFBPKgl37vr9uvsGxLeUUzu25BwDaNJ8umAgPT09QE3uq5YE6l+1KerzaoWFhWpza9lw9XEAeEZUhH7CQFa+vVGez+HU7/FpJWEAAAAAgJeZTQEypleUtu0+VCSzUtPl8zV7pdBe4fIxVqcd0ba24TaZPKijTB7c0fB9FwAAAOo2Y0WaVFTqX1O+nnQBAM2Az7ck0Jvgry4eOJkNgOdFR3i2JUG5YcKAX/zKAwAAANBMdG4bJg+P7y2pD4yWJy7qI4nR4W49/lChXV6av0OGPrVAbvnoN1m5O6fWIgsAAADUrdheIR+mpuvu69gqRMb0iuYUAmjyfDphIC4uropJfsD/RUfadG9vrJYE5U6n7u0UDAAAAADwRaG2QLlicLyWFrBqT67MTE2XHzZmGa50O5a637e/H9A2VXQwJSVeLuwXox0XAAAAxj7/da8UlOknPV0zNEFLhwKAps6n3zmmp6cXeXsMAOrPKBrzcJFdHM5KsZhNHmlJ0NDPAwAAAAANSS2aGNy5tbapRLaPVmbIR6sytCQBV23NKpS//3ejPDVvq1w8MFamJMdrSQYAAACozVlZJe8s3aN7WiKCAuWSgXGcMgDNArNnABpddKR+wYBKylRFAx5rSUDBAAAAAAA/Kry+c0x3WXbvKHn58v5yWkJLtx5faK+Q95alyajnf5Ep76yUnzZnaxfFAQAA8IefNmdJRm6J7umYPDietCYAzYZPJwwAaBqiwvULBpSs/DJpHxncoM9nNygYsARSIwUAAADAv6jWauOTOmjb5v0FMis1Xb5au09KHfqt2PQs2XFY22JaBMuVyfEy6bQ4aRVqbdRxAwAA+Lq3l+inCwSaAuTqIQkeHw8AeAuzZwAaXYsQi3aRS4+K2Wxoqs2BHhsJAwAAAAD8WK8OETJ9Ql9JfWC0PHReL0loHeLW4/fllcrT32+V5Onz5a+frpf1mXmNNlYAAABftjbjiKxJP6K7TxVqGqXmAkBTRMIAAI/04YyOCNKNd1IJAx5rSUDCAAAAAIAmIDLYItcO6yTThiTIkp2HZebyNFmw7aDW9s3V90xf/LZX25LiWsjU5Hg595T2EmQxN/bQAQAAfMLbS/XTBZTrhnfy6FgAwNsoGADgEYYFAwV2jyUMWEgYAAAAANCEmEwBckb3ttqWmVsiH6xMl9mrMyWvxOHyMVTKwF8z8+SJeVu0VgVXDO4osS3dSy4AAADwJ+p103cbDujuG9KltfTuEOnxMQGAN9GSAIBHRBlEODVGSwISBgAAAAA0N3GtQuT+s3tK6v2j5dmLT5G+Me5d6M4tLpfXFu2S059ZKNfNWCNLdhySykoXIwsAAAD8yHvL0sToZQ7pAgCaI79LGCgoKJCVK1eaf/vtN/Phw4cD8vPzA+x2u9vx6O+//37Dz1ICMBQdYdO9vVFaEhgkDNCSAAAAAEBTp9oKXDIwTi4eECvrMvNk5op0+fb3A4bvk46lLp7/vCVb2zq3CZUpKfEycUCsRARZGn3sAAAAja2gzCGzV2fo7uvSNlRGdG/HNwFAs+M3BQOrVq0yPffcc7Y5c+YEVlRUnPRxqqqqKBgAvCAqwvsJA7QkAAAAANBcqMUS/Tu21La/n9tTa1XwYWq67HejaHv34WJ59OvN8uwP2+Si/jEyNSVBekSHN+q4AQAAGtMnqzKkuNypu++64Z21lk8A0Nz4RcHAI488Yn3yySdtTqdTm/CvfuNb/XH150c7ep/RfQB4TrQnWxIYrJyxBdKFBQAAAEDz0ybMJreM7Co3nt5Z5m89KDNXpMmynTkuP76k3CkfrszQtkGdWslVKQkytncURdkAAMCvOJyVWjsCPa1DrVqBJAA0Rz5fMPD4449bH3vsMduxE/7VSQFHf36sY/fr3QeAdxMGVDVnYZlDwhsw3lK98NNjMVM0BAAAAKD5CjSbZFzvaG3bebBQZq1Ily9+2ydFdteTHFftydW2qAibTB4UL5cPipN2Bu/3AAAAfMm8DQfkgEHa0pXJ8VprJwBojnx6ue2aNWtMjzzyiE1N/FcnClx++eWOb7/9tmTz5s3FRxcAzJw5s1TdtmTJkpLXXnut7NJLL62wWq01RQKJiYmVixYtKtm1a1fRzp07i7z4ZQHNUnQdF5AaOmXAqCWB1ezTv/IAAAAAwGO6tguXRy/oI6kPjJbHL+gt3dqFufX47AK7vPjzdhny1AL5y8drZXVaLgs1AACAz1JzRW8v2aO7zxpokikp8R4fEwD4Cp9OGJg+fbrt6BYE7777btnVV1/t0LtvVFRUlSoKUB8PHTrUeeONNzqysrICbrnllqD//ve/gdu2bTNddtllwT/99FNJ79699WcTATSadhFaUIiurHy7drGq0QsGAqkQBQAAAICjhdkCZUpKgraqLnV3rtau4MfN2eKsdC2lsaKySr5ev1/bEqPD5aohCXJBvw4SYvXpS04AAKCZUQlJG/bl6+6b0D9Ga+EEAM2Vzy63tdvtMm/evMDqdIFLLrmkwqhYwEh0dHTVF198UXrPPfeUq8IDVUBw7rnnhhQUFDTewAHosgWapVWoVXdfVgMnDDic+he2aEkAAAAAAPrUtZeULq3ltSsHyNJ7R8pfRnWVNmH67+GMbM0qlPu/3CCDn5wvj3+zWdIOF3O6AQCAT3jLIF1AuXZYJ4+OBQB8jc8WDKSmpppV0UB1wsDtt99uP9ljPfXUU/YRI0Y41ceZmZkBjz76KKVigBdEGbQlaOiWBHbDhAGf/ZUHAAAAAD6jfWSw/HVsD1l23yh56bJ+MiC+pVuPLyyrkHeW7pERzy2Sq95dJfO3uJ5YAAAA0NB2HyqS+VuzdfeN6NFWukU1XPotAPgjn50927FjR83YQkJCJCUlpc42AuXl5XUe79FHH9UKDlQBwrvvvmt1ONwKKwDQAKIN2hJk5Td0woBBwYDZZ3/lAQAAAIBPJsVd0C9Gvrh5iHzzl2EyaWCcBFnce1/1y/ZDcu2MNTLiuYXyxi+75Ehx3ddvAAAAGtq7y/bI/9amHuf64Z054QCaPZ+dPcvNzQ2ojsSLj4/Xnf0zmf4cvt1u1+5vZPjw4c6WLVtqfxJUS4Lly5fTzBzwsOjIII+0JCgnYQAAAAAAGlSfmEh5+uJTJPX+0fL3c3pKx1Yhbj0+M7dUpn+3VZKnz5e7P1svG/bq9xAGAABoSKpY8fNf9+ru69k+QoZ0ac0JB9Ds+WzBgGpHUC08PFy39is8PLymZcGhQ4fqLBhQ4uLiao6zefNmn/3agebWkuBgQxcMGCUM0JIAAAAAAOqlRYhVrj+9syz62wh57+rTZGSPthJwwisytVvIffbrXhn/ylK56NVl8t+1e8VeoXWRBAAAaHAfpKZLmUP/evF1wzppi1YBoLkLFB8VERFRM7lfUlISYHSf/Px8bV9GRsYJCwDM5j9DBXJycvgrAPhIwUBDJgyovphGvTEttCQAAAAAgAZhMgXIyMR22paeU6xdjP90zV7JL3W9BeTajDxt++c3W+SyQXFyxeB46dAimO8QAABoEKooccaKdN197cJtMj6pA2caANT7O189CzExMTUzfnl5ebqT+126dKkpC1u9evUJWwzs2bOn5jiBgT5bKwE0WdEGBQOHCu1SYZAK4C5HHcchYQAAAAAAGl5861D5+7m9tHYFT0/sK73aR7j1+JzicvnPwl0y7OkFcuOsNbJs5+GaREkAAICTNWfdfjlc9Gea9dGuGpLA9WIA8PWCgT59+mizfuoN4r59+wJKS0uPu09SUpKz+j7Lly835+bmGh7vhx9+MB9deBAVFcU7T8BHEgZUIMDhovIGeQ4Vb2nESsIAAAAAADSaYKtZJp3WUb69bZh8cXOKXNCvg1jMrgc8qveGP2zKliveXiljXlwsM5anSWGZ64kFAAAA1dS80TtL9ui/ZrGY5YrBHTlZAODrBQPdu3evbNGiRVX1L/a1a9celyBw7rnnVqh/VY8ZVVBw9913685G5uTkyF/+8pego3vRJCcn0yAP8LDoSP2CgYZsS0DCAAAAAAB4l7r+MiC+lbx0WX9Zft9o+euY7oaJc0Z2HiySh+dukuQn58tDX22UHdmFjTZeAADQ9CzZcVi2Gbx+uHRgrLQIsXp8TADgq3y2YMBkMsnpp59eM6k/b96843oIjBo1ypmQkFBTVPD+++9bxo0bF/zVV18Fbt261fT777+bXnnlFcupp54atnPnTlP1m9a+fftW9uzZs2HyzwG4rGWIxTDmKSu/YQoGykkYAAAAAACf0TbcJn8Z3U2W3jtSXrviVEnp3NqtxxeXO2VWarqWOHD5m6ny3YYDDdbSDgAANF1vLdmte7taVzptaCePjwcAfNlxk/C+5MILL6yYO3euNsYvvvgi8J///Kf92KKC5557ruziiy8OVoUAqmjg559/DlTb0fdTt1fvV4958sknG2ZmEoBb1P/DqAibZOYe32Ik2wMJAxaDYgUAAAAAQOMKNJvk7L7ttW17dqHMWpEuX/62VysIcNWK3Tna1j4ySCYP6iiXDeqoFSQAAAAcbVtWoZYwoGdsryhJaBPKCQOAo/j07NnEiRMdLVu2rAoJCZG9e/eaFi5ceFxbggkTJlT87W9/K68uClDUx0dv1cUCyiOPPGI/55xzaEcAeIlRDGVDtSQgYQAAAAAAfFv3qHB5/MI+kvrAaHn0/N7Spa17F+0P5JfJ8z9tlyFPzZfbP1krv6YfqbnuAwAA8LZBuoBy/fDOnCAA8KeEgfDwcMnJySk60f2eeeYZe2JiYuUDDzxgO3jw4B9VA0dRbxo7duxY9fTTT5dNmjSpotEGDOCEogwKBhoqYcBeV0sCEgYAAAAAwGeEB1nkqiEJMjUlXpbvypGZK9Lkp83ZUuni3L/DWSVz1u3Xtt4dIrTjnJ8UI8HW49abAACAZuJgYZn22kBPUlwLGRDf0uNjAgBf59WCgdtvv912zTXXOJKSkurdfE4d58orr3QsWLDAvGLFCvPBgwdNqlAgKiqqcujQoc6RI0c6LRZLwwwcgM8WDNTVksBq9ulQFQAAAABollQy5NCubbRtX16pfLQyXT5ZlSk5xeUuH2PT/gK594sN8uS8rXLpwFi5Mjle4lsTNwwAQHOj2h6VG1wjvn54p5qkagCAjxQMvPzyy9ZXXnnF2qdPn8qrrrrKMWXKFEfbtm1POkPOarXKWWed5VRbw44UQKO3JMj3QEsCEgYAAAAAwKfFtAiWu8clym2ju8m8DQdk5op0WZuR5/Lj80sd8taSPfL20j0yontbmTokQc7o1lZMJiYHAABo6krLnfJBarrha4yzekd7fEwA4A+8vtxWpQBs2LDBdPfdd9tiY2PDxo8fH/z5558HOhwObw8NQCOIijRKGLA3yPFVJKUedW3IzAUiAAAAAPALtkCzXNQ/Vv77f0Pl61uHySUDYsXmRhF4VZXIwm2HZNp7q2Xk84vkrcW7Ja/E9cQCAADgfz7/ba8cKdGfW5o2NEECSaAFAN8sGFDxL2pThQOqSGDevHmBkyZNCm7fvn3Yrbfealu9erXXxwig8RMGiuwV2lZf5U79gBHSBQAAAADAP/WNjZRnL0mS1PtHy/1nJ0pcq2C3Hp+eUyJPzNsiydPny72f/y4b9+U32lgBAIB3VFZWybtL9+juC7cFyqTT4jw+JgDwF16djD/rrLMqzGazVixQXTigqM9zc3MDXnvtNWtycnJonz59Qp955hnrgQMHyI8DmmjBQEO1JTBqSWClehQAAAAA/FrLUKvceEYXWfS3kfLOVQPljO5t3Xp8maNSZq/JlPNeXioTX1suc9btq7OtHQAA8B/ztx6UPYeLdfddNihOwoMsHh8TAPgLrxYMzJs3rzQzM7PomWeesfft27dSFQocXTxQ/fnmzZtN999/vy0+Pj7s7LPPDp49e3ag3d4w8eUAPKtdhM1wX3ZBAxQMGLQkIGEAAAAAAJoG1W5udM8omXHNIFn4txFy7bBOEhEU6NYxfk0/Ird/sk6GPLVAXvhxW4MUsAMAAO95e8luw9cNVw/t5PHxAIA/8Xrcf1RUVNXf/va38nXr1hWr7fbbby9Xt6lCAeXo4oGKigr58ccfAydPnqy1LLjpppuCVqxY4fWvAYDrgixmaRmiX81JwgAAAAAAwB2d2oTKQ+f1ktQHRsv0CX2lZ/sItx5/uMgu/16wU4Y+vUBu/uBXWbErR7sGBQAA/MeGvfmyck+u7r5z+7aXmBbutTMCgObGpybbTznllMoXX3zRrlIHvv7665JLLrmkIigoqOaN2tEtC/Ly8gLeeusty7Bhw0ITExNDn3zySevevXtpWQD4gSiDtgTZhY3XksAS6FO/7gAAAAAADSjEGiiXD+oo824bJp/dlCLjkzpIoMn1y0TOyir5bmOWXP5Wqoz712KZtSJNiuwVfI8AAPADbxmkCyjXDSddAABOxCdn0Mxms5x77rnO2bNnlx44cKDw1VdfLRs6dKhT7dNrWbB9+3bTQw89ZOvUqVPYmDFjQj788MPAsjKi5AC/KxhogAhIh1O/YMBq9slfdwAAAACABqSuF52W0Epevry/LL9vlNx5ZndpF27cGk/P9uwieWjOJkl+cr48PGej7DxYxPcIAAAftT+vVL7dcEB336BOreSU2BYeHxMA+Bufn0GLiIiQm266ybFkyZKSHTt2FP3973+3JyQk6LYscDqdsmDBAvPUqVNVy4Lw6667LmjJkiVmb38NAGqLNigYyCpovIQBKwkDAAAAANCstIsIktvP7CbL7hsl/5l8qgzu1Mqtx6uEgRkr0uXMF36RK95Ole83ZkmFQZE6AADwjveXp2lJQXquH97Z4+MBAH/k8wUDR+vcuXPV448/Xr5r166iRYsWlUybNs2hCgr0Whbk5+fLe++9ZxkxYkRIt27dQh977DFreno6LQsAHxAVaVQwYK/3scsNLt5YSBgAAAAAgGZJvR8895T2MvvGFPn+juFyxeCOEmJ1b33Jsp05ctMHv8rpzyyU/yzcKYeL6v/+FQAA1E9hmUM+Xpmhu69Tm1AZndiOUwwATa1g4Ginn36685133ilTLQs++OCD0rFjx1aYTKZaLQsU9fmuXbtMjz76qK1Lly5hI0eODJkxY4bF2+MHmrPoRmxJQMIAAAAAAMBIYnSEPHFRX0l9YLQ8PL6XdG4T6tbJ2p9fJs/+sE2GTF8gd85eJ2szjtQsZAEAAJ716Zq9Umiv0N13zbBOYjKxhhQAmnTBQLWgoCCZPHlyxffff1+akZFRNH36dHvv3r0r1Zu1o4sH1MeVlZXyyy+/mK+55hr92UoAHhEdqd8/8lCR3TA+qr4JAzZaEgAAAAAA/iciyCLThnaSn+86Q2ZdO0jG9IoSd+YU1HvP/67dJxe9ulzOf2WZfLYmU8ocTs4vAAAeotoEvbt0j+6+FiEWufjUWL4XANBcCgaO1r59+6p77723/Pfffy9es2ZN8W233Vbetm3bqmNTBwB4V5RBwoAqFqhvrKOjgpYEAAAAAADXqJWHw7u1lbemDpTF94yUm0d0kZYh7gVTbtiXL3d//rskT58v0+dtkczcEk4/AACN7IdN2bIvr1R335WD4yXYzfZDANCcNamCgaOdeuqplWeffXbFGWecQXk34CctCZSserYlMEoYsJqb7K87AAAAAEADiG0ZIveelSgr7h8tz1+SJEmxkW49Pq/EIW8s3i2nP7tQrn1/tfyy/ZBU1jNFDwAAHE8tEn1ryW7D68BTh8Rz2gDADYHSxGzevNn0/vvvWz755BPLvn37iBQAfFCrUKtYzAHicB5/4SS7oH4FAw6DggELLQkAAAAAAC4Isphl4oBYbVuXmSczV6TJN78fkHKDRLtjVVWJzN96UNsSWofIlJQEuXhArEQGu5dcAAAA9P2afkT7G63n/H4dpF04XakBoNkVDOTk5MgHH3xgUdtvv/1mrq4wq6ZaEVR/HhQUJOedd16F90YLQP2fVC/a9CKj6lswYDe4gEPCAAAAAADAXf3iWki/uH7y4Lm9ZPbqTPkgNd0w/lhPWk6JPP7NZnnuh21yYf8OMiU5QXp1iAacbBkAAQAASURBVOAbAQBAPby9ZI/hvuuGd+LcAkBzKRhwOBzy9ddfB86YMcPyww8/BKrPjy0SUNRtahs0aJBz6tSpjsmTJztatGjhxZEDUKIj9QsGsupZMGC04sNKwgAAAAAAoB5JeTeP6CI3nN5ZFmw9qKUOLNlx2OXHlzqc8vGqTG07LaGlTE1JkHG9o3mvCgCAm9JziuWHzVm6+4Z3ayOJ0RTmAUCTLxhYuXKlacaMGdbPPvssMDc3V6sKqC4UOLZIICYmpkoVCEybNs2RmJjoWm4cAI+IjtCPhcrKtzdKSwKrmQ4lAAAAAID6MZsCZEyvKG3bdahIZq1Ily9+3SuFdtfDLFenHdG2tuE2mTyoo0we3FGiDN4jAwCA2t5dukdr/6PnuuGdOV0A0FQLBjIzMwNmzpyptRzYvn276dgigeqWA2oLDg6W888/33H11Vc7xowZ4zSZtLsD8DFGF0Pq25KAhAEAAAAAgCd0aRsmj5zfW+4e10O+WrdPZi5Pl23ZhS4//lChXV6av0P+s3CnjOsTLVOT42VQp1Y1C2IAAEBt+SUO+XTNXt3T0j0qTE7v1oZTBgBNqWCgpKREPvvsM4sqFFi8eLG5srLSsOWAkpKSorUcuPzyyx0REUTOAL4uOtKme3t9WxI4nPrlpRYzxUMAAAAAgIYXaguUKwbHa2kBq/bkyswV6fL9pixxVhosfzxGRWWVfPv7AW1LjA6XKSnxcmG/GO24AADgTx+uStfa/Oi5blhniu4A4CT53DuPn3/+2ayKBL766itLcXGx1NVyIC4uruqKK65QLQfKu3Xr5tq7MAC+nTCQT8IAAAAAAMD/qOtWgzu31ras/DL5aFWGfLwqQ0sScNXWrEL5+383ylPztsrFA2NlSnK8dG4b1qjjBgDAH6hk2RnL03T3tQmzyQX9O3h8TADQVPhEwcC2bdtM7733nuXjjz+27N27N6CulgMhISFy4YUXai0HzjzzTP1SMgA+L9qgYED1fSy2V5z0Sgq7s1L3dmsgCQMAAAAAAM+IjgySu8Z0l1tHdpUfNmXJzBVpsjrtiMuPV++N31uWpm3Du7WRqSkJMiqxnZhNtCsAADRP3/y+X7IL9IvwrkqJF1ug2eNjAoCmwqsFAy+//LLlgw8+sKxZs8asVyRQfZv6eNiwYVrLgUmTJjnCw8O9OWwAjZgwoGQXlJ30CgpHhUHBAC0JAAAAAAAeporXxyd10LbN+wtkVmqafLV2v2Gcsp4lOw5rW0yLYLkyOV4mnRYnrUKtjTpuAAB8iZonemvJHt19QRaTXJEc7/ExAUBT4tWCgdtvvz2oOj1AObpIQElISFAtB8qnTZvm6Ny5My0HgCa22sJIVj0KBspJGAAAAAAA+KBeHSJk+oRT5L6zespnv2bKB6npkpZT4vLj9+WVytPfb5UXf94u40/pIFNT4iUprkWjjhkAAF+wYleObDlQoLtv4qmxFNIBQFNoSXB0y4GwsDC56KKLtJYDI0eOpOUA0EQFWcwSGWyR/FKHbsJAfXpZ6SFhAAAAAADgCyJDLHLd8M5yzdBOsnjHIZm1Il0WbDso/1s/49L73i9+26ttqmBganK8nHtKe+19NgAATdFbS3Yb7rt2WCePjgUAmiKvFwxUtxw444wznFdddZXjkksucYSGhnp7WAA8IDoiSLdgICtfvxeVKxwGCQMWWhIAAAAAAHyIyRQgI3q007bM3BItcWD2mkzJKzn+fbKR9Zl58tfMPHli3hatVcEVgztKbMuQRh03AACetPNgoSzcdkh335k92510Ui0AwEcKBrp06VJ5xRVXaGkCqv2AN8cCwPOiIoNkW3ahZxIGAk0nfUwAAAAAABpTXKsQuf+cnnLnmO4yd/1+mbkiTTbu049e1pNbXC6vLdolb/yyS0b3jNLaFQzr2qam/ScAAP7qnaV7DPepxB4AgJ8XDOzYsaPYm88PwLuiI2y6t2fl16NggIQBAAAAAICfUm0FLh0YJ5cMiJW1mXlau4Jvfz9g+F73WJVVIj9tzta2zm1DZUpyvEwcECsRQZZGHzsAAA3tcJFdvvhtn+6+PjERMrhTK046ADQAltwC8GpLAj1ZjZAwYCNhAAAAAADgJ1QywKkdW8qLk/rJ8vtHyd3jekiHSP330EZ2HyqWR7/eLMlPzpe//3eDbMs6PuEPAABfpgrnjK73Xj+8M0k6ANAUEgYANG+qJYGek21JUFVVZbjqgpYEAAAAAAB/1CbMJreM7Co3nt5Zft5yUGalpsmynTkuP76k3CkfrszQNrUSc2pKgoztHSUWM+uIAAC+q8zhlA9S03X3tY8MknP6tvf4mACgqaJgAIDXRIXrFwwcLLRLZWWVmEzu9Vp0VlZJVZX+Pi6EAAAAAAD8WaDZJGf1ida2nQcLtVWXKqa5yF7h8jFW7snVtqgIm0weFC+XD46TdgbvzQEA8Kb/rt0nOcXluvuuHpLA9V4AaECUEgPwmmiDhAE18X+42O728erq6UjCAAAAAACgqejaLlwevaCPpD4wWh6/oLd0axfm1uOzC+zy4s/bZehTC+QvH6+V1Wm5WmofAAC+QC0me2fpHt19oVazXDaoo8fHBABNGQkDALwmKsJ4FUN2vt3tVQ6OCuOLGxaze2kFAAAAAAD4ujBboExJSZArk+Nlxe4cLXXgx83ZWiG+KxzOKvl6/X5t69k+QqamxMsF/TpIiJVLhgAA7/ll+yHZebBId9+k0zpKZLDF42MCgKaMV/8AvKZ1qFWbyFcXKI6VVVAmfSXSrePZnU7DfbZAAlUAAAAAAE1TQECADOnSRtsO5JfKRysz5ONVGXK4SD/KWc+WAwVy/5cbZPq8LXLJwDiZkhwvCW1CG3XcAADoeWvJbt3bVQfbaUMTOGkA0MCYQQPgNSZTgGGKgCoYcFd5RR0tCcxmt48HAAAAAIC/aR8ZLH8d20OW3TdKXrqsnwyIb+nW4wvKKrQY6BHPLZKr3l0lC7a6nlgAAEB9bdqfL8t35ejuO7tPe4lrFcJJBoAGRsIAAK+KirDJvrzS427Pzne/YEAvqaCaJZCWBAAAAACA5sMWaJYL+sVo28Z9+Vq7gjnr90mZw7jYXi8SWm1xrYLlysHxcunAOGkZam3UcQMAmrd3luwx3Hft8E4eHQsANBcUDDQzpaWlsnTpUvOWLVtMR44cCbBarRIXF1eZnJzs7Nq1K+Xi8LjoSE8lDBCoAgAAAABonvrERMrTF58i95+TKJ+t2SuzUtMlI7fE5cdn5pbK9O+2ygs/bZfzkzrI1JQE6RvrXhtBAABOJCu/TOau36+7TyXmnNrRvdQcAIBrKBjwggcffND2xBNPnHQ59pVXXumYNWuWW7Op2dnZAY888oht1qxZluLiYt379O/fv/LBBx+0T5gwoeJkxwa4KypCv2Agu6ELBgIpGAAAAAAANG8tQqxy/emd5dphnbTkgJkr0mTR9kNS5eISEntFpXz2615t69+xhUxNiZdz+rbX0gwAAKivGSvSpMKgDc71pAsAQKOhYKAZmD9/vnnSpEnBOTk5dWayr1271jRx4sTgK664wvHOO++U2Ww2zw0SzVaDFgw4jQsGLCQMAAAAAACgMZkCZGRiO21LzymWD1LT5dM1eyW/1OHyGVqbkadt//xmi1w2KE6uGBwvHVoEc4YBACel2F4hH6am6+7r2CpExvSK5swCQCNhyW0T98svv5jHjx8fcmyxQIsWLSQpKakyPj6+ymyuXQX+4YcfWlSBQWWl6z3tgJMVbVAwoOKn3EVLAgAAAAAA3BPfOlT+fm4vSb1/tDw9sa/0ah/h1uNzisvlPwt3ybCnF8iNs9bIsp2HpcrVyAIAAP7n81/3SkGZfvjxNUMTxGyqcz0kAKAeSBjwAU899ZS9X79+TlfvHxMT49K7rtzcXLn88suDS0tLa27r2LFj1Ysvvlh24YUXVphMf9SLZGRkBDz++OO2t99+21J9vzlz5gQ+99xz1nvuuafc3a8HaIiEAfXisLTcKcFW12MNHQYJA4GmAG31BAAAAAAA0Kfef086raNcOjBOfss4IjNXpMu8DQfE4XRt8l8lSP+wKVvburYLkynJ8TLh1BgJD6q53AQAgC5nZZW8s3SP7r6IoEC5ZGAcZw4AGhEFAz5g4MCBztGjR7tcMOCqp556ynbgwIGaWdKEhISqJUuWFMfGxtZ6p6eKCN56662yuLi4yocffrimD8GTTz5pu+6668pbtWrV0EMDakRH6hcMKFkFZdKpTWi9EwasgYSpAAAAAADgioCAABkQ30rbHjy3l3yyKkM+XJmhvUd31c6DRfLw3E3yzPdbZcKpsTI1JV66RYXzDQAA6Pppc5Zk5Jbo7ps8OF5CbUxlAUBj8vtZtIKCAm2FfFpaWoDdbvf2cHxGdnZ2wGuvvWY9+rY33nij9NhigaM9+OCD5cOGDaspXMjPz5enn366poAA8GRLgpNpS1BukDBAwQAAAAAAAO5rG26Tv4zuJkvvHSmvXXGqpHRu7dbji8udMis1Xca8uFgufzNVvttwQCoM3rsDAJqvt5fsMUyOvXpIgsfHAwDNjd8VDPz000/mm266KahPnz6hVqs1vGXLluGdOnUK69KlS9jixYt1s8tXrVplWrhwoVlt69at87uv+WR89NFHgUVFRTWfq0KAsWPH1plioFoU/OMf/6hVdTFjxgxLZSVv5NC4kYcqVkpPthurF+pqSWAxN4v/9gAAAAAANIpAs0nO7ttePr4hWX6883St3UCoGy0ElRW7c+TmD3+T4c8slJfn75BDhSz8AQCIrM04ImvSj+ieivFJHepMqAUANAy/yXFZtmyZ+f/+7/+CNm7cqM38VVVVHReXZuSTTz6xvPTSS9pq+4iICNm/f39hcHCwNGVz586t1SDummuucbjyONUaQbUuUIkN1UkFy5cvNx+dPAA0NPWir6DszwKXau7EHSp2o5YEFAwAAAAAANAgukeFy+MX9pF7zuohX/62T2auSJNdh4pdfvyB/DJ5/qft8u8FO+Scvu1lakqCnNqxRZ3X9gAAzS9dQLlueCePjgUAmiu/WHb7/PPPW0eNGhWiigXcKRSodscdd5SbzWatyEC1MPjss8/8plDiZBQWFmoFFkffNm7cuApXHqtSBkaNGlXrvl9//XWTPl/wviiDtgTutiQwShigJQEAAAAAAA0rPMgiVw1JkJ/vOkM+vG6wjOsdJSY35vwdziqZs26/THxtuZz38lKZvTpDSstZrwIAzUlmbol8t/GA7r4hXVpL7w6RHh8TADRHPl8w8Oabb1ruvvtum8Px5wJ5Nfl/2mmnOSdMmFBxbAGBno4dO1adccYZTqPV903Nhg0bzEefL5UY0KFDhxOfqP8ZOnRorXdn69ev9/mfEzTNgoGDhe4VDJSTMAAAAAAAgEepxTxDu7aRN6YMlCX3jpJbRnaR1qFa0KfLNu0vkHu/2CDJ0+fLE99ulvQc1xMLAAD+671laVJpMHNBugAAeI5PrxzfvXt3wG233RZUnSKg/lVpAQ888IC9devW2m0mkynclZSBCRMmOObPn29WBQYLFy50r8maB5SVlcmuXbtMhw8fDrBYLNKmTZvKmJiYqtDQULePtXnz5loT/ImJiW6VZ/fq1avW/bdt2+Zz5wtNS3QDJQwYFgwEUvMCAAAAAEBji2kRLHePS5TbRneTeRsOyIzl6bIuM8/lx+eXOuStJXvk7aV7ZET3tjJ1SIKc0a2tmNyJLgAA+IWCMoeWLqOnS9tQGdG9ncfHBADNlU8XDDz00EO28vLymqj8WbNmlV5++eUuResf6+iEgby8vIAdO3YEdOvWzeVV943p1ltvDUpLSzOpooGjBQYGSv/+/Z2qncCtt97qiIqKcmm827ZtqzU7GhcX59bXqRIZjv48IyMjoLS0VIKDg905DOCyqEj9goHsAnuDtCSwmLmwAAAAAACAp9gCzXJR/1ht27A3X2auSJM56/cbFvofSwWKLtx2SNviW4fIlYPj5ZKBsdIixL3kAgCA7/pkVYYUG7SiuW54Z4rFAMCDfLZgwG63y5w5cyzV6QHXXnut42SLBZQePXpUhoSESElJifb55s2bzd26dTvp4zWkrVu36i5/rqiokNWrV5vV9vzzz9tUusJjjz1mV4UEdTl06FCt2dGYmBjX3o39T3R0dJV6DvX8SmVlpajkA3cLD/RkZWUFHDu+E9mxYwfLw5tpwkB2QZlUVla5/OKQhAEAAAAAAHxL39hIefaSJHngnJ7y6ZpMmZWaLnuPlLr8+PScEnli3hZ5/qdtckFSjExJiZc+MfS0BgB/phZ+qXYEelRbm4v6x3h8TADQnPlswcCSJUvM1ZP7qmjgnnvucW+p8THMZrOaCK/cvXu3Nvm8d+9ev1pyrFb4T58+3bps2TLzN998UxIeHm5436Kiolpfm7ttDVSag0oTKCwsPPaY9S4YeOWVV6xPPPEE5eBwqWCgorJKcorLpW24zaUzZjdMGKDmBAAAAAAAb2oZapUbz+iirRpdtO2gzFiRLou3H3L58WWOSpm9JlPbBsS3lKkp8XJ2n/a0IQQAP6Ta1hwwaEd7ZXK8BFnokgwAnuSzBQPVE/tKhw4dqrp06VLvyerIyD+rjwsKCrxaMKCKIAYPHuw8++yzK9S/vXr1qmzdunWVmqxXq/l//fVXVRgQ+MEHH1iOblWwePFi86WXXhr89ddflxolDRQVFdX6PCgoyO1zpx5TWFhYc46OLh4AGlpUpHFBgEoZcLVgwFGh/6NuC6RgAAAAAAAAX2A2BcjonlHatudwsXyQmq4lDxSWuR4E+mv6EW17PGyLTB4UJ5MHx0u0QbtDAIBvqaqqkreX7NHdZw00aUkyAADP8tlZtOrYejWxrpIBGvr4KmbfW8aNG1exefPm4hUrVpT84x//KB83bpxTxf2rlglBQUESGxtbdcEFF1S89dZbZdu2bStKSUmp1cjn+++/D3z55ZctRse32+21iiGsVvcX9NtstSdoS0tL/SqRAf6lTahNAg3aDmQZVJrqKXc6DV9oAgAAAAAA39KpTag8dF4vWfnAaJk+oa8kRhsnauo5XGSXfy/YKUOfXiA3f/CrrNiVo01EAQB818o9ubJhX77uvgn9Y6RNmGuLxwAAzSBh4OgJ6/Ly8gaZrM7Nza05jlrNL14yfPhw/VlNHR07dqyaP39+yciRI0NWrlxZk8Mzffp02w033ODQazdgs9lqfW3l5eVuj9Fut9c7pUDPrbfeWj5p0iSHO4/ZsWOHaeLEicEN8fzwTSZTgLQLt8l+neKArALXCwaMEgZoSQAAAAAAgO8KsQbK5YM6ymWnxcma9CMyY3mafL8xS2tV6ApnZZV8tzFL27pHhcmUlASt/3WYzWcvfQJAs2WULqBcO6yTR8cCAPiDz75qjoqK0iIAVFXw/v37610wcOTIEcnMzAxQiQX/O77flBsHBwfLzJkzS3v37h1WUVFRk8CgkgYmTpx4XF5bWFhYrc/LysrcPn/HPiY83L0KbyPR0dFVamuQg6FJaRcRpFswcNCNgoFyp35yiNVMwgAAAAAAAL5OXbc7LaGVtqnrAR+typCPVmbIwcLaC1vqsj27SB76aqM8/d1WuXhArNYLu2u72tfKAADesftQkczfmq27b0SPttItqmHmIQAA7vHZWbSuXbtWHp0MsHnz5nqN9bvvvgtUbQiqY8kGDRrk8ip/X9C9e/eqc889t1ZxwI8//qhb8BEWFlZrQr64uNit51LnqbS0tM5jAg0tOkK/16A7CQPlFQYFA7QkAAAAAADA7xYW3HFmd1l23yj5z+RTZVCnVm49vsheIe8vT5MzX/hFrng7VX7YlCUVBgsNAACe8e6yPWLUOeb64Z35NgCAl/hswcCgQYMqW7ZsWVWdCPDmm29a6nO8559/3nZ0MUJMTIzfTYCPGjWqVsHA9u3bdb9/bdu2rfW17du3z63vc1ZWVkB1koFiMpmOOybQ0KIjjQoG7PVOGKAlAQAAAAAA/km9pz/3lPby6Y0p8v0dw2Xy4I4SbKnp2umSZTtz5MZZv8rpzyyU/yzcKTlFrl9rAAA0jCPF5fL5r3t19/VsHyFDurTmVAOAl/hswYDZbJbx48dXqEQAtb3++uvWNWvWnNR4H374YevatWu1x6oChCuuuMIhfqhjx461Ju1VWwK9+/Xo0aPWrGlGRoZbLQnS09MDjn1e1RYBaExRBgkD2TptCtxNGLCRMAAAAAAAgN9LjI6QJy/qK6kPjJZ/nNdLOrcJdevxqhXisz9sk5TpC+Su2etkbcaRmjRSAEDj+iA1Xcoc+tdvrxvWSZu7AQB4h88WDCgPP/yw3Wq1an8oysvL5ZxzzglZvny5yyXEapX83XffbfvnP/9pq/5jExkZWXXHHXeUix+yWCy13sE4HPp1Dz179qz1V3fr1q1ulV1v2bKl1v27d+/uV+0b4J+iI2tCQE66JYGDhAEAAAAAAJq8yGCLXDOsk/x81xky69pBcmbPKDG5Mc+kEgq/XLtPLnp1uVzwn2Xy2ZpMKXNw+QsAGou9wikzVqTr7msXbpPxSR04+QDgRT5dMNCpU6eq++67z64qfdWE/+HDhwPOOOOMkMmTJwd99913ZvW5ul91JbDT6dRW3S9btsz82GOPWbt06RL2wgsvWKtTCtQxXnzxRXtERIT4owMHDtT6fhm1Cejbt6/TYrHUSgzYt2+fy2+b1Pk7+vOkpCQavMFrCQP5pQ6X37QbJQxYSRgAAAAAAKDJMZkCZHi3tvL2VQPll7tHyk1ndJGWIe51Nf19b77c/fnvkjx9vkz/botk5pY02ngBoLmas26/HDZoB3PVkASu3wKAlwWKj3vkkUfKd+3aZfrwww8tasJfFQXMnj3boraj76cKAs4999yQY29T1OPUx7fddlv5VVdd5ZftCPQm8mNjY3ULBlRBxNChQ52LFi2quf+PP/4YOG3atBN+7ZWVlbJgwYJaPxfnn39+Rf1GDpxYtEHBgJKVXyYJLsQMqhUCeigYAAAAAACgaYtrFSL3nZ0od5zZTb75/YDMWpEm6/fmu/z4vBKHvPHLbnlz8W4ZndhOpqQkyPCubbSiBADAyVNzM+8s2aO7L9hilisGd+T0AoCX+XTCQLX333+/7P777y8/uofN0akB1Vv1bUfvU9S/jz/+uF2lC4ifOnLkiHz11Ve1iiRGjRplOJE/fvz4Wvveffddl8qr58+fb05LS6s50e3atatKSUkhkw1eSxhwpy2BUcKAxewXv+oAAAAAAEA9BVnMcvGAWJlz6zD56pahMuHUGLcWEqj1Rz9vOShXvbtKRr/wi7yzdI+WfggAODlLdhyWbdmFuvsuHRgrLUKsnFoA8DK/mEUzm83yxBNP2BcuXFgyevRoZ3VywLGqCweqqfudccYZTvW4v//97+Xix+66666gvLy8ms+tVqtKVDAsGJg8ebIjNPTPFdlLly41//TTT7USCvTSBR577LFajeSnTp3qUOcfaGyhtkAJt+mHnmS7WjBAwgAAAAAAAPiffnEt5IVL+8mK+0bJPWf1kJgWwW6dmz2Hi+XxbzZL8pPz5f4vN8iWAwWcWwBw01tLduverqZypg3txPkEAB/gFwUD1YYPH+786aefSjZu3Fj89NNP29Uq+q5du1a2atWqSk1qh4eHS0JCQtWIESOcjzzyiH3lypXFqlhAPU58xD//+U/rqlWrXD7vDodD7rjjDtv7779fKyHg+uuvL4+JiakyjHePjq666aabahVJ3HDDDcF79+4NqGtsqrCg+vPIyEi57777/DaVAf4nKjKoXgUDDqOCATPxgQAAAAAANFetw2zyfyO6yuJ7RsqbUwbI8G5t3Hp8qcMpH6/KkLNfWiKXvr5Cvl6/3zDlEADwp21ZhVrCgJ6xvaJcakMLAGh8+st5fVyvXr0qe/XqVX733XeLv/nxxx8DH3roIVtycrLz4osvrhgzZkxFz549Ky2W2h0DVJrAN998E/jss8/afv/991oFBp07d6565JFHTpiYcP/999s/+OADS3Z2tjZbqloNDB06NPRf//pX2QUXXFBhMv1x2IyMjIDHH3/c9vbbb9caxL333mtv3bp1A33lwIlFRwTJzoNFx92ele9a3YrRm3V3ogcBAAAAAEDTZDYFyNje0dq261CRzFqRLl/8ulcK7YYhnsdZlZarbW3DbTJ5UEeZPLhjnW0WAaA5e9sgXUC5fnhnj44FANDECgaagtTUVLPaRMRms9lEpQVERERoSQm5ubkB6enpAapFwLGioqKq5s2bV9KmTRvDdIFqarL/448/Lj3nnHNCysrKaooDJkyYENyiRQuJj4+vzM/PD8jMzAxwOmuHMJx33nkV9957r1+3cYD/MXqD7XJLAqOCAdpqAAAAAACAo3RpGyaPnN9b7h7XQ/67dp9WPGDUY1vPoUK7vDR/h/xn4U4Z1ydapibHy6BOrWq1SwWA5uxgYZnMWbdfd19SXAsZEN/S42MCAOijYMAH2O122b17t3o3Uec7inHjxlW8//77ZardgKvHHjlypHPu3LklkyZNCj5y5EjA0QkGeXl5usuuJ02apJ6ntDqBAPCU6Eib7u1ZLrck0P+vYaElAQAAAAAA0BFqC5Qrk+PlisEdZeWeXK1w4PtNWeKsdO3yW0VllXz7+wFtS4wOlykp8XJhvxjtuADQnKnfp+UGLWSvH96JAisA8CE+PSM8YsSIkOptzZo19Rrr6tWrTdXHGjlyZIh4yQMPPGC//vrrHaoNgUoTOJGwsDCZMGFCxYIFC0q+//77UneKBaqNGTPGuWnTpuIbbrjBERJi/KUnJSVVfvrpp6WffPJJaVAQUWrwTksCPVn59UwYoCUBAAAAAACog0oGSO7cWv5zxamy7N5RctvoblrbAXdszSqUv/93oyQ/OV8e/XqT7D50fNtFAGgOSsud8kFquu6+mBbBclbvaI+PCQBgzKdLXRcvXmyujvE6enX8yVCPV8dTH3szGuyss85yqk19XFxcLBs3bjSlpaWZ9u/fH1BUVKS1IWjRokVVq1atqnr16lWpJvEDA+v/bWrfvn3VG2+8Ufbiiy+WLV261Lx582ZTXl5egNVqldjY2Mrk5GRn9+7d3S5GADzRkkDFV1VWVonJZPx/t6qqyrBilYIBAAAAAADgqujIILlrTHe5dWRX+WFTlsxckSar0464/PhCe4W8tyxN24Z3ayNTUxJkVGI7MddxXQMAmpLPf9srR0ocuvumDU2QQLNPr2UFgGbHpwsGqicBm2rvr9DQUBk8eHCl2jz1nCphYOzYsU61eeo5gfoWDKhWA7kl5dImzOZ2OwLFygtQAAAAAADgJrUAYXxSB23bvL9AZqWmyVdr90upw/XLakt2HNY2taJWtT6YdFqctAq18r0A0GSphV/vLt2juy/cFqj9HgQA+BafL+NqqsUCAPQr+I2cqC2BUbqAQsIAAAAAAACoj14dImT6hFMk9f7/Z+8+wKMq0zaO35lJJyGFklBDMyBFUFSqDRSwF8CKHbdZ1i3W3c/VtbuuW9xddxUroKvoruLaAUU6ioD0GkJNgFDS68x3vTOGJDATJsn0+f+u61whc9rLSUiG897neUbrtxeeqG5tmtbxdNehMj396XoNfXK2fvXOSq3ccYgvCICwNHv9XuXsL3G57urTuyg5PsbvYwIAhHiFAW+prq4+8mdvlPgH4H2mgoApz1djs7tsSyCluN23qtp9YCCGCgMAAAAAAMALUhJjNPmMHrplRHd9vWmfpi7K1ZwNe2X3sNFnZbVN732307EM7JKqG4Zm6cKTOig+xtFJFQBC3kvztrp83dz3vWlEd7+PBwBwfBEzc75v374j1RRatWrl4Vt4AP5k3jS2S4pTXuGx1QTyDlc0ui8VBgAAAAAAgL9YLFE6u3d7x7LjQKmmLc7V29/u0CE3PbtdMVUGfrXjkB7/eJ2jRPd1Q7qqc1rTKhcAQDD5fuchLc054HLdhQM6ONqzAACCT9C3JPCWOXPmWGtbHHTs2JHAABCkMty0JXAVIjg6oe9OLBUGAAAAAACAj3RJT9QDF5zoaFfwzIST1L9T6ybtf6CkUi98tUVnPvOlbnvjW83btE92T0sWAEAQmTIvx+26yWdQXQAAglXIVBgwE/1NVVpaqq1bt1reeeed6OnTp8fUHuOkk06q8cEQAXhBZus4rXTxev7h4wQGahoJDERHTDYKAAAAAAAEiGkrcOWpXTRxcGct33HI0a7go+/3NHrPoj7TofGLtfmOpUe7Vrp+aJbGD+6s1vT7BhACdh8q00er9rhcd3r3dJ3UOdXvYwIAhEhgwGq1Jje2vjZNO3bs2BbV46o9jgkNXH755dUtORYA38lsTYUBAAAAAAAQusz9x1O6pjmW31x4ot7+ZoemL87V7uM8DFHf1n0leuTDtfrDZxt0+cmddMO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v3t0WE0Ov0KB3zgPSvGclW7Vvz3PCuKb/MDc3ay3Rrse2b73vAgPljQQGqDAQGBf8QcpdIBXt8f6xSc4CACJJMP5ONTekuo10LrVsNulgTl07g9rWBgdyTHpUvmN3BlPNsm5m3csxreqqEJh2BrVBgoQ0H44FAAAAQMQrPyxNvULK+953l8L8X86EBY4OdwOhfr8BPkdgIMTFxMQcc5etY8eOdtNCID8/3xEsMGGBb775xjpy5MgGwQJ35s+fb63/+cCBA6kuECpuXyo9f4rvjm8m2S9/oen7mafQ0ntK+zccu870wPWVRisM0JIgIMxEgnnT+uoFUvkh7x2X5CwAINKEyu9U8zR/m57OpX4brcoSae/6ugBBbZjAPHHjS1Ul0s5vnEt9rTvVVU0wFQna95XaniBZCU4DAAAA8MKDbdPGS7u/c79NXHLjLXY9eWrbTMRSWQCRcr8BXkVgIIQUFhbq4MGDDdoLZGZmunws5/zzz69+7bXXjtzd+vzzzz0ODMyePbvB98XFF19c1fxRw6/Mjdgrp0rvXO/9Y1vjpFs+bf4P83a93QQG1stn3FUYMH+XaM8qbsAHzI34mz92vkn2RkqR5CwAIFKF8u/U2FZS58HOpZZpvWb+Ho6WBvWW/Rslm4//S1K4y7ls+rzuNUuMs7VWbRWC2jBBUoYU1aSubwAAAAAiVUWxNH3isaHl+szDdub/dntWSgv+4ny621NZI6QRd1PiHd4Vyvcb0CwEBkLIhx9+GG03N9F+0LZtW7upJuBq20suuaRBYOCNN96Iffjhhystx+nXuWnTpqh58+YdqTBgWhFcdNFFPq5xD68yT255OzRg0o23fNayH+YmMLBOwVFhgOoCgWe+l366UPrkXmnVjOYfh+QsACDShdPvVDMJ37qjcznhvLrXqyulgk0/BAhMRQJTjWCNVLTbt+MxIYX8Vc6lvoT0uvBAbZig3YlSbKJvxwMAAAAgtJjKam9eJe1Y7H6btG7SjR9KyZnOxfSQN//nMT3kdy2Tdq9o+JS3eUq74yCp02Bnr3nz/xHAF8LpfgOOi8BAiCgtLdUjjzzS4JHoCy64oNpqbdA9oEGFgU6dOtl37drlePQlNzc3asqUKTE/+tGPGn0053e/+11c/VDCZZddVp2amuqtvwb8GRq48zvptYulol0tO1b2+dJl/2j5D3PzdJYrBZulmmrJGu2bvlDuWisg8Mz31PgpzjcMJGcBAOB3amPtrWrbBejKutdLDzRsZ+D48zqpqtS3301lB6Rt85zLEVHOal+mlcGRIEE/KTXL2ZYBAAAAQGSpKpPeulrKne9+m9Su0o3/k1I6NXzdUeHsIeefzXxNZbEzSG3+bxSbRMUz+A/38CMGgQE/++Uvfxl35ZVXVg0dOtTm6T4FBQWaOHFi4qZNm47caTJBgV/+8peV7vaJj4/X/fffX3HnnXfG1752//33xw8fPrymf//+Ls89derU6Lfeeium/jl+//vfV3j8l0NwMTcsf7VW+vJJ52RsdVnT9k/pIl34nPdKGZkKA67UVEoHt0lte8nrqDAQGkxqluQsAAD8Tm3OjYvuZziXWjabdDCnrp3B3h8+Hsgxd9p8+F1mdwZhzbJuZt3LMa0atjNwBAr6SglpPhwLAAAAgICqKpf+fa2U83Xj999NWCC1y/ErsZkKwHTYRSBxDz/sERjws1mzZkX/6U9/ij311FNrJk6cWD169OjqAQMG2GJjYxtsZ7PZtH79esvbb78d/fe//z22oKCgQZPMO++8s3LgwIGNhg5MNYF//OMfsevWrXMEDQ4ePKgzzzwz8Y9//GPFpEmTqky7gdpAwrPPPhv3zDPPNBjE5MmTq/r06eNxsAFB6pwHnMvaD6W5TzvLuVaXu3/yvsfZ0tkPeL+UUZteUpRFsrv4ltq33jeBgXI3LQmoMBCcSM4CAMDv1JYyT/Ob4KxZTNWt+qVA966vq0RQ296gfmlPX6gqcfYqPbpfaevODYME5qN5v2w9kt8GAAAAEIqqK6S3J0lb5rjfJrmjdONMKS3LnyMDWo57+GGLwECAfPvtt1azmKlLExbo2LGjPSUlxR4bG2svKiqK2rVrl6WoqMjlvmay/9lnnz3uk//muDNmzCg744wzEg8ePOgIHJiPt9xyS/xdd90V3717d1t5ebm2bdtmqapq2KnABBqee+45N7PKCEl9L3YutU9eFeVJxXudZYxad5LiW/u2lFFMgrMf04GtrgMDJ17k/XNSYSB0kZwFAIDfqd4U20rqPNi51DKlPYv21IUHTJ9Q8+f9GyRbtW+/Awt3OpdNn9e9Zo2V2vb+IURQL0yQlEHJUQAAACAUmLYB79wobf7C/TZJmdJN/5PSe/hzZID3cQ8/rBAYCAKVlZVm0t7M1DY6W9u6dWs99thj5bfffnuVxcM+mP369bPNmjWr9PLLL0/cvn37keMXFxdr1apVLg9yzjnn1Lz33nuliYmJTf/LIDSY75+Ujs7Fn8wNUJeBgQ2+OZ/bCgMpvjkfAAAAQuvmRuuOzuWE8xre5Nu/Udq7tl5FgrVS0W7fjse06spf5VzqS2zzQyuDHyoRmKVdHymW/68BAAAAQaOmSnr3ZmnjJ+63adVeuvFDZ0U0AAgiBAb87K233ir74IMPok1rgmXLllkLC91MaP4gKirKMel/3XXXVd18881VGRkZTW68ecopp9hWrVpV/Nhjj8VNmTIl1rQmcKVXr162X/3qV5WmlYGngQSgSdr1dv2GyTzF5QvuKgyYnk8AAACAK6YCV2Z/56Ir614vPVDXzmBv7cd1UlWpb69jaYG0bZ5zOSLKeZPRhAfa/xAiMEtqljMcDAAAAMB/aqql9yZL6//nfpvEts42BO2y+coACDoEBvzMTP7369ev8sEHH6ysqanRxo0bLZs2bbLk5uZGFRYWRpnWAMnJyTLtCUzLANMaICWl5U9Dm+oEzzzzTMVjjz1WsWjRIqupLlBQUBBltVod7RAGDx5cM3DgQBfN5QEvMk9CubJvo7NNgrdvbla4buvhaL8AAAAANEViutT9DOdSy7yHPZhTFyQwFQlMZYIDOabngQ+vr10q2Oxc1n5Q93JsktT+xLp2Bo7KBH2lhDQfjgUAAACIYLYa6f2fSGvfd7+NeT9+wwfO9+oAEIQIDASQmaw/8cQTbWbx1zljY2N11lln1ZjFX+cEGlQYcKW6TDq8XUrr5qeWBAQGAAAA4AUm8Gqe9DdL30vqXq8olvat/6Glwdq6MEH5Id9e9spiaec3zqW+1p1/CBHUa23QppdkjfHteAAAAIBwDwt8cLu0aob7beJTnGEBRwUzAAhOBAYA+E/bRsot7dvg/cCAu5YEVBgAAACAL8UlSZ1PdS617HapaE9deMDxca2zPZet2rdfj8KdzmXTZ3WvWWOltr3r2hnULkkZpjeeb8cDAAAAhDpTbezDu6SVb7nfJi5Fuv59qcNAf44MAJqMwAAA/944TekiHd7hOjCQPda756PCAAAAAIKFmYRv3dG5nHBe3evVldL+jc5WBkeCBGuc4QJfqqmU8lc5l/oS2ziDA+3rhQhMa7HYRN+OBwAAAAgVJgz80S+l5dPcbxObLF3/H6nTKf4cGQA0C4EBAP5vS+AuMODthCcVBgAAABDsomOd5UkdJUqvrHu99EBdeMAECUygYO86qarUt+MpLZByvnYuR0Q52y44AgT9pfamtUE/KTXL2ZYBAAAAiKSwwMf3SMtedb9NTCtp0rsNK44BQBAjMADAv8zTSZtnHfu66fHq7f6tsrsvBQUAAAAEs8R0qfsZzqV+j9SD2xoGCcxH85q7975eYZcKNjuXtR/UvRyb9EN4wCz9f6hM0FdKSPXhWAAAAIAAhgU+fUD65iX328QkStfNkLoO9efIAKBFCAwA8H+FAVdMhQHzhstb/VLdVRcw4lt75xwAAACAP1mszif9zdL3krrXK4qdAVxHgGBtXZig/JBvx2NCujuXOpf6Wneua2dQu7TpJVljfDseAAAAwFfMvesv/k9a8oL7baITpGvflrqN4OsAIKQQGADgX23dBAYqi6TC3VJKJ++cp7yRwEAcgQEAAACEkbgkZ7nT+iVPzQ1N8/7atDKorURglv0bJVu1b8dTuNO5bPqs7jVrrPP/AkcHCZIyvBcaBgAAAHzBvLee/Xtp4fPut7HGSde8JXU/k68BgJBDYACAf7XLdr9u/wbvBQaoMAAAAIBIZibhzXtrs5xwXt3r1ZXO0EBtFQJHoGCNVLTHt+OpqZTyVzmX+hLb/BAe6P9De4N+zjZmsYm+HQ8AAADgqa+elOY/5369Ccde/abU8xyuKYCQRGAAgH8lpElJmVJxnuu2BD1H+bbCgCVGio73zjkAAACAUBMdK2X2dy66qu710gN1VQhqKxLsXSdVl/l2PKUFUs7XzqVWlEVK7yllmABBf2eIwIQJUrMki8W34wEAAADqm/sHae7T7q+Jud985VTphHO5bgBCFoEBAP7XrrebwMB6753DXYWB+NaUPAUAAACOlpgudT/DudSy1UgHt/0QIKjX2uBgjm+vn90mFWxyLms/qHs9NqmuCkHtYj5PSOXrCQAAAO+b/yfpy8fcr7dESxNfk3qP4+oDCGkEBgD4nykxmjPXdYUBbyk/7Pr1uNbeOwcAAAAQzixWqU1P59L30rrXK4qdYd/aAEHtUn7It+OpLJZ2LnUu9bXu3DBEYJY2vSRrjG/HAwAAgPC18G/SrIfdr4+ySuNflk68yJ+jAgCfIDAAwP/aZbt+3dx0tNu9UwGgsQoDAAAAAJovLknqfKpzqWXexxfurmtpsNdUJFgj7d8o2ap9e7ULdzqXTZ817CNrKpuZlgZHqhL0l5LaU3EMAAAAjVv8T+nz37hfb1poXfGi1O8yriSAsEBgAEBgKgy4UnZQKtkvJbVr+TnK3QQGqDAAAAAAeJ8J/aZ0ci7ZY+per66Q9m+qCxKYjyZMULTHt1+Fmkopb5VzqS+xTV14oLalQfsTpZgE344HAAAAoeGbKdKn9zWyQZR02T+lARP8OCgA8C0CAwCCJzBQW2XAG4EBtxUGUlp+bAAAAACeiY6TMvs7F11V93pJgbTXhAjW1gsSrJOqy3x7ZUsLpJyvnUv9J8TSe0oZfeuCBGZJ6SpZLL4dDwAAAILHstekj37V+DaX/k0aWO99LQCEAQIDAPyvVVvnkz3mZp2rwED3M1p+DioMAAAAAMGrVRup+5nOpZatRjq4rS5AULsczPHtWOw2qWCTc1n7Qd3rsUn12hn8sJjPE1J9Ox4AAAD43/Jp0oc/b3ybi/8inTzJXyMCAL8hMAAgcFUGchcc+/q+Dd45vrsKA3HJ3jk+AAAAAO+yWKU2PZ1L30vrXq8odlYfcFQkqF1WS+WHffsVqCyWdi51LvWldDkqSNBfatNLsnKLBQAAICSt/Lf0wR2Nb3PhH6XBN/lrRADgV/xvFkBgtM12HRjY76XAgLsKA/GtvXN8AAAAAP4RlyR1Oc251LLbpcLddeGBvaa1wRpp/0bJVu3b8Rze4Vw2fVb3mjVWatfbGR44EiboLyW1l6KifDseAAAANN+qd6X3f2reYLrf5vxnpNMmc5UBhC0CAwACV2HAFa9VGChy/XocgQEAAAAg5JlJ+JROziV7TN3r1RXO0ED+2oatDYrzfDuemkopb5VzqS+xrZRhAgT96yoSmP8LxST4djwAAAA4vjX/lf7zI2eLKnfGPC4N+TFXE0BYIzAAIDDM0zeuFOdLpQekxPSWHb/CTXlSKgwAAAAA4Ss6Tsoc4Fx0Vd3rJQVHtTRY42xzUF3m2/GU7pdyvnYutaIsUnrPei0NflhSukoWi2/HAwAAAKd1/5PemyzZa9xfkXMfloYfp1UBAIQBAgMAgqvCgGGeCOo61DctCagwAAAAAESeVm2k7mc6l1q2GulAzrFBgoM5vh2LeYKtYJNzWft+3euxyVL7E+uFCExVgr5SfIpvxwMAABBpNnwqzbip8VZW5/xWGvkLf44KAAKGwACAwEjOlOJSXFcC2Le+ZYEB08/UXUsCKgwAAAAAMCxWqW0v59L30rprYv4vsXe9s6XB3rU/BAlWS+Vuqph5S2WRtHOpc6kvpYszQNC+b12QoE0vycotHQAAgCbbNEt653rJVuV+m7Puk866h4sLIGLwv0sAges52i5b2vnNsev2bWzZsStL3JeSMiEFAAAAAHAnLlnqcppzqR9KLtwl5ZsAweq6agSmSkBjT6Z5w+EdzmXjp3WvWeOc/59yVCGoDRP0l5LaO/+vBQAAgGNtmSP9+1qpptL91Rn5S+nsB7h6ACIKgQEAgdOut5vAwPqWHbfCTTsCgwoDAAAAAJrKTMKndHYu2WPqXq+ucLZUq9/SwCzFeb69xjUVUt4q51JfYtt6LQ1+WEw7uJgE344HAAAg2G2dK711jfN9lDvD75RGP0QAE0DEITAAIHDMjStX9m1o2XHLGwkMxLVu2bEBAAAAoFZ0nJQ5wLnUV1Ig7a0NEJiKBGulveuk6jLfXrvS/VLOXOdSK8oipfesa2eQ8UNrg5SuksXC1xIAAIS/bQukt66WqsvdbzPkp9J5jxIWABCRCAwACL7AQOFO56R/c6sBUGEAAAAAQCC1aiN1P9O51LLVSAdynAGCvWvrwgQHt/l2LHabs3WCWda+X/d6bLIzPOBoZ1AvTBBPGzcAABBGti+Wpk+Uqkrdb3PabdK4JwkLAIhYBAYABE7bbPfr9m+SOg/2boWBKKsUk9i8YwIAAABAS1isUttezqXfZXWvVxRJe9f/UIlgzQ9hgtVS+WHfXu/KImnHEudSX0qXunYGjjBBf6lNL8nKLSQAABBidnwjTZsgVZW432bwzdIFfyAsACCi8b89AIFjbkSZCXxX6c79G5ofGKhwc2PNVCwwvUcBAAAAIFjEJUtdTnMutex2qXDXD1UI6i37N0r2Gt+O5/AO57Lx07rXrHFSu951QYLaigRJ7X07FgAAgOba9Z007QpnSNKdk6+XLnyOe8YAIh6BAQCBY/plmioDe1Ycu27f+uYf112FgbhmtjgAAAAAAH8yQeeUzs4le2zd69UVztBAbTuD/B9aGxTn+XY8NRVS3vfOpb7Etg3bGZg/m9ZzMQm+HQ8AAEBj9qyUpl7WeOvagddIF//VeY8aACIcgQEAgWVuJrkMDGxo/jHdvRE0FQYAAAAAIFRFx0mZA5xLfSX7G7YzcPx5vVRd5tvxlO6XcuY6l1pRFim9Z70ggfnYV0rN4uk9AADge3mrpTcubby904CJ0qV/JywAAD8gMAAgsExZS1d8UmEgpfnHBAAAAIBg1aqt1OMs51LLViMdyKkXIPghTHBwm2/HYrdJBZucy9r3616PTa6rQtC+b11Vgnj+nwYAALxk7zr9f3v3ASdXVfeP/2zL7qaTSguhJSQkgDTpCKFLLwoiIEhVH31QbCioDz9REQGx/S2AFEGqEOFBqSICSn0oIZUE0ijpZbN9d/6vs5PdndnsJLvJzrZ5v1+v80r27p07Z+/CZOaez/1+w+0nhFCxPPM+E04O4aTfhpBf4LQDrCUwAHTPwMDyuSHUVGxcKUsVBgAAgFwXL4IP2zE5JpzUvL1qdfJiekNbg5RRtZ678DpC7B88/8XkSDVo1NoqBGvHiAkhDN0xhAKXrACAdlg8M4TbTgihfGnmfcYfH8Ipf/A+A6AFn76Arm9J0KpECEtmhbDFrh1YYUBLAgAAIMcVDwhh1MeTo1EiEcKqhWvDA7EiQaxG8HYIS2aGkKjL7nxWzk+OmX9v3lZQnAyXpwYJYkWC/iOyOxcAoGdaOjuE244PYc2izPuMPSaEU28JoaCoM2cG0CMIDABdK/axjBeD6qrW/d7iGRsXGMhUYSBeGAMAACBdXl4Ig7ZOjrFHNW+vrUp+LmtsZ9AQKJgaQtmH2T2D8fPhh28mR6q+w5rDAw1/7pwMoW9MZToAoHdYNieEW49b//uTMUeG8OnbQijs05kzA+gxBAaArhXLTA4bk7z41NLi6Rt3zEwVBkpUGAAAAGizwuJkiLtlkHvNkuZWBosa/5wWQm1ldk9u+ZIQ3v1ncjTKy0+2MGhsZ9BYkWDwNskgBADQey1/L4Rbjw9h9fuZ99lhUgifviP5vgaAVgkMAF0vlprsyMBAxgoDAgMAAACbrN+wELb/RHI0qq9L3uHXGCRoDBPEC/nZlKhPtk6I4+0Hm7f3GZCsQNAYIGgIE+wcQsmg7M4HAOgcK+Yn2xCsWpB5n+0ODuGMu0IoKvFbAVgPgQGg6w3bqfXt8YJPRwYGVBgAAADIjvyCZPW4OCac1Ly9anWy+kBDS4OpzWGCqpXZ/U1Urw5h/ovJkWrQNulBgtjeYMgOyep3AEDPsHJhCLcdF8KKeZn3GX1gCJ+5W+sigDbwaQjoHhUGWrN0dgi11e3vLZWpJYEKAwAAAJ2reEAIoz6eHI0SiRBWLVwbHpiy9s+pydB4oi6781k5Lzlm/r15W0Fx8nNpDA80hAhioGBiCP1HZHcuAED7rfogWVlgfVWMttkvhDPvCaFPP2cYoA0EBoCuN3xc69vjhaJls0MYMb7tx4oXnjJWGFB6EgAAoMvl5YUwaOvkGHtU8/baqhAWz2huZ9BYjaDso+zOp64qhA/fTI5U/YaHMGJteKCxIkH8/KqsMQB0jbJFIdx+QvKacSZb7x3CZ+8Lobh/Z84MoEcTGAC63pDtQ8gvDKG+dt3vLZ7evsBATUXrx4lUGAAAAOi+CotD2GLX5Ei1ZklzeKAxTBDbHNRWZnc+axaH8O4/k6NRXn4IQ3dMhgdGNLY1mBDC4G2SQQgAIEv/Li9JVhZYXxvbLfcI4awHkhWOAGgzgQGg68WWA7Fn5JIZ634v3l3SHpmqC0QlA9s/NwAAALpWv2EhbP+J5GhUHyvSzUkPEsT2BivmZncuifrkQkUcbz+YHlCPYffGAEGsShC/VukOADZd+bIQbj8xeXNZJlvsFsLZf/FvL8BGEBgAuofhYzsmMFC5nsCACgMAAAC9Q35BCMPGJMeEk5q3V61OVh+I4YGGEMHU5J9VK7M7nxhen/9icqQatM3aAMHOzUGCGJgvcEkOANqkYnkyLBD/bc9k5C4hnP1QCKWbOakAG8GnE6B7iH0gpz287nYVBgAAAGirWIJ41MeTo1EiEcLKBc3tDBorEiyZFUKiLrvnduW85Jj5t+ZtBcUhDN8pGR5oChNMDKH/iOzOBQB6mooVIdxxcggfvpl5n+HjQzjnoRD6DunMmQH0KgIDQPcJDLRm6awQ6mrbfvdFZYa7RmKfyT79N35+AAAA9Ex5eSEMHpUcOx3dvL2mMtlaoLGdwaK11QjKPsrufOqqkgsfLRc/+g1PBghGNLY1mJD8rFxUkt35AEB3FCvJ/unUEN7/v8z7DBsbwuf+mmxfBMBGExgAuod4d0Vr6qpDWP5eCMN2bHsZyEx3mcSLRAAAABDFhfgtdk2OVGuWNFchaAwTxJ7JtZXZPW9rFocw55nkSA2/D92xOUDQGCYYvI3PuAD0XrHF0J2nhbDwlcz7xH8fP/ewCj0AHUBgAOge4hu8EBf0E+t+L16YaWtgICZPW1M8aNPmBwAAQG6Idylu/4nkaFRfF8KyOcnwQEOIIFYjmBLCirnZnUuiPlkFIY63H2zeXjwwhBGxlUEcMUQwMYQR40Mo8dkXgB6uek0Id346hPkvZt5ns+2SYYEBm3fmzAB6LYEBoHsoKg1hs21DWP7uut9bMiOEcNymVRgoGbhp8wMAACB35ReEMGxMckw4OT20vmhaCItSKxJMDaEqQ7u8jhI/+87/T3KkGrRNczWChjDBxBCG7ND2Nn8A0JWqy0O46/QQ5r2QeZ/Bo0M495EQBm7ZmTMD6NV8WgC6j9ibsbXAwOIYGAibWGFAYAAAAIAOFsPp2+yTHI0SiRBWLmhuZ7AoViN4O4Qls0JI1GX3V7ByXnLM/FvztoLiEEaMa25n0Dj6j8juXACgPWoqQ7j7zBDe+1fmfQaNSlYWGLS1cwvQgQQGgO5j+E7pFzVSWxK0lQoDAAAAdKW8vBAGj0qOnY5OXwiJrQUagwTxzxgmKPsou/OpqwrhgzeSI1W/4SntDNa2NohB/qKS7M4HAFqqrQrhns+GMOcfmc/NwK2SYYHNRjt/AB1MYADoPuKFidYsnhlCfX0I+fkbPoYKAwAAAHRHcSF+i12TI1XZ4pSWBrEawZRkcL62MrvzWbM4hDnPJEejvIIQhu64tp1BSphg8DbJIAQAdLTa6hDuPSeEd57MvE//zZNhgSHbOf8AWSAwAHQfw8e2vr22IoSV89uWHs1UYaB4wKbNDQAAALKh//AQ+h8SwvaHNG+rrwth2ZzmSgSNY8Xc7P4OYsuEJTOS4+0H09v8NVYhaAgTrA0SxJYMALCx6mpCuP+8EGb+PfM+/UaEcO4jIQzdwXkGyBKBAaD7GJYhMBAtntG2wEDlyta3u4gBAABAT5FfEMKwMckx4eT0qnqLpiWDBLGdQWOQIFN4vqPE48//T3KkipUHRsQQQcoYskMIBS45ArABdbUhPHBBCNMfybxP32HJygLx30MAssa7d6D7iFUABo1KVhNoKZZjHHvkJlQYcNcDAAAAPVwMw2+zT3I0SiRCWLlgbXhgbUWCGCZYMitZMSCbVsxLjpl/a95WUBzCiHHNVQgaWxvESgoA0BgWePCiEKY+lPl8lA4J4XN/Tf6bAkBWCQwA3cvwnTIEBma07fFVq1vfrsIAAAAAvVFeXgiDRyXHTkc3b6+pTLYW+GhqemuDNYuyO5+6qhA+eCM5UvUb3hweiH/GMMHwcSEUlWR3PgB0L7HtzuQvhjDlgcz7lAwO4ZzJyX8vAMg6gQGge4kXC955svUKA20RyzO2pnjQps0LAAAAepK4EL/FbsmRqmxxCIvWhgcaR/zMXVuZ3fmsWRzCnGeSo1FeQQhDd1wbJNi5OUwQqw/GIAQAvUt9fQh//XIIb96TeZ94Hfech0LYYtfOnBlAThMYALqXYWNb375kZrLM4oYuGGRqSaDCAAAAACRbA/Q/JITtD0kvDb1sTosgwZRku4Fsii0TYhWEON7+S3pbwaZ2BmtH/Npne4CeHRZ45NIQXr8z8z59BoRw9l9C2HL3zpwZQM4TGAC6X4WBTEGA1R+EMHDLzI+N5Rbrqlv/XrzYAAAAAKyroDCE4WOTY8LJ6VX8Fk1rbmmwKLY3eDtzWL+jxOPP/09ypBq8TbIKQVOYYGIIQ7ZPzh+A7iveCPbo10N47bbM+/TpH8JZD4Sw9V6dOTMAVBgAup14cSKTWCJxfYGB9V2wcBcCAAAAtE/8LL3NPsmRuuizcn4IH01tDhLEsfSdZMWAbIoVD+KY8WjztoLiEEaMa25n0BAmmJispABA14v/bvz92yG8cnPmfYr6hvDZ+9L/vQGg04jfAt1L6WYh9N88hLIP1/3e4hkh7DAp82PjnQ+ZqDAAAAAAmy62Cox3+sex09HpVf9ia4GmlgZrx5pF2T3rdVUhfPBGcqTqNyKEkWvDA41tDYbtFEJRSXbnA0B6WODxK0J48beZz0phaQhn3hvC6P2dOYAuIjAAdD/Dd8oQGJi+/sdVrcz8PRUGAAAAIHviQvwWuyVHqrLFISxKDRFMCWHR9ORCfzbFoMKcOJ5p3pZXEMLQHZsDBI1j0KhkEAKAjg0LPPU/Ifz7V5n3KSwJ4TN/DmG7g5x5gC4kMAB0z8DAu/9cd/vimet/XMYKA3kh9BnQIVMDAAAA2iG2Buh/SAjbH9K8ra42hGVz1oYHpjYHCWK7gWyKLRNiFYQ43v5LelXChlYGjSGCiSGMGO/mA4BN8Y8fhfDcDZm/X9AnhDPuDGGHQ51ngC4mMAB0z8BAaxZPSyZTM6X+qzIEBooHhJCf33HzAwAAADZeQWEIw8cmRzgl/UaARdOS4YHGigQxUJDp835Hicef/5/kSBXbLjSEBxrDBBNDGLJ9cv4AZPbMNSE8+9PM388vCuH0P4Ww4+HOIkA34N0t0P0MH9f69orlIaxZkrw7oT0VBuKdAgAAAED3FtsJbrNPcjSKNw6snJ/S0mDtWDorhER9ducTKx7EMePR9PLZ8UaHGB5orEgwYkLmaxUAueZf14XwzI8yfz+/MIRP3xbC2KM6c1YArIfAANBzAgPR4umZP4RnuuMgXnAAAAAAep5YZTDe6R/HTsc0b6+pTLYWaBkkWLMou/OprQzhgzeSI1W/ESktDdaOYTuFUFSS3fkAdCfP/yKEp67K/P28ghBOuyWEccd25qwA2ACBAaD76TcshNIhIVQsW/d78WLAdge1/jgVBgAAACA3xIX4LXZLjlRli5pbGTSECKaEsGh6CHVV2Z1PDCrMieMf6QtjQ3dcN0gwaFTmdosAPdW/fxPCE1dm/n5efgin/iGEnU/szFkB0AYCA0D3rTIw74V1ty+ekfkxKgwAAABAbus/Ijl2OLR5W11tCMvmJMMDjZUIFr2dbDeQTYm65I0Pcbz9l/TWiQ2tDHZeGyKYGMKI8SokAj3XS38I4bHL17NDXggn/TaEiad24qQAaCuBAaB7iv0AWw0MTM/8GBUGAAAAgJYKCkMYPjY5Jp6Sch1hZQiLpq3b1qB6dXbPYbzhYd6/kyNVbLsQwwONlQhGTAhhyPbJ+QN0V6/8MYRHv76BsMBvQtjt9E6cFADt4d0m0H0rDLRmvRUGVra+vXhAx8wJAAAA6D1KBoWwzb7J0SiRCGHl/OZ2Bh+tbW2wdFYIifrszidWPIhjxqPN2wpLktdIUlsaxCBB/+HZnQtAW7x2RwiPXLr+fU74RQgfO9P5BOjGBAaA7lthoDVlH4VQsTyE0s3aXmGgZGDHzg0AAADonfLyknf6x7HTMc3bayqSNzEsWhsgaGxvsGZxdudTWxnCB68nR6p+I9JDBHEM2ymEopLszgeg0et/DuGvX17/+Tj2+hD2OMc5A+jmBAaAnhUYiBbPDGGbfVov6dea2BsQAAAAYGMVlYaw5ceSI1XZouZWBg1hgikhLJoeQl1Vds/1mkUhzInjH83b8gpCGDYmhBE7rw0RrG1vMGjrZBACoKO8eV8Ik78Yy7Jk3ueYa0PY+3znHKAHEBgAuqcBWyQX+lsLASyeniEwsDpziUEAAACAjtZ/RHLscGjztrraEJbNbg4SNIQJ3k62G8imRF3ymkkcb/+leXvxoBBGrg0RNIQJJoYwYryKjMDGefvBEB68aP1tWo76UQj7XOQMA/QQAgNA9xST77HKwIKX1/1eLAHYmkwtCVQYAAAAADpLQWHymkYcE09p3l65MoRF09a2M2hsbfB2CNUZboDoKFUrQ5j37+RIFdsuNFYhaAgTTAhh6A4h5Bdkdz5AzzX1ryHcf/76wwKH/08I+32pM2cFwCYSGAC6r4yBgemt75+pJUGJlgQAAABAF4sVELfZNzkaJRLJygON7QwaQwRL31n/glxHiM8bx4xHm7cVloQwfNzaIEFKa4N+w7I7F6D7m/5oCPefl6xmksmkK0I48NLOnBUAHUBgAOi+4gfU1rRWYaC2OoTaytb3V2EAAAAA6K4VFjcbnRw7HdO8vaYief2joZ1BSphgzeLszideW/ng9eRI1X/k2nYGE5rDBMN2CqGoJLvzAbqHmY+HcO85IdTXZt7nE98O4eBvdOasAOggAgNA9xU/eLZm1YIQqlaHUDxgw9UFIhUGAAAAgJ6kqDSELT+WHKnKFjVXIWgIE8QxPYS6quzOp+yj5Jjzj+ZteQUhDBuztp3Bzs3tDQZtnQxCAL3DO0+FcM9ZIdTXZN7noMtCOOTbnTkrADqQwADQvVsSZLJkZghb7ZneBzATFQYAAACA3qD/iOTY4dDmbXW1ISybvbYKwdTmMMHKedmdSyxLHttGNrSOfKB5e/GglHYGMUwQx3g3dEBPNOeZEO4+c/2hpP2/EsKkKwWFAHowgQGg+xo0KoSiviHUlK/7vViWLzUwoMIAAAAAkIsKCpM3XcQx8dT0mysWTWtuZ9AwpoZQvTq786laGcK8fydHqsGjm9sZNLY2GLJ9CPkF2Z0PsHHeey6Eu87I3AY22vdLIRxxlbAAQA8nMAB0X/n5IQwbu27fvKghvZ6icj0tCVQYAAAAAHJNyaAQttk3ORolEiGsmNfczqAxSLD0nRAS9dmdz4q5yTHjf5u3FZaEMHxcczuDhjDBxBD6DcvuXID1m/vvEO78dAi1FZn3+fjFIRx1tbAAQC8gMAB0b/FDY6uBgZnpX2eqMNCnv6Q6AAAAQJSXF8Jmo5Nj3Cebz0lNRbKaY2OAIIYJPpwSQvmS7J63eOdyvO7T8tpP/5EhjEipRBD/jBUUCov9HiHb5r8cwp2nhVCzJvM+e30+hGOuERYA6CUEBoDubfjY1re3tcKA6gIAAAAA61dUGsKWH0uOVGWL1rY0mLo2TDAleU2mrjq7Z7Tso+SY84/mbXkFIQwbkwwPNIQJ1gYJBm1t0RI6ysJXQ/jTKSFUl2XeZ49zQvjkdf6/A+hFBAaA7l9hoDXL30um3+MH2vVVGCgZmL25AQAAAPRm/UeE0H9SCDtMat5WVxvCstlrgwRvN4cJVs7L7lwSdcmwQsNNJA80by8etLaVwYTmigQjxodQPCC784He5v3XQ7jj5MzXWaPdzgzhuBuTrWQB6DUEBoCeGRgIiRCWzAphi12TX6owAAAAAJB9BYXJ9gBxTDy1eXvFihAWTUu2M2hsbRDDBNWrszufqpUhzPt3cqQaPHptFYKU1gZDtte6Elrz4Vsh3HFSCJUrM5+fXT4dwom/EhYA6IUEBoDuLX64KygOoa5q3e/F3nqNgQEVBgAAAAC6TungEEbvlxyNEokQVsxrDhA0hgmWvhNCoj6781kxNzlm/G/ztsKS5M0pje0MGke/YdmdC3RnMdhz+4khVCzPvM+EU0I46f8TuAHopQQGgO6fWh+6Y/IDZUtLZjT/PVP6tVhLAgAAAIAukZcXwmajk2PcJ5u3xzaTsbVAYzuDxvYG5UuyO5/ayhA+eD05UvUfmQwOjNi5OUwQKygUFmd3PtDV4g1Zt58QQvnSzPuMPyGEU36fvE4LQK/kFR7o/uIHtNYCAw0968L6KwzoVwcAAADQvRSVhrDl7smRqmxRc3igIUwwJXn9p646u/Mp+yg5Zj/dvC2vIIRhY1IqEUxMBgoGbZ0MQkBPF9u93nZ8CGsWZ95np0+GcOrNIRQUdebMAOhkAgNA9xdLxWVKwDaqzBAYKFFhAAAAAKBH6D8ihP6TQthhUvO2utoQls1OCRKsDROsnJfduSTqkmGFOKY80Ly9eNDaAMHOKUGC8W5aoWdZOjsZFohBmUzGHBnCp24NobBPZ84MgC4gMAD0jAoDmd7Y1lYn37RmrDAwKKtTAwAAACCLYhn0eG0ojomnNm+vWBHComnJIMGixtYGU0OoXp3dX0fVyhDmvZAcqQaPbm5n0BAmmBjCkO31fKf7Wf5eCLedEMLqDzLvs8NhIXz6Dm05AHKEwADQcysMxKR3TJjHFHdVhg+DKgwAAAAA9D6lg0MYvV9yNKqvT1Ye+KgxQLA2TLD0nRAS9dmdz4q5yTHjf5u3FZYkr1uNaGxrsHb0G5bduUDG/07nhXDr8SGsWpD5HG1/SAhn3BlCUYnzCJAjBAaA7i+msWPfuBgQaK0tQfzglaklQbGWBAAAAAA5IT8/hM22TY5xn2zeXlORbC3QWIWgsb1B+ZLszqe2MoT3/y85UvUf2RweaAwTxAoKhcXZnQ+5beXCZBuC9bXz2PagEM74cwhFpZ05MwC6mMAA0P3FlgNDdwhhyczWAwNRppYEKgwAAAAA5La4+Lnl7smRqmxRc3igccRgQV11ducT+8bHMfvp5m3xZplhY9e2M4ghgokhjNg5hEFbh5CXl9350Put+iCE245LtiPIZJv9QvjM3SH06duZMwOgGxAYAHqGmLJuNTAQP8TVhFBT3vrjVBgAAAAAoDX9R4TQf1IIO0xq3havMy2d3dzOoDFIsHJ+ds9hrKy5eFpyTHmgeXvxoJR2BjuvDRKMD6F4gN8pbbP6o2RlgWVzMu+z9cdD+Ox9IRT3d1YBcpDAANAzDB8XwrSHW68wULU68+NUGAAAAACgrQqKQhgxLjlSVawIYdG05ooEjWGC6rLsntuqlSHMeyE5UsW2C43tDBpHbOuZX5Dd+dCzlC1OhgWWzsq8z1Z7hnDW/UIoADlMYADoOYGB1ix9J4SK5Zkfp8IAAAAAAJuqdHAIo/dLjkb19cl+8A1VCKY2hwmWzQ4hUZ/dcx5Ly8cx43+btxWWJoMOMTzQFCaYGEK/odmdC93TmqUh3H5iCEvWtnRtzRa7hXDWX0IoGdSZMwOgmxEYAHqG2MOtNXVVIXzwRubHebMLAAAAQDbk5yfv9I9j3LHN22sqkm00G9sZNIwpIZQvze7vobYihPf/LzlS9R+ZUokgtjTYOdn+s7A4u/Oh65QvC+GOE0NY9HbmfUbuEsLZDyXDMADkNIEBoGcYNiaEkBcbuq37vQUvZ36cfm4AAAAAdKai0hC23D05GiUSIZQtSi7gpoYIYrvNuurszqfso+SY/XTztryC5A06DSGCnZNBgvj3gVuFkBevwdFjxfYZd5wcwodvZd4nhkbOmRxC3yGdOTMAuimBAaDnfNCKae3l7677vfkvZXhM32TfOQAAAADoSnERfsDI5NhhUvP2upoQls5ubmewKLY2eDuElfOzO59EXQiLpyXHlBbVOpvaGawdI8a7KaenqFwZwp9OCeGD1zPvM2ynEM75q1YVADQRGAB6juHjWg8MfPhm6/sXD8z6lAAAAABgo8WbXUaMS45dTku/S7wxPNA44tfVZdlfcJ73QnKkijfypAUJJoYwZLsQ8guyOx/armp1CH86LYSFr2beZ+iOIXzuryH0H+7MAtBEYADoOWJvtZl/W3d7prJtJQIDAAAAAPRAsa/86P2To1F9fQgr56WHCOJYNjuERH1257P8veSY8b/N2wpLk0GHxgBBLHMf/+w3NLtzYV3Va0K489MhLMhQiTUasn0In3s4hAGbO4MApBEYAHpWYKA9VBgAAAAAoLfIz0/e6R/HuGObt9dUhLB4ekqIYG17g/Kl2Z1PbUUI7/9fcqTqv3kII3duDhLEP4eNDaGwOLvzyVXV5SHcdfq6VSFSDR6dDAsM3LIzZwZADyEwAPTewIAKAwAAAAD0dkWlIWy5e3I0SiRCKFvUHB5oaG8wJYTFMzJX6+woZR8mx+ynm7flF4YwdMzaEMHaSgTx7wO3CiEvL7vz6c1iWOTuz4Tw3r8y7zNomxDOfSSEQVt35swA6EEEBoCeIyaR20OFAQAAAAByUVyEHzAyOXY8rHl7XU0IS2c3BwkawwQr52d3PvW1ISyelhxTUraXDAphRAwRTEhpbTA+hOL+2Z1Pb1BTGcI9Z4Uw55nM+8RAxuf+GsLgbTpzZgD0MAIDQM9RPCCEQaPa/gEm7g8AAAAAJBUUhTBiXHLsclrzWalYsbYKwdvpQYLqsuyeucqVyVL6Lcvpx7YLjVUIRqytSDBkuxDyC3r3bzJWhqhanQx2xN9VvL7ZWgWG2qoQ7j0nhHeezHysAVsk2xDE8wYA6yEwAPS8KgNtDQzEhDIAAAAAsH6lg0MYvX9yNKqvD2HlvJQQQaxKMDWEZbNDSNRn94wufy85pj/SvK2wNBl0aKxE0BAmmBBCv6GhR4vn9q37Q1j4aggfvBFC5Yrm75UMDmGL3ULYas8QdvlUsp1DDBPcd14Isx7LfMz+I5NhgaE7dMqPAEDPJjAA9CzDx4Uw+6m27aslAQAAAABsnPz85J3+cYw7tnl7dXkIi6enVCRY296gfGl2z3RtRQjv/19ypOq/+doQwdpKBPHv8aajwuLQrc18LITnfr5udYVUMTzw7j+T47nrQ9hm32QVgvkvZn5M32EhnPPXEIaNycq0Aeh9BAaAnmX4Tm3ft2RgNmcCAAAAALmnT98QttojORrFReyyRc3hgYYwwZQQFs8Ioa46u/Mp+zA5Um8yyi8MYeiYtUGCxooEO4cwcKvWS/x3pvJlITz6jRCm3N/+x877z/q/XzokWVkgVmIAgDYSGAB6XoWBtlJhAAAAAACyLy7CDxiZHDse1rw9ls9f+k5KW4O1Y9WC7M6nvjaExdOSI3VhPrYwjeGBETs3BwlGjA+huH/oFB9OCeHO00JY/UHHHzu2LzhncjIYAQDtIDAA9CzDx7Z9XxUGAAAAAKDrFBQlF+Tj2OW05u0Vy0NYNC2lpcHUZFWC6rLszqdyZQhzn0+OVLHtQmM7g4YwwcQQhmwXQn5Bx4YFbj022WagoxUPCuGch0LYYteOPzYAvZ7AANCzlG4WQv+RIZR9tOF9VRgAAAAAgO55jW/0/snRqL4+hBVz17YzaAwSvB3CsjkhJOqzO5/l7yXH9EeatxWWJoMO8Y79pjDBhBD6Dd24NgSxskA2wgIhL4RP3RrClrtn4dgA5AKBAaDnGb5T2wIDKgwAAAAAQM+Qn5+8qz+Occc2b68uD2Hx9GR4oCFMMCV5t37FsuzOp7YihPdfS45U/Tdf284gZQwbG0JhceZjPfqN7LQhaJAI4Y27QthxUpaOD0BvJzAA9DzDx4Xw7rMb3k+FAQAAAADo2fr0DWGrPZKjUSKRvKGooRJBylgyI4S66uzOp+zD5Jj9VPO2/MJkaKChncGEtRUJdg5h4FYhzHo8hCn3Z3dOb90Xwi6fCmHsUdl9HgB6JYGBLlZfXx/efffdvDfffLNg/vz5eStWrMgrLi4Om222WWLs2LH1++yzT11paWmHPueqVavCv/71r4IZM2YUxL/H42+77bb1BxxwQN3WW2+d6NAng2xVGGiLkkHOPwAAAAD0Nnl5IQzYPDl2PKx5e11NCEvfSWlpsLa9waoF2Z1PfW2y+kEcqeGAeH0yhhs6w/M3CgwAsFEEBrrAsmXLwgMPPFD02GOPFT7zzDMFS5cuzcu0b1FRUTj66KNrL7300upJkybVbcrzzp49O+/KK68sjs9dXb1uyjIvLy8cdNBBdT/4wQ+qDj300E16Lsh6hYG2eP4XIex2RjLNCwAAAAD0bgVFIYwYnxy7nNa8vWJ5CIumpQQJYnuDaSFUl2V3PpUrQ6eZ+3wyIOFaKADtlJdIJFa190FsvEsuuaTkj3/8Y6sL9hvy2c9+tubXv/515aBB7b9r+s9//nPhBRdcUFpeXr7BfWNw4Gtf+1r1T3/606r82Deqi7311lv5u+66a7/Gr6dMmRImTJjQtZOi68x8LIRnrw1hwcttf8w2+4dw4FdDGHtkNmcGAAAAAPQU9fUhrJi7NjwwtTlIsHR27HkQeqSDLgvhsO919SwA2Ahvv/12mDhxYtPXb7755ppddtmlPnQCFQY62UsvvVTQWligoKAgbL755okRI0Ykampqwvz58/NXrkxPH955551FM2bMyH/66afLBwwY0ObnvPvuuwvPOuus0tj+INWwYcMSsQXB4sWL895///0YHmnYHv+87rrr+sR5/uIXv6ja6B8WOlL5shAe/cbG9fua90IId72Q7ON1zE9D6DvE7wYAAAAAclm8WW7Idskx/rjm7dXlISyevrYaQUpFgoplodtb+GpXzwCAHkhgoAsNHjw4nH766TXHHnts7Sc+8YnagQMHNn2vtrY2/POf/yz4/ve/X/z8888XNG5/5ZVXCs4555zSBx98sKItzzFr1qy8WFkgNSwQ0yjXX3995eGHH97UdmDatGn5l19+efHkyZOb/pv45S9/2Se2KPjUpz5V20E/MmycD6eEcOdpIaz+YNPO4Fv3hfDecyGc9UAII1WpAAAAAABa6NM3hK32SI5G8Wa7so/WhgemNocJYrCgvqb7nML3X0/ONS9jF2QAWIeWBJ1sjz326Lds2bK8yy+/vOrss8+u6du373r3j8GB2Mbg5ptvLkrd/sQTT5SnLvhncvrpp5fee++9TSGAPffcs/6pp55a01p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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from qsdsan.unit_operations import DynamicInfluent as DI\n", "qs.set_thermo(bsm1.cmps)\n", "DI1 = DI(outs=('dynamic_stream',))\n", "sys_di = qs.System(path=(DI1,))\n", "sys_di.set_dynamic_tracker(DI1)\n", "sys_di.simulate(t_span=(0, 10))\n", "DI1.scope.plot_time_series(('S_NH', 'S_S'))" ] }, { "cell_type": "markdown", "id": "e11-di-inspect-intro", "metadata": {}, "source": [ "#### 3.2.1. What does `DynamicInfluent()` default to?\n", "\n", "Constructing `DI()` with no arguments loaded a ready-made BSM1-style influent time series. That's a real file shipped with QSDsan; we can see it directly." ] }, { "cell_type": "code", "execution_count": 39, "id": "e11-di-inspect-code", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:10.613921Z", "iopub.status.busy": "2026-05-30T20:38:10.613921Z", "iopub.status.idle": "2026-05-30T20:38:10.626839Z", "shell.execute_reply": "2026-05-30T20:38:10.626839Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C:\\Users\\Yalin\\Documents\\Coding\\QSDsan-platform\\QSDsan\\qsdsan\\data\\sanunit_data/_inf_dry_2006.tsv\n", "rows: 1345, columns: ['t', 'S_I', 'S_S', 'X_I', 'X_S', 'X_BH', 'S_NH', 'S_ND', 'X_ND', 'S_ALK', 'Q']\n" ] }, { "data": { "text/html": [ "
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5 rows × 11 columns

\n", "
" ], "text/plain": [ " t S_I S_S X_I X_S ... S_NH S_ND X_ND S_ALK Q\n", "0 0 30 63.6 58.5 224 ... 30.2 6.36 11.8 84 21477\n", "1 0.0104 30 61.7 58.5 224 ... 30.2 6.17 11.8 84 21474\n", "2 0.0208 30 61.7 53.1 224 ... 31 6.17 11.6 84 19620\n", "3 0.0312 30 62.2 51.5 221 ... 31.7 6.22 11.4 84 19334\n", "4 0.0417 30 64.6 49.7 218 ... 33.2 6.46 11.2 84 18978\n", "\n", "[5 rows x 11 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from qsdsan.unit_operations.dynamic._influent import dynamic_inf_path\n", "print(dynamic_inf_path)\n", "inf_df = pd.read_csv(dynamic_inf_path, sep='\\t')\n", "print(f'rows: {len(inf_df)}, columns: {list(inf_df.columns)}')\n", "inf_df.head()\n" ] }, { "cell_type": "markdown", "id": "e11-di-custom-intro", "metadata": {}, "source": [ "#### 3.2.2. Supplying your own time series\n", "\n", "To drive a system with custom influent dynamics, write a CSV with a `t` column and one column per component (plus `Q` for total volumetric flow), then pass `data_file=` to `DynamicInfluent`. Below we feed a step change in `S_S` into the same `CompleteMixTank` from §2.5 and watch the tank smooth the step out." ] }, { "cell_type": "code", "execution_count": 40, "id": "e11-di-custom-code", "metadata": { "execution": { "iopub.execute_input": "2026-05-30T20:38:10.626839Z", "iopub.status.busy": "2026-05-30T20:38:10.626839Z", "iopub.status.idle": "2026-05-30T20:38:10.851322Z", "shell.execute_reply": "2026-05-30T20:38:10.851322Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Author a 10-day step-change time series: S_S jumps from 50 to 300 mg/L at t=4 d.\n", "custom_path = os.path.join(tmpdir, 'custom_inf.csv')\n", "t_step = np.linspace(0, 10, 201)\n", "S_S_step = np.where(t_step < 4.0, 50.0, 300.0)\n", "custom_df = pd.DataFrame({'t': t_step, 'S_S': S_S_step, 'Q': 1000.0})\n", "custom_df.to_csv(custom_path, index=False)\n", "\n", "DI_step = DI(ID='DI_step', outs=('step_inf',), data_file=custom_path)\n", "tank2 = CompleteMixTank(ID='T2', ins=DI_step-0, V=200, isdynamic=True)\n", "tank2.set_init_conc(S_S=50.0)\n", "sys_di2 = qs.System('di_step_sys', path=(DI_step, tank2))\n", "sys_di2.set_dynamic_tracker(DI_step, tank2)\n", "sys_di2.simulate(t_span=(0, 10), method='BDF',\n", " t_eval=np.linspace(0, 10, 201),\n", " state_reset_hook='reset_cache')\n", "\n", "# Both `DI_step.scope` and `tank2.scope` are SanUnitScopes whose `.header`\n", "# entries look like ('id', 'S_S [mg/L]'); split off the units suffix to match\n", "# by component name.\n", "def col(scope, name):\n", " return next(i for i, h in enumerate(scope.header) if h[1].split()[0] == name)\n", "\n", "S_S_in_idx = col(DI_step.scope, 'S_S')\n", "S_S_out_idx = col(tank2.scope, 'S_S')\n", "\n", "t_in = DI_step.scope.time_series\n", "S_S_in = DI_step.scope.record[:, S_S_in_idx]\n", "t_out = tank2.scope.time_series\n", "S_S_out = tank2.scope.record[:, S_S_out_idx]\n", "\n", "fig, ax = plt.subplots(figsize=(7, 3.2))\n", "ax.plot(t_in, S_S_in, '-o', label='influent (DI)', markersize=3)\n", "ax.plot(t_out, S_S_out, '-o', label='tank outlet (CMT)', markersize=3)\n", "ax.set_ylabel('S_S [mg/L]'); ax.set_xlabel('time [d]')\n", "ax.set_title('Step change in influent S_S smoothed by a 200 m3 CSTR')\n", "ax.legend()\n", "fig.tight_layout()\n" ] }, { "cell_type": "markdown", "id": "nav-footer-11_dynamic_simulation", "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 }