{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Synthetic Pumping Test - Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "### What is TTim\n", "\n", "TTim uses the Laplace Transform Analytic Element Method (AEM) to derive a solution to transient flow in multi-layered systems. TTim uses discrete features such as Wells and Line-elements to represent real-world aquifer features of interest.\n", "\n", "\n", "In this notebook we demonstrate:\n", "\n", "* How to specify a simple conceptual model consisting of one confining layer and one well\n", "* Simulate the model\n", "* Calibrate a aquifer parameters by providing data from observation wells\n", "* Generate Confidence Intervals from calibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To achieve this we will create a Model, sample some observations add noise and try to find the model parameters through fitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by importing the required libraries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Import the Required Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt # Plotting library\n", "import numpy as np # Numpy\n", "\n", "import ttim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Set Model Parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the model parameters with dimensions in **meters** and time in **days**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "H = 7 # aquifer thickness [m]\n", "k = 70 # hydraulic conductivity [m/d]\n", "S = 1e-4 # specific storage fo the aquifer\n", "Q = 788 # constant discharge [m/d]\n", "d1 = 30 # distance of observation well 1 to pumpinq well\n", "d2 = 90 # distance of observation well 2 to pumpinq well (same as Oude Korendijk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Loading Data:\n", "\n", "- Since we are modelling a synthetic example. We will just borrow the time interval from another pumping test\n", "\n", "* Data consistis of two columns:\n", " * First column is time in minutes\n", " * Second column is the piezometer level in meters above mean sea level [amsl]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data1 = np.loadtxt(\"data/piezometer_h30.txt\", skiprows=1)\n", "t = data1[:, 0] / 60 / 24 # convert min to days" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen above, we have loaded the data as a numpy array. That is the preffered format for loading data into TTim." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Creating a Conceptual Model\n", "\n", "We will model our aquifer using ModelMaq, which is the 2d model interface from TTim. It assumes horizontal stacking of layers consiting of one aquifer and a leaky aquitard." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ml = ttim.ModelMaq(\n", " kaq=k, # Hydraulic Conductivity of the aquifer\n", " # Top and bottom dimensions of the aquifer layer.\n", " # The leaky aquitard can have 0 thickness:\n", " z=[-18, -25],\n", " Saq=1e-4, # Specific storage of the aquifers\n", " # the minimum time for which heads can be computed after any change\n", " # in boundary condition:\n", " tmin=1e-5,\n", " tmax=1, # The maximum time for which heads will be computed\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we add the Well element at position (0,0) with screen radius of 10 cm" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "w = ttim.Well(\n", " ml, # Model where we add the well element\n", " xw=0, # Position x\n", " yw=0, # Position y\n", " rw=0.1, # Well radius,\n", " tsandQ=[(0, 788)], # Tuple describing starting time and discharge\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now 'solve' the model and compute the heads at the two observation locations" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ml.solve(silent=\"False\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# To compute the heads at the specified time intervals and location, we use the 'head'\n", "# method\n", "h1 = ml.head(\n", " x=d1, # location of observation well 1\n", " y=0,\n", " t=t, # Time array that the heads will be returned for\n", ")\n", "h2 = ml.head(\n", " d2, 0, t\n", ") # Computing heads at distance d2, and time array t for observation well 2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-2.28275005e-04, -7.70478488e-03, -3.18554789e-02,\n", " -5.15317124e-02, -7.77470940e-02, -1.06832914e-01,\n", " -1.36233705e-01, -1.57179797e-01, -1.76793138e-01,\n", " -1.96853417e-01, -2.16516484e-01, -2.50176995e-01,\n", " -2.78596120e-01, -3.02578736e-01, -3.08282745e-01,\n", " -3.25241029e-01, -3.58423647e-01, -3.97875642e-01,\n", " -4.48679367e-01, -4.73964012e-01, -5.01394438e-01,\n", " -5.21357145e-01, -5.47533636e-01, -5.86237506e-01,\n", " -6.08113174e-01, -6.56622524e-01, -6.90311043e-01,\n", " -7.28971868e-01, -7.54845212e-01, -7.78144650e-01,\n", " -8.14919239e-01, -8.43451038e-01, -8.68180109e-01,\n", " -8.84950599e-01]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can take a look at the data:\n", "\n", "h1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The head method output a numpy array with dimensions [number of aquifer layers, number of time data]. In this case we only have one row" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Demonstration of Calibration with TTim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To demonstrate the capability of TTim for deriving aquifer parameters with drawdown data, we will first add noise to the sampled data. We will test the model performance with two standard devitations: 0.02 and 0.05" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# np.savetxt('data/syn_30_0.0.txt', h1[0])\n", "# np.savetxt('data/syn_90_0.0.txt', h2[0])\n", "# print(h2[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating the arrays with noise for sigma = 0.02" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "rnd = np.random.default_rng(5) # Adding a Random seed\n", "he12 = h1[0] - rnd.random(len(t)) * 0.02\n", "he22 = h2[0] - rnd.random(len(t)) * 0.02\n", "np.savetxt(\"data/syn_p30_0.02.txt\", he12)\n", "np.savetxt(\"data/syn_p90_0.02.txt\", he22)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating the arrays with noise for sigma = 0.05" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "rnd = np.random.default_rng(4) # Adding a Random seed\n", "he15 = h1[0] - rnd.random(len(t)) * 0.05\n", "he25 = h2[0] - rnd.random(len(t)) * 0.05\n", "np.savetxt(\"data/syn_p30_0.05.txt\", he15)\n", "np.savetxt(\"data/syn_p90_0.05.txt\", he25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting and checking the noise added data:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8, 5))\n", "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with $\\sigma$ =0.02\")\n", "plt.semilogx(t, h1[0], label=\"modelled heads at 30 m (obs 1)\")\n", "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with $\\sigma$ =0.02\")\n", "plt.semilogx(t, h2[0], label=\"modelled heads at 90 m (obs 2)\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "fig.title(\"Drawdown model and observations with noise: $\\sigma$ = 0.02\");" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8, 5))\n", "plt.semilogx(t, he15, \".\", label=\"obs at 30 m with $\\sigma$ =0.05\")\n", "plt.semilogx(t, h1[0], label=\"modelled heads at 30 m (obs 1)\")\n", "plt.semilogx(t, he25, \".\", label=\"obs at 90 m with $\\sigma$ =0.05\")\n", "plt.semilogx(t, h2[0], label=\"modelled heads at 90 m (obs 2)\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "fig.title(\"Drawdown model and observations with noise: $\\sigma$ = 0.05\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5.1: Calibration of the model using the noisy data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Calibrate the model using the $\\sigma $ = 0.02 noisy data from observation well 1.\n", "* We calibrate the model by creating a `Calibrate` object with the initial model as input.\n", "* Then we set the parameter initial values using the `set_parameter` method\n", "* we add the observation data with the `series` method\n", "* And fit" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...............................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 28\n", " # data points = 34\n", " # variables = 2\n", " chi-square = 0.01117172\n", " reduced chi-square = 3.4912e-04\n", " Akaike info crit = -268.704829\n", " Bayesian info crit = -265.652108\n", "[[Variables]]\n", " kaq0: 69.9141328 +/- 1.09423849 (1.57%) (init = 10)\n", " Saq0: 1.0170e-04 +/- 5.4838e-06 (5.39%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.852\n" ] } ], "source": [ "ca23 = ttim.Calibrate(ml) # TTim class for model calibration\n", "# Setting initial parameters values for calibration\n", "ca23.set_parameter(name=\"kaq0\", initial=10) # Hydraulic Conductivity\n", "ca23.set_parameter(name=\"Saq0\", initial=1e-3) # Specific Storage\n", "\n", "ca23.series( # Adding the observations for calibration\n", " name=\"obs1\", # Observation well 1\n", " x=d1, # Location\n", " y=0,\n", " t=t, # Time Array\n", " h=he12, # Drawdown noisy data for well 1\n", " layer=0, # Aquifer layer where we have the observations from\n", ")\n", "ca23.fit(report=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can already see the results from the calibration if we set `report = True` in the `fit` method. Besides fit statistics, we also get the Variables, with confidence interval and the correlations between variables during fitting process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also check the calibrated parameters:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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optimalstdperc_stdpminpmaxinitialparray
kaq069.9141331.0942381.565118-infinf10[69.91413282570136]
Saq00.0001020.0000055.39192-infinf0.001[0.00010170348919178826]
\n", "
" ], "text/plain": [ " optimal std perc_std pmin pmax initial \\\n", "kaq0 69.914133 1.094238 1.565118 -inf inf 10 \n", "Saq0 0.000102 0.000005 5.39192 -inf inf 0.001 \n", "\n", " parray \n", "kaq0 [69.91413282570136] \n", "Saq0 [0.00010170348919178826] " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ca23.parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `display` function returns a data frame with initial and optimal values for each parameter, besides the standard deviation of the estimate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now compare the model performance for both observation wells" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rmse: 0.01812677527586899\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca23.rmse()) # Return the RMSE error for the calibration\n", "h123 = ml.head(d1, 0, t) # Compute drawdown of the calibrated model for obs 1\n", "h223 = ml.head(d2, 0, t) # Compute drawdown for obs 2\n", "plt.figure(figsize=(8, 5))\n", "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with $\\sigma$ = 0.02\")\n", "plt.semilogx(t, h123[0], label=\"modelled drawdown at 30 m\")\n", "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with $\\sigma$ = 0.02\")\n", "plt.semilogx(t, h223[0], label=\"modelled drawdown at 90 m\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\n", " \"Ttim analysis with synthetic data, \"\n", " \"Calibrated model with $\\sigma$ = 0.02 errors at 30 m.\"\n", ")\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we do the same procedure as [before](#sig002obs1) to calibrate the model, but now using the observation data from well 2:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".......................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 36\n", " # data points = 34\n", " # variables = 2\n", " chi-square = 0.00859548\n", " reduced chi-square = 2.6861e-04\n", " Akaike info crit = -277.617900\n", " Bayesian info crit = -274.565179\n", "[[Variables]]\n", " kaq0: 70.7905238 +/- 1.71072660 (2.42%) (init = 10)\n", " Saq0: 9.3336e-05 +/- 5.1366e-06 (5.50%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.831\n" ] }, { "data": { "text/html": [ "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.7905241.7107272.416604-infinf10[70.79052382809591]
Saq00.0000930.0000055.503366-infinf0.001[9.333600107013827e-05]
\n", "
" ], "text/plain": [ " optimal std perc_std pmin pmax initial \\\n", "kaq0 70.790524 1.710727 2.416604 -inf inf 10 \n", "Saq0 0.000093 0.000005 5.503366 -inf inf 0.001 \n", "\n", " parray \n", "kaq0 [70.79052382809591] \n", "Saq0 [9.333600107013827e-05] " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ca29 = ttim.Calibrate(ml)\n", "ca29.set_parameter(name=\"kaq0\", initial=10)\n", "ca29.set_parameter(name=\"Saq0\", initial=1e-3)\n", "ca29.series(name=\"obs2\", x=d2, y=0, t=t, h=he22, layer=0)\n", "ca29.fit(report=True)\n", "display(ca29.parameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can once again check the model performance with the new calibrated model" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rmse: 0.015899943725520456\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca29.rmse())\n", "h129 = ml.head(d1, 0, t)\n", "h229 = ml.head(d2, 0, t)\n", "plt.figure(figsize=(8, 5))\n", "plt.semilogx(t, he15, \".\", label=\"obs at 30 m with sig=0.02\")\n", "plt.semilogx(t, h129[0], label=\"ttim at 30 m\")\n", "plt.semilogx(t, he25, \".\", label=\"obs at 90 m with sig=0.02\")\n", "plt.semilogx(t, h229[0], label=\"ttim at 90 m\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data, sig=0.02 errors at 90 m.\")\n", "plt.legend(loc=\"best\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5.2: Calibrate with two datasets simultaneously" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TTim can also analyse the pumping tests using drawdown data from more than one borehole." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we calibrate the model using the data without error from both observation wells." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "............................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 25\n", " # data points = 68\n", " # variables = 2\n", " chi-square = 7.7331e-15\n", " reduced chi-square = 1.1717e-16\n", " Akaike info crit = -2492.46835\n", " Bayesian info crit = -2488.02934\n", "[[Variables]]\n", " kaq0: 69.9999996 +/- 5.2525e-07 (0.00%) (init = 10)\n", " Saq0: 1.0000e-04 +/- 2.2749e-12 (0.00%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.830\n" ] }, { "data": { "text/html": [ "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.05.252545e-070.000001-infinf10[69.99999956044262]
Saq00.00012.274863e-120.000002-infinf0.001[0.00010000000009794474]
\n", "
" ], "text/plain": [ " optimal std perc_std pmin pmax initial \\\n", "kaq0 70.0 5.252545e-07 0.000001 -inf inf 10 \n", "Saq0 0.0001 2.274863e-12 0.000002 -inf inf 0.001 \n", "\n", " parray \n", "kaq0 [69.99999956044262] \n", "Saq0 [0.00010000000009794474] " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ca0 = ttim.Calibrate(ml)\n", "ca0.set_parameter(name=\"kaq0\", initial=10)\n", "ca0.set_parameter(name=\"Saq0\", initial=1e-3)\n", "ca0.series(name=\"obs1\", x=d1, y=0, t=t, h=h1[0], layer=0)\n", "ca0.series(name=\"obs2\", x=d2, y=0, t=t, h=h2[0], layer=0)\n", "ca0.fit(report=True)\n", "display(ca0.parameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The obtained results match exactly the input parameters" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rmse: 1.0664077889093849e-08\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca0.rmse())\n", "h1n = ml.head(d1, 0, t)\n", "h2n = ml.head(d2, 0, t)\n", "plt.figure(figsize=(7, 4))\n", "plt.semilogx(t, h1[0], \"b.\", label=\"obs at 30 m no errors\")\n", "plt.semilogx(t, h1n[0], color=\"r\", label=\"ttim at 30 m\")\n", "plt.semilogx(t, h2[0], \"g.\", label=\"obs at 90 m no errors\")\n", "plt.semilogx(t, h2n[0], color=\"orange\", label=\"ttim at 90 m\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data without errors.\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We do the same, but now with drawdowns with errors with $\\sigma=0.02$." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...........................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 24\n", " # data points = 68\n", " # variables = 2\n", " chi-square = 0.02036601\n", " reduced chi-square = 3.0858e-04\n", " Akaike info crit = -547.710892\n", " Bayesian info crit = -543.271877\n", "[[Variables]]\n", " kaq0: 70.5085864 +/- 0.85773221 (1.22%) (init = 10)\n", " Saq0: 9.7107e-05 +/- 3.6049e-06 (3.71%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.830\n" ] }, { "data": { "text/html": [ "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.5085860.8577321.216493-infinf10[70.50858641493859]
Saq00.0000970.0000043.712304-infinf0.001[9.71065523694604e-05]
\n", "
" ], "text/plain": [ " optimal std perc_std pmin pmax initial \\\n", "kaq0 70.508586 0.857732 1.216493 -inf inf 10 \n", "Saq0 0.000097 0.000004 3.712304 -inf inf 0.001 \n", "\n", " parray \n", "kaq0 [70.50858641493859] \n", "Saq0 [9.71065523694604e-05] " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ca2 = ttim.Calibrate(ml)\n", "ca2.set_parameter(name=\"kaq0\", initial=10)\n", "ca2.set_parameter(name=\"Saq0\", initial=1e-3)\n", "ca2.series(name=\"obs1\", x=d1, y=0, t=t, h=he12, layer=0)\n", "ca2.series(name=\"obs2\", x=d2, y=0, t=t, h=he22, layer=0)\n", "ca2.fit()\n", "display(ca2.parameters)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rmse: 0.017306074039873633\n" ] }, { "data": { "image/png": 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fJjo6mpCQEKKjozly5IjbcvPnz6du3brUrl2b0aNHJz8+ePBgrrzySho3bkyvXr04evRojmMCBxO9iAQC7wFdgfrATSJSP02xrkCIa7kHeD9PgwRid8UyavkoYnfF5vWuPRYbC6NG2Vt/4sSx8WSfBeEzo1RuyizRN2zYkLi4ODZs2MD8+fO59957uXDhAgD3338/48aNY8uWLWzZsoX58/NmRtvmzZvz9ttv53g7o0ePJioqii1bthAVFZUqiSdJSEhg4MCBzJs3j02bNjF58mQ2bdoEQHR0NBs3buSXX36hTp06jBo1KscxgbOt7q8C/jLGbAMQkSlAT2BTijI9gYnG/qxbJSJlRKRSXk1zGLsrllPzW3FNEMhfcOpCSYoHFIaAQAgMyPw2IADy4HLcqVNQYtdprilxCoktzqndxShePPf3m7z/+NOcOn+K4oWKUzy4WIo1ksHfgKRZJwEpbgPsbdJCgKt86seOnDvG4V2rqG8S2ft7IAdqdeSSklUhoLBdAj24DSwKhUpDcDkILgvBZSAg43+J2F2xRE2MIj4hnuDAYBbftpiIahFZLqOUp2JjISYGIiMhwgsfo9dff51PP/0UgLvuuotHHnkEgAsXLtC/f39++ukn6tSpw8SJEylWrBhDhgxh1qxZBAUF0alTJ1599dVU21uzZg2PPPIIZ86coWjRonz22WfUrFmTZ555hjNnzrBixQqGDh3KjTfemPycYsX++544e/Zs8jXnffv2cfz4cSJcL/S2225jxowZdO3aNdU+n3vuObZv386+ffv4888/ef3111m1ahXz5s2jSpUqzJ49O9OuZ9988w3PP/88gYGBlC5dmmXLlhETE8Orr77KnDlzOHDgADfffDOHDh2iRYsWzJ8/n3Xr1lGhQoWLvr8zZ84kJiYGgP79+xMZGcmYMWPSvWe1a9emVq1aAPTt25eZM2dSv359OnXqlFyuZcuWfPvttxfdpyecTPRVgF0p7u8Gwj0oUwVIl+hF5B7sWT+XX365VwKM2RFDhfNw3IAYCD5ymroHT0NCgmcbCAqCQkEQVMjNbSHX+kLpyxUqZP8OCrJLJo0v/txzmO3FfgNJhEIBXDjaliYVy3nl9V/MoTOHWXHgNxJNIgESQJvL21K+aDkgZXVbmqq3tFVxJtGWSXWbaG+T/k5MTL3eJHL2xF4qBSYSKFBYEih0aA2c+AUSzkHiOXtrLmT9RRUqlSLxu24L29sze37m1uJn+feCYU/COVZvnUNE1Zapjk/MjhjiE+JJMAnEJ8QTsyNGE73KlthYiIqC+HgIDobFi3OW7NetW8dnn33G6tWrMcYQHh5O+/btKVu2LJs3b+aTTz6hdevW3HHHHYwdO5Y77riD6dOn88cffyAibquRr7zySpYtW0ZQUBDff/89//vf/5g6dSovvPACcXFxvPvuu25jWb16NXfccQc7d+7k888/JygoiD179lC1atXkMlWrVmXPnj1un79161aWLFnCpk2biIiIYOrUqbz88sv06tWL7777jmuvvTbD9+GFF15gwYIFVKlSxe1rev755+nQoQNDhw5l/vz5jBs3Lnld27ZtOXHiRLrnvPrqq3Ts2JH9+/dTqVIlACpVqsS///6bruyePXuoVq1aqte5evXqdOU+/fTTVD+QcsLJRO8ue6W9IONJGfugMeOAcQDNmzf3yoWd8icjuWdvUQiIh8RgPmy9mLpdI+DCBThxAo4ft8uxY//9nXY5lFGZQ3DypGeBlCgBpUqlX0qX5psi+xl9icEEAAnCfXU68X67/3nj5V/UuOWjGL5vBQkGAkUYcWVnhrYdmif73pHuzPm79AnVJKZO/ClvE89BwlmIPwrxRyD+sL09dzj1/WN7ku93SDxPh0uTNp4Ie1+Cr1+HYpdD8cuh2OX0E2FH6QC2xRu2JRQisnr7PHk/lO+JibFJPiHB3sbE5CzRr1ixgl69elHcVeXXu3dvli9fTo8ePahWrRqtW7cGoF+/frz99ts88sgjFClShLvuuotrrrmG7t27p9vmsWPH6N+/P1u2bEFEOH/+vEexhIeH89tvv/H777/Tv39/unbt6vZ6fEYtzLt27UqhQoVo1KgRCQkJdOnSBYBGjRqxY8eOTPfdunVrBgwYwA033EDv3r3TrV+xYkVyu4EuXbpQtmzZ5HXLly/36PVlxpPX+eKLLxIUFMQtt9yS4/2Bs4l+N1Atxf2qwN5slMk1hzZEEPD5YhIvjyHg70gOFY+wrQaCgqBsWbvkREKCTfYpfwBk9qMhZZm9e+H4cf6v+GHe7GOINxCcmMBtL74Eny+FVq3st0J4OJQu7ZX3I63IGpEEBwYnJ9vIGpG5sh93IqpFsPi2xcTsiCGyRqT7s2YJgKCiQNGc79AYSDjNuh3f8/PORbQqX40rixaDUzvh9N9w6m/YN49qZ/bxYfJEkWdhdTf4oz6Urg+lXLelGxB7aBcxO5dmHLvye5GR9kw+6Yw+MjJn28usYVvaRCMiBAUFsWbNGhYvXsyUKVN49913+eGHH1KVGz58OFdffTXTp09nx44dRGYxyHr16lG8eHE2btxI1apV2b17d/K63bt3U7lyZbfPK1y4MAABAQEUKlQoOf6AgIDk6/0Z+eCDD1i9ejXfffcdYWFhbNiwIdX6zN6ni53RV6xYkX379lGpUiX27dvHpZdemq5s1apV2bXrv4rqtK9zwoQJzJkzh8WLF3utO6aTiX4tECIiNYE9QF/g5jRlZgEPuq7fhwPH8ur6PNh/rMIjIojfE+GVf7R0AgNtEs5BIo4AFv/9IzHrpxP5bzEi2v9r6/yef94mJxFo0OC/xN+qFYSEZHo5wON9e5Jsc1FEtYi826cIBBWnWe2eNKvdM+NyCefgzB44uR2O/Q7HN8GxTbB7Fpz7JLlYw0S4cA5W/RpEuZbPUbfurVCsmleOi/INERG2ut5b1+jbtWvHgAEDGDJkCMYYpk+fzueffw7A33//TWxsLBEREUyePJk2bdpw8uRJTp8+Tbdu3WjZsiW1a9dOt81jx45RpUoVAMaPH5/8eMmSJd0mRIDt27dTrVo1goKC2LlzJ5s3b6ZGjRpUqFCBkiVLsmrVKsLDw5k4cSIPPfRQtl/v0KFDueqqq+jVq1eqx7du3Up4eDjh4eHMnj07VdIFaNOmDV9//TVPPfUUCxcuTNVy/mJn9D169GDChAkMGTKECRMm0LNn+u+KFi1asGXLFrZv306VKlWYMmUKX375JWBb448ZM4alS5emasuQY8YYxxagG/AnsBV42vXYfcB9rr8F2zJ/K/Ar0NyT7TZr1sx4y8qVxrz0kr0tUI4dM2bRImOef96YLl2MKV3aGJv6jSlf3pju3e0LW7LEmJMnnY7WP5w5YMz+ZWbu3J7mnffErPwYc+ZzjJnkWqZeZkxMD2M2vmjMvkXGxB93OmLlRZs2bXI6BPPaa6+ZBg0amAYNGpg33njDGGPM9u3bTb169cy9995rGjVqZHr37m1OnTpl9u7da1q0aGEaNWpkGjZsaMaPH59ueytXrjQhISGmVatWZtiwYaZ69erGGGMOHTpkmjdvbkJDQ82UKVNSPWfixImmfv36JjQ01DRp0sRMnz49ed3atWtNgwYNTK1atczAgQNNYmJiun0+++yz5pVXXkm+X7x4cbfrrrnmGrPSzRd3r169TMOGDU2DBg3MoEGDTGJiolmyZIm55pprjDHG7N+/33To0ME0adLEPPLII6ZSpUrm7NmzHr2/Bw8eNB06dDC1a9c2HTp0MIcOHTLGGLNnzx7TtWvX5HLfffedCQkJMbVq1TIjR45MfvyKK64wVatWNaGhoSY0NNTce++9bvfj7rMExJkMcqKYPOqnmJeaN29u4uLinA4jf0lMhD/+gJUr7RIba++DrVkIDU191l+9eq6fXcbuinWsNsBJKVvmFw8sxPJe79I46CwcXA2H18DxzQAkEMCpUg0pdfm1cFlHKB8OgcHOBq+y7ffff6devXpOh+E3OnfuzIIFC7L8vHPnzhEYGEhQUBCxsbHcf//96ar3nebusyQi64wxzd2V10Tvzw4fhlWr/kv8q1fb/noAlSr9l/QjIqBpUyhSxGu79vduaJn9yFmzbQEjZvSgdfB5OhaHZoUFIRGCisMl7WzSvywKyjTWqv4CRBN9wbBlyxZuuOEGEhMTCQ4OZuzYsbRo0cLpsFLJaqLX2ev8Wbly0K2bXcD2Jvj1V5v0k5L/tGl2XXCwTfYpz/ozaCjjifzQDc3JGoXM2hcs3rOeeScTmGMMww4H8kr7//ForSbwz2LY/z389DgAxwLLcOqyLlSuPwgqhLvGGFBK5URISAg//fST02F4lSZ69Z+gIGjSxC4PPGAf278/deJ/7z14/XW77vLLbcJv3x6uvx7Kl/d4V0622If8XaOQ9r1pWasrVIuAarZR0bq/ZjLuu+v5v2JHib4wBfZMgWJVoVofuPx6qNBSk75SKpkmepW5ihXh2mvtAravz08//Zf8ly+HKVNg0CDo3h3694euXW0NQCacbrGfH2oUMnKx92bhvk18ciyRcUehbGAAE5pfx/8VPQdbxsLmN6FoFah2nU36l7TSpK+Un9NEr7ImONj2zQ8PB9fwmfz8M0yYAJMmwfTpUKEC3HyzTfpNmmR4HTlPu8el4XSNwsVk9t6kjP2sBFOhwaP2jP/8cdg9G3Z9A399CH++DUUr/Zf0K7S2wzMrpfyKNsZT3nP+PCxYYJP+rFn27L9hQ5vwb7nFNvBLwdvjeGdVQW71f9HYzx+HPd/B39/Avnl2FMAil0GNW6D2PVCqTt4H7ee0MZ7ylqw2xnO0H31uLd7sR6+y6dAhY8aONSY83PbdDwgwpmtXY6ZMMebMGbNypTFFixoTGGhvC9w4BQVJ/HFjtk82Zum1xnwZZPvsf9/BmJ1fG3PhnNPR+Q2n+9EfOXLEvPfee8n3t2/fbiZNmpR8f+3ateahhx7y+n6nT59ufvvtN7fr3n//fdOwYUMTGhpqWrdunarc+PHjTe3atU3t2rXd9uH3Z1ntR+94Us6NRRN9PvPHH8YMHWpM1ar2I1e6tFnf4h7TJuBHA4kmMNCO3aPywOl9dkCeGdVdg/RUNOanocac2OZ0ZD7P6US/fft206BBg+T7KQeJyU39+/c333zzjdt1x44dS/575syZpnPnzsYYO+BOzZo1zaFDh8zhw4dNzZo1zeHDh3M91oIiq4leW+mo3Fe3Lrz0EuzYAYsWQY8eNP71C5YntuYPrmRgwPt0iDjjdJT+oehl0OB/8H9bof13dhCe38fArCtgSTc7VG9iNmb9U/nekCFD2Lp1K2FhYQwePJghQ4awfPlywsLCeOONN4iJiUmeuOa5556jf//+dOrUiRo1ajBt2jSefPJJGjVqRJcuXdxOXvPRRx/RokULQkNDue666zh9+jQrV65k1qxZDB48mLCwMLZu3ZrqOaVKlUr++9SpU8ljuy9YsIDo6GjKlStH2bJliY6Odjs3fWRkJI8++ijt2rWjXr16rF27lt69exMSEsKwYcO8+fYVaNoYT+WdwEDo2BE6diTwvff4a8xUSk54n7d2PwA3PGtb7j/wgO3fr3JXQCBU6WaXU7tg68ew9SNY1tO22q99N9S+D4pWdDpS3/TII+Dt0dbCwuDNNzNcPXr0aDZu3Jg8ylvKOdiT7qeU1alge/fuzd133w3AsGHD+OSTT3jooYfo0aMH3bt3p0+fPm7jeu+993j99deJj49PnjTH3VSuGU1ZGxwczLJly3jrrbfo2bMn69ato1y5clxxxRU8+uijlM9Ct19fpWf0yhklS1J75AAq/73Ktshr3hyGD7d98x99FP7+2+kI/UfxatD4eei5E9pOgzIN4dfnYGZ1WHMfHN/idITKAVmdCnbjxo20bduWRo0aMWnSJH777TeP9jNw4EC2bt3KmDFjGDlyJODZVK5JevTokRxXgwYNqFSpEoULF6ZWrVrpJqzxV3pGr5wlYgfcad8efvkFXn0V3n3XLjfdBIMHQ6NGTkfpEy7aUj+gkB2Up1ovOP4n/PEabBsPf42Dar2h/lNQPv1QoAW594JjMjnzzi+yOhXsgAEDmDFjBqGhoYwfPz5dDcHF9O3bl/vvvx+wZ/Apn7979+4Mp8BNGWfS35nF6Y/0jF7lH40bw8SJsHUrPPigHX63cWM7RO/SpXbuPZUtSSMBDl8ynKiJUcTuis38CaXqwFUfQs8dUH8I/PM9LLgKvr8a9s5LPhZZ3q5yTNqpYzObSjY7Tpw4QaVKlTh//jyTJk3yaD9btvxXW/Tdd98REhIC2AlpkqaIPXLkCAsXLqRz585ei9XfaKJX+c/ll8Mbb9jq+5EjIS7OdrZv2dIm/4QEpyMscNyNBOiRopdB2Etw7S5o8hqc/AtiusG8UNj+BUu3L87edlWeK1++PK1bt6Zhw4YMHjyYxo0bExQURGhoKG+88UaOtz9ixAjCw8OJjo7myiuvTH68b9++vPLKKzRp0iRdY7x3332XBg0aEBYWxuuvv86ECRMAKFeuHMOHD6dFixa0aNGCZ555hnLadifbdMAclSN5MujNmTN2EJ5XX7Vn+yEh8MQTrKpzG0tiizg24E5BktWx/TOsjk+Ih51T4PeX4dhvnC1SmUd3HeCzYwkEBBbOV3MG5Dc6YI7yFp2mFk30eSU2FqKi7AB4wcGweHEuJ9yEBHtGP2YMrFvHfirytjzMuMIPMeuHEprsL8LTa+ke/Sgwifzx82uU3vIWlc7v4UhgWQ5d8QC1mz4HAdr0xx1N9MpbsprotepeZVtMjE3yCQn2Nottb7IuMNDOkrd2LZPu/IGfaMKL5n/8erY2x0a/b4fgVRmKqBbB0LZDL3rG7Uk1f+zu1TT97lmq/b6PXv8EU6hoRWr/+SJ81wB2TAaTSOyuWEYtH6XX7ZVymCZ6lW2RkfZMPjDQ3mbQKNb7RKh159X0LjqP1gGxbAmoS5dZD0CDBvDtt9poL4eSJs0JlMAMJ/z578dAIrNPJvBOmVuh7XQILAwrb+b0zBDe/jaS4UuGaSM9pRymiV5lW0SEra4fMSIPqu0z2Hf3kS0JWh4Dc+ZA4cL2jL9lyzyoXvBdSdPkjrh6RIbX3NP9GKh5NVS7FrpugNZTOB1/gskV44mtmkjLQue0kZ5SDtJr9Mp3JCTA55/bgXd274auXWH0aNtFT3ldZtf8Y/9ewSfTO/BMmfNcXggOl29LuYiPoFRdh6J1nl6jV96i1+iV/woMhAED4M8/4eWXbWvBsDD7mI6053WZXfOPuLwNd/Zeylc1nmdnjfsod2yDvX6/9kE4eyDvg1XKj2miV76naFE7ot62bfDEEzBlCtSpY/8+fNjp6PxGRLUIBrd/huqt3ocef9mx8//6AGbXht9GwwWdyCg/2LFjBw0bNvTqNjds2MDcuXPdrouPj+f222+nUaNGhIaGphoBb926dTRq1IjatWszaNAgt0Ph5oa4uDgGDRqU4+0cPnyY6OhoQkJCiI6O5siRI27LzZ8/n7p161K7dm1Gjx6d/PjgwYO58sorady4Mb169eLo0aM5jgk00StfVrasPbPfssUOp/v661Crlu2ed0aTTJ4qcim0eBe6bYRLI+HnoTCnLmz/Akyi09EpL8ss0X/00UcA/PrrryxatIjHH3+cxET7Gbj//vsZN24cW7ZsYcuWLW5nrMsNzZs35+23387xdkaPHk1UVBRbtmwhKioqVRJPkpCQwMCBA5k3bx6bNm1i8uTJbNq0CYDo6Gg2btzIL7/8Qp06dRg1alSOYwJN9MofVKsGn30GP/8MbdvCkCF20J1PP9VR9vJa6Suh/UyIWmKTf+ytdmjd/TFOR5YvebuL4uuvv07Dhg1p2LAhb6YYb//ChQv079+fxo0b06dPH06fPg3YqW3r169P48aNeeKJJ9Jtb82aNbRq1YomTZrQqlUrNm/eTHx8PM888wxfffUVYWFhfPXVV6mes2nTJqKiogC49NJLKVOmDHFxcezbt4/jx48TERGBiHDbbbcxY8aMdPvMzhS6KX3zzTc0bNiQ0NBQ2rVrB5Bqit4DBw4QHR1N06ZNuffee6levToHDx706P2dOXMm/fv3B6B///5u41+zZg21a9emVq1aBAcH07dvX2bOnAlAp06dCAqy41C0bNmS3bt3e7Tfi8poovqCvDRr1swolaGYGGPCw40BY5o2NWbVKqcj8k+JCcZs+9yY6dWMmYQxMT2MOfaH01Hlmk2bNmWp/Mq/V5qiI4uawOcDTdGRRc3Kv1fmaP9xcXGmYcOG5uTJk+bEiROmfv36Zv369Wb79u0GMCtWrDDGGHP77bebV155xRw6dMjUqVPHJCYmGmOMOXLkSLptHjt2zJw/f94YY8yiRYtM7969jTHGfPbZZ2bgwIFu4/jwww9Nnz59zPnz5822bdtM6dKlzbfffmvWrl1roqKiksstW7bMXHPNNeme/+yzz5rWrVub+Ph4s2HDBlO0aFEzd+5cY4wx1157rZk+fXqm70PDhg3N7t27U72mJUuWJO9r4MCB5qWXXjLGGDNv3jwDmAMHDhhjjGnTpo0JDQ1NtyxatMgYY0zp0qVT7atMmTLp9v/NN9+YO++8M/n+xIkT3b5X3bt3N59//rnb1+DuswTEmQxyop7RK//Tvr1tqDd5Mvzzj+2Od9ddcODijcRiY2HUKHurckgCoGY/6L4ZQkfB/iUwtxFsGAIXTjkdneOyPT9BBlasWEGvXr0oXrw4JUqUoHfv3ixfvhyAatWq0bp1awD69evHihUrKFWqFEWKFOGuu+5i2rRpFCtWLN02jx07xvXXX0/Dhg159NFHPZqa9o477qBq1ao0b96cRx55hFatWhEUFJSlqWmzOoVuSq1bt2bAgAF89NFHJLip0VuxYgV9+/YFoEuXLpQtWzZ53fLly9mwYUO6pWPHjhd93Uk8eZ0vvvgiQUFB3HLLLR5vNzOa6JV/EoG+feGPP2wjvQkToG5deP/9DKvzk4b8HT7c3mqy95KgotBgiG2wV6MfbBoDc+rDrul+PfiRJwMXZYW7BJMkbaIREYKCglizZg3XXXcdM2bMSE6mKQ0fPpyrr76ajRs3Mnv2bM6ePXvROIKCgnjjjTfYsGEDM2fO5OjRo4SEhFC1atVUVdW7d++mcuXKbreR1Sl0U/rggw8YOXIku3btIiwsjEOHDqVan9n71LZtW8LCwtIt33//PQAVK1Zk3759AOzbt49LL7003TaqVq3Krl27MnydEyZMYM6cOUyaNCnDHzpZpYle+beSJeGVV+z1+7AweOABuOoqWLUqXdE8H/LX3xS5FFp+Ch2XQ3BpWN4blnaHk9ucjswRngxclBXt2rVjxowZnD59mlOnTjF9+nTatm0LwN9//02s65fr5MmTadOmDSdPnuTYsWN069aNN998kw0bNqTb5rFjx6hSpQoA48ePT348s6lpk/YPsGjRIoKCgqhfvz6VKlWiZMmSrFq1CmMMEydOpGfPntl+vUOHDmX69OnpHt+6dSvh4eG88MILVKhQIVXSBWjTpg1ff/01QPJUuUkudkbfo0eP5Bn4JkyY4Db+Fi1asGXLFrZv3058fDxTpkyhR48egG2NP2bMGGbNmuW2BiW7NNErBVC/vh1qb8oUW50fEQF33pmqOt+xIX/9zaVtoMt6aPo6/LvMnt3/+gIkXPxs0dd4Oj+BJ5o2bcqAAQO46qqrCA8P56677qJJkyYA1KtXjwkTJtC4cWMOHz7M/fffz4kTJ+jevTuNGzemffv2bqeyffLJJxk6dCitW7dOVQ1+9dVXs2nTJreN8f7991+aNm1KvXr1GDNmDJ9//nnyuvfff5+77rqL2rVrc8UVV9C1a9dsv95ff/2Vyy67LN3jgwcPplGjRjRs2JB27doRGhqaav2zzz7LwoULadq0KfPmzUv+AeKJIUOGsGjRIkJCQli0aBFDhgwBYO/evXTr1g2wNRrvvvsunTt3pl69etxwww00aNAAgAcffJATJ04QHR1NWFgY9913X7Zff0o6Mp5SaZ04Ycf1feMNKFECXnwR7r0XAgPzZlpe9Z/Te2D94/D3V1DiCmj+LlROX4VcEOjIeHmrc+fOLFiwIMvPO3fuHIGBgQQFBREbG8v999/vtjbDSToynlI5VbKk7X//88/QpAkMHJhcnR8RAUOHapLPM8WqQJsp0GGRnf42piss7wOndl38ucqvZSfJg72M0aJFC0JDQxk0aFByv/+CTBO98lsXbUHvQXW+8p5M+4xf1hG6/gyhL8LeufBdPdj0CiTq1MTKu0JCQvjpp5/4+eefWbt2LS1atHA6pBzTRK/8ksct6EXgxhtt6/zBg2HiRNs6/9NP/bpFuLfF7oolamIUw5cMz3ha28DC0OB/cM0mqBgFG56EeU1g/9K8D1ipAkQTvfJLWW5Bn7I6v2FDe2bfsSP89Ve6otrXPuuy1Ge8RA07ul67Wba//eJIfp3RlLXb5uVVuEoVKJrolV/Kdgv6+vXtr4IPPoC4OGjUyE6F6xp2U/vaZ0+2+oxX/T9Wh37K6CNB1D31E9V/7Maf60dqTYtSaWiiV34pIsJefh8xwt5mqXFdQIBthf/779Ctm22d16IFxMVpX/tsym6f8R92rWLYIUPTv2Hneajzx3BYfh2c+SeXI1aq4NBEr/xWjlvQV64MU6fCtGnw778QHk7/nx+jTKFT2tc+G7LTZzypJuCP84F02FeEnTUHuhrr1Yftn2f57N7bk8jkJ0ePHmXs2LHJ93fs2MGXX36ZfN9bU7WmNWPGjOTZ2dLauXMnUVFRNG7cmMjIyFQj402YMIGQkBBCQkKSB6FR2ZTRIPgFedFJbVSeO3rUmPvuMwbMmcuqmykD5pmVOZuDRHlo5d8rzUvLXvpv0pdjfxizsLWdKGdJN2NO7fJ4O96cRCatrE5q423bt283DRo0SL6fciKX3NS/f3/zzTffuF3Xp08fM378eGOMMYsXLzb9+vUzxhhz6NAhU7NmTXPo0CFz+PBhU7NmTXP48OFcj7Wg0EltlHJC6dJ2nPxlyyhSpig3ju9KxLjbIcXwmSp3pKsJKFUXOi6DZm/Z6W+/awB/fXTRs3tvTyKT3wwZMoStW7cSFhbG4MGDGTJkCMuXLycsLIw33ngj1VSt2ZkK9qOPPkruf37ddddx+vRpVq5cyaxZsxg8eDBhYWFs3bo11XNSTll79dVXJ0/XumDBAqKjoylXrhxly5YlOjra7dz0kZGRPProo7Rr14569eqxdu1aevfuTUhICMOGDfP2W1hgBTkdgFI+pW1b2LDBXvwfPRoWLoQPPwTXF6jKIxIAdQdBle6w+i5Ycw/s/ArCP4ISNd0+JekyQHxCvFcmkcnUukfgyAbvbrNsGDR7M8PVo0ePZuPGjcmjvMXExPDqq68yZ86c5Pspbd26lSVLlrBp0yYiIiKYOnUqL7/8Mr169eK7777j2muvTVW+d+/e3H333QAMGzaMTz75hIceeogePXrQvXt3+vTpky6m0NBQpk6dysMPP8z06dM5ceIEhw4dYs+ePVSrVi25XNWqVdmzZ4/b1xUcHMyyZct466236NmzJ+vWraNcuXJcccUVPProo5QvXz7z980P6Bm9Ut5WuDCMHAmrV0P58vB//we33QaHDzsdmf8pUQs6LIarPoRDa+w0uJvfBZOYrqi3J5Ep6LI6FezGjRtp27YtjRo1YtKkSR5NWfvqq6+ydOlSmjRpwtKlS6lSpUqWp6xNmhCmUaNGNGjQgEqVKlG4cGFq1aqVbsIaf6Vn9ErllmbNbBe8F1+El16CRYtst7wczMilskEEat8DlbraM/t1D9mx88M/hVIhqYpGVIvImwSfyZl3fpHVqWAHDBjAjBkzCA0NZfz48elqCNypXLky06ZNA+DkyZNMnTqV0qVLU7Vq1VTP3717N5EZtGxNGWfS35nF6Y/0jF6p3BQcDM8/D2vXQsWKcO21cMstrJ1/SAfVyWvFq0HkXGg5Ho5uhHmN4ffXIDHhok/1BWmnjs1sKtnsOHHiBJUqVeL8+fNMmjTJo/0cPHiQxERbuzJq1CjuuOMOwE5IkzRF7JEjR1i4cCGdO3f2Wqz+RhO9UnkhLMwm++efJ/Hrb7i8a33WDpuhg+rkNRGo1R+6b4JKneGnJ2BRKzh68Wrmgq58+fK0bt2ahg0bMnjwYBo3bkxQUBChoaFup6DNqhEjRhAeHk50dDRXXnll8uN9+/bllVdeoUmTJuka48XExFC3bl3q1KnD/v37efrppwEoV64cw4cPp0WLFrRo0YJnnnmGcuXK5ThGf6XT1CqVxz4e9AtN3xlAU35iotzGwWFv8dgLZZwOy/8YA39/DXEPwvnj0PAZqP8kBBTKld3pNLXKW3SaWqXyuQY3NSayyGpGyDPcbCYx8MNG9vp9Cjpefh4Qgeo32klyqvaCX4bBgqvgyC9OR6aUV2miVyqPRUTAgh8KEfTi82z6OJbC5UpAp07wwANw6pRH4+XrDwEvKnKJnfO+7XQ4sw8WNIdNYzK8du/Lo+cp3+RIq3sRKQd8BdQAdgA3GGOOpClTDZgIXAYkAuOMMW/lbaRK5Y6IiKShd1vAzeth2DB44w1YuJAtHScQH9861Xj5KYfpTfohEB9v2/pleax+5V61a+GSNrD2ftgwBPbMhoiJtoueS9J0ukl97bPaDc8Yk2E3MaU8kZ3L7U6d0Q8BFhtjQoDFrvtpXQAeN8bUA1oCA0Wkfh7GqFTeKFoUXnvNZvTERG4d15aX5UmKBZx1O16+TpyTi4pUgDZfQ8TntmX+3MapRtXLyeh5RYoU4dChQ9n6olYKbJI/dOgQRYoUydLznOpH3xOIdP09AYgBnkpZwBizD9jn+vuEiPwOVAHcz46gVEHXrh38/DPyxBM8Nu4V+lWcx76XvyA0IjRVsaQpdpPO6HXiHC8TgZr94NL2sOp22/d+90wI/zhHo+dVrVqV3bt3c+DAgdyLXfm8IkWKULVq1Sw9x5FW9yJy1BhTJsX9I8aYspmUrwEsAxoaY45nUOYe4B6Ayy+/vNnOnTu9GrNSeWrePLjjDjua3siR8NhjEBiYvDo21p7JR0ZqtX2uMonw57uw4SkIKg4tPiBWqhCzI4bIGpF+P3qeyj8ya3Wfa4leRL7HXl9P62lggqeJXkRKAEuBF40x0zzZt3avUz7h4EE77/20adC+PUyYANWrOx2Vfzr2B8TeCofjoEY/aP4OBJdxOiqlkjnSvc4Y09EY09DNMhPYLyKVXMFVAv7NIPBCwFRgkqdJXimfUaECfPstfPYZrFsHjRvDF19keY515QWlr4ROK6HRc7BzMswNhX+XOR2VUh5xqjHeLKC/6+/+wMy0BcQ2Tf0E+N0Y83oexqZU/iECAwbAL79Ao0Zw663Qt69OkOOEgELQ6FmI/tH+/X0kbBgKCfHpimoXPJWfOJXoRwPRIrIFiHbdR0Qqi8hcV5nWwK1ABxHZ4Fq6OROuUg6rWROWLrWT40ybZpP+9987HZV/qhAOXTfAFXfAptF2CN3jm5NXJ3XBG75kOFETozTZK8c5kuiNMYeMMVHGmBDX7WHX43uNMd1cf68wxogxprExJsy1zM18y0r5sMBAGDrUTn9bqhRER8OTT9rm9ypvFSoB4R9D26lwcjvMawJbPgBjctQFT6ncoCPjKVXQNG1qr9nfdx+88optdr9588Wfp7yvWm/o9ut/A+0s60nHyqEEBwYTKIFZ7oKnVG7QSW2UKshmzoQ774QzZ+Ctt+zfOvJa3jOJsPlt2w0vuCy/X/EkM46f0y54Ks/opDZK+aqePW1DvYgIuPtu6NNHG+o5QQLgykeg81oofAn1fnucoUX3ElEpzOnIlNJEr1SBV7kyLFwIL78Ms2fbbnhLljgdlX8q2xi6rIW6D9uBdhZcZYfSVcpBmuiV8gUBATB4MKxaBcWL21lvhg7VhnpOCCwCzd6EyLlw7l9Y0AL+HKvjHyjHaKJXypc0bQrr19tr9aNHQ+vWsGULoFPb5rnKXaHrL3BpJMQNhGU94exBp6NSfkgTvVK+pnhx+OgjO6re1q3QpAl/Pf0pUR1MpnPcq1xQtCJEfgdN34B9C2BeY/hnsdNRKT+jiV4pX3XddbahXosW1H7pTiacu5GSCUd0atu8ltRQr9MqKFQKfoi28927GVFPqdygiV4pX1a1Knz/PTvvG8W1ZjobCKNd4I86ta0TyjWBLuug9t2waQx83xZObnM6KuUHNNEr5esCA6n+/hD++PhHSpULYnFCeyKWvAQJCU5H5n+CisNVH0Kbb+ywufOawM6vnI5K+ThN9Er5iUZ3XkXZbeuRPn3g6aehUyfYt8/psPzT5X3sePmlG8CPfWH1XXDhlNNRKR+liV4pf1K6NEyeDB9/bFvkhYbCvHlOR+WfStSAjkuhwf9g66cwvzkc+cXpqJQP0kSvlL8Rsd3v1q2Dyy6Dbt3giSdS9bnXrni5K3ka2z1xEPoidFgE8UftADuuPvc61a3yFh3rXil/duaMTfJjx0Lz5jBlCrH/XkFUlM37wcGweLEdYTcjsbG2FX9kZObllJU0jW18QjzBgcEsvm2xHQ//7L8QOwD2zeNQ+fY0Xr+a/efPpy6jVAZ0rHullHtFi8J778HUqfDXX9CkCf++NZn4eNtW72Jd8WJjbb987Z/vuQynsS1yKUTOgSavUubQCmIrnyW8sE51q3JOE71SCnr3hg0boFEjen51M59yByUDThEcTKZd8WJi8PhHgbIia0RmPI2tBEC9x/ktbBznEZZWhf+VEyKrt3MsXlXwaaJXSlnVq8PSpfD009yaOJ5t5VuwctzGTKvjIyNt9X5gIBf9UaCsiGoRLL5tMSOuHpFhlXzj+ndwqN1C/izWiBfKXSDirxds1b5S2aDX6JVS6S1eDLfcAseOwTvvZDrPvV6jz0XGwNaPIG4QFC4Hrb6EipFOR6Xyocyu0WuiV0q5t38/9OsH338PN90EH34IJUs6HZV/OvIL/HgDnNgCDZ+BBsMgINDpqFQ+oo3xlFJZV7EizJ8PI0fCV1/ZmfF++snpqPxT2cbQOQ6q3wK/PgdLOsEZHexIeUYTvVIqY4GBdhS9mBjbFa9lS9tK3wdrAvO9QiUgYgKEfwoHY2FeGOxb5HRUqgDQRK+Uuri2bW2r/KgoePBBuP56OHrU6aj8jwhccTt0iYPCl8CSzvDzMEi84HRkKh/TRK+U8kyFCjBnDrz8MsycCU2awNq1Tkfln0rXh85r4Io74LcXYXEHOL3b6ahUPqWJXinluYAAGDwYli2DxERo3RreesttVb4Oo5vLgopB+McQ8QUcWW+r8vfMdToqlQ9poldKZV1EBGvH/cSWK7rAI4/YAXeOHElerSPm5aGat0CX9VC0Kiy9Bn56EhLPOx2Vykc00Sulsiw2Ftr3Kke9P2fyVNBrJM6eY1vlu6rydcS8PFaqDnReBSH3w++vwPft4dQup6NS+YQmeqVUliUn8kThNfMYX9y9zGb11q3hnXeIbG90xLy8FlgEWoyF1lPg6EatylfJPEr0IhIgIk1E5BoR6SAiFXM7MKVU/pV26NuQ2yJsq/zOnWHQICJe60PMjKOMGHHx2e+Ul1W/Ebqsg2LVbFX+hiHaKt/PZToynohcATwFdAS2AAeAIkAd4DTwITDBGJOY+6F6TkfGUyr3uR36NjERXn8dhgyxY+d//TU0a+ZglH7swhlY/wj8NQ4uaWPP9ItVcToqlUuyPQSuiEwG3geWmzQFReRS4GbgiDFmghfjzTFN9Eo5bOVK6NvXDqP75ptw330ZjpWvctmOL2HNPRBY1LbQr9zZ6YhULsj2ELjGmJuMMcvSJnnXun+NMW/mtySvlMoHWrWC9ettk/sHHrAT5Jw44XRU/qnGzXb43CKXQUwX+Plprcr3M55eow8UkR4iMkhEHktacjs4pVQBljTAzksv2bHyW7SAX391Oir/VPpK6Lwaat0Bv70EP0TDmX+cjkrlEU9b3c8GBgDlgZIpFqWUylhAAAwdalvkHTsG4eEwfrzTUfmnoGLEVrmLOeX6kHAwFuY1gf1LnY5K5YEgD8tVNcY0ztVIlFK+KzLSznx3881w++2wfLmd575YMacj8xuxu2KJmhhFfEI8TYoEsSwkmKI/dIDGI6H+UyDa29pXeXpk54lIp1yNRCnl2y67DBYtgmHD4LPP7Ex4mzc7HZXfiNkRQ3xCPAkmgZ/OXmBsuduh2vXw8/9gaQ84d9jpEFUu8TTRrwKmi8gZETkuIidE5HhuBqaU8kGBgTBiBMydC3v3QvPm9vq98orYXbGMWj6K2F3pxxyOrBFJcGAwgRJIcGAwrWp1htaTofm78M9CmN8UDq5xIGqV2zLtXpdcSGQbcC3wq7sW+PmNdq9TqgDYtct2wVu5EgYOhNdeg8KFnY6qwEpZNR8cGMzi2xYTUS0iXZmYHTFE1ohMve7gGvjxBjizF5q8DnUGanfIAibb3etS2AJsLAhJXilVQFSrZkfcefxxeO89aNMGtm93OqoCK2XVfHxCPDE7YtKViagWwdC2Q9P9AKDCVXZinMs6wbqH4Me+cF67Q/oKTxP9PiBGRIZq9zqllNcUKgSvvgozZsCWLXZinNmznY6qQEpbNR9ZIzJrGyhcDtrPgtBRsOtbmN8cjmp3SF/gaaLfDiwGgtHudUopb+vZ0w6wU6sW9Ohhu+Rd0EFdsiKiWgSLb1vMiKtHuK2294gEQIMh0OEHOH8cFoTDtvFej1XlLY+u0Rc0eo1eqQLq7Fk7v/2HH0L79jBlim2tr/LemX9g5c2wf4kdaKf5uxBU1OmoVAayfY1eRMaJSKMM1hUXkTtE5BZvBKmUUhQpAh98ABMnwpo10KQJLNVBXRxR9DK4ehE0eBq2fQoLW8LxLU5HpbLhYlX3Y4HhIvK7iHwjImNF5FMRWQ6sxFbff5vrUSqlfF5sLIwaZW+59Vab6EuVgg4dYMwYOzOeylsBgRA6EiLnwundML8Zm9e/mGEXPpU/edq9rgTQHKgEnAF+N8bk25EutOpeqYIlNtbOfxMfb+e3T57D/sQJuOsuO91tjx52+NyyZZ0O1z+d+psTP3Sj5InfePuoMPxIYebf9kP22gIor8tx9zpjzEljTIwxZrIxZkZ+TvJKqYInJsYm+YQEexsT41pRsqS9Tv/22zBvnp3bfv16ByP1Y8UvZ2zpG3nrqDCojGFBpbOs+2um01EpD+jgxkopx0VG2jP5wEB7GxmZYqUIPPQQLFtmW+K3agXjxoEPNiTO79rV7MjQI0W4cV8ADYLh3gMfwr5FToelLkITvVLKcRERtrp+xIgU1fZptWxpz+bbt4d774X+/eHUqTyP1Z8ldeELu2okf7WYTKHiVWBJZ/h1BBhtQ5FfOdK9TkTKAV8BNYAdwA3GmCMZlA0E4oA9xpjunmxfr9Er5cMSEuDFF+G556BBA/j2W6hb1+mo/NOFU7DmPtjxBVTqAq2+gMLlnY7KL+X4Gr2I1BGRj0RkoYj8kLTkIKYhwGJjTAh2IJ4hmZR9GPg9B/tSSvmSwEB45hlYsAD++cdOjPP1105H5Z+CikPERGjxPuz/AebpxDj5kadV998A64FhwOAUS3b1BCa4/p6AnTAnHRGpClwDfJyDfSmlfFF0tK3Kb9QIbrzRDrQTH+90VP5HBELug+gf7d/ft4E/xya3ochsRj2VN4I8LHfBGPO+F/db0RizD8AYs09ELs2g3JvAk3gw3K6I3APcA3D55Zd7KUylVL6WNDHOU0/Bm2/C2rX27L5KFacj8z/lm9uJcVb2g7iBcOBHVle5k6hJ3TOdUU/lPk/P6GeLyAMiUklEyiUtmT1BRL4XkY1ulp6e7FBEugP/GmPWeVLeGDPOGNPcGNP8kksu8eQpSilfEBwMb7xh57X/+Wc7Mc4PObmyqLKtcDmInAONR8DOydRacxM1A85lOqOeyn2eJvr+2Kr6lcA615JpazdjTEdjTEM3y0xgv4hUAnDd/utmE62BHiKyA5gCdBCRLzyMVynlb264wZ7Rly9vq/VHj9bR9JwgAdBwGHRYSBmJZ1W1RPqWlOzNqKe8wtMBc2q6WWrlYL+zsD8ecN2mG3XBGDPUGFPVGFMD6Av8YIzpl4N9KqV8Xb16dujc66+3M+D16gVHj3r89FTD8Kqcuawjha75FVOmEZMvM2xt/X9EVG7mdFR+ydNW98tF5EUR6SIi3piedjQQLSJbgGjXfUSksojM9cL2lVL+qkQJmDzZjqY3d65tlb9hw0WfljQM7/Dh9laTvRcUq0qpbuug7sNU2vs1LL4aTu9xOiq/k5Wq+83AdcBKEYkTkTeyu1NjzCFjTJQxJsR1e9j1+F5jTDc35WM87UOvlFLJo+ktXWqnvo2IsOPkZyLDYXhVzgQUgmZvQuspcPRnmN/UTn2r8oynVffbgEXYPu/LgGJAvVyMSymlcq5VK9sFr1UruP12uOcem/jdyHQYXpVz1W+EzmsguBz80BE2jdFhjPOIp1X3W4EZQEXgE6ChMaZLLsallFIeueh19UsvhYUL7TX7jz6C1q1h+/Z0xTwahlflTOn6NtlX6wMbhsDyXhB/1OmofJ6n09Q+DLQBqgF/AEuBZcaYrbkbXvboELhK+YcMp7fNyKxZcNttEBAAX3wB3dJdKVR5wRjY/Bb8NBiKV4e206BsY6ejKtC8MU3tW8aY64GO2K51zwF/ei1CpZTKhixfV+/RA9atg8svh2uusUPpJiTkQaQqFRG48hGIWgIJp2FhS9g20emofJanVfevichqYDUQBjwDhORiXEopdVHZuq5+xRW2KmDAAFtP37UrHDyYu4Eq9y5tA11+gvLhsKq/nSAnwX0bCpV9nlbdX4+tqt+f+yHlnFbdK+U/YmPtmXxkZBavqxsDn3wCDz5or+N/8w2Eh+dSlCpTiRfgl2G2gV655tD2W1ulrzyWWdW9x9PUikgPoJ3r7lJjzGwvxed1muiVUh5btw769IE9e+x4+fffb6uWVd7bNcOe2UsQtJoElbXNt6e8MU3tKOx0sZtcyyDXY0opVbA1a2aTfXQ0DBwIt94Kp045HZV/qnYtdI6DYlUgphv88hwYHcY4pzwdMOcaINoY86kx5lOgi+sxpZQq+MqVg9mz4YUX4MsvoWVL2LLF6aj8U6kQ6LQKavSDjc9DzDVw7rDTURVoniZ6gDIp/i7t5TiUUspZAQF2/Nt582DvXjt07owZTkfln4KKQcQEaDEW9i+G+c3g8HqnoyqwPE30o4CfRGS8iEzAdrF7KffCUkoph3TubEfTq1PHToozZAhcuOB0VP5HBELuh47LwVyAha1g6ydOR1UgedqPfjLQEpjmWiKMMVNyMzCllHJM9eqwYgXcey+MGQOdOsG/7mbTVrkldlcso5aPIvZMInRZD5e2hdV32cXVBS+5zC6dgSgzQZmtFJGmaR7a7bqtLCKVjTFal6KU8k2FC8MHH9jr9fffD02b2i54OjZurovdFUvUxCjiE+IJDgxm8W2LiYicD78+A7+9BId/Yn3tp4n6ul/qMtX02LhzsTP611zLe9jBcsYBH7n+fjt3Q1NKqXxgwADbWb9wYWjfHt59VydjyWUxO2KIT4gnwSQQnxBPzI4YCAiE0Beh3Uw4uZV66/rRofC51GWUW5kmemPM1caYq4GdQFNjTHNjTDOgCfBXXgSolFKOCwuDuDh7/f6hh6BfP+2Cl4sia0QSHBhMoAQSHBhMZI3I/1ZW7QFd4kgsWoVZlRJ5rpxQOLBQ6jIqFU9HxttgjAm72GP5hQ6Yo5TKFYmJdqq84cOhQQOYNg1C0o8Gnu3R+lSy2F2xxOyIIbJGpPsq+Qun+TemD5f+O48jZSMo22EOFC6XtW34kByPjCcik4FTwBeAAfoBJYwxN3kzUG/RRK+Uyi2xsbDzo4VcN/UmCnEBJk6Enj1Trc/SjHoq+4yBvz6EdYOgaBU7C165JkAG1/l9ONnneGQ84HbgN+zoeI9gR8e73SvRKaVUAZGUxPtN7ETDc+s5WbkOXHst/O9/ybPgZXlGPZV9IhBy339d8Ba1gq2fARlc5/dTnib6VsCHxpheruUNY4xOMaSU8ispk/jWC9UZe9NyuOceW53fpQscOJC9GfVUzlQIt13wKrSC1XfAmnu5+vKIjK/z+5lMu9elMAD4QEQOActdywpjzJHcCkwppfKbpCSeVC3fNroIRHxoZ7174AFo1oyIb79l8eKr9Bp9XityCVy9AH4ZDptG0/Lwepbf8AUL/9nsF9foM+Px7HUAIlIZ6AM8AVQ2xnj6QyFP6TV6pVRuybCh3fr1cN11dvjct96yg+3oLHjOSJoFL6AQtPoSKnVyOqJc543GeP2AtkAj4CCwAlhujMmXwxFpoldKOeLwYbjlFpg/H/r3h/ffh6JF0xXTVvl54PifsLw3HNsEoSOh/hCQrEzvUrB4I9EfBLYCHwBLjDE7vBqhl2miV0o5JjHRzoL3wgsQGgpTp0KtWsmrtVV+HrpwClbfDTsnQ5UedqKc4DJOR5Urctzq3hhTAbgDKAK8KCJrRORzL8aolFK+ISAAnnsO5syBHTvsfPdz5yav1lb5eSioOLSaBM3egr1zYX4LOPqr01HlOY8SvYiUAi4HqgM1sNPUJuZeWEopVcB16wbr1kGNGtC9u03+iYnaKj+viUDdQRC1BC6chAXhsONLp6PKU55W3f+CvS6/AlhmjNl9kac4SqvulVL5xunTtkX+hAnQtSt88QWxm8vpNXonnNkHK26EA8uhzkPQ5FUIDHY6Kq/I8TX6gkYTvVIqXzEGxo2z4+RXqWKHzm3SxOmo/FPiefjpKdj8hu133+YbKFbZ6ahyLMfX6EXkEhF5RUTmisgPSYt3w1RKKR8lYrvbLV8OFy5Aq1YwfrzTUfmngELQ7HVoPQWO/gzzm8K/y5yOKld52tdgEvAHUBN4HtgBrM2lmJRSyjeFh9v+9q1awe23w333wblzbovGxtoB92LzZSdmH1D9Rui0GgqVhsUd4I83fHb6YU8TfXljzCfAeWPMUmPMHUDLXIxLKaV80yWXwIIF8NRT8OGH0K4d7NqVqkhSF7zhw+2tJvtcUqYBdFlru96tfwx+7AvnTzodldd5mujPu273icg1ItIEqJpLMSmllG8LCoLRo20f+99/h6ZN4Yf/roZqF7w8VKgUtJ0KYWNg17ew4Co4vtnpqLzK00Q/UkRKA49jh7/9GHg016JSSil/0Ls3rFljz/Kjo+Hll8EY7YKX10Sg/pNw9SI4d9D2t981zemovOaiiV5EAoEQY8wxY8xGY8zVxphmxphZeRCfUkr5tiuvtMm+Tx9bnd+nDxENjrN4MYwYoSPn5anLOrAudBx7A0rD8utgwxBIvOB0VDl20URvjEkAeuRBLEop5Z9KlIApU+D112HmTGjRgojSmxg6VJN8XordFUvbr26m9qa9fHw8EDaNgSWd4ewBp0PLEU+r7leKyLsi0lZEmiYtuRqZUkr5ExF49FF7Cn/0KFx1FXz9tdNR+ZWYHTHEJ8RzxiRy378wp+x1cOBH2wXv4Bqnw8s2TxN9K6AB8ALwmmt5NbeCUkopv9W+ve2CFxoKN94Ijz9u+96rXBdZI5LgwGACJZDgwGDKN3wcOq0ECYLv28Jf4wpkFzwdGU8ppfKj+Hib5N991yb/r76CihWdjsrnxe6KJWZHDJE1Iomo5rpucu4QrLwF9i2AWrdD8/cgKP30w07K9hC4IvJYZhs2xryew9hyhSZ6pZTP+OILuOceKFvWdsdrqUOYOCIxATY+DxtHQNmmrK/9NAv+2Zz6B4GDcjIEbknX0hy4H6jiWu4D6nszSKWUUm7062dHzClSxA6uM3Zsgaw+LvACAqHxC9B+NheO/0n1VdexfPUwoiZGEbsrf49olGmiN8Y8b4x5HqgANDXGPG6MeRxohg6Yo5RSeSM0FOLibF/7gQNhwAA4c8bpqPxTle58fMm97L4Acyol8kSps8Rsz99Tv3jaGO9yID7F/XjsvPRKKaXyQtmyMHu2ndf+88/tePnbtjkdlV8KDbmODvuKMPmk8EJ5w72n50P8UafDypCnif5zYI2IPCcizwKrgQm5F5ZSSql0AgLg2WdhzhzYsQOaN4d585JX60Q4eSOiWgRzbv2Bv+uNZHutRyl3ZJUdTe/oRqdDc8vjVveufvNtXXeXGWN+yrWockgb4ymlfN62bXYI3V9+geeeIzZqGFHRAcTH22FzdUS9PPTvClhxPZw/DuEfQ42b8jyEHM9HD2CMWW+Mecu15Nskr5RSfqFWLVi50jbWe/ZZLrmzB8XOHdGJcJxwaRvouh7KNYWVN8O6RyDx/EWfllc8TvRKKaXymWLFYMIEeO89am1dyGrTgrCAX3QiHCcUrQRRP0Ddh2HzW7A4Cs7843RUgCZ6pZQq2ETggQcIWLaUauXPsCawJT8P/kKr7Z0QUAiavQmtJsHhdXbo3AM/Oh2VJnqllPIJEREEb1xPoYgWhLxwKwwaZOvwVd6rcTN0XgWBxeH7SNj8jqNjHziS6EWknIgsEpEtrtuyGZQrIyLfisgfIvK7iOhvVKWUykjFivD993ZynHfegQ4dYN8+p6PyT2UaQZe1UKkLrBsEsbfBhdOOhOLUGf0QYLExJgRY7LrvzlvAfGPMlUAo8HsexaeUUgVToUJ2utvJk+Gnn6BpU1ixwumo/FNwGWg/Exq9ADsmwcIIOLGV2F2xjFo+Ks9G1HNkUhsR2QxEGmP2iUglIMYYUzdNmVLAz0Atk8UgtXudUkoBGzfaLnjbt8Nrr8FDD9lr+irv7Z0HK2/hQuIFrt99jtknEwgODGbxbYu9Mla+V7rXeVlFY8w+ANftpW7K1AIOAJ+JyE8i8rGIFM9ogyJyj4jEiUjcgQMHcidqpZQqSBo2hLVroVs3ePhh2xXv1Cmno/JPlbtClzgOSgmmVoznrlIJxCfEE7MjJtd3nWuJXkS+F5GNbpaeHm4iCGgKvG+MaQKcIuMqfowx44wxzY0xzS+55BIvvAKllPIBpUvD9OkwcqStzo+IgL/+cjoqv5Cuir5ELXa2mMQnJwJZfiaA4MBgImtE5nocQbm1YWNMx4zWich+EamUour+XzfFdgO7jTGrXfe/JZNEr5RSKgMBAfD003bI3JtusreTJsE11zgdmc+K3RVL1MQo4hPiU1XRh9e4msRrltMv7Zz3ucipqvtZQH/X3/2BmWkLGGP+AXaJSNK1+yhgU96Ep5RSPqhzZ1i3DmrWhO7d7QQ5iYlOR+WTYnbEEJ8QT4JJX0UfUS2CoW2H5tk89k4l+tFAtIhsAaJd9xGRyiIyN0W5h4BJIvILEAa8lNeBKqWUT6lZ0w6d278/PP88/N//wZEjTkflcyJrRBIcGEygBOZZFX1GHGl1n9u01b1SSqUXG2vHwI+MhIiWBj74wDbSq1YNpk2z894rr4ndFUtMHlXRZ9bqXhO9Ukr5gdhYiIoi/ex2sbHQp489q//oI7jlFqdDVdmQH7vXKaWUykMxMTbJp5vdLiLCXrdv0cJ2v9Ohc32OJnqllPIDkZH2TD4wkPSz2112mQ6d68M00SullB+IiLDV9SNGpKi2T8nd0Lk/Oj/zmso5TfRKKeUnIiJg6FA3ST6lvn1h1SooUcKe9r/j7MxrKuc00SullEqtUSM7dG7Xrvaa/a23wmlnZl5TOaeJXimlVHplysCMGbau/8svbTXA1q1OR6WyQRO9Ukop9wICYNgwmDsXdu2yQ+fOnXvx56l8RRO9UkqpzHXpAnFxUKOGHTr3+ed16NwCRBO9Ukqpi6tVy7bCv/VWO0Z+jx46dG4BoYleKaWUZ4oVg/Hj4b33YMECO8jOL784HZW6CE30SimlPBa7Shh17AF+fW+pbYnfsqVtrKfyrVybj14ppZRvSTle/ojgViz7ej3NX77Bjo+/Zg288oodeEflK3pGr5RSyiNpx8tf9Otldpi9hx+Gt96yvwL++cfpMFUamuiVUkp5xO14+YUKwZtvwqRJtmV+s2b21F/lG5rolVJKeSTT8fJvvtkOnVu0KLRvD2PH6tC5+YReo1dKKeWxiIhMxspv3NgOnduvHwwcCKtXwwcf2OSvHKNn9EoppbynbFmYPdv2tf/8c06GtWbs4O1am+8gTfRKKaW8KyAAnn2W31+ezfk/t9P31WaMilygyd4hmuiVUkrlihnnryE8II7dVGVGfFfin31Rh851gCZ6pZRSuSIyEnYXvoI2AbF8HXgT7RcNg9694dgxp0PzK5rolVJK5YqkVvpDRxan+rIvbDe8OXPgqqtg0yanw/MbYnyw+0Pz5s1NXFyc02EopZRKa9kyuOEGOHkSPvsMrr/e6Yh8goisM8Y0d7dOz+iVUkrlnXbtYN06aNTIJvzBg+HCBaej8mma6JVSSuWtKlVg6VJ44AF49VXo1AkOHHA6Kp+liV4ppVTeCw62091+9hmsXGmHzl271umofJImeqWUUs4ZMMAm+oAAaNMGPv4YsMPljxqlw+Z7gw6Bq5RSyllNm9rr9jfdBHffzf7Za+i68B1Oni9McLCbcfVVlugZvVJKKeeVLw/z5sHQoVSc9RGLzralUsIu4uPt9Lgq+zTRK6WUyh8CA+Gll9g8ahp1+YN1NKNj4BI7Ha7KNk30Siml8pW6Q3qx9cs1BFxSnnkXOhLx46s65W0OaKJXSimV7zS56UoqbF2D9Opl+9rfeKMdZEdlmSZ6pZRS+VPJkvDNNzBmDEydCuHh8OefTkdV4GiiV0oplX+JwJNPwsKFsH8/tGgBM2c6HVWBooleKaVU/hcVZbvghYTAtdfC8OGQkOB0VAWCJnqllFIFQ/XqsGIF3HEHjBzJ0dbX8MbwwzqozkVooldKKVVwFCkCH3/M1ic/pNjqH+g5sjmPXr1Bk30mNNErpZQqWET4usw9RAYsJ5h4lpyL4MAbXzgdVb6liV4ppVSBExkJGwqHc1XAOtYGhNPjm1th0CA4f97p0PIdTfRKKaUKnIgIOwb+QyMrUijme3jsMXjnHejQAfbtczq8fEUntVFKKVUgRUQkTXYTBG1fg6uusg31mjWDb7+FVq2cDjFf0DN6pZRSvuHGG2H1aiheHNq3Z9sT7zHqJeP3DfU00SullPIdDRvC2rUcDu9CrdcepMqwAVzT4YxfJ3tN9EoppXxLmTJ82HUmz8nz9DOf8/3Z1vw0bbvTUTlGE71SSimfE9khgJeLPEPPgDnUZDt3j2tuh9H1Q5rolVJK+ZykVvmtRnZj+9dxFKpRFbp0gZdegsREp8PLU44kehEpJyKLRGSL67ZsBuUeFZHfRGSjiEwWkSJ5HatSSqmCKSIChg6FptdfAStXwk03wdNPw3XXwfHjToeXZ5w6ox8CLDbGhACLXfdTEZEqwCCguTGmIRAI9M3TKJVSSvmG4sXhiy/gzTdh9mw7C96mTU5HlSecSvQ9gQmuvycA12ZQLggoKiJBQDFgb+6HppRSyieJwMMPww8/wLFjtt/9t986HVWucyrRVzTG7ANw3V6atoAxZg/wKvA3sA84ZozJsCWFiNwjInEiEnfgwIFcClsppVSB166dnfK2USO4/np46im4cMHpqHJNriV6EfnedW097dLTw+eXxZ751wQqA8VFpF9G5Y0x44wxzY0xzS+55BLvvAillFK+qUoViImB+++Hl1+2DfV89CQx14bANcZ0zGidiOwXkUrGmH0iUgn4102xjsB2Y8wB13OmAa0AnaJIKaVUzhUuDGPH2ir8++6zQ+dOmwbNmzsdmVc5VXU/C+jv+rs/MNNNmb+BliJSTEQEiAJ+z6P4lFJK+YsBA+DHHyEgANq0gU8/dToir3Iq0Y8GokVkCxDtuo+IVBaRuQDGmNXAt8B64FdXrOOcCVcppZRPa9YM4uKgbVu48057hn/unNNReYUYY5yOweuaN29u4uLinA5DKaVUQZOQAMOGwejREB5uW+VXrep0VBclIuuMMW6vOejIeEoppVSSwEAYNcom+N9+s2f6MTFOR5UjmuiVUkopl9hYm+djK18Ha9ZA2bLQsSO88QakqAFPLlcAZsXLtVb3SimlVEESGwtRURAfD8HBsHhxPSLWrIH+/eGxx2zi//hjYn8pnqacHW43v9IzeqWUUgpbQx8fby/Tx8e7auxLlYKpU+1kOF99BS1b8vPUv9KXy8c00SullFJAZKQ9Qw8MtLeRka4VAQF2dpz582HvXu76sDk9Ar9LXy6f0kSvlFJK8d/UtiNGZFAd36kTxMURVLsm0+K7s6T9cyxelJivq+1Br9ErpZRSySIiLnK9vWZNO+XtfffRduLzUDTOzopXpkxehZhlekavlFJKZUXRojB+PLz7LixYYIfM/fVXp6PKkCZ6pZRSKqtEYOBA2xLv9Glo2RKmTHE6Krc00SullFLZ1bq1nfK2aVO46SbbDe/8eaejSkUTvVJKKZUTlSrBDz/AoEF2YJ3oaNi/3+mokmmiV0oppXKqUCF46y34/HM7sE6zZvz68ep8MXqeJnqllFLKW/r1g5UrOWuCqXN3O3Y+PY6oKGeTvSZ6pZRSypvCwnj/jjiW0IEPzL28c/Yuli8661g4muiVUkopL2vZrRx9iszhRRnGneYTHpjcFv7+25FYNNErpZRSXhYRAYt+CCTgxRH8MXoGJfb+aae8Xbw4z2PRkfGUUkqpXPDfKHs94do10Ls3dOrEzvtH82XlJ4i8WvJk+FxN9EoppVRuq1sXVq3iUM87qP7ek5yS80QV+V+eTHGrVfdKKaVUXihZknEdv+YheZcPzd15NsWtntErpZRSeSTyaiGqyEDi4/NuiltN9EoppVQeSZoKNybGJnm9Rq+UUkr5mItOhetleo1eKaWU8mGa6JVSSikfpoleKaWU8mGa6JVSSikfpoleKaWU8mGa6JVSSikfpoleKaWU8mGa6JVSSikfJsYYp2PwOhE5AOxM83Bp4FgmT8tsfUbrPH28AnAwk33npou97tzelqfPyavj4+4xXzg+vnBs3D3u5LEBPT4Xe0z/d3JWzpv/OyHGmNJut2SM8YsFGJfd9Rmt8/RxIC6/vu7c3panz8mr45PBYwX++PjCsXH3uJPHRo+PR4/p/04+ODYX25Y/Vd3PzsH6jNZl9XEneDOW7GzL0+fk1fHJT8cGvBePLxwbT/aV1/T4eL6fvKbHxsNt+WTVfX4jInHGmOZOx6Hc0+OTf+mxyd/0+BQM/nRG76RxTgegMqXHJ//SY5O/6fEpAPSMXimllPJhekavlFJK+TBN9EoppZQP00SvlFJK+TBN9EoppZQP00SfD4hIcRFZJyLdnY5FpSYi9UTkAxH5VkTudzoe9R8RuVZEPhKRmSLSyel4VGoiUktEPhGRb52Oxd9pos8BEflURP4VkY1pHu8iIptF5C8RGeLBpp4Cvs6dKP2XN46PMeZ3Y8x9wA2A9hf2Ei8dmxnGmLuBAcCNuRiu3/HS8dlmjLkzdyNVntDudTkgIu2Ak8BEY0xD12OBwJ9ANLAbWAvcBAQCo9Js4g6gMXa86CLAQWPMnLyJ3vd54/gYY/4VkR7AEOBdY8yXeRW/L/PWsXE97zVgkjFmfR6F7/O8fHy+Ncb0yavYVXpBTgdQkBljlolIjTQPXwX8ZYzZBiAiU4CexphRQLqqeRG5GigO1AfOiMhcY0xi7kbuH7xxfFzbmQXMEpHvAE30XuCl/x0BRgPzNMl7l7f+d1T+oIne+6oAu1Lc3w2EZ1TYGPM0gIgMwJ7Ra5LPXVk6PiISCfQGCgNzczMwlbVjAzwEdARKi0htY8wHuRmcyvL/TnngRaCJiAx1/SBQDtBE733i5rGLXh8xxoz3fijKjSwdH2NMDBCTW8GoVLJ6bN4G3s69cFQaWT0+h4D7ci8c5SltjOd9u4FqKe5XBfY6FItKT49P/qXHJn/T41NAaaL3vrVAiIjUFJFgoC8wy+GY1H/0+ORfemzyNz0+BZQm+hwQkclALFBXRHaLyJ3GmAvAg8AC4Hfga2PMb07G6a/0+ORfemzyNz0+vkW71ymllFI+TM/olVJKKR+miV4ppZTyYZrolVJKKR+miV4ppZTyYZrolVJKKR+miV4ppZTyYZrolfJjIlJGRB5Icb9ybs0f7po//pkM1p103V4iIvNzY/9K+StN9Er5tzJAcqI3xuzNxSlFnwTGZlbAGHMA2CcirXMpBqX8jiZ6pfzbaOAKEdkgIq+ISA0R2Qh2RkURmSEis0Vku4g8KCKPichPIrJKRMq5yl0hIvNFZJ2ILBeRK9PuRETqAOeMMQdd92uKSKyIrBWREWmKzwBuydVXrZQf0USvlH8bAmw1xoQZYwa7Wd8QuBk7F/mLwGljTBPs8Ki3ucqMAx4yxjQDnsD9WXtrIOWc8W8B7xtjWgD/pCkbB7TN5utRSqWh09QqpTKzxBhzAjghIseA2a7HfwUai0gJoBXwjUjyLKaF3WynEnAgxf3WwHWuvz8HxqRY9y9Q2TvhK6U00SulMnMuxd+JKe4nYr8/AoCjxpiwi2znDFA6zWMZTbRRxFVeKeUFWnWvlH87AZTM7pONMceB7SJyPYBYoW6K/g7UTnH/R+w0p5D+enwdYGN2Y1JKpaaJXik/Zow5BPwoIhtF5JVsbuYW4E4R+Rn4DejppswyoIn8V7//MDBQRNaS/kz/auC7bMailEpDp6lVSuUJEXkLmG2M+f4i5ZYBPY0xR/ImMqV8m57RK6XyyktAscwKiMglwOua5JXyHj2jV0oppXyYntErpZRSPkwTvVJKKeXDNNErpZRSPkwTvVJKKeXDNNErpZRSPuz/AbX7ZpVrR7CyAAAAAElFTkSuQmCC", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca2.rmse())\n", "h12 = ml.head(d1, 0, t)\n", "h22 = ml.head(d2, 0, t)\n", "plt.figure(figsize=(8, 5))\n", "plt.semilogx(t, he12, \"b.\", label=\"obs at 30 m, sig=0.02\")\n", "plt.semilogx(t, h12[0], color=\"r\", label=\"ttim at 30 m\")\n", "plt.semilogx(t, he22, \"g.\", label=\"obs at 90 m, sig=0.02\")\n", "plt.semilogx(t, h22[0], color=\"orange\", label=\"ttim at 90 m\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data and errors with sig=0.02\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drawdowns with errors with $\\sigma=0.05$." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".........................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 22\n", " # data points = 68\n", " # variables = 2\n", " chi-square = 0.16477216\n", " reduced chi-square = 0.00249655\n", " Akaike info crit = -405.543554\n", " Bayesian info crit = -401.104539\n", "[[Variables]]\n", " kaq0: 70.3176085 +/- 2.43430886 (3.46%) (init = 10)\n", " Saq0: 9.8232e-05 +/- 1.0351e-05 (10.54%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.830\n" ] }, { "data": { "text/html": [ "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.3176082.4343093.461877-infinf10[70.31760849447059]
Saq00.0000980.00001010.537516-infinf0.001[9.823189067091308e-05]
\n", "
" ], "text/plain": [ " optimal std perc_std pmin pmax initial \\\n", "kaq0 70.317608 2.434309 3.461877 -inf inf 10 \n", "Saq0 0.000098 0.000010 10.537516 -inf inf 0.001 \n", "\n", " parray \n", "kaq0 [70.31760849447059] \n", "Saq0 [9.823189067091308e-05] " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ca5 = ttim.Calibrate(ml)\n", "ca5.set_parameter(name=\"kaq0\", initial=10)\n", "ca5.set_parameter(name=\"Saq0\", initial=1e-3)\n", "ca5.series(name=\"obs1\", x=d1, y=0, t=t, h=he15, layer=0)\n", "ca5.series(name=\"obs2\", x=d2, y=0, t=t, h=he25, layer=0)\n", "ca5.fit()\n", "display(ca5.parameters)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rmse: 0.049225196392244215\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca5.rmse())\n", "h15 = ml.head(d1, 0, t)\n", "h25 = ml.head(d2, 0, t)\n", "plt.figure(figsize=(8, 5))\n", "plt.semilogx(t, he15, \"b.\", label=\"obs at 30 m\")\n", "plt.semilogx(t, h15[0], color=\"r\", label=\"ttim at 30 m\")\n", "plt.semilogx(t, he25, \"g.\", label=\"obs at 90 m\")\n", "plt.semilogx(t, h25[0], color=\"orange\", label=\"ttim at 90 m\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data and errors with sig=0.05\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Model performed also well for $\\sigma = 0.05$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Final Remarks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we have succesfully:\n", "\n", "* Initiated a TTim model instance using the `Model` class\n", "* Sampled observation data from the model and defined the synthetic data by adding noise.\n", "* Calibrated the model with the `Calibrate` class using one and two calibration wells\n", "* Inspected the calibration performance" ] } ], "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": "3.11.10" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 2 }