{ "cells": [ { "cell_type": "markdown", "id": "edd8871e-c5c8-41c0-b21a-8cebc129b30e", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "# HowTo: Make a cross-section model with `ttim`\n", " \n", "This HowTo describes how to make a cross-section model with `ttim`.\n", "\n", "We start by importing the required packages:" ] }, { "cell_type": "code", "execution_count": 1, "id": "64c3b56a-ec12-4da3-ad88-64ae7fd4193d", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import ttim as ttim\n", "\n", "#\n", "plt.rcParams[\"figure.figsize\"] = (4, 3) # set default figure size\n", "plt.rcParams[\"font.size\"] = 8 # set default font size" ] }, { "cell_type": "markdown", "id": "c8abce62-4971-441d-b45f-5a3dfb9486fa", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "We will create an xsection model for a two-aquifer system with a river with a leaky river bottom (see Figure).\n", "The model is divided in three sections: the left side of the river, the river, and the right side of the river.\n", "The resistance of the top semi-confining layer is different than the resistance of the leaky river bottom. \n", "The aquifer parameters are defined in the table and code cell below. " ] }, { "cell_type": "code", "execution_count": 2, "id": "0278d562-ffdf-4495-a842-1eb9f9815329", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.colors as mcolors\n", "\n", "\n", "def gradient_fill(x, y, fill_color=\"C0\", ax=None, **kwargs):\n", " if ax is None:\n", " ax = plt.gca()\n", "\n", " z = np.empty((100, 1, 4), dtype=float)\n", " rgb = mcolors.colorConverter.to_rgb(fill_color)\n", " z[:, :, :3] = rgb\n", " z[:, :, -1] = np.linspace(0.1, 0.5, 100)[:, None]\n", "\n", " xmin, xmax, ymin, ymax = x[0], x[1], y[0], y[1]\n", " ax.imshow(\n", " z,\n", " aspect=\"auto\",\n", " extent=[xmin, xmax, ymin, ymax],\n", " origin=\"lower\",\n", " )\n", "\n", "\n", "def arrow(xystart, xyend, text=\"\", arrow=\"<-\", color=\"k\", **kwds):\n", " plt.annotate(\n", " text,\n", " xy=xystart,\n", " xytext=xyend,\n", " arrowprops={\"arrowstyle\": arrow, \"shrinkA\": 0, \"shrinkB\": 0, \"color\": color},\n", " color=color,\n", " **kwds,\n", " )\n", "\n", "\n", "def solution12():\n", " plt.figure(figsize=(8, 3))\n", "\n", " plt.fill(\n", " [-0.1, 0.3, 0.3, -0.1],\n", " [0.8, 0.8, 1.2, 1.2],\n", " color=\"grey\",\n", " fill=False,\n", " hatch=\"//\",\n", " )\n", " plt.fill(\n", " [0.3, 0.7, 0.7, 0.3], [0.8, 0.8, 1.0, 1.0], color=\"grey\", fill=False, hatch=\"//\"\n", " )\n", " plt.fill(\n", " [0.7, 1.1, 1.1, 0.7], [0.8, 0.8, 1.2, 1.2], color=\"grey\", fill=False, hatch=\"//\"\n", " )\n", "\n", " plt.fill(\n", " [-0.1, 1.1, 1.1, -0.1],\n", " [0, 0, -0.2, -0.2],\n", " color=\"grey\",\n", " fill=False,\n", " hatch=\"///\",\n", " )\n", " plt.fill(\n", " [-0.1, 1.1, 1.1, -0.1],\n", " [-1, -1, -0.8, -0.8],\n", " color=\"grey\",\n", " fill=False,\n", " hatch=\"xxx\",\n", " )\n", " plt.plot([-0.1, 0.3], [1.1, 1.1], \"C0\")\n", " plt.plot([0.3, 0.7], [1.4, 1.4], \"C0\")\n", " plt.plot([0.7, 1.1], [1.1, 1.1], \"C0\")\n", " gradient_fill([0.3, 0.7], [1, 1.4])\n", " plt.plot([0.3, 0.3], [0.8, 1.5], \"k\")\n", " plt.plot([0.7, 0.7], [0.8, 1.5], \"k\")\n", " plt.text(0.1, 1.35, \"$h_0$\", ha=\"center\")\n", " plt.text(0.5, 1.55, \"$h_r(t)$\", ha=\"center\")\n", " plt.text(0.9, 1.35, \"$h_0$\", ha=\"center\")\n", " plt.plot([0.5, 0.5], [-1.3, -1.1], \"k\")\n", " arrow((0.5, -1.2), (0.7, -1.2), \"$x$\", va=\"center\")\n", " plt.text(-0.1, 0.4, \"left\", va=\"center\")\n", " plt.text(0.5, 0.25, \"river\", va=\"center\", ha=\"center\")\n", " plt.text(1.1, 0.4, \"right\", ha=\"right\", va=\"center\")\n", " arrow((0.3, 0.45), (0.7, 0.45), arrow=\"<->\")\n", " plt.text(0.5, 0.55, \"$2L$\", ha=\"center\")\n", " plt.ylim(-1.3, 1.5)\n", " plt.axis(\"off\")\n", "\n", "\n", "solution12() # plt.savefig('riverxsection.svg')" ] }, { "cell_type": "markdown", "id": "d9c645d3-a790-42ea-be9b-509ac94aec48", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "| Parameter | left/right | river |\n", "| :------------: | :------: | ----: |\n", "| $c_0$ (d) | 500 | 200 |\n", "| $k_0$ (m/d) | 10 | 10 |\n", "| $S_{s0}$ (m$^{-1}$) | 1e-4 | 1e-4 |\n", "| $c_1$ (d) | 1000 | 1000 |\n", "| $k_1$ (m/d) | 20 | 20|\n", "| $S_{s1}$ (m$^{-1}$) | 1e-4 | 1e-4 |" ] }, { "cell_type": "code", "execution_count": 3, "id": "675937ac-6786-4ffb-9c7d-bba705009055", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "zside = [2, 0, -10, -12, -22] # elevation of top and bottoms of layers, m\n", "zriver = [1, 0, -10, -12, -22] # elevation of top and bottoms of layers, m\n", "cside0 = 500 # resistance of top semi-confining layer, d\n", "criver0 = 200 # resistance of leaky river bottom, d\n", "k0 = 10 # hydraulic conductivity of first aquifer, m/d\n", "Ss0 = 1e-4 # specific storage of first aquifer, m^(-1)\n", "c1 = 1000 # resistance of leaky layer between aquifers, d\n", "k1 = 20 # hydraulic conductivity of second aquifer, m/d\n", "Ss1 = 1e-4 # specific storage of second aquifer, m^(-1)\n", "L = 50 # half width of river, m" ] }, { "cell_type": "markdown", "id": "86d97286-0870-4cea-9b47-fae0be9284ab", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "An cross-sectional model is created with two aquifer layer, \n", "and is stored in the variable `ml`. Next, `XsectionMaq` sections are defined for the three sections of the model. Make sure that the entire cross-section is covered by an `XsectionMaq`. The head above the semi-confining top is fixed on the left and right side, which means that the head change equals zero. The head in the river varies with time and is specified with the `tsandhstar` keyword. `tsandhstar` must be a list of 2-tuples, where each tuple consists of `(time, head in river starting at that time)`. Alternatively, `tsandhstar` can be an array with $N$ rows and two columns, where $N$ is the number of times that a different water level in the river is specified. " ] }, { "cell_type": "code", "execution_count": 4, "id": "88304f3d-5614-474f-a7b5-98cf6600393b", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self.neq 8\n", "solution complete\n" ] } ], "source": [ "ml = ttim.ModelXsection(naq=2, tmin=1e-3, tmax=1e2)\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=-np.inf,\n", " x2=-L,\n", " kaq=[k0, k1],\n", " z=zside,\n", " Saq=[Ss0, Ss1],\n", " c=[cside0, c1],\n", " topboundary=\"semi\",\n", " name=\"left\",\n", ")\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=-L,\n", " x2=L,\n", " kaq=[k0, k1],\n", " z=zriver,\n", " Saq=[Ss0, Ss1],\n", " c=[criver0, c1],\n", " topboundary=\"semi\",\n", " tsandhstar=[(0, 1)],\n", " name=\"river\",\n", ")\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=L,\n", " x2=np.inf,\n", " kaq=[k0, k1],\n", " z=zside,\n", " Saq=[Ss0, Ss1],\n", " c=[cside0, c1],\n", " topboundary=\"semi\",\n", " name=\"right\",\n", ")\n", "\n", "ml.solve() # solve the model" ] }, { "cell_type": "markdown", "id": "d4290951-4131-4f52-8f80-32fa703c3707", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Compute the head at 100 points in space between $-200$ and $+200$ for 5 times" ] }, { "cell_type": "code", "execution_count": 5, "id": "bca9d78a-d7cb-45e2-8c1f-782f16c84c41", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "ng = 100\n", "nt = 5\n", "x = np.linspace(-200, 200, ng)\n", "y = np.zeros(ng)\n", "t = np.logspace(-2, 1, nt)\n", "h = ml.headalongline(x, y, t)" ] }, { "cell_type": "code", "execution_count": 6, "id": "062663d5-7b27-4a2e-96f6-81b5e78673f8", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "for i, itime in enumerate(range(5)):\n", " plt.plot(x, h[0, itime], \"C\" + str(i), label=f\"{t[itime]:.3f}\")\n", " plt.plot(x, h[1, itime], \"C\" + str(i), ls=\"--\")\n", "plt.legend(title=\"time (d)\")\n", "plt.axhline(0, color=\"k\", lw=1)\n", "plt.axvspan(-L, L, color=[0.9, 0.9, 0.9]);" ] }, { "cell_type": "markdown", "id": "7ce042e0-e249-4ff3-aaff-15081642f163", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Compute and plot the head at $x=50$ for time varying from 0.001 to 10 with 99 steps, equall spaced in log-space." ] }, { "cell_type": "code", "execution_count": 7, "id": "d2fe572d-b7c4-43f0-8a71-7a369cd31bb8", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nt = 100\n", "t = np.logspace(-3, 1, nt)\n", "h = ml.head(x=50, y=0, t=t)\n", "plt.semilogx(t, h[0])\n", "plt.semilogx(t, h[1])\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", "plt.grid()" ] }, { "cell_type": "markdown", "id": "e4837450-b195-4d95-bb1e-cd87a8984f9f", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "### Sythetic calibration example\n", "A synthetic example is presented \n", "The head in the river is varied 20 times (daily) over a period of 20 days. " ] }, { "cell_type": "code", "execution_count": 8, "id": "e0d89768-445c-49ab-adb0-bddb4b2c27a2", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ts = np.arange(0, 20)\n", "rng = np.random.default_rng(seed=22)\n", "hstar = rng.integers(low=0, high=10, size=20) * 0.1\n", "tsandh = list(zip(ts, hstar, strict=True))\n", "plt.figure(figsize=(8, 3))\n", "plt.step(ts, hstar, where=\"post\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"river level (m)\")\n", "plt.grid()" ] }, { "cell_type": "markdown", "id": "980e6691-f4f9-40b6-953d-eccc14d63bfe", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "The head is computed at $x=50$ in the top aquifer from day 10 till day 20 with intervals of 0.1 day. This is the synthetic truth. A normally distributed error is added to the synthetic truth and this is called the (synthetic) observed head `hsyn`, which is plotted. " ] }, { "cell_type": "code", "execution_count": 9, "id": "bdc9457b-f263-4edc-a7a5-c9a08eb43c59", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self.neq 8\n", "solution complete\n" ] }, { "data": { "image/png": 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u4woLC93/HjZsmCZPnqz3339fkvTmm29qypQpKigokCRVVlaqpKRENTU1Htvs7OxUZ2en+3F7e7skyel0yul0BvX8POlpw4i2zCA9PV3p6emSzPN/QgzNjxj+gxnfgxIxNDviZ35Gx9CfdgxLZFtaWjR69GglJFxu0mKxKDs7W83Nzb0S2SudO3dOr7zyirZt2yZJam5uVk5Ojvv7VqtVZ86cce8R3ldNTY1Wr17d7/nt27crOTk5CGflm4aGBsPaQmgQw8va2tp09uxZZWRkKDU1Ndzd8QsxND9iaG7Ez/yMiuHFixd9PtbQnb0sFkuvx1eOfe2rvb1dc+bMUVVVlW688UavrzGQlStX6sEHH+z1mllZWbrjjjuUkpLiR88D43Q61dDQoFmzZikxMTHk7SH4iOE/bNiwQcuWLXN/cKytrdWiRYvC3a1BEUPzI4bmRvzMz+gY9txB94VhiWxWVpYcDocuXbqkhIQEuVwutbS0KDs7u9+xFy5c0OzZs1VaWtorEc3OztaOHTvcj+12u6677jqP1VhJSkpKUlJSUr/nExMTDX0zGd0egi/WY+hwONxJrHR5HdKeoT1mmTAU6zGMBsTQ3Iif+RkVQ3/aMGyyV1pamgoLC7Vx40ZJ0pYtW2S1WvsNK+jo6NDs2bNVXFys6urqXt+bPXu29u/fr+PHj0uS1q5dq/nz5xvSfyCWsQ4pACASGbpqQV1dnerq6pSfn681a9a4t0hcvHixXnvtNUnSM888o3379unVV1/V5MmTNXnyZPfKBCNGjND69es1d+5c2Ww2nTlzRo888oiRpwDEJNYhBQBEIkPHyI4bN0579uzp9/z69evd/161apVWrVrl9TVKS0tVWloakv4B8KxnHdKKigp1dXWxDikAICIYmsgCMK+ysjIVFxerqalJNpuNJBYAEHYksgB8lpmZSQILAIgYho6RBQAAAIKFRBYAAACmRCILAAAAUyKRBQAAgCmRyALox+FwaOfOnXI4HOHuSkhE+/kBQKwgkQUCEM2JUH19vXJyclRUVKScnBz3xiXRItrPDwBiCYks4KdoToQcDofKy8vd29F2d3eroqIiahL2aD8/AIg1JLKAH6I9EWpsbHSfW4+uri41NTWFqUfBFe3nBwCxhkQW8EO0J0J5eXmKi+t9WYiPj5fNZgtTj4Ir2s8PAGINiSzgh2hPhDIzM7Vu3TrFx8dLunxudXV1UbObV7Sf35WieRw3APQgkQX8EAuJUFlZmex2u3bu3Cm73a6ysrJwdymoov38pOgexw0AV0oIdwcAsykrK1NxcbGamppks9miKontkZmZGZXn1SOaz8/bOO7i4uKoPWcAsYtEFghANCdCMLeBxnHzOwsg2viVyHZ0dOill17Sn/70JzkcDl199dWaNGmS5s2bp5tvvjlUfQQA+KhnHPeVyWw0jeMGEBwOh0ONjY3Ky8sz9Ydcn8fIrlmzRpMmTdJf//pXffOb31R1dbWWL1+u1NRUrVy5UjNnztTx48dD2VcAwCBiYRw3gKGJpnH0Pldkhw8fruPHjysxMbHX83PmzFFVVZWOHTumlpYWFRQUBL2TAADfxcI4bgCBibZx9D4nssuXLx/w+9dff72uv/76IXcIMKtouU2D6MA4bgCeRNs4+oAme/3xj3/U+++/r0uXLrmfq6ysDFqnALOpr693f8KNi4vTunXronJZJwCAuUXbOHq/E9l7771X7777riZNmuQeg2WxWILeMcAsou02DQAgevWMo6+oqFBXV5dP4+gdDoeOHDmiiRMnasyYMQb2dnB+J7IHDx7Uu+++605igVgXbbdpAADRzZ9x9FfecfzpT38acXcc/d7Zy2az6bPPPgtFXwBTivZtawEAkc/fbakzMzM1c+bMQSuxnu44RtLW135XZJ966inddtttuu222zRs2LBezwOxKJDbNAAABMtA8zSGMhHZDHcc/a7I/uhHP1JmZqZGjhypL33pS+4vIJaVlZXJbrdr586dstvtEXXbBQAQvQaqmg51vVgz3HH0uyJ75swZHTt2LKDGGhsb9cADD6itrU0jR47U888/rxtuuKHXMXa7XQsXLtQ777yjvLw8HThwoNf3bDabxo8f735uy5Ytys3NDag/QDCx3BEAwAhXVlm9VU337Nkz5InIZrjj6HdFdvLkyTp79mxAjVVUVKi8vFwnTpxQVVWVx6pVSkqKfv7zn+vFF1/0+BojR47UoUOH3F8ksQCCxd8xZgBgtL5V1gMHDnismrpcLq/DArzxdA0sKytTY2OjHnvsMTU2NkbcHUe/E9nz589r/PjxKi0t1d133+3+Gkxra6sOHjyoBQsWSJLmzZunU6dOyW639zruy1/+sm699VaGKwAwVDRt2QggOnkaRrBy5Uo9+eST/balnjFjhl/DAga6BmZmZmrChAkRVYntEdA6svfee6/fDbW0tGj06NFKSLjcpMViUXZ2tpqbm2W1Wn1+nfb2dt10003q6urS3LlztWrVKq9LgXV2dqqzs7PXz0qS0+mU0+n0+xz81dOGEW0hNIIRQ4fDwVahYeRLDL2NMSsqKiJmEYBrqbkRv+A5duyYxyrr5MmT1djYqJMnTyo3N9d93aqtrVVlZaV7WMDatWuVnp7eLxaDXQONjqE/7fidyD7wwAP+/ohb340TXC6XXz+fkZEhh8OhtLQ0nT9/Xvfcc49++ctfqqqqyuPxNTU1Wr16db/nt2/fruTkZL/aHoqGhgbD2kJoBBrDhoYGrV27Vi6XSxaLRZWVlZo1a1aQewdfDBTDI0eOePzjsGnTJk2YMCHUXYOProxhW1ubzp49q4yMDKWmpoaxV/AVfwuHrq2tTRaLpVf+FBcXp9OnT+vTTz+VJB0+fFiHDx+WJKWnp6uurq7Xe2Xbtm39XtfXa6BRMbx48aLPx1pcPmaTTz31lJYvX+41AfzrX/+qDz/8UCUlJR6/39raqry8PH388cdKSEiQy+VSRkaG9u7d67Eiu2vXLj300EO9Jnv1tXnzZr344ot6/fXXPX7fU0U2KytLbW1tSklJGeBsg8PpdKqhoUGzZs1SYmJiyNtD8A0lhg6HQzabrd82gI2NjVT5DORLDIlVZOsbww0bNmjZsmXupYZqa2u1aNGicHcTXvC3MLg2bNjQr8o61N//wa6BRsewvb1dqamp+uSTTwbN13yuyF599dUaP368brvtNs2YMUOjRo3SZ599puPHj2vbtm1KS0vTs88+6/Xn09LSVFhYqI0bN2rhwoXasmWLrFarX8MKWltbde211yoxMVGdnZ3aunWrCgsLvR6flJSkpKSkfs8nJiYa+mYyuj0EXyAxtNvtHj/hnj59OuK2+IsFA8VwzJgxHmfmEqfIkpiYqA8//NCdxEqXb4FWVlaqpKSEDx0Rjr+FA/N1vdfy8nKVlJQEdciar9dAo2LoTxs+J7I//OEPtWTJEr3yyivatWuXHA6HkpOTNXHiRP3Hf/xHryWxvKmrq9PChQv1xBNPKCUlRS+88IIkafHixSotLVVpaak6OzuVm5urzs5OffLJJ8rMzNT3vvc91dTUaPfu3Xr00UcVHx+vS5cuqaioSKtWrfL5ZGGMoSy+HE161t/r+wk3ktbfwz/4s2UjwscMC7QD/hpoQwNPQrHco1mvgX6NkR02bJgWLFjgXnnAX+PGjdOePXv6Pb9+/Xr3v5OSkrwufXPXXXfprrvuCqhtGMPfN2M0M8P6e+iNtYAjHx8QEW28TbTyZ73XYDHjNdDv5bcAb8ywJ7PR2PErOrC+bOTo+YDYd6khs/3xBXoMdJcBg/N71QLAG275eWbGT7j4B+4yRB6z3gIFPOEuw9BQkUXQmGFPZsAf3GWIXJmZmZo5cyZJLEyPuwxDQyKLoOHNiGjDLT8ARmAYWuB8Hlpw00039dvQ4Er79u0LSodgbtzyQzThlh8Ao3gahsYqQIPzOZH9xS9+IUl64403dOLECX3/+9+XJD3//POaNGlSaHoHU2JMKKIFK08ACBfG5/vG50T29ttvlyT97Gc/044dO9zV2TvvvFP/8i//EpreAUCYcZcBgNEiaUmuSOf3qgUOh0Off/65rr76akmXt4Fl4gMQHNxGikzcZQBgJFYB8p3fk73uueceTZ8+XTU1NaqpqdEtt9yi+fPnh6JvpudwOHTkyBESffikvr5eOTk5KioqUk5Ojurr68PdJQBAGLAKkO/8TmR//vOf67HHHlNbW5s++ugj92P0Vl9fL5vNpurqatlsNpISDIhlngAgNviywQqrAPkuoA0R5syZozlz5gS7L1GDsS3wF7eRACD6+TOBi/H5vvE7kW1ra9Pq1av1v//7v/r888/dz7P81j+QlMBfLPMEANEtkCIX4/MH5/fQgu9///vKzMzUuXPnVF1drbS0NBUXF4eib6bF2Bb4i9tI8IUvtyQBRCY2WAkNvxPZ5uZmPfzwwxo2bJjmzJmjrVu36u233w5F30yLpASBYGcXDITJgIC5UeQKDb8T2auuukqSlJSUpPPnzyshIYHqgAdlZWVqbGzUY489psbGRpIS+IT94+EJkwEB86PIFRp+j5EdN26czp8/rwULFmjatGm65pprVFhYGIq+mV5mZqYmTJjALymAIWHcPRAdmMAVfH4nsv/5n/8pSfrxj3+sKVOm6O9//7u++c1vBr1jABBNhrLZBZMBgejBBK7g8ntogSQdOnRIL774om655Rbdcsstam1tDXa/ACBqDHV8K7ckAcAzvxPZ5557Tg888ICqq6slSefPn9d9990X9I4BQDQI1vhWJgMCQH9+J7J1dXXau3evUlJSJEm5ublUZAHg/+u7RFYwl9xhMiAA9BbQqgVXX311r+cSEgLaIAyAj4KxfihrkIaepyEELLkDAKHjdyL71a9+VSdOnJDFYpF0efJXVlZW0DsG4LJgrB/KGqSh520IgSTGtwJAiPhdSv31r3+te++9V++9956sVquSk5P1+uuvh6JvQMwLZEtDf15DUsAz6dHbQEMIWHIHAELD70TWZrNp7969eu+99+RyuTRu3Dh3pQFAcAVj/VBvr/HMM8/oV7/6lbq7uxUXF6d169YxgWgIBlsiiyV3ACD4Alp+S5KuueYaDR8+XGfOnFFzc3Mw+wTg/wvG+EpPrxEXF+dOYiV2igpE3zHHLJEFAMbzO5F9/vnnNXLkSE2YMEFf//rX9fWvf11TpkwJRd+AmBeM5MjTazz44INBm0kfi7yNOWaJLAA9mGBrDL8T2ccee0z79u3Txx9/rI8++kgfffSRz8tvNTY2asaMGcrPz9fUqVN19OjRfsfY7XbNnDlT11xzjccE+Y033lBBQYFsNpvmzZunjo4Of08BMBV/kyNPF8++r/HjH/+YmfQBGmxdWJbIAsAEW+MEtGpBQUFBQI1VVFSovLxcJ06cUFVVlcc/yCkpKfr5z3+uF198sd/3Ojo6VFZWpt///vdqampSRkaGHn/88YD6AoSbP5/WfU2OBrp4Xvka3AYPXDDXhQUQfYK1CQp84/Nkr4sXL0qS7rrrLv3mN7/Rvffeq2HDhrm/n5ycPODPt7a26uDBg9q+fbskad68eVq+fLnsdrusVqv7uC9/+cu69dZbtWvXrn6v8eabb2rKlCnuRLqyslIlJSWqqanx2GZnZ6c6Ozvdj9vb2yVJTqdTTqdz8JMeop42jGgLoRGqGG7YsEHLli1zT7Sqra3VokWLhvSa3i6eRUVFHhPU+++/X0VFRTp58qRyc3OVmZkZlb+rwY6h1Wr1OKkrJycnKv//IgHXUnOLtfgdO3bM44fd48ePKz09PUy9GhqjY+hPOz4nssOHD5fFYpHL5ZIk/ehHP3I/tlgs6urqGvDnW1paNHr0aPfmCRaLRdnZ2Wpubu6VyA6kublZOTk57sdWq1VnzpxxJwN91dTUaPXq1f2e3759+6CJdzA1NDQY1hZCI5gxbGtr09KlS93vpe7ubi1btkzx8fFKTU31+7XOnj2rjIwMnT171uPFc9OmTZowYcKAr3P48GEdPnzYvxMxmWDGcNmyZaqtrXVfe5YuXRoT/4fhxrXU3GIlfm1tbb3yJenyBNvTp09r27ZtYezZ0BkVw57iqS98TmT7/oEMRM8mCj2uDHKgrzGQlStX6sEHH3Q/bm9vV1ZWlu644w73Fruh5HQ61dDQoFmzZikxMTHk7SH4QhHDXbt29fvd7+7uVk5Ojm6//XafX6dvVfeJJ57wWCm87777YnrIQChiWFJSop/85Ce9qtkIHa6l5haL8evq6lJlZaW6uroUHx+vtWvX6v777w93twJmdAx77qD7wrC9ZbOysuRwOHTp0iUlJCTI5XKppaVF2dnZPr9Gdna2duzY4X5st9t13XXXeazGSlJSUpKSkpL6PZ+YmGjom8no9hB8wYzh9ddf7zHhLCgo8LkNh8PhTmKly4nwqlWr9OSTT2rFihXui2ddXZ3GjBkTlH6bXbDfh2PGjImI/1uHwxEzm1pwLTW3WIpfeXm5SkpKom4TFKNi6E8bAa8j66+0tDQVFhZq48aNkqQtW7bIarX6PKxAkmbPnq39+/fr+PHjkqS1a9dq/vz5oeguEDLBmGjlbcLRlClTWP4pCvg6EZCZ0UDkYgUTYxiWyEpSXV2d6urqlJ+frzVr1rgvuosXL9Zrr70m6fIErczMTH3nO9/R4cOHlZmZqZUrV0qSRowYofXr12vu3Lmy2Ww6c+aMHnnkESNPIShYWw5DXW90oI0SuHiam6/JKTOjPeP6CsQWw4YWSNK4ceO0Z8+efs+vX7/e/e+kpKQBL0ClpaUqLS0NSf+MUF9f7/7jw7agsW0oW5b2VHUrKip6DSMgeTU3b8lpcXFxv9gGY/viaMP1FYg9hlZkYx0VFAQTu0iZX9/qoT9r1AZj++JowvUViE0ksgZiIXUEG8MIzMvTEAJ/klM2teiN6ysQm0hkDUQFBYDkvXooya/klKr8P3B9BWITiayBBqugMEkBiA0DVQ/9TU6pyl9GhRqITYZO9sLlCkpxcXG/teWYpADEjp7qYd+1hHuqh0OZCBjLvF1fAUQvKrJh0LeCwiQFILZQPQwdKtRAbKEiGwFYRgeIPVQPAWDoSGQjwGC3GQFEJ4YQAMDQMLQgAnCbEQAAwH9UZCMEtxkBAAD8QyIbQbjNCAAA4DuGFgAAgJjFGu7mRiILAACihj+JqaetomEuJLIAACAq+JOYsoZ7dCCRBQBENW4dxwZ/E9OB1nDnd8Y8SGQBAFGLW8exY6DE1JOeNdyvFB8frwMHDvA7YyIksgCAqMSt49jiLTH1trmQpzXca2pq9PDDD/M7YyIksibFbQ8AGJi/FTqYWyCbC5WVlclut2vnzp2y2+2aMmUKvzMmwzqyEc7hcKixsVF5eXnuN2N9fb27yhAXF6d169aprKwszD0FgMjC9t+xJ5DNhfqu4R7I74ynv9UwBhXZCOZpbBe3ygDAN2z/HZsyMzM1c+bMgOIcyO8M47DDi0Q2QnlLWN9++21uewCAj/reOubuFQbjz+8MxaXwY2hBhPI2tstisXCrDAD8wPbf0StUt/R9/Z0ZaBw2v3PGoCIbobzNvpw+fTq3ygAAMS8ct/T7TrT2d6UEBB+JbIQaaJwOt8oAALGkbwIZjlv6nhJnxmGHH0MLIthAsy+5VQYAiAWeVuoZO3asobf0vSXOxcXFAa2UgOAxtCLb2NioGTNmKD8/X1OnTtXRo0c9HldfX6+8vDzl5uaqvLxcly5dkiTZ7XYlJCRo8uTJ7q+TJ08aeQqGG8rsSwAAzMxbAjl8+HBDb+kPtiYxf6vDx9BEtqKiQuXl5Tpx4oSqqqo83hI/deqUqqurtXv3bjU1NencuXO9xr2MHDlShw4dcn/l5uYaeQoAgCjBxjKRz1sC+emnnxp6S5+xsJHLsES2tbVVBw8e1IIFCyRJ8+bN06lTp2S323sd98orr+hb3/qW0tPTZbFYtHTpUm3evNmobgIAYgBrf0auKz9gDJRAGjlfhLGwkcuwMbItLS0aPXq0EhIuN2mxWJSdna3m5mZZrVb3cc3NzcrJyXE/tlqtam5udj9ub2/XTTfdpK6uLs2dO1erVq1y/2L11dnZqc7Ozl4/K0lOp1NOpzOYp+dRTxtGtIXQIIbmRwzNL9gx9Ha7uqioiMQkBPyJ34YNG7Rs2TL3eNja2lrV1taqsrJSXV1dio+P19q1a5Weni6n06n09HSlp6f79PoOh2NI41jvv/9+FRUV6eTJk8rNzVVmZmbMXFeMvo76046hk70sFkuvxy6Xa9DjrjwmIyNDDodDaWlpOn/+vO655x798pe/VFVVlcfXqamp0erVq/s9v337diUnJwdyCgFpaGgwrC2EBjE0P2JofsGK4ZEjRzzert60aZMmTJgQlDbQX9/4tbW16ezZs8rIyFBqaqra2tq0dOlS99/97u5uLVu2TOvWrVNdXV2vY7dt2+Z322vXrpXL5ZLFYlFlZaVmzZoV8LkcPnxYhw8fDvjnzcqo6+jFixd9Ptbi8pZNBllra6vy8vL08ccfKyEhQS6XSxkZGdq7d2+viuzTTz8tu92u3/72t5Kkbdu26amnntKuXbv6vebmzZv14osv6vXXX/fYpqeKbFZWltra2pSSkhLU8/PE6XSqoaFBs2bNUmJiYsjbQ/ARQ/MjhuYX7Bg6HA7ZbLZ+G8s0NjZSkQ0BT/HzVHkdM2aM7rjjjn4/39DQoNtvvz3g9on30Bl9HW1vb1dqaqo++eSTQfM1wyqyaWlpKiws1MaNG7Vw4UJt2bJFVqu1VxIrXR47e+utt+rRRx9VWlqannvuOc2fP1/S5WT42muvVWJiojo7O7V161YVFhZ6bTMpKUlJSUn9nk9MTDT0D5rR7SH4iKH5EUPzC1YMx4wZo3Xr1qmiosJ9u7qurk5jxowJQi/hTU/8HA6HO4mVLldeKysrtWfPHo87VxYUFAwp7na73WMF/vTp08TcT0ZdR/1pw9BVC+rq6lRXV6f8/HytWbPGPbh+8eLFeu211yRJY8eO1erVq3XLLbcoNzdXaWlp7gHcu3fvVmFhoSZNmqQbb7xRo0aN0qpVq4w8BQBAFGBjmfAxeiUCVhyIboaOkR03bpz27NnT7/n169f3erxkyRItWbKk33F33XWX7rrrrpD1LxqEat9pAIg2bCwTHj2JZd/Kq81m08yZM4O+uUDPigN9K/DEPjqwRW0UiYR9pwEAGMhgS1mFYnMBKvDRi0Q2SkTKvtMAAAwmHIklu29FJxLZKDHY9nnBFo7EGQAQPUgsEQwkslEi1IPZ+w4hMDpxBgAA6ItENkoMNOZoqONYPQ0hYBYogFjCfAAgMpHIRhFPY46GOo7V2xACSew7DSAq9U1amQ8ARC4S2Shz5ZijYIxjHWgIAbNAAUSbvknr008/zXwAIIIZuo4sjDVQEupr5XSg9f4k1mEEED08ffhfsWLFkK+jAEKHimwUC2Qca99baoOt9wcA0cLTh//u7u4Br6OMnQXCi0Q2ivmbhHobB8YQAgCxwNuH/yeffNLjdZSxs0D4kchGOV+T0MHG07LeH4Bo5+3D/0MPPdTvOhrIHASjq7dUixELSGRjgLck9MqLHOvCAoD3D/99r6P+XjONrt5SLUasIJGNUX0vcgcOHGBdWACQb3eg/JmDMFj1NtiVU3ZeRCwhkY1Bni5yK1eu9DoODADQmz9zEAaq3oaicsodNsQSlt+KQd4uclOmTJHdbldTU5NsNhtJLAAMoKysTMXFxYNeM70tY/ilL33JY+W0uLh4SNffwZZNBKIJFdkYNNAtMSZ1AYDvfLlmeqvednR0DFg5DXTIAcsmIpaQyMYgLnIAYCxPk8gGKioMdcgByyYiVpDIxigucgBgrL7VW29FBUlBmawVjDts/laFWfILRiORjWEMIwCA8PJUVIiUyVr+VoVZ8gvhQCILAEAY9S0qBLK9eLD5u4QXS34hXEhkAQCIIJEwj8HfqnCkVJERe1h+CwCAIOvZMTEvLy+gBNTXpb1C1Td/l/BiyS+ECxVZAACCKFhjRT3NYxjqZCpf++ZvVTgSqsiITSSyAAAESSjHig41Qfa3b/6ubuPteIfDoSNHjjBeFiFBIgsAQJCEaqxoMBLkQPrm7+o2fY+vr6+XzWZTdXW1e31cIJhIZAEACJJQrTgQjATZ6NUQWMkARjA0kW1sbNSMGTOUn5+vqVOn6ujRox6Pq6+vV15ennJzc1VeXq5Lly65v/fGG2+ooKBANptN8+bNU0dHh1HdBwBgQKEaKxqMJNTocaysZAAjGJrIVlRUqLy8XCdOnFBVVZXH8TanTp1SdXW1du/eraamJp07d859K6Kjo0NlZWX6/e9/r6amJmVkZOjxxx838hQAABhQKHZODFYSGqy++TLpLBLWw0X0M2z5rdbWVh08eFDbt2+XJM2bN0/Lly+X3W6X1Wp1H/fKK6/oW9/6ltLT0yVJS5cu1VNPPaWKigq9+eabmjJligoKCiRJlZWVKikpUU1Njcc2Ozs71dnZ6X7c3t4uSXI6nXI6naE4zV562jCiLYQGMTQ/Ymh+Zoxhenq6++9YsPp9//33q6ioSCdPnlRubq4yMzMDeu2h9m3Dhg1atmyZuru7FRcXp9raWi1atMhjO7W1taqsrFRXV5fi4+O1du1apaenmyqWMP496E87hiWyLS0tGj16tBISLjdpsViUnZ2t5ubmXolsc3OzcnJy3I+tVquam5u9fu/MmTPuN1NfNTU1Wr16db/nt2/fruTk5GCd2qAaGhoMawuhQQzNjxiaHzH8h8OHD+vw4cOGt9vW1qalS5fK5XJJujzuddmyZYqPj1dqamq/49PT01VXV6ezZ88qIyNDqamp2rZtm9HdRpAY9R68ePGiz8cauiGCxWLp9bjnjTDQcX2P6fsaA1m5cqUefPBB9+P29nZlZWXpjjvuUEpKis+vEyin06mGhgbNmjVLiYmJIW8PwUcMzY8Ymh8xjBy7du3q93e5u7tbOTk5uv322z3+DPEzP6Nj2HMH3ReGJbJZWVlyOBy6dOmSEhIS5HK51NLSouzs7F7HZWdny263ux+fPn3afUx2drZ27Njh/p7dbtd1113nsRorSUlJSUpKSur3fGJioqFvJqPbQ/ARQ/MjhuZHDMPv+uuv97iDV0FBwaCxIX7mZ1QM/WnDsMleaWlpKiws1MaNGyVJW7ZskdVq7TWsQLo8dvbVV1/Vhx9+KJfLpeeee07z58+XJM2ePVv79+/X8ePHJUlr1651fw8AAIQWO3gh0hg6tKCurk4LFy7UE088oZSUFL3wwguSpMWLF6u0tFSlpaUaO3asVq9erVtuuUXd3d0qKipyz6ocMWKE1q9fr7lz5+rSpUuaMGGC+zUAAEDolZWVqbi4WE1NTbLZbCSxCCtDE9lx48Zpz549/Z5fv359r8dLlizRkiVLPL5GT8ILAADCIzMzkwQWEYGdvQAAAGBKJLIAAMCnTQ6ASEMiCwCAyQ01Ca2vr1dOTo6KioqUk5Pj3lETiHQksgAAmNhQk1CHw6Hy8nL3klrd3d2qqKigMgtTIJEFAMCkgpGENjY29loXVpK6urrU1NQU1L4CoUAiCwCASQUjCc3Ly+u3sVB8fLxsNltQ+giEEoksAAAmFYwklE0OYGYksgAAmFSwktCysjLZ7Xbt3LlTdrvdvREREOkM3RABAAAEV7B22mKTA5gRiSwAACZHEopYxdACAAAAmBKJLAAAAEyJRBYAAACmRCILAAAAUyKRBQAAgCnF1KoFLpdLktTe3m5Ie06nUxcvXlR7e7sSExMNaRPBRQzNjxiaHzE0N+JnfkbHsCdP68nbBhJTieyFCxckSVlZWWHuCQAAAAZy4cIFXXPNNQMeY3H5ku5Gie7ubn3wwQcaMWKELBZLyNtrb29XVlaWWlpalJKSEvL2EHzE0PyIofkRQ3MjfuZndAxdLpcuXLig0aNH99uCua+YqsjGxcWFZcHolJQU3rwmRwzNjxiaHzE0N+JnfkbGcLBKbA8mewEAAMCUSGQBAABgSiSyIZSUlKSf/vSnSkpKCndXECBiaH7E0PyIobkRP/OL5BjG1GQvAAAARA8qsgAAADAlElkAAACYEoksAAAATIlENkh+9KMfyWq1ymKx6G9/+5v7+dbWVs2ePVt5eXkaP368du/eHcZeYiDeYvj9739f48aN0+TJk/VP//RPOnToUPg6iQF5i2GPF154QRaLRW+88UYYegdfeIuhy+XSz372M+Xn52v8+PGaOXNm+DoJr7zF78CBA5o+fboKCwt1/fXX66mnngpjLzGQzz//XHPnzlV+fr4mT56s2bNny263S4rMnIZENki+/e1va/fu3crJyen1/IoVKzRt2jQ1NjZqw4YNuu+++3Tp0qUw9RID8RbDuXPn6t1339WhQ4dUVVWlu+++O0w9xGC8xVCSHA6H6urqNG3atDD0DL7yFsNnn31WR44c0d/+9jf97W9/0+bNm8PUQwzEW/yWLFmilStX6p133tGf//xn/eIXv9DRo0fD1EsMpry8XO+9954OHTqkO++8U+Xl5ZIiM6eJqZ29Qumf/umfPD7/8ssv69SpU5Kkm266Senp6dq9ezfVhAjkLYalpaXuf0+bNk2nT59Wd3f3oNvmwXjeYihdvjD/+7//ux5++GEDewR/eYvh008/rV27dumqq66SJGVkZBjZLfhooPfg//3f/0mSPv30U1111VX68pe/bFCv4I9hw4appKTE/XjatGn69a9/LSkycxr+EofQxx9/rO7ubn31q191P2e1WtXc3BzGXmEonnnmGZWUlJDEmkxtba2+9rWv6eabbw53VxCA9vZ2ffTRR3r11Vc1bdo0TZs2TS+99FK4uwU/bNiwQdXV1crOzlZ+fr5qamo0atSocHcLPnj22Wc1Z86ciM1pqMiGmMVi6fWYZXvNa+PGjXr55Zf1P//zP+HuCvxw6tQp/e53v9Of//zncHcFAXI6nfriiy/02Wefae/evWpubtb06dP1ta99TePHjw939+CDp59+Wk8//bTuvvtuvf/++5o5c6amTp2qcePGhbtrGMATTzyhxsZGPffcc/rss88iMqehrBRCX/nKVyRJH330kfu506dPKzs7O1xdQoBeeuklrV69Wg0NDUpLSwt3d+CHPXv26IMPPtD1118vq9WqvXv3qqysTL/73e/C3TX46Ctf+YqGDx+uBQsWSJKys7N1yy236MCBA2HuGXzR1tamV1991T2/YOzYsbr55pv19ttvh7lnGMgvfvELbd26VW+++aaSk5MjNqchkQ2x73znO/rtb38rSdq/f7/OnTunW2+9Ncy9gj9efvll/du//Zv++Mc/hv0NC//de++9OnfunOx2u+x2u6ZNm6b6+notWbIk3F2DH7773e/qv//7vyVJf//737Vv3z5NnDgxzL2CL6699loNGzZMb731lqTLie3evXuppkewX/3qV9q8ebMaGho0cuRI9/ORmNOwRW2Q/OAHP9Af/vAHnTt3TqmpqRo+fLiampr04Ycf6nvf+55OnTqlq666SmvXrtXtt98e7u7CA28xTExM1KhRo9yfRiXpT3/6U6/HiAzeYnilmTNn6qGHHtKdd94Zpl5iIN5i2NbWpkWLFrknmvzwhz9URUVFmHuLvrzF749//KMefvhhXbp0SU6nUxUVFfrxj38c7u7CA4fDoaysLI0dO1YjRoyQJCUlJekvf/lLROY0JLIAAAAwJYYWAAAAwJRIZAEAAGBKJLIAAAAwJRJZAAAAmBKJLAAAAEyJRBYAAACmRCILAAAAUyKRBQAD/exnP9MXX3zhfvzoo4/qpZdeCmmbH3zwgaZOnaru7m6P37dYLOro6JDL5dJtt93m3nQAACIdGyIAgIEsFosuXLig4cOHG9ZmZWWlpk2bpvvvv3/QPm3dulWvvfaann/+ecP6BwCBoiILAAZZunSpJGnGjBmaPHmyWltbtXDhQv3mN7+RdLla+93vfld33nmnbDab7r77br3zzjsqKirS2LFj9eCDD7pf69y5c7r77rs1depUTZw4UY8++qjHNj///HO99NJL+va3v+1+buvWrSooKND06dP12GOP9Tp+zpw52rZtmy5cuBDs0weAoCORBQCDPPfcc5Kkt99+W4cOHVJaWlq/Yw4cOKBNmzbpvffe03vvvacVK1bozTff1JEjR7Rx40adOHFCkvTAAw9o+fLl2rdvnw4ePKh9+/bp1Vdf7fd6+/fvl81mU3JysiSptbVVS5Ys0R/+8Aft2bNHSUlJvY5PTEzU+PHj9ec//znYpw8AQZcQ7g4AAP6huLhY11xzjSRp4sSJmjRpkpKSkpSUlKRx48bp/fff13XXXacdO3boww8/dP9cR0eHjh8/3u/1HA6HRo0a5X68d+9e3XjjjRo3bpwkqby8XA8//HCvnxk1apQcDkcoTg8AgopEFgAiyLBhw9z/jo+P7/f40qVL6u7ulsVi0f79+5WYmDjg6yUnJ+uzzz5zP/ZlWsTnn3+uq6++OoDeA4CxGFoAAAYaMWKEPvnkkyG/xm233aY1a9a4n/vggw88VlEnTZrUq1I7ffp0vfPOO+4hCuvXr+/3M8eOHdOkSZOG1EcAMAKJLAAY6Cc/+YmKiorck70CtWnTJh07dkwTJkzQhAkTNG/ePH388cf9jrNarfrqV7+qd999V5KUlpamdevWac6cOZoxY4bi4nr/GbDb7ZKk8ePHB9w3ADAKy28BQJR7+eWX9dZbb+m3v/3toMeuWLFCeXl5KisrM6BnADA0jJEFgCh3991368MPP1R3d3e/Cmxfo0eP1qJFiwzqGQAMDRVZAAAAmBJjZAEAAGBKJLIAAAAwJRJZAAAAmBKJLAAAAEyJRBYAAACmRCILAAAAUyKRBQAAgCmRyAIAAMCU/h+TupWMOCgXCQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ml = ttim.ModelXsection(naq=2, tmin=1e-3, tmax=1e2)\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=-np.inf,\n", " x2=-L,\n", " kaq=[k0, k1],\n", " z=zside,\n", " Saq=[Ss0, Ss1],\n", " c=[cside0, c1],\n", " topboundary=\"semi\",\n", " name=\"left\",\n", ")\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=-L,\n", " x2=L,\n", " kaq=[k0, k1],\n", " z=zriver,\n", " Saq=[Ss0, Ss1],\n", " c=[criver0, c1],\n", " topboundary=\"semi\",\n", " tsandhstar=tsandh,\n", " name=\"river\",\n", ")\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=L,\n", " x2=np.inf,\n", " kaq=[k0, k1],\n", " z=zside,\n", " Saq=[Ss0, Ss1],\n", " c=[cside0, c1],\n", " topboundary=\"semi\",\n", " name=\"right\",\n", ")\n", "\n", "ml.solve()\n", "\n", "tsyn = np.linspace(10, 20, 101)\n", "xsyn = 50\n", "hexact = ml.head(xsyn, 0, tsyn, layers=0)\n", "hsyn = hexact[0] + 0.01 * rng.standard_normal(len(tsyn))\n", "#\n", "plt.figure(figsize=(8, 3))\n", "plt.plot(tsyn, hsyn, \"k.\", label=\"layer 0\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", "plt.grid()" ] }, { "cell_type": "markdown", "id": "29b4b6b5-97dd-420d-8e21-0a773e3780df", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Next, we pretend that we don't quite know the resistance of the semi-confining layer on the left and right sides nor do we know the resistance of the leaky river bed. We will create a `ttim` model and use the observed head to \n", "calibrate the model and estimate the resistance of the semi-confining layer the leaky stream bed. As as first guess, we use $c=100$ d for both." ] }, { "cell_type": "code", "execution_count": 10, "id": "79930651-2658-4fe7-97bb-000d8ff6e205", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "right: XsectionMaq [50, inf]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crivguess = 100.0\n", "csideguess = 100.0\n", "\n", "ml = ttim.ModelXsection(naq=2, tmin=1e-3, tmax=1e2)\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=-np.inf,\n", " x2=-L,\n", " kaq=[k0, k1],\n", " z=zside,\n", " Saq=[Ss0, Ss1],\n", " c=[csideguess, c1],\n", " topboundary=\"semi\",\n", " name=\"left\",\n", ")\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=-L,\n", " x2=L,\n", " kaq=[k0, k1],\n", " z=zriver,\n", " Saq=[Ss0, Ss1],\n", " c=[crivguess, c1],\n", " topboundary=\"semi\",\n", " tsandhstar=tsandh,\n", " name=\"river\",\n", ")\n", "\n", "ttim.XsectionMaq(\n", " model=ml,\n", " x1=L,\n", " x2=np.inf,\n", " kaq=[k0, k1],\n", " z=zside,\n", " Saq=[Ss0, Ss1],\n", " c=[csideguess, c1],\n", " topboundary=\"semi\",\n", " name=\"right\",\n", ")" ] }, { "cell_type": "markdown", "id": "564f41b8-a18e-4a9b-8387-a94bc5283459", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Next, we create a `Calibrate` object for the model. We add the series to use for calibration. Next, we add two parameters to be calibrated. The first one on the resistance of the leaky stream bed. The second one is the resistance of the semi-confining layer on the left and right sides; the same value is used for both sides by specifying `inhoms=(\"left\", \"right\")`. Note that the inhoms are specified by the name (string) used to create them. Finally, the model is calibrated using the `fit` function. " ] }, { "cell_type": "code", "execution_count": 11, "id": "42fe85a8-528a-40e8-9d0d-d92726df22d9", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".............................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 26\n", " # data points = 101\n", " # variables = 2\n", " chi-square = 0.01077326\n", " reduced chi-square = 1.0882e-04\n", " Akaike info crit = -919.726682\n", " Bayesian info crit = -914.496441\n", "[[Variables]]\n", " c_river: 198.354633 +/- 4.77943674 (2.41%) (init = 12)\n", " c_left_right: 521.772791 +/- 31.6324449 (6.06%) (init = 100)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(c_river, c_left_right) = +0.9057\n" ] } ], "source": [ "cal = ttim.Calibrate(ml)\n", "cal.series(\n", " name=\"synthetic\",\n", " x=xsyn,\n", " y=0,\n", " layer=0,\n", " t=tsyn,\n", " h=hsyn,\n", ")\n", "\n", "cal.set_parameter(\n", " name=\"c\",\n", " layers=[0],\n", " initial=12,\n", " pmin=1.0,\n", " pmax=1000.0,\n", " inhoms=(\"river\"),\n", ")\n", "\n", "cal.set_parameter(\n", " \"c\",\n", " layers=[0],\n", " initial=csideguess,\n", " pmin=1.0,\n", " pmax=1000.0,\n", " inhoms=(\"left\", \"right\"),\n", ")\n", "\n", "cal.fit(report=True)" ] }, { "cell_type": "markdown", "id": "96a8d512-c5e7-4248-97ff-66de137ed2f4", "metadata": {}, "source": [ "All inhomogeneities are stored in a dictionary `inhomdict`, which is stored in `ml.aq`. For example, the resistance of the leaky layer on the left and right side of the river may be retrieved as follows." ] }, { "cell_type": "code", "execution_count": 12, "id": "10d9dcc9-a07c-43c9-b01f-3c2ba47322d5", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "resistance values on left side: [ 521.7727914 1000. ]\n", "resistance falues on right side: [ 521.7727914 1000. ]\n" ] } ], "source": [ "print(\"resistance values on left side: \", ml.aq.inhomdict[\"left\"].c)\n", "print(\"resistance falues on right side: \", ml.aq.inhomdict[\"right\"].c)" ] }, { "cell_type": "markdown", "id": "e04a20bf-115a-47a8-be05-c62f927b749d", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Finally, the fitted head is plotted together with the 'observed' heads and the 'truth'. " ] }, { "cell_type": "code", "execution_count": 13, "id": "7e1a203f-6590-4cbd-b26c-ae7da65f6f8e", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hmodel = ml.head(xsyn, 0, tsyn, 0)\n", "plt.figure(figsize=(8, 3))\n", "plt.plot(tsyn, hsyn, \"k.\", label=\"synthetic\")\n", "plt.plot(tsyn, hmodel[0], label=\"fit\")\n", "plt.plot(tsyn, hexact[0], \"--\", label=\"truth\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 5 }