diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml index 145587d..ee010c0 100644 --- a/.github/workflows/pytest.yaml +++ b/.github/workflows/pytest.yaml @@ -26,9 +26,35 @@ jobs: - name: Install pycalphad development version if: matrix.pycalphad_develop_version run: python -m pip install git+https://github.com/pycalphad/pycalphad.git@develop + - name: Save pycalphad version + run: | + echo "PYCALPHAD_VERSION=$(python -c "from importlib_metadata import version;print(version('pycalphad'))")" >> $GITHUB_ENV - run: python -m pip install build - run: python -m build --wheel - run: python -m pip install dist/*.whl - run: python -m pip install pytest - run: python -m pip list - - run: python -m pytest -v --pyargs kawin + # pytest: + # - The `--import-mode=append` and `--pyargs kawin` flags test the installed package over the local one + # - The `--cov` flag is required to turn on coverage + - run: pytest -v --import-mode=append --cov --cov-config=pyproject.toml --pyargs kawin + - run: coverage xml + - uses: actions/upload-artifact@v4 + with: + name: coverage-${{ matrix.os }}-${{ matrix.python-version }}-pycalphad-${{ env.PYCALPHAD_VERSION }} + path: coverage.xml + + Upload-Coverage: + runs-on: ubuntu-latest + needs: [Tests] + steps: + # The source code _must_ be checked out for coverage to be processed at Codecov. + - uses: actions/checkout@v4 + - name: Download artifacts + uses: actions/download-artifact@v4 + with: + pattern: coverage-* + - name: Upload to Codecov + uses: codecov/codecov-action@v3 + with: + fail_ci_if_error: true diff --git a/examples/01_Binary_Precipitation.ipynb b/examples/01_Binary_Precipitation.ipynb new file mode 100644 index 0000000..28460c5 --- /dev/null +++ b/examples/01_Binary_Precipitation.ipynb @@ -0,0 +1,263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Binary Precipitation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example - The Al-Zr system\n", + "\n", + "In the Al-Zr system, $Al_3Zr$ can precipitate into an $\\alpha$-Al (FCC) matrix. The Thermodynamics module provides some functions to interface with pyCalphad in defining the driving force and interfacial composition. However, it is also possible to use user-defined functions for the driving force and nucleation as long as the function parameters and return values are consistent with the ones provides by the Thermodynamics module. Calphad models for the Al-Zr system was obtained from the STGE database and Wang et al [1,2].\n", + "\n", + "For a binary system, one hyperparameter that may need to be set in the Thermodynamics module for calculating interfacial compositions is the guess composition when finding a tie-line. The $Al_3Zr$ phase has a fixed composition at 25 at.% Zr, so the guess composition can be set to 24 at.% Zr." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import BinaryThermodynamics\n", + "from kawin.precipitation import PrecipitateModel, VolumeParameter\n", + "import numpy as np\n", + "\n", + "therm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'], drivingForceMethod='tangent')\n", + "therm.setGuessComposition(0.24)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the model\n", + "\n", + "Initializing the KWN model requires the solute elements and precipitate phases. In this case, since we only have one element and one precipitate phase, we can use the default parameters where the element is named 'solute' and the precipitate phase is named 'beta'.\n", + "\n", + "For multi-element system, the order of the elements must be in the same order as defined in the Thermodynamics object. For multi-phase systems, the name of the phases must correspond to the names of precipitate phases defined in the thermodynamic database." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#Create model\n", + "model = PrecipitateModel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Inputs\n", + "\n", + "The interfacial energy and diffusivity are from Robson and Prangnell [3]. Although the diffusivity for this system is only a function of temperature, it needs to be defined as a function of both composition and temperature (to keep consistent with systems that use composition dependent diffusivity). Since we're manually defining the diffusivity, we'll set addDiffusivity to False when inputting the Thermodynamics object. This tells the model to ignore any diffusivity parameters in the .tdb file when dealing with binary systems.\n", + "\n", + "$ x_0 = 0.4 \\: \\text{at.\\%} $\n", + "\n", + "$ T = 450 \\: ^oC = 723.15 \\: K $\n", + "\n", + "$ \\gamma = 0.1 \\: J/m^2 $\n", + "\n", + "$ D = 0.0768 \\, exp\\left(- \\frac{242000}{R T}\\right) \\: m^2/s $\n", + "\n", + "$ a = 0.405 \\: nm = 0.405\\mathrm{e}{-9} \\: m $\n", + "\n", + "4 atoms per unit cell\n", + "\n", + "Dislocation density $ \\rho_D = 1e15 $\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "xInit = 4e-3 #Initial composition (mole fraction)\n", + "model.setInitialComposition(xInit)\n", + "\n", + "T = 450 + 273.15 #Temperature (K)\n", + "model.setTemperature(T)\n", + "\n", + "gamma = 0.1 #Interfacial energy (J/m2)\n", + "model.setInterfacialEnergy(gamma)\n", + "\n", + "D0 = 0.0768 #Diffusivity pre-factor (m2/s)\n", + "Q = 242000 #Activation energy (J/mol)\n", + "Diff = lambda x, T: D0 * np.exp(-Q / (8.314 * T))\n", + "model.setDiffusivity(Diff)\n", + "\n", + "a = 0.405e-9 #Lattice parameter\n", + "Va = a**3 #Atomic volume of FCC-Al\n", + "Vb = a**3 #Assume Al3Zr has same unit volume as FCC-Al\n", + "atomsPerCell = 4 #Atoms in an FCC unit cell\n", + "model.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, atomsPerCell)\n", + "model.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, atomsPerCell)\n", + "\n", + "#Average grain size (um) and dislocation density (1e15)\n", + "model.setNucleationDensity(grainSize = 1, dislocationDensity = 1e15)\n", + "model.setNucleationSite('dislocations')\n", + "\n", + "#Set thermodynamic functions\n", + "model.setThermodynamics(therm, addDiffusivity=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the Model\n", + "\n", + "Now we can run the model. The current status of the model may be output by setting \"verbose\" to True with \"vIt\" being how many iterations will pass before the current status of the model is printed out." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "0\t0.0e+00\t\t0.0\t\t723\t\t0.4000\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t5.7737e+03\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "3675\t1.8e+06\t\t48.5\t\t723\t\t0.0126\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t1.374e+22\t\t1.5504\t\t6.1126e-09\t3.2902e+02\n", + "\n" + ] + } + ], + "source": [ + "model.solve(500*3600, verbose=True, vIt=10000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "We can now plot the results. Here, we are plotting the precipitate density, volume fraction and average radius as a function of time and the size distribution density at the final time.\n", + "\n", + "Everything will be plotted on a logarithmic time scale.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", + "\n", + "model.plot(axes[0,0], 'Precipitate Density')\n", + "model.plot(axes[0,1], 'Volume Fraction')\n", + "model.plot(axes[1,0], 'Average Radius', label='Average Radius')\n", + "model.plot(axes[1,0], 'Critical Radius', label='Critical Radius')\n", + "axes[1,0].legend()\n", + "model.plot(axes[1,1], 'Size Distribution Density')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving\n", + "\n", + "The model can be saved into a numpy .npz format or a .csv format.\n", + "\n", + "$ PrecipitateModel.save(filename, compressed=True) $ or \n", + "\n", + "$ PrecipitateModel.save(filename, toCSV=True) $\n", + "\n", + "
\n", + "\n", + "To load the model, just make sure to add the file extension.\n", + "\n", + "$ model = PrecipitateModel.load('file.npz') $ or\n", + "\n", + "$ model = PrecipitateModel.load('file.csv') $" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", + "2. T. Wang, Z. Jin and J. Zhao, “Thermodynamic Assessment of the Al-Zr Binary System” *Journal of Phase Equilibria* 22 (2001) p. 544\n", + "3. J. D. Robson and P. B. Prangnell, “Dispersoid Precipitation and Process Modeling in Zirconium Containing Commercial Aluminum Alloys” *Acta Materialia* 49 (2001) p. 599" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('base')", + "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": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/02_Multicomponent_Precipitation.ipynb b/examples/02_Multicomponent_Precipitation.ipynb new file mode 100644 index 0000000..1d8b7c0 --- /dev/null +++ b/examples/02_Multicomponent_Precipitation.ipynb @@ -0,0 +1,336 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multicomponent Precipitation\n", + "\n", + "This example will use a ternary system (Ni-Cr-Al); however, the setup for any multicomponent system is mostly the same." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example - The Ni-Cr-Al system\n", + "\n", + "In the Ni-Cr-Al system, $Ni_3(Al,Cr)$ can precipitate into an $\\gamma$-Ni (FCC) matrix. As with binary precipitatation, the Thermodynamics module provides some functions to interface with pyCalphad in defining the driving force, growth rate and interfacial composition. Similarly, it is also possible to use user-defined functions for the driving force and nucleation as long as the function parameters and return values are consistent with the ones provides by the Thermodynamics module. Calphad models for the Ni-Cr-Al system was obtained from the STGE database and Dupin et al [1,2]. Mobility data for the Ni-Cr-Al system was obtained from Engstrom and Agren [3]." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import MulticomponentThermodynamics\n", + "from kawin.precipitation import PrecipitateModel, VolumeParameter\n", + "import numpy as np\n", + "\n", + "elements = ['NI', 'AL', 'CR', 'VA']\n", + "phases = ['FCC_A1', 'FCC_L12']\n", + "\n", + "therm = MulticomponentThermodynamics('NiCrAl.tdb', elements, phases)\n", + "\n", + "model = PrecipitateModel(elements=['Al', 'Cr'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Inputs\n", + "\n", + "Setting up model parameters is the same as for binary systems. The only difference is that the initial composition needs to be set as an array where the elements in the array will correspond to the same order of elements when the model was defined. In this case, [0.10, 0.085] corresponds to Ni-10Al-8.5Cr (at.%)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model.setInitialComposition([0.098, 0.083])\n", + "model.setInterfacialEnergy(0.023)\n", + "\n", + "T = 1073\n", + "model.setTemperature(T)\n", + "\n", + "a = 0.352e-9 #Lattice parameter\n", + "Va = a**3 #Atomic volume of FCC-Ni\n", + "Vb = Va #Assume Ni3Al has same unit volume as FCC-Ni\n", + "atomsPerCell = 4 #Atoms in an FCC unit cell\n", + "model.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, atomsPerCell)\n", + "model.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, atomsPerCell)\n", + "\n", + "#Set nucleation sites to dislocations and use defualt value of 5e12 m/m3\n", + "#model.setNucleationSite('dislocations')\n", + "#model.setNucleationDensity(dislocationDensity=5e12)\n", + "model.setNucleationSite('bulk')\n", + "model.setNucleationDensity(bulkN0=1e30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Surrogate Modeling\n", + "\n", + "For efficiency, a surrogate model can be made on the driving force and interfacial composition. The surrogate models uses radial-basis function (RBF) interpolation and the scale and basis function can be defined (using RBF interpolation from Scipy). \n", + "\n", + "For multicomponent systems, a surrogate on the driving force and the various terms derived from the curvature of the free energy surface to calculate growth rate and interfacial composition (which will be referred to as \"curvature factors\") can be made. Both surrogates will need a set of compositions and temperatures to be trained on. When defining the range to train the surrogate model on, it is recommended to extend the range beyond what is expected to occur during the precipitate simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import MulticomponentSurrogate, generateTrainingPoints\n", + "\n", + "surr = MulticomponentSurrogate(therm)\n", + "\n", + "#Train driving force surrogate\n", + "xAl = np.linspace(0.02, 0.12, 8)\n", + "xCr = np.linspace(0.02, 0.12, 8)\n", + "xTrain = generateTrainingPoints(xAl, xCr)\n", + "surr.trainDrivingForce(xTrain, T)\n", + "\n", + "#Train curvature factors surrogate\n", + "xAl = np.linspace(0.05, 0.23, 16)\n", + "xCr = np.linspace(0, 0.12, 16)\n", + "xTrain = generateTrainingPoints(xAl, xCr)\n", + "surr.trainCurvature(xTrain, T)\n", + "\n", + "model.setSurrogate(surr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the Model\n", + "\n", + "Solving the model is the same as for binary precipitation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAl\tCr\t\n", + "0\t0.0e+00\t\t0.0\t\t1073\t\t9.8000\t8.3000\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t2.4397e+02\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAl\tCr\t\n", + "5000\t1.3e+04\t\t34.9\t\t1073\t\t8.8266\t8.5647\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t6.346e+20\t\t11.5153\t\t3.2934e-08\t9.0621e+00\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAl\tCr\t\n", + "7694\t1.0e+06\t\t53.7\t\t1073\t\t8.7978\t8.5718\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t8.751e+18\t\t11.8685\t\t1.3883e-07\t2.1499e+00\n", + "\n" + ] + } + ], + "source": [ + "model.solve(1e6, verbose=True, vIt = 5000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "Plotting is also the same as with binary precipitation. Note that plotting composition will plot all components." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", + "\n", + "bounds = [1e-1, 1e6]\n", + "model.plot(axes[0,0], 'Precipitate Density', bounds)\n", + "model.plot(axes[0,1], 'Volume Fraction', bounds)\n", + "model.plot(axes[1,0], 'Average Radius', bounds, color='C0', label='Avg. R')\n", + "model.plot(axes[1,0], 'Critical Radius', bounds, color='C1', label='R*')\n", + "axes[1,0].legend(loc='upper left')\n", + "model.plot(axes[1,1], 'Composition', bounds)\n", + "model.plot(axes[1,1], 'Eq Composition Alpha', bounds, color='k', linestyle='--')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the $Ni_3(Al,Cr)$ precipiates are non-stoichiometric, there are two ways to compute the composition in the matrix. The first way (done above) is to assume infinitely fast diffusion in the precipitates where the composition throughout a particle is the same as the surface composition, which is computed from equilibrium.\n", + "\n", + "The other way is to account for the time-dependent history of the surface composition. So as the precipitate grows, only the volume that is added to the precipitate has the surface composition. We can simulate this by the setInfinitePrecipitateDiffusivity function, which can be applied to all precipitates or a specified one." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAl\tCr\t\n", + "0\t0.0e+00\t\t0.0\t\t1073\t\t9.8000\t8.3000\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t2.4397e+02\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAl\tCr\t\n", + "5000\t1.3e+04\t\t21.3\t\t1073\t\t8.8416\t8.5239\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t6.389e+20\t\t11.6912\t\t3.3030e-08\t9.0377e+00\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAl\tCr\t\n", + "7690\t1.0e+06\t\t32.1\t\t1073\t\t8.8139\t8.5284\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t8.851e+18\t\t12.0534\t\t1.3903e-07\t2.1473e+00\n", + "\n" + ] + } + ], + "source": [ + "model_nodiff = PrecipitateModel(elements=['Al', 'Cr'])\n", + "\n", + "model_nodiff.setInitialComposition([0.098, 0.083])\n", + "model_nodiff.setInterfacialEnergy(0.023)\n", + "model_nodiff.setTemperature(T)\n", + "model_nodiff.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, atomsPerCell)\n", + "model_nodiff.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, atomsPerCell)\n", + "model_nodiff.setNucleationSite('bulk')\n", + "model_nodiff.setNucleationDensity(bulkN0=1e30)\n", + "model_nodiff.setSurrogate(surr)\n", + "\n", + "model_nodiff.setInfinitePrecipitateDiffusivity(False)\n", + "\n", + "model_nodiff.solve(1e6, verbose=True, vIt = 5000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When plotting the matrix composition under these two assumptions shows that the matrix composition under the no diffusion assumption will never reach the equilibrium matrix composition. This is due to how the precipitates will never homogenize to the equilibrium precipitate composition." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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T2aIpAP/ddpJfDmRZOCIhhBA1TRJ1Pbf46UjcHPT3p8d+tYPMnEILRySEEKImSaKu5xxsNXw7pgdqFZRqFeLnbUUrz1cLIUSDIYm6AWjr58pb8R0AyMwtYtgXqRaOSAjREGzYsAGVSlWrE3S8+uqr+Pr6olKpDEOM1oQTJ06gUqmMRjrbsmULHTt2xNbWlvj4+CrLrI0k6gZicFQw/Tv5A7D5yCWmfb/PwhEJIeqDlJQUNBoNcXFxd7Sf6iT1AwcO8Nprr/Hpp5+SkZFBv379brvNfffdh0qlQqVSYW9vT7Nmzejfvz8rVqwwqhcYGEhGRgYdOnQwlCUmJhIREcHx48dZtGhRlWXWRhJ1A/LR4C50aKZ/2H5xykkWbT1h2YCEEFZvwYIFTJgwgY0bN3Lu3Lk6PfbRo/rRFQcMGICfnx/29vYmbffMM8+QkZHB0aNH+e6772jfvj1/+9vfGD16tKGORqPBz88PG5vrY0wcPXqU3r1707x5czw8PKosszYWT9Tz5s0jJCQEBwcHoqKiSE29dbPt8uXLCQsLw8HBgY4dO/Ljjz8arc/KymL48OEEBATg5ORE3759SU9Pr7CflJQUevfujbOzM25ubvTs2ZPCwvrdEUulUrFybA98XfX/2N9fe5BTlwosHJUQwlrl5eWRlJTE2LFjiYuLq9ErykWLFuHh4cFPP/1Eu3btcHFxoW/fvmRkZAD6Ju/+/fsDoFarUalUJu/byckJPz8/mjdvzt133827777Lp59+yueff84vv/wCGDd9l7+/dOkSTz/9NCqVikWLFlVaZo0smqiTkpJITExk+vTp7Ny5k86dOxMbG8v58+crrb9161YGDx7MyJEjSUtLIz4+nvj4ePbt0zfzKopCfHw8x44d4/vvvyctLY3g4GBiYmKMJkZPSUmhb9++9OnTh9TUVP744w/Gjx+PWm3xv1vumK2Nhv+90JOgpk7kl2h5csE2MnOKLB2WEI2GoigUlJRZZDF34KNly5YRFhZGaGgoTz31FAsXLqzRwZMKCgqYOXMm//3vf9m4cSOnTp3ipZdeAuCll17iiy++ACAjI8OQwKtr2LBhNGnSpEITOFxvBndzc2P27NlkZGTw+OOPVyhLSEi4oxhqi0XHnfzggw945plnGDFiBADz58/nhx9+YOHChUyePLlC/X//+9/07duXSZMmAfDGG2+wbt065s6dy/z580lPT2fbtm3s27eP8PBwAD755BP8/Pz45ptvGDVqFAAvvvgizz//vNExQkNDa/t060xTZzu+HRvNoPkpnLhUwFMLfidp9N14upjWrCSEqL7CUi3tp/1kkWPvfz0WJzvTf60vWLCAp556CoC+ffuSk5PDb7/9xn333Vcj8ZSWljJ//nxatWoFwPjx43n99dcBcHFxMTQ1+/n53fGx1Go1bdu25cSJExXWlTeDq1Qq3N3dDcdzdnauUGaNLHYJWVJSwo4dO4iJibkejFpNTEwMKSkplW6TkpJiVB8gNjbWUL+4uBjAaMB0tVqNvb09mzdvBvQDyv/+++/4+PjQo0cPfH196dWrl2F9Q+Hj6sBXo6Lwd3fgyPk8hi5MJbugxNJhCSGsxKFDh0hNTWXw4MEA2NjYkJCQwIIFC2rsGE5OToYkDeDv719li2lNUBTFrCb0+sJiV9QXL15Eq9Xi6+trVO7r68vBgwcr3SYzM7PS+pmZmQCEhYURFBTElClT+PTTT3F2dubDDz/kzJkzhmaVY8eOAfr7IzNnziQiIoLFixfzwAMPsG/fPtq0aVPpsYuLiw1/CABVzh5jTZo3ceLrUVEM+jSFP8/lMvjz3/lqZKRcWQtRixxtNex/PdZixzbVggULKCsrIyAgwFCmKAr29vbMnTsXd3f3O47H1tbW6LNKpaq1eQm0Wi3p6el07969VvZvSfX/puwNbG1tWbFiBYcPH6Zp06Y4OTmxfv16+vXrZ7j/rNPpBwN59tlnGTFiBF26dOHDDz8kNDSUhQsXVrnvGTNm4O7ublgCAwPr5JzuVEtvF5Y8czdeLvYcyMjlb59t43yu3LMWoraoVCqc7Gwssph6NVlWVsbixYuZNWsWu3btMiy7d+8mICCAb775ppZ/SjXvyy+/5MqVKwwcONDSodQ4iyVqLy8vNBoNWVnG41NnZWVVea/Az8/vtvW7du3Krl27yM7OJiMjg7Vr13Lp0iVatmwJ6JteANq3b2+0n3bt2nHq1Kkq450yZQo5OTmG5fTp06afrIW19XVl2bN34+fmQPr5PBI+28bpy9IbXIjGas2aNVy5coWRI0fSoUMHo2XgwIE12vxtjpUrVxIWFnbbegUFBWRmZnLmzBm2bdvGP//5T8aMGcPYsWO5//776yDSumWxRG1nZ0fXrl1JTk42lOl0OpKTk4mOjq50m+joaKP6AOvWrau0vru7O97e3qSnp7N9+3YGDBgAQEhICAEBARw6dMio/uHDhwkODq4yXnt7e9zc3IyW+qSltwvLno2meRNHjl/MZ8DczSzYdMzSYQkhLGDBggXExMRU2rw9cOBAtm/fzp49e+o8rpycnAq/myvz+eef4+/vT6tWrXjsscfYv38/SUlJfPzxx3UQpQUoFrR06VLF3t5eWbRokbJ//35l9OjRioeHh5KZmakoiqIMGTJEmTx5sqH+li1bFBsbG2XmzJnKgQMHlOnTpyu2trbK3r17DXWWLVumrF+/Xjl69KiyatUqJTg4WHnssceMjvvhhx8qbm5uyvLly5X09HTl5ZdfVhwcHJQjR46YHHtOTo4CKDk5OXf4U6hbGdmFSp8Pf1OC/7lGCf7nGmXS8l2WDkmIeq2wsFDZv3+/UlhYaOlQRC261fdc2/nAoo9nJSQkcOHCBaZNm0ZmZiYRERGsXbvW0GHs1KlTRs829+jRgyVLlvDyyy8zdepU2rRpw6pVq4yGiMvIyCAxMZGsrCz8/f0ZOnQor7zyitFxX3jhBYqKinjxxRe5fPkynTt3Zt26dUa9ExsqP3cHkkbfTb9/byIjp4hl289wICOXZc9G42jGYx1CCCHqhkpRaqkLXgOXm5uLu7s7OTk59a4ZHKCkTMugT7ex63Q2AK4ONiwdfTfhAXfe01OIxqSoqIjjx4/TokULo0dDG5p+/fqxadOmStdNnTqVqVOn1nFEdetW33Nt5wO5hGqk7Gw0rHyuB2+s2c/CLSe4WlRG/zmbmfpQO0bd29LS4QkhrMx//vOfKodZbtq0aR1H07hIom7EVCoV0/qHc09rL8Z8tYNSrcKbPxxgxc4zLB4ZhZc8by2EuKZZs2aWDqHRalDPUYvqeaCdL5v+cT/Bnk4A7M+4yt1vJ7Mp/YKFIxNCCGHWFXV2djYrV65k06ZNnDx5koKCAry9venSpQuxsbH06NGjtuIUtczP3ZENL93HB+sOM2/9Ecp0CsMXpvJMz1Y8/0Brs8YPFkIIUXNMuqI+d+4co0aNwt/fnzfffJPCwkIiIiJ44IEHaN68OevXr+fBBx+kffv2JCUl1XbMopaoVCr+3ieU5L/fx31tvdEqMP+3ozz4wUZ+2JOBTif9DoUQoq6ZdJnUpUsXhg0bxo4dOyqM6FWusLCQVatWMXv2bE6fPm2YykzUPy28nFn0dCTr9mfx6uo/OZtdyLglO2nv78bf+7Sld5hPgxz4XgghrJFJj2ddunQJT09Pk3dqbv36qL4/nmWqgpIyPv3tGAs2HyevuAyAMD9XhvUIYUBEgDSJi0avsTye1dhZ8vEsk5q+zU26DT1JNyZOdja8+GBbNv3jfsb0aoWjrYaDmVeZsmIvkW8l0+v99Uxcmsa+szm1NiuOEKLh++yzzwgMDEStVjN79uwa3bdKpWLVqlWGzwcPHuTuu+/GwcGBiIiIKsusRbV7fV+9epVJkybRvXt37rrrLiZMmMDFixdrMjZhRZo42zG5XxjbpjzAy3HtCPZ0Iq+4jJOXCvh+1zkenrOZjq/+zHNf7yDt1BVJ2kLUA5mZmUyYMIGWLVtib29PYGAg/fv3rzCngjlOnDiBSqVi165dJm+Tm5vL+PHj+ec//8nZs2cZPXr0bbcZPnw4KpUKlUqFra0tvr6+PPjggyxcuNAwS2K5jIwM+vXrZ/g8ffp0nJ2dOXTokOFcKyuzFtVut3zmmWdwdHTktddeo7S0lM8++4wnn3ySn376qSbjE1bG3cmWUfe25Ol7WrD12EU+2XCUP45foUSrI6+4jB/3ZvLj3kzcHGzo19Gfl/q0xdtVmgOFsDYnTpzgnnvuwcPDg/fff5+OHTtSWlrKTz/9xLhx4zh48GCl25WWllaYZ/pOnTp1itLSUuLi4gwzHJqib9++fPHFF2i1WrKysli7di0TJ07k22+/ZfXq1djY6FPczTMyHj16lLi4OKOJmCorsxqmDgr+wQcfKDqdzvC5ZcuWSllZmeHzgQMHFHd39xoagtz61ddJOWqDVqtT1h/IUp76zzalzb9+NEz4EfzPNUrIP9coT36+TbmUV2TpMIWoFfV1Uo5+/fopzZo1U/Ly8iqsu3LliuE9oHz88cdK//79FScnJ2X69Om33O/x48cVQElLS1MURVHWr1+vAMovv/yidO3aVXF0dFSio6OVgwcPKoqiKF988YUCGC3Hjx+/bfzDhg1TBgwYUKE8OTlZAZTPP//c6BxWrlxpeH/jMn369ErLbmbJSTlMbvo+evQoUVFRpKWlAfDggw8SFxfH/PnzmTNnDkOHDiU2NrZm/noQ9YpareK+MB/+OzKKA6/3ZeHwbkS1bIpapf9Xv/nIRQZ+spUNh85bOlQhap+iQEm+ZRYTbzldvnyZtWvXMm7cOJydnSus9/DwMPr86quv8uijj7J3716efvrpav1Y/vWvfzFr1iy2b9+OjY2NYT8JCQn88ssvAKSmppKRkUFgYGC1jgHQu3dvOnfuzIoVKypdn5GRQXh4OH//+9/JyMjgpZdeqrTMmpjc9D137ly2bdvG008/zf3338+MGTP46quvWLduHVqtlscff5zx48fXZqyiHtCoVfQO86V3mC85hSXM+N9BftidwfGLBQz/4g8e6ujHW/EdaeJsZ+lQhagdpQXwdoBljj31HNhVTLw3O3LkCIqiEBYWZtJun3jiCUaMGHFHob311lv06tULgMmTJxMXF0dRURGOjo6GDsje3t4VmqmrIywsrMr5tP38/LCxscHFxcVwLBcXlwpl1sSszmR33303f/zxB56enkRHRxMSEsJ3333HqlWrmDRpEo6OjrUVp6iH3B3teOexTmyZ0ptn7m2BjVrFj3sz6fvvjWw9Kh0PhbAUxczOnt26dbvjY3bq1Mnwvvw+9PnztdPKpihKgxrrwezOZDY2NvzrX/9i0KBBjBkzhi+//JK5c+da5V8hwjq4Odjyr7j2PNK5GROT0jh2IZ8hC1J5Y0AHnogKsnR4QtQsWyf9la2ljm2CNm3aoFKpquwwdrPKmsfNdWMHtPIkenPv7Jpy4MABWrRoUSv7tgSTr6h3795N9+7dcXV15Z577kGn05GcnExcXBw9evTgk08+qc04RQPQsbk7P0y4l0e7NEOrU5i6ci8z/ndAHuUSDYtKpW9+tsRi4lVk06ZNiY2NZd68eeTn51dYn52dXcM/lLrz66+/snfvXgYOHGjpUGqMyYn66aef5t577+WPP/7g8ccfZ8yYMQCMGDGC33//nS1bthAdHV1rgYqGwdFOwweDOpP4YFsAPv3tGK+v2S/JWog6Nm/ePLRaLZGRkXz33Xekp6dz4MABPvroI4v/Lk9NTSUsLIyzZ8/esl5xcTGZmZmcPXuWnTt38vbbbzNgwAAefvhhhg4dWkfR1j6Tm74PHz5MUlISrVu3pk2bNkYjx3h7e/PVV1/x888/10aMooFRqVQ8/0AbfFztmbxiL19sOcGJi/nMe/IuGZJUiDrSsmVLdu7cyVtvvWXo7ezt7U3Xrl0t3kJaUFDAoUOHKC0tvWW9tWvX4u/vj42NDU2aNKFz58589NFHDBs2DLW64czibNJY3wD9+/cnPz+fv/3tb/z6669oNBq+/vrr2o7PajWWsb5r29LUU0xesReAUF9Xfpx4Lxp1w+kEIho+Geu7cbD6sb4BFi9ezF133cX3339Py5YtLf4Xl2gY/hYZxFN36zuUHcq6ypivdlg4IiGEsC4mtzM2adKEmTNn1mYsopF6Y0AHjl7IJ+XoJdbtz2L+b0cZ06uVpcMSQlRizJgxfPXVV5Wue+qpp5g/f34dR9Twmdz0LYxJ03fNKtPq6PX+es5mF6FSwZrxfyG8mbulwxLithpb0/f58+fJzc2tdJ2bmxs+Pj51HFHdsGTTd4313GnXrh2HDx9Gq9XW1C5FI2KjUfPtmB70fH89pVqFJ/7zO79PfQAHW42lQxNC3MDHx6fBJmNrVWPd4mbMmMHChQtraneiEfL3cGR2QgQAOYWlJC7bZdF4hBDCGtTYFXV8fHxN7Uo0YnGdAliZdpZfDpznx72Z/H7sElEtPS0dlhBCWIzZV9S9e/eudNSa3NxcevfuXa0g5s2bR0hICA4ODkRFRZGamnrL+suXLycsLAwHBwc6duzIjz/+aLQ+KyuL4cOHExAQgJOTE3379iU9Pb3SfSmKQr9+/VCpVKxatapa8YuaNWfwXTjZ6Zu8x3y1A51OulEIIRovsxP1hg0bKCkpqVBeVFTEpk2bzA4gKSmJxMREpk+fzs6dO+ncuTOxsbFVDta+detWBg8ezMiRI0lLSyM+Pp74+Hj27dsH6BNvfHw8x44d4/vvvyctLY3g4GBiYmIqHSpv9uzZDWrw9obA0U7DrMc7A3CloJQdp65YOCIhhLAck3t9l08ZFhERwa+//krTpk0N67RaLWvXruXTTz/lxIkTZgUQFRVF9+7dmTt3LqAfpD0wMJAJEyYwefLkCvUTEhLIz89nzZo1hrK7776biIgI5s+fz+HDhwkNDWXfvn2Eh4cb9unn58fbb7/NqFGjDNvt2rWLhx9+mO3bt+Pv78/KlStNbsKXXt+175/f7iZp+xnC/FxZM+Ev2GgazkhDouFobL2+G6t6MeBJREQEXbp0QaVS0bt3byIiIgxL165defPNN5k2bZpZBy8pKWHHjh3ExMRcD0itJiYmhpSUlEq3SUlJMaoPEBsba6hfXFwMYPSDVKvV2Nvbs3nzZkNZQUEBTzzxBPPmzTNp5q/i4mJyc3ONFlG7/tmvHR5OthzMvMpX205aOhwhGp0NGzagUqnq1SQdixYtwsPDw6jss88+IzAwELVabRj+urIya2Vyoj5+/DhHjx5FURRSU1M5fvy4YTl79iy5ubk8/fTTZh384sWLaLVafH19jcp9fX3JzMysdJvMzMxb1g8LCyMoKIgpU6Zw5coVSkpKePfddzlz5gwZGRmGbV588UV69OjBgAEDTIp1xowZuLu7G5bAwEBzTlVUQ1NnO17qEwrArJ8Pc+FqsYUjEqLhSUlJQaPREBcXd0f7KU/q4eHhFR7T9fDwYNGiRXe0f5VKZVicnZ1p06YNw4cPZ8cO49EMExISOHz4sOFzbm4u48eP55///Cdnz55l9OjRlZZZM5MTdXBwMCEhIeh0Orp160ZwcLBh8ff3R6OxjuddbW1tWbFiBYcPH6Zp06Y4OTmxfv16+vXrZxikffXq1fz6669m/RU1ZcoUcnJyDMvp06dr6QzEjQZHBtGhmRtXi8t453+mzZ0rhDDdggULmDBhAhs3buTcuTufR/vYsWMsXry4BiKr6IsvviAjI4M///yTefPmkZeXR1RUlNHxHB0djZ7zPnXqFKWlpcTFxeHv74+Tk1OlZdas2jf99u/fz9q1a1m9erXRYg4vLy80Gg1ZWVlG5VlZWVU2R/v5+d22fteuXdm1axfZ2dlkZGSwdu1aLl26RMuWLQH9fKVHjx7Fw8MDGxsbbGz0T6kNHDiQ++67r9Lj2tvb4+bmZrSI2qdRq3hjQAcAvtt5hu0nLls4IiEajry8PJKSkhg7dixxcXF3fNULMGHCBKZPn264DVmZU6dOMWDAAFxcXHBzc2PQoEEVfq9XxsPDAz8/P0JCQujTpw/ffvstTz75JOPHj+fKFX2n0xubvhctWkTHjh0B/WxhKpWq0jJz+1bVNbMT9bFjx+jcuTMdOnQgLi7O0Ov60Ucf5dFHHzVrX3Z2dnTt2pXk5GRDmU6nIzk5ucr5UKOjo43qA6xbt67S+u7u7nh7e5Oens727dsNzdyTJ09mz5497Nq1y7AAfPjhh3zxxRdmnYOofV2CmpDQTX+r4ZXv/6RMq7NwRELcXkFJmUlLqYn/nnU65Zb7qY5ly5YRFhZGaGgoTz31FAsXLrzjueFfeOEFysrKmDNnTqXrdTodAwYM4PLly/z222+sW7eOY8eOkZCQUK3jvfjii1y9epV169ZVWJeQkMAvv/wC6Oe4zsjI4PHHH69QZu23Ms0e8GTixIm0aNGC5ORkWrRoQWpqKpcuXeLvf/97tSbtSExMZNiwYXTr1o3IyEhmz55Nfn4+I0aMAGDo0KE0a9aMGTNmGI7fq1cvZs2aRVxcHEuXLmX79u189tlnhn0uX74cb29vgoKC2Lt3LxMnTiQ+Pp4+ffoA+qvyyq7Yg4KCaNGihdnnIGrfP/qGsvbPTA5k5PL176cY1iPE0iEJcUvtp/1kUr3XB4QzNDrktvWOXMijz4cbq1x/4h3z7zEvWLCAp556CoC+ffuSk5PDb7/9VmXLoimcnJyYPn06U6dO5ZlnnsHd3XjM/uTkZPbu3cvx48cNCXLx4sWEh4fzxx9/0L17d7OOFxYWBlDpVbGjoyOenvoBk7y9vQ2/9ysrs2ZmX1GnpKTw+uuv4+XlhVqtRq1W85e//IUZM2bw/PPPmx1AQkICM2fOZNq0aURERLBr1y7Wrl1r6DB26tQpo05gPXr0YMmSJXz22Wd07tyZb7/9llWrVtGhQwdDnYyMDIYMGUJYWBjPP/88Q4YM4ZtvvjE7NmE9PF3seSlW37Fs5s+HOHmp4jPxQgjTHTp0iNTUVAYPHgyAjY0NCQkJLFiw4I73PXLkSDw9PXn33XcrrDtw4ACBgYFGV7Ht27fHw8ODAwcOmH2s8haAhjwehtlX1FqtFldXV0B/j/ncuXOEhoYSHBzMoUOHqhXE+PHjGT9+fKXrNmzYUKHs8ccf5/HHH69yf88//7zZfzTIJGLW74nIIJb8fpIDGVfp9+9NbH85Bie7GhsFV4gatf/1WJPq2Zo4PkBrbxeT92mKBQsWUFZWRkBAgKFMURTs7e2ZO3duhSthc9jY2PDWW28xfPjwKn+315Ty5N6QW0PNvqLu0KEDu3fvBvSDlbz33nts2bKF119/3dBZS4jaoFGrmPZwewAKSrT8a+VeC0ckRNWc7GxMWkxN1Gq16pb7MUdZWRmLFy9m1qxZRn11du/eTUBAQI20QD7++OOEh4fz2muvGZW3a9eO06dPGz05s3//frKzs2nfvr3Zx5k9ezZubm4VxtdoSMy+HHn55ZcNQ3G+/vrrPPzww9x77714enqSlJRU4wEKcaPoVl78pbUnm49cYmXaOUb3bEk7f5m3WghzrFmzhitXrjBy5MgKV84DBw5kwYIFjBkz5o6P88477xAba9wKEBMTQ8eOHXnyySeZPXs2ZWVlPPfcc/Tq1Ytu3brdcn/Z2dlkZmZSXFzM4cOH+fTTT1m1ahWLFy+uMMhJQ2L2FXVsbCyPPfYYAK1bt+bgwYNcvHiR8+fPV3tSDiHM8e+/dcFGrb8fNfarnXLbQggzLViwgJiYmEqbtwcOHMj27dsNw0bfid69e9O7d2/Kyq73SlepVHz//fc0adKEnj17EhMTQ8uWLU260BsxYgT+/v6EhYUxduxYXFxcSE1N5YknnrjjWK2ZyWN9C2My1rdlfZR8mA/W6WdEe2NAOENM6DUrRG2Qsb4bB6sf63vMmDGcOXPGpB0mJSXx9ddf31FQQtzOuPvb4ONqD8CbPxwgp7DUwhEJIUTtMClRe3t7Ex4ezkMPPcQnn3zCH3/8wdmzZ7l06RJHjhxh9erV/OMf/yAoKIgPP/zQMOqLELVFo1bx0eAuABSX6Zj4TZqFIxKiYevXrx8uLi6VLm+//balw2vQTG76zsrK4j//+Q9Lly5l//79RutcXV2JiYlh1KhR9O3bt1YCtTbS9G0dnv7iD349pJ+7/OMn7+Khjv4Wjkg0No2l6fvs2bMUFhZWuq5p06ZGUx83RJZs+q7WPeorV65w6tQpCgsL8fLyolWrVg36YfPKSKK2DjmFpUTPSKagRIuPqz2/T32g0f1bFJbVWBJ1Y2fJRF2t0SKaNGlCkyZNajoWIczm7mjLh4MieParHZy/WsyOk1foFtKw/7IXQjQu1Z49SwhrEdvBj792bQ7ApG/3VHuCAiGEsEaSqEWD8MrD7fFzc+D4xXxeXf2npcMRQogaI4laNAjujrZ8kNAZlQqWbT/D97vOWjokIYSoEZKoRYPRo5UXE3q3AWDqir0cvygzbAkh6j9J1KJBeb53ayJbNCW/RMv4JTspKtVaOiQhhAVs2LABlUpFdna2oWzVqlW0bt0ajUbDCy+8UGWZtTE7UWdlZTFkyBACAgKwsbFBo9EYLUJYko1GzUd/60ITJ1v+PJcr96uFuIXMzEwmTJhAy5Ytsbe3JzAwkP79+5OcnFztfZ44cQKVSoWPjw9Xr141WhcREcGrr756RzGHhISgUqlQqVQ4OjoSEhLCoEGD+PXXX43q9ejRg4yMDKPxzJ999ln++te/cvr0ad54440qy6yN2Y9nDR8+nFOnTvHKK6/g7+8vz6wKq+Pn7sC//9aFYV+ksvSP03QJ8mBARDMcbOUPSSHKnThxgnvuuQcPDw/ef/99OnbsSGlpKT/99BPjxo3j4MGDlW5XWlqKra3tbfd/9epVZs6cWWGay5rw+uuv88wzz1BSUsKJEyf46quviImJ4Y033uBf//oXAHZ2dvj5+Rm2ycvL4/z588TGxhrm4K6szCopZnJxcVHS0tLM3azBycnJUQAlJyfH0qGIKnz0y2El+J9rlBaT1ygD5m5WyrQ6S4ckGqDCwkJl//79SmFhoaVDMUu/fv2UZs2aKXl5eRXWXblyxfAeUD7++GOlf//+ipOTkzJ9+vRb7vf48eMKoEyaNElxcXFRsrKyDOs6d+5stP3ly5eVIUOGKB4eHoqjo6PSt29f5fDhw7fcf3BwsPLhhx9WKJ82bZqiVquVgwcPKoqiKOvXr1cA5cqVK4b3Ny5VlVXlVt9zbecDs5u+AwMDZVpBUS+Mu781d7doik6BXaezeWPN/ttvJERNKck3bdGaOKGMTnfr/Zjh8uXLrF27lnHjxuHs7Fxh/c1zO7/66qs8+uij7N27l6efftqkYwwePJjWrVvz+uuvV1ln+PDhbN++ndWrV5OSkoKiKDz00EOUlpo/yc7EiRNRFIXvv/++wroePXpw6NAhAL777jsyMjKqLLNGZjd9z549m8mTJ/Ppp58SEhJSCyEJUTPUahWfDu3GA7M2cDGvhEVbT9A9pAlxnay4iUs0HG+b+O/soZkQ+czt6108BB/fXfX6V3NMOx5w5MgRFEUhLCzMpPpPPPEEI0aMMHn/oJ93+p133qF///68+OKLtGrVymh9eno6q1evZsuWLYYE+fXXXxMYGMiqVat4/PHHzTpe06ZN8fHx4cSJExXW2dnZ4ePjY6hX3iReWZk1MvuKOiEhgQ0bNtCqVStcXV0Ng7E3hkHZRf3j7mjL4qej0Fz7l/5C0i7Ss67eeiMhGjhzW0W7detWrePExsbyl7/8hVdeeaXCugMHDmBjY0NUVJShzNPTk9DQUA4cOFCt4ymK0iD7TVXrilqI+qR9gBszHu3EP77bQ6lWYciCVH5O7Imbw+07xAhRbVPPmVZPY2daPa9Q0/d5G23atEGlUlXZYexmlTWPm+qdd94hOjqaSZMmVXsfprh06RIXLlygRYsWtXocSzA7UQ8bNqw24hCiVg3qHkjqiUt8u+MsmblFPPfVThY/HYla3fD++hZWwq76ya1SanWN7bNp06bExsYyb948nn/++QqJODs7u8J96uqKjIzkscceY/LkyUbl7dq1o6ysjN9//93Q9H3p0iUOHTpE+/btzT7Ov//9b9RqNfHx8TURtlWp1uxZWq2WVatWGZonwsPDeeSRR+Q5amHV3nq0I3vO5HA4K4/NRy7y7+R0XnywraXDEsIi5s2bxz333ENkZCSvv/46nTp1oqysjHXr1vHJJ59Uu/m5Mm+99Rbh4eHY2FxPOW3atGHAgAE888wzfPrpp7i6ujJ58mSaNWvGgAEDbrm/q1evkpmZSWlpKcePH+err77iP//5DzNmzKB169Y1Fre1MPse9ZEjR2jXrh1Dhw5lxYoVrFixgqeeeorw8HCOHj1aGzEKUSPsbTQsGhGJs53+D8p/J6ezbn+mhaMSwjJatmzJzp07uf/++/n73/9Ohw4dePDBB0lOTuaTTz6p0WO1bduWp59+mqKiIqPyL774gq5du/Lwww8THR2Noij8+OOPt31Oe9q0afj7+9O6dWuGDBlCTk4OycnJ/POf/6zRuK2FSjGzV8FDDz2Eoih8/fXXhs5jly5d4qmnnkKtVvPDDz/USqDWprYnChe1Z8uRizz5n98BmNC7NX/vE2rhiER9VlRUxPHjx2nRogUODg6WDkfUklt9z7WdD8y+ov7tt9947733jHp4e3p68s477/Dbb79VK4h58+YREhKCg4MDUVFRpKam3rL+8uXLCQsLw8HBgY4dO/Ljjz8arc/KymL48OEEBATg5ORE3759SU9PN6y/fPkyEyZMIDQ0FEdHR4KCgnj++efJyTH98QZRf93T2ou/99E3eX/62zF2n862bEBCCHELZidqe3v7CuO3gn4oNjs7E3sv3iApKYnExESmT5/Ozp076dy5M7GxsZw/f77S+lu3bmXw4MGMHDmStLQ04uPjiY+PZ9++fYC+e358fDzHjh3j+++/Jy0tjeDgYGJiYsjP1w8KcO7cOc6dO8fMmTPZt28fixYtYu3atYwcOdLs+EX9NP7+1vRp70uJVsfYr3ZwMa/Y0iEJUS+MGTMGFxeXSpcxY8ZYOryGydyhzIYMGaKEh4cr27ZtU3Q6naLT6ZSUlBSlQ4cOyrBhw8weGi0yMlIZN26c4bNWq1UCAgKUGTNmVFp/0KBBSlxcnFFZVFSU8uyzzyqKoiiHDh1SAGXfvn1G+/T29lY+//zzKuNYtmyZYmdnp5SWlpoUtwwhWv/lFJYo972/Xgn+5xrl8U+2KsWlWkuHJOqh+jqEaHVlZWUp6enplS43Dhfa0NSrIUQ/+ugjWrVqRXR0NA4ODjg4OHDPPffQunVr/v3vf5u1r5KSEnbs2EFMTIyhTK1WExMTQ0pKSqXbpKSkGNUH/UP15fWLi/VXRjfeQ1Cr1djb27N58+YqYym/t3Bjr8QbFRcXk5uba7SI+s3NwZbPh3bFxd6G1BOXee3/ZKYtIW7Hx8eH1q1bV7qUj/QlapbZidrDw4Pvv/+eQ4cO8e233/Ltt99y6NAhVq5caTSdmCkuXryIVqvF19fXqNzX15fMzMp742ZmZt6yflhYGEFBQUyZMoUrV65QUlLCu+++y5kzZ8jIyKgyjjfeeIPRo0dXGeuMGTNwd3c3LIGBgeacqrBSrX1c+fffIlCp4OvfT/HVtpOWDkkIIYyYnajLtWnThv79+9O/f3+rem7N1taWFStWcPjwYZo2bYqTkxPr16+nX79+qNUVTzc3N5e4uDjat29/y3lSp0yZQk5OjmE5ffp0LZ6FqEsPtPPlpWs9v19d/Se/H7tk4YhEfaTIZEUNmiW/X5MGPElMTOSNN97A2dmZxMTEW9b94IMPTD64l5cXGo2GrKwso/KsrKwqB0j38/O7bf2uXbuya9cucnJyKCkpwdvbm6ioqArj1V69epW+ffvi6urKypUrb/nsnr29Pfb29iafm6hfnruvFQczr/J/u88x9uudfD/uHpo3cWyQ4waLmlX+e6OgoABHR0cLRyNqS0lJCYBFBvYyKVGnpaUZph1LS0ursYPb2dnRtWtXkpOTDcO+6XQ6kpOTGT9+fKXbREdHk5yczAsvvGAoW7duHdHR0RXqljfFp6ens337dt544w3DutzcXGJjY7G3t2f16tXy/GMjp1KpeG9gJ45dyOPPc7k8Pj+FwKaO/HdkFA62MuKeqJpGo8HDw8PwpIqTk5P8gdfA6HQ6Lly4gJOTU5X9mGqT2QOe1LSkpCSGDRvGp59+SmRkJLNnz2bZsmUcPHgQX19fhg4dSrNmzZgxYwagfzyrV69evPPOO8TFxbF06VLefvttdu7cSYcOHQD9c9be3t4EBQWxd+9eJk6cSNeuXfnuu+8AfZLu06cPBQUFrFy50micW29vb5P+YpIBTxqmjJxCBszdwvmr+k6JD7bzZf6QrmhkTHBxC4qikJmZSXZ2tqVDEbVErVbTokWLSh9Dru18YPafBk8//TT//ve/cXV1NSrPz89nwoQJLFy40Kz9JSQkcOHCBaZNm0ZmZiYRERGsXbvW0GHs1KlTRveWe/TowZIlS3j55ZeZOnUqbdq0YdWqVYYkDZCRkUFiYiJZWVn4+/szdOhQo2nWdu7cye+/60emuvn++vHjx2We7UbM392RxSMjeXTeVgpLtaw7kMU/vt3N+3/tLBN4iCqpVCr8/f3x8fExtD6KhsXOzq7Sfk51wewrao1GQ0ZGRoVu+BcvXsTPz4+ysrIaDdBayRV1w7b1yEWGLPwdrU7/+am7g3hjQAdp0hRCVGA1Q4jm5uaSk5ODoihcvXrV6HniK1eu8OOPP8ozdKLB6NHaiw8GRRg+f7XtFG/9cEB69goh6pzJTd8eHh6oVCpUKhVt21acGlClUvHaa6/VaHBCWNKAiGZcuFrMmz/op/v7z+bj2NuqealPqFxZCyHqjMmJev369SiKQu/evfnuu++MJuWws7MjODiYgICAWglSCEsZdW9LCkq0fLDuMADz1h+lqFTHy3HtJFkLIeqEyYm6V69egL6zVVBQkPySEo3GhN6tKS7TMm+9fr71BZuP06+DH91Cmt5mSyGEuHMmJeo9e/bQoUMH1Go1OTk57N27t8q6nTp1qrHghLAGKpWKl/qEUlSqY8Hm4wD8eS5XErUQok6YlKgjIiLIzMzEx8eHiIgIVCpVpZ1qVCoVWq22xoMUwtJUKhUvx7VDUWDhluNMX/0necVljLvfeobPFUI0TCYl6uPHj+Pt7W14L0RjpFKpeOXhdrjYa/jo1yO8/9Mh8orL+EesdC4TQtQekxJ1cHBwpe+FaGxUKhWJfUJxtrdhxv8O8smGo+QUlvL6I+HYaCwzGIIQomEz+zfLl19+yQ8//GD4/I9//AMPDw969OjByZMyRaBoHJ7t1Yo34zugUsGS30/x7H93UFDSOAb7EULULbMT9dtvv22YISYlJYW5c+fy3nvv4eXlxYsvvljjAQphrZ66O5iPn7gLexs1yQfP87fPtnHh2hjhQghRU8xO1KdPnzaMj71q1Sr++te/Mnr0aGbMmMGmTZtqPEAhrFm/jv4seSaKJk627DmTw2OfbOH345eY9fMhSsvHHxVCiDtgdqJ2cXHh0qVLAPz88888+OCDADg4OFBYWFiz0QlRD3QNbsqK5+4h2NOJ05cLeeLz35nz6xGGLkjlcn6JpcMTQtRzZifqBx98kFGjRjFq1CgOHz7MQw89BMCff/4ps06JRquFlzPfje1BRKAHWp3+0cWUY5foP2czO05esXB0Qoj6zOxEPW/ePKKjo7lw4QLfffcdnp6eAOzYsYPBgwfXeIBC1BdeLvZ888zd9OvgZyg7m13IoE9T+HjDEXQ6mdBDCGE+s6e5FHoyzaWoiqIofLzhKO//dMio/N42Xswa1BkfVwcLRSaEqA21nQ+qlaizs7NZsGABBw7oZxUKDw/n6aefxt3dvcYDtFaSqMXt/Hb4AhOW7CS36PpjW14udrz9aEf6hPvdYkshRH1iNfNRl9u+fTutWrXiww8/5PLly1y+fJkPPviAVq1asXPnzhoPUIj6qldbb9ZMuJfwgOv/cS/mlTD6vzv47fAFC0YmhKhPzL6ivvfee2ndujWff/45Njb6gc3KysoYNWoUx44dY+PGjbUSqLWRK2phquIyLe+vPcR/rk3o4WSnYcVzPQjzk383QjQEVtf07ejoSFpaGmFhYUbl+/fvp1u3bhQUFNRogNZKErUw14ZD53lp+W4u5pVgZ6Pm7w+2ZdS9LdGoZZxwIeozq2v6dnNz49SpUxXKT58+jaura40EJURDdF+oD/+b2JP7Qr0pKdMx438HGfjJVtKzrlo6NCGEFTM7USckJDBy5EiSkpI4ffo0p0+fZunSpYwaNUoezxLiNrxd7flieHfeG9gJVwcbdp3OJu6jzXy84QhlMpKZEKISZjd9l5SUMGnSJObPn09Zmb43q62tLWPHjuWdd97B3t6+VgK1NtL0Le5URk4hU1fsZf0hfcey8AA33nq0IxGBHgCUlOmws5EZuYSwdlZ3j7pcQUEBR48eBaBVq1Y4OTnVaGDWThK1qAmKorBi51le+78/yS0qQ6WCwZFBPBkZxIhFfzD2vlY8dXcwtjKFphBWy2oTNejvSwMEBgbWWED1hSRqUZMuXC1mxv8OsGLnWQAcbNQUlembwlv7uPByXDvuC/WxZIhCiCpYXWeysrIyXnnlFdzd3QkJCSEkJAR3d3defvllSktLazxAIRoDb1d7PhgUwdLRd9PGx8WQpG3UKo6cz2P4F38wZMHv7DmTbdlAhRB1zuxEPWHCBD777DPee+890tLSSEtL47333mPBggU8//zz1Qpi3rx5hISE4ODgQFRUFKmpqbesv3z5csLCwnBwcKBjx478+OOPRuuzsrIYPnw4AQEBODk50bdvX9LT043qFBUVMW7cODw9PXFxcWHgwIFkZWVVK34hasrdLT354fl7mdwvDEdbDWXXxgdXqWBT+kUembuF577ewZHzeRaOVAhRV8xu+nZ3d2fp0qX069fPqPzHH39k8ODB5OTkmBVAUlISQ4cOZf78+URFRTF79myWL1/OoUOH8PGp2NS3detWevbsyYwZM3j44YdZsmQJ7777Ljt37qRDhw4oikKPHj2wtbVl1qxZuLm58cEHH7B27Vr279+Ps7MzAGPHjuWHH35g0aJFuLu7M378eNRqNVu2bDEpbmn6FrXtXHYh7609yKpd5wDQqFRor/13Vavg8a6BTOobipdL4+jAKYS1srp71D4+Pvz222+0a9fOqPzAgQP07NmTCxfMGxoxKiqK7t27M3fuXAB0Oh2BgYFMmDCByZMnV6ifkJBAfn4+a9asMZTdfffdREREMH/+fA4fPkxoaCj79u0jPDzcsE8/Pz/efvttRo0aRU5ODt7e3ixZsoS//vWvABw8eJB27dqRkpLC3Xfffdu4JVGLupJ26gpv/nDAMF2mvY2a4jIdznYafvvH/ZKohbAwq7tHPX78eN544w2Ki4sNZcXFxbz11luMHz/erH2VlJSwY8cOYmJirgekVhMTE0NKSkql26SkpBjVB4iNjTXUL4/LweH6DEVqtRp7e3s2b94M6KfkLC0tNdpPWFgYQUFBVR63uLiY3Nxco0WIutAlqAnfjolm3hN30byJI8XX7l872WtIOXpJps8UooEzO1GnpaWxZs0amjdvTkxMDDExMTRv3pz/+7//Y/fu3Tz22GOG5XYuXryIVqvF19fXqNzX15fMzMxKt8nMzLxl/fKEO2XKFK5cuUJJSQnvvvsuZ86cISMjw7APOzs7PDw8TD7ujBkzcHd3NyyNsae7sByVSkVcJ39+SezF1IfCaOJky4WrJUz4Jo2HPtrEuv1ZyIy1QjRMNuZu4OHhwcCBA43KrClp2drasmLFCkaOHEnTpk3RaDTExMTQr1+/O/pFNmXKFBITEw2fc3Nzreq8RePgYKthdM9WDI4M4ostJ/h84zEOZl7lmcXb6RzowQsxbbivrTcqlYwfLkRDYXai/uKLL2rs4F5eXmg0mgq9rbOysvDzq3y+Xj8/v9vW79q1K7t27SInJ4eSkhK8vb2JioqiW7duhn2UlJSQnZ1tdFV9q+Pa29s3mlHXhPVzdbDl+QfaMDQ6mE83HmPRlhPsPp3NiC/+oL2/G2Pva8VDHf3RqFX8sj+LKwUlPBIRgL2NxtKhCyHMZNHhjuzs7OjatSvJycmGMp1OR3JyMtHR0ZVuEx0dbVQfYN26dZXWd3d3x9vbm/T0dLZv386AAQMAfSK3tbU12s+hQ4c4depUlccVwhp5ONnxz75hbPzH/Yz6Swuc7DTsz8hlwjdpxHzwG9/8fpL3fjrIpG/3cM876/koOZ0LV4tvv2MhhNUwu9f3pUuXmDZtGuvXr+f8+fPodMYTCVy+fNmsAJKSkhg2bBiffvopkZGRzJ49m2XLlnHw4EF8fX0ZOnQozZo1Y8aMGYD+8axevXrxzjvvEBcXx9KlS3n77bcNj2eB/jlrb29vgoKC2Lt3LxMnTqRr16589913huOOHTuWH3/8kUWLFuHm5saECRMM+zeF9PoW1uhKfglfppxg0dYTZBfoByBysdegUqm4WqQfm99GrSI23I8nooKIbumJWqbZFOKO1HY+MLvpe8iQIRw5coSRI0fi6+t7x/fCEhISuHDhAtOmTSMzM5OIiAjWrl1r6DB26tQp1OrrF/49evRgyZIlvPzyy0ydOpU2bdqwatUqQ5IGyMjIIDExkaysLPz9/Rk6dCivvPKK0XE//PBD1Go1AwcOpLi4mNjYWD7++OM7OhchLK2Jsx0vxLTlmXtb8k3qKT7fdIysXP0VtK1GhbujLRfzSvhhbwY/7M0gxNOJwZFB/LVrczzlMS8hrJLZV9Surq5s3ryZzp0711ZM9YJcUYv6oLhMy6q0s3yx5QQHM6/Pe+3v7kB2QSmFpVpAn8R/SexFsKezpUIVot6yuivqsLAwCgsLazwQIUTNs7fRkNA9iEHdAtl27DKLth5n3f4sMnKKAGjqbIe9jRpXBxuCmjauGfCEqC/MvqL+448/mDx5MtOmTaNDhw7Y2toarW8sV5dyRS3qq9OXC/hq20m+ST1F7rX71moVxLTz5W+RgfRs442NTKsphMmsbgjR9PR0nnjiCXbu3GlUrigKKpUKrVZbowFaK0nUor4rKCnj/3afI+mP0+w8lW0o93Nz4PFuzRnULZDAm66yFUUhu6CUJs52dRytENbL6hJ1ZGQkNjY2TJw4sdLOZL169arRAK2VJGrRkBzOukrSH6dZsfMMVwquT1fbPaQJj3ZpTlxHf9ydbNl7Jof4j7cQ3dKThzr6ExvuK53QRKNndYnaycmJtLQ0QkNDazyY+kQStWiIisu0rNufRdIfp9l85CLlvx3sNGoeaOeDi70Ny3ecMdTXqFVEhjTlgXY+PNDOlxZe0hlNND5Wl6h79uzJtGnTKkyM0dhIohYNXUZOId/vOsfKnWc5lHW9x7ibgw2tvF3ILizl+MV8o21aejnTO8yHmPa+3N3Ss65DFsIirC5RL1++nFdffZVJkybRsWPHCp3JOnXqVKMBWitJ1KKxUBSF/Rm5rNx5lu93nzMa2czD0ZYWXs4Ua3WkZ12lVKv/ddItuAnfju1hqZCFqFNWl6hvHHzEsBOVSjqTCdEIlGl1bDl6idW7zvHLgSxyCq/fz3ax0xDm74aNRsUD7Xx55t6WFoxUiLpjdYn65MmTt1wfHBx8RwHVF5KoRWNXqtWRevwya/dl8tOfmZy/4UrbRq2ia3AT7g/z4b5Qb0J9XSsdxbCgpIzU45fpGtwEVwfbCuuFqA+sLlELPUnUQlyn0ynsOpPNT/syWbc/i2M33bv2d3fgvlBv7gv1oUcrT0NS3nj4AkMXpqJWQfsANyJDPIls0ZTuIU2kN7moN6wyUR89epTZs2dz4MABANq3b8/EiRNp1apVjQdorSRRC1G1U5cK2HD4POsPnmfr0UsUl12fvEejVtGpuTv3tPLCRq3iu51nOH2l4miHrbydiWzhyV1BHnQJ8qCVt4vMsy2sktUl6p9++olHHnmEiIgI7rnnHgC2bNnC7t27+b//+z8efPDBGg/SGkmiFsI0RaVath27xIZDF9hw6DwnLhUYrbezUdOxmRv+7o6U6RSOXcjjcFaeUR0Xext2T++DRmb6ElbI6hJ1ly5diI2N5Z133jEqnzx5Mj///HOFEcsaKknUQlTPmSsFbD16ia1HLrL16CWje9sAznYaOjRzx8fNHq0OMnMKaeJkx4Lh3S0UsRC3ZnWJ2sHBgb1799KmTRuj8sOHD9OpUyeKiopqNEBrJYlaiDunKApHL+Sz9ehFth65RMqxS0Y9yUHfVB7m50L3EE+6hTShW3BT/Nwdqtzn4/O3Ym+jIbyZG+EB7rT3dyPE00nGLxe1xupmz/L29mbXrl0VEvWuXbvw8fGpscCEEA2fSqWitY8LrX1cGBodglancDjrKttPXmH7ictsP3GFs9mF/HnuKn+eu8qirScA/XjkHZu707m5O52ae9CxmTtNnO0oKClj+8krKApsPnLRcBw7jZqW3s609XUl1M+VNj4u3BXcBC/psCbqAbMT9TPPPMPo0aM5duwYPXroBzTYsmUL7777LomJiTUeoBCi8dCoVbTzd6OdvxtD7tY/6pmRU8j2E9cS98krHMjIJTO3iMz9Razbn2XYNqipEx2auTE8OhiNRk1OQSnp5/M4lHmVwlItBzOv6ufk3q2vP/Pxzvy1a3NLnKYQZjG76VtRFGbPns2sWbM4d+4cAAEBAUyaNInnn3++0fTKlKZvISwjv7iMP8/lsudMNnvO5LDnTHaFDmrlAtwdCPVzpXkTRxztbNDqdFzMK+HI+TzeHdiJDs3cb3u8I+fz+O3wBUI8nQjxciawiRN2NtKMLq6zunvUN7p6VT/+r6ura40FVF9IohbCeuQUlLL3bA57zmaz53QOe8/mcDa74iNfAPY2atr6utLO35W2vvqlja8Lfm4OlV5ofP37Sf61cp/hs0atws/NgeZNHGnexOnaq/59ZIum0jO9EbKaRF1YWMi6deu4//77KyTm3NxcNmzYQGxsLPb2jeOejyRqIaxbblEphzKvciAjlwMZ+tfyZvDKuNrb0NrXhTY+LrTxcSXEy5kWXk4cu5DPyrSznLhUwImL+VVub2+j5uAbfRtNq6K4zmo6k3322WesXr2aRx55pMI6Nzc3PvroI06fPs24ceNqNEAhhKgONwdbuoc0pXtIU0OZTqdw8nIBBzNyOZCRy+GsPNLPX+XEpQKuFpeRdiqbtFPZRvtRqyDAw5EWXs7cFeSOl4s9TnY2qFRQWKIjM7eQM1cKUalUJifpiUvT2H7iCv7uDvi6O+Dnpl/K3/u42uPpYoeLvY0kfmH6FXVkZCSvvPIK/fv3r3T9mjVreP3110lNTa3RAK2VXFEL0XCUlOk4cSmfw1lXSc/K48iFPE5czOfExXzyS6qeaEitAl83B5p56Ju/m11rAm/moX/fzMMRB1tNhe0e/XhLhT8IKtOvgx+fPNXVpHMonxhJ1D2ruaJOT0+nc+fOVa7v1KkT6enpNRKUEELUJbtr963b+hrf1lMUhYt5JZy4lM/xi/mcvJTPiYsFHL+Yz4lL+RSUaMnIKSIjp4jtJ69Uum8vF/trCdyR5tcS+t+6BzG4exBlOh0FJVqycovIzC0mK6eIzNwiLuYVU1CixcPJ9IlKOr36MxqNiiZOdng42eLhaEsTJzvcnWyvlznZ0bm5O8Geznf08xJ1y+REXVZWxoULFwgKCqp0/YULFygrK6uxwIQQwtJUKhXervZ4u9obNaHD9SR+NruQM1cKOHtF3wR+Nrvw2vsC8ku0XMwr5mJeMbtPZ1d6DI1ahbeLPb7uDvi62hPq54qfuwMejrZ4uthxOOsqPq72uDvaVnnFXFym5Wqx/vdvdkFppXXKvfZIOMN63D5Rn88t4vNNx3Cxt8XVwQYXBxvcHGxwdbDFxd4G12vvXR1ssLdRy9V8LTI5UYeHh/PLL7/QtWvlzTA///wz4eHhNRaYEEJYsxuTeESgR4X1iqKQU1jKmWsJ/MyVgmtJvZCs3CKycou4cLUYrU7RPxeee+tRHW3UKjxd7PB01t+/9nKxx9PZDk8XezydbZmdEIGN5tp9ckUhv1jLlYISsgtLyS4o4Up+KdmFJQR5Opl0fhk5RXy+6bhJdb8dE023m/6Qqcz+c7msP3QeR1sNzvYaHO1scLLV4GSnwdFOg5OdjeG9p7OdJP9rTE7UTz/9NImJiYSHh/Pwww8brfu///s/3nrrLT744AOzA5g3bx7vv/8+mZmZdO7cmTlz5hAZGVll/eXLl/PKK69w4sQJ2rRpw7vvvstDDz1kWJ+Xl8fkyZNZtWoVly5dokWLFjz//POMGTPGUCczM5NJkyaxbt06rl69SmhoKP/6178YOHCg2fELIURlVCoVHk52eDjZVfm8dplWx6X8EjJzigzJOyu3+FpTeBHnc4vJzC0ip7CUMp1ybV1xpfu6mYu9DU2cbfFw1Dd7N3Gyo62vK2mnsjl+Id9onYeTHU2cbHF1sDU8XtbU2Y7RPVtytaiUq0VlXC0qI6+4zPA5r6jMcBVv6lziu05n8/5Ph0yqe/CNvpXe37/ZtzvOsPXIRext1djbaLC3UesX2xve22ho4mzHg+19TTp2SZkOG7UKtZU8amdyoh49ejQbN27kkUceISwsjNDQUAAOHjzI4cOHGTRoEKNHjzbr4ElJSSQmJjJ//nyioqKYPXs2sbGxHDp0qNLhSLdu3crgwYOZMWMGDz/8MEuWLCE+Pp6dO3fSoUMHABITE/n111/56quvCAkJ4eeff+a5554jICDA0GN96NChZGdns3r1ary8vFiyZAmDBg1i+/btdOnSxaxzEEKI6rLRqPF1c8DXreqxy0HftH05v4RLeSXXmtJLuJRXzKV8/edLeSVcyi82rC/VKuQV6xPraSp/nrwq15u1rzdtuzrY0qyJI64ONrhdK3NzsMXZXoOtWk2pVseZKwW4OtjibKepclz1EC8nBnVrTkGJlsISLfklZRSWaCm4thSWaikoKaNUq2Bv4qAyaaeusCLt7G3rtfZxMTlRD1nwO78fv4xGrcJWo8JWo0/4tpryRV/WK9SbKf3ambTPO2H2gCfLli1jyZIlpKenoygKbdu25YknnmDQoEFmHzwqKoru3bszd+5cAHQ6HYGBgUyYMIHJkydXqJ+QkEB+fj5r1qwxlN19991EREQwf/58ADp06EBCQgKvvPKKoU7Xrl3p168fb775JgAuLi588sknDBkyxFDH09OTd999l1GjRpkUu/T6FkJYI0VRyC0q42JeMdkF+mbv7IJSrhSUkFOof71SUErOtbLyOrfq3W4uOxs1TnYanK81ZTtda9Yub+52vqGZ28leX8+xvL69BgcbNa4OtobtHO00ONiqsdNUvBe+5chF9p/LpbhMS3GZTr+U3vC+TEtxqQ4/dwfeerSjSfEP/GQrO6roHHijRzoH8NHgLtbT67vcoEGDqpWUb1ZSUsKOHTuYMmWKoUytVhMTE0NKSkql26SkpFQYTzw2NpZVq1YZPvfo0YPVq1fz9NNPExAQwIYNGzh8+DAffvihUZ2kpCTi4uLw8PBg2bJlFBUVcd99993xeQkhhCWpVCrcHW1xdzS9xzjom3uvFpWSW1R2Q3N3+edr7wtvWFd8vUm8fF2JVmfYV0mZ7rYd28w/N/3AMg7XmrUdbDU42Giwt1UbXu1t9End3kb/B0JTZztD3c82HjVaX36VbHfDq51GzbSH26NTFFQ3HLf8klanKJRqFUq0Ojyd62aAL7MTdU25ePEiWq0WX1/jpghfX18OHjxY6TaZmZmV1s/MzDR8njNnDqNHj6Z58+bY2NigVqv5/PPP6dmzp6HOsmXLSEhIwNPTExsbG5ycnFi5ciWtW7euMt7i4mKKi6/fG8rNzTXrfIUQwprZ2aj1HdPuYEax4jItBcVaCkq1FBSXkV+ib8q+uayw5Nq64jJDs3d+Sfn7a/VvKNPq9FlSUaCoVEdRqa6mTttsKhX6pG5I8CrUZebdXjCXxRJ1bZkzZw7btm1j9erVBAcHs3HjRsaNG0dAQAAxMTEAvPLKK2RnZ/PLL7/g5eXFqlWrGDRoEJs2baJjx8qbRmbMmMFrr71Wa3FXdgfi5jJT6txJmaIohkWn06HT6ar9/uYyrVaLVqulrKzM6LWysqpe76SuTlcz/7HVan3TW2Wv5q6ryX3d6bobX015b05dS+xDWIb+KlVDkxrcp6Lor16Ly3QUleqbsYvLtBTd8Fp0ran7xtfK1hffuL5Mq7/y1yqUlOko1eoMLQGG91r9cuOvSkW53mLAtWs3XfGte+zfKYslai8vLzQaDVlZWUblWVlZ+Pn5VbqNn5/fLesXFhYydepUVq5cSVxcHKAfiGXXrl3MnDmTmJgYjh49yty5c9m3b5/hcbLOnTuzadMm5s2bZ7jXfbMpU6YYNbvn5uYSGBhIQkICtrbmNTHdrLJfLDeX3e5zTdW51S/3O0lAarUaGxsbNBoNGo3G8P52r3Z2dibXvdW68ljuxI1/xFT1au66mtzXjevK/zgxJ4Yby015X93tansfdaX8D11r/8NApVJV+KO8Nstu/GPpxteaLLvxuNX9bAfYm1BfARRUKKhR1Bp0qNGp1OhQw7XPxaWlnKb2WCxR29nZ0bVrV5KTk4mPjwf0ncmSk5MZP358pdtER0eTnJzMCy+8YChbt24d0dHRAJSWllJaWopabdxbUKPRGP4DFxTop8O7VZ3K2NvbVzrhSFJSknQmE0KIa8oTdvkfT9Utq2rdze9v97m668ypm5uby6oF/6bWKGY6f/58lev27Nlj1r6WLl2q2NvbK4sWLVL279+vjB49WvHw8FAyMzMVRVGUIUOGKJMnTzbU37Jli2JjY6PMnDlTOXDggDJ9+nTF1tZW2bt3r6FOr169lPDwcGX9+vXKsWPHlC+++EJxcHBQPv74Y0VRFKWkpERp3bq1cu+99yq///67cuTIEWXmzJmKSqVSfvjhB5Njz8nJUQAlJyfHrHMWQgjRsNR2PjA7Ufv6+ipr1qypUP7+++8rDg4OZgcwZ84cJSgoSLGzs1MiIyOVbdu2Gdb16tVLGTZsmFH9ZcuWKW3btlXs7OyU8PDwCsk1IyNDGT58uBIQEKA4ODgooaGhyqxZsxSdTmeoc/jwYeWxxx5TfHx8FCcnJ6VTp07K4sWLzYpbErUQQghFqf18YPZz1O+99x7Tpk1jxIgRfPDBB1y+fJmhQ4eyd+9ePv30Ux599NHauPC3OvIctRBCCKj9fGB2ogZIS0tjyJAhFBcXc/nyZaKioli4cGGVncAaIknUQgghoPbzgWljtN2kdevWdOjQgRMnTpCbm0tCQkKjStJCCCFEXTE7UW/ZssUw9/SePXv45JNPmDBhAgkJCVy5cvsh14QQQghhOrMTde/evUlISGDbtm20a9eOUaNGkZaWxqlTp6ocLEQIIYQQ1WP2c9Q///wzvXr1Mipr1aoVW7Zs4a233qqxwIQQQghRzc5kQjqTCSGE0LOK2bM++ugjRo8ejYODAx999FGV9VQqFRMmTKix4IQQQojGzqQr6hYtWrB9+3Y8PT1p0aJF1TtTqTh27FiNBmit5IpaCCEEWMkV9fHjxyt9L4QQQojaZVav79LSUlq1asWBAwdqKx4hhBBC3MCsRG1ra0tRUe3OuymEEEKI68x+jnrcuHG8++67lJWV1UY8QgghhLiB2c9R//HHHyQnJ/Pzzz/TsWNHnJ2djdavWLGixoITQgghGjuzE7WHhwcDBw6sjViEEEIIcROzE/UXX3xRG3EIIYQQohLVGus7Ozu7Qnlubi69e/euiZiEEEIIcY3ZiXrDhg2UlJRUKC8qKmLTpk01EpQQQggh9Exu+t6zZ4/h/f79+8nMzDR81mq1rF27lmbNmtVsdEIIIUQjZ3KijoiIQKVSoVKpKm3idnR0ZM6cOTUanBBCCNHYmZyojx8/jqIotGzZktTUVLy9vQ3r7Ozs8PHxQaPR1EqQQgghRGNlcqIODg4GQKfT1VowQgghhDBm9uNZ5fbv38+pU6cqdCx75JFH7jgoIYQQQuiZnaiPHTvGo48+yt69e1GpVJTPkqlSqQB9xzIhhBBC1AyzH8+aOHEiLVq04Pz58zg5OfHnn3+yceNGunXrxoYNG2ohRCGEEKLxMvuKOiUlhV9//RUvLy/UajVqtZq//OUvzJgxg+eff560tLTaiFMIIYRolMy+otZqtbi6ugLg5eXFuXPnAH1ns0OHDpkdwLx58wgJCcHBwYGoqChSU1NvWX/58uWEhYXh4OBAx44d+fHHH43W5+XlMX78eJo3b46joyPt27dn/vz5FfaTkpJC7969cXZ2xs3NjZ49e1JYWGh2/EIIIURtMjtRd+jQgd27dwMQFRXFe++9x5YtW3j99ddp2bKlWftKSkoiMTGR6dOns3PnTjp37kxsbCznz5+vtP7WrVsZPHgwI0eOJC0tjfj4eOLj49m3b5+hTmJiImvXruWrr77iwIEDvPDCC4wfP57Vq1cb6qSkpNC3b1/69OlDamoqf/zxB+PHj0etNvvHIYQQQtQuxUxr165VvvvuO0VRFCU9PV0JDQ1VVCqV4uXlpSQnJ5u1r8jISGXcuHGGz1qtVgkICFBmzJhRaf1BgwYpcXFxRmVRUVHKs88+a/gcHh6uvP7660Z17rrrLuVf//qX0TYvv/yyWbHeLCcnRwGUnJycO9qPEEKI+q2284HZl5CxsbE89thjALRu3ZqDBw9y8eJFzp8/b9akHCUlJezYsYOYmBhDmVqtJiYmhpSUlEq3SUlJMapfHs+N9Xv06MHq1as5e/YsiqKwfv16Dh8+TJ8+fQA4f/48v//+Oz4+PvTo0QNfX1969erF5s2bTY5dCCGEqCs10tbbtGlTw+NZprp48SJarRZfX1+jcl9fX6NxxG+UmZl52/pz5syhffv2NG/eHDs7O/r27cu8efPo2bMnoH+8DODVV1/lmWeeYe3atdx111088MADpKenVxlvcXExubm5RosQQghR20zu9f3000+bVG/hwoXVDqYmzJkzh23btrF69WqCg4PZuHEj48aNIyAggJiYGMPIas8++ywjRowAoEuXLiQnJ7Nw4UJmzJhR6X5nzJjBa6+9VmfnIYQQQoAZiXrRokUEBwfTpUsXwyAnd8LLywuNRkNWVpZReVZWFn5+fpVu4+fnd8v6hYWFTJ06lZUrVxIXFwdAp06d2LVrFzNnziQmJgZ/f38A2rdvb7Sfdu3acerUqSrjnTJlComJiYbPubm5BAYGmni2QgghRPWYnKjHjh3LN998w/HjxxkxYgRPPfUUTZs2rfaB7ezs6Nq1K8nJycTHxwP6ccSTk5MZP358pdtER0eTnJzMCy+8YChbt24d0dHRAJSWllJaWlqh97ZGozFcSYeEhBAQEFDhUbLDhw/Tr1+/KuO1t7fH3t7e3NMUQggh7ojJiXrevHl88MEHrFixgoULFzJlyhTi4uIYOXIkffr0MfseNegfpRo2bBjdunUjMjKS2bNnk5+fb2iSHjp0KM2aNTM0R0+cOJFevXoxa9Ys4uLiWLp0Kdu3b+ezzz4DwM3NjV69ejFp0iQcHR0JDg7mt99+Y/HixXzwwQeAfqjTSZMmMX36dDp37kxERARffvklBw8e5NtvvzX7HIQQQtwBRQFFd33Raa+9v/aqu2GdoUxrvI3Z22krHrf8M8pNZcoNZTesu7Hsan6t/ojMGpnM3t6ewYMHM3jwYE6ePMmiRYt47rnnKCsr488//8TFxcWsgyckJHDhwgWmTZtGZmYmERERrF271tBh7NSpU0ZXxz169GDJkiW8/PLLTJ06lTZt2rBq1So6dOhgqLN06VKmTJnCk08+yeXLlwkODuatt95izJgxhjovvPACRUVFvPjii1y+fJnOnTuzbt06WrVqZVb8QgjrpygKWq2W0tJSysrKKCsrq/S9VqtFp9MZXsuXW32urbo6rRZFW4pKV4ZKKYXy97pS1Ir+VaUrQ61or30uQ63oP6uuvWoof9WhUenQoEOt6FCrdKjRoUa59qp/r0GHSqUv06CgUekXdfkrGMo0XC831Lux7Kbt1YBapaBCQa0CNQqqa69q86/xrE/xnd8OvhWVUs0bzqdPn+aLL75g0aJFlJSUcPDgQbMTdX2Wm5uLu7s7ffv2xdbW1lB+c8tCZS0Nt6tTE9uoVCo0Gg02NjZoNBqjpabLTKnr4OCAk5MTzs7OODk54eTkhL29fbVaYsSd0+l0t0xapr6vzW3Kysru6ByVGyYMsrGxwcbGBltb20rfly/lwyLbalTYqbTYq3XYqnXYqxVs1TpsVTrsVFrs1DpsVVps0GKn0mGjKsMW/WcblRYbyvSLon/VKPr3GkrRKKVodGVolFLUSikaXSmqawn3xqQrbqJS37Bo9K9qDahUlZSpjRejsvJ6akClX4fqhvU3vDcc98ayinVzC0pxH7mcnJwc3Nzcav7UzUnUxcXFhqbvzZs38/DDDzNixAj69u3b6Eb1Kk/UN34xN/8oK/vR3q5OTW1T/he6Vqs1XC3cuNR1WVFREQUFBUZLUVGRUfw3Jm1FUbCzszMk9RsT/M2Ls7Mzjo6O2NraGn7RajQaw3tzP98Ye3nCMOX9zZ9rI4FptdoKP6ebf3amUKvVRgmqqgR2q8Rm7jbmbq/RaG5/XooCpQVQfBWKcvWvpflQcm0pLbj+3vA5D0oKqlh37b222KyfZ53Q2F1bbEFte/294dW2kjo3lKltQGOjfzUsGn3iuvHzzesrLbvhs+p2da4dQ6WqImFqbkh6lSXb8mRsvX/UV5YPapLJTd/PPfccS5cuJTAwkKeffppvvvkGLy+vGg+oPjPlyliYrrS0lPz8/AoJvnzJz88nJyeHjIwMCgoKKCsrq7RJsaqmxqrKbkxi5S0DVb2vqqw82Tg5OdVoAmtwfxCXFkLhFSi8ADlXoChHn2yLc/VL0bXXGxOx0bqr+vuNtcnGAWzsr7063PTZHmwdjT8b1buhzLaSbW0c9EnUxv6GZGpXMQmrbaw6UYnaZfIVtVqtJigoiC5dutwyAa1YsaLGgrNmtf0XlBD1irYU8i9C/oVribd8uXzD++yb1l2BsqKaOb5KDfZuYO8Kds76xdYJ7FzAzsm093bOYOt87b2Lfp2NvSRIcVtWc0U9dOhQuUIUojEpLYSrGXA1E/Kyrifi/AvX3t/wuSi7+sdRacCxCTh6gIO7PuE6XEu69u76V8Pna6/l9crX2TpJQhUNllkDngghGgBtqT7x5mZcT8RGr9eWohzz9qvSgJMnOHtdS7xNwMFDn4DLP1e22LtKkhXiFsx6PEsIUQ8U50HOacg+pV9yTkP26euveVmAiX1IbRzBzR9cfMHZ+4bF69pyQ5mDx7WetMIqlD8DXP4MsUqtv999O9oyyD5ZyXPJNz27rNOCdyg4mTDw1dUsOLHpWlyVPbN8w7PK4Y/qW0xu51waHN9YcXsF432r1HDf5NvvD2Dvt3D690r2edP+3ZvB/VNN22cNkEQtRH2j00HuGbh8DC4d1b9eOXE9KRdeuf0+1Lbg6geu/lW/uvnrm5cb69VuwWV9pzVtKWhLri2lUFZ8/b22RH9Pu3XM7fcHsHUuXD4KujJ9otOV3bDc8Dn4Hrg38fb7A/joLv2th8r2eaPeL0PPSbffX/4FmHOXacdO+Ara9b99vfN/wncjTdtn8D2mJepT22DdtNvXU2lMT9THN8LOL29fzydcErUQjZ6iQO5ZuHTkejIuT8xXTtz+8SEHD/AIBPega6+B11/dA/VN1NZ69avTXX9UyslT/0jR7Zz+Aw79qE+iZYVQWqTvqFa+lH/WlcGYTabF8curpv/SNjVRH/wBTm29fT17MzoklXfYux1Tn8RVa/R9A4wep7r5ueVrn20dTdunYxMIuffaH30qjJ5NRmX83tR9erWFzk/csM+b9mN0DBO1jdW3Ht0cJypQcX3fzt6m77MGSKIWwpJ0Wn0z44XDcOEgXDgEFw/pP5dcrXo7tS00CQHPVtC0FTRtAR5B1xJxc30Hq7pSVqx/VMruWo/p28n6E5LfuPa887WEfOP7ssLrdcel6ptXbydjF2z+wLR4ddprg1zchq2Tvhf4zc8n3/y+aQvTjgsQMRha9Kzkeeabnj32CDZ9nyP+p3+tbH8qjf4PMpVa/yiYKVx8YErVExRVS0AXGL6mZvfZ+gH9UpPC4vSLlZFELURdKc6D8/shcw9k7oPMvfqkdWNiupHaBpq0ME7Gnq2gaUt9QjYl2dypknxY//a155uvPbt84/vi3OuPWA1cAB3/ato+D//PtOOXFphWz68jRI296blmxxs+O1x/lhkTm/L7vaNfatJdQ2t2fwA+7Wp+n8KqSKIWojaUFumv8s78AWd36JPypaNU2olLY69vxvMOvWEJ0ydkUzr/3Exbqr/PmHf+2pIFBRf191wLL0PBFWjbB7oOv/2+VGpImWvacU1Nqk1bQv9/X3tm+dpVeGXvbR1Nvz8edLd+EaIBkkQtRE3IOaPv3HI6VZ+cM/eCrrRiPRc//dXfjUvTljV3dfzZ/XBu5+3ruXiblqhtHOCeF8DeRX/f0sHtpmedb3g19RycvUw7thACkEQtRPVcOQknt8CJLfrHTrJPVqzj7A3NI6F5N/DvrE/KLj6V76+sRN+TO+esvhNZzplrr2f1+4mfZ1pcGjv9q0qj387F59qjVV7g2FT/KI1TU/DtcOv9lFOp4MHXTKsrhKgVkqiFMEVRjv7RjSO/wNFf9Y9C3UilBr9OEBgFgdeSs0fwrZtud/4X9iTpk37umWvPflbCPcj0OAf+R99k7NjUent1CyHMIolaiMooir4H9qEfIP2Xa4Mg3DD5g0qj78ka8hf9EhilbwIufwTGlHuruWevDwIB+s5P7s3ArZm+57ZbgP69hxmJ2iPQ9LpCiHpBErUQ5RRFf3/3wP/BgTVwKd14vWdraHXtkZDge/Q9ii8chIzd8Oub+t7cWX/C2K2mJcywOP0jVk1a6F9dfBrv4CJCiCpJohaNm06nv1r+c6V+MIrcM9fXaeyg5X36QRBa9dY/L3z6dzi4Rv/I0vn9+pGpbnbxkGmJurwzmRBC3IIkatH4KIr+0am93+oTdO7Z6+tsnaHNg/phEdv0uT5wyKWj8HElj//Yu4N/J/39af9O+sTr2aZOTkMI0ThIohaNx/mDsO9b2PedfjjOcnau+mbo8Hj9FXRlQxg2banvHOYRBM27Q0CEPjk3CZHmaiFErZJELRq2y8dg3wr9cv7P6+U2jtDiXn0P7egJ10asugWVCibulqQshKhzkqhFw5N3HvYu1185n91xvVxlo78SdvSAy8ch/We4eNi0GYVAkrQQwiIkUYuGQVuqT7xpX8Hhn254lEqlv2dsY6efXvDs9uvbqG300zkWXwV7V4uELYQQtyOJWtRvBZdh+0JI/Uw/pnU5rzD9uNb55+HS4evlrv76KQnb9IGWvUyb91YIISxIErWon66cgG2f6Ef3Ks3Xlzn7QOe/QZen9FMUzu6gH5gk6O5ryflB/dCZ0oQthKhHJFGL+uVcGmz5CPavuj7kpm9HuOd5CH/UeLapJ5brO4s5elgiUiGEqBFWMRjwvHnzCAkJwcHBgaioKFJTU29Zf/ny5YSFheHg4EDHjh358ccfjdbn5eUxfvx4mjdvjqOjI+3bt2f+/PmV7ktRFPr164dKpWLVqlU1dUqiJul0+vvOix6Gz+6DP1fok3Sr3jBkJYzZBJ0GVZwSsm0fSdJCiHrP4ok6KSmJxMREpk+fzs6dO+ncuTOxsbGcP3++0vpbt25l8ODBjBw5krS0NOLj44mPj2ffvn2GOomJiaxdu5avvvqKAwcO8MILLzB+/HhWr15dYX+zZ89GJU2h1qm0CHYsgo+jYMmg6+Nid3wcxmzWJ+lWvaUpWwjRoKkURalkJvu6ExUVRffu3Zk7Vz85vU6nIzAwkAkTJjB58uQK9RMSEsjPz2fNmjWGsrvvvpuIiAjDVXOHDh1ISEjglVdeMdTp2rUr/fr148033zSU7dq1i4cffpjt27fj7+/PypUriY+PNynu3Nxc3N3dycnJwc3NrTqnLqpy8Qjs+hp2fAmFlyquf/Jb/f1mIYSwArWdDyx6RV1SUsKOHTuIiYkxlKnVamJiYkhJSal0m5SUFKP6ALGxsUb1e/TowerVqzl79iyKorB+/XoOHz5Mnz59DHUKCgp44oknmDdvHn5+freNtbi4mNzcXKNF1KArJ2DbfPhPH5jbFTZ/UDFJt7wPBi3WvwohRCNh0c5kFy9eRKvV4uvra1Tu6+vLwYMHK90mMzOz0vqZmZmGz3PmzGH06NE0b94cGxsb1Go1n3/+OT179jTUefHFF+nRowcDBgwwKdYZM2bw2muvmXpq4nZyzugnuDidCsc26GehqoyTp74X913DwLNVnYYohBDWoEH2+p4zZw7btm1j9erVBAcHs3HjRsaNG0dAQAAxMTGsXr2aX3/9lbS0NJP3OWXKFBITEw2fc3NzCQyUuX+rpC3TP8N8NVO/5JzRjwJ28ZB+nucbn3kG/WNUDm5QeEU/vGdYHHT8q35aSRs7y5yDEEJYAYsmai8vLzQaDVlZxr+0s7KyqmyO9vPzu2X9wsJCpk6dysqVK4mLiwOgU6dO7Nq1i5kzZxITE8Ovv/7K0aNH8fDwMNrPwIEDuffee9mwYUOF49rb22Nvb1/NM21AivMg+xRkn4Ts0/rhOvOuJeO8C/rm6qJs/Whft6LS6GeaCozSP+fc6n7I3AelBRDyF7BzrpPTEUIIa2fRRG1nZ0fXrl1JTk42dOLS6XQkJyczfvz4SreJjo4mOTmZF154wVC2bt06oqOjASgtLaW0tBS12vj2u0ajQafTP3c7efJkRo0aZbS+Y8eOfPjhh/Tv37+Gzq6eK8qBrP2QtQ+y/tQvl49CQSWdu27HryO4B4FXG/AOBa+24NOuYjJucW/NxC6EEA2IxZu+ExMTGTZsGN26dSMyMpLZs2eTn5/PiBEjABg6dCjNmjVjxowZAEycOJFevXoxa9Ys4uLiWLp0Kdu3b+ezzz4DwM3NjV69ejFp0iQcHR0JDg7mt99+Y/HixXzwwQeA/qq8siv2oKAgWrRoUUdnbkVKiyBjNxxbr5+nOXMf5Jyuur6DBzg2gSvHr5epNGDvoh+S07GpfqhO9+bg4gORo+V5ZiGEqCaLJ+qEhAQuXLjAtGnTyMzMJCIigrVr1xo6jJ06dcro6rhHjx4sWbKEl19+malTp9KmTRtWrVpFhw4dDHWWLl3KlClTePLJJ7l8+TLBwcG89dZbjBkzps7PzyqVFOifSf5zBZxM0TdlU8lTeu6B4BuuX3za66+EmwTrk3FRDlw6Cs7e4NRUP2SnPM8shBA1zuLPUddX9eo5akXRXzHvSdKP8HX5OKCrvG6rB+DeRH1ydmxSp2EKIUR9VNv5wOJX1KKW6LRwKgUOrIED/we5ZyrWUdvq7xuH/EU/aUXz7vqrYyGEEFZDEnVDotPByc2wZxkc+tG445dKAyjg2RraxELHgeDXGdQWH0VWCCHELUiibgguHYXd38DuJMg5db3csQm07QftHgafcHDzBxt5xEwIIeoTSdT1VVkpbJ0NR5L1Tdzl7N2hw6MQ/hgE3wMa+YqFEKI+k9/i9U3OOVj7Tzj0P9CV6stUan0nsIjBEPoQ2DpaNkYhhBA1RhJ1fZH+C6ybBuf/NC4P6QmPfQpuAZaJSwghRK2SRG3NtKWwcRb88SkUXL5errGFsIehz9vgLglaCCEaMknU1qgwG35+BXYvAV3Z9XJnb4geB9ET5N6zEEI0EvLb3lqUlcDx32DfCti/Sj85RTn/CHjwdWjZy1LRCSGEsBBJ1Jag08HVDP0MVBm79fMyH12vn3WqnHd7aHYXPDAdXH0sFqoQQgjLkkR9p/7zIDja6BNvST6GMbMrG5hVKdMPPKIo13ts38jZB9oPgA4D9VM/ytjZQgjR6EmivlMXDoC9OQlVq39RafSzS3mHXpuTOVqfnNWaWglTCCFE/SSJ+k4lLAFXZ7hyQj+j1K04NtGPDuYTDi6+0iFMCCHEbUmmuFMte4K1z54lhBCi3pIZGYQQQggrJolaCCGEsGKSqIUQQggrJolaCCGEsGKSqIUQQggrJolaCCGEsGKSqIUQQggrJolaCCGEsGKSqIUQQggrJolaCCGEsGKSqIUQQggrZhWJet68eYSEhODg4EBUVBSpqam3rL98+XLCwsJwcHCgY8eO/Pjjj0br8/LyGD9+PM2bN8fR0ZH27dszf/58w/rLly8zYcIEQkNDcXR0JCgoiOeff56cnNtMqiGEEELUMYsn6qSkJBITE5k+fTo7d+6kc+fOxMbGcv78+Urrb926lcGDBzNy5EjS0tKIj48nPj6effv2GeokJiaydu1avvrqKw4cOMALL7zA+PHjWb16NQDnzp3j3LlzzJw5k3379rFo0SLWrl3LyJEj6+SchRBCCFOpFEVRLBlAVFQU3bt3Z+7cuQDodDoCAwOZMGECkydPrlA/ISGB/Px81qxZYyi7++67iYiIMFw1d+jQgYSEBF555RVDna5du9KvXz/efPPNSuNYvnw5Tz31FPn5+djY3H5SsdzcXNzd3cnJycFNZs8SQohGq7bzgUWvqEtKStixYwcxMTGGMrVaTUxMDCkpKZVuk5KSYlQfIDY21qh+jx49WL16NWfPnkVRFNavX8/hw4fp06dPlbGU/4CrStLFxcXk5uYaLUIIIURts2iivnjxIlqtFl9fX6NyX19fMjMzK90mMzPztvXnzJlD+/btad68OXZ2dvTt25d58+bRs2fPKuN44403GD16dJWxzpgxA3d3d8MSGBho6mkKIYQQ1Wbxe9S1Yc6cOWzbto3Vq1ezY8cOZs2axbhx4/jll18q1M3NzSUuLo727dvz6quvVrnPKVOmkJOTY1hOnz5di2cghBBC6N3+Zmwt8vLyQqPRkJWVZVSelZWFn59fpdv4+fndsn5hYSFTp05l5cqVxMXFAdCpUyd27drFzJkzjZrNr169St++fXF1dWXlypXY2tpWGau9vT329vbVOk8hhBCiuix6RW1nZ0fXrl1JTk42lOl0OpKTk4mOjq50m+joaKP6AOvWrTPULy0tpbS0FLXa+NQ0Gg06nc7wOTc3lz59+mBnZ8fq1atxcHCoqdMSQgghaoxFr6hB/yjVsGHD6NatG5GRkcyePZv8/HxGjBgBwNChQ2nWrBkzZswAYOLEifTq1YtZs2YRFxfH0qVL2b59O5999hkAbm5u9OrVi0mTJuHo6EhwcDC//fYbixcv5oMPPgCuJ+mCggK++uoro85h3t7eaDQaC/wkhBBCiEooVmDOnDlKUFCQYmdnp0RGRirbtm0zrOvVq5cybNgwo/rLli1T2rZtq9jZ2Snh4eHKDz/8YLQ+IyNDGT58uBIQEKA4ODgooaGhyqxZsxSdTqcoiqKsX79eASpdjh8/blLMOTk5CqDk5OTc0bkLIYSo32o7H1j8Oer6Sp6jFkIIAQ38OWohhBBC3JrF71HXV+UNETLwiRBCNG7leaC2GqglUVfTpUuXAGTgEyGEEIA+L7i7u9f4fiVRV1PTpk0BOHXq1B19Md27d+ePP/6odp2q1t1cfqvPN79PTk4mMDCQ06dP39H9FlPO7Xb1Klt3u7KqzrX8NTc3t87OT7676n93lb2X7+725Lur++/ul19+ISgoyJAXapok6moqf07b3d39jv7RaTSa225/qzpVrbu5/Fafq3rv5uZW6+d2u3qVrbtdWVXnenN5XZyffHfV/+5u9Z3Kd1e9uE2tJ9+ded9d+cXazeN31BTpTGZh48aNu6M6Va27ufxWn6t6f6dM3Ze553e7sqrOtSbPzdT9yXdnXlldnJup+5Pvzrwy+e5qjzyeVU0N+fGshnxu0LDPryGfGzTs82vI5wYN+/zk8SwrZW9vz/Tp0xvk+N8N+dygYZ9fQz43aNjn15DPDRr2+dX2uckVtRBCCGHF5IpaCCGEsGKSqIUQQggrJolaCCGEsGKSqIUQQggrJom6Djz66KM0adKEv/71r5YOpUasWbOG0NBQ2rRpw3/+8x9Lh1OjGtp3daPTp09z33330b59ezp16sTy5cstHVKNyc7Oplu3bkRERNChQwc+//xzS4dUKwoKCggODuall16ydCg1KiQkhE6dOhEREcH9999v6XBq3PHjx7n//vtp3749HTt2JD8/36ztpdd3HdiwYQNXr17lyy+/5Ntvv7V0OHekrKyM9u3bs379etzd3enatStbt27F09PT0qHViIb0Xd0sIyODrKwsIiIiyMzMpGvXrhw+fBhnZ2dLh3bHtFotxcXFODk5kZ+fT4cOHdi+fXuD+XdZ7l//+hdHjhwhMDCQmTNnWjqcGhMSEsK+fftwcXGxdCi1olevXrz55pvce++9XL58GTc3N2xsTB8YVK6o68B9992Hq6urpcOoEampqYSHh9OsWTNcXFzo168fP//8s6XDqjEN6bu6mb+/PxEREQD4+fnh5eXF5cuXLRtUDdFoNDg5OQFQXFyMoii1NpORpaSnp3Pw4EH69etn6VCEGf78809sbW259957Af08EeYkaZBEzcaNG+nfvz8BAQGoVCpWrVpVoc68efMICQnBwcGBqKgoUlNT6z7QGnKn53vu3DmaNWtm+NysWTPOnj1bF6HfVkP/Lmvy/Hbs2IFWq7Wa2d9q4tyys7Pp3LkzzZs3Z9KkSXh5edVR9LdXE+f30ksvMWPGjDqK2HQ1cW4qlYpevXrRvXt3vv766zqK3DR3en7p6em4uLjQv39/7rrrLt5++22zY2j0iTo/P5/OnTszb968StcnJSWRmJjI9OnT2blzJ507dyY2Npbz588b6pTfF7t5OXfuXF2dhslq4nytVUM+N6i587t8+TJDhw7ls88+q4uwTVIT5+bh4cHu3bs5fvw4S5YsISsrq67Cv607Pb/vv/+etm3b0rZt27oM2yQ18d1t3ryZHTt2sHr1at5++2327NlTV+Hf1p2eX1lZGZs2beLjjz8mJSWFdevWsW7dOvOCUIQBoKxcudKoLDIyUhk3bpzhs1arVQICApQZM2aYte/169crAwcOrIkwa0x1znfLli1KfHy8Yf3EiROVr7/+uk7iNcedfJfW+F3drLrnV1RUpNx7773K4sWL6ypUs9XE/8OxY8cqy5cvr80wq6065zd58mSlefPmSnBwsOLp6am4ubkpr732Wl2GbZKa+O5eeukl5YsvvqjFKKuvOue3detWpU+fPob17733nvLee++ZddxGf0V9KyUlJezYsYOYmBhDmVqtJiYmhpSUFAtGVjtMOd/IyEj27dvH2bNnycvL43//+x+xsbGWCtlkDf27NOX8FEVh+PDh9O7dmyFDhlgqVLOZcm5ZWVlcvXoVgJycHDZu3EhoaKhF4jWXKec3Y8YMTp8+zYkTJ5g5cybPPPMM06ZNs1TIJjPl3PLz8w3fXV5eHr/++ivh4eEWiddcppxf9+7dOX/+PFeuXEGn07Fx40batWtn1nFkPupbuHjxIlqtFl9fX6NyX19fDh48aPJ+YmJi2L17N/n5+TRv3pzly5cTHR1d0+HeMVPO18bGhlmzZnH//fej0+n4xz/+US961pr6XdaX7+pmppzfli1bSEpKolOnTob7bP/973/p2LFjXYdrFlPO7eTJk4wePdrQiWzChAlWf17laur3jDUy5dyysrJ49NFHAX3v/WeeeYbu3bvXeazVYervzLfffpuePXuiKAp9+vTh4YcfNus4kqjrwC+//GLpEGrUI488wiOPPGLpMGpFQ/uubvSXv/wFnU5n6TBqRWRkJLt27bJ0GHVi+PDhlg6hRrVs2ZLdu3dbOoxa1a9fvzvqrS9N37fg5eWFRqOp0CklKysLPz8/C0VVexry+Tbkc4OGfX4N+dygYZ9fQz43qLvzk0R9C3Z2dnTt2pXk5GRDmU6nIzk5uV40h5qrIZ9vQz43aNjn15DPDRr2+TXkc4O6O79G3/Sdl5fHkSNHDJ+PHz/Orl27aNq0KUFBQSQmJjJs2DC6detGZGQks2fPJj8/nxEjRlgw6upryOfbkM8NGvb5NeRzg4Z9fg353MBKzq96ndQbjvXr1ytAhWXYsGGGOnPmzFGCgoIUOzs7JTIyUtm2bZvlAr5DDfl8G/K5KUrDPr+GfG6K0rDPryGfm6JYx/nJWN9CCCGEFZN71EIIIYQVk0QthBBCWDFJ1EIIIYQVk0QthBBCWDFJ1EIIIYQVk0QthBBCWDFJ1EIIIYQVk0QthBBCWDFJ1EIIIYQVk0QtRCM0fPhw4uPjLXb8IUOG8Pbbb5tU929/+xuzZs2q5YiEsF4yhKgQDYxKpbrl+unTp/Piiy+iKAoeHh51E9QNdu/eTe/evTl58iQuLi63rb9v3z569uzJ8ePHcXd3r4MIhbAukqiFaGAyMzMN75OSkpg2bRqHDh0ylLm4uJiUIGvLqFGjsLGxYf78+SZv0717d4YPH864ceNqMTIhrJM0fQvRwPj5+RkWd3d3VCqVUZmLi0uFpu/77ruPCRMm8MILL9CkSRN8fX35/PPPDdP1ubq60rp1a/73v/8ZHWvfvn3069cPFxcXfH19GTJkCBcvXqwyNq1Wy7fffkv//v2Nyj/++GPatGmDg4MDvr6+/PWvfzVa379/f5YuXXrnPxwh6iFJ1EIIAL788ku8vLxITU1lwoQJjB07lscff5wePXqwc+dO+vTpw5AhQygoKAAgOzub3r1706VLF7Zv387atWvJyspi0KBBVR5jz5495OTk0K1bN0PZ9u3bef7553n99dc5dOgQa9eupWfPnkbbRUZGkpqaSnFxce2cvBBWTBK1EAKAzp078/LLL9OmTRumTJmCg4MDXl5ePPPMM7Rp04Zp06Zx6dIl9uzZA8DcuXPp0qULb7/9NmFhYXTp0oWFCxeyfv16Dh8+XOkxTp48iUajwcfHx1B26tQpnJ2defjhhwkODqZLly48//zzRtsFBARQUlJi1KwvRGMhiVoIAUCnTp0M7zUaDZ6ennTs2NFQ5uvrC8D58+cBfaew9evXG+55u7i4EBYWBsDRo0crPUZhYSH29vZGHd4efPBBgoODadmyJUOGDOHrr782XLWXc3R0BKhQLkRjIIlaCAGAra2t0WeVSmVUVp5cdTodAHl5efTv359du3YZLenp6RWarst5eXlRUFBASUmJoczV1ZWdO3fyzTff4O/vz7Rp0+jcuTPZ2dmGOpcvXwbA29u7Rs5ViPpEErUQolruuusu/vzzT0JCQmjdurXR4uzsXOk2ERERAOzfv9+o3MbGhpiYGN577z327NnDiRMn+PXXXw3r9+3bR/PmzfHy8qq18xHCWkmiFkJUy7hx47h8+TKDBw/mjz/+4OjRo/z000+MGDECrVZb6Tbe3t7cddddbN682VC2Zs0aPvroI3bt2sXJkydZvHgxOp2O0NBQQ51NmzbRp0+fWj8nIayRJGohRLUEBASwZcsWtFotffr0oWPHjrzwwgt4eHigVlf9q2XUqFF8/fXXhs8eHh6sWLGC3r17065dO+bPn88333xDeHg4AEVFRaxatYpnnnmm1s9JCGskA54IIepUYWEhoaGhJCUlER0dfdv6n3zyCStXruTnn3+ug+iEsD5yRS2EqFOOjo4sXrz4lgOj3MjW1pY5c+bUclRCWC+5ohZCCCGsmFxRCyGEEFZMErUQQghhxSRRCyGEEFZMErUQQghhxSRRCyGEEFZMErUQQghhxSRRCyGEEFZMErUQQghhxSRRCyGEEFZMErUQQghhxf4f1sg6ukY+HEgAAAA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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(5, 4))\n", + "\n", + "bounds = [1e-1, 1e6]\n", + "model.plot(axes, 'Eq Composition Alpha', bounds, color='k', linewidth=0.5)\n", + "model.plot(axes, 'Composition', bounds, label='Inf. Diff')\n", + "model_nodiff.plot(axes, 'Composition', bounds, label='No Diff', linestyle=(0,(5,5)))\n", + "\n", + "axes.legend(loc='upper right')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", + "2. N. Dupin, I. Ansara and B. Sundman, \"Thermodynamic Re-assessment of the Ternary System Al-Cr-Ni\" *Calphad* 25 (2001) p. 279\n", + "3. A. Engstrom and J. Agren, \"Assessment of Diffusional Mobilities in Face-centered Cubic Ni-Cr-Al Alloys\" *Z. Metallkd.* 87 (1996) p. 92" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('base')", + "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": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/03_Multiphase_Precipitation.ipynb b/examples/03_Multiphase_Precipitation.ipynb new file mode 100644 index 0000000..30b0084 --- /dev/null +++ b/examples/03_Multiphase_Precipitation.ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiphase Systems\n", + "\n", + "Kawin supports the usage of multiple phases. Nucleation and growth rate are handled for each precipitate phase independently. Coupling comes from the mass balance where all precipitates contribute to the overall mass changes in the system.\n", + "\n", + "In the Al-Mg-Si system, several phases can form including: $ \\beta' $, $ \\beta\" $, B', U1 and U2. To model precipitation of these phases, they must be defined in the .tdb file, the Thermodynamics module and the PrecipitateModel module.\n", + "\n", + "When defining the thermodynamics module, the first phase in the list of phases will be the parent phase." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import MulticomponentThermodynamics\n", + "\n", + "phases = ['FCC_A1', 'MGSI_B_P', 'MG5SI6_B_DP', 'B_PRIME_L', 'U1_PHASE', 'U2_PHASE']\n", + "therm = MulticomponentThermodynamics('AlMgSi.tdb', ['AL', 'MG', 'SI'], phases, drivingForceMethod='approximate')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In defining the precipitate model, all precipitate phases must be included. Since we already have our list of phases, we can use that and remove the parent phase." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.precipitation import PrecipitateModel, VolumeParameter\n", + "\n", + "model = PrecipitateModel(phases=phases[1:], elements=['MG', 'SI'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model inputs\n", + "\n", + "Setting up parameters for the parent phase and overall system is the same as for single phase systems. Here, it is just the composition (Al-0.72Mg-0.57Si in mol. %), molar volume ($1e$-$5\\text{ }m^3/mol$).\n", + "\n", + "The temperature will be divided into two stages: a 16 hour temper at $175\\text{ }^oC$, followed by a 1 hour ramp up to $250 ^oC$. To do this, there needs to be three time designations: $175\\text{ }^oC$ at 0 hours, $175\\text{ }^oC$ at 16 hours and $250\\text{ }^oC$ at 17 hours. The temperature can be plotted to show the profile over time. Here, a parameter called timeUnits is passed to convert the time from seconds to either minutes or hours." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model.setInitialComposition([0.0072, 0.0057])\n", + "model.setVolumeAlpha(1e-5, VolumeParameter.MOLAR_VOLUME, 4)\n", + "\n", + "lowTemp = 175+273.15\n", + "highTemp = 250+273.15\n", + "model.setTemperature(([0, 16, 17], [lowTemp, lowTemp, highTemp]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setting parameters for each precipitate phase is similar to single phase systems except that the phase has to be defined when inputting parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "gamma = {\n", + " 'MGSI_B_P': 0.18,\n", + " 'MG5SI6_B_DP': 0.084,\n", + " 'B_PRIME_L': 0.18,\n", + " 'U1_PHASE': 0.18,\n", + " 'U2_PHASE': 0.18\n", + " }\n", + "\n", + "for i in range(len(phases)-1):\n", + " model.setInterfacialEnergy(gamma[phases[i+1]], phase=phases[i+1])\n", + " model.setVolumeBeta(1e-5, VolumeParameter.MOLAR_VOLUME, 4, phase=phases[i+1])\n", + " model.setThermodynamics(therm, phase=phases[i+1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the model\n", + "\n", + "As with single precipitate phase systems, running the model is exactly the same.\n", + "\n", + "kawin currently implements two iterative methods for solving a model: Explicit euler and 4th order Runga Kutta. The Runga Kutta method is used by default, but we can input a different iterative method when solving. Here, we'll use explicit euler to have the model solve a bit faster.\n", + "- Another note: the solverType parameter in the solve function can take in either a SolverType enumerator or an Iterator from kawin.solver.Iterator which allows for custom iteration schemes that are not yet implemented in kawin to be used." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nucleation density not set.\n", + "Setting nucleation density assuming grain size of 100 um and dislocation density of 5e+12 #/m2\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMG\tSI\t\n", + "0\t0.0e+00\t\t0.0\t\t448\t\t0.7200\t0.5700\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tMGSI_B_P\t0.000e+00\t\t0.0000\t\t0.0000e+00\t1.2935e+04\n", + "\tMG5SI6_B_DP\t0.000e+00\t\t0.0000\t\t0.0000e+00\t6.4812e+03\n", + "\tB_PRIME_L\t0.000e+00\t\t0.0000\t\t0.0000e+00\t8.0247e+03\n", + "\tU1_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t7.5291e+03\n", + "\tU2_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t7.1709e+03\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1200: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / (t - self.time[startIndex]))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMG\tSI\t\n", + "10000\t6.1e+04\t\t266.0\t\t523\t\t0.0619\t0.2062\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tMGSI_B_P\t2.059e+22\t\t1.0246\t\t4.8723e-09\t7.3873e+02\n", + "\tMG5SI6_B_DP\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-4.5913e+03\n", + "\tB_PRIME_L\t2.364e+04\t\t0.0000\t\t1.1430e-09\t-2.0480e+03\n", + "\tU1_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t1.2776e+03\n", + "\tU2_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-4.8765e+02\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMG\tSI\t\n", + "13685\t9.0e+04\t\t327.0\t\t523\t\t0.0566\t0.2032\t\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tMGSI_B_P\t4.596e+21\t\t1.0328\t\t7.6556e-09\t4.6515e+02\n", + "\tMG5SI6_B_DP\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-4.8030e+03\n", + "\tB_PRIME_L\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-2.2562e+03\n", + "\tU1_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t1.1745e+03\n", + "\tU2_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-6.3866e+02\n", + "\n" + ] + } + ], + "source": [ + "from kawin.solver import SolverType\n", + "\n", + "model.solve(25*3600, solverType=SolverType.EXPLICITEULER, verbose=True, vIt=10000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "Plotting is also the same as with single phase systems. The major difference is each phase will be plotted for the radius, volume fraction, precipitate density, nucleation rate and particle size distribution. In addition, the total amount of some variables, such as the precipitate density and volume fraction, can be plotted." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+GMz+GEwmdAYjeoMRvd6A3mDQfjYY0RsM1Gvairb1Q9DrdBh0Cga9gl5R0OsVjDodOp2CUa8luJZbx5J1Ng2T0YTRaMRkMmI0mTAZjBiNBgxGAwaDgUZxzWjdrgV6nYJepyXEeUntuZ9Bp1MY03IjBoMeo9GA0Whwn0uvnV9vwGTSzhkUGICfxYJO0RJkpdjptF1gavlyu4yMDGaW6wznVOmk+0JsNhvbtm1j2rRzH3adTkf//v3ZtGkToP1jueOOO7jyyisZNWqU57jk5GT8/f0JCgoiPT2ddevWcd999xX7WlarFavV6nmckeG98f1l8eyjD/L0q28D8MyoK3jyw5UoJj+fxiSEKNnfc66H3HRoFefrUIQQQghxAdk2J/uStWU+VbSh0fk5XS5PUl0Sp92KUa/DbNB57k2en/WY3I9Du3chxJmOxWLGbLFgMVuw+Fnw8/PDz3LuPigwgGsGX4JBp8NkUDDqdZ6bSa/DaFAw6HQY9QrK3d97+1fDbd2e8vo5m/br4fVzViXVNuk+deoUTqez0JDwqKgo9u7dC8CGDRtYsmQJ7dq1Y/ny5QD85z//IScnh7vvvttTQO3BBx+kbdu2xb7WrFmzeOaZZyrsvVyMN154wpNwv3rXVUx65zswmHwclRCiVAwWIB3sUsFcCCGEqMrSc+yen+fd3gmXCvn7VHNvupT0ybde8Bxmk5GQoABCgwLRnd9VXpSrWwB3ly1gUaVV26S7NHr37o3LVfQVqJ07d5b6PNOmTSM+Pt7zOCMjw1OQrTJ9NO9NHn7iBQCeG9WbSe98Kwm3ENWJKUC7t8la3UIIIURVlpGrJd1RwRZC/Yv4vh1gIiY8uJKjEtVVKS65VE0RERHo9XqSk5MLbE9OTiY62rsFisxmM8HBwQVulU21ZvLlgtcBiL++A49/sBIM5kqPQwhRDgaLdu+0+TYOIcro7bffpnHjxlgsFrp168bmzZsvePzSpUtp1aoVFouFtm3b8t133xXY/+WXX3LNNdcQHh6OoihFXhDPzc3lgQceIDw8nMDAQG666aZCbb8QQnjb8WMnOLI1gZR/fvd1KKIGqLZJt8lkolOnTiQkJHi2uVwuEhIS6NGjhs0JcLlQ1r3Cl/e25tMJvXj1kx9RzAG+jkoIcbF07iqY51XeFKI6yFsx5KmnnmL79u20b9+eAQMGcPLkySKP37hxI7fccgvjx49nx44dDBs2jGHDhrF7927PMVlZWfTu3ZuXXnqp2Nd95JFH+Prrr1m6dClr167lxIkTRa5oIoQQ3vTHzm1s+uBJ1vznNV+HImqAKj28PDMzk/3793seHzp0iJ07dxIWFkbDhg2Jj49nzJgxdO7cma5duzJnzhyysrI81cxrjF3/hePb0BvN3PrUIgis6+uIhBBlobivc7ocvo1DiDIozYoh+b3xxhsMHDiQKVOmAPDcc8+xatUq5s6dy7x58wA8RU4PHz5c5Gump6czf/58PvvsM6688koAFi5cyCWXXMKvv/5K9+7dvf02hRACAKtVG5VmNFl8HImoCap0T/fWrVvp2LEjHTt2BCA+Pp6OHTsyY8YMAEaOHMmrr77KjBkz6NChAzt37mTlypXVbr3tC0raBTs+0X7ucT9ENPNtPEKIssvr6Valp1tUL3krhvTv39+z7fwVQ863adOmAscDDBgwoNjji7Jt2zbsdnuB87Rq1YqGDRsWex6r1UpGRkaBmxBCXCy7Q7tAXqoCaEKUoEr3dPft2xdVVS94zIQJE5gwYUIlRVTJrGdh7cvaz02vhGb9L3y8EKJq07n/5BZT4FGIqqo0K4acLykpqcjjk5KSSv26SUlJmEwmQkNDS32eqrTiiBCi+nK41wiTpFt4g3yKqrLN70POGQiuD93v93U0QojyUqSnW4iKNm3aNNLT0z23o0eP+jokIUQ15HBqbbVer/dxJKImqNI93bXasa1w4Gft596PgFHmkwhR7XkKqcmcblG9lGXFkOjo6HKvMBIdHY3NZiMtLa1Ab/eFzmM2mzGbZXUPIUT5OGR4ufAiSbqrInsObHxL+7n19RDZyrfx+IjD6cLuVLE5XTicLlTApaqg/R+XqqKq5+4BtN0XnpIgKo6CUvT2ojdfYPu5HUqB7SW/bv5jijm80I7inl/cU4qLr8DxBeLQHvy84S/+89VWQr+xc9e0WEBrzA0GA3qDHr1Oj8FgIDAwkJCQEBRFwWLQYdBLgy98K/+KIcOGDQPOrRhS3BSvHj16kJCQwMMPP+zZtmrVqotaYaRTp04YjUYSEhK46aabANi3bx9HjhypeSuVCCGqFM/wcunpFl4gSXcVk52djd9fS1GyT0NgFFw22tcheYWqqqRl20nJtJJy1sqpTCunM21kWh1kWh2czdXuc+1ObA4XdqdLS7CFqEH8tx3my23JsO1HFiz7sdjjmva5gc63TvE8DjQbCLIYCbYYqBNgIjLIQmSQmbpBZrJPH6dtiyYE+vtVxlsQtVhJK4aMHj2a+vXrM2vWLAAmTpxInz59eO211xgyZAiLFy9m69atvP/++55zpqamcuTIEU6cOAFoCTVoPdzR0dGEhIQwfvx44uPjCQsLIzg4mAcffJAePXpI5XIhRIXK6+nWS0+38AJJuquYB++9k8PbVvH+mDY07TceDNVviFx6jp2jqdkcO5PN0dQcjp7J5tiZHLJtZR9Sq9cp6BQFRdH6DHXuXkRtG57tiqJtE5Uv/zWS/KMNirt2kn9zwWOKfkLB8+ffXszxpXhu4dco+bULvkbR77O447u2a87GTds4ngl2xaiN2lBdqC4XqsvpvrnQ6Qv+ac67OJWYXvB8ToedLyb0BSCgTiQR9WKp37AxjRvH0aJ5c9pe0oLL2rSkYUykDI8T5TZy5EhSUlKYMWMGSUlJdOjQocCKIUeOHCnwOevZsyefffYZTzzxBNOnT6d58+YsX76cNm3aeI5ZsWJFgWU+b775ZgCeeuopnn76aQBef/11dDodN910E1arlQEDBvDOO+9UwjsWQtRmUkhNeJOillQeXBSSkZFBSEgI6enpBAcHe+28v//+Ox07dkRVVTbOHkWPhz8qfqxrFWBzuDh6JpvDp7L497SWWB89k83ZXHuRxysohAWaqBuo9dCFB5gIshgJshgIMBsIshjwM+oxGXSY9DqMBh1GvYJJryswnFeIamv9a1qths7joM2Nns3F/Rl2qZBlc5CRYycjx0F6jp3ULBspZ3M5edbKgUOHWfDwDThsuRd8WaNfIEMfeoFeVw0kKthMVJCFyGAz0cEWwgJMMny9klVUGyKKJr9vIURZ3Pv4LN6bOZ2OVwxg+9qVvg5H+IA32w/p6a4iVFVl8sQHUFWVkV1j6DHm6SqVcOfanRw+ncXhU1kcOpXNoVOZHE/LwekqnCwoKEQGm4mt40+DOn7EhvkTW8efmFALRvlyL2o1979pteCSYcVdVNIrEGwxEmwxQp2ijmjFu/dkcfBoIjv+3Muuv/7h7/37OXzoIIlHj3Aq8QjZZ1Kw52SS5jKx+dDpQmfQKQoRgWYtGQ+24ExP4sTfu2jepBGXNIujdbPG+Fmq34gbIYQQojx6D7qJxNC2XHlpfV+HImoASbqriJUrV7J67QZMBh2zHh4FYU18FovTpXI0NZu/k8/yz8lMDpzMJDE9t8gCZYFmI03qBtAwzN+dXPtRv44fZoMUnRCiEMX7F510Oh3NGtWnWaP6jBh8VaH9aWcz2b57H+Y60Zx16EhK13rJkzOsnDybi93p4uTZXE6ezWXX8XQOrF/B1k9eyhezgn9IOKF1o4mIqk90/frENmhAXKNG3HDjDdQN9ifU3ygX1IQQQtQoBpMFv5BwQkJCfR2KqAEk6a4CVFVlxvTHAHjwqkbEDazcNbnTc+zsP3mWf5Iz+Ts5kwMpmVgdhdcRDgswERcRQKPwAJpEBNA4IoDwAJMM/RaitJSie7orUmhQIFf26FTkPlVVOZNtJzkjLxHP5cfEBqS06cyZk0lkpibjctjJTjtFdtopTvyzmz/cz9XpDfxhbIninusWaDYQ6m+ijr+RUH8Tof5G6vibqONvIvPUcQJNelrExRIeKsN7hRBCVH15XU06nXzPFeUnSXcV8MMPP7B15y78TDoeu/dWqNOoQl8vPcfOX4kZ7DmRwZ7EDI6dyS50jJ9RT/OoIJpFBtI8MpAmdQMJ8TNWaFxC1Hh5Pd1VpJSGoiiEBZgICzBxSYy27f86T4CntCWgnE4nB48ksufAIf4+cJiDh//l6NFjJJ44Ro7VRkSwH+nZdhwul6fY27EzhV9n4/tPcnRbAgAGsz/+wXUIqhNOcJ1wwsIjiKgbSWRkJC1bteLqa64h2GIkxE+r9yDzzYUQQvhCXlMtKbfwBkm6fUxVVZ57egYA9/ZtRN0rxnn9NXJsTnYdT2fX8XT2nMjgeFrhJLt+qD/NowJpERVI88ggGtTxkx5sIbyu8nu6y0Ov19M8rgHN4xpA/8uLPEZVVTKtDtKy7ZzJthV5v8NkQG+y4LTl4rBmk5GSTUbKcY6fd66YNj3Y4ogtsC3AZCDYT1suLchiJNBiINBs4JcVn+FnMlC3bgRRdcOpFxVJ/ei6xMZEEhoUKH+/hBBClEvetEppT4Q3SNLtY2vWrGHjb1swG3RMGTPEK3O5VVXl2JkcdhxN4/ejaexNyihU8KxhmD+tY4JpXS+EVjFBWqEmIUTF8szprho93d6gKIp7FQIjsWH+RR7z9HUrcblcpKSmc+Docf49lsSxxCQSk5JJSk4m5WQKqadTiGjcioZh/qS7q7WrqGTZHGTZCi+X9tWbL2LLyijy9fRGM+bAYPyDQukx9Db6Xncz/mYDASY9fiYD/iY9fiY9Ae6f/U16zHqF3Mx0YuqG4e9n8favSQghRDWT99VZcm7hDZJ0+9jrs18DYNzlDYjpdWuZz+NwuvjzRAZbDqey40gap7OsBfZHBVto3yCUNvUlyRbCZ3wwp7uq0Ol0REXUISqiDj07tinx+Lwe9PR8y6VluYewn7U6ONpnAGmppzmbnkZWRho5mWnkZqajOp047Vayz6SQfSaFI8mn2Hw4tcTXy0pN4ptp2jJueqMZk38gloAg/AIC8Q8MJiAwiMCgYIJDQhl40620aNnyXAJv1BPgSer1+JsM6GUOoBBCVG/u8eWKDDAXXiBJtw+pqkrfNvXZ/ZsfE6/vBLHdLur5uXYnfxxLZ/Oh02w/kka2zeHZZ9TraF0vmI6xobSPDSUmxM/b4QshLlYVm9NdleXvQS9qubRR3/630La83vSjSSc5nnSSpJOnCI9pSFBkfXJsTrJsDrKtTrJtTrLtDm2b1UmO3UFO8rkLlU67lZx0Kznppyliijo5UW2JOn3hZdRMBj2p+7aw6dPZ5xL3oCCCgoJp1aHzxf46hBBCVDJPITXJuYUXSNLtQ4qiEH9FKA8374eu882gK7lgkMulsvtEOmv3pbDl3zPY8lUZD/Uz0blxHTo1qkPresGybJcQVU7t7emuDPl70zu3aXmRz+6E9bGbSD59huSUVFJOnyEl9Qynz5zhdGoaZ9LSSEtPJz09g97d2+MXHn4ukbc53T87PX+TbQ4nKSeTST1+sNArnUi3FtomhBCianG58uZ0+zgQUSNI0u1LZ5Mg+U9tKYKmV17w0PRsOz/sSWLNvpOkZtk82yODLHSJC6NbXBjNI6V4kBBVWi0eXl4dmE1GGsZE0jAmsszncDhd5Ni1JPzfy6PZNagLp9PSSD2TRlpaGmfOpBPZqBlvrV/uvcCFEEJ4XV5PtwwvF94gSbcvHfhJu49pDwERRR6SmJ7D/3ae4Jd/TuFwaV/UA80GejSNoE+LujStGyCJthDVheffqgwvr6kMeh1Beh1BFiORwXF0uSSu0DEZGRm8Nf0+H0QnhBCitP7atok/E9bQxHoF/9fldl+HI6o5Sbp9RVVhv7ZuLc2uKrT7TJaNL7Yd4+d9J3G55382iwxkcNsYujQOwyhr1wpR/XjmdEtPtxBCCFGV/bl1A7tXfECsvxMelKRblI8k3b5y+gBkJoPeBA17eja7XCo/7kni881HsbrnBnZsWIcbOtanRVSQr6IVQnhF3vBy6ekWQgghqjLVPcJUJyNKhRdI0u0r/27Q7ht0BqO2JuzJs7nM/Wk/fyefBaB5ZBC3dmvIJTHBvopSCOFNUr1cCCGEqBacTq3zS1eKQsdClESSbl858qt237AHAH8cS+PNhH/ItDrwM+q5tVsj+l8SKfO1hahJZE63EEIIUS2o7qlger2sBiTKT5JuX0g7CulHQaeHBl3YsP8Ub/+8H5eq0qRuII/0b0HdoAuvASuEqIZkTrcQQghRLUhPt/AmSbp9Ia+XO7odG45qQ8pVVHo3i+DuK5piMsg/biFqJpnTLYQQQlQHeet066SnW3iBJN2VSFVVBg8eTNvAM0y5PAhbq3a8u+YAKipXtYrizsvjZDi5EDWZ9HQLIYQQ1ULe8HLp6RbeIJ+iSrRnzx5WrlzJG8u3oNcrzN0bhMPlokvjMEm4hagNZE63EEIIUS24ZHi58CL5FFWiZcuWAXDNpRGkKiEcsgYTE+LHA/2aScItRG0gPd1CCCFEteByLxmm18nwclF+knRXoq+++gqAIe3r8mtuQxRFx719mmIxyj9mIWoHmdMthBBCVAdGkwWTfxBmi8XXoYgaQOZ0V5Ljx4+zc+dOFAU6NgriB2NzBlwaRcvoIF+HJoSoLHkjWqSnWwghhKjSbrh/OlED7mZEz8a+DkXUANLTXUlWrlwJQIeGwQRaDKQFX8LILg19HJUQolLJnG4hhBCiWnB5RqXJFFBRfpJ0V5Lvv/8egJ7NQknVRzC816X4mWRYuRC1iszpFkIIIaqFvJxbyi4Jb5Dh5ZXAbrezatUqAHo3r0NGUHMGNw33cVRCiMqXN7zct1EIIYQQNU1WVha5ublYrdYCN4fDwSWXXIK/v/9FnU/6uYU3SdJdCTZu3EhGRgYhgRYurR+I4dLOUq1ciNpIerqFEELUQKqqYrfbMZlMpTp+xYoV7N27l5ycnCJveQlz+/bteeWVV0p1znbt2nHw4MEi94WHh7Ns2TL69OlT6veU19Wtk+/swgsk6a4E3333HQC9m4XgZ9LTtF1XH0ckhPAJKaQmhBDCR1RVJTs7m8zMTLKysmjUqBF6fclTHZcsWcLixYvJysoiMzOT7OxssrOzCyXK0dHRnDhxolSxLFy4kOXLl5d4nMPhKNX5AE/Cr9frMZvNmM1mTCYTOTk5nD59mptuuol9+/YRHl660aYuGV4uvMgrSbfdbicpKYns7Gzq1q1LWFiYN05bY9xzzz3sOXGGG82bCA4JRQlt5OuQhBA+IS23EEKIirN161bmzp1Leno6aWlppKenF/jZ6XR6jj158iR169Yt8Zx///13qRLknJycUsfZr18/goOD8fPzw9/fHz8/P8/NYrFgsVgwmUzUr1+/1OfcsWMHRqOx0IWE3NxcunTpwu7du3n33Xd54oknSnU+1T3AXJJu4Q1lTrrPnj3LJ598wuLFi9m8eTM2mw1VVVEUhQYNGnDNNddw991306VLF2/GWy1lm8O5vEtrumftpU7DNvKvV4haTyZ1CyGE0LhcLk6ePMnx48cL3BITE0lJSeHUqVPMnj2b7t27l3iupKQkPvrooxKPCwgIIDs7u1TxDR48mLp16xIYGEhAQAABAQEFkuT8SXNpPfTQQ6U+trQsxaynbbFYeOyxx3j44YfJyMgo9fnOFVKT7+2i/MqUdM+ePZsXXniBpk2bMnToUKZPn069evXw8/MjNTWV3bt3s379eq655hq6devGW2+9RfPmzb0de4mOHj3KqFGjOHnyJAaDgSeffJIRI0YA8M033zBp0iRcLhePPfYYd955Z4XFsfbvFKIdxwnxM2Ks26zCXkcIUcV5hpdL0i2EELXdkSNH6N27N4mJiSUOoz569Gipku5LL72UF198kdDQUEJCQjy3vMfBwcEEBASg05V+AaNOnTrRqVOnUh9fFd18883ccsstpRpOn8czvLyCYhK1S5mS7i1btrBu3TouvfTSIvd37dqVcePGMW/ePBYuXMj69et9knQbDAbmzJlDhw4dSEpKolOnTgwePBiz2Ux8fDw///wzISEhdOrUiRtuuKHUczwuhtXhZOvhVG5xnCA01AhhTbz+GkIIIYQQwvfsdjtnz54t1VTLqKgojh07hqqq6HQ6oqKiqF+/vudWr1496tatS926denWrVupXj8uLo7HHnusvG+jxjEYLj7lOTe8XNJuUX5lSro///zzUh1nNpu59957y/ISXhETE0NMTAwA0dHRREREkJqaytGjR7n00ks980QGDRrEjz/+yC233OL1GHYeSSPXZqcByfiZzBDe1OuvIYSoLvIabunpFkKImiA1NZVffvmFjRs3smnTJrZs2cKoUaN47733Snyu2Wxmy5YtREVFER0dXabEUFSctUs+YM+ff9DT/0H6tLje1+GIaq70Y0t8YN26dQwdOpR69eqhKEqRRRzefvttGjdujMVioVu3bmzevLnIc23btg2n00lsbCwnTpwoUJihfv36HD9+vELew8YDpwl3phBmAcVggaB6FfI6QohqQIaXCyFEteZ0Ovn11195+umn6dGjB3Xr1uX666/npZdeYt26deTk5BS7bFVROnXqRIMGDSThroIO7t7C0a0JJB8/6utQRA1w0f/Cz5w5g6qqhIWFkZKSwvr162nZsmWxQ83LIysri/bt2zNu3DhuvPHGQvuXLFlCfHw88+bNo1u3bsyZM4cBAwawb98+IiMjPcelpqYyevRoPvjgA6/HeCEOp4vfj6XRzHmCED8D1ImDi5hDI4SoaaSnWwghqqt3332X559/vtCyWC1btqR379707NmTnj170qJFCx9FKLzJ5dSW99Tp5bu7KL+LSro//PBDZs6cCcCUKVP49NNPad++PU899RQTJ070ejGyQYMGMWjQoGL3z549m7vuuouxY8cCMG/ePL799lsWLFjA1KlTAbBarQwbNoypU6fSs2dPAOrVq1egZ/v48eN07er9tbP3Jp0l1+6ksZKMxaiX+dxCCCGEENWUoiicOHGCkJAQrr76agYOHMg111xDbGysr0MTFUBVtaRbryt98TUhinNRSfebb77Jn3/+SU5ODg0bNuTQoUPUrVuX9PR0+vTpU6EVwM9ns9nYtm0b06ZN82zT6XT079+fTZs2AaCqKnfccQdXXnklo0aN8hzXtWtXdu/ezfHjxwkJCeH777/nySefLPa1rFYrVqvV87i0yw3sPJoGwCX+GSgoUEfW5xaiVpPh5UIIUW2NGDGC+vXrc80112A2m30djqhgLpe2prn0dAtvuKhPkcFgwM/Pj7CwMJo1a0bdunUBCAkJqfTKfqdOncLpdBIVFVVge1RUFElJSQBs2LCBJUuWsHz5cjp06ECHDh3YtWsXBoOB1157jX79+tGhQwcmTZp0wcrls2bNKrDsQmmvaOYl3bH6VG1DSIOLf6NCiBpEKqAKIUR1FR4eztChQyXhriVU95phF7PMmBDFuaiebr1eT25uLhaLhbVr13q2Z2Zmej0wb+jduzcul6vIfddddx3XXXddqc4zbdo04uPjPY8zMjJKTLzXbtzM8rdfpH6rzoR1O6X1cIXI8CMhBMicbiGEqDryvtsKkV/e8PKLWdNciOJc1Kdo9erVnqt7ISEhnu3Z2dm8//773o2sBBEREej1epKTkwtsT05OJjo62quvZTabCQ4OLnAryRdff8+BtV9xevt3GBQFjH7gV8ercQkhqhkZXi6EEFVGZmYmU6ZMoUWLFqWeOihqhuzs7BKPcTm14eV6SbqFF1zUp6i4YeSRkZF06dLFa0GVhslkolOnTiQkJHi2uVwuEhIS6NGjR6XGUpS8eeVdW8dpG0IanPvCLYQQQgghfGbdunW0b9+eV199laNHj/Lf//7X1yGJSrBv3z6io6Np2rRpiceqqgwvF97jlUUBc3Nz+eOPPzh58mSh4dylHcJdlMzMTPbv3+95fOjQIXbu3ElYWBgNGzYkPj6eMWPG0LlzZ7p27cqcOXPIysryVDP3pX27dgBwVdsY4IwMLRdCIEuGCSGEb9lsNqZNm8brr7+Oqqo0bNiQd999l8GDB/s6NFEJQkNDSU5ORlEU7HY7RqOx2GPzCqnppZCa8IJyJ90rV65k9OjRnDp1qtA+RVFwuodmlMXWrVvp16+f53HevOoxY8awaNEiRo4cSUpKCjNmzCApKYkOHTqwcuXKQsXVKtuxxCQyU0+CotC/eQCkIkm3ECJfzi1JtxBCVLYTJ04wYsQINm7cCMCdd97Ja6+9Vqppg6JmqFu3LkajEbvdTlJS0gVrNEU1bold1RMSGlaJEYqaqtxJ94MPPsiIESOYMWOG15Pdvn37eoZ2FGfChAlMmDDBq69bXgm/bAYgODKWSCVN2xhS33cBCSGqCOnpFkIIX9i0aRM33ngjSUlJhISE8PHHH5drNKbwElUFlwOcdnDZwenQHrscoDrB5b6pznPbXU5QXe59JR3rAlSIagNRrdHpdMTExHDkyBGOHz9+waR70P1PczAlkw6dW1Xe70PUWOVOupOTk4mPj/d573JVsnHLdgAaNmuFkqktX0aQd4u7CSGEEEKIkq1YsYKRI0eSm5tLmzZt+Oqrr2jWrJmvw6oaXC5wWsFhBacNHLlaAuzIzbct/71V218gSbaft81exDaHdo4Cz7FpSXJl6TIeLr2B+vXrc+TIEY4dO3bBw/M6/ip7WWRRM5U76R4+fDhr1qwpVUGC2uKPP34HoHXrS8DqnpMeKEm3ELWeNNxCCFHp/ve//5Gbm8uQIUNYsmQJAQEBvg6p7FQV7DlgPavd7FnaY3u2+/78n8977Mh1J9k27b4yk97SUBTQGbSbogdd3i3vsQF0unw/G84d49mW717RgzUDjm6GrQuhfmcaNGgAwPHjxy8YSt5gW2m5hTeUO+meO3cuI0aMYP369bRt27ZQQYKHHnqovC9R7Rzc+ycA3Vs3BvaDKRBM/j6NSQhRhcicbiGEqDTz5s2jXbt23H///RcsnOUTqgq2LMhJhZwzkO2+z0mF3HSwZp5LsG2ZWgJZUW2I3gh6MxhM591btH0Gi7ZNZ9Qe6wygN7l/NoLe/TgvGS5yn/tnndH9WO9+Xfe+ilqea9VTcHwb/PU19etrUz5LnXRL1i28oNxJ9+eff86PP/6IxWJhzZo1BYZgKIpS65Lu3FwrKccOAtCvTT04BgTJ0HshBMj1clHdvf3227zyyiskJSXRvn173nrrLbp27Vrs8UuXLuXJJ5/k8OHDNG/enJdeeqlAlWhVVXnqqaf44IMPSEtLo1evXrz77rs0b97cc8zff//NlClT2LBhAzabjXbt2vHcc88VKLQqxIUYjUYmTpzouwBsWZB+HDKT4WwSZCad+zn7tDYE+2LpTWAOBGOA1rFj9AODn3ZvdD8ucPPXkmajn/Zcg8V9b9ZuelPNzi5bX68l3YfXU7/eZUApkm53/RVdTf69iEpT7qT78ccf55lnnmHq1KnoZPF4DiSm0KBDH3LTUmgbZdSSbhlaLoSAc19opKdbVENLliwhPj6eefPm0a1bN+bMmcOAAQPYt28fkZGRhY7fuHEjt9xyC7NmzeLaa6/ls88+Y9iwYWzfvp02bdoA8PLLL/Pmm2/y0UcfERcXx5NPPsmAAQPYs2cPFosFgGuvvZbmzZvz008/4efnx5w5c7j22ms5cOAA0dHSvooqxOWEM4ch9aB2n3YE0v7Veq9LYvQH/zDwCwO/OuBfByyhYA7WkmtzkDZy0hICpgAtURalF91Ou+BgPUt0oNYWnzx58oJPcUlTLbyo3Em3zWZj5MiRknC7WfUB9Lz7eRqFBaDP3qRtlJ5uIYQQ1dzs2bO56667GDt2LKAN2f32229ZsGABU6dOLXT8G2+8wcCBA5kyZQoAzz33HKtWrWLu3LnMmzcPVVWZM2cOTzzxBNdffz0AH3/8MVFRUSxfvpybb76ZU6dO8c8//zB//nzatWsHwIsvvsg777zD7t27JekWvmXLhqRdcHIPpOyDU39rxcGK4ldHK6obFA2BUVqHTFAUBNTV9kkSXbH0Bi3xPvobkbp0oOSkW4aXC28qd9I9ZswYlixZwvTp070RT7WXmJYLQEyoRRs6BNofVyGEkCXDRDVls9nYtm0b06ZN82zT6XT079+fTZs2FfmcTZs2ER8fX2DbgAEDWL58OQCHDh0iKSmJ/v37e/aHhITQrVs3Nm3axM0330x4eDgtW7bk448/5rLLLsNsNvPee+8RGRlJp06dvP9GhbgQVdV6sY9uhhM7IOWvwiOXjH4Q3hzqNILQRu77hlrvtPCtuq3g6G9cGuZk9uzZNGrU6IKHy/By4U3lTrqdTicvv/wyP/zwA+3atStUoGL27NnlfYlqJTE9B4CYEAscz0u65Uq8EAIZXi6qrVOnTuF0OgstDxoVFcXevXuLfE5SUlKRxyclJXn2520r7hhFUVi9ejXDhg0jKCgInU5HZGQkK1eupE6dOkW+rtVqxWq1eh5nZGRcxDsV4jyqCmcOwaH1cHi9Ng87v6AYiG4LkZdA3ZYQEitdo1VVuLbSUn39KR55ZGaJh0tPt/Cmcifdu3btomPHjgDs3r27wL7auK5dYrq7pzvED+f+NOymcDCFQW6ujyMT1YXRaESv1/s6DFEhpKdbiIuhqioPPPAAkZGRrF+/Hj8/Pz788EOGDh3Kli1biImJKfScWbNm8cwzz1z0azmdTuz2MhS0EjWTLQeObYZD6yAjX8EtvxiIulS7RbbCGFpP2uzqIqyJdp9xQpsaUMLKQp51uqUIqvCCMifdM2bM4Prrr+fnn3/2ZjzVXpI76Q5zpfN3k7HakgmnsuH0IR9HJqqT0NBQoqOja+WFKyFE1RMREYFeryc5ObnA9uTk5GLnVUdHR1/w+Lz75OTkAslzcnIyHTp0AOCnn37im2++4cyZMwQHBwPwzjvvsGrVKj766KMi55JPmzatwLD2jIwMYmNji31vqqqSlJREWlpasceIqm316tX89ttvTJkyBZPJVL6TuZzuda1zAQtEXgOR5KvybT7X9XkyE07+LW12deEXqhWiy03XLqRENL/g4V88exeZGWnccel/aRl9WeXEKGqsMifdx44dY9CgQZhMJoYOHcr111/PlVdeWf4/dtVYrt3J6Swr3aN16Jy5REbF4G82otRp6OvQRDWhqirZ2dme4h5F9eKIaky+kIlqymQy0alTJxISEhg2bBgALpeLhIQEJkyYUORzevToQUJCAg8//LBn26pVq+jRowcAcXFxREdHk5CQ4EmyMzIy+O2337jvvvsAyM7OBihUrFWn0+FyuYp8XbPZjNlc+qJUeQl3ZGQk/v7+kjhVM8eOHWPGjBmkpaXRrl27si9V67RryZgtEwjWbjqju2p4gLae9Hmkza6GguuXOulOPX6QnIwzOO3FFMcT4iKUOelesGABLpeLDRs28PXXXzNx4kQSExO5+uqruf7667n22msJCwvzZqxVXspZKyYddIgyERURRrg+S1v30L3siRCl4efnB2hVNSMjI2XYWk0kc7pFNRQfH8+YMWPo3LkzXbt2Zc6cOWRlZXmqmY8ePZr69esza9YsACZOnEifPn147bXXGDJkCIsXL2br1q28//77gDYF7eGHH+b555+nefPmniXD6tWr50nse/ToQZ06dRgzZgwzZszAz8+PDz74gEOHDjFkyJByvyen0+lJuMPDw8t9PlG5VFXloYceIi0tja5du/LII48Uqi1UIpcTcs5AThqgglGvFUPzC9PuS7gII212NRNcT6s2n5FY4qGq+8KeTl/u2bhClG9Ot06n4/LLL+fyyy/n5Zdf5q+//uLrr7/mvffe4+6776Zr165cd9113HLLLdSvX99bMVdZp7NsBBjBqNfhbzaCgyKvjApREn9/bZ6R3W6XBrxGkTndovoaOXIkKSkpzJgxg6SkJDp06MDKlSs9hdCOHDlSoEe6Z8+efPbZZzzxxBNMnz6d5s2bs3z5cs8a3QCPPvooWVlZ3H333aSlpdG7d29WrlzpWaM7IiKClStX8vjjj3PllVdit9u59NJL+d///kf79u3L/Z7y5nDn/c0V1ctHH33E999/j9lsZtGiRReXcKsqWM9C9ikt8YZza2Ub/S4qDmmzq5Fgdz6Sf55+MfKSbr1eRr+I8vPqpZtLLrmESy65hEcffZSUlBRWrFjBihUrAJg8ebI3X6pKOp1pRQH0OgVFdf8B18nVMXHxZHhjDSXVy0U1N2HChGKHk69Zs6bQthEjRjBixIhiz6coCs8++yzPPvtsscd07tyZH3744aJjvRjyN7f6OX78uGfqwjPPPMMll1xS+ic7HZB1EmxZ2mO9UVsv2+hfpmlA8vmpRoLrafcZJ0o8VFW1pNsgPd3CCyrsU1S3bl3Gjx/P+PHjK+olqpyk1LOoqqqt55c310ySbiGEEEIIr5o0aRLp6el06dKFSZMmlf6Jtiw4mwyqE1DAv442lFwS59ohyF34MetkiYfmVS/Xn1dTQoiy8MqnaMKECaSmpnrjVNXaW889yof3XMlXXywB1aFtlKT7ghRFYfny5b4OQ4hKIsPLhRDVU1Vqr9esWcOSJUtQFIX33nsPg6EU37VUFbJPaz2cqlOrRB4aC/7hknDXJv7uelM5adqIhwtQ3dMO9HpJukX5lflTdOzYMc/Pn332GZmZmQC0bduWo0ePlj+yauhk4gkcNisBAQHn5gdVk6RbUZQL3p5++ulin3v48GEURWHnzp2VFq8Q1ZIMLxdClFNtb68dDoenQvk999xDx44dS36S6oKziZDt7iCyBENIAy3xFrWLJRQUHdlWJzt+W8evv/5a7KGenm4ZXi68oMyfolatWhEeHk6vXr3Izc3l6NGjNGzYkMOHD3sKk9Q2qSe1+SH1YmK0pFtHtSmklph4rorjkiVLmDFjBvv27fNsCwwM9EVYQtQw0tMthCif2t5ev/fee+zatYuwsDCef/75kp/gcmq9245cQIHASC3pFrWTooB/GDv//pte915FXFwcBw8eLPJQV15PtwwvF15Q5k9RWloaS5cupVOnTrhcLgYPHkyLFi2wWq388MMPJCcnezPOKs/lcnH2lPaeY2Ji3HOFqDY93dHR0Z5bSEgIiqJ4HkdGRjJ79mwaNGiA2Wz2VKzNExcXB0DHjh1RFIW+ffsCsGXLFq6++moiIiIICQmhT58+bN++3RdvTwghhKgRant7PXz4cMaNG8fMmTNLXubN6YD0Y1rCreggpL4k3AL8w4kM1kY55K2xXiSXu6fbIEm3KL8yf4rsdjtdu3Zl0qRJ+Pn5sWPHDhYuXIher2fBggXExcXRsmVLb8ZapSWmpOJy2ACIqhuBpycrX9KdlZVVqltpRwq4XK4Lnsdb3njjDV577TVeffVV/vjjDwYMGMB1113HP//8A8DmzZsBWL16NYmJiXz55ZcAnD17ljFjxvDLL7/w66+/0rx5cwYPHszZs2e9FpsQQghRESqzzfaW2tBeR0VFMX/+fO65554LH+hyaMtCOW3ad7GQBhe9FJioofzCiHIn3Rf6N9iy7zCaXH49/v4BlRmdqKHK3A0bGhpKhw4d6NWrFzabjZycHHr16oXBYGDJkiXUr1+fLVu2eDPWKu3QMW1oucnij5/FpG1UdAWKc5R2yNfcuXN54IEHSjzur7/+KrDe6flUL80bffXVV3nssce4+eabAXjppZf4+eefmTNnDm+//TZ169YFIDw8nOjoaM/zrrzyygLnef/99wkNDWXt2rVce+21XolNiGpF5nSLSpaWlsbmzZs5efIkrrxVNdxGjx7to6iqh8pss6W99jKXs3DCrb+INbxFzRYQQaBFj8VsJNdqJyUlRavHdJ6eo6eSZXMQGhrigyBFTVPmpPv48eNs2rSJjRs34nA46NSpE126dMFms7F9+3YaNGhA7969vRlrlXb4qJZ0B4aGawU7oNrM576QjIwMTpw4Qa9evQps79WrF7///vsFn5ucnMwTTzzBmjVrOHnyJE6nk+zsbI4cOVKRIQtRhcmcblF5vv76a2677TYyMzMJDg4usJawoiiSdNcw0l67qS73HG6b9j0suL4k3KIgvzAURSEyNIAjyWkkJyfTuHHjQoe53BfDFKS6vSi/MifdERERDB06lKFDhzJv3jzWrVvHX3/9xejRo5k8eTKjRo2ia9eurF271pvxVllHTyQBEFQn/FwvllIw6c6r8F4Sk8lUquMuueSSUp/TF8aMGcPp06d54403aNSoEWazmR49emCz2XwdmhC+IT3dohJNmjTJM/fV39/f1+FUO7Wpza4x7bWqQmbyuTncwfXBULr/PqIW8asDQFSoP0eS04qd153XUsuKcsIbvFblKyQkhP/7v/9j/Pjx/PTTT/j7+9eahBvgRKJWRC00LKLYnu6ihq6Uh06n8/o5zxccHEy9evXYsGEDffr08WzfsGEDXbt2Bc594XA6nQWeu2HDBt555x0GDx4MwNGjRzl16lSFxiuEEEJz/PhxHnroIUm4y6i6tdnSXqMtCWZ1X9gIipElwUTRLNpw8cgg7d9DccWfXXl9aJUSlKjpvJJ0//HHH9SvXx+ARo0aYTQaiY6OZuTIkd44fbWQd5UsLKL4pLu6mjJlCk899RRNmzalQ4cOLFy4kJ07d/Lpp58CEBkZiZ+fHytXrqRBgwZYLBZCQkJo3rw5//nPf+jcuTMZGRlMmTIFPz8pYiJqMxleLirPgAED2Lp1K02aNPF1KKKS1Or22poJOe51uAOjwCQXm0Qx3El3VJCWBl2wgjkUmJojRFl5JemOjY31/Lx7925vnLLaadWtH50y9fS9vMO5pFupGUn3Qw89RHp6OpMmTeLkyZO0bt2aFStW0Lx5cwAMBgNvvvkmzz77LDNmzODyyy9nzZo1zJ8/n7vvvpvLLruM2NhYZs6cyeTJk338boTwIU/OLUm3qHhDhgxhypQp7Nmzh7Zt22I0FpzXet111/koMlFRam177bRrw8oBLKGyLJi4sLyebn+tUS52eHnenG7JuYUXKGoZSmYeOXKEhg0blvr448ePe3rCa4KMjAxCQkJIT08nOFj7wz571d9sPnSaO3s0oL7jOHHRoVhCozzzRoS4GLm5uRw6dIi4uDgsFouvwxHecnw7rJoBdRrB9W/7OhrhI0W1IRVBpyt+VVBFUQoNMa6pLvT7lr+1NYCqnluL22DRKpVXcpYkn6Nqxp4Lnw7n9R8OEr/4L26++WY+//zzQofd/uFvOFwu3r71MsIDZapCbeTN9rpM63R36dKFe+6554JLgqWnp/PBBx/Qpk0bli1bVuYAq4uMHG2dzgCzodhCakIIIURlcblcxd5qS8ItqrevvvqKVatWXXhJtZzUc4XTgqKlW1KUzGgBvYlbutfj9w2reeutt4o8THVPBdPJZ0p4QZmGl+/Zs4cXXniBq6++GovFQqdOnahXrx4Wi4UzZ86wZ88e/vzzTy677DJefvllT2GOmiwjV0u6Ay0GOFuz5nQLIYQQQlQmp9PJI488wr///svixYuLrhPkyNWKpwEERsrSYKL0LCFEh9iIblYfIiKKPMSVN1tUcm7hBWXq6Q4PD2f27NkkJiYyd+5cmjdvzqlTp/jnn38AuO2229i2bRubNm2qFQk3QLZV6zXwN+prXCE1IYSXeJYM820YovZYu3YtQ4cOpVmzZjRr1ozrrruO9evX+zosIUr07bff8u+//xIWFlZ0/YG85cEAzIFgDqrcAEX15p7XTW56sYfk9XRLITXhDeUqpObn58fw4cMZPny4t+KptrJsDgD8jHpsNayQmhDCW6R6uag8n3zyCWPHjuXGG2/koYceArSloa666ioWLVrErbfe6uMIhShe3pDfO++8s+hK6jmp4LBp37UC6lZydKLaKyHpPn78OEvu6YnOYOS9UVmVGJioqby2TndtZnO4sDu1RNtf58Dzz1d6uoUQ+Xl6uiXpFhXvhRde4OWXX+aRRx7xbHvooYeYPXs2zz33nCTdosr666+/WL16NTqdjvvuu6/wAU47ZJ/Rfg6sCzr5OisuUglJtytvbDmgk45u4QVlGl4uCsqyar3cCgpmNce9VeFcr5YQQghRuQ4ePMjQoUMLbb/uuus4dOiQDyISonTefltb3WHo0KE0bty48AFZKYAKRn8wBVZqbKKG8AvV7nPTitxtd2jTRhVFkeHlwisk6faCvKHlAWY9it2ddCs6qbwghDiPDC8XlSc2NpaEhIRC21evXk1sbKwPIhKiZGfPnuWjjz4C4MEHHyx8gC1LuwEERMh3LVE2ZvfyT8X0dOet8KDo9NKFJrxCxuN4QbbNXUTNZABbtrZRkesZQojzyPByUYkmTZrEQw89xM6dO+nZsyegzeletGgRb7zxho+jE6JoS5YsITMzkxYtWnDllVcW3Kmq7l5utJ5Kg6ydLMrIEqrdF5N0O/KWVVQUWTJMeEWNzwxvuOEG6tSpU6DY2759++jQoYPn5ufnx/Lly8v8GkeOJ3LqwC7s6clgl6RbCFEcabhF5bnvvvtYvHgxu3bt4uGHH+bhhx9m9+7dLFmyhHvuucfX4QlRpAULFgAwfvz4wsN6c9O1+dw6PfiF+SA6UWPkVbu3ni1yd4Gebmm6hReUu6d7zJgxjB8/niuuuMIb8XjdxIkTGTdunGeoEkDLli3ZuXMnAJmZmTRu3Jirr766zK+x9qcEEl6+n8MdekDf5wAD6CTpFkIUR3q6ReW44YYbuOGGG3wdhhCl8tdff7Fp0yb0ej2jR48uuFN1aRXLQUu4pVitKA+Le3h5MUm3o8Cc7soKStRk5c4M09PT6d+/P82bN2fmzJkcP37cG3F5Td++fQkKKn7txhUrVnDVVVcREBBQ5tc4napV0AwMCq7WPd133HEHiqJw7733Ftr3wAMPoCgKd9xxh2dbUlISEydOpFmzZlgsFqKioujVqxfvvvsu2dnZnuN+//13rrvuOiIjI7FYLDRu3JiRI0dy8uRJAA4fPoyiKJ4LIReSd2zezWQy0axZM55//nnUUg7Zffrppz3PNxgMNG7cmEceeYTMzMxSPV8IIYTwtZrYZjdu3Jj//Oc/PP7440RHRwP52mydHkNkCxpf1o9Hpj0tbbYoH3dP958HjvLqq6+yePHiArud7urlinSiCS8p9ydp+fLlHD9+nPvuu48lS5bQuHFjBg0axBdffIHdbi/XudetW8fQoUOpV68eiqIUOQT87bffpnHjxlgsFrp168bmzZsv6jX++9//MnLkyHLFmXpGu/IaFBIKeYXUqunI/djYWBYvXkxOTo5nW25uLp999hkNGzb0bDt48CAdO3bkxx9/ZObMmezYsYNNmzbx6KOP8s0337B69WoAUlJSuOqqqwgLC+OHH37gr7/+YuHChdSrV4+srLKve7h69WoSExP5559/eOaZZ3jhhRc8Q9JK49JLLyUxMZHDhw/z0ksv8f777zNp0qQyxyNEqcicblHBwsLCOHXqFAB16tQhLCys2Juo/mpam+3n58ftt9/OM888U2D7pZdeSuKfmzi8/WdeeuE53v/gA2mzRfm4k+4t/yQzZcoUFi1aVGC3Z3i5opM53cIrvFJIrW7dusTHxxMfH8/27dtZuHAho0aNIjAwkNtvv53777+f5s2bX/R5s7KyaN++PePGjePGG28stH/JkiXEx8czb948unXrxpw5cxgwYAD79u0jMjKyxPNnZGSwcePGQle3LlZ6WhoAISGhWiE1PdWypxvgsssu48CBA3z55ZfcdtttAHz55Zc0bNiQuLg4z3H3338/BoOBrVu3Fhgl0KRJE66//nrPFewNGzaQnp7Ohx9+iMGgfdzi4uLo169fueIMDw/3XAVv1KgRCxcuZPv27YwfP75UzzcYDJ7njxw5koSEBFasWMF7771XrriEuDCpXi4q1uuvv+4Z3fX666/LUjc1XK1ps3UK0ZHhoDcxsm1vEtZvkjZblI9J+zsZHaIV40tKSiqw+9ycbp1UYxFe4dXq5YmJiaxatYpVq1ah1+sZPHgwu3btonXr1rz88ss88sgjF3W+QYMGMWjQoGL3z549m7vuuouxY8cCMG/ePL799lsWLFjA1KlTSzz///73P6655hosFstFxXW+zLPafJDg4CCwZ2lJd77hKKqqYnW4yvUaZWU26C76S9e4ceNYuHChpwFfsGABY8eOZc2aNQCcPn3ac7W8uGH5ea8ZHR2Nw+Hgq6++Yvjw4RXyBXDr1q1s27at8Pyvi+Dn54fNZvNiVEIUQXq6RQUbM2aM5+f8Q4tF6fmqzS5Lew21oM1WXaC6K0n7h4OiSJstyk+nA1MgMSEZgJbD5Bddrz6X3RyP3miWi5fCK8qddNvtdlasWMHChQv58ccfadeuHQ8//DC33norwcFakYKvvvqKcePGXXTSfSE2m41t27Yxbdo0zzadTkf//v3ZtGlTqc7x3//+l7vvvrvE46xWK1ar1fM4IyOjwP7sbG3IVVBQsDa83EKBnm6rw8UdCy9u2Lu3LBrbFYvx4oqN3H777UybNo1///0X0K58L1682NOA79+/H1VVadmyZYHnRUREkJubC2jzyV566SW6d+/O9OnTufXWW7n33nvp2rUrV155JaNHjyYqKqrM76tnz57odDpsNht2u5277767zEn3tm3b+OyzzwovTSKEENWYXq8nMTGx0Miv06dPExkZ6enJEQX5qs0uS3sNtaDNdli1C5V6E5gCpM0W3mMO9PR0p6Sk4HA4PCM8IiIiaN5PW/lIJzm38IJyJ90xMTG4XC5uueUWNm/eTIcOHQod069fP0JDQ8v7UgWcOnUKp9NZqBGIiopi7969nsf9+/fn999/JysriwYNGrB06VJ69OhBeno6mzdvZtmyZSW+1qxZswrNL8ovxz3PKTAwAGzuKojVdHg5aNMFhgwZwqJFi1BVlSFDhhAREVHi8zZv3ozL5eK2224rcJHihRdeID4+np9++onffvuNefPmMXPmTNatW0fbtm3LFOOSJUu45JJLsNvt7N69mwcffJA6derw4osvlur5u3btIjAwEKfTic1mY8iQIcydO7dMsQhRejK8XFSe4opLWq1WTCZTJUcjKkqNbrNVFzhy2fXX3wTGXipttvAucxARQSZ0Oh0ul4uUlBRiYmIAcEkzLbys3En3xIkTmTRpEv7+/gW2q6rK0aNHadiwIaGhoRw6dKi8L1UmecVBzhcSEkJycnKpzjFt2jTi4+M9jzMyMoiNjfU8zsnRku6QoCCwn3RvPZd0mw06Fo3tepGRe4fZULbkf9y4cUyYMAHQitXl16xZMxRFYd++fQW2N2nSBNCGap8vPDycESNGMGLECGbOnEnHjh159dVXCyzldjFiY2Np1qwZAJdccgkHDhzgySef5Omnny7VdIGWLVuyYsUKDAYD9erVky+gonLI8HJRCd58801AGzL84YcfEhgY6NnndDpZt24drVq18lV4VZ6v2uyyttdQg9ts61lQXbRs1oQV33yHwWiUNlt4jzkYvU4hMjyUpJRUkpKSPEl33rVxBUWGlwuvKHfS/fTTT3PPPfcUSrpTU1OJi4ursOFrERER6PX6QolzcnKyp1iHt5jNZsxmc7H7re6qodqcbveyG/nmdCuKUqYhY740cOBAbDYbiqIwYMCAAvvCw8O5+uqrmTt3Lg8++OBFL7dmMplo2rRpuSqhnk+v1+NwOLDZbKVKuvOWLRGicknDLSre66+/DmgXv+fNm4def679MZlMNG7cmHnz5vkqvCpP2uyCfNZmqypka0uymswWmpWhIK8QF+SuYB4dHuxJuvO45OK48LJyJ93FDV/LzMwsd4GyCzGZTHTq1ImEhASGDRsGgMvlIiEhwXO1t7JYc7REOyQ48NySYdV4eDloDeJff/3l+fl877zzDr169aJz5848/fTTtGvXDp1Ox5YtW9i7dy+dOnUC4JtvvmHx4sXcfPPNtGjRAlVV+frrr/nuu+9YuHBhmeM7ffo0SUlJOBwOdu3axRtvvEG/fv08dQSEqNqkMRcVJ29kWb9+/fjyyy+pU6eOjyMSFa1Gttm2LHDZAR3oqtdFEFFNmLXPX0xYEDspXEwNCvShCVEuZU6684ZbK4rCjBkzCvR0O51OfvvttyLnd1+MzMxM9u/f73l86NAhdu7cSVhYGA0bNiQ+Pp4xY8bQuXNnunbtypw5c8jKyvJUM68sAx95lZOnUunYoT1s+ULbWAOGolyoMWzatCk7duxg5syZTJs2jWPHjmE2m2ndujWTJ0/m/vvvB6B169b4+/szadIkjh49itlspnnz5nz44YeMGjWqzLH1798f0L5cxMTEMHjwYF544YUyn08IIWqan3/+2dchiEpUXdvsnJycIoe4k5um3RuKH2koRLnk9XSHajlMUT3dioxQE16iqMV1VZcgb83GtWvX0qNHjwLza/KGr02ePLlM63PnWbNmTZFrQ44ZM8aziP3cuXN55ZVXSEpKokOHDrz55pt069atzK9ZGhkZGYSEhJCenk5wcDB3frSFTKuD165vRvhPUzjUbCxxbbpg8fMv+WRCFCE3N5dDhw4RFxdXoSNGRCU79Q9884i27M3/lW1upKj+zm9DKspNN91E165deeyxxwpsf/nll9myZQtLly6tsNeuSi70+5a/tb61f/9+2rdvz4033sjHH398bu6swwppR7Sf6zQGvdFnMZaGfI6qqb3fwq/v8viqTN7/8U8eeeQRpk+fDsCpTCsTPtuOUa/jP+MrNq8QVZc32+sy93TnXUEfO3Ysb7zxRoV8cejbt2+xw9fzTJgwodKHk58v166t52nhXPXP6j68XAhREaR6uag869at4+mnny60fdCgQbz22muVH5AQ51m2bBnZ2dkkJycXLFaV18ttDqzyCbeoxtw93c/f1pUXPl1TYFde+iFF1IS3lDszXLhwYa2eR+twunC43Em3Ytc2yj/QMrv33nsJDAws8nbvvfeW6hzFPT8wMJD169dX8DsQ4gKkermoRJmZmUVWeTYajWRkZPggIlHTlLfN/vLLLwFt1GSB50c3IbBRB9Zv/bOi34KozdxJt2LLLLRL9QwvF8I7ytTTHR8fz3PPPUdAQECBpbSKMnv27DIFVl3kOlyen82uXByA/BMtu2effZbJkycXua+0F3d27txZ7L769euXJSwhhKh22rZty5IlS5gxY0aB7YsXL6Z169Y+ikrUJOVps48ePcrmzZtRFIU1a9ZQt25dbUduula13GCifqvO3g5ZiHPchdSwni20KyMjg/QThwgKCqrkoERNVaake8eOHdjtds/PxakNQzJy7dqSaAadDoPLqiXdMrS8zCIjI4mMjCzXOWQpMCGEgCeffJIbb7yRAwcOcOWVVwKQkJDA559/Xmvmc4uKVZ42+6uvvgKgV69e9OjRQ9uoqpD2LziDITASLFIbR1QgT9KdoX328uUta39OYOUztxHZrB1MGuqjAEVNUqakO39F1NpeHTUv6bYYdfmWC6v5FxuEEOUhw8tFxRs6dCjLly9n5syZfPHFF/j5+dGuXTtWr15Nnz59fB2eqOWWLVsGaAX/PBy54LRrnRcm6WEUFcwcqN27nNpnz3iuir7doY1d1RukpoDwjnKv052Tk4Oqqp4lw/7991+++uorWrduzTXXXFPuAKs6q3t4udmoB3veHDlJuoUQRZA53aKSDRkyhCFDhvg6DCEKSE5O9tRYufHGG8/tyE3X7s2BskCyqHgGi7YGvMupDTHPl3RbrTYAdJJ0Cy8p91+066+/no8//hiAtLQ0unbtymuvvcb111/Pu+++W+4AqzpPT7dBr10lA+npFkIUQ/42CCHE//73P1RVpXPnzjRs2FDb6HJCXkErc4jvghO1h6IUO6/bbtOSbunpFt5S7p7u7du38/rrrwPwxRdfEB0dzY4dO1i2bBkzZszgvvvuK3eQVVnecmHm/MPLa8AXa1VVUVFxqS7Pz3n3gHavUvBxDaZU8n9Tq92KzWljX+o+FGPh1y4qnqJqKJQ67iIOK/I1LuL3UK54ynG+4l5DURRtn3LuOAXFc968xwX2K0qh487flz+2/I/z/qfT6TDqjJj15vJf5RTiIjidTl5//XX++9//cuTIEWzuL5F5UlNTfRSZqO3y5nMX6OW2ntVGAelNYDD7KDJR65iDIefMuVEWbjZJuoWXlTvpzs7O9lT2+/HHH7nxxhvR6XR0796df//9t9wBVnUH9//D/rVfoW/WBBq5C35U4Z5uVdUSabvLjsPlwO6y41Sd2s3lxKW6cKrOEtdHFxXLaXNy1naWL/d8SZozzdfhCC8xOOwEkkJ7h40xvg5G1HjPPPMMH374IZMmTeKJJ57g8ccf5/DhwyxfvrxQRXMhKovNZuPXX38FYNiwYed2WN1T9CwhVfp7lKhh3MuGnd/TnZd06wzlTpWEALyQdDdr1ozly5dzww038MMPP/DII48AcPLkyVqxfvcfO7ay7bNXSO/YEwaP1jZWkcZCVVXsLjs2pw2rU+s5tbvsuFRXyU/Ox9Nrl79nz90jWOBxsSco81vwHi9fQ6jonn2HzoFep6deQD1CKDjMrjwXRIqKu7Tnu5jnluf3U9Q5S3u+4mL0bHePzvCM0Mi/DzwjO/LOlTeiI//jAvvP+7mokSD5z+9QnaThZK0rg0HZJ4n0L1+lfiEu5NNPP+WDDz5gyJAhPP3009xyyy00bdqUdu3a8euvv/LQQw/5OkRRC5lMJo4fP8769etp1aqVttFh1W4o55IgISpDCUm39HQLr1HLaenSparRaFR1Op169dVXe7bPnDlTHThwYHlPXyWlp6ergJqenq7GP/uaCqitu1+pqutfV3M+G6Pu2blFzcnJ8UlsdqddzbBmqMlZyeq/6f+qh9IOFXk7knFETcxMVFOyU9TUnFQ1PTddvW3UbSqg3nX3XardaVedLqfqcrlUVVXV+++/XwXUMWPGeF4rMTFRfeihh9SmTZuqZrNZjYyMVHv27Km+8847alZWlue4Pn36qGipi+d2zz33FIj7yy+/VLt166YGBwergYGBauvWrdWJEyd69i9cuFANCQkp8Jzc3Fx1+vTpasOGDVWTyaQ2atRInT9/fql+T0899VSBeIKDg9XevXura9asKfXvulGjRp7nWywWtVGjRuqIESPUhISEAscdOnSowGuFhYWpV199tbp9+/Ziz52Tk6Pu2bPHZ58j4T0ul0u1OqxqhjVDTTmxQ71rYWd17Efd1N2ndvs6NOEj+duQiuTv76/++++/qqqqanR0tLpt2zZVVVX1wIEDanBwcIW+dlVyod93df5bO2bMmCLbU1Wthm12Zoqqpvytqukninyv0maLCrPhTVVdOERdMWeSOmjQIPXZZ59VVVVVJ02boQJqq77DfByg8CVvttflnmI4fPhwjhw5wtatW1m5cqVn+1VXXeWZ612T5eRo87hNZrPPlgyzu+ykW9M5kXmCY2ePcTrnNNn2bFyqC0VRMOvNBJmCiPCLoF5gPRoGNyQ2KJbogGgi/CKoY6lDsDkYg85AbGws/13yX+xWOzpFh6Io5Obm8tlnn50rdgIcPHiQjh078uOPPzJz5kx27NjBpk2bePTRR/nmm29YvXp1gRjvuusuEhMTPbeXX37Zsy8hIYGRI0dy0003sXnzZrZt28YLL7zgWQu+OP/3f/9HQkIC8+fPZ9++fXz++ee0bNmy1L+3Sy+91BPPpk2baN68Oddeey3p6eklP9nt2WefJTExkX379vHxxx8TGhpK//79eeGFFwodu3r1ahITE/nhhx/IzMxk0KBBpKWllfq1RPWkKAomvcn9bzCMphgBlSxblq9DEzVcgwYNSExMBKBp06b8+OOPAGzZsgWzWebM1gSxsbEsXrzY810EqH5ttqqe62W8QC+3tNmiQrg/cydPnuT777/3TH2wO7TPs0F6uoWXeGWiQnR0NNHR0QW2de3a1RunrvKyc7SK5SazBezZ7q0VXy5JVVVyHDmctZ0lx5FTYJ9Zb8ZisOBn8MOsNxdZgKo4l112GQcOHODLL7/ktttuA+DLL7+kYcOGxMXFeY67//77MRgMbN26lYCAAM/2Jk2acP311xcaIuzv71/oM5Ln66+/plevXkyZMsWzrUWLFgXnep1n5cqVrF27loMHDxIWFgZA48aNS/0+AQwGgyem6Ohonn32WRYuXMjff/9Nly5dSnWOoKAgzzkaNmzIFVdcQUxMDDNmzGD48OEFLgKEh4d7/q28+uqr9OrVi99++40BAwZcVNyievNzz7c4/9+tEN52ww03kJCQQLdu3XjwwQe5/fbbmT9/PkeOHPFMBRPVW41os23Z4HK41+b2L/Yc0maLCuFOuqODteQ6KSkJyD+nW5Ju4R1eyQ4TEhKYPn06d955J+PGjStwq+lyc7UvzuYL9XSrKthzvXJT7TlkZp/ieNpBUjKOkpubhuKwYgbCDP408IsgxlyHOno/LGrZplOPGzeOhQsXeh4vWLCAsWPHeh6fPn2aH3/8kQceeKBA453f+Yn+p59+SkREBG3atGHatGlkZ2d79kVHR/Pnn3+ye/fuUse4YsUKOnfuzMsvv0z9+vVp0aIFkydPLnC1/2JYrVYWLlxIaGjoRfWWF2XixImoqsr//ve/Yo/x89PWgjy/mrCo+fzdf3azHdklHClE+bz44otMnz4dgJEjR7J+/Xruu+8+vvjiC1588cUynfPtt9+mcePGWCwWunXrxubNmy94/NKlS2nVqhUWi4W2bdvy3XffFdivqiozZswgJiYGPz8/+vfvzz///FPoPN9++y3dunXDz8+POnXqXDDBKzcvttkXdStjvY5q32Z7erkDtcS7FKTNFl7jXjIsOlD77OUl3SNuHUPfR96kzVU3+Sw0UbOUu6f7mWee4dlnn6Vz587ExMRcVK9qTWDN1Xq6zWYLOIpZMsxhhU+Hl/u1nKoTh8uBXnURjla8TK/To1f06IprqG77AoyWi3qd22+/nWnTpnmqz2/YsIHFixezZs0aAPbv34+qqoUauoiICHLdv48HHniAl156CYBbb72VRo0aUa9ePf744w8ee+wx9u3bx5dffgnAgw8+yPr162nbti2NGjWie/fuXHPNNdx2223FDoE8ePAgv/zyCxaLha+++opTp05x//33c/r06QJfPi5k165dBAYGAueq8C9ZsqTcBQDDwsKIjIzk8OHDRe5PS0vjueeeIzAwsNaMCBF5FE9Pd7Zdkm5Rcex2O/fccw9PPvmkp8eze/fudO/evcznXLJkCfHx8cybN49u3boxZ84cBgwYwL59+4iMLFwUcOPGjdxyyy3MmjWLa6+9ls8++4xhw4axfft22rRpA8DLL7/Mm2++yUcffURcXBxPPvkkAwYMYM+ePVgsWtu1bNky7rrrLmbOnMmVV16Jw+G4qITvonmpzb5oZWivobq32adY+OqT2kHmC7e90maLCuH+3MVoHy2Sk5NxOp3ENmpMVKvOhAdd/L9JIYpS7qR73rx5LFq0iFGjRnkjnmonr8GyWMzuypt4fU636q5C7lSd2undybZBZ6iQ9aPr1q3LkCFDWLRoEaqqMmTIECIiIkp83ubNm3G5XNx2221YrVbP9rvvvtvzc9u2bYmJieGqq67iwIEDNG3alICAAL799lsOHDjAzz//zK+//sqkSZN444032LRpE/7+hYebuVzafPVPP/2UkBCtuvfs2bMZPnw477zzjueq9IW0bNmSFStWAHD27FmWLFnCiBEj+Pnnn+ncuXOJz78QVVULXYDq2bMnOp2OrKwsmjRpwpIlS4iKiirX64hqRlE8Pd0yvFxUJKPRyLJly3jyySe9ds7Zs2dz1113eXpR582bx7fffsuCBQuYOnVqoePfeOMNBg4c6BmG/Nxzz7Fq1Srmzp3LvHnzUFWVOXPm8MQTT3D99dcD8PHHHxMVFcXy5cu5+eabcTgcTJw4kVdeeYXx48d7zt26dWuvva/qrtq32c9Owi8gEAwXTm6kzRYVwj28PNJP+47tdDo5ffo0Ktr3yFrWlygqULmTbpvNRs+ePb0RS7WU11BZ/PzAcQrQFz7IYNauYJfl/E4rKTmncLocAASaggg1h6BXinidohjKVixn3LhxTJgwAdCGE+bXrFkzFEVh3759BbY3adIEoMSEt1u3boB29b1p06ae7U2bNqVp06bceeedPP7447Ro0YIlS5YUGCaXJyYmhvr163sab4BLLrkEVVU5duwYzZs3L/E9mkwmmjVr5nncsWNHli9fzpw5c/jkk09KfH5xTp8+TUpKSoH5dKD1ErVu3Zrw8HBCQ0PLfH5RnSkyp1tUmmHDhrF8+XKvzN+22Wxs27aNadOmebbpdDr69+/Ppk2binzOpk2biI+PL7BtwIABLF++HIBDhw6RlJRE//79PftDQkLo1q0bmzZt4uabb2b79u0cP34cnU5Hx44dSUpKokOHDrzyyiue3vLzWa3WAklkRkbGxb3ZcrTZ5VLG9hqqeZt9IonmbTqWmN1Imy0qhFnr4jY4sggPD+f06dOcPHkSY0QjAHSSdQsvKfec7jvvvJPPPvvMG7FUS1ar1tPtZ7GA0z3X5/x/oIqiDRm7yFs2LpJsaTh0evSmAKJCGhEeVA+9KaD05ynjH4uBAwdis9mw2+2FioaEh4dz9dVXM3fuXLKyLr4C886dOwGtES5O48aN8ff3L/b8vXr14sSJE2RmZnq2/f333+h0Oho0aHDRMeXR6/Vlnhee54033kCn0xWacxgbG0vTpk2l8a7lTO6k2+q0lnCkEOXTvHlznn32WYYPH86sWbN48803C9wuxqlTp3A6nYV6+qKiojxzIM+XlJR0wePz7i90zMGDBwF4+umneeKJJ/jmm2+oU6cOffv2JTU1tcjXnTVrFiEhIZ5bbGzsRb3XsrbZ5b6V48t9tWyz9+7V2ux60WVem1vabFFuedMabFmev0W7d+/GVcYaC0IUp9w93bm5ubz//vusXr2adu3aYTQWrPI3e/bs8r5ElWbNdfd0W/zAkQtGP8pWvqygLFsWKTkpAPgZ/Ijwi0CvK2Xvthfo9Xr++usvz8/ne+edd+jVqxedO3fm6aefpl27duh0OrZs2cLevXvp1KkTAAcOHOCzzz5j8ODBhIeH88cff/DII49wxRVX0K5dO0D7MpWdnc3gwYNp1KgRaWlpvPnmm9jtdq6++uoi47v11lt57rnnGDt2LM888wynTp1iypQpjBs3rlRDywEcDofni13eULU9e/bw2GOPlfr3dPbsWZKSkrDb7Rw6dIhPPvmEDz/8kFmzZhW4Ii9EnrwrnSrSoIuKNX/+fEJDQ9m2bRvbtm0rsE9RFB566CEfRVZ6LpcLgMcff5ybbtIKGi1cuJAGDRqwdOlS7rnnnkLPmTZtWoEe9oyMjItPvKuZqt5mjxw5kmeeeaZgm/3oFMbdehN+QcGl6uWXNltUiHwXfPr3vZw9e/bwwAMPsGzVBkCGlwvvKXfS/ccff9ChQweAQoVNakNRtVsefZkmw6dwQ49GsNNdxbWc7zvHnuNJuAOMAUT4Rfjkd3mh4iRNmzZlx44dzJw5k2nTpnHs2DHMZjOtW7dm8uTJ3H///YA2HGz16tXMmTOHrKwsYmNjuemmm3jiiSc85+rTpw9vv/02o0ePJjk5mTp16njWEy2uKmlgYCCrVq3iwQcfpHPnzoSHh/N///d/PP/886V+f3/++afnyr2/vz9Nmzbl3XffZfTo0aU+x4wZM5gxYwYmk4no6Gi6d+9OQkIC/fr1K/U5RC2iuKswqGqhJXqE8BaXy4VOp+PQoUNeO2dERAR6vZ7k5OQC25OTk4tdWio6OvqCx+fdJycnF+hFTU5O9nyvyNuefw632WymSZMmHDlypMjXNZvNtXId8qrcZu/Zs4eUlBTWrVt3rs0eNpjnH50ApsBSvT9ps0WF0OnB6A/2bF58YhJff/8jhw4dYu1PqyC4c4XUThK1k6LKN7+LlpGRQUhICOnp6byz4QQ7j57h/l71uWLL/eSaIzjU/lHimjTxVF69GFaHlaTsJFRV9WnCLXwrNzeXQ4cOERcXV6bPkaiiMhJZ9+VtLFIy6dD2dh66rOr3NArvy9+GlLfyclH0ej2JiYmeiuJTpkxh2rRpnvWRy6pbt2507dqVt956C9CS+4YNGzJhwoQiC6mNHDmS7Oxsvv76a8+2nj170q5dO08htXr16jF58mQmTZoEaL+byMhIFi1axM033+x5/Pbbb3sKqdntdho0aMBzzz1XoOhXcS70+5a/tZXj6aef5plnnmH48OEsXboUXE5IPQSoENqwXPPZqwL5HFVzX4yHzGQY9DLbjuWQmpqKrk4DPtieRmwdf14Z0d7XEQof8WZ7Xe6e7trO7tSGvpkVx7mNZUySHS4HydnJqKqKxWCRhFuIGijvqrkMLxcV5fxr6e+99x733XdfuZPu+Ph4xowZQ+fOnenataunNzSvcNbo0aOpX78+s2bNArT1j/v06cNrr73GkCFDWLx4MVu3buX9998HtNFwDz/8MM8//zzNmzf3LBlWr149z/za4OBg7r33Xp566iliY2Np1KgRr7zyCgAjRowo1/sRlefHH38EODff3JYFqKA3VfuEW9QAlmAt6baepVMnrXDgrmPpQJoMLxde45Wke/369bz33nscOHCAL774gvr16/Of//yHuLg4evfu7Y2XqLLykm4Tdm2Drmy/UlVVSclOwaW6MOqNRPpHSsJdDnlreRbl+++/5/LLL7/g8z/99NMi5woCNGrUiD///LNc8YlaKt+/aZfq8mEgojbx1oC2kSNHkpKSwowZMzxVxFeuXOkpPnTkyBF0unP1WXv27Mlnn33GE088wfTp02nevDnLly8vUHX80UcfJSsri7vvvpu0tDR69+7NypUrC/QWvvLKKxgMBkaNGkVOTg7dunXjp59+ok6dOl55X6JinTlzht9++w3In3S7C6q5K0dLmy18Km+Kg+1cob+8C+MyvFx4S7mT7mXLljFq1Chuu+02duzY4VmmIz09nZkzZ/Ldd9+VO8iqrFDSrTeV6TypualYnVYURSHSLxKdUu7C8rVaXrXVotSvX7/E51933XWeZVLOd36xQCEuhvzLFtXZhAkTPEtTnW/NmjWFto0YMeKCPdKKovDss8/y7LPPFnuM0Wjk1Vdf5dVXX73oeIXvJSQk4HK5uOSSS7SCdi4n2LK1ne5kR9ps4VOepPtc9f2Na3/m6Pa/qNulu4+CEjVNuZPu559/nnnz5jF69GgWL17s2d6rV6+LKmpVXdkc2pUwM+7h5WVIunMcOZy1nQWgrl9djHppIMqrvFVIg4KCCAoq2xImQhTv3DVzKachKtKMGTPw9/cHtDW2X3jhhQJrJEPNX11EVA15Q8uvueYabYM9G21oudHznUnabOFTpgDtPl/Sfd/tNwIQG/UecKUPghI1TbmT7n379nHFFVcU2h4SEkJaWlp5T1/lnevpdq+5e5FJt0t1cSrnFABBpiD8jf5ejU8IUTXJnG5RUa644gr27dvnedyzZ0/Petd5ZPqSqAyqqvLDDz8A+YaWW91DeE2Bsh6TqBo8Sbf22cx/UTwsuoEvIhI1ULmT7ujoaPbv30/jxo0LbP/ll19o0qRJeU9f5eUl3QbVqW24yF7qM7lncLqcGHQG6lhkfpoQNZqSr4ya9HSLClLUMG8hfGHfvn0cOXIEs9lMnz59wOUCu7s30Vy6pcKEqHDnDS/fsmWLZ1dQWIQvIhI1ULmnF951111MnDiR3377DUVROHHiBJ9++imTJ0/mvvvu80aMVdq5pPvie7ptTptnWHm4X7jM4xaixjs3vNyFFFITQtRseUPLe/furU13sGeDqoLOCHqpWi6qiPOGl+cfGWS2yAhU4R3l7umeOnUqLpeLq666iuzsbK644grMZjOTJ0/mwQcf9EaMVZrTpfVWGVR3ITVD6ZJuVVVJzU0FwN/oj5/Br0LiE0JULTqphCqEqCXWr18PwNVXX61t8FQtD5Ch5aLqOC/pzsjIAKBeu97SYguvKXfSrSgKjz/+OFOmTGH//v1kZmbSunXrCy7/UJNs+vQ1XKrK2UtuJAxK3dOd48gh15ELIMPKhahFpJCaEKK2+PTTT3n44Ydp2LCh1sPtqVoe4NvAhMjvvCXDTp8+DYA5METqXwivKVfS7XK5WLRoEV9++SWHDx9GURTi4uIYPnw4o0aNqhUf1L9/+gJVdWF/zF0gpBRzulVVJc2aBkCwORijTqqVC1Er5PubKIXUhBA1nclkolevXtoDew6oTlB0IKP7RFWSr6dbVVW+//57AIz+QTIgQ3hNmScRq6rKddddx5133snx48dp27Ytl156Kf/++y933HEHN9xwgzfjrJLsdgeqqs3LNOvzCqmV3NOd48jB5rShKAohppASj68sd9xxB4qieG7h4eEMHDiQP/74o1TPz//ckJAQevXqxU8//VTk+Y1GI3FxcTz66KPk5uYWOs/y5csLnffXX38tcJzVaiU8PBxFUQoUDsofR/5b/iXtirNmzRoURakVlfeFL8iSYUII76h2bfbZVMJbdEWJaMaatWuLjEPabOET+ZJuu93umRaRnZqMTrJu4SVlTroXLVrEunXrSEhIYMeOHXz++ecsXryY33//ndWrV/PTTz/x8ccfezPWKifHavP87Kdzf4HWlZx05/VyB5mC0Ov0FRFamQ0cOJDExEQSExNJSEjAYDBw7bXXlvr5CxcuJDExkQ0bNhAREcG1115boCBF3vkPHjzI66+/znvvvcdTTz1V4nljY2NZuHBhgW1fffVVsdMY8uLIfxs2bFip34cQFULJl3RLT7eoBOvXr+f222+nR48eHD9+HID//Oc//PLLLz6OTHhDtWqzv/ySwICii1JJmy18Kt+SYSajkdtvvx2AplcMkzndwmvKnHR//vnnTJ8+nX79+hXad+WVVzJ16lQ+/fTTcgVX1Vlt55Jus85didhw4aHiVocVm1N7XrApuMJiKyuz2Ux0dDTR0dF06NCBqVOncvToUVJSUkr1/NDQUKKjo2nTpg3vvvsuOTk5rFq1qtD5Y2NjGTZsGP379y+wvzhjxoxh8eLF5OTkeLYtWLCAMWPGXDCO/DeLxVKq9yBERVIAVFkyTFS8ZcuWMWDAAPz8/NixYwdWq7bKRnp6OjNnzvRxdMIbqk2b7bCx4NMljBlZ9ChIabOFT5nydeDYs1mwYAGfr/qN6Eu6oNNJ2i28o8xJ9x9//MHAgQOL3T9o0CB+//33sp6+WsjJtXp+tniGlxdeAkNVVaxOK1anlVM5p7A5bRh1Rpyq07O9om7l+WKfmZnJJ598QrNmzQgPD7/o5/v5aXO2bPkuTuS3e/duNm7ciMlU8uiATp060bhxY5YtWwbAkSNHWLduHaNGjbrouITwpbw/utLTLSra888/z7x58/jggw8wGs9dEO7Vqxfbt2/3YWRVW/42uzJv5b0QV6Xb7IP7WLdpC6NuHXnRcQlR4Qwm0LnLXNmyMBqN1GsYByA93cJrylxILTU1laioqGL3R0VFcebMmbKevlrIzWuYFAUjDu3nIuZ021w27lt9n6chBzDpTZWyLve7/d/FfBFrYX7zzTeeIdtZWVnExMTwzTffoNNdXKzZ2dk88cQT6PV6+vTpU+j8DocDq9WKTqdj7ty5pTrnuHHjWLBgAbfffjuLFi1i8ODB1K1bt8hjb7nlFvT6gkP39+zZo1VQFaIKkJ5uUdH27dvHFVdcUWh7SEiIzIG9gLw2u7JdbHsN1anN/ojB/ftQN6boNljabOFzpgDITXdXMI/0XBavDUWhReUoc9bndDoxGIrP2fV6PQ6Ho6yn95obbriBOnXqMHz48EL7srOzadSoEZMnTy7TuXPdc7p1eiO4h4xfqHq5U9V6wxVFQVf2X32F6tevHzt37mTnzp1s3ryZAQMGMGjQIP79999SPf+WW24hMDCQoKAgli1bxvz582nXrl2h8//222+MGTOGsWPHctNNN5Xq3LfffjubNm3i4MGDLFq0iHHjxhV77Ouvv+55H3m3evXqlep1hKgwipKvlJoQFSs6Opr9+/cX2v7LL7/QpEkTH0QkvK1atNn7/2bRZ0sYd+twMBU9p1vabOFznmXDtLW6Xe4L45JzC28pc0+3qqrccccdmM1FX5XNmzvmaxMnTmTcuHF89NFHhfa98MILdO/evczntlrtAOj0hnNJt67wr9SkM/Fu/3dJzEzE5rRRx1KHYHPlzOc2laKwW34BAQE0a9bM8/jDDz8kJCSEDz74gOeff77E57/++uv079+fkJCQInuh859/wYIFtG/fnvnz5zN+/PgSzx0eHs61117L+PHjyc3NZdCgQZw9e7bIY6Ojowu8DyGqirz224XLp3GImu+uu+5i4sSJLFiwAEVROHHiBJs2bWLy5Mk8+eSTvg6vysprs33xuhererTZd5Kba2PQgKs5W0zHhLTZwufyVTAHyOvqlindwlvKnHQXV8Aqv9GjR5f19F7Tt2/fAstJ5fnnn3/Yu3cvQ4cOZffu3WU6d45VWzZDbzCAU0vA0Rk5f6pm/p5tk95EHUsdDEUk51WRoijodLoCBcwu5GIaTp1Ox/Tp04mPj+fWW2/1zCe7kHHjxjF48GAee+yxQkPRhKj6ZMkwUXmmTp2Ky+XiqquuIjs7myuuuAKz2czkyZN58MEHfR1elaUoykUP864qqmyb/eBd6P2Coejp4kL4njlIu89JA/L1dMvoNOElZc78zl8KoiKsW7eOV155hW3btpGYmMhXX31VaAmJt99+m1deeYWkpCTat2/PW2+9RdeuXUs89+TJk3nllVfYuHFjmePT6QwEx8Rh8Q88l3TrDVDEqPosu3blzGKwVOmE22q1kpSUBMCZM2eYO3cumZmZDB06tEJeb8SIEUyZMoW33367VMP8Bw4cSEpKCsHBFx4pkJaW5nkfeYKCgggICChVXLt27SIoKMjzWFEU2rdvX6rnCnEhnuZbkm5RwRRF4fHHH2fKlCns37+fzMxMWrduXexSi6L6qapt9qOPPorT6eTGG24gZd9mggP9wRgAttwij5c2W/hcQIR2n30aONdES0+38Jaqm/2hFQVp374948aN48Ybbyy0f8mSJcTHxzNv3jy6devGnDlzGDBgAPv27SMyMrLY8/7vf/+jRYsWtGjRolxJd71GjRn09KeEB5rB9bm2UVf00Kksh5Z0BxhL14D4ysqVK4mJiQG0Bq9Vq1YsXbqUvn37VsjrGQwGJkyYwMsvv8x9991XYgOrKAoRERElnnfs2LGFts2aNYupU6eWKq7ziw9VlRoFopqTdbqFD5hMJlq3bu3rMEQFqKpt9j///IPVakVx5BIRFgo6PRjMQNFJt7TZwuf8CybdeT3dMqlbeEuVTroHDRrEoEGDit0/e/Zs7rrrLs8f63nz5vHtt9+yYMGCC/6h/vXXX1m8eDFLly4lMzMTu91OcHAwM2bMuKj4XC7tH6ReUfINLy/8K3W4HNjd+/0NRRcRqQoWLVrEokWLyvz8kobLFnfuqVOnFvjvdf55LnTe0NDQizq+JH379pVhv6ICnWu85XMmKlpubi5vvfUWP//8MydPnsTlKlhHQJYNq96qYpudmpqKoiioqkq/fv3APcoPoz8oirTZoury9HSfAs7NFJWebuEtVTrpvhCbzca2bduYNm2aZ5tOp6N///5s2rTpgs+dNWsWs2bNArRGZffu3RdMuK1Wa4HCcBkZGQA43Um3Qa+AK294eeF5xtn2bADMejN6ncxDFqI207kTbymkJira+PHj+fHHHxk+fDhdu3aVpW9EhVu/fj2qqtKqVSutB/6Mu4q6qWqP8hMCvzravXtOtypzuoWXVduk+9SpUzidzkJrhUdFRbF3717P4/79+/P777+TlZVFgwYNWLp0KT169Lio15o1axbPPPNMoe0Opzvp1uXr6VYK/0pzHFpBEz9jyUVHqqqZM2cyc+bMIvddfvnlfP/995UcUdkMGjSI9evXF7lv+vTpTJ8+vZIjErWKIoXUROX55ptv+O677+jVq5evQxGVzFdt9tq1awG0tb6d9nMruxjLNspP2mxRaQwW7d79mZU53cLbqm3SXVqrV6++4P477rijxHNMmzaN+Ph4z+OMjAxiY2NxuIfq6XW6Ytfpdqkucp3aHKaqPLS8JPfeey//93//V+S+0lQwrSo+/PDDYqu6hoWFVXI0ojbKN8Dch1GI2qB+/foFikuJ2sNXbfa6desAd9LtHuWHwaLN6S4DabNFpTG4VyxwaN/ZXTKlW3iZV5Lu9evX895773HgwAG++OIL6tevz3/+8x/i4uLo3bu3N16ikIiICPR6PcnJyQW2JycnEx0d7dXXMpvNRa5H7hlerlPAlm/JMOyeY2xOG6qqolf0GIspslYdhIWF1YgGrn79+r4OQdRqMqdbVJ7XXnuNxx57jHnz5tGoUSNfhyMqkS/a7PT0dHbs2AG4C5vZ3Em3qewdDtJmi0rjSbq16aR5xU5lWo7wFl15T7Bs2TIGDBiAn58fO3bs8Mx9Tk9PL3ZokzeYTCY6depEQkKCZ5vL5SIhIeGih4+XldM9JVOvU8DlrpKpL3gdI9d9xcxsMMs/XCGEVC8XlaZz587k5ubSpEkTgoKCPIlYTbmIKqqWDRs24HK5aNq0KfXr1TvX013FV20RAgB9waTbJU208LJy93Q///zzzJs3j9GjR7N48WLP9l69evH888+X69yZmZns37/f8/jQoUPs3LmTsLAwGjZsSHx8PGPGjKFz58507dqVOXPmkJWVVeTSExWhQE93MdXL84aWW/SWSolJCFGFKYrnSqf0dIuKdsstt3D8+HFmzpxJVFSUXPgVFarg0PIcUF35lgoToorL+5w6baCqnjZaJ383hZeUO+net29fofURAUJCQkhLSyvXubdu3aotOeGWN696zJgxLFq0iJEjR5KSksKMGTNISkqiQ4cOrFy5slBxtYricP+D1OuVc3O68yXdqqpidWpXzCwGSbqFEOcab5cq1ctFxdq4cSObNm2iffv2vg5F1AIFiqh5ern9ZVKsqB7yXxxy2pBluoW3lTvpjo6OZv/+/TRu3LjA9l9++YUmTZqU69ylWX9xwoQJTJgwoVyvU1YO9/hyo5Lvy3O+Qmp587l1iq5az+cWQniPTpYfEZWkVatWxRahEsLb5s6dy88//0z//v3Prc8tS4WJ6kKfL+l25HqmgEn1cuEt5Z7TfddddzFx4kR+++03FEXhxIkTfPrpp0yePJn77rvPGzFWWRsSVvLTaw/w82fvnNuYb8mwvF5us17mcwshClJlnW5RwV588UUmTZrEmjVrOH36NBkZGQVuQnhTp06dmDx5MvWi6oIjb6mw6rO6iajldLpzo1UdNtwLFMk63cJryt3TPXXqVFwuF1dddRXZ2dlcccUVmM1mJk+ezIMPPuiNGKusk0knSPl7ByfrRQHu5TD0hZNuk97kg+iEEFWOrNMtKtHAgQMBuOqqqwpsV1UVRVFwOp2+CEvUdAWWCqvxK9OKmsRgAVumu6dba62lz0x4S7l7uhVF4fHHHyc1NZXdu3fz66+/kpKSwnPPPeeN+Ko0m12rWG40uBNuRSmwFqXNpV3pNeurRxGRvn378vDDDxfavmjRIkJDQwH4888/uemmm2jcuDGKojBnzpyLeo077rgDRVFQFAWTyUSzZs149tlncTi03+WaNWtQFKXIegCNGzcu8vVmzZqFXq/nlVdeKbTP6XTy4osv0qpVK/z8/AgLC6Nbt258+OGHRcaU/5b3hVUI71GkermoND///DM///wzP/30U4Fb3jZRvVXZNtu9VFjj9r2kzRbVi8HdSea04VJlyTDhXeW+BHnkyBFiY2MxmUy0bt260L6GDRuW9yWqLIddq1huMugAl3uNbo1LdWF3VzSvST3d2dnZNGnShBEjRvDII4+U6RwDBw5k4cKFWK1WvvvuOx544AGMRiPTpk0r0/kWLFjAo48+yoIFC5gyZUqBfc888wzvvfcec+fOpXPnzmRkZLB161bOnDlTZEz5FbU2uxDlIj3dohL16dPH1yEIH6v0NltVz/V0F5OsSJstqqy8oseOXFC17+4yp1t4S7mT7ri4OBITE4mMjCyw/fTp08TFxdXo4WsO93vT5/2LzJdcO5zaVWCdokOv6As9t7rq0qULXbp0AbSpBWVhNpuJjo4G4L777uOrr75ixYoVZUq6165dS05ODs8++ywff/wxGzdupGfPnp79K1as4P7772fEiBGebUVV8s0fkxAV6Vz7LUm3qFh5SzgVp6iVR0TNUulttiNXWypM0UMRc2GlzRZVWt73eHsuLjUIKOpTLETZlDvpzpsbdr7MzEwslpq9TJazUNJ97teZN7Q8r5fbZbVWbnBuislU5YfG+Pn5cfr06TI9d/78+dxyyy0YjUZuueUW5s+fX6ABj46O5qeffuL++++nbt263gpZiDKTnm5RWfr27VtoW/72oCZfFC8PVVVRbbZKf93q0F5DCW12Xi+3qegCatJmiyotuD6cOQzHtqDqBgOgk65u4SVlTrrz1sxWFIUnn3wSf39/zz6n08lvv/1Ghw4dyh1gVZb3hcWod0+Nzze83O6yg6Il3arNxtG77vZFiMR+8D5KFR1ypaoqCQkJ/PDDD4WK7jVo0KDQ8dnZ2QUeZ2Rk8MUXX7Bp0yYAbr/9di6//HLeeOMNAgMDAZg9ezbDhw8nOjqaSy+9lJ49e3L99dczaNCgAuf65ptvPM/JM336dKZPn17u9ynEOfnW6ZaeblHBzh+Sa7fb2bFjB08++SQvvPCCj6Kq+nzVZlfl9hpK22ar2iAeRZE2W1Q/LQfCvxtg/2qUJn0B6ekW3lPmpHvHjh2A9kd4165dmEznhlabTCbat2/P5MmTyx9hFeZ0DyHPy7nzr9HtcDlAj7Y+t3y3LiCvsbTb7bhcLm699VaefvrpAsesX7+eoKCgAtvO77X5/PPPadq0qWfoWYcOHWjUqBFLlixh/PjxALRu3Zrdu3ezbds2NmzYwLp16xg6dCh33HFHgcIs/fr149133y1w/rCwMC+9YyHcFEXW6RaVJiQkpNC2q6++GpPJRHx8PNu2bfNBVKK6KXWbHeAPaUe1DSH16XvV1QWOkTZbVHkxHbTe7ozjRJz5HWhYLUafiOqhzEn3zz//DMDYsWN54403CA4O9lpQ1YXTqS3iZygq6VYdKCiYdCYUvYnYD973QYTacLXSCg4OJj09vdD2tLS0Ir+8lVVeY2kymahXrx4GQ+GPYVxcnKf6ap7zj5s/fz5//vlnge0ul4sFCxZ4GnAAnU7nmdf28MMP88knnzBq1Cgef/xx4uLiAAgICKBZs2Zee49CFCev+Xapsk638I2oqCj27dvn6zCqLMXkmzb7YtprqIJttp8BwnTad6E6jaXNFtWPokBoQ8g4jt6R7dkkhDeUe053XvXIPXv2cOTIEWznzYO67rrryvsSVZbLPbzcoBQspOZSXbhcLvToMegM2nIWVXjIWJ6WLVvy448/Ftq+fft2WrRo4bXX8UZjuWvXLrZu3cqaNWsKXN1OTU2lb9++7N27l1atWhX53Lwq+1lZWeWKQYiLd656OTKnW1SwP/74o8BjVVVJTEzkxRdfrPHTv8pD2uyCLtRmr1y5EoBt27ZxVbe22kajf6HjpM0W1YZ76V9F1b7j6yTrFl5S7qT70KFDDBs2jF27dqEoiqc4UN5wjJpcqKVeXHMaXNaXpnENgAOeOd15PVh6nR69rvpULr/vvvuYO3cuDz30EHfeeSdms5lvv/2Wzz//nK+//hoAm83Gnj17PD8fP36cnTt3EhgYWKlXnefPn0/Xrl2LrL7bpUsX5s+fzyuvvMLw4cPp1asXPXv2JDo6mkOHDjFt2jRatGhRoIG3Wq0kJSUVOI/BYCAiIqLC34uoXWSdblFZOnToUKBdztO9e3cWLFjgo6iEt1SFNjshIQFwJ92XNdU2FpF0S5stqg33ikOqS8tfJOUW3qIr+ZALe+ihh4iLi+PkyZP4+/vz559/sm7dOjp37syaNWu8EGLV1bX/dfS6ZyYDruytbXAPL3e4tLnexnyF1aqDJk2asG7dOvbu3Uv//v3p1q0b//3vf1m6dCkDBw4E4MSJE3Ts2JGOHTuSmJjIq6++SseOHbnzzjsrLU6bzcYnn3zCTTfdVOT+m266iY8//hi73c6AAQP4+uuvGTp0KC1atGDMmDG0atWKH3/8scAQt5UrVxITE1Pg1rt378p6S6K2yHfFXIaXi4p26NAhDh48yKFDhzh06BD//vsv2dnZbNy4sdheRVF9+LrNttvt7N69G4Ce3buC067tMBasXC5ttqhW8nq6XdLTLbxLUcu5bk1ERAQ//fQT7dq1IyQkhM2bN9OyZUt++uknJk2a5Cm4VpNkZGQQEhLCm9/vZMORbCY0OEzvk59C/U7kXj6NP//+k9CYUEIDQwn3C/d1uKIays3N5dChQ8TFxdX4pfdqFZeT5I+HMI1TWOpdxjvX+KbWg/CtvDYkPT29VtZDqWwX+n3L39qy++233+jevTt16tTh1LED6LJPgcECobG+Dq3SyeeoBtnwJvzzI2sCBjLvTCduvKwB/9e59n2mhcab7XW5h5c7nU5PlemIiAhOnDhBy5YtadSoUY0v1JJ3uUKP++que0630z0PxKAr969XCFGjKJ7hRTK8XFSEN998s9THPvTQQxUYiajp1q1bB8Dll1+OzpmrbTQVHlouRLXi6enWRq1KT7fwlnJnhW3atOH3338nLi6Obt268fLLL2MymXj//fdp0qSJN2KsspzurFuXN0zUnWTnDRutbUn3kSNHPAVPirJnzx4aNmxYiREJUcXka7xVGV4uKsDrr79equMURZGku5Yrb5u9du1aAPpccQXYcrSNRcznFqJacX93zyukJim38JZyZ4VPPPGEp6Lks88+y7XXXsvll19OeHg4S5YsKXeAVZnTpSXderSrYXlXx2prT3e9evXYuXPnBfcLUdsp0oSLCnTo0CFfhyCqifK02U6nk/Xr1wPQp3cPUJ2g6LTh5UJUZ4o2Hk1R3T3d5a5+JYSm3FnhgAEDPD83a9aMvXv3kpqaSp06dWr8gvKe4eX5erptTpunUqxBqV1Jt8FgkHUzhSiBp3q5LBkmKtH5K4sIUZ42+/fffycjI4Pg4GA6tG4O1jStgJp8vkR1l7fqkPu7vVwoF95S7us3R44cKfTlMSwsDEVROHLkSHlPX6XlvW0d7mXRdAbSctO0nxXQKXJ5TAiRT/7q5TKnW1SCjz/+mLZt2+Ln54efnx/t2rXjP//5j6/DEtVc3nzu3r17o3dZtY0ytFzUBJ7h5e6kW3Ju4SXl7oqNi4sjMTGRyMjIAttPnz5NXFxcjV6nO294uU49l3SfsZ4BtF5u6VEQQpzP81dBerpFBZs9ezZPPvkkEyZMoFevXgD88ssv3HvvvZw6dYpHHnnExxGK6ipvPvcVl18OdncRNUm6RU3gXqcbdyE1+S4vvKXcSbeqqkV+IDMzM2v8sgl5Pfx6T0+3njO5ZzBgQJ/3j1YIIfKR6uWisrz11lu8++67jB492rPtuuuu49JLL+Xpp5+WpFuU2dChQ1EUhf59ewOq1juoN/o6LCHK77xCajrJuYWXlHn8c3x8PPHx8SiKwpNPPul5HB8fz8SJExk5ciQdOnTwYqhVz7nq5eeS7jRrGgB6nSTdQojiyZxuUdESExPp2bNnoe09e/YkMTGxTOd8++23ady4MRaLhW7durF58+YLHr906VJatWqFxWKhbdu2fPfddwX2q6rKjBkziImJwc/Pj/79+/PPP/8UeS6r1UqHDh1QFOWCBcBExRs3bhxffvklndq21DYY/WUcrqgZPEuG5VUvl8+18I4yJ907duxgx44dqKrKrl27PI937NjB3r17ad++PYsWLfJiqFXPf19/guWTB/P9+u3aBp2Bs/azQO2rXC6EKB1pvkVladasGf/9738LbV+yZAnNmze/6PMtWbKE+Ph4nnrqKbZv30779u0ZMGAAJ0+eLPL4jRs3cssttzB+/Hh27NjBsGHDGDZsGLt37/Yc8/LLL/Pmm28yb948fvvtNwICAhgwYAC5ubmFzvfoo4/KKhhVjT1bu5f1uUVNkZd0S0+38LIyZ4Y///wzAGPHjuWNN94gODjYa0FVFzln07GeTcNht2kbdAbOWtNAD7ry16gTQtRAiqKAKsPLRcV75plnGDlyJOvWrfPM6d6wYQMJCQlFJuMlmT17NnfddRdjx44FYN68eXz77bcsWLCAqVOnFjr+jTfeYODAgUyZMgWA5557jlWrVjF37lzmzZuHqqrMmTOHJ554guuvvx7QCr9FRUWxfPlybr75Zs+5vv/+e3788UeWLVvG9//P3n3HN1WvDxz/nIzuvQddQNlTkLIU0Mp04EUEFxsXoIgLVHChXNSrKCIoMvQqgvxUxHFRWaIsZW9klN0N3TPJ+f2RNlBaoDRpk7bP+2VeTc58cgz55jnf9b//XXfsohqYDGAo+f2jd7VvLELYilI26ZYGHMJWrM4MFy1aVC8TbgCTyTyyoa70Kmp0ZBZlArWzeXnPnj2ZOHFiueWLFy/Gx8cHgPnz53PTTTfh6+uLr68v8fHx12xeePk5FEVBURRcXFxo0aIFH330UYXnupyiKKxYsaLc8kceeQStVsvy5cvLrcvLy2PKlCk0atQIFxcXAgMD6dGjB99//32FMV36ePTRRyv9voSovIsluDQxF9WhtCZ50KBBbN26lYCAAFasWMGKFSsICAjgr7/+4u67776uYxYVFbF9+3bi4+MtyzQaDfHx8WzevLnCfTZv3lxmezBPM1q6fUJCAklJSWW28fb2Ji4urswxk5OTGTt2LP/9739xc5Ma1VJ2L7OL8lj81bf4NOpo6Qd7KSmzRa1U+lku7Toq7dOEjVSppnvSpEm8/vrruLu7M2nSpKtu++6771YpsNrAVNLfQ1f671HRkFuUC651d7qw9evXc99999G1a1dcXFyYOXMmvXv3Zv/+/YSHh1fqGGPHjuW1114jLy+Pzz//nHHjxuHr68t999133fHk5eWxdOlSnnvuORYuXMjgwYPLrH/00UfZunUrs2fPpkWLFqSnp7Np0ybS09MrjOlS8uNOVAdFWsGIatamTRtuvPFGxowZw9ChQ/niiy+sPmZaWhpGo5Hg4OAyy4ODgzl06FCF+yQlJVW4fVJSkmV96bIrbaOqKiNGjODRRx+lY8eOnDhx4pqxFhYWUlhYaHmdlZV1zX3qqmots2/vZd7gOnISKbOFw7M0LzdXrEnzcmErVUq6d+7cSXFxseX5ldT1YfZNxrI13UVAntHcv6mujl7+5Zdflnn96aef8s0337BmzZoyI+RejZubGyEhIQC88sorLFmyhJUrV1Yp6V6+fDktWrRg8uTJhIWFcfr0aSIiIizrV65cyfvvv0///v0BiI6OpkOHDleNSYjqdGnKbVJNdfa7QtjP77//zqJFi3j66ad56qmnuOeeexg9ejQ33XSTvUO7brNnzyY7O5spU6ZUep8ZM2bw6quvVmNUtUe1ltl94kq2qPxvPSmzhcMrSbo1MmWYsLEqVbmsW7fO0nRp3bp1lsfatWtZu3Ztmdd1mankH6ROY24immkqsqy7tKZbVVUMxUa7PKq7+WpeXh7FxcX4+flV+Riurq4UFRVde8MKLFiwgAcffBBvb2/69etXbvC+kJAQfv75Z7Kzs6scnxDVRfp1i+pw0003sXDhQhITE5k9ezYJCQn06NGDJk2aMHPmTEst8vUICAhAq9WSnJxcZnlycvIVk5+QkJCrbl/692rbrF27ls2bN+Ps7IxOp6Nx48YAdOzYkeHDh1d43ilTppCZmWl5nD59+rreq73K7JrobmKzMruwAEzGa298GSmzhcMrvREuNd3CxmwyxPaCBQt47733LNN8xMbGMnHiRMaMGWOLwzssS023Ujbp1iiaMnfGjAYTP87eXfMBArdPaItOX301ac8//zxhYWHl+u1VhtFo5KuvvmLPnj08/PDDluWZmZl4eHhcc/8jR46wZcsWvv32WwAefPBBJk2axEsvvWS5/p988gkPPPAA/v7+tG3blu7du3PPPfdYBhUq9dFHH/Hpp5+WWfbxxx/zwAMPXPf7EuJqLk4/Igm3qF7u7u6MHDmSkSNHcvToURYtWsScOXOYOnUqffv2ZeXKlZU+lpOTEx06dGDNmjUMHDgQMI9rsmbNGsaPH1/hPl26dGHNmjVl+h3/9ttvdOnSBYCYmBhCQkJYs2aNZYrRrKwstm7dymOPPQbABx98wPTp0y37nzt3jj59+rBs2TLi4uKoiLOzM87OzpV+b5ezV5ld3eU12LDMHvGgeaHWScpsUbdY5uk2gCIDqQnbsTrpnjZtGu+++y4TJkywFKSbN2/mqaee4tSpU+X63NQlpX269SWV2heM+UDd7c99uX//+98sXbqU9evX4+LiUun9SgvLoqIitFotTz31lOUHFoCnpyc7duwot9/lU9wsXLiQPn36EBAQAED//v0ZPXo0a9eu5dZbbwXg5ptv5vjx42zZsoVNmzaxZs0a3n//fV599VWmTp1qOdYDDzzAiy++WOb4l/czFMIWSkcvBxlITdScxo0b88ILLxAVFcWUKVP46aefrvsYkyZNYvjw4XTs2JFOnToxa9YscnNzLaOZDxs2jPDwcGbMmAHAk08+SY8ePfjPf/7DgAEDWLp0Kdu2beOTTz4BzP8WJk6cyPTp04mNjSUmJoapU6cSFhZmSewjIyPLxFCa3DVq1IgGDRpU9XLUSzYts0feB4Z80OqlzBZ1i+by0csl6xa2YXXSPXfuXObPn1+mP+6dd95JmzZtmDBhQp1OutXLRi/PNJrnFb086dbqNNw+oW2NxnbpuSvLy8uLzMzMcsszMjLw9vYus+ydd97h3//+N6tXr6ZNmzbXFVNpYenq6kpoaCgaTdkYNRqNpQnhlRiNRj777DOSkpLQ6XRlli9cuNBSgAPo9XpuuukmbrrpJp5//nmmT5/Oa6+9xvPPP4+TkxNgHjH3WucUwqZk2jBRQzZs2MDChQv55ptv0Gg03HvvvYwePfq6jzNkyBBSU1OZNm0aSUlJtGvXjlWrVlmSnVOnTpX5Pu/atStLlizhpZde4oUXXiA2NpYVK1bQqlUryzbPPfccubm5PPzww2RkZNC9e3dWrVp1XUmhrdmrzL6e8hpqvsw+dOgQOTk59OrZE40x17xS5yxltqhbtObPmNZUDFoZu1zYjtVJd3FxMR07diy3vEOHDhgMBmsP79CMRvNdMK1iTr6zS5qXX35XTFGUam8yZgtNmzbl119/Lbd8x44dNGnSxPL6rbfe4o033uCXX36p8P/9tdiisCzt87Vz50602ovXdt++fYwcOZKMjIwrTj3WokULDAYDBQUFlgJciJqiSBEuasC5c+dYvHgxixcv5ujRo3Tt2pUPPviAe++9F3d39yofd/z48VdsTr5+/fpyywYPHlxuhOpLKYrCa6+9Vukb9NHR0dXeQkTK7LJKy+x///vfLFiwgGcnPcVbUx4z1whWMFVYRaTMFrWGznzDT2vpMipltrANq5Puhx56iLlz55abGqy0X05d1qrXQFLPnycm0Aikk2M0T1GiqaVTAj322GN8+OGHPPHEE4wZMwZnZ2d++uknvvrqK3744QcAZs6cybRp01iyZAnR0dGWAXk8PDwq1afLVhYsWMCAAQNo27ZsbUSLFi146qmn+PLLLxk3bhw9e/bkvvvuo2PHjvj7+3PgwAFeeOEFevXqVWZ++by8vHKDCzk7O+Pr61sj70fUH5ePXi6ErfXr14/Vq1cTEBDAsGHDGDVqFE2bNrV3WMLGarrM3rBhAwA9ut1oXqB3q3SHVymzRa1RknTrTebWq5JzC1uxSXa4YMECWrVqxZgxYxgzZgytW7dm/vz5aDQaJk2aZHnUNS17DaT1nWNpFGIuuHKuUNNdWzRs2JANGzZw6NAh4uPjiYuL4+uvv2b58uX07dsXMHcnKCoq4p577iE0NNTyeOedd2oszuTkZH766ScGDRpUbp1Go+Huu+9mwYIFAPTp04fPPvuM3r1707x5cyZMmECfPn34+uuvy+w3f/78Mu8nNDS0SlOYCXFNl3w/SPNyUR30ej3/93//x5kzZ5g5c6Yk3HVUTZbZiYmJHDlyBEVR6NahtXmhvnLzYkuZLWoVd/OYAy6GLHRqkdR0C5tRVCvbafXq1atyJ1KUOjOFWFZWFt7e3jy6YAMXDDrmeX2GT8EZ3o5qRqLRxLCQYTRr3MyufdJE7VZQUEBCQgIxMTHyOapjiv/7Lx4xnoLAZszpMx9Xnau9QxI1rLQMyczMLFN7J6rH1a63fNdWzrJlyxg6dCjt27dnx6/LzAt9o0Grt2tcjkI+R3WIqsJX93EiMZn3XMczpHcPujYOsHdUwk5sWV5b3bx83bp11h6i1jKV3K7QlIxwmGssBPTSZ1MIcUWXThkmo5cLIWqD33//HYCbu5lnqUGrl4Rb1E2KAj4RqOeSCTQm19rWq8Lx1M7Ox9fh7rvvxtfXl3vuuadSy6+HqfQHs2oeMC7nCqOX1xd//PGHpZ9YRQ8hBNK8XAjhEK6nzLb05+5ySX9uIeoqVz8AXEz50qdb2EyVaronTZrE66+/jru7+zX7al8+wFpNe/LJJxk1ahSfffZZpZZfj9KkW1MyGFKOIR83rWe9renu2LEju3btsncYQji0+vntIIRwNJUts9PS0ti/fz8AN3UqmW5Mkm5Rl2l0gIoWo5TZwmaqlHTv3LmT4uJiy/MrcYQmGT179qxwGpMrLb8epUm3ohopQqXIZMSN+lvT7erqKvNmCnENMnq5EMIRVLbM/uOPPwBo2bIFAT6e5oV6GYtC1GEaDaoKGozotPbPZUTdUKWk+9J+3NXZp3vDhg28/fbbbN++ncTERL777jsGDhxYZps5c+bw9ttvk5SURNu2bZk9ezadOnWqtpguZTQBGlBUA9mYQFHQonWImw1CCEclzcuFELVHadNyS39unYt5jm4h6iqNDhXQqkZ0mvpZkSZsz6E/Sbm5ubRt25Y5c+ZUuH7ZsmVMmjSJl19+mR07dtC2bVv69OlDSkpKjcSnXlLTnYsJFHAtufsrAyQJa8jnp+5SpAAXwuHId+6Vubu7ExISws2dO5oXOEnT8svJ56eO0ehQVdBKTbewIat//c2YMYOFCxeWW75w4UJmzpxp1bH79evH9OnTufvuuytc/+677zJ27FhGjhxJixYtmDdvHm5ubhXGUx0ubV6ejQlQ0GjNlzQvL69GYhB1U+nnR6+X0WHrnkvGL5cfakLYVel3rJTZVzZ9+nTOnT3L4NtvNS+Q/tzlSJldx2h0qKhoVCN6rdwoF7Zh9ZRhH3/8MUuWLCm3vGXLlgwdOpTnn3/e2lNUqKioiO3btzNlyhTLMo1GQ3x8PJs3b7bpuQoLCyksLLS8zsrKAiA36wKKUyEmrYE8rQqKgouTCz4+Ppbadjc3N2luLipNVVXy8vJISUnBx8cHrVaa8NVdMmWYEPam1WqlzK4MQyEYjBSjgAEoma2lvpMyu47SaEv6dJvQauT7QNiG1Ul3UlISoaGh5ZYHBgaSmJho7eGvKC0tDaPRSHBwcJnlwcHBHDp0yPI6Pj6e3bt3k5ubS4MGDVi+fDldunS54vKKzJgxg1dffbXc8h+m3ofJUMy5t24hP1AFFNx0boSEhADUWDN3Uff4+PhYPkei7lFQpDe3EA5CyuxKKMqDohzQOUGGDAB5OSmz6xiNDlU1j16uly5hwkasTrojIiLYuHEjMTExZZZv3LiRsLAwaw9vtdWrV1/X8opMmTKlzNRoWVlZREREoJrMBY9OUcnDnHS76FxQFIXQ0FCCgoIso7wLUVl6vV7ultdlSknzchVMyI9XIexNyuxK+HMWpB6CVvdATHt7R+NQpMyugywDqZmkT7ewGauT7rFjxzJx4kSKi4u55ZZbAFizZg3PPfccTz/9tNUBXklAQABarZbk5OQyy5OTk21+t9HZ2RlnZ+dyy1WTEQCtRiG/ZPRyV93FaTS0Wq18EQshLnPJ6OXSvFwIhyFl9hUYiiDxLzAWQYPW4OJi74iEqF6KFlPplGHSvFzYiNVJ97PPPkt6ejqPP/44RUVFALi4uPD8888zefJkqwO8EicnJzp06MCaNWss04iZTCbWrFnD+PHjq+28FdFrS5JuyibdQghRESnChRC1RupBc8Lt6gs+kfaORojqp9UDqnnKMBlITdiI1Um3oijMnDmTqVOncvDgQVxdXYmNja2wZvh65eTkcPToUcvrhIQEdu3ahZ+fH5GRkUyaNInhw4fTsWNHOnXqxKxZs8jNzWXkyJFWn/t6aDUaClDL1XQLIUQ5imKZNkLm6RZCOLxzu8x/w9qBDDIn6gFVKR1IzSgDqQmbsTrpBvjjjz/4+OOPOX78OMuXL8fZ2Zn//ve/xMTE0L179yofd9u2bfTq1cvyurRf9fDhw1m8eDFDhgwhNTWVadOmkZSURLt27Vi1alW5wdWqm06DpU+3JN1CiMqS5uVCCId3bqf5b2g7u4YhRE0xKeZb41pJuoUNWd1m4ptvvqFPnz64urqyY8cOy9RamZmZvPnmm1Ydu2fPnqiqWu6xePFiyzbjx4/n5MmTFBYWsnXrVuLi4qw6Z1VoNAoFigoKknQLIYQQom4ozIb0khaHYTKAmqgfTIp5bAeNakIrrTuEjVhd0z19+nTmzZvHsGHDWLp0qWV5t27dmD59urWHrxU0ikJ+SS/Nupx0q6pKQW4xORcKKcgtpiCnmMI8A4YiI0aDCUOxCZPBhNGggmreXgUwqSX7Y359ee1eBV9oyhVeXPGrTym/0ZW3vfRpJQ5e0fEuOZ9Or0HnpEGn16LVa9A7aXB21+PiocfVwwlXTz1OLjZpVCLqCA0KoGJSZfRyIYTjOX36NAARxpPmBT6R4OZnx4iEqDmqYv7NpsWAzBgmbMXqTODw4cPcfPPN5ZZ7e3uTkZFh7eFrBa0G8hVzIlmXkm5jsYn0czmknsom/Vwu2Wn5FBca7R1WreTsrsfT3wUvfxe8A93wD3fH3ccZRe6g1j+X/D+XPt1CCEf07rvvMmvWLKY8dBtv3uIkTctFvWLEXNOtVY1S0y1sxuqkOyQkhKNHjxIdHV1m+Z9//knDhg2tPXytYK7pNv94dtO52Tka65hMKiknsjh98DxJxzIxGi6riVMU3H2ccHV3wsVDh7ObHr2zuYZXq9Og1WvQaBU0JV9S5m4xivk/RbnqGCxX7N56yYoym1wxX7lke7XCxVfY+irnusIOqqpiLDbX8huKTBiLjRQVGinMLSY/x9waoCjfQGFuMYW5xaSdyrbs6+Sqwy/MnaAoL4JjvHD3tn7wQVEbKDJ6uRDCoa1btw6ANv7FgJM0LRf1irGkT7cGk/TpFjZjk3m6n3zySRYuXIiiKJw7d47NmzfzzDPPMHXqVFvE6LA6jXgJX00eOs1BS9Ltoqud81cai02c3J/O0e0p5GUWWpa7eDgRGOlBYIQn3kFuePg5o5XpE65LcZGRnPMFZKUVkJWWz4XkPDKScinKN5B0LJOkY5kAePi6EBzjRVisD35h7lILXodZRi+XgdSEEA4mPT2d3bt3A9AzWmu+ex7c0s5RCVFz1NKabkzyW0zYjNVJ9+TJkzGZTNx6663k5eVx88034+zszDPPPMOECRNsEaPDatCuBw30maiagxSVNC9309eumm5VVTlz6AL7/zhHQY55nnW9i47IFn40aOaLT7CbfOFYSe+kxTfEHd8Qd8syk9FERnI+aWezSU7IIv1sLjkXCsi5UMCxHSm4eDgR3sSH8Ka++IbI/4M6RZqXCyEc2O+//w5A80YRhHi7QFBzcKpdv22EsIaxZCA1HdKlUtiOTebpfvHFF3n22Wc5evQoOTk5tGjRAg8PD1vE5/AUVAoV83RhAC7a2lPTnZtZyPZVJzl/NgcAVy8nYjsGE9XSH61earOrk0arwS/MHb8wd5rcGEJxoZGUk1kkHc8k8WgmBTlFHNuRwrEdKbh5OxPZwo/Ilv64eTnZO3RhK5JvCyEcUGnT8l6twswLpD+3qGdMJe3RdMhgp8J2rEq6i4uL6du3L/PmzSM2NpYWLVrYKq5aQ6OaMDfGVtBr9Gg1WjtHVDkn96WzZ90ZjMVGtHoNTTqF0LhDEFqdJNv2oHfWEt7El/AmvhgNJpJPZHH28AWSjmeSl1nIoc2JHNqSSECEJ1Gt/Alr5CM3RmotxdK8XEYvF0I4mtKk+5bokt8zYe3sF4wQdmCp6VakplvYjlVJt16vZ8+ePbaKpVbSYqKwpMrKSeP4tZAmo4m968+SsDsVAP9wD27oGyWDeDkQrU5DWGMfwhr7YCw2ce5oBif3pZN2Opu0U+bHbmctDZr6Et06AO8gV2l+XptY/l+p0rxcCOFQUlJS2L9/PwA9GrmDkwcENLVzVELULBOlU4bJjXFhO1Y3L3/wwQdZsGAB//73v20RT62jVUqSbkXBWefYiWtxkZGt3x8n7XQ2KNC8axhNbgxGkZEZHZZWryGiuR8Rzf3IzSzk1P7znNqfTn52ESf2pHFiTxrega5EtQ6gQTNfmQ+8lpB/cUIIR7R+/XoA2sRGEODpZK7llomKRT1jLGmPppU+3cKGrP6FbjAYWLhwIatXr6ZDhw64u7uXWf/uu+9aewqHpgGKMPfpdtY6btJdVGBg07fHyEjKRavX0rF/NKGNvO0dlrgO7t7ONO8aSrMuIaSdzuHkvjTO/pNBZmo+e9aeZt+Gs4Q38SGqVQD+4TL6uSPTlKTdMnq5EMKRWPpzNw8wLwjvYMdohLCPi0m31HQL27E66d63bx833HADAP/880+ZdfXhR79WMZbUdINeo7d3OBUqKjCw8f+OkpmSh95FR7dBjfEJlpFIaytFUQiM9CQw0pM2vQycPnSeE3vTyU7L5/SB85w+cB53X2eiWwUQ0cIPF3fH/FzWXzJ6uRDCMR05cgSAXlEltdthN9gxGiHso7RPtyTdwpasTrpL74rWVxoo6dOtOOQc3UaDia0rj5OZkoeTq45u98TiHehq77CEjTi56mjUPoiG7QK5kJTHyb3pnDl8ntwLhez/4ywHNp4jpKE3Ua39CY7ykq4EjqAe3IwUQtROq1evJuH3pQQdWgw+UeDub++QhKhxBkqnDDPYORJRl1Q56TaZTLz99tusXLmSoqIibr31Vl5++WVcXetXQqdTjCXNy8FJ61gDqamqyvZVJ0k/k4POSSsJdx2mKAp+oe74hbrTqmc4Zw9f4OTedC4k5ZJ4NIPEoxm4ejoR2dKfqFYy9Zh9yejlQgjHFaNNBGcdhEstt6ifjCVJt0ZquoUNVTnpfuONN3jllVeIj4/H1dWV999/n5SUFBYuXGjL+Bxa6pFdnHY5T2Erc023o41efnhLEuf+uYCiUYi7s6Ek3PWE3klLdOsAolsHkJmaz8l96Zw+eJ787CIOb0nk8NZEgqK8iG7tT0hDbzRaGSTHXqR5uRDCoagqnN1hfi5Jt6inTGpp83IZSE3YTpWT7s8//5yPPvqIRx55BDA3SRowYACffvopmnoy0uUfc57lb1c3/vVRJ1Acq3l5ckIWh7YkAtD+tkgCIz3tHJGwB+9AV9r0akDLm8LMU4/tNU89lnIii5QTWTi56iy1355+jvP5reukgbkQwiFdOAH5F0DrBEEt7R2NEHZRjAYtknQL26py0n3q1Cn69+9veR0fH4+iKJw7d44GDRrYJLjaQKNRHG6e7rysIrb97wSoEN0mgMiW0iervtPqNEQ08yOimR+5GYWc3JfOqf3pFOQWc3RbMke3JeMf7kFUa3/CY33R6uvHjTO7UJTSsctl9HIhhGM5u938N6Q16BzjN40QNc1UknRrVNXc+kPGYhE2UOWk22Aw4OJStmZMr9dTXFxsdVC1iaIoloHUHKFPt6qq7PjlJMUFBnyC3Wjdq/7cABGV4+7jTIvuYTTrGkpyQhYn96aRlJBJ+tkc0s/msGfdGSKa+RHVyl9Gua8Wl4xeLkm3EMKRWJqWy1Rhov4yoEVPSa5tMoBWZoER1qty0q2qKiNGjMDZ+eLc1AUFBTz66KNl5ur+9ttvrYvQwSmKYh5ITdE4xDzdCbvTSDudjVanoWP/aLTSX1dcgUajENrIm9BG3uTnFHFq/3lO7ksnL7OQhN2pJOxOxTvIjahW/kQ090PvrLV3yHWG3DMXQjic4nxI2W9+Lkm3qMdKRy8HRZJuYTNVTrqHDx9ebtmDDz5oVTC1kUa5pHm5nWu6czMK2b/hLAAtbgrDw1f66IrKcfVwomlcCE06BZN2OocTe9M4dySDzJQ89qzNY/+Gs4Q18SG6dQB+Ye4o0tSq6hQFDQqoYJKRUYUQjiJxD5iM4BEMXmH2jkYIuzGWzDFiqekWwgaqnHQvWrTIlnHUXgolU4Ypdq3pVlWV3WtOYzSYCIjwpGG7QLvFImovRVEIjPQkMNKTwnwDpw+c5+TeNLLPF3D6wHlOHziPh58LUa38iWzhh7Ob3P21hjQvF0I4jHOXNC2XG6uiHjOqJUk3mG9ECWEDVU66hZlG0ZhruhX7Jt1JxzJJOZmFolFoFx8hNZHCas6uOhp3CKLRDYFcSMzjxN40zh6+QM75AvZvOMuBP88R2sibqNYBBEV6omjkM1c5ijQvF0I4jPPnz+Pr44NyZpt5gUwVJuo5I+a5uqWmW9iSJN1WUhTs3rzcWGxi7/ozAMR2DJZm5cKmFEXBL8wdvzB3WvdqwNlDFzixL52MpFzOHcng3JEMXNz1hDf1JaK5H95BrnLT5xpKr47M0y2EsLdbbrmF9NQU/m9UDHGxgRDa1t4hCWFXRqOKUdEBJjDWrwGiRfWRpNtKmtKB1OzYvPzo9mTysopw8XCiSVywXWIQ9YPeSUt0mwCi2wSQmZrPyX1pnD54gYLcYo7tSOHYjhQ8/Vxo0NyPiOZ+uHnZf0R/h6NcknJL83IhhB0lJyeze/duABoGtoTQNqB3tXNUQtiXUVUxKDoUpQiMRfYOR9QRknRbSdEoFKOCAno7jG5YlG/gyLYUAFreHIZOLyNMi5rhHehKm14RtLo5nOQT2Zw+eJ6kYxlkny/g4MZzHNx4Dv9wDyKa+xHWxAcnF/m6MZPm5UIIx7Bq1SoAbmgUTKCXMzToZOeIhLA/k0k1Ny8HaV4ubEZ+BVshfvKn3Op2hEzWAwp6Tc0n3f/8nYyhyIhXoCsNmvrW+PmF0Gg1lqnHiguNnDuSwemD50k7k22Z+3v32tOENPQmorkfwTFeaHX1eyq70ncvo5cLIezpp59+AmBAS0/zgghJuoUwmFQMih7Il5puYTOSdFvBKySKcNfzpJU0L9dpavZyFuQUk7ArFYAW3cKkH62wO72zlqhW/kS18ic/u4gzhy5w+uB5stLySTyaQeLRDHTOWsIa+xDe1JfACA809XkueWldLoSwk+LiYn755RcABrQJAt8o8Aiyc1RC2J9JLanpVhRJuoXNSNJtJQ3qxeblNVzT/c/fyRgNJvxC3QmO8arRcwtxLa6eTsTeGEzsjcFkpuZz5tB5c//vnCJO7U/n1P509C46wmJ9aNDUl4AGHvVjBHRFkQbmQgi727RpE1lZWQR4u9MxxkealgtRwlhS062ADKQmbEaSbitpMGH+51izzcuL8g2c3JsGQLOuoVLLLRyad6Ar3oHhtOgeRvrZHM4ezuDsPxcsn+OTe9NwdtMTFmuuAfcPd6/Dn+mLKbdJleblQgj7KG1a3q91AFqNIk3LhShhMKkY0JmnGpGabmEjknRbSYOKoaR5eU0m3cd3p2I0mPAOdCUw0rPGzltZqqpizMjAmJaGMTsHU34ean4+pvx8VKMRjCZUk/kvqgnVaAJHHMlZUcxfuopiTgIVBVCusVwpeXrJaxTQKGhcXNC4uqK4uqJxc0Pr6Yk2IACNU/0Y5VtRFAIaeBLQwJPWvRqQdjqbs4czOHc0g8K8YhJ2p5KwOxUXDz3hTXwJb+KLb6hb3UrAL3krMmWYEMJeLP25W/mBsxcENLVzREI4BpNJxaRoSgZSM9o7HFFHSNJtJQ1Gc9Jdg6OXG4tNHN9p7svd+MZgh0hIDGlp5O/ZS9HxYxQeT8CQlIRqkBEfK0vj5YkuIACniAicoqNxiozEKToaRV/zg/PVFI1GISjKi6AoL9re2oCUU9mcPXyBxGOZFORcnILM1dOJ8CY+hMb64BdaN2rAze9AEm5R+8yZM4e3336bpKQk2rZty+zZs+nU6co1pMuXL2fq1KmcOHGC2NhYZs6cSf/+/S3rVVXl5ZdfZv78+WRkZNCtWzfmzp1LbGwsACdOnOD1119n7dq1JCUlERYWxoMPPsiLL76IUz25WVkdTpw4wYEDB9BqNfRuFQgNOoKmHo+vIcQljKqKCY3594a0SBM2Ikm31UrvgCnolJq5nKcOpFOUb8DVy4nwJvYbsdyYk0PO77+Tt2ULRSdPld9AUdD5+6Hx8kLj6obG1RWNmytodShaDSga0GpQNFrzX1slU7Y4jnrJHMqqas6PVBVQL667dHm5dRUsN6moBQWY8vIw5edjys/DmJmJml+AKSuboqxsio4nwO8bzG/DyQmX5s1wadkK1xvaow+quwPcaLQaQmK8CYnxxmg0kXLiYgKen13E0e0pHN2egou7ntDGPoTF+uDfwANNLe0Dbhm9XApzUYssW7aMSZMmMW/ePOLi4pg1axZ9+vTh8OHDBFXw/bRp0ybuu+8+ZsyYwe23386SJUsYOHAgO3bsoFWrVgC89dZbfPDBB3z22WfExMQwdepU+vTpw4EDB3BxceHQoUOYTCY+/vhjGjduzL59+xg7diy5ubm88847NX0J6ozNmzcD0LVJEL7uemlaLsQlTCYVlZKablVquoVtKKrqiG16HVtWVhbe3t78a9ZvDHX9nf85/Q/cA/l40Mpqb2Kuqirr/nuIrLR8WvVoQOMONZ+IGS5cIHPF9+T++SdqcckAE4qCc5MmuDRrilNMQ/QNwtH5+aHo5L7O1aiqiik3D2NaKsUpKRSfOkXRyZMUJiRgysous61z48a4dY7DvWs3tB7udoq4ZhmLTSSfyOLckQskHc/CUHSx8NO76Aht5E1YrA+BkZ61Zxqy78fz+oXtJPg14InOL9IuqJ29IxI1rLQMyczMxMur9gyCGRcXx4033siHH34IgMlkIiIiggkTJjB58uRy2w8ZMoTc3Fx+/PFHy7LOnTvTrl075s2bh6qqhIWF8fTTT/PMM88AkJmZSXBwMIsXL2bo0KEVxvH2228zd+5cjh8/Xqm4a+v1rm7nDv5N2v89TZsoXxj6FTi52TskIRzC4o0J+G2aTmfnkwT1nwyN4+0dkrATW5YfkhFZyVRa063UTE33+cRcstLy0eo0RLb0q/bzXUotLibzhx/J+vln1CLzwBJOUVF43NILtw4d0MqPmeumKApaD3e0Hu44RUdDSTNNVVUpPnOGgn37yN+1m4JDhyg8epTCo0fJ+Ho57l274tn7NpwaNLDvG6hmWr2GsFhzzbbRaCL1VDaJRzJJPJZBUb7BMgq6Vq8lpKEXYY19CI7xQuektXfoV6aUDKSmSp9uUXsUFRWxfft2pkyZYlmm0WiIj4+31JpebvPmzUyaNKnMsj59+rBixQoAEhISSEpKIj7+4g9ab29v4uLi2Lx58xWT7szMTPz8rlz+FRYWUlhYaHmdlZV1zfdXH4UVHiUswgvC2kvCLcQljCqYFI25L5jUTQobkaTbSirmfss6RVsjfU1P7DGPWB7e1Bcnl5r731d08iRpn3xC8ekzgLnW1WfIvTg3aVIn+tg6GkVRzP27IyLw6tcPw4UL5P31Nzkbfqf49Bly1q8nZ/16XG9oj8/dd+MUFWXvkKud9pIm6G1NEaSfzSHxaAbnjmRQkFPM2cMXOHv4Ahqtua94SCNvQhp64+LueP3iS8cvl4ZGorZIS0vDaDQSHBxcZnlwcDCHDh2qcJ+kpKQKt09KSrKsL112pW0ud/ToUWbPnn3VpuUzZszg1VdfvfobEnByo/lvVDf7xiGEg7E0L1eQgdSEzUjSbYVjf3zPloAT0Bn0NVDLXZRv4OzhCwBEtwmo9vOVyvlzI+cXLkA1GNF4euI3bBhunW6UZLsG6Xx98erTG8/et1H4zz9k//Iredu3k79jJ/k7duLWsSM+9w5GHxJi71BrhEajEBjhSWCEJ617NiAjOY9zR8wJeG5GIUnHM0k6ngkK+Ia4E9LQm9BG3nj6uzjA5/bi+aWmW4jKO3v2LH379mXw4MGMHTv2ittNmTKlTA17VlYWERERNRFi7ZGVCBdOmMdAiYizdzRCOBSDyTyQGijSp1vYTL1Nut977z0+/fRTVFUlPj6e999//7p/jO/+5kNyY0K4qXM4Ok31N2c9deA8JqOKd6ArviHV3xRMVVUyln1N1s8/A+Darh3+Y0ZLM3I7UhQFl6ZNcWnalOJz58j8/ntyt2wlb9s28nftxLNPX7zvvAONq6u9Q60xiqLgG+KOb4g7LbqHkZVWQOKxDJKOZZKRnMeFxFwuJOZycOM53LycLDXgAQ080Gjt0A9co7OMXi413aK2CAgIQKvVkpycXGZ5cnIyIVe42RcSEnLV7Uv/JicnExoaWmabdu3aldnv3Llz9OrVi65du/LJJ59cNVZnZ2ecnZ0r9b7qrdJa7pA24CJluhCXMqnmKcM0IKOXC5upJSMP2VZqaioffvgh27dvZ+/evWzfvp0tW7ZU6VhKyRWsiZru0wfPAxDVOqDaa+tUVeX8osWWhNv7zjsIfGqiJNwORB8WRsBjjxH6xhu4tm2DajCS9dNPnHt+Mrlb/6qXCZ2iKHgHutKscyg9H2hG34db0y4+kuAYbzQ6DXlZRRzfmcqmb47y89y9/PVjAqcPnKeooAant9PqLQW51HSL2sLJyYkOHTqwZs0ayzKTycSaNWvo0qVLhft06dKlzPYAv/32m2X7mJgYQkJCymyTlZXF1q1byxzz7Nmz9OzZkw4dOrBo0SI0MrWV9U5uMv+N6mrfOIRwQEaTiopibpgmzcuFjdTbmm6DwUBBQQEAxcXFFU53Uhmlua++mmu6s9LzyUzJQ9EohDet3mnCVFXl/IIF5Gz4AxQF/zFj8Lipe7WeU1SdU4Nwgp5+mvxduzj/5RIMycmkzZmD29aO+A0fhtbb294h2o2Lh57oNgFEtwnAUGwk9VQOiccySD6eRWFeMef+ucC5fy6YP+dh7oQ0MjdD9/B1qb6gFAce5E2Iq5g0aRLDhw+nY8eOdOrUiVmzZpGbm8vIkSMBGDZsGOHh4cyYMQOAJ598kh49evCf//yHAQMGsHTpUrZt22apqVYUhYkTJzJ9+nRiY2MtU4aFhYUxcOBA4GLCHRUVxTvvvENqaqolnivVsItryE2DtH/MzyMrvmEiRH1mMJrM83SD1HQLm6mVSfeGDRt4++232b59O4mJiXz33XeWArrUnDlzePvtt0lKSqJt27bMnj2bTiUjQwcGBvLMM88QGRmJTqfj0UcfpVGjRlWK5WLSXb2X8swhc1/uoGgvnF2r91wZy762JNwBjz6C+xVqMYRjcW3XjrCWLcn84Ucyf1hJ3rZtFBw6hN+wh3Dv3Nne4dmdTq8ltCSpVlWVC0l55r7fxzLJSssn/WwO6Wdz2L/hLO6+zoQ28iGkoTd+Ye62nQ9coy0pyKV5uahdhgwZQmpqKtOmTSMpKYl27dqxatUqy0Bop06dKlML3bVrV5YsWcJLL73ECy+8QGxsLCtWrLDM0Q3w3HPPkZuby8MPP0xGRgbdu3dn1apVuLiYb3z99ttvHD16lKNHj9Lgstka5N9PFZXWcgc1B7eanQVFCEdnMqkcSsomBA1OOo0k3cJmamXSnZubS9u2bRk1ahT/+te/yq1ftmwZkyZNYt68ecTFxTFr1iz69OnD4cOHCQoK4sKFC/z444+cOHECV1dX+vXrx4YNG7j55puvOxZL0l2NzctVVeVMSdPyiGbVW8udvXatpUm5/9ixknDXMopej8+/7satww2kf/opRSdPkfbRXPL37sVv2DA00s8RMNew+YW64xfqTotuYeRmFpJ8PIvE45mknc4m90IhR7clc3RbMnoXHcHRXoQ28iYo2gu9s5U11Vr9xT7d0rxc1DLjx49n/PjxFa5bv359uWWDBw9m8ODBVzyeoii89tprvPbaaxWuHzFiBCNGjKhKqOJKpGm5EFd0NiOfrIJidFotbk46SbqFzdTKpLtfv37069fviuvfffddxo4da2nyNm/ePH766ScWLlzI5MmTWb16NY0bN7bM8zlgwAC2bNlSpaS7dCBiXTXWdJ9PzCUvqwitXktoI59qO0/BgQOc/+xzAHwG/QuP7jKNSG3lFBVFyMsvk/n9SjJXriT3jz8pOnqMgPHjcJJRfMtx93amYftAGrYPpLjISEpCFkkJmSQdz6K4wMCZQ+c5c+g8ikbBv4EHoSWDsbl7V+EmRmnzclWSbiFEzUlISCDCzw1d8j7zgijpNibE5fKLzX24nfR6NIoifbqFzdS50UiKiorYvn078fHxlmUajYb4+Hg2b94MQEREBJs2baKgoACj0cj69etp2rTpFY9ZWFhIVlZWmUcpy0Bq1Zh0n/snA4DQRt5o9dXzv8yYkUHa3Lmgqrh364bXnXdWy3lEzVF0OnwG/YvgKZPR+vhQnJhI0suvkL1unb1Dc2h6Jy3hTX3p0Deafo+2pvu9TWjcMRgPXxdUk0raqWz2rjvDbwv288fX/5CfXXR9J5DRy4UQNUxVVXr16kVIdCzbEjIgqAV4BNo7LCEcTmGxuWZboy29QS413cI26lzSnZaWhtFotPQxKxUcHExSUhIAnTt3pn///rRv3542bdrQqFEj7rxKkjljxgy8vb0tjzLzfVr6dOtt/l7AXFAmHssEICzWp3rOYTKRNu9jjJlZ6Bs0wG/kCAeYy1jYikuzZoS+MR3Xtm1RDQbOL1pM+uLFqIYaHLG7ltJoFAIaeNDq5nDiR7YgfmQLWvVogH8DD1Ag/UwOv391mKz0/Os4qNY8PIvUdAshasjmzZs5efIkhQUFtAz3hIY97B2SEA6p0GCu2dZqSyrTJOkWNlLnku7KeuONNzh48CD79+/ngw8+uGqSOWXKFDIzMy2P06dPW9Yplubl1TMicVZaPnmZhWh0GoKiPavlHNm//krBgQMozs4Ejh+HxsmpWs4j7Efr6UngpKfwGXwPKAo5a9eRPOPfGDMy7B1areLh60LjDkHcdG8T4ke0xNPPhYKcYv5YdoSz/1yoXM21pVWMiuTcQoia8NVXXwEwsH0Qrs56iJam5UJUpNBQWtNdkiKp0rxc2EadS7oDAgLQarUkJyeXWZ6cnFzl6UWcnZ3x8vIq87Co5pruxKPmWu6gSE90etsn9sXJyWT83zcA+N5/H/qwMJufQzgGRVHwvuMOgiY9hcbVlcIjR0h8+RUKjx+3d2i1koevM92HNMEn2I3iAgN//5jAkb+Tr71jafNyqekWQtQAg8HA119/DcB9cWEQfgO41N+pJIW4mqKSpFurkZpuYVt1Lul2cnKiQ4cOrFmzxrLMZDKxZs0auth4JO5/zfqNhyeYp2LSVdPo5YlHMwAIrYam5aqqcn7hQtSiIlxatMCjZ0+bn0M4Hte2bQl59RX0YWEYL1wg+c0Z5G3fbu+waiVnVx3dBscS087cN/LwliQKcouvvtOlfbol6RZCVLP169eTkpKCv6czt7UMgIY97R2SEA7rYvPykoouGUhN2EitTLpzcnLYtWsXu3btAswjcu7atYtTp04BMGnSJObPn89nn33GwYMHeeyxx8jNzbWMZm5TJTXd2moYSC03s5DM1HxQIKSh7e9K527YQMHBQyhOTviNGin9uOsRfUgIIS9Pw7VtG9SiIlI/mE3WL7/aO6xaSe+kpU2vBviEuGM0mDixJ+3qO2guGb1cBlITQlSz0qbl93QIRu/sAhFxdo5ICMdV2rxcq5OabmFbtTLp3rZtG+3bt6d9+/aAOclu374906ZNA2DIkCG88847TJs2jXbt2rFr1y5WrVpVbnA1WzBh/seoU2zf9DvlhHmUdL8wD5xdbZvUm/LzubB8OWCeHkwfFGTT4wvHp3F1JXDiRDx69QJV5cKXX3L+iy9RTVLAXC9FUWhUUtt96sD5qyfTMk+3EKKGFBYW8s035i5k98WFQWRn0LvaOSohHJcl6ZbRy4WN1cp5unv27HnNGqLx48czfvz4ao/FpJb2/aiGpPtkNgDBUbYfQC3z+5WYsrLRhQTjedttNj++qB0UrRa/EcPRBQWSsexrsn/9FUN6GgGPPSYD6l2nsFgfdq/VkpdZSPrZXAIaeFS8oUZnvtsptdxCiGr2888/k5mZSbivKzc18YPG8dfeSYh6rEiSblFNamVNtyMxKeYfzhob9+k2GU2knjIn3UHRXtfY+voUJ6eQ/esvAPjdfz+KrlbeexE2oigK3gMGEPD44yg6Hfnbd5Dy1tuYcnPtHVqtotVrLNP6nT5w/sobWgZdlOblQojqtXDhQgAe7BKGxjMQQtvZNyAhHFy5mm7p0y1sRJJuK1mal9u4pvtCUh6GIiN6Fx0+QW42PXbmt9+gGoy4tGqFS9u2Nj22qL3cO8cR9Pxz5pHN//mH5H//G2Nmpr3DqlUimvsBcPbIBYyGK9wdv7RPtzQvF0JUE5PJhF6vR6tRGNm9gbmWWyM/+4S4mtKB1HS6khvkUtMtbES+fa1U+qNZZ+OB1FJOmvtzB0V5omhsN8BZ8dmz5G7ZCoDvkHtl8DRRhkvTpgS/+AJaby+KTp4iafobGFJT7R1WrREQ4YGLhxOGQiNnD1+oeCOtExoUGUhNCFGtNBoN334+j+T342ka6iFNy4WohILiy5uXS023sA1Juq1kLKnp1ii2vZQpJ0qalkfZtml5xncrQFVx69gBp6gomx5b1A1OkZEEv/QSuoAADMnJJE1/g+KzZ+0dVq2gKAoxbQMA2Pv7WXIuFFSwUel3hdR0CyGq2dHV+Hs4QUgb8AyxdzRCOLT95zLZcdJ8w9zTtWRcG6npFjYiSbeVLDXdNuzTXVRg4EKyuT9tkA0HUSs6fZq8v/4CwPvuu212XFH36IODCZ76EvrwcIwXLpA0/Q0Kjx+3d1i1QuOOQXgHuVFcYGD/n+fKb6AopTMNStIthKg+JiMcKZkKMlYGTBXiWpZvO4PBZKJzQ38aBpVUesmMLsJGJOm2kqnkR7NWa7uk+/y5XFDB3dcZV0/bjSCduXIlAG6dOuEUEWGz44q6SefrS/CLL+LUqCGm3FySZ86k4PA/9g7L4Wm1Gjr0NbciSTySQdqZnHLbWL54JecWQlSX01shNw2cPSGqm72jEcLhpeYUAnB7m1AURUYvF7YlSbcVdi7/gG1/ngFAa8N5ukt/pPuHXWHKoSowpKWR9/c2ALzvvMNmxxV1m9bDneDnn8eleXPU/AJS3nmHgoMH7R2Ww/MKcCWylT8Af/+YgKGo4j5hUtMthKg2B380/23SB3QyBaQQV6OqKln5xQB4ueovGfRUkm5hG5J0WyFh4w8cK5kayJYDqZ0/a066rzjPbxVk/formEy4tGyJU2SkzY4r6j6NiwuBT0/CpVUr1MJCUv7zLvl799k7LIfX6uZwdM5aCvOKK6ztBkm6hRDVJOMUJO0xP2/a376xCFELFBSbKDaaE2wvF/3F8VdkIDVhI5J0W6n0R7PWRn26jcUmLiTnAeBvo6TblJtLzvr1AHj162uTY4r6RePkRNDEJ3Ft2xa1qIjU994jf/due4fl0JxcdIQ19gFgy4pjZUYztwylJqOXCyGqQ2ktd2Rn8AiybyxC1AKZJbXcTjotLnrtxaRb5ukWNiJJt5VK/01qbVTTfT4xF9Wk4uKhx83LNs3Bcv74A7WgEH14OC6tW9vkmKL+UZycCHzyCVw73IBqMJD6/vvk7dhh77AcWuyNwZbnJ/all1svNd1CCJsryoVja83Pm91u31iEqCWyCsxJt7dryfzclppuaV4ubEOSbiuV/mTWamzTpzv9XEl/7nAPm8yhraoq2WvNha9n79tkXm5hFUWnI3DcONw6dUI1GEmdPZvcrX/ZOyyH5ennwo23xwDmViylSscvl6RbCGFzh/8HhgLwjoDQtvaORoha4eyFfAD83UsqvCTpFjYmSbe1SnJYrUZvk8OlnzVPFeYfbpum5YWHDmFISkZxcca9c2ebHFPUb4pOR8Bjj+LetQsYTaR99BG5mzfbOyyHpXMy35AzGi4W3DJ6uRDC1nbv3k3v2+JZt3yeeUHrQSA32oWolO0l83O3Dvc2L7Ak3VJQC9uQpNtalqTb+ublqqqSkWROuv1C3a0+HmDpy+3epSsaV1ebHFMIRavF/+GH8bj5JlBV0uZ9TO6WLfYOyyFpdeavWUNx+X5hUtMthLCVmTNn8tvqNXzy60Fw84eYnvYOSYhaochgYs/ZTABuiPI1L5SB1ISN2W7I7Xqq9CezzgZThuVmFFJcaESj0+AV4GL18YzZ2eT9/TcAnr16Wn08IS6laDT4jR4NQM6GP0ibOw9QcO8cZ9/AHIxOby64iwsqSLrlDroQwgaOHz/OsmXLAHi+f0NoeTdo5SeeEJVxJCWbIoMRXzcnov3dzAtLu43KQGrCRqSm21o2rOm+kGQetdwn0BWN1vr/NbkbN6IajDhFR+MUHW318YS4nKIo+I0ejftN3UtqvOdJH+/LePg6gwJF+QYyU819xiyjl0tNtxDCBt555x1MJhN9WwfSrnG4eW5uIUSlnD5v/v3dKPCS8ZQUmadb2JYk3daqjqQ7xM3qYwHk/PknAB49e9jkeEJURFEU/EePxr17dzCZSJs7l9y/JPEupXPS4hNs7i7yz19JZdZJ0i2EsNa5c+dYuHAhAJP7N4IWd4JeupMJUVmnSpLuCL9L/t3IQGrCxiTptoJnSBRuPuZk2xZJd2l/bt9g6/tzF505S/Gp0yg6LW6dpLmvqF6KRoP/mNG4d+tmTrw/mmvp2iCgUftAAHLOFwCWe3XSvFwIYbXp06dTWFhI91hfbm4dCS3usndIQtQqx1PNv79jAi4ZxFiSbmFjknRb4bbJn9Lx7gjA+tHLTUYTGSVNT31Dra/pzt28CQCX1m3QethmUDYhrkbRaPAfO8aSeKd+9BF527bZOyyH4OlvHqOhIM8AgAYZUVgIYb3jx48zf/58AN4Y1BSlzWBwkjJfiMpKySrgZLq5prtx0KVJd0k5LQOpCRuRpNtKppLmodbWdGelFWAymNA5a3H3cbbqWKqqklcyhZN7165WHUuI62FJvEumE0udM0cSb8DF3XxTrjCvuMzsI9K8XAhhjVdffRWDwUDvlgHc3LYhNLvd3iEJUav8feICKiotw7zxK52jGy4OpCYt0oSNSNJtJWPJj2adlTXdGSkl/bmD3C4O4lBFhUeOYEhLR3FxxrV9O6uOJcT1UjQa/B9+GPcunS8m3jt22Dssu3JyLbkpp0Jx0cXl0rxcCFFVu3fv5osvvgDMtdy0HQp662c+EaI+KZ2f+4ZI37IrpHm5sDFJuq1ku5puc9Ny70DrBz8preV269ARjZPTNbYWwvYuT7zTPvyQvB077R2W3Wg0CorGfDPNYJTRy4UQ1jt//jzhgd4MvjGUju1aQ5N+9g5JiFrlQm4RBxOzALgxWpJuUb0k6baSrZLu0qmErE26VVUlb7u5VlHmSxb2pGi1+D/8MG6d41ANRtI+nF2vE2/nktrurIyLX7tS0y2EqKpe7Rtz6LXOfPRQK+g0VublFuI6fbfzLCoqTYM9CfK6rJVIadIt83QLG5FvaCuoqBgxX0RrBlJTVZWsNPOoxl4B1iXdRceOYczIQHF1waVFC6uOJYS1FK2WgEceIc2kkvfXX6R9OJuACRNwa9/e3qHVOJ8QN5KOZZKTgwyjJoSwjqrClrm4OWlwa9Qdwm+wd0RCWEVVVUyq+a9RVVFVMJUsM6kqqqn09cXtTOrFZeW2v3S9iTLbZBcY2HnqAhuPpQEwqEOD8gHJPN3CxiTptopa8lDQWVHTXZBTTHGBARQFzwDr+mPlbdsOgGvbtih66/qZC2ELilZLwGOPkgbmxHv2bAKfeALXdu3sHVqN8vJ3JelYJllZF8cul+blQogq+ecXSNoDWifo9LC9o6kzrpnIXUfiZzSZfyOWPlcr3K70eck2mF+bn5cmjJeeq/Q5JYnpxe1Nask+pisknpcdw3Lu691eVS3xqaXPS97Dpccwqipcsr1JBbj4/GKs5v3tVR7e2zGCNg18yq+Q5uXCxiTptoKqGizPNVYk3Zkl/bk9/ZzRaqve4l9VVfJ2mJNutw4dq3wcIWxN0WoJePQR0lSVvL//JvWDD+pdjXfpYGqXtlST5uVCiOuWmwbbFpif3zAMPEPsG4+NGE0qGXlF5BYaySk0kFtkIKfQQH6RkSKDiSKjiSKDieKSv6XPi03mRK70UZrkWR5q2XUG4yXbqBcTP5N8Hzs8BQWtRkFRQFEUtApoFPNrjaKYHxrzOvPry9eb/7rotUT7u9E9NrDsNGFlTlY6ZZgk3cI2JOm2ysV/iDpt1Qcsy7JRf27DuXMYkpJRdFpc27S26lhC2Jqi05lrvBXFUuMdMH4CbjfUj8RbozUX4EbTJWW51HQLIa6HqsKmD6A4HwKbQfM77R3RdcsrMnAiLY+T6bmcPJ9HUmYBqTmFXMgtqhWJb2lyd3kip1zyvKLE7+I+oNUogGJ5rlCSRJY812gu296cZVqea0oG5tRqzMsUlJJ9zN2XzImpeX/t5fFqKohXuVqieul6Lp6nEttb3k+Za1L6fszLtcolSXSZ91Ny3EveT42Smm5hY5J0W8VgaSaqLZ3PrwpKRy63tj936bRMLi1bonG1fhR0IWxN0enMNd5wsY93PUm8tTpzAW4ygkZn/uaQpFsIcV32fQNnd4BWD92eAI3jj4drMJrYdy6LfWcz2X8ukxNpeVf87tMoCh7OOtyddZa/bk5anHQanLSaMn/1Wg16rYJep0GrKOg05sRTW/pQLnleksRalmuVkuT1YlKqURQUzeWJdPlEUtQTGunTLWxLkm4r/PHhi0TfmEmbPiHWNS8vqen2srKmO297SX/uG2RAFeG4LtZ4Q97W0sR7PG51/HNbWtNtuqT8lublQohKSzkIOz43P+/0CPhE2jeeq1BVlYOJ2Ww8msbWhHRyCg1l1gd4OBPt706UvxvhPq4EeDoT4OGMj6veUosrhF1JTbewMUm6rZB+7AAB0YFoUC7+47xOJqOJnAuFgHmgpaoyZmdTlHACoN4NUCVqH3Mf75Km5lu2kjp7NoHjx+PWoYO9Q6s2Wl1p0q3I6OVCiErLyMjAx8kEv880JwAxN0OTPvYOq0Kn0vP442gqm46lk55TaFnu6+ZEuwgfWoV70yLUC1/3qnfJE6JGSNItbEySbhvQQJWT7tzMIlSTilavwdWz6qONF+zfD6qKPqIBOl/fKh9HiJpSOp1YuqKQu3kLqR9+SOC4cbh1rJuDAGpKBkk0Gi9OGSY13UKIq0lNTSUurhN3tfblnbtC0fqEQ5fxFweGcACp2YVsOpbGn0fSOH0hz7LczUlHpxg/ujcOoEWol9Rgi9pF5ukWNiZJt5UUpSTprmLdVc558/zcHr4uVvUVyt+zFwDX1m2qfAwhapqi1eL/sHm6m9zNW0idM4fAxx/H7cYb7RyZ7VXYvFz6dAshriA3N5e77rqThIQTrMxJYert0fjFvwpObvYOjeyCYrYcP8/Go2kcSsqyLNdpNNwQ6UP32EDaRfjgpHP8PudCVEjm6RY2Jkm3DVjTvDz7gjnp9vRzrvL5VVWlYO8eABm1XNQ6ilaL/yOPgKKQu2kzqXM+IuDxx3Dv1MneodlUhTXdknQLISqQm5vLHXfczubNW/B11/Pz053xu/N18Aq1W0wFxUZ2nLrAxqNp7D6diaHkDqKCQoswL7o3DqBTjB/uzvLTUtQB0rxc2Jh8M1pLAS1UOenOOW/u8+Th51LlEIpPncKYmYXi7IxzbGyVjyOEvSgaTUmNt0Lupk2kfTQXVHCPqzuJd2lNt2oqbR0jzcuFEOVlZWUx8K67WLd+PZ4uOn6aFEfT+96A4JY1HkuRwcSu0xlsPpbO9lMXKDJcbGobE+BOt8YBdGnoj79H1SsOhHBIMk+3sDFJuq2kKEpJn+6qNQ3PLmle7ulb9aS7tGm5S/PmKPqq9wsXwp7MifdYc433xo2kzZ0L1J3E++Lc3BdJTbcQ4lInT57k9gH92bf/AJ4uOn55tjNdxrwNkZ1rLIaCYiP7zmayNeE8206cJ7/4YqId5OlCt8b+dGscQANf+zdzF6LaXFqZpqoONY6CqJ0k6baWUtq8/Pr/MaqqerFPtxU13QX79gHg0rpVlY8hhCNQNBr8x44BuJh4qyrunePsHJkNqVUdAUIIUZetWrWK4Q89SEpaOqE+zvzwdHc6jJwJ4dU/q0NKdgE7Tmaw8/QFDpzLoth4sXbP38OZzg396drIn4YB7jJXtagfSufpBvNgalpJmYR16u0nKDo6Gi8vLzQaDb6+vqxbt65Kx1Gqlm8DUJRvoLjQ3MHTw6dqTbPUoiIKjx4FwLVlzTc9E8LWLIm3RiH3jz9JmzsXtbgYj5u62zs0mymdNEyalwshAD6e+xGPPj4OgLYRXvwwpQ8RQ98C3+hqOV9GXhEHErM4cM78OJeZX2Z9kKcLN0T60KVRAE2CPSTRFvWPcknSrRqpxymTsJF6/QnatGkTHh4eVh9HW8V6q+yS/txuXs5o9VXrE16YkIBaXIzW2wtdqP0GWBHClhSNBv/Ro1E0GnJ+30D6/PmoRYV43nqrvUOrstIfrdK8XAhhoapw5m96GdfjpNPwcI8I3np2NK49J4Kzp01OYTCaOH0hn+OpORxLzeFQUjbnMsom2RpFoUmwJzdE+XJDpA/hPq6SaIv6TXdJZZixqOxrIaqgXifd1mrQtj0+Dc5baq2uV2nTcmtGLi84eBAA52bNpIAUdYqi0eA3ahSKkzPZv/3G+c8+Ry0sxKt/f3uHZiVF5ukWtdacOXN4++23SUpKom3btsyePZtOV5lpYPny5UydOpUTJ04QGxvLzJkz6X/Jv2FVVXn55ZeZP38+GRkZdOvWjblz5xJ7yaCg58+fZ8KECfzwww9oNBoGDRrE+++/b5Ob5nZjNMCpzbB3OZw/ThMPOPDOABrdPhEa9qr6ODEFxZy5kM/ZC/mcvpDH8dRcTqTnlmkuDuZvoSh/N1qEedEyzIumIV54yKjjQlyk0Zr7dasmMBTa7CaYqL9q5Tfshg0bePvtt9m+fTuJiYl89913DBw4sMw21/phoCgKPXr0QKPRMHHiRB544IHrjqPr8FHovRZWuaY7J8Nc0+1exablAIUHDwHmQdSEqGsURcH3wQdQXJzJ+uFHLixdhqmgEO+7B9bem0zqJaOXS023qEWWLVvGpEmTmDdvHnFxccyaNYs+ffpw+PBhgoKCym2/adMm7rvvPmbMmMHtt9/OkiVLGDhwIDt27KBVK/MYJG+99RYffPABn332GTExMUydOpU+ffpw4MABXFzMY5088MADJCYm8ttvv1FcXMzIkSN5+OGHWbJkSY2+f6sZDZCyH05tgeO/Q2HJ/NZaJ2h+O43aDL3mHNyqqpKVbyA1p4CU7EJSSx7nMgo4cyGPrILiCvdzd9LRMNCdRoEeNA7yoFmoJNlCXJPOGYrzzUm3EFaqld+4ubm5tG3bllGjRvGvf/2r3PrK/DD4888/CQ8PJzExkfj4eFq3bk2bNm2uMxLznWNNFX/852Zal3Rf2p/bpVmzKh1DCEenKAq+gwejcXEhY/n/kbliBWpRIT5DhtSuxLsWhSpERd59913Gjh3LyJEjAZg3bx4//fQTCxcuZPLkyeW2f//99+nbty/PPvssAK+//jq//fYbH374IfPmzUNVVWbNmsVLL73EXXfdBcDnn39OcHAwK1asYOjQoRw8eJBVq1bx999/07FjRwBmz55N//79eeeddwgLC6uhd195WVlZnDh6hL3bN7Hzr008N6QbQWoKJO8v++Pd1Qc1tg+FsbeTp3EnL9dA3oVssgsMZOUXk5FfRGZeMRn5xWTkFZOVX0xablGZabsqEuDhTANfN8J9XYnxd6dRkDshXi616/tSCEegdTIn3cYie0ci6oBamXT369ePfv36XXF9ZX4YhIeHAxAaGkr//v3ZsWPHdSfdCgagZPTyKsjLNP8jdveuWtIt/blFfeJ9xx0oeicuLFlC1s//Qy0sxHfYsFrzQ/LSKcOkebmobYqKiti+fTtTpkyxLNNoNMTHx7N58+YK99m8eTOTJk0qs6xPnz6sWLECgISEBJKSkoiPj7es9/b2Ji4ujs2bNzN06FA2b96Mj4+PJeEGiI+PR6PRsHXrVu6+++5Kv4fH77kHvVaHqqrmByqoKiZVxfxPUaVxeDhNIiIu7mT5N6qW+ZObn883f/5BfkEh+YX55BUWkpOXT3p2NrmFZX+gOyen0D46DHDDqHiTq/UkS+NLluKB4a9k4NNKxe9R8gAFJ60GZ70GZ50GF70WF50GV2cdrjotWoMCGUACZAO7Kn2FhBBlnPYCgxOc/BicvewdjbCDvIICmx2rVibdV1OZHwa5ubmYTCY8PT3Jyclh7dq13HvvvVc8ZmFhIYWFF+9OZ2ZmAlBcmI+ab6TYYCQrK+u6Y01NSsdQZMSgFJCVdf2JQ8b27WQXFeEWGUV2dvZ17y9ErdO1CzqjgQtffEn2/1aReSEDv+HDULTaa+9rZ1nZ+eQV5GA05mLMN1BkUsnLyavSd4eo3Ur/n9emmy5paWkYjUaCg4PLLA8ODubQoUMV7pOUlFTh9klJSZb1pcuuts3lTdd1Oh1+fn6WbS53pTL7y99+u+p7BOjb4jYCC9tdc7ucwhx+3bb9iutd9a6EeAXTwK8BhqIbSExtUKYziQbwKbeXUv6Zcvma8o1mijFSjJFsKm5aLoSoIrUpSDewei2/2HazzdS5pLsyPwySk5Mtd8eNRiNjx47lxhtvvOIxZ8yYwauvvlpu+bdTXrI8n/mod9WDfrHquwKwbCk896yVBxGiFlq2FJ6aaO8oquxLNjCOcfYOQ9hJeno63t5WlB2iQlcqsytj1YHfWHXg2sn5teQX55OQfoKE9BP8ceRPq48nhBDCfmxRXte5pLsyGjZsyO7duyu9/ZQpU8o0kTOZTJw/fx5/f/9a07TVkWRlZREREcHp06fx8pLmOtdLrp/15BpaR66fdTIzM4mMjMTPz8/eoVRaQEAAWq2W5OTkMsuTk5MJCQmpcJ+QkJCrbl/6Nzk5mdBLukglJyfTrl07yzYpKSlljmEwGDh//vwVzytltm3Jv3fryPWzjlw/68j1s44ty+s6l3RX5YfBtTg7O+PsXLbftY+PT1VDFCW8vLzkC8AKcv2sJ9fQOnL9rKPRaK69kYNwcnKiQ4cOrFmzxjJbiMlkYs2aNYwfP77Cfbp06cKaNWuYOHGiZdlvv/1Gly5dAIiJiSEkJIQ1a9ZYkuysrCy2bt3KY489ZjlGRkYG27dvp0OHDgCsXbsWk8lEXFxcheeVMrt6yL9368j1s45cP+vI9bOOLcrr2lPiV9KlPwxKlf4wKC3ohRBCCHF9Jk2axPz58/nss884ePAgjz32GLm5uZZBS4cNG1ZmPJUnn3ySVatW8Z///IdDhw7xyiuvsG3bNkuSrigKEydOZPr06axcuZK9e/cybNgwwsLCLIl98+bN6du3L2PHjuWvv/5i48aNjB8/nqFDhzrkyOVCCCFERWplTXdOTg5HS6bKAvMIqLt27cLPz4/IyEgmTZrE8OHD6dixI506dWLWrFllfhgIIYQQ4voMGTKE1NRUpk2bRlJSEu3atWPVqlWWMVROnTpVpjaga9euLFmyhJdeeokXXniB2NhYVqxYYZmjG+C5554jNzeXhx9+mIyMDLp3786qVassc3QDfPnll4wfP55bb70VjUbDoEGD+OCDD2rujQshhBBWqpVJ97Zt2+jVq5fldWnfreHDh7N48eJr/jAQ9uXs7MzLL79crvmfqBy5ftaTa2gduX7Wqc3Xb/z48VdsTr5+/fpyywYPHszgwYOveDxFUXjttdd47bXXrriNn58fS5Ysue5YhW3U5s+rI5DrZx25ftaR62cdW14/Ra1Nc5YIIYQQQgghhBC1SJ3r0y2EEEIIIYQQQjgKSbqFEEIIIYQQQohqIkm3EEIIIYQQQghRTSTpFjXmlVdeQVGUMo9mzZrZOyyHtWHDBu644w7CwsJQFIUVK1aUWa+qKtOmTSM0NBRXV1fi4+M5cuSIfYJ1QNe6fiNGjCj3eezbt699gnVAM2bM4MYbb8TT05OgoCAGDhzI4cOHy2xTUFDAuHHj8Pf3x8PDg0GDBpGcnGyniB1LZa5fz549y30GH330UTtFLERZUmZfHymzrSNldtVJeW29miizJekWNaply5YkJiZaHn/++ae9Q3JYubm5tG3bljlz5lS4/q233uKDDz5g3rx5bN26FXd3d/r06UNBQUENR+qYrnX9APr27Vvm8/jVV1/VYISO7ffff2fcuHFs2bKF3377jeLiYnr37k1ubq5lm6eeeooffviB5cuX8/vvv3Pu3Dn+9a9/2TFqx1GZ6wcwduzYMp/Bt956y04RC1GelNmVJ2W2daTMrjopr61XI2W2KkQNefnll9W2bdvaO4xaCVC/++47y2uTyaSGhISob7/9tmVZRkaG6uzsrH711Vd2iNCxXX79VFVVhw8frt511112iac2SklJUQH1999/V1XV/HnT6/Xq8uXLLdscPHhQBdTNmzfbK0yHdfn1U1VV7dGjh/rkk0/aLyghrkLK7KqTMts6UmZbR8pr61VHmS013aJGHTlyhLCwMBo2bMgDDzzAqVOn7B1SrZSQkEBSUhLx8fGWZd7e3sTFxbF582Y7Rla7rF+/nqCgIJo2bcpjjz1Genq6vUNyWJmZmYB5zmSA7du3U1xcXOYz2KxZMyIjI+UzWIHLr1+pL7/8koCAAFq1asWUKVPIy8uzR3hCVEjKbNuQMts2pMyuHCmvrVcdZbbOphEKcRVxcXEsXryYpk2bkpiYyKuvvspNN93Evn378PT0tHd4tUpSUhIAwcHBZZYHBwdb1omr69u3L//617+IiYnh2LFjvPDCC/Tr14/Nmzej1WrtHZ5DMZlMTJw4kW7dutGqVSvA/Bl0cnLCx8enzLbyGSyvousHcP/99xMVFUVYWBh79uzh+eef5/Dhw3z77bd2jFYIMymzbUfKbOtJmV05Ul5br7rKbEm6RY3p16+f5XmbNm2Ii4sjKiqKr7/+mtGjR9sxMlEfDR061PK8devWtGnThkaNGrF+/XpuvfVWO0bmeMaNG8e+ffukP2cVXen6Pfzww5bnrVu3JjQ0lFtvvZVjx47RqFGjmg5TiDKkzBaORMrsypHy2nrVVWZL83JhNz4+PjRp0oSjR4/aO5RaJyQkBKDcyJPJycmWdeL6NGzYkICAAPk8Xmb8+PH8+OOPrFu3jgYNGliWh4SEUFRUREZGRpnt5TNY1pWuX0Xi4uIA5DMoHJKU2VUnZbbtSZldnpTX1qvOMluSbmE3OTk5HDt2jNDQUHuHUuvExMQQEhLCmjVrLMuysrLYunUrXbp0sWNktdeZM2dIT0+Xz2MJVVUZP3483333HWvXriUmJqbM+g4dOqDX68t8Bg8fPsypU6fkM8i1r19Fdu3aBSCfQeGQpMyuOimzbU/K7IukvLZeTZTZ0rxc1JhnnnmGO+64g6ioKM6dO8fLL7+MVqvlvvvus3doDiknJ6fM3bOEhAR27dqFn58fkZGRTJw4kenTpxMbG0tMTAxTp04lLCyMgQMH2i9oB3K16+fn58err77KoEGDCAkJ4dixYzz33HM0btyYPn362DFqxzFu3DiWLFnC999/j6enp6Xfl7e3N66urnh7ezN69GgmTZqEn58fXl5eTJgwgS5dutC5c2c7R29/17p+x44dY8mSJfTv3x9/f3/27NnDU089xc0330ybNm3sHL0QUmZfLymzrSNldtVJeW29GimzrRxRXYhKGzJkiBoaGqo6OTmp4eHh6pAhQ9SjR4/aOyyHtW7dOhUo9xg+fLiqquYpSKZOnaoGBwerzs7O6q233qoePnzYvkE7kKtdv7y8PLV3795qYGCgqtfr1aioKHXs2LFqUlKSvcN2GBVdO0BdtGiRZZv8/Hz18ccfV319fVU3Nzf17rvvVhMTE+0XtAO51vU7deqUevPNN6t+fn6qs7Oz2rhxY/XZZ59VMzMz7Ru4ECWkzL4+UmZbR8rsqpPy2no1UWYrJScSQgghhBBCCCGEjUmfbiGEEEIIIYQQoppI0i2EEEIIIYQQQlQTSbqFEEIIIYQQQohqIkm3EEIIIYQQQghRTSTpFkIIIYQQQgghqokk3UIIIYQQQgghRDWRpFsIIYQQQgghhKgmknQLIYQQQgghhBDVRJJuIeqRESNGMHDgQLud/6GHHuLNN9+0vI6OjmbWrFk2PceBAwdo0KABubm5Nj2uEEIIUZOkzBai7pCkW4g6QlGUqz5eeeUV3n//fRYvXmyX+Hbv3s3PP//ME088Ua3nadGiBZ07d+bdd9+t1vMIIYQQVSVltpmU2aK+kKRbiDoiMTHR8pg1axZeXl5llj3zzDN4e3vj4+Njl/hmz57N4MGD8fDwsOo4xcXF19xm5MiRzJ07F4PBYNW5hBBCiOogZfZFUmaL+kCSbiHqiJCQEMvD29sbRVHKLPPw8CjXVK1nz55MmDCBiRMn4uvrS3BwMPPnzyc3N5eRI0fi6elJ48aN+d///lfmXPv27aNfv354eHgQHBzMQw89RFpa2hVjMxqN/N///R933HFHuXV5eXmMGjUKT09PIiMj+eSTTyzrTpw4gaIoLFu2jB49euDi4sKXX37JyZMnueOOO/D19cXd3Z2WLVvy888/W/a77bbbOH/+PL///rsVV1QIIYSoHlJmS5kt6hdJuoWo5z777DMCAgL466+/mDBhAo899hiDBw+ma9eu7Nixg969e/PQQw+Rl5cHQEZGBrfccgvt27dn27ZtrFq1iuTkZO69994rnmPPnj1kZmbSsWPHcuv+85//0LFjR3bu3Mnjjz/OY489xuHDh8tsM3nyZJ588kkOHjxInz59GDduHIWFhWzYsIG9e/cyc+bMMnfjnZycaNeuHX/88YeNrpIQQghhf1JmC1FLqUKIOmfRokWqt7d3ueXDhw9X77rrLsvrHj16qN27d7e8NhgMqru7u/rQQw9ZliUmJqqAunnzZlVVVfX1119Xe/fuXea4p0+fVgH18OHDFcbz3XffqVqtVjWZTGWWR0VFqQ8++KDltclkUoOCgtS5c+eqqqqqCQkJKqDOmjWrzH6tW7dWX3nllatcAVW9++671REjRlx1GyGEEMLepMyWMlvUfTp7JvxCCPtr06aN5blWq8Xf35/WrVtblgUHBwOQkpICmAdXWbduXYX9vI4dO0aTJk3KLc/Pz8fZ2RlFUa56/tLmdaXnKnX53fYnnniCxx57jF9//ZX4+HgGDRpU5jgArq6uljv9QgghRF0gZbYQtZM0LxeintPr9WVeK4pSZllpoWsymQDIycnhjjvuYNeuXWUeR44c4eabb67wHAEBAeTl5VFUVFSp85eeq5S7u3uZ12PGjOH48eM89NBD7N27l44dOzJ79uwy25w/f57AwMCrvXUhhBCiVpEyW4jaSZJuIcR1ueGGG9i/fz/R0dE0bty4zOPygrZUu3btAPN8nLYSERHBo48+yrfffsvTTz/N/Pnzy6zft28f7du3t9n5hBBCiNpGymwhHIMk3UKI6zJu3DjOnz/Pfffdx99//82xY8f45ZdfGDlyJEajscJ9AgMDueGGG/jzzz9tEsPEiRP55ZdfSEhIYMeOHaxbt47mzZtb1p84cYKzZ88SHx9vk/MJIYQQtZGU2UI4Bkm6hRDXJSwsjI0bN2I0GunduzetW7dm4sSJ+Pj4oNFc+StlzJgxfPnllzaJwWg0Mm7cOJo3b07fvn1p0qQJH330kWX9V199Re/evYmKirLJ+YQQQojaSMpsIRyDoqqqau8ghBB1X35+Pk2bNmXZsmV06dKl2s5TVFREbGwsS5YsoVu3btV2HiGEEKKukjJbCNuSmm4hRI1wdXXl888/Jy0trVrPc+rUKV544QUpvIUQQogqkjJbCNuSmm4hhBBCCCGEEKKaSE23EEIIIYQQQghRTSTpFkIIIYQQQgghqokk3UIIIYQQQgghRDWRpFsIIYQQQgghhKgmknQLIYQQQgghhBDVRJJuIYQQQgghhBCimkjSLYQQQgghhBBCVBNJuoUQQgghhBBCiGoiSbcQQgghhBBCCFFNJOkWQgghhBBCCCGqiSTdQgghhBBCCCFENZGkWwghhBBCCCGEqCaSdAshhBBCCCGEENVEkm4hhBBCCCGEEKKaSNIthBBCCCGEEEJUE0m6hRBCCCGEEEKIaiJJt5U2bNjAHXfcQVhYGIqisGLFimo9X3Z2NhMnTiQqKgpXV1e6du3K33//Xa3nFEIIIYQQQghRNZJ0Wyk3N5e2bdsyZ86cGjnfmDFj+O233/jvf//L3r176d27N/Hx8Zw9e7ZGzi+EEEIIIYQQovIUVVVVewdRVyiKwnfffcfAgQMtywoLC3nxxRf56quvyMjIoFWrVsycOZOePXte9/Hz8/Px9PTk+++/Z8CAAZblHTp0oF+/fkyfPt0G70IIIYQQQgghhK1ITXc1Gz9+PJs3b2bp0qXs2bOHwYMH07dvX44cOXLdxzIYDBiNRlxcXMosd3V15c8//7RVyEIIIYQQQgghbERqum3o8pruU6dO0bBhQ06dOkVYWJhlu/j4eDp16sSbb7553efo2rUrTk5OLFmyhODgYL766iuGDx9O48aNOXz4sK3eihBCCCGEEEIIG5Ca7mq0d+9ejEYjTZo0wcPDw/L4/fffOXbsGACHDh1CUZSrPiZPnmw55n//+19UVSU8PBxnZ2c++OAD7rvvPjQa+V8phBBCCCGEEI5GZ+8A6rKcnBy0Wi3bt29Hq9WWWefh4QFAw4YNOXjw4FWP4+/vb3neqFEjfv/9d3Jzc8nKyiI0NJQhQ4bQsGFD278BIYQQQgghhBBWkaS7GrVv3x6j0UhKSgo33XRThds4OTnRrFmz6z62u7s77u7uXLhwgV9++YW33nrL2nCFEEIIIYQQQtiYJN1WysnJ4ejRo5bXCQkJ7Nq1Cz8/P5o0acIDDzzAsGHD+M9//kP79u1JTU1lzZo1tGnTpswI5JX1yy+/oKoqTZs25ejRozz77LM0a9aMkSNH2vJtCSGEEEIIIYSwARlIzUrr16+nV69e5ZYPHz6cxYsXU1xczPTp0/n88885e/YsAQEBdO7cmVdffZXWrVtf9/m+/vprpkyZwpkzZ/Dz82PQoEG88cYbeHt72+LtCCGEEEIIIYSwIUm6hRBCCCGEEEKIaiJDXgshhBBCCCGEENVEkm4hhBBCCCGEEKKayEBqVWAymTh37hyenp4oimLvcIQQQtQiqqqSnZ1NWFgYGo3c+65uUmYLIYSoCluW15J0V8G5c+eIiIiwdxhCCCFqsdOnT9OgQQN7h1HnSZkthBDCGrYoryXprgJPT0/A/D/Ay8vLztEIIYSoTbKysoiIiLCUJaJ6SZkthBCiKmxZXkvSXQWlzdO8vLykABdCCFEl0tS5ZkiZLYQQwhq2KK+lM5kQQgghhBBCCFFNJOkWQgghhBBCCCGqiSTdQgghhBBCCCFENZE+3dXIaDRSXFxs7zBELaPX69FqtfYOQwghhBBC1BEmk4mioiJ7h+FwnJycamT6Tkm6q4GqqiQlJZGRkWHvUEQt5ePjQ0hIiAy0JIQQQgghrFJUVERCQgImk8neoTgcjUZDTEwMTk5O1XoeSbqrQWnCHRQUhJubmyROotJUVSUvL4+UlBQAQkND7RyREEIIIYSorVRVJTExEa1WS0RERI3U6tYWJpOJc+fOkZiYSGRkZLXmbJJ025jRaLQk3P7+/vYOR9RCrq6uAKSkpBAUFCRNzYUQQgghRJUYDAby8vIICwvDzc3N3uE4nMDAQM6dO4fBYECv11fbeeRWh42V9uGWD7WwRunnR8YEEEIIIYQQVWU0GgGqvfl0bVV6XUqvU3WRpLuaSJNyYQ35/AghhBBCCFuR35YVq6nrUi+TbqPRyNSpU4mJicHV1ZVGjRrx+uuvo6qqvUMTQgghhBBCCFGH1Ms+3TNnzmTu3Ll89tlntGzZkm3btjFy5Ei8vb154okn7B2eEEIIIYQQQog6ol7WdG/atIm77rqLAQMGEB0dzT333EPv3r3566+/7B2aXY0YMQJFUXj00UfLrRs3bhyKojBixAjLsqSkJJ588kkaN26Mi4sLwcHBdOvWjblz55KXl2fZbvfu3dx5550EBQXh4uJCdHQ0Q4YMsYzQfeLECRRFYdeuXdeMsXTb0oeTkxONGzdm+vTplW6p8Morr1j21+l0REdH89RTT5GTk1Op/YUQQgghhBDVp7ryEnuplzXdXbt25ZNPPuGff/6hSZMm7N69mz///JN33323wu0LCwspLCy0vM7KyqqpUGtcREQES5cu5b333rOMol1QUMCSJUuIjIy0bHf8+HG6deuGj48Pb775Jq1bt8bZ2Zm9e/fyySefEB4ezp133klqaiq33nort99+O7/88gs+Pj6cOHGClStXkpubW+U4V69eTcuWLSksLOTPP/9kzJgxhIaGMnr06Ert37JlS1avXo3BYGDjxo2MGjWKvLw8Pv744yrHJIQQQgghhLANW+cl9lQvk+7JkyeTlZVFs2bN0Gq1GI1G3njjDR544IEKt58xYwavvvpqDUdpHzfccAPHjh3j22+/tVyPb7/9lsjISGJiYizbPf744+h0OrZt24a7u7tlecOGDbnrrrsstc4bN24kMzOTTz/9FJ3O/HGLiYmhV69eVsXp7+9PSEgIAFFRUSxatIgdO3ZUOunW6XSW/YcMGcKaNWtYuXKlJN1CCCGEEEI4AFvnJfZUL5uXf/3113z55ZcsWbKEHTt28Nlnn/HOO+/w2WefVbj9lClTyMzMtDxOnz59XedTVZW8IoNdHlX5kI0aNYpFixZZXi9cuJCRI0daXqenp/Prr78ybty4Mh/sS5WOBBgSEoLBYOC7776rtg/8tm3b2L59O3FxcVU+hqurK0VFRTaMSgghhBBCCMdSn/MSe6qXNd3PPvsskydPZujQoQC0bt2akydPMmPGDIYPH15ue2dnZ5ydnat8vvxiIy2m/VLl/a1x4LU+uDld3//mBx98kClTpnDy5EnAXFu9dOlS1q9fD8DRo0dRVZWmTZuW2S8gIICCggLA3Ndi5syZdO7cmRdeeIH777+fRx99lE6dOnHLLbcwbNgwgoODq/y+unbtikajoaioiOLiYh5++GGGDRtWpWNt376dJUuWcMstt1Q5HiGEEEIIIRxdfc5L7KleJt15eXloNGUr+bVaLSaTyU4ROZbAwEAGDBjA4sWLUVWVAQMGEBAQcM39/vrrL0wmEw888ECZPvBvvPEGkyZNYu3atWzdupV58+bx5ptvsmHDBlq3bl2lGJctW0bz5s0pLi5m3759TJgwAV9fX/79739Xav+9e/fi4eGB0WikqKiIAQMG8OGHH1YpFiGEEEIIIYTt2TovsZd6mXTfcccdvPHGG0RGRtKyZUt27tzJu+++y6hRo6rlfK56LQde61Mtx67Muati1KhRjB8/HoA5c+aUWde4cWMUReHw4cNlljds2NB8zpKBDi7l7+/P4MGDGTx4MG+++Sbt27e/apP+a4mIiKBx48YANG/enGPHjjF16lReeeUVXFxcrrl/06ZNWblyJTqdjrCwMJycnKoUhxBCCCGEELWF5CX2US+T7tmzZzN16lQef/xxUlJSCAsL45FHHmHatGnVcj5FUa67KYW99e3bl6KiIhRFoU+fsv8w/f39ue222/jwww+ZMGHCFftPXImTkxONGjWyavTyy2m1WgwGA0VFRZVKukunGhNCCCGEEKK+kLzEPmrXFbcRT09PZs2axaxZs+wdisPSarUcPHjQ8vxyH330Ed26daNjx4688sortGnTBo1Gw99//82hQ4fo0KEDAD/++CNLly5l6NChNGnSBFVV+eGHH/j555/LDIpwvdLT00lKSsJgMLB3717ef/99evXqhZeXV5WPKYQQQgghhHAstspL7KleJt2icq6WwDZq1IidO3fy5ptvMmXKFM6cOYOzszMtWrTgmWee4fHHHwegRYsWuLm58fTTT3P69GmcnZ2JjY3l008/5aGHHqpybPHx8YD5H15oaCj9+/fnjTfeqPLxhBBCCCGEEI7JFnmJPSmqI0xcVstkZWXh7e1NZmZmuQ9AQUEBCQkJxMTEVKqZsxAVkc+REHXX1coQYXtyvYUQ9Zn8pry6q10fW5Yf9XKebiGEEEIIIYQQoiZI83LhUB599FG++OKLCtc9+OCDzJs375rH8PDwuOK6//3vf9x0001Vjk8IIUTtNOazv3Fy9UBRzAMJKYCigOaS55cuV1DQaMx/uXw7zK9dnLR4OOtwd9Lh4aLDw1mLn7szod4uBHu54O/uhEaj2PV9CyGEsD9JuoVDee2113jmmWcqXFfZZh27du264rrw8PCqhCWEEKKW23L8PBrngho9p16rEOnnRrMQL5qGeNIq3IuO0X54uehrNA4hhBD2JUm3cChBQUEEBQVZdQyZCkwI4ahMJpUvt5ywdxj10sxBrXF198SkqqgqqICp5ImKikmlZLn5Oapq3sZk/quqJduXPDeqKgXFRnILDeQUGsgpNJJTUEx6bhGJmQWk5RRSbFQ5lprLsdRcftqbCIBGgZZh3twUG0D/1qG0DPNCUaQ2XAgh6jJJuoUQQogacD63iKeW7WLd3pP2DqVeGtAmrEYHUis2mkjOKuBoSg6Hk7I5nJTNztMZJKTlsvdsJnvPZvLR+mPEBLhzb8cIht4Yga+7U43FJ4QQouZI0i2EEEJUsx2nLjDuyx0kZhbgpJMxTOsDvVZDA183Gvi60bPpxRZcSZkFbD6exq/7k1l7KIWEtFxmrjrErNX/MKhDA564JZYQbxlhWAgh6hIp+YUQQohqoqoqC/9M4N55m0nMLKBhgDtLH46zd1hVNmfOHKKjo3FxcSEuLo6//vrrqtsvX76cZs2a4eLiQuvWrfn555/LrFdVlWnTphEaGoqrqyvx8fEcOXLEsn79+vXmwc0qePz999/V8h6rW4i3C3e3b8DcBzuwfeptvHVPG1qGeVFoMLFk6yl6vL2OGf87SE6hwd6hCiGEsBFJuoUQQohqkF1QzONf7uC1Hw9gMKkMaBPK9+O70SS4ds4VvWzZMiZNmsTLL7/Mjh07aNu2LX369CElJaXC7Tdt2sR9993H6NGj2blzJwMHDmTgwIHs27fPss1bb73FBx98wLx589i6dSvu7u706dOHggLzgGddu3YlMTGxzGPMmDHExMTQsWPHGnnf1cnDWce9HSP4cUJ3lj7cmRujfSk0mPj49+P0fvd31h2q+NoKIYSoXRRVLRkVRFTa1SZKlwnohS3I50iI2m37yQs8s3w3CWm56LUKL/ZvzvCu0SiKctUyxJHFxcVx44038uGHHwJgMpmIiIhgwoQJTJ48udz2Q4YMITc3lx9//NGyrHPnzrRr14558+ahqiphYWE8/fTTllkrMjMzCQ4OZvHixQwdOrTcMYuLiwkPD2fChAlMnTq1UnHXpuutqiprD6Xw6g8HOHU+D4ChN0bwyp0tcdFr7RydEKI2kt+UV3e162PL8kNquoUQQggbyS008MrK/dwzbxMJabmE+7jy9SNdGNEtplaPUF1UVMT27duJj4+3LNNoNMTHx7N58+YK99m8eXOZ7QH69Olj2T4hIYGkpKQy23h7exMXF3fFY65cuZL09HRGjhxp7VtySIqicGvzYH6ZeDNjb4pBUWDp36e5+6NNnEzPtXd4QgghqkiSbmExYsQIFEXh0UcfLbdu3LhxKIrCiBEjLMuSkpJ48sknady4MS4uLgQHB9OtWzfmzp1LXl6eZbuePXuW64t3+Tm+++47OnfujLe3N56enrRs2ZKJEyda1i9evBgfH58y+xQWFvLiiy8SFRWFs7Mz0dHRLFy4sFLv9ZVXXikTj7e3NzfddBO///57pfYHiI6Otuzv6upKdHQ09957L2vXri2z3YkTJ8qcy9/fn969e7Nz585Kn0sI4fi2HE+n93sbWLzpBKoK93RowE9PdKd9pK+9Q7NaWloaRqOR4ODgMsuDg4NJSkqqcJ+kpKSrbl/693qOuWDBAvr06UODBg2uGGthYSFZWVllHrWNq5OWFwe04L+j4vB3d+JgYhZ3f7SJnacu2Ds0IYSoMampqTz22GNERkbi7OxMSEgIffr0YePGjYD5t/isWbPsG2QlSdItyoiIiGDp0qXk5+dblhUUFLBkyRIiIyMty44fP0779u359ddfefPNN9m5cyebN2/mueee48cff2T16tVljjt27NgyffLeeusty7o1a9YwZMgQBg0axF9//cX27dt54403KC4uvmqs9957L2vWrGHBggUcPnyYr776iqZNm1b6vbZs2dISz+bNm4mNjeX2228nMzOz0sd47bXXSExM5PDhw3z++ef4+PgQHx/PG2+8UW7b1atXk5iYyC+//EJOTg79+vUjIyOj0ucSQjgmk0nlw7VHuH/+Fs5m5NPA15X/ju7EO4Pb4uMmU0DZypkzZ/jll18YPXr0VbebMWMG3t7elkdEREQNRWh73WMD+OmJm2jTwJvzuUXcN38Law4m2zssIYSoEYMGDWLnzp189tln/PPPP6xcuZKePXuSnp5u79Cum0wZJsq44YYbOHbsGN9++y0PPPAAAN9++y2RkZHExMRYtnv88cfR6XRs27YNd3d3y/KGDRty1113cflQAW5uboSEhFR4zh9++IFu3brx7LPPWpY1adKEgQMHXjHOVatW8fvvv3P8+HH8/PwA892u66HT6SwxhYSE8Nprr7Fo0SL++ecfbrzxxkodw9PT03KMyMhIbr75ZkJDQ5k2bRr33HNPmZsA/v7+hISEEBISwjvvvEO3bt3YunUrffr0ua64hRCOIy2nkKeW7eKPI2kADLqhAa8PbImbU90qXgMCAtBqtSQnl034kpOTr/jdHhISctXtS/8mJycTGhpaZpt27dqVO96iRYvw9/fnzjvvvGqsU6ZMYdKkSZbXWVlZtTrxDvF24auxnRm3ZAfrD6fy8H+3M+f+G+jbquLrLoQQdUFGRgZ//PEH69evp0ePHgBERUXRqVMnO0dWNVLTXRNUFYpy7fOowjh5o0aNYtGiRZbXCxcuLNN/Lj09nV9//ZVx48aVSbgvdXnfxS+//JKAgABatWrFlClTyjQ/DwkJYf/+/WVGtL2WlStX0rFjR9566y3Cw8Np0qQJzzzzTJka+utRWFjIokWL8PHxua7a8oo8+eSTqKrK999/f8VtXF1dAXM/SSFE7bTnTAZ3zP6TP46k4aLX8PY9bfjPvW3rXMIN4OTkRIcOHVizZo1lmclkYs2aNXTp0qXCfbp06VJme4DffvvNsn1MTAwhISFltsnKymLr1q3ljqmqKosWLWLYsGHo9fqrxurs7IyXl1eZR23n7qxj/rCODGwXhtGkMuGrHaw+IDXeQogqqCV5iYeHBx4eHqxYsYLCwsJqvCA1o+79MnBExXnwZph9zv3COXCqODG+kgcffJApU6Zw8uRJADZu3MjSpUtZv349AEePHkVV1XLJaUBAgGWal3HjxjFz5kwA7r//fqKioggLC2PPnj08//zzHD58mG+//RaACRMm8Mcff9C6dWuioqLo3LkzvXv35oEHHsDZ2bnCGI8fP86ff/6Ji4sL3333HWlpaTz++OOkp6eXuWFwNXv37sXDwwOAvLw8PD09WbZsmdU/0Pz8/AgKCuLEiRMVrs/IyOD111/Hw8Oj1t6tE6K++27nGSZ/s5dCg4mGge7Me7ADTYI97R1WtZo0aRLDhw+nY8eOdOrUiVmzZpGbm2u5KTts2DDCw8OZMWMGYL4B2aNHD/7zn/8wYMAAli5dyrZt2/jkk08A883ZiRMnMn36dGJjY4mJiWHq1KmEhYWVa+m0du1aEhISGDNmTI2+Z0ei12r4z73tMKrww+5zPP7lDj4f3YnODf3tHZoQojapJXmJTqdj8eLFjB076LRUswAAvL9JREFUlnnz5nHDDTfQo0cPhg4dSps2bao5UNuTpFuUExgYyIABA1i8eDGqqjJgwAACAgKuud9ff/2FyWTigQceKHNH6uGHH7Y8b926NaGhodx6660cO3aMRo0a4e7uzk8//cSxY8dYt24dW7Zs4emnn+b9999n8+bNuLm5lTuXyWRCURS+/PJLvL29AXj33Xe55557+Oijjyw1yVfTtGlTVq5cCUB2djbLli1j8ODBrFu3zur5X1VVLVfb37VrVzQaDbm5uTRs2JBly5aVG0BICOHYDEYT//7fIT79MwGA+OZBvDukHV4uV699rQuGDBlCamoq06ZNIykpiXbt2rFq1SrL99ipU6fQaC42oOvatStLlizhpZde4oUXXiA2NpYVK1bQqlUryzbPPfccubm5PPzww2RkZNC9e3dWrVpVbtqWBQsW0LVrV5o1a1Yzb9ZBaTUK793blmKDiVX7k3jkv9v59vGuNAr0sHdoQghhc4MGDWLAgAH88ccfbNmyhf/973+89dZbfPrpp2UGd64NJOmuCXo3850de527CkaNGsX48eMBmDNnTpl1jRs3RlEUDh8+XGZ5w4YNAa6Z8MbFxQHmGvNGjRpZljdq1IhGjRoxZswYXnzxRZo0acKyZcsqnBomNDSU8PBwS8IN0Lx5c1RV5cyZM8TGxl7zPTo5OdG4cWPL6/bt27NixQpmzZrFF198cc39ryQ9PZ3U1NQyfeABli1bRosWLfD39y83ErsQwvFdyC1iwlc7+fOouf/2hFsa81R8EzSa2jsV2PUaP368pWy4XGlrqEsNHjyYwYMHX/F4iqLw2muv8dprr131vEuWLLmuOOsynVbDrKHtuG/+FnaeymDkor/57vGu+HtU3DJMCCHKqGV5iYuLC7fddhu33XYbU6dOZcyYMbz88suSdIsKKMp1N/G2t759+1JUVISiKOUG+vL39+e2227jww8/ZMKECVfs130lu3btAigzcM7loqOjcXNzIze34nlJu3XrxvLly8nJybE0Ef/nn3/QaDRXnUrmWrRabZX7hZd6//330Wg05ZpHRkRElLnJIISoPQ4lZTH2822cPp+Pm5OW/wxuS7/WV/4OE6I6uei1zB/Wkbs/2sip83k8uXQXn43qhLYe3QASQlRRLcxLLtWiRQtWrFhh7zCumyTdokJarZaDBw9anl/uo48+olu3bnTs2JFXXnmFNm3aoNFo+Pvvvzl06BAdOnQA4NixYyxZsoT+/fvj7+/Pnj17eOqpp7j55pst/TFeeeUV8vLy6N+/P1FRUWRkZPDBBx9QXFzMbbfdVmF8999/P6+//jojR47k1VdfJS0tjWeffZZRo0ZVqmk5gMFgsMwFW9q8/MCBAzz//POVvk7Z2dkkJSVRXFxMQkICX3zxBZ9++ikzZswoU4suhKi9/rc3kaeX7yavyEiknxufDOtAs5DaPziXqN0CPJxZMPxG7vpwI38eTeP9NUeYdFsTe4clhBA2kZ6ezuDBgxk1ahRt2rTB09OTbdu28dZbb3HXXXfZO7zrJkm3uKKrDSjWqFEjdu7cyZtvvsmUKVM4c+YMzs7OtGjRgmeeeYbHH38cMDfhXr16tWXAnYiICAYNGsRLL71kOVaPHj2YM2cOw4YNIzk5GV9fX8sc4FcaSdzDw4PffvuNCRMm0LFjR/z9/bn33nuZPn16pd/f/v37LbXtbm5uNGrUiLlz5zJs2LBKH2PatGlMmzYNJycnQkJC6Ny5M2vWrKFXr16VPoYQwjEVG03MWv0Pc9YdA6B74wA+vL+9zL0tHEaTYE9m/Ks1E5ftYvbaI3SI8qVHk0B7hyWEEFbz8PAgLi6O9957j2PHjlFcXExERARjx47lhRdesHd4101RL59QWVxTVlYW3t7eZGZmlktMCwoKSEhIICYmptxAMEJUlnyOhLCvoynZTPp6N3vOZAIw9qYYnu/bDJ3W+pk2r1aGCNurD9f7he/2smTrKQI8nPj1qR74ucuNISGEmfymvLqrXR9blh8yT7cQQghRQlVVPt98gv4f/MmeM5l4u+qZfV97XhzQwiYJtxDVYdrtLWgS7EFaThFTV+yzdzhCCCEuI78gRJ3k4eFxxccff/xxzf2//PLLK+7fsmXLGngHQoiaVlBs5Jnle5j2/X6KDCZ6NAnk16du5o62dprPVIhKctFr+c/gdug0Cj/tTeSH3XYamVgIIUSFpE+3qJNKR0ivSHh4+DX3v/POOy1Tm11Or6/78/EKUd8kZubz6H+3s/tMJhoFXujfnNHdY1AUGQ1a1A6tG3gz/pbGzFp9hKnf76NzQ38CPWUaMSGEcASSdIs6ydqRwz09PfH09LRRNEIIR7brdAZjPttGWk4hPm56PrzvBrrHBtg7LCGu27hejfntQDL7z2Ux4+eDvDuknb1DEkIIgTQvF0IIUY/9sj+JoZ9sJi2nkGYhnvwwvrsk3KLW0ms1vHF3axQFvt15li3H0+0dkhDCQcjY2RWrqetSL5Pu6OhoFEUp9xg3bpy9QxNCCFEDVFVlwZ8JPPrFdgqKTfRsGsj/PdaVCD83e4cmhFXaRfhwf6dIAF5asY8ig8nOEQkh7Emr1QJQVFRk50gcU+l1Kb1O1aVeNi//+++/MRqNltf79u3jtttuY/DgwXaMSgghRE0wmlRe//EAizedAOCBuEhevbOljE4u6ozn+jRj1b4kjqbksGhjAo/0aGTvkIQQdqLT6XBzcyM1NRW9Xo9GI2VdKZPJRGpqKm5ubuh01ZsW18ukOzAwsMzrf//73zRq1IgePXrYKSIhhBA1Ia/IwBNf7WT1wRQApvRrxsM3N5QB00Sd4u2m5/l+zXju//bw4bqj3NsxAl+Zu1uIeklRFEJDQ0lISODkyZP2DsfhaDQaIiMjq/13QL1Mui9VVFTEF198waRJk654sQsLCyksLLS8zsrKqqnwhBBC2EhKVgGjP9vG3rOZOOs0vDekHf1bh9o7LCGqxaAbGrBo4wkOJmbxwdojvHyHTHcpRH3l5OREbGysNDGvgJOTU43U/tf7pHvFihVkZGQwYsSIK24zY8YMXn311ZoLSgghhE39k5zNyEV/czYjHz93J+YP60iHKF97hyVEtdFqFF7s35wHF2zliy0nGd4lmugAd3uHJYSwE41Gg4uLi73DqLfqfaP+BQsW0K9fP8LCwq64zZQpU8jMzLQ8Tp8+XYMR1pwRI0aUGVjO39+fvn37smfPnkrtf+m+3t7edOvWjbVr11Z4fL1eT0xMDM899xwFBQXljrNixYpyx92yZUuZ7QoLC/H390dRFNavX19hHJc+li5des33sH79ehRFISMjo1LvWQjh2FRV5f+2n2HgnI2czcinYYA73z3eVRJuUS90jw2gR5NAio0qb/1yyN7hCCFEvVWvk+6TJ0+yevVqxowZc9XtnJ2d8fLyKvOoq/r27UtiYiKJiYmsWbMGnU7H7bffXun9Fy1aRGJiIhs3biQgIIDbb7+d48ePlzv+8ePHee+99/j44495+eWXr3nciIgIFi1aVGbZd999h4eHx1XjuPQxcODASr8PIUTtl1No4Mmlu3hm+W7yiox0aejPN491JcpfavtE/fFC/+ZoFPh5bxJ7zmTYOxwhhKiX6nXSvWjRIoKCghgwYIC9Q3EYzs7OhISEEBISQrt27Zg8eTKnT58mNTW1Uvv7+PgQEhJCq1atmDt3Lvn5+fz222/ljh8REcHAgQOJj48vs/5Khg8fztKlS8nPz7csW7hwIcOHD79qHJc+pEmNEPVHYmY+98zdxMrd59BqFJ7p3YQvxsTJYFKi3mka4snAduEAvL/6iJ2jEUKI+qneJt0mk4lFixYxfPjwah8iXlVV8orz7PKwZsL3nJwcvvjiCxo3boy/v/917+/q6gpceV7Affv2sWnTJpycrv0juEOHDkRHR/PNN98AcOrUKTZs2MBDDz103XEJIeq2g4lZ3D1nE4eSsgnwcObrRzoz/pZYtBoZoVzUTxNujUWjwJpDKew+nWHvcIQQot6ptwOprV69mlOnTjFq1KhqP1e+IZ+4JXHVfp6KbL1/K256t0pv/+OPP1qabOfm5hIaGsqPP/543aP65eXl8dJLL6HVastMxVZ6fIPBQGFhIRqNhg8//LBSxxw1ahQLFy7kwQcfZPHixfTv37/c9G+l7rvvvnKT3B84cIDIyMjreh9CiNrlzyNpPPrFdnIKDTQO8mDRiBuJ8Kv8d6AQdVFMgDsD24fz7Y6zzFr9D4tGdrJ3SEIIUa/U26S7d+/eVtUC11W9evVi7ty5AFy4cIGPPvqIfv368ddffxEVFXXN/UuT3fz8fAIDA1mwYAFt2rQpd/zc3Fzee+89dDodgwYNqlRsDz74IJMnT+b48eMsXryYDz744Irbvvfee8THx5dZdrXB8oQQtd//9ibyxNKdFBtV4mL8+OShjni76e0dlhAO4YlbYvl+1znWHU5l1+kM2kX42DskIYSoN+pt0l2TXHWubL1/q93OfT3c3d1p3Lix5fWnn36Kt7c38+fPZ/r06dfcvzTZ9fb2rrAW+tLjL1y4kLZt27JgwQJGjx59zWP7+/tz++23M3r0aAoKCujXrx/Z2dkVbhsSElLmfQgh6rav/z7N5G/3YFKhX6sQZg1th7NOe+0dhagnogPcGdgunG92nGH2miMsGHGjvUMSQoh6Q5LuGqAoynU18XYkiqKg0WjKDGB2NdeT7Go0Gl544QUmTZrE/fffb+kDfjWjRo2if//+PP/88+Wajwsh6qdPNhzjzZ/N0yEN6RjBm/9qLf23hajA+Fsa8+3OM6w5lMI/ydk0Cfa0d0hCCFEvSNItyigsLCQpKQkwNy//8MMPycnJ4Y477qiW8w0ePJhnn32WOXPm8Mwzz1xz+759+5KamnrNadsyMjIs76OUp6cn7u6Vmypo7969eHpe/DGiKApt27at1L5CiJqhqipv/3KYj9YfA+CRmxsyuV8zFEUSbiEqEhPgTp8WIazan8QnG47zzmAp14QQoiZI0i3KWLVqFaGhoYA5SW3WrBnLly+nZ8+e1XI+nU7H+PHjeeutt3jssceumRQrikJAQMA1jzty5Mhyy2bMmMHkyZMrFdfNN99c5rVWq8VgMFRqXyFE9TOaVKZ+v48lW08B8HzfZjzWs5GdoxLC8T3SoyGr9ifx/a6zPNO7KSHeMp2mEEJUN0WV0cSuW1ZWFt7e3mRmZparcS0oKCAhIYGYmBiZF1pUmXyOhLiyIoOJSV/v4sc9iSgKvDGwNffH1Z6ZCa5Whgjbk+td3r3zNvPXifM8cnNDpvRvbu9whBDCIdmy/Ki383QLIYSofTLzihnz+TZ+3JOIXqvwwdD2tSrhFsIRPNKjIQBLtp4iq6DYztEIIUTdJ0m3qJQ333wTDw+PCh/9+vWzd3iV1q9fvyu+jzfffNPe4QkhrmLf2Uxu//APNvyTiotew/xhHbmjrUwFKMT16tU0iMZBHmQXGlj61yl7hyOEEHWe9OkWlfLoo49y7733VriuMqOOO4pPP/30iiOx+/n51XA0QojKWncohce+3E5BsYkIP1fmPtCBVuHe9g5LiFpJo1EYe1MMz3+zl883n2R094Yy4r8QQlQjSbpFpfj5+dWJpDQ8PNzeIQghrtOKnWd5ZvluDCaVm5sEMntoe7zd9PYOS4ha7a524cz43yHOXMhn7aEUbmsRbO+QhBCizpLm5UIIIRzWZ5tOMHHZLgwmlbvahbFgeEdJuIWwARe9liE3RgDw+eYT9g1GCCHqOEm6hRBCOKT5G47z8sr9AAzvEsV797ZDr5Viy57mzJlDdHQ0Li4uxMXF8ddff111++XLl9OsWTNcXFxo3bo1P//8c5n1qqoybdo0QkNDcXV1JT4+niNHjpQ7zk8//URcXByurq74+voycOBAW76teuvBuCgUBf44ksbRlBx7hyOEEHWW/HoRQgjhcOZvOM4bPx8EYHyvxrxyZ0s00ufUrpYtW8akSZN4+eWX2bFjB23btqVPnz6kpKRUuP2mTZu47777GD16NDt37mTgwIEMHDiQffv2WbZ56623+OCDD5g3bx5bt27F3d2dPn36UFBQYNnmm2++4aGHHmLkyJHs3r2bjRs3cv/991f7+60PIvzcuLWZuVn5F1tO2jkaIYSou2Se7iqQebpFdZPPkajPPtlwjDd/PgTAE7fG8lR8LIpSdxLumpg3OiMjg++++44//viDkydPkpeXR2BgIO3bt6dPnz507dr1uo8ZFxfHjf/P3n3HN1W9Dxz/JGmb7hQoXVBogbL3KktlFAuCiAMQBwgKylcZoqIooCBaRFBAVARBcCCIIKIiyI8pe5Rp2RTKaAsF2pTuJvn9kTZt6IBCmjTt83697usm597c+wSR5Mk55zlt2jB37lwA9Ho9gYGBjBw5knfeeafA+QMGDCAlJYU///zT1NauXTuaN2/OvHnzMBgMBAQE8MYbb/Dmm28CkJSUhK+vL4sXL+bpp58mOzuboKAgJk+ezIsvvnhPfxayTnfx/j19jecX7sVd7cDud7vhrpZyP0IIAbJOtxBCiHIqf8I9ulsIY7vXLVcJd2m7cuUKL730Ev7+/kydOpW0tDSaN29Ot27dqF69Ops3b6Z79+40bNiQ5cuX3/V1MzMzOXDgAGFhYaY2pVJJWFgYu3btKvQ1u3btMjsfIDw83HR+dHQ0cXFxZudoNBpCQ0NN50RGRnL58mWUSiUtWrTA39+fnj17mvWWi/vTsbY3taq6cSsjm1WRl2wdjhBClEvyc6YQQogy4ZutZ4n4Oy/hfr17XRtHZH9atGjB4MGDOXDgAA0bNiz0nLS0NFavXs2sWbO4ePGiqZe5OAkJCeh0Onx9zStc+/r6cuLEiUJfExcXV+j5cXFxpuO5bUWdc+7cOQA++OADPvvsM4KCgpg5cyadO3fm1KlTha6qkZGRQUZGhum5Vqu94/uryJRKBc+3q8nkP6L4ee9Fnm9XU37oEkIIC5OebmHSuXNnxowZU6B98eLFeHl5AfDff//x5JNPEhQUhEKhYNasWSW6xwsvvIBCoUChUODk5ESdOnWYMmUK2dnZAGzZsgWFQkFiYmKB1wYFBRV6v4iICFQqFZ9++mmBYzqdjmnTplG/fn1cXFyoXLkyoaGhfPvtt4XGlH/r0aNHid6bEOLeScJtGVFRUUyfPr3IhBvAxcWFgQMHsmvXLoYMGWLF6EpOr9cD8N577/Hkk0/SqlUrvvvuOxQKBStWrCj0NREREWg0GtMWGBhozZDt0uMtquHkoOR4rJYjl5JsHY4QQpQ7knSLEklNTaVWrVpMmzYNPz+/e7pGjx49iI2N5fTp07zxxht88MEHhSbMd2vRokWMGzeORYsWFTg2efJkPv/8cz788EOioqLYvHkzw4cPL5DU58aUf/v555/vOSYhxN3Ln3CPCZOE+35UqVKlVM739vZGpVIRHx9v1h4fH1/kZ4Gfn1+x5+fuizvH398fwOxHBLVaTa1atYiJiSn0vuPHjycpKcm0Xbx48a7eY0Xm5erEI42Nf+bL9hX+5yqEEOLeSdItSqRNmzZ8+umnPP3006jV6nu6hlqtxs/Pj5o1azJixAjCwsJYs2bNPV1r69atpKWlMWXKFLRaLTt37jQ7vmbNGv73v//Rr18/goODadasGS+++GKB4ZS5MeXfKlWqdE8xCSHuTpZOz6Tfj5kl3GPCJOG2tOTkZN566y3atGlDy5YtGTlyJAkJCSW6hpOTE61atWLjxo2mNr1ez8aNG2nfvn2hr2nfvr3Z+QAbNmwwnR8cHIyfn5/ZOVqtlj179pjOadWqFWq1mpMnT5rOycrK4vz589SsWbPQ+6rVajw9Pc02cWdPt60BwJpDV0jJyLZxNEIIUb7InG4rMBgMGNLSbHJvhYtLmZ+b5eLiwvXr1+/ptQsXLmTgwIE4OjoycOBAFi5caFaV18/Pj02bNvG///2PqlWrWipkIcR9SsvUMfyH/fx72pj8vd2jPiM617ZxVOXTsGHDcHFxYfLkyWRlZTF//nyeffZZ1q9fX6LrjB07lsGDB9O6dWvatm3LrFmzSElJMQ1RHzRoENWqVSMiIgKA0aNH89BDDzFz5kx69erFsmXL2L9/P/PnzwdAoVAwZswYpk6dSkhICMHBwUycOJGAgADTOtyenp688sorvP/++wQGBlKzZk3TyKh+/fpZ6E9IAIQGV6aWtxvnElL44/AVUxIuhBDi/knSbQWGtDROtmxlk3vXizyAwtXVJve+E4PBwMaNG1m/fj0jR440O1a9evUC56emppo912q1/Prrr6Yqt8899xwPPPAAs2fPxt3dHYDPPvuMp556Cj8/Pxo1akSHDh147LHH6Nmzp9m1/vzzT9Nrcr377ru8++679/0+hRDmUjOzGbp4H7vP3cDVScWsAc15uNG9TVcRBX3++eeMGTPG9IPrvn37OHXqFCqVCoB69erRrl27El93wIABXLt2jUmTJhEXF0fz5s1Zt26dqRBaTEwMSmXeALoOHTqwdOlSJkyYwLvvvktISAirV6+mcePGpnPGjRtHSkqKadpPp06dWLdundlSiZ9++ikODg48//zzpKWlERoayqZNm2Q0koUpFAoGtAkk4u8T/LzvoiTdQghhQZJ0C6vLTXCzsrLQ6/U888wzfPDBB2bn/Pvvv3h4eJi1de7c2ez5zz//TO3atWnWrBkAzZs3p2bNmixfvty0nmvDhg05duwYBw4cYMeOHWzbto1HH32UF154wayYWpcuXfj666/Nrl9YVVwhxP1JychmyOJ97I2+gbvagSVD29Cqpvy/Zklnz54lNDSUb775hhYtWtC9e3d69epF3759ycrK4ocffiA8PPyerv3aa6/x2muvFXpsy5YtBdr69etXbI+0QqFgypQpTJkypchzHB0dmTFjBjNmzChxvKJknmxVnRn/nOTwxUSOx2pp4C9D84UQwhIk6bYChYsL9SIP2Ozed8vT05OkpIJVSxMTE9FoNBaLKTfBdXJyIiAgAAeHgn8Ng4ODTRXTc91+3sKFC/nvv//M2vV6PYsWLTIl3WBcS7ZNmza0adOGMWPG8OOPP/L888/z3nvvERwcDICbmxt16tSx2HsUQhSUnqXjxSXGhNtD7cCSF9vSsob0Vlra3Llz2b17N0OHDqVLly5ERETw448/smHDBnQ6Hf369SsycRYVm7e7mu4NfVl7NI5le2OY/FjjO79ICCHEHUnSbQUKhaLMDvHOr169evzzzz8F2iMjI6lb13LFjSyR4B49epT9+/ezZcsWsx7pGzdu0LlzZ06cOEH9+vULfW1uFdyUlJT7ikEIcfeydHpeWxrJ7nPGHu7vX2xLC0m4S027du3Yt28fn3zyCe3bt+fTTz9l5cqVtg5L2IGn29Rg7dE4fjt4mfGPNMDZUWXrkIQQwu5J0i1MRowYwdy5cxk1ahQvvfQSarWav/76i59//pk//vgDgMzMTKKiokyPL1++zKFDh3B3d7dqT/HChQtp27YtDz74YIFjbdq0YeHChXz66ac89dRTdOzYkQ4dOuDn50d0dDTjx4+nbt26Zkl5RkYGcXFxZtdxcHDA29u71N+LEOWdXm/gzRWH+b/jV1E7KFk4uLUk3Fbg4ODAe++9R//+/XnllVdYsmQJc+fOveflHkXF0KmON9W8XLicmMaGqHgebRZg65CEEMLuyZJhwqRWrVps27aNEydOEBYWRmhoKL/88gsrVqygR48eAFy5coUWLVrQokULYmNjmTFjBi1atOCll16yWpyZmZn8+OOPPPnkk4Uef/LJJ/n+++/JysoiPDycP/74g0cffZS6desyePBg6tevzz///GM2LH3dunX4+/ubbZ06dbLWWxKi3DIYDLy/5j9+P3QFB6WCr59rSWitkq0lLUrm8OHDtGnTBg8PDzp27Gha2qtXr1506NChQP0KIfJTKhU80bIaACsjL9k4GiGEKB8UBoPBYOsg7I1Wq0Wj0ZCUlFRg/c/09HSio6MJDg42q74qREnI3yNRXsxYf5K5m8+gUMCsAc15rHk1W4dkc8V9hlhCq1ateOihhxg+fDjr1q3j999/Z/PmzQBcu3aN119/nbNnz5pWfijvTH/eJ3fg6ekBCiWgAIWikMcU0V7EYwc1OLqBsnz1YUQnpNBlxhaUCtg1vhu+nvI5JISoeCz5eS3Dy4UQQpSK+dvOMnfzGQA+fKyxJNxWcurUKZYvX06dOnUICQlh1qxZpmNVq1blxx9/LLR+R7n3XQ9QK0rhwgpwcge1h3FzqwqaauBZDTTVoWp98G0ILvYzpSLY243WNSux/8JNVh+8zMsP1bZ1SEIIYdcqbNJ9+fJl3n77bf7++29SU1OpU6cO3333Ha1bt7Z1aHYrJibGVKSsMFFRUdSoIet+ClERLNsbw8drTwDwVng9nmtX08YRVRydO3dm+PDhPP3002zatImOHTsWOOfhhx+2QWQ25lYV1ErAAAY9GAw5jw23PdbnPS5wbr7HJgbITDZuyUDCycLv71kdaoRC8INQqzNUCirFN3v/nmxVnf0XbvLrgUsMf7CWad13IYQQJVchk+6bN2/SsWNHunTpwt9//03VqlU5ffo0lSrZz6/QZVFAQACHDh0q9rgQovz7Zf9Fxv92FICXH6zF/zpLL5k1ff/993z00Uf8/vvvNGvWjHfeecfWIZUNow6CJYfzGwyQnQ4ZyTmb1rhPjgftZeOWGAPxUZAUA9pLcOwSHMupIu/fHJo8BY2fAk9/y8VlIb2a+vPBmv84ffUWRy4l0SzQy9YhCSGE3aqQSfcnn3xCYGAg3333naktd71mce8cHBxkrWshKjCDwcDsjaeZ9X+nARjYtgbv9KwvPWRWVqlSJWbMmGHrMMo/hQIcXYybu0/x56YnQewROL8dorfCxb0Qe8i4/d8H0PhJaP8a+De1QuB3x9PZkfBGfqw5fIWVkZck6RZCiPtQvip/3KU1a9bQunVr+vXrh4+PDy1atGDBggW2DksIIezaV1vOmhLu17rU4aO+jSXhFgLAWQPBD0CX8TB0Hbx5CnrNhOptQZ8NR5bDNw/AiiFw87ytozV5qlV1AH4/dIWMbJ2NoxFCCPtVIZPuc+fO8fXXXxMSEsL69esZMWIEo0aNYsmSJYWen5GRgVarNduEEELkWbLzPJ+uN85lfe+RBrwZXg+lUhLusqhBgwaoVCpbh1GxuXlDm5fgpQ0wbBM0egJQwH+rYG4b2DoddFm2jpKOdbzx83QmKS2LTcev2jocIYSwWxUy6dbr9bRs2ZKPP/6YFi1aMHz4cIYNG8a8efMKPT8iIgKNRmPaAgMDrRyxEEKUXasiL/H+mv8AGN0thGEP1rJxRKI4ERERLFq0yNZhiFzVWkG/7+CVf40F1nSZsPkj+DYMrp2yaWgqpYLHc9bs/vWArNkthBD3qkIm3f7+/gWqbDdo0ICYmJhCzx8/fjxJSUmm7eLFi9YIUwghyrwdZxIY9+sRAIZ2DGZMWIiNIxJ30rdvXwYPHmzrMMTt/JrA86vhiW/B2cs433tBVzjxl03DerKlcYj5llPXuJacYdNYhBDCXlXIpLtjx46cPGm+pMepU6eoWbPwJW3UajWenp5mmxBCVHQn45J55YcDZOsNPNosgAm9Gsgc7jKka9euJCYmFmjXarV07drV+gGJO1MooGk/+N9uCHrAuAzZsmdg26c5S5VZXx0fd5oHeqHTG/j90GWbxCCEEPbOLqqXJyYm8ttvv/Hvv/9y4cIFUlNTqVq1Ki1atCA8PJwOHTqU6Hqvv/46HTp04OOPP6Z///7s3buX+fPnM3/+/FJ6B0IIUb7Ea9MZ8t1ekjOyaRtUmRn9msoc7jJmy5YtZGZmFmhPT0/n33//tUFE4q55+sPzv8E/E2HP17BpKqQlwsNTjYm5lT3VqjqHLiby64FLvPSATB8RQoiSKtM93VeuXOGll17C39+fqVOnkpaWRvPmzenWrRvVq1dn8+bNdO/enYYNG7J8+fK7vm6bNm347bff+Pnnn2ncuDEffvghs2bN4tlnny3Fd1P2de7cmTFjxhRoX7x4MV5eXgAsWLCABx54gEqVKlGpUiXCwsLYu3dvie6hUChQKBQ4OzvTsGFDvvrqq0LvdTuFQsHq1asLtL/88suoVCpWrFhR4Fhqairjx4+ndu3aODs7U7VqVR566CF+//33QmPKv73yyit3/b6EqEhuZWQz5Lt9XElKp1ZVN+YPaoXaQQpzlRVHjhzhyBHjkP+oqCjT8yNHjnDw4EEWLlxItWrVbByluCOVI/ScBj0+MT7fNRf+HGOTHu/eTf1xUik5EZdM1BUpJiuEECVVpnu6W7RoweDBgzlw4ECBOdi50tLSWL16NbNmzeLixYu8+eabd3Xt3r1707t3b0uGWyFs2bKFgQMH0qFDB5ydnfnkk094+OGH+e+//+76S9ywYcOYMmUKqampfP/997z66qtUqlSJgQMHljie1NRUli1bxrhx41i0aBH9+vUzO/7KK6+wZ88evvjiCxo2bMj169fZuXMn169fLzSm/FxdXUscjxDlXbZOz6s/RRIVq8Xb3YklQ9ri5epk67BEPs2bNzf9eFjYMHIXFxe++OILG0Qm7km7V8DZE35/FQ4sBrUHdP/Qqj3eXq5OhDX0Ye3ROFZFXqJhQOHfyYQQQhSuTCfdUVFRVKlSpdhzXFxcGDhwIAMHDiyQSAnL++mnn8yef/vtt6xcuZKNGzcyaNCgu7qGq6srfn5+AHzwwQcsXbqUNWvW3FPSvWLFCho2bMg777xDQEAAFy9eNKsuv2bNGmbPns0jjzwCQFBQEK1atSo2JiFE4QwGAxN/P8bWU9dwdlSycHAbAivLj1NlTXR0NAaDgVq1arF3716qVq1qOubk5ISPj48sGWZvmj8DBr0x8d75BbhUhgfGWjWEJ1pUZ+3ROFYfusI7PevjoCrTgyWFEKJMKdNJ950S7vs931oMBgPZmXqb3NvBSVmqhY1SU1PJysqicuXK93wNFxeXQucd3o2FCxfy3HPPodFo6NmzJ4sXL2bixImm435+fqxdu5YnnngCDw+Pe45RCAFfbTnLz3svolDAnKdb0CzQy9YhiULkFgXV623zuSNKSYvnIO0m/DMBNk6GKrWh4WNWu/1D9apSxc2JhFsZ/Hs6gS71fax2byGEsHdlOum+3ZUrV9i+fTtXr14t8GVi1KhRNorqzrIz9cwfvdUm9x4++yEc1aXXo/H2228TEBBAWFhYiV+r0+n4+eefOXLkCMOHDze1JyUl4e7ufsfXnz59mt27d7Nq1SoAnnvuOcaOHcuECRNMPzTMnz+fZ599lipVqtCsWTM6derEU089RceOHc2u9dVXX/Htt9+atX3zzTcVfp6/ELnmbzvLp+uNqz588GgjHm4kI0PsRVRUFDExMQV+3OzTp4+NIhL3rMNI0F6B3V/BbyOgcm3wa2yVWzuqlPRpHsB3O86zMvKSJN1CCFECdpN0L168mJdffhknJyeqVKli1nurUCjKdNJdXk2bNo1ly5axZcsWnJ2d7/p1uQluZmYmKpWK119/nREjRpiOe3h4EBkZWeB1ISHm6/8uWrSI8PBwvL29AXjkkUd48cUX2bRpE926dQPgwQcf5Ny5c+zevZudO3eyceNGZs+ezeTJk816xJ999lnee+89s+v7+vre9XsSojz7estZPll3AoD/da7N4A5Btg1I3JVz587x+OOPc/ToURQKBYacAly5n586nc6W4Yl71f1DuBoF57bAsoEwfCu43vtos5J4smV1vttxnn+i4klKy0Lj4miV+wohhL2zm6R74sSJTJo0ifHjx6NU2tc8IgcnJcNnP2Sze98tT09PkpKSCrQnJiai0WjM2mbMmMG0adP4v//7P5o2bVqimHITXBcXF/z9/Qv891QqldSpU6fYa+h0OpYsWUJcXBwODg5m7YsWLTIl3QCOjo488MADPPDAA7z99ttMnTqVKVOm8Pbbb+PkZCwApdFo7nhPISqi3w5eMiXcb4XX49Uu8v+JvRg9ejTBwcFs3LiR4OBg9u7dy/Xr13njjTeYMWOGrcMT90rlAE99Bwu6wM3zxorm/ZZYpbBaowBP6vq6cyr+FmuPxjKwbY1Sv6cQQpQHdpN0p6am8vTTT9tdwg3GXoXSHOJtKfXq1eOff/4p0B4ZGUndunVNz6dPn85HH33E+vXrad26dYnvY4kEd+3atSQnJ3Pw4EGzgkDHjh1jyJAhJCYmFrn0WMOGDcnOziY9Pd2UdAshCtpxJoFxvxqXnhr2QLAk3HZm165dbNq0CW9vb5RKJUqlkk6dOhEREcGoUaM4ePCgrUMU98q1sjHxXtgdon6Hgz9Ay7srZno/FAoFT7asTsTfJ1gVeUmSbiGEuEt2k8G++OKLha7DLCxnxIgRnDp1ilGjRnHkyBFOnjzJZ599xs8//8wbb7wBwCeffMLEiRNZtGgRQUFBxMXFERcXx61bt6wa68KFC+nVqxfNmjWjcePGpq1///54eXmZqqx37tyZb775hgMHDnD+/HnWrl3Lu+++S5cuXfD09DRdLzU11fRecrebN29a9T0JUZacuXqLV344QJbOQO+m/ozv2cDWIYkS0ul0pgKS3t7eXLlyBTAWWjt58qQtQxOWUK0ldM2ZJvX325Bwxiq37duiGkoF7Dt/kwvXU6xyTyGEsHd209MdERFB7969WbduHU2aNMHR0Xwe0WeffWajyMqPWrVqsW3bNt577z3CwsLIzMykfv36rFixgh49egDw9ddfk5mZyVNPPWX22vfff58PPvjAKnHGx8fz119/sXTp0gLHlEoljz/+OAsXLuTVV18lPDycJUuW8O6775KamkpAQAC9e/dm0qRJZq9bsGABCxYsMGsLDw9n3bp1pfpehCiLktKyGP79fpIzsmkTVImZ/ZuhVFpvTWBhGY0bN+bw4cMEBwcTGhrK9OnTcXJyYv78+dSqVcvW4QlL6DAKzm6E6G2w5jV4YS2U8ohAX09nOtbx5t/TCayKvMzr3eve+UVCCFHBKQy5lVXKuKlTpzJp0iTq1auHr69vgUJqmzZtslosWq0WjUZDUlKSWW8pQHp6OtHR0QQHB5eouJgQ+cnfI2ErOr2BoYv3sfXUNQI0zqwZ2Qlvd7WtwypXivsMsaT169eTkpLCE088wZkzZ+jduzenTp2iSpUqLF++nK5du5bavcsSa/1520ziRfgyFLJSoPcsaD2k1G/5+6HLjF52iMDKLmx7q0upLk0qhBC2YsnPD7vp6Z45cyaLFi3ihRdesHUoQghRbk1fd4Ktp67h7Khk/qDWknDbsfDwcNPjOnXqcOLECW7cuEGlSpUkSSpPvAKh20RY9w5seB/q9QSP0l3S7+GGfrg5qbh4I41952/SNtg61dOFEMJe2c2cbrVaXWBtZVG2/Pvvv7i7uxe5CSHKtt8OXuKbbecA+PSpZjSuprnDK4S9qVy5siTc5VHb4RDQEjKSjPO7S5mLk4pHmvgDsCryUqnfTwgh7J3dJN2jR4/miy++sHUYohitW7fm0KFDRW5CiLLr8MVE3l55FIBXu9Tm0WYBNo5I3ItXXnmFS5fuLglavny5qeiksHNKFfSZAwolRK2G89tL/ZZPtqoOwF9HYknPkjXfhRCiOHYzvHzv3r1s2rSJP//8k0aNGhUopLZq1SobRSZyubi4yFrXQtihq9p0hv+wn8xsPWENfHijez1bhyTuUdWqVWnUqBEdO3bk0UcfpXXr1gQEBODs7MzNmzeJiopi+/btLFu2jICAAObPn2/rkIWl+DWBVkNg/0JYNx6GbzEm46WkbVBlqnm5cDkxjX+i4ukjP9QJIUSR7Kan28vLiyeeeIKHHnoIb29vNBqN2SaEEKLk0jJ1vPzjAeK1GYT4uPP5gOZSqdyOffjhh5w6dYqOHTvy1Vdf0a5dO2rUqIGPjw/16tVj0KBBnDt3jvnz57N7926aNm1aout/+eWXBAUF4ezsTGhoKHv37i32/BUrVlC/fn2cnZ1p0qQJa9euNTtuMBiYNGkS/v7+uLi4EBYWxunTp83OCQoKQqFQmG3Tpk0rUdwVRpd3Qa2BuCNw+OdSvZVSqeCJltUAGWIuhBB3Yjc93d99952tQygROykKL8oo+fsjrCE1M5sXF+/nYEwins4OLBjUGg9nxzu/UJRpvr6+vPfee7z33nvcvHmTmJgY0tLS8Pb2pnbt2vc8p3v58uWMHTuWefPmERoayqxZswgPD+fkyZP4+PgUOH/nzp0MHDjQtOTn0qVL6du3L5GRkTRu3BiA6dOnM2fOHJYsWUJwcDATJ04kPDycqKgos5UbpkyZwrBhw0zPc9cfF7dx84aH3oJ/JsDGKdCwL6hLr6bKEy2r88WmM2w7dY2r2nR8PGW1DSGEKIzd9HTbi9xh76mpqTaORNiz3L8/t0+jEMJSDAYDb/16hF3nruPmpGLhC20I8nazdVjCwipVqkSzZs1o164dderUua8iap999hnDhg1jyJAhNGzYkHnz5uHq6sqiRYsKPX/27Nn06NGDt956iwYNGvDhhx/SsmVL5s6dCxj/Ds6aNYsJEybw2GOP0bRpU77//nuuXLnC6tWrza7l4eGBn5+faXNzk7+rRWr7MlQKhlvxsPebUr1VsLcbLWt4oTfA74eulOq9hBDCnpXpnu4ePXrwwQcf0K5du2LPS05O5quvvsLd3Z1XX33VStEVTqVS4eXlxdWrVwFwdXWVSrHirhkMBlJTU7l69SpeXl6oVKU3H09UbAu3R/PXkVgcVQq+G9KWNkGy5I8oWmZmJgcOHGD8+PGmNqVSSVhYGLt27Sr0Nbt27WLs2LFmbeHh4aaEOjo6mri4OMLCwkzHNRoNoaGh7Nq1i6efftrUPm3aND788ENq1KjBM888w+uvv46DQ5n+CmM7Dk7GYearhsGOOdDmJXAuvWl4T7SsTmRMIisjLzHswVqldh8hhLBnZfoTq1+/fjz55JNoNJpiC8KsXbuWXr168emnn9o6ZAD8/IzrY+Ym3kKUlJeXl+nvkRCWtuvsdSL+PgHAxN4NZY1dcUcJCQnodDp8fX3N2n19fTlx4kShr4mLiyv0/Li4ONPx3LaizgEYNWoULVu2pHLlyuzcuZPx48cTGxvLZ599Vuh9MzIyyMjIMD3XarV3+S7LkcZPwr8z4doJ2PUVdBl/59fco0ebBjDljyhOxCXz35UkGgVInR0hhLhdmU66X3zxRZ577jlWrFjB8uXLmT9/PklJSQAoFAoaNmxIeHg4+/bto0GDBjaONo9CocDf3x8fHx+ysrJsHY6wM46OjtLDLUpNbFIaI3+ORKc38ESLajzfrqatQxKiWPl7y5s2bYqTkxMvv/wyERERqNXqAudHREQwefJka4ZY9ihV0Hk8rBgMu76E0JfBtXR+XNO4OhLW0Ie1R+NYFXlZkm4hhChEmU66AdRqNc899xzPPfccAElJSaSlpVGlSpUyP99VpVJJ8iSEKDMysnX876dIEm5l0sDfk48ebyLTX8Rd8fb2RqVSER8fb9YeHx9f5KgcPz+/Ys/P3cfHx+Pv7292TvPmzYuMJTQ0lOzsbM6fP0+9egWXtxs/frxZoq7VagkMDCz+DZZHDfoYlxGLOwq75kK3SaV2qydaVGft0Th+P3SZ8T3r46CSkkFCCJGf3f2rqNFo8PPzK/MJtxBClDUf/hllqlQ+77mWuDjJj4Li7jg5OdGqVSs2btxoatPr9WzcuJH27dsX+pr27dubnQ+wYcMG0/nBwcH4+fmZnaPVatmzZ0+R1wQ4dOgQSqWy0IrpYPyx3tPT02yrkJRKeOgd4+N930J66Q2zf6heVaq4OZFwK5Ntp6+V2n2EEMJe2V3SLYQQouR+PXCJH3fHoFDA7KdbULOKVH8u7+Lj43n++ecJCAjAwcHBNPrqXkdhjR07lgULFrBkyRKOHz/OiBEjSElJYciQIQAMGjTIrNDa6NGjWbduHTNnzuTEiRN88MEH7N+/n9deew0wTsUaM2YMU6dOZc2aNRw9epRBgwYREBBA3759AWMxtlmzZnH48GHOnTvHTz/9xOuvv85zzz1HpUqV7v8Pqbyr9wh414X0JDiwuNRu46hS0qd5AAArIy+X2n2EEMJelfnh5UIIIe7PsctJvPfbUQBGdwuhS/3CewhF+fLCCy8QExPDxIkT8ff3v++pBAMGDODatWtMmjSJuLg4mjdvzrp160yF0GJiYlAq837L79ChA0uXLmXChAm8++67hISEsHr1atMa3QDjxo0jJSWF4cOHk5iYSKdOnVi3bp1pjW61Ws2yZcv44IMPyMjIIDg4mNdff71AVXRRBKUSOoyCNa/B7q+Mc7sdCs6Dt4QnW1bnux3n2RAVT1JaFhoXGZEohBC5FAaDwWDrIOyNVqtFo9GQlJRUcYetCSHsws2UTB6du51LN9PoWt+Hbwe1RqmUedy2ZK3PEA8PD/79999i50dXBBX+Mzs7A2Y3g+RY6DMXWj5fKrcxGAz0mPUvJ+OT+fjxJjwTWqNU7iOEENZiyc8PGV4uhBDllE5vYPTyQ1y6mUaNyq583r+5JNwVSGBgIPK7usBBDe1fNT7eMRv0+lK5jUKh4ImW1QBYFXmpVO4hhBD2ym6S7osXL3LpUt4/4nv37mXMmDHMnz/fhlEJIUTZNev/TrHt1DWcHZV883wrNK4y3LMimTVrFu+88w7nz5+3dSjC1lq9AM4auH4azmwotdv0bVENpQL2X7jJhesppXYfIYSwN3aTdD/zzDNs3rwZgLi4OLp3787evXt57733mDJlio2jE0KIsuWPw1f4YtMZAKY90ZQG/hVwWG0FN2DAALZs2ULt2rXx8PCgcuXKZpuoQNQe0CJnWPne0uus8PV0plNIVUAKqgkhRH52U0jt2LFjtG3bFoBffvmFxo0bs2PHDv755x9eeeUVJk0qvfUnhRDCnvx7+hpjlh8C4IUOQfRtUc22AQmbmDVrlq1DEGVJm5dg15dw5v8g4Qx41ymV2zzZshrbTl1jVeQlxnQLkSktQgiBHSXdWVlZqNXGipv/93//R58+fQCoX78+sbGxJbrWBx98wOTJk83a6tWrx4kTJywTrBBC2Mjp+GT+92MkOr2Bx5oHMLF3Q1uHJGxk8ODBtg5BlCWVg6FuOJxaB/sWQM9PSuU2Dzf0w13twKWbaey/cJO2wTKqQggh7GZ4eaNGjZg3bx7//vsvGzZsoEePHgBcuXKFKlWq3NP1YmNjTdv27dstHbIQQlhVwq0Mhi7ZR3JGNm2DKjP9qaaopJepQtPpdKxcuZKpU6cydepUfvvtN3Q6na3DErbSdrhxf/AnyEgulVu4OKl4pIkfACsPSEE1IYQAO0q6P/nkE7755hs6d+7MwIEDadasGQBr1qwxDTsvCQcHB/z8/Eybt7e3pUMWQgirSc/SMfz7/Vy8kUbNKq7Me74VageVrcMSNnTmzBkaNGjAoEGDWLVqFatWreK5556jUaNGnD171tbhCVuo1QWq1IHMZDi8rNRu80TL6gD8dTSW9Cz5kUcIIewm6e7cuTMJCQkkJCSwaNEiU/vw4cOZN29eia93+vRpAgICqFWrFs8++ywxMTGWDFcIIaxGrzfw5orDRMYk4unswMLBbajs5mTrsISNjRo1itq1a3Px4kUiIyOJjIwkJiaG4OBgRo0aZevwhC0olXm93fsWQiktKdc2qDLVvFy4lZHNumNxpXIPIYSwJ3aTdAOoVCoqVapk1hYUFISPj0+JrhMaGsrixYtZt24dX3/9NdHR0TzwwAMkJxc+1CojIwOtVmu2CSFEWfHZhlP8eSQWB6WCec+3oo6Pu61DEmXA1q1bmT59ulml8ipVqjBt2jS2bt1qw8iETTUdAA4ucO04XD5QKrdQKhX0bx0IwNI90qkhhBB2U0gtODgYhaLouYnnzp2762v17NnT9Lhp06aEhoZSs2ZNfvnlF1588cUC50dERBQovCaEEGXBL/svMnezcWmwiCea0KG2TJURRmq1utAfk2/duoWTk4yEqLBcvKBRXzj8MxxYDNVbl8ptBrQJZM6m0+w9f4PT8cmE+HqUyn2EEMIe2E1P95gxYxg9erRp+9///kf79u1JSkpi+PDh93VtLy8v6taty5kzZwo9Pn78eJKSkkzbxYsX7+t+QghhCTvPJPDuqqMAvNalDv1yepaEAOjduzfDhw9nz549GAwGDAYDu3fv5pVXXjGtACIqqJaDjPtjq0qtoJqfxplu9Y0jEX+S3m4hRAVnNz3do0ePLrT9yy+/ZP/+/fd17Vu3bnH27Fmef/75Qo+r1WrTcmVCCFEWnLl6i1d+PEC23kDvpv6M7V7X1iGJMmbOnDkMHjyY9u3b4+joCEB2djZ9+vRh9uzZNo5O2FSN9saCatfPGBPvVqWzvNwzoTX4JyqeVZGXeKdnfZwdpbijEKJispue7qL07NmTlStXlug1b775Jlu3buX8+fPs3LmTxx9/HJVKxcCBA0spSiGEMDpz9RafrDvBbwcvcTkx7Z6uEa9NZ8SPB9CmZ9Oyhhcz+jVDKUuDidt4eXnx+++/c/LkSX799Vd+/fVXTp48yW+//YZGo7F1eMKWFIq83u7I70vtNg+GVKV6JRe06dn8eSS21O4jhBBlnd30dBfl119/NSsSczcuXbrEwIEDuX79OlWrVqVTp07s3r2bqlWrllKUQghh9MaKwxy+mGh6Xr2SC13q+dC3RTVa1vAqtnYFwNFLSQz/YT+xSel4uzvx1bOtpPdIFCskJISQkBBbhyHKmmYDYeMUuLwf4v8D30YWv4VSqWBg2xp8uv4kP+25wFOtqlv8HkIIYQ/sJulu0aKF2ZdRg8FAXFwc165d46uvvirRtZYtK721KYUQoih6vYHjV4yrH3i7O3EzNYtLN9P4YfcFfth9gSbVNLwZXo8HQ7wLJN/pWTrmbDzNN9vOodMbqF3Vje9eaIufxtkWb0WUUWPHjuXDDz/Ezc2NsWPHFnvuZ599ZqWoRJnk7gP1HoHjayDyB+g5rVRu0791IJ9vOMXBmESOx2pp4O9ZKvcRQoiyzG6S7r59+5o9VyqVVK1alc6dO1O/fn3bBCWEECVwPSWTTJ0ehQJ2je9GRraefdE3+OPwFf4+FsfRy0kMXrSXljW8eKFjME2radCmZ7E3+gY/7L7AheupAPRq4s/HjzdB4+po43ckypqDBw+SlZVleixEsVo8Z0y6j/0KD08FleW/Flb1UBPeyI+/jsby4+4LfPR4E4vfQwghyjqFwWAw2DoIe6PVatFoNCQlJeHpKb/YCiHuzqn4ZB7+fBuVXB05OOlhs2PXb2Xw1Zaz/Lj7AhnZ+kJfX9VDzdS+jQlv5GeNcEUpkc8Q65I/72LosmBmfUhNgGd/hZDupXKbXWevM3DBblwcVewa3xUvV1myTghR9lny86NMF1LTarVmj4vbhBCirMvMSaadHAr+01vFXc3E3g35d1wXRnSuTX0/D1wcVXi7O9GuVmWm9m3M5jc7S8It7trQoUMLXac7JSWFoUOH2iAiUeaoHKHxk8bHh0tv6l27WpVp4O9JWpaOZftk2VVRtt1IyWTt0ViydIX/AC7EvSjTPd0qlYrY2Fh8fHxQKpWFFhgyGAwoFAp0Op3V4pJfzYUQ9+JgzE0e/2on1Su5sP3trrYOR9iItT5D8n+G5peQkICfnx/Z2dmldu+yRD6z7+DyAVjQFRxc4K3ToPYoldus2H+Rt349gr/GmW3juuCoKtP9PqKCSs/SUX/iOgDmPtOC3k0DbByRsCVLfn6U6TndmzZtMlUm37x5s42jEUKI+5OlM/7G6SRfNkUp0mq1GAwGDAYDycnJODvnFdvT6XSsXbu2QCIuKrCAlnlrdh//A5o/Uyq3ebRZAJ+sO0FsUjrr/4uTZEaUSbvOXTc9zq2jIoQllOmk+6GHHir0sRBC2KPcoWrSwyNKk5eXcek5hUJB3bp1CxxXKBRMnjzZBpGJMkmhgKYDYPNHcGR5qSXdzo4qng2tyeyNp1m0PVqSblEmRV3Jm7KarSuzg4GFHSrTSfeRI0fu+tymTZuWYiRCCHH/MnOTbofi1+IW4n5s3rwZg8FA165dWblypWnEGICTkxM1a9YkIEASHpFPk37GpPvcVtBeAc/S+fvxbLsafL3lLJExiRyMuUmLGpVK5T5C3KuEWxmmx6lZFWMKjrCOMp10N2/eHIVCYZq3XRxrzukWQoh7kZUtPd2i9OWODIuOjqZGjRp3/PwUgsrBENgOLu6Go79Cx1GlchsfD2cebRbAyshLLNpxni8k6RZlyKWbqew6mze8PDVDcgthOWX6m190dDTnzp0jOjqalStXEhwczFdffcXBgwc5ePAgX331FbVr12blypW2DlUIIe4od063JN2itBw5cgS93vjjTlJSEkePHuXIkSOFbkKYadrfuD/yS6neZminIAD+OnKFC9dTSvVeQpTEG78c5kRc3ooPqZmSdAvLKdM93TVr1jQ97tevH3PmzOGRRx4xtTVt2pTAwEAmTpxI3759bRChEELcvdw53epClgwTwhKaN29OXFwcPj4+ZqPFbmftVT+EHWj0OPw9DuKPQsJp8A4pndsEaHioblW2nrrGvK1niXhCpgcK28vW6TkYkwhAl3pV2XzyGqmZMrxcWI7dfPM7evQowcHBBdqDg4OJioqyQURCCFEymVJITZSy6OhoqlatanqcO1rs9u3cuXM2jlSUOa6VoVZn4+Njq0r1Vq91rQPArwcuEZuUVqr3EuJuXLqZRqZOj7Ojkp5N/AHp6RaWZTff/Bo0aEBERASZmZmmtszMTCIiImjQoIENIxNCiLuTV71c5tiK0lGzZk3THO6aNWsWuwlRQKMnjPv/fivV27QJqkzb4Mpk6QzM3yY/AAnbOxFnrFoe7O2Ou9o4EDhNkm5hQXaTdM+bN4/169dTvXp1wsLCCAsLo3r16qxfv5558+bZOjwhhLgjKaQmrGnJkiX89ddfpufjxo3Dy8uLDh06cOHCBRtGJsqs+r1A6QjXjsPV46V6q9e6GHu7f94bY1YxWghb2JlTQK1NUCVcnVQApMjwcmFBdvPNr23btpw7d46pU6fStGlTmjZtykcffcS5c+do27atrcMTQog7yi2k5iRJt7CCjz/+GBcXFwB27drF3LlzmT59Ot7e3rz++us2jk6USS5eUKeb8XEpDzF/IMSbptU1pGfpWSC93cLGImNuAtCuVhVcnaSnW1hemS6kdjs3NzeGDx9u6zCEEOKeyJxuYU0XL16kTh1jb+Lq1at56qmnGD58OB07dqRz5862DU6UXY2egFPrjEPMu7wLpbTknEKhYExYCEMX72fxzvMM7RSMr6dzqdxLiOJk6/Scir8FQKMAT5LTjT3c0tMtLMnuvvlFRUWxbt061qxZY7YJIURZl5k7vNxB5nSL0ufu7s7168Yhk//88w/du3cHwNnZmbS0eyte9eWXXxIUFISzszOhoaHs3bu32PNXrFhB/fr1cXZ2pkmTJqxdu9bsuMFgYNKkSfj7++Pi4kJYWBinT58u9FoZGRmmiuyHDh26p/jFXajXE1RquH4a4o+V6q261POhVc1KZGTr+WJT4f/dhSht56+nkJmtx81JRWAlV9PwcimkJizJbpLuc+fO0axZMxo3bkyvXr3o27cvffv25fHHH+fxxx+3dXhCCHFHWdLTLayoe/fuvPTSS7z00kucOnXKtOTmf//9R1BQUImvt3z5csaOHcv7779PZGQkzZo1Izw8nKtXrxZ6/s6dOxk4cCAvvvgiBw8eNH1uHzuWl8hNnz6dOXPmMG/ePPbs2YObmxvh4eGkp6cXuN64ceMICAgocdyihJw9IcT4A01pDzFXKBS8FV4PgGV7LxJzPbVU7ydEYY7HGtfmrufngVKpMBteXtiSi0LcC7v55jd69GiCg4O5evUqrq6u/Pfff2zbto3WrVuzZcsWW4cnhBB3lJt0y5xuYQ1ffvkl7du359q1a6xcuZIqVaoAcODAAQYOHFji63322WcMGzaMIUOG0LBhQ+bNm4erqyuLFi0q9PzZs2fTo0cP3nrrLRo0aMCHH35Iy5YtmTt3LmDs5Z41axYTJkzgscceo2nTpnz//fdcuXKF1atXm13r77//5p9//mHGjBkljlvcg8a5VcxXQSknHe1qVeGBEG+y9QY+/79TpXovIQpzMi4v6QZwVRt7urP1BtO0MCHul91889u1axdTpkzB29sbpVKJUqmkU6dOREREMGrUKFuHJ4QQd5RbSE16uoU1eHl5MXfuXH7//Xd69Ohhap88eTLvvfdeia6VmZnJgQMHCAsLM7UplUrCwsLYtWtXoa/ZtWuX2fkA4eHhpvOjo6OJi4szO0ej0RAaGmp2zfj4eIYNG8YPP/yAq6trieIW9ygkHBxc4OZ5iD1U6rcbF14fgNWHLnP0UlKp30+I/E7FG5PuEJ+cpNtRZTomxdSEpdjNNz+dToeHh/F/Bm9vb65cuQIY1yE9efKkLUMTQoi7IoXUhLUlJiYyc+ZM0zDzzz//nKSkkic1CQkJ6HQ6fH19zdp9fX2Ji4sr9DVxcXHFnp+7L+4cg8HACy+8wCuvvELr1q3vKtaMjAy0Wq3ZJkpI7Q51w42PS3mIOUCT6hr6Ng/AYIDJf/wnQ3qFVZ2+aiyiVtfXmGc4qJSoHYyf07lF1YS4X3bzza9x48YcPnwYgNDQUKZPn86OHTuYMmUKtWrVsnF0QghxZ1lSSE1Y0f79+6lduzaff/45N27c4MaNG3z22WfUrl2byMhIW4d3V7744guSk5MZP378Xb8mIiICjUZj2gIDA0sxwnLMNMR8dakPMQd4u2d9XBxV7L9wkz+PxJb6/YQASM/SceF6CgB1fd1N7Z4ujgBo07NsEpcof+wm6Z4wYQJ6vfEL65QpU4iOjuaBBx5g7dq1zJ4928bRCSHEncmcbmFNr7/+On369OH8+fOsWrWKVatWER0dTe/evRkzZkyJruXt7Y1KpSI+Pt6sPT4+Hj8/v0Jf4+fnV+z5ufviztm0aRO7du1CrVbj4OBgWgKtdevWDB48uND7jh8/nqSkJNN28eLFEr1XkaNOd3B0haQYqwwx99e4MKJzbQAi1h6XYb3CKs5eu4XeABoXR6p6qE3tmpykOylNkm5hGXbzzS88PJwnnjD+6lqnTh1OnDhBQkICV69epVu3bjaOTggh7kzmdAtr2r9/P2+//TYODg6mNgcHB8aNG8f+/ftLdC0nJydatWrFxo0bTW16vZ6NGzfSvn37Ql/Tvn17s/MBNmzYYDo/ODgYPz8/s3O0Wi179uwxnTNnzhwOHz7MoUOHOHTokGnJseXLl/PRRx8Vel+1Wo2np6fZJu6Bk2teFfMo6yzNOvzBWlTzcuFKUjpfbTljlXuKiu1MztDyEB93FPnWpPdwNv67KcPLhaXY9Te/ypUrExcXx2uvvWbrUIQQ4o5kTrewJk9PT2JiYgq0X7x40VQjpSTGjh3LggULWLJkCcePH2fEiBGkpKQwZMgQAAYNGmQ2DHz06NGsW7eOmTNncuLECT744AP2799v+sxWKBSMGTOGqVOnsmbNGo4ePcqgQYMICAigb9++ANSoUYPGjRubtrp16wJQu3ZtqlevXuL3IEqoQR/j/vgaqwwxd3ZUMbF3AwC+3nLWVFVaiNISnWAcWl6rqptZe+7ndLZO6gsIy3C48ym2999//7F582acnJzo378/Xl5eJCQkMHXqVL755huZ0y2EsAt563TLnG5R+gYMGMCLL77IjBkz6NChAwA7duzgrbfeuqclwwYMGMC1a9eYNGkScXFxNG/enHXr1pkKocXExKBU5v2g1KFDB5YuXcqECRN49913CQkJYfXq1TRu3Nh0zrhx40hJSWH48OEkJibSqVMn1q1bh7Oz832+e2ERdcNBpYbrZ+DqcfBtWOq3DG/kR/eGvmyIimf8qiP8+koHlEr5N1OUjvM5SXeQ9+1Jt/HvXLZelgwTllHmk+41a9bw1FNPkZ1tHN4xffp0FixYQP/+/WnVqhW//fab2VIoQghRVpnmdDtIT7cofTNmzEChUDBo0CDTZ6ijoyMjRoxg2rRp93TN1157rcjRZVu2bCnQ1q9fP/r161fk9RQKBVOmTGHKlCl3df+goCCpbG1Nag+o3RVO/Q1Rv1sl6VYoFEx5rBG7zl4nMiaRH/dcYFD7oFK/r6iYoq+nAlDrtqTbIecHxCzp6RYWUua/+U2dOpVXX30VrVbLZ599xrlz5xg1ahRr165l3bp1knALIexGVrbxw1sKqQlrcHJyYvbs2dy8edM0J/rGjRt8/vnnqNXqO19ACICG+YaYW4m/xoVxPeoBMO3vE6beSCEsyWAwEH3NOKf79p5uh5zRFTrp6RYWUua/+Z08eZJXX30Vd3d3Ro4ciVKp5PPPP6dNmza2Dk0IIUpE5nQLW3B1dcXLywsvLy9cXV1tHY6wN/V6gtIBrkZBgvWKmz0XWpN2tSqTmqljzPJDZOsk+RGWdTM1C21OobSalW9LunOGl0tPt7CUMv/NLzk52VR5VKVS4eLiYvE53NOmTTMVdBFCiNJimtMtw8uFFWRnZzNx4kQ0Gg1BQUEEBQWh0WiYMGECWVmyDI64Sy6VIPhB4+Pjv1vttkqlgpn9m+Ph7MChi4l8sUmqmQvLyi2i5q9xxsVJZXbMwVRITX7sEZZR5ud0A6xfvx6NRgPkLVFy7Ngxs3P69OlzT9fet28f33zzDU2bNr3vOIUQojhSSE1Y08iRI1m1ahXTp083LcG1a9cuPvjgA65fv87XX39t4wiF3WjQB85uMi4d9sAbVrttNS8XPnq8CaN+PsgXm07TKcSbNkGVrXZ/Ub6ZiqhVcStwzFGZW0hNerqFZdhF0j148GCz5y+//LLZc4VCgU6nK/F1b926xbPPPsuCBQuYOnXqfcUohBB3kjtMTeZ0C2tYunQpy5Yto2fPnqa2pk2bEhgYyMCBAyXpFnevfm/4ayzEHoKbF6BSTavduk+zADafuMpvBy/zv58i+WtkJ3w8pbq9uH/nrxdeuRxAlVNITZJuYSll/pufXq+/43YvCTfAq6++Sq9evQgLC7Nw1EIIUVBmtszpFtajVqsJCgoq0B4cHIyTk5P1AxL2y70q1DAuO8fxP6x++6l9G1PX151ryRn876dI07+lQtyP3OHlwd4Fa12YlgyT4eXCQirsN79ly5YRGRlJRETEHc/NyMhAq9WabUIIUVJSSE1Y02uvvcaHH35IRkaGqS0jI4OPPvqoyGW/hChSw8eMeytWMc/lpnbgm+db4+HswP4LN5ny53+ydJy4b6ae7kKGl0shNWFpdjG83NIuXrzI6NGj2bBhA87Odx6iFBERweTJk60QmRCiPMtbp1vmdIvSd/DgQTZu3Ej16tVp1qwZAIcPHyYzM5Nu3brxxBNPmM5dtWqVrcIU9qJBb/j7Lbi4B7Sx4Olv1dsHe7sxa0BzXlyynx93x1CjsivDH6xt1RhE+WEwGDifYFyjO7iQ4eUOpuHl0tMtLKNCJt0HDhzg6tWrtGzZ0tSm0+nYtm0bc+fOJSMjA5Uqr4rh+PHjGTt2rOm5VqslMDDQqjELIexflgwvF1bk5eXFk08+adYmn13innkGQPW2cGkvnPgT2g6zegjdGvjy3iMN+GjtcT5eewJfT2cea17N6nEI+5dwK5NbGdkoFBBYueDwcgcppCYsrEIm3d26dePo0aNmbUOGDKF+/fq8/fbbZgk3GOfFqdVqa4YohCiHcoepSdItrOG7776zdQiivGnYx5h0R/1uk6Qb4KUHgolNSmfRjmjeXHEYT2dHutT3sUkswn7lDi0P0Ljg7KgqcDxvyTBJuoVlVMhvfh4eHjRu3Nhsc3Nzo0qVKjRu3NjW4QkhyiGDwSBzuoUQ9q3Bo8b9hR2QkmCTEBQKBRN6NaBXU3+ydAZe/uEAm07E2yQWYb+ir+UWUSs4tBwg92NaJz3dwkLs6ptfYmIi3377LePHj+fGjRsAREZGcvnyZRtHJoQQxcs/RE2WDBPWcP36dV599VUaNmyIt7c3lStXNtuEKLFKQeDfDAx6OPGXzcJQKhXMGtCcno39yNTpefmHA/xflCTe4u5Fm5YLKzi0HEClMA4v10vBPmEhdjO8/MiRI4SFhaHRaDh//jzDhg2jcuXKrFq1ipiYGL7//vv7uv6WLVssE6gQQhQiK9+yI45SSE1YwfPPP8+ZM2d48cUX8fX1RaGQv3fCAhr0gdjDxiHmrQbbLAxHlZI5A1swetlB1h6N4+UfDzC1b2MGtq1hs5iE/ThvWi7MvdDjsk63sDS7SbrHjh3LCy+8wPTp0/Hw8DC1P/LIIzzzzDM2jEwIIe4sKzvvg1uGlwtr+Pfff9m+fbupcrkQFtHwMdj0IURvhbSb4FLJZqE4qpTMfroFzo5HWBV5mfGrjnLxRipvPlwPpVJ+ZBJFK26NbsgbXq6XpFtYiN1889u3bx8vv/xygfZq1aoRFxdng4iEEOLuZebr6XaQL4PCCurXr09aWpqtwxDljXcI+DQEfTacXGfraHBUKZnZrxmjuoUA8NWWswz/YT+JqZk2jkyUVQaDgQvXjcuFFbZGN2D60UbmdAtLsZukW61Wo9VqC7SfOnWKqlWr2iAiIYS4e6Y1ulVKGeYrrOKrr77ivffeY+vWrVy/fh2tVmu2CXHPGvQx7o+vsW0cORQKBWO71+XTp5ri5KDk/45fpdec7RyMuWnr0EQZFKdNJy1Lh0qpoHql4ud062ROt7AQu0m6+/Tpw5QpU8jKygKM/8DGxMTw9ttvF1iHVAghyhpT0u1gN//sCjvn5eWFVqula9eu+Pj4UKlSJSpVqoSXlxeVKtluSLAoBxo+Ztyf2QgZybaNJZ9+rQNZNaIDNau4cjkxjafm7WL6uhOkZ+lsHZooQ87lVC6vUdm1yM9kVU5PtwwvF5ZiN3O6Z86cyVNPPYWPjw9paWk89NBDxMXF0b59ez766CNbhyeEEMXKMi0XJr3cwjqeffZZHB0dWbp0qRRSE5bl0wCq1IHrZ+DUemjylK0jMmlcTcMfIzvx3m/H+OPwFb7acpb1/8UR8URT2gZL1X4B567dAqBWEcuFAShNPd1WCUlUAHaTdGs0GjZs2MD27ds5cuQIt27domXLloSFhdk6NCGEuKPMnEJqUkRNWMuxY8c4ePAg9erVs3UoorxRKIxDzLd/ZqxiXoaSbgBPZ0e+GNiCXk38mfj7Mc5eS6H/N7vo0ciPd3rWJ6iYZEuUf2dzerpr+xReuRzyerp1en2R5whREnaTdOfq1KkTnTp1snUYQghRInk93ZJ0C+to3bo1Fy9elKRblI6GjxmT7jP/B5kp4FT2Etkejf1oX6sKn6w/wbK9Maz7L46NJ+Lp1zqQVx6sTY0qhc/nFeXb2bvp6ZZCasLC7CbpnjNnTqHtCoUCZ2dn6tSpw4MPPohKpbJyZEIIcWcyp1tY28iRIxk9ejRvvfUWTZo0wdHR0ex406ZNbRSZKBf8m4FXDUiMMSbeufO8yxiNqyMfP96Ewe2D+HjtcbaeusbSPTEs2xvDo80CGNoxmKbVNTL9ogLJndNdq2rRPd0OpqTbKiGJCsBuku7PP/+ca9eukZqaaioAc/PmTVxdXXF3d+fq1avUqlWLzZs3ExgYaONohRDCXKbM6RZWNmDAAACGDh1qalMoFBgMBhQKBTqdFJcS90GhMCbaO7+AqDVlNunOVc/PgyVD27Ln3HW+2nKWraeu8fuhK/x+6AoN/T0Z2DaQPs2roXFxvPPFhN1KycjmSpJxKcXaVYvu6c6tXq6X6uXCQuymy+Xjjz+mTZs2nD59muvXr3P9+nVOnTpFaGgos2fPJiYmBj8/P15//XVbhyqEEAVk6WROt7Cu6OjoAtu5c+dMeyHuW4OcRPvUeshKt20sdym0VhWWDG3LnyM70bd5AE4OSqJitUz8/T9aT93AC9/t5Zd9F7mZIut8l0fHLidhMIC/xpkq7uoiz5Ph5cLS7Kane8KECaxcuZLatWub2urUqcOMGTN48sknOXfuHNOnT5flw4QQZVJmtszpFtZVs2ZNW4cgyrtqrcCzGmgvw7nNUK+nrSO6a42raZj1dAs+SM1kVeRllu+7yMn4ZLacvMaWk9dQroKm1b3oVMebTiHetKjhhdpBpjDau8OXEgFoVt2r2PO8bp3hN6dJLMt8B2hb6nGJ8s9uku7Y2Fiys7MLtGdnZxMXFwdAQEAAycllZ71IIYTIZZrTLUm3sKKzZ88ya9Ysjh8/DkDDhg0ZPXq02Q/YQtwzpRIaPAp75hmHmNtR0p3Ly9WJoZ2CGdopmDNXk/n7aBxrj8VxPFbLoYuJHLqYyNzNZ1A7KGlSTUPzQC9a1KhE8xpeBGicZS64nTl8MQmAZoFexZ4XtvkxUEKLuJeAfqUfmCj37Cbp7tKlCy+//DLffvstLVq0AODgwYOMGDGCrl27AnD06FGCg4NtGaYQQhTKVL3cQb6gCetYv349ffr0oXnz5nTs2BGAHTt20KhRI/744w+6d+9u4whFudDwMWPSffIvyM4EBydbR3TP6vh4MLKbByO7hXAlMY0dZxLYcSaB7Weuk3Arg/0XbrL/wk0gGgAPZwfq+npQ19edOj7GfY3KrgR4uciopjLIYDCwJ/oGAC1qeBV3onUCEhWK3STdCxcu5Pnnn6dVq1amCqzZ2dl069aNhQsXAuDu7s7MmTNtGaYQQhRKhpcLa3vnnXd4/fXXmTZtWoH2t99+W5JuYRmBoeDmAylX4fw2qBNm64gsIsDLhX6tA+nXOhCDwcC5hBQOxSSaer+Px2pJTs/mwIWbHLhw0+y1SgX4a1yoVsmFwEquVKvkgo+HmqoeatO+qodahqtb2fHYZBJuZeDiqCo+6b5+1moxiYrDbpJuPz8/NmzYwIkTJzh16hQA9erVM1t/tEuXLrYKTwghiiWF1IS1HT9+nF9++aVA+9ChQ5k1a5b1AxLlk1JlHGK+fyFE/V5uku78FAoFtau6U7uqO0+2qg5ARraO6IQUTsXf4nR8Mqfikzlz9RaXbqaRka3ncmIalxPT2JvTs1oYjYsj3u5OeLk6oXFxxMvFEU8XR7xcjY81ro54uTjh6eKAm9oBN6ecvVqFk0opQ9tLaO3RWAA61vEu/geP62esFJGoSOwm6c5Vv3596tevb+swhBCiRGROt7C2qlWrcujQIUJCQszaDx06hI+Pj42iEuVSwz7GpPvEX9Drc1DZ3dfLElM7qKjv50l9P0+zdoPBwLVbGVy8kcalm6lcvJHKlaR0riVnmG2ZOj1JaVkkpWUBKSW+v4NSgZvaAXe1A65OKlMynpuYOzsqcXZUGTcHVb7n+dodVTg75D13yTmuztmXp8Q+M1vPyshLAPRpHlD8ybfiTQ+1Cg88izlViLtlV/8qXrp0iTVr1hATE0NmpvlSDp999pmNohJCiDvLG15ePr7AiLJv2LBhDB8+nHPnztGhQwfAOKf7k08+YezYsTaOTpQrNTuBS2VIvQ4XdkCth2wdkc0oFAp8PJzx8XCmVc1KhZ5jMBjQpmVz7VY615Izc5Jv4z4xNYvEnGQ8KTWLxLRMtGnZpGZmcysjm/Qs42dJtt6QL2kvrfdiHJ2lVilxcsjbHFXGhDz3udrB/Llj7mNVzrEijud/naNKiYNKYdwrFabnDkoljioFDjntDkrjY8d8x+7mh4H5284Sm5SOj4eahxv6Fn/yraumhw4ULOIsxL2wm6R748aN9OnTh1q1anHixAkaN27M+fPnMRgMtGzZ0tbhCSFEsVIyjR/crmq7+WdX2LmJEyfi4eHBzJkzGT9+PGBc5eODDz5g1KhR93TNL7/8kk8//ZS4uDiaNWvGF198Qdu2RS+ns2LFCiZOnMj58+cJCQnhk08+4ZFHHjEdNxgMvP/++yxYsIDExEQ6duzI119/bdY736dPHw4dOsTVq1epVKkSYWFhfPLJJwQE3KG3SliPygHq94KDP8DxNRU66b4bCoUCjatx+HidEg46ydbpSc3SkZKRnbMZH9/KyCY1U5ezNybn6Vk64z5bR3qmzrg3td/2ODvvce7S1AaD8QfjzGw9ZFj+z8FSVDnJeF6ibkzIc5N4vcHAheupALz5cD2cHe8wl/5WXN61DbrSDF1UIHbz7W/8+PG8+eabTJ48GQ8PD1auXImPjw/PPvssPXr0sHV4QghRrJSMbJTqOPalLeXkjUnUq1zvzi8S4j4oFApef/11Xn/9ddNymh4eHvd8veXLlzN27FjmzZtHaGgos2bNIjw8nJMnTxY6XH3nzp0MHDiQiIgIevfuzdKlS+nbty+RkZE0btwYgOnTpzNnzhyWLFlCcHAwEydOJDw8nKioKJydnQFjvZZ3330Xf39/Ll++zJtvvslTTz3Fzp077/m9iFLQ8LGcpPsP6PmpcTkxYXEOKiWeKiWezo6lcn2DwUCWzkBals6YcOv0psTb+FxHRs7jLJ3B1JZ7PCPfa7Lyv1anN70u93n+4xnZenR6A9l6A1k6Pdk6A9l64z2ydXqy9Ma9vpDC4jq9AZ3eQEbOiLLCKBXwWpc69Gtd/c5/CLGHTQ+lp1tYisJgsI+6+B4eHhw6dIjatWtTqVIltm/fTqNGjTh8+DCPPfYY58+ft1osWq0WjUZDUlISnp4y00MIcWfjVx1hTcJolOoEAj0CWfvEWluHJGyktD9D0tLS2LBhA126dCmQZGu1WrZs2UJ4eDhqtbpE1w0NDaVNmzbMnTsXAL1eT2BgICNHjuSdd94pcP6AAQNISUnhzz//NLW1a9eO5s2bM2/ePAwGAwEBAbzxxhu8+eabACQlJeHr68vixYt5+umnC41jzZo19O3bl4yMDNNqJsWRz2wryc6ET+tARhIMWQc129s6IlEO6XMS8/wJeWGJui63LWdfy9sdP43znW+QroVPgiB/D/ekm/IjUgVlyc8Pu/kb5ObmZprH7e/vz9mzeeX8ExISbBWWEELclZQMHUq18d+qi8kXbRyNKM/mz5/P7NmzC+3V9vT0ZM6cOXz77bclumZmZiYHDhwgLCyvMrVSqSQsLIxdu3YV+ppdu3aZnQ8QHh5uOj86Opq4uDizczQaDaGhoUVe88aNG/z000906NDhrhJuYUUOTlCvp/Hx8TW2jUWUW0qlAicHJa5ODmhcHKnirsbX05nqlVwJ8najjo8HDfw9aVxNQ4salWgTVJkOtb3vLuEGSDgNBh06B7e8Nn3pzZsXFYfdJN3t2rVj+/btADzyyCO88cYbfPTRRwwdOpR27drZODohhCheZjHD3oSwpJ9++okxY8YUeXzMmDEsWbKkRNdMSEhAp9Ph62tegMjX15e4uLhCXxMXF1fs+bn7u7nm22+/jZubG1WqVCEmJobff/+9yFgzMjLQarVmm7CSho8Z91FrjBOChbA3acYl3jLd/PLadJlFnCzE3bObpPuzzz4jNDQUgMmTJ9OtWzeWL19OUFAQCxcutHF0QghRvGy9JN3COk6fPk2zZs2KPN60aVNOnz5txYju31tvvcXBgwf5559/UKlUDBo0iKJmx0VERKDRaExbYGCglaOtwGp3BSd30F6CywdsHY0QJZd2E4BsF++8Np30dIv7ZxdJt06n49KlS9SoUQMwDjWfN28eR44cYeXKldSsWdPGEQohRPGup8gv5cI6srOzuXbtWpHHr127RnZ2yYoDeXt7o1KpiI+PN2uPj4/Hz8+v0Nf4+fkVe37u/m6u6e3tTd26denevTvLli1j7dq17N69u9D7jh8/nqSkJNN28aJM57AaR2eom1Pc9r/fbBuLEPciwzgyRq/2Qm/IWYpML8XUxP2zi6RbpVLx8MMPc/PmTVuHIoQQ9+SqtgyvtyLKlUaNGvF///d/RR7/559/aNSoUYmu6eTkRKtWrdi4caOpTa/Xs3HjRtq3L7xgVvv27c3OB9iwYYPp/ODgYPz8/MzO0Wq17Nmzp8hr5t4XjMPIC6NWq/H09DTbhBU1ftK4P7YKZISPsDfZxn9XDA4uZJGztJgMLxcWYBdJN0Djxo05d+6crcMQQoh7ok2X4WnCOoYOHcqHH35oVjU81x9//GGqh1JSY8eOZcGCBSxZsoTjx48zYsQIUlJSGDJkCACDBg0yrQcOMHr0aNatW8fMmTM5ceIEH3zwAfv37+e1114DjEuajRkzhqlTp7JmzRqOHj3KoEGDCAgIoG/fvgDs2bOHuXPncujQIS5cuMCmTZsYOHAgtWvXLjYxFzZUpxs4ayD5CsQUXhBPiDIrOx0Ag4OabEm6hQXZzTrdU6dO5c033+TDDz+kVatWuLm5mR2XX7KFEGVZlk6P1FoW1jB8+HC2bdtGnz59qF+/PvXqGdeEP3HiBKdOnaJ///4MHz68xNcdMGAA165dY9KkScTFxdG8eXPWrVtnKoQWExODMt+yOh06dGDp0qVMmDCBd999l5CQEFavXm1aoxtg3LhxpKSkMHz4cBITE+nUqRPr1q0zrdHt6urKqlWreP/990lJScHf358ePXowYcKEEi95JqzEQQ0NHoWDP8KxXyGoo60jEuLu5fR04+Ccl3TrdUWfL8Rdspt1uvN/kCsUCtNjg8GAQqFAp7Pe/xCy5qcQoqRqv7sW13pvm54fHXzUhtEIW7LWZ8gvv/zC0qVLOX36NAaDgbp16/LMM8/Qv3//UrtnWSSf2TZwdhP88Di4VoE3ToJKfnIUdmLDJNgxm5vNhqM7tAxvhRZG7ALfhraOTNiAJT8/7Kane/PmzRa71tdff83XX3/N+fPnAeP8t0mTJtGzZ0+L3UMIIXLp9QZ0+rzfNx2UdvNPr7Bj/fv3r3AJtigjgh4Et6qQcg3ObYWQsDu/RoiywNTTnW94uazTLSzAbr75PfTQQxa7VvXq1Zk2bRohISEYDAaWLFnCY489xsGDB0tcXEYIIe4kS68H8goKOSql10cIUY6pHKBhX9i3AI6tlKRb2I+cOd3mw8ulerm4f3ZTSA3g33//5bnnnqNDhw5cvnwZgB9++IHt27eX6DqPPvoojzzyCCEhIdStW5ePPvoId3f3IpcfEUKI+5GlM4Ay75dytUrmogohyrncKuYn/oSsdNvGIsTdyj+n25BbSE2SbnH/7CbpXrlyJeHh4bi4uBAZGWlaKiQpKYmPP/74nq+r0+lYtmwZKSkpRVZCzcjIQKvVmm1CCHG3snV6FIq8pFuGlwshyr3AUPCsblz3+MwGW0cjxN3JSjPuZXi5sDC7SbqnTp3KvHnzWLBgAY6OeUMzO3bsSGRkZImvd/ToUdzd3VGr1bzyyiv89ttvNGxYeJGEiIgINBqNaQsMDLzn9yGEqHgydXrIl3TbSf1KIYS4d0olNH7c+PjYStvGIsTdSI7PW+bO2UuGlwuLspuk++TJkzz44IMF2jUaDYmJiSW+Xr169Th06BB79uxhxIgRDB48mKioqELPHT9+PElJSabt4sWLJb6fEKLiytIZUOQbXq436Is5WwjLuHbtWpHHjh6V6vnCCnKHmJ9cBxnJto1FiDtZ9w7cioeq9ckKeSTfOt2SdIv7ZzdJt5+fH2fOnCnQvn37dmrVqlXi6zk5OVGnTh1atWpFREQEzZo1Y/bs2YWeq1ar8fT0NNuEEOJuZd/W060zyJqfovQ1adKEv/76q0D7jBkzaNu2rQ0iEhWOf3OoUgey0yBqja2jEaJ48ceM+4c/ArWbDC8XFmU3SfewYcMYPXo0e/bsQaFQcOXKFX766SfefPNNRowYcd/X1+v1pnniQghhSVk6vVlPtwwvF9YwduxYnnzySUaMGEFaWhqXL1+mW7duTJ8+naVLl9o6PFERKBTQ7Gnj48M/2zYWIe4k7aZx7+GHSqEgKyfpNugybRiUKC/spprPO++8g16vp1u3bqSmpvLggw+iVqt58803GTlyZImuNX78eHr27EmNGjVITk5m6dKlbNmyhfXr15dS9EKIisxYvTzvQ1t6uoU1jBs3ju7du/P888/TtGlTbty4QWhoKEeOHMHPz8/W4YmKounTsGkqnP8XEmPAq4atIxKiIIMhL+l2qYRSoUCXk3Trs0193kLcM7vp6VYoFLz33nvcuHGDY8eOsXv3bq5du8aHH35Y4mtdvXqVQYMGUa9ePbp168a+fftYv3493bt3L4XIhRAVnbGnOy/pljndwlrq1KlD48aNOX/+PFqtlgEDBkjCLazLKxCCHjA+PrLctrEIUZTMW3kF01wro1QqyMpZMkyvk+Hl4v7ZTU/3jz/+yBNPPIGrq2uRVcbv1sKFCy0UlRBC3FnWbXO6JekW1rBjxw6ee+45KleuzJEjR9ixYwcjR45k7dq1zJs3j0qVKtk6RFFRNH/G2NN9eBk88KZx2LkQZUnqDePewRkcXVDqsvIVUpOkW9w/u+npfv311/Hx8eGZZ55h7dq16HQyPFMIYR+M1culp1tYV9euXRkwYAC7d++mQYMGvPTSSxw8eJCYmBiaNGli6/BERdLgUXB0hetn4NJ+W0cjREH5hpYDqJQKU9Ktl+rlwgLsJumOjY1l2bJlKBQK+vfvj7+/P6+++io7d+60dWhCCFGsLJ1e5nQLq/vnn3+YNm0ajo6OprbatWuzY8cOXn75ZRtGJioctYcx8QYpqCbKprScnu6cpFupyEu6pZCasAS7SbodHBzo3bs3P/30E1evXuXzzz/n/PnzdOnShdq1a9s6PCGEKFKWTo9Cdcv03IBBKpiLUvfQQw8V2q5UKpk4caKVoxEVXrOBxv2xlZAtq8WIMubGOeNeEwjcnnRLT7e4f3Yzpzs/V1dXwsPDuXnzJhcuXOD48eO2DkkIIYqUpTOgdEwya9Mb9KgUUg9VWNacOXMYPnw4zs7OzJkzp8jzFApFiVf+EOK+BD8IHgGQfAVO/AmNn7R1RELkuXrCuPepD5gPLzfInG5hAXaVdKempvLbb7/x008/sXHjRgIDAxk4cCC//vqrrUMTQogiGYeXm/fs6A16VLIIibCwzz//nGeffRZnZ2c+//zzIs+TpFtYnVIFLZ6FbZ/CgSWSdIuyJeGUce9dDwClArINknQLy7GbpPvpp5/mzz//xNXVlf79+zNx4kTat29v67CEEOKOYhPTUTldM2vTI8XUhOVFR0cX+liIMqHlINg2A6K3wvWzUEWmB4oyIjnWuNdUB4w/TGbnjEYz6CXpFvfPbuZ0q1QqfvnlF2JjY5k7d65Zwn3s2DEbRiaEEMVbGXkJhaPWrE2nl2JqovRkZWVRu3ZtmX4lyhavGlAnzPg48nvbxiJEfsnxxr2Hv6kpO7dvMlvmdIv7ZzdJ908//cQjjzyCSmX81Sk5OZn58+fTtm1bmjVrZuPohBCicAaDgeiEW2brdIMsGyZKl6OjI+np6bYOQ4iCWr1g3B/6CbKlKrQoAzJTICOn7oqHn6lZnzunWy9Jt7h/dpN059q2bRuDBw/G39+fGTNm0LVrV3bv3m3rsIQQolBZOgMZukwUCvNq5TK8XJS2V199lU8++YRs6aURZUndcHD3g5RrcHKtraMRApLjjHtHN+PydjlMw8tlTrewALuY0x0XF8fixYtZuHAhWq2W/v37k5GRwerVq2nYsKGtwxNCiCKlZepQKAv25uj1knSL0rVv3z42btzIP//8Q5MmTXBzczM7vmrVKhtFJio0lSO0eA7+nQEHFkOjvraOSFR0uUm3hy8oFKZmvSInTZI53cICynxP96OPPkq9evU4cuQIs2bN4sqVK3zxxRe2DksIIe5KalY2KIxJt5PSydSuM8icblG6vLy8ePLJJwkPDycgIACNRmO2CWEzLZ8HFHBuc976yELYyq3cpNvfrDl3eDnS0y0soMz3dP/999+MGjWKESNGEBISYutwhBCiRPL3dLs4upCdmY3eoJc53aLUfffdd7YOQYjCVQqC2l3h7EbYtxDCP7J1RKIiM/V0+5k1Z+f0dBt0MkVH3L8y39O9fft2kpOTadWqFaGhocydO5eEhARbhyWEEHclNVMHSuOv5C4OLihz/tk1YCjuZULct65du5KYmFigXavV0rVrV+sHJER+oS8b95E/QMYt28YiKrbc5cJu7+mW4eXCgsp80t2uXTsWLFhAbGwsL7/8MsuWLSMgIAC9Xs+GDRtITk62dYhCCFGktKy8nm5nlTOKnPli0tMtStuWLVvIzCxYTyA9PZ1///3XBhEJkU+d7lC5trFq9OGfbR2NqMiK6OnW5Q4vl+rlwgLKfNKdy83NjaFDh7J9+3aOHj3KG2+8wbRp0/Dx8aFPnz62Dk8IIQqVlqkzzel2cXBBqTD+sytJtygtR44c4ciRIwBERUWZnh85coSDBw+ycOFCqlWrZuMoRYWnVEK7EcbHu78GKS4pbCU36XY3T7pNPd0yvFxYQJmf012YevXqMX36dCIiIvjjjz9YtGiRrUMSQohCpeaf0y1Jt7CC5s2bo1AoUCgUhQ4jd3FxkYKkomxoNhA2fgg3zsLpf6BeD1tHJCqipEvGvWeAWbNOkdvTLcPLxf2zy6Q7l0qlom/fvvTt29fWoQghRKHOJdxC5Wqszuvj6sPJmycBMBhkTrcoHdHR0RgMBmrVqsXevXupWrWq6ZiTkxM+Pj6oVCobRihEDrU7tBoEO7+A3V9J0i2sz2AA7RXjY011s0PS0y0sya6TbiGEKMv2nb/BjPUncal1AoA+tfuw4/IOAPRIT7coHTVr1gRkLXhhJ9q+DLu+guitEP8f+DaydUSiIklJAF0GoCjQ052dmybpC9bGEKKkJOkWQohS8n9R8egN4OiUgQ4I9AiUQmrC6qKiooiJiSlQVE3qoYgywSsQGjwKUathxxx44htbRyQqEm3O0HJ3X1A5mh1KUboDoMpIsnZUohyym0JqQghhb2KT0gE9OtIA8HDykDndwmrOnTtHs2bNaNy4Mb169TJNx3r88cd5/PHH7+maX375JUFBQTg7OxMaGsrevXuLPX/FihXUr18fZ2dnmjRpwtq1a82OGwwGJk2ahL+/Py4uLoSFhXH69GnT8fPnz/Piiy8SHByMi4sLtWvX5v333y+0KruwYx1HG/dHV8CNaNvGIiqW3PncmoLFJZNVGgAc0m9YMyJRTklPtxBClJLEtCxQZpieS9ItrGn06NEEBwezceNGgoOD2bt3L9evX+eNN95gxowZJb7e8uXLGTt2LPPmzSM0NJRZs2YRHh7OyZMn8fHxKXD+zp07GThwIBEREfTu3ZulS5fSt29fIiMjady4MQDTp09nzpw5LFmyhODgYCZOnEh4eDhRUVE4Oztz4sQJ9Ho933zzDXXq1OHYsWMMGzaMlJSUe3oPooyq1hJqd4Wzm2DHbHh0lq0jEhWFNmeN7tuGlgNolZ4AOCdfgOm1c1oNxnngBn3OY0ABKJTGDUXeY4USFPmeK1WgdDBuCpX5c6XqLo7n2ytUhbfnPlYUc22lQ048DoWfU+xrb7+2g3ElAoXK/L0W+LNQGLcKTJJuIYQoJUmpmShUxl5utUqNk8oJBTK8XFjHrl272LRpE97e3iiVSpRKJZ06dSIiIoJRo0Zx8ODBEl3vs88+Y9iwYQwZMgSAefPm8ddff7Fo0SLeeeedAufPnj2bHj168NZbbwHw4YcfsmHDBubOncu8efMwGAzMmjWLCRMm8NhjjwHw/fff4+vry+rVq3n66afp0aMHPXrkFdeqVasWJ0+e5Ouvv5aku7x54E1j0n3oJ3hoXKFJkBAWp71s3HsW7OmOUwYQrfclWBkPqQlWDqycKjYpz7cv7scLhaKI195+3dv3xfwYYGpXmO/TdBZ765J0CyFEKTAYDMQmpaNQpgPg7pgzNyxnCRIDUr1clC6dToeHhwcA3t7eXLlyhXr16lGzZk1OnjxZomtlZmZy4MABxo8fb2pTKpWEhYWxa9euQl+za9cuxo4da9YWHh7O6tWrAWOV9bi4OMLCwkzHNRoNoaGh7Nq1i6effrrQ6yYlJVG5cuUSxS/sQFBHqNEeYnbBzrnQ42NbRyQqguScnm4P/4LHlA48njmFxY950zzYr5CEL+cxOT3fBn1eL7ipJzznsV4PBh3odaDPztl0OW35npsdz855bXbh55hee/v+Xq992/PC4s3/3KDLeZ8lYPqzsRMZlvuuJkm3EEKUgos30rianIGTu3F4uYeTMfmRQmrCWho3bszhw4cJDg4mNDSU6dOn4+TkxPz586lVq1aJrpWQkIBOp8PX19es3dfXlxMnThT6mri4uELPj4uLMx3PbSvqnNudOXOGL774othe7oyMDDIy8qZ1aLXaIs8VZcwDb8JPT8L+RcZ53h6+d36NEPcjd7mwQkZWKJUKEvFAW6UZ+FUtcFyQ92NCYT86mH54KOJHidt/mDDcdi63nWs6ZrjD8fzHbt8Xdd+c/e3TB5JTYdrLFvmjkqRbCCFKwZ7o6wAE+6i4Ql7SnTunW9bpFqVtwoQJpKSkADBlyhR69+7NAw88QJUqVVi+fLmNoyu5y5cv06NHD/r168ewYcOKPC8iIoLJkydbMTJhMXW6QfU2cGkfbPsUeskUAlHKiku6c6Yg6+TzumhKJeW6LrdWC1gm6S7Hf0pCCGE7e6NvAHrUmmMAeKqNBVlyk26dwXLzhIQoTHh4OE888QQAderU4cSJEyQkJHD16lW6du1aomt5e3ujUqmIj483a4+Pj8fPz6/Q1/j5+RV7fu7+bq555coVunTpQocOHZg/f36xsY4fP56kpCTTdvHixTu/QVE2KBTQbZLx8YHFcPO8LaMR5Z3BUOzwclVO1q3XS9It7p8k3UIIUQr2nb+Bg+dhotO3oVQoeab+MwBSSE3YVOXKlU1THErCycmJVq1asXHjRlObXq9n48aNtG/fvtDXtG/f3ux8gA0bNpjODw4Oxs/Pz+wcrVbLnj17zK55+fJlOnfuTKtWrfjuu+9QKov/6qJWq/H09DTbhB0JfhBqdQF9FmyZZutoRHmWngRZqcbHhfR0500Hs2ZQoryS4eVCCGFhaZk6zl9PRe1jrIrar24/Hqz+IJBveLkUUhOlZOjQoXd13qJFi0p03bFjxzJ48GBat25N27ZtmTVrFikpKaZq5oMGDaJatWpEREQAxiXLHnroIWbOnEmvXr1YtmwZ+/fvN/VUKxQKxowZw9SpUwkJCTEtGRYQEEDfvn2BvIS7Zs2azJgxg2vXrpniKaqHXZQD3SbBuc1weBl0GAW+DW0dkSiPcoeWu1QCR5cCh1W5w8sl6xYWUCGT7oiICFatWsWJEydwcXGhQ4cOfPLJJ9SrV8/WoQkhyoHoBOM8WrVzMgagpmdN0zFZp1uUtsWLF1OzZk1atGhh0doBAwYM4Nq1a0yaNIm4uDiaN2/OunXrTIXQYmJizHqhO3TowNKlS5kwYQLvvvsuISEhrF692rRGN8C4ceNISUlh+PDhJCYm0qlTJ9atW4ezszNg7Bk/c+YMZ86coXr16mbxSF2EcqxaS2j4GET9Duvfhed/q/Br/IpSkJyTdHsUvjxd7vBy+bdGWEKFTLq3bt3Kq6++Sps2bcjOzubdd9/l4YcfJioqCjc3N1uHJ4Swc2ev3QLA2fkWaYCva14FXkm6RWkbMWIEP//8M9HR0QwZMoTnnnvOYktsvfbaa7z22muFHtuyZUuBtn79+tGvX78ir6dQKJgyZQpTpkwp9PgLL7zACy+8cC+hCnsXNhlOrjP2eJ9cC/V72ToiUd5oc+ZzF7EmfO7wcimkJiyhQs7pXrduHS+88AKNGjWiWbNmLF68mJiYGA4cOGDr0IQQ5cCxy0nGBw7GvY+rj+mYVC8Xpe3LL78kNjaWcePG8ccffxAYGEj//v1Zv369/L0T9qNyMHTI+YFn3XjISrdtPKL8MVUuL2SNbkAlc7qFBVXIpPt2SUnGL8ZF9QRkZGSg1WrNNiGEKMqe6BsoHLSkG24AEOCe9yt63i/nUr1clB61Ws3AgQPZsGEDUVFRNGrUiP/9738EBQVx69YtW4cnxN3pNNY49DfxAuz6wtbRiPLmDsPLc2fLSPVyYQkVPunW6/WMGTOGjh07ms0zyy8iIgKNRmPaAgMDrRylEMJepGRkc+xyEg4eRzGgp3nV5uY93UhPt7AupVKJQqHAYDCg08mPPcKOqN2he87Ug20z4Ua0beMR5csdhpcrTT3d8nkt7l+FT7pfffVVjh07xrJly4o8R9b8FELcrYMxiWTrDXh4xgHQPsB8OSXTnG5kTrcoPRkZGfz88890796dunXrcvToUebOnUtMTAzu7u62Dk+Iu9fkKQh6ALLT4I9RxrWVhbAE0/Dy4pNuqV4uLKFCFlLL9dprr/Hnn3+ybdu2AlVR81Or1ajVaitGJoSwV3ujrwPg4BZDlgGaeDcxO5637qck3aJ0/O9//2PZsmUEBgYydOhQfv75Z7y9vW0dlhD3RqGAPnPgqw4QvQ0il0CrF2wdlSgPTMPLi5jTrZSebmE5FTLpNhgMjBw5kt9++40tW7YQHBxs65CEEOXE7nM3UKhSSDPEA9C0alOz4zK8XJS2efPmUaNGDWrVqsXWrVvZunVroeetWrXKypEJcY8q14KuE+Cf9+CfiVCnO2iq2ToqYc+yMyDV+CN50T3dxr10dAtLqJBJ96uvvsrSpUv5/fff8fDwIC7OOAxUo9Hg4uJi4+iEEPbqZkomB2JuonS5BECQZxAatcbsnNx1jKWnW5SWQYMGmUZUCFFutBsB//0Gl/fD6hHw/Oq8SldClFRyznxulRpcKhV6igwvF5ZUIZPur7/+GoDOnTubtX/33XeyHqgQ4p79fugyOr2BAJ94kik4tBzyerplTrcoLYsXL7Z1CEJYnlIFfb+C+Z0heivs+BweeMPWUQl7ZSqi5m+cwlCI3OHlMjJNWEKFTLrlfx4hhKVdvJHKF5vOAODhdZbkdGhWtVmB80yF1KSnWwghSqZqPeg5Hda8Bps+gpqdoEaoraMS9ugOy4WB9HQLy6qQSbcQonzS6w1k6w3o9Aay9fqcvSFvr8trz8p5nKUzkK0z7rP0erJzn+sNZGXriz0nU2cgM1vPhespbD+TQEa2njoBGVxJP4lSoaRbzW4FYpRCakIIcR9aPGfs6T66An4dCsO3gHtVW0cl7E3+nu4iKE2F1KwRkCjvJOkWohwpSdJp1p6TSBbanvtcV0S72fFC2nPvV+D6+kJeX1R8Oe26Itpzntt6EEurmpVo1/IA35+ANn5t8HYpWDE6t6dbRtwIIcQ9UCig12dwORJunIVfnodBv4ODrDIjSiB3TncRlcshfyE1+bwW90+SbiEKYTAYyMjWk5GlJyNbR0a2nkyd8XmmTk9mtrE9M9v4OPdYhk5Pts6YwN7ea5rbU5qd2643P2Z8jaHA67N0+rxzb3t9WUs6yyqVUoFKqcDBbK/ESaXAQaXEQaXAUanE0UGBg1KJoypn76DEUanAIec8R6UCR5XS+FiVd66/xpkWNSpRz9+ZXqsmAvBY7ccKjcU0p1t6uoUQ4t44e8LAZfBtGMTsgj/HwmNzi5ybK0QBd1ijG0ClkCXDhOVI0i3KPZ3eQGxSGheup3ItOYOEWxlcT8nk+q0MbqRkkZKRTUpmtnGfoSMlM5vUTF25msPjcHvSqVIWkoQak0hVTpJZaLvp9cakteDrc9oLvP6281VFtN/x+ncRn8q8XanAapWcvzn8DVfTruLj6kN4UHih55iGl0shNSGEuHdV68JTi2BpPzj0I1QOggffsnVUwl6Yerr9ijxFYZrTbY2ARHknSbcoV/R6A1GxWvadv8HBmESiYrXEXE8l8z7+xVQoQO2gxEmlxMlBhdpBaXyeb298rMJRZewJdVQZE8bbe0Rzk0lHs2Pm5znkXMPB1Kua//W5181rK0tJZ0VlMBhYdXoVXx3+CoAxLcfgpHIq9FyVQmV6jRBCiPsQEgbhEbDubdg0FZy9oO0wW0cl7IH2zoXUVDkr0klPt7AESbqF3TMYDOy/cJM1h66wISqeOG16gXOcVEqqV3bBX+NMZTc1Vdyc8HZ3opKbE+5qB9ycHHBTO+CudsBVrTLunVQ4O6pwUCokaa2AdHodKdkp3Mq8xa2sW6RkpZCcmVxgfyP9Bvvi9hGTHAPAkyFP0rtW7yKva/rl3KCzyvsQQohyrd0rkHodtk2HtW+Ckzs0H2jrqERZZjBAcpzxcXGF1HJHppWjkY/CdiTpFnYrS6fnt8jLLNoRzYm4ZFO7m5OKNsGVaVmjEs0Cvajl7UaAl4tpvUVRvhkMBlKzU7mVmZMoZyWTkpmzz5cw38q6ZUqobz/3VtYtUrNTS3RfFwcXhjcdztDGQ4v9kUYKqQkhhIV1eRcytLBnHqweAVmp0OZFW0clyqrUG6DLMD4urpCaMvdHcvm8FvdPkm5hd/R6A38ejeXzDaeITkgBwNlRSe+mATzSxI8Otb1xdlTZOEpRUgaDgQxdRpFJsFmSnO+xWSKdeYuU7BSLFilzUjrh7uSOu6M7bo5ueDh54Obohruju6m9UZVGtPVvi4eTxx2vJ4XUhBDCwhQK4zBzvQ72LYC/xkJ6InQaK8XVREHay8a9q3exVe/zCqlZIyhR3knSLezKybhkxq86QmRMIgBV3JwY/mAtnm5TA42ro22Dq8Cy9FnFDsO+PWEuquc5W59tsZhUClWRSXL+x/nPMe0dPXBzMr6mqLnZ90oKqQkhRClQKuGRT8HFC7Z9ChunQOJF6DkdHCz777iwc4nG6WBUqlnsaaYlwyTrFhYgSbewC+lZOr7YdJpvtp4jW2/AzUnFKw/VZkinYNzV8tf4XuXOWy4sCS5uGPbtvc0ZucO0LECBAjdHt2ITZjennMTY0a3QRNrdyR1nlXOZnIsvw8uFEKKUKBTQdQK4VIL178GB7+DaCej/Pbj72Do6UVbkJt1eNYo9LXd4uRRSE5Yg2Yoo807GJTPy50hOxd8CILyRLx/0aYS/xsXGkdnO/cxbzt9W0nnLd+Li4JKXJN+hN9ksWc53rqujqykxLY9y35sMLxdCiFLS/lWoUgdWvmRcx/ubB6HvV1C7q60jE2XB3SbdCpnTLSxHkm5RZhkMBn7cE8PUP6PIyNZT1UPN1L6NCW9U9JqK9iJDl0FyZjLaTC3Jmclm2+1tt89ftsa8ZbNe5cIS5kISaldHVxyVMsT/TnKTbqleLoQQpahuOAzbBMuegYRT8MPjEPoKhH0AjhX3R3vBXSfduQV4JecWliBJtyiTElMzGffrEf6Jigegc72qzOjXDG/3ogteWFtadhpJGUkkZiQat/REtJnaQhPp25PpTH2mRWJQKVRFJsi3D7cukECX4rxlUbTcQmoyvFwIIUqZdwgM3wobJsK+b43VzU+tM87zrhtu6+iErZiS7uLndOfOUNPJnG5hAZJ0izJn97nrvL78ELFJ6TiqFLzTswFDOgSZ5taUBoPBwK2sW1xLu8b1tOskpCVwPe06NzNukpSRxM30m+YJdkbifc9jVqDAw8kDDycPPJ08TY/NNkePYhPnsjpvWRTNVEhNhpcLIUTpc3KFXjOhbg9YMwpunoel/aFuT+g+GarWs3WEwtrutqc7d3i5JN3CAiTpFmVGtk7PnI2nmbv5DHoD1PJ2Y87AFjSuprm/6+qzuZp6lSu3rhCbEktsSixXU6+SkJZglmTfSxLtoHDAy9kLL7UXGrUGjZMGT7VnwWTa0aNAW3mfuywKZyqkhnyICyGE1YR0h9f2wtbpsPsrOPU3nF4PTfrBQ29Dldq2jlBYQ8p1yEgCFKAJLPbU3DndMjJNWIIk3aJMuHQzldHLDnHgwk0AnmpVncl9GuF2l5XJM3QZXNBe4HzSec5rz3M+6TyXb102Jdh3O3/W3dEdbxdvvF28qeJSBS+1MaGu5FwJjVpjep67uTm6SU+zKBEppCaEEDai9oCHP4QWzxmXFDvxJxxZDkdXQL1HIPRlCHpA1vYuz65GGfeVgoyjIIqRO8JSCqkJS5CkW9jcX0dieWfVEZLTs/FQOzD18cY81rxaoefqDXpitDGcuHGC4zeOc+rmKaKTorly60qxPYcOSgf83fwJcAvA390fX1dfqrpUNSXXuXsXBymuIkqXAhleLoQQNlW1Hjz9E1w5CJsjjD3eJ/40bj4Nofkz0Pgp8PS3daTC0nKTbp+GdzxVZZoOVpoBiYpCkm5hMwm3Mpj8RxR/HL4CQIsaXsx5ugWBlfN+ebyRfoODVw9yMP4gRxKOcPLGySKXufJw9CBIE0SwJpggzyCqe1Q3JtruAXi7eMtQblEmSE+3EEKUEQEt4Nlf4OoJ2DsfDv9sTMr+mQD/TITgB6HhY8aia5rqto5WWIIp6W5wx1NzSwnpJesWFiBJt7A6vd7AyshLfLT2OImpWSgVMKJzbcaE1SU1O5l10evYFbuLyPhIzmvPF3i9WqWmbqW61K9cn/qV6xOsCSZYE0wV5yoy1FuUeZJ0CyFEGeNTH3p/Bt0mwrGVcGQFXNwN0VuN21+ATyOo+7Bx+HlgW+NQdWF/4v8z7u8m6Vbm9nRL0i3unyTdwmoys/VsOnGV2RtPczxWC0A9P3dGhrtxKXMLQ9d/xJGEIwWSkdqa2rTwbUELnxY0rNyQIE0QDkr5qyvskynpRpJuIYQoU1wqQZuXjNvN83BsFZxaD5f2wtX/jNv2z0GhBL8mUKODsbfcr4lxeTKVo63fgShOdgbEHjE+Dmhxx9OVpurlpRmUqCgkcxGlymAw8N8VLSsjL/H7oSvcSMkEDHh4JNC60UUSDHsZv/e82Wtqa2rTsVpH2vi1oXnV5ng5e9kidCFKRW7SnamzzFrtQgghSkGlIHhgrHFLvQFn/g/ObISYncYlp2IPG7dcKrWx99S3sbESeuVaeXsnN5u9DZFP7BHQZYBrFeN/lztQ5cxKlOrlwhIk6RalIuFWBqsPXubXA5c4EZdsbFSmUcnvGB5V93Mz+wL7k4zNTkonOlTrwIPVH6RTQCf83aVwiSi/jl8/DsC3R79ldMvRNo5GiJL58ssv+fTTT4mLi6NZs2Z88cUXtG3btsjzV6xYwcSJEzl//jwhISF88sknPPLII6bjBoOB999/nwULFpCYmEjHjh35+uuvCQkJMZ3z0Ucf8ddff3Ho0CGcnJxITEwszbcoREGulaFpf+MGkHQZYnbBxT0QdxTijkFmMsQeMm63c/cDr0Dw8AfPasYCbZ7VjM89/MDNG9SeUjW9tJ3dZNzX7HBXf9amnm5JuoUFSNItLCZ3+PivBy6x5eRVsnMKT6hdL1M9KJKbiv1k6TO4mQ2OSkc6VutIeFA4nat3xt3J3cbRC2EdRxKOmB4bDAapQyDsxvLlyxk7dizz5s0jNDSUWbNmER4ezsmTJ/Hx8Slw/s6dOxk4cCARERH07t2bpUuX0rdvXyIjI2ncuDEA06dPZ86cOSxZsoTg4GAmTpxIeHg4UVFRODs7A5CZmUm/fv1o3749CxcutOp7FqJQmmrQ5CnjBqDXQ+J5Y/J99TjcOAs3zsH1s5B2A27FGbfiKB2MPbCmrbJx71IZnD2NSbnaA5w1xr3aI69N7QFKVam/bbt3ap1xX6f7XZ2eN7xckm5x/xQGGTNRYlqtFo1GQ1JSEp6enrYOx6Zyh4//euASvx+6zM3UrJwjekJqXsKxyjYuph0znV/Hqw5P1X2K3rV6o1FrbBO0EDbU9qe2pGWnAfD3E39T3UMq4lY09voZEhoaSps2bZg7dy4Aer2ewMBARo4cyTvvvFPg/AEDBpCSksKff/5pamvXrh3Nmzdn3rx5GAwGAgICeOONN3jzzTcBSEpKwtfXl8WLF/P000+bXW/x4sWMGTOmxD3d9vrnLcqJtJvGBDzpMiTHgvYyaGNBewWSr0ByPGSl3P99nNyNm6OLcTi7o0vOlvPYyRUcXYtuc3ABB3W+zdk4ZL5Am5N99sjHHYN5HY0/brweBR6+d3zJ4h3RfPBHFL2a+PPlsy2tEKQoayz5+SE93eKeXL+VwW+3Dx8Hqno40rLhBS7q/+DirWhIAweFAz2Ce/B0/adp6t1UevZEhaZWqU1J9/R905nTdY6NIxLizjIzMzlw4ADjx483tSmVSsLCwti1a1ehr9m1axdjx441awsPD2f16tUAREdHExcXR1hYmOm4RqMhNDSUXbt2FUi6hbBLLpWgWivjVpSsNOO88dTr+bac52k3IF0LGcmQoTVu+Z/n1gfJvGXcrCF/Mq7Kl5A7OOUl5rnPVWrjc5VjzuaUt1fmb3MClUO+x445x51ue12+85W3nW+6roP5DwNZabA+59+u+r3uKuEGUCmlp1tYjiTd4q4ZDAZ2nbvO0j0xrP8vjiyd8R8hJ5WSsIY+1K11gS3xC9iZdBYAN0c3+tXtx7MNnsXPzc+WoQthZDAYv6Bkp0NWunGfu2WlQ3aasbppVs4+O62I83K3DOMXKu+6xrl+rpVLFM7RhKOl9EaFsKyEhAR0Oh2+vuZfVn19fTlx4kShr4mLiyv0/Li4ONPx3LaizrkXGRkZZGRkmJ5rtdp7vpYQVuHoYhyyrqlW8tdmZxgT8PQkyEyBrNScLQ0yU/M9v70tzdjDntuW+5mmy8j5/EuH7JzPS12G+T11OedlFB5SmWBK6h0g4xYYdMYfArpOuutL5HYSyZJhwhIk6RZ3dDMlk5WRl1i6N4Zz1/KGQDWtrqFf60B8fc6zMGo6C09FAeDh6MHgRoN5psEzeDjJOpbiLul1xi8Cmbd/Qcj5UlBoW0ohXyLyP86XPOcm05TSh+e6t0GtgZAweHCccd3X213chyFf1fJGVRqVTixCVGARERFMnjzZ1mEIYR25Pc5u3qV3D9MP1vkS8twfsHPbCkvWs9NBlwX6LOP5uvz7fI/1RbTrMnOO5bZlgi4777E+3+Pb5bbnznr08Ie+X4F3nbt+2ypZp1tYUIVMurdt28ann37KgQMHiI2N5bfffqNv3762DqtMMRgM7Dt/k6V7LrD2WByZ2cZFCt2cVDzWohrPtK2Bm/sNZuyfzrZ/twHg4uDCcw2eY3CjwTJfu7wyGIwfohk5w9gyU/KGtGXkPk8xVnHNTMlJhgtLjG9vSy38Q7O0ObiAo3POMLicrdDnOXPdHHPnvOXsVU6QmgCn/oH4o5CRBMdWwom/4NkVEPyg+f0WhmEIrAYOxoI3Gbf3HghRRnl7e6NSqYiPjzdrj4+Px8+v8JFMfn5+xZ6fu4+Pj8ff39/snObNm99zrOPHjzcb1q7VagkMDLzn6wlR4SkUecl9WWQw5CTgRSTsji7GpLuExeZUpp7u0ghaVDQVMulOSUmhWbNmDB06lCeeeMLW4ZQpyelZrIq8zI+7L3D6at7coEYBnjwbWpM+zQPQK1L55vA3LD2+lGxDNg4KB56u/zQvNXmJKi5VbBi9KJReX/g8sPSctsx8yXJGcr5EOqWQZDpniFapUuQVdjEVecnZnG4rAmNWMOa2wjAOzjlJsnPBZNnRxbLFYLpOhJvRxoI4W6fBuS2w7Fl4cUNej7fBwIbEMfROrM/ypp+iU2XJWt3Cbjg5OdGqVSs2btxo+pFar9ezceNGXnvttUJf0759ezZu3MiYMWNMbRs2bKB9+/YABAcH4+fnx8aNG01JtlarZc+ePYwYMeKeY1Wr1ajVZTQ5EEJYnkKRN6cbV4teFmROt7CMCpl09+zZk549e9o6jDIlOiGFJTvP8+uBS9zKyAbAxVFFn2YBPBNag6bVNegNeladWcUXkV9wM+MmAA9Wf5A3W79JsCbYluGXb3qdca5W2k1jYZW0m8YtPcnYs5quNU+iM5LN2zKT73yPe+HoZkx41e7GfW7lVNPjnOOFJcuFVlF1y0uS7a3YnkIBlWsZt4HL4ccn4MIOWDMShvwN698F7xBOpT+EJ1AjsSHRVQ6Tpc+646WFKCvGjh3L4MGDad26NW3btmXWrFmkpKQwZMgQAAYNGkS1atWIiIgAYPTo0Tz00EPMnDmTXr16sWzZMvbv38/8+fMB43zJMWPGMHXqVEJCQkxLhgUEBJiNPouJieHGjRvExMSg0+k4dOgQAHXq1MHdXZabFEKUDhleLiypQibdwkivN/DvmQQW74hm88lrpvbaVd0Y3CGIvi2q4ensCEBkfCQReyM4ccNYMCdYE8y4NuPoVK2TTWK3W3qdMXFOuZZvSzBWJ81NpvMn1mk3jMm1JajUeWt95l/zU+1RMGkuKpHObXd0A6XSMnGVN47O8MQC+DIULu2F316GY7/mHPwNAKXB+GcnKzYKezJgwACuXbvGpEmTiIuLo3nz5qxbt85UCC0mJgZlvn8XOnTowNKlS5kwYQLvvvsuISEhrF692rRGN8C4ceNISUlh+PDhJCYm0qlTJ9atW2daoxtg0qRJLFmyxPS8RYsWAGzevJnOnTuX8rsWQlRUSimkJiyowq/TrVAo7jinu7BKqIGBgXa75md6lo4V+y/y3c7zpsJoCgV0refDCx2D6FTH21Sx8VrqNT478Bl/njOus+rh5MGrzV+lf73+OCodbfYeypx0bd6am9orxjU4b0+sU64Zl/+410Jeak9w8TJWy3apBM6anARaY55I50+o8x8rq3Oxyqu9C2Dtmxj0cH6DN+k3ndjU+UsA3DudYYbuCxpVacSy3stsHKiwNlk32rrkz1sIcS/WHL7CqJ8P0q5WZZYNb2/rcIQNyDrdVlZeKqFq07P4cfcFFm2PJuGWcS6ph9qBfq0DGdS+JkHebqZzs/RZ/Hz8Z746/BUpWSkoUPBEyBOMbjmaSs6VbPUWrM9gMPY8J12E5FjQXs5LqrWXc9qulHxtTJfK4FY1Z6sCrlWMbbkJtWu+xy6Vjcm2Sn7ksCttXoKTf5O261/SbzqZHfJSeYIO9Aa9jYITQgghRHFyRpdLITVhEZJ03wV7r4R6MyWThdujWbLrPMnpxvna1bxcGPZAME+1DsRdbf7XYF/cPj7e8zFnEs8A0MS7Ce+Gvktj78YFrm339Hq4FQeJF42JdWJMzv5i3j4r5c7XAWOvskcAeAaApz+4++ZLrL2Ne1dvY4Ktkv/1yj2FAnpMQ7GvY8Fj8gkuhBBClGmm6uXymS0sQL753wV7rYSakpHNou3RzN92juSc4mh1fNz5X+faPNosAEeV+Zzc+JR4Zu6fyd/n/wbAS+3FmJZjeDzkcZQKO56/q9cZE+jrZ+D62bz9jXPG3uq7qSDt5pOTTFfLS6o9qxmXoPCsZnzu5Hbn64gKI3HlSq59+SX65GDgtmJ2OuMHuKG01gwXQgghxH1RyJxuYUEVMum+desWZ86cMT2Pjo7m0KFDVK5cmRo1atgwMsvIyNaxdE8MX24+YxpG3sDfk9Hd6vBwQz+USvPK0Jm6TH46/hPzDs8jNTsVpUJJv7r9GNlipP2tt52uhfhjEHsE4o5C3GG4drL4xFqhNCbOmkDwCgSvGnmPNTVAU91YHEuIErg6axa6awmFH8z5/K7gJTWEEEKIMiu3erlOPqqFBVTIpHv//v106dLF9Dx36PjgwYNZvHixjaKyjI3H45nyZxQXrqcCULOKK2O71+XRpgEFkm2DwcD/xfwfn+3/jEu3LgHQrGoz3gt9jwZVGlg99hIzGIw92Bd2QcxO4z7hZOHnqpyMyzlVqQNVahv3lWsZE2yPABnuLSwvs5jlwAx6UIAemdMthBBClEW5A0LlB3JhCRUy0+jcuXO5+x8oOiGFKX/8Z1r6y8dDzeiwEPq3DiwwjBzg6LWjzNg/g8irkQBUdanKqJaj6FO7T9keSp56A85shDMb4Px24/Dw23lWB78m4N8U/JqCbyNjcq1UWT9eUWHl/hvjER5O8vr1ZscUegOo5INcCCGEKKtyh5frZE63sIAKmXSXJzq9gW//PcfMf06RqdPjqFIwtFMwI7uGFCiQBhB7K5bZB2fz17m/AHBWOTOk8RBeaPQCro6u1g7/zgwGiP8PTq+HU/8Y1z3OX/FZ6QD+zaBGe6jZAQJDjUXLhLC13IT6thEmACScBV/rhiOEEEKIu6fO6bTKyJZRaeL+SdJtxy4npjF2+SH2RN8A4MG6VXn/0YbUrupe4NyEtAQWHFnAilMryNIbh732qd2HkS1G4ufmZ9W47ygzFaK35SXa2kvmx30aQsjDULsLVG8jBcxE2ZSTdCsKGTmiuHkefGXJMCGEEKKs8nI1LveZmHoXBXeFuANJuu3U74cuM2H1MZLTs3F1UvH+ow3p3zrQNBQmV2J6Iov+W8TPx38mXZcOQBu/NrzR+g0aVWlki9ALd/MCnP4HTq2H8/9CdnreMQdnCH7QmGjXDTcOFReirNPnJNSKgj3dBtNehqwJIYQQZVEVd2PSnXArk2e/3Y0y5/PcYMj7/DaYCqMWbHNQKXB2UKF2VKJ2UKF2UOLsaNyrHZSocx/nbzOdn/81eY9zjzuplAVqNYmyTZJuO5OUlsXE1cdYc/gKAM0DvZg1oDlB3ua9vTfSb7D0+FJ+PP4jKTnrTDf1bsrIliMJ9QstkJxbXVa6sfjZmY1wekPBAmiawLwkO+gBcCqDQ9+FKIYpnVYW0tNd1kaXCCGEEMJMZTcnPJ0d0KZns+PMdVuHU4CTKjd5z0vKnXKTeJUSx/9v786jo6jyvoF/q6qXdPaEkIQACTsEDLtERgVHMgk6L0eUUWQYBhDxiAFFR2dGnwF09NV3nKMPyjhydM64PK+g4usy4zAogwguEQVEBDFCCItkgexk6a3qvn9Ud6U73YSl0+ks388591T1rdtVt6+NN7+ue2+ZJJgVGSZZhsUkwSTLMCsyzIonX5FgUWRj3/+YDIvieY9JhlmWoMgSTIoEWdLzva8VWYIitR43yRIUWdbzjNf6VpZ9X8uQJUQ+JukkDLq7kaKSavzmzX0oq7dDkSWsuHYYlv90GEw+C6WdaDiBV797Fe8eeRcO1QEAGJU8CismrMDV/a+O3Be7pRYo+xo4tRc4uQso/QRwt7QelxR9PvaIfGB4AZCaHfQOIVG34f2pO8jXWMj8Xy8REVFXZlZkvHzbFOw5VouUOP2ut+Tp1H3/RPX+bS355EuQ4NY0OFwa7G4VDpcGh1uF3bN1uDXYXfprp7s1z+Hdd/nsu1vP47v+qlPV4FQ1nHV0RmuEj+ITlPtuvYG5IkuQJUD2BPeypAfvsqS/V5IkKH77EmQZkD0/BMje93jKeN+vSAg4l7esfi7A1dLUYZ+Tf/l1Aw63iqc//AEvfHIUQuiPAfvvueMxMTMJgL4C8u7K3dj4/Ub85/h/jOEtY/qMwW2X3Ya8rLzOXZHc2QxU7NcD7FN7gLK9QM3RwHJx/YBhM4BhecCQawBbUufVkSjcPMPLg83pRksDAK5eTkRE1JVNzEwy/t6ONCEE3JrwBOHtBOmefbemwaUKuFQNblWDs82+W9XgUn3L6FuXJuDyvN+ptu6rmoCq6XVQfZI7YF8LWuZcvGW64sx5zdHcYedi0N3F/VB5Fve8vg+HyvU/0m+9fCBW/a/RiLGaUO+oxz9K/oFNP2xCaX2p8Z6r+1+NxZctxuS0yeG/s62pwJli4NRuPcD+cQ9w+jtAqIFlkwYBGROB/pP0IDttDO9mU89lrF4eJOh22QPziIiIiM5BkiRj+HewJxR1ZUIIaAJwaxo0DUYQHzyA9w/aXaqAEPq+JgDN2PckDVCFgOY5rorW8qomIDx5+n6w88BzHhFwnubGs/jj2o5pg+71X6wX0TSBV4qO4Yl/fw+nW0NyjAX/56Yc/HRUH3xW9ik2l27GRyc+MoaQ20w2XD/4eszPno/hScPDV7GzFcCPXwE/eoLssq8BZ2Ngudh0oP9ET5A9Qd9GJ4evXkRdjTG83LPwSrAiXEiNiIiIerjWIeCKJ0dpt3xX0dDQgD920LkYdHdBlQ123L/pG3xyuAoAcPXIeMy5sgWfV63HI29uRYOzwSg7Mmkkbhl5C64ffD1iLYGPCguJpgKnDwEnvwBO7NLnYtcdBwAIIcElrHAJG1ymoXCljIMr+TK4EkbBFTcULiUBLocKt12F6zsVrr3VcDkq4XKoenKqUF0CmqrpvyipAkITUFUBobbmA4DkmaMhyfp8Dn1fn2uhmGWYzArMVhkmi+JJ+r7ZIsNiMyEqxgxrtBlRMSZYY8ye1yaYrUqvWbyBOp8IeE63z3dNSP5liIiIiKjHYtDdxbz/TRn+61870CSOIzqtHEMGVOOg/TvsK3IZZVJsKZg5aCZ+PuTnGJ08GhD6nXGn3W0Er5oqoLo1uF0aVJfmv+/S4HarcDs1T1Lhdmlwt9jhrq2Au+403A21cDedhVtV4BJRcImpcImf6vuIhluz+Fe83PdFmSd1bYpJRnSCBbGJVkQnWPVtov46NsmK+JRoxCRYIPGRDHQp2nlOtzcA551uIiIiop6PQXcHOltjx86NxWhp1ANkobU+r897R0toAOCZX6CpcKpOON0uOFUnmpwOCOHGXCiQMAQShgKlEiTcBBNMsEhWmGGGBBliJ7BTO4Md2sdh+CR9PelCCJgkDSbJDZOkQhFumOCCItxQhAsmzQlFc0JRHXpy26G47ZDddkhuB2SXC3A7Ibmcnq0DkqZCEiokvbEgJBkCEiDJ+r6iALICoZggFAs0cxQ0iw2aJRqaJQqa2QbNFAXVZIVbscGlRMElWeGEFS6Y4VRN0IQE1a3hbLUdZ6vPPb9WMcuIT7EhoW9rSkyNRkKaDXFJUQzI6dzaDi/3GVXhXf1U83zHiYiIiKjnYtDdgY7uO4Nj317Kc/wUKLAhHrbzltSXJ7uAP9SFBllzeZIbsuaC4tm25jmhqC6/rXdf0ZyQPXkm1e4Jmn2CZ0+SNVewJyJ1aQKAJlvgtMTBaYmHw5oIhyUBDmsCHNYkOGP6wBGVjBYlHqoLqC1vQm154CMDFJOMhFQ9CE9MsyEhNRqJadFITI2GLc7Moeu9XcDwct9jHF5ORERE1Fsw6A7B6s9Ww2F26HerVSeSfxiOEbgK5Qkl2Jf+EYQkoA8gFbjpcxWjTgpI0PMgvPto3Q+WBwFJaJ6kXuC+z3n0kwGS54abJDxbALICSVEAkxmSNQpyVDSkqGhINjMksxmSxQLJbAbMifprbzKZ/V+bzZBMJkiW1tcwmYKU8dm3eN7Tpkzr+yyQTAqgaRCaBqiqvnW7/V+rKoSqQjgc0Ox2n60TwmHX9+16ntbUBO1sA9SzjdAaGqA2ercV0Op+gNbkH1hrkgy7NRktthS02PoaqdmzVd0m1JQ1oaYsMCC32BT9jrg3EE/Tg/P4FBus0aZeG5Brmj5nX1U1YxqEd05/67x+n2M+x4WxrwW8R/M95vM+v/N43idUzwqWatvrB6+T97zB6uS9Rv8RSfjJnKGI7+Pzw5l3eLmxernvMHPPne4L+QGNiIiIiLo1Bt0h+PD4h1BsravvjWvWV+dusFThePIBKJKCRGsi0mPSkVFUiT61p1E263IcGTkaXxwWqKy1QQgzTCYZN04YgJsmDYDVrMCIiiXJs/HuS4C9Aag/AdQdh1R3HKgt1Z+B3VgB7/rIkgRIioBsEpBNgJQyBEjNBlJHt277DAUUcwRaresSLhfctbVQq6vhrq6BWl0Fd3UN3NVVUKtr4K46DVf5N3CVlEG1O+CwJqE5Og3NtlQ9GI9ORbMtFfaoZDhbgNPHz+L08bMB15FlCVFxZthiLbDFmWGLNcMcZYLZosAcpcBsbV0QTpb1ReO8W2MxOVnSpy14Hn8gNP2uqXcfnkcoCCGMOf++gad/wKoFDSa9+XqQ7FPWL5BtJzBtGwhrIvgS3j1Ayd7TOP5tFa79dTaGX57W5g62d3h52xwOLyciIiLqDRh0h2hG5gzkZ+XDqljR8KUZZSfc+Nmgn+HxeSsQY44x7miW/vNO2HEa22pHYMORHEACYtIV/GLSANx+9RAMTI5uPanqBupPAtVHgKof9HTGs22uCqyEDCAeQHx/T1DtE2CnjAQs0YHvoQCS2QxzairMqantlhNCQKuvh6usDK7ycrjKyuEqK4PzxPdwHtuClpNlaDEloiU6Fc1GMJ6GluhUOC3x0DSB5nonmuudnfTJujgJkBX9hwVZkT1bPUnefe8xT57ie8zneGCez7G2+ca5gr+vNU9ucy5JP5fnvE67G7veO4qyw3X4z8vfwRZnRv8RiT6fL3D1cu8eh5cTERER9XwMukM0KnkUrh9yPQBgz6FjKMNRxFhiEGuJhRACXxytxptfncSEH85gMoAzTS4kDpBx+8R4LMoxIbbpB+Dgh0DtsdZU/yMg1HNfNH4AkDIc6DtS36aOAVJHAbakTvjEJEkSlMREKImJiBo9OuC4cLv1IPzYMSM5SovgPHoMzsoqOC2xcJlj4TTHweXZdytWaIoVIqEPRFwStOh4aFYbJIsVMFshzBbAZAYgGUOavY9NkzwjImRZHw0hSf6PWZM8ed6gNVhAagSSfsd8yrcX3LY5T7sBbZDguicsRjf73gn48O8HcWT3afx7/be4/s4xEJD0aR5y4OrlkuBCakRERES9BYPuEMmaG2iuAdx2iEZ9EbX6ymP4f69swbGTp4CWWoyT6jFGlAAAHot5HanSOkh7VWBvOydWLEDyUKDvCCDFJ/UZBlg7+Hnc1KEkkwmWzExYMjOBadP8jmnNzXAePw5naSkcx47BWXoMztJSOEtLA+aUB6MkJEBJStKDft9tQiKU+HjIMdGQowOTFB0N2WbT5+kHCQJ7Et+5/8KtAqobQlVb5/8beRqgedYG8CShCf0RA5qmHxf++UJVAe++5z1KfDyisrMxY2E2muudKDtch3fX7oc07b9hddQj5rQFpuxFSKotNupo3OnuqePtiYiIiMjAoDtE0sd/At77LwCAaLwZwC+RdOpjXNO4Xi/gmTZ9AsloQhSiXPWQhApIMhCTCiQN8qQsn/1BQGx60Dtk1L3J0dGIys5GVHa2X74QAmpVFRylpXCWHoOrrAzuigq4Kir0bWUlhN0Otb4ean19aJVQFGORvNatuXUBO7NZD8xlGZAl/TnTiqLfkZba5PvuSxIgBITQ9LnbQsAzsVwPLjXhfX6eHrR65pt7y8AzJ90bHBtBsuoG3D4L6bU5buR5jiMCQ7bluDgk3DgbBbfdiU/fL0PJ12egwQy7LQV2B4C0y1GZdnnA+3inm4iIiKjnY9AdIu8dK6dQYBcWAEAdYnE4eiLik/qiT99UmOLSgAOfARXHgWsfAm78hR5wK2x+0kmSBFPfvjD17YuYKVMCjnvnkbvPnIFaV6cv+FZXB7WuHmptLdTaWmhNjdCamvVV2pub/ZJwOFpPpqoQLS0QLS2d+Am7AFnWV+tXFP9t2x8XJMmTJwf98cHvBwdJgru8HO4zZ1D76v+gZc9ezHjlZVw7fxj2T50BhyURpoIbcXJ/JSrSLoeQ9X/zkqQH2wy6iYiIiHo+Rn0hakEUbkndgEGp8RhxRgO+qUXSlFswfN4qv3LifxYDOK6vGh6fEZnKUrflO4/8Ugi3W398mssF4XRBuJyerQvC6fTfulw+w659h1K32Q9SRvIEovokc+8cc88dcs8K/JLssxq/JLeu0C/L+vsUGZJi0h8Zpyj6viIDvnkmk34tT57+6DtPnskEqW1grShhG1YvNA2N27ejfNVq2A8exI/L7sKAv6yDzV4Dm70GyfG1iC3+vxhW8jY+uerPAAAZ+poNHF5ORERE1PMx6A5RjNWMN++6GgCw6x9Hsfub2uALQ2n6Ha2esGgUdT+SyQQllmsBhIMky4ibMQOm9HSc+PVCNH/1FSoff7z1uGf1crO7Gf1P7cCp/tMhCf0HAK5eTkRERNTzcdJwiCSfxwAJzfOc7GAFPUE352kT9Uy2MWPQ79E/AgCavtjVekA69795Di8nIiIi6vkYAYZI9mlC700rSQoMu407Wu38AU5E3ZupXz8A+nB+g88PbZJxZ1v/fwSHlxMRERH1fIwAQ+QfYHv+gA7Wqt7h5QqbnKinkkz6jB2/oNvvNzjPaBg+p5uIiIio12AEGCL/4eWevCB3ujm8nKjnM4Jul6s1r5073Qy6qbt57rnnMGjQIERFRSE3Nxdffvllu+U3bdqEUaNGISoqCjk5Odi8ebPfcSEEVq9ejX79+sFmsyEvLw+HDx/2K1NTU4P58+cjPj4eiYmJWLJkCRobGzv8sxEREYULI8AQyZLv8HLPXawgMXfr8HIupEbUYykKAP+g239KSZvh5VxIjbqRN954A/fddx/WrFmDvXv3Yty4cSgoKMDp06eDlv/8888xb948LFmyBF9//TVmz56N2bNn48CBA0aZJ598Es8++yzWr1+PXbt2ISYmBgUFBbDb7UaZ+fPn4+DBg9i6dSvef/997Ny5E3fccUfYPy8REVFHYdAdItkniG5vTnfr6uVscqKeSjKZ9R2/oDtwCoo3RwPvdFP38fTTT2Pp0qVYvHgxRo8ejfXr1yM6Ohp///vfg5Z/5plnMHPmTDzwwAPIzs7Go48+iokTJ+Ivf/kLAP1Hp7Vr1+IPf/gDbrjhBowdOxavvvoqysrK8O677wIADh06hC1btuBvf/sbcnNzcdVVV2HdunV4/fXXUVZW1lkfnYiIKCSMAEPkN7y8nTvdHF5O1PNJJiUw0+cxgZL3xrbnkWEcXk7dhdPpxJ49e5CXl2fkybKMvLw8FBUVBX1PUVGRX3kAKCgoMMqXlpaioqLCr0xCQgJyc3ONMkVFRUhMTMTkyZONMnl5eZBlGbt27QIREVF30KsjwIudmxZM0DvdQZ7FzdXLiXo+SQkMuv1HtwQG2RxiTt1BVVUVVFVFWlqaX35aWhoqKiqCvqeioqLd8t7t+cqkpqb6HTeZTEhOTj7ndR0OBxoaGvwSERFRJPXaCPBi56adi+TbhJo3sA5S0BhezjndRD2Wd3i5n2D/5n3WguBjw4g61BNPPIGEhAQjDRw4MNJVIiKiXq7XBt0XOzftXGT54uZ0c3g5Uc8VdHi57/Egd7U5xJy6g5SUFCiKgsrKSr/8yspKpKenB31Penp6u+W92/OVaftjuNvtRk1NzTmv++CDD6K+vt5IJ0+evMBPSUREFB6mSFcgErxz0x588EEj73xz04K59uAYSKZoHHhpKwCgulQCIMNZcgQNH/o/8kQ9e1bf4fByoh7L+8gwgywHXUit7uwA5H+bAwDY/L8fhxz0bjj1VM0+K3N3FxaLBZMmTcK2bdswe/ZsAICmadi2bRuWL18e9D1Tp07Ftm3bsHLlSiNv69atmDp1KgBg8ODBSE9Px7Zt2zB+/HgAQENDA3bt2oVly5YZ56irq8OePXswadIkAMBHH30ETdOQm5sb9LpWqxVWq7UDPjUREVHH6JVBd3tz077//vuA8g6HAw6Hw3hdX18PAEiv/QWOWqJx9BP/P6Aq3/sXiv/yUdBrn3XYoXJ+GVGPpLW0oFFVjdeSoiA6ymrk2Z3NaHE2oRQj0K9mBADgh5qIVJUiyO5sBtD95vPfd999WLhwISZPnowpU6Zg7dq1aGpqwuLFiwEAv/71r9G/f3888cQTAIB77rkH06dPx1NPPYWf//zneP3117F792688MILAPRRYStXrsRjjz2G4cOHY/DgwVi1ahUyMjKMwD47OxszZ87E0qVLsX79erhcLixfvhy33norMjIyLqje3nbm3G4iIroY3n6jQ/pr0QudOnVKABCff/65X/4DDzwgpkyZElB+zZo1AvotKiYmJiYmpg5JJSUlndXtdZh169aJzMxMYbFYxJQpU8QXX3xhHJs+fbpYuHChX/k333xTjBgxQlgsFjFmzBjxr3/9y++4pmli1apVIi0tTVitVjFjxgxRXFzsV6a6ulrMmzdPxMbGivj4eLF48WJx9uzZC67zyZMnI/7fmomJiYmp+6aO6K8lIbrZT+0dwOl0Ijo6Gm+99ZbxazoALFy4EHV1dXjvvff8yre9061pGmpqatCnT5/g87epXQ0NDRg4cCBOnjyJ+Pj4SFen22H7hY5tGBq2X2jq6+uRmZmJ2tpaJCYmRro6PZ6maSgrK0NcXBz77EvAf++hYfuFhu0XGrZfaDqyv+6Vw8svdm5asPlh/EMpdPHx8fwfQAjYfqFjG4aG7RcamQtrdgpZljFgwIBIV6Pb47/30LD9QsP2Cw3bLzQd0V/3yqAbOP/cNCIiIiIiIqJQ9dqge+7cuThz5gxWr16NiooKjB8/Hlu2bAlYXI2IiIiIiIjoUvXaoBsAli9ffs5HnVD4WK1WrFmzho90uURsv9CxDUPD9gsN24+6E35fQ8P2Cw3bLzRsv9B0ZPv1yoXUiIiIiIiIiDoDV3EhIiIiIiIiChMG3URERERERERhwqCbiIiIiIiIKEwYdFOnefjhhyFJkl8aNWpUpKvVZe3cuROzZs1CRkYGJEnCu+++63dcCIHVq1ejX79+sNlsyMvLw+HDhyNT2S7ofO23aNGigO/jzJkzI1PZLuiJJ57A5Zdfjri4OKSmpmL27NkoLi72K2O321FYWIg+ffogNjYWc+bMQWVlZYRq3LVcSPtdc801Ad/BO++8M0I1JvLHPvvisM8ODfvsS8f+OnSd0Wcz6KZONWbMGJSXlxvp008/jXSVuqympiaMGzcOzz33XNDjTz75JJ599lmsX78eu3btQkxMDAoKCmC32zu5pl3T+doPAGbOnOn3fdy4cWMn1rBr27FjBwoLC/HFF19g69atcLlcyM/PR1NTk1Hm3nvvxT//+U9s2rQJO3bsQFlZGW666aYI1rrruJD2A4ClS5f6fQeffPLJCNWYKBD77AvHPjs07LMvHfvr0HVKny2IOsmaNWvEuHHjIl2NbgmAeOedd4zXmqaJ9PR08ec//9nIq6urE1arVWzcuDECNeza2rafEEIsXLhQ3HDDDRGpT3d0+vRpAUDs2LFDCKF/38xms9i0aZNR5tChQwKAKCoqilQ1u6y27SeEENOnTxf33HNP5CpF1A722ZeOfXZo2GeHhv116MLRZ/NON3Wqw4cPIyMjA0OGDMH8+fNx4sSJSFepWyotLUVFRQXy8vKMvISEBOTm5qKoqCiCNetePv74Y6SmpmLkyJFYtmwZqqurI12lLqu+vh4AkJycDADYs2cPXC6X33dw1KhRyMzM5HcwiLbt5/Xaa68hJSUFl112GR588EE0NzdHonpEQbHP7hjsszsG++wLw/46dOHos00dWkOiduTm5uLll1/GyJEjUV5ejkceeQRXX301Dhw4gLi4uEhXr1upqKgAAKSlpfnlp6WlGceofTNnzsRNN92EwYMHo6SkBA899BCuu+46FBUVQVGUSFevS9E0DStXrsSVV16Jyy67DID+HbRYLEhMTPQry+9goGDtBwC//OUvkZWVhYyMDOzfvx+/+93vUFxcjLfffjuCtSXSsc/uOOyzQ8c++8Kwvw5duPpsBt3Uaa677jpjf+zYscjNzUVWVhbefPNNLFmyJII1o97o1ltvNfZzcnIwduxYDB06FB9//DFmzJgRwZp1PYWFhThw4ADnc16ic7XfHXfcYezn5OSgX79+mDFjBkpKSjB06NDOriaRH/bZ1JWwz74w7K9DF64+m8PLKWISExMxYsQIHDlyJNJV6XbS09MBIGDlycrKSuMYXZwhQ4YgJSWF38c2li9fjvfffx/bt2/HgAEDjPz09HQ4nU7U1dX5led30N+52i+Y3NxcAOB3kLok9tmXjn12x2OfHYj9dejC2Wcz6KaIaWxsRElJCfr16xfpqnQ7gwcPRnp6OrZt22bkNTQ0YNeuXZg6dWoEa9Z9/fjjj6iurub30UMIgeXLl+Odd97BRx99hMGDB/sdnzRpEsxms993sLi4GCdOnOB3EOdvv2D27dsHAPwOUpfEPvvSsc/ueOyzW7G/Dl1n9NkcXk6d5v7778esWbOQlZWFsrIyrFmzBoqiYN68eZGuWpfU2Njo9+tZaWkp9u3bh+TkZGRmZmLlypV47LHHMHz4cAwePBirVq1CRkYGZs+eHblKdyHttV9ycjIeeeQRzJkzB+np6SgpKcFvf/tbDBs2DAUFBRGsdddRWFiIDRs24L333kNcXJwx7yshIQE2mw0JCQlYsmQJ7rvvPiQnJyM+Ph4rVqzA1KlTccUVV0S49pF3vvYrKSnBhg0bcP3116NPnz7Yv38/7r33XkybNg1jx46NcO2J2GdfLPbZoWGffenYX4euU/rsEFdUJ7pgc+fOFf369RMWi0X0799fzJ07Vxw5ciTS1eqytm/fLgAEpIULFwoh9EeQrFq1SqSlpQmr1SpmzJghiouLI1vpLqS99mtubhb5+fmib9++wmw2i6ysLLF06VJRUVER6Wp3GcHaDoB46aWXjDItLS3irrvuEklJSSI6OlrceOONory8PHKV7kLO134nTpwQ06ZNE8nJycJqtYphw4aJBx54QNTX10e24kQe7LMvDvvs0LDPvnTsr0PXGX225LkQEREREREREXUwzukmIiIiIiIiChMG3URERERERERhwqCbiIiIiIiIKEwYdBMRERERERGFCYNuIiIiIiIiojBh0E1EREREREQUJgy6iYiIiIiIiMKEQTcRERERERFRmDDoJupFFi1ahNmzZ0fs+gsWLMDjjz9uvB40aBDWrl3bodf47rvvMGDAADQ1NXXoeYmIiDoT+2yinoNBN1EPIUlSu+nhhx/GM888g5dffjki9fvmm2+wefNm3H333WG9zujRo3HFFVfg6aefDut1iIiILhX7bB37bOotGHQT9RDl5eVGWrt2LeLj4/3y7r//fiQkJCAxMTEi9Vu3bh1uvvlmxMbGhnQel8t13jKLFy/G888/D7fbHdK1iIiIwoF9div22dQbMOgm6iHS09ONlJCQAEmS/PJiY2MDhqpdc801WLFiBVauXImkpCSkpaXhxRdfRFNTExYvXoy4uDgMGzYM//73v/2udeDAAVx33XWIjY1FWloaFixYgKqqqnPWTVVVvPXWW5g1a1bAsebmZtx2222Ii4tDZmYmXnjhBePYsWPHIEkS3njjDUyfPh1RUVF47bXXcPz4ccyaNQtJSUmIiYnBmDFjsHnzZuN9P/vZz1BTU4MdO3aE0KJEREThwT6bfTb1Lgy6iXq5V155BSkpKfjyyy+xYsUKLFu2DDfffDN+8pOfYO/evcjPz8eCBQvQ3NwMAKirq8O1116LCRMmYPfu3diyZQsqKytxyy23nPMa+/fvR319PSZPnhxw7KmnnsLkyZPx9ddf46677sKyZctQXFzsV+b3v/897rnnHhw6dAgFBQUoLCyEw+HAzp078e233+JPf/qT36/xFosF48ePxyeffNJBrURERBR57LOJuilBRD3OSy+9JBISEgLyFy5cKG644Qbj9fTp08VVV11lvHa73SImJkYsWLDAyCsvLxcARFFRkRBCiEcffVTk5+f7nffkyZMCgCguLg5an3feeUcoiiI0TfPLz8rKEr/61a+M15qmidTUVPH8888LIYQoLS0VAMTatWv93peTkyMefvjhdlpAiBtvvFEsWrSo3TJERESRxj6bfTb1fKZIBvxEFHljx4419hVFQZ8+fZCTk2PkpaWlAQBOnz4NQF9cZfv27UHneZWUlGDEiBEB+S0tLbBarZAkqd3re4fXea/l1fbX9rvvvhvLli3Dhx9+iLy8PMyZM8fvPABgs9mMX/qJiIh6AvbZRN0Th5cT9XJms9nvtSRJfnneTlfTNABAY2MjZs2ahX379vmlw4cPY9q0aUGvkZKSgubmZjidzgu6vvdaXjExMX6vb7/9dhw9ehQLFizAt99+i8mTJ2PdunV+ZWpqatC3b9/2PjoREVG3wj6bqHti0E1EF2XixIk4ePAgBg0ahGHDhvmlth2t1/jx4wHoz+PsKAMHDsSdd96Jt99+G7/5zW/w4osv+h0/cOAAJkyY0GHXIyIi6m7YZxN1DQy6ieiiFBYWoqamBvPmzcNXX32FkpISfPDBB1i8eDFUVQ36nr59+2LixIn49NNPO6QOK1euxAcffIDS0lLs3bsX27dvR3Z2tnH82LFjOHXqFPLy8jrkekRERN0R+2yiroFBNxFdlIyMDHz22WdQVRX5+fnIycnBypUrkZiYCFk+9/9Sbr/9drz22msdUgdVVVFYWIjs7GzMnDkTI0aMwF//+lfj+MaNG5Gfn4+srKwOuR4REVF3xD6bqGuQhBAi0pUgop6vpaUFI0eOxBtvvIGpU6eG7TpOpxPDhw/Hhg0bcOWVV4btOkRERD0V+2yijsU73UTUKWw2G1599VVUVVWF9TonTpzAQw89xM6biIjoErHPJupYvNNNREREREREFCa8001EREREREQUJgy6iYiIiIiIiMKEQTcRERERERFRmDDoJiIiIiIiIgoTBt1EREREREREYcKgm4iIiIiIiChMGHQTERERERERhQmDbiIiIiIiIqIwYdBNREREREREFCYMuomIiIiIiIjC5P8DjfAHq5fNqGUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", + "\n", + "model.plot(axes[0,0], 'Total Precipitate Density', timeUnits='h', label='Total', color='k', linestyle=(0,(5,5)), zorder=6)\n", + "model.plot(axes[0,0], 'Precipitate Density', timeUnits='h', alpha=0.75)\n", + "axes[0,0].set_ylim([1e5, 1e25])\n", + "axes[0,0].set_xscale('linear')\n", + "axes[0,0].set_yscale('log')\n", + "\n", + "model.plot(axes[0,1], 'Total Volume Fraction', timeUnits='h', label='Total', color='k', linestyle=(0,(5,5)), zorder=6)\n", + "model.plot(axes[0,1], 'Volume Fraction', timeUnits='h', alpha=0.75)\n", + "axes[0,1].set_xscale('linear')\n", + "\n", + "model.plot(axes[1,0], 'Average Radius', timeUnits='h')\n", + "axes[1,0].set_xscale('linear')\n", + "\n", + "model.plot(axes[1,1], 'Composition', timeUnits='h')\n", + "axes[1,1].set_xscale('linear')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. E. Povoden-Karadeniz et al, \"Calphad modeling of metastable phases in the Al-Mg-Si system\" *Calphad* 43 (2013) p. 94\n", + "2. Q. Du et al, \"Modeling over-ageing in Al-Mg-Si alloys by a multi-phase Calphad-coupled Kampmann-Wagner Numerical model\" *Acta Materialia* 122 (2017) p. 178" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 64-bit", + "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": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "822df1fa43a9cb3d4c4a5882bc10c066bf8074b03729cc74aeda55033a52fda7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/04_Precipitation_with_Elastic_Energy.ipynb b/examples/04_Precipitation_with_Elastic_Energy.ipynb new file mode 100644 index 0000000..f91b34a --- /dev/null +++ b/examples/04_Precipitation_with_Elastic_Energy.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Precipitation with Elastic Energy\n", + "\n", + "This example will cover adding a strain energy term to the KWN model. This strain energy term will also be used to calculate the aspect ratio as a function of precipitate radius." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example - The Cu-Ti system\n", + "\n", + "In copper alloys with dilute amounts of titanium, formation of $\\beta$-$Cu_4Ti$, a needle-like precipitate, can occur. Due to volume differences between the precipitate and the parent phase, the parent phase is put under strain. This strain comes with an elastic energy that serves to reduce the driving force for nucleation. In addition, the aspect ratio of the $\\beta$ precipitates depends on the size of the precipitate to minimize the elastic and interfacial energy contributions.\n", + "\n", + "To setup the KWN, the PrecipitateModel and BinaryThermodynamics will need to be defined. For BinaryThermodynamics, a mobility correction factor of 100 will be applied. This is to represent the presence of excess quench-in vacancies, which will speed up diffusion." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import BinaryThermodynamics\n", + "from kawin.precipitation import PrecipitateModel, VolumeParameter, ShapeFactor\n", + "\n", + "model = PrecipitateModel(phases=['CU4TI'], elements=['TI'])\n", + "\n", + "therm = BinaryThermodynamics('CuTi.tdb', ['CU', 'TI'], ['FCC_A1', 'CU4TI'], interfacialCompMethod='equilibrium', drivingForceMethod='approximate')\n", + "therm.setMobilityCorrection('all', 100)\n", + "therm.setGuessComposition(0.15)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Inputs\n", + "\n", + "For model inputs, the composition will be Cu-1.9Ti (at.%) and the temperature will be $350\\text{ }^oC$. The molar volume of the matrix phase will be that of FCC copper with 2 atoms per unit cell. For the $\\beta$-$Cu_4Ti$ precipitates, the atomic volume and atoms per unit cell are taken from Ref. 5 from the SpringerMaterials database. Bulk nucleation will be assumed with $1e30\\text{ }sites/m^3$." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model.setInitialComposition(0.019)\n", + "model.setTemperature(350 + 273.15)\n", + "model.setInterfacialEnergy(0.035)\n", + "model.setThermodynamics(therm)\n", + "\n", + "VmAlpha = 7.11e-6\n", + "model.setVolumeAlpha(VmAlpha, VolumeParameter.MOLAR_VOLUME, 4)\n", + "\n", + "VaBeta = 0.25334e-27\n", + "model.setVolumeBeta(VaBeta, VolumeParameter.ATOMIC_VOLUME, 20)\n", + "\n", + "model.setNucleationSite('bulk')\n", + "model.setNucleationDensity(bulkN0=1e30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elastic Energy\n", + "\n", + "Elastic energy has to be defined by a separate object, StrainEnergy. Here, the elastic constants and eigenstrains can be defined. It is important to check the order of the axes in the eigenstrains. For needle-like precipitates, the axes are (short axis, short axis, long axis). For plate-like precipitates, the axes are (long axis, long axis, short axis).\n", + "\n", + "When inputting the StrainEnergy object into the KWN model, setting \"calculateAspectRatio\" to True will allow for the aspect ratio to be calculated from the elastic energy. Otherwise, the aspect ratio will be taken from what was defined when defining the precipitate shape." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.precipitation import StrainEnergy\n", + "\n", + "se = StrainEnergy()\n", + "se.setElasticConstants(168.4e9, 121.4e9, 75.4e9)\n", + "se.setEigenstrain([0.022, 0.022, 0.003])\n", + "\n", + "model.setStrainEnergy(se, calculateAspectRatio=True)\n", + "\n", + "#Set precipitate shape\n", + "#Since we're calculating the aspect ratio, it does not have to be defined\n", + "#Otherwise, a constant value or function can be inputted\n", + "model.setPrecipitateShape(ShapeFactor.NEEDLE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "0\t0.0e+00\t\t0.0\t\t623\t\t1.9000\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tCU4TI\t0.000e+00\t\t0.0000\t\t0.0000e+00\t1.9660e+03\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "4571\t1.0e+05\t\t108.0\t\t623\t\t0.1757\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tCU4TI\t1.455e+23\t\t9.3695\t\t5.1198e-09\t1.2258e+02\n", + "\n" + ] + } + ], + "source": [ + "model.solve(1e5, verbose=True, vIt=5000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "As with the other examples, plotting is the same. Some additional things:\n", + "1. The variable 'timeUnits' is set to 'min' to plot in minutes rather than seconds\n", + "2. The equilibrium matrix composition is plotted to compare with the actual composition.\n", + "3. The mean aspect ratio and aspect ratio as a function of radius is plotted" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", + "\n", + "model.plot(axes[0,0], 'Precipitate Density', bounds=[1e-2, 1e4], timeUnits='min')\n", + "axes[0,0].set_ylim([1e10, 1e28])\n", + "axes[0,0].set_yscale('log')\n", + "\n", + "model.plot(axes[0,1], 'Composition', bounds=[1e-2, 1e4], timeUnits='min', label='Composition')\n", + "model.plot(axes[0,1], 'Eq Composition Alpha', bounds=[1e-2, 1e4], timeUnits='min', label='Equilibrium')\n", + "axes[0,1].legend()\n", + "\n", + "model.plot(axes[1,0], 'Average Radius', bounds=[1e-2, 1e4], timeUnits='min', label='Radius')\n", + "axes[1,0].set_ylim([0, 7e-9])\n", + "\n", + "ax1 = axes[1,0].twinx()\n", + "model.plot(ax1, 'Aspect Ratio', bounds=[1e-2, 1e4], timeUnits='min', label='Aspect Ratio', color='C1')\n", + "ax1.set_ylim([1,4])\n", + "\n", + "model.plot(axes[1,1], 'Size Distribution Density', label='PSD')\n", + "\n", + "ax2 = axes[1,1].twinx()\n", + "model.plot(ax2, 'Aspect Ratio Distribution', label='Aspect Ratio', color='C1')\n", + "axes[1,1].set_xlim([0, 1.5e-8])\n", + "ax2.set_ylim([1,7])\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", + "2. J. Wang et al, \"Experimental Investigation and Thermodynamic Assessment of the Cu-Sn-Ti Ternary System\" *Calphad* 35 (2011) p. 82\n", + "3. J. Wang et al, \"Assessment of Atomic Mobilities in FCC Cu-Fe and CuTi Alloys\" *Journal of Phase Equilibria and Diffusion* 32 (2011) p. 30\n", + "4. K. Wu, Q. Chen and P. Mason, \"Simulation of Precipitate Kinetics with Non-Spherical Particles\" *Journal of Phase Equilibria and Diffusion* 39 (2018) p. 571\n", + "5. Eremenko V.N., Buyanov Y.I., Prima S.B., \"Phase diagram of the system titanium-copper\" *Soviet Powder Metallurgy and Metal Ceramics* 5 (1966) p. 494" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('base')", + "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": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/05_Strength_Modeling.ipynb b/examples/05_Strength_Modeling.ipynb new file mode 100644 index 0000000..172e7dd --- /dev/null +++ b/examples/05_Strength_Modeling.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Strength Modeling\n", + "## Example - The Al-Sc system\n", + "\n", + "Precipitates obstruct dislocation movement and thus can increase the strength of an alloy in a process known at age/precipitation hardening. There are several mechanisms for how precipitates create an obstable for dislocations.\n", + "\n", + "The two main mechanisms involved are dislocation cutting and dislocation bowing. In the cutting mechanism, the dislocation cuts through the precipitate. Based off differences in properties of the matrix and precipitate phase, an additional force is required for the dislocation to cut through the precipitate. In the dislocation bowing mechanism (Orowan strengthening), the dislocation bows around the precipitate, creating a dislocation loop when it crosses over.\n", + "\n", + "In the Al-Sc system, $Al_3Sc$ can precipitate into an $\\alpha$-Al (FCC) matrix. Setting up the model will be similar to the Binary Precipitation example. Here, the time will be simulated up to 250 hours." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.precipitation import PrecipitateModel, VolumeParameter\n", + "from kawin.thermo import BinaryThermodynamics\n", + "import numpy as np\n", + "\n", + "therm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'SC'], ['FCC_A1', 'AL3SC'])\n", + "therm.setGuessComposition(0.24)\n", + "model = PrecipitateModel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As with the binary precipitation example, the model inputs are supplied here: initial composition, temperature, interfacial energy, molar volume, diffusivity and thermodynamics." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model.setInitialComposition(0.002)\n", + "model.setTemperature(400+273.15)\n", + "model.setInterfacialEnergy(0.1)\n", + "\n", + "Va = (0.405e-9)**3\n", + "Vb = (0.4196e-9)**3\n", + "model.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, 4)\n", + "model.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, 4)\n", + "\n", + "diff = lambda x, T: 1.9e-4 * np.exp(-164000 / (8.314*T)) \n", + "model.setDiffusivity(diff)\n", + "\n", + "model.setThermodynamics(therm, addDiffusivity=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The strength model is implemented in the kawin.Strength module. For all strengthening mechanisms, parameters for the dislocation line tension is needed. This includes the shear modulus, the Burgers vector and the poisson ratio.\n", + "\n", + "There are several dislocation cutting mechanisms, where each is divided into a weak+coherent and strong+coherent contribution:\n", + "- Coherency - lattice misfit between matrix and precipitate creates a strain field that interacts with the dislocation\n", + " - Requires lattice misfit strain\n", + "- Modulus - dislocation energies differs between matrix and precipitate due to differences in the shear modulus\n", + " - Requires shear modulus of preciptiate phase\n", + "- Anti-phase boundary - an ordered precipitate will form an anti-phase boundary if a dislocation cuts through\n", + " - Requires anti-phase boundary energy\n", + "- Stacking fault energy (SFE) - partial dislocations that creates stacking faults will have different energies if the SFE differs between the matrix and precipitate\n", + " - Requires SFE of matrix and precipitate and Burgers vector of precipitate\n", + "- Interfacial energy (IE) - the surface area of a precipitate increases slightly if a dislocation cuts through it\n", + " - Requires interfacial energy between matrix and precipitate\n", + "\n", + "The differences between the weak+coherent and strong+coherent mechanisms is based off how must resistance a particle will give to dislocation cutting. \n", + "\n", + "For dislocation bowing, the precipitate becomes large and incoherent with the matrix. This mechanism is based off Orowan strengthening and requires no additional parameters apart from the parameters needed to define the dislocation line tension.\n", + "\n", + "For the Al-Sc system, parameters will be included for the coherency, modulus, anti-phase boundary and interfacial energy mechanism.\n", + "\n", + "The precipitate and strength model can be integrated by the StrengthModel.insertStrength function. This adds functions for the precipitate model to perform certain calculations necessary for the strength model. This includes the mean projected radius and inter-particle distance on a slip plane.\n", + "- Note: parameters for the strengthening mechanisms are not actually required for the precipitate model. The strength model will still work if the two models are combined first, then the precipitate model is solved and the strength parameters are added at the end." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.precipitation.coupling import StrengthModel\n", + "\n", + "sm = StrengthModel()\n", + "sm.setDislocationParameters(G=25.4e9, b=0.286e-9, nu=0.34)\n", + "sm.setCoherencyParameters(eps=2/3*0.0125)\n", + "sm.setModulusParameters(Gp=67.9e9)\n", + "sm.setAPBParameters(yAPB=0.5)\n", + "sm.setInterfacialParameters(gamma=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the strengthening models can be done as a function of the particle radius or a function of time (if a solved precipitate model is supplied). For plotting over radius, the mean projected radius and inter-particle distance are needed. \n", + "\n", + "Estimating the inter-particle distance from the mean projected radius can be done by:\n", + "$$ L_s = r_{ss} \\left(\\sqrt{\\frac{3\\pi}{4f}} - \\frac{\\pi}{2} \\right) $$\n", + "Where f is the volume fraction of precipitates (taken to be 0.75% for Al-0.2Sc at.%).\n", + "\n", + "In the KWN model, the mean projected radius and inter-particle distance is be determined from the particle size distribution by:\n", + "$$ r_{ss} = \\sqrt{\\frac{2}{3}} \\frac{\\sum{n_i r^2_i}}{\\sum{n_i r_i}} $$\n", + "$$ L_s =\\sqrt{\\frac{ln{3}}{2\\pi\\sum{n_i r_i}} + (2r_{ss})^2} - 2r_{ss} $$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\coupling\\Strength.py:490: RuntimeWarning: divide by zero encountered in divide\n", + " return self.J * self.G * self.b / (2 * np.pi * np.sqrt(1 - self.nu) * Ls) * np.log(2 * r / self.ri)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "fig, ax = plt.subplots(3,2,figsize=(10,10))\n", + "rs = np.linspace(0, 14e-9, 1000)\n", + "ls = rs * (np.sqrt(3*np.pi/4/0.0075) - np.pi/2)\n", + "contributions = [['Coherency', 'Modulus'], ['APB', 'Interfacial'], ['Orowan', 'All']]\n", + "for i in range(3):\n", + " for j in range(2):\n", + " sm.plotPrecipitateStrengthOverR(ax[i,j], rs, ls, contribution=contributions[i][j])\n", + "ax[1,1].set_ylim([0, 50])\n", + "ax[2,0].set_ylim([0, 600])\n", + "ax[2,1].set_ylim([0, 1500])\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The strength model can be added as a coupling function to the KWN model. After this, the KWN model can be solved." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nucleation density not set.\n", + "Setting nucleation density assuming grain size of 100 um and dislocation density of 5e+12 #/m2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "0\t0.0e+00\t\t0.0\t\t673\t\t0.2000\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t3.6624e+03\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "3768\t9.0e+05\t\t71.9\t\t673\t\t0.0165\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t7.216e+19\t\t0.7344\t\t2.8289e-08\t8.2115e+01\n", + "\n" + ] + } + ], + "source": [ + "model.addCouplingModel(sm)\n", + "model.solve(250*3600, verbose=True, vIt=5000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the strength contributions are done through the StrengthModel object. In plotContributions is set to False, then the overall strength contribution will be plotting. If True, then the strength contributions from the precipitate hardening mechanisms, solid solution strengthening and the base strength will be plotted. Since the solid solution strengthening and base strength was not included in the model, only the precipitate hardening mechanisms contributed to the overall strength." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\coupling\\Strength.py:490: RuntimeWarning: divide by zero encountered in divide\n", + " return self.J * self.G * self.b / (2 * np.pi * np.sqrt(1 - self.nu) * Ls) * np.log(2 * r / self.ri)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", + "\n", + "model.plot(axes[0,0], 'Precipitate Density', timeUnits='h')\n", + "model.plot(axes[0,1], 'Volume Fraction', timeUnits='h')\n", + "model.plot(axes[1,0], 'Average Radius', timeUnits='h')\n", + "sm.plotStrength(axes[1,1], model, timeUnits='h', plotContributions=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The individual strengthening mechanisms can be plotted as a function of time as well as the precipitate radius. Rather than including the mean projected radius and inter-particle distance, the solved precipitate model is inserted into the plotting function.\n", + "\n", + "Here, we can see that the interfacial energy had very little contribution to the strength from dislocation cutting compared to the other three mechanisms. However, the strength was mainly governed by Orowan strengthening." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3,2,figsize=(10,10))\n", + "contributions = [['Coherency', 'Modulus'], ['APB', 'Interfacial'], ['Orowan', 'All']]\n", + "for i in range(3):\n", + " for j in range(2):\n", + " sm.plotPrecipitateStrengthOverTime(ax[i,j], model, timeUnits='h', strengthUnits='MPa', contribution=contributions[i][j])\n", + "ax[2,1].set_ylim([0,400])\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", + "2. H. Bo et al, \"Experimental study and thermodynamic modeling of the Al-Sc-Zr system\" *Computational Materials Science* 133 (2017) p. 82\n", + "3. M. R. Ahmadi et al, \"A model for precipitate strengthening in multi-particle systems\" *Computational Materials Science* 91 (2014) p. 173\n", + "4. D. Seidman et al, \"Precipitation strengthening at ambient and elevated temperatures of heat-treatable Al(Sc) alloys\" *Acta Materialia* 50 (2002) p. 4021\n", + "5. K. Deane et al, \"Utilization of bayesian optimization and KWN modeling for increased efficiency of Al-Sc precipitation strengthening\" *Metals* 12 (2022) p. 975\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "reduced_base", + "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": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c9273d58247b0edfb9b15033609c9520285d9dcdb8321aca7ebc3803de582268" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/06_Single_Phase_Diffusion.ipynb b/examples/06_Single_Phase_Diffusion.ipynb new file mode 100644 index 0000000..d37aaff --- /dev/null +++ b/examples/06_Single_Phase_Diffusion.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Single Phase Diffusion\n", + "\n", + "## Example - NiCrAl System\n", + "\n", + "Along with precipitation, kawin also supports one dimensional diffusion models. In this example, a diffusion couple will be simulated between two different NiCrAl compositions. Both phases will be FCC.\n", + "\n", + "Note: Fluxes are calculated on a volume fixed frame of reference. In this frame of reference, the location of the Matano plane is fixed. If a lattice fixed frame of reference is used, then the movement of the Matano plane would move (this would be similar to the Smigelskas–Kirkendall experiments).\n", + "\n", + "## Setup\n", + "\n", + "The diffusion model handles the mesh creation and interfaces with the Thermodynamics module to compute fluxes from mobility and the curvature of the Gibbs free energy surface\n", + "\n", + "Loading the Thermodynamics object is the same as done for creating a precipitation model. The GeneralThermodynamics object can be used here since the functions necessary for the diffusion model are the same for binary and multicomponent systems." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import GeneralThermodynamics\n", + "\n", + "therm = GeneralThermodynamics('NiCrAl.tdb', ['NI', 'CR', 'AL'], ['FCC_A1'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step is to create the diffusion model. The model requires the z-coordinates, elements and phases upon initialization. Initial conditions can be added with the composition either as a step function, linear function, delta function or a user-defined function. Finally, boundary conditions are assumed to be no-flux conditions; however, constant flux or composition may also be defined.\n", + "\n", + "Defining the initial and boundary conditions must specify the element it is being applied to.\n", + "\n", + "Here, a diffusion couple composed of Ni-7.7Cr-5.4Al / Ni-35.9Cr-6.2Al will be used.\n", + "\n", + "Plotting functions are stored in the diffusion object and can be used to look at the initial conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 0.4)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kawin.diffusion import SinglePhaseModel\n", + "from kawin.solver import SolverType\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#Define mesh spanning between -1mm to 1mm with 50 volume elements\n", + "m = SinglePhaseModel([-1e-3, 1e-3], 100, ['NI', 'CR', 'AL'], ['FCC_A1'])\n", + "\n", + "#Define Cr and Al composition, with step-wise change at z=0\n", + "m.setCompositionStep(0.077, 0.359, 0, 'CR')\n", + "m.setCompositionStep(0.054, 0.062, 0, 'AL')\n", + "\n", + "m.setThermodynamics(therm)\n", + "m.setTemperature(1200 + 273.15)\n", + "\n", + "fig, axL = plt.subplots(1, 1)\n", + "axL, axR = m.plotTwoAxis(['AL'], ['CR'], zScale = 1/1000, axL = axL)\n", + "axL.set_xlim([-1, 1])\n", + "axL.set_xlabel('Distance (mm)')\n", + "axL.set_ylim([0, 0.1])\n", + "axR.set_ylim([0, 0.4])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the initial and boundary conditions, the temperature and Thermodynamics object must be supplied to the diffusion model.\n", + "\n", + "Similar to the precipitation model, progress on the simulation can be outputted by setting verbose to True and setting vIt to the number of iterations before a status update on the model is outputted." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration\tSim Time (h)\tRun time (s)\n", + "0\t\t0.0e+00\t\t0.0\n", + "100\t\t2.9e+01\t\t5.7\n", + "200\t\t5.7e+01\t\t17.2\n", + "300\t\t8.6e+01\t\t22.7\n", + "349\t\t1.0e+02\t\t24.2\n" + ] + } + ], + "source": [ + "m.solve(100*3600, solverType = SolverType.EXPLICITEULER, verbose=True, vIt=100)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting\n", + "\n", + "Plotting the final composition profile is the same as plotting the initial profile." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axL = plt.subplots(1, 1)\n", + "axL, axR = m.plotTwoAxis(['AL'], ['CR'], zScale = 1/1000, axL=axL)\n", + "axL.set_xlim([-1, 1])\n", + "axL.set_xlabel('Distance (mm)')\n", + "axL.set_ylim([0, 0.1])\n", + "axR.set_ylim([0, 0.4])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. A. Borgenstam, A. Engstrom, L. Hoglund, J. Agren, \"DICTRA, a Tool for Simulation of Diffusional Transformations in Alloys\" *Journal of Phase Equilibria* 21 (2000) p. 269\n", + "2. A. Engstrom and J. Agren, \"Assessment of Diffusional MObilities in Face-Centered Cubic Ni-Cr-Al Alloys\" *Z. Metallkd.* 87 (1996) p. 92" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('base')", + "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": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/07_Homogenization_Model.ipynb b/examples/07_Homogenization_Model.ipynb new file mode 100644 index 0000000..83d214b --- /dev/null +++ b/examples/07_Homogenization_Model.ipynb @@ -0,0 +1,271 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homogenization Model\n", + "\n", + "## Example - Fe-Cr-Ni system\n", + "\n", + "The homogenization model can simulate multiphase diffusion without having to resort to more complex methods such as phase field modeling. The model relies on the assumption that every volume element is in local equilibrium. Then fluxes are determined by the mobility and chemical potential gradient.\n", + "\n", + "$$ J_k = -\\Gamma_k^* \\frac{\\partial \\mu_k^{eq}}{\\partial z} $$\n", + "\n", + "$$ \\Gamma_k^\\phi = M_k^\\phi x_k^\\phi $$\n", + "\n", + "$$ \\Gamma_k^* = f(\\Gamma_k^\\alpha, \\Gamma_k^\\beta, ...) $$\n", + "\n", + "$\\Gamma_k^*$ is an average mobility term that assumes certain geometry in the system. The following averaging functions are available in kawin:\n", + "\n", + "1. Upper Wiener - assumes phases are continuous layers parallel to flux\n", + "2. Lower Wiener - assumes phases are continuous layers orthogonal to flux\n", + "3. Upper Hashin-Shtrikman - assumes a matrix of the phase with the fastest mobility with spheres of all other phases\n", + "4. Lower Hashin-Shtrikman - assumes a matrix of the phase with the slowest mobility with spheres of all other phases\n", + "5. Labyrinth - assumes phases as precipitates\n", + "\n", + "Note that the Hashin-Shtrikman bounds are much narrower than the Wiener bounds.\n", + "\n", + "The fluxes are calculated in a lattice fixed frame of reference. To convert to a volume fixed frame, the flux is then defined by:\n", + "\n", + "$$ J_k^v = J_k - x_k \\sum{J_j} $$\n", + "\n", + "In this example a Fe-25.7Cr-6.5Ni / Fe-42.3Cr-27.6Ni diffusion couple will be simulated using the lower and upper Hashin-Shtrikman bounds. Both sides of the diffusion couple are $\\alpha+\\gamma$.\n", + "\n", + "The first step is the load the thermodynamic database." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import GeneralThermodynamics\n", + "from kawin.diffusion import HomogenizationModel\n", + "from kawin.solver import SolverType\n", + "import matplotlib.pyplot as plt\n", + "\n", + "elements = ['FE', 'CR', 'NI']\n", + "phases = ['FCC_A1', 'BCC_A2']\n", + "\n", + "therm = GeneralThermodynamics('FeCrNi.tdb', elements, phases)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Defining the homogenization model is similar to defining the single phase diffusion model where the bounds of the domain, the number of volume elements, the defined elements and the defined phases are needed.\n", + "\n", + "As with the single phase diffusion model, inputting the composition profile and parameters are also the same. The only difference is that two extra parameters will be defined for the homogenization model:\n", + "\n", + "Smoothing factor ($\\varepsilon$) - this factor allows for the composition to smooth out when the chemical potential gradient is zero but the composition gradient is non-zero (in n-phase regions where n is the number of components). This can be viewed as an ideal contribution where the composition smoothes out to maximize entropy. By default, it is set to 0.05, but here, we will set it to 0.01.\n", + "\n", + "Mobility function - this defined which of the above mentioned mobility functions to use. We will start with the lower Hashin-Shtrikman bounds.\n", + "\n", + "Solving the model is also similar to the single phase diffusion model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration\tSim Time (h)\tRun time (s)\n", + "0\t\t0.0e+00\t\t0.0\n", + "252\t\t1.0e+02\t\t58.1\n" + ] + } + ], + "source": [ + "ml = HomogenizationModel([-5e-4, 5e-4], 200, elements, phases)\n", + "ml.setCompositionStep(0.257, 0.423, 0, 'CR')\n", + "ml.setCompositionStep(0.065, 0.276, 0, 'NI')\n", + "ml.setTemperature(1100+273.15)\n", + "ml.setThermodynamics(therm)\n", + "ml.eps = 0.01\n", + "\n", + "ml.setMobilityFunction('hashin lower')\n", + "ml.solve(100*3600, solverType=SolverType.EXPLICITEULER, verbose=True, vIt=500)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next model will be the exact same except the mobility function will be switched to the upper Hashin-Shtrikman bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration\tSim Time (h)\tRun time (s)\n", + "0\t\t0.0e+00\t\t0.0\n", + "500\t\t1.3e+01\t\t55.6\n", + "1000\t\t2.7e+01\t\t80.2\n", + "1500\t\t4.2e+01\t\t97.4\n", + "2000\t\t5.7e+01\t\t110.2\n", + "2500\t\t7.1e+01\t\t120.3\n", + "3000\t\t8.6e+01\t\t130.8\n", + "3483\t\t1.0e+02\t\t139.5\n" + ] + } + ], + "source": [ + "mu = HomogenizationModel([-5e-4, 5e-4], 200, elements, phases)\n", + "mu.setCompositionStep(0.257, 0.423, 0, 'CR')\n", + "mu.setCompositionStep(0.065, 0.276, 0, 'NI')\n", + "mu.setTemperature(1100+273.15)\n", + "mu.setThermodynamics(therm)\n", + "ml.eps = 0.01\n", + "\n", + "mu.setMobilityFunction('hashin upper')\n", + "mu.solve(100*3600, solverType=SolverType.EXPLICITEULER, verbose=True, vIt=500)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To compare the two mobility functions, the Cr composition, Ni composition and $\\alpha$ phase fraction profile will be plotted. By default, the plotting functions will plot all components or phases; however, an individual component or phase can be defined to have it be the only thing that is plotted.\n", + "\n", + "Here, we can see that the upper Hashin-Shtrikman bounds gives a smoother Cr and Ni profile. Additionally, the lower Hashin-Shtrikman bounds shows a pure $\\gamma$ layer near the interface of around 4-6 $\\mu m$." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,3, figsize=(15,4))\n", + "ml.plot(ax[0], plotElement='CR', label='lower')\n", + "ml.plot(ax[1], plotElement='NI', label='lower')\n", + "ml.plotPhases(ax[2], plotPhase='BCC_A2', label='lower')\n", + "\n", + "mu.plot(ax[0], plotElement='CR', label='upper')\n", + "mu.plot(ax[1], plotElement='NI', label='upper')\n", + "mu.plotPhases(ax[2], plotPhase='BCC_A2', label='upper')\n", + "\n", + "ax[0].set_ylabel('Composition CR (%at)')\n", + "ax[0].set_ylim([0.2, 0.45])\n", + "ax[1].set_ylabel('Composition NI (%at)')\n", + "ax[1].set_ylim([0, 0.35])\n", + "ax[2].set_ylabel(r'Fraction $\\alpha$')\n", + "ax[2].set_ylim([0, 0.8])\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the composition profile on a phase diagram to further show the diffusion path and compare both mobility functions. Using the triangular plotting feature in pycalphad, the Fe-Cr-Ni ternary phase diagram can be plotted and the diffusion paths of the two homogenization models can be superimposed on top." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pycalphad import equilibrium, Database, ternplot, variables as v\n", + "from pycalphad.plot import triangular\n", + "from pycalphad.plot.utils import phase_legend\n", + "\n", + "fig = plt.figure(figsize=(6,6))\n", + "ax = fig.add_subplot(projection='triangular')\n", + "\n", + "conds = {v.T: 1100+273.15, v.P:101325, v.X('CR'): (0,1,0.015), v.X('NI'): (0,1,0.015)}\n", + "ternplot(therm.db, ['FE', 'CR', 'NI', 'VA'], phases, conds, x=v.X('CR'), y=v.X('NI'), ax = ax)\n", + "\n", + "ln1, = ax.plot(ml.getX('CR'), ml.getX('NI'), label='lower')\n", + "ln2, = ax.plot(mu.getX('CR'), mu.getX('NI'), label='upper')\n", + "\n", + "#The pycalphad ternplot function will automatically add a legend for the phases,\n", + "#but the legend has to be added again to add labels for the diffusion paths\n", + "handles, _ = phase_legend(phases)\n", + "ax.legend(handles = handles + [ln1, ln2])\n", + "\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. H. Larsson and L. Hoglund, \"Multiphase diffusion simulations in 1D using the DICTRA homogenization model\" *Calphad* 33 (2009) p. 495\n", + "2. H. Larsson and A. Engstrom, \"A homogenization approach to diffusion simulations applied to $\\alpha+\\gamma$ Fe-Cr-Ni diffusion couples\" *Acta Materialia* 54 (2006) p. 2431" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 64-bit", + "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": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "822df1fa43a9cb3d4c4a5882bc10c066bf8074b03729cc74aeda55033a52fda7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/08_Model_Coupling.ipynb b/examples/08_Model_Coupling.ipynb new file mode 100644 index 0000000..0810d54 --- /dev/null +++ b/examples/08_Model_Coupling.ipynb @@ -0,0 +1,421 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling Coupling\n", + "\n", + "## Exampling - Grain growth with Zener pinning\n", + "\n", + "There are three ways to couple two models in kawin. We'll show this by coupling a KWN model with a grain growth model, where grain growth is inhibited by Zener pinning.\n", + "\n", + "First let's set up the two models.\n", + "\n", + "The first model is the KWN model with the Al-Sc system that was used in the Strength Modeling example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import BinaryThermodynamics\n", + "from kawin.precipitation import PrecipitateModel, VolumeParameter\n", + "import numpy as np\n", + "\n", + "therm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'SC'], ['FCC_A1', 'AL3SC'])\n", + "therm.setGuessComposition(0.24)\n", + "\n", + "precModel = PrecipitateModel()\n", + "\n", + "precModel.setInitialComposition(0.002)\n", + "precModel.setTemperature(400+273.15)\n", + "precModel.setInterfacialEnergy(0.1)\n", + "\n", + "Va = (0.405e-9)**3\n", + "Vb = (0.4196e-9)**3\n", + "precModel.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, 4)\n", + "precModel.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, 4)\n", + "\n", + "diff = lambda x, T: 1.9e-4 * np.exp(-164000 / (8.314*T)) \n", + "precModel.setDiffusivity(diff)\n", + "\n", + "precModel.setThermodynamics(therm, addDiffusivity=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second model is a grain growth model with a grain size distribution (radius) following $ ln X = Normal(ln(1*10^{-6}), 0.2) $ with a grain boundary mobility of 1e-14 $m^4/Js$.\n", + "\n", + "The grain growth model follows the implementation from K. Wu, J. Jeppson and P. Mason, J. Phase Equilib. Diffus. 43 (2022) 866-875. The growth rate of a grain of size $R_i$ is defined as:\n", + "$$ \\frac{dR_i}{dt} = \\alpha M \\gamma \\left(\\frac{1}{R_{Cr}} - \\frac{1}{R_i} \\right) $$\n", + "\n", + "Where $\\alpha$ is a correction factor when fitting to experimental data, $M$ is the grain boundary mobility ($m^4/Js$) and $\\gamma$ is the grain boundary energy ($J/m^2$). The default values for $\\alpha$, $M$ and $\\gamma$ are $1$, $1e{-14} m^4/Js$ and $0.5 J/m^2$ respectively.\n", + "\n", + "To satisfy volume conservation, $R_{Cr}$ is defined as:\n", + "$$ R_{Cr} = \\frac{\\sum{n_i R_i^2}}{\\sum{n_i R_i}} $$\n", + "\n", + "With the average radius defined as:\n", + "$$ R_m = \\left(\\frac{\\sum{n_i R_i^3}}{\\sum{n_i}}\\right)^{1/3} $$\n", + "\n", + "Precipitates create a pinning force, which can be defined as the inverse of the Zener radius ($R_z$):\n", + "$$ z = \\frac{1}{R_z} = \\frac{1}{K} \\frac{f^m}{r_{avg}} $$\n", + "\n", + "Where $f$ and $r_{avg}$ is the volume fraction and average radius of the precipitates respectively. The terms $K$ and $m$ are values that correspond to the spatial distribution of precipitates in the alloy. By default, they are $4/3$ and $1$ respectively. For multi-phase systems, $z$ is calculated for each phase and summed together. Then $R_z$ is the inverse of the summed $z$.\n", + "\n", + "The growth rate accounting for this pinning force is then defined as:\n", + "$$ \\frac{dR_i}{dt} = \\alpha M \\gamma \\left(\\frac{1}{R_{Cr}} - \\frac{1}{R_i} \\pm \\frac{1}{R_z} \\right) $$\n", + "\n", + "Where $\\frac{1}{R_z}$ is subtracted if $\\left(\\frac{1}{R_{Cr}} - \\frac{1}{R_i} - \\frac{1}{R_z} \\right)$ is greater than 0 (inhibiting grain growth), $\\frac{1}{R_z}$ is added if $\\left(\\frac{1}{R_{Cr}} - \\frac{1}{R_i} + \\frac{1}{R_z} \\right)$ is less than 0 (inhibiting grain dissolution), and $\\frac{dR_i}{dt}$ is 0 between these two limits." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kawin.precipitation.coupling.GrainGrowth import GrainGrowthModel\n", + "import matplotlib.pyplot as plt\n", + "\n", + "grainModel = GrainGrowthModel(cMin=1e-10, cMax=0.5e-5)\n", + "grainModel.setGrainBoundaryMobility(1e-14)\n", + "data = np.random.lognormal(mean=np.log(1e-6), sigma=0.2, size=100000)\n", + "grainModel.LoadDistribution(data)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(4,4))\n", + "\n", + "#Solve model for 250 hours to create a reference to unpinned grain growth\n", + "grainModel.solve(250*3600)\n", + "t_noPin = np.array(grainModel.time/3600)\n", + "r_noPin = np.array(grainModel.avgR)\n", + "\n", + "grainModel.plotRadiusvsTime(ax, timeUnits='h')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Method 1 - Loose Coupling\n", + "\n", + "The easiest way to couple these two models is to solve each one seperately for a fixed amount of time and update the models with the new values. For our system, only the grain growth model needs information from the precipitate model. While easy to implement, this is a very loose couple and the fidelity of the coupling is dependent on the time interval before updating the models. Here, we use a fairly coarse step size with 50 steps on a log scale." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nucleation density not set.\n", + "Setting nucleation density assuming grain size of 100 um and dislocation density of 5e+12 #/m2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + } + ], + "source": [ + "#Reset models\n", + "precModel.reset()\n", + "grainModel.reset()\n", + "\n", + "#Set up an array of fixed times on a log scale up to 9e5 seconds\n", + "times = np.concatenate(([0], np.logspace(np.log10(9e0), np.log10(9e5), 50)))\n", + "\n", + "for i in range(len(times)-1):\n", + " precModel.solve(times[i+1] - times[i])\n", + " grainModel.computeZenerRadius(precModel)\n", + " grainModel.solve(times[i+1] - times[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the un-pinned grain growth showed parabolic behavior, the pinned grain growth grows in a step-wise fashion growing very quickly, then plateauing, then growth quickly again. This occurs as the during the coarsening step, the grains will continue to growth until reaching the Zener radius, at which the growth rate will plateau. As the precipitate coarsens, the Zener radius increases (reducing the pinning force), allowing to grains to quickly grow and catch up to the Zener radius. This process will continue to repeat itself as the precipitates coarsen." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 6e-05)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(4,4))\n", + "ax.plot(t_noPin, r_noPin, label='No pinning')\n", + "grainModel.plotRadiusvsTime(ax, timeUnits='h', label='Pinning')\n", + "ax.legend()\n", + "ax.set_ylim([0, 6e-5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Method 2 - Dependent coupling\n", + "\n", + "The next way these two models can be coupled in kawin is by making one model dependent off the other. Here, we'll make the grain growth model dependent off the precipitate model.\n", + "\n", + "Implementation-wise, this requires the grain growth model to have a function called \"updateCoupledModel\" which will take the precipitate model as an input.\n", + "\n", + "This type of coupling is similar to method 1, except that instead of running each model for a fixed amount of time, the precipitate model will first update a single iteration. Then the grain growth model will be solved for the amount of time that was solved for in that iteration. You can think of it as method 1, but with much finer intervals of time that are determined by the precipitate model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "0\t0.0e+00\t\t0.0\t\t673\t\t0.2000\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t3.6624e+03\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "2000\t4.0e+02\t\t25.2\t\t673\t\t0.1605\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t2.474e+20\t\t0.1590\t\t1.1460e-08\t3.3350e+03\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "3768\t9.0e+05\t\t37.5\t\t673\t\t0.0165\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t7.218e+19\t\t0.7344\t\t2.8281e-08\t8.1794e+01\n", + "\n" + ] + } + ], + "source": [ + "#Reset models\n", + "precModel.reset()\n", + "grainModel.reset()\n", + "\n", + "#Add grain growth model to the precipitate model to be updated every iteration\n", + "precModel.addCouplingModel(grainModel)\n", + "\n", + "precModel.solve(9e5, verbose=True, vIt=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 6e-05)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(4,4))\n", + "ax.plot(t_noPin, r_noPin, label='No pinning')\n", + "grainModel.plotRadiusvsTime(ax, timeUnits='h', label='Pinning')\n", + "ax.legend()\n", + "ax.set_ylim([0, 6e-5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Method 3 - Tight coupling\n", + "\n", + "kawin supplies a Coupler class that allows control over how the models affect each other within a single iteration when solving. This can enable tigher coupling between multiple models.\n", + "\n", + "Here, we'll create a custom model class that inherits the Coupler. We can then overload the getdXdt function to compute the Zener radius of the grain growth model using the current particle size distribution of the precipitate model. (Note: we use the parameter x in the getdXdt function rather than using the particle size distribution from the precipitate model itself since x is the most recent values of the model. For iteration schemes that have multiple steps such as the Runga Kutta methods, x will include the intermediate values generated during the iteration)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration\tSim Time(s)\tRun Time(s)\n", + "0\t\t0.0e+00\t\t0.0\n", + "2000\t\t2.7e+02\t\t21.3\n", + "4000\t\t1.1e+03\t\t31.9\n", + "6000\t\t1.4e+04\t\t40.2\n", + "8000\t\t2.5e+05\t\t50.6\n", + "10000\t\t8.7e+05\t\t59.3\n", + "10054\t\t9.0e+05\t\t59.5\n" + ] + } + ], + "source": [ + "from kawin.GenericModel import Coupler\n", + "\n", + "class CustomCoupledModel(Coupler):\n", + " def __init__(self, precModel, grainModel):\n", + " '''\n", + " Custom model that inherits the coupler\n", + " The coupler takes a list of models, but we can separate them\n", + " in this derived class\n", + " '''\n", + " self.precModel = precModel\n", + " self.grainModel = grainModel\n", + " super().__init__([precModel, grainModel])\n", + "\n", + " def getdXdt(self, t, x):\n", + " '''\n", + " Here we overload the getdXdt function by computing the zener\n", + " radius in the grain growth model before compute dXdt for the two\n", + " models\n", + " '''\n", + " self.grainModel.computeZenerRadiusByN(self.precModel, x[0])\n", + " return super().getdXdt(t, x)\n", + " \n", + "#Reset models\n", + "precModel.reset()\n", + "precModel.clearCouplingModels()\n", + "grainModel.reset()\n", + "\n", + "#Create the coupled model and solve\n", + "coupledModel = CustomCoupledModel(precModel, grainModel)\n", + "coupledModel.solve(9e5, verbose=True, vIt=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 6e-05)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RryZL0c/0W7ZsiQMHDihfP61rImkPLy8vnDhxQuwyiOotMbsQ3xy9iZ8v3oFMrmjlDmtij/Hdm6FvS3cY60BPnPoQPWFNTEw42BcRNZgz6XlYdSQVhxL/ujmxa4Az3uneDJ2bOel9DzTRQz85ORmenp6wsLBAp06dMH/+fHh7e9e6rlQqhVQqVb6uyxCkOnydmlTEnzU9iSAIOJ5yH8sPpeCPtDwAins1XwzxwDvdmyG0ieEMKy5q6Hfo0AFxcXEICgpCVlYW5syZg65du+LKlSuwtbWtsf78+fNrXAN4kkd3lJaWlsLS0lKtdZN2Ki1VTEjx+N3EZLjkcgEHE3Ow4lCycnYqM2MjvNy2Md7q1gy+zrrZ7fJ5aFWXzfz8fPj4+GDx4sUYN25cjeW1nel7eXk9sWtTVlYW8vPz4erqCisrK73/b5uhEgQBpaWlyMnJgYODAzw8PMQuiUQmkwv49XIWVh5OQWJ2EQDAwtQII9p7461ufvCw194TQYPqsung4IDAwECkpKTUutzc3Bzm5nXvI/voWsHTBhYj/eHg4MDrQwauUibHjoTbWH0kFTfvlwAAbMxNEN3JB+Ne8FXOTmXItCr0i4uLkZqaiujoaLVsTyKRwMPDA66urqisrFTLNkk7mZqa8m5gA1ZeKcO2s5lYffQmbueXAVBMRRjT2RdjOzeFvRWb/B4RNfSnTp2KgQMHwsfHB3fu3MGsWbNgbGyMESNGqHU/xsbGDAQiPVQircLG0xlY8/tN3CtSNP0625jjrW6+GNnBBzbmWnVeqxVE/Y78+eefGDFiBHJzc+Hi4oIXXngBp06dgouLi5hlEZGWKyirxPr4dPxwIg0PHk4y7mlvgXd6NMOwCC+tHtpYbKKG/ubNm8XcPRHpmAclFfj+eBrWxaejSKqYtKSpkxXG92iGIa2bwMyEkwE+C//vQ0Ra736xFN/9nob1J9NRWiEDAAS62SC2pz8GhHrAxJhhX1cMfSLSWjlF5Vhz9CY2nM5AWaUi7Ft42GFSb3/0beGuE5OWaBuGPhFpneyCcqw+mopNf2RAWqUYBC2siT0m9QpA72BX3nPzHBj6RKQ1bueXYfWRVGw5k4kKmSLsW3s74L3eAege6MKwVwOGPhGJ7s8Hpfj6cAq2n/tTOdF4+6aOmNQ7AF389X8QtIbE0Cci0WQVlGHFoRRsPZupDPvOzZwwqXcAOvo5iVydfmLoE1GDyyksx8ojqdh4OkPZjPOCvzPeiwxAu6aOIlen3xj6RNRgcoulWH00Ff85dUs5S1V7X0dM6RPIM/sGwtAnIo17UFKBb3+/ibj4v/rZt/Z2wAd9gthm38AY+kSkMQVllfj+eBp+OJ6G4od30IY1scfkPoHowd44omDoE5HaFUursPZ4Gr79/SYKyxVhH+xhhyl9AhHJfvaiYugTkdqUVciw7mQ6vjmaqhwILcDVBpP7BKJ/S95Bqw0Y+kT03Cqq5NhyJgPLDqUohzj2c7bGe5EBeCnME8YMe63B0CeiepPLBfxy6Q6+/O0GMvIUcxQ3aWSJ9yMDMTjckwOhaSGGPhGpTBAEHE7KwaK9Sco5aJ1tzDGptz9ea+fNIY61GEOfiFTyR1oeFu1NxNlbDwAAthYmeKd7M8R0aQorM0aKtuNPiIjq5OqdAnyxLwmHk+4BAMxNjDC2S1OM794MDlZmIldHdcXQJ6KnSr9fgsX7b+Dni3cAAMZGEgxv54VJvQLgbm8hcnWkKoY+EdUqp7AcSw8mY8uZTFTJFYOh/aOVJ6b0CURTZ2uRq6P6YugTUTXF0iqsOXYT3x67qZytqmeQC6b2C0JLT3uRq6PnxdAnIgBAlUyOzWcyseRAMu4XK/rat/F2wLT+zdGBg6HpDYY+kYETBAH7r93Fgr2JuHmvBADQ1MkK0/o3R/8Qdw6ZoGcY+kQGLCHjAebvTsQf6XkAAEdrM7zXOwAjO3jDlDdW6SWGPpEBSr9fgs/3JeHXy1kAAAtTI7zxgh/e7u4HWwtTkasjTWLoExmQvJIKLDuYjA2nb6FSJkAiAV5t2wST+wTCw95S7PKoATD0iQxARZUc6+LTsexQMooeDnXcI8gF06Oao7m7ncjVUUNi6BPpsUcXaeftvo70XMWAaC097fDxi8Ho4u8scnUkBoY+kZ66nlWIubuuIT41FwDgYmuOj/oF4eU2TTiuvQFj6BPpmfvFUnz52w1sOZMBuQCYmRjhza6+GN/DHzbm/JM3dPwNINITFVVyxMWnYfnBFBQ9nI92QJgHpvdvDi9HK5GrI23B0CfScYIg4FBiDj779TrS7iturgptbI+ZA1ugXVNHkasjbcPQJ9JhKTnF+HTXNRy7oRju2NnGHB/1D8IrbLenJ2DoE+mggrJKLDuYjHXx6aiSCzAzNsI/X/BFbM9mvLmKnoqhT6RDqmRybPojA18dSEZeSQUAIDLYDZ8MCOZwx1QnDH0iHXH0xj18tusaknOKAQD+rjb410st0D3QReTKSJcw9Im0XEpOET779TqOPJymsJGVKSb3CcSI9hwUjVTH0CfSUg9KKrDkwA3893QGZHIBJkYSjOncFJN6BcDeiu32VD8MfSItU1Elx/qT6Vh2MBmFD8fJ6dPCDR+/GAxfttvTc2LoE2kJQRBw4HoO5u3+q799c3dbzHypBTpznBxSk3qFfkZGBm7duoXS0lK4uLigZcuWMDc3V3dtRAbjelYhPvv1Gk6kKMbJcbYxw9S+QXg1wgvG7G9PalTnq0Dp6emYNm0afHx84Ovri+7duyMqKgoRERGwt7dHnz59sG3bNsjl8noVsmDBAkgkErz//vv1+jyRLnpQUoFPdl7GgGW/40RKLsxMjDC+RzMcntoDr7X3ZuCT2tUp9CdNmoRWrVohLS0Nn332Ga5du4aCggJUVFQgOzsbu3fvxgsvvICZM2ciLCwMZ86cUamIM2fO4JtvvkFYWFi9DoJI18jkAv5z6hZ6fnkE/z2lGBjtxVB3HJzSHdP6N+cNVqQxdWresba2xs2bN+Hk5FRjmaurK3r16oVevXph1qxZ2Lt3LzIzM9GuXbs6FVBcXIxRo0bh22+/xWeffaZa9UQ66Ex6Hmb9dBXXsgoBKNrtZw1siU7Nav59EalbnUJ//vz5dd5g//79VSogNjYWAwYMQGRk5DNDXyqVQiqVKl8XFhaqtC8iMWUXlGPBnuvYeeEOAMDOwgRT+gTi9Y4+MGF/e2ogovbe2bx5M86fP1/n5qD58+djzpw5Gq6KSL2kVTL8cDwdyw8lo7RCBokEeK2dF6b2DYKTDTtAUMNSOfRzc3Mxc+ZMHD58GDk5OTUu3Obl5dVpO5mZmXjvvfewf/9+WFhY1OkzM2bMwJQpU5SvCwsL4eXlVffiiRrY4cQcfLrrmrILZmtvB8z5R0uENXEQtzAyWCqHfnR0NFJSUjBu3Di4ublBIqlf74Jz584hJycHbdq0Ub4nk8lw7NgxrFixAlKpFMbGxtU+Y25uzq6hpBPS75dg7q5rOJiYA0Ax5PGMqOYY0roxhzwmUakc+r///juOHz+OVq1aPdeOe/fujcuXL1d7LyYmBs2bN8e0adNqBD6RLiivlOHrwyn45uhNVMjkMDGS4J8v+OLdXv7skUNaQeXQb968OcrKyp57x7a2tggJCan2nrW1NZycnGq8T6QLDl6/i9m/XEVmnuLvo2uAM2YNbAl/VxuRKyP6i8qhv3LlSkyfPh0zZ85ESEgITE2rn73Y2dmprTgiXfDng1LM+eUa9l+7CwDwsLfAzJdaoH+Ie72bP4k0ReXQd3BwQGFhIXr16lXtfUEQIJFIIJPJ6l3MkSNH6v1ZooZWUSXHd8dvYtnBZJRXKppyxr3gi0m9A2BtzmGtSDup/Js5atQomJqaYuPGjc91IZdIl8Wn3Me/frqC1HuKXjntfR3x2eAQBLrZilwZ0dOpHPpXrlxBQkICgoKCNFEPkVbLKSzHv3dfx08Pb7BytjHDxy8GY0jrxjwBIp2gcuhHREQgMzOToU8GRSYX8J+T6fjytxsoklbBSAJEd/TBlL5BsLdkrxzSHSqH/rvvvov33nsPH374IUJDQ2tcyOWgaaRvrt0pxIwdl3ExMx8A0MrLAZ8NCkFoE3txCyOqB4kgCIIqHzAyqjlGiEQiUcuFXFUVFhbC3t4eBQUF7DVEaldWIcPSg8n49vebkMkF2Jqb4KOo5hjV3ps3WJHGaDrXVD7TT0tLU3sRRNrm2I17+L+dl5V97l8MdcesgS3hZle3IUOItJXKoe/j46OJOoi0wv1iKT7bdU05EqaHvQU+HRSCPi3cRK6MSD3qFPqnTp1Cx44d67TB0tJSpKWloWXLls9VGFFDEgQB2879iXm7ryO/tBISCTC2c1N80DcINuxzT3qkToN4R0dHo1+/fti2bRtKSkpqXefatWv4+OOP0axZM5w7d06tRRJp0s17xRj57Wl8tP0S8ksrEexhhx0TumDWwJYMfNI7dfqNvnbtGlatWoVPPvkEI0eORGBgIDw9PWFhYYEHDx4gMTERxcXFGDJkCH777TeEhoZqum6i51Ypk2PNsZtYejAZFVVyWJgaYXJkIP75gi9MOakJ6SmVe++cPXsWx48fx61bt1BWVgZnZ2e0bt0aPXv2hKOjo6bqrBV771B9Xb1TgI+2X8LVO4rZ17oGOOPfg0Ph7WQlcmVk6LSu905ERAQiIiLUXghRQ5BWyfD1oRSsPJKKKrkAe0tTzHypBYa24R21ZBjYYEkG40JmPj7afhE37hYDAPq3dMeng1vC1ZbdMMlwMPRJ75VXyvDV/hv49vebkAuAk7UZ5g4OwYuhHmKXRtTgGPqk186k5+Gj7ZeUc9QODvfEzIEt4WhtJnJlROJg6JNeKpFW4fN9SVh3Mh2CALjZmePfg0MRyZusyMCpJfTz8/Ph4OCgjk0RPbdTN3Px4faLyiEUhkd44eMBwRwNkwh1vDnr7xYuXIgtW7YoXw8bNgxOTk5o3LgxLl68qNbiiFRRXinD3F3XMOLbU8jMK0NjB0v8Z1x7LHwljIFP9JDKob969Wp4eXkBAPbv34/9+/djz549iIqKwocffqj2Aonq4mJmPgYs+x3fH0+DIACvtfPCvsnd0DXARezSiLSKys072dnZytDftWsXhg0bhr59+6Jp06bo0KGD2gskepqKKjmWH0rGyiOpkMkFuNqaY+HLYejZ3FXs0oi0kspn+o0aNUJmZiYAYO/evYiMjASgGLCqIcfSJ0rMLsTgr09g+aEUyOQC/tHKE79N7sbAJ3oKlc/0hw4dipEjRyIgIAC5ubmIiooCACQkJMDf31/tBRI9TiYXsObYTXy1/wYqZHI0sjLFZ4NDMSCM/e6JnkXl0P/qq6/QtGlTZGZmYtGiRbCxsQEAZGVlYcKECWovkOjv0u6X4IOtF3A+Ix8AEBnsinlDQ3lXLVEdqTzgmjbhgGuGQxAEbD6TiU9/uYayShlszU0wc2ALvNK2CcfMIb2idQOurV+//qnLR48eXe9iiGqTV1KB6f+7hN+u3QUAdPJzwhfDWqGxg6XIlRHpHpXP9Bs1alTtdWVlJUpLS2FmZgYrKyvk5eWptcCn4Zm+/jt24x6mbruInCIpTI0l+Khfc4x7wZcTk5Pe0roz/QcPHtR4Lzk5GePHj2c/fVKb8koZFu1Nwg8n0gAA/q42WDI8HCGN7UWujEi3qWUYhoCAACxYsACvv/46EhMT1bFJMmBJ2UV4b3MCErOLAADRHX3w8YvBsDQzFrkyIt2ntgHXTExMcOfOHXVtjgyQIAiIi0/H/D2JqKiSw8naDIteCUPvYA6SRqQuKof+zz//XO21IAjIysrCihUr0KVLF7UVRoblXpEUU7ddxNEb9wAAPYNcsOiVVnCxNRe5MiL9onLoDx48uNpriUQCFxcX9OrVC19++aW66iIDcjz5Pt7fcgH3i6UwNzHC/w0IRnRHH3bFJNIAlUNfLpdrog4yQFUyOb46cAMrj6RCEIAgN1ssH9kagW62YpdGpLc4iQqJ4k5+GSZtSsDZW4reYCPae2PWwBawMOXFWiJNqlPoT5kyBXPnzoW1tTWmTJny1HUXL16slsJIf+2/dhcfbr+I/NJK2JqbYP7LoXgpzFPssogMQp1CPyEhAZWVlcqvn4RtsPQ00ioZFuxJxNoT6QCAsCb2WDGiDbydrMQtjMiAcOwdahDp90swcdN5XLldCAAY94IvpvVvDjMTlUf3JtJrWndHLpGqfr2UhWn/u4RiaRUcrEzx5aut2PeeSCR1Cv2hQ4fWeYM//vhjvYsh/VJRJce83dcRF58OAGjf1BFLR4TDw54DpRGJpU6hb2//13gngiBgx44dsLe3R0REBADg3LlzyM/PV+kfB9Jvd/LLELvxPBIejns/vkczfNAnECbGbM4hElOdQn/t2rXKr6dNm4Zhw4Zh9erVMDZWdK+TyWSYMGEC29UJAHD0xj28vzkBD0orYWdhgsXDwhHZgs05RNpA5Qu5Li4uOH78OIKCgqq9n5SUhM6dOyM3N1etBT4NL+RqF5lcwNKDyVh+KBmCAIQ0tsOqUW3h5cjeOUR1pelcU/n/2lVVVbWOpJmYmKjy3bqrVq1CWFgY7OzsYGdnh06dOmHPnj2qlkRaILdYirFr/8Cyg4rAH9XBG9vf6czAJ9IyKvfeiYmJwbhx45Camor27dsDAE6fPo0FCxYgJiZGpW01adIECxYsQEBAAARBwLp16zBo0CAkJCSgZcuWqpZGIrmQmY/x/z2HrIJyWJoa499DQjC0TROxyyKiWqjcvCOXy/HFF19g6dKlyMrKAgB4eHjgvffewwcffKBs568vR0dHfP755xg3btwz12Xzjvi2ns3EJzuuoEImh5+LNVaNaosgd46dQ1Rfms6157o5q7BQcaONOgqTyWTYtm0bxowZg4SEBLRo0aLGOlKpFFKptNr+vby8GPoiqJTJMXfXNaw/eQsA0KeFGxYPawVbC1ORKyPSbVp9c5Y6Crp8+TI6deqE8vJy2NjYYMeOHbUGPgDMnz8fc+bMee590vO5VyRF7Ibz+CNdMR/y5MhAvNvLn/PWEumAep3pb9++HVu3bkVGRgYqKiqqLTt//rxK26qoqEBGRgYKCgqwfft2fPfddzh69CjP9LXUxcx8vPOw/d7W3ARfDWd3TCJ10rreO8uWLUNMTAzc3NyQkJCA9u3bw8nJCTdv3kRUVJTKBZiZmcHf3x9t27bF/Pnz0apVKyxdurTWdc3NzZU9fR49qOFsO5uJV785iayCcjRzscbOiV0Y+EQ6RuXQX7lyJdasWYPly5fDzMwMH330Efbv349JkyahoKDguQuSy+XVzuZJfJUyOWb/fBUfbr+Eiio5IoPdsDO2C5q52IhdGhGpSOU2/YyMDHTu3BkAYGlpiaKiIgBAdHQ0OnbsiBUrVtR5WzNmzEBUVBS8vb1RVFSEjRs34siRI9i3b5+qZZGG5JdWYMKG84hPVdx0935kACb1CmD7PZGOUjn03d3dkZeXBx8fH3h7e+PUqVNo1aoV0tLSoOrlgZycHIwePRpZWVmwt7dHWFgY9u3bhz59+qhaFmnAzXvFGLfuLNLul8DazBhfDQ9H35buYpdFRM9B5dDv1asXfv75Z7Ru3RoxMTGYPHkytm/fjrNnz6o84Nr333+v6u6pgZxIuY/x/z2HwvIqNHawxHdjIhDswWsoRLquXjdnyeVymJgo/r3YvHkz4uPjERAQgLfffhtmZmYaKbQ2vDlLM/5z6hZm/3wVMrmANt4O+CY6Ai625mKXRWQQtPrmrMfdvn0bjRs3Vtfmnomhr15VD2+4WvfwhqshrRtj/tBQTlZO1IC0rstmbbKzs/Huu+8iICBAHZsjERSUVSIm7owy8D/sF4TFw1ox8In0TJ1D/8GDBxgxYgScnZ3h6emJZcuWQS6XY+bMmfDz88OZM2eqjbtPuiMzrxRDV57A78n3YWlqjNWvt0FsT39OdE+kh+p8IXf69OmIj4/H2LFjsW/fPkyePBl79+6FkZERDh06hI4dO2qyTtKQi5n5GLfuDO4XV8DdzgLfjYlASGP7Z3+QiHRSnUN/z549iIuLQ69evTBx4kT4+fkhPDwc8+bN02R9pEEHrt3Fu5sSUFYpQ7CHHdaObQd3ewuxyyIiDapz6N+5cwfBwcEAgKZNm8LCwgKvv/66xgojzfrPyXTM+vkq5ALQLdAFX49szREyiQxAnUNfEARlN00AMDY2hqWlpUaKIs2RywUs3JeIb47eBAAMi2iCfw8JhSknLCcyCCqFfu/evZXBX1ZWhoEDB9bol6/qKJvUcMorZZi67SJ2XVJMfjOlj2JIZF6wJTIcdQ79WbNmVXs9aNAgtRdDmpNfWoG31p/DH+l5MDGSYOHLYXi5Lac0JDI0ar05q6Hx5qy6yS4ox+gfTuPG3WLYmptgdXRbdPF3FrssIqqFVs+cRdrv5r1iRH//B27nl8HV1hzrx7VHc3f+A0lkqBj6euzynwUYu/YP5JZUwNfZGuv/2R5ejlZil0VEImLo66n4lPt4c/1ZlFTIENLYDnEx7eFsw0HTiAwdQ18P7bmchfc2X0CFTI5Ofk5YM7ot++ATEQCGvt7ZeDoDn+y8DLkA9G/pjiWvhXPQNCJSqlfoHzx4EAcPHkROTg7kcnm1ZT/88INaCiPVrTmWinm7EwEAI9p747PBITDmtIZE9Dcqh/6cOXPw6aefIiIiAh4eHryxRwsIgoBlB1Pw1YEbAIAJPZrhw35B/NkQUQ0qh/7q1asRFxeH6OhoTdRDKhIEAQv3JmH10VQAwNS+gZjYi/MaEFHtVA79iooKdO7cWRO1kIrkcgGf7rqGuPh0AMAnA4LxRlc/cYsiIq2m8ihbb7zxBjZu3KiJWkgFMrmAj3dcVgb+Z4NDGPhE9Ewqn+mXl5djzZo1OHDgAMLCwmBqWr0r4OLFi9VWHNWuSibH1G0XsfPCHRhJgEWvtMIrHEeHiOpA5dC/dOkSwsPDAQBXrlyptowXDjWvSibHe1su4NdLWTAxkmDJa+F4KcxT7LKISEeoHPqHDx/WRB1UB1UyOSZvvYhfL2XB1FiCr0e2Qd+W7mKXRUQ6hDdn6QiZXMAH2y7il4t3YGoswcpRbdGnhZvYZRGRjqlT6A8dOhRxcXGws7PD0KFDn7rujz/+qJbC6C8yuYAPt13ETxfuwMRIghUj2zDwiahe6hT69vb2yvZ6e3t7jRZE1cnlAqb97xJ+TLgNYyMJlo9ojX5s0iGieuIkKlpMLhcw/cdL2Hr2TxgbSbDstdYYEOYhdllEpEGazjXOhq2lBEHAJz9dwdazf8JIAiwZHs7AJ6LnVq8Ludu3b8fWrVuRkZGBioqKass4MfrzEwTFnbYbT2dAIgG+Gh6Oga3YLZOInp/KZ/rLli1DTEwM3NzckJCQgPbt28PJyQk3b95EVFSUJmo0OEsOJGPtiXQAwMKXwzAovLG4BRGR3lA59FeuXIk1a9Zg+fLlMDMzw0cffYT9+/dj0qRJKCgo0ESNBmXtiTQsPZgMAJg7qCWGRXiJXBER6ROVQz8jI0M54JqlpSWKiooAANHR0di0aZN6qzMwOxNuY84v1wAAH/QJRHSnpuIWRER6R+XQd3d3R15eHgDA29sbp06dAgCkpaVBhzsCie5Q4l1M3XYRABDTpSkm9vIXuSIi0kcqh36vXr3w888/AwBiYmIwefJk9OnTB8OHD8eQIUPUXqAhOJOeh/H/PY8quYAhrRvjXwNacBwjItIIlfvpy+VyyOVymJgoOv5s3rwZ8fHxCAgIwNtvvw0zMzONFFobfeinn5RdhFdWx6OovAq9mrvim+i2MDVmT1oiQ6XpXFMp9KuqqjBv3jz885//RJMm4g/lq+uhf7ewHEO+PoE7BeWI8GmE/4zrAEszTmJOZMi06uYsExMTLFq0CFVVVWovxNAUS6sQs/YM7hSUw8/FGt+NiWDgE5HGqdyO0Lt3bxw9elQTtRiMKpkcsRvO41pWIZxtzLAupj0crBquWYyIDJfKd+RGRUVh+vTpuHz5Mtq2bQtra+tqy//xj3+orTh9JAgC/vXTVRy9cQ8Wpkb4fkw7eDlaiV0WERkIlS/kGhk9+T8HEokEMpmsztuaP38+fvzxRyQmJsLS0hKdO3fGwoULERQUVKfP62Kb/sojKVi0NwkSCfDN6205CQoRVaNVbfrAX713anuoEvgAcPToUcTGxuLUqVPYv38/Kisr0bdvX5SUlKhalk7YdzUbi/YmAQBmvtSCgU9EDU7UmbP27t1b7XVcXBxcXV1x7tw5dOvWTaSqNCP5bhGmbLkAABjTyQcxXXzFLYiIDFKdQ7+srAwHDx7ESy+9BACYMWMGpFKpcrmxsTHmzp0LCwuLehfzaOweR0fHWpdLpdJq+ywsLKz3vhpSQWkl3lx/FiUVMnT0c8QnL7UQuyQiMlB1Dv1169bh119/VYb+ihUr0LJlS1haWgIAEhMT4enpicmTJ9erELlcjvfffx9dunRBSEhIrevMnz8fc+bMqdf2xSKTC5i0OQHpuaVo7GCJr0e24c1XRCSaOqfPhg0b8NZbb1V7b+PGjTh8+DAOHz6Mzz//HFu3bq13IbGxsbhy5Qo2b978xHVmzJiBgoIC5SMzM7Pe+2son+9LUvbU+Sa6LZxszMUuiYgMWJ1DPyUlBaGhocrXFhYW1XrytG/fHteuXatXERMnTsSuXbtw+PDhp97pa25uDjs7u2oPbfbLxTtYfTQVgGJc/JDGnF+YiMRV5+ad/Pz8au3p9+7dq7ZcLpdXW14XgiDg3XffxY4dO3DkyBH4+urPxc2rdwrw4XbFqJlvd/fjRChEpBXqfKbfpEkTXLly5YnLL126pPJ4PLGxsfjvf/+LjRs3wtbWFtnZ2cjOzkZZWZlK29E2ucVSvLX+HMor5egW6IKP+jUXuyQiIgAqhP6LL76ImTNnory8vMaysrIyzJkzBwMGDFBp56tWrUJBQQF69OgBDw8P5WPLli0qbUebVMrkmLgxAbfzy9DUyQrLX2sNYyMOk0xE2qHOd+TevXsX4eHhMDMzw8SJExEYGAgASEpKwooVK1BVVYWEhAS4ublptOC/08Y7cufuuobvj6fB2swYO2K7INDNVuySiEiHaDrX6tym7+bmhvj4eIwfPx7Tp09XzpIlkUjQp08frFy5skEDXxvtvpyF74+nAQC+HBbOwCciraPSHbm+vr7Yu3cv8vLykJKSAgDw9/d/4s1UhuTmvWJ8tP0SAMWF2/4hHGKBiLRPvYZhcHR0RPv27dVdi84qrajC+P+eR7G0Cu19HfFh37oNGEdE1NB4a+hzEgQBn+y4gqS7RXC2MceKEa1hwjtuiUhLMZ2e06Y/MvFjwm0YSYAVI1vD1a7+Yw8REWkaQ/85JGUXYc4vVwEAH/Zrjo5+TiJXRET0dAz9eiqvlGHSpgRIq+ToHuiCt7v5iV0SEdEzMfTracGexIft+Gb44tVWMOINWESkAxj69XAo8S7i4tMBAF+82gouthw5k4h0A0NfRTmF5Zi6TdEf/59dfNEjyFXkioiI6o6hrwJBEDB1+yXklVQg2MMO06LYH5+IdAtDXwWb/sjEsRv3YG5ihOUjwmFuYix2SUREKmHo11FmXin+/atikpiP+jeHvyvH1SEi3cPQrwO5XMC0/11CSYUM7Zs6IqZzU7FLIiKqF4Z+HWw4fQvxqbmwNDXGolfC2D2TiHQWQ/8ZMnJLMW93IgBgelRzNHW2FrkiIqL6Y+g/hSAImP7jJZRVytDRzxHRHX3ELomI6Lkw9J/ix/O3EZ+aCwtTIyx6mXfdEpHuY+g/QV5JBT572Fvnvd6B8HayErkiIqLnx9B/gnm7r+NBaSWC3GzxRldfscshIlILhn4tTqbmYvu5PwEA84aGwpSTohCRnmCaPaaiSo7/23kZADCqgzfa+jQSuSIiIvVh6D8mLj4NN++VwNnGHB/1by52OUREasXQ/5t7RVIsO5gCAPiofxDsLU1FroiISL0Y+n/zxb4kFEurENbEHq+0aSJ2OUREasfQf+jynwXYei4TADBrYEv2yScivcTQh+LO2zm/XIUgAIPDPXnxloj0FkMfwJ4r2Th76wEsTY0xLYoXb4lIfxl86FfJ5PhiXxIA4M1ufvCwtxS5IiIizTH40N969k/cvF8CR2szvMk7b4lIzxl06JdVyLDkwA0AwMSe/rC1YBdNItJvJmIXIKa18WnIKZKiSSNLjOroLXY5RKQL5DJAWgiUF9R8VJQAVVJAVvHXo6oCkD16rwoQ5NUfEP72WgBKpBot32BDv6C0EquPpAIApvQJ5CTnmiIIQHEOUHhb8VxZAlSWA1VlgPzRL7zw17Pw2HuPb6vmDp6xTi2fedY6NT5Sn200VK1i7kcd29Di70mVtPZglxbWsn01ktbhe/0cDDb0fziRhsLyKgS52WJQeGOxy9Evsirgxl7g8jbgVjxQkiN2RUTqZ2IJWNhXf5hZAybmgLHZX8/Kr00BI1PAyBiABJAY/e0hefgwAorLgQWxmitbY1vWYkXllVh7Ig0A8G5vfxjzRiz1STkA7JkO5Cb/7U0JYOuueJjZACYWgKkFIDFW/KJD8oxnVN/W4561Tq0/3sfXedY2atuIitvQ+v2ouI167UeHvifGpoCFw99C/e9f2ymCXBMKCwEw9NVq/clbKCyvQjMXa0SFeIhdjn4QBODwPODYIsVrS0eg9etA0IuAZzhgyq6wRNrA4EK/RFqF736/CQCY2Itn+WohCMBvnwAnVyheR4wDImcrzoaISKsYXOj/99QtPCithI+TFQaGeYpdjn44suCvwH9pCRARI2o5RPRkBtVPX1olw3fHFW35sT38YcIZsZ5f/HLg6ALF1/0XMvCJtJxBpd4vF7Nwr0gKNztzDG7NHjvP7ewPimYdAOj1CdDxHXHrIaJnEjX0jx07hoEDB8LT0xMSiQQ7d+7U2L4EQVC25Y/p3BRmJgb17536JfwX2DVZ8XWX94GuU0Uth4jqRtTkKykpQatWrfD1119rfF/xqblIzC6CpakxRrbn3bf1JgjAsc+Bnx52KeswXnHRti5dAIlIdKJeyI2KikJUVFSD7OvRWf6wiCZwsDJrkH3qFUEA/jwDHJgN3DqheK/jBKDfPAY+kQ7Rqd47UqkUUulf41IUFtbtduj0+yU4nHQPEgkQ04UjadbZg3Tg6k4g6wJwJ0HxGgBMrRRhz4u2RDpHp0J//vz5mDNnjsqf23pWMQ1itwAXNHW2VndZ+unuNeC7SMVYOY+YWAAhLwM9pgMObCIj0kU6FfozZszAlClTlK8LCwvh5eX11M9UyeTYfu5PAMBr7Z6+Lv3N8a8Uge/aEgh9RXFXrWcbwNJB7MqI6DnoVOibm5vD3Fy18S6OJN1DTpEUTtZm6B3spqHK9IwgKMbQAYABXwI+ncSth4jURu/7LW552LQzpHVjdtOsq5J7QFmeYsS/xm3EroaI1EjUM/3i4mKkpKQoX6elpeHChQtwdHSEt/fztxnnFJXjUKJiWN/hbNqpu/sPR8i099LcSIJEJApRQ//s2bPo2bOn8vWj9voxY8YgLi7uubf/v3O3IZMLaOPtgAA32+fensHIU3RvhVMzcesgIrUTNfR79OgBoS4z8tSDIAjYfk7RtMOzfBUVZSme7ThUBZG+0dtG7qt3CpF6rwRmJkZ4MZRj5qukKFvxbOsubh1EpHZ6G/q/XLwDAOjd3BW2FqYiV6Njiu8qnm3Y24lI3+hl6MvlgjL0/9GKY+arrPjhnLYMfSK9o5ehfy7jAe4UlMPW3AQ9m7uKXY7uKb2veLZ2FrcOIlI7vQz9XQ/P8vu2dIeFqbHI1eig4nuKZ2v+g0mkb/Qu9AVBwP5rijbpqBBeiFRZZTlQUaT42tpJ3FqISO30LvSvZRXiTkE5LEyN8EIAmydU9qhpx8gEsHAQtRQiUj+9C/0D1xQXIbsGuLBppz4eXcS1duE4+UR6SP9C/7qiaacPB1ernxJexCXSZ3oV+ncLy3H5dgEkErDXTn0p++jzegiRPtKr0D+RojhLDW1sDxdbDhRWL4+GYLDl/5SI9JFehX58ai4AoHMzNk3UW+FtxTPH3SHSS3oT+oIgIP7hmX4Xf3Y1rLcHtxTP9hykjkgf6U3op+eW4k5BOUyNJYjwcRS7HN2Vl6p4dvQTtw4i0gi9Cf34VMVZfmvvRrA0Y1fNeikvBPIzFF+7BIlbCxFphN6E/rn0BwCAjn5s2qm32+cUz/Ze7LJJpKf0JvQTMvMBAK29HUStQ6dd/1nx3LSruHUQkcaIOnOWujwoqUDa/RIAQHgTB3GLqY1cBlzdATxIU3wtCAAEFZ9Rz8/9/RlPXl5ZCiT+qthPq9ca6jtDRA1ML0L/8p0CAICvszUaWZuJXE0tdrwNXN4mdhV1EzoM8O0mdhVEpCH6EfqZitAP93IQt5DalOb9FfgthwIW9g/HtJHU8Rl1W09i9IRlqPu+bD2AFoM45g6RHtOL0L+eXQgACGtiL3Iltbh7VfHs4AO8ulbcWojI4OnFhdyUnGIAQJC7rciV1CL/4c1OTs3ErYOICHoS+rfzywAAQW5aGPopBxXPdpyrl4jEpxehLwiAs40ZnGy0cJC1R+PTV0nFrYOICHoS+gAQqI1n+QBw67ji2SlA3DqIiMDQbzi2HJ+eiMSnF713AKCpk1X1N1IOKNrTZRWKh1wGCPKHz7K/Xivfk//t/Yc3UNVYVwbIHz7X2JZQy7qyv+pxDW7YbwgRUS30JvS9HP8W+vdTgA3DFMGrLVxbiF0BEZGehn7Sr4rAN7cHOr4DGJkCRsaKh8QIkPz9a6OHXz++/PH16vGZlIPAb/+nqMncRpxvDBHR3+hN6DdpZPnXixu/KZ57/R/Q4W1xCgIUTToeYYA15+slIu2gF6HvZG0KK7OHh5J3E8iIV3wd0Fe8oh7hODZEpEX0oveOs63FXy9+/1JxIdW/D+DoK15RRERaSC9C38nm4cia0iLgwibF192milcQEZGW0ovQd7QyAypKgW1jFRdwbT2AJu3FLouISOvoRZu+n3EOEPcicCcBMLEEXo1T9KQhIqJq9CL037o+FjCXAJaNgBGbAe+OYpdERKSV9CL0AQB+PYB/LAccvMWuhIhIa+lFG0iqfwww+icGPhHRM+hF6Fe5hIhdAhGRTtCL0Lf0DBS7BCIinaAXoe/l30rsEoiIdIJWhP7XX3+Npk2bwsLCAh06dMAff/yh0ucl7J5JRFQnoqflli1bMGXKFMyaNQvnz59Hq1at0K9fP+Tk5IhdGhGR3hE99BcvXow333wTMTExaNGiBVavXg0rKyv88MMPYpdGRKR3RO2nX1FRgXPnzmHGjBnK94yMjBAZGYmTJ0/WWF8qlUIq/WuC8YKCAgBAYWGh5oslImoAj/JMEASNbF/U0L9//z5kMhnc3Nyqve/m5obExMQa68+fPx9z5syp8b6Xl5fGaiQiEkNubi7s7e3Vvl2duiN3xowZmDJlivK1XC5HXl4enJycIJFIRKysYRUWFsLLywuZmZmws7MTu5wGw+PmcRuCgoICeHt7w9HRUSPbFzX0nZ2dYWxsjLt371Z7/+7du3B3d6+xvrm5OczNzau95+DgoMkStZqdnZ1B/TE8wuM2LIZ63EYa6pUo6oVcMzMztG3bFgcPHlS+J5fLcfDgQXTq1EnEyoiI9JPozTtTpkzBmDFjEBERgfbt22PJkiUoKSlBTEyM2KUREekd0UN/+PDhuHfvHmbOnIns7GyEh4dj7969NS7u0l/Mzc0xa9asGk1d+o7HzeM2BJo+bomgqX5BRESkdUS/OYuIiBoOQ5+IyIAw9ImIDAhDn4jIgDD0tdTs2bMhkUiqPZo3b65cXl5ejtjYWDg5OcHGxgYvv/xyjZvcdMWxY8cwcOBAeHp6QiKRYOfOndWWC4KAmTNnwsPDA5aWloiMjERycnK1dfLy8jBq1CjY2dnBwcEB48aNQ3FxcQMeheqeddxjx46t8TvQv3//auvo2nHPnz8f7dq1g62tLVxdXTF48GAkJSVVW6cuv9sZGRkYMGAArKys4Orqig8//BBVVVUNeSgqqctx9+jRo8bP+5133qm2jjqOm6GvxVq2bImsrCzl4/jx48plkydPxi+//IJt27bh6NGjuHPnDoYOHSpitfVXUlKCVq1a4euvv651+aJFi7Bs2TKsXr0ap0+fhrW1Nfr164fy8nLlOqNGjcLVq1exf/9+7Nq1C8eOHcNbb73VUIdQL886bgDo379/td+BTZs2VVuua8d99OhRxMbG4tSpU9i/fz8qKyvRt29flJSUKNd51u+2TCbDgAEDUFFRgfj4eKxbtw5xcXGYOXOmGIdUJ3U5bgB48803q/28Fy1apFymtuMWSCvNmjVLaNWqVa3L8vPzBVNTU2Hbtm3K965fvy4AEE6ePNlAFWoGAGHHjh3K13K5XHB3dxc+//xz5Xv5+fmCubm5sGnTJkEQBOHatWsCAOHMmTPKdfbs2SNIJBLh9u3bDVb783j8uAVBEMaMGSMMGjToiZ/Rh+POyckRAAhHjx4VBKFuv9u7d+8WjIyMhOzsbOU6q1atEuzs7ASpVNqwB1BPjx+3IAhC9+7dhffee++Jn1HXcfNMX4slJyfD09MTfn5+GDVqFDIyMgAA586dQ2VlJSIjI5XrNm/eHN7e3rUOSa3L0tLSkJ2dXe1Y7e3t0aFDB+Wxnjx5Eg4ODoiIiFCuExkZCSMjI5w+fbrBa1anI0eOwNXVFUFBQRg/fjxyc3OVy/ThuB8Nj/5ocLG6/G6fPHkSoaGh1W7g7NevHwoLC3H16tUGrL7+Hj/uRzZs2ABnZ2eEhIRgxowZKC0tVS5T13GLfkcu1a5Dhw6Ii4tDUFAQsrKyMGfOHHTt2hVXrlxBdnY2zMzMagw25+bmhuzsbHEK1pBHx1Pb8NuPlmVnZ8PV1bXachMTEzg6Our096N///4YOnQofH19kZqaio8//hhRUVE4efIkjI2Ndf645XI53n//fXTp0gUhISEAUKff7ezs7Fp/Hx4t03a1HTcAjBw5Ej4+PvD09MSlS5cwbdo0JCUl4ccffwSgvuNm6GupqKgo5ddhYWHo0KEDfHx8sHXrVlhaWopYGTWU1157Tfl1aGgowsLC0KxZMxw5cgS9e/cWsTL1iI2NxZUrV6pdqzIETzruv1+LCQ0NhYeHB3r37o3U1FQ0a9ZMbftn846OcHBwQGBgIFJSUuDu7o6Kigrk5+dXW+dJQ1LrskfH87Tht93d3WvMqVxVVYW8vDy9+n74+fnB2dkZKSkpAHT7uCdOnIhdu3bh8OHDaNKkifL9uvxuu7u71/r78GiZNnvScdemQ4cOAFDt562O42bo64ji4mKkpqbCw8MDbdu2hampabUhqZOSkpCRkaF3Q1L7+vrC3d292rEWFhbi9OnTymPt1KkT8vPzce7cOeU6hw4dglwuV/7h6IM///wTubm58PDwAKCbxy0IAiZOnIgdO3bg0KFD8PX1rba8Lr/bnTp1wuXLl6v9g7d//37Y2dmhRYsWDXMgKnrWcdfmwoULAFDt562W467HhWdqAB988IFw5MgRIS0tTThx4oQQGRkpODs7Czk5OYIgCMI777wjeHt7C4cOHRLOnj0rdOrUSejUqZPIVddPUVGRkJCQICQkJAgAhMWLFwsJCQnCrVu3BEEQhAULFggODg7CTz/9JFy6dEkYNGiQ4OvrK5SVlSm30b9/f6F169bC6dOnhePHjwsBAQHCiBEjxDqkOnnacRcVFQlTp04VTp48KaSlpQkHDhwQ2rRpIwQEBAjl5eXKbejacY8fP16wt7cXjhw5ImRlZSkfpaWlynWe9btdVVUlhISECH379hUuXLgg7N27V3BxcRFmzJghxiHVybOOOyUlRfj000+Fs2fPCmlpacJPP/0k+Pn5Cd26dVNuQ13HzdDXUsOHDxc8PDwEMzMzoXHjxsLw4cOFlJQU5fKysjJhwoQJQqNGjQQrKythyJAhQlZWlogV19/hw4cFADUeY8aMEQRB0W3zX//6l+Dm5iaYm5sLvXv3FpKSkqptIzc3VxgxYoRgY2Mj2NnZCTExMUJRUZEIR1N3Tzvu0tJSoW/fvoKLi4tgamoq+Pj4CG+++Wa17nqCoHvHXdvxAhDWrl2rXKcuv9vp6elCVFSUYGlpKTg7OwsffPCBUFlZ2cBHU3fPOu6MjAyhW7dugqOjo2Bubi74+/sLH374oVBQUFBtO+o4bg6tTERkQNimT0RkQBj6REQGhKFPRGRAGPpERAaEoU9EZEAY+kREBoShT0RkQBj6REQGhKFPOm/s2LEYPHiwaPuPjo7GvHnzlK+bNm2KJUuWqHUf165dQ5MmTWrMtESkKoY+abXH5wx9/DF79mwsXboUcXFxotR38eJF7N69G5MmTdLoflq0aIGOHTti8eLFGt0P6T+GPmm1v88XumTJEtjZ2VV7b+rUqbC3t68x6UZDWb58OV599VXY2Ng813YqKyufuU5MTAxWrVql1ROAk/Zj6JNWc3d3Vz7s7e0hkUiqvWdjY1OjeadHjx5499138f7776NRo0Zwc3PDt99+i5KSEsTExMDW1hb+/v7Ys2dPtX1duXIFUVFRsLGxgZubG6Kjo3H//v0n1iaTybB9+3YMHDiwxrLS0lL885//hK2tLby9vbFmzRrlsvT0dEgkEmzZsgXdu3eHhYUFNmzYgFu3bmHgwIFo1KgRrK2t0bJlS+zevVv5uT59+iAvLw9Hjx59ju8oGTqGPumldevWwdnZGX/88QfeffddjB8/Hq+++io6d+6M8+fPo2/fvoiOjlbOQZqfn49evXqhdevWOHv2LPbu3Yu7d+9i2LBhT9zHpUuXUFBQUG2O2ke+/PJLREREICEhARMmTMD48eORlJRUbZ3p06fjvffew/Xr19GvXz/ExsZCKpXi2LFjuHz5MhYuXFjtfxBmZmYIDw/H77//rqbvEhkk9QwcSqR5a9euFezt7Wu8P2bMGGHQoEHK1927dxdeeOEF5euqqirB2tpaiI6OVr6XlZUlABBOnjwpCIIgzJ07V+jbt2+17WZmZgoAagzj/MiOHTsEY2NjQS6XV3vfx8dHeP3115Wv5XK54OrqKqxatUoQBEFIS0sTAAhLliyp9rnQ0FBh9uzZT/kOCMKQIUOEsWPHPnUdoqfhHLmkl8LCwpRfGxsbw8nJCaGhocr3Hk0o/WgWoosXL+Lw4cO1ts2npqYiMDCwxvtlZWUwNzeHRCJ56v4fNUk9PrXh4/9DmDRpEsaPH4/ffvsNkZGRePnll6ttBwAsLS2V/zshqg8275BeMjU1rfZaIpFUe+9RUMvlcgCK6SgHDhyICxcuVHskJyejW7dute7D2dkZpaWlqKioqNP+H+3rEWtr62qv33jjDdy8eRPR0dG4fPkyIiIisHz58mrr5OXlwcXF5WmHTvRUDH0iAG3atMHVq1fRtGlT+Pv7V3s8Hs6PhIeHA1D0oVcXLy8vvPPOO/jxxx/xwQcf4Ntvv622/MqVK2jdurXa9keGh6FPBCA2NhZ5eXkYMWIEzpw5g9TUVOzbtw8xMTGQyWS1fsbFxQVt2rTB8ePH1VLD+++/j3379iEtLQ3nz5/H4cOHERwcrFyenp6O27dvIzIyUi37I8PE0CcC4OnpiRMnTkAmk6Fv374IDQ3F+++/DwcHBxgZPfnP5I033sCGDRvUUoNMJkNsbCyCg4PRv39/BAYGYuXKlcrlmzZtQt++feHj46OW/ZFh4hy5RM+hrKwMQUFB2LJlCzp16qSx/VRUVCAgIAAbN25Ely5dNLYf0n880yd6DpaWlli/fv1Tb+JSh4yMDHz88ccMfHpuPNMnIjIgPNMnIjIgDH0iIgPC0CciMiAMfSIiA8LQJyIyIAx9IiIDwtAnIjIgDH0iIgPC0CciMiD/D0xHTPHfMz5fAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(4,4))\n", + "ax.plot(t_noPin, r_noPin, label='No pinning')\n", + "grainModel.plotRadiusvsTime(ax, timeUnits='h', label='Pinning')\n", + "ax.legend()\n", + "ax.set_ylim([0, 6e-5])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "calphad", + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/09_Thermodynamics.ipynb b/examples/09_Thermodynamics.ipynb new file mode 100644 index 0000000..ddad367 --- /dev/null +++ b/examples/09_Thermodynamics.ipynb @@ -0,0 +1,558 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Thermodynamics\n", + "\n", + "The thermodynamic modules interfaces with pycalphad to perform important calculations for the KWN model. They are split into two classes to handle binary and multicomponent systems.\n", + "\n", + "Setting up a Thermodynamics object requires the database, the elements involved (where first element will be the reference element) and the phases involved (where the first phase will be the matrix phase). For systems where the parent and precipitate phases are handled by an order/disorder model (ex. $\\gamma$ and $\\gamma$' in nickel-based alloys), the matrix phase is assumed to be the disordered part of the model.\n", + "\n", + "For multicomponent systems, any compositions that is used as a parameter or as a return value will be in the same order of solutes that was used when creating the Thermodynamics object. In the example below, all compositions will be in the order [xCr, xAl]. If the solutes were ordered as ['Ni', 'Al', 'Cr'] in the constructor, then all compositions will be in the order [xAl, xCr]." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import BinaryThermodynamics, MulticomponentThermodynamics\n", + "\n", + "binaryTherm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'])\n", + "multiTherm = MulticomponentThermodynamics('NiCrAl.tdb', ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyperparameters\n", + "\n", + "### Sampling density\n", + "\n", + "When calculating equilibrium, pycalphad samples the free energy surfaces of each phase to find a suitable starting point for the free energy minimization procedure. The sampling density (defined as the number of samples to create per degree of freedom in the free energy model) can influence the accuracy of the equilibrium results and the computation time. A low sampling density may lead to inaccurate results while a high sampling density may result in slow calculations. By default, the Thermodynamics object sets the sampling density to 500.\n", + "\n", + "There is a second sampling density parameter that is used when calculating the driving force using the sampling method. By default, it is set to 2000. This sampling density is set to be higher than for the sampling density used for solving equilibrium because the samples themselves are used in the driving force calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#Change sampling density\n", + "multiTherm.setEQSamplingDensity(500)\n", + "\n", + "#Change driving force sampling density\n", + "multiTherm.setDFSamplingDensity(2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Moblity correction factors\n", + "\n", + "For mobility terms, a correction factor can be applied to each element. This may be useful in parameter assessment, sensitivity analysis or in cases where the mobility will be known to be higher (e.g. higher vacancy concentrations from a solutionizing/quenching treatment)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#Change mobility factor for Cr\n", + "multiTherm.setMobilityCorrection('Cr', 1)\n", + "\n", + "#Change mobility factor for all components\n", + "multiTherm.setMobilityCorrection('all', 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Starting conditions for Binary Systems\n", + "\n", + "For BinaryThermodynamics, the interfacial composition is independent of the composition of the system and is calculated by solving equilibrium at several compositions until a 2-phase region is found. By default, it samples the composition in intervals of 0.1. The starting compositions can be manually set to always be inside the 2-phase region to improve computation time." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#Change starting conditions for BinaryThermodynamics\n", + "\n", + "#Compositions between 0 and 0.5 at intervals of 0.015\n", + "binaryTherm.setGuessComposition((0, 0.5, 0.015))\n", + "\n", + "#Single composition at 0.24\n", + "binaryTherm.setGuessComposition(0.24)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Driving Force Calculations\n", + "\n", + "### Nucleation\n", + "\n", + "Nucleation of a precipitate results in a reduction in Gibbs free energy that scales with the precipitate volume and an increase in the free energy that scales with the surface, creating a barrier for nucleation.\n", + "\n", + "$$\\Delta G = -\\frac{4}{3}\\pi R^3 \\Delta G_{vol} + 4\\pi R^2 \\gamma$$\n", + "\n", + "The height of this barrier, $\\Delta G^{*}$, can be used to find the nucleation rate.\n", + "\n", + "$$J_N = N_0 Z \\beta exp\\left(-\\frac{\\Delta G^{*}}{k_B T}\\right) exp\\left(-\\frac{\\tau}{t}\\right)$$\n", + "\n", + "The driving force is defined as the maximum difference in Gibbs free energy between the chemical potential hyperplane computed for the matrix ($\\alpha$) and precipitate ($\\beta$) phase separately. This can also be defined as the difference in the Gibbs free energy when the chemical potential hyperplanes of each phase are parallel. The chemical potential of the $\\alpha$ is computed at the matrix composition while the chemical potential of the $\\beta$ phase is computed at the composition which maximizes the driving force (Rheingans and Mittemeijer, 2015).\n", + "\n", + "$$\\Delta G_m = \\sum{x_A^\\beta \\, \\mu_A^\\alpha (\\boldsymbol{x}^\\alpha) - x_A^\\beta \\, \\mu_A^\\beta (\\boldsymbol{x}^\\beta)} = \\left(\\frac{2 \\gamma}{R^*} + \\Delta G_{el}\\right) V_m^\\beta$$\n", + "\n", + "Four different methods are available for driving force calculations: tangent, approximate, sampling and curvature.\n", + "\n", + "### Tangent method\n", + "\n", + "Rather than calculating equilibrium of the precipitate phase to have a parallel chemical potential hyperplane, the tangent method solves for the energy offset that puts the precipitate phase on the chemical potential hyperplane of the matrix phase. This is the default method when the Thermodynamics object is created.\n", + "\n", + "### Approximate method\n", + "\n", + "The approximate method assumes that the composition of a newly nucleated precipitate is near the equilibrium composition. \n", + "\n", + "$$ \\Delta G_M = \\sum_{A}{x_{eq}^\\beta \\, \\mu_A^\\alpha \\left(\\boldsymbol{x^\\alpha}\\right) - x_{eq}^\\beta \\, \\mu_A^\\beta \\left(\\boldsymbol{x_{eq}^\\beta}\\right)} $$\n", + "\n", + "### Sampling method\n", + "\n", + "The sampling method approximates calculating the driving force by the parallel tangent method. Rather than finding the composition of the precipitate phase that gives the same chemical potential as for the parent phase, the maximum difference between Gibbs free energy of the precipitate phase and the chemical potential hyperplane of the parent phase is found. This is the only method of the three that can calculate negative driving forces and is used by the other two methods if the precipitate phase is unstable.\n", + "$$ \\Delta G_M = argmax \\left(\\sum_{A}{x_A^\\beta \\, \\mu_A^\\alpha \\left(\\boldsymbol{x^\\alpha}\\right)} - G_M^\\beta \\left(\\boldsymbol{x^\\beta}\\right) \\right) $$\n", + "\n", + "### Curvature method\n", + "\n", + "The curvature method determines the local curvature of the free energy surface of the parent phase at the given composition and calculates driving force based off the equilibrium composition of the parent and precipitate phase. This is only valid for small supersaturations and non-dilute systems and thus, is not recommended.\n", + "$$ \\Delta G_M = \\boldsymbol{\\left(x^\\alpha - x_{eq}^\\alpha\\right)} \\boldsymbol{\\nabla^2} G_M^\\alpha \\boldsymbol{\\left(x_{eq}^\\beta - x_{eq}^\\alpha\\right)} $$\n", + "\n", + "### Binary System\n", + "\n", + "For a binary system, the driving force method is defined as:\n", + "\n", + "$ \\Delta G_M, x^\\beta = BinaryThermodynamics.getDrivingForce(x, T, returnComp) $\n", + "\n", + "The example below compares the three methods on the Al-Zr system. The approximate and sampling method gives the same values for the driving force. This is due to the $Al_3Zr$ having zero degrees of freedom for the composition, so the calculation of the driving force ends up being the same. The curvature method is only accurate near the equilibrium composition (where the driving force is 0), but at higher concentrations, it greatly over predicts the driving force. This is due to the high curvature at low concentrations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "#Plot comparison of driving force methods for Al-Zr system\n", + "\n", + "#Driving force methods\n", + "DGmethods = ['tangent', 'approximate', 'sampling', 'curvature']\n", + "\n", + "x = np.linspace(1e-5, 1e-2, 100)\n", + "\n", + "fig1 = plt.figure(1, figsize=(6, 5))\n", + "ax1 = fig1.add_subplot(111)\n", + "\n", + "for m in DGmethods:\n", + " #Clear cache before using a different method\n", + " binaryTherm.clearCache()\n", + " binaryTherm.setDrivingForceMethod(m)\n", + "\n", + " #Calculate driving force (x and T must be same shape)\n", + " dg, _ = binaryTherm.getDrivingForce(x, np.ones(100) * 673.15)\n", + " ax1.plot(x, dg, label=m)\n", + "\n", + "ax1.set_xlim([0, 0.01])\n", + "ax1.set_ylim([-1000, 10000])\n", + "ax1.set_xlabel('xZr (mole fraction)')\n", + "ax1.set_ylabel('Driving Force (J/mol)')\n", + "ax1.legend(DGmethods)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multicomponent systems\n", + "\n", + "For multicomponent systems, the driving force method is defined as:\n", + "\n", + "$ \\Delta G_M, \\boldsymbol{x^\\beta} = MulticomponentThermodynamics.getDrivingForce(\\boldsymbol{x}, T, returnComp) $\n", + "\n", + "This is similar to the method for binary systems except that the composition must be an array of the solute components. Below is an example of the different driving force methods in the Ni-Cr-Al system. Because the equilibrium composition is non-dilute, the curvature method gives similar values to the other two methods. Once the driving force becomes negative (no driving force for nucleation), the three methods converge since the sampling method is used if the precipitate is unstable." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Driving force for NiCrAl system\n", + "\n", + "#Create points to calculate driving force at\n", + "# x and T must be same length\n", + "comps = np.array([[0.08, 0.1] for i in range(100)])\n", + "T = np.linspace(700, 1200, 100)\n", + "\n", + "fig2 = plt.figure(2, figsize=(6, 5))\n", + "ax2 = fig2.add_subplot(111)\n", + "\n", + "for m in DGmethods:\n", + " #Clear cache before switching method\n", + " multiTherm.clearCache()\n", + " multiTherm.setDrivingForceMethod(m)\n", + "\n", + " #Calculate driving force\n", + " dg, xP = multiTherm.getDrivingForce(comps, T)\n", + " ax2.plot(T, dg, label=m)\n", + "\n", + "ax2.set_xlim([700, 1200])\n", + "ax2.set_ylim([-500, 2500])\n", + "ax2.set_xlabel('Temperature (K)')\n", + "ax2.set_ylabel('Driving Force (J/mol)')\n", + "ax2.legend(DGmethods)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interfacial Composition and Precipitate Growth Rates\n", + "\n", + "### Binary Systems\n", + "\n", + "Assuming diffusion controlled growth, the growth rate of a spherical preciptiate in a binary system can be written as:\n", + "\n", + "$$ \\frac{dR}{dt} = \\frac{D}{R} \\frac{x - x_R^\\alpha}{x_R^\\beta - x_R^\\alpha} $$\n", + "\n", + "Where $x_R^\\alpha$ and $x_R^\\beta$ is the interfacial composition of the matrix and precipitate phase respectively. For binary systems, the interfacial composition is independent of the composition of the system. This becomes useful in the KWN model as these values can be calculated beforehand and used for determining the growth rate, rather than calculating them at every iteration.\n", + "\n", + "Determining the interfacial composition requires solving for equilibrium while accounting for the Gibbs-Thompson effect (proportional to 1/r). Elastic energy can also be accounted for.\n", + "\n", + "$$\\mu_i^\\alpha (\\boldsymbol{x_R^\\alpha}) = \\mu_i^\\beta (\\boldsymbol{x_R^\\beta}) + \\left(\\frac{2 \\gamma}{R} + \\Delta G_{el}\\right) V_m^\\beta$$\n", + "\n", + "For a binary system, the interfacial composition can be calculated from the curvature of the Gibbs free energy surfaces similar to the curvature method for calculating driving force:\n", + "$$ \\left(\\frac{2 \\gamma}{R} + \\Delta G_{el}\\right) V_m^\\beta = \\boldsymbol{\\left(x^\\alpha - x_{eq}^\\alpha\\right)} \\boldsymbol{\\nabla^2} G_M^\\alpha \\boldsymbol{\\left(x_{eq}^\\beta - x_{eq}^\\alpha\\right)} $$\n", + "\n", + "For composition of the precipitate:\n", + "$$ \\boldsymbol{\\nabla^2} G_M^\\beta \\boldsymbol{\\left(x^\\beta - x_{eq}^\\beta\\right)} = \\boldsymbol{\\nabla^2} G_M^\\alpha \\boldsymbol{\\left(x^\\alpha - x_{eq}^\\alpha\\right)} $$\n", + "\n", + "As with the curvature method for calculating driving force, the curvature method for calculating interfacial composition is only valid for small supersaturations and non-dilute systems. Additionally, while these two equations can be generalized to multicomponent systems, they are generally indeterminate and the interfacial compositions in multicomponent systems cannot be determined by the free energy curvature alone.\n", + "\n", + "The interfacial composition method for binary systems is defined as:\n", + "\n", + "$ x^\\alpha, x^\\beta = BinaryThermodynamics.getInterfacialComposition(T, G_{TH}) $\n", + "\n", + "Where $G_{TH}$ is the free energy contribution from the Gibbs-Thomson effect. The example below compares the two methods for calculating the interfacial composition in the matrix phase. If $G_{TH}$ is too large such that the precipitate phase becomes unstable, then the function will return -1 for both the matrix and precipitate compostion." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Interfacial composition for Al-Zr system\n", + "\n", + "#Different methods for calculating interfacial composition\n", + "ICmethods = ['equilibrium', 'curvature']\n", + "\n", + "#Get Gibbs-Thomson contribution from radius\n", + "gamma = 0.1 #Interfacial energy between FCC-Al and Al3Zr\n", + "Vm = 1e-5 #Molar volume\n", + "R = np.linspace(1e-10, 5e-9, 100) #Radius\n", + "G = 2 * gamma * Vm / R #Contribution from Gibbs-Thomson effect\n", + "\n", + "fig3 = plt.figure(3, figsize=(6, 5))\n", + "ax3 = fig3.add_subplot(111)\n", + "\n", + "for m in ICmethods:\n", + " binaryTherm.clearCache()\n", + " binaryTherm.setInterfacialMethod(m)\n", + "\n", + " #Calculate interfacial composition\n", + " xM, xP = binaryTherm.getInterfacialComposition(673.15, G)\n", + " ax3.plot(R[xM != -1], xM[xM != -1], label=m)\n", + "\n", + "ax3.set_xlim([0, 5e-9])\n", + "ax3.set_ylim([0, 0.001])\n", + "ax3.set_xlabel('Radius (m)')\n", + "ax3.set_ylabel('Matrix composition of Zr (mole fraction)')\n", + "ax3.legend(ICmethods)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multicomponent Systems\n", + "\n", + "The governing equations for solving multicomponent precipitate is the same as for binary precipitation. However, calculating interfacial composition through solving for equilibrium requires the solution to the following equations for each component.\n", + "\n", + "$$\\frac{dR}{dt} = \\sum_{j}{\\frac{D_{ij}}{R} \\frac{x_i - x_{R,i}^{\\alpha}}{x_{R,j}^{\\beta} - x_{R,j}^{\\alpha}}}$$\n", + "\n", + "$$\\mu_i^\\alpha (\\boldsymbol{x_R^\\alpha}) = \\mu_i^\\beta (\\boldsymbol{x_R^\\beta}) + \\left(\\frac{2 \\gamma}{R} + \\Delta G_{el}\\right) V_m^\\beta$$\n", + "\n", + "This gives 2N-1 equations to solve, which can be time consuming and, in worst cases, a solution may not be found. At small saturations, the growth rate can be determined through local expansion of the chemical potential at equilibrium (Philippe and Voorhees, 2013). The growth rate (assuming $\\Delta G_{el} = 0$ for simplicity) then becomes:\n", + "\n", + "$$\\frac{dR}{dt}=\\frac{1}{R (\\boldsymbol{\\Delta \\overline{x}})^T M^{-1} \\boldsymbol{\\Delta \\overline{x}}}\\left(\\Delta G_m - \\frac{2 \\gamma V_m^\\beta}{R}\\right)$$\n", + "\n", + "\n", + "$$\\boldsymbol{\\Delta \\overline{x} = x_{\\infty}^{\\beta} - x_{\\infty}^{\\alpha}}$$\n", + "\n", + "Where $\\boldsymbol{x_{\\infty}^{\\alpha}}$ and $\\boldsymbol{x_{\\infty}^{\\beta}}$ are the equilibrium compositions of $\\alpha$ and $\\beta$ on a planar interface and $M^{-1} = \\boldsymbol{\\nabla^2} G^{\\alpha} * D^{-1}$, where $\\boldsymbol{\\nabla^2} G^{\\alpha}$ is the curvature of the free energy surface of phase $\\alpha$ and $D^{-1}$ is the inverse of the interdiffusivity matrix.\n", + "\n", + "Interfacial compositions can be determined by the following equations, which are needed for solving mass balance.\n", + "\n", + "$$\\boldsymbol{x^{\\alpha}} = \\boldsymbol{x} - \\frac{D^{-1} \\boldsymbol{\\Delta \\overline{x}}}{(\\boldsymbol{\\Delta \\overline{x}})^T M^{-1} \\boldsymbol{\\Delta \\overline{x}}} \\left(\\Delta G_m - \\frac{2 \\gamma V_m^\\beta}{R}\\right)$$\n", + "\n", + "\n", + "$$\\boldsymbol{x^\\beta} = \\boldsymbol{x_{\\infty}^{\\beta}} + \\left(\\boldsymbol{\\nabla^2} G^\\beta \\right)^{-1} \\boldsymbol{\\nabla^2} G^{\\alpha}\\left(\\boldsymbol{x-x_{\\infty}^{\\alpha}}\\right)$$\n", + "\n", + "The growth rate and interfacial composition method for multicomponent systems is defined as:\n", + "\n", + "$ \\frac{dR}{dt}, \\boldsymbol{x^\\alpha}, \\boldsymbol{x^\\beta}, \\boldsymbol{x_{\\infty}^{\\alpha}}, \\boldsymbol{x_{\\infty}^{\\beta}} = MulticomponentThermodynamics.getGrowthAndInterfacialComposition(\\boldsymbol{x}, T, \\Delta G_M, R, G_{TH}) $\n", + "\n", + "Where $\\Delta G_M$ is the driving force at composition $\\boldsymbol{x}$ and temperature $T$, $R$ is the precipitate radius and $G_{TH}$ is the free energy contribution from the Gibbs-Thomson effect corresponding to $R$." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Set driving force method since driving forces are required for \n", + "# calculating growth rate and interfacial compositions\n", + "multiTherm.setDrivingForceMethod('approximate')\n", + "\n", + "#Gibbs-Thomson contribution from radius\n", + "gamma = 0.023 #Interfacial energy between FCC-Ni and Ni3Al\n", + "Vm = 1e-5 #Molar volume\n", + "R = np.linspace(1e-10, 3e-8, 300)\n", + "G = 2 * gamma * Vm / R\n", + "\n", + "fig4 = plt.figure(4, figsize=(6, 5))\n", + "ax4 = fig4.add_subplot(111)\n", + "\n", + "#Calculate growth rate for different sets of compositions\n", + "xset = {'Ni-3Cr-5Al': [0.03, 0.1], 'Ni-3Cr-15Al': [0.03, 0.15], 'Ni-3Cr-17.5Al': [0.03, 0.175], 'Ni-3Cr-20Al': [0.03, 0.2]}\n", + "T = 1273\n", + "for x in xset:\n", + " #Clear cache since the compositions are quite different in values\n", + " multiTherm.clearCache()\n", + "\n", + " #Calculate driving force and growth rate\n", + " dg, xb = multiTherm.getDrivingForce(xset[x], T, returnComp=True)\n", + " res = multiTherm.getGrowthAndInterfacialComposition(xset[x], T, dg, R, G, searchDir = xb)\n", + " if res is not None:\n", + " gr, ca, cb, _, _ = res\n", + " ax4.plot(R, gr, label=x)\n", + "\n", + "ax4.set_xlim([0, 3e-8])\n", + "ax4.set_ylim([-1.4e-6, 1.4e-6])\n", + "ax4.set_xlabel('Radius (m)')\n", + "ax4.set_ylabel('Growth Rate (m/s)')\n", + "ax4.legend(xset.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interdiffusivity\n", + "\n", + "For binary systems, the interdiffusivity (as used in the growth rate equation) must be defined separately from the other thermodynamic/kinetic terms. To be used in the Thermodynamics module, parameters for the diffusivity/mobility must be defined in the TDB database file (either as 'MF'/'MQ' for mobility or 'DF'/'DQ' for diffusivity). The method is defined as:\n", + "\n", + "$ D = BinaryThermodynamics.getInterdiffusivity(x, T) $\n", + "\n", + "The reference element for the interdiffusivity will be the first element in the list of elements used to define the Thermodynamics modules.\n", + "\n", + "This method is also available for multicomponent systems, where $x$ must be defined as an array of the solute components and the method returns the interdiffusivity matrix of the solute components; however, it is not need for the KWN model as it is already accounted for when calculating the growth rate and interfacial compositions. The example below shows usage of this method for the Al-Zr system." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$ln(D (m/s^2))$')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Calculate interdiffusivity for various temperatures\n", + "T = np.linspace(500, 1000, 100)\n", + "d = binaryTherm.getInterdiffusivity(np.ones(100)*0.01, T)\n", + "\n", + "fig5 = plt.figure(5, figsize=(6, 5))\n", + "ax5 = fig5.add_subplot(111)\n", + "\n", + "#Arrhennius plot of diffusivities\n", + "ax5.plot(1/T, np.log(d))\n", + "\n", + "ax5.set_xlim([1/1000, 1/500])\n", + "ax5.set_xlabel('1/T ($K^{-1}$)')\n", + "ax5.set_ylabel('$ln(D (m/s^2))$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage in the KWN model\n", + "\n", + "The thermodynamics modules can be easily used in the KWN model as:\n", + "\n", + "$ KWNModel.setThermodynamics(Thermodynamics) $\n", + "\n", + "For binary systems, the interdiffusivity must be defined separately. This is to allow for user-defined functions. The interdiffusivity method can be inputted by:\n", + "\n", + "$ KWNModel.setDiffusivity(BinaryThermodynamics.getInterdiffusivity) $" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('base')", + "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": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/10_Surrogates.ipynb b/examples/10_Surrogates.ipynb new file mode 100644 index 0000000..72de88c --- /dev/null +++ b/examples/10_Surrogates.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Surrogates\n", + "\n", + "Surrogates can be contructed in place of thermodynamic functions to reduce computational time of the KWN model. This is useful for sensitivity analysis where certain parameters need to be pertubated often.\n", + "\n", + "As with the Thermodynamics module, the Surrogates module are split into two classes for binary and multicomponent systems." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Binary Systems\n", + "\n", + "Surrogates for driving force, interfacial composition and diffusivity can be created for binary systems.\n", + "\n", + "Both the Binary and Multicomponent surrogates require the thermodynamic functions for the various terms. While these can be user-defined, it is easiest to use a Thermodynamics object." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import BinaryThermodynamics\n", + "from kawin.thermo import BinarySurrogate\n", + "\n", + "#Load TDB file into a Thermodynamics object\n", + "binaryTherm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'])\n", + "binaryTherm.setGuessComposition(0.24)\n", + "\n", + "#Create Surrogate object\n", + "binarySurr = BinarySurrogate(binaryTherm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Driving force\n", + "\n", + "Training a surrogate model for driving forces in a binary system requires a set of compositions and temperatures (or a single temperature for isothermal systems). An additional parameter called 'scale' will convert the set of training compositions into linear or logarithmic spacing. This will allow for training on both dilute (logarithmic spacing) and non-dilute (linear spacing) systems." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "#Train driving forces\n", + "T = 723.15\n", + "xtrain = np.logspace(-5, -2, 20)\n", + "binarySurr.trainDrivingForce(xtrain, [T], scale='log')\n", + "\n", + "#Compare surrogate and thermodynamics modules\n", + "xTest = np.linspace(1e-7, 1.5e-2, 100)\n", + "binaryTherm.clearCache()\n", + "dgTherm, _ = binaryTherm.getDrivingForce(xTest, np.ones(100)*T)\n", + "dgSurr, _ = binarySurr.getDrivingForce(xTest, np.ones(100)*T)\n", + "\n", + "fig1 = plt.figure(1, figsize=(6, 5))\n", + "ax1 = fig1.add_subplot(111)\n", + "ax1.plot(xTest, dgTherm, label='Thermodynamics', linewidth=2, alpha=0.75)\n", + "ax1.plot(xTest, dgSurr, label='Surrogate', color='k', linestyle=(0,(5,5)), linewidth=2)\n", + "ax1.set_xlim([0, 0.014])\n", + "ax1.set_ylim([-10000, 10000])\n", + "ax1.set_xlabel('xZr (mole fraction)')\n", + "ax1.set_ylabel('Driving Force (J/mol)')\n", + "ax1.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interfacial composition\n", + "\n", + "Training a surrogate for interfacial compositions requires a set of temperatures and free energy contributions. For the free energy contributions, it may be useful to setup the KWN model first, then calling $ KWNBase.particleGibbs(R) $ where R is a set of radii. In practice, R should encompass a larger domain than what is set for the particle size distribution in the KWN model in case the particle size distribution is updated to include large size classes during a simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Create training points\n", + "T = 723.15\n", + "gamma = 0.1\n", + "Vm = 1e-5\n", + "R = np.linspace(1e-10, 1e-8, 100)\n", + "G = 2 * gamma * Vm / R\n", + "\n", + "#Train surrogate\n", + "binarySurr.trainInterfacialComposition([T], G, scale='log')\n", + "\n", + "#Compare surrogate and thermodynamics modules\n", + "Gtest = np.linspace(1000, 25000, 100)\n", + "Rtest = 2 * gamma * Vm / Gtest\n", + "binaryTherm.clearCache()\n", + "xMTherm, _ = binaryTherm.getInterfacialComposition(T, Gtest)\n", + "xMSurr, _ = binarySurr.getInterfacialComposition(T, Gtest)\n", + "\n", + "fig2 = plt.figure(2, figsize=(6, 5))\n", + "ax2 = fig2.add_subplot(111)\n", + "ax2.plot(Rtest[xMTherm != -1], xMTherm[xMTherm != -1], label='Thermodynamics', linewidth=2, alpha=0.75)\n", + "ax2.plot(Rtest[xMSurr != -1], xMSurr[xMSurr != -1], label='Surrogate', color='k', linestyle=(0,(5,5)), linewidth=2)\n", + "ax2.set_xlim([0, 1e-9])\n", + "ax2.set_ylim([0, 0.2])\n", + "ax2.set_xlabel('Radius (m)')\n", + "ax2.set_ylabel('Matrix Composition of Zr (mole fraction)')\n", + "ax2.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interdiffusivity\n", + "\n", + "Training a surrogate on the interdiffusivity requires a set of compositions and temperatures. If the interdiffusivity only depends on temperature, then only a single value for the composition is required." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Train interdiffusivity\n", + "Ttrain = np.linspace(500, 1000, 10)\n", + "binarySurr.trainInterdiffusivity([0.01], Ttrain, scale='log')\n", + "\n", + "#Compare surrogate and thermodynamics modules\n", + "Ttest = np.linspace(500, 1000, 100)\n", + "binaryTherm.clearCache()\n", + "dTherm = binaryTherm.getInterdiffusivity(np.ones(100)*0.01, Ttest)\n", + "dSurr = binarySurr.getInterdiffusivity(np.ones(100)*0.01, Ttest)\n", + "\n", + "fig3 = plt.figure(3, figsize=(6, 5))\n", + "ax3 = fig3.add_subplot(111)\n", + "ax3.plot(1/Ttest, np.log(dTherm), label='Thermodynamics', linewidth=2, alpha=0.75)\n", + "ax3.plot(1/Ttest, np.log(dSurr), label='Surrogate', color='k', linestyle=(0,(5,5)), linewidth=2)\n", + "ax3.set_xlim([1/1000, 1/500])\n", + "ax3.set_xlabel('1/T ($K^{-1}$)')\n", + "ax3.set_ylabel('$ln(D (m^2/s))$')\n", + "ax3.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multicomponent Systems\n", + "\n", + "Surrogates for driving force, interfacial composition, growth rate and impingement factor can be created for multicomponent systems. Note that as the interfacial composition, growth rate and impingement factor can all be determined by a single equilibrium calculation, these terms are grouped into 'curvature factors'. This is similar to how these terms are handled in the Thermodynamics module.\n", + "\n", + "As with the Binary surrogates, the multicomponent surrogate object only requires a MulticomponentThermodynamics object." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import MulticomponentThermodynamics\n", + "from kawin.thermo import MulticomponentSurrogate\n", + "\n", + "multiTherm = MulticomponentThermodynamics('NiCrAl.tdb', ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'])\n", + "multiSurr = MulticomponentSurrogate(multiTherm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Driving force\n", + "\n", + "Training a surrogate for driving force calculations requires a set of compositions and temperatures. The difference between the Binary and Multicomponent surrogate objects is that the set of compositions for a multicomponent systems is a 2D array of size m x n, where m is the number of training points and n is the number of solutes.\n", + "\n", + "A utility function is provided to create a cartesian product of multiple arrays for each solute." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import generateTrainingPoints\n", + "\n", + "#Create training points\n", + "T = 1273.15\n", + "xCr = np.linspace(0.01, 0.05, 8)\n", + "xAl = np.linspace(0.1, 0.2, 8)\n", + "xTrain = generateTrainingPoints(xCr, xAl)\n", + "\n", + "#Train driving force\n", + "multiSurr.trainDrivingForce(xTrain, [T])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Curvature factors\n", + "\n", + "The growth rate, interfacial composition, interdiffusivity and impingement rate can all be determined from the curvature of the Gibbs free energy surface. Thus, these terms are lumped into a single group that will be referred to as 'curvature factors'. Training the curvature factors only requires a set of compositions and temperatures." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Create training points\n", + "T = 1273.15\n", + "xCr = np.linspace(0.01, 0.05, 16)\n", + "xAl = np.linspace(0.1, 0.2, 16)\n", + "xTrain = generateTrainingPoints(xCr, xAl)\n", + "\n", + "#Train curvature surrogate\n", + "multiSurr.trainCurvature(xTrain, T)\n", + "\n", + "#Compare growth rate from surrogate and thermodynamics modules\n", + "xTest = [0.03, 0.175] #Ni-3Cr-17.5Al\n", + "\n", + "gamma = 0.023 #Interfacial energy between FCC-Ni and Ni3Al\n", + "Vm = 1e-5 #Molar volume\n", + "Rtest = np.linspace(1e-10, 3e-8, 300)\n", + "Gtest = 2 * gamma * Vm / Rtest\n", + "\n", + "multiTherm.clearCache()\n", + "dgTherm, xb = multiTherm.getDrivingForce(xTest, T, returnComp=True)\n", + "grTherm, caTherm, cbTherm, _, _ = multiTherm.getGrowthAndInterfacialComposition(xTest, T, dgTherm, Rtest, Gtest, searchDir=xb)\n", + "\n", + "dgSurr, _ = multiSurr.getDrivingForce(xTest, T)\n", + "grSurr, caSurr, cbSurr, _, _ = multiSurr.getGrowthAndInterfacialComposition(xTest, T, dgSurr, Rtest, Gtest)\n", + "\n", + "fig4 = plt.figure(4, figsize=(6, 5))\n", + "ax4 = fig4.add_subplot(111)\n", + "ax4.plot(Rtest, grTherm, label='Thermodynamics', linewidth=2, alpha=0.75)\n", + "ax4.plot(Rtest, grSurr, label='Surrogate', color='k', linestyle=(0,(5,5)), linewidth=2)\n", + "ax4.set_xlim([0, 3e-8])\n", + "ax4.set_ylim([-2e-6, 2e-6])\n", + "ax4.set_xlabel('Radius (m)')\n", + "ax4.set_ylabel('Growth Rate (m/s)')\n", + "ax4.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyperparameters\n", + "\n", + "The surrogates are created through scipy's radial basis functions. The same hyperparameters used in the scipy's implementation can be used for these surrogates. These include: 'function', 'epsilon', 'smooth'. 'function' is the basis function to use, 'epsilon' is the scale between training points (the surrogates will automatically scale the training points such that the optimal value for 'epsilon' should be near 1), and 'smooth' allows for smoothing the interpolation (a value of 0 means that the surrogate will cross all training points). When training the surrogates, these are set as additional parameters. For example:\n", + "\n", + "$ Surrogate.trainDrivingForce(x, T, function='linear', epsilon=1, smooth=0) $\n", + "\n", + "If a surrogate is already trained, the hyperparameters can be changed without the need for re-training.\n", + "\n", + "$ Surrogate.changeDrivingForceHyperparameters(function, epsilon, smooth) $" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving and loading\n", + "\n", + "The surrogates can be saved and loaded for later usage. These will not retain the thermodynamic functions used for the training, so re-training of the surrogate cannot be done after saving/loading; however, the hyperparameters can still be changed.\n", + "\n", + "$ Surrogate.save(filename) $\n", + "\n", + "$ surr = Surrogate.load(filename) $" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage in the KWN Model\n", + "\n", + "As with the Thermodynamics module, the Surrogate objects can be easily used in the KWN model by:\n", + "\n", + "$ KWNModel.setSurrogate(Surrogate) $\n", + "\n", + "For binary systems, the interdiffusivity also has to be inputted separately.\n", + "\n", + "$ KWNModel.setDiffusivity(BinarySurrogate.getInterdiffusivity) $" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/11_Extra_Factors.ipynb b/examples/11_Extra_Factors.ipynb new file mode 100644 index 0000000..6972c18 --- /dev/null +++ b/examples/11_Extra_Factors.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extra Factors in the KWN Model\n", + "\n", + "The default options in the KWN model assumes bulk nucleation and a spherical precipitate. However, in real-life systems, these assumptions may not be true. Several options are present to model heterogenous nucleation and non-spherical precipitate shapes.\n", + "\n", + "## Nucleation\n", + "\n", + "The options for the nucleation site density includes the following: 'bulk', 'dislocations', 'grain_boundaries', 'grain_edges', 'grain_corners'\n", + "\n", + "The nucleation site density ($N_0$) for bulk nucleation is determined by the number of solutes in the bulk lattice. For dislocation, $N_0$ depends on the dislocation density. $N_0$ for grain boundaries, edges and corners depends on the grain size [1]. For grain boundary nucleation, the change in surface energy accounts for both the creation of the precipitate/matrix interface and removal of grain boundary, for which the grain boundary energy must be defined [2]. By default, the grain boundary energy is set to 0.3 $J/m^2$.\n", + "\n", + "While the KWNModel will automatically calculate the nucleation site densities for each site type, these values can be manually set." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.precipitation import PrecipitateBase\n", + "\n", + "model = PrecipitateBase()\n", + "\n", + "#Change nucleation site type to grain boundaries\n", + "model.setNucleationSite('grain_boundaries')\n", + "\n", + "#Set grain boundary energy for nucleation on grain boundaries/edges/corners\n", + "model.setGrainBoundaryEnergy(0.3)\n", + "\n", + "#Change dislocation density and grain size\n", + "model.setNucleationDensity(grainSize = 10, aspectRatio = 1, dislocationDensity = 5e12)\n", + "\n", + "#Manually set nucleation site density for each site type\n", + "model.bulkN0 = 1e30 #Bulk nucleation site density\n", + "model.dislocationN0 = 1e30 #Site density on dislocations\n", + "model.GBareaN0 = 1e30 #Site density on grain boundaries\n", + "model.GBedgeN0 = 1e30 #Site density on grain edges\n", + "model.GBcornerN0 = 1e30 #Site density on grain corners" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shape factors\n", + "\n", + "Currently, the KWN model has support for ellipsoidal (prolate/needle and oblate/plate) and cuboidal precipitates. For cuboidal precipitates the cubic factor currently set constant at $\\sqrt{2}$ [3,4]. These shapes are defined in the KWN model by their equivalent spherical radius ($R_{sph}$) and an aspect ratio ($\\alpha$), where the $\\alpha$ can either be constant or as a function of $R_{sph}$.\n", + "\n", + "The aspect ratio is defined as the ratio of the long axis over the short axis. Conversion between the radius along the short axis ($r$) and the equivalent spherical radius ($R_{sph}$) is given by:\n", + "\n", + "Needle: $ R_{sph} = \\sqrt[3]{\\alpha} r $\n", + "\n", + "Plate: $ R_{sph} = \\sqrt[3]{\\alpha^2} r $\n", + "\n", + "Cuboidal: $ R_{sph} = \\sqrt[3]{\\frac{3 \\alpha}{4 \\pi}} r $\n", + "\n", + "Deviation from a spherical precipitate changes both the thermodynamics (Gibbs-Thomson effect) and kinetics (growth rate). The free energy contribution from the Gibbs-Thomson effect is given by:\n", + "\n", + "$$ \\Delta G_{TH} = g(\\alpha) \\frac{2 \\gamma V_M^\\beta}{R_{sph}} $$\n", + "\n", + "The changes in the growth rate is given by:\n", + "\n", + "$$ \\frac{dR}{dt} = f(\\alpha) \\frac{dR_{sph}}{dt} $$\n", + "\n", + "The functions of $g(\\alpha)$ and $f(\\alpha)$ are taken from Ref. 3 and 4." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.precipitation import ShapeFactor\n", + "#Change precipitate shape\n", + "model.setPrecipitateShape(ShapeFactor.NEEDLE, ratio = 1.5)\n", + "model.setPrecipitateShape(ShapeFactor.PLATE, ratio = 1.5)\n", + "model.setPrecipitateShape(ShapeFactor.CUBIC, ratio = 1.5)\n", + "\n", + "#Remove aspect ratio and set to spherical shape\n", + "model.setPrecipitateShape(ShapeFactor.SPHERE)\n", + "\n", + "#Radius-dependent aspect ratio\n", + "ar = lambda r: 2.3 * (r/1e-9)**1.1\n", + "model.setPrecipitateShape(ShapeFactor.NEEDLE, ratio = ar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Strain Energy\n", + "\n", + "Molar volume differences between the matrix and precipitate phase can induce strains, which reduces the driving force for nucleation. For spherical and cuboidal precipitates, the strain energy can be calculated by Khachaturyan's approximation. For ellipsoidal precipitates, the strain energy can be calculated using Eshelby's tensor [4,5].\n", + "\n", + "Similar to the Thermodynamics and Surrogate modules, the strain energy is calculated using a module separated from KWNBase. Inserting the strain energy parameters requires creating and setting up a StrainEnergy object, then inserting it into the KWN model for a specified phase.\n", + "\n", + "The StrainEnergy object requires the elastic constants and eigenstrains to be defined. External stresses can also be defined if applicable. The eigenstrains and external stress can be defined as a tensor (3x3), values along the three axes (array of length 3), or a single value to be applied on all 3 axes. The elastic constants can be defined using its 6x6 tensor, the three elastic constants ($c_{11}$, $c_{12}$ and $c_{44}$), or by at least two moduli (e.g. elastic modulus, poission ratio, shear modulus, bulk modulus, etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.precipitation import StrainEnergy\n", + "\n", + "#Create StrainEnergy object\n", + "se = StrainEnergy()\n", + "\n", + "#Set elastic tensor by its elastic modulus and possion ratio\n", + "se.setModuli(E = 100e9, nu = -0.3)\n", + "\n", + "#Set eigenstrains\n", + "# [[0.01, 0.00, 0.00]\n", + "# [0.00, 0.01, 0.00]\n", + "# [0.00, 0.00, 0.02]]\n", + "se.setEigenstrain([0.01, 0.01, 0.02])\n", + "\n", + "#Insert StrainEnergy object into KWN model\n", + "model.setStrainEnergy(se)\n", + "\n", + "#Use strain energy to calculate aspect ratio (for plate- and needle-like precipitates)\n", + "#This will override the aspect ratio that was defined when setting the precipitate shape\n", + "#We'll also still need to input the precipitate shape\n", + "model.setStrainEnergy(se, calculateAspectRatio=True)\n", + "model.setPrecipitateShape(ShapeFactor.NEEDLE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Strain Energy Example (Cu-Ti system)\n", + "\n", + "In the Cu-Ti system (dilute Ti), the needle-like $Cu_4Ti$ precipitate creates lattice strains in the Cu-matrix. The following parameters are applicable to this system (from K. Wu et al (2018)):\n", + "\n", + "Eigenstrains of the $Cu_4Ti$ precipitate:\n", + "\n", + "$ \\epsilon_{11} = 0.022 $\n", + "\n", + "$ \\epsilon_{22} = 0.022 $\n", + "\n", + "$ \\epsilon_{33} = 0.003 $\n", + "\n", + "Elastic constants for the Cu matrix\n", + "\n", + "$ c_{11} = 168.4 \\quad GPa $\n", + "\n", + "$ c_{12} = 121.4 \\quad GPa $\n", + "\n", + "$ c_{44} = 75.4 \\quad GPa $\n", + "\n", + "We can use these values to determine the strain energy of the $Cu_4Ti$ precipitate for any given aspect ratio. In this example, we'll vary the aspect ratio from 1 to 2 and calculate the strain energy. The volume of the precipitate will be set constant to the volume of a sphere with a radius of 4 nm." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "#By default, StrainEnergy outputs 0\n", + "#This is changed within the KWN model before the model is solved for\n", + "#However, we can manually change it. For this example, we need to set it to the calculate for ellipsoidal shapes\n", + "se.setEllipsoidal()\n", + "\n", + "#Set elastic tensor by c11, c12 and c44 values\n", + "se.setElasticConstants(168.4e9, 121.4e9, 75.4e9)\n", + "\n", + "#Set eigenstrains\n", + "se.setEigenstrain([0.022, 0.022, 0.003])\n", + "\n", + "#Setup strain energy parameters\n", + "se.setup()\n", + "\n", + "#Aspect ratio\n", + "aspect = np.linspace(1, 2, 100)\n", + "\n", + "#Equivalent spherical radius of 4 nm\n", + "rSph = 4e-9 / np.cbrt(aspect)\n", + "r = np.array([rSph, rSph, aspect*rSph]).T\n", + "\n", + "E = se.strainEnergy(r)\n", + "\n", + "plt.plot(aspect, E)\n", + "plt.xlim([1, 2])\n", + "plt.ylim([1.0e-17, 1.5e-17])\n", + "plt.xlabel('Aspect Ratio')\n", + "plt.ylabel('Strain Energy (J)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating Aspect Ratio from Strain Energy\n", + "\n", + "The aspect ratio for plate- and needle-like precipitates can be determined by minimizing the energy contributions from the strain and interfacial energy contributions.\n", + "\n", + "$$ \\alpha = argmin\\left( \\frac{4}{3}\\pi R_{sph}^{3} \\Delta G_{el}(\\alpha) + 4 \\pi R_{sph}^{2} g(\\alpha) \\gamma \\right) $$\n", + "\n", + "Where $R_{sph}$ is the equivalent spherical radius. The strain energy module has two options for calculating the equilibrium aspect ratio: iterative or searching. The iterative method (StrainEnergy.eqAR_byGR) performs a Golden Section search to find the minimum. The search method (StrainEnergy.eqAR_bySearch) will calculate the net energy contribution for a number of aspect ratios and will return the aspect ratio that gives the minimum. By default, this method is accurate up to 2 significant digits. In addition, due to caching, this method is also faster than the iterative method for large number of calculations.\n", + "\n", + "## Example ($\\gamma''$ in IN718)\n", + "\n", + "The $\\gamma''$ precipitate in IN718 are plate shape where the aspect ratio depends on the size of the precipitate. Using the elastic properties of IN718 (shear modulus of 57.1 GPa and Poisson's ratio of 0.33) and the eigenstrain of the $\\gamma''$ precipitate, the relationship between the aspect ratio and precipitate diameter (long axis) can be found." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kawin.precipitation import ShapeFactor\n", + "\n", + "#Strain energy parameters\n", + "se = StrainEnergy()\n", + "se.setEigenstrain([6.67e-3, 6.67e-3, 2.86e-2])\n", + "se.setModuli(G=57.1e9, nu=0.33)\n", + "se.setEllipsoidal()\n", + "se.setup()\n", + "\n", + "#Shape factor parameters (only the shape needs to be defined)\n", + "sf = ShapeFactor()\n", + "sf.setPlateShape()\n", + "\n", + "#Calculate equilibrium aspect ratio\n", + "gamma = 0.02375\n", + "Rsph = np.linspace(1e-10, 40e-9, 100)\n", + "eqAR = se.eqAR_bySearch(Rsph, gamma, sf)\n", + "\n", + "#Convert spherical radius to diameter of the plate\n", + "R = 2*Rsph*eqAR / np.cbrt(eqAR**2)\n", + "\n", + "#Plot diameter vs. aspect ratio\n", + "plt.plot(R, eqAR)\n", + "plt.xlim([0, 40e-9])\n", + "plt.ylim([1, 9])\n", + "plt.xlabel('Diameter (m)')\n", + "plt.ylabel('Aspect Ratio')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. Kozeschnik, Ernst et al. Precipitation Modeling, Momentum Press, 2012\n", + "2. P. J. Clemm and J. C. Fisher, \"The Influence of Grain Boundaries on the Nucleation of Secondary Phases\" *Acta Metallurgica* 3 (1955) p. 70\n", + "3. B. Holmedal, E. Osmundsen, Q. Du, \"Precipitation of Non-Spherical Particles in Aluminum Alloys Part I: Generalization of the Kampmann-Wagner Numerical Model\" *Metallurgical and Materials Transactions A* 47 (2016) p. 581\n", + "4. K. Wu, Q. Chen and P. Mason, \"Simulation of Precipitation Kinetics with Non-Spherical Particles\" *J. Phase Equilib. Diffus.* 39 (2018) p. 571\n", + "5. C. Weinberger, W. Cai and D. Barnett, ME340B Lecture Notes - Elasticity of Microscopic Structures, Standford University 2005. http://micro.standford.edu/~caiwei/me340b/content/me340b-notes_v01.pdf" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('base')", + "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": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/12_Custom_Iterators.ipynb b/examples/12_Custom_Iterators.ipynb new file mode 100644 index 0000000..a3fcbdf --- /dev/null +++ b/examples/12_Custom_Iterators.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Custom Iterators\n", + "\n", + "kawin currently comes with two built-in iterators when solving a model: Explicit Euler and 4th order Runga Kutta. However, custom iterators can be made and used in the solve function.\n", + "\n", + "We'll use the Al-Zr system from the Binary Precipitation example as a use-case." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from kawin.thermo import BinaryThermodynamics\n", + "from kawin.precipitation import PrecipitateModel, VolumeParameter\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#Set up thermodynamics\n", + "therm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'])\n", + "therm.setGuessComposition(0.24)\n", + "\n", + "#Set up model with parameters\n", + "model = PrecipitateModel()\n", + "\n", + "model.setInitialComposition(4e-3)\n", + "model.setTemperature(450 + 273.15)\n", + "model.setInterfacialEnergy(0.1)\n", + "Diff = lambda x, T: 0.0768 * np.exp(-242000 / (8.314 * T))\n", + "model.setDiffusivity(Diff)\n", + "a = 0.405e-9 #Lattice parameter\n", + "model.setVolumeAlpha(a**3, VolumeParameter.ATOMIC_VOLUME, 4)\n", + "model.setVolumeBeta(a**3, VolumeParameter.ATOMIC_VOLUME, 4)\n", + "\n", + "model.setNucleationDensity(grainSize = 1, dislocationDensity = 1e15)\n", + "model.setNucleationSite('dislocations')\n", + "\n", + "model.setThermodynamics(therm, addDiffusivity=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's solve the model using the Explicit Euler method for a comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "0\t0.0e+00\t\t0.0\t\t723\t\t0.4000\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t5.7737e+03\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "3831\t1.8e+06\t\t36.8\t\t723\t\t0.0126\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t1.395e+22\t\t1.5504\t\t6.0887e-09\t3.2954e+02\n", + "\n" + ] + } + ], + "source": [ + "from kawin.solver import SolverType\n", + "\n", + "model.solve(500*3600, solverType=SolverType.EXPLICITEULER, verbose=True, vIt=5000)\n", + "eTime = np.array(model.time)\n", + "eR = np.array(model.avgR)\n", + "eN = np.array(model.precipitateDensity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The custom iteration function will need to take in the following parameters:\n", + "- f : function that takes in ($t$, $X$) and returns $dX/dt$ (and $\\Delta t$)\n", + "- t : current time (float)\n", + "- X : current $X$ (list - can be a nested list)\n", + "- updateX : function that takes in ($X$, $dX/dt$ and $\\Delta t$) and returns $X_{new}$\n", + "\n", + "Notes:\n", + "- The functions f and updateX are implemented by the Solver object, so these do not need to be implemented\n", + "- While $X_{new}$ can be found by $X + dX/dt * \\Delta t$, the updateX function will apply any corrections needed to $dX/dt$ defined by the GenericModel to avoid improper values of $X_{new}$\n", + "\n", + "We'll create a custom function that uses the Midpoint iterative scheme. This goes by:\n", + "$$ X_{n+1} = X_n + \\Delta t * f\\left(t + \\frac{\\Delta t}{2}, X_n + \\frac{\\Delta t}{2} * \\frac{dX_n}{dt}\\right) $$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def MidpointIterator(f, t, Xn, updateX):\n", + " #Get dXdt at Xn along with dt\n", + " dXdt, dt = f(t, Xn, True)\n", + "\n", + " #Calculate X + dXdt * dt/2 and get dXdt at midpoint\n", + " Xmid = updateX(Xn, dXdt, dt/2)\n", + " dXdt_mid = f(t, Xmid)\n", + "\n", + " #Calculate X + dXdt_mid * dt\n", + " return updateX(Xn, dXdt_mid, dt), dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can solve the model with this new iteration by replacing solverType with this function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ury3\\OneDrive - LLNL\\Documents\\Projects\\U-C Modeling\\kawin-development\\kawin\\kawin\\precipitation\\KWNBase.py:1162: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.exp(-tau / t)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "0\t0.0e+00\t\t0.0\t\t723\t\t0.4000\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t5.7737e+03\n", + "\n", + "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", + "3751\t1.8e+06\t\t26.6\t\t723\t\t0.0126\n", + "\n", + "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", + "\tbeta\t1.386e+22\t\t1.5505\t\t6.0962e-09\t3.2700e+02\n", + "\n" + ] + } + ], + "source": [ + "model.reset()\n", + "\n", + "model.solve(500*3600, solverType=MidpointIterator, verbose=True, vIt=5000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we could compare the results from the Explicit Euler with the Midpoint iterators." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(8,4))\n", + "\n", + "ax[0].plot(eTime, eN, label='Explicit Euler', alpha=0.75)\n", + "model.plot(ax[0], 'Precipitate Density', label='Midpoint', color='k', linestyle=(0,(5,5)), linewidth=2)\n", + "\n", + "ax[1].plot(eTime, eR, label='Explicit Euler', alpha=0.75)\n", + "model.plot(ax[1], 'Average Radius', label='Midpoint', color='k', linestyle=(0,(5,5)), linewidth=2)\n", + "\n", + "ax[0].legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "calphad", + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Binary Precipitation.ipynb b/examples/Binary Precipitation.ipynb deleted file mode 100644 index b32ed47..0000000 --- a/examples/Binary Precipitation.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Binary Precipitation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example - The Al-Zr system\n", - "\n", - "In the Al-Zr system, $Al_3Zr$ can precipitate into an $\\alpha$-Al (FCC) matrix. The Thermodynamics module provides some functions to interface with pyCalphad in defining the driving force and interfacial composition. However, it is also possible to use user-defined functions for the driving force and nucleation as long as the function parameters and return values are consistent with the ones provides by the Thermodynamics module. Calphad models for the Al-Zr system was obtained from the STGE database and Wang et al [1,2].\n", - "\n", - "For a binary system, one hyperparameter that may need to be set in the Thermodynamics module for calculating interfacial compositions is the guess composition when finding a tie-line. The $Al_3Zr$ phase has a fixed composition at 25 at.% Zr, so the guess composition can be set to 24 at.% Zr." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import BinaryThermodynamics\n", - "from kawin.KWNEuler import PrecipitateModel\n", - "import numpy as np\n", - "\n", - "therm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'])\n", - "therm.setGuessComposition(0.24)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the model\n", - "\n", - "Inititializing the KWN model will require the initial and final time, the number of time steps, the bounds for the particle size distribution (PSD) and the number of bins for the PSD.\n", - "\n", - "The Al-Zr system will be ran at $450\\text{ }^oC$ for 500 hours. 25,000 steps will be used. Adaptive time stepping is on by default; however, it tends to work well if if the model is initialized with at least 5,000 - 10,000 steps. The PSD will be composed of 100 bins ranging from 0.1 to 10nm. Bins will be added automatically to the PSD during the simulation to account for particles growing larger than the initial range.\n", - "\n", - "The time can be either on a linear or a logarithmic scale." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#Initial and final time in seconds\n", - "t0 = 0\n", - "tf = 500*3600\n", - "steps = 2.5e4\n", - "\n", - "#Create model\n", - "model = PrecipitateModel(t0, tf, steps, linearTimeSpacing = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Inputs\n", - "\n", - "The interfacial energy and diffusivity are from Robson and Prangnell [3]. Although the diffusivity for this system is only a function of temperature, it needs to be defined as a function of both composition and temperature (to keep consistent with systems that use composition dependent diffusivity). Since we're manually defining the diffusivity, we'll set addDiffusivity to False when inputting the Thermodynamics object. This tells the model to ignore any diffusivity parameters in the .tdb file when dealing with binary systems.\n", - "\n", - "$ x_0 = 0.4 \\: \\text{at.\\%} $\n", - "\n", - "$ T = 450 \\: ^oC = 723.15 \\: K $\n", - "\n", - "$ \\gamma = 0.1 \\: J/m^2 $\n", - "\n", - "$ D = 0.0768 \\, exp\\left(- \\frac{242000}{R T}\\right) \\: m^2/s $\n", - "\n", - "$ a = 0.405 \\: nm = 0.405\\mathrm{e}{-9} \\: m $\n", - "\n", - "4 atoms per unit cell\n", - "\n", - "Dislocation density $ \\rho_D = 1e15 $\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "xInit = 4e-3 #Initial composition (mole fraction)\n", - "model.setInitialComposition(xInit)\n", - "\n", - "T = 450 + 273.15 #Temperature (K)\n", - "model.setTemperature(T)\n", - "\n", - "gamma = 0.1 #Interfacial energy (J/m2)\n", - "model.setInterfacialEnergy(gamma)\n", - "\n", - "D0 = 0.0768 #Diffusivity pre-factor (m2/s)\n", - "Q = 242000 #Activation energy (J/mol)\n", - "Diff = lambda x, T: D0 * np.exp(-Q / (8.314 * T))\n", - "model.setDiffusivity(Diff)\n", - "\n", - "a = 0.405e-9 #Lattice parameter\n", - "Va = a**3 #Atomic volume of FCC-Al\n", - "Vb = a**3 #Assume Al3Zr has same unit volume as FCC-Al\n", - "atomsPerCell = 4 #Atoms in an FCC unit cell\n", - "model.setVaAlpha(Va, atomsPerCell)\n", - "model.setVaBeta(Vb, atomsPerCell)\n", - "\n", - "#Average grain size (um) and dislocation density (1e15)\n", - "model.setNucleationDensity(grainSize = 1, dislocationDensity = 1e15)\n", - "model.setNucleationSite('dislocations')\n", - "\n", - "#Set thermodynamic functions\n", - "model.setThermodynamics(therm, addDiffusivity=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the Model\n", - "\n", - "Now we can run the model. The current status of the model may be output by setting \"verbose\" to True with \"vIt\" being how many iterations will pass before the current status of the model is printed out." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N\tTime (s)\tTemperature (K)\tMatrix Comp\n", - "10000\t6.9e+05\t\t723\t\t0.0138\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tbeta\t3.924e+22\t\t1.5457\t\t4.3538e-09\t4.6562e+02\n", - "\n", - "N\tTime (s)\tTemperature (K)\tMatrix Comp\n", - "20000\t1.4e+06\t\t723\t\t0.0130\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tbeta\t2.119e+22\t\t1.5487\t\t5.3297e-09\t3.8005e+02\n", - "\n", - "Finished in 68.545 seconds.\n" - ] - } - ], - "source": [ - "model.solve(verbose=True, vIt=10000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting\n", - "\n", - "We can now plot the results. Here, we are plotting the precipitate density, volume fraction and average radius as a function of time and the size distribution density at the final time.\n", - "\n", - "Everything will be plotted on a logarithmic time scale.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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TbiKq1ffHrgMAnurRziASbl2YmojRs701era31pQpylU4ciUHP/6dhkMXspGeW4KtR65h65FrcLExxXhfF7zg1wFO1qYCRk5ERGQYMvNK8K+dpxB7OUdT1sXRAn1d26KTgwWcrE1hbymFmYkYZiYVP5BLxCKIIIKRqCJJNapcVFV0P0GtpJ3QPvDnBxZhrc6j6tYlUa7udSJ9YNJNRDUqV6rwS3IGAGDiAFeBo2kcJhIjDO3qgKFdHVCiUCLm0i3sPXMT+85kIe1OCT6PvIjV+y9imJcjgod4wtfNRuiQiYiIBHH6Rh4mbzmGu8VlkEqMMOkxN7zymBs87MyFDo3IoDHpJqIaJaXlIq+kDG3NjDGwk53Q4TQ6UxMxgnq0Q1CPdihR9MSf/2Qi4u80HEu5g8izNxF59ib83NoieEhHDPNy4C/hRETUalzIKsDETUdRIC9HD2crrJ3Yl8k2UR0x6SaiGh2+kA0ACOxsD3Erm6NjaiLG83074Pm+HXA5uxBfx1zFzyfSkXD9LqZvT4CPaxv8+6lueMzTVuhQiYiIGlVeSRne+CYBBfJy9HNviy1T+8FSZix0WETNhpHQARCR4Tp6tWKF78Fd7AWORFidHCzw6bjeiP33UMwc4glTYzGSUnPx0sajmLr1OC7dLBA6RCIiokbz2V/ncf12Mdq3McXGSX5MuInqiUk3EVWrTKnCP+l5AIC+rm2EDcZAOFjJsHikF6L+9ThefcwVEiMRDl+4hZFfxODTP8+jWFEudIhERER6lZyWi++PpwIAVr7YB23NTQSOiKj5YdJNRNW6kFUAebkKVjIJ3G05Z+tBDlYyfPxsL+wPGYInvRxRrlIjPOoKnvw8CvvOZAkdHhERkd6E7b8ItRp4zqc9/DtyShVRQzDpJqJqJaXlAgD6uLThnos1cLczx9dT/PD1ZD90aGuKjLxSvPFNIkJ+TEZ+aZnQ4REREenkn/Q8HL5wC0YiYN6wzkKHQ9RsMekmomqdvpELAOjToY2gcTQHT3Z3ROSCIZg5xBNGIuDnE+l4anU0jjywhykREVFzs/XINQDAmD7OcOdK5UQNxqSbiKp1KbsQAODlZCVwJM2DqYkYi0d6YWewP9xszZCRV4pXvj6Gz/ddgFKlFjo8IiKieimUl2PP6UwAwGR/N4GjIWremHQTURVqtRqXb1Yk3Z0dLQSOpnnxdbPBnrmBmDjAFQDw5cHLmLLlOG4XygWOjIiIqO7+OJWBkjIlPO3N0de1rdDhEDVrTLqJqIqb+XIUyMshNhJxEbUGMJdKsPy5XvjiJW+YGosRezkHo9fE4vSNPKFDIyIiqpP/naro5R7XtwNEIq7tQqQLJt1EVMWl7Ip9p91tzWAi4T8TDTXWuz1+nTMQnvbmyMovxfgN8dh/9qbQYREREdWqUF6OY1fvAABG9GgncDREzR+/TRNRFZcqh5Y7WAocSfPXxdESv84eiMDOdigpU+KNbxKwPf6a0GERERHVKObiLSiUKrjbmqGjPUe8EemKSTcRVZGSUwQA8GRDqxeWMmNsmdoPL/VzgUoNfPDrmXv7nnKBNSIiMjwHzmcDAIZ5OXJoOZEeMOkmoioy80oAAO3bmgocScthLDZC6PO98PbwLgCAsP2X8N+9F5h4ExGRwYm/chsAMKSLvcCRELUMTLqJqIr03FIAgHMbJt36JBKJ8NawznhvtBcAYN3hK1i+5xwTbyIiMhjpuSVIzy2B2EgEXzeuWk6kD0y6iaiKyp5uZ2sm3Y1heqAnlo7tAQDYFJOCLw5cEjgiIiKiCn+nVCyg1tPZCuZSicDRELUMTLqJSEuJQonc4jIAgFMbmcDRtFyT/d3x0TMViXfY/kv4hourERGRATh+rSLp7uduI3AkRC0Hk24i0pJxr5fbQiqBlcxY4GhatikB7pg3rDMA4IPfzuB/pzIEjoiIiFq7xGt3AQD9PJh0E+mLQSfd0dHRGDNmDJydnSESifDLL7/UWv/nn3/G8OHDYW9vDysrK/j7+2Pv3r1NEyxRC5GRW5F0O1mzl7spzH+yMyb7u0GtBt7+8SRO3cgVOiQiAMC6devg4eEBmUwGX19fxMTE1Fo/KioKvr6+kMlk8PT0RHh4uNbrZ86cwbhx4+Du7g6RSISwsLBazxcaGgqRSIT58+fr+E6IqK5KFEpcyi4AAHi7tBE2GKIWxKCT7qKiIvTp0wdr166tU/3o6GgMHz4ce/bsQWJiIoYOHYoxY8YgKSmpkSMlajkyuYhakxKJRPjPmB4Y1s0B8nIV3tieiOz8UqHDolYuIiIC8+fPx5IlS5CUlITAwECMHDkSqamp1dZPSUnBqFGjEBgYiKSkJLz77ruYO3cudu3apalTXFwMT09PfPrpp2jXrl2t1//777+xceNG9O7dW6/vi4hqdz4rHyo1YGdhAgdLqdDhELUYBp10jxw5Eh9//DGef/75OtUPCwvDO++8g379+qFz585Yvnw5OnfujN9//72RIyVqOSqHlztzPneTERuJEPaSNzo5WCArvxRvfJOI0jKl0GFRK7Zq1SpMmzYN06dPh5eXF8LCwuDi4oL169dXWz88PByurq4ICwuDl5cXpk+fjtdffx0rV67U1OnXrx/++9//4qWXXoJUWvOX+cLCQrzyyivYtGkT2rblyslETelMRj4AoLuzNffnJtIjg066daVSqVBQUAAbG85JIaqryp5uJ65c3qQsZcb4erIfrE2NkZyWi0/+OCd0SNRKKRQKJCYmIigoSKs8KCgIcXFx1R4THx9fpf6IESOQkJCAsrKyel1/9uzZGD16NJ588sk61ZfL5cjPz9d6EFHDVCbdPZytBI6EqGVp0Un3559/jqKiIowfP77GOmysibRV9nRzTnfTc7czx5qXfQAA3xy9jj9PZwocEbVGOTk5UCqVcHR01Cp3dHREVlZWtcdkZWVVW7+8vBw5OTl1vvYPP/yAEydOIDQ0tM7HhIaGwtraWvNwcXGp87FEpO1sRh4AJt1E+tZik+4dO3bgww8/REREBBwcHGqsx8aaSFvlQmrtOadbEEO62CN4SEcAwDu7TiHtTrHAEVFr9fDQUrVaXetw0+rqV1dek7S0NMybNw/ffvstZLK6/+i3ePFi5OXlaR5paWl1PpaI7lOq1DifVbGIWg9na4GjIWpZWmTSHRERgWnTpuHHH3985PA0NtZE96nVamTm3RtezqRbMG8HdUFf1zYoKC3HWzuSUK5UCR0StSJ2dnYQi8VVerWzs7Or9GZXateuXbX1JRIJbG1t63TdxMREZGdnw9fXFxKJBBKJBFFRUVizZg0kEgmUyurXOZBKpbCystJ6EFH93bhbDHm5CiYSI7jamAkdDlGL0uKS7h07dmDq1Kn4/vvvMXr06EfWZ2NNdF9+STmKFRVfbDm8XDjGYiOsedkHljIJktNysSH6qtAhUStiYmICX19fREZGapVHRkYiICCg2mP8/f2r1N+3bx/8/PxgbGxcp+sOGzYMp0+fRnJysubh5+eHV155BcnJyRCLxQ17Q0RUJ1dvFQEAPO3MITbiImpE+iQROoDaFBYW4vLly5rnKSkpSE5Oho2NDVxdXbF48WKkp6dj+/btACoS7smTJ+OLL77AY489pvnV3dTUFNbWHCZD9Cjp94aW25ibQGbML7hC6tDWDB+O6YG3d55E2P6LGOblgG7t+KMgNY2QkBBMmjQJfn5+8Pf3x8aNG5Gamorg4GAAqNL+BgcHY+3atQgJCcGMGTMQHx+PzZs3Y8eOHZpzKhQKnD17VvPn9PR0JCcnw8LCAp06dYKlpSV69uypFYe5uTlsbW2rlBOR/l25VQgA8LQ3FzgSopbHoHu6ExIS4OPjAx+fioWFQkJC4OPjgw8++AAAkJmZqbVn6IYNG1BeXo7Zs2fDyclJ85g3b54g8RM1N5ncLsygPN+3PZ70ckCZUo2FO0+ijMPMqYlMmDABYWFhWLp0Kby9vREdHY09e/bAzc0NQNX218PDA3v27MHhw4fh7e2NZcuWYc2aNRg3bpymTkZGhqZNz8zMxMqVK+Hj44Pp06c3+fsjoqqu3Ovp7mhvIXAkRC2PSF250gkBAPLz82FtbY28vDwONadW55uj1/H+L/9geHdHbJrsJ3Q4BCA7vxTDV0cjr6QMIcO7YO6wzkKHRHrENkf/eE+JGmbChngcS7mD1RP64DmfDkKHQ9Qs1LXNMeiebiJqWpn3hpc7cz63wXCwkuGjZ3oAANYeuoxrOUUCR0RERC0Re7qJGg+TbiLSqNwujCuXG5ax3s4I7GwHRbkK7//6DzhAiYiI9CmvpAw5hXIAgIcd53QT6RuTbiLSyLi3XZgzk26DIhKJsHRsT5hIjBBzKQd/nM4UOiQiImpBKkdROVhKYSmr244DRFR3TLqJSEOzkBqHlxscDztzvDmkIwBg6e9nUVBaJnBERETUUqTdLQYA7s9N1Ej0knSXlZUhLS0NFy5cwJ07d/RxSiJqYiqVGln3ero5vNwwvfl4R7jbmiG7QI6w/ZeEDoeIiFqI9LsVP7p3aMv2n6gxNDjpLiwsxIYNG/D444/D2toa7u7u6N69O+zt7eHm5oYZM2bg77//1mesRNSIcgrlKFOqYSQCHC2lQodD1ZAZi/HhvUXV/i/uGq7e21OViIhIFzc0STd7uokaQ4OS7tWrV8Pd3R2bNm3CE088gZ9//hnJycm4cOEC4uPj8Z///Afl5eUYPnw4nnrqKVy6xB4ZIkNXOZ/b0UoGiZgzTwzV410d8HhXe5Sr1Fi+57zQ4RARUQtw497w8vbs6SZqFJKGHBQXF4dDhw6hV69e1b7ev39/vP766wgPD8fmzZsRFRWFzp25tyyRIavcLsyJ87kN3nujvRBzKQf7z91E3OUcBHSyEzokIiJqxm5weDlRo2pQ0r1z58461ZNKpZg1a1ZDLkFETSyD87mbjU4OlnhlgCu2x1/H0v+dxR9zAyE2EgkdFhERNUNqtRrpuRxeTtSYOIaUiADc36ObK5c3D/Of7AIrmQTnswqwMyFN6HCIiKiZultchmKFEgDg3IbfAYgaQ72T7rt372pWKL916xZ27dqFf/75R++BEVHT0mwXxp7uZsHG3ARzh1VM2/k88iJK7n1hIiIiqo/K+dwOllJIJWKBoyFqmeqVdH/99dfw8/ODr68v1q9fj+eeew4HDhzASy+9hI0bNzZWjETUBDJy7w0vt2bS3VxM9ndHh7amuFUgx7a4a0KHQ0REzRDncxM1vnrN6f7yyy9x5swZFBcXw9XVFSkpKbC3t0d+fj4GDx6MN954o7HiJKJGdr+nm0PLmgsTiREWPNkFb+88ifCoK5g4wBXWpsZCh0VERM1I5R7d7Tmfm6jR1KunWywWQyaTwcbGBp06dYK9vT0AwMrKCiIRF/Ehaq4U5SpkF8gBsKe7uXnWpz26OFogr6QMG6OvCB0OERE1MzfzK0e68Ud3osZSr6RbIpGgtLTigxkVFaUpLygo0G9URNSkbuaXQq0GTMRGsDU3ETocqgexkQhvB3UFAGyJvYbsglKBIyIioubk5r0f3R0spQJHQtRy1SvpPnjwIKTSig+ktbW1prykpASbN2/Wb2RE1GQyNduFyWDEraeanaDujvB2aYOSMiW+OnhZ6HCIiKgZqezpdrRiTzdRY6nXnG4LC4tqy62srFBeXo7//e9/UKlUWq8988wzDY+OiJpE5XxuDi1rnkQiEd4Z0RUTvz6G74+nYnqgJ1xsODePiIgeLZtJN1Gjq1fSXZ2//voLkydPRk5OTpXXRCIRlEpuY0Nk6NI1e3RzPndzFdDJDoM62SH2cg6+PHgJn73QR+iQiIjIwKnVatzMrxhe7mjF4eVEjaXe+3Q/bM6cOXjxxReRmZkJlUql9WDCTdQ8ZObeH15OzdeC4V0AAD+fSEfq7WKBoyEiIkNXIC9HSVnF93UHS34HIGosOifd2dnZCAkJgaOjoz7iISIB3N8ujD3dzZmvW1sEdrZDuUqNrw5xbjcREdWucmi5lUwCUxOxwNEQtVw6J90vvPACDh8+rIdQiEgoGfd6ujm8vPmb/2RnAMCuEzeQdoe93UREVLP7Q8vZy03UmHSe07127Vq8+OKLiImJQa9evWBsbKz1+ty5c3W9BBE1sozKhdQ4vLzZ83WzQWBnO8RcysFXhy7j03G9hQ6JiIgMFFcuJ2oaOifd33//Pfbu3QtTU1McPnwYItH97YZEIhGTbiIDV6JQIre4DADgxJ7uFmHesM6IuZSDnxJvYPbQTlzJnIiIqlXZ0+3ARdSIGpXOw8vfe+89LF26FHl5ebh27RpSUlI0j6tXr+ojRiJqRJW93BZSCaxkOv8ORwbAz90GgzpVzO1ed5hzu4mIqHrs6SZqGjon3QqFAhMmTICRkc6nIiIBaFYut5ZpjVSh5m3evbndOxNu4MZdzu0mIqKqbhVU9HTbW7Cnm6gx6ZwpT5kyBREREfqIhYgEkJFbOZ+bQ8tbkn7uNgjoaHtvJfMrQodDREQG6HZRRdJta2EicCRELZvOSbdSqcRnn32GIUOG4K233kJISIjWg4gMW+XwcmdrDi1raeY/WbFv90+JacjKKxU4Gmpu1q1bBw8PD8hkMvj6+iImJqbW+lFRUfD19YVMJoOnpyfCw8O1Xj9z5gzGjRsHd3d3iEQihIWFVTlHaGgo+vXrB0tLSzg4OODZZ5/FhQsX9Pm2iOgBtwsVAAA79nQTNSqdk+7Tp0/Dx8cHRkZG+Oeff5CUlKR5JCcn6yFEImpMlcPLuUd3y9Pfwwb9PWxQplRjUwzX2KC6i4iIwPz587FkyRIkJSUhMDAQI0eORGpqarX1U1JSMGrUKAQGBiIpKQnvvvsu5s6di127dmnqFBcXw9PTE59++inatWtX7XmioqIwe/ZsHD16FJGRkSgvL0dQUBCKiooa5X0StXZ3iiqSbvZ0EzUunVdNOnTokD7iICKBaLYLY093izR7aCccTzmO74+lYvbQTrAx5xcrerRVq1Zh2rRpmD59OgAgLCwMe/fuxfr16xEaGlqlfnh4OFxdXTW9115eXkhISMDKlSsxbtw4AEC/fv3Qr18/AMCiRYuqve5ff/2l9Xzr1q1wcHBAYmIiBg8erK+3R0QAlCo17hRXJN1sG4gaV4N7ut99910cP35cn7EQkQAq53Szp7tlGtzZDj3bW6GkTIltR1KEDoeaAYVCgcTERAQFBWmVBwUFIS4urtpj4uPjq9QfMWIEEhISUFZW1uBY8vLyAAA2NjY11pHL5cjPz9d6ENGj5RYroFZX/NnGjEk3UWNqcNKdmZmJp59+Gk5OTnjjjTfwxx9/QC6X6zM2ImpkarUamXn3Vy+nlkckEmH2450AANvirqGgtOEJELUOOTk5UCqVcHR01Cp3dHREVlZWtcdkZWVVW7+8vBw5OTkNikOtViMkJASDBg1Cz549a6wXGhoKa2trzcPFxaVB1yNqbW7fG1rexswYEjF3ISJqTA3+hG3duhU3b97Ejz/+iDZt2uDtt9+GnZ0dnn/+eWzbtq3BjSwRNZ38knIUK5QA2NPdko3o0Q4d7c2RX1qO745VPyeX6GEPbyGoVqtr3VawuvrVldfVnDlzcOrUKezYsaPWeosXL0ZeXp7mkZaW1qDrEbU2lYuo2XJoOVGj0+lnLZFIhMDAQHz22Wc4f/48jh8/jsceewybNm1C+/btMXjwYKxcuRLp6ekNOn90dDTGjBkDZ2dniEQi/PLLL4885lGrpxLRfZXzuW3MTSAzFgscDTUWIyMR3rzX2/11TApKy5QCR0SGzM7ODmKxuEqvdnZ2dpXe7Ert2rWrtr5EIoGtrW29Y3jrrbfw22+/4dChQ+jQoUOtdaVSKaysrLQeRPRomu3CzLlyOVFj0+tYEi8vL7zzzjs4cuQIbty4gSlTpiAmJuaRv1LXpKioCH369MHatWvrVL8uq6cS0X2aPbo5tLzFG+vtjPZtTJFTKMfOBPYEUs1MTEzg6+uLyMhIrfLIyEgEBARUe4y/v3+V+vv27YOfnx+MjY3rfG21Wo05c+bg559/xsGDB+Hh4VH/N0BEdcKVy4majs6rl9fE3t4e06ZNw7Rp0xp8jpEjR2LkyJF1rl+X1VOJ6L4MzXxuDi1v6YzFRpg5xBMf/HoG4VFX8VJ/VxhzDh/VICQkBJMmTYKfnx/8/f2xceNGpKamIjg4GEDFkO709HRs374dABAcHIy1a9ciJCQEM2bMQHx8PDZv3qz1o7tCocDZs2c1f05PT0dycjIsLCzQqVPFSIzZs2fj+++/x6+//gpLS0tN77m1tTVMTfnvFJE+5RRy5XKipqKXb1xz5szBnTt39HEqnTTW6qlELVWmZuVy9nS3BuP9XGBnYYL03BL8lpwhdDhkwCZMmICwsDAsXboU3t7eiI6Oxp49e+Dm5gagYjHVB/fs9vDwwJ49e3D48GF4e3tj2bJlWLNmjdYP3hkZGfDx8YGPjw8yMzOxcuVK+Pj4aLYlA4D169cjLy8Pjz/+OJycnDSPiIiIpnvzRK3E7cJ7w8stOLycqLE1uKf7xo0bmnlW33//Pd555x3Y2NigV69e2LNnjyCrhz5q9VQnJ6cqx8jlcq1V17nVCLUmlSuXcxG11kFmLMa0QZ5Y8dd5rDt8Gc/5tIeRUcMWuaKWb9asWZg1a1a1r23btq1K2ZAhQ3DixIkaz+fu7q5ZXK0mj3qdiPRHM7ycPd1Eja7BPd3dunWDm5sbJk6ciNLSUs1qodeuXRO0V7m+q6dyqxFqzdI5p7vVefUxV1jKJLhyqwj7zla//RMREbV8mtXLOaebqNE1OOnOy8vDzp074evrC5VKhVGjRqFLly6Qy+XYu3dvjXt5NqaGrJ7KrUaoNcvMqxxezp7u1sJSZoypAe4AgLWHLrNnkYiolapcvZxzuokaX4OT7rKyMvTv3x9vv/02TE1NkZSUhK1bt0IsFmPLli3o2LEjunbtqs9YH6khq6dyqxFqrVQqNbI0C6mxp7s1eW2gB0yNxfgnPR8xl3KEDoeIiARw+97wcjvO6SZqdA2e021lZQUfHx8MHDgQCoUCxcXFGDhwICQSCSIiItChQwccP35cp+AKCwtx+fJlzfOUlBQkJyfDxsYGrq6uDVo9lYgq5BTJUaZUw0gEOFox6W5NbMxN8FJ/F2w9cg1fHbqMwV3shQ6J9CA3NxfHjx9HdnY2VCqV1muTJ08WKCoiMkRKlRp5JRXTQduY1X1bPyJqmAYn3RkZGYiPj0dcXBzKy8vh5+eHfv36QaFQ4MSJE3BxccGgQYN0Ci4hIQFDhw7VPA8JCQEATJkyBdu2batx9dQFCxbgq6++grOzc5XVU4moQmZuRS+3g6WMW0e1QjMCPfHt0es4lnIHidfvwtetrdAhkQ5+//13vPLKKygqKoKlpaXWOiYikYhJNxFpKSgtQ+XsojamHF5O1NhEaj1M6Gvbti2io6Nx7tw5TJ48Ge3atcPNmzfRv39/REVF6SPOJpOfnw9ra2vk5eVxqDm1aH+ezsSb352Aj2sb7J41UOhwSADv/HQSPybcwJNeDvh6Sj+hw2mV9NXmdOnSBaNGjcLy5cthZmamxwibH7bjRI92/XYRhvz3MMxMxDi79CmhwyFqtura5uite8va2hrjx4+HsbExDh48iJSUlBq3GiEi4WVUbhdmzUXUWquZQzpCJAL2n8vG+Sxul9icpaenY+7cua0+4Saiusktvje03JRDy4magl6S7lOnTmn27HZzc4OxsTHatWuHCRMm6OP0RNQIMnMrVy7nfO7WqqO9BUb1dAIArD98ReBoSBcjRoxAQkKC0GEQUTORe28+t7UZh5YTNYUGz+l+0IN7W//zzz/6OCURNbJMzcrl7Oluzd58vCP+OJ2J309m4O3hXeFqy57S5mj06NH417/+hbNnz6JXr15Vdux45plnBIqMiAxRbnHFyuXs6SZqGg1KulNTU+Hq6lrn+unp6Wjfvn1DLkVEjSSdPd0EoGd7awzuYo/oi7ewIfoKPnmul9AhUQPMmDEDALB06dIqr4lEIiiVyqYOiYgMGFcuJ2paDRpe3q9fP8yYMaPWLcHy8vKwadMm9OzZEz///HODAySixpGZV5F0s6ebZj/eEQCwM+EGsvNLBY6GGkKlUtX4YMJNRA/LK2bSTdSUGtTTfe7cOSxfvhxPPfUUjI2N4efnB2dnZ8hkMty9exdnz57FmTNn4Ofnh//+978YOXKkvuMmIh2UKVXILpADAJzbMOlu7fp72MDXrS0Sr9/F5tgULB7lJXRIRETUiDRzurldGFGTaFBPt42NDVauXImMjAysX78eXbp0QU5ODi5dugQAeOWVV5CYmIgjR44w4SYyQDfzS6FWAyZiI9ias8Ft7UQiEWbd6+3+9uh1TQ8INS9RUVEYM2YMOnXqhM6dO+OZZ55BTEyM0GERkQGqXL3cmnO6iZqETgupyWQyPP/883j++ef1FQ8RNYGM3IohxO2sZTAyEgkcDRmCJ7o5oFs7S5zPKsD/xV/D3GGdhQ6J6uHbb7/Fa6+9hueffx5z586FWq1GXFwchg0bhm3btmHixIlCh0hEBiSv5N5CahxeTtQk9LZPNxE1H/fnc3MRNaogEonw5r3e7q1HUlCsKBc4IqqPTz75BJ999hkiIiIwd+5czJs3DxEREfj000+xbNkyocMjIgPDfbqJmhaTbqJWqLKnuz3nc9MDRvdygquNGe4Wl+GH42lCh0P1cPXqVYwZM6ZK+TPPPIOUlBQBIiIiQ3Z/n24m3URNgUk3USuk6enmdmH0AInYCDOHeAIANsVchaJcJXBEVFcuLi44cOBAlfIDBw7AxcVFgIiIyJBptgzjQmpETUKnOd1E1Dxl5HK7MKreuL4d8MX+S8jMK8UvSekY348JW3Pw9ttvY+7cuUhOTkZAQABEIhFiY2Oxbds2fPHFF0KHR0QGRK1Wc8swoibGpJuoFaocXu7Mnm56iMxYjOmBHli+5zzCo65gnG8HiLnYnsF788030a5dO3z++ef48ccfAQBeXl6IiIjA2LFjBY6OiAxJSZkSCmXFSCauXk7UNHQeXj516lRER0frIxYiaiKVw8u5RzdVZ+IAN1ibGuNqThH2nskSOhyqo+eeew6xsbG4ffs2bt++jdjYWCbcRFRF5SJqxmIRzEzEAkdD1DronHQXFBQgKCgInTt3xvLly5Genq6PuIiokZQolLh7r8Hl8HKqjoVUgin+bgCArw5dhlqtFjgiIiLSl/t7dJtAJOJIJqKmoHPSvWvXLqSnp2POnDnYuXMn3N3dMXLkSPz0008oKyvTR4xEpEcZ93q5zU3EsJJxhglVb+pAD5gai3EmIx/Rl3KEDoeqYWNjg5yciv83bdu2hY2NTY0PIqJKudyjm6jJ6eUbt62tLebNm4d58+YhKSkJW7ZswaRJk2BhYYFXX30Vs2bNQufOnfVxKSLSUea9+dxObUz5CzfVyMbcBC/3d8WWIylYd+gyhnSxFzokesjq1athaWmp+TM/z0RUF/kl3KObqKnptZsrMzMT+/btw759+yAWizFq1CicOXMG3bt3x2effYYFCxbo83JE1ACVPd1O1lxEjWo3Y7AHvjl6DcdS7iDx+h34urHH1JBMmTJF8+epU6cKFwgRNSv3h5cz6SZqKjoPLy8rK8OuXbvw9NNPw83NDTt37sSCBQuQmZmJ//u//8O+ffvwzTffYOnSpfqIl4h0VLldWIe2nM9NtXOyNsXzPh0AAOsOXRE4GqqNWCxGdnZ2lfLbt29DLOZCSUR0X0FpOQDAklPMiJqMzp82JycnqFQqvPzyyzh+/Di8vb2r1BkxYgTatGmj66WISA8qk25nLqJGdTBziCd+TEzDgfPZOJ+Vj27trIQOiapR02J3crkcJiYmTRwNERmygtKKnm5LGXu6iZqKzj3d8+bNw40bN/DVV19pJdxqtRqpqakAKhZ4SUlJ0fVSRKQH9/foZtJNj+Zpb4FRPZ0AAOsPs7fb0KxZswZr1qyBSCTC119/rXm+Zs0arF69GrNnz0a3bt0adO5169bBw8MDMpkMvr6+iImJqbV+VFQUfH19IZPJ4OnpifDwcK3Xz5w5g3HjxsHd3R0ikQhhYWF6uS4R1U8+e7qJmpzOn7YPP/wQM2fOhJmZmVb5nTt34OHhAaVSqesliEiPND3dTLqpjt58vCP+OJ2J309mIGR4F7jZmgsdEt2zevVqABU/dIeHh2sNJTcxMYG7u3uV5LcuIiIiMH/+fKxbtw4DBw7Ehg0bMHLkSJw9exaurq5V6qekpGDUqFGYMWMGvv32Wxw5cgSzZs2Cvb09xo0bBwAoLi6Gp6cnXnzxxRrXeKnvdYmo/u4PL2dPN1FT0bmnu6YhbYWFhZDJuFATkSFRq9VIv5d0t2fSTXXUs701hnSxh0oNbIi+KnQ49ICUlBSkpKRgyJAhOHnypOZ5SkoKLly4gL1792LAgAH1Pu+qVaswbdo0TJ8+HV5eXggLC4OLiwvWr19fbf3w8HC4uroiLCwMXl5emD59Ol5//XWsXLlSU6dfv37473//i5deeglSqVQv1yWi+rs/vJw93URNpcGftpCQEACASCTCBx98oNXTrVQqcezYsWrndxORcO4UKSAvV0EkAhytq//SS1SdWY93RNTFW/gp4QbmD+sMByv+qGpIDh06pLdzKRQKJCYmYtGiRVrlQUFBiIuLq/aY+Ph4BAUFaZWNGDECmzdvRllZGYyNH92j1pDrAhXz1uVyueZ5fn7+I69F1JpxITWiptfgnu6kpCQkJSVBrVbj9OnTmudJSUk4f/48+vTpg23btukxVCLSVWUvt72FFFIJVzSmuuvvYQNft7ZQKFX4OpZrdBiaF154AZ9++mmV8v/+97948cUX63WunJwcKJVKODo6apU7OjoiKyur2mOysrKqrV9eXo6cnJxGuy4AhIaGwtraWvNwcXGp0/WIWqsCeUVPtxWHlxM1mQb/xFX5q/prr72GL774AlZWXNGWyNBxPjc1lEgkwuyhHfH6tgR8d/Q6Zj/eCdZm/MJmKKKiovCf//ynSvlTTz2lNcS7PkQikdZztVpdpexR9asr1/d1Fy9erBl9B1T0dDPxJqoZe7qJmp7Oc7q3bt3KhJuomUi/t3I553NTQwzt6oBu7SxRpFDi/+KvCR0OPaCwsLDarcGMjY3rPdzazs4OYrG4Su9ydnZ2lV7oSu3atau2vkQiga2tbaNdFwCkUimsrKy0HkRUMy6kRtT0GvQTV0hICJYtWwZzc3OtX5ers2rVqgYFRkT6d7+nm/Nxqf5EIhHefLwj5v2QjK1HUjA90ANmJuwpMQQ9e/ZEREQEPvjgA63yH374Ad27d6/XuUxMTODr64vIyEg899xzmvLIyEiMHTu22mP8/f3x+++/a5Xt27cPfn5+dZrP3dDrElH9qNVqLqRGJIAGfdqSkpJQVlam+XNN6jukjIgaF4eXk65G93LCqsiLuH67GDuOp2HaIA+hQyIA77//PsaNG4crV67giSeeAAAcOHAAO3bswM6dO+t9vpCQEEyaNAl+fn7w9/fHxo0bkZqaiuDgYAAVQ7rT09Oxfft2AEBwcDDWrl2LkJAQzJgxA/Hx8di8eTN27NihOadCocDZs2c1f05PT0dycjIsLCzQqVOnOl2XiHQjL1ehTFkx9YNJN1HTadCn7cFVUvW5YioRNa4MbhdGOpKIjTBzcEe8u/s0vo65ikmPucFEovNMJdLRM888g19++QXLly/HTz/9BFNTU/Tu3Rv79+/HkCFD6n2+CRMm4Pbt21i6dCkyMzPRs2dP7NmzB25ubgCAzMxMpKamaup7eHhgz549WLBgAb766is4OztjzZo1mj26ASAjIwM+Pj6a5ytXrsTKlSsxZMgQHD58uE7XJSLd5JdUdJqJRIA5RyoRNRmRuqaNtuuopKQEarVas2XY9evXsXv3bnTv3r3K9iHNQX5+PqytrZGXl8d5YdTi+H28HzmFcvzvrUHo2d5a6HComZKXKxG44hCyC+T4bFxvjO/HRasaim2O/vGeEtXscnYhnlwVBUuZBKc/HCF0OETNXl3bHJ27J8aOHasZXpabm4v+/fvj888/x9ixY7F+/XpdT09EelJapkROYcVetuzpJl1IJWLMCPQEAKyPugKlSqffbomIqIlUzufmdmFETUvnpPvEiRMIDAwEAPz0009o164drl+/ju3bt2PNmjU6B7hu3Tp4eHhAJpPB19cXMTExtdb/7rvv0KdPH5iZmcHJyQmvvfYabt++rXMcRM1dVl7FyuWmxmK04VZPpKOXB7jC2tQYKTlF+OufmvdQpqahVCqxcuVK9O/fH+3atYONjY3Wg4gI4HZhRELROekuLi6GpaUlgIqVSp9//nkYGRnhsccew/Xr13U6d0REBObPn48lS5YgKSkJgYGBGDlypNY8sgfFxsZi8uTJmDZtGs6cOYOdO3fi77//xvTp03WKg6gleHDlci5ySLqykEowJcAdALD20GWo2NstqI8++girVq3C+PHjkZeXh5CQEE17/OGHHwodHhEZCCbdRMLQOenu1KkTfvnlF6SlpWHv3r2aedzZ2dk6z6VatWoVpk2bhunTp8PLywthYWFwcXGpcdj60aNH4e7ujrlz58LDwwODBg3CzJkzkZCQoFMcRC1BOlcuJz17faA7zE3EOJeZj8hzN4UOp1X77rvvsGnTJixcuBASiQQvv/wyvv76a3zwwQc4evSo0OERkYG4v10YR7wRNSWdk+4PPvgACxcuhLu7OwYMGAB/f38AFb3eD65SWl8KhQKJiYlVFmMLCgpCXFxctccEBATgxo0b2LNnD9RqNW7evImffvoJo0ePbnAcRC1FRm7F8HLO5yZ9aWNmountXnPgEnRcl5N0kJWVhV69egEALCwskJeXBwB4+umn8ccffwgZGhEZEPZ0EwlD56T7hRdeQGpqKhISEvDXX39pyocNG4bVq1c3+Lw5OTlQKpVwdHTUKnd0dERWVvXzBwMCAvDdd99hwoQJMDExQbt27dCmTRt8+eWXNV5HLpcjPz9f60HUEnGPbmoM0wM9YWYixpmMfBw4ly10OK1Whw4dkJmZCaBiBNq+ffsAAH///TekUqmQoRGRAbnf082km6gp6WVz1Xbt2sHHxwdGRvdP179/f3Tr1k3ncz8891StVtc4H/Xs2bOYO3cuPvjgAyQmJuKvv/5CSkoKgoODazx/aGgorK2tNQ8XF259Qy0Th5dTY7AxN8Fkf3cAwBfs7RbMc889hwMHDgAA5s2bh/fffx+dO3fG5MmT8frrrwscHREZinxNTzeHlxM1Jb38zHXgwAEcOHAA2dnZUKlUWq9t2bKlQee0s7ODWCyu0qudnZ1dpfe7UmhoKAYOHIh//etfAIDevXvD3NwcgYGB+Pjjj+Hk5FTlmMWLFyMkJETzPD8/n4k3tUgPLqRGpE8zAj3wf3HXcDo9D4cuZOOJbtX/G02N59NPP9X8+YUXXoCLiwuOHDmCTp064ZlnnhEwMiIyJBxeTiQMnXu6P/roIwQFBeHAgQPIycnB3bt3tR4NZWJiAl9fX0RGRmqVR0ZGIiAgoNpjiouLtXrbAUAsFgNAjb0vUqkUVlZWWg+ilkatVmt6ujmnm/TN1kKKyf5uAIAv9rO3u6mVlZXhtddew9WrVzVlAwYMQEhICBNuItLChdSIhKHzz1zh4eHYtm0bJk2apI94tISEhGDSpEnw8/ODv78/Nm7ciNTUVM1w8cWLFyM9PR3bt28HAIwZMwYzZszA+vXrMWLECGRmZmL+/Pno378/nJ2d9R4fUXNxp0gBeXnFKJR21uzpJv2bMdgT2+Ov4+SNPBy+eAtDuzoIHVKrYWxsjN27d+P9998XOhQiMnCVPd1W7OkmalI693QrFIoae551NWHCBISFhWHp0qXw9vZGdHQ09uzZAze3ih6VzMxMrT27p06dilWrVmHt2rXo2bMnXnzxRXTt2hU///xzo8RH1FxUrlxubymFVCIWOBpqiewspHj1MVcA7O0WwnPPPYdffvlF6DCIyMAVyLmQGpEQdP7ETZ8+Hd9//32j/cI+a9YszJo1q9rXtm3bVqXsrbfewltvvdUosRA1V1xEjZrCG4M74puj15GclovoSzkY0sVe6JBajU6dOmHZsmWIi4uDr68vzM3NtV6fO3euQJERkSEp4EJqRILQOekuLS3Fxo0bsX//fvTu3RvGxtof4lWrVul6CSLSUeUiah2YdFMjsreU4pUBbtgcm4Iv9l/E4M52Ne42Qfr19ddfo02bNkhMTERiYqLWayKRiEk3EQHgQmpEQtH5E3fq1Cl4e3sDAP755x+t1/hli8gwcOVyaiozh3ji26PXcSI1F7GXcxDYmb3djUmlUsHIyAgpKSlCh0JEzUAhe7qJBKFz0n3o0CF9xEFEjSgjj8PLqWk4WMowcYArth65hi/2X8KgTuztbkzGxsbIzMyEg0PFwnX/+te/sHjxYtjY2AgcGREZGkW5CgplxaKqFibs6SZqSjovpEZEhi/9bkXS7WTNpJsaX/CQjjCRGCHh+l3EXbktdDgt2sML1m3YsAG5ubnCBENEBq1IXq75s7mUi6oSNSW9JN0xMTF49dVX4e/vj/T0dADAN998g9jYWH2cnoh0dONe0t2hLZNuanyOVjJM7F+xknnY/otcybwJ8V4TUU0K7yXdUokRJGL2uxE1JZ0/cbt27cKIESNgamqKpKQkyOVyAEBBQQGWL1+uc4BEpJsieTluFykAAC42ZgJHQ61FZW/339fuIvZyjtDhEBG1ekWKiqTbXMqh5URNTedP3ccff4zw8HBMnjwZP/zwg6Y8ICAAS5cu1fX0RKSjyu3CrGQSWJty4RRqGu2sZXh1gBu2HEnB5/sucm53I/rggw9gZlbxg5pCocAnn3wCa2trrTrcSYSIiuRKABxaTiQEnZPuCxcuYPDgwVXKraysOK+MyACk3SkGAHRoy15ualpvPt4RO46nIjktFwfPZ2OYl6PQIbU4gwcPxoULFzTPAwICcPXqVa06/LGDiID7c7rNuYgaUZPT+VPn5OSEy5cvw93dXas8NjYWnp6eup6eiHRUmXS72HA+NzUte0spJge4YUPUVayKvIgnujkwAdSzw4cPCx0CETUTlUm3BYeXEzU5ned0z5w5E/PmzcOxY8cgEomQkZGB7777DgsXLsSsWbP0ESMR6eD+Imrs6aamN3NwR5ibiHEmIx97z2QJHQ4RUatVuZAa53QTNT2dP3XvvPMO8vLyMHToUJSWlmLw4MGQSqVYuHAh5syZo48YiUgHaXfv9XRz5XISgI25CV4f5IEvD17G6shLCOreDkZG7O0mImpqmuHlnNNN1OT0sl/AJ598gpycHBw/fhxHjx7FrVu3sGzZMn2cmoh0VNnTzZXLSSjTB3nCUibBhZsF+N/pTKHDISJqlYoU9xZS45xuoianU9KtUqmwZcsWPP300+jfvz9ee+01fPzxx/j555+5VyiRgeBCaiQ0azNjzAisWOMjbP9FlCtVAkdERNT6FHF4OZFgGpx0q9VqPPPMM5g+fTrS09PRq1cv9OjRA9evX8fUqVPx3HPP6TNOImqAvJIy5JdWNLIdOLycBPTaQHe0MTPG1VtF+DU5Q+hwiIhaHS6kRiScBifd27ZtQ3R0NA4cOICkpCTs2LEDP/zwA06ePIn9+/fj4MGD2L59uz5jJaJ6unFvPreNuQl/2SZBWcqMMXNwRwDAFwcuoYy93XoXExODV199Ff7+/khPTwcAfPPNN4iNjW3Q+datWwcPDw/IZDL4+voiJiam1vpRUVHw9fWFTCaDp6cnwsPDq9TZtWsXunfvDqlUiu7du2P37t1ar5eXl+O9996Dh4cHTE1N4enpiaVLl0Kl4t8XIl0Vavbp5vcBoqbW4KR7x44dePfddzF06NAqrz3xxBNYtGgRvvvuO52CIyLdpN25N5+bvdxkAKYEuMHOwgSpd4rxU+INocNpUXbt2oURI0bA1NQUSUlJkMvlAICCggIsX7683ueLiIjA/PnzsWTJEiQlJSEwMBAjR45EampqtfVTUlIwatQoBAYGIikpCe+++y7mzp2LXbt2aerEx8djwoQJmDRpEk6ePIlJkyZh/PjxOHbsmKbOihUrEB4ejrVr1+LcuXP47LPP8N///hdffvllvd8DEWnjQmpEwmlw0n3q1Ck89dRTNb4+cuRInDx5sqGnJyI9qOzp7sBF1MgAmJlI8ObjnQAAXx64BHm5UuCIWo6PP/4Y4eHh2LRpE4yNjTXlAQEBOHHiRL3Pt2rVKkybNg3Tp0+Hl5cXwsLC4OLigvXr11dbPzw8HK6urggLC4OXlxemT5+O119/HStXrtTUCQsLw/Dhw7F48WJ069YNixcvxrBhwxAWFqapEx8fj7Fjx2L06NFwd3fHCy+8gKCgICQkJNT7PRCRtiLFvaSbC6kRNbkGJ9137tyBo6Njja87Ojri7t27DT09EenB/T262dNNhuGVAa5wtJIiI68UEX+nCR1Oi3HhwgUMHjy4SrmVlRVyc3PrdS6FQoHExEQEBQVplQcFBSEuLq7aY+Lj46vUHzFiBBISElBWVlZrnQfPOWjQIBw4cAAXL14EAJw8eRKxsbEYNWpUjfHK5XLk5+drPYioKi6kRiScBifdSqUSEknNH1qxWIzy8vKGnp6I9KBy5XIXrlxOBkJmLMacoRW93WsPXkZpGXu79cHJyQmXL1+uUh4bGwtPT896nSsnJwdKpbLKD+uOjo7Iysqq9pisrKxq65eXlyMnJ6fWOg+e89///jdefvlldOvWDcbGxvDx8cH8+fPx8ssv1xhvaGgorK2tNQ8XF5d6vV+i1qLo3pxuLqRG1PQa/KlTq9WYOnUqpFJpta9XzicjIuFwj24yROP7uSA86irSc0vw7dHrmB5Yv6SQqpo5cybmzZuHLVu2QCQSISMjA/Hx8Vi4cCE++OCDBp1TJBJpPVer1VXKHlX/4fJHnTMiIgLffvstvv/+e/To0QPJycmYP38+nJ2dMWXKlGqvu3jxYoSEhGie5+fnM/EmqkYh53QTCabBSXdNjd+DJk+e3NDTE5GO1Go10irndHN4ORkQqUSMt57ohEU/n0Z41BVMHOAKM84x1Mk777yDvLw8DB06FKWlpRg8eDCkUikWLlyIOXPm1OtcdnZ2EIvFVXq1s7Oza5xW1q5du2rrSyQS2Nra1lrnwXP+61//wqJFi/DSSy8BAHr16oXr168jNDS0xu8dUqm0xg4AIrpPM6ebPd1ETa7Bn7qtW7fqMw4i0rO7xWUoVlQMJWvfhkk3GZZxvh2w7vAVpN4pxv/FXcebj3cUOqRm75NPPsGSJUtw9uxZqFQqdO/eHRYWFvU+j4mJCXx9fREZGYnnnntOUx4ZGYmxY8dWe4y/vz9+//13rbJ9+/bBz89Ps7Cbv78/IiMjsWDBAq06AQEBmufFxcUwMtKe+SYWi7llGJEeFHPLMCLBNHhONxEZtsr53A6WUsiMOZSMDIux2AjzhnUGAGyIvoKC0jKBI2oZzMzM4Ofnh/79+zco4a4UEhKCr7/+Glu2bMG5c+ewYMECpKamIjg4GEDFkO4HR7MFBwfj+vXrCAkJwblz57BlyxZs3rwZCxcu1NSZN28e9u3bhxUrVuD8+fNYsWIF9u/fj/nz52vqjBkzBp988gn++OMPXLt2Dbt378aqVau0kn8iqj9FuQoKZcWPVxYcWUTU5PipI2qhKoeWcz43Gapnfdrjq8OXcfVWEbYeuYa595Jwqr/S0lJ8+eWXOHToELKzs6v0DNd327AJEybg9u3bWLp0KTIzM9GzZ0/s2bMHbm5uAIDMzEytPbs9PDywZ88eLFiwAF999RWcnZ2xZs0ajBs3TlMnICAAP/zwA9577z28//776NixIyIiIjBgwABNnS+//BLvv/8+Zs2ahezsbDg7O2PmzJkNnpdORBUqVy4HADPO6SZqciJ15UonBKBiARZra2vk5eXByspK6HCIGiw86go+/fM8nvV2RthLPkKHQ1St305mYO6OJFjKJIh95wlYmxk/+qAWRF9tzsSJExEZGYkXXngBjo6OVRYs+89//qNrqM0G23GiqtLuFCPws0MwkRjh4scjhQ6HqMWoa5vDnm6iFqpyeHkHbhdGBuzpXk746uBlXLhZgK9jr+LtoK5Ch9Qs/fHHH9izZw8GDhwodChEZIAq13jhdmFEwuCcbqIWKvVe0u1qy6SbDJeRkQgLhlcMK98Sm4I7RQqBI2qe2rdvD0tLS6HDICIDxe3CiISll6Q7JiYGr776Kvz9/ZGeng4A+OabbxAbG6uP0xNRA1y7XQQAcLc1FzgSotqN6NEOPZytUKRQYkP0FaHDaZY+//xz/Pvf/8b169eFDoWIDFDlnG5zLqJGJAidk+5du3ZhxIgRMDU1RVJSEuRyOQCgoKAAy5cv1zlAIqo/RbkK6XdLAADu7OkmAycSiRAyvAsAYHvcddwqkAscUfPj5+eH0tJSeHp6wtLSEjY2NloPImrdKpNuDi8nEobOn7yPP/4Y4eHhmDx5Mn744QdNeUBAAJYuXarr6YmoAdJzS6BSA6bGYthbSoUOh+iRnujmgD4ubXAyLRfhUVfw/tPdhQ6pWXn55ZeRnp6O5cuXV7uQGhG1bpXDy82YdBMJQudP3oULFzB48OAq5VZWVsjNzdX19ETUAJVDy91szfjlm5qFyt7uKVuO49uj1/HGYE84WsmEDqvZiIuLQ3x8PPr06SN0KERkgO4vpMY53URC0Hl4uZOTEy5fvlylPDY2Fp6enrqenoga4HrO/aSbqLkY3NkOfm5tIS9X4atDVdsVqlm3bt1QUlIidBhEZKAKOaebSFA6J90zZ87EvHnzcOzYMYhEImRkZOC7777DwoULMWvWLJ0DXLduHTw8PCCTyeDr64uYmJha68vlcixZsgRubm6QSqXo2LEjtmzZonMcRM3JtdsVK5e7cRE1akZEIhFCgirmdv9wPA3puUwi6+rTTz/F22+/jcOHD+P27dvIz8/XehBR66ZZSI3Dy4kEofMn75133kFeXh6GDh2K0tJSDB48GFKpFAsXLsScOXN0OndERATmz5+PdevWYeDAgdiwYQNGjhyJs2fPwtXVtdpjxo8fj5s3b2Lz5s3o1KkTsrOzUV5erlMcRM1N5XZh7Omm5iagox38PW0Rf/U21h68hNDnewsdUrPw1FNPAQCGDRumVa5WqyESiaBUKoUIi4gMBBdSIxKWXj55n3zyCZYsWYKzZ89CpVKhe/fusLCw0Pm8q1atwrRp0zB9+nQAQFhYGPbu3Yv169cjNDS0Sv2//voLUVFRuHr1qma1Vnd3d53jIGpuuF0YNWdvB3XBC+Hx2JlwA28O6cS95uvg0KFDQodARAasUF7xw5sZ53QTCULnpDs1NRUuLi4wMzODn59flddq6pF+FIVCgcTERCxatEirPCgoCHFxcdUe89tvv8HPzw+fffYZvvnmG5ibm+OZZ57BsmXLYGpq2qA4iJobpUqNtHs93a42TFao+fFzt8HgLvaIvngLXxy4hM/Hc3GwRxkyZIjQIRCRAStWsKebSEg6f/I8PDyQmZkJBwcHrfLbt2/Dw8OjwUPacnJyoFQq4ejoqFXu6OiIrKysao+5evUqYmNjIZPJsHv3buTk5GDWrFm4c+dOjfO65XK5Zm9xAJz7Rs1eZl4JypRqGItFcG7DH5uoeQoZ3gXRF29hd9INzBraER3tdR891ZJFR0fX+np1u4wQUevBhdSIhKXzJ69yvtjDCgsLIZPpvt3Lw+eu6XoAoFKpIBKJ8N1338Ha2hpAxRD1F154AV999VW1vd2hoaH46KOPdI6TyFBcv7eImouNGcRG3C6MmidvlzZ40ssB+89l44v9l7DmZR+hQzJojz/+eJWyB9tKzukmat24kBqRsBr8yQsJCQFQ0ai///77MDO7P4xVqVTi2LFj8Pb2bnBgdnZ2EIvFVXq1s7Ozq/R+V3JyckL79u01CTcAeHl5Qa1W48aNG+jcuXOVYxYvXqx5L0BFT7eLi0uD4yYSmmaPbg4tp2ZuwfAu2H8uG7+fysDsoZ3QtZ2l0CEZrLt372o9LysrQ1JSEt5//3188sknAkVFRIaiSF65TzeTbiIhNPiTl5SUBKCi5/n06dMwMTHRvGZiYoI+ffpg4cKFDQ7MxMQEvr6+iIyMxHPPPacpj4yMxNixY6s9ZuDAgdi5cycKCws1C7ldvHgRRkZG6NChQ7XHSKVSSKXSBsdJZGhSuV0YtRA9nK0xsmc7/PlPFr44cBHrXvEVOiSD9eCPzZWGDx8OqVSKBQsWIDExUYCoiMhQVA4v50JqRMJocNJduVLqa6+9hi+++AJWVlZ6C6pSSEgIJk2aBD8/P/j7+2Pjxo1ITU1FcHAwgIpe6vT0dGzfvh0AMHHiRCxbtgyvvfYaPvroI+Tk5OBf//oXXn/9dS6kRq3G/ZXL2dNNzd/8J7vgrzNZ2HM6C2cy8tDDuWpySTWzt7fHhQsXhA6DiATGhdSIhKXzJ2/r1q0AgLNnzyI1NRUKhULr9WeeeabB554wYQJu376NpUuXIjMzEz179sSePXvg5uYGAMjMzERqaqqmvoWFBSIjI/HWW2/Bz88Ptra2GD9+PD7++OMGx0DU3FTO6XazY083NX9d21ni6d7O+P1kBlZHXsLXU/wefVArdOrUKa3narUamZmZ+PTTT9GnD1d/J2rtKoeXc043kTB0/uSlpKTg2WefxenTpyESiaBWqwHcX8BF18VbZs2ahVmzZlX72rZt26qUdevWDZGRkTpdk6i5UqvV95NuzummFmL+k53xx6kM7D93EyfTctHHpY3QIRkcb29vrTa40mOPPVbj7h1E1DooylVQKFUAAAuuXk4kCCNdTzB37lx4eHjg5s2bMDMzw5kzZxAdHQ0/Pz8cPnxYDyESUV3dKpCjpEwJIxHQoS2TbmoZOtpb4Fmf9gCAVZEXBY7GMKWkpODq1atISUlBSkoKrl+/juLiYsTFxaFbt25Ch0dEAqpcuRzgnG4ioej8c1d8fDwOHjwIe3t7GBkZwcjICIMGDUJoaCjmzp2rWXCNiBrflVsV87ldbMxgItH5NzUigzFvWGf8mpyBqIu3kHj9DnzdbIQOyaBUTrsiInpY0b353CYSIxiL+d2ASAg6J91KpVKzUridnR0yMjLQtWtXuLm5cfEWoiZ2NacQAODJ+dzUwrjZmuNF3w744e80fL7vIr6f8ZjQIQluzZo1da47d+7cRoyEiAwZtwsjEp7On76ePXvi1KlT8PT0xIABA/DZZ5/BxMQEGzduhKenpz5iJKI6upJd0dPtaW8hcCRE+jfniU7YdeIG4q7cRvyV2/DvaCt0SIJavXp1neqJRCIm3UStWOV2YeYcWk4kGJ2T7vfeew9FRRVf9D/++GM8/fTTCAwMhK2tLSIiInQOkIjqTtPTbc+ebmp5OrQ1w0v9XPHN0etYvf8iHvN8TLNoZ2uUkpIidAhE1AxUzuk25yJqRILR+dM3YsQIzZ89PT1x9uxZ3LlzB23btm3VX4aIhHD13pxuTzv2dFPLNGtoR0QkpOF4yh3EXbmNgZ3shA7J4Dy8iwgRtW6apJvDy4kEo/NqCqmpqVW2KLGxsYFIJNLaQ5uIGpe8XIkbdyu2C+vInm5qoZysTTGxvyuAipXMH25/WrPt27ejV69eMDU1hampKXr37o1vvvlG6LCISGBFCu7RTSQ0nZNuDw8P3Lp1q0r57du34eHhoevpiaiOrt8uhkpdsVCKvaVU6HCIGs2sxztCKjFC4vW7iLpYtf1pjVatWoU333wTo0aNwo8//oiIiAg89dRTCA4OrvPc74etW7cOHh4ekMlk8PX1RUxMTK31o6Ki4OvrC5lMBk9PT4SHh1eps2vXLnTv3h1SqRTdu3fH7t27q9RJT0/Hq6++CltbW5iZmcHb2xuJiYkNeg9EdL+n24JzuokEo3PSrVarqx3CVlhYCJlMpuvpiaiOrt66P5+bw0qpJXOwkmHSYxVbZK1mbzcA4Msvv8T69euxYsUKPPPMMxg7diw+++wzrFu3rl6rnFeKiIjA/PnzsWTJEiQlJSEwMBAjR46scQRbSkoKRo0ahcDAQCQlJeHdd9/F3LlzsWvXLk2d+Ph4TJgwAZMmTcLJkycxadIkjB8/HseOHdPUuXv3LgYOHAhjY2P8+eefOHv2LD7//HO0adOm3u+BiCoUck43keAa/OkLCQkBUDFn7P3334eZmZnmNaVSiWPHjsHb21vnAImobq5o5nNzaDm1fMGPd8R3x1Jx8kYeDp7PxjAvR6FDElRmZiYCAgKqlAcEBCAzM7Pe51u1ahWmTZuG6dOnAwDCwsKwd+9erF+/HqGhoVXqh4eHw9XVFWFhYQAALy8vJCQkYOXKlRg3bpzmHMOHD8fixYsBAIsXL0ZUVBTCwsKwY8cOAMCKFSvg4uKCrVu3as7t7u5e7/iJ6D7O6SYSXoN7upOSkpCUlAS1Wo3Tp09rniclJeH8+fPo06cPtm3bpsdQiag2lYuodeR2YdQK2FlIMSXAHQDndgNAp06d8OOPP1Ypj4iIQOfOnet1LoVCgcTERAQFBWmVBwUFIS4urtpj4uPjq9QfMWIEEhISUFZWVmudB8/522+/wc/PDy+++CIcHBzg4+ODTZs21St+ItJWxC3DiATX4J+8Dh06BAB47bXX8MUXX8DKykpvQRFR/d3fLoxJN7UObwz2xDfx13AmIx/7zt7EiB7thA5JMB999BEmTJiA6OhoDBw4ECKRCLGxsThw4EC1yXhtcnJyoFQq4eioPXrA0dERWVlZ1R6TlZVVbf3y8nLk5OTAycmpxjoPnvPq1atYv349QkJC8O677+L48eOYO3cupFIpJk+eXO215XI55HK55nl+fn693i9RS8eF1IiEp/Oc7q1btzLhJhKYWq3GlWzu0U2ti425CV4bWLFg5+rIi1CpWl9vd3JyMgBg3LhxOHbsGOzs7PDLL7/g559/hp2dHY4fP47nnnuuQed+eG2ImtZwqa3+w+WPOqdKpULfvn2xfPly+Pj4YObMmZgxYwbWr19f43VDQ0NhbW2tebi4uDz6zRG1IvcXUmPSTSSUBn36QkJCsGzZMpibm2vmdtdk1apVDQqMiOrudpEC+aXlEIkAD87pplZkeqAH/i/uGs5nFeDPf7IwureT0CE1qb59+8LHxwfTp0/HxIkT8e233+p8Tjs7O4jF4iq92tnZ2VV6qiu1a9eu2voSiQS2tra11nnwnE5OTujevbtWHS8vL60F2R62ePFire8i+fn5TLyJHsCF1IiE16Ce7qSkJM0crQfncj/8qPwFnogaV+V8bmdrU8iMOWeLWo82ZiZ4fVBFb3fY/otQtrLe7iNHjqBv375YtGgRnJycMGnSJM30r4YyMTGBr68vIiMjtcojIyOrXawNAPz9/avU37dvH/z8/GBsbFxrnQfPOXDgQFy4cEGrzsWLF+Hm5lZjvFKpFFZWVloPIrqPc7qJhNegn7webNAf/HN1Q8mIqPE9uF0YUWvz+iAPbD2SgkvZhfjfqQyM9W4vdEhNxt/fH/7+/lizZg1+/PFHbN26FU8++STc3d3x+uuvY8qUKejQoUO9zxsSEoJJkybBz88P/v7+2LhxI1JTUxEcHAygonc5PT0d27dvBwAEBwdj7dq1CAkJwYwZMxAfH4/NmzdrViUHgHnz5mHw4MFYsWIFxo4di19//RX79+9HbGysps6CBQsQEBCA5cuXY/z48Th+/Dg2btyIjRs36niniFqvIjnndBMJTec53QCwefNm9OzZEzKZDDKZDD179sTXX3+tj1MTUR1cuZd0c+Vyao2sTY0xI9ATAPDF/ksoV6oEjqjpmZqaYsqUKTh8+DAuXryIl19+GRs2bICHhwdGjRpV7/NNmDABYWFhWLp0Kby9vREdHY09e/ZoepwzMzO19uz28PDAnj17cPjwYXh7e2PZsmVYs2aNZrswoGL7sh9++AFbt25F7969sW3bNkRERGDAgAGaOv369cPu3buxY8cO9OzZE8uWLUNYWBheeeUVHe4OUetWpOCWYURCE6l13Gfl/fffx+rVq/HWW2/B398fQMW2IGvXrsW8efPw8ccf6yXQppKfnw9ra2vk5eVxiBo1G1O2HEfUxVv45LmeeGVAzcMwiVqqQnk5Bq04iNziMqwa3wfP961/764QGqvNKSwsxHfffYd3330Xubm5UCqVeju3oWM7TqTNZ+k+3C0uw74Fg9HF0VLocIhalLq2OTr/5LV+/Xps2rQJL7/8sqbsmWeeQe/evfHWW281u6SbqDm6dLMAANCVjSm1UhZSCWYO7ogVf53HFwcu4Zk+zpCI9TKYq1mJiorCli1bsGvXLojFYowfPx7Tpk0TOiwiEhCHlxMJT+dvJEqlEn5+flXKfX19UV5eruvpiegR8kvLkJFXCgDozKSbWrHJ/m6wNTfB9dvF+PlEutDhNJm0tDQsW7YMHTt2xNChQ3HlyhV8+eWXyMjIwKZNm/DYY48JHSIRCURRroLi3pQbC65eTiQYnZPuV199tdr9Mzdu3Mg5WERN4NLNivncjlZSWJsaCxwNkXDMpRIED+kIAFhz8BIU5S1/bvfw4cPh4eGBdevW4YUXXsC5c+cQGxuL1157DebmXFiRqLWrXLkc4OrlRELSy09emzdvxr59+zS/ph89ehRpaWmYPHmy1t6Z3LObSP8u3htaznlaRMCrj7lhQ/RV3Lhbgp8Sb2DiAFehQ2pUpqam2LVrF55++mmIxfxCTUTaKvfolkqMWuWUGyJDoXPS/c8//6Bv374AgCtXrgAA7O3tYW9vj3/++UdTj9uIETUOJt1E95maiDHr8Y5Y+r+zWHvwEsb5todU0nKT0d9++03oEIjIgBUrOJ+byBDo/Al8cJ9uImp6lcPLuYgaUYWJA1yxIfoKMvJK8ePfaZjk7y50SEREgqjs6ebQciJhcZwJUTN34V5Pd2dH7tFNBAAyYzFmD+0EAFh76DJKy1rPdllERA+qnNNtzkXUiATVoE9gSEgIli1bBnNzc60529XhPG6ixnO3SIFbBXIAXLmc6EET+rkg/HBFb/f3x1Lx+iAPoUMiImpylUm3BYeXEwmqQZ/ApKQklJWVaf5cE87jJmpclfO527cxZYNK9ACpRIw5T3TGu7tPY93hK3i5vytMTTi8kohal/vDy/kdgUhIDfoEPjiPm3O6iYRzMfvefO527OUmetgLvh2w7vBl3Lhbgm+PXseMwZ5Ch0RE1KQqF1LjD/NEwuKcbqJm7BLncxPVyERihLlPdAYAhEdd0dqvloioNeBCakSGQeekOzQ0FFu2bKlSvmXLFqxYsULX0xNRLS5k3dsuzIE93UTVea5ve7jZmuF2kQLb468LHQ4RUZOq/LHRjAupEQlK56R7w4YN6NatW5XyHj16IDw8XNfTE1EN1Go1zt9Lujm8nKh6xmLt3u68kjKBIyIiajpcSI3IMOicdGdlZcHJyalKub29PTIzM3U9PRHVIDOvFHklZZAYiTi8nKgWz/q0R2cHC+SVlCE86orQ4RARNZlCecWcbi6kRiQsnZNuFxcXHDlypEr5kSNH4OzsrOvpsW7dOnh4eEAmk8HX1xcxMTF1Ou7IkSOQSCTw9vbWOQYiQ3Q2Ix8A0MnBAlIJ52oR1URsJMI7T1WMyNoSm4KsvFKBIyIiahr3e7r5PYFISDon3dOnT8f8+fOxdetWXL9+HdevX8eWLVuwYMECzJgxQ6dzR0REYP78+ViyZAmSkpIQGBiIkSNHIjU1tdbj8vLyMHnyZAwbNkyn6xMZsrOZFUl3dycrgSMhMnxPejmgn3tbyMtVCNt/UehwiIiaRJGCW4YRGQKdk+533nkH06ZNw6xZs+Dp6QlPT0+89dZbmDt3LhYtWqTTuVetWoVp06Zh+vTp8PLyQlhYGFxcXLB+/fpaj5s5cyYmTpwIf39/na5PZMjOVSbdzky6iR5FJBJh0ciK3u4fE9JwObtA4IiIiBpfEffpJjIIOifdIpEIK1aswK1bt3D06FGcPHkSd+7cwQcffACRSNTg8yoUCiQmJiIoKEirPCgoCHFxcTUet3XrVly5cgX/+c9/GnxtouaAPd1E9ePrZoOg7o5QqYHP/rogdDhERI2uqHJON1cvJxKUXvbpjomJQXBwMObNm4e2bdtCKpXim2++QWxsbIPPmZOTA6VSCUdHR61yR0dHZGVlVXvMpUuXsGjRInz33XeQSOr2j4tcLkd+fr7Wg8jQFZSW4frtYgCAF5Nuojp756muMBIB+87eRMK1O0KHQ0TUqLhPN5Fh0Dnp3rVrF0aMGAFTU1OcOHECcrkcAFBQUIDly5frHODDveVqtbraHnSlUomJEyfio48+QpcuXep8/tDQUFhbW2seLi4uOsdM1Ngq9+d2spahrbmJwNEQNR+dHCwx3q/i3/lP/zwPtVotcERERI2nck43twwjEpbOSffHH3+M8PBwbNq0CcbGxprygIAAnDhxosHntbOzg1gsrtKrnZ2dXaX3G6hI8hMSEjBnzhxIJBJIJBIsXboUJ0+ehEQiwcGDB6u9zuLFi5GXl6d5pKWlNThmoqbCoeVEDTf/yS6QSoyQcP0u9p/LFjocIqJGwzndRIZB56T7woULGDx4cJVyKysr5ObmNvi8JiYm8PX1RWRkpFZ5ZGQkAgICqr3e6dOnkZycrHkEBweja9euSE5OxoABA6q9jlQqhZWVldaDyNBVbhfGoeVE9dfOWobXB3kAAFb8dR7lSpXAERER6Z+iXIUyZcVoHibdRMLS+RPo5OSEy5cvw93dXas8NjYWnp6eOp07JCQEkyZNgp+fH/z9/bFx40akpqYiODgYQEUvdXp6OrZv3w4jIyP07NlT63gHBwfIZLIq5UTN3akbeQCAnu2ZdBM1RPCQjvjheCouZxdix/FUTPJ3FzokIiK9quzlBgBzE87pJhKSzj3dM2fOxLx583Ds2DGIRCJkZGTgu+++w8KFCzFr1iydzj1hwgSEhYVh6dKl8Pb2RnR0NPbs2QM3NzcAQGZm5iP37CZqaUrLlLhws2JOd+8ObYQNhqiZsjY1xoLhFet/rIq8iLySMoEjIiLSr8pF1GTGRpCI9bJ2MhE1kEith1VklixZgtWrV6O0tBRAxZDthQsXYtmyZToH2NTy8/NhbW2NvLw8DjUng3Qi9S6eXxcHOwsT/L3kSZ225iNqzcqVKjz1RQwuZxdiRqAHlozu3uQxsM3RP95Togrns/LxVFgMbM1NkPj+cKHDIWqR6trm6OVnr08++QQ5OTk4fvw4jh49ilu3bjXLhJuoOTiVlgugopebCTdRw0nERlgy2gsAsC3uGq7lFAkcERGR/nARNSLDoVPSXVZWhqFDh+LixYswMzODn58f+vfvDwsLC33FR0QPqZzP3au9tcCREDV/Q7s6YHAXe5Qp1Qj985zQ4RiUdevWwcPDAzKZDL6+voiJiam1flRUFHx9fSGTyeDp6Ynw8PAqdXbt2oXu3btDKpWie/fu2L17d43nCw0NhUgkwvz583V9K0StUqFcCYBJN5Eh0CnpNjY2xj///MPeNqImdCq9Iunu48Kkm0gf3hvtBbGRCHvP3ET8ldtCh2MQIiIiMH/+fCxZsgRJSUkIDAzEyJEja1xHJSUlBaNGjUJgYCCSkpLw7rvvYu7cudi1a5emTnx8PCZMmIBJkybh5MmTmDRpEsaPH49jx45VOd/ff/+NjRs3onfv3o32HolaumJ55R7dXESNSGg6Dy+fPHkyNm/erI9YiOgRCuXluHKrEADQq30bYYMhaiG6OFri5f4uAICP/zgLpUrnpU6avVWrVmHatGmYPn06vLy8EBYWBhcXF6xfv77a+uHh4XB1dUVYWBi8vLwwffp0vP7661i5cqWmTlhYGIYPH47FixejW7duWLx4MYYNG4awsDCtcxUWFuKVV17Bpk2b0LZt28Z8m0QtWiGHlxMZDJ2TboVCgfXr18PX1xczZ85ESEiI1oOI9Of0jTyo1YCztQz2llKhwyFqMRY82QWWMgnOZORj14kbQocjKIVCgcTERAQFBWmVBwUFIS4urtpj4uPjq9QfMWIEEhISUFZWVmudh885e/ZsjB49Gk8++aSub4WoVWPSTWQ4dP4U/vPPP+jbty8A4OLFi1qvcdg5kX6dTs8FwK3CiPTN1kKKt57ohOV7zuO/ey9gdC+nVvtFNScnB0qlEo6Ojlrljo6OyMrKqvaYrKysauuXl5cjJycHTk5ONdZ58Jw//PADTpw4gb///rvO8crlcsjlcs3z/Pz8Oh9L1JIVllYk3Vay1vlvGZEh0flTeOjQIX3EQUR1cLJyEbUOnM9NpG9TAtzx3bFUXL9djPWHr2DhiK5ChySoh384V6vVtf6YXl39h8trO2daWhrmzZuHffv2QSaT1TnO0NBQfPTRR3WuT9RaFNzr6baUGQscCRE1eHh5cXExZs+ejfbt28PBwQETJ05ETk6OPmMjooecupELAOjDnm4ivZNKxFg8smILsU0xV5F2p1jgiIRhZ2cHsVhcpVc7Ozu7Sk91pXbt2lVbXyKRwNbWttY6ledMTExEdnY2fH19IZFIIJFIEBUVhTVr1kAikUCpVFZ77cWLFyMvL0/zSEtLa9D7JmppCkorpnZYtNJRO0SGpMFJ93/+8x9s27YNo0ePxksvvYTIyEi8+eab+oyNiB6QXVCKtDslEInY003UWEb0cERAR1vIy1X48LczQocjCBMTE/j6+iIyMlKrPDIyEgEBAdUe4+/vX6X+vn374OfnB2Nj41rrVJ5z2LBhOH36NJKTkzUPPz8/vPLKK0hOToZYXP0KzFKpFFZWVloPIgLySyt7upl0EwmtwZ/Cn3/+GZs3b8ZLL70EAHj11VcxcOBAKJXKGhtGImq4xGt3AQBdHS1hbcqhYkSNQSQSYenYHhj5RQwOnM9G5NmbGN69+t7dliwkJASTJk2Cn58f/P39sXHjRqSmpiI4OBhARe9yeno6tm/fDgAIDg7G2rVrERISghkzZiA+Ph6bN2/Gjh07NOecN28eBg8ejBUrVmDs2LH49ddfsX//fsTGxgIALC0t0bNnT604zM3NYWtrW6WciB6toJTDy4kMRYN7utPS0hAYGKh53r9/f0gkEmRkZOglMCLSlnC9Iun2deMWOkSNqZODJaYHegIAPvztDEoU1Q9rbskmTJiAsLAwLF26FN7e3oiOjsaePXvg5uYGAMjMzNTas9vDwwN79uzB4cOH4e3tjWXLlmHNmjUYN26cpk5AQAB++OEHbN26Fb1798a2bdsQERGBAQMGNPn7I2oNCu8NL2dPN5HwGvwpVCqVMDEx0T6ZRILy8nKdgyKiqiqTbj93Jt1Eje2tJzrht+QMpOeWYO2hS/jXiG5Ch9TkZs2ahVmzZlX72rZt26qUDRkyBCdOnKj1nC+88AJeeOGFOsdw+PDhOtclIm0FHF5OZDAa/ClUq9WYOnUqpNL7ewWXlpYiODgY5ubmmrKff/5ZtwiJCCUKJc6kV6xc7udmI3A0RC2fmYkEH4zpjpnfJGJj9FU837cDOtpbCB0WEVGdaZJuKYeXEwmtwUn3lClTqpS9+uqrOgVDRNU7eSMX5So1HCyl6NDWVOhwiFqFoO6OGNrVHocu3MKS3afx/fTHYGRU85ZZRESGpIDDy4kMRoM/hVu3btVnHERUi8QHhpbXtk8uEelPxaJqPRG0OhpHr97Bjr9T8coAN6HDIiJ6JKVKjaJ761Ew6SYSXoMXUiOippNw7Q4AwJdDy4malIuNGf41oisAIHTPeWTklggcERHRoxWW3l9jiauXEwmPSTeRgVOp1DiRmguAK5cTCWFKgDv6urZBobwcS3afhlqtFjokIqJaFcgrhpZLJUYwkfDrPpHQ+CkkMnBnM/ORV1IGcxMxejhbCR0OUasjNhLhsxd6w0RshEMXbmF3UrrQIRER1Yp7dBMZFibdRAbu6NXbAIB+HjYwFvMjSySETg6WmPdkZwDA0v+dRXZ+qcARERHVjNuFERkWfoMnMnDxVyqS7oCOtgJHQtS6vTHYEz2crZBbXIZ//XSKw8yJyGBx5XIiw8Kkm8iAlStVOJZSsYiav6edwNEQtW7GYiOETfCGVGKEqIu3sD3+utAhERFViz3dRIaFSTeRAfsnIx+F8nJYySTozvncRILr7GiJxSO7AQCW7zmHSzcLBI6IiKgqTU+3lHO6iQwBk24iA1Y5tHyApy3ERtyfm8gQTAlwx+Au9pCXqzDvh2TIy5VCh0REpKVAzp5uIkPCpJvIgMVdyQEA+HtyPjeRoRCJRFj5Qm+0NTPG2cx8fL7votAhERFpyS/h6uVEhoRJN5GBUpSrkHDtLgAgoBOTbiJD4mAlw6fjegMANkZfxYFzNwWOiIjovtxiBQCgrRmTbiJDwKSbyEAlXr+LkjIlbM1N0MXBUuhwiOghI3q0w9QAdwDAgohkpN0pFjYgIqJ7cosr5nS3YdJNZBCYdBMZqMMXswEAQ7rYw4jzuYkM0rujvODt0gb5peWY/f0Jzu8mIoOQW1LR021tZiJwJEQEMOkmMliHz98CAAzpai9wJERUExOJEb56pS/amBnj1I08fPy/c0KHRER0v6fblD3dRIaASTeRAcrILcGFmwUwEgGDOzPpJjJk7duYYvUEbwDAN0ev4+cTN4QNiIhavbwSDi8nMiRMuokM0OELFb3c3i5t0NacQ8OIDN3Qrg5464lOAIBFP5/GidS7AkdERK3Z/Z5ufocgMgRMuokM0KELFfO5h3Z1EDgSIqqrBU92wfDujlCUq/DG9kRk5pUIHRIRtUKlZUqUlFWsL2HNnm4ig8Ckm8jAyMuViLtcsT/340y6iZoNIyMRVk/wRldHS+QUyjFjewJKFFxYjYiaVv69oeVGIsBSKhE4GiICmkHSvW7dOnh4eEAmk8HX1xcxMTE11v35558xfPhw2Nvbw8rKCv7+/ti7d28TRkuku7jLt1GkUMLBUooezlZCh0NE9WAhleDrKX6wMTfBP+n5WLjzJFQqtdBhEVErknsv6bY2NebuJ0QGwqCT7oiICMyfPx9LlixBUlISAgMDMXLkSKSmplZbPzo6GsOHD8eePXuQmJiIoUOHYsyYMUhKSmriyIka7q9/sgBU7AHMxpKo+XGxMcP6V/rCWCzCH6cz8ckermhORE3n/h7dnM9NZCgMOuletWoVpk2bhunTp8PLywthYWFwcXHB+vXrq60fFhaGd955B/369UPnzp2xfPlydO7cGb///nsTR07UMOVKFSLP3QQAPNWzncDREFFDDfC0xWcv9AYAbI5NwaboqwJHREStRW7xvT26uV0YkcEw2KRboVAgMTERQUFBWuVBQUGIi4ur0zlUKhUKCgpgY2PTGCES6d3f1+7iTpECbcyM0d+Df2+JmrPnfDrg3VHdAACf7DmHX5LSBY6IiFqDyp7utlxEjchgGOzqCjk5OVAqlXB0dNQqd3R0RFZWVp3O8fnnn6OoqAjjx4+vsY5cLodcLtc8z8/Pb1jARHqw90zF3+0nvRxhLDbY38SIqI5mBHriZr4cm2NTsHDnSbQ1N8GQLvZCh0VELVhuCXu6iQyNwX+rF4m057Sq1eoqZdXZsWMHPvzwQ0RERMDBoeYVoENDQ2Ftba15uLi46BwzUUOo1WpN0v1UDw4tJ2oJRCIRlozywpg+zihXqfHG9gTEXckROiwiasFuF1Yk3bYWUoEjIaJKBpt029nZQSwWV+nVzs7OrtL7/bCIiAhMmzYNP/74I5588sla6y5evBh5eXmaR1pams6xEzVE4vW7yMwrhbmJGIM62wkdDhHpiZGRCJ+/2AfDujlAXq7CtG0JOJ5yR+iwHqk+u4cAQFRUFHx9fSGTyeDp6Ynw8PAqdXbt2oXu3btDKpWie/fu2L17t9broaGh6NevHywtLeHg4IBnn30WFy5c0Ov7ImrpbhVWjOC0Y9JNZDAMNuk2MTGBr68vIiMjtcojIyMREBBQ43E7duzA1KlT8f3332P06NGPvI5UKoWVlZXWg0gIvyRXzPcc0bMdZMZigaMhIn0ykRjhq1f6YnAXe5SUKfHa1uNIvH5X6LBqVN/dQ1JSUjBq1CgEBgYiKSkJ7777LubOnYtdu3Zp6sTHx2PChAmYNGkSTp48iUmTJmH8+PE4duyYpk5UVBRmz56No0ePIjIyEuXl5QgKCkJRUVGjv2eiliJH09PN1cuJDIVIrVYb7AaiERERmDRpEsLDw+Hv74+NGzdi06ZNOHPmDNzc3LB48WKkp6dj+/btACoS7smTJ+OLL77A888/rzmPqakprK2t63TN/Px8WFtbIy8vjwk4NZkypQr9P9mPu8Vl2P56fwzmnE+iFqm0TInXt/2NuCu3YSmV4Kvx3TCkp7vBtTkDBgxA3759tXYL8fLywrPPPovQ0NAq9f/973/jt99+w7lz97dHCw4OxsmTJxEfHw8AmDBhAvLz8/Hnn39q6jz11FNo27YtduzYUW0ct27dgoODA6KiojB48OA6xc52nFq7UV/E4GxmPrZO7Yeh3WqeYklEuqtrm2OwPd1ARQMdFhaGpUuXwtvbG9HR0dizZw/c3NwAAJmZmVq/um/YsAHl5eWYPXs2nJycNI958+YJ9RaI6iT64i3cLS6DnYUUAR1thQ6HiBqJzFiMr6f4ob+HDQrk5Xhje4LQIVXRkN1D4uPjq9QfMWIEEhISUFZWVmud2nYkycvLAwDuQkJUDzkcXk5kcAx29fJKs2bNwqxZs6p9bdu2bVrPDx8+3PgBETWC3fe2EnqmjzMkXLWcqEUzM5Fg22v98Mb2RESfqX64tpAasntIVlZWtfXLy8uRk5MDJyenGuvUdE61Wo2QkBAMGjQIPXv2rDFe7kJCdJ9Kpcadoorh5XaWHF5OZCj47Z5IYHeLFNh35iYA4Dmf9gJHQ0RNwcxEgq+n+GFIF8NdNLG+u4dUV//h8vqcc86cOTh16lSNQ88rcRcSovvySspQrqr47Nmas6ebyFAw6SYS2K4TN6BQqtCzvRV6dajb2gNE1PzJjMVYPcFH6DCqaMjuIe3atau2vkQiga2tba11qjvnW2+9hd9++w2HDh1Chw4dao2Xu5AQ3Vc5tNxKJoGJhF/ziQwFP41EAlKr1fj+eMXw0pf7uwocDRE1NUP8UtyQ3UP8/f2r1N+3bx/8/PxgbGxca50Hz6lWqzFnzhz8/PPPOHjwIDw8PB4ZL3chIbpPs12YJXu5iQyJwc/pJmrJjqfcwdVbRTAzEWOsN4eWE5FhCAkJwaRJk+Dn56fZPSQ1NRXBwcEAUGX3kODgYKxduxYhISGYMWMG4uPjsXnzZq2h4fPmzcPgwYOxYsUKjB07Fr/++iv279+P2NhYTZ3Zs2fj+++/x6+//gpLS0tNz7i1tTVMTU2b8A4QNU+3720XxkXUiAwLk24iAVX2co/1doaFlB9HIjIMEyZMwO3bt7F06VJkZmaiZ8+ete4e4uHhgT179mDBggX46quv4OzsjDVr1mDcuHGaOgEBAfjhhx/w3nvv4f3330fHjh0RERGBAQMGaOpUblH2+OOPa8WzdetWTJ06tfHeMFELcTO/FABgz6SbyKAY9D7dQuD+ntRUbhXIMfDTg1AoVfh9ziDO5yZqhdjm6B/vKbVmb+1Iwu8nM/D28C54a1hnocMhavFaxD7dRC3Z9vhrUChV8HZpw4SbiIiIdJZ47Q4AwNetrcCRENGDmHQTCaBYUY5vjl4HAMwc7ClwNERERNTcZeSWICOvFGIjEbxd2wgdDhE9gEk3kQB2JtxAbnEZ3GzNENSjndDhEBERUTOXeP0uAKC7kxXMTLhODJEhYdJN1MTKlCp8HXsVADB9kAfERiKBIyIiIqLmLoFDy4kMFpNuoib2U+INpN0pgZ2FCV7wdRE6HCIiImoBjly5DQAY4GEjcCRE9DAm3URNqLRMiTUHLgEAZj3eCaYmYoEjIiIiouYuM68El7MLYSQCAjraCR0OET2ESTdRE9pxPBWZeaVoZyXDxAGuQodDRERELUDMpRwAQO8ObWBtZixwNET0MCbdRE2koLQMXx26DAB4a1gnyIzZy01ERES6q0y6Azuzl5vIEDHpJmoiYfsvIadQAXdbM7zIudxERESkB2VKFWIu3QIABHa2FzgaIqoOk26iJnAhqwDb4q4BAD58pgdMJPzoERERke6OXb2D3OIy2JqbcOVyIgPFb/5EjUytVuODX/+BUqXGiB6OeLyrg9AhERERUQvx15lMAMDw7o7chpTIQDHpJmpkv53MwLGUO5AZG+H9p7sLHQ4RERG1ECqVGnvP3AQAPNWzncDREFFNmHQTNaKsvFL857czAIA5QzuhQ1szgSMiIiKiliLuym3cKpDDUibhVmFEBoxJN1EjUanUCPkxGbnFZejV3hpvDO4odEhERETUgkQkpAEAxno7c70YIgPGTydRI9kYcxVxV27D1FiML17yZmNIREREepNbrMDeM1kAgAl+rgJHQ0S1YRZA1AhiL+Xgv3svAAA+fKY7PO0tBI6IiIiIWpKfEm9AUa6Cl5MVera3EjocIqoFk24iPbucXYA3v0uEUqXGcz7tMd6Pe3ITERGR/ijKVdgcmwIAmPSYG0QirlpOZMiYdBPp0e1COV7floCC0nL4ubXFp+N6sSEkIiIivfrtZAYy80phbynF833bCx0OET0Ck24iPblVIMfETceQeqcYrjZm2DDJF1KJWOiwiIiIqAVRlKvw1aHLAIDXB3pAZszvGkSGTiJ0AEQtwc38UkzcdBRXbhXBwVKKra/1g62FVOiwiIiIqIX59uh1pOQUwc7CBK8+xgXUiJoDJt1EOrp0swDTtyfg+u1iOFvL8P2Mx+BuZy50WERERNTC3MwvRdj+iwCAt4O6wlJmLHBERFQXTLqJdLDndCb+tfMkihRKdGhrih0zHoOLjZnQYREREVELo1arsWjXKeSXlqNXe2su1ErUjDDpJmqAEoUSn++7gK/vrRz6mKcN1k7sCzsOKSciIqJGsCnmKg5duAUTsRE+H98HYiMu1ErUXDDpJqqnQ+ez8f6v/+DG3RIAwBuDPfHOiK6QiLkuIREREelf5Nmb+PTP8wCAJaO90MXRUuCIiKg+mHQT1dE/6XlYc+AS9p29CQBwtpbho7E9Mby7o8CRERERUUsVefYmZn2XCJUaeLm/Cyb7uwkdEhHVE5Nuolqo1WrEXbmN8KgriLmUAwAQG4kwbZAH5g3rDHMpP0JERESkfyqVGhtjruKzv85DpQZG93LCsrE9IRJxWDlRc2Pw42HXrVsHDw8PyGQy+Pr6IiYmptb6UVFR8PX1hUwmg6enJ8LDw5soUmop1Go1Tt/IQ+if5xD42SG88vUxxFzKgdhIhLHezvhzXiDeHeXFhJuIWrTGaH937dqF7t27QyqVonv37ti9e7fO1yVqiS5kFWD8hnh8+mdFwv1SPxd88ZI3p7IRNVMGnTVERERg/vz5WLduHQYOHIgNGzZg5MiROHv2LFxdq+5LmJKSglGjRmHGjBn49ttvceTIEcyaNQv29vYYN26cAO+AmoNypQopOUVIvH4XCdfv4ujV25r52gBgaizGi34dMCPQkyuTE1Gr0Bjtb3x8PCZMmIBly5bhueeew+7duzF+/HjExsZiwIABDbouUUuiVKlxLOU2/i/uGvadvQm1GjAzEeO90d3xcn8X9nATNWMitVqtFjqImgwYMAB9+/bF+vXrNWVeXl549tlnERoaWqX+v//9b/z22284d+6cpiw4OBgnT55EfHx8na6Zn58Pa2tr5OXlwcrKSvc3QYJTq9XIKylDdoEc2flyZBeU4trtYlzOLsDl7EJcyymGQqnSOkZmbIRh3RwxurcThnZ1gKmJWKDoiaglM9Q2pzHa3wkTJiA/Px9//vmnps5TTz2Ftm3bYseOHQ26bnUM9Z4SycuVKJIrkVuswK0COXIKFbhVUIpbhXJcv12MYyl3cKtArqk/qlc7vDe6O5zbmAoYNRHVpq5tjsH2dCsUCiQmJmLRokVa5UFBQYiLi6v2mPj4eAQFBWmVjRgxAps3b0ZZWRmMjY3rfP1NMVdgaq69MmR9f56o6feM2s5T00s1HaOu4Yia69eipnj1FFPtx9T/GmXlaiiUSsjLVFAoVZCXqSAvV0JerkJBaTnyS8uQX1KG/NJyKFW1/88zNRajdwdr+Lm3hZ+bDQZ42sDMxGA/HkREjaax2t/4+HgsWLCgSp2wsLAGX7c2aw9cgszcot7H1VVT9Fg0RbdIbW223q7RJO+j8alUaiiUKpQr1ShXqaAor/hvubKyXIVylRqKchXk5SoUK8pRJFeiUF6OYkU5ypSPjtLa1BijerXD6wM90JkrlBO1GAabVeTk5ECpVMLRUXtlaEdHR2RlZVV7TFZWVrX1y8vLkZOTAycnpyrHyOVyyOX3f1XMy8sDAKz+4xSMpBxK3JJYySSwt5TC3lIKZ2tTdHQwh4e9BTxtzeHcxhRGD+x3WV5ajPxSAYMlolYhPz8fQM0/0gqhsdrfmupUnrMh1wVqbsfXRf7DdpwMkrlUDDsLKWzNTWBnYQJbCykcrWTo4WwNX7e2MJEYAVBr/n0gIsNV13bcYJPuSg/PX1Gr1bXOaamufnXllUJDQ/HRRx9VKU9fP7WekRIRETVMQUEBrK2thQ5DS2O0v3U5Z32vy3aciIiE9qh23GCTbjs7O4jF4iq/bmdnZ1f5FbxSu3btqq0vkUhga2tb7TGLFy9GSEiI5rlKpcKdO3dga2uraeT79euHv//+u97voT7HPapuTa/Xp/zhsgef5+fnw8XFBWlpaY02B66h97E+xzb0Ptb0miHex5ri0vexvJf6O641fL5ri1dfx+n772R15bXd28a4j2q1GgUFBXB2dtbL+fShsdrfmupUnrMh1wUe3Y7z30v9MeR7yX8v61+3JdxLQ/47WdNrhngfa4pL38e2xHtZ13bcYJNuExMT+Pr6IjIyEs8995ymPDIyEmPHjq32GH9/f/z+++9aZfv27YOfn1+N87mlUimkUqlWWZs2bbSei8XiBv2Pqc9xj6pb0+v1KX+4rLo6VlZWjfaBbuh9rM+xDb2PNb1miPexpmvq+1jeS/0d1xo+3zVdU5/H6fvvZHXldbm3+r6PhtbD3Vjtr7+/PyIjI7Xmde/btw8BAQENvi7w6Hac/17qjyHfS/57Wf+6LeFeGvLfyZpeM8T7WNM19X1sS72XdWnHDTbpBoCQkBBMmjQJfn5+8Pf3x8aNG5Gamorg4GAAFb9up6enY/v27QAqVkpdu3YtQkJCMGPGDMTHx2Pz5s2aVVEbavbs2Y1+3KPq1vR6fcofLmvo+2ooXa5X12Mbeh9res0Q76Ou1+S9rD2GxjiuNXy+dbmmUH8nqyuv69/dlq4x2t958+Zh8ODBWLFiBcaOHYtff/0V+/fvR2xsbJ2v2xD891J/DPle8t/L+tdtCffSkP9O1vSaId5HXa/Je1kHagP31Vdfqd3c3NQmJibqvn37qqOiojSvTZkyRT1kyBCt+ocPH1b7+PioTUxM1O7u7ur169c3ccTNU15enhqAOi8vT+hQmjXeR/3hvdQf3kv9aG33sTHa3507d6q7du2qNjY2Vnfr1k29a9euel2Xqtfa/m42Jt5L/eG91A/eR/0R8l4a9D7d1HTkcjlCQ0OxePHiKsP0qO54H/WH91J/eC/1g/eRDBX/buoP76X+8F7qB++j/gh5L5l0ExERERERETUSI6EDICIiIiIiImqpmHQTERERERERNRIm3URERERERESNhEk3ERERERERUSNh0k21SktLw+OPP47u3bujd+/e2Llzp9AhNVsFBQXo168fvL290atXL2zatEnokJq14uJiuLm5YeHChUKH0qxJJBJ4e3vD29sb06dPFzqcZi0lJQVDhw5F9+7d0atXLxQVFQkdEhHbcT1hG65/bMf1g+24fjR2G87Vy6lWmZmZuHnzJry9vZGdnY2+ffviwoULMDc3Fzq0ZkepVEIul8PMzAzFxcXo2bMn/v77b9ja2godWrO0ZMkSXLp0Ca6urli5cqXQ4TRbdnZ2yMnJETqMFmHIkCH4+OOPERgYiDt37sDKygoSiUTosKiVYzuuH2zD9Y/tuH6wHdePxm7D2dNNtXJycoK3tzcAwMHBATY2Nrhz546wQTVTYrEYZmZmAIDS0lIolUrwN6+GuXTpEs6fP49Ro0YJHQoRAODMmTMwNjZGYGAgAMDGxoYJNxkEtuP6wTZcv9iOkyFpijacSXcLFx0djTFjxsDZ2RkikQi//PJLlTrr1q2Dh4cHZDIZfH19ERMTU+25EhISoFKp4OLi0shRGyZ93Mvc3Fz06dMHHTp0wDvvvAM7O7smit5w6OM+Lly4EKGhoU0UseHSx73Mz8+Hr68vBg0ahKioqCaK3PDoei8vXboECwsLPPPMM+jbty+WL1/ehNFTS8Z2XD/YhusP23H9YTuuH82hDWfS3cIVFRWhT58+WLt2bbWvR0REYP78+ViyZAmSkpIQGBiIkSNHIjU1Vave7du3MXnyZGzcuLEpwjZI+riXbdq0wcmTJ5GSkoLvv/8eN2/ebKrwDYau9/HXX39Fly5d0KVLl6YM2yDp4+/ktWvXkJiYiPDwcEyePBn5+flNFb5B0fVelpWVISYmBl999RXi4+MRGRmJyMjIpnwL1EKxHdcPtuH6w3Zcf9iO60ezaMPV1GoAUO/evVurrH///urg4GCtsm7duqkXLVqkeV5aWqoODAxUb9++vSnCbBYaei8fFBwcrP7xxx8bK8RmoSH3cdGiReoOHTqo3dzc1La2tmorKyv1Rx991FQhGyx9/J186qmn1H///XdjhdhsNORexsXFqUeMGKF57bPPPlN/9tlnjR4rtS5sx/WDbbj+sB3XH7bj+mGobTh7ulsxhUKBxMREBAUFaZUHBQUhLi4OAKBWqzF16lQ88cQTmDRpkhBhNgt1uZc3b97U/PqYn5+P6OhodO3atcljNWR1uY+hoaFIS0vDtWvXsHLlSsyYMQMffPCBEOEatLrcy7t370IulwMAbty4gbNnz8LT07PJYzV0dbmX/fr1w82bN3H37l2oVCpER0fDy8tLiHCpFWE7rh9sw/WH7bj+sB3XD0Npw7nKSyuWk5MDpVIJR0dHrXJHR0dkZWUBAI4cOYKIiAj07t1bMz/im2++Qa9evZo6XINWl3t548YNTJs2DWq1Gmq1GnPmzEHv3r2FCNdg1eU+Ut3U5V6eO3cOM2fOhJGREUQiEb744gvY2NgIEa5Bq8u9lEgkWL58OQYPHgy1Wo2goCA8/fTTQoRLrQjbcf1gG64/bMf1h+24fhhKG86kmyASibSeq9VqTdmgQYOgUqmECKtZqu1e+vr6Ijk5WYComp/a7uODpk6d2kQRNV+13cuAgACcPn1aiLCapUf9vRw5ciRGjhzZ1GERsR3XE7bh+sN2XH/YjuuH0G04h5e3YnZ2dhCLxVV+eczOzq7yaxDVjvdSP3gf9Yf3Un94L8lQ8e+mfvA+6g/vpf7wXuqHodxHJt2tmImJCXx9fauszhcZGYmAgACBomqeeC/1g/dRf3gv9Yf3kgwV/27qB++j/vBe6g/vpX4Yyn3k8PIWrrCwEJcvX9Y8T0lJQXJyMmxsbODq6oqQkBBMmjQJfn5+8Pf/f/buO76pev/j+CtJ94ZSyipQ9t4goIi4EQQFvQ4U3HvB5foTr3tx9aoo1ysuBFEU9AouFFCQpeyNbGhpgUKB7p0m5/dH2mhlCDTtSZP38/HII8k5J9/zScSefPL9fj/fPrz33nukpKRwzz33mBi1d9Jn6Rn6HD1Hn6Xn6LMUb6V/m56hz9Fz9Fl6jj5Lz6gRn6NHa6GL1/n5558N4LjbqFGj3Mf897//NZo0aWIEBQUZ3bp1MxYvXmxewF5Mn6Vn6HP0HH2WnqPPUryV/m16hj5Hz9Fn6Tn6LD2jJnyOFsMwDE8n8iIiIiIiIiKiOd0iIiIiIiIiVUZJt4iIiIiIiEgVUdItIiIiIiIiUkWUdIuIiIiIiIhUESXdIiIiIiIiIlVESbeIiIiIiIhIFVHSLSIiIiIiIlJFlHSLiIiIiIiIVBEl3SIiIiIiIiJVREm3iB975pln6NKli2nnf/LJJ7nrrrtO69ixY8fy0EMPVXFEIiIiNYeu4yI1g8UwDMPsIETE8ywWyyn3jxo1irfeeovi4mJiY2OrKarfHT58mJYtW7Jp0yaaNm36l8enp6fTvHlzNm3aRGJiYtUHKCIiYiJdx0V8h5JuER916NAh9+OZM2fy1FNPsWPHDve20NBQoqOjzQgNgJdeeonFixczb968037N8OHDadGiBS+//HIVRiYiImI+XcdFfIeGl4v4qHr16rlv0dHRWCyW47b9eVjaLbfcwlVXXcVLL71EfHw8MTExPPvss5SWlvKPf/yD2rVr06hRIz788MMK5zpw4ADXXXcdtWrVIjY2lqFDh5KcnHzK+GbMmMGQIUMqbPvf//5Hx44dCQ0NJTY2losvvpj8/Hz3/iFDhvDZZ59V+rMRERHxdrqOi/gOJd0iUsHChQs5ePAgS5Ys4fXXX+eZZ55h8ODB1KpVi5UrV3LPPfdwzz33kJqaCkBBQQEDBgwgIiKCJUuWsGzZMiIiIrj88sspKSk54TkyMzPZsmULPXr0cG9LS0vjhhtu4LbbbmPbtm0sWrSIYcOG8cfBOL169SI1NZV9+/ZV7YcgIiJSQ+k6LuJ9lHSLSAW1a9dm4sSJtG7dmttuu43WrVtTUFDA448/TsuWLRk3bhxBQUH88ssvgOuXbqvVygcffEDHjh1p27YtU6ZMISUlhUWLFp3wHPv27cMwDBo0aODelpaWRmlpKcOGDaNp06Z07NiR++67j4iICPcxDRs2BPjLX99FRET8la7jIt4nwOwARMS7tG/fHqv199/j4uPj6dChg/u5zWYjNjaW9PR0ANauXcvu3buJjIys0E5RURF79uw54TkKCwsBCAkJcW/r3LkzF110ER07duSyyy7j0ksv5ZprrqFWrVruY0JDQwHXr/IiIiJyPF3HRbyPkm4RqSAwMLDCc4vFcsJtTqcTAKfTSffu3Zk+ffpxbcXFxZ3wHHXq1AFcw9PKj7HZbPz444/8+uuvzJ8/n//85z/885//ZOXKle4qpxkZGadsV0RExN/pOi7ifTS8XEQqpVu3buzatYu6devSokWLCreTVVVt3rw5UVFRbN26tcJ2i8XCueeey7PPPsv69esJCgpi9uzZ7v1btmwhMDCQ9u3bV+l7EhER8Re6jotUPSXdIlIpI0aMoE6dOgwdOpSlS5eSlJTE4sWLefjhh9m/f/8JX2O1Wrn44otZtmyZe9vKlSt56aWXWLNmDSkpKcyaNYsjR47Qtm1b9zFLly6lX79+7uFpIiIiUjm6jotUPSXdIlIpYWFhLFmyhMaNGzNs2DDatm3LbbfdRmFhIVFRUSd93V133cWMGTPcw9uioqJYsmQJV1xxBa1ateKJJ57gtddeY+DAge7XfPbZZ9x5551V/p5ERET8ha7jIlXPYvyxjr+ISDUxDIPevXvzyCOPcMMNN/zl8XPmzOEf//gHmzZtIiBA5ShERETMpOu4yOlTT7eImMJisfDee+9RWlp6Wsfn5+czZcoUXahFRES8gK7jIqdPPd0iIiIiIiIiVUQ93SIiIiIiIiJVREm3iIiIiIiISBVR0i0iIiIiIiJSRZR0i4iIiIiIiFQRJd0iIiIiIiIiVURJt4iIiIiIiEgVUdItIiIiIiIiUkWUdIuIiIiIiIhUESXdIiIiIiIiIlVESbeIiIiIiIhIFVHSLSIiIiIiIlJFlHSLiIiIiIiIVBEl3SIiIiIiIiJVREm3iIiIiIiISBVR0i0iIiIiIiJSRZR0i4iIiIiIiFQRJd2nsGTJEq688koaNGiAxWLhq6++qtLz5ebm8sgjj9CkSRNCQ0Pp27cvq1evrtJzioiI+KLKXsN37NjBgAEDiI+PJyQkhGbNmvHEE09gt9srHLd48WK6d+/uPuadd97x4LsQERFfoKT7FPLz8+ncuTNvvfVWtZzvjjvu4Mcff+Tjjz9m8+bNXHrppVx88cUcOHCgWs4vIiLiKyp7DQ8MDGTkyJHMnz+fHTt28MYbb/D+++/z9NNPu49JSkriiiuuoF+/fqxfv57HH3+chx56iC+//NJTb0NERHyAxTAMw+wgagKLxcLs2bO56qqr3NtKSkp44oknmD59OllZWXTo0IGXX36ZCy644IzbLywsJDIykq+//ppBgwa5t3fp0oXBgwfzwgsveOBdiIiI+B9PXcPHjBnD6tWrWbp0KQD/93//xzfffMO2bdvcx9xzzz1s3LiR5cuXV9XbERGRGkY93ZVw66238ssvvzBjxgw2bdrEtddey+WXX86uXbvOuK3S0lIcDgchISEVtoeGhrJs2TJPhSwiIiKc+TV89+7dzJ07l/79+7u3LV++nEsvvbTCcZdddhlr1qw5bhi6iIj4LyXdZ2nPnj189tlnfPHFF/Tr14/mzZszduxYzjvvPKZMmXLG7UVGRtKnTx+ef/55Dh48iMPh4JNPPmHlypWkpaVVwTsQERHxT2dyDe/bty8hISG0bNmSfv368dxzz7n3HTp0iPj4+ArHx8fHU1paytGjR6vlvYiIiPdT0n2W1q1bh2EYtGrVioiICPdt8eLF7NmzB4Dk5GQsFsspbw888IC7zY8//hjDMGjYsCHBwcFMnDiRG2+8EZvNZtbbFBER8Tmncw0vN3PmTNatW8enn37KnDlzePXVVyvst1gsFZ6Xz9r783YREfFfAWYHUFM5nU5sNhtr1649LimOiIgAoGHDhhXmeZ1IrVq13I+bN2/O4sWLyc/PJycnh/r163PdddeRmJjo+TcgIiLip07nGl4uISEBgHbt2uFwOLjrrrv4+9//js1mo169ehw6dKjC8enp6QQEBBAbG1u1b0JERGoMJd1nqWvXrjgcDtLT0+nXr98JjwkMDKRNmzZn3HZ4eDjh4eFkZmYyb948XnnllcqGKyIiImVO5xp+IoZhYLfb3b3Zffr04dtvv61wzPz58+nRoweBgYEejVlERGouJd2nkJeXx+7du93Pk5KS2LBhA7Vr16ZVq1aMGDGCkSNH8tprr9G1a1eOHj3KwoUL6dixI1dcccUZn2/evHkYhkHr1q3ZvXs3//jHP2jdujW33nqrJ9+WiIiIz6vsNXz69OkEBgbSsWNHgoODWbt2LePGjeO6664jIMD19emee+7hrbfeYsyYMdx5550sX76cyZMn89lnn5n1tkVExAtpybBTWLRoEQMGDDhu+6hRo5g6dSp2u50XXniBadOmceDAAWJjY+nTpw/PPvssHTt2POPzff7554wbN479+/dTu3Zthg8fzosvvkh0dLQn3o6IiIjfqOw1fObMmbzyyivs3LkTwzBo0qQJN910E6NHj66w0sjixYsZPXo0v/32Gw0aNOD//u//uOeee6rzrYqIiJdT0i0iIiIiIiJSRVS9XERERERERKSKKOkWERERERERqSIqpPYnTqeTgwcPEhkZqTU2RUSkShmGQW5uLg0aNMBq1e/gnqDruIiIVJfTvY4r6f6TgwcPutfkFBERqQ6pqak0atTI7DB8gq7jIiJS3f7qOq6k+08iIyMB1wcXFRVlcjQiIuLLcnJySEhIcF97pPJ0HRcRkepyutdxJd1/Uj4ULSoqShdrERGpFhoG7Tm6jouISHX7q+u4JpCJiIiIiIiIVBEl3SIiIiIiIiJVREm3iIiIiIiISBXRnO6zYBgGpaWlOBwOs0MRH2ez2QgICNB8TxERERGRGsrnku4DBw7wf//3f/zwww8UFhbSqlUrJk+eTPfu3T3SfklJCWlpaRQUFHikPZG/EhYWRv369QkKCjI7FBEREREROUM+lXRnZmZy7rnnMmDAAH744Qfq1q3Lnj17iImJ8Uj7TqeTpKQkbDYbDRo0ICgoSD2QUmUMw6CkpIQjR46QlJREy5YtsVo1I0REREREpCbxqaT75ZdfJiEhgSlTpri3NW3a1GPtl5SU4HQ6SUhIICwszGPtipxMaGgogYGB7Nu3j5KSEkJCQswOSUREREREzoBPdZt988039OjRg2uvvZa6devStWtX3n///VO+pri4mJycnAq3v6LeRqlO+vcmIiIiIlJz+dS3+b179zJp0iRatmzJvHnzuOeee3jooYeYNm3aSV8zfvx4oqOj3beEhIRqjFhERERERER8mU8l3U6nk27duvHSSy/RtWtX7r77bu68804mTZp00teMGzeO7Oxs9y01NbUaI5aaKDk5GYvFwoYNGwBYtGgRFouFrKwsU+MSERERERHv41NJd/369WnXrl2FbW3btiUlJeWkrwkODiYqKqrCzVf9+uuv2Gw2Lr/8crNDqXLliXH5LTo6mt69e/Ptt996/Fx9+/YlLS2N6Ohoj7ctIiIiIiI1m08l3eeeey47duyosG3nzp00adLEpIi8y4cffsiDDz7IsmXLTvlDhCc4HA6cTmeVnuN0/PTTT6SlpbFy5Up69erF8OHD2bJli0fPERQURL169VTJXkREREREjuNTSffo0aNZsWIFL730Ert37+bTTz/lvffe4/777zc7NNPl5+fz+eefc++99zJ48GCmTp3q3tenTx8ee+yxCscfOXKEwMBAfv75Z8BVuf3RRx+lYcOGhIeHc84557Bo0SL38VOnTiUmJobvvvuOdu3aERwczL59+1i9ejWXXHIJderUITo6mv79+7Nu3boK59q+fTvnnXceISEhtGvXjp9++gmLxcJXX33lPubAgQNcd9111KpVi9jYWIYOHUpycvJfvu/Y2Fjq1atHmzZtePHFF7Hb7e73BDB37lzOO+88YmJiiI2NZfDgwezZs6dCG6tWraJr166EhITQo0cP1q9fX2H/n4eXP/PMM3Tp0qXCMW+88UaFSvqLFi2iV69ehIeHExMTw7nnnsu+ffv+8v2IiIiIiEjN4lNJd8+ePZk9ezafffYZHTp04Pnnn+eNN95gxIgRVXZOwzAoKCmt9pthGGcU58yZM2ndujWtW7fmpptuYsqUKe42RowYwWeffVahzZkzZxIfH0///v0BuPXWW/nll1+YMWMGmzZt4tprr+Xyyy9n165d7tcUFBQwfvx4PvjgA3777Tfq1q1Lbm4uo0aNYunSpaxYsYKWLVtyxRVXkJubC7jm4V911VWEhYWxcuVK3nvvPf75z39WiL2goIABAwYQERHBkiVLWLZsGREREVx++eWUlJSc1vu32+3uSvaBgYHu7fn5+YwZM4bVq1ezYMECrFYrV199tbuXPj8/n8GDB9O6dWvWrl3LM888w9ixY8/os/+z0tJSrrrqKvr378+mTZtYvnw5d911l3rKRURERER8kE+t0w0wePBgBg8eXG3nK7Q7aPfUvGo7X7mtz11GWNDp/+ebPHkyN910EwCXX345eXl5LFiwgIsvvpjrrruO0aNHs2zZMvr16wfAp59+yo033ojVamXPnj189tln7N+/nwYNGgAwduxY5s6dy5QpU3jppZcAV2L79ttv07lzZ/d5L7zwwgpxvPvuu9SqVYvFixczePBg5s+fz549e1i0aBH16tUD4MUXX+SSSy5xv2bGjBlYrVY++OADd2I6ZcoUYmJiWLRoEZdeeulJ33ffvn2xWq0UFhbidDpp2rQpf/vb39z7hw8fftznVLduXbZu3UqHDh2YPn06DoeDDz/8kLCwMNq3b8/+/fu59957T/uz/7OcnByys7MZPHgwzZs3B1y1B0RERERExPf4VE+3nNiOHTtYtWoV119/PQABAQFcd911fPjhhwDExcVxySWXMH36dACSkpJYvny5e4TAunXrMAyDVq1aERER4b4tXry4wlDsoKAgOnXqVOHc6enp3HPPPbRq1cq9LFteXp57TvmOHTtISEhwJ9wAvXr1qtDG2rVr2b17N5GRke5z165dm6KiouOGgv/ZzJkzWb9+Pd988w0tWrTggw8+oHbt2u79e/bs4cYbb6RZs2ZERUWRmJgI4I5v27ZtdO7cmbCwMPdr+vTp81cf+SnVrl2bW265hcsuu4wrr7ySN998k7S0tEq1KSIiIiIi3snnerqrW2igja3PXWbKeU/X5MmTKS0tpWHDhu5thmEQGBhIZmYmtWrVYsSIETz88MP85z//4dNPP6V9+/buHmun04nNZmPt2rXYbBXPGxER8XtMoaHHDZG+5ZZbOHLkCG+88QZNmjQhODiYPn36uIeFG4bxl8OqnU4n3bt3d/8o8EdxcXGnfG1CQgItW7akZcuWREREMHz4cLZu3UrdunUBuPLKK0lISOD999+nQYMGOJ1OOnToUCG+M2W1Wo97nd1ur/B8ypQpPPTQQ8ydO5eZM2fyxBNP8OOPP9K7d+8zPp+IiIiIiHgvJd2VZLFYzmiYd3UrLS1l2rRpvPbaa8cNwx4+fDjTp0/ngQce4KqrruLuu+9m7ty5fPrpp9x8883u47p27YrD4SA9Pd09/Px0LV26lLfffpsrrrgCgNTUVI4ePere36ZNG1JSUjh8+DDx8fEArF69ukIb3bp1Y+bMmdStW7dSS7r179+fDh068OKLL/Lmm29y7Ngxtm3bxrvvvut+X8uWLavwmnbt2vHxxx9TWFhIaGgoACtWrDjleeLi4jh06FCFHxTK1/T+o65du9K1a1fGjRtHnz59+PTTT5V0i4iIiIj4GA0v93HfffcdmZmZ3H777XTo0KHC7ZprrmHy5MkAhIeHM3ToUJ588km2bdvGjTfe6G6jVatWjBgxgpEjRzJr1iySkpJYvXo1L7/8Mt9///0pz9+iRQs+/vhjtm3bxsqVKxkxYoQ7eQW45JJLaN68OaNGjWLTpk388ssv7kJq5QnriBEjqFOnDkOHDmXp0qUkJSWxePFiHn74Yfbv339Gn8ff//533n33XQ4cOOCuhP7ee++xe/duFi5cyJgxYyocXz6v/fbbb2fr1q18//33vPrqq6c8xwUXXMCRI0d45ZVX2LNnD//973/54Ycf3PuTkpIYN24cy5cvZ9++fcyfP5+dO3dqXreIiIiIiA9S0u3jJk+ezMUXX0x0dPRx+4YPH86GDRvcS3iNGDGCjRs30q9fPxo3blzh2ClTpjBy5Ej+/ve/07p1a4YMGcLKlStJSEg45fk//PBDMjMz6dq1KzfffDMPPfSQe2g3gM1m46uvviIvL4+ePXtyxx138MQTTwAQEhICQFhYGEuWLKFx48YMGzaMtm3bctttt1FYWHjGPd+DBw+madOmvPjii1itVmbMmMHatWvp0KEDo0eP5t///neF4yMiIvj222/ZunUrXbt25Z///Ccvv/zyKc/Rtm1b3n77bf773//SuXNnVq1aVaHieVhYGNu3b2f48OG0atWKu+66iwceeIC77777jN6LiIiIiIh4P4txNpNWfVhOTg7R0dFkZ2cfl9AVFRWRlJREYmKiOyEUz/vll18477zz2L17t7u6tz/TvzsR37Vm1356tko44TVHzs6pruMiIiKedLrXHO+djCx+Y/bs2URERNCyZUt2797Nww8/zLnnnquEW0R83rRf95kdgoiIiFQxJd1iutzcXB599FFSU1OpU6cOF198Ma+99prZYYmIVKmsghK+33LI7DBERESkiinpFtONHDmSkSNHmh2GiEi1+nxNKo5S+18fKCIiIjWaCqmJiIhUM4fT4OMV+xhi+8XsUESkzO70XF6bv4M7PlrDNxsPorJHIuIp6ukWERGpZot2pJOaUcCEwJ+ZaHYwIn5uQ2oWj8/azNa0HPe2n7YdZsaqFJ4b2oEWdSNMjE5EfIF6ukVERKrZR8v30d2yk1akmB2KiF/bn1nAbVNXszUthwCrhYvb1uWO8xIJDrDy655jDHxzCT9uPWx2mCJSw6mnW0REpBrtPZLHkp1HmBg4H0rNjkbEfxWUlHLXtLVk5JfQvkEUH99+DrXDgwAY2acpT3y9hSU7j/D47M30SqxNdGigyRGLSE2lnm4REZFq9PGKfcSRyRW2VWaHIuK3DMPgH//bxNa0HGLDg3hvZA93wg3QODaM90d2p1mdcI7kFvPa/B0mRisiNZ2SbhERkWqSX1zK/9bs50bbQgJwQKNeZock4pemLd/HnE1pBFgtTLqpOw1jQo87JjjAxgtXdwBcP5ZtSM2q5ihFxFco6ZZTWrRoERaLhaysrFMe17RpU9544w2PnfeCCy7gkUce8Vh7Z2Lq1KnExMS4nz/zzDN06dLFlFhExLfMXn+AouIiRgYtdG3oPsrcgET8UGGJg/8s3AXA41e0pVdi7ZMe27d5HYZ1bYhhwOOzNlPqcFZXmCLiQ5R0+4lDhw7x4IMP0qxZM4KDg0lISODKK69kwYIFp3xd3759SUtLIzo6Gjg+IS23evVq7rrrrqoI/YSmTp2KxWJx3+Lj47nyyiv57bffPH6usWPH/uXnJCLyVwzD4OPl+7jcuopYIxMi4qHVQLPDEvE701fu42heCQm1Q7m5T5O/PP7xQW2JDg1ka1oOM1anVkOEIuJrlHT7geTkZLp3787ChQt55ZVX2Lx5M3PnzmXAgAHcf//9J32d3W4nKCiIevXqYbFYTnmOuLg4wsLCPB36KUVFRZGWlsbBgweZM2cO+fn5DBo0iJKSEo+eJyIigtjYWI+2KSL+Z2VSBjsO53JL4I+uDd1vhYCgU79IRDyqsMTBO4v3AnD/BS0ItP31V+E6EcE8eGELAD5dqRUHROTMKen2A/fddx8Wi4VVq1ZxzTXX0KpVK9q3b8+YMWNYsWKF+ziLxcI777zD0KFDCQ8P54UXXqgwvHzRokXceuutZGdnu3uYn3nmGeD44eVZWVncddddxMfHExISQocOHfjuu+8AOHbsGDfccAONGjUiLCyMjh078tlnn53x+7JYLNSrV4/69evTo0cPRo8ezb59+9ix4/diJ6+//jodO3YkPDychIQE7rvvPvLy8iq0M3XqVBo3bkxYWBhXX301x44dq7D/z8PLTzT0/aqrruKWW25xP3/77bdp2bIlISEhxMfHc80115zx+xMR3zLllyTaWZLpbtkB1gDofovZIYn4nU9XpXA0r5iGMaEM69botF83vFsjgmxWtqbl8NvB7CqMUER8kZYMqyzDAHtB9Z83MAz+ovcZICMjg7lz5/Liiy8SHh5+3P4/DxV/+umnGT9+PBMmTMBms5GUlOTe17dvX9544w2eeuopd2IbERFxXJtOp5OBAweSm5vLJ598QvPmzdm6dSs2mw2AoqIiunfvzv/93/8RFRXFnDlzuPnmm2nWrBnnnHPOmXwKbllZWXz66acABAb+vqSH1Wpl4sSJNG3alKSkJO677z4effRR3n77bQBWrlzJbbfdxksvvcSwYcOYO3cuTz/99FnFUG7NmjU89NBDfPzxx/Tt25eMjAyWLl1aqTZFpGbbdyyf+VsP8++Aua4NbYdAVH3IyTE3MBE/UmR38M7iPQDcP6AFQQGn3/dUKzyIi9vV5fvNh/hizX7aD4muqjBFxAcp6a4sewG81KD6z/v4QQg6Pon+s927d2MYBm3atDmtZm+88UZuu+029/M/Jt1BQUFER0e7e5hP5qeffmLVqlVs27aNVq1aAdCsWTP3/oYNGzJ27Fj38wcffJC5c+fyxRdfnFHSnZ2dTUREBIZhUFDg+uFjyJAhFd7rH3ukExMTef7557n33nvdSfebb77JZZddxmOPPQZAq1at+PXXX5k7d+5px/FnKSkphIeHM3jwYCIjI2nSpAldu3Y96/ZEpOab8ksydYwshtqWuzb0OfnUHhGpGp+vSeVIrquX+5rup9/LXe7aHgl8v/kQX284wONXtD2jpF1E/Jv+Wvg4wzAA/nJOdrkePXpU+pwbNmygUaNG7oT7zxwOBy+++CKdOnUiNjaWiIgI5s+fT0rKmc2TioyMZMOGDaxdu5Z33nmH5s2b884771Q45ueff+aSSy6hYcOGREZGMnLkSI4dO0Z+fj4A27Zto0+fPhVe8+fnZ+qSSy6hSZMmNGvWjJtvvpnp06e7fxQQEf+TXWjn8zWp3BTwI4HYXcuENar831pf9Mwzz1QokvlXP/KKnIkv1+4H4I5+iWeVMJ/fMo74qGAyC+ws2HbY0+GJiA9TT3dlBYa5ep3NOO9paNmyJRaLhW3btnHVVVf95fEnGoJ+pkJDj1/r8o9ee+01JkyYwBtvvOGeb/3II4+ccQE0q9VKixauwiZt2rTh0KFDXHfddSxZsgSAffv2ccUVV3DPPffw/PPPU7t2bZYtW8btt9+O3W4Hfv9R4kzP++fXlbcHrh8D1q1bx6JFi5g/fz5PPfUUzzzzDKtXrz5h5XcR8W0zVqXgKClkVOgCMIA+95kdkldr3749P/30k/t5+dQkkcpIOVbAxv3ZWC0wuNPZjVC0WS0M69aISYv28MXa/QzsWN/DUYqIr1JPd2VZLK5h3tV9O82e69q1a3PZZZfx3//+1927+0d/tf72nwUFBeFwOE55TKdOndi/fz87d+484f6lS5cydOhQbrrpJjp37kyzZs3YtWvXGcVxIqNHj2bjxo3Mnj0bcM2tLi0t5bXXXqN37960atWKgwcr/kDSrl27CsXkgOOe/1lcXBxpaWnu5w6Hgy1btlQ4JiAggIsvvphXXnmFTZs2kZyczMKFCyvz9kSkBrI7nHz0azJX2X4hxsiB6MbQ5kqzw/JqAQEB1KtXz32Li4szOyTxAd9tdl3/+zSPJS4y+KzbubZsWPqiHemk5xR5JDYR8X1Kuv3A22+/jcPhoFevXnz55Zfs2rWLbdu2MXHixDMeSt20aVPy8vJYsGABR48ePeGw6f79+3P++eczfPhwfvzxR5KSkvjhhx/c86RbtGjBjz/+yK+//sq2bdu4++67OXToUKXfZ1RUFHfccQdPP/00hmHQvHlzSktL+c9//sPevXv5+OOPjxt+/tBDDzF37lxeeeUVdu7cyVtvvfWX87kvvPBC5syZw5w5c9i+fTv33XdfhR8vvvvuOyZOnMiGDRvYt28f06ZNw+l00rp160q/RxGpWX7YcoiD2YXcFVj2d+Wcu8CmQWansmvXLho0aEBiYiLXX389e/fuNTsk8QHfbXT9WH62vdzlmsVF0L1JLZwGfL3BhJGOIlIjKen2A4mJiaxbt44BAwbw97//nQ4dOnDJJZewYMECJk2adEZt9e3bl3vuuYfrrruOuLg4XnnllRMe9+WXX9KzZ09uuOEG2rVrx6OPPuruIX/yySfp1q0bl112GRdccAH16tU7raHvp+Phhx9m27ZtfPHFF3Tp0oXXX3+dl19+mQ4dOjB9+nTGjx9f4fjevXvzwQcf8J///IcuXbowf/58nnjiiVOe47bbbmPUqFGMHDmS/v37k5iYyIABA9z7Y2JimDVrFhdeeCFt27blnXfe4bPPPqN9+/YeeY8iUjMYhsHkpXvpZ91Mc1IhKAK6jTQ7LK92zjnnMG3aNObNm8f777/PoUOH6Nu373FLOf5RcXExOTk5FW4if7TnSB5b03IIsFq4vH3lawRc2ck1rHzRzvRKtyUi/sFinM2kVh+Wk5NDdHQ02dnZREVFVdhXVFREUlISiYmJhISEmBSh+Bv9uxOpmdYkZ3DNO8uZFvQy51s3wjn3wsB/VTjmVNccgfz8fJo3b86jjz7KmDFjTnjMM888w7PPPnvcdn2mUu7Nn3Yx4aedXNA6jqm39qp0e3uO5HHRa4sJslnZ8PQlhAVp9IqIvzrd67h6ukVERKrAB0uTaGHZ70q4scA5d5sdUo0THh5Ox44dT1n3Y9y4cWRnZ7tvqamp1Rih1ATfbXINA6/s0PJyzeqE0zAmlBKHk5VJGR5pU0R8m5JuERERD0s5VsD8rYe4zfaDa0ObQVA70dygaqDi4mK2bdtG/fonrxIdHBxMVFRUhZtIuR2HctmVnkeQzcql7eM90qbFYuH8VnUAWLLziEfaFBHfpqRbRETEw95fupdaRjbXBP7i2tDnfnMDqiHGjh3L4sWLSUpKYuXKlVxzzTXk5OQwatQos0OTGmrOZlcBtfNbxREVEuixds9v6aqqv3TXUY+1KSK+S5NQREREPOhoXjGfr0nl/oB5BBkl0KAbND6zlSL81f79+7nhhhs4evQocXFx9O7dmxUrVtCkSROzQ5MaanFZT/RlHurlLte3eR2sFtidnsfBrEIaxIR6tH0R8S1KukVERDxo6i/J2EoLuDX0JzCA8x4Bi8XssGqEGTNmmB2C+JDsAjub92cBcF7LOh5tOzoskM4JMaxPyWLpriNc17OxR9sXEd+i4eVnQQXfpTrp35tIzZFXXMq05cncYFtIpJEHtZtDm8FmhyXil5bvPYrTgOZx4dSP9nxPdPkQ8yUaYi4if0FJ9xkIDHTNBSooKDA5EvEn5f/eyv/9iYj3+mxlCoVFRdwdVFZA7dyHwGozNygRP1U+37pfWXLsaeXF1H7ZfRSHUz+Qi8jJaXj5GbDZbMTExJCeng5AWFgYFg0ZlCpiGAYFBQWkp6cTExODzaYv7iLerLjUwQfL9nKl9VfqGscgIh46XW92WCJ+65fdrqT73BaeHVpernOjGCJDAsgqsLP5QDZdEmKq5DwiUvMp6T5D9erVA3An3iJVLSYmxv3vTkS819frD5KeU8j9oXNcc7l73wuBIWaHJeKXUjMKSD5WgM1qoXez2lVyjgCblXOb12Hub4dYtuuIkm4ROSkl3WfIYrFQv3596tati91uNzsc8XGBgYHq4RapAZxOg3eW7GGAdQPNjVQIjoIet5kdlojfKu/l7pIQQ6QHlwr7s3Oa1Wbub4dYl5JVZecQkZpPSfdZstlsSoZERASA+VsPs/dIPq+GfOfa0ONWCIk2NygRP7a0LOk+r4qGlpfr2rgWAOtTMjEMQ9MOReSEVEhNRESkEgzD4J3Fe+hm2Uk3toMtCM651+ywRPyW02nwa3nS7eGlwv6sXf0oggKsZBbY2XdMhXZF5MR8Kul+5plnsFgsFW6aCysiIlXp1z3H2JCaxf2B37o2dLoOouqbG5SIH9ualkNmgZ2I4IAqn2cdFGClQ4MoANanZlbpuUSk5vKppBugffv2pKWluW+bN282OyQREfFhby7YRVvLPi6yrgUscO7DZock4tfKlwrr3aw2gbaq/6rbJaF8iHlWlZ9LRGomn5vTHRAQoN5tERGpFiv2HmNVUgaTgr5ybegwDOq0NDUmEX+3KukYAH2aV+3Q8nJdG8fAL7AhNataziciNY/P9XTv2rWLBg0akJiYyPXXX8/evXvNDklERHzUfxbuooVlP5dZV7k29BtrbkAifs7hNFizzzXMu1fTqlkq7M+6No4BYOvBHIrsjmo5p4jULD6VdJ9zzjlMmzaNefPm8f7773Po0CH69u3LsWPHTvqa4uJicnJyKtxERET+ytp9Gfyy+xgPBX6NFQPaXgnx7cwOS8Sv7TycS25RKWFBNtrWj6yWczaMCSUuMphSp8GWA9nVck4RqVl8KukeOHAgw4cPp2PHjlx88cXMmTMHgI8++uikrxk/fjzR0dHuW0JCQnWFKyIiNdjEBbtJtKQx2LrcteH8f5gbkIiwJjkDgG6NaxFQDfO5ASwWC13LCrZpXreInIhPJd1/Fh4eTseOHdm1a9dJjxk3bhzZ2dnuW2pqajVGKCIiNdGG1CwW7zzCAwFfY8UJrS6H+p3NDqva2O12UlNT2bFjBxkZGWaHI+K2Otk1tLxnNQ0tL+der1sVzEXkBHw66S4uLmbbtm3Ur3/ypVuCg4OJioqqcBMRETmV/yzYRYLlMFfZfnFtOP9RcwOqBnl5ebz77rtccMEFREdH07RpU9q1a0dcXBxNmjThzjvvZPXq1WaHKX6uvKe7Z9Na1Xre8nnd6ukWkRPxqaR77NixLF68mKSkJFauXMk111xDTk4Oo0aNMjs0ERHxEVsOZLNgezr3BXyDDQc0vwgadTc7rCo1YcIEmjZtyvvvv8+FF17IrFmz2LBhAzt27GD58uU8/fTTlJaWcskll3D55ZefcoSZSFU5kFXIwewibFYLXcqS4OrSqVE0VgukZReRll1YrecWEe/nU0uG7d+/nxtuuIGjR48SFxdH7969WbFiBU2aNDE7NBER8RETF+yiAUe51rbUtaG/7/dy//rrr/z888907NjxhPt79erFbbfdxjvvvMPkyZNZvHgxLVtq6TSpXuW93B0aRBEWVL1fccOCAmhTL4qtaTlsSMmifsfQaj2/iHg3n0q6Z8yYYXYIIiLiw9anZDJ/62FeCPyGAEqhaT9o3NvssKrcF198cVrHBQcHc99991VxNCIntirJlXT3qOb53OW6NI5xJd37sxjY8eRTG0XE//jU8HIREZGqYhgGL8/dTiPLEa4PWOTa2P//TI1JRH63xqQiauXaN3DVBdqWlmvK+UXEeynpFhEROQ2Ldx5hxd4MHg6cTYBRCon9IbGf2WFVuxdeeMHsEESOk11gZ8dhV7Lbo5qLqJVrV9+VdG89mGPK+UXEe/nU8HIREZGq4HQavDx3B00taQy3ls3lvvAJc4OqBo8+WnG+umEYfPDBB+TkuJKKV155xYywRI6zNsU1tLxZnXDqRASbEkPrepFYLHA0r5j03CLqRoaYEoeIeB8l3SIiIn/h200H2ZaWw1vBs7HigJaXQUIvs8Oqcp9//jm9e/fmiiuuwDAMAAICAmjfvr3JkYlUVL4+t1m93OAqppYYG87eo/lsS8tV0i0ibhpeLiIicgolpU5em7+Tlpb9DLKUrcs94HFzg6om27Zto0WLFnz77bece+65jBo1isjISEaNGqXlOMWrrE9xJd3dm5iXdAO0dc/r1hBzEfmderpFREROYcbqFFIyCpgcOguLYUDbIdCgi9lhVYvQ0FBeeOEFdu/ezdixY2ndujUOh8PssEQqcDgNNu3PBqBrY3OT7nb1o5izKU1Jt4hUoKRbRETkJPKLS5m4YBftLUlcZKwALH7Ty/1HLVq04KuvvuKbb77BZrOZHY5IBTsP51JQ4iAiOIDmcRGmxtK2fiSgYmoiUpGSbhERkZP4cFkSR/NKmBg+GxxAx2uhbluzwzLNkCFDGDJkiNlhiFSwPiULgM4J0disFlNjaVc/GoC9R/MpsjsICdSPVCKipFtEROSEMvJLeHfJXrpZdtLXsQYsNrjgMbPDMl1WVhaTJ0/m0KFDJCYm0qVLFzp37kx4eLjZoYmfKp/P3TXB3KHlAPFRwdQKCySzwM7Ow7l0ahRjdkgi4gWUdIuIiJzAf3/eTV6xnRciZkIp0HUExDY3OyzTDRs2jM2bN9OzZ09++OEHdu7cidPppFmzZnTp0oXPP//c7BDFz2xIzQKgS0KMqXEAWCwW2jWI4pfdx9iWlqOkW0QAJd0iIiLH2Z9ZwMfL93GZdTXtSrdBYBhc4H9zuU9k5cqVLF68mB49egBQXFzMb7/9xsaNG9m4caPJ0Ym/ySmys/tIHgBdGseYG0yZtvXKk+5cs0MRES+hpFtERORPJvy4C6ejhGfCv3DN5e7zAETVNzssr9ChQwes1t9XHA0ODqZbt25069bNxKjEX21KzcYwIKF2KHUigs0OB4C29V3LhqmYmoiU0zrdIiIif7DjUC6z1u/nBttC6jsOQHgcnPuQ2WF5jZdffpknn3ySoqIis0MR8ar53OXa/WGtbsMwTI5GRLyBkm4REZE/+Pe8HYQbBTwa8pVrwwWPQXCkqTF5k8TERHJzc2nbti2PP/44X3/9NSkpKWaHJX5qvRfN5y7XPC6CQJuF3OJS9mcWmh2OiHgBJd0iIiJl1iRn8NO2w9wTOIdIRxbEtoBuo8wOy6sMHz6c1NRUBgwYwKpVq7j99ttJTEwkNjaWCy+80OzwxI8YhuEuotbVS+ZzAwQFWGlRt2y97jQNMRcRzekWEREBXF/gX567nXgyuCvgezCAi58BW6DZoXmVrVu3smLFCjp16uTelpKSwvr169mwYYN5gYnfSckoICO/hCCb1T2k21u0qx/FtrQctqXlcFn7emaHIyImU9ItIiIC/LwjndXJmfw76EuCjGJI6A1tBpsdltfp2bMneXl5FbY1btyYxo0bM3ToUJOiEn9U3svdrkEUwQE2c4P5k9b1IgDYlZ73F0eKiD/Q8HIREfF7DqfBK3N30MqSynDrYtfGS58Hi8XcwLzQI488wjPPPENmZqbZoYifW5+SBXjXfO5yLcuGl+8+rKRbRNTTLSIiwjcbD7D9UC7TQmZgxQnthkJCL7PD8krDhw8HoGXLlgwZMoTevXvTtWtXOnXqRHCwdyzZJP5h4/4swDuT7hZ1XT3de4/mUepwEmBTP5eIP1PSLSIifi270M6Lc7bTx/ob57MerAFw0dNmh+W1kpKS2LBhAxs3bmTDhg28/PLLJCcnY7PZaNOmDZs2bTI7RPEDdofTvQ52p0bRJkdzvIYxoYQG2ii0O9iXUUDzuAizQxIREynpFhERv/ba/B0czSvik7CZ4AR63Aaxzc0Oy2s1adKEJk2aVJi/nZuby4YNG5RwS7XZeTiX4lInkSEBNI0NNzuc41itFlrUjWDzgWx2Hc5T0i3i5zTWRURE/NbG1Cw+XrGPwdYVtHHuhsBwOP9Rs8PySo8//jirVq064b7IyEj69evH/fffX81Rib/atD8bcPVyW63eWXuhZdkQ893puSZHIiJmU9ItIiJ+yeE0eOKrLUQYBbwYOt218dyHISLO3MC8VFpaGoMHD6Z+/frcddddzJkzh+LiYrPDEj+1qWw+d8eGMabGcSot413F1FTBXESUdIuIiF/6ZMU+Nh/I5p8hnxPtyIDYFnDeI2aH5bWmTJnC4cOH+fzzz4mJieHvf/87derUYdiwYUydOpWjR4+aHaL4kfKe7s5eOJ+7XHlP9y5VMBfxe0q6RUTE7xzMKuTf83bQxbKb6/jRtXHwBAhQ9e1TsVgs9OvXj1deeYXt27ezatUqevfuzfvvv0/Dhg05//zzefXVVzlw4IDZoYoPK7I72HHINWS7kxdWLi/XMt6VdO85kofDaZgcjYiYSUm3iIj4FcMweHz2ZgqLi5kQPhULBnS+ARLPNzu0Gqdt27Y8+uij/PLLL+zfv59Ro0axdOlSPvvsM4+0P378eCwWC4888ohH2hPfsDUth1KnQZ2IIBpEh5gdzkk1qhVGcICV4lIn+zMLzA5HREzkFdXLk5OTWbp0KcnJyRQUFBAXF0fXrl3p06cPISHe+8dURERqnlnrDrBoxxHuCZxHYuleCK0Fl75gdlg1ygMPPMBzzz1H7dq13dvi4uK4/fbbuf322z1yjtWrV/Pee+/RqVMnj7QnvmNTahYAHRtGY7F4ZxE1AJvVQvO4CLam5bDrcB5NvLDKuohUD1N7uj/99FN69+5Ns2bN+Mc//sFXX33F0qVL+eCDD7j88suJj4/nvvvuY9++fWaGKSIiPiI9p4hnv/2NBMth/h74P9fGS56H8DrmBlYD7N+/3/34008/JS/PNU+1Y8eOpKamevRceXl5jBgxgvfff59atWp5tG2p+TYdKK9cHmNuIKehfIi5iqmJ+DfTku5u3brx+uuvc9NNN5GcnMyhQ4dYu3Yty5YtY+vWreTk5PD111/jdDrp0aMHX3zxhVmhioiIDzAMg39+tYXcohImhU8m0FkETc6DLiPMDq1GaNOmDU2aNOHGG2+kqKjInWgnJydjt9s9eq7777+fQYMGcfHFF3u0XfEN7iJqCd5bRK2cu5ialg0T8WumDS9//vnnGTRo0En3BwcHc8EFF3DBBRfwwgsvkJSUVI3RiYiIr/l2Uxo/bj3MbYE/0qF0i2tN7qFvgVXlTU5HdnY2a9euZenSpcyaNYsrrriC+Ph4iouLmTdvHldffTX16tWr9HlmzJjBunXrWL169WkdX1xcXGHpspycnErHIN4rr7iUPUfKRll48XJh5VrUdS0btls93SJ+zbRvGqdKuP+sTp069OzZswqjERERX5aWXciTX22hseUw4wJnujZe8izUTjQ3sBrEbrfTq1cv/v73vxMaGsr69euZMmUKNpuNDz/8kObNm9O6detKnSM1NZWHH36YTz755LRruowfP57o6Gj3LSEhoVIxiHfbvD8bw4AG0SHERXr/agPlw8t3p+fhVAVzEb/lFYXUyqWnp5Oeno7T6aywXUVURETkbDmdBmO/2EhOYTHTIyYTWFoETftBD88U/PIXUVFRdO3alXPPPZeSkhIKCgo499xzCQgIYObMmTRq1IhVq1ZV6hxr164lPT2d7t27u7c5HA6WLFnCW2+9RXFxMTabrcJrxo0bx5gxY9zPc3JylHj7sM0HsoCaMZ8boEntMAJtFgpKHBzMLqRRrTCzQxIRE3hF0r127VpGjRrFtm3bMAzXr4AWiwXDMLBYLDgcDpMjFBGRmmrysiR+2X2Mu4I0rLwyDh48yPLly/n1118pLS2lR48e9OzZk5KSEtatW0dCQgLnnXdepc5x0UUXsXnz5grbbr31Vtq0acP//d//HZdwg2s6WnCw9/d4imdsLJvP3akGzOcGCLBZaVYngh2Hc9mVnqekW8RPecU3jltvvZVWrVrx66+/snfvXpKSkirci4iInI2tB3P497wdtLKk8n8BZWtHX/Is1Gpqalw1UZ06dbjyyisZP348YWFhrF69mgcffBCLxcLYsWOJioqif//+lTpHZGQkHTp0qHALDw8nNjaWDh06eOidSE22aX8WAJ1qwHzuci3KK5gfVjE1EX/lFT3dSUlJzJo1ixYtWpgdioiI+Igiu4NHZq7H4ijiw8h3sNlLoMUl0PMOs0PzCdHR0fztb3/j9ttvZ+HChYSFhbF48WKzwxIflpFfQmpGIQAdG9WMnm6AFnGupHvvkXyTIxERs3hF0n3RRRexceNGJd0iIuIxT3/9GzsP5/Fy2Ewa2ZMgvC5cNQksFrNDq/E2bdpEw4YNAWjSpAmBgYHUq1eP6667zuPnWrRokcfblJppc9n63Il1wokODTQ5mtPXLC4cUNIt4s+8Iun+4IMPGDVqFFu2bKFDhw4EBlb8QzpkyBCTIhMRkZrof2v3M3NNKhfZ1nGd8wfXxqsmQUScuYH5iD8WKtuyZYuJkYg/2ZSaBUCnGtTLDdCsTllP91EtGybir7wi6f71119ZtmwZP/zww3H7VEhNRETOxPZDOTzx1WbqkslboR9AKdD7fmh5sdmhiUgllBdR69iwZiXdiWU93UfzSsgutNeoXnoR8QyvKKT20EMPcfPNN5OWlobT6axwU8ItIiKnK6+4lPumr6PUXsIn0W8TWpoF9TrCxU+bHVqNlpKSckbHHzhwoIoiEX9WvlxY54QYU+M4UxHBAcRHuSrs7z2i3m4Rf+QVSfexY8cYPXo08fHxZociIiI1lGEYPPq/jew9ks8L4Z/Tqvg3CI6Caz+CAC0pVRk9e/bkzjvvPOU63NnZ2bz//vt06NCBWbNmVWN04g8O5xRxOKcYqwXaN4gyO5wzVj7EPOmo5nWL+COvSLqHDRvGzz//7PF2x48fj8Vi4ZFHHvF42yIi4l0mLtjN95sPMSRgBdc7vnNtvPodiG1ubmA+YNu2bURHR3P55ZcTHx/PoEGDuPPOO3nwwQe56aab6NatG3Xr1mXq1Kn8+9//5sEHHzQ7ZPExG8vmc7eKjyQsyCtmR56RRBVTE/FrXvFXq1WrVowbN45ly5bRsWPH4wqpPfTQQ2fc5urVq3nvvffo1KmTp8IUEREv9cPmNCb8tJPmlgO8Fvw+OIDzRkObQWaH5hNq167Nq6++ygsvvMD333/P0qVLSU5OprCwkDp16jBixAguu+wyraUtVWZTDZ3PXa5ZnbKkW8XURPySVyTdH3zwARERESxevPi4NT4tFssZJ915eXmMGDGC999/nxdeeMGToYqIiJf57WA2Yz7fSDR5zIz6D4HFhZB4Pgx4wuzQfE5ISAjDhg1j2LBhZocifmZT2XJhnWrYfO5yzbVWt4hf84qkOykpyaPt3X///QwaNIiLL774L5Pu4uJiiouL3c9zcnI8GouIiFSd9Jwi7vxoDXZ7MV9Gv02d4hSIToDhH4LNKy5xIlJJhmGwaX8WAJ1r2HJh5crX6k46mo/TaWC1WkyOSESqk1fM6fakGTNmsG7dOsaPH39ax48fP57o6Gj37Y9rj4qIiPfKKbIzaspqDmYX8mbEx7Qr3gBBEXDjTK3HLeJDUjMKySqwE2Sz0rpepNnhnJWGMaEE2iwUlzo5kFVodjgiUs1MS7r/9a9/UVBQcFrHrly5kjlz5vzlcampqTz88MN88sknhISEnFbb48aNIzs7231LTU09rdeJiIh5iksd3D1tLdvScng4bD6DSn8EixWu+RDi25sdnoh40MayXu429SMJDrCZG8xZCrBZaRL7e2+3iPgX05LurVu30rhxY+69915++OEHjhw54t5XWlrKpk2bePvtt+nbty/XX389UVF/vTzE2rVrSU9Pp3v37gQEBBAQEMDixYuZOHEiAQEBJ1zzOzg4mKioqAo3ERHxXk6nwZjPN7J87zGGBa/mEec0145LX4RWl5kbnIh43Oby+dw1dGh5OXcxNa3VLeJ3TEu6p02bxsKFC3E6nYwYMYJ69eoRFBREZGQkwcHBdO3alQ8//JBbbrmF7du3069fv79s86KLLmLz5s1s2LDBfevRowcjRoxgw4YN2Gw189dRERFxMQyDZ779jTmb0ugfsIVXbW9hwYAet0Pve80Ozy/ccsstLFmyxOwwxI+ULxfWqVGMqXFUVrPyYmrq6RbxO6ZWmenUqRPvvvsu77zzDps2baqw/EiXLl2oU6fOGbUXGRl53HIl4eHhxMbGahkTEZEazjAMnv9uG9OW76OTdS+TQ97AWmqHdkPhin+DRYWJqkNubi6XXnopCQkJ3HrrrYwaNYqGDRuaHZb4KIfTYEtZT3fnGp90a61uEX/lFaVdLRYLnTt3pnPnzmaHIiIiXsgwDP71w3Y+/CWJ5pYDfB7xGgElBZDYH4a9D1aNZKouX375JceOHeOTTz5h6tSpPP3001x88cXcfvvtDB06lMDAQLNDFB+y90ge+SUOQgNtNC9LWmsqDS8X8V8+V738zxYtWsQbb7xhdhgiInKWDMPgtfk7eXfJXppZDvJt1CuElGRCg65w/XQICDY7RL8TGxvLww8/zPr161m1ahUtWrTg5ptvpkGDBowePZpdu3aZHaL4iE37Xb3cHRpGEWCr2V9by4eXH8wuorDk+DpDIuK7avZfLxER8WmGYfDS99t46+fdNLMc5LvIlwkrPgJ128OI/0FwzVw+yFekpaUxf/585s+fj81m44orruC3336jXbt2TJgwwezwxAeUr89d0+dzA9QODyImzDUSRBXMRfyLkm4REfFKDqfBY19u5v2lSWUJ978IKylLuEd9A+FnVvdDPMNut/Pll18yePBgmjRpwhdffMHo0aNJS0vjo48+Yv78+Xz88cc899xzZocqPmDjft+oXF7OPcT8qIaYi/gTr5jTLSIi8kclpU5Gz9zAnM1ptLOm8GXkq4QWH1XC7QXq16+P0+nkhhtuYNWqVXTp0uW4Yy677DJiYmKqPTbxLSWlTram5QC+0dMNkFgngnUpWSqmJuJnvDLpzsnJYeHChbRu3Zq2bduaHY6IiFSj3CI7901fx9JdR+kTsINpoa8TWJyrhNtLTJgwgWuvvZaQkJCTHlOrVi2SkpKqMSrxRTsP51JS6iQqJICmsWFmh+MR5RXMkzW8XMSveMXw8r/97W+89dZbABQWFtKjRw/+9re/0alTJ7788kuToxMRkepyMKuQa99ZztJdRxkYuIFPgv9FoD0XEnrDrXOUcHuB/v37Exx8fPE6wzBISUkxISLxVZvcQ8tjsPjIkoCJ7uHlSrpF/IlXJN1LliyhX79+AMyePRvDMMjKymLixIm88MILJkcnIiLVYcuBbK767y9sP5TLLWG/8HbAa9gcxdDqcrh5NoTWMjtEARITEzly5Mhx2zMyMkhMTDQhIvFVvxdR84353ABNY8t6uo8p6RbxJ16RdGdnZ1O7dm0A5s6dy/DhwwkLC2PQoEFadkRExA/8uPUw176znCO5hYyPns0zzv9iMRzQ+Ua47hMI8o2hpb7AMIwT9jrm5eWdcsi5yJna5GNF1ACa1nH9LcsqsJOZX2JyNCJSXbxiTndCQgLLly+ndu3azJ07lxkzZgCQmZmpC7iIiA8zDIO3F+3h1fk7CDGKmFnrQ3oVLnPtPG80XPgUWL3i92G/N2bMGAAsFgtPPvkkYWG//xDicDhYuXLlCYuqiZyNIruDHYdzAd8pogYQFhRAfFQwh3OKSTqWT63wILNDEpFq4BVJ9yOPPMKIESOIiIigSZMmXHDBBYBr2HnHjh3NDU5ERKpEXnEp//hiIz9sOUQ8GcyqNZGGhTvBFgRXToQuN5gdovzB+vXrAdcPJZs3byYo6PdkISgoiM6dOzN27FizwhMf89vBHBxOgzoRwdSP9q0OmMQ64RzOKSb5aD7dGmvajIg/8Iqk+7777qNXr16kpqZyySWXYC3r1WjWrJnmdIuI+KC9R/K4++O17ErPo3vAHj4O/w9hhekQFgvXTYcmfcwOUf7k559/BuDWW2/lzTffJCoqyuSIxJeVz+fu3CjaZ4qolUusE86KvRmqYC7iR7wi6Qbo0aMHPXr0qLBt0KBBJkUjIiJV5aethxk9cwO5xXbuCV/Mo8YUrMV2iGsLN86AWk3NDlFOYcqUKWaHIH6gfD53Rx+az12uvJha0rECkyMRkeriFUn3bbfddsr9H374YTVFIiIiVaXU4eSNn3bx1s+7CaGYqTGfcEHRAtfOtlfC0LchRL2n3mjMmDE8//zzhIeHu+d2n8zrr79eTVGJL9uYmgVAl4QYU+OoCuXLhiUdzTM5EhGpLl6RdGdmZlZ4brfb2bJlC1lZWVx44YUmRSUiIp6Sll3Iw59tYFVyBo0th/k8+r/UK9oNFitc/Az0fQh8bAipL1m/fj12u939+GR8bRiwmCO7wO5ex7qzDxVRK1eedCcfLTjpagAi4lu8IumePXv2cducTif33XcfzZo1MyEiERHxlJ+3pzPm8w1kFtgZFLyRN4LeJrAoF8Lj4JopkNjP7BDlL5TP5/7zY5GqsLFsPnfT2DCfrO6dUDsMi8VVTPJoXglxkcFmhyQiVcxr12GxWq2MHj2aCRMmmB2KiIicBbvDyfjvt3Hr1NXkFRTyWsz/+K/lZQLtuZBwDty9RAl3DVRYWEhBwe9zUfft28cbb7zB/PnzTYxKfMmGsqHlnX1waDlASKCNhjGhACSpmJqIX/DapBtgz549lJaWmh2GiIicof2ZBfzt3eW8u2QvCZbDLKo9nuFFs1w7z7kHRn0HUQ3MDVLOytChQ5k2bRoAWVlZ9OrVi9dee42hQ4cyadIkk6MTX+DL87nL/T7EXEm3iD/wiuHlfy7KYhgGaWlpzJkzh1GjRpkUlYiInI05m9IYN2sTOUWlXBuyipcCPiCwIA9CYuCqt6GNVqaoydatW+cehfa///2PevXqsX79er788kueeuop7r33XpMjlJrMMAyf7+kGVwXzpbuOknRMSbeIP/CKpPvPRVmsVitxcXG89tprf1nZXEREvENukZ2nv/mNWesOEEIx78XM4NKieVAKNO4Dw96HmASzw5RKKigoIDIyEoD58+czbNgwrFYrvXv3Zt++fSZHJzXd/sxCjuWXEGiz0K6+765m0FQ93SJ+xSuSbhVlERGp2dYkZ/DIzA3szyykrTWFT6LfJbYwCbDA+WOh/2Ng84pLjlRSixYt+Oqrr7j66quZN28eo0ePBiA9PZ2oKN9NkqR6lPdyt60fRUigzdxgqlAz97JhSrpF/IG+AYmIyFmzO5xMXLCL//68G6dh8GDkYkY7P8JaWAwR9WDYe9Csv9lhigc99dRT3HjjjYwePZqLLrqIPn36AK5e765du5ocndR0/jCfG/7Q030sH6fTwGrVsmEivsy0pLtbt24sWLCAWrVq0bVr11OuUbhu3bpqjExERE7H3iN5jJ65gY37s4kji2l1PqJt3krXzhaXwNXvQHgdc4MUj7vmmms477zzSEtLo3Pnzu7tF110EVdffbWJkYkvcM/n9sH1uf+oUa1QbFYLRXYnh3OLqB8danZIIlKFTEu6hw4dSnCwa13Cq666yqwwRETkDDmdBp+s3Mf477dTaHcwNGQdrwRNJjgvE2zBcPEzrgrlVq9eIEMqoV69etSrV6/Ctl69epkUjfgKu8PJloPZAHRpHGNuMFUs0Galce0wko7mk3QkX0m3iI8zLel++umnT/hYRES8V8qxAv7xv42sTMognEKm1P6cAQXzoASI7+gaTh7fzuwwpYotWLCABQsWkJ6ejtPprLDvww8/NCkqqel2HMqlyO4kMiSAxNhws8Opck1jy5LuY/n0baFRQSK+THO6RUTkLzmdBh+v2Me/fnD1bvcJ3M17Ee8RWbAfsMC5D8GAf0JAsNmhShV79tlnee655+jRowf169c/5fQwkTOxcX8W4Bpa7g9znJvWCYcdR1TBXMQPmJZ016pV67Qv1BkZGVUcjYiInMwfe7eDsPN6nR+4Ov9zLIVOiE5wzd1uep7ZYUo1eeedd5g6dSo333yz2aGIj9mQkgX4fhG1cr9XMC8wORIRqWqmJd1vvPGG+/GxY8d44YUXuOyyy9xVUJcvX868efN48sknTYpQRMS/OZwGH/2azL/n7aDQ7qBnUDLvRU2mVt4e1wGdrocrXoGQaHMDlWpVUlJC3759zQ5DfJC7p9tPku6m7qQ7z+RIRKSqmZZ0jxo1yv14+PDhPPfcczzwwAPubQ899BBvvfUWP/30k3sNUBERqR6b92fz+OzNbD6QTTAlvFnne4bk/w9LnhPC6sCg16D9VWaHKSa44447+PTTT/WjuHhUbpGdXemu5LNzgn/8kNe0bN56akYhDqeBzQ+G1Iv4K6+Y0z1v3jxefvnl47ZfdtllPPbYYyZEJCLin/KKS3l9/k6m/pqE04DzQvby3/DJROcluQ7oMBwG/hvCY80NVExTVFTEe++9x08//USnTp0IDAyssP/11183KTKpyTYfyMYwoGFMKHUjQ8wOp1o0iAklyGalxOHkYFYhCbXDzA5JRKqIV6znEhsby+zZs4/b/tVXXxEbqy92IiLVYf5vh7jk9cV8+EsSQUYxk+t/xceWp4jOT4LwunDddLjmQyXcfm7Tpk106dIFq9XKli1bWL9+vfu2YcOGSrU9adIkOnXqRFRUFFFRUfTp04cffvjBM4GLVytfn9tf5nMD2KwWmsS6Eu29KqYm4tO8oqf72Wef5fbbb2fRokXuOd0rVqxg7ty5fPDBByZHJyLi2/YeyeOFOdtYuD0dgL9F/8azAR8RmrnfdUCn6+Dyf0FYbROjFG/x888/V1nbjRo14l//+hctWrQA4KOPPmLo0KGsX7+e9u3bV9l5xXwby5JufxlaXq5pnXB2peeRfDSf/q3izA5HRKqIVyTdt9xyC23btmXixInMmjULwzBo164dv/zyC+ecc47Z4YmI+KScIjv/WbCLqb8mY3cYNLRm8mG9/9E642coBqIaueZut77c7FDFyyxdupR3332XvXv38sUXX9CwYUM+/vhjEhMTOe+8s69kf+WVV1Z4/uKLLzJp0iRWrFihpNvH/d7TXcvcQKpZoruYmnq6RXyZVyTdAOeccw7Tp083OwwREZ/ndBp8sTaVf8/bwdG8Eqw4ebH+r9yQNw1rRh5YbNDnPuj/GARHmB2ueJkvv/ySm2++mREjRrBu3TqKi4sByM3N5aWXXuL777/3yHkcDgdffPEF+fn57lFw4psOZRdxOKcYm9VCh4ZRZodTrZR0i/gHr0m6yxUWFmK32ytsi4ryrz/AIiJVwTAMFu88witzd7A1LQeAobWSeSHkEyIzt7oOatQTBk+Aeh1NjFS82QsvvMA777zDyJEjmTFjhnt73759ee655yrd/ubNm+nTpw9FRUVEREQwe/Zs2rVrd9Lji4uL3Yk/QE5OTqVjkOq1ITUTgFbxkYQFed1X0ypVXsE8+ZiSbhFf5hV/2QoKCnj00Uf5/PPPOXbs2HH7HQ6HCVGJiPiOtfsyeWXudlYmZQDQOjiDSfFf0yz9RyjEtdb2xc9At1vA6hU1NsVL7dixg/PPP/+47VFRUWRlZVW6/datW7NhwwaysrL48ssvGTVqFIsXLz5p4j1+/HieffbZSp9XzLMhNRuALn42nxt+7+nen1mI3eEk0Ka/vyK+yCv+z/7HP/7BwoULefvttwkODuaDDz7g2WefpUGDBkybNs3s8EREaqxtaTncOW0Nwyf9ysqkDGICSvgkcT5zA/7uSrgtVuh+Kzy4DnrcpoRb/lL9+vXZvXv3cduXLVtGs2bNKt1+UFAQLVq0oEePHowfP57OnTvz5ptvnvT4cePGkZ2d7b6lpqZWOgapXhv9sHJ5ufioYEIDbTicBqkZBWaHIyJVxCt6ur/99lumTZvGBRdcwG233Ua/fv1o0aIFTZo0Yfr06YwYMcLsEEVEapS1+zJ5++fdLCirSB5sKeXlpuu5Mns6tjTXNhLPh8vGQ70OJkYqNc3dd9/Nww8/zIcffojFYuHgwYMsX76csWPH8tRTT3n8fIZhVBg+/mfBwcEEBwd7/LxSPRxOg037swDo7IdJt8VioWmdcLal5ZB0NJ9mcaqjIeKLvCLpzsjIIDExEXANT8vIcA1/PO+887j33nvNDE1EpMYwDIOlu44yadEelu91TdWxWZw803gz1xd8SmBaWQ9graZw6YvQZhBYLOYFLDXSo48+SnZ2NgMGDKCoqIjzzz+f4OBgxo4dywMPPFCpth9//HEGDhxIQkICubm5zJgxg0WLFjF37lwPRS/eZs+RPPJLHIQF2WhZN9LscEyRWCfMnXSLiG/yiqS7WbNmJCcn06RJE9q1a8fnn39Or169+Pbbb4mJiTE7PBERr5ZbZGfWugNMW57MniOuL23BVifPNNvO8PyZBB3e5Towoh70/wd0HQkBQSZGLDXdiy++yD//+U+2bt2K0+mkXbt2RERUvofu8OHD3HzzzaSlpREdHU2nTp2YO3cul1xyiQeiFm+0ISULgI4No7FZ/fNHQBVTE/F9XpF033rrrWzcuJH+/fszbtw4Bg0axH/+8x9KS0t5/fXXzQ5PRMTrGIbBhtQsZq07wKx1+8kvcRWcjA0q5bkmG7ks+3MC9pf1bIfWgvNGQ887ISjMxKjFl4SFhdGjRw+Ptjl58mSPtifeb70fz+cup2XDRHyfVyTdo0ePdj8eMGAA27dvZ82aNTRv3pzOnTufdjuTJk1i0qRJJCcnA9C+fXueeuopBg4c6OmQRURMkXw0n282HmT2+gMVvqD1iS1gXPxKOhyajTX1qGtjWB3ofQ/0ustVnVykkpxOJ1OnTmXWrFkkJydjsVhITEzkmmuu4eabb8ai6Qpyhtbtcy0X1q1JLZMjMU950p18VIXURHyVVyTdf9a4cWMaN24MwP/+9z+uueaa03pdo0aN+Ne//kWLFi0A+Oijjxg6dCjr16+nffv2VRaviEhVcTgN1qVk8tO2w/y09bB7+DhARCA81DSVa5zzqXXwZyx7na4dMU2g74PQ9SYIDDUpcvE1hmEwZMgQvv/+ezp37kzHjh0xDINt27Zxyy23MGvWLL766iuzw5QaJLvQzs70XAC6NfbfpLtpWdJ9MLuQIruDkECbyRGJiKeZnnSXlpayY8cOAgMDadWqlXv7119/zVNPPcX27dtPO+m+8sorKzx/8cUXmTRpEitWrFDSLSI1QnGpgy0HcliZdIxVSRmsSc4kr7jUvT/QanBzo3RGhK2iWfpPWFKP/P7ixPNdy361uRJspv95Fx8zdepUlixZwoIFCxgwYECFfQsXLuSqq65i2rRpjBw50qQIpabZkJqFYUCT2DDiIv23An1seBCRIQHkFpWSklFAq3j/LCgn4stM/Va2detWBg8ezL59+wAYOnQokyZN4m9/+xsbN27kjjvu4Lvvvjurth0OB1988QX5+fn06dPHk2GLiFSaw2lwMKuQfccK2H4oh60Hc9ialsPu9DxKnUaFYxuElHBHg31cFLiJhGO/Yk1P+31naG3ofL0r2a7TsprfhfiTzz77jMcff/y4hBvgwgsv5LHHHmP69OlKuuW0uYeW+3EvN7iWDUusE86m/dnsPZKvpFvEB5madD/22GMkJiYyceJEpk+fzsyZM9myZQs33XQT3333HZGRZ/5HZ/PmzfTp04eioiIiIiKYPXs27dq1O+nxxcXFFdb/zMnJOav3IiJSzjAMcgpLSc8tIj232HWfU+xKsjMKSDlWQGpmAXaHcaJX0z4sh6vrHKBP8B4Si7YSenQLloOO3w8JioS2g6HDcGh2AdgCq+utiR/btGkTr7zyykn3Dxw4kIkTJ1ZjRFLTrUvRfO5yTWNdSbcqmIv4JlOT7lWrVvH999/TrVs3zjvvPGbOnMk//vEP7rzzzrNus3Xr1mzYsIGsrCy+/PJLRo0axeLFi0+aeI8fP55nn332rM8nImfH7nCSVWAns6CEYruTUqeTUqdBqcPAYoEAq4UAm5UAqwWb1XLS578/thBgtWK1UOliTg6nQaHdQWGJgyK761b+PL+klOxCO1kFrlt2ob3seQlZhXaO5hWTnlNMcanzFGcwiCKf1rYMOkXm0TX8GO0CDtKwNIWo3D1YS3Ig/U8viW0JLS6GlhdDk/MgMKRS71HkTGVkZBAfH3/S/fHx8WRmZlZjRFKTOZwG68uWC+vu5z3d8Mdiakq6RXyRqUl3eno6DRs2BCAmJoawsDD69+9fqTaDgoLchdR69OjB6tWrefPNN3n33XdPePy4ceMYM2aM+3lOTg4JCQmVikFEKioscbBi7zF+2X3UPYQ6Pbf4r194lv6YhNusFgJt5Ym6lQDb70k7QJHdSZHdQXGpE7uj/HaiHuhTs+IkggJqW3JpTy61rbk0DMonIbiQeoH51LXlUYcsYh3pRBQdxlZa9sWqqOxWobEAqNcRGvWCRj0hoRfUalK5D0WkkhwOBwEBJ//aYLPZKC0tPel+kT/aeTiXvOJSwoNstK6n4dTlSfdeJd0iPsnUpNtisWC1Wt3PrVYrgYGeHSZpGEaF4eN/FhwcTHCw/xbvEKlK+zMLeG/JXmavO0Bu8fFfxi0WiA4NJDTQViFJBih1uHq+HU4DHCX0cGygl7GJpsZB6nGUMIoIogQrBnYCsBsBlBDgeowNOwGUGIGUlAZQUuraXkIgJYbrcTGBZdtcr3VgJdhiJ8RSQnCAnRBKCLbYCbfaCbW4biFlt1CLnWDsBFFMoFFCoLMEq3GSZKO47HYiYbEQ1RBqJ0JcW4hrDXFtILY5BOjvkngXwzC45ZZbTnrNPNW1VuTP1pbN5+7auJb7774/a6qebhGfZmrSbRgGrVq1cg8FzcvLo2vXrhUScXANaTsdjz/+OAMHDiQhIYHc3FxmzJjBokWLmDt3rsdjF5GTczgNJi3azX8W7nYPs24YE8r5reLo1jiGFnUjSKgdRkxoIAE268kbMgzYNBMWPA85+13bTvbdrKq/sxllt78SFAlhtV0JdXgd1335LaIuRDeCqEYQ1QCCwqo4aBHPGTVq1F8eoyJqcrq0PndFibGupDs9t5j84lLCg7UChYgvMfX/6ClTpni0vcOHD3PzzTeTlpZGdHQ0nTp1Yu7cuVxyySUePY+InFxukZ37pq9j6a6jAPRuVpsHBrSkb/NYrGfSm+F0wnePwLqPXM/D60KbQdCwG8Q0huBICAgBLOC0g8MOjhLXrbTk98fubcVlx5TdlxaX7Svb5nS41rQOCIaA8vsQ19zpgD/egk9yXCgEhmmutfgsT1+zxb+tLS+i1jjG3EC8RHRYILXDg8jILyH5WD7tG0SbHZKIeJCpSffp/Gp+JiZPnuzR9kTkzOQXl3LLlNWs3ZdJaKCNF67qwLBuDc+usNmPT7oSbosVBjwOfR5UQisi4gOO5Baz71gB4BpeLi5NY8PIyC8h6aiSbhFfo7ErIuIRhmEweuYG1u7LJCokgOl39KZjo7P80rB3ESx/y/X4qneg83Uei1NERMxVvlRYq/gIokO15GG5pnXCWZeSpXndIj7oFJMpRURO35Rfkpm/9TBBNitTb+t19gm30wHfla0o0ON2JdwiIj6mfD53d83nrqBZWTG1pKMFJkciIp6mpFtEKu1AViGvzNsOwD8HtaVbZYYL/jYbMvZAaC245FkPRSgiIt5idbKrQG73JrVNjsS7NHUn3XkmRyIinqakW0Qq7aU52yiyO+nZtBYj+1RiPWnDgGVvuB73vs9VLE1ERHxGkd3B5gPZAPRqqqT7j5qWVTBPPqaebhFfozndIlIpvx3MZs7mNKwWeHZIh7Mrmlbu0CY4vBlswdDrTs8FKSIetWDBAhYsWEB6ejpOp7PCvg8//NCkqKQmWJ+Shd1hEB8VTELtULPD8SrlPd0Z+SVkF9o1313Eh5iWdI8ZM+a0j3399derMBIRqYx3F+8FYFCnBrRrEFW5xjbOdN23HugaXi4iXufZZ5/lueeeo0ePHtSvX79yP7SJ3ykfWt6zaW392/mTiOAA6kYGk55bTPLRfDonxJgdkoh4iGlJ9/r160/rOP1BFvFe+zML+G7TQQDuPr9Z5RozDNj+netxx2srGZmIVJV33nmHqVOncvPNN5sditRAf0y65XhN64STnltMkpJuEZ9iWtL9888/m3VqEfGQL9cewGlA3+axdGhYyTVFM/ZC1j6wBkKzCzwSn4h4XklJCX379jU7DKmBSh1Od+VyJd0nlhgbzqqkDJK0bJiIT1EhNRE5K06nwf/WpQLwtx4JlW9w9wLXfePeEBxR+fZEpErccccdfPrpp2aHITXQ1rQc8kscRIYE0LqeCmWeSGJceTE1Jd0ivsRrCqmtXr2aL774gpSUFEpKSirsmzVrlklRicjJrNmXSWpGIRHBAVzWvl7lGjMM+OEfrsctLqp8cCJSZYqKinjvvff46aef6NSpE4GBFYs9qQ6LnMzqZFcvd48mtbBZNX3wRMormKunW8S3eEXSPWPGDEaOHMmll17Kjz/+yKWXXsquXbs4dOgQV199tdnhicgJzN1yCIDL2tcjNMhWucaO7fn9cesrKteWiFSpTZs20aVLFwC2bNlSYZ/qsMiprE4qm8+dqKHlJ5NY5/ek2zAM/T8l4iO8Iul+6aWXmDBhAvfffz+RkZG8+eabJCYmcvfdd1O/fn2zwxORPzEMgwXbDwNwSbv4yjdYnP3747jWlW9PRKqMarLI2TAMw11ETetzn1yT2DAsFsgtKiUjv4TYiGCzQxIRD/CKOd179uxh0KBBAAQHB5Ofn4/FYmH06NG89957JkcnIn+2Oz2PfccKCLJZ6deyTuUb/GWi614F1EREfNLeo/kcyy8hKMBKx0aVLLzpw0ICbTSIdq1friHmIr7DK3q6a9euTW5uLgANGzZky5YtdOzYkaysLAoKCkyOTkT+bMH2dAD6NI8lPNgDf0aObHfdx7WpfFsiUuWysrKYPHky27Ztw2Kx0LZtW26//Xaio5VMyYmVDy3vkhBDcEAlpyT5uKZ1wjiQVUjS0Xx6aFSAiE/wip7ufv368eOPPwLwt7/9jYcffpg777yTG264gYsuUlElEW/z655jAPRvFVf5xuyFcHSX63Gn6yrfnohUqTVr1tC8eXMmTJhARkYGR48eZcKECTRv3px169aZHZ54qVVJGlp+usqLqamCuYjv8Iqe7rfeeouioiIAxo0bR2BgIMuWLWPYsGE8+eSTJkcnIn9U6nCytmxeXu9msZVvMGMvGA7X4/pdKt+eiFSp0aNHM2TIEN5//30CAlxfI0pLS7njjjt45JFHWLJkickRircxDIMVe10/1nrkuuHjyoupJR/VaE8RX+EVSXft2r//6mm1Wnn00Ud59NFHTYxIRE6mfJ3VKE+ts5q2yXVfvzNYvWLwjYicwpo1ayok3AABAQE8+uij9OjRw8TIxFulZBRwMLuIQJuF7k1qmR2O1ytPuvdqTreIz/CKb7jff/898+bNO277/Pnz+eGHH0yISEROpnyIYM+mtT2zzurhsiWHNJ9bpEaIiooiJSXluO2pqalERnrghzjxOeW93F0SYiq/xKQfaFqWdO875lo2TERqPq9Iuh977DEcDsdx251OJ4899pgJEYnIyawsn5fnqXVWM/a67hN6eaY9EalS1113HbfffjszZ84kNTWV/fv3M2PGDO644w5uuOEGs8MTL7R8j4aWn4mEWmHYrBYKShyk5xabHY6IeIBXDC/ftWsX7dq1O257mzZt2L17twkRicjJbNqfBUA3Tw0RPLDWdR/bwjPtiUiVevXVV7FYLIwcOZLS0lIAAgMDuffee/nXv/5lcnTibVzzuV0/1vZR0n1aggKsNKoVyr5jBew9kk98VIjZIYlIJXlFT3d0dDR79+49bvvu3bsJDw83ISIROZH03CIO5xRjsUC7+lGVb7C0GPIOux7X61T59kSkygUFBfHmm2+SmZnJhg0bWL9+PRkZGUyYMIHg4GCzwxMvk3ysgEM5RQTZrJ77sdYPqIK5iG/xiqR7yJAhPPLII+zZs8e9bffu3fz9739nyJAhJkYmIn/024EcAJrHRXhmfe60ja778DgI1ZcxkZokLCyMjh070qlTJ8LCwswOR7yUez534xhCAjWf+3T9XsFcSbeIL/CK4eX//ve/ufzyy2nTpg2NGjUCYP/+/fTr149XX33V5OhEpNzmA9kAdGjggV5ugMx9rvs6rcDigaJsIlIlxowZw/PPP094eDhjxow55bGvv/56NUUlNYHmc5+d8qQ7SUm3iE/wiqQ7OjqaX3/9lR9//JGNGzcSGhpKp06dOP/8880OTUT+wJ10N4z2TIOHN7vu67T0THsiUiXWr1+P3W53Pz4Zi348kz/44/rcms99Zpoq6RbxKV6RdIPrQn3ppZdy6aWXmh2KiJzEb2VJd0dPJd3HyqaU1G3vmfZEpEr8/PPP7scfffQRjRo1wmqtOEPNMAxSU1OrOzTxYnuP5pOeW0xQgJWujWPMDqdGSSyb070vowCn08DqiSU6RcQ0piXdEydO5K677iIkJISJEyee8tiHHnqomqISkZPJKbJzMLsIgDaeKKIGvy8XVruZZ9oTkSqXmJhIWloadevWrbA9IyODxMTEEy4BKv6pvJe7m+Zzn7GGtUIJtFkoKXVyMLuQRrVUN0GkJjMt6Z4wYQIjRowgJCSECRMmnPQ4i8WipFvEC+xOzwMgPiqY6NDAyjdoGJCR5HpcO7Hy7YlItTAM44Tb8/LyCAnR0kbyu193az732bJZLTSuHcaeI/kkHc1X0i1Sw5mWdCclJZ3wsYh4p/Kku0XdCM80mHsISgvBYoOYxp5pU0SqTHkBNYvFwlNPPVWhYrnD4WDlypV06dLFpOjE2zicBr/sOQpAv5Z1TI6mZkqsE86eI/kkH82nX8s4s8MRkUrwiiXDnnvuOQoKCo7bXlhYyHPPPWdCRCLyZ3vKku6WdSM902DqCtd9TALYPNBzLiJVav369axfvx7DMNi8ebP7+fr169m+fTudO3dm6tSpZocpXmLrwRyyCuxEBAfQqVGM2eHUSL9XMD/+O7KI1CxeUUjt2Wef5Z577jlunc+CggKeffZZnnrqKZMiE5Fyu8qS7uae6uk+stN1H53gmfZEpEqVF1O79dZbefPNN4mK8lBtB/FJS3cfAVxDywNtXtHHU+OUVzBPPqYK5iI1nVck3YZhnHCZkY0bN1K7dm0TIhKRP9vt7un2UNJd4mqPuNaeaU9EqsWUKVPMDkFqgGW7NLS8ssormGvZMJGaz9Sku1atWlgsFiwWC61ataqQeDscDvLy8rjnnntMjFBEAIrsDlIzXcPbPDanOyvFdV+7uWfaE5Fq8VfTvjQ6TQpLHKxJzgTgPCXdZy0xzpV0p2YUUOpwEqARAyI1lqlJ9xtvvIFhGNx22208++yzREf/vvZvUFAQTZs2pU+fPiZGKCIAe47kYRhQKyyQ2PAgzzSaWVZAsVZTz7QnItVi9uzZFZ7b7XaSkpIICAigefPmlUq6x48fz6xZs9i+fTuhoaH07duXl19+mdatNSKmJlmVnEGJw0mD6BCalQ2RljMXHxlCSKCVIruT/ZmF7uHmIlLzmJp0jxo1CnCt+dm3b18CA1VMScQb7TniGtrWPC7ihFNBzkpmsutey4WJ1Cjr168/bltOTg633HILV199daXaXrx4Mffffz89e/aktLSUf/7zn1x66aVs3bqV8HAlHDXFsl2u+dzntqjjuWuGH7JaLTSNDWf7oVySjuYr6RapwUxLunNyctxFWLp27UphYSGFhYUnPFbFWkTMlZrhGlreJNZDF/zCLCjKdj2OaeKZNkXENFFRUTz33HMMHjyYm2+++azbmTt3boXnU6ZMoW7duqxdu5bzzz+/smFKNVlWtj63hpZX3h+T7gFmByMiZ820pLtWrVqkpaVRt25dYmJiTvhLaHmBNYfDYUKEIlIu5Zgr6W5cO+wvjjxN5fO5w+tCkIfaFBFTZWVlkZ2d7dE2y9tTUdWa40huMdvScgBXT7dUTrOyed17j+aZHImIVIZpSffChQvdF9HyZUhExDuVF1FrHBvqmQaz9rnuYxp7pj0RqTYTJ06s8NwwDNLS0vj444+5/PLLPXYewzAYM2YM5513Hh06dDjpccXFxRQXF7uf5+TkeCwGOXO/7nFVLW9XP4o6EcEmR1PzlRcv3XVYSbdITWZa0t2/f38ASktLWbRoEbfddhsJCVqvV8QbpWRUUU+3km6RGmfChAkVnlutVuLi4hg1ahTjxo3z2HkeeOABNm3axLJly0553Pjx43n22Wc9dl6pnCU7tVSYJ5Un3XuOKOkWqclMX6c7ICCAV1991V1UTUS8i93h5GCWq95CQi0l3SL+LikpqcrP8eCDD/LNN9+wZMkSGjVqdMpjx40bx5gxY9zPc3Jy9CO+SZxOg8U70wHo3yrO5Gh8Q/M4V9J9NK+EzPwSanlqBRERqVZeseDfRRddxKJFi8wOQ0RO4GBWIU4DggOsxEV6aKigkm4Rn2AYBoZheLS9Bx54gFmzZrFw4UISE/96dYPg4GCioqIq3MQcWw5mczSvhPAgGz2aah6+J4QHB9AwxjW1a7d6u0VqLK9IugcOHMi4ceMYO3Ysn332Gd98802F2+kaP348PXv2JDIykrp163LVVVexY8eOKoxcxPelZrh6uRvXDvPgcmHlc7pVuVykJpo8eTIdOnQgJCSEkJAQOnTowAcffFDpdu+//34++eQTPv30UyIjIzl06BCHDh066eom4l0W7XAtFXZeyzoEBXjFV0yfUD7EfHe6km6Rmsr04eUA9957LwCvv/76cfvOpHq51vcU8bzy+dwJnprPbRi/93TXUtItUtM8+eSTTJgwgQcffJA+ffoAsHz5ckaPHk1ycjIvvPDCWbc9adIkAC644IIK26dMmcItt9xy1u1K9fh5h2to+YDWdU2OxLe0qBvB4p1HlHSL1GBekXQ7nU6PtKP1PUU8z+NF1AozoSTX9Tj61HM1RcT7TJo0iffff58bbrjBvW3IkCF06tSJBx98sFJJtyeHqkv1ysgvYUNqFgAXKOn2KHcFcyXdIjWWVyTdVeV01vfUUiMip5bq6Z7u8l7uiHgI9NASZCJSbRwOBz169Dhue/fu3SktLTUhIvEGS3YewTCgTb1I6kWHmB2OT2lZXsFcSbdIjWXqhJuFCxfSrl27Eya62dnZtG/fniVLlpxV26e7vuf48eOJjo5231TxVKQiLRcmIn900003uYeB/9F7773HiBEjTIhIvMGi8qHlbdTL7WnlPd0HsgrJL9YPWyI1kak93W+88QZ33nnnCSuNRkdHc/fddzNhwoSzGhp+uut7aqkRkVNLzSzv6fZQr3RWeRE1Jd0iNcUfr5MWi4UPPviA+fPn07t3bwBWrFhBamoqI0eONCtEMZHDabB4p6uImuZze15MWBB1IoI5mlfMniN5dGoUY3ZIInKGTE26N27cyMsvv3zS/ZdeeimvvvrqGbd7Jut7BgcHExzsoWWQRHxMdqGdrAI74ME1unf84LpX0i1SY6xfv77C8+7duwOwZ88eAOLi4oiLi+O3336r9tjEfBv3Z5FZYCcyJIBujWPMDscntagbztG8YnanK+kWqYlMTboPHz5MYGDgSfcHBARw5MiR027PMAwefPBBZs+ezaJFi05rfU8RObny+dx1IoIID/bQn4uSsjlpgR5K4kWkyv38889mhyBebNF219Dy81vFEWDTUmFVoUXdCFbszVAxNZEaytS/jA0bNmTz5s0n3b9p0ybq169/2u1pfU8Rz/J4ETWA0hLXff0unmtTRERMM3/rYQAu1NDyKtOybiSgtbpFaipTe7qvuOIKnnrqKQYOHEhISMVKl4WFhTz99NMMHjz4tNvT+p4inuWez+2poeWGAdn7XY9rNfVMmyJS5caMGcPzzz9PeHh4hfndJ/L6669XU1TiDVIzCth+KBeb1cKFKqJWZVqogrlIjWZq0v3EE08wa9YsWrVqxQMPPEDr1q2xWCxs27aN//73vzgcDv75z3+ednta31PEszxeubwo6w9rdDf0TJsiUuXWr1+P3W53Pz4Zi8VSXSGJl/ixrJe7R5Na1AoPMjka31W+bFjysXyKSx0EB9hMjkhEzoSpSXd8fDy//vor9957L+PGjXMnzRaLhcsuu4y3336b+Ph4M0MU8WspGa6pGZ5bLizVdR8WC0HhnmlTRKpc+Zzu8sT73XffpVWrVmaGJF5i/tZDAFzavp7Jkfi2uMhgIkMCyC0qJfloAa3rRZodkoicAVOTboAmTZrw/fffk5mZye7duzEMg5YtW1KrVi2zQxPxex6f051dlnRHa1k+kZooMDCQLVu2qEdbAMgqKGF1ciYAl7ZTJ0lVslgstKgbwfqULHan5ynpFqlhvKbEZK1atejZsye9evVSwi3iBRxOgwOZrp5uj63RXT6fO0ZJt0hNNXLkSCZPnmx2GOIFFm5Px+E0aFMv0rMFN+WEyoeY7zyca3IkInKmTO/pFhHvdDiniBKHkwCrhfrRHkq6s1Jc99Fao1ukpiopKeGDDz7gxx9/pEePHoSHV5wqokJq/mP+b6753Jeol7tatIp39W4r6RapeZR0i8gJlRdRa1QrFJvVQ0NJy4eXq6dbpMbasmUL3bp1A2Dnzp0V9mnYuf8osjtYsusIAJe203zu6tCmXhQA2w8p6RapaZR0i8gJpVTFGt3lhdSiG3muTRGpVuVF1cS//brnKAUlDupHh9ChYZTZ4fiFNvVdPd3Jx/IpLHEQGqQK5iI1hdfM6RYR7+LxImqgQmoiPiAlJeWkS3SmpKRUczRilrlbXFXLL24brxEO1aRORDB1IoIwDA0xF6lplHSLyAmlenqNbnsh5LuGIhKjOd0iNVViYiJHjhw5bvuxY8dITEw0ISKpbnaHk3ll87kHdtTQ8ur0+xDzHJMjEZEzoaRbRE7IPby8lqeWCzvgug8Mh1CtUCBSUxmGccKezby8PEJCQkyISKrbL7uPkl1op05EEOckxpodjl9pU7ZUmOZ1i9QsmtMtIidUnnQ3ifVU0l027DQmATQUUaTGGTNmDOAqlvbkk08SFvb73waHw8HKlSvp0qWLSdFJdfp+cxoAl3eo57lCm3Jaytfn3p6mpFukJlHSLSLHySsu5WheCQCNPZV0ZyS57mOaeKY9EalW69evB1w93Zs3byYoKMi9LygoiM6dOzN27FizwpNqUlL6+9DyQR0bmByN/2lb//fh5ScbdSIi3kdJt4gcp3w+d0xYIFEhgZ5pNGOv6z62hWfaE5FqVV61/NZbb+XNN98kKkoVq/3RL3vKh5YH0yuxttnh+J0WdSOwWiCzwM6R3GLqRmlKh0hNoDndInKcfcfKhpZ7snJ5Ttmcbi0XJlKjvf322wQE/P6b/b59+3jjjTeYP3++iVFJdZmzyTW0fKCGlpsiJNBGYp1wQPO6RWoSJd0icpyqWS5sv+teSbdIjTZ06FCmTZsGQFZWFr169eK1115j6NChTJo0yeTopCqVlDqZ/5trqbBBneqbHI3/UgVzkZpHSbeIHGdfRj7gwSJq8Hv18uiGnmtTRKrdunXr6NevHwD/+9//qFevHvv27WPatGlMnDjR5OikKv2y+yg5RaXERQbTs6mGlpuljYqpidQ4SrpF5Djlw8s9tkZ3aQnkuXpHiE7wTJsiYoqCggIiI11f+ufPn8+wYcOwWq307t2bffv2mRydVKVvNx4ENLTcbK21bJhIjaOkW0SOUz68vHHtcM80mJ0KhhMCwyA8zjNtiogpWrRowVdffUVqairz5s3j0ksvBSA9PV3F1XxYQUkpc8uGlg/toqrlZiqvYL47PQ+7w2lyNCJyOpR0i0gFpQ4n+zMLAQ8OL89Mdt3Xaqo1ukVquKeeeoqxY8fStGlTzjnnHPr06QO4er27du1qcnRSVeb9doiCEgdNYsPo1riW2eH4tYYxoYQH2ShxOEk+mm92OCJyGpR0i0gFadlFlDoNgmxW4j21FEl50q01ukVqvGuuuYaUlBTWrFnD3Llz3dsvuugiJkyYYGJkUpVmrXPV5bi6a0OtDW0yq9XiHmK+TUPMRWoEJd0iUkFK2dDyRrVDPTdn74893SJS49WrV4+uXbtitf7+NaJXr160adPGxKikqqTnFPHL7qOAK+kW87UpG2K+LU0VzEVqgoC/PkRE/ElKhoeLqIGSbpEabsyYMTz//POEh4czZsyYUx77+uuvV1NUUl2+3nAQpwHdm9SiSayHan1IpbRv4Eq6txzINjkSETkdSrpFpILyyuVNlHSLSJn169djt9vdj09Gw45906z1vw8tF+/QsWE0AJsPZGMYhv7fE/FySrpFpIKUsjW6G3uyNyOrbBkhJd0iNdLPP/98wsfi+7al5bAtLYcgm5XBneqbHY6UaV0vkkCbhawCO/szC0nw5A/lIuJxmtMtIhV4fHh5YSYUlQ1/i2nsmTZFxBROp5MPP/yQwYMH06FDBzp27MiQIUOYNm0ahmGYHZ5UgVnr9gMwoE0cMWFBJkcj5YIDbO5iaps1xFzE6ynpFhE3wzB+H17u6eXCIuIhSL/Ei9RUhmEwZMgQ7rjjDg4cOEDHjh1p3749+/bt45ZbbuHqq682O0TxsJJSp7tq+TXdE0yORv6sY8MYQEm3SE2g4eUi4rYqKYPcolJCAq2e6+nWfG4RnzB16lSWLFnCggULGDBgQIV9Cxcu5KqrrmLatGmMHDnSpAjF037cephj+SXERwUzoHWc2eHIn3RsGM1nqJiaSE2gnm4RcZu2wjX3+uquDQkJtHmmUSXdIj7hs88+4/HHHz8u4Qa48MILeeyxx5g+fboJkUlVmbE6BYBruycQYNNXRm9TXkxt0/5sTe8Q8XL6CyoiABzKLmLelkMA3Ny7qecaVtIt4hM2bdrE5ZdfftL9AwcOZOPGjdUYkVSl1IwClu5yrc19XU8NLfdGrepFEGSzkl3oKqYmIt5LSbeIADBteTKlToNeTWvTrmz9T4/I2Ou6V9ItUqNlZGQQHx9/0v3x8fFkZmZWY0RSlcp7ufu1rKPK2F5KxdREag4l3SJCQUkpn65yfcG67bxEzzZ+rCzpjm3h2XZFpFo5HA4CAk5eCsZms1FaWlqNEUlVKXU4+WKNq2r59T216oQ36/CHIeYi4r1USE1E+HLdAbIK7DSuHcYl7U7ek3XGSgogx/XFTUm3SM1mGAa33HILwcHBJ9xfXFxczRFJVVm4PZ303GJiw4M8e00Qj+vUKJrPVqmYmoi3U9It4uecToMpy5IAuKVvU2xWi+caLx9aHhIDYbU9166IVLtRo0b95TGqXO4bPlnpGvk0vHsjggI0KNKblRdT23zAVUzNYvHgNVxEPEZJt4ifW7Qznb1H84kMDuBvni6Wc2y361693CI13pQpU8wOQarB7vQ8luw8gsUCN53TxOxw5C+0io90F1NLzSikcazm34t4I/18KeLHDMPgvz/vAeD6XglEBHv4d7hju1z3sc09266IiFSJj35NBuDitvFK4GqAoAArbeqrmJqIt1PSLeLHlu85xtp9mQQFWLmzXzPPn+DITtd9nVaeb1tERDwqu9DOl+tcdThu7dvU3GDktJUPMd+4P8vcQETkpJR0i/ixiQtdPdHX90ygblSI509wdIfrPq6N59sWERGP+mJNKgUlDlrFR9CneazZ4chp6ta4FgBrkjNMjkRETkZJt4ifWp2cwYq9GQTaLNzTvwqGfzudcLRseHlca8+3LyI+acmSJVx55ZU0aNAAi8XCV199ZXZIfsHhlUnIpAAAL4dJREFUNPhoeTIAt/RNVEGuGqRnU1eh0s0HsimyO0yORkROREm3iJ+auMCVEF/TvRENYkI9f4Kc/WAvAGsg1PLw2t8i4rPy8/Pp3Lkzb731ltmh+JWF29NJzSgkOjSQq7s2NDscOQMJtUOJiwzG7jC0XreIl1L1chE/tCopg6W7jmKzWri3fxVVFk/f7rqPbQE2/akRkdMzcOBABg4caHYYfuf9Ja4lHq/vmUBokM3kaORMWCwWejatxfebD7FmXwa9ErVEp4i38bmebg1LEzk1p9PghTlbAfhbj4Sqq06b/pvrPr591bQvIgIUFxeTk5NT4SZnZk1yBquSXdONbj1XI5Nqou5NXIn2muRMkyMRkRPxuaRbw9JETu3rjQfYtD+biOAAxlxShVXFD5cn3e2q7hwi4vfGjx9PdHS0+5aQkGB2SDXOpEWupSOHd2tEvegqKKopVa5nU1cxtbX7MnE6DZOjEZE/87kxnxqWJnJyhSUOXpnrqih+34DmxEUGV93JDrt606mrnm4RqTrjxo1jzJgx7uc5OTlKvM/A9kM5LNiejsUCd51fBUtHSrVoWz+K0EAb2YV2dh/Jo1V8pNkhicgf+FxPt4ic3AdL95KWXUTDmFBuq8ohhKXFcLRsjW71dItIFQoODiYqKqrCTU7fO2W93Fd0qE+zuAiTo5GzFWiz0rVxDOBanUREvIvfJ92aCyb+Ij2niEmLXV+u/m9gG0ICq7BQTvpWcNohJAai1eMkIuKNUjMK+HZTGgD3XlAFS0dKterRpGyIueZ1i3gdv0+6NRdM/MWr83dQUOKga+MYruxUv2pPlrbRdd+gC2itVxE5A3l5eWzYsIENGzYAkJSUxIYNG0hJSTE3MB/09qI9OJwG/VrWoUPDaLPDkUrqUbZe9+p96ukW8TZ+n3SPGzeO7Oxs9y01NdXskEQ87reD2Xyxdj8ATwxqh6WqE+GDG1z39TtX7XlExOesWbOGrl270rVrVwDGjBlD165deeqpp0yOzLekHCvgizWu7zwPXtjS5GjEE7o2jsFqgdSMQg7nFJkdjoj8gc8VUjtTwcHBBAdXYTEpEZMZhsGLc7ZhGDC4U326lw0/q1JpG1z3SrpF5AxdcMEFGIaqL1e1NxfsorSsl1vrOvuGyJBA2tSLYmtaDmuSMxlU1aPaROS0+VxPt4aliVS0YFs6v+45RlCAlf+7vE3Vn9BeBIe2uB437F715xMRkTOyOz2P2etdo5/GXtra5GjEk8qXDlux95jJkYjIH/lc0q1haSK/szucvPT9NgBuPy+RhNphVX/StA2uImrhdSGmSdWfT0REzsgbP+3EacAl7eLpnBBjdjjiQee2qAPA0l1HTI5ERP7I54aXa1iayO+mr9jH3qP5xIYHcV91Vabdv9p1n9BLRdRERLzMtrQcviurWD7mklYmRyOe1qd5LAFWC8nHCkg5VkDj2Gr4sV1E/pLP9XSLiEt2gZ03FuwCYMylrYgMCayeE6escN036lk95xMRkdP2rx+2A64aH23ra01zXxMZEki3stoti9XbLeI1lHSL+KiJC3eRVWCnVXwE1/WopqXwDANSlrseN+5TPecUEZHTsmhHOot3HiHQZuHvmsvts85vWTbEfKeSbhFvoaRbxAclHc1n2vJkAP45qB0Btmr6Xz19KxQcg4BQaNC1es4pIiJ/qdTh5IU5rhoft/RtSmKdcJMjkqpyfqs4AH7dcwy7w2lyNCICSrpFfNK/ftiG3WHQv1Uc/csuvtViz8+u+6bnQkBQ9Z1XRERO6dNVKexOz6N2eBAPaF1un9ahQTS1w4PIKy5lfUqW2eGICEq6RXzOir3HmPfbYWxWC/8c1LZ6T763LOlufmH1nldERE4qu8DOhB93AjD64pZEh1ZTjQ8xhdVq4byyKuZLNMRcxCso6RbxIU6nwQtztgJwQ68EWsVHVt/J7YWQ/IvrcbMB1XdeERE5pQk/7SSzwE7LuhHc0Kux2eFINSgfYr5ExdREvIKSbhEfMnv9AbYcyCEyOIBHLq7mpWD2LobSQohqBHWruYddREROaNP+LHeNj6eurMYaH2KqfmXF1DYfyCYjv8TkaEREf3lFfERBSSn/nrcDgPsvbEGdiODqDWDHHNd964Fan1tExAuUOpw8PnszTgOGdmlAv5bVWONDTBUfFUKbepEYBizbfdTscET8npJuER/x/pIkDuUU0ahWKLf0bVq9J3c6YMdc1+M2V1TvuUVE5ISm/prMlgM5RIUE8MSgdmaHI9Wsf2vXjyw/bj1sciQioqRbxAccyi7incV7AHhsYBtCAm3VG0DSYshPh5AYaHJe9Z5bRESOcyCrkNfLiqc9fkVb4iKrefSTmO6KDvUBWLDtMIUlDpOjEfFvSrpFarDiUgc/bE7j7o/XUGh30L1JLQZ1rF/9gWyc6brvMExLhYmImMwwDMbN2kxBiYOeTWvxtx4JZockJujUKJpGtUIpKHGwaEe62eGI+LUAswMQkTNjGAabD2Tz5dr9fL3xIFkFdgCCA6w8NbgdluqeT12SD9u+dT3udH31nltERI7zycoUluw8QnCAlfHDOmK1qs6GP7JYLAzqVJ93F+/lu01pDDTjR3kRAZR0i9QITqfBxv1ZzN1yiLm/HWLfsQL3vvioYIZ1a8SNvRqTUDus+oPb9DnY86F2M0joVf3nFxERt6Sj+bw0ZxsA/3d5G1rUrcalI8XrDO7YgHcX72XB9sMUlJQSFqSv/iJm0P95Il4qr7iUlXuPsXjnEeb/dphDOUXufcEBVi5rX4/h3RtxXos62MzqxXA6YcXbrsc971DVchERE5U6nIyeuYFCu4O+zWOrv6imeJ0ODaNoXDuMlIwCFm5PZ3CnBmaHJOKXlHSLeAm7w8mG1CyW7TrKL7uPsiE1i1Kn4d4fHmTjwrbxDOxQj/6t4ggP9oL/fXfOhaM7ISgSut5sdjQiIn7tPwt3syE1i8jgAP59bWcNKxf3EPNJi/YwZ1Oakm4Rk3jBt3YR/3Qou4gNqZmsT81iQ0oWmw9kU/Cn6qJNYsM4t0UdLmpTl3Nb1Kn+quSnYhiw9FXX4y43QEiUufGIiPixpbuOMHHhLgCeu6o9DWNCTY5IvMWgjq6ke+H2dPKLS73jR3sRP6P/60SqmN3hZN+xfHYezmPHoVy2H8phY2p2heHi5WqHB9G3eSzntajDuS3qmDNH+3RtnAEH1kJAKPR9yOxoRET8Vlp2IQ/P2IBhwA29Eri6ayOzQxIv0r5BFIl1wkk6ms9P2w4ztEtDs0MS8TtKukU8oLjUwaHsIg5kFXIwq4gDmYXsOZLHzsO57D2ST4nDedxrrBZoXS+KLgkxdE2IoUvjGFrERdSM4YAl+bDwBdfjztdDjJajERExg93h5P7p68jIL6Fd/SievrK92SGJl7FYLFzZqT4TF+5mxqpUJd0iJlDSLfIXDMPgWH4JB7MKOZhVyIGsIvfjg9mux0dyi0/ZRliQjZbxkbSqG0HrepF0bBhNx0bRNbeK6NcPQM5+iKgHlzxndjQiIn7r+e+2si4li8iQACbd1M27piGJ17i+V2Pe+nk3y/ceY+fhXFrFq6q9SHWqod/4Rc6MYRjklzjIKbSTU2Qnp7CU3KLfH+cU2sktLj1uf1ahnbTsIkpKj++p/rPgACsNY0JpEBNK/egQEuPCaR0fSav4SBrGhNaMHuzTsXEG/DbL9fiq/2out4iISab+ksS05fsAeO3azjSJDTc5IvFWDWJCubRdPeb+dohpy5N54aqOZock4leUdMtJOZwGJaVOSkqdFDsc2B2/Py8pdWJ3OokKCaRuVDCRwQFYTrBclMNpUFBSSmGJg0K7g4Kye8MwiA4NJCYsiOAAK0V2J0V2177CEtdxuUV2CkocBAVYCQm0Uuow3PsL7Q5yi1xJcqnTwDAMHIZBkd3pTpxzi0orJNB/KAR+xiwWqBsZTIOypLphWWJd/rhBTCi1wgJP+Bn4lMNbYfbdrscWK7S42Nx4RET81MLth3nuu60APDawDZe2r2dyROLtRvZpwtzfDjFr3QEevbwNUSGBZock4jeUdJ/EyMkrCQw9s1+MjbNM6iqRC2Kc5UkNoLQ8iXY4K96XPXacQZYaEmglLjKYQKv19+S6xHHCucxmCrRZiAoJJCo0kKiQAKJCA4kMCTjltnpRIcRHhRAUYDU7fHMd2wNTB/3+/PGD5sUiIuLHfjuYzYOfrsdpwHU9Erj7/GZmhyQ1QJ/msbSoG8Hu9Dxmrd3PLecmmh2SiN9Q0n0S61KysAaXmB2G17BYIMhmdd0CXLcAm4WsAlePcpHdSWpG4SlfHxpoIyzIRmiQDQsWsgpKyCkqdR8TGujaFxpoIyTQSmRIIOHBNuylrh7uAJvFdUygjZAgmytBDgkkwGbBarFgsVgIDrASXZ40hwYSFRJIdKjruMiQQEICrb7fG10V9q+B6ddCYYbr+d93QqCWoxERqW57j+Qx6sNV5Jc46Ns8luev6qDrmpwWi8XCqD5NePLr35i2fB8j+zT1nalvIl5OSfdJTLiuM+ERZ1Nk4uz+eFXmenm2Lw38QwId+IeEOrhsW4UE22o56UW9sMTBkdxi0nOLcDgNwoICCA2yEhoU4E60gwNOnOyWOpzYHYaSYW/lsMOSf8OSV8EoW0P8gTUQGW9uXCIifmj//7d373E934sfwF/fb9+uUimkpORSQpJyCRlDmOvpmGb7SZitbWatpTEOY5umjePS6Li1OSNlk+1szjCbLjhORe5zLRUJkaiU6vP7w/Fd3/qWvt++d6/n49Fj9f7cXr33ffv0/nze78/nXhn+b/Mx3Hn45EnlG/7Ph6OwSCF/6eOEFb9cwNU7pUi7fAdD3NpoOxLRc4Gd7gaM7N4OVlZ8QFRTmJsYwdnOAs52ir9TWmIkhoQPWtUtNdXAvRwgMw44thGo/t+T2Y1bAHP+C1jz/a9ERJp2q+QRXtt8DDfuP0LnNi3wz1n9YG3OObmkGEtTCSb7OOHrIzlYc/AS/Lu25k0PIg1gp7sh6VuAFnWHzzYwx7nBedVaWl8XMzU6PZz1qvr1G9mmpvrJe7YflwGPy//8vrIUeHgLKLsDCHXm4vt/AAxbCIh5hYSISNOe3uG+VlQGp1bm+Pb1/rCzNNV2LNJTbw3tjJ3puci8dg/7zxViFB/CR6R27HQ35NclgCmv/NFzSiwBnP0ABy/ghQ/5WjAiIi25cvshpv3vDnd7G3PseH0AHKz5TA1Snr2VGWYNdsVXv19B9C9/YHi3tpAYcZoCkTqx090QjwmAhYmcBQ10xBscmqOl9ZU6RoM7UtH+G7uIYcj1qsp6UnR9OcvERk+GihubAyYWst+3aANYtgNatOZdbSIiLTtz/T5C4v6LOw8r0blNC3z7en92uEkl3nyhM7Yfy8WV26X4LjMfr/Rz1nYkIoPGTndDJq0HOKebiIiItGDf2ZsI25mF8sfV6OFohW0z+3FIOamMlZkx5gzrgk9/Po+//3oRE3u3h7kJL7YTqQvHkhARERHpCEEQsP7QZbz5z0yUP66Gf9fWiH9jADvcpHLT/FzQ3sYchSUV+Or3y9qOQ2TQ2OkmIiIi0gEPHj3Gu/EnEP3LBQBAsJ8L4kL6wsqMTykn1TOVGGHhWA8AwPpDl5GRc1fLiYgMFzvdRERERFp2Ov8+xq1Lw0+nCiARi7BsYg8sm9iTD7gitXrJ0wGBfdqjRgDCErJQ8uixtiMRGST+S05ERESkJVXVNYhNvoLADYdxragM7W3MkRjqh2C/jtqORs+JpRN6oIOtOfLvlWPJD2e1HYfIILHTTURERKQFf9wsQeCGI/j833/gcbWAgO722DvXH32cW2k7Gj1HWpoZY3WQN4zEIiSduI74/+ZqOxKRweHTy4mIiIg06GFFFb76/TI2p17F42oBVmYSLBrXHS/7OEGk6GsjiVTAx6UV3hveFasOXMTCpNNoYSrBBC9HbcciMhjsdBMRERFpQHWNgO8z8xG97wLuPKwAAIzwsMdnf+kJeyszLaej5927L3bBzZJH2HEsF+8nZMFMIkZAj3bajkVkENjpJiIiIlKj6hoBP526gXW/XcblWw8BAB3tLLBwbHeM8GjLu9ukE0QiET6d2BOPKqux+8R1zNlxAiuneGE873gTNRs73URERERqUF5ZjR+yrmNT6lVcuV0KALAyk+DdF7ti+sCOMJHw0TqkW8RiEaIn98KjqmrsPX0T78afQHrOXSwc6wFTiZG24xHpLXa6iYiIiFQo+04pEtLzsDM9F8VlT17BZG1ujNcHu2L6oI587zbpNImRGGtf8UZHu4tYf+gKth29huO59xD9Vy90d7TSdjwivcRONxEREVEz3XrwCPvO3MTuE9dxIrdYWu7UyhzBfi6Y2s8ZLdnZJj0hMRIjcnQ39HW1RXhCFs5cL8HYdamY4OWI8JFucLFroe2IRHrFIMc1rV+/Hq6urjAzM4OPjw9SU1O1HYmIiIiaSB/O41XVNcjKK0bMb5cw6avD6PfZQfzth7M4kVsMsQh4wa0NNgX7InneMLwxpDM73KSXhrm3xc9z/THeyxGCAPyQdQPDVybjjW0Z+OXMTVRUVWs7IpFeMLg73QkJCQgLC8P69esxaNAg/OMf/8CYMWNw7tw5ODs7azseERERNUIXz+OCIODWgwqczr+PU9fv42ReMTJy7qK0UrbD4eVkjfFejpjQ2xFtW/Jp5GQYHG3MsW6qN94c0gnR+y4g5eJt7D9XiP3nCmFtbgy/Tnbo38kW/Vxt0aWtJed+E8khEgRB0HYIVerfvz/69OmDDRs2SMs8PDwwadIkREVFPXP7kpISWFtb4/79+7Cy4rwVIiJSH55z6lP3ebyyqgZFpRW4W1qJqmoBVTUCqqprUF3z5PtHj6tR+KACN++X40bxI1y9/RBXb5fiQUVVvX1Zmxujn6stXuzWFi92a8vXftFz4Y+bJUg6fh17sq6jsKRCZpmRWARnWwt0btMCDtbmsLcyRZuWpmhpZowWphJYmhqhhakELUwkaGEqgcRIBCORCEbiJ18SsYhP8ye90tTzuEHd6a6srERmZibmz58vUx4QEIAjR47I3aaiogIVFX/+g3H//n0ATyqQiIhInZ6eawzs+rfSVH0eX7n/AvacuC5d9rimBg8fKTccViwCOrexhIeDFXo4toSPiy3c7FtCLH7aQahESUmlUvsm0ieOFsA7g9sjdKAjTubdQ8a1e8i8dg8n84vxsLwaV66X4sr120rvXyx60nkXi0UwEkFuJ7xeiZx+uryuu0hOad3dy92OFwKoAZN62AJ49nncoDrdd+7cQXV1Nezt7WXK7e3tcfPmTbnbREVFYenSpfXKO3TooJaMREREdT148ADW1tbajqF1un4evwbgN5XvlYiI9NWp//33Wedxg+p0P1X3apQgCA1eoVqwYAHCw8OlP9fU1ODu3buws7OTbtO3b1+kp6crnEOR7Z61bkPLFSmvW1b755KSEnTo0AF5eXlqG+KobD0qsq2y9djQMl2sx4ZyqXpb1qXqtnse2ndjeVW1nao/k/LKG6tbddSjIAh48OABHB0dVbI/Q9Gc83hxcTFcXFyQm5urdxcyNNVW1UWf8+tzdkC/8+tzdkC/8+tzdkA38jf1PG5Qne7WrVvDyMio3tXwW7du1btq/pSpqSlMTU1lymxsbGR+NjIyUup/pCLbPWvdhpYrUl63TN46VlZWavvQKluPimyrbD02tEwX67GhY6p6W9al6rZ7Htp3Q8dU5Xaq/kzKK29K3aq6HvWtY6hOqjqPA0/qVR//iATU31bVTZ/z63N2QL/z63N2QL/z63N2QPv5m3IeN6hXhpmYmMDHxwcHDhyQKT9w4AAGDhyo9H7feecdtW/3rHUbWq5Ied0yZX8vZTXneE3dVtl6bGiZLtZjc4/Jumw8gzq2ex7ad3OOqa3PpLzypn52ST3UdR4nIiLSJoN7enlCQgKmTZuG2NhY+Pn5YePGjdi0aRPOnj0LFxcXbcfTWXyCrmqwHlWHdak6rEvVYD1qRnPP4/r8/0mfswP6nV+fswP6nV+fswP6nV+fswP6ld+ghpcDQFBQEIqKirBs2TIUFBSgZ8+e2Lt3Lzvcz2BqaoolS5bIHaJHTcd6VB3WpeqwLlWD9agZzT2P6/P/J33ODuh3fn3ODuh3fn3ODuh3fn3ODuhXfoO7001ERERERESkKwxqTjcRERERERGRLmGnm4iIiIiIiEhN2OkmIiIiIiIiUhN2uomIiIiIiIjUhJ1ualReXh6GDh2K7t27o1evXti1a5e2I+mtBw8eoG/fvujduzc8PT2xadMmbUfSa2VlZXBxcUFERIS2o+g1iUSC3r17o3fv3nj99de1HUevZWdnY9iwYejevTs8PT1RWlqq7UgGaf369XB1dYWZmRl8fHyQmpra6PrJycnw8fGBmZkZOnXqhNjYWA0llU+R/Lt378bIkSPRpk0bWFlZwc/PD/v27dNgWlmK1v1Thw8flv5bo02K5q+oqMDChQvh4uICU1NTdO7cGVu3btVQ2voUzb99+3Z4eXnBwsICDg4OmDFjBoqKijSU9k8pKSkYP348HB0dIRKJsGfPnmduoyvtVtHsutZmlan7p7TdbpXJrmtttjZ2uqlREokEq1evxrlz5/Drr7/i/fff5x+SSrKwsEBycjKysrJw7NgxREVFaeXkZyg+++wz9O/fX9sx9J6NjQ2ysrKQlZWFzZs3azuOXgsJCcGyZctw7tw5JCcn68UrTPRNQkICwsLCsHDhQpw4cQL+/v4YM2YMcnNz5a6fnZ2Nl156Cf7+/jhx4gQ++ugjzJ07F99//72Gkz+haP6UlBSMHDkSe/fuRWZmJoYNG4bx48fjxIkTGk6uePan7t+/j+DgYAwfPlxDSeVTJv+UKVNw8OBBbNmyBRcuXEB8fDy6deumwdR/UjR/WloagoODMWvWLJw9exa7du1Cenq6Vi6ulpaWwsvLCzExMU1aX5faraLZdanNAornf0oX2q0y2XWpzdYjECnA09NTyM3N1XYMvVdUVCQ4OzsLt2/f1nYUvXTx4kUhMDBQiIuLEz744ANtx9FrdnZ22o5gEM6cOSMMHz5c2zEMXr9+/YTQ0FCZsm7dugnz58+Xu35kZKTQrVs3mbI333xTGDBggNoyNkbR/PJ0795dWLp0qaqjPZOy2YOCgoRFixYJS5YsEby8vNSYsHGK5v/3v/8tWFtbC0VFRZqI90yK5v/iiy+ETp06yZStXbtWcHJyUlvGpgAgJCUlNbqOrrXbp5qSXR5ttdm6FMmvK+32qaZk17U2WxfvdBu4pgzNaOpwpYyMDNTU1KBDhw5qTq2bVFGXxcXF8PLygpOTEyIjI9G6dWsNpdcdqqjHiIgIREVFaSix7lJFXZaUlMDHxweDBw9GcnKyhpLrnubW5aVLl2BpaYkJEyagT58+WL58uQbTPx8qKyuRmZmJgIAAmfKAgAAcOXJE7jZHjx6tt/6oUaOQkZGBx48fqy2rPMrkr6umpgYPHjyAra2tOiI2SNnscXFxuHLlCpYsWaLuiI1SJv+PP/4IX19fREdHo3379nBzc0NERATKy8s1EVmGMvkHDhyI/Px87N27F4IgoLCwEN999x3Gjh2ricjNokvttrm01WabQ1faraJ0qc3Kw063gXvW0IymDlcqKipCcHAwNm7cqInYOkkVdWljY4OTJ08iOzsbO3bsQGFhoabi64zm1uMPP/wANzc3uLm5aTK2TlLFZzInJweZmZmIjY1FcHAwSkpKNBVfpzS3Lh8/fozU1FR89dVXOHr0KA4cOIADBw5o8lcweHfu3EF1dTXs7e1lyu3t7XHz5k2529y8eVPu+lVVVbhz547assqjTP66Vq5cidLSUkyZMkUdERukTPZLly5h/vz52L59OyQSiSZiNkiZ/FevXkVaWhrOnDmDpKQkrF69Gt999x3eeecdTUSWoUz+gQMHYvv27QgKCoKJiQnatWsHGxsbrFu3ThORm0WX2m1zaavNKkuX2q2idKnNyqXtW+2kOZAzNKMpw5UePXok+Pv7C9u2bdNETL2gbF3WFhoaKiQmJqorol5Qph7nz58vODk5CS4uLoKdnZ1gZWWlE8O2tE0Vn8nRo0cL6enp6oqoN5SpyyNHjgijRo2SLouOjhaio6PVnvV5cv36dQGAcOTIEZnyTz/9VHB3d5e7TdeuXYXly5fLlKWlpQkAhIKCArVllUeZ/LXt2LFDsLCwEA4cOKCuiA1SNHtVVZXg6+srbNiwQVqmzWGqytT9yJEjBTMzM6G4uFha9v333wsikUgoKytTa966lMl/9uxZwcHBQYiOjhZOnjwp/PLLL4Knp6cwc+ZMTURukLx/X+vSpXZbW1Oy16bNNivPs/LrWrutrSl1r0ttVh7e6X6ONWW4kiAICAkJwYsvvohp06ZpI6ZeaEpdFhYWSu8ilpSUICUlBe7u7hrPqsuaUo9RUVHIy8tDTk4OvvzyS8yePRuLFy/WRlyd1pS6vHfvHioqKgAA+fn5OHfuHDp16qTxrLquKXXZt29fFBYW4t69e6ipqUFKSgo8PDy0EddgtW7dGkZGRvXu7N26daveXbGn2rVrJ3d9iUQCOzs7tWWVR5n8TyUkJGDWrFlITEzEiBEj1BlTLkWzP3jwABkZGZgzZw4kEgkkEgmWLVuGkydPQiKR4LffftNUdADK1b2DgwPat28Pa2traZmHhwcEQUB+fr5a89alTP6oqCgMGjQI8+bNQ69evTBq1CisX78eW7duRUFBgSZiK02X2q2ytN1mlaFr7VZRutRm5WGn+znWlOFKhw8fRkJCAvbs2SN9rdDp06e1EVenNaUu8/PzMWTIEHh5eWHw4MGYM2cOevXqpY24OksVwy/piabU5fnz5+Hr6wsvLy+MGzcOa9as0at5Z5rSlLqUSCRYvnw5hgwZgl69eqFr164YN26cNuIaLBMTE/j4+NQbtn/gwAEMHDhQ7jZ+fn711t+/fz98fX1hbGystqzyKJMfAOLj4xESEoIdO3ZobT6uotmtrKxw+vRp6ZsRsrKyEBoaCnd3d2RlZWn8zRPK1P2gQYNw48YNPHz4UFp28eJFiMViODk5qTVvXcrkLysrg1gs+2e+kZERgCc3VHSZLrVbZehCm1WGrrVbRelSm5VHvwbrk1qIRCKZnwVBkJYNHjwYNTU12oillxqrSx8fH2RlZWkhlf5prB5rCwkJ0VAi/dVYXQ4cOJAX0RTwrM/lmDFjMGbMGE3Heq6Eh4dj2rRp8PX1hZ+fHzZu3Ijc3FyEhoYCABYsWIDr169j27ZtAIDQ0FDExMQgPDwcs2fPxtGjR7FlyxbEx8frRf74+HgEBwdjzZo1GDBggPQij7m5uczdHF3LLhaL0bNnT5nt27ZtCzMzs3rlmqJo3b/66qv45JNPMGPGDCxduhR37tzBvHnzMHPmTJibm+t8/vHjx2P27NnYsGEDRo0ahYKCAoSFhaFfv35wdHTUaPaHDx/i8uXL0p+zs7ORlZUFW1tbODs763S7VTS7LrVZRfPrWrtVtO51rc3Wo7WB7aRxqDMfoqKiQjAyMhJ2794ts97cuXOFIUOGaDidfmFdqgbrUXVYl6rDutRtX331leDi4iKYmJgIffr0EZKTk6XLpk+fLrzwwgsy6x86dEjw9vYWTExMhI4dO8rMV9QGRfK/8MILAoB6X9OnT9d8cEHxuq9NF+aGKpr//PnzwogRIwRzc3PByclJCA8P1+rcUEXzr127Vujevbtgbm4uODg4CK+99pqQn5+v4dSC8Pvvvzf6Odbldqtodl1rs8rUfW3abLfKZNe1NlubSBB0fIwJqYxIJEJSUhImTZokLevfvz98fHywfv16aVn37t0xceJEvpKpEaxL1WA9qg7rUnVYl0RERKRKHF5u4J41NONZw5XoT6xL1WA9qg7rUnVYl0RERKQ22r7VTur1rKEZgtD4cCX6E+tSNViPqsO6VB3WJREREakLh5cTERERERERqQlfGUZERERERESkJux0ExEREREREakJO91EREREREREasJONxEREREREZGasNNNREREREREeiElJQXjx4+Ho6MjRCIR9uzZo9bjVVVVYdGiRXB1dYW5uTk6deqEZcuWoaampsn7YKebiIiIiMiAhISEYNKkSdKfhw4dirCwMLUes7KyEl26dMHhw4ebtZ+IiAjMnTtXRanIEJWWlsLLywsxMTEaOd6KFSsQGxuLmJgYnD9/HtHR0fjiiy+wbt26Ju+DnW4iIiIiIg0LCQmBSCSCSCSCRCKBs7Mz3nrrLdy7d0/lx9q9ezc++eQTle+3to0bN8LFxQWDBg1q1n4iIyMRFxeH7OxsFSUjQzNmzBh8+umnCAwMlLu8srISkZGRaN++PVq0aIH+/fvj0KFDSh/v6NGjmDhxIsaOHYuOHTti8uTJCAgIQEZGRpP3wU43EREREZEWjB49GgUFBcjJycHmzZvxr3/9C2+//bbKj2Nra4uWLVuqfL+1rVu3Dq+//nqz99O2bVsEBAQgNjZWBanoeTRjxgwcPnwYO3fuxKlTp/Dyyy9j9OjRuHTpklL7Gzx4MA4ePIiLFy8CAE6ePIm0tDS89NJLTd4HO91ERERERFpgamqKdu3awcnJCQEBAQgKCsL+/fuly6urqzFr1izpXFJ3d3esWbNGZh/V1dUIDw+HjY0N7OzsEBkZCUEQZNapO7xc3jxYGxsbfP311wCe3CmcM2cOHBwcYGZmho4dOyIqKqrB3+P48eO4fPkyxo4dKy3LycmBSCRCYmIi/P39YW5ujr59++LixYtIT0+Hr68vLC0tMXr0aNy+fVtmfxMmTEB8fHxTqpBIxpUrVxAfH49du3bB398fnTt3RkREBAYPHoy4uDil9vnhhx9i6tSp6NatG4yNjeHt7Y2wsDBMnTq1yftgp5voOfbxxx+jd+/eWjv+3/72N7zxxhtNWpdzvIiIyJBdvXoVv/zyC4yNjaVlNTU1cHJyQmJiIs6dO4fFixfjo48+QmJionSdlStXYuvWrdiyZQvS0tJw9+5dJCUlNSvL2rVr8eOPPyIxMREXLlzAt99+i44dOza4fkpKCtzc3GBlZVVv2ZIlS7Bo0SIcP34cEokEU6dORWRkJNasWYPU1FRcuXIFixcvltmmX79+yMvLw7Vr15r1e9Dz5/jx4xAEAW5ubrC0tJR+JScn48qVKwD+vCDU2NecOXOk+0xISMC3336LHTt24Pjx4/jmm2/w5Zdf4ptvvmlyLonKf1Mi0gkikajR5dOnT0dMTAzeffddDSWSVVhYiDVr1uDUqVNNWj8yMhKdO3fG+++/D1dXVzWnIyIiUr+ffvoJlpaWqK6uxqNHjwAAq1atki43NjbG0qVLpT+7urriyJEjSExMxJQpUwAAq1evxoIFC/DXv/4VABAbG4t9+/Y1K1dubi66du2KwYMHQyQSwcXFpdH1c3Jy4OjoKHdZREQERo0aBQB47733MHXqVBw8eFA693vWrFnSO+xPtW/fXrrfZx2bqLaamhoYGRkhMzMTRkZGMsssLS0BPPl8nT9/vtH9tGrVSvr9vHnzMH/+fLzyyisAAE9PT1y7dg1RUVGYPn16k3Kx001koAoKCqTfJyQkYPHixbhw4YK0zNzcXHr1Txu2bNkCPz+/Rq+c11Z7jteKFSvUG46IiEgDhg0bhg0bNqCsrAybN2/GxYsX610Mj42NxebNm3Ht2jWUl5ejsrJSOkrt/v37KCgogJ+fn3R9iUQCX1/fekPMFRESEoKRI0fC3d0do0ePxrhx4xAQENDg+uXl5TAzM5O7rFevXtLv7e3tATzptNQuu3Xrlsw25ubmAICysjKlfwd6Pnl7e6O6uhq3bt2Cv7+/3HWMjY3RrVu3Ju+zrKwMYrHsAHEjIyO+MoyIgHbt2km/rK2tIRKJ6pXVHV7+9BUjy5cvh729PWxsbLB06VJUVVVh3rx5sLW1hZOTE7Zu3SpzrOvXryMoKAitWrWCnZ0dJk6ciJycnEbz7dy5ExMmTJAp++677+Dp6Qlzc3PY2dlhxIgRKC0tlS7nHC8iIjIkLVq0QJcuXdCrVy+sXbsWFRUVMne2ExMT8f7772PmzJnYv38/srKyMGPGDFRWVjbruCKRqF6n/PHjx9Lv+/Tpg+zsbHzyyScoLy/HlClTMHny5Ab317p16waful57uPzTUXh1y+p2Xu7evQsAaNOmTRN/I3qePHz4EFlZWcjKygIAZGdnIysrC7m5uXBzc8Nrr72G4OBg7N69G9nZ2UhPT8eKFSuwd+9epY43fvx4fPbZZ/j555+Rk5ODpKQkrFq1Cn/5y1+avA92uolIxm+//YYbN24gJSUFq1atwscff4xx48ahVatWOHbsGEJDQxEaGoq8vDwAT67+DRs2DJaWlkhJSUFaWpr0wSgN/VFw7949nDlzBr6+vtKygoICTJ06FTNnzsT58+dx6NAhBAYGyvxRwDleRERkyJYsWYIvv/wSN27cAACkpqZi4MCBePvtt+Ht7Y0uXbpI56UCgLW1NRwcHPCf//xHWlZVVYXMzMxGj9OmTRuZEXGXLl2qd1fZysoKQUFB2LRpExISEvD9999LO8N1eXt7448//mjW3fXazpw5A2NjY/To0UMl+yPDkpGRAW9vb3h7ewMAwsPD4e3tLX02QFxcHIKDg/HBBx/A3d0dEyZMwLFjx9ChQweljrdu3TpMnjwZb7/9Njw8PBAREYE333xTodfwcXg5EcmwtbXF2rVrIRaL4e7ujujoaJSVleGjjz4CACxYsACff/45Dh8+jFdeeQU7d+6EWCzG5s2bpVew4+LiYGNjg0OHDskdjnbt2jUIgiAz/6ugoABVVVUIDAyUzt+qPfwM4BwvIiIybEOHDkWPHj2wfPlyxMTEoEuXLti2bRv27dsHV1dX/POf/0R6errMs03ee+89fP755+jatSs8PDywatUqFBcXN3qcF198ETExMRgwYABqamrw4Ycfytx9/vvf/w4HBwf07t0bYrEYu3btQrt27WBjYyN3f8OGDUNpaSnOnj2Lnj17NrseUlNTpU88J6pr6NChjV7gefoshNqjRpqjZcuWWL16NVavXq30Pninm4hk9OjRQ2beir29vUzn18jICHZ2dtL5V5mZmbh8+TJatmwpnSNua2uLR48eyVyNr628vBwAZOZ/eXl5Yfjw4fD09MTLL7+MTZs21RuqxjleRERk6MLDw7Fp0ybk5eUhNDQUgYGBCAoKQv/+/VFUVFTvPd4ffPABgoODERISAj8/P7Rs2fKZw15XrlyJDh06YMiQIXj11VcREREBCwsL6XJLS0usWLECvr6+6Nu3L3JycrB3795681qfsrOzQ2BgILZv3978CgAQHx+P2bNnq2RfRLpAJKhqHAgR6ayvv/4aYWFh9a58f/zxx9izZ490TkxISAiKi4tl3t05dOhQ9O7dW+bqXseOHREWFoawsDC89dZbOH78uNwTbZs2bWBtbV2v/NKlS3Bzc8OFCxfg5uYmLRcEAUeOHMH+/fuRlJSEmzdv4tixY9Ir+oWFhWjXrp30/Z5ERESkG06fPo0RI0ZIL8Qr6+eff8a8efNw6tQpSCQclEuGgXe6iahZ+vTpg0uXLqFt27bo0qWLzJe8DjcAdO7cGVZWVjh37pxMuUgkwqBBg7B06VKcOHECJiYmMu8a5RwvIiIi3eTp6Yno6OhnPkj1WUpLSxEXF8cONxkUdrqJqFlee+01tG7dGhMnTkRqaiqys7ORnJyM9957D/n5+XK3EYvFGDFiBNLS0qRlx44dw/Lly5GRkYHc3Fzs3r0bt2/fhoeHh3QdzvEiIiLSXdOnT6/3PBZFTZkyBf3791dRIiLdwE43ETWLhYUFUlJS4OzsjMDAQHh4eGDmzJkoLy+HlZVVg9u98cYb2Llzp/Q1IVZWVkhJScFLL70ENzc3LFq0CCtXrsSYMWOk23COFxERERHpG87pJiKtEAQBAwYMQFhYGKZOnfrM9TnHi4iIiIj0Ee90E5FWiEQibNy4EVVVVU1an3O8iIiIiEgf8U43ERERERERkZrwTjcRERERERGRmrDTTURERERERKQm7HQTERERERERqQk73URERERERERqwk43ERERERERkZqw001ERERERESkJux0ExEREREREakJO91EREREREREasJONxEREREREZGasNNNREREREREpCb/D/RMcRGyYbV4AAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", - "\n", - "model.plot(axes[0,0], 'Precipitate Density')\n", - "model.plot(axes[0,1], 'Volume Fraction')\n", - "model.plot(axes[1,0], 'Average Radius', label='Average Radius')\n", - "model.plot(axes[1,0], 'Critical Radius', label='Critical Radius')\n", - "axes[1,0].legend()\n", - "model.plot(axes[1,1], 'Size Distribution Density')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Saving\n", - "\n", - "The model can be saved into a numpy .npz format or a .csv format.\n", - "\n", - "$ PrecipitateModel.save(filename, compressed=True) $ or \n", - "\n", - "$ PrecipitateModel.save(filename, toCSV=True) $\n", - "\n", - "
\n", - "\n", - "To load the model, just make sure to add the file extension.\n", - "\n", - "$ model = PrecipitateModel.load('file.npz') $ or\n", - "\n", - "$ model = PrecipitateModel.load('file.csv') $" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", - "2. T. Wang, Z. Jin and J. Zhao, “Thermodynamic Assessment of the Al-Zr Binary System” *Journal of Phase Equilibria* 22 (2001) p. 544\n", - "3. J. D. Robson and P. B. Prangnell, “Dispersoid Precipitation and Process Modeling in Zirconium Containing Commercial Aluminum Alloys” *Acta Materialia* 49 (2001) p. 599" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Extra Factors.ipynb b/examples/Extra Factors.ipynb deleted file mode 100644 index 727a713..0000000 --- a/examples/Extra Factors.ipynb +++ /dev/null @@ -1,317 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extra Factors in the KWN Model\n", - "\n", - "The default options in the KWN model assumes bulk nucleation and a spherical precipitate. However, in real-life systems, these assumptions may not be true. Several options are present to model heterogenous nucleation and non-spherical precipitate shapes.\n", - "\n", - "## Nucleation\n", - "\n", - "The options for the nucleation site density includes the following: 'bulk', 'dislocations', 'grain_boundaries', 'grain_edges', 'grain_corners'\n", - "\n", - "The nucleation site density ($N_0$) for bulk nucleation is determined by the number of solutes in the bulk lattice. For dislocation, $N_0$ depends on the dislocation density. $N_0$ for grain boundaries, edges and corners depends on the grain size [1]. For grain boundary nucleation, the change in surface energy accounts for both the creation of the precipitate/matrix interface and removal of grain boundary, for which the grain boundary energy must be defined [2]. By default, the grain boundary energy is set to 0.3 $J/m^2$.\n", - "\n", - "While the KWNModel will automatically calculate the nucleation site densities for each site type, these values can be manually set." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.KWNBase import PrecipitateBase\n", - "\n", - "model = PrecipitateBase(t0 = 0, tf = 100, steps = 2e3, linearTimeSpacing = True)\n", - "\n", - "#Change nucleation site type to grain boundaries\n", - "model.setNucleationSite('grain_boundaries')\n", - "\n", - "#Set grain boundary energy for nucleation on grain boundaries/edges/corners\n", - "model.setGrainBoundaryEnergy(0.3)\n", - "\n", - "#Change dislocation density and grain size\n", - "model.setNucleationDensity(grainSize = 10, aspectRatio = 1, dislocationDensity = 5e12)\n", - "\n", - "#Manually set nucleation site density for each site type\n", - "model.bulkN0 = 1e30 #Bulk nucleation site density\n", - "model.dislocationN0 = 1e30 #Site density on dislocations\n", - "model.GBareaN0 = 1e30 #Site density on grain boundaries\n", - "model.GBedgeN0 = 1e30 #Site density on grain edges\n", - "model.GBcornerN0 = 1e30 #Site density on grain corners" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Shape factors\n", - "\n", - "Currently, the KWN model has support for ellipsoidal (prolate/needle and oblate/plate) and cuboidal precipitates. For cuboidal precipitates the cubic factor currently set constant at $\\sqrt{2}$ [3,4]. These shapes are defined in the KWN model by their equivalent spherical radius ($R_{sph}$) and an aspect ratio ($\\alpha$), where the $\\alpha$ can either be constant or as a function of $R_{sph}$.\n", - "\n", - "The aspect ratio is defined as the ratio of the long axis over the short axis. Conversion between the radius along the short axis ($r$) and the equivalent spherical radius ($R_{sph}$) is given by:\n", - "\n", - "Needle: $ R_{sph} = \\sqrt[3]{\\alpha} r $\n", - "\n", - "Plate: $ R_{sph} = \\sqrt[3]{\\alpha^2} r $\n", - "\n", - "Cuboidal: $ R_{sph} = \\sqrt[3]{\\frac{3 \\alpha}{4 \\pi}} r $\n", - "\n", - "Deviation from a spherical precipitate changes both the thermodynamics (Gibbs-Thomson effect) and kinetics (growth rate). The free energy contribution from the Gibbs-Thomson effect is given by:\n", - "\n", - "$$ \\Delta G_{TH} = g(\\alpha) \\frac{2 \\gamma V_M^\\beta}{R_{sph}} $$\n", - "\n", - "The changes in the growth rate is given by:\n", - "\n", - "$$ \\frac{dR}{dt} = f(\\alpha) \\frac{dR_{sph}}{dt} $$\n", - "\n", - "The functions of $g(\\alpha)$ and $f(\\alpha)$ are taken from Ref. 3 and 4." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#Change precipitate shape\n", - "model.setAspectRatioNeedle(ratio = 1.5)\n", - "model.setAspectRatioPlate(ratio = 1.5)\n", - "model.setAspectRatioCuboidal(ratio = 1.5)\n", - "\n", - "#Remove aspect ratio and set to spherical shape\n", - "model.setSpherical()\n", - "\n", - "#Radius-dependent aspect ratio\n", - "ar = lambda r: 2.3 * (r/1e-9)**1.1\n", - "model.setAspectRatioNeedle(ratio = ar)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Strain Energy\n", - "\n", - "Molar volume differences between the matrix and precipitate phase can induce strains, which reduces the driving force for nucleation. For spherical and cuboidal precipitates, the strain energy can be calculated by Khachaturyan's approximation. For ellipsoidal precipitates, the strain energy can be calculated using Eshelby's tensor [4,5].\n", - "\n", - "Similar to the Thermodynamics and Surrogate modules, the strain energy is calculated using a module separated from KWNBase. Inserting the strain energy parameters requires creating and setting up a StrainEnergy object, then inserting it into the KWN model for a specified phase.\n", - "\n", - "The StrainEnergy object requires the elastic constants and eigenstrains to be defined. External stresses can also be defined if applicable. The eigenstrains and external stress can be defined as a tensor (3x3), values along the three axes (array of length 3), or a single value to be applied on all 3 axes. The elastic constants can be defined using its 6x6 tensor, the three elastic constants ($c_{11}$, $c_{12}$ and $c_{44}$), or by at least two moduli (e.g. elastic modulus, poission ratio, shear modulus, bulk modulus, etc.)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.ElasticFactors import StrainEnergy\n", - "\n", - "#Create StrainEnergy object\n", - "se = StrainEnergy()\n", - "\n", - "#Set elastic tensor by its elastic modulus and possion ratio\n", - "se.setModuli(E = 100e9, nu = -0.3)\n", - "\n", - "#Set eigenstrains\n", - "# [[0.01, 0.00, 0.00]\n", - "# [0.00, 0.01, 0.00]\n", - "# [0.00, 0.00, 0.02]]\n", - "se.setEigenstrain([0.01, 0.01, 0.02])\n", - "\n", - "#Insert StrainEnergy object into KWN model\n", - "model.setStrainEnergy(se)\n", - "\n", - "#Use strain energy to calculate aspect ratio (for plate- and needle-like precipitates)\n", - "#This will override the aspect ratio that was defined when setting the precipitate shape\n", - "model.setStrainEnergy(se, calculateAspectRatio=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Strain Energy Example (Cu-Ti system)\n", - "\n", - "In the Cu-Ti system (dilute Ti), the needle-like $Cu_4Ti$ precipitate creates lattice strains in the Cu-matrix. The following parameters are applicable to this system (from K. Wu et al (2018)):\n", - "\n", - "Eigenstrains of the $Cu_4Ti$ precipitate:\n", - "\n", - "$ \\epsilon_{11} = 0.022 $\n", - "\n", - "$ \\epsilon_{22} = 0.022 $\n", - "\n", - "$ \\epsilon_{33} = 0.003 $\n", - "\n", - "Elastic constants for the Cu matrix\n", - "\n", - "$ c_{11} = 168.4 \\quad GPa $\n", - "\n", - "$ c_{12} = 121.4 \\quad GPa $\n", - "\n", - "$ c_{44} = 75.4 \\quad GPa $\n", - "\n", - "We can use these values to determine the strain energy of the $Cu_4Ti$ precipitate for any given aspect ratio. In this example, we'll vary the aspect ratio from 1 to 2 and calculate the strain energy. The volume of the precipitate will be set constant to the volume of a sphere with a radius of 4 nm." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "#By default, StrainEnergy outputs 0\n", - "#This is changed within the KWN model before the model is solved for\n", - "#However, we can manually change it. For this example, we need to set it to the calculate for ellipsoidal shapes\n", - "se.setEllipsoidal()\n", - "\n", - "#Set elastic tensor by c11, c12 and c44 values\n", - "se.setElasticConstants(168.4e9, 121.4e9, 75.4e9)\n", - "\n", - "#Set eigenstrains\n", - "se.setEigenstrain([0.022, 0.022, 0.003])\n", - "\n", - "#Setup strain energy parameters\n", - "se.setup()\n", - "\n", - "#Aspect ratio\n", - "aspect = np.linspace(1, 2, 100)\n", - "\n", - "#Equivalent spherical radius of 4 nm\n", - "rSph = 4e-9 / np.cbrt(aspect)\n", - "r = np.array([rSph, rSph, aspect*rSph]).T\n", - "\n", - "E = se.strainEnergy(r)\n", - "\n", - "plt.plot(aspect, E)\n", - "plt.xlim([1, 2])\n", - "plt.ylim([1.0e-17, 1.5e-17])\n", - "plt.xlabel('Aspect Ratio')\n", - "plt.ylabel('Strain Energy (J)')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculating Aspect Ratio from Strain Energy\n", - "\n", - "The aspect ratio for plate- and needle-like precipitates can be determined by minimizing the energy contributions from the strain and interfacial energy contributions.\n", - "\n", - "$$ \\alpha = argmin\\left( \\frac{4}{3}\\pi R_{sph}^{3} \\Delta G_{el}(\\alpha) + 4 \\pi R_{sph}^{2} g(\\alpha) \\gamma \\right) $$\n", - "\n", - "Where $R_{sph}$ is the equivalent spherical radius. The strain energy module has two options for calculating the equilibrium aspect ratio: iterative or searching. The iterative method (StrainEnergy.eqAR_byGR) performs a Golden Section search to find the minimum. The search method (StrainEnergy.eqAR_bySearch) will calculate the net energy contribution for a number of aspect ratios and will return the aspect ratio that gives the minimum. By default, this method is accurate up to 2 significant digits. In addition, due to caching, this method is also faster than the iterative method for large number of calculations.\n", - "\n", - "## Example ($\\gamma''$ in IN718)\n", - "\n", - "The $\\gamma''$ precipitate in IN718 are plate shape where the aspect ratio depends on the size of the precipitate. Using the elastic properties of IN718 (shear modulus of 57.1 GPa and Poisson's ratio of 0.33) and the eigenstrain of the $\\gamma''$ precipitate, the relationship between the aspect ratio and precipitate diameter (long axis) can be found." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from kawin.ShapeFactors import ShapeFactor\n", - "\n", - "#Strain energy parameters\n", - "se = StrainEnergy()\n", - "se.setEigenstrain([6.67e-3, 6.67e-3, 2.86e-2])\n", - "se.setModuli(G=57.1e9, nu=0.33)\n", - "se.setEllipsoidal()\n", - "se.setup()\n", - "\n", - "#Shape factor parameters (only the shape needs to be defined)\n", - "sf = ShapeFactor()\n", - "sf.setPlateShape()\n", - "\n", - "#Calculate equilibrium aspect ratio\n", - "gamma = 0.02375\n", - "Rsph = np.linspace(1e-10, 40e-9, 100)\n", - "eqAR = se.eqAR_bySearch(Rsph, gamma, sf)\n", - "\n", - "#Convert spherical radius to diameter of the plate\n", - "R = 2*Rsph / np.cbrt(eqAR**2)*eqAR\n", - "\n", - "#Plot diameter vs. aspect ratio\n", - "plt.plot(R, eqAR)\n", - "plt.xlim([0, 40e-9])\n", - "plt.ylim([1, 9])\n", - "plt.xlabel('Diameter (m)')\n", - "plt.ylabel('Aspect Ratio')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. Kozeschnik, Ernst et al. Precipitation Modeling, Momentum Press, 2012\n", - "2. P. J. Clemm and J. C. Fisher, \"The Influence of Grain Boundaries on the Nucleation of Secondary Phases\" *Acta Metallurgica* 3 (1955) p. 70\n", - "3. B. Holmedal, E. Osmundsen, Q. Du, \"Precipitation of Non-Spherical Particles in Aluminum Alloys Part I: Generalization of the Kampmann-Wagner Numerical Model\" *Metallurgical and Materials Transactions A* 47 (2016) p. 581\n", - "4. K. Wu, Q. Chen and P. Mason, \"Simulation of Precipitation Kinetics with Non-Spherical Particles\" *J. Phase Equilib. Diffus.* 39 (2018) p. 571\n", - "5. C. Weinberger, W. Cai and D. Barnett, ME340B Lecture Notes - Elasticity of Microscopic Structures, Standford University 2005. http://micro.standford.edu/~caiwei/me340b/content/me340b-notes_v01.pdf" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "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": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Homogenization Model.ipynb b/examples/Homogenization Model.ipynb deleted file mode 100644 index 3a64f7c..0000000 --- a/examples/Homogenization Model.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Homogenization Model\n", - "\n", - "## Example - Fe-Cr-Ni system\n", - "\n", - "The homogenization model can simulate multiphase diffusion without having to resort to more complex methods such as phase field modeling. The model relies on the assumption that every volume element is in local equilibrium. Then fluxes are determined by the mobility and chemical potential gradient.\n", - "\n", - "$$ J_k = -\\Gamma_k^* \\frac{\\partial \\mu_k^{eq}}{\\partial z} $$\n", - "\n", - "$$ \\Gamma_k^\\phi = M_k^\\phi x_k^\\phi $$\n", - "\n", - "$$ \\Gamma_k^* = f(\\Gamma_k^\\alpha, \\Gamma_k^\\beta, ...) $$\n", - "\n", - "$\\Gamma_k^*$ is an average mobility term that assumes certain geometry in the system. The following averaging functions are available in kawin:\n", - "\n", - "1. Upper Wiener - assumes phases are continuous layers parallel to flux\n", - "2. Lower Wiener - assumes phases are continuous layers orthogonal to flux\n", - "3. Upper Hashin-Shtrikman - assumes a matrix of the phase with the fastest mobility with spheres of all other phases\n", - "4. Lower Hashin-Shtrikman - assumes a matrix of the phase with the slowest mobility with spheres of all other phases\n", - "5. Labyrinth - assumes phases as precipitates\n", - "\n", - "Note that the Hashin-Shtrikman bounds are much narrower than the Wiener bounds.\n", - "\n", - "The fluxes are calculated in a lattice fixed frame of reference. To convert to a volume fixed frame, the flux is then defined by:\n", - "\n", - "$$ J_k^v = J_k - x_k \\sum{J_j} $$\n", - "\n", - "In this example a Fe-25.7Cr-6.5Ni / Fe-42.3Cr-27.6Ni diffusion couple will be simulated using the lower and upper Hashin-Shtrikman bounds. Both sides of the diffusion couple are $\\alpha+\\gamma$.\n", - "\n", - "The first step is the load the thermodynamic database." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import GeneralThermodynamics\n", - "from kawin.Diffusion import HomogenizationModel\n", - "import matplotlib.pyplot as plt\n", - "\n", - "elements = ['FE', 'CR', 'NI']\n", - "phases = ['FCC_A1', 'BCC_A2']\n", - "\n", - "therm = GeneralThermodynamics('FeCrNi.tdb', elements, phases)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Defining the homogenization model is similar to defining the single phase diffusion model where the bounds of the domain, the number of volume elements, the defined elements and the defined phases are needed.\n", - "\n", - "As with the single phase diffusion model, inputting the composition profile and parameters are also the same. The only difference is that two extra parameters will be defined for the homogenization model:\n", - "\n", - "Smoothing factor ($\\varepsilon$) - this factor allows for the composition to smooth out when the chemical potential gradient is zero but the composition gradient is non-zero (in n-phase regions where n is the number of components). This can be viewed as an ideal contribution where the composition smoothes out to maximize entropy. By default, it is set to 0.05, but here, we will set it to 0.01.\n", - "\n", - "Mobility function - this defined which of the above mentioned mobility functions to use. We will start with the lower Hashin-Shtrikman bounds.\n", - "\n", - "Solving the model is also similar to the single phase diffusion model." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\nury\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pycalphad\\core\\utils.py:54: RuntimeWarning: invalid value encountered in divide\n", - " pts[:, cur_idx:end_idx] /= pts[:, cur_idx:end_idx].sum(axis=1)[:, None]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration\tSim Time (h)\tRun time (s)\n", - "0\t\t0.000\t\t0.000\n", - "253\t\t100.000\t\t26.615\n" - ] - } - ], - "source": [ - "ml = HomogenizationModel([-5e-4, 5e-4], 200, elements, phases)\n", - "ml.setCompositionStep(0.257, 0.423, 0, 'CR')\n", - "ml.setCompositionStep(0.065, 0.276, 0, 'NI')\n", - "ml.setTemperature(1100+273.15)\n", - "ml.setThermodynamics(therm)\n", - "ml.eps = 0.01\n", - "\n", - "ml.setMobilityFunction('hashin lower')\n", - "ml.solve(100*3600, True, 500)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next model will be the exact same except the mobility function will be switched to the upper Hashin-Shtrikman bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration\tSim Time (h)\tRun time (s)\n", - "0\t\t0.000\t\t0.000\n", - "500\t\t12.879\t\t48.213\n", - "1000\t\t27.378\t\t75.341\n", - "1500\t\t42.197\t\t94.020\n", - "2000\t\t56.994\t\t106.260\n", - "2500\t\t71.294\t\t115.187\n", - "3000\t\t85.527\t\t124.803\n", - "3490\t\t100.000\t\t132.300\n" - ] - } - ], - "source": [ - "mu = HomogenizationModel([-5e-4, 5e-4], 200, elements, phases)\n", - "mu.setCompositionStep(0.257, 0.423, 0, 'CR')\n", - "mu.setCompositionStep(0.065, 0.276, 0, 'NI')\n", - "mu.setTemperature(1100+273.15)\n", - "mu.setThermodynamics(therm)\n", - "ml.eps = 0.01\n", - "\n", - "mu.setMobilityFunction('hashin upper')\n", - "mu.solve(100*3600, True, 500)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To compare the two mobility functions, the Cr composition, Ni composition and $\\alpha$ phase fraction profile will be plotted. By default, the plotting functions will plot all components or phases; however, an individual component or phase can be defined to have it be the only thing that is plotted.\n", - "\n", - "Here, we can see that the upper Hashin-Shtrikman bounds gives a smoother Cr and Ni profile. Additionally, the lower Hashin-Shtrikman bounds shows a pure $\\gamma$ layer near the interface of around 4-6 $\\mu m$." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "fig, ax = plt.subplots(1,3, figsize=(15,4))\n", - "ml.plot(ax[0], plotElement='CR', label='lower')\n", - "ml.plot(ax[1], plotElement='NI', label='lower')\n", - "ml.plotPhases(ax[2], plotPhase='BCC_A2', label='lower')\n", - "\n", - "mu.plot(ax[0], plotElement='CR', label='upper')\n", - "mu.plot(ax[1], plotElement='NI', label='upper')\n", - "mu.plotPhases(ax[2], plotPhase='BCC_A2', label='upper')\n", - "\n", - "ax[0].set_ylabel('Composition CR (%at)')\n", - "ax[0].set_ylim([0.2, 0.45])\n", - "ax[1].set_ylabel('Composition NI (%at)')\n", - "ax[1].set_ylim([0, 0.35])\n", - "ax[2].set_ylabel(r'Fraction $\\alpha$')\n", - "ax[2].set_ylim([0, 0.8])\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot the composition profile on a phase diagram to further show the diffusion path and compare both mobility functions. Using the triangular plotting feature in pycalphad, the Fe-Cr-Ni ternary phase diagram can be plotted and the diffusion paths of the two homogenization models can be superimposed on top." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pycalphad import equilibrium, Database, ternplot, variables as v\n", - "from pycalphad.plot import triangular\n", - "from pycalphad.plot.utils import phase_legend\n", - "\n", - "fig = plt.figure(figsize=(6,6))\n", - "ax = fig.add_subplot(projection='triangular')\n", - "\n", - "conds = {v.T: 1100+273.15, v.P:101325, v.X('CR'): (0,1,0.015), v.X('NI'): (0,1,0.015)}\n", - "ternplot(therm.db, ['FE', 'CR', 'NI', 'VA'], phases, conds, x=v.X('CR'), y=v.X('NI'), ax = ax)\n", - "\n", - "ln1, = ax.plot(ml.getX('CR'), ml.getX('NI'), label='lower')\n", - "ln2, = ax.plot(mu.getX('CR'), mu.getX('NI'), label='upper')\n", - "\n", - "#The pycalphad ternplot function will automatically add a legend for the phases,\n", - "#but the legend has to be added again to add labels for the diffusion paths\n", - "handles, _ = phase_legend(phases)\n", - "ax.legend(handles = handles + [ln1, ln2])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. H. Larsson and L. Hoglund, \"Multiphase diffusion simulations in 1D using the DICTRA homogenization model\" *Calphad* 33 (2009) p. 495\n", - "2. H. Larsson and A. Engstrom, \"A homogenization approach to diffusion simulations applied to $\\alpha+\\gamma$ Fe-Cr-Ni diffusion couples\" *Acta Materialia* 54 (2006) p. 2431" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "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": "3.10.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "822df1fa43a9cb3d4c4a5882bc10c066bf8074b03729cc74aeda55033a52fda7" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Multicomponent Precipitation.ipynb b/examples/Multicomponent Precipitation.ipynb deleted file mode 100644 index 9122875..0000000 --- a/examples/Multicomponent Precipitation.ipynb +++ /dev/null @@ -1,251 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multicomponent Precipitation\n", - "\n", - "This example will use a ternary system (Ni-Cr-Al); however, the setup for any multicomponent system is mostly the same." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example - The Ni-Cr-Al system\n", - "\n", - "In the Ni-Cr-Al system, $Ni_3(Al,Cr)$ can precipitate into an $\\gamma$-Ni (FCC) matrix. As with binary precipitatation, the Thermodynamics module provides some functions to interface with pyCalphad in defining the driving force, growth rate and interfacial composition. Similarly, it is also possible to use user-defined functions for the driving force and nucleation as long as the function parameters and return values are consistent with the ones provides by the Thermodynamics module. Calphad models for the Ni-Cr-Al system was obtained from the STGE database and Dupin et al [1,2]. Mobility data for the Ni-Cr-Al system was obtained from Engstrom and Agren [3]." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import MulticomponentThermodynamics\n", - "from kawin.KWNEuler import PrecipitateModel\n", - "import numpy as np\n", - "\n", - "elements = ['NI', 'AL', 'CR', 'VA']\n", - "phases = ['FCC_A1', 'FCC_L12']\n", - "\n", - "therm = MulticomponentThermodynamics('NiCrAl.tdb', elements, phases)\n", - "\n", - "t0, tf, steps = 1e-1, 1e6, 2e4\n", - "model = PrecipitateModel(t0, tf, steps, elements=['Al', 'Cr'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Inputs\n", - "\n", - "Setting up model parameters is the same as for binary systems. The only difference is that the initial composition needs to be set as an array where the elements in the array will correspond to the same order of elements when the model was defined. In this case, [0.10, 0.085] corresponds to Ni-10Al-8.5Cr (at.%)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model.setInitialComposition([0.098, 0.083])\n", - "model.setInterfacialEnergy(0.023)\n", - "\n", - "T = 1073\n", - "model.setTemperature(T)\n", - "\n", - "a = 0.352e-9 #Lattice parameter\n", - "Va = a**3 #Atomic volume of FCC-Ni\n", - "Vb = Va #Assume Ni3Al has same unit volume as FCC-Ni\n", - "atomsPerCell = 4 #Atoms in an FCC unit cell\n", - "model.setVaAlpha(Va, atomsPerCell)\n", - "model.setVaBeta(Vb, atomsPerCell)\n", - "\n", - "#Set nucleation sites to dislocations and use defualt value of 5e12 m/m3\n", - "#model.setNucleationSite('dislocations')\n", - "#model.setNucleationDensity(dislocationDensity=5e12)\n", - "model.setNucleationSite('bulk')\n", - "model.setNucleationDensity(bulkN0=1e30)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Surrogate Modeling\n", - "\n", - "For efficiency, a surrogate model can be made on the driving force and interfacial composition. The surrogate models uses radial-basis function (RBF) interpolation and the scale and basis function can be defined (using RBF interpolation from Scipy). \n", - "\n", - "For multicomponent systems, a surrogate on the driving force and the various terms derived from the curvature of the free energy surface to calculate growth rate and interfacial composition (which will be referred to as \"curvature factors\") can be made. Both surrogates will need a set of compositions and temperatures to be trained on. When defining the range to train the surrogate model on, it is recommended to extend the range beyond what is expected to occur during the precipitate simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\nury\\Anaconda3\\lib\\site-packages\\pycalphad\\core\\utils.py:54: RuntimeWarning: invalid value encountered in divide\n", - " pts[:, cur_idx:end_idx] /= pts[:, cur_idx:end_idx].sum(axis=1)[:, None]\n" - ] - } - ], - "source": [ - "from kawin.Surrogate import MulticomponentSurrogate, generateTrainingPoints\n", - "\n", - "surr = MulticomponentSurrogate(therm)\n", - "\n", - "#Train driving force surrogate\n", - "xAl = np.linspace(0.02, 0.12, 8)\n", - "xCr = np.linspace(0.02, 0.12, 8)\n", - "xTrain = generateTrainingPoints(xAl, xCr)\n", - "surr.trainDrivingForce(xTrain, T)\n", - "\n", - "#Train curvature factors surrogate\n", - "xAl = np.linspace(0.05, 0.23, 16)\n", - "xCr = np.linspace(0, 0.12, 16)\n", - "xTrain = generateTrainingPoints(xAl, xCr)\n", - "surr.trainCurvature(xTrain, T)\n", - "\n", - "model.setSurrogate(surr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the Model\n", - "\n", - "Solving the model is the same as for binary precipitation." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N\tTime (s)\tTemperature (K)\tAl\tCr\t\n", - "5000\t5.6e+00\t\t1073\t\t9.5379\t8.3736\t\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tbeta\t3.499e+23\t\t3.0692\t\t2.6677e-09\t1.3989e+02\n", - "\n", - "N\tTime (s)\tTemperature (K)\tAl\tCr\t\n", - "10000\t3.2e+02\t\t1073\t\t8.9259\t8.5398\t\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tbeta\t2.290e+22\t\t10.3059\t\t9.6989e-09\t3.1439e+01\n", - "\n", - "N\tTime (s)\tTemperature (K)\tAl\tCr\t\n", - "15000\t1.8e+04\t\t1073\t\t8.8234\t8.5655\t\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tbeta\t4.761e+20\t\t11.5545\t\t3.6454e-08\t8.2386e+00\n", - "\n", - "N\tTime (s)\tTemperature (K)\tAl\tCr\t\n", - "20000\t1.0e+06\t\t1073\t\t8.7976\t8.5719\t\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tbeta\t8.772e+18\t\t11.8705\t\t1.3935e-07\t2.1532e+00\n", - "\n", - "Finished in 24.537 seconds.\n" - ] - } - ], - "source": [ - "model.solve(verbose=True, vIt = 5000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting\n", - "\n", - "Plotting is also the same as with binary precipitation. Note that plotting composition will plot all components." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", - "\n", - "model.plot(axes[0,0], 'Precipitate Density')\n", - "model.plot(axes[0,1], 'Volume Fraction')\n", - "model.plot(axes[1,0], 'Average Radius', color='C0', label='Avg. R')\n", - "model.plot(axes[1,0], 'Critical Radius', color='C1', label='R*')\n", - "axes[1,0].legend(loc='upper left')\n", - "model.plot(axes[1,1], 'Size Distribution Density', color='C0')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", - "2. N. Dupin, I. Ansara and B. Sundman, \"Thermodynamic Re-assessment of the Ternary System Al-Cr-Ni\" *Calphad* 25 (2001) p. 279\n", - "3. A. Engstrom and J. Agren, \"Assessment of Diffusional Mobilities in Face-centered Cubic Ni-Cr-Al Alloys\" *Z. Metallkd.* 87 (1996) p. 92" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "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": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Multiphase Precipitation.ipynb b/examples/Multiphase Precipitation.ipynb deleted file mode 100644 index bb42ec9..0000000 --- a/examples/Multiphase Precipitation.ipynb +++ /dev/null @@ -1,264 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multiphase Systems\n", - "\n", - "Kawin supports the usage of multiple phases. Nucleation and growth rate are handled for each precipitate phase independently. Coupling comes from the mass balance where all precipitates contribute to the overall mass changes in the system.\n", - "\n", - "In the Al-Mg-Si system, several phases can form including: $ \\beta' $, $ \\beta\" $, B', U1 and U2. To model precipitation of these phases, they must be defined in the .tdb file, the Thermodynamics module and the PrecipitateModel module.\n", - "\n", - "When defining the thermodynamics module, the first phase in the list of phases will be the parent phase." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import MulticomponentThermodynamics\n", - "\n", - "phases = ['FCC_A1', 'MGSI_B_P', 'MG5SI6_B_DP', 'B_PRIME_L', 'U1_PHASE', 'U2_PHASE']\n", - "therm = MulticomponentThermodynamics('AlMgSi.tdb', ['AL', 'MG', 'SI'], phases, drivingForceMethod='approximate')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In defining the precipitate model, all precipitate phases must be included. Since we already have our list of phases, we can use that and remove the parent phase." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.KWNEuler import PrecipitateModel\n", - "\n", - "model = PrecipitateModel(0, 25*3600, 1e4, phases=phases[1:], elements=['MG', 'SI'], linearTimeSpacing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model inputs\n", - "\n", - "Setting up parameters for the parent phase and overall system is the same as for single phase systems. Here, it is just the composition (Al-0.72Mg-0.57Si in mol. %), molar volume ($1e$-$5\\text{ }m^3/mol$).\n", - "\n", - "The temperature will be divided into two stages: a 16 hour temper at $175\\text{ }^oC$, followed by a 1 hour ramp up to $250 ^oC$. To do this, there needs to be three time designations: $175\\text{ }^oC$ at 0 hours, $175\\text{ }^oC$ at 16 hours and $250\\text{ }^oC$ at 17 hours. The temperature can be plotted to show the profile over time. Here, a parameter called timeUnits is passed to convert the time from seconds to either minutes or hours." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "model.setInitialComposition([0.0072, 0.0057])\n", - "model.setVmAlpha(1e-5, 4)\n", - "\n", - "lowTemp = 175+273.15\n", - "highTemp = 250+273.15\n", - "model.setTemperatureArray([0, 16, 17], [lowTemp, lowTemp, highTemp])\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n", - "model.plot(ax, 'Temperature', timeUnits='h')\n", - "ax.set_ylim([400, 550])\n", - "ax.set_xscale('linear')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setting parameters for each precipitate phase is similar to single phase systems except that the phase has to be defined when inputting parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "gamma = {\n", - " 'MGSI_B_P': 0.18,\n", - " 'MG5SI6_B_DP': 0.084,\n", - " 'B_PRIME_L': 0.18,\n", - " 'U1_PHASE': 0.18,\n", - " 'U2_PHASE': 0.18\n", - " }\n", - "\n", - "for i in range(len(phases)-1):\n", - " model.setInterfacialEnergy(gamma[phases[i+1]], phase=phases[i+1])\n", - " model.setVmBeta(1e-5, 4, phase=phases[i+1])\n", - " model.setThermodynamics(therm, phase=phases[i+1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the model\n", - "\n", - "As with single precipitate phase systems, running the model is exactly the same." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nucleation density not set.\n", - "Setting nucleation density assuming grain size of 100 um and dislocation density of 5e+12 #/m2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\nury\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pycalphad\\core\\utils.py:54: RuntimeWarning: invalid value encountered in divide\n", - " pts[:, cur_idx:end_idx] /= pts[:, cur_idx:end_idx].sum(axis=1)[:, None]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N\tTime (s)\tTemperature (K)\tMG\tSI\t\n", - "5000\t4.5e+04\t\t448\t\t0.3118\t0.1024\t\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tMGSI_B_P\t2.050e+22\t\t0.0595\t\t1.7575e-09\t8.6466e+03\n", - "\tMG5SI6_B_DP\t1.279e+24\t\t0.8200\t\t1.0870e-09\t1.5759e+03\n", - "\tB_PRIME_L\t1.172e+16\t\t0.0000\t\t1.5682e-09\t4.1972e+03\n", - "\tU1_PHASE\t3.080e+08\t\t0.0000\t\t3.8188e-10\t4.3610e+03\n", - "\tU2_PHASE\t1.222e+09\t\t0.0000\t\t4.7452e-10\t4.0113e+03\n", - "\n", - "N\tTime (s)\tTemperature (K)\tMG\tSI\t\n", - "10000\t8.6e+04\t\t523\t\t0.0571\t0.2035\t\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tMGSI_B_P\t5.299e+21\t\t1.0321\t\t7.3370e-09\t4.9014e+02\n", - "\tMG5SI6_B_DP\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-4.7837e+03\n", - "\tB_PRIME_L\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-2.5215e+03\n", - "\tU1_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t1.1839e+03\n", - "\tU2_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-8.9218e+02\n", - "\n", - "Finished in 307.622 seconds.\n" - ] - } - ], - "source": [ - "model.solve(verbose=True, vIt=5000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting\n", - "\n", - "Plotting is also the same as with single phase systems. The major difference is each phase will be plotted for the radius, volume fraction, precipitate density, nucleation rate and particle size distribution. In addition, the total amount of some variables, such as the precipitate density and volume fraction, can be plotted." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", - "\n", - "model.plot(axes[0,0], 'Total Precipitate Density', timeUnits='h', label='Total', color='k', linestyle=':', zorder=6)\n", - "model.plot(axes[0,0], 'Precipitate Density', timeUnits='h')\n", - "axes[0,0].set_ylim([1e5, 1e25])\n", - "axes[0,0].set_xscale('linear')\n", - "axes[0,0].set_yscale('log')\n", - "\n", - "model.plot(axes[0,1], 'Total Volume Fraction', timeUnits='h', label='Total', color='k', linestyle=':', zorder=6)\n", - "model.plot(axes[0,1], 'Volume Fraction', timeUnits='h')\n", - "axes[0,1].set_xscale('linear')\n", - "\n", - "model.plot(axes[1,0], 'Average Radius', timeUnits='h')\n", - "axes[1,0].set_xscale('linear')\n", - "\n", - "model.plot(axes[1,1], 'Composition', timeUnits='h')\n", - "axes[1,1].set_xscale('linear')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. E. Povoden-Karadeniz et al, \"Calphad modeling of metastable phases in the Al-Mg-Si system\" *Calphad* 43 (2013) p. 94\n", - "2. Q. Du et al, \"Modeling over-ageing in Al-Mg-Si alloys by a multi-phase Calphad-coupled Kampmann-Wagner Numerical model\" *Acta Materialia* 122 (2017) p. 178" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "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": "3.10.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "822df1fa43a9cb3d4c4a5882bc10c066bf8074b03729cc74aeda55033a52fda7" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Precipitation with Elastic Energy.ipynb b/examples/Precipitation with Elastic Energy.ipynb deleted file mode 100644 index fc5e2fd..0000000 --- a/examples/Precipitation with Elastic Energy.ipynb +++ /dev/null @@ -1,244 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Precipitation with Elastic Energy\n", - "\n", - "This example will cover adding a strain energy term to the KWN model. This strain energy term will also be used to calculate the aspect ratio as a function of precipitate radius." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example - The Cu-Ti system\n", - "\n", - "In copper alloys with dilute amounts of titanium, formation of $\\beta$-$Cu_4Ti$, a needle-like precipitate, can occur. Due to volume differences between the precipitate and the parent phase, the parent phase is put under strain. This strain comes with an elastic energy that serves to reduce the driving force for nucleation. In addition, the aspect ratio of the $\\beta$ precipitates depends on the size of the precipitate to minimize the elastic and interfacial energy contributions.\n", - "\n", - "To setup the KWN, the PrecipitateModel and BinaryThermodynamics will need to be defined. For BinaryThermodynamics, a mobility correction factor of 100 will be applied. This is to represent the presence of excess quench-in vacancies, which will speed up diffusion." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import BinaryThermodynamics\n", - "from kawin.KWNEuler import PrecipitateModel\n", - "\n", - "model = PrecipitateModel(1e-3, 1e5, 5000, phases=['CU4TI'], linearTimeSpacing=False, elements=['TI'])\n", - "\n", - "therm = BinaryThermodynamics('CuTi.tdb', ['CU', 'TI'], ['FCC_A1', 'CU4TI'], interfacialCompMethod='equilibrium')\n", - "therm.setMobilityCorrection('all', 100)\n", - "therm.setGuessComposition(0.15)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Inputs\n", - "\n", - "For model inputs, the composition will be Cu-1.9Ti (at.%) and the temperature will be $350\\text{ }^oC$. The molar volume of the matrix phase will be that of FCC copper with 2 atoms per unit cell. For the $\\beta$-$Cu_4Ti$ precipitates, the atomic volume and atoms per unit cell are taken from Ref. 5 from the SpringerMaterials database. Bulk nucleation will be assumed with $1e30\\text{ }sites/m^3$." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model.setInitialComposition(0.019)\n", - "model.setTemperature(350 + 273.15)\n", - "model.setInterfacialEnergy(0.035)\n", - "model.setThermodynamics(therm)\n", - "\n", - "VmAlpha = 7.11e-6\n", - "model.setVmAlpha(VmAlpha, 4)\n", - "\n", - "VaBeta = 0.25334e-27\n", - "model.setVaBeta(VaBeta, 20)\n", - "\n", - "model.setNucleationSite('bulk')\n", - "model.setNucleationDensity(bulkN0=1e30)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Elastic Energy\n", - "\n", - "Elastic energy has to be defined by a separate object, StrainEnergy. Here, the elastic constants and eigenstrains can be defined. It is important to check the order of the axes in the eigenstrains. For needle-like precipitates, the axes are (short axis, short axis, long axis). For plate-like precipitates, the axes are (long axis, long axis, short axis).\n", - "\n", - "When inputting the StrainEnergy object into the KWN model, setting \"calculateAspectRatio\" to True will allow for the aspect ratio to be calculated from the elastic energy. Otherwise, the aspect ratio will be taken from what was defined when defining the precipitate shape." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.ElasticFactors import StrainEnergy\n", - "\n", - "se = StrainEnergy()\n", - "se.setElasticConstants(168.4e9, 121.4e9, 75.4e9)\n", - "se.setEigenstrain([0.022, 0.022, 0.003])\n", - "\n", - "model.setStrainEnergy(se, calculateAspectRatio=True)\n", - "\n", - "#Set precipitate shape\n", - "#Since we're calculating the aspect ratio, it does not have to be defined\n", - "#Otherwise, a constant value or function can be inputted\n", - "model.setAspectRatioNeedle()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\nury\\Anaconda3\\lib\\site-packages\\pycalphad\\core\\utils.py:54: RuntimeWarning: invalid value encountered in divide\n", - " pts[:, cur_idx:end_idx] /= pts[:, cur_idx:end_idx].sum(axis=1)[:, None]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N\tTime (s)\tTemperature (K)\tMatrix Comp\n", - "2000\t1.6e+00\t\t623\t\t1.9000\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tCU4TI\t0.000e+00\t\t0.0000\t\t0.0000e+00\t1.9730e+03\n", - "\n", - "N\tTime (s)\tTemperature (K)\tMatrix Comp\n", - "4000\t2.3e+02\t\t623\t\t0.3379\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tCU4TI\t2.506e+25\t\t8.3499\t\t8.6947e-10\t6.3285e+02\n", - "\n", - "Finished in 44.440 seconds.\n" - ] - } - ], - "source": [ - "model.solve(verbose=True, vIt=2000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting\n", - "\n", - "As with the other examples, plotting is the same. Some additional things:\n", - "1. The variable 'timeUnits' is set to 'min' to plot in minutes rather than seconds\n", - "2. The equilibrium matrix composition is plotted to compare with the actual composition.\n", - "3. The mean aspect ratio and aspect ratio as a function of radius is plotted" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", - "\n", - "model.plot(axes[0,0], 'Precipitate Density', bounds=[1e-2, 1e4], timeUnits='min')\n", - "axes[0,0].set_ylim([1e10, 1e28])\n", - "axes[0,0].set_yscale('log')\n", - "\n", - "model.plot(axes[0,1], 'Composition', bounds=[1e-2, 1e4], timeUnits='min', label='Composition')\n", - "model.plot(axes[0,1], 'Eq Composition Alpha', bounds=[1e-2, 1e4], timeUnits='min', label='Equilibrium')\n", - "axes[0,1].legend()\n", - "\n", - "model.plot(axes[1,0], 'Average Radius', bounds=[1e-2, 1e4], timeUnits='min', label='Radius')\n", - "axes[1,0].set_ylim([0, 7e-9])\n", - "\n", - "ax1 = axes[1,0].twinx()\n", - "model.plot(ax1, 'Aspect Ratio', bounds=[1e-2, 1e4], timeUnits='min', label='Aspect Ratio', linestyle=':')\n", - "ax1.set_ylim([1,4])\n", - "\n", - "model.plot(axes[1,1], 'Size Distribution Density', label='PSD')\n", - "\n", - "ax2 = axes[1,1].twinx()\n", - "model.plot(ax2, 'Aspect Ratio Distribution', label='Aspect Ratio', linestyle=':')\n", - "axes[1,1].set_xlim([0, 1.5e-8])\n", - "ax2.set_ylim([1,7])\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", - "2. J. Wang et al, \"Experimental Investigation and Thermodynamic Assessment of the Cu-Sn-Ti Ternary System\" *Calphad* 35 (2011) p. 82\n", - "3. J. Wang et al, \"Assessment of Atomic Mobilities in FCC Cu-Fe and CuTi Alloys\" *Journal of Phase Equilibria and Diffusion* 32 (2011) p. 30\n", - "4. K. Wu, Q. Chen and P. Mason, \"Simulation of Precipitate Kinetics with Non-Spherical Particles\" *Journal of Phase Equilibria and Diffusion* 39 (2018) p. 571\n", - "5. Eremenko V.N., Buyanov Y.I., Prima S.B., \"Phase diagram of the system titanium-copper\" *Soviet Powder Metallurgy and Metal Ceramics* 5 (1966) p. 494" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "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": "3.9.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Single Phase Diffusion.ipynb b/examples/Single Phase Diffusion.ipynb deleted file mode 100644 index ac07a17..0000000 --- a/examples/Single Phase Diffusion.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Single Phase Diffusion\n", - "\n", - "## Example - NiCrAl System\n", - "\n", - "Along with precipitation, kawin also supports one dimensional diffusion models. In this example, a diffusion couple will be simulated between two different NiCrAl compositions. Both phases will be FCC.\n", - "\n", - "Note: Fluxes are calculated on a volume fixed frame of reference. In this frame of reference, the location of the Matano plane is fixed. If a lattice fixed frame of reference is used, then the movement of the Matano plane would move (this would be similar to the Smigelskas–Kirkendall experiments).\n", - "\n", - "## Setup\n", - "\n", - "The diffusion model handles the mesh creation and interfaces with the Thermodynamics module to compute fluxes from mobility and the curvature of the Gibbs free energy surface\n", - "\n", - "Loading the Thermodynamics object is the same as done for creating a precipitation model. The GeneralThermodynamics object can be used here since the functions necessary for the diffusion model are the same for binary and multicomponent systems." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import GeneralThermodynamics\n", - "\n", - "therm = GeneralThermodynamics('NiCrAl.tdb', ['NI', 'CR', 'AL'], ['FCC_A1'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next step is to create the diffusion model. The model requires the z-coordinates, elements and phases upon initialization. Initial conditions can be added with the composition either as a step function, linear function, delta function or a user-defined function. Finally, boundary conditions are assumed to be no-flux conditions; however, constant flux or composition may also be defined.\n", - "\n", - "Defining the initial and boundary conditions must specify the element it is being applied to.\n", - "\n", - "Here, a diffusion couple composed of Ni-7.7Cr-5.4Al / Ni-35.9Cr-6.2Al will be used.\n", - "\n", - "Plotting functions are stored in the diffusion object and can be used to look at the initial conditions." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 0.4)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from kawin.Diffusion import SinglePhaseModel\n", - "import matplotlib.pyplot as plt\n", - "\n", - "#Define mesh spanning between -1mm to 1mm with 50 volume elements\n", - "m = SinglePhaseModel([-1e-3, 1e-3], 100, ['NI', 'CR', 'AL'], ['FCC_A1'])\n", - "\n", - "#Define Cr and Al composition, with step-wise change at z=0\n", - "m.setCompositionStep(0.077, 0.359, 0, 'CR')\n", - "m.setCompositionStep(0.054, 0.062, 0, 'AL')\n", - "\n", - "m.setThermodynamics(therm)\n", - "m.setTemperature(1200 + 273.15)\n", - "\n", - "fig, axL = plt.subplots(1, 1)\n", - "axL, axR = m.plotTwoAxis(axL, ['AL'], ['CR'], zScale = 1/1000)\n", - "axL.set_xlim([-1, 1])\n", - "axL.set_xlabel('Distance (mm)')\n", - "axL.set_ylim([0, 0.1])\n", - "axR.set_ylim([0, 0.4])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to the initial and boundary conditions, the temperature and Thermodynamics object must be supplied to the diffusion model.\n", - "\n", - "Similar to the precipitation model, progress on the simulation can be outputted by setting verbose to True and setting vIt to the number of iterations before a status update on the model is outputted." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration\tSim Time (h)\tRun time (s)\n", - "0\t\t0.000\t\t0.000\n", - "100\t\t28.638\t\t4.114\n", - "200\t\t57.276\t\t7.580\n", - "300\t\t85.924\t\t9.422\n", - "349\t\t100.000\t\t9.936\n" - ] - } - ], - "source": [ - "m.solve(100*3600, verbose=True, vIt=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting\n", - "\n", - "Plotting the final composition profile is the same as plotting the initial profile." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axL = plt.subplots(1, 1)\n", - "axL, axR = m.plotTwoAxis(axL, ['AL'], ['CR'], zScale = 1/1000)\n", - "axL.set_xlim([-1, 1])\n", - "axL.set_xlabel('Distance (mm)')\n", - "axL.set_ylim([0, 0.1])\n", - "axR.set_ylim([0, 0.4])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. A. Borgenstam, A. Engstrom, L. Hoglund, J. Agren, \"DICTRA, a Tool for Simulation of Diffusional Transformations in Alloys\" *Journal of Phase Equilibria* 21 (2000) p. 269\n", - "2. A. Engstrom and J. Agren, \"Assessment of Diffusional MObilities in Face-Centered Cubic Ni-Cr-Al Alloys\" *Z. Metallkd.* 87 (1996) p. 92" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "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": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Strength Modeling.ipynb b/examples/Strength Modeling.ipynb deleted file mode 100644 index f83fcef..0000000 --- a/examples/Strength Modeling.ipynb +++ /dev/null @@ -1,310 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Strength Modeling\n", - "## Example - The Al-Sc system\n", - "\n", - "Precipitates obstruct dislocation movement and thus can increase the strength of an alloy in a process known at age/precipitation hardening. There are several mechanisms for how precipitates create an obstable for dislocations.\n", - "\n", - "The two main mechanisms involved are dislocation cutting and dislocation bowing. In the cutting mechanism, the dislocation cuts through the precipitate. Based off differences in properties of the matrix and precipitate phase, an additional force is required for the dislocation to cut through the precipitate. In the dislocation bowing mechanism (Orowan strengthening), the dislocation bows around the precipitate, creating a dislocation loop when it crosses over.\n", - "\n", - "In the Al-Sc system, $Al_3Sc$ can precipitate into an $\\alpha$-Al (FCC) matrix. Setting up the model will be similar to the Binary Precipitation example. Here, the time will be simulated up to 250 hours." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Cannot use 0 as an initial time when using logarithmic time spacing\n", - "\tSetting t0 to 9.000e-01\n" - ] - } - ], - "source": [ - "from kawin.KWNEuler import PrecipitateModel\n", - "from kawin.Thermodynamics import BinaryThermodynamics\n", - "import numpy as np\n", - "\n", - "therm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'SC'], ['FCC_A1', 'AL3SC'])\n", - "therm.setGuessComposition(0.24)\n", - "model = PrecipitateModel(0, 250*3600, 1e4, linearTimeSpacing=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As with the binary precipitation example, the model inputs are supplied here: initial composition, temperature, interfacial energy, molar volume, diffusivity and thermodynamics." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "model.setInitialComposition(0.002)\n", - "model.setTemperature(400+273.15)\n", - "model.setInterfacialEnergy(0.1)\n", - "\n", - "Va = (0.405e-9)**3\n", - "Vb = (0.4196e-9)**3\n", - "model.setVaAlpha(Va, 4)\n", - "model.setVaBeta(Vb, 4)\n", - "\n", - "diff = lambda x, T: 1.9e-4 * np.exp(-164000 / (8.314*T)) \n", - "model.setDiffusivity(diff)\n", - "\n", - "model.setThermodynamics(therm, addDiffusivity=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The strength model is implemented in the kawin.Strength module. For all strengthening mechanisms, parameters for the dislocation line tension is needed. This includes the shear modulus, the Burgers vector and the poisson ratio.\n", - "\n", - "There are several dislocation cutting mechanisms, where each is divided into a weak+coherent and strong+coherent contribution:\n", - "- Coherency - lattice misfit between matrix and precipitate creates a strain field that interacts with the dislocation\n", - " - Requires lattice misfit strain\n", - "- Modulus - dislocation energies differs between matrix and precipitate due to differences in the shear modulus\n", - " - Requires shear modulus of preciptiate phase\n", - "- Anti-phase boundary - an ordered precipitate will form an anti-phase boundary if a dislocation cuts through\n", - " - Requires anti-phase boundary energy\n", - "- Stacking fault energy (SFE) - partial dislocations that creates stacking faults will have different energies if the SFE differs between the matrix and precipitate\n", - " - Requires SFE of matrix and precipitate and Burgers vector of precipitate\n", - "- Interfacial energy (IE) - the surface area of a precipitate increases slightly if a dislocation cuts through it\n", - " - Requires interfacial energy between matrix and precipitate\n", - "\n", - "The differences between the weak+coherent and strong+coherent mechanisms is based off how must resistance a particle will give to dislocation cutting. \n", - "\n", - "For dislocation bowing, the precipitate becomes large and incoherent with the matrix. This mechanism is based off Orowan strengthening and requires no additional parameters apart from the parameters needed to define the dislocation line tension.\n", - "\n", - "For the Al-Sc system, parameters will be included for the coherency, modulus, anti-phase boundary and interfacial energy mechanism.\n", - "\n", - "The precipitate and strength model can be integrated by the StrengthModel.insertStrength function. This adds functions for the precipitate model to perform certain calculations necessary for the strength model. This includes the mean projected radius and inter-particle distance on a slip plane.\n", - "- Note: parameters for the strengthening mechanisms are not actually required for the precipitate model. The strength model will still work if the two models are combined first, then the precipitate model is solved and the strength parameters are added at the end." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Strength import StrengthModel\n", - "\n", - "sm = StrengthModel()\n", - "sm.setDislocationParameters(G=25.4e9, b=0.286e-9, nu=0.34)\n", - "sm.setCoherencyParameters(eps=2/3*0.0125)\n", - "sm.setModulusParameters(Gp=67.9e9)\n", - "sm.setAPBParameters(yAPB=0.5)\n", - "sm.setInterfacialParameters(gamma=0.1)\n", - "\n", - "sm.insertStrength(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the strengthening models can be done as a function of the particle radius or a function of time (if a solved precipitate model is supplied). For plotting over radius, the mean projected radius and inter-particle distance are needed. \n", - "\n", - "Estimating the inter-particle distance from the mean projected radius can be done by:\n", - "$$ L_s = r_{ss} \\left(\\sqrt{\\frac{3\\pi}{4f}} - \\frac{\\pi}{2} \\right) $$\n", - "Where f is the volume fraction of precipitates (taken to be 0.75% for Al-0.2Sc at.%).\n", - "\n", - "In the KWN model, the mean projected radius and inter-particle distance is be determined from the particle size distribution by:\n", - "$$ r_{ss} = \\sqrt{\\frac{2}{3}} \\frac{\\sum{n_i r^2_i}}{\\sum{n_i r_i}} $$\n", - "$$ L_s =\\sqrt{\\frac{ln{3}}{2\\pi\\sum{n_i r_i}} + (2r_{ss})^2} - 2r_{ss} $$" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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4ubskIiLmF4C16CbmDqdPn+bmm2/m7rvvpnfv3gQHB7Nx40ZmzZrF9ddfD5jdyb788ksuu+wybDYb4eHh1b7XiBEj6N27N+PHj2fu3LkUFxczZcoUhgwZQr9+/WpUnuDgYCZMmMDvf/97IiIiaNmyJc888wxeXl7VfgsvTYBCt4g0Nqq/m2z9re7lzqIu5iIiNRYUFMTAgQOZM2cOV155JQkJCfzf//0f9957L6+++ioAL730EqtWrSIuLo4+ffqc870sFgvLly8nPDycK6+8khEjRtChQwc++OCDWpVp9uzZDBo0iDFjxjBixAguu+wyunfvjp+fvlB1tpkzZ2KxWKpM52IYBs8++yyxsbH4+/szdOhQtm/fXuV1drudhx56iKioKAIDA7nuuus4evRo3Qqhe7pFRGpN9XfdWIzG1uHdA2RlZREaGkrm48GE2Mq+Qfnddght496CiUizU1BQwIEDB4iPj29UlUtTkJubS+vWrXnppZeYNGlStcec79/fUVdkZhISEtIQRfYIGzZsYNy4cYSEhDBs2DDmzp0LwIsvvsgLL7zAwoUL6dKlC88//zxff/01u3btcgyg85vf/IbPPvuMhQsXEhkZybRp00hLSyMxMRFvb+8LnrtK/X3Pcuj4CxdeqYjIuan+dp3GWH+rpdtZCtXSLSLiyTZv3sySJUvYt28fmzZtYvz48QCO7nJSfzk5OYwfP5633nqrSndDwzCYO3cuTz31FGPHjiUhIYFFixaRl5fH4sWLAcjMzGT+/Pm89NJLjBgxgj59+vD++++zbds2Vq9eXfvCFBc667JERMSNPKH+Vuiur/KBB9S9XETE4/31r3/loosuYsSIEeTm5rJu3TqioqLcXawm44EHHuCaa65hxIgRVfYfOHCAlJQURo0a5dhns9kYMmQI69evByAxMZGioqIqx8TGxpKQkOA45kx2u52srKwqi0NxgROvTERE3Kmx198aSK2+fIPMwK3QLSLi0fr06UNiYqK7i9FkLV26lE2bNrFhw4aznktJSQEgOjq6yv7o6GgOHTrkOMbX1/esAXmio6Mdrz/TzJkzee6556ovUIlaukVEmgJPqL/V0l1ftiBzrdAtIiJSrSNHjvDwww/z/vvvn/fexermWb3Q6LPnO+aJJ54gMzPTsRw5cqTiSbV0i4hIA1Hori/fstCte7pFRESqlZiYSGpqKn379sVqtWK1Wlm7di0vv/wyVqvV0cJ9Zot1amqq47mYmBgKCwtJT08/5zFnstlshISEVFkcNGWYiIg0EIXu+rKZI6pSlO/ecohIs6aJKNxD/+41M3z4cLZt28aWLVscS79+/Rg/fjxbtmyhQ4cOxMTEsGrVKsdrCgsLWbt2LYMHDwagb9+++Pj4VDkmOTmZpKQkxzG1otAtIo2A6hH3aOh/d93TXV/lLd1Fue4th4g0Sz4+PgDk5eXh7+/v5tI0P3l5Zi+n8p+DVC84OJiEhIQq+wIDA4mMjHTsnzp1KjNmzKBz58507tyZGTNmEBAQwG233QZAaGgokyZNYtq0aURGRhIREcH06dPp1avXWQOz1UixviwXEfdR/e1eDV1/K3TXl+OeblXeItLwvL29CQsLIzU1FYCAgIAL3gMr9WcYBnl5eaSmphIWFlajOaLl/B599FHy8/OZMmUK6enpDBw4kJUrVzrm6AaYM2cOVquVcePGkZ+fz/Dhw1m4cGHd/v3tOU4svYhI7aj+dg931d8K3fVVPmWY7ukWETeJiYkBcFTc0nDCwsIc//5SO2vWrKny2GKx8Oyzz/Lss8+e8zV+fn688sorvPLKK/UvQKF6qImIe6n+dp+Grr8VuutL3ctFxM0sFgutWrWiZcuWFBUVubs4zYaPj49auD1ZoVq6RcS9VH+7hzvqb4Xu+vIta+lW93IRcTNvb2+FQJGasme7uwQiIoDq7+ZAo5fXl0/ZwAfqpiYiIuI51NItIiINRKG7vnwCzbVaukVERDyHviwXEZEG4nGh+9ixY9x+++1ERkYSEBDAxRdfTGJiouN5wzB49tlniY2Nxd/fn6FDh7J9+/Yq72G323nooYeIiooiMDCQ6667jqNHj9atQOUt3UUaSE1ERMRjaPRyERFpIB4VutPT07nsssvw8fHhP//5Dzt27OCll14iLCzMccysWbOYPXs2r776Khs2bCAmJoaRI0eSnV1x79bUqVNZtmwZS5cu5ZtvviEnJ4cxY8ZQUlJSyxJZwLe8pVuhW0RExGOoe7mIiDQQjxpI7cUXXyQuLo4FCxY49rVv396xbRgGc+fO5amnnmLs2LEALFq0iOjoaBYvXszkyZPJzMxk/vz5vPfee4wYMQKA999/n7i4OFavXs1VV11Vu0Lpnm4RERHPo4HURESkgXhUS/enn35Kv379uPnmm2nZsiV9+vThrbfecjx/4MABUlJSGDVqlGOfzWZjyJAhrF+/HoDExESKioqqHBMbG0tCQoLjmJqzgC3Y3FQ3NREREc+hL8tFRKSBeFTo3r9/P2+88QadO3fmv//9L/fffz+//e1veffddwFISUkBIDo6usrroqOjHc+lpKTg6+tLeHj4OY85k91uJysrq8riUB661U1NRETEc5QWQbHd3aUQEZFmwKO6l5eWltKvXz9mzJgBQJ8+fdi+fTtvvPEGd955p+M4i8VS5XWGYZy170znO2bmzJk899xz1b/QFmKu7VnVPy8iIiKNkz0HrDZ3l0JERJo4j2rpbtWqFT169Kiyr3v37hw+fBiAmJgYgLNarFNTUx2t3zExMRQWFpKenn7OY870xBNPkJmZ6ViOHDlS9owFfIPMTXs2GEZ9Lk9EREQaUqHu6xYREdfzqNB92WWXsWvXrir7du/eTbt27QCIj48nJiaGVatWOZ4vLCxk7dq1DB48GIC+ffvi4+NT5Zjk5GSSkpIcx5zJZrMREhJSZal4sqx7uVGqEcxFREQ8ie7rFhGRBuBR3ct/97vfMXjwYGbMmMG4ceP48ccfmTdvHvPmzQPMbuVTp05lxowZdO7cmc6dOzNjxgwCAgK47bbbAAgNDWXSpElMmzaNyMhIIiIimD59Or169XKMZl5jlvIpwyyAYXZTK59CTERERBo3DYIqIiINwKNCd//+/Vm2bBlPPPEEf/zjH4mPj2fu3LmMHz/eccyjjz5Kfn4+U6ZMIT09nYEDB7Jy5UqCg4Mdx8yZMwer1cq4cePIz89n+PDhLFy4EG9v79oXymIx7+u2Z5pdzIOr76IuIiIijUxBhrtLICIizYDFMHQjcm1lZWURGhpK5hPhhMxIg9k9IOsY3PsVtL7E3cUTEZFGwFFXZGZWvS1J3MbxM3k8mBCbBW54Ay6+zd3FEhGRRsQV9bdH3dPdaDnm6taALCIiIh4j95S7SyAiIs2AQnd9lE8xprm6RUREPE/eaXeXQEREmgGFbmdQS7eIiIjnyVNLt4iIuJ5Cd72c0dKt0C0iItLoFRplA6fmqqVbRERcT6HbGXzLQ3eWe8shIiIiF5ROWb2t7uUiItIAFLrr48x7ujXfp4iISKOXYQSZG+peLiIiDUCh2xnUvVxERMRjnDbK6m11LxcRkQag0F0vuqdbRETE06RTNu+qPROKC91bGBERafIUup1BoVtERMRjZBoB4O1rPshJcW9hRESkyVPodgbHPN0K3SIiIo1dKV4Q3Mp8kHnMvYUREZEmT6G7Ps4aSE2hW0RExCOEtjHXWQrdIiLiWlZ3F6BJUOgWERHxLCGtzbVCt4hIs1VQVMLJbDup2QWkZtlJzbZzJMX5g2wqdNeLWrpFREQ8UmhZ6Fb3chGRJsUwDLLtxWUhuoCT2fayYG0nNavAXJdtZxUUn/X6Unue08vkktBdVFRESkoKeXl5tGjRgoiICFecpvEoD90FWe4th4iIiNSMWrpFRDxKRZguICXTzomsAk44WqgrWqpTswsoKCqt8fv6Wr1oGWwrW/wItRYxy8lld1rozsnJ4R//+AdLlizhxx9/xG63O55r06YNo0aN4r777qN///7OOqX7ld/T7RdmrovzodgOVpvbiiQiIiLnZ2CpCN2ZR91bGBERoaCohNQsOylZBWaYdiz2Ktv5RSU1fs9gm5UWITZaBNloGeJXEaxDzHBdHrJD/K1YynMdkJWV1ThD95w5c3jhhRdo37491113HY8//jitW7fG39+ftLQ0kpKSWLduHSNHjuTSSy/llVdeoXPnzs44deNgCwGLFxilkJ8BwdHuLpGIiIicT3g7c51+AAyj4ot0ERFxmuKSUk7lFDrCdGpWQdm2veyxGbQz84tq/J4hflaiQ/wcixmiy4J02XaLYBsBvo3nTmqnlGT9+vV89dVX9OrVq9rnBwwYwN13380bb7zBO++8w9q1a5tI6C6roL28zNbu/DTIT1foFhERaezC4811QSbkpUFgpHvLIyLiQQzDIDO/iORMM0SnlgXpM7dP5dgxjJq9p83qRUyoH9Fl4TmmUqiuvN2YwnRNOaXEH330UY2O8/PzY8qUKc44ZePjH14RukVERKTRMrCAbwCEtIGso5C2T6FbRKSMYRik5xWRnJlPckYByVkFpGTmmwG7bEnOLKhxV29vL0tZt24/ooNtZrAu6+5dvh1dTTfvpsRlXxPs2LGDw4cPU1hYWGX/dddd56pTNrzK/1P4h5trhW4REfFwzWZA1MgOZug+vRfiBri7NCIiLldaanA6t7AsOJtB2gzT+Y5W6+TMAgqLazYQWXiAD9Ehfo4W6uhQP6JDbEQHm/tahtiIDLTh7dU0w3RNOT1079+/n1/96lds27YNi8WCUdafoPxbi5KSmt/87lEUukVExIM1pwFRHT0dIzvBga/h9D53FkdExClKSg1O5dirhujMAo5Xenwiq4Cikpr1944K8qVVqD8xoX60CvWrWIf4Ox77+Xi7+KqaBqeH7ocffpj4+HhWr15Nhw4d+PHHHzl9+jTTpk3jr3/9q7NP52Zq6RYREc/XbAdEjSy7hpM/u7ccIiI1kGMv5nhGPsfS8zmWkc9xx1LAsYx8TmQVUFx64UBtsUCLIButQv2qCdVmoG4ZYsNmVaB2FqeH7u+++47//e9/tGjRAi8vL7y8vLj88suZOXMmv/3tb9m8ebOzT9k4KHSLiIiHqumAqG+++Sbz589vOgOixiSY6xNJ7i2HiDR7JaUGJ7IKzFBdFqTLQ3V5wM4qKL7g+3hZcHT3jq2ulTrUn5bBNny8vRrgqqSc00N3SUkJQUFBAERFRXH8+HG6du1Ku3bt2LVrl7NP516Vb01Q6BYREQ9V0wFRbTZbkxgQ1SivwKPLQnf6QXMUc79Qt5VJRJq27IIiR5CurpU6JauAkhq0Uof6+xAb5k/rMD9ah/kTW2Xxo0WQDasCdaPj9NCdkJDA1q1b6dChAwMHDmTWrFn4+voyb948OnToUK/3fvbZZ3nuueeq7IuOjiYlJQUwR9p77rnnmDdvHunp6QwcOJDXXnuNnj17Oo632+1Mnz6dJUuWkJ+fz/Dhw3n99ddp06ZNvcqm0C0iIuJhAiIgNA4yj0BKErS/zN0lEhEPZBgGWfnFHEnP42h6HkfT88sWc7umrdRWL4vZQh3mT+uypTxMtw7zp1WYP0E2z5suS1wQuv/whz+Qm5sLwPPPP8+YMWO44ooriIyM5IMPPqj3+/fs2ZPVq1c7Hnt7V9xrMGvWLGbPns3ChQvp0qULzz//PCNHjmTXrl0EBwcDMHXqVD777DOWLl1KZGQk06ZNY8yYMSQmJlZ5r1pT6BYRkSakKc9CYlTuqhbTqyx0b1PoFpFzyioo4miaGaSPVArUR9PzOZqWR7b9wqG6cit1bJVQbW63CNYo302V00P3VVdd5dju0KEDO3bsIC0tjfDwcKfMu2a1WomJiTlrv2EYzJ07l6eeeoqxY8cCsGjRIqKjo1m8eDGTJ08mMzOT+fPn89577zFixAgA3n//feLi4li9enWVsteMBlITEZGmpdnNQhLTG3Z9YYZuEWm2cuzFZpBOyy9rsa4I1kfS8mrUUh0V5Evr8ADiwv1pEx5Am3B/Wof700at1M1eg/zknTm/5549e4iNjcVmszFw4EBmzJhBhw4dOHDgACkpKYwaNcpxrM1mY8iQIaxfv57JkyeTmJhIUVFRlWNiY2NJSEhg/fr1dQjdlSh0i4hIE9C8ZiHBbOkGON5EB3oVEQCKSko5lp7P4bQ8DqXlcaRsOZpuhuyMvKILvkdEoC9twv2JKwvUbcrCdVyEP63DAvD31WjfUj2nhe68vDx+//vfs3z5coqKihgxYgQvv/wyUVFRzjoFAwcO5N1336VLly6cOHGC559/nsGDB7N9+3bHfd3R0dFVXhMdHc2hQ4cASElJwdfXl/Dw8LOOKX99dex2e5X5SrOysswNS3Ut3Rl1vDoRERH3aw6zkFQZqqhN2bzjqTvML879w6t7iYh4gKyCIg6fzuPQ6TwOp+VxOC3XDNmn8ziekc+FxikLC/A5I1Sb67iIAFqH+ROolmqpI6f9n/PMM8+wcOFCxo8fj7+/P4sXL+Y3v/lNjUdErYnRo0c7tnv16sWgQYPo2LEjixYt4tJLLwU4qwu7YRgX7NZ+oWNmzpx51gBuZymvpO2ZUFIM3vqlFBERz9OsZiEBCI6GyE5wei8c/gG6Xu3uEonIOZSWGiRnFXD4dNVAfaSs9fpCrdV+Pl60jQigbUQAcWXripZqf4L9fBroSqS5cVoy/OSTT5g/fz6//vWvARg/fjyXXXYZJSUl9Rug7DwCAwPp1asXe/bs4YYbbgDM1uxWrVo5jklNTXW0fsfExFBYWEh6enqV1u7U1FQGDx58zvM88cQTPPLII47HWVlZxMXFUeWe7srTjBRkQmBk/S5ORETEDVwxC8kbb7zBG2+8wcGDBwFzUNSnn37a8WV6Q88+UmUgNYB2g83QfehbhW4RNyssLuVIeh4HT+Vy4FSuI1AfTjPvty4sKT3v66OCfB3Bum1kIG0jAmgXGUC7iABaBNucMsaUSG05LXQfOXKEK664wvF4wIABWK1Wjh8/XhZQnc9ut7Nz506uuOIK4uPjiYmJYdWqVfTp0weAwsJC1q5dy4svvghA37598fHxYdWqVYwbNw6A5ORkkpKSmDVr1jnPY7PZsNls5y+MtxVsoWZLd95phW4REfFIrpiFpE2bNvz5z3+mU6dOgDnQ6fXXX8/mzZvp2bOne2cfAWh3GWx6Fw6tr9/7iEiNFJeUcjQ9nwOnczl4ylwOnDaD9tH0vPN2A7d6WWgT7k/byEDaOcJ1gCNoqwu4NEZO+7+ypKQEX1/fqm9utVJcfOGR/mpq+vTpXHvttbRt25bU1FSef/55srKymDBhAhaLhalTpzJjxgw6d+5M586dmTFjBgEBAdx2220AhIaGMmnSJKZNm0ZkZCQRERFMnz6dXr16OUYzr5UzvykLjDJDd+5JaNHFCVcsIiLSsFwxC8m1115b5fELL7zAG2+8wffff0+PHj3cMPvIGdqV9XY7vtnsrVa595qI1ElJqcHxjHwOlgXrA6fyOHAqh4Nl3cGLz5OsA3y9aRcZSHxUAO3KW6vLuoTHhvlrWi3xOE4L3YZhMHHixCotwgUFBdx///0EBgY69n3yySd1PsfRo0e59dZbOXXqFC1atODSSy/l+++/p127dgA8+uij5OfnM2XKFEf3tJUrVzq+JQeYM2cOVquVcePGObqnLVy40Dld4ANbQNo+M3SLiIh4kPMNiOrMWUhKSkr46KOPyM3NZdCgQS6dfeScA6Ge2b08rC1EdobTe2Df/6Dnr5x2vSJNmWEYnMyxsy81l/2nchzh+uDpXA6fzjtvV3Cb1Yv2kYG0jwqgfVQg8ZGB5joqkJbqBi5NjNNC94QJE87ad/vttzvr7QFYunTpeZ+3WCw8++yzPPvss+c8xs/Pj1deeYVXXnnFCSU648MgqIW5VugWEREPU3lAVD8/P5YsWeLUAVG3bdvGoEGDKCgoICgoiGXLltGjRw/Wrze7dLti9pEaDYRarstV8N0e2P1fhW6RMxQWl3I4LZe9qbnsO5lTtuSyPzWHbPu5e7X6ensRF+FPfFRgWcAOpEOUuY4J8cNLLdbSTDgtdC9YsMBZb+W5Alua65xU95ZDRESkls4cEPX222936oCoXbt2ZcuWLWRkZPDxxx8zYcIE1q5d63jeFbOPnGsg1Go7tXa5Gr57FfashNIS8NJ8u9L8pOcWsv9UDvvOCNeH0/IoOUd3cC8LxEUEEF/WSl0esOOjAtUVXKSMRhqoj7Pu6VZLt4iIeCZXD4jq6+vrGEitX79+bNiwgb/97W889thjgGtmH6nRQKjl2l5q3suddxoOfw/tL6vtJYp4hNJSg6Pp+ew9mX1WuE7LLTzn6wJ9venYMoiOLYLo2CKQDi3M7XaRAfj56EsqkfNxWui+++67a3TcO++846xTNj7qXi4iIh6qIQZErcwwDOx2u0tnH6kVbx/odi1seR+2faTQLR6vPFzvSc1m94kc9pzIZk9qDntTc8gvKjnn61qH+dOhRaAjXHdsEUTHlkG6z1qkHpwWuhcuXEi7du3o06cPhnGecf6blDNbutW9XEREPJMrB0R98sknGT16NHFxcWRnZ7N06VLWrFnDihUr3DL7yDn/Sul1kxm6dyyH0bPA6nuuI0UajdJSg2MZ+ew+URauU7PZc+L84drX6kWHqEA6lbVcl4fsDi0CCfBVR1gRZ3Pab9X999/P0qVL2b9/P3fffTe33367U0c79QhBZaFbLd0iIuJhXDkg6okTJ7jjjjtITk4mNDSU3r17s2LFCkaOHAk0gtlHysVfCUHRkHMC9q6Cbtc4771F6skwzHC9K8Vssd59ogbh2tuLDi0C6RIdTJfoIDq1NNdtIwKwens18BWINF8Ww4nN0na7nU8++YR33nmH9evXc8011zBp0iRGjRrVpLqjZGVlERoaSuafOhDyh30VT5zeB69cAr5B8OQx9xVQRETczlFXZGYSEhLi7uIIFT+Tv0wfx/S/fFD9QSv/D9a/DB2Hwx11n+ZUpD5y7MXsSsnm55Qsfk4uW6dkk11Q/e0e5eG6c3QwXVoGmWuFa5E6cUX97dT+IzabjVtvvZVbb72VQ4cOsXDhQqZMmUJRURE7duwgKCjImadrfMoHUivMgcI88A1wb3lERESkdvrdDetfgX1fwqm9ENXJ3SWSJqy01OBQWh4/J2exMyWbn5PNcH04La/a461eFjq2CKJzdJCj9bpzdDDtFK5FGjWX3bRhsViwWCwYhkFpaamrTtO42ILB6gfFBWYXc9927i6RiIhIjWhA1DIR8dB5FOz5L2ycD1fPdHeJpInIKihi5/EsdpYF650p2exOyT5n1/DoEBtdY0LoHhNMt1bBdIsJoWOLIHytCtcinsapobty9/JvvvmGMWPG8Oqrr3L11Vfj5dUUPyDO6DJvsZit3ZlHzNAdrtAtIiKeoTkNiGqcWX+facB9Zuje/D4MeQz8wxqkXNJ0nMqxk3Qsk+3Hs9h+3FwfOl1967XN6kXXmGC6xZjBujxgRwRqID+RpsJpoXvKlCksXbqUtm3bctddd7F06VIiIyOd9faeozx0awRzERHxIBoQtZKOv4CWPSB1B/zwJgx93N0lkkaqfHCz7cez2F4WspOOZ3Iiy17t8a3D/OneKoTurSoCdvvIQLy9ms7YRyJyNqeF7jfffJO2bdsSHx/P2rVrWbt2bbXH1WWqkUarusHhgluZ6+zkhi2LiIhIPbz++uvMmTPH0WPtiSeeaLIDol6QlxcMeRQ+mgjfvQ4D71drt2AYBgdP57H1aAY7ysL19uNZZOQVnXWsxQLxUYEkxIbSMzaEhNah9GgVQrhar0WaJaeF7jvvvLN5VcjnEhJrrrOOu7ccIiIitdRcBkS9YPdygO7XQ4vucHInfPcq/OIPri+YNCopmQX8dDSDn45ksPVoJluPZpBVzejhVi8LXaKDHeG6Z2wI3VuFEGjTfNciYnLap8HChQud9VYepJpKW6FbRESagGY5IGplXl7wi6fgg9vN0cz73KGxWpqwzLwifjqawdajGfx0NJOfjmSQmn12F3Ffqxc9WoXQq3VFC3bn6CBsVifOFy8iTY5TQvfhw4dp27ZtjY8/duwYrVu3dsapG5+QsuvK0jzdIiLiWZrfgKgX0G0MtL8CDq6DVU/DuEXuLpE4QUFRCduPZ7LlSGZZK3YGB6sZ5MzLAl2ig7moTRi940K5qE0YXWOC8dHUXCJSS04J3f379+e6667j3nvvZcCAAdUek5mZyYcffsjf/vY3Jk+ezEMPPeSMU7tXdd3p1dItIiIeSAOiVsNigav/DH+/AnYsh93/hS5XubtUUksnsgrYdCidxEPpbDqcTtKxLApLzu690S4ygN5twrioTSgXxYXRMzaEAF91EReR+nPKJ8nOnTuZMWMGV199NT4+PvTr14/Y2Fj8/PxIT09nx44dbN++nX79+vGXv/yF0aNHO+O0jVPl0G0Y1QdzERGRRqZZDohaEzEJcOkU877uzx6GKd+Bf7i7SyXnUFRSys/J2SQeSmPT4QwSD6VzLCP/rOMiA33p0zasrBU7jN6tQzXImYi4jFNCd0REBH/96195/vnn+eKLL1i3bh0HDx4kPz+fqKgoxo8fz1VXXUVCQoIzTteInKeluygXCjI12qmIiHiE5jQgao0GUqvsF3+A3Svg9F74z+Mw9u+uKZjUWmZ+EYmH0th40GzJ/uloBgVFVVuxvSzQNSaES9qG0bddOJe0DaddZECz+f9dRNzPqX1m/Pz8GDt2LGPHjnXm23oWH3/wj4D8NLO1W6FbREQ8QPMcELWGfPzhhjfgnatg61LoOAwu+rW7S9Usnc6x8+OBNH44kMaPB9LYmZKFYVQ9JsTPyiVl4bpvu3AuigsjSCOJi4gb6ROoPs71DWlI64rQHd2jYcskIiIiF1CHFs64AXDlo7D2z/DZVIjpBdE9nV4yqSols4AfDpx2hOy9qTlnHdMhKpB+7c2A3bddOB2igvDyUiu2iDQeCt2uEBILJ7ZpBHMREfEImoWkhoY8Ckc3wL4vzanE7vkSAiLcXaomJTkzn2/3nub7/af58UAah9POHlW8W0wwA+IjHEvLYD83lFREpOYUuuvlXC3d5YOpKXSLiEjj12xnIaktL28Y+xbMGwpp+2HxLTDhU7P7udRJRl4h3+8/zTd7T7F+72n2n8qt8ryXBRJahzKgvRmw+7eP0IBnIuJxFLpdISzOXGccdm85REREaqC5zUJiXPiQcwuMhPEfwTuj4OiP8M9JcMt7ZiCXC8ovLGHDwTS+3WeG7KTjmVXuyfayQK82YQzuGMnA+Aj6tgsn2M/HfQUWEXEChW5XCG9vrtMPurMUIiIiNdJ8ZyGpo5bd4Nal8O4NsOvfsOx+c6A1b/1ZdSbDMNiZnM2a3al8vfskmw5lnDVHdqeWQVzWMZLLOkUxsEMkof4K2SLStLi8dnjooYd45plniIqKcvp7z5w5kyeffJKHH36YuXPnAuaH+3PPPce8efNIT09n4MCBvPbaa/TsWTHYid1uZ/r06SxZsoT8/HyGDx/O66+/Tps2bWpXgHON0eEI3YdqfU0iIiLu0lxmITEMJwyy1W4w3PQOfDQBtn0IRgn8ap6CN5CZV8Q3e0+xZlcqa3efJDXbXuX5VqF+DO4YxeWdIxncMYroEN2TLSJNm8trhqFDh3LVVVfxq1/9imnTpuHv75z7njZs2MC8efPo3bt3lf2zZs1i9uzZLFy4kC5duvD8888zcuRIdu3aRXBwMABTp07ls88+Y+nSpURGRjJt2jTGjBlDYmIi3t5O6B4W1t5cZx+HogLwUWUiIiLS5HQfAzcvgo8mQtLHZp1/49vgG+DukjWo0lKDHclZrNmVyppdJ9l8JIOS0oo+4/4+3gzuGMmQri24vFMU8VGBmiNbRJoVl4fuG2+8keuvv5433niDSy+9lIceeohJkybV68M2JyeH8ePH89Zbb/H888879huGwdy5c3nqqacc39IvWrSI6OhoFi9ezOTJk8nMzGT+/Pm89957jBgxAoD333+fuLg4Vq9ezVVXXVWLkpzjGgIiwDcYCrPN+7pbdKnrpYqIiEhj1n0M3PI+fHiH2dV84TVm1/PgaHeXzKUKikr4Zs8pVu04wZc/p3Iqp2prdqeWQQzt0oKhXVvSr304fj66511Emi+vhjiJ1Wpl7NixTJs2jccff5yEhAS++OKLOr/fAw88wDXXXOMIzeUOHDhASkoKo0aNcuyz2WwMGTKE9evXA5CYmEhRUVGVY2JjY0lISHAcU28WC4S3M7cz1MVcRESkMTHqMk/3+XS9Gu78FPwj4PgmeHs4pCQ59xyNwOkcOx9uPMK9727k4j+u5J53N/LBxiOcyrET6OvNyB7RvPCrBL55bBirHxnCH8b04PLOUQrcItLsubyle/To0ezcuZM2bdowYMAAXnnlFbp06cJrr73GqlWrmDNnTq3eb+nSpWzatIkNGzac9VxKSgoA0dFVv12Ojo7m0KFDjmN8fX0JDw8/65jy15/Jbrdjt1d8g5uVlWVunK+1Prw9nEjSYGoiIiLNQbtBcM9q+MfNkLbPDN7XvAR9bnd3yerlwKlcVu1IYdWOEyQeSqdSr3Fah/kzskc0I7pHMyA+Al9rg7TliIh4HJeH7v/7v/9j4MCBZ90r/c4779CtW7dahe4jR47w8MMPs3LlSvz8zn2f9Jld1w3DuGB39vMdM3PmTJ577rkalxPQCOYiIiKNVL2mDDufyI5m8P7kXti7Gv71ABxaD6NngS3IVWd1un0nc/j31mS+2JbMzynZVZ7rGRvCyB7RjOwRTY9WIbo3W0SkBlz+leTkyZPJzq74wE5PT2fjxo0Ate5inpiYSGpqKn379sVqtWK1Wlm7di0vv/wyVqvV0cJ9Zot1amqq47mYmBgKCwtJT08/5zFneuKJJ8jMzHQsR44cKXvmPBVNWFn38rQDtbpGERGRxuT222939PD64osvWLZsmZtL1MgFRMBtH8Ev/g8sXrDlH/DGYDiwzt0lO699J3N45cs9XD33a4a/tJbZq3bzc0o2Vi8Ll3eK4rnrevLt47/g37+9gqkjutAzNlSBW0Skhlze0m21WgkLC3M8Dg0NZfLkySQmJtKhQ4davdfw4cPZtm1blX133XUX3bp147HHHqNDhw7ExMSwatUq+vTpA0BhYSFr167lxRdfBKBv3774+PiwatUqxo0bB0BycjJJSUnMmjWr2vPabDZsNlutykpkR3N9em/tXiciItKIbN26lZCQEHbs2MH06dO54oorWLt2rWOqTqmGlxdcOR3aXgrLfmOO77JoDAyYDMOfbjSt3gdP5fL51uN8vrVqi7bVy8JlnaK4pncrRvWIJizA142lFBHxfC4P3W3atOHbb7/lsssuA8DLy4vCwsI6vVdwcDAJCQlV9gUGBhIZGenYP3XqVGbMmEHnzp3p3LkzM2bMICAggNtuuw0wQ/+kSZOYNm0akZGRREREMH36dHr16nXWwGwXdL5veKM6m+u0/VBSrHk7RUTEI/n4+GAYBgsXLuSpp55i/Pjx9O3b193FqhenD6R2Lu0vh998C6v+DxIXwo9/h58/h1HPQ89fnf/vCBdJzy3k823JLNt0lE2HMxz7HUG7VytG9VTQFhFxJpcnwVdffZUxY8YwaNAg+vfvz7Zt22jbtq3Lzvfoo4+Sn5/PlClTSE9PZ+DAgaxcudIxRzfAnDlzsFqtjBs3jvz8fIYPH87ChQudM0d3uZA2YPWH4nzzG+7ylm8REREPct9999G/f3/S0tJ45plnAMjNzXVzqTyIXwhc+zfofi18/oj5N8E/74KN78Av/wItu7u8CPbiEr76OZVPNh3jq12pFJWYd7V7WeCyTlFc2ztWQVtExIUshmG4bDyRckVFRSxbtoxt27YRHR3NxIkTCQpqHF2r6iIrK4vQ0FAyX+xNyKM/nfvANy6HE9vg1g/M6URERKTZcNQVmZmEhIS4uzj1kpGRgdVqJSgoiL179/KnP/2JRYsWubtYtVb+M/njtDv5v7+6ofxF+fDt3+CbOVBcYN7zfdGtMOSxiqlGncQwDLYdy+SDDUf4fGsymflFjue6twphbJ/WXH9xLC1Dzj0wrYhIc+SK+tvlLd0///wzn332GWFhYYwePZqEhASPDty1EtXZDN2ndit0i4iIR5o4cSIJCQn07NmThIQEOnXq5JGBu1Hw8Yehj8NFv4aVf4Cdn5kDrW39EPrdDVdMg+DqB3WtqayCIv61+RhLfjzCjuQsx/7oEBs3XNyaX13Smm4xnv0lkIiIp2mQebrvueceMjIy+Pvf/862bdvIzc1l165drj51A7jAvVhRXcz1qd2uL4qIiIgL3HfffWzdupXPPvuMZ555ht27d9OjRw/Wr1/v7qJ5rvD2cMv7cHQjfPkcHPjavN970yK4eDwMfggi4mv8doZhsOlwOkt+PMLnW49TUFQKgK+3F6N7xXBz3zgGdYzE20ujjYuIuIPLQ3dMTAxPPfVUlX0lJSWuPm3jUD6YmkYwFxERDzV48GAGDx7sePz111/z5ZdfurFE9efy++pqqk0/mPAZ7F8DX/4Jjm2EjfMhcQH0uAEuexhiLz7ny/MLS1i+5RiL1h+sMvp455ZB/HpAW8b2aU14oO7TFhFxN5eH7quuuor33nuPO+64w7HPqQOWNWbloTt1JxiGW0YpFRERqY/MzExCQ0Mdj6+88krmzZvnxhI1QR2GQvwQOLjOvOd772rY/om5xA2E/vdAj+vBak5fejQ9j/e+P8TSH4847tX28/FiTO9Ybh0QxyVtwzWHtohII+Ly0P3jjz/yzjvv8NxzzzFgwAB69epFr169GDNmjKtP3QAu1L28K3hZoSADso5BaJsGKZWIiIizXHnlleTm5tKlSxcSEhLw8/Pjp5/OM4ioR2iEgdRigfgrzSVlmxm+ty+DIz/AkR8wVjxOcvyNvJ59BYv3eFNa1lwfF+HPhEHtublfHKH+Pu69BhERqZbLQ/cXX3wBmKPAJSUlkZSUxKpVq5pI6L4AHz/zvu7UHZCSpNAtIiIe43e/+x0XXXQRCxYsoEuXLhw+fJikpCTS09P57LPP3F28pi2mF9z4Nox6ntLERdh/eAf/vBRit8/jeeZxnU9XtkZcTcehd3Bl7066V1tEpJFzeehOS0vj7bffxtfXl6lTp1a5L8zj1aTrVnSCGbpPbNMI5iIi4jGGDh3K1q1b+fe//82OHTvw8vKiZ8+e9O7dm6SkJNq3b+/uIjZpRSWl/Gt3MW9uupQD6d0Z7rWJO6xfcpnXNgZ47WJAxi747E3YPRp63wIdf2F+2S8iIo2Oy0P3TTfdxI033sjrr7/O1KlTSUpKYsmSJbzwwguuPnXjEJMA2z40W7pFREQ8xPXXX8/111/veJyfn09SUhJbt27lyy+/9Ogea0Zj7F5exl5cwgcbjvDmmn0czywAINjPRudBv6bb4CfwKj0F2z6Cn5bCyZ2wY7m5+AZDl1HQ/TroPBJ8A916HSIiUsHloTs7O5sHHniAt99+G4CEhAS++OKLJhK6a1Bpx/Qy1ycUukVExHP5+/vTv39/+vfv7+6iNElFJaV8nHiUl7/c4wjbUUE27rkintsGtiXEr/x+7dZw+VRzZPOUrWb43r4cso9D0sfmYvWHTsPNAN5pBARGuuuyRESEBgjdLVu25Pjx41VG0SwoKHD1aRuP6LLQfXofFObqm2cREfEoEydOJCEhgZ49e5KQkEBcXJy7i1RvjWbKMKCk1OBfW44xd/UeDqflARAdYuPBYZ24uV8cfj7nmPHFYoFWF5nLqBfgWCLs/Bfs+BQyDsHPn5sLFmjdFzqPMlvAW10MXl4Ndn0iItIAoXvOnDlMmDCB1NRUPvjgA1asWEG3bt1cfdqGUZN7uoNaQFA05JyAEzsgTi0EIiLiOe677z62bt3KZ599xjPPPMPu3bvp3r073333nbuLVi+GYbh9Wq2vdqUy49872ZOaA0BUkC+/GdqJ8QPbnjtsV8fLy/z7Iq4/jPyT2QK+41PY/V9zTJljG81lzQwIbGG2fscPMUdKD23toqsTEZFyLg3dpaWlfPfdd/z73/9m+fLlbNu2jX79+nHXXXe58rSNT3RCWejeptAtIiIeZfDgwVUGQf3666/58ssv3Vgi5zCMmn137gq7UrJ54YudfL37JACh/j7cP6QjEwa3I8C3nn+aVW4BH/5/kHXcnPd7z0rYtwZyT8JPS8wFIKKDGb7bX2Gug1rW7/wiInIWl4ZuLy8vFixYwIQJExg3bhzjxo1z5ekar9iLYd+XZtevfne7uzQiIiI1lpmZSWhoqOPxlVdeybx589xYovozsLili/npHDuzV+1myY+HKTXAx9vCXZfF88CwTq6bYzskFi6501yKC815v/euhoPr4PhmSNtvLokLzeOjukK7QRA30FwiOrjv2wkRkSbC5d3LBw4cyKuvvsqDDz7o6lM1Xm3KWrePbnRvOURERGrpyiuvJDc3ly5dupCQkICfnx9bt251d7HqzTAMajQgqhOUlhp8uPEIM//zM5n5RQCMTojh8dHdaBfZgGO9WH0h/gpzASjIhEPfwYGv4eDXkLINTu0yl/IQHhBZFsAHmOvYPuDj33BlFhFpAlweurdt28aSJUv461//yuDBg+nVqxe9evXy6KlGHGr6zW/rfub65M+QnwH+Ya4qkYiIiFMcPnyYtm3b8tNPP1FSUsKuXbtISkoiLS2NTz/91N3Fq5eGbOXefSKbJz/ZxsZD6QD0aBXCM9f2YGCHRjCiuF8odL3aXADy0uDQt3DkR3M5vhnyTsOuL8wFwMsKLXuYvfhi+5gDs0X3BKvNXVchItLouTx0f/GF+SGdlZVFUlISSUlJrFq1qmmE7poKagHh8ZB+wOxi3mm4u0skIiJyXnfeeSeHDh0iLi6O3r17O5ZrrrmGwMDatc7OnDmTTz75hJ9//hl/f38GDx7Miy++SNeuXR3HGIbBc889x7x580hPT2fgwIG89tpr9OzZ03GM3W5n+vTpLFmyhPz8fIYPH87rr79OmzZtan19rg7e9uISXv5yD39fu5/iUoMAX28eGdmFiYPbY/VupKOHB0RA92vNBaDYDslbzS7pR743g3jOCXOgtpStsOld8zgvH4juURHCYy+GFt3Bx89dVyIi0qi4PHSnpaXx9ttv4+vry9SpU6sMxuL5atEtrU1/M3Qf3ajQLSIijd6aNWsAcxaSNWvWEBAQwIcffsjatWvp0KEDu3btqvF7rV27lgceeID+/ftTXFzMU089xahRo9ixY4cjwM+aNYvZs2ezcOFCunTpwvPPP8/IkSPZtWsXwcHBAEydOpXPPvuMpUuXEhkZybRp0xgzZgyJiYl4e9ditG/MgdRcZcfxLB75cAs/p2QDMLJHNM9d15PYMA/rlm21VYyKzoPmP1rmETi+xWwFTy5b56dD8k/mUs7iDZGdzDAe3RNa9jTXYW11j7iINDsuD9033XQTN954I6+//jpTp04lKSmJJUuW8MILL7j61I1Lm/6w7UM4+qO7SyIiIlJj7777Lps3b3Y8XrlyJYsXL67Ve6xYsaLK4wULFtCyZUsSExO58sorMQyDuXPn8tRTTzF27FgAFi1aRHR0NIsXL2by5MlkZmYyf/583nvvPUaMGAHA+++/T1xcHKtXr+aqq66qcXnMgdScn7pLSg3+/vU+5qzaTVGJQWSgLy/8KoGrE1o5/VxuYbGYoTmsLfS4ztxnGJBxuGoIT/7JDOLl94dvX1bxHr7BZhBvWRbGo7pAi67m9KoK4yLSRLk8dGdnZ/PAAw/w9ttvA5CQkMAXX3zRNEJ3bSqH8qnCjm6A0lJzTk0REZFGzs/Pj127djm6go8aNYonn3yyXu+ZmZkJQEREBAAHDhwgJSWFUaNGOY6x2WwMGTKE9evXM3nyZBITEykqKqpyTGxsLAkJCaxfv75WoRuc39KdnJnPb5dsZsNB897tkT2imTm2F1FBTfxeZ4sFwtuZS88bzH2GAdnJcGIHnEiC1B1wYjuc3AWF2WXd1X+o+j62UIjqbIbwqM5mEI/qAuHtwdtFI7uLiDQQl4fuli1bcvz4cSyVAmpBQYGrT9v4RCeAT4A5UujJnea3uyIiIo3c22+/zc0338ywYcPo3bs327dvr9f7GYbBI488wuWXX05CQgIAKSkpAERHR1c5Njo6mkOHDjmO8fX1JTw8/Kxjyl9/Jrvdjt1udzzOysoyy+DkUcu/3n2SqR9sIS23kCCblWev68mNl7Su8rdPs2KxmFOVhcRC5xEV+0uK4NSeihCeugNO7Yb0g2DPhGMbzaUyLx9z2rKozhDZ0RwjJ6KDuYTEglftbisQEXEHl4fuOXPmMGHCBFJTU/nggw9YsWIF3bp1c/VpGx9vH2g7yJyv+8DXCt0iIuIRevbsyY8//siyZcvYvn07cXFx/Oc//6nz+z344INs3bqVb7755qznzgyphmFcMLie75iZM2fy3HPP1bmsF1JSavDK//bwty/3YBjQMzaEN8b3pW1kgMvO6dG8ywZci+4BvW6q2F9sh9P7zABeZdkDRXkV3dTPej9fsyW8chCPKNsOa6sWchFpNFweurt06cK///1vli9fzrZt2+jXrx933XWXq0/bQGr5DXb8lRWh+9LfuKZIIiIiTlRcXMw///lPTp48yRVXXMGoUaPq3IL70EMP8emnn/L1119XGXE8JiYGMFuzW7WquP85NTXV0fodExNDYWEh6enpVVq7U1NTzzlI6xNPPMEjjzzieJyVlUVcXBxQ/+7lOfZiHl6ymS9/TgXg1gFteebaHvj5qOW11qy2ijBeWWkpZB0rC917IO2AOSht2n5IPwQlhRUB/UwWbwhtDWHtIDSu7F70uIrtkNbmvOUiIg3A5aH7559/5rPPPiMsLIzRo0eTkJBAQEAz/QY4/kpzffAbKCkGb5f/84uIiNTLrbfeSmRkJN26deOf//wnjz32GB9++CFdunSp8XsYhsFDDz3EsmXLWLNmDfHx8VWej4+PJyYmhlWrVtGnTx8ACgsLWbt2LS+++CIAffv2xcfHh1WrVjFu3DgAkpOTSUpKYtasWdWe12azYbOdfU+1AfUaSO14Rj6TFm1kZ3IWNqsXM37Vixv71n7aMrkALy8zKIfFQacRVZ8rLYHMo2YAT9tfFsbLl/1QnG8O8JZx+BxvXtYFvnIgD2tbKZTHgm/tpsYTETkXl6e+0aNHc88995CRkcHf//53tm3bRm5ubq2mGin3xhtv8MYbb3Dw4EHA7PL29NNPM3r0aKDh5/is9S1hrS4Cv1Dzvu7kn6BN39qfU0REpAHt2rWLrVu3Oh5v2rSJ++67zzGlWE088MADLF68mH/9618EBwc77sEODQ3F398fi8XC1KlTmTFjBp07d6Zz587MmDGDgIAAbrvtNsexkyZNYtq0aURGRhIREcH06dPp1auXYzTz2qhrS/e2o5lMWrSB1Gw7UUE23rqzL33ahl/4heJcXt4VA7h1HFb1OcOA7BTIOFQRvDMOm9OdZRwx18UFZit61jFzDvLq+IWaLeIhsRDcqmI7pDWEtDK3/cI06rqIXJDLQ3erVq146qmnquwrKSmp03u1adOGP//5z3Tq1AkwpxO5/vrr2bx5Mz179mzwOT5rzcsb2l8BP38OB9YodIuISKMXFBTEvn376NixIwCXXHIJaWlptXqPN954A4ChQ4dW2b9gwQImTpwIwKOPPkp+fj5TpkxxfHG+cuVKR/0N5jgxVquVcePGOb44X7hwYR3qb0ud2rl/2H+auxduILewhC7RQbwzsT9twptp773GzGIpC8WtoO2lZz9vGJB7sppAfrgilBfmmI0kBZnmgG/n4hNQFshjqwb04GhzGrTyxVf/n4g0ZxbDcPakGaZp06Zx0UUXsWvXLrp06cKECRNccRoiIiL4y1/+wt13301sbCxTp07lscceA8xW7ejoaF588UXHHJ8tWrTgvffe45ZbbgHg+PHjxMXF8cUXX9R4upGsrCxCQ0PJnH0pIb/7rnYF/mEe/Of3Zvie+HntXisiIh7DUVdkZhISEuLu4tTZtm3buO222xg9ejTdu3dn586d7Nixg88/97w6rPxn8uS0e3j8+dcJ9qv5QFtrdqVy//uJFBSVMqhDJH+/sy8htXi9eJiCLMg6XtYaftycAq18u3zJr8WXT7YQCGoJQTHmOjimmsfR4B+haWVF3MwV9bfLWrqvvPJKtm7dyu7du/nnP//JCy+8QL9+/ejVqxe9evVizJgx9Xr/kpISPvroI3Jzcxk0aJBb5visk84j4T/AofWQnw7+6pImIiKNT35+Pv7+/vTq1YuNGzeyfPlydu7cSceOHXn22WfdXbx6q02Lw6odJ5jyj0SKSgx+0a0lr4+/RAOmNXV+IebS8jwz7hTlVwTwM0N5TirknDCX4gKwZ5nL6b3nP6+XFQJbmi3lgS0hMMpcAqIgsMUZj6PAx9+51y0iLuGy0H399ddz/fXXOx7n5+eTlJTE1q1b+fLLL+scurdt28agQYMoKCggKCiIZcuW0aNHD9avXw84f45POPc8n3W6hyciHlp0g5M/w94vq06ZISIi0kgEBQXRsWNHevbsSUJCAgkJCdx888107doVq9WzBwKtzTzd3+49xQP/2ERRicE1vVox55aL8bWqJVIwA29kR3M5F8Mww3ZOqnmfeXkQzzkB2SeqPs47DaXFkH3cXGrCN+iMUB5prqt9HGWOFC8iDc7ltWZxcTFLly7l5MmT9OjRg7vvvrvOU40AdO3alS1btpCRkcHHH3/MhAkTWLt2reN5Z8/xCS6Y57PL1Wbo3r1CoVtERBqlSZMmkZiYSN++fYmMjGTt2rW89tpr7N69mxYtWrBt2zZ3F7FeanJz3ebD6dz77kYKS0q5qmc0f/v1xVi9FbilFiwWc0A2v1CI6nz+Y0uKqraQ554sW06b67xTVR+XFpn3nhfmQPrBmpXHJxACIsyeluVr/4iy7TPXZcfYQtXlXaSeXB66q5tq5KOPPqJz5wt88JyDr6+vYyC1fv36sWHDBv72t7857uN29hyfcL55Puv45UGXq+HbubBnlaYOExGRRmnevHkcPHiQP/3pT3z77bc8/fTTDBo0CDCn6vJkhuM/57Y3NZuJCzaQV1jC5Z2iePnWPgrc4lrePubc4qGtL3xseQt6bnkQP1UpmJ+q9Ph0xfNGCRTlQmauOVhcTVm8wT/s3KHcP8J83i/M/HKh8raXbsMQgQYI3dVNNXLvvffWaqqR8zEMA7vd7rI5PuHc83zWWdwA8wMqPw0Or6+Yv1tERKQRad++PfPnz2fv3r386U9/YsaMGfzlL3+hW7fz3OfqIUrP09SdnlvIpEUbycwvok/bMP5+R19sVoUHaUQqt6Cfr3t7udJSsGdCXpo5plD5Oj+tbPvMddkxRblmWM87bS6na1lOW8gZYTzUfHyukF5528evlicTabxcHrqdMdVIuSeffJLRo0cTFxdHdnY2S5cuZc2aNaxYscItc3zWeV5GL2/odg1sfg+SPlHoFhGRRmfPnj3s2rWLXbt2sXPnTg4fPkxWVhbbtm1rEqE7v6iE6oYyLSop5YHFmzh0Oo824f68fWc/Am3qkSYezsurrCt5LQfwLbafI5SfEc4LMqEgw1znZ5hhHSoGkMusQ5mtflXDuK1scDtbCNiCK22X7w+u9FyouW31rcOJRZzP5bXI3//+d2644YYqU420bdu2Tu914sQJ7rjjDpKTkwkNDaV3796sWLGCkSNHAg09x2c9JYw1Q/fOT+GXfzG7FImIiDQSXbt2pXfv3owbN47f/va3dO/eHR+fplJXWcgrLK72mZlf/Mz6facJ9PXm7Qn9iAzSwFPSjFltFXOe10ZxoRm28zPKwnhGpe3MC2xnAoY56ntOirnUlbetaiCvHNSrBPdzhHjfIHOtbvJSTy6bp7syu93umGqkVatW3HHHHQQEBLj6tC7jmLtt7uWEPLyubm9SUgwvdTXvvbn9Y+hUh5Z2ERFptDx9nu6XXnqJ7du3k5SUxMGDB2nXrp1jFPOEhISGmWbTycp/Jo9Pu49bp75I7zZhVZ7/du8pxr/9AwDz7ujLqJ4xbiilSDNXWgqF2RUBvDyw27PM+dPt2RUt6AVla3t21e3CHOeWyeoPtqCyEF62rrztCOhB4BsIvsFnHB9s7i/fpxDfqHnUPN1Lly6lpKSE8ePHY7PZuOWWW1x1Ks/kbYUe18PG+ZC0TKFbREQahc8//5xf/vKXTJs2rcr+/fv3k5SURFJSEu+9955Hhu5yBhZy7SVV9mUVFPH7j34C4PZL2ypwi7iLl1fF/ep1VVpSEc4rB3VHMK8mqBdkmfe9V95fWtYjpjjfXHJPOucafQIuHNLLn/MJKDsm0Bx93jfgjH1l2+o126i5LHQ///zzVabyKrd48WJKS0u5/fbbXXVqz9HrJjN071gOo180f7lERETc6Prrryc5OZmWLVtW2d+hQwc6dOjAdddd56aSOdeZ3cv/+NkOjmcW0C4ygCd/2d1NpRIRp/AqH3E9rO7vYRjmPe3l07LZK6+zoTC3Yrv8ucLcipb26vaVh/iiPHPJTXXG1Zq8fMxA7htUFsQDykJ6YKXtgIrAXzm8VxfiK2+rZb7eXBa6LRYLkZGRZ+0fM2YMQ4YMaRqhux7zjQPQdhBEdIC0/bB9GVxyh3PKJSIiUkcNcNdZo5BXWNHSvXJ7Cv9MPIrFAi/dfBEBvho4TaTZs1jMEdR9/CAwqv7vV9cQX5hrBvTCPHNf+XZR2XPlQb60qKw7fl1GrbsAb1ulQO9ftgRUrK1+Z+9zHFfN8T7+Zpf9ys9ZbfXPVo2Yy2oVPz+/s+bDBggJCaG0tNRVp/UsFgtcciesfhY2vavQLSIijcKWLVu47LLLCAwMdOw7duwY3bt3Jysry40lcw6Dipbu0zl2nly2DYD7ruxAv/YRbiyZiDRZzg7x5YoLywJ4XllAL9suKgvpju3KAT63mn2Vgnz5tlGW2UrskG83R6t3GUtZIPc7I7SfI6Q71tUcX/4lwLnWbuiK77LQ/cADDzB27Fg+/PBDWrRo4dhf1+nCGicnfBtz0W3w5Z/g6I+QuhNaqkubiIi41+jRo7FYLLRv357evXvTtWtXDh06RFhYmLuL5jS59hIMw+CpZUmcyimka3Qwj4zs4u5iiYjUjtXXXGo7HdyFOFrmc6uG+uJ8KMov6yJfvi6o9LjSc45jqzu+bLu0qPyE5nmKcqn9hPC1ZPGuJoz7lQV7Pyh0fkR2WeieOHEixcXFDBgwgEGDBtG7d29KS0tZvHgxjzzyiKtO63mCo6HraPj5c7O1++qZ7i6RiIg0c7t37yY1NZVt27axdetWtm3bRmlpKfPmzXN30ZzCAE7n2lm+5Rgrtqfg421h9i0XYbPqvkUREaBqyzxn3zLsNCVFVYN58TkCfLWPL3B8cUHFurig4pxGSUU3/+rYnX+bldNDd/mop15eXtxzzz3ceuutfPjhhyQlJREYGMhbb73FoEGDnH1a93DWfQeXTDBD9+Z/wLAnzWkHRERE3CQ4OJiOHTs2nfq6Gmt3n+Td7w4B8PDwzvSMrcdIySIiUjfePubi5+KpNQ2jagh3rAvKWuQrrTPS4M/3O/X0Tg/dZ456GhgYyF133eXs0zQtnUZAZCc4vRc2vw+X/sbdJRIRkWbq2muvxcen6U89k3TMvDf94rgw7h/S0c2lERERl7JYKu77vpCsLMC5odvLqe9G8xn11OSklm4vL7h0irn9/etQUnz+40VERFzkX//611mDoDY1Rln9HRHoy2vjL8Hq7fQ/h0RERBxcUsts2bKF3NzcKvuOHTtGSIiLuw14sotuBf8IyDgMP3/m7tKIiIg0WZfEhfH0mB589tDltA6rQauHiIhIPbhkILXmMOop4Ny55HwDYMC9sPZF+GYO9LihSc9VJyIi4i5RwX6MvDze3cUQEZFmwiWhu6mPeuoyAybDd69B8k/w87+h+xh3l0hERERERETqwSWhuzmMempyckt0YCQMnAzrXoKvZkDXX5r3e4uIiIiIiIhHcnqiay6jnrrMoAfBFgKp22HHcneXRkREpAnS7VsiItJwnB66m8Oopw6uuOc6IAIGPWBu/+9PUGx3/jlERESaMUVuERFpSOq73BgNegCCoiFtP/zwd3eXRkREpEkxNFCpiIg0IIXuenFRpW0LhuFPm9trZ0FOqmvOIyIi0gwpcouISENS6G6sLroNWl0Mhdmw+ll3l0ZERERERETqQKG7sfLygl/+BbDAln/Avq/cXSIREZGmQd3LRUSkASl014er6+y4ATDgXnP7s99CYa6LTygiIiIiIiLOpNDd2A1/GkLjIOMwfPknd5dGRESkCVBLt4iINByF7nppgErbFgxj5prbP7ypbuYiIiIiIiIeRKHbE3QeAZdMAAxYNhlyTrq7RCIiIp5LDd0iItKAPCp0z5w5k/79+xMcHEzLli254YYb2LVrV5VjDMPg2WefJTY2Fn9/f4YOHcr27durHGO323nooYeIiooiMDCQ6667jqNHj9a+QA05EMvVf4YW3SDnBCy/H0pLG+7cIiIiTYpSt4iINByPCt1r167lgQce4Pvvv2fVqlUUFxczatQocnMrBhibNWsWs2fP5tVXX2XDhg3ExMQwcuRIsrOzHcdMnTqVZcuWsXTpUr755htycnIYM2YMJSUl7rismvENgJsWgNUP9q6GdS+5u0QiIiIeSZFbREQakkeF7hUrVjBx4kR69uzJRRddxIIFCzh8+DCJiYmA2co9d+5cnnrqKcaOHUtCQgKLFi0iLy+PxYsXA5CZmcn8+fN56aWXGDFiBH369OH9999n27ZtrF692p2Xd2HRPWD0LHP7q+fh53+7tzwiIiIeyNCUYSIi0oA8KnSfKTMzE4CIiAgADhw4QEpKCqNGjXIcY7PZGDJkCOvXrwcgMTGRoqKiKsfExsaSkJDgOOZMdrudrKysKovb9J0A/cumEfvkPjix/fzHi4iISBWK3CIi0pA8NnQbhsEjjzzC5ZdfTkJCAgApKSkAREdHVzk2Ojra8VxKSgq+vr6Eh4ef85gzzZw5k9DQUMcSFxdnPuGub8qvngnxV0JhDvxjHGTW4X50ERERERERcTmPDd0PPvggW7duZcmSJWc9ZzkjDBuGcda+M53vmCeeeILMzEzHcuTIkboX3Bm8feDmRRDZGbKOwnu/gtzT7i2TiIiIhzDU1i0iIg3II0P3Qw89xKeffspXX31FmzZtHPtjYmIAzmqxTk1NdbR+x8TEUFhYSHp6+jmPOZPNZiMkJKTKYnJjpR0QAXcsg5DWcGo3/OMmsGdf+HUiIiLNnEWhW0REGpBHhW7DMHjwwQf55JNP+N///kd8fHyV5+Pj44mJiWHVqlWOfYWFhaxdu5bBgwcD0LdvX3x8fKock5ycTFJSkuMYjxEWZwZv/wg4vgnevwkK3Hi/uYiIiCdQ5hYRkQbkUaH7gQce4P3332fx4sUEBweTkpJCSkoK+fn5gNmtfOrUqcyYMYNly5aRlJTExIkTCQgI4LbbbgMgNDSUSZMmMW3aNL788ks2b97M7bffTq9evRgxYkTtCtQYRj9t0RVu/xhsoXDke3jvBshPv+DLRERERERExPWs7i5AbbzxxhsADB06tMr+BQsWMHHiRAAeffRR8vPzmTJlCunp6QwcOJCVK1cSHBzsOH7OnDlYrVbGjRtHfn4+w4cPZ+HChXh7ezfUpThX60tgwqfmvd3HEmHRtXD7JxDU0t0lExERERERadYshmEY7i6Ep8nKyiI0NJTMN0cTMvkLdxenwont8O71kHsSwtrC+H+aLeEiItLgHHVFZmalsUDEncp/Jl++9yK/uP1RdxdHREQaIVfU3x7VvbzxaQTdyyuL7gl3rYDweMg4DPNHwoF17i6ViIiIiIhIs6XQ3dREdYJ7VkPcQCjINLucb1wA6tAgIiJiamTfmYuISNOm0F0fjWEgteoERsGdn0LPX0FpEXw+FZZPgcI8d5dMRESaoa+//pprr72W2NhYLBYLy5cvr/K8YRg8++yzxMbG4u/vz9ChQ9m+fXuVY+x2Ow899BBRUVEEBgZy3XXXcfTo0Qa8ChERkbpR6G6qfPzgxndgxLNg8YKfFpvdzU/tdXfJRESkmcnNzeWiiy7i1Vdfrfb5WbNmMXv2bF599VU2bNhATEwMI0eOJDs723HM1KlTWbZsGUuXLuWbb74hJyeHMWPGUFJSUocSNdIvzUVEpElS6K6XRl5pe3nB5b8zW70DW8KJJHjzcvjxLXU3FxGRBjN69Gief/55xo4de9ZzhmEwd+5cnnrqKcaOHUtCQgKLFi0iLy+PxYsXA5CZmcn8+fN56aWXGDFiBH369OH9999n27ZtrF69uqEvR0REpFYUupuD+Ctg8tcQPwSK8+GL6ea93pnH3F0yERFp5g4cOEBKSgqjRo1y7LPZbAwZMoT169cDkJiYSFFRUZVjYmNjSUhIcBxTK438O3MREWlaFLrro7He012dkFZwx3IY/Rew+sP+r+D1QbBhPpSWurt0IiLSTKWkpAAQHR1dZX90dLTjuZSUFHx9fQkPDz/nMdWx2+1kZWVVWUweVH+LiIjHU+huTry8YOB9cP86aN0X7Jnw70fMe72Tt7q7dCIi0oxZzvgi2zCMs/ad6ULHzJw5k9DQUMcSFxfnlLKKiIjUhkJ3vXjoN+VRnWHSKhg9C3yD4dhGmDcEvvg95J52d+lERKQZiYmJATirxTo1NdXR+h0TE0NhYSHp6ennPKY6TzzxBJmZmY7lyJEjTi69iIjIhSl0N1de3jBwMjy4wZxazCiFH+fByxfDN3OgqMDdJRQRkWYgPj6emJgYVq1a5dhXWFjI2rVrGTx4MAB9+/bFx8enyjHJyckkJSU5jqmOzWYjJCSkymLy0C/NRUTEI1ndXQDP1gQq7ZBWcPNC6DsRVv4BUrbB6mfNe72HPg69bwFvHzcXUkSkYRmGQV5hCdkFxeTYi8rWxeQUFJNdUEx22XaOvYgcezFZBeWPK9YZmRnuvoxGIycnh717K6asPHDgAFu2bCEiIoK2bdsydepUZsyYQefOnencuTMzZswgICCA2267DYDQ0FAmTZrEtGnTiIyMJCIigunTp9OrVy9GjBhR+wJ50pgsIiLi8RS6xdRhKNz3NWz9AP73J8g8Av96ANbOgiunw0W3KnyLiEcoKTXIKSgmq6AiLGeXbWcXFJFVHpyrfd58Xa69mNJ6zqxYaq/L/NFN08aNGxk2bJjj8SOPPALAhAkTWLhwIY8++ij5+flMmTKF9PR0Bg4cyMqVKwkODna8Zs6cOVitVsaNG0d+fj7Dhw9n4cKFeHt716FECt0iItJwLIahCZtrKysri9DQUDLn3UDIvcvcXRznK8o3u5p/+zLknTL3hbaFy6ea4ds3wK3FE5Gmq6iktEogziooqmhdLg/G9qrhOadSYM4uKCK30Hlh19vLQrCflSCbuQT7WQn28zEf+1kJLtsfVGl/+fFGUR59OrYmMzOzUrdmcafy+vvLxbP5xa2/c3dxRESkEXJkPSfW32rplrP5+MNlD0P/e2DjAvj2b5B52Bzp/H/PQ7+7oP+9Ztd0EZEyhcWljjCclV/RsmyG5KqtzZVDcuXnC4qcN4WhzepFsJ8PIX7lodhKsM3HEZyDy/aFlG0HVd5vM7f9fLwuOIL2uWRladgUERERUeiW8/ENhMEPQv9JkLgIvn8dMg7BupfMIN5zrDkYW+u+uj9OpAkob2XOyi8iq6CIrPzisnURmdXsyzrj2Pwi57Uw+/t4O0JxeRAOqRSUy/cFlYXjkDOCdLCfD75WhV45B1VZIiLSgBS666O5BE0ff7j0fhhwL/z8bzN8H/4Otn1oLi17wiV3Qu9xEBDh7tKKNFvFJaXmoF75xZVC8vnDcmZ+xXN5TuqWXdENu3IQrtyCXM3+yi3ONitWbwVmERERaRoUuqXmvLyhx3XmcmwT/PB32LEcUrfDisdg1dPmcxePh/grzeNFpMYMw6CgqJSM/EIy84vIyDNDsRmMK4XlcwTpHHuxU8oR6OtNiL8PIX4+hPhbCXVsmy3KlZ+r2G8+VmAWT1DXWwZERETqQqG7Xppxpd36Ehj7dxj9Z9j6EWxaBCeSYNtH5hIUbXY/73WTup9Ls1NYXOoIy5llAbpyiM7Iq+iynZFfdV9hSf3vaQ7w9a4mFJthOfQ8gbm8pVmhWURERMR5FLqlfvzDYeB9Ztfz45th07uwfRnknIAf3jCX8PaQcBN0vxZaXaQALh6hpNQgu6Do7MCcXykw553dIp2ZX/9u2lYvC6H+PoQGmCE59Bxhubp9wX5WfBSaRS5A9ZCIiDQche76UHisYLGYrd+tL4HRs2Df/8wW711fQPpBWPdXcwmNg66/hG6/hHaXae5vcbnSUoNsezEZeYWk5xWRnldobucWVdlXtSW6kGx7MfWZUNFigWCbldAAH8L8fauE6LBKQToswAzLYf6+jucDfb3V/VXEpfT7JSIiDUehW5zP6gtdrzaXwlzY9R+z9Xvf/yDzCPz4d3PxC4XOV0GnEdDxFxDUwt0ll0auoKiEjLwiMvLPDs3l21X3mSG6pLTu6TnA17tKa3NYQOVt37LAfPbzwX4+eHvpD3sRERGR5k6hu170B/UF+Qaa93X3ugmK8mH/GnME9F3/gbxTFSOgA8T0NsN3p+EQNxCsNrcWXVzHMMzW57ScQkc4Tq8SmittVwrX9ZmSKsDXm/AAs8U5PNAMzOEBPoQH+Dq2KwK1ryNEa9opkSZIPUlERKQBKXRLw/Hxh66jzaW0BI78CLtXmC3gKVsrlm/ngk8gtBsM7S8zu6HH9lFX9EasqKSU9LxC0nJrtqTnFVJUUrfWZ28vi9myXBaYzbBceV1pO9DHEbT9fDSavoiIiIg0PI8K3V9//TV/+ctfSExMJDk5mWXLlnHDDTc4njcMg+eee4558+aRnp7OwIEDee211+jZs6fjGLvdzvTp01myZAn5+fkMHz6c119/nTZt2tS+QPqmvO68vKHdIHMZ+RzkpJqt4Hu/NEN4birsXWUuAD4B0Ka/GcDbX2aOiO7j79ZLaKoMwyC3sIT03EJO5xY61mm5dtJyi85YmyE6q6BuU1WVtz6HOVqcfc4KzWcG6WCbFS912xYRERERD+FRoTs3N5eLLrqIu+66ixtvvPGs52fNmsXs2bNZuHAhXbp04fnnn2fkyJHs2rWL4OBgAKZOncpnn33G0qVLiYyMZNq0aYwZM4bExES8vdUS5jZBLaH3OHMxDHP6sQPr4NC3cGg95KfBgbXmAuDta3ZHb9MPWveDNn0hPF5fhJxDQVEJp3MLOZ1j51SOnVM5hZzKsXO6bJ2WW8jpsq7ep3MLKSyu/bRVFguEB/gSEehLRPk6qNJ2NYtan0XELVRViIhIA/Ko0D169GhGjx5d7XOGYTB37lyeeuopxo4dC8CiRYuIjo5m8eLFTJ48mczMTObPn897773HiBEjAHj//feJi4tj9erVXHXVVQ12LXIeFgvE9DKXQVOgtBRO7TID+MGyEJ6TAsc2mks5/wizBbw8iMf2gcBI912HCxmGQVZBcVmIPneYPp1byKlsO9n22rdE26xeRJYH50AbEQE+5jrwzLUZoEP9NXCYiHgGi1K3iIg0II8K3edz4MABUlJSGDVqlGOfzWZjyJAhrF+/nsmTJ5OYmEhRUVGVY2JjY0lISGD9+vXnDN12ux273e54nJWV5boLkbN5eUHL7ubS/x6zJTz9ABxNNEP30Y3mveD5aVW7pAOEtK4I8DG9zNbx8PaNskXcMAwy8opIzbZzMrs8RFcO0pUCdh1ao328LUQF2YgM8jXXgTaign2JKg/OQb5myC5bAnybzMeDiEgVRiOsA0REpOlqMn9Vp6SkABAdHV1lf3R0NIcOHXIc4+vrS3h4+FnHlL++OjNnzuS55547+wlV2u5hsUBEB3PpfbO5r9gOKUkVIfzYRkjbD1nHzGX3iorX20IgOsEM4dE9zTAf1QX8w1xS3MLiUk7lmEE6NdtOanZBxXaWnZM5dk5mFXAyx17rwcWCbFaignyJDLJVWpvbUWXb5SE7xM+quZ9FRFDvchERaVhNJnSXOzNUGIZxwaBxoWOeeOIJHnnkEcfjrKws4uLi6ldQcS6rzbyvu01fGDjZ3FeQBSe2Q8q2spHRt0HqDrBnweH15lJZcCto0a1s6WqG8RZdwT/8rNOVDzaWmlXgaJmuHKhPVgrUabmFtbqU8AAfWgTbaBFc1hpdFpxbVG6lLlvrnmgRERERkcatyYTumJgYwGzNbtWqlWN/amqqo/U7JiaGwsJC0tPTq7R2p6amMnjw4HO+t81mw2bTnNEexy+kYoT0ciVFcGq3GcCTt8LJnXByl9kanp1sLvu/qvI2eb5RnPRry3GvWPaXxvBzYQt+yotkV2EUdnxrVBSrl4UWwTZaBttoEexXadtctwwx90UF+WKzKkiLiLiSev2IiEhDajKhOz4+npiYGFatWkWfPn0AKCwsZO3atbz44osA9O3bFx8fH1atWsW4ceMASE5OJikpiVmzZrmt7NJwcostpHi140RgNCktLyfFr4ATIQVkpJ/CN30voTn7iLEfpJPlGJ28jtHGcoqAwlO0KzxFOzbhiO9eUGqzkEIkydZYTtviyAlsS2FIPER0wC+qHZER4WWh2o8wfx9NcyUiIiIi0gx5VOjOyclh7969jscHDhxgy5YtRERE0LZtW6ZOncqMGTPo3LkznTt3ZsaMGQQEBHDbbbcBEBoayqRJk5g2bRqRkZFEREQwffp0evXq5RjNXDyXvbiElMwCjmXkczyjgOMZ+RzPyOdYRj7JmQWcyCy4wCjercqWy/H2stAywEa7oFIu9j9BF+8TtLUkE110nPCCw/hnH8K7MItYThFbcgrytkIecBLYV/Z2gS0grG3Z0q5iHd4OQttonnEREbfRl6AiItJwPCp0b9y4kWHDhjkel99nPWHCBBYuXMijjz5Kfn4+U6ZMIT09nYEDB7Jy5UrHHN0Ac+bMwWq1Mm7cOPLz8xk+fDgLFy6s2xzd6p7WYAzDIC23kOMZ5aG6bMnM51hZwD6Zbb/wG2EOPhYdYiMm1I/oED9ahfoRE2Jux5RtRwbZzj/9lWFA3mk4vQ/S9lVdpx8CeybknjSXY4nnKEh0RSgPiTVHWq+8DooGL3U1FxERERHxZBbDMGo3XLKQlZVFaGgomQtuIWTiUncXp0kwDIOTOXaOpOVzJC3PXNLzHC3WxzLysddgiiw/Hy9iw/xpHeZPbKg/sWH+xIb50SrU3wzUoX4E2Rrgu6b8DMg4DBmHytaHzTBevq8w58LvYfE2B3cLiT0jlJdtB8eYwdzHz+WXIyK156grMjMJCQlxd3GEip/J2n++zpU3/sbdxRERkUbIFfW3R7V0Nz5q6a6N7IIijqTlczgtj6Pp5cG64nFB0YVDdctgW0WoDvMrC9Xlj/0JD/BpHAPk+IeZS6veZz9nGJCfXjWQZyWXTW923Fyyk8Eogayj5nI+fqEQFANBLc0QHlxpu/ISEKHeGSIigKH6W0REGpBCtzhNeWv1wVN5HDyVy4HTuRw+ncfhslbrjLyi877eYoFWIX60iQigbUQAceEBtAmvCNXRobamMbK3xWIG4IAIiO1T/TGlJZCTWhbCj5atK4XyzGOQkwIlhVCQaS6ndp3/vF4+ZWG8LJAHREFgZNk6yrwHPSDS3A6IAt8A51+7iIiIiEgzo9BdH82w1bD83uqDp3M5UClcHzyVy6HTeeScd6Aycw7quIgAcwkPIC7Cn7hwM2THhvnja/VqoCtp5Ly8IaSVudC3+mMMAwoyIPsE5JwwQ3pOSqXtExXP5adBaVFZcD9WszL4BJwdzCuH8sAo8I8w5zH3Dzdb9r19nPQPICLiOha1dIuISANS6JZqFZWUcuh0LntTc9hzIod9J3M4cCqXA6dyySo4d7C2WKB1mD/xUYG0jwykXaQZsNtGmK3WwX4KZU5jsVQE3pbdzn9scSHkplYN5LmnzMHgck9B3ilzXb5dUghFeZB52Fxqyje4IoCXly2gcjA/x2K11eufQkRERESksVLorhfP/6a8oKiEfSdz2JtasexJzeHgqVyKS889xl5sqB/towJpHxVIfGTZOsoM2E2iC3hTY/U1pykLbXPhYw0D7NllQfx0RSB3rCsF9fx0cynINF9bmG0utQnqYLaq+4WZ96f7hYJfCNhCKrb9Qis9Dj37OZ+AZtnzRETqSB8XIiLSgBS6m4nSUoNDaXn8nJzFzuQsdiRns/tENkfS8zjX+PUBvt50ahnkWDpEBREfZbZe+/koWDdZFktZmA2BiA41e01piRm8y0N4bRaj1GxVL8qD7ON1LLP3+cO5LRhsQeAbZO5zbAeZrfPlj30DFd5FmgX9nouISMNR6K6PRvrHeY692BGud6ZkszM5i10p2eQVllR7fKi/D51bBtE5OoiOLYLoHB1Mp5ZBxIb6NY6RwKXx8/KuGByuNkpLzZbx/HTISwN7FhRkmQH+rO3MM7bL1kaJuZSH+HqxVArjQZXCevC5g3rlxz4BZnD3DazY1lzrIo2Q6jYREWk4Ct0erqCohO3HM/npSCZbj2aw9Wgm+0/lVnusr9WLrtHBdG8VTLeYELq1CqZzy2CignwVrsU9vLwqWqXD29f+9YZhtpCfN5xnmt3l7Tnm/Oj27LL1GY+NUsCo6CLvLN42cyR4n0Bz7RtYsV0ezH0CKh0TWPX4KscEVg32CvQidaMqT0REGpBCd700bK1dUmqw+0Q2mw9nsPVoBj8dzWT3iWxKqrn3OjrERvdWIRVLTDDxUYFYvTU6uDQhFktFAA1pVff3KQ/v1QVze1kIdzyXc8bjSttFeVCYay6U/V6W2CHf7oRW+Gp428DHzwziVj/w8TcXa9nax6/Stn/ZMQHn2H+BYzQyvYiIiEidKHQ3YvbiErYezeTHA2lsPJjGxkPpZFczcnhUkI2L2oTSu00YveNC6d06lMggjQYtUmOVwzvR9X8/w4DiAijMg6LcM9Z5ZkB3bOeWrSsdU5h7xvFnHGOUmucpsZtL+UB2rmTxriaMl2/7mWHdajPX3r5VH1fZPvO5SmvvavZZ/czAr944IiIi4qEUuuvDyX8EFpWUsuVIBuv2nOK7faf46WgmhcWlVY4J9PXmorgwcykL2q1077VI42KxVLQQE+nc9zYMKLZXBPOiAijOh6KypbigbGC6yvvrcYzjvCXO73pfY5ayAF6LoF5+vLdP2TGVt33NLwbOue1bdvw5tr2s+hLAw+nHJyIiDUmh280OnMpl3Z6TfL37FN/vP02OvWpLdlSQL/3bRziW7q2C1UVcpDmzWMq6lPvh9EB/pvKAf1YYPyOYF9vN7WL7GduV1iWF1e8vPsf+EnvlgpQ9VwA0QKv+BVkqhfHzBPlCJTsRERFR6G5whmGw7Vgm/92ewn+3n2Bvak6V58MDfLisUxSXd4piYIdI2kcGqBVbRNyjcsD3D2/YcxvGeYL6ecJ9cVmX+yphv7BsX6H5uMp2YdlrKm8XnX28UbnXkVHRtf987OeYj1Hcz6Ivr0VEpOEodNeDUcOB1AzDYPvxLD7ZdIwVSckczyxwPGf1stC/fQRXdIniik4t6BkbgpeXQraINHOWSl3KG4OS4hoG9krbmRnw57vcXXIRERFxM4XuetiRnMWg8zx/OsfOx5uO8nHiMXadqLgPMsDXm6FdW3BVzxiGdm1JqL9GBRYRadS8reZCQM1fk5UFKHSLiIg0dwrd9bDvVG61oXtXSjbvfHOAZVuOOQZC87V6MbJHNDdc3JorOkfh56P5dUVERNzBoom6RUSkASl0O9HPKVn89b+7WL0z1bGvV+tQbh3Qlmt6tSI0QC3aIiIiIiIizYlCdz2UD5FzOsfOjC9+5pPNRzEM8LLA1Qkx3H1ZPH3bhWsgNBERkcZE1bKIiDQghe56+uyn4zzz6XbScgsBuKZXKx4Z1YWOLYLcXDIRERERERFxN4XuerHw0JLNAHSLCWbm2F70advA0+qIiIhIragDmoiINCSF7nryssCDv+jMg8M64WvVvJ8iIiKNn1K3iIg0HIXuejCw8OHkQfRrH+HuooiIiEiNKXSLiEjDUeiuhz5xYfRS4BYREfEwCt0iItJwmm1/6Ndff534+Hj8/Pzo27cv69atq/V7eHk1238+ERERt6l3Ha7MLSIiDahZpsYPPviAqVOn8tRTT7F582auuOIKRo8ezeHDh2v1Pt5eqrVFREQakrPqcBERkYbSLEP37NmzmTRpEvfccw/du3dn7ty5xMXF8cYbb9TqfdTSLSIi0rCcUYdb1NQtIiINqNnd011YWEhiYiKPP/54lf2jRo1i/fr11b7Gbrdjt9sdjzMzMwHIKygkKyvLdYUVERGPVV4/GIbh5pI0HbWtw89Vf+fm5av+FhGRarmi/m52ofvUqVOUlJQQHR1dZX90dDQpKSnVvmbmzJk899xzZ+3vM/VDmPqhS8opIiJNw+nTpwkNDXV3MZqE2tbh56q/R91yP3C/q4opIiJNgDPr72YXustZLFW7lhmGcda+ck888QSPPPKI43FGRgbt2rXj8OHDTfYPqaysLOLi4jhy5AghISHuLo7T6fo8X1O/Rl2f58vMzKRt27ZERGiWC2eraR2u+rvp/W7p+jxfU7/Gpn590PSv0RX1d7ML3VFRUXh7e5/1jXhqaupZ35yXs9ls2Gy2s/aHhoY2yf/RKgsJCWnS16jr83xN/Rp1fZ5P4384T23rcNXfTfcadX2er6lfY1O/Pmj61+jM+rvZ/SXg6+tL3759WbVqVZX9q1atYvDgwW4qlYiIiFyI6nAREfFEza6lG+CRRx7hjjvuoF+/fgwaNIh58+Zx+PBh7r9f93eJiIg0ZqrDRUTE0zTL0H3LLbdw+vRp/vjHP5KcnExCQgJffPEF7dq1q9HrbTYbzzzzTLVd1pqKpn6Nuj7P19SvUdfn+ZrDNbpDferw5vAzaerXqOvzfE39Gpv69UHTv0ZXXJ/F0FwmIiIiIiIiIi7R7O7pFhEREREREWkoCt0iIiIiIiIiLqLQLSIiIiIiIuIiCt0iIiIiIiIiLqLQfQ6vv/468fHx+Pn50bdvX9atW3fe49euXUvfvn3x8/OjQ4cOvPnmmw1U0rqpzfV98sknjBw5khYtWhASEsKgQYP473//24ClrZva/gzLffvtt1itVi6++GLXFrCeant9drudp556inbt2mGz2ejYsSPvvPNOA5W2bmp7jf/4xz+46KKLCAgIoFWrVtx1112cPn26gUpbO19//TXXXnstsbGxWCwWli9ffsHXeNLnTG2vz9M+Z+ry8yvnKZ8xnkr1dwVP+70qp/q7KtXfjYvq76o87XPGbfW3IWdZunSp4ePjY7z11lvGjh07jIcfftgIDAw0Dh06VO3x+/fvNwICAoyHH37Y2LFjh/HWW28ZPj4+xj//+c8GLnnN1Pb6Hn74YePFF180fvzxR2P37t3GE088Yfj4+BibNm1q4JLXXG2vsVxGRobRoUMHY9SoUcZFF13UMIWtg7pc33XXXWcMHDjQWLVqlXHgwAHjhx9+ML799tsGLHXt1PYa161bZ3h5eRl/+9vfjP379xvr1q0zevbsadxwww0NXPKa+eKLL4ynnnrK+Pjjjw3AWLZs2XmP97TPmdpen6d9ztT2+sp5ymeMp1L9XZWn/V4Zhurv6qj+blxUf1flaZ8z7qq/FbqrMWDAAOP++++vsq9bt27G448/Xu3xjz76qNGtW7cq+yZPnmxceumlLitjfdT2+qrTo0cP47nnnnN20Zymrtd4yy23GH/4wx+MZ555plFX2rW9vv/85z9GaGiocfr06YYonlPU9hr/8pe/GB06dKiy7+WXXzbatGnjsjI6S00+9D3tc6ay2lRqlTX2z5lytbk+T/mM8VSqvy+ssf9eqf6uSvV346b6u3qN/XOmXEPW3+pefobCwkISExMZNWpUlf2jRo1i/fr11b7mu+++O+v4q666io0bN1JUVOSystZFXa7vTKWlpWRnZxMREeGKItZbXa9xwYIF7Nu3j2eeecbVRayXulzfp59+Sr9+/Zg1axatW7emS5cuTJ8+nfz8/IYocq3V5RoHDx7M0aNH+eKLLzAMgxMnTvDPf/6Ta665piGK7HKe9DnjDI39c6YuPOUzxlOp/r6wxv57pfr7bKq/PZ8nfc44Q2P/nKkLZ3zGWJ1Ynibh1KlTlJSUEB0dXWV/dHQ0KSkp1b4mJSWl2uOLi4s5deoUrVq1cll5a6su13eml156idzcXMaNG+eKItZbXa5xz549PP7446xbtw6rtXH/WtTl+vbv388333yDn58fy5Yt49SpU0yZMoW0tLRGeV9YXa5x8ODB/OMf/+CWW26hoKCA4uJirrvuOl555ZWGKLLLedLnjDM09s+Z2vKkzxhPpfr7whr775Xq77Op/vZ8nvQ54wyN/XOmtpz1GaOW7nOwWCxVHhuGcda+Cx1f3f7GorbXV27JkiU8++yzfPDBB7Rs2dJVxXOKml5jSUkJt912G8899xxdunRpqOLVW21+hqWlpVgsFv7xj38wYMAAfvnLXzJ79mwWLlzYaL8th9pd444dO/jtb3/L008/TWJiIitWrODAgQPcf//9DVHUBuFpnzN15UmfMzXhqZ8xnkr1d/U86fdK9XcF1d9Ng6d9ztSVJ33O1IQzP2Ma91eCbhAVFYW3t/dZ38alpqae9S1VuZiYmGqPt1qtREZGuqysdVGX6yv3wQcfMGnSJD766CNGjBjhymLWS22vMTs7m40bN7J582YefPBBwKzkDMPAarWycuVKfvGLXzRI2WuiLj/DVq1a0bp1a0JDQx37unfvjmEYHD16lM6dO7u0zLVVl2ucOXMml112Gb///e8B6N27N4GBgVxxxRU8//zzHv9Nsid9ztSHp3zO1IanfcZ4KtXf5+Ypv1eqv8+m+lv1t6fwlM+Z2nDmZ4xaus/g6+tL3759WbVqVZX9q1atYvDgwdW+ZtCgQWcdv3LlSvr164ePj4/LyloXdbk+ML+5mjhxIosXL27099jU9hpDQkLYtm0bW7ZscSz3338/Xbt2ZcuWLQwcOLChil4jdfkZXnbZZRw/fpycnBzHvt27d+Pl5UWbNm1cWt66qMs15uXl4eVV9SPN29sbqPhG2ZN50udMXXnS50xteNpnjKdS/V09T/q9Uv19NtXfqr89gSd9ztSGUz9jaj30WjNQPtXB/PnzjR07dhhTp041AgMDjYMHDxqGYRiPP/64cccddziOL58K4He/+52xY8cOY/78+Y16KoDaXt/ixYsNq9VqvPbaa0ZycrJjycjIcNclXFBtr/FMjX3009peX3Z2ttGmTRvjpptuMrZv326sXbvW6Ny5s3HPPfe46xIuqLbXuGDBAsNqtRqvv/66sW/fPuObb74x+vXrZwwYMMBdl3Be2dnZxubNm43NmzcbgDF79mxj8+bNjilVPP1zprbX52mfM7W9vjM19s8YT6X627N/rwxD9bfqb9Xf7qb62zWfMQrd5/Daa68Z7dq1M3x9fY1LLrnEWLt2reO5CRMmGEOGDKly/Jo1a4w+ffoYvr6+Rvv27Y033nijgUtcO7W5viFDhhjAWcuECRMavuC1UNufYWWNvdI2jNpf386dO40RI0YY/v7+Rps2bYxHHnnEyMvLa+BS105tr/Hll182evToYfj7+xutWrUyxo8fbxw9erSBS10zX3311Xl/rzz9c6a21+dpnzN1+flV5gmfMZ5K9fcQx2NP+70qp/p7SJXjVX83Lqq/Pftzxl31t8UwmkC/DREREREREZFGSPd0i4iIiIiIiLiIQreIiIiIiIiIiyh0i4iIiIiIiLiIQreIiIiIiIiIiyh0i4iIiIiIiLiIQreIiIiIiIiIiyh0i4iIiIiIiLiIQreIiDQZX3/9Nddeey2xsbFYLBaWL1/u0vMVFxfzhz/8gfj4ePz9/enQoQN//OMfKS0tdel5RUREmpKmXn8rdIvIWSZOnMgNN9zgeDx06FCmTp3q0nMWFhbSqVMnvv3223q9z/Tp0/ntb3/rpFKJp8nNzeWiiy7i1VdfbZDzvfjii7z55pu8+uqr7Ny5k1mzZvGXv/yFV155pUHOLyJSmepv8VRNvf5W6BbxUBMnTsRisWCxWLBarbRt25bf/OY3pKenO/1cn3zyCX/605+c/r6VzZs3j3bt2nHZZZfV630effRRFixYwIEDB5xUMvEko0eP5vnnn2fs2LHVPl9YWMijjz5K69atCQwMZODAgaxZs6bO5/vuu++4/vrrueaaa2jfvj033XQTo0aNYuPGjXV+TxFp2lR/V0/1d/PW1OtvhW4RD3b11VeTnJzMwYMHefvtt/nss8+YMmWK088TERFBcHCw09+3sldeeYV77rmn3u/TsmVLRo0axZtvvumEUklTc9ddd/Htt9+ydOlStm7dys0338zVV1/Nnj176vR+l19+OV9++SW7d+8G4KeffuKbb77hl7/8pTOLLSJNjOrvs6n+lvPx9PpboVvEg9lsNmJiYmjTpg2jRo3illtuYeXKlY7nS0pKmDRpkuN+la5du/K3v/2tynuUlJTwyCOPEBYWRmRkJI8++iiGYVQ55szuadXdaxMWFsbChQsB89vIBx98kFatWuHn50f79u2ZOXPmOa9j06ZN7N27l2uuucax7+DBg1gsFj788EOuuOIK/P396d+/P7t372bDhg3069ePoKAgrr76ak6ePFnl/a677jqWLFlSk39CaUb27dvHkiVL+Oijj7jiiivo2LEj06dP5/LLL2fBggV1es/HHnuMW2+9lW7duuHj40OfPn2YOnUqt956q5NLLyJNiepv1d9Sc02h/lboFmki9u/fz4oVK/Dx8XHsKy0tpU2bNnz44Yfs2LGDp59+mieffJIPP/zQccxLL73EO++8w/z58/nmm29IS0tj2bJl9SrLyy+/zKeffsqHH37Irl27eP/992nfvv05j//666/p0qULISEhZz33zDPP8Ic//IFNmzZhtVq59dZbefTRR/nb3/7GunXr2LdvH08//XSV1wwYMIAjR45w6NChel2HNC2bNm3CMAy6dOlCUFCQY1m7di379u0DKv5YPN/y4IMPOt7zgw8+4P3332fx4sVs2rSJRYsW8de//pVFixa56zJFxMOo/q6g+luq0xTqb6tL3lVEGsTnn39OUFAQJSUlFBQUADB79mzH8z4+Pjz33HOOx/Hx8axfv54PP/yQcePGATB37lyeeOIJbrzxRgDefPNN/vvf/9arXIcPH6Zz585cfvnlWCwW2rVrd97jDx48SGxsbLXPTZ8+nauuugqAhx9+mFtvvZUvv/zSce/YpEmTHN/Ql2vdurXjfS90bmk+SktL8fb2JjExEW9v7yrPBQUFAeb/Ozt37jzv+4SHhzu2f//73/P444/z61//GoBevXpx6NAhZs6cyYQJE5x8BSLSVKj+Vv0tNdcU6m+FbhEPNmzYMN544w3y8vJ4++232b17Nw899FCVY958803efvttDh06RH5+PoWFhVx88cUAZGZmkpyczKBBgxzHW61W+vXrd1YXtdqYOHEiI0eOpGvXrlx99dWMGTOGUaNGnfP4/Px8/Pz8qn2ud+/eju3o6GjA/GCsvC81NbXKa/z9/QHIy8ur8zVI09OnTx9KSkpITU3liiuuqPYYHx8funXrVuP3zMvLw8uraqcxb29vTRkmIuel+lv1t9RcU6i/1b1cxIMFBgbSqVMnevfuzcsvv4zdbq/yzfiHH37I7373O+6++25WrlzJli1buOuuuygsLKzXeS0Wy1mVelFRkWP7kksu4cCBA/zpT38iPz+fcePGcdNNN53z/aKios45amvl7nYWi6XafWd+QKalpQHQokWLGl6RNBU5OTls2bKFLVu2AHDgwAG2bNnC4cOH6dKlC+PHj+fOO+/kk08+4cCBA2zYsIEXX3yRL774ok7nu/baa3nhhRf497//zcGDB1m2bBmzZ8/mV7/6lROvSkSaGtXfqr+lqqZefyt0izQhzzzzDH/96185fvw4AOvWrWPw4MFMmTKFPn360KlTJ8e9LwChoaG0atWK77//3rGvuLiYxMTE856nRYsWJCcnOx7v2bPnrG+lQ0JCuOWWW3jrrbf44IMP+Pjjjx2V6Zn69OnDzz//XK9v5ytLSkrCx8eHnj17OuX9xHNs3LiRPn360KdPHwAeeeQR+vTp47hvcMGCBdx5551MmzaNrl27ct111/HDDz8QFxdXp/O98sor3HTTTUyZMoXu3bszffp0Jk+e7PIpekSkaVH9bVL93Xw19fpb3ctFmpChQ4fSs2dPZsyYwauvvkqnTp149913+e9//0t8fDzvvfceGzZsID4+3vGahx9+mD//+c907tyZ7t27M3v2bDIyMs57nl/84he8+uqrXHrppZSWlvLYY49V+fZ6zpw5tGrViosvvhgvLy8++ugjYmJiCAsLq/b9hg0bRm5uLtu3bychIaHe/w7r1q1zjJgqzcvQoUPP+8df+X2SlVuU6iM4OJi5c+cyd+5cp7yfiDRPqr9Nqr+br6Zef6ulW6SJeeSRR3jrrbc4cuQI999/P2PHjuWWW25h4MCBnD59+qx5QKdNm8add97JxIkTGTRoEMHBwRfsWvPSSy8RFxfHlVdeyW233cb06dMJCAhwPB8UFMSLL75Iv3796N+/PwcPHuSLL744696ZcpGRkYwdO5Z//OMf9f8HAJYsWcK9997rlPcSERFpCKq/VX9L02UxnNUfRESkHrZt28aIESPYu3cvwcHBdX6ff//73/z+979n69atWK3qzCMiIuJKqr9FLkwt3SLSKPTq1YtZs2Zx8ODBer1Pbm4uCxYsUIUtIiLSAFR/i1yYWrpFREREREREXEQt3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIiIiIiIuotAtIiIiIiIi4iIK3SIiIuJUX3/9Nddeey2xsbFYLBaWL19+wdesXbuWvn374ufnR4cOHXjzzTfPOubjjz+mR48e2Gw2evTowbJly1xQehEREedS6BYRERGnys3N5aKLLuLVV1+t0fEHDhzgl7/8JVdccQWbN2/mySef5Le//S0ff/yx45jvvvuOW265hTvuuIOffvqJO+64g3HjxvHDDz+46jJEREScwmIYhuHuQoiIiEjTZLFYWLZsGTfccMM5j3nsscf49NNP2blzp2Pf/fffz08//cR3330HwC233EJWVhb/+c9/HMdcffXVhIeHs2TJEpeVX0REpL6s7i6AJyotLeX48eMEBwdjsVjcXRwREWmEDMMgOzub2NhYvLzUsex8vvvuO0aNGlVl31VXXcX8+fMpKirCx8eH7777jt/97nf/z96dh9d07X8cf2cOwSGJJEIQGjETURGqKIJW0YlWm06ucrXVFB3cTnTg0qu0VJWr0hpK+2tp3apKW2PNIa2pxpgTMUQiRMb9++NwOBJkTk58Xs+zn5yz9tr7fFcOZ+V71t5r5agzefLkG543LS2NtLQ0y/Ps7GzOnj2Lh4eH+m8REclVcfTfSroL4MSJE/j5+ZV2GCIiYgOOHj1KrVq1SjuMMi0+Ph5vb2+rMm9vbzIzMzl9+jQ1atS4YZ34+PgbnnfcuHGMGTOmWGIWEZHyrSj7byXdBVC5cmXA/EZUqVKllKMREZGyKDk5GT8/P0ufITd3/cjzlbvfri3Prc7NRqxHjRrF8OHDLc+TkpKoXbu2+m8REbmh4ui/lXQXwJUOvkqVKuq0RUTkpnQZ8635+PjkGLFOSEjA0dERDw+Pm9a5fvT7Wi4uLri4uOQoV/8tIiK3UpT9t24yExERkVIVGhpKVFSUVdny5ctp3bo1Tk5ON63Trl27EotTRESkIDTSLSIiIkUqJSWF/fv3W57HxsYSExODu7s7tWvXZtSoURw/fpyvvvoKMM9UPnXqVIYPH86gQYNYv349s2bNspqV/KWXXuLuu+9m/Pjx9OnThx9++IFff/2VtWvXlnj7RERE8kMj3SIiIlKktmzZQlBQEEFBQQAMHz6coKAg3n77bQDi4uI4cuSIpb6/vz9Lly5l5cqVtGzZkvfee49PPvmEhx56yFKnXbt2LFiwgNmzZ9O8eXMiIyNZuHAhISEhJds4ERGRfNI63QWQnJyMyWQiKSnppveEZWVlkZGRUYKR3d6cnJxwcHAo7TBERIC89xVScvSeiIjIrRRHX6HLy4uBYRjEx8dz7ty50g7ltlO1alV8fHw0cZGIiIiIiJQJSrqLwZWE28vLi4oVKyoBLAGGYXDx4kUSEhIAqFGjRilHJCIiIiIioqS7yGVlZVkS7ivLnEjJqFChAmBeQsbLy0uXmouIiIiISKnTRGpF7Mo93BUrVizlSG5PV37vupdeRERERETKAiXdxUSXlJcO/d5FpKw4dvZiaYcgIiIiZYAuLxcRESkClzKy2HDwDKv2nmLVnlPsP36qtEMSERGRMkBJt5SqTp060bJlSyZPnlzaoYiI5IthGBw8fYGVe06xau8pNh48Q1pmtmW/g72uvBEREREl3XLZ9OnTeeWVV0hMTMTR0fzPIiUlhWrVqtG2bVvWrFljqbtmzRruvvtu9uzZQ4MGDUorZBGREnchLZM/9p82j2bvPcWxxFSr/b4mVzoGVqdjg+o09XLG76NSClRERETKDCXdAkDnzp1JSUlhy5YttG3bFjAn1z4+PmzevJmLFy9aJilbuXIlvr6+SrhFpNwzDIMDpy6wck8CK/YksCn2LBlZhmW/s4M9bfzd6XQ50b7Dq5Jlbonk5OTSCltERETKEE2kJgAEBgbi6+vLypUrLWUrV66kT58+1K9fn3Xr1lmVd+7cmfT0dF599VVq1qyJm5sbISEhVsefOXOGxx57jFq1alGxYkWaNWvG119/fdM4li1bhslk4quvvirqJoqI5ElqehYr/k7g7R92cPeHK+j60Sre/2k3f+w/Q0aWQW33ijwVWocvnm5NzDvdmPuPEP7RoR4B3pU1maOIiIjkoJHuEmAYBqkZWSX+uhWcHPL1B2CnTp1YsWIFr7/+OgArVqzg1VdfJTs7mxUrVtC1a1fS09NZv349U6ZM4ZlnnuHQoUMsWLAAX19fFi1aRI8ePdi+fTsBAQFcunSJ4OBgXnvtNapUqcJPP/1EeHg49erVIyQkJMfrL1iwgOeee445c+bQp0+fIvs9iIjcypEzF1lxeTR7/QHre7OdHewJqedOp0AvOgdWx9/TTcm1iIiI5JmS7hKQmpFF47d/KfHX3fVudyo65/0t7tSpEy+//DKZmZmkpqaybds27r77brKysvjkk08A2LBhA6mpqXTq1IlBgwZx7NgxfH19ARg5ciTLli1j9uzZjB07lpo1azJy5EjL+V988UWWLVvGt99+myPpnjZtGv/617/44Ycf6Ny5cxG0XkTkxtIys9gUe5YVf59i5d4EDp66YLXf1+RK54ZedA70IrS+B24u6i5FRESkYPRXhFh07tyZCxcusHnzZhITE2nQoAFeXl507NiR8PBwLly4wMqVK6lduzZbt27FMIwc93WnpaXh4eEBQFZWFv/+979ZuHAhx48fJy0tjbS0NNzc3KyO+e677zh58iRr166lTZs2JdZeEbm9HD+Xar43++9TrDtwmovpV69AcrS3o3XdanQO9KJzQy8Crrk3W0RERKQwlHSXgApODux6t3upvG5+3HHHHdSqVYsVK1aQmJhIx44dAfDx8cHf358//viDFStWcM8995CdnY2DgwPR0dE4OFi/TqVKlQCYOHEikyZNYvLkyTRr1gw3NzciIiJIT0+3qt+yZUu2bt3K7NmzufPOO/WHrogUiYysbKIPJ7JiTwIr/z7FnpPnrfZXr+xC58DqdA70on2AJ1VcnUopUhERESnPylTSvXr1aj788EOio6OJi4tj0aJF9O3b16rO7t27ee2111i1ahXZ2dk0adKEb775htq1awPmkdaRI0fy9ddfk5qaSpcuXZg2bRq1atWynCMxMZFhw4bx448/AtC7d2+mTJlC1apVi6VddnZ2+brMuzR17tyZlStXkpiYyCuvvGIp79ixI7/88gsbNmzgmWeeISgoiKysLBISEujQoUOu51qzZg19+vThiSeeACA7O5t9+/bRqFEjq3r169dn4sSJdOrUCQcHB6ZOnVp8DRSRcu10Shor/k7g978TWLvvNOfTMi377O2gVe1qdAqsTqdAL5r4VtGXfCIiIlLsylQmeOHCBVq0aMEzzzzDQw89lGP/gQMHuOuuuxg4cCBjxozBZDKxe/duXF1dLXUiIiJYsmQJCxYswMPDgxEjRtCrVy+rEdkBAwZw7Ngxli1bBsBzzz1HeHg4S5YsKZmGlmGdO3fm+eefJyMjwzLSDeak+5///CeXLl2ic+fO+Pn58fjjj/Pkk08yceJEgoKCOH36NL///jvNmjXj3nvv5Y477uC7775j3bp1VKtWjY8++oj4+PgcSTdAgwYNWLFiBZ06dcLR0ZHJkyeXYKtFxFYZhsHuuPP8/vdJft2dwJ/HzmFcXdELdzdnOjWoTqeGXtwd4EnVis6lF6yIiIjclspU0t2zZ0969ux5w/1vvPEG9957LxMmTLCU1atXz/I4KSmJWbNmMWfOHLp27QrA3Llz8fPz49dff6V79+7s3r2bZcuWsWHDBstkXjNnziQ0NJQ9e/YQGBhYTK2zDZ07dyY1NZWGDRvi7e1tKe/YsSPnz5+nfv36+Pn5ATB79mzef/99RowYwfHjx/Hw8CA0NJR7770XgLfeeovY2Fi6d+9OxYoVee655+jbty9JSUm5vnZgYCC///67ZcR74sSJxd9gEbE5lzKyWH/wDL/tPsnvuxM4kXTJan/TmlW4p6E39zT0onlNE/b2Gs0WERGR0lOmku6byc7O5qeffuLVV1+le/fubNu2DX9/f0aNGmW5BD06OpqMjAzCwsIsx/n6+tK0aVPWrVtH9+7dWb9+PSaTyWr27LZt22IymVi3bt1tn3TXrVsX49phostq1aqVo9zJyYkxY8YwZsyYXM/l7u7O4sWLb/p6167rDdCoUSNOnjyZr5hFpPxLSL7E738n8Nvly8avXYbR1cmeu+7wtCTaPibXm5xJREREpGTZTNKdkJBASkoK//73v3n//fcZP348y5Yt48EHH2TFihV07NiR+Ph4nJ2dqVatmtWx3t7exMfHAxAfH4+Xl1eO83t5eVnqXO/KrNtXJCcnF2HLRETkeoZhsPNEMr/tTuC3v0/y1zHrK2R8qrhyTyMvujbyol19T1zzOXGkiIiISEmxmaQ7OzsbgD59+vDyyy8D5lmv161bx/Tp063uP76eYRhWk+XkNnHO9XWuNW7cuBuO5oqISNFITc/ij/2n+e3vBH7/+yQnk9Os9reoZaJLI/NotiZBExEREVthM0m3p6cnjo6ONG7c2Kq8UaNGrF27FjAvbZWenk5iYqLVaHdCQgLt2rWz1Mnt8uVTp05Z3cN8rVGjRjF8+HDL8+TkZMt9zSIiUnBxSanmy8Z3J/DH/tOkZWZb9lVwcqBDgCddGpnXzvaqrMvGRURExPbYTNLt7OzMnXfeyZ49e6zK9+7dS506dQAIDg7GycmJqKgo+vXrB0BcXBw7duywTL4WGhpKUlISmzZtok2bNgBs3LiRpKQkS2J+PRcXF1xcXIqraSIit40rs41H7TpJ1O54dhy3vl3H1+RKl0bedGnkRdt6HrpsXERERGxemUq6U1JS2L9/v+V5bGwsMTExuLu7U7t2bV555RX69+/P3XffTefOnVm2bBlLliyxTMZlMpkYOHAgI0aMwMPDA3d3d0aOHEmzZs0ss5k3atSIHj16MGjQID7//HPAvGRYr169bvtJ1EREikNmVjabDp01J9q7TnIsMdWyz84OWvpVpevly8Yb+lTWZePlxLRp0/jwww+Ji4ujSZMmTJ48mQ4dOuRa9+mnn+bLL7/MUd64cWN27twJQGRkJM8880yOOqmpqVZLh4qIiJQ1ZSrp3rJlC507d7Y8v3JJ91NPPUVkZCQPPPAA06dPZ9y4cQwbNozAwEC+++477rrrLssxkyZNwtHRkX79+pGamkqXLl2IjIy0rNENMG/ePIYNG2aZ5bx3795MnTq1hFopIlL+XUjLZPXeU0TtOslvfyeQlJph2efiaE+HAE+6NfbmnobeVK+sK4nKm4ULFxIREcG0adNo3749n3/+OT179mTXrl3Url07R/2PP/6Yf//735bnmZmZtGjRgkceecSqXpUqVXJc8aaEW0REyjo7I7f1oeSmkpOTMZlMJCUlUaVKFat9ly5dIjY2Fn9/f/0hUAr0+xcpPQnnL/Hb7gSW74znjwNnSL/m/uxqFZ3o0sibbo296RDgSUXnMvWdb7G4WV9R3oWEhNCqVSs+++wzS1mjRo3o27cv48aNu+Xxixcv5sEHHyQ2NtZyC1lkZCQRERGcO3euwHHdzu+JiIjkTXH0FeX/rx4RESkWhmFw4FQKyy9fNh5z9BzXfo1bx6Mi3S4n2sF1quHoYF96wUqJSU9PJzo6mtdff92qPCwsjHXr1uXpHLNmzaJr166WhPuKlJQU6tSpQ1ZWFi1btuS9994jKCjohufRkp8iIlIWKOkWEZE8y8o22HYkkahdJ1m+6ySxpy9Y7W9Ry0S3xt50a+xDA+9Kuj/7NnT69GmysrJyrAji7e1NfHz8LY+Pi4vj559/Zv78+VblDRs2JDIykmbNmpGcnMzHH39M+/bt+fPPPwkICMj1XFryU0REygIl3SIiclOXMszrZy/feZJfd5/kzIV0yz4nBzva1Tffn921kTc+Jt3WIWbXf+FiGEaevoSJjIykatWq9O3b16q8bdu2tG3b1vK8ffv2tGrViilTpvDJJ5/kei4t+SkiImWBkm6xSEhI4K233uLnn3/m5MmTVKtWjRYtWjB69GhCQ0Oxs7Nj0aJFOf4QEpHy50JaJiv2JLBsRzwr/k7gQnqWZV9lV0fuaehFt8bedGxQncquTqUYqZQ1np6eODg45BjVTkhIyDH6fT3DMPjiiy8IDw/H2dn5pnXt7e2588472bdv3w3raMlPEREpC5R0i8VDDz1ERkYGX375JfXq1ePkyZP89ttvnD17Ns/nyMjIwMlJf4CL2KJzF9P5dbc50V6975TVRGg+VVwJa+JNWGMfQuq546T7s+UGnJ2dCQ4OJioqigceeMBSHhUVRZ8+fW567KpVq9i/fz8DBw685esYhkFMTAzNmjUrdMwiIiLFSUm3AHDu3DnWrl3LypUr6dixIwB16tShTZs2ANStWxfA8gdUnTp1OHToEKNHj2bx4sUMGzaM999/n0OHDpGVlcXRo0d58cUX+e2337C3t6dHjx5MmTLFMspx5bgRI0bw1ltvkZiYSM+ePZk5cyaVK1cG4Pz58wwZMoTFixdTpUoVXn31VX744QdatmzJ5MmTS/YXJFJOJSRf4pddJ/llRzzrD54hK/vqTGh1PSrSvakPPZr40KJWVeztdX+25M3w4cMJDw+ndevWhIaGMmPGDI4cOcKQIUMA82Xfx48f56uvvrI6btasWYSEhNC0adMc5xwzZgxt27YlICCA5ORkPvnkE2JiYvj0009LpE0iIiIFpaS7JBgGZFws+dd1qgh5nMSoUqVKVKpUicWLF9O2bdscl+Nt3rwZLy8vZs+eTY8ePazWPd+/fz/ffPMN3333naW8b9++uLm5sWrVKjIzMxk6dCj9+/dn5cqVluMOHDjA4sWL+d///kdiYiL9+vXj3//+Nx988AFg/qPtjz/+4Mcff8Tb25u3336brVu30rJly8L9XkRuc0fOXOSXnfEs2xnP1iOJVjOON/SpTI+mPvRo6kOgd2VNhCYF0r9/f86cOcO7775LXFwcTZs2ZenSpZbZyOPi4jhy5IjVMUlJSXz33Xd8/PHHuZ7z3LlzPPfcc8THx2MymQgKCmL16tWWL4dFRETKKiXdJSHjIoz1LfnX/dcJcHbLU1VHR0ciIyMZNGgQ06dPp1WrVnTs2JFHH32U5s2bU716dQCqVq2Kj4+P1bHp6enMmTPHUicqKoq//vqL2NhYy4Q1c+bMoUmTJmzevJk777wTgOzsbCIjIy0j2+Hh4fz222988MEHnD9/ni+//JL58+fTpUsXAGbPno2vbyn8HkVsnGEY7EtIYdmOeJbtiGdXnPWySUG1q9KjiQ/dm/hQ1zNvnxkitzJ06FCGDh2a677IyMgcZSaTiYsXb/wF9aRJk5g0aVJRhSciIlJilHSLxUMPPcR9993HmjVrWL9+PcuWLWPChAn897//5emnn77hcXXq1LEk3AC7d+/Gz8/PaobYxo0bU7VqVXbv3m1JuuvWrWtJuAFq1KhBQkICAAcPHiQjI8NqBMNkMhEYGFhUzRUp1wzD4K9jSSzbGc8vO+I5eM3SXg72doT4u9OjqQ9hjX0047iIiIhIMVLSXRKcKppHnUvjdfPJ1dWVbt260a1bN95++23+8Y9/8M4779w06XZzsx4Zu9GyMNeXXz/hmp2dHdnZ2Za6V8quP4eI5M4wDLYdPcfSv+L4eUc8x8+lWvY5O9jTIcCT7k196NrIG3e3m88MLSIiIiJFQ0l3SbCzy/Nl3mVN48aNWbx4MWBOkrOysm5+wOVjjhw5wtGjRy2j3bt27SIpKYlGjRrl6XXr16+Pk5MTmzZtspwjOTmZffv2WSZ6ExHIzr6caG+P4+ftcZxIumTZV9HZgc4NvejRxIdOgVraS0RERKQ0KOkWAM6cOcMjjzzCs88+S/PmzalcuTJbtmxhwoQJliVe6taty2+//Ub79u1xcXGhWrVquZ6ra9euNG/enMcff5zJkydbJlLr2LEjrVu3zlM8lStX5qmnnuKVV17B3d0dLy8v3nnnHezt7TWxk9z2zIl2Ij/9Fc/PO+KIuybRdnN2oEsjb+5tVoNOgdVxdXK4yZlEREREpLgp6RbAPHt5SEgIkyZN4sCBA2RkZODn58egQYP417/+BcDEiRMZPnw4M2fOpGbNmhw6dCjXc9nZ2bF48WJefPFF7r77bqslw/Ljo48+YsiQIfTq1cuyZNjRo0dxddX9p3L7yc422HokkZ+2x/Hz9njik60T7a6NzYl2xwZKtEVERETKEjtDN8nmW3JyMiaTiaSkJKpUqWK179KlS8TGxuLv76/ksIhduHCBmjVrMnHiRAYOHJhrHf3+pTzJzjaIPpLIT3/FsWyHdaJdycWRro28uLdZDe5Wol0m3ayvkNKh90RERG6lOPoKjXRLmbVt2zb+/vtv2rRpQ1JSEu+++y6A5XJ3kfIoO9tgy+FE8z3aO+I4mZxm2Xcl0b6vuS8dAjyVaIuIiIjYACXdUqb95z//Yc+ePTg7OxMcHMyaNWvw9PQs7bBEipRhGGw9co4lf57IkWhXdnG0XDquRFtERETE9ijpljIrKCiI6Ojo0g5DpFgYhsGuuGR+/PME//szzmp5r8oujnS7kmg38MTFUYm2iIiIiK1S0i0iUoL2J6Sw5M8TLPnrBAdPXbCUV3R2IKyxN72a+yrRFhERESlHlHQXE81PVzr0e5ey6OjZi/zvrziW/HmCXXHJlnJnR3vuCfSid0tfOgd6UcFZibaIiIhIeWNf2gFca/Xq1dx///34+vpalp26kcGDB2NnZ8fkyZOtytPS0njxxRfx9PTEzc2N3r17c+zYMas6iYmJhIeHYzKZMJlMhIeHc+7cuSJpg5OTEwAXL14skvNJ/lz5vV95H0RKS0LyJWb/EcuD0/6gw4QVjF/2N7viknG0t6NzYHU+6teC6De7Mj08mHub1VDCLSIiIlJOlamR7gsXLtCiRQueeeYZHnrooRvWW7x4MRs3bsTX1zfHvoiICJYsWcKCBQvw8PBgxIgR9OrVi+joaBwczH/UDhgwgGPHjrFs2TIAnnvuOcLDw1myZEmh2+Dg4EDVqlVJSEgAoGLFitjZ2RX6vHJzhmFw8eJFEhISqFq1quW9FilJiRfSWbYznh9jTrAh9gxXLryws4O2/h7c38KXnk19qObmXLqBioiIiEiJKVNJd8+ePenZs+dN6xw/fpwXXniBX375hfvuu89qX1JSErNmzWLOnDl07doVgLlz5+Ln58evv/5K9+7d2b17N8uWLWPDhg2EhIQAMHPmTEJDQ9mzZw+BgYGFboePjw+AJfGWklO1alXL71+kJFxIy2T5LnOivWbfaTKzr97i0Kp2Ve5v4ct9zWrgVUXrxouIiIjcjspU0n0r2dnZhIeH88orr9CkSZMc+6Ojo8nIyCAsLMxS5uvrS9OmTVm3bh3du3dn/fr1mEwmS8IN0LZtW0wmE+vWrSuSpNvOzo4aNWrg5eVFRkZGoc8neePk5KQRbikRGVnZrN13msUxx1m+8ySpGVmWfY1rVOH+Fr70al4DP/eKpRiliIiIiJQFNpV0jx8/HkdHR4YNG5br/vj4eJydnalWrZpVube3N/Hx8ZY6Xl5eOY718vKy1LleWloaaWlX181NTk7Otd71HBwclASKlBOGYRBz9Bw/xJxgyZ8nOHMh3bKvrkdF+rSsyf0tfLnDq1IpRikiIiIiZY3NJN3R0dF8/PHHbN26Nd/3SBuGYXVMbsdfX+da48aNY8yYMfkLWETKhdjTF1i87Tg/xBzn0JmrEyR6uDlzfwtf+gbVpEUtk+ZuEBEREZFc2UzSvWbNGhISEqhdu7alLCsrixEjRjB58mQOHTqEj48P6enpJCYmWo12JyQk0K5dO8B8v/XJkydznP/UqVN4e3vn+tqjRo1i+PDhlufJycn4+fkVVdNEpIw5nZLG//48weKYE8QcPWcpr+DkQPcm3vQJqsldd3ji5FCmFoAQERERkTLIZpLu8PBwy+RoV3Tv3p3w8HCeeeYZAIKDg3FyciIqKop+/foBEBcXx44dO5gwYQIAoaGhJCUlsWnTJtq0aQPAxo0bSUpKsiTm13NxccHFxaW4miYiZcDF9Eyidp1k0bbjrNl3mqzLE6LZ20GHgOo8EFSTbo29cXOxmY9NERERESkDytRfjykpKezfv9/yPDY2lpiYGNzd3alduzYeHh5W9Z2cnPDx8bFMfmYymRg4cCAjRozAw8MDd3d3Ro4cSbNmzSwJe6NGjejRoweDBg3i888/B8xLhvXq1atIJlETEduRmZXNHwfOsHjbcX7ZGc/F9KsTorWoZaJvUE16NfelemV96SYiIiIiBVOmku4tW7bQuXNny/Mrl3Q/9dRTREZG5ukckyZNwtHRkX79+pGamkqXLl2IjIy0mtBs3rx5DBs2zDLLee/evZk6dWrRNUREyrS9J8/zXfQxFm07TsL5q5Mk1rk8IVrflr7Uq64J0URERESk8OwMwzBuXU2ulZycjMlkIikpiSpVqpR2OCKSB2cvpPNjzHG+23qc7ceTLOXVKjpZJkQL8quqCdGkyKivKHv0noiIyK0UR1+hWYBEpNxKz8xm+c54Bs/ZQsjYXxm9ZBfbjyfhaG9HWGNvPg8PZuO/uvJun6a0ql1NCbdIEZo2bRr+/v64uroSHBzMmjVrblh35cqV2NnZ5dj+/vtvq3rfffcdjRs3xsXFhcaNG7No0aLiboaIiEihlanLy0VECsswDHaeSOb/oo/x458nOHvNetpNa1bhoVa16N3CF49Kuk9bpLgsXLiQiIgIpk2bRvv27fn888/p2bMnu3btslqF5Hp79uyxGlWoXr265fH69evp378/7733Hg888ACLFi2iX79+rF27lpCQkGJtj4iISGHo8vIC0OVpImVPwvlL/LDtBN9tPcbf8ect5Z6VXHiwVU0ealWLQJ/KpRih3G5u574iJCSEVq1a8dlnn1nKGjVqRN++fRk3blyO+itXrqRz584kJiZStWrVXM/Zv39/kpOT+fnnny1lPXr0oFq1anz99dd5iut2fk9ERCRviqOv0Ei3iNisSxlZ/Lr7JN9FH2P1Nct8OTva062xNw+3qkWHAE8ctZ62SIlJT08nOjqa119/3ao8LCyMdevW3fTYoKAgLl26ROPGjXnzzTetJlddv349L7/8slX97t27M3ny5CKLXUREpDgo6RYRm7PzRBLfbD7K4pgTJKVmWMqDalfl4eBa9Grmi6miUylGKHL7On36NFlZWXh7e1uVe3t7Ex8fn+sxNWrUYMaMGQQHB5OWlsacOXPo0qULK1eu5O677wYgPj4+X+cESEtLIy3t6goFycnJBW2WiIhIgSnpFhGbkHQxgx/+PM7CzUfZeeLqH841TK482KomD7aqRX0t8yVSZlw/MaFhGDecrDAwMJDAwEDL89DQUI4ePcp//vMfS9Kd33MCjBs3jjFjxhQkfBERkSKjpFtEyqzsbIMNB8+wcMtRlu2IJy0zGwAnBzvCGvvQ704/7rrDEwd7zTouUlZ4enri4OCQYwQ6ISEhx0j1zbRt25a5c+danvv4+OT7nKNGjWL48OGW58nJyfj5+eU5BhERkaKgpFtEypy4pFT+b8sxvo0+xpGzFy3lgd6V6XenHw8E1cTdzbkUIxSRG3F2diY4OJioqCgeeOABS3lUVBR9+vTJ83m2bdtGjRo1LM9DQ0OJioqyuq97+fLltGvX7obncHFxwcVFKxWIiEjpUtItImVCemY2v+0+ycItR1m99xSX50Sjsosj97f0pX9rP5rXMmktbREbMHz4cMLDw2ndujWhoaHMmDGDI0eOMGTIEMA8An38+HG++uorACZPnkzdunVp0qQJ6enpzJ07l++++47vvvvOcs6XXnqJu+++m/Hjx9OnTx9++OEHfv31V9auXVsqbRQREckrJd0iUqr2nTzPws1HWbTtOGeuWVO7jb87/Vv7cW+zGlRwdijFCEUkv/r378+ZM2d49913iYuLo2nTpixdupQ6deoAEBcXx5EjRyz109PTGTlyJMePH6dChQo0adKEn376iXvvvddSp127dixYsIA333yTt956i/r167Nw4UKt0S0iImWe1ukuAK3zKVI4lzKy+OmvOOZvOkL04URLuVdlFx4KrkW/1n74e7qVYoQihae+ouzReyIiIreidbpFxKbtO3me+ZuO8P3W45alvhzs7binoRf9W/vRKbC61tQWERERkXJFSbeIFKtLGVn8vCOO+RuPsPnQ1VHtmlUr8FgbP/q19sOrimspRigiIiIiUnyUdItIsdifkMLXm47w3dZjnLtoPao9IKQ2dwdU11JfIiIiIlLuKekWkSKTlpnFsh3xzNt4hE2xZy3lviZXHm1Tm36t/fAxaVRbRERERG4fSrpFpNBiT19g/sbD/F/0MRIvj2rb22EZ1e7YwEuj2iIiIiJyW1LSLSIFkpVt8Nvuk8zZcJg1+05bymuYXOl/p/lebd+qFUoxQhERERGR0qekW0Ty5UxKGgs2H2X+xiMcP5cKgJ0ddGpQncdD6mgGchERERGRayjpFpFbMgyDbUfPMWf9YX76K470rGwAqlV0ot+dfjwRUgc/94qlHKWIiIiISNlTpoajVq9ezf3334+vry92dnYsXrzYsi8jI4PXXnuNZs2a4ebmhq+vL08++SQnTpywOkdaWhovvvginp6euLm50bt3b44dO2ZVJzExkfDwcEwmEyaTifDwcM6dO1cCLRSxLZcysvhm81Hun7qWB6etY9G246RnZdOilon/PNKC9aO6MKpnIyXcIiIiIiI3UKZGui9cuECLFi145plneOihh6z2Xbx4ka1bt/LWW2/RokULEhMTiYiIoHfv3mzZssVSLyIigiVLlrBgwQI8PDwYMWIEvXr1Ijo6GgcHBwAGDBjAsWPHWLZsGQDPPfcc4eHhLFmypOQaK1KGHTlzkbkbD/PNlqOW5b6cHe25v7kvT4bWoYVf1dINUERERETERtgZhmGUdhC5sbOzY9GiRfTt2/eGdTZv3kybNm04fPgwtWvXJikpierVqzNnzhz69+8PwIkTJ/Dz82Pp0qV0796d3bt307hxYzZs2EBISAgAGzZsIDQ0lL///pvAwMBbxpacnIzJZCIpKYkqVaoUSXtFSlt2tsGqfaf4at0hVu49xZVPhlrVKvBE2zr0a+2Hu5tz6QYpYkPUV5Q9ek9ERORWiqOvKFMj3fmVlJSEnZ0dVatWBSA6OpqMjAzCwsIsdXx9fWnatCnr1q2je/furF+/HpPJZEm4Adq2bYvJZGLdunV5SrpFypMLaZl8v/UYs9cd4uCpC5byjg2q82RoHToFarkvEREREZGCstmk+9KlS7z++usMGDDA8g1EfHw8zs7OVKtWzaqut7c38fHxljpeXl45zufl5WWpc720tDTS0tIsz5OTk4uqGSKl5ljiRb5af5ivNx3h/KVMACq7OPJIaz/CQ+vg7+lWyhGKiIiIiNg+m0y6MzIyePTRR8nOzmbatGm3rG8YBnZ2V0fqrn18ozrXGjduHGPGjCl4wCJlhGEYbD6UyOw/YvllZzzZly8hr+tRkWfa+/NQcC0qudjkx4KIiIiISJlkc39dZ2Rk0K9fP2JjY/n999+trrP38fEhPT2dxMREq9HuhIQE2rVrZ6lz8uTJHOc9deoU3t7eub7mqFGjGD58uOV5cnIyfn5+RdUkkWKXlpnF//6MY/a6WHYcv3qlxl13ePJM+7p0DvTCXpeQi4iIiIgUOZtKuq8k3Pv27WPFihV4eHhY7Q8ODsbJyYmoqCj69esHQFxcHDt27GDChAkAhIaGkpSUxKZNm2jTpg0AGzduJCkpyZKYX8/FxQUXF5dibJlI8Th1Po15Gw8zd8MRTqeYb5FwcbTnwVY1ebqdP4E+lUs5QhERERGR8q1MJd0pKSns37/f8jw2NpaYmBjc3d3x9fXl4YcfZuvWrfzvf/8jKyvLcg+2u7s7zs7OmEwmBg4cyIgRI/Dw8MDd3Z2RI0fSrFkzunbtCkCjRo3o0aMHgwYN4vPPPwfMS4b16tVLk6hJufF3fDL/XRPLjzEnSM/KBsC7igtPhtblsTa1NQu5iIiIiEgJKVNJ95YtW+jcubPl+ZVLup966ilGjx7Njz/+CEDLli2tjluxYgWdOnUCYNKkSTg6OtKvXz9SU1Pp0qULkZGRljW6AebNm8ewYcMss5z37t2bqVOnFmPLRIqfYRj8sf8MM9YcZPXeU5byln5VeaZ9Xe5tVgMnB/tSjFBERERE5PZTZtfpLsu0zqeUJRlZ2fz0VxwzVh9kV5z5fm17O+jZtAYDO/jTqna1W5xBRIqD+oqyR++JiIjcitbpFhGL85cyWLDpKF/8EUtc0iUAKjg50P9OP55t709tj4qlHKGIiIiIiCjpFrExcUmpRP5xiPkbj3A+zby+tmclF55uV4fHQ+pQTfdri4iIiIiUGUq6RWzE7rhkZq45yI8xJ8i8vMB2/epuPHd3Pfq0rImrk8MtziAiIiIiIiVNSbdIGWYYBhtjz/LZygOsumZytBB/d567u57W1xYRERERKeM0lbFIGWQYBr/tPsnD09fz6IwNrNp7Cns76NW8Bj88356Fg0Pp0shbCbeIlFnTpk3D398fV1dXgoODWbNmzQ3rfv/993Tr1o3q1atTpUoVQkND+eWXX6zqREZGYmdnl2O7dOlScTdFRESkUDTSLVKGZGZl89P2OD5beYC/488D4OxoT7/WtXiuQ31NjiYiNmHhwoVEREQwbdo02rdvz+eff07Pnj3ZtWsXtWvXzlF/9erVdOvWjbFjx1K1alVmz57N/fffz8aNGwkKCrLUq1KlCnv27LE61tXVtdjbIyIiUhhaMqwAtOSIFLW0zCy+iz7O9FUHOHL2IgBuzg48EVqHge398aqiPypFbM3t3FeEhITQqlUrPvvsM0tZo0aN6Nu3L+PGjcvTOZo0aUL//v15++23AfNId0REBOfOnStwXLfzeyIiInlTppYMy8jIID4+nosXL1K9enXc3d2LJCCR28mFtEzmbzzCzDUHSTifBkC1ik48296fJ0PrYqroVMoRiogtKs0+Oj09nejoaF5//XWr8rCwMNatW5enc2RnZ3P+/PkccaekpFCnTh2ysrJo2bIl7733ntVI+PXS0tJIS0uzPE9OTs5HS0RERIpGvpLulJQU5s2bx9dff82mTZusOrJatWoRFhbGc889x5133lnkgYqUJ4kX0olcd4jIdYdISs0AoIbJlUEd6vFoGz8qOuvODxHJn7LSR58+fZqsrCy8vb2tyr29vYmPj8/TOSZOnMiFCxfo16+fpaxhw4ZERkbSrFkzkpOT+fjjj2nfvj1//vknAQEBuZ5n3LhxjBkzpuCNERERKQJ5/st+0qRJfPDBB9StW5fevXvz+uuvU7NmTSpUqMDZs2fZsWMHa9asoVu3brRt25YpU6bcsBMUuV2dvZDOzDUH+WrdIS6kZwHg7+nGPzvWp29QTZwdNbehiORfWeyj7eysJ3o0DCNHWW6+/vprRo8ezQ8//ICXl5elvG3btrRt29byvH379rRq1YopU6bwySef5HquUaNGMXz4cMvz5ORk/Pz88tsUERGRQslz0r1u3TpWrFhBs2bNct3fpk0bnn32WT777DO++OILVq1apaRb5LLTKWnMXH2QORsOc/Fyst2oRhVe6HwHPZr64KBZyEWkEPLaR0+fPp1Zs2YVax/t6emJg4NDjlHthISEHKPf11u4cCEDBw7k22+/pWvXrjeta29vz5133sm+fftuWMfFxQUXF5e8By8iIlIM8px0f/vtt3mq5+rqytChQwsckEh5cup8GjNWH2DuhiOkZpiT7aY1q/BSlwZ0beSVp1EfEZFbyWsf7eLiUux9tLOzM8HBwURFRfHAAw9YyqOioujTp88Nj/v666959tln+frrr7nvvvtu+TqGYRATE3PDLxpERETKikLdOLpr1y6OHDlCenq6VXnv3r0LFZSIrUs4f4nPVx1k3sbDXMrIBqB5LRMvdQngnoZKtkWkfBs+fDjh4eG0bt2a0NBQZsyYwZEjRxgyZAhgvuz7+PHjfPXVV4A54X7yySf5+OOPadu2rWWUvEKFCphMJgDGjBlD27ZtCQgIIDk5mU8++YSYmBg+/fTT0mmkiIhIHhUo6T548CAPPPAA27dvx87Ojiurjl1JJLKysoouQhEbcjL5EtNXHWD+xiOkZZqT7ZZ+VXmpawCdGlRXsi0iJaY0vxjv378/Z86c4d133yUuLo6mTZuydOlS6tSpA0BcXBxHjhyx1P/888/JzMzk+eef5/nnn7eUP/XUU0RGRgJw7tw5nnvuOeLj4zGZTAQFBbF69WratGlT7O0REREpjAKt033//ffj4ODAzJkzqVevHps2beLMmTOMGDGC//znP3To0KE4Yi0ztM6nXC/h/CWmrTjA/E1HSL+cbLeqXZWXujbg7gBPJdsit6HS6iv0xfiNqf8WEZFbKTPrdK9fv57ff/+d6tWrY29vj729PXfddRfjxo1j2LBhbNu2rUiCEynrEi+kM331Ab5cd8hyGfmddavxUpcGtL/DQ8m2iJS4l156CX9/f3799ddcvxgXERGRklWgpDsrK4tKlSoB5llKT5w4QWBgIHXq1GHPnj1FGqBIWZR8KYNZa2KZtTaWlLRMAIJqV2VkWCDt6ivZFpHSoy/GRUREypYCJd1Nmzblr7/+ol69eoSEhDBhwgScnZ2ZMWMG9erVK+oYRcqMi+mZfLnuMJ+vPsC5ixkANK5RhZHdG9A5UBOkiUjp0xfjIiIiZUuBku4333yTCxcuAPD+++/Tq1cvOnTogIeHBwsXLizSAEXKgrTMLOZvPMKnKw5wOiUNgPrV3RjeLZCeTX2w1zrbIlJG6ItxERGRssW+IAd1796dBx98EIB69eqxa9cuTp8+TUJCAvfcc0+Bg1m9ejX3338/vr6+2NnZsXjxYqv9hmEwevRofH19qVChAp06dWLnzp1WddLS0njxxRfx9PTEzc2N3r17c+zYMas6iYmJhIeHYzKZMJlMhIeHc+7cuQLHLeVXRlY2X286QucPVzJmyS5Op6Th516BiY+0YPnLHbmveQ0l3CJSprz55ptkZ5vnmHj//fc5fPgwHTp0YOnSpXzyySelHJ2IiMjtp0BJd27c3d0LfWnthQsXaNGiBVOnTs11/4QJE/joo4+YOnUqmzdvxsfHh27dunH+/HlLnYiICBYtWsSCBQtYu3YtKSkp9OrVy2q21gEDBhATE8OyZctYtmwZMTExhIeHFyp2KV8Mw+Dn7XF0n7SaUd9v50TSJXyquPLBA035fUQnHgquhYOSbREpg4rri3EREREpmHwtGXbx4kVeeeUVFi9eTEZGBl27duWTTz7B09Oz6AOzs2PRokX07dsXMCdBvr6+RERE8NprrwHmUW1vb2/Gjx/P4MGDSUpKonr16syZM4f+/fsDcOLECfz8/Fi6dCndu3dn9+7dNG7cmA0bNhASEgLAhg0bCA0N5e+//yYwMPCWsWnJkfJtU+xZxv28m21HzgHg7ubM0E71eaJtHVydHEo3OBGxGSXdV5RkH22r1H+LiMitFEdfka+R7nfeeYfIyEjuu+8+HnvsMaKiovjnP/9ZJIHcSmxsLPHx8YSFhVnKXFxc6NixI+vWrQMgOjqajIwMqzq+vr40bdrUUmf9+vWYTCZLwg3Qtm1bTCaTpY7cnvadPM8/vtxMv8/Xs+3IOSo4OfDiPXew6pVO/KNDPSXcIlKmXdtHP/rooyXaR4uIiMiN5Wsite+//55Zs2bx6KOPAvD444/Tvn17srKycHAo3oQkPj4eAG9vb6tyb29vDh8+bKnj7OxMtWrVctS5cnx8fDxeXl45zu/l5WWpc720tDTS0tIsz5OTkwveEClz4pJSmRS1l/+LPka2AQ72dvS/04+ILgF4VXEt7fBERPLk+j76iSeeKLE+WkRERG4sX0n30aNH6dChg+V5mzZtcHR0tFzCXRKuv2/cMIxb3kt+fZ3c6t/sPOPGjWPMmDEFiFbKsqTUDKavOsAXa2NJyzRPOtSjiQ+v9AikfvVKpRydiEj+lIU+WkRERHLK1+XlWVlZODs7W5U5OjqSmZlZpEHlxsfHByDHaHRCQoJl9NvHx4f09HQSExNvWufkyZM5zn/q1Kkco+hXjBo1iqSkJMt29OjRQrdHSk9GVjaRf8TS6cMVfLbyAGmZ2dxZtxrf/bMd08ODlXCLiE0qzT5aREREbixfI92GYfD000/j4uJiKbt06RJDhgzBzc3NUvb9998XXYSX+fv74+PjQ1RUFEFBQQCkp6ezatUqxo8fD0BwcDBOTk5ERUXRr18/AOLi4tixYwcTJkwAIDQ0lKSkJDZt2kSbNm0A2LhxI0lJSbRr1y7X13ZxcbFqs9gmwzBYuecU7/+0iwOnzOvM3+FVidd6NKRrI69Cz74vIlKaSrOPFhERkRvLV9L91FNP5Sh74okniiyYlJQU9u/fb3keGxtLTEwM7u7u1K5dm4iICMaOHUtAQAABAQGMHTuWihUrMmDAAABMJhMDBw5kxIgReHh44O7uzsiRI2nWrBldu3YFoFGjRvTo0YNBgwbx+eefA/Dcc8/Rq1evPM1cLrZp78nzvPe/XazZdxoADzdnhoc1oH9rPxwdimzlPBGRUlPcfbSIiIgUTL6S7tmzZxdXHABs2bKFzp07W54PHz4cMP8hERkZyauvvkpqaipDhw4lMTGRkJAQli9fTuXKlS3HTJo0CUdHR/r160dqaipdunQhMjLSahKZefPmMWzYMMss5717977h2uBi286kpDHp173M33iEbAOcHOx4tr0/z99zB1VcnUo7PBGRIlPcfbSIiIgUTL7W6RYzrfNZ9qVnZvPlukN88vs+zl8y38/Yo4kPo+5tSB0Pt1scLSJSeOoryh69JyIicivF0Vfka6T72WefzVO9L774okDBiBSWYRhE7TrJ2KW7OXTmIgBNfKvwVq/GtK3nUcrRiYgUH/XRIiIiZVO+ku7IyEjq1KlDUFAQGiCXsib29AVG/7iTVXtPAVC9sguvdA/koVa1cLDXJGkiUr6pjxYRESmb8pV0DxkyhAULFnDw4EGeffZZnnjiCdzd3YsrNpE8uZieyacr9jNzdSzpWdk4O9jzjw7+DO18B5Vc8vVPXETEZqmPFhERKZvyNW3ztGnTiIuL47XXXmPJkiX4+fnRr18/fvnlF32rLiXOMAyWbo+j68RVfLriAOlZ2XRsUJ1fXr6bV3s0VMItIrcV9dEiIiJlU6EmUjt8+DCRkZF89dVXZGRksGvXLipVqlSU8ZVJmoil9O1POM/oH3exdr95CbBa1Srwdq/GdGvsrfW2RaRMKO2+4nbto2+mtN8TEREp+0p9IrXr2dnZYWdnh2EYZGdnF0lAIjdzIS2TT37bx6y1sWRmGzg72jOkY32GdqqPq5PDrU8gInKbUB8tIiJSNuTr8nKAtLQ0vv76a7p160ZgYCDbt29n6tSpHDly5Lb/Bl2KV9Suk3T7aBWfrz5IZrZB10Ze/PpyR4Z3a6CEW0QE9dEiIiJlUb6S7qFDh1KjRg3Gjx9Pr169OHbsGN9++y333nsv9vb5zt9F8iQuKZXBc7Yw6KstnEi6hJ97Bb54ujX/fepOantULO3wRETKhLLWR0+bNg1/f39cXV0JDg5mzZo1N62/atUqgoODcXV1pV69ekyfPj1Hne+++47GjRvj4uJC48aNWbRoUXGFLyIiUmTydU+3vb09tWvXJigo6Kb3zX7//fdFElxZpXvCSkZWtsFX6w/xn1/2cCE9C0d7OwbdXY9h9wRQwVkj2yJStpV0X1GW+uiFCxcSHh7OtGnTaN++PZ9//jn//e9/2bVrF7Vr185RPzY2lqZNmzJo0CAGDx7MH3/8wdChQ/n666956KGHAFi/fj0dOnTgvffe44EHHmDRokW8/fbbrF27lpCQkDzFpf5bRERupTj6inwl3U8//XSeJqmaPXt2oYIq69RpF78dx5P416Lt/HUsCYBWtasy9sFmNPTR71tEbENJ9xVlqY8OCQmhVatWfPbZZ5ayRo0a0bdvX8aNG5ej/muvvcaPP/7I7t27LWVDhgzhzz//ZP369QD079+f5ORkfv75Z0udHj16UK1aNb7++us8xXXlPYk7dUb9t4iI5Co5OZka1T1KbyK1yMjIInlRkRu5kJbJpKi9fPFHLNkGVHZ15LUeDRnQpjb29pqVXETkRspKH52enk50dDSvv/66VXlYWBjr1q3L9Zj169cTFhZmVda9e3dmzZpFRkYGTk5OrF+/npdffjlHncmTJ98wlrS0NNLS0izPk5OTAWjzwW/Yu+j2JBERySk77WKRnzPPN3kdOXIkXyc+fvx4voOR29vafacJm7Sa/641J9z3Na/Bb8M78kTbOkq4RURuoiz10adPnyYrKwtvb2+rcm9vb+Lj43M9Jj4+Ptf6mZmZnD59+qZ1bnROgHHjxmEymSybn59fQZokIiJSKHke6b7zzjvp3bs3gwYNok2bNrnWSUpK4ptvvuHjjz9m8ODBvPjii0UWqJRfyZcyGPvTbhZsPgpAzaoVeL9vUzo39CrlyEREbENZ7KOvv9TdMIybXv6eW/3ry/N7zlGjRjF8+HDL8+TkZPz8/Nj0RhddXi4iIrlKTk6mxuSiPWeek+7du3czduxYevTogZOTE61bt8bX1xdXV1cSExPZtWsXO3fupHXr1nz44Yf07NmzaCOVcmnFngT+9f124pIuAfBUaB1e7dEQN5dCLSEvInJbKUt9tKenJw4ODjlGoBMSEnKMVF/h4+OTa31HR0c8PDxuWudG5wRwcXHBxcUlR3lFZ0cqOqufERGRnDKLoX/I8+Xl7u7u/Oc//+HEiRN89tlnNGjQgNOnT7Nv3z4AHn/8caKjo/njjz+UcMstJV3MYPg3MTwzezNxSZeo41GRhc+1ZUyfpkq4RUTyqSz10c7OzgQHBxMVFWVVHhUVRbt27XI9JjQ0NEf95cuX07p1a5ycnG5a50bnFBERKSvyNXu5mGn28sJZvjOeNxbv4NT5NOzs4Nn2/owMC9QyYCJSrtzOfcWVJcOmT59OaGgoM2bMYObMmezcuZM6deowatQojh8/zldffQVcXTJs8ODBDBo0iPXr1zNkyBCrJcPWrVvH3XffzQcffECfPn344YcfePPNN7VkmIiIFKni6Cs0pCglJik1g3d+2MHimBMA1KvuxocPNye4jnspRyYiIkWpf//+nDlzhnfffZe4uDiaNm3K0qVLqVOnDgBxcXFWk7/5+/uzdOlSXn75ZT799FN8fX355JNPLAk3QLt27ViwYAFvvvkmb731FvXr12fhwoV5TrhFRERKi0a6C0DflOffH/tPM/LbP4lLuoS9HQy6ux4vd22Aq5NGt0WkfFJfUfboPRERkVvRSHdZs2kGBD0AHvVLO5Iy61JGFhOW7eGLP2IBqOtRkY/6t6RV7WqlHJmIiIiIiEjxU9JdGL+9C7vmwbBtpR1JmbTjeBIvL4xhX0IKAI+H1OaN+xppxlgREREREblt5Hn28rIgMzOTN998E39/fypUqEC9evV49913yc7OttQxDIPRo0fj6+tLhQoV6NSpEzt37rQ6T1paGi+++CKenp64ubnRu3dvjh07VrCgzh4sTJPKpaxsg09X7OeBaX+wLyEFz0ouzH76Tj54oJkSbhGREnLp0qXSDkFEREQogqQ7LS2tKOLIk/HjxzN9+nSmTp3K7t27mTBhAh9++CFTpkyx1JkwYQIfffQRU6dOZfPmzfj4+NCtWzfOnz9vqRMREcGiRYtYsGABa9euJSUlhV69epGVlVWwwNLO37rObeL4uVQenbGeD3/ZQ0aWQfcm3ix/+W46N/Qq7dBERG4r7du3tzy+fqktERERKTmFSrojIiKoVq0awcHBHDx4kGHDhvHoo48yc+bMoorPyvr16+nTpw/33XcfdevW5eGHHyYsLIwtW7YA5lHuyZMn88Ybb/Dggw/StGlTvvzySy5evMj8+fMBSEpKYtasWUycOJGuXbsSFBTE3Llz2b59O7/++mvBAjt3tKiaaNOW7Yin5+TVbD6USCUXR/7zSAumPxGMu5tzaYcmInLbufYqsNdee81qX1hYWEmHIyIictsqVNK9ZMkSTp8+zccff0z79u2pXr06/fr1IyoqinfeeaeoYrS46667+O2339i7dy8Af/75J2vXruXee+8FzOt8xsfHW/0x4eLiQseOHVm3bh0A0dHRZGRkWNXx9fWladOmljrXS0tLIzk52Wqzcu5IrsfdLi5lZPHW4h0MmRtN8qVMWvhVZemwDjwcXAs7O7vSDk9E5LZ07efv9QuVnDp1qqTDERERuW0V6gZbk8lExYoVueuuu6hSpQpvvfUWAL1796Zt27aMGTOmSIK84rXXXiMpKYmGDRvi4OBAVlYWH3zwAY899hgA8fHxAHh7e1sd5+3tzeHDhy11nJ2dqVatWo46V46/3rhx427elqTbd6R7f8J5Xpi/jb/jzZfYD+5YjxHdAnF2tKnpAkREyp1Tp06xZMkSmjVrluMLUH0hKiIiUnIKlXSfOnWK//3vfzRv3hw3N7erJ3V0zPGtelFYuHAhc+fOZf78+TRp0oSYmBgiIiLw9fXlqaeestS7/o8JwzBu+QfGzeqMGjWK4cOHW54nJyfj5+d3tULiofw3xsYZhsE3W47yzo87uZSRjWclZyb2a0nHBtVLOzQREQFefvllFi1axHvvvcfBgwdp164dgYGBBAYGcvbs2dIOT0RE5LZRqKT7Zh36mTNniipGi1deeYXXX3+dRx99FIBmzZpx+PBhxo0bx1NPPYWPjw9gHs2uUaOG5biEhATL6LePjw/p6ekkJiZajXYnJCTQrl27XF/XxcUFFxeXGwd2m410X0zP5F/fb2dxzAkAOgR4MrFfC7wqu5ZyZCIicsW1XxYDHDx4kB07drBjxw6rSdZERESkeBXqGuCWLVuyf/9+nJyceO2114iMjOSBBx4gOzu7WDr0ixcvYm9vHbKDg4Nlshh/f398fHysZmlNT09n1apVloQ6ODgYJycnqzpxcXHs2LHjhkn3LSUVcLkxG3TgVAp9P/2DxTEncLC347UeDfnymTZKuEVEypjrb4uqV68evXv35l//+hfz5s0rpahERERuP4Ua6R46dChjx47F19eX6dOnM3fuXN5991169+5dVPFZuf/++/nggw+oXbs2TZo0Ydu2bXz00Uc8++yzgPmy8oiICMaOHUtAQAABAQGMHTuWihUrMmDAAMB8H/rAgQMZMWIEHh4euLu7M3LkSJo1a0bXrl0LFthtMnv5z9vjeOX//iIlLROvyi5MHdCKNv7upR2WiIjk4ocffrBMajpw4EBmzZpVyhGJiIjcngqVdLu6uvLggw8CcOedd9K+fXvefffdIgksN1OmTOGtt95i6NChJCQk4Ovry+DBg3n77bctdV599VVSU1MZOnQoiYmJhISEsHz5cipXrmypM2nSJBwdHenXrx+pqal06dKFyMhIHBwcChbYhQTIuARO5XO0NyMrmwnL/mbmmlgAQvzdmTIgSKPbIiI2Ytu2baUdgoiIyG3LzijEjGe1atVi0qRJNGzYkAYNGhAaGsrWrVuLMr4yKTk5GZPJRNLrlanicnnytRe3gkf90g2sGCQkX+KF+dvYdMg86c7gu+vxSvdAHB00O7mIyM1Y+oqkJKpUqVLir+/n58e0adNo1qwZDz744G3RP99Kab8nIiJS9hVHX1Goke7hw4fz888/8+GHH7Jnzx7S0tLo168fLVq0oHnz5tx///1FEmSZVqUmJB83r9VdzpLubUcSGTwnmoTzaVRyceQ/jzSnR9Matz5QRERKXUREhNVkp+3bt6dRo0aW7d577y3tEEVERG4LhU66r3XtzKgLFiy4PZJuj/rmpLuczWD+XfQxRi3aTnpmNg28KzH9iWDqVa9U2mGJiEgejRgxwur5tX30/PnzlXSLiIiUkEIl3derV6+eZXbU20Y1f4hdXW5mMM/KNvj3z7st92+HNfbmo/4tqeRSpP9URESkmA0ZMoSIiAgaNmwIQEpKChcvXiQ8PBw/P79Sjk5EROT2USQ35p49e5a1a9cyY8YMIiIi6NatG7Vq1SqKU5d9VS//4VIOZjBPSs3g2cjNloR72D13MP2JYCXcIiI26Pfff7ck3H/99Rft2rXjiy++oGPHjqxcubJ0gxMREbmNFCqb6ty5M3v27MHNzY1GjRrRsGFD5s+fz+LFiwkICCiqGMs2U23zTxu/vPzAqRQGfbmFg6cv4Opkz8RHWnJfc92/LSJiq66d/GXWrFk888wzTJkyhYMHD/Lkk0+ydu3aUoxORETk9lGopLtatWr4+/szfvx47rrrLgC+/fZb2rVrVyTB2QR3f/PPMwdKN45CWH/gDIPnbCH5Uia+JldmPNmapjVNpR2WiIgUQu3atfn1119p164d33//PXPmzAHMt4JdvHixlKMTERG5fRTq8vLvv/+eTz/9lPHjx9O9e3c2btyInZ1dUcVmA+zA4w7zw/MnIC2ldMMpgEXbjvHkFxtJvpRJq9pV+fHFu5Rwi4iUA5988gmjR4+mWrVqBAQE0KlTJwAyMzM5f/586QYnIiJyGyn0Pd0tW7ZkyZIlvPvuu7z99tucPHmSjRs3FkVsNsAOKrpDBXfz07MHSzecfDAMgym/7ePlhX+SkWVwX7MazB/UFs9KLqUdmoiIFML//vc/srOzqVWrFmvXriU5OZnff//dsv/333+nY8eOpRihiIjI7aVASfeVDv1aISEh/PLLLyxbtoxRo0bRtWvXIgmwbLs8qu95+f71M/tKL5R8yMjK5rXv/mJi1F4ABt9djymPBeHq5FDKkYmISGH16dOH06dPW567uFh/mRoWFsZ///vfkg5LRETktlWgpPv6Dv1aHTp04Pfff+df//pXoQKzCVcupb9yibkN3Nd9/pJ5hvJvthzD3g7e69uUUfc2wt7+drotQESk/DIMo1RfPzExkfDwcEwmEyaTifDwcM6dO3fD+hkZGbz22ms0a9YMNzc3fH19efLJJzlx4oRVvU6dOmFnZ2e1Pfroo8XcGhERkcIrUNKdlw79nnvuKcipbZMl6d5funHcwumUNB6buYE1+05T0dmB/z7VmvC2dUo7LBERKWIxMTFcuHDBquz48eNWM5oXlwEDBhATE8OyZctYtmwZMTExhIeH37D+xYsX2bp1K2+99RZbt27l+++/Z+/evfTu3TtH3UGDBhEXF2fZPv/88+JsioiISJEo8OzlMTExtG/fHjc3N0vZ8ePHadSoEcnJyUUSXJl3/Uj36bJ7efnxc6mE/3cjB09fwMPNmchn2tCsliZMExEpj3r27ImdnR1169alefPmBAYGcvjwYapWrVqsr7t7926WLVvGhg0bCAkJAWDmzJmEhoayZ88eAgMDcxxjMpmIioqyKpsyZQpt2rThyJEj1K5d21JesWJFfHx8irUNIiIiRa3ASXdpdehly/WXl+8Hw7iajJcR+xPOEz5rE3FJl6hZtQJzBrahXvVKpR2WiIgUk71795KQkMD27dv566+/2L59O9nZ2cyYMaNYX3f9+vWYTCZLwg3Qtm1bTCYT69atyzXpzk1SUhJ2dnY5/qaYN28ec+fOxdvbm549e/LOO+9QuXLlomyCiIhIkStw0l1aHXqZ5HEH2DtBWjKcOwLVys4l238ePcfTszeReDGDO7wqMWdgG2qYKpR2WCIiUowqV65M/fr1CQ0NLdHXjY+Px8vLK0e5l5cX8fHxeTrHpUuXeP311xkwYIDV5fCPP/44/v7++Pj4sGPHDkaNGsWff/6ZY5T8WmlpaaSlpVme3zZX4omISJlS4KS7tDr0MuXKiLajM3g1hPjtEP9XmUm61x84wz++3MyF9Cxa1DIx+5k2uLs5l3ZYIiJSjO6//36cnJyK9JyjR49mzJgxN62zefNmAOxyudrLMIxcy6+XkZHBo48+SnZ2NtOmTbPaN2jQIMvjpk2bEhAQQOvWrdm6dSutWrXK9Xzjxo27ZdwiIiLFrUBJd3F06Lbpmj8gfFpcTrq3Q6P7Sy+ky9buO80/vtrMpYxs2t/hwefhrankUuDvWERExEb88MMPRX7OF1544ZYzhdetW5e//vqLkydP5th36tQpvL29b3p8RkYG/fr1IzY2lt9///2Wk761atUKJycn9u3bd8Oke9SoUQwfPtzyPDk5GT8/v5ueV0REpKgVKAsrjg7dJl37rb1PM/PP+O2lE8s1Vu89xaCvtpCWmc09Db2Y9ngrrcEtIiIF5unpiaen5y3rhYaGkpSUxKZNm2jTpg0AGzduJCkpiXbt2t3wuCsJ9759+1ixYgUeHh63fK2dO3eSkZFBjRo1bljHxcUlxzrlIiIiJa1AS4bJFdck3TWam3/G/VU6oVy2Yk8C/7iccHdt5MVnTyjhFhGRktGoUSN69OjBoEGD2LBhAxs2bGDQoEH06tXLahK1hg0bsmjRIgAyMzN5+OGH2bJlC/PmzSMrK4v4+Hji4+NJT08H4MCBA7z77rts2bKFQ4cOsXTpUh555BGCgoJo3759qbRVREQkr5R0F8a1I93eTcw/k4/BxbOlEs5vu08y+Kto0jOzCWvszbTHg3FxVMItIiIlZ968eTRr1oywsDDCwsJo3rw5c+bMsaqzZ88ekpKSADh27Bg//vgjx44do2XLltSoUcOyrVu3DgBnZ2d+++03unfvTmBgIMOGDSMsLIxff/0VBwf1cyIiUrbZXNJ9/PhxnnjiCTw8PKhYsSItW7YkOjrast8wDEaPHo2vry8VKlSgU6dO7Ny50+ocaWlpvPjii3h6euLm5kbv3r05duxYAaK5Jul2NYF7ffPjY1sKcK7CWfF3AkPmRpOelU3Ppj58+ngrnB1t7u0VEREb5+7uzty5c0lOTiY5OZm5c+fmWPrLMAyefvppwHwvuGEYuW6dOnUCwM/Pj1WrVnHmzBnS0tLYv38/H3/8Me7u7iXbOBERkQKwqawsMTGR9u3b4+TkxM8//8yuXbuYOHGiVWc+YcIEPvroI6ZOncrmzZvx8fGhW7dunD9/3lInIiKCRYsWsWDBAtauXUtKSgq9evUiKysrfwFdPxNr7cszuR9ZX8AWFsz6A2cYMjeajCyD+5rV4JPHgnBysKm3VkREREREpFyyqemsx48fj5+fH7Nnz7aU1a1b1/LYMAwmT57MG2+8wYMPPgjAl19+ibe3N/Pnz2fw4MEkJSUxa9Ys5syZQ9euXQGYO3cufn5+/Prrr3Tv3j0fEV2fdIdAzFw4urGgTcy3bUcS+ceXmy33cE9+tKUSbhERERERkTLCprKzH3/8kdatW/PII4/g5eVFUFAQM2fOtOyPjY0lPj6esLAwS5mLiwsdO3a03BcWHR1NRkaGVR1fX1+aNm1qqXO9tLQ0y2VyVzYgR86NX1vzz+PRkJle+Abfwq4TyTz1xSYupGfR/g4Ppg5opYRbRERERESkDLGpDO3gwYN89tlnBAQE8MsvvzBkyBCGDRvGV199BUB8fDxAjrVAvb29Lfvi4+NxdnamWrVqN6xzvXHjxmEymSzb1TU+r8u6PQOggjtkXoK4PwvZ2ps7cCqFJ7/YSPKlTILrVGNGeGvNUi4iIiIiIlLG2FTSnZ2dTatWrRg7dixBQUEMHjyYQYMG8dlnn1nVs7vuXmvDMHKUXe9mdUaNGkVSUpJlO3r0aO4nsbODOpfXIY1dmac2FURC8iWenLWJ0ynpNPGtwhdP34mbi03dKSAiIiIiInJbsKmku0aNGjRu3NiqrFGjRhw5cgQAHx8fgBwj1gkJCZbRbx8fH9LT00lMTLxhneu5uLhQpUoVqw3IOZEaQP3O5p/7f89X2/IqJS2TZyI3c/xcKnU9KvLVs20wVXAqltcSERERERGRwrGppLt9+/bs2bPHqmzv3r3UqVMHAH9/f3x8fIiKirLsT09PZ9WqVbRrZx6BDg4OxsnJyapOXFwcO3bssNTJu9yS7i7mn8c2waXkfJ7v5jKysvnn3Gh2nkjGs5IzXz7bBo9KLkX6GiIiIiIiIlJ0bOqa5Jdffpl27doxduxY+vXrx6ZNm5gxYwYzZswAzJeVR0REMHbsWAICAggICGDs2LFUrFiRAQMGAGAymRg4cCAjRozAw8MDd3d3Ro4cSbNmzSyzmedZbiPd7v7gXg/OHoTY1dCoV2GbDZgvf3/tu79Ys+80FZwcmPXUndTxcCuSc4uIiIiIiEjxsKmk+84772TRokWMGjWKd999F39/fyZPnszjjz9uqfPqq6+SmprK0KFDSUxMJCQkhOXLl1O5cmVLnUmTJuHo6Ei/fv1ITU2lS5cuREZG4uCQ34nIbnCf+B1dYdMM2PNzkSXdk3/dx/dbj+Ngb8e0x1vRwq9qkZxXREREREREio+dYRhGaQdha5KTkzGZTCS960+Vtw7mrBC7Gr68H1yrwiv7waFw91z/9Fccz8/fCsC/H2zGo21qF+p8IiJS/Cx9RVLS1blApFTpPRERkVspjr7Cpu7pLntuMNJdpz24VYdL5+DgqkK9wo7jSYz4NgaAf9zlr4RbRERERETEhijpLowbrUJm7wCN+5gf71xU4NOfOp/Gc19t4VJGNh0bVGfUvY0KfC4REREREREpeUq6C+Uma383ecD88+//QWZ6vs+clpnF4DlbOJF0iXrV3fjksSAc7G++1riIiIiIiIiULUq6CyO32cuvqB0KlWuYLzH/+3/5PvXoH3ex9cg5Krs68t8nW2stbhERERERERukpLu42DtAULj5cfTsfB26aNsxvt50BDs7mPJYEPWqVyqGAEVERERERKS4KekulFtc7t3qSbCzN89mfnp/ns647+R5/vX9DgBevCeAToFehQ1SRERERERESomS7sK42eXlAFX94I5u5sdbvrjl6S6mZzJ03lZSM7Jof4cHL3UJKIIgRUREREREpLQo6S6UPExsduc/zD+3fgmpiTesZhgGby7ewb6EFLwquzC5vyZOExERERERsXVKugvjViPdAAHdwLsppKfAppk3rPbjnyf4futx7C/fx129sksRBioiIiIiIiKlQUl3oeQh6bazg7teNj/eMA3SUnJUOX4ulTcXm+/jfqlLA0LqeRRlkCIiIiIiIlJKlHQXRl6v/m7yALjXM19evmGa1a7sbIOR3/zJ+UuZBNWuyvOd6xd9nCIiIiIiIlIqlHQXSh6zbnsHuOdN8+M/PoaUBMuuL/6IZf3BM1RwcmBSv5Y4OugtERERERERKS+U4RVGXu7pvqLJg+Dbynxv94qxgHl5sAnL9gDwVq/G1PV0K44oRURESkxiYiLh4eGYTCZMJhPh4eGcO3fupsc8/fTT2NnZWW1t27a1qpOWlsaLL76Ip6cnbm5u9O7dm2PHjhVjS0RERIqGku5CyUfSbWcHYe+bH0dHknV4I69+9xfpWdnc09CLx9r4FU+IIiIiJWjAgAHExMSwbNkyli1bRkxMDOHh4bc8rkePHsTFxVm2pUuXWu2PiIhg0aJFLFiwgLVr15KSkkKvXr3IysoqrqaIiIgUCcfSDsCm5WekG6Bue2j5OMTM4/w3Q9hxZjSVXFz54IGm2OX3XCIiImXM7t27WbZsGRs2bCAkJASAmTNnEhoayp49ewgMDLzhsS4uLvj4+OS6LykpiVmzZjFnzhy6du0KwNy5c/Hz8+PXX3+le/fuRd8YERGRIqKR7pIW9j5ZFTyoeuEgQx1+4LWeDalhqlDaUYmIiBTa+vXrMZlMloQboG3btphMJtatW3fTY1euXImXlxcNGjRg0KBBJCRcnf8kOjqajIwMwsLCLGW+vr40bdr0lucVEREpbUq6CyX/o9NGhWr8120IAMOcFvG4z4miDkpERKRUxMfH4+XllaPcy8uL+Pj4Gx7Xs2dP5s2bx++//87EiRPZvHkz99xzD2lpaZbzOjs7U61aNavjvL29b3retLQ0kpOTrTYREZGSpqS7UPKfdP+6O4FxxxrzY3Z7HMjG/vt/wMWzxRCbiIhI0Rg9enSOic6u37Zs2QKQ6+1ShmHc9Daq/v37c99999G0aVPuv/9+fv75Z/bu3ctPP/1007hudd5x48ZZJnQzmUz4+Wn+FBERKXm6p7tQ8pd0p2Vm8f5PuwA7DoS8Bwf+AWcPwqLB8NgC89JiIiIiZcwLL7zAo48+etM6devW5a+//uLkyZM59p06dQpvb+88v16NGjWoU6cO+/btA8DHx4f09HQSExOtRrsTEhJo167dDc8zatQohg8fbnmenJysxFtEREqcTY90jxs3Djs7OyIiIixlhmEwevRofH19qVChAp06dWLnzp1WxxXZsiP5nPxs1tpYDp+5iFdlFwZ1bQGPRIKjK+xbDsvfzP/ri4iIlABPT08aNmx4083V1ZXQ0FCSkpLYtGmT5diNGzeSlJR00+T4emfOnOHo0aPUqFEDgODgYJycnIiKirLUiYuLY8eOHTc9r4uLC1WqVLHaRERESprNJt2bN29mxowZNG/e3Kp8woQJfPTRR0ydOpXNmzfj4+NDt27dOH/+vKVO0S07kvek+2TyJab+vh+A13s2pJKLI9RoAQ9MN1fYMA02zczn64uIiJQdjRo1okePHgwaNIgNGzawYcMGBg0aRK9evaxmLm/YsCGLFi0CICUlhZEjR7J+/XoOHTrEypUruf/++/H09OSBBx4AwGQyMXDgQEaMGMFvv/3Gtm3beOKJJ2jWrJllNnMREZGyyiaT7pSUFB5//HFmzpxpdZmZYRhMnjyZN954gwcffJCmTZvy5ZdfcvHiRebPnw9cXXZk4sSJdO3alaCgIObOncv27dv59ddf8xdIPka6xy/7m4vpWQTVrkrfljWv7mjyANxzeZR76SsQMz9/MYiIiJQh8+bNo1mzZoSFhREWFkbz5s2ZM2eOVZ09e/aQlJQEgIODA9u3b6dPnz40aNCAp556igYNGrB+/XoqV65sOWbSpEn07duXfv360b59eypWrMiSJUtwcNCtWSIiUrbZ5D3dzz//PPfddx9du3bl/ffft5THxsYSHx9vtaSIi4sLHTt2ZN26dQwePPiWy47kttZnWlqaZQZVwDL7aUp6Fnm5UO3v+GQWbTsOwDv3N8He/rpkvcNISDkFmz6HH54HB2do9nBefhUiIiJliru7O3Pnzr1pHcMwLI8rVKjAL7/8csvzurq6MmXKFKZMmVLoGEVEREqSzY10L1iwgK1btzJu3Lgc+64sG3L9ZC3XLilSkGVHbjT76YmkS3mKeeLyvRgG3NvMh5Z+VXNWsLODnuMh+GkwsuH752DrnJz1RERERERExKbYVNJ99OhRXnrpJebOnYurq+sN612/fMitlhS5VZ1Ro0aRlJRk2Y4ePWo+Jg8xbz2SSNSuk9jbwfBugTeuaGcH902CoHAwsuDHF2D1f8DIy6uIiIiIiIhIWWRTSXd0dDQJCQkEBwfj6OiIo6Mjq1at4pNPPsHR0dEywn39iHVCQoJl37XLjtyozvUKM/vpf37ZA8BDrWpxh1elm1e2t4feU+Cuy8ub/P6eOfnOyNuIuoiIiIiIiJQtNpV0d+nShe3btxMTE2PZWrduzeOPP05MTAz16tXDx8fHakmR9PR0Vq1aZVlSpKDLjuTu5qPnGw6eYd2BMzg72PNS14A8ntIOur4DPcaDnT1smwuze0JSAZY0ExERERERkVJlUxOpVa5cmaZNm1qVubm54eHhYSmPiIhg7NixBAQEEBAQwNixY6lYsSIDBgwArJcd8fDwwN3dnZEjRxZo2RHjFkn3tJUHAHikdS1qVauYr3PTdghUbwD/9yyc2Aqfd4Q+n0Jgj/ydR0REREREREqNTSXdefHqq6+SmprK0KFDSUxMJCQkhOXLl+dYdsTR0ZF+/fqRmppKly5diIyMzPeyIze723rH8SRW7z2FvR0Mvrt+wRpT/x54biUsfALit8PX/aHVU9B9LLjc4lJ1ERERERERKXV2hqGZuvIrOTkZk8nE+leb0Hb8jlzrPD9/Kz/9FUeflr58/GhQ4V4w45L5/u71nwIGVKsLvSZD/c6FO6+IiBSbK31FUlJSvuYCkeKj90RERG6lOPoKm7qnu6y50bcVh89c4OftcQD8s1MBR7mv5eQK3T+Ap36EKrUg8RDM6Wu+9Dw5rvDnFxERERERkWKhpLsQbnRP99wNh8k24O4G1WnoU4TfpPvfDUPXQZvB5knWdnwHU++ENR9B+sWiex0REREREREpEkq6CyG3pDs1PYtvtphnGn+6XZ2if1FXE9w7wXyvd83WkH4efhsDU1rBltmQlVH0rykiIiIiIiIFoqS7EHK7vPzHP4+TlJqBn3sFOjbwKr4Xr9ECBkbBAzOgam04Hwf/i4BP20D0l5CZVnyvLSIiIiIiInmipLtQrEe6DcPgq/WHAXgipA4O9jdfUqzQ7O2hRX94YQv0nAAVPeHsQVgyDCY3hz8+gbTzxRuDiIiIiIiI3JCS7kK4/vLy7ceT2HkiGRdHe/q19iu5QBxdIGQwvPQndB8HVWpCSjxEvQUfNYalr0DC3yUXj4iIiIiIiABKuovUd9Hme7l7NPWhmptzyQfgUglCh8KwGOjzKXgEQFoybJoB00Jg9n3mydcyLpV8bCIiIiIiIrchx9IOwJZde093emY2P/55AoAHW9UqnYCucHSGoCegxQCIXQmbZ8GepXB4rXlzNUHjvtDiUfBra75MXURERERERIqcku5CuPby8pV7Eki8mIFXZRfa1/coxaiuYW8P9e8xb0nHYeuXsG0uJF9+vPVL8yRszR6BRvdDjZZgV8z3oYuIiIiIiNxGlHQXwrVJ96JtxwHoG1QTR4cyOHJsqgmd/wUdXzePdv+5EHb9AOeOwJqJ5s3kBw3vg4a9oHYoOOifh4iIiIiISGEoqyqEK5eXp6ZnsWJPAgC9W/iWXkB5YW8P/nebt3s/NF92vusH2P8rJB2FjdPNWwV3uKMr3NHFPFJeqRiXPxMRERERESmnlHQXwpWR7lV7T3EpI5uaVSvQxLdKKUeVD84VodnD5i0jFQ6sgL//Z07EU8/C9m/MG4B3M7jjHqjfBfzagFOF0o1dRERERETEBijpLhRz0v3LznjAPGu5na3eE+1UARrea96yMuHoBtj/Gxz4DeL+hJPbzdsfH4ODM9QMNl+CXqe9OQl3taEvG0REREREREqIku5CSs/M5rfdJwFz0l0uODhC3bvMW9d3IOUUHFwBB343j4anxMOR9eZt7UdgZw8+zaFOO3MyXrMVVPPXpGwiIiIiInLbU9JdCAawKfYsyZcy8azkTKva1Uo7pOJRqTo072feDAPOHoTD68zbkXWQeAjiYszbFRWqXU7Ag8G3lflnpeql1AAREREREZHSoaS7EAzDjtX7TgHQKdALB/vbYGTXzg486pu3VuHmsuQTlxPwDXBiK8Rvh9RE8+Rs+3+9emxlX/BpCt5Nr/70uAPsHUqnLSIiIiIiIsVMSXchrd5rTro7BHiWciSlqIrv1QnZADLT4OROOB4Nx7eaf57eC+dPmLd9y68e6+gKXo3MCbh3E/BsANUDoUpNXZ4uImKDEhMTGTZsGD/++CMAvXv3ZsqUKVStWvWGx9xoPpQJEybwyiuvANCpUydWrVpltb9///4sWLCgaAIXEREpJkq6C8EA/o4/D8Bdd9zGSff1HF3M93XXbHW17FKyORE/ucO8xe+AhF2QcRFObDNv13JyA88AcwLu2eBqMu5eDxycSrY9IiKSZwMGDODYsWMsW7YMgOeee47w8HCWLFlyw2Pi4uKsnv/8888MHDiQhx56yKp80KBBvPvuu5bnFSpoJQ0RESn7lHQXwpUlw5r4VsGjkkspR1PGuVaBOqHm7YrsLPP94PHbzYl4wm7ziPjZg5BxIed94gD2jmDyA3d/82Rt7vWuPq5W17wMmoiIlIrdu3ezbNkyNmzYQEhICAAzZ84kNDSUPXv2EBgYmOtxPj7WE5H+8MMPdO7cmXr16lmVV6xYMUddERGRss6mku5x48bx/fff8/fff1OhQgXatWvH+PHjrTpxwzAYM2YMM2bMIDExkZCQED799FOaNGliqZOWlsbIkSP5+uuvSU1NpUuXLkybNo1atWrlKx7j8s8OAZogrEDsHa7eH96k79XyrAw4Gwun95iT8FN7Lz/eB+kpkBhr3nJTyeeahNzfnKCbapm3KjXB0blEmiYicjtav349JpPJknADtG3bFpPJxLp1626YdF/r5MmT/PTTT3z55Zc59s2bN4+5c+fi7e1Nz549eeedd6hcufINz5WWlkZaWprleXJycj5bJCIiUng2lXSvWrWK559/njvvvJPMzEzeeOMNwsLC2LVrF25uboD5/q+PPvqIyMhIGjRowPvvv0+3bt3Ys2ePpWOOiIhgyZIlLFiwAA8PD0aMGEGvXr2Ijo7GwSHvk3pdGem+re/nLg4OTlC9gXm7lmHA+TjzSPjZy4n32Vjz88RYuJRkXs7sypJmOdhBZZ/LSbjf1Z9Vr0nMXavqXnIRkQKKj4/Hy8srR7mXlxfx8fF5OseXX35J5cqVefDBB63KH3/8cfz9/fHx8WHHjh2MGjWKP//8k6ioqBuea9y4cYwZMyZ/jRARESliNpV0X7k/7IrZs2fj5eVFdHQ0d999N4ZhMHnyZN544w1LZ/3ll1/i7e3N/PnzGTx4MElJScyaNYs5c+bQtWtXAObOnYufnx+//vor3bt3z3M8BnZUcHKgdd1yulRYWWNnZ560rYqveQ3x6108ezURT4w1X7qedOzqlnnJnLSfj4Njm3N/DccK5sS8cg3rn1V8rZ87uxVrU0VEypLRo0ffMnndvNn8uZrbpGiGYdxwsrTrffHFFzz++OO4urpalQ8aNMjyuGnTpgQEBNC6dWu2bt1Kq1atrj8NAKNGjWL48OGW58nJyfj5+eUpDhERkaJiU0n39ZKSkgBwd3cHIDY2lvj4eMLCwix1XFxc6NixI+vWrWPw4MFER0eTkZFhVcfX15emTZuybt26XJPum12eFlrfAxdHLXlVJlR0N281g3PuMwy4cBqSjl7eLifi545cfXzxNGSm3vzy9StcqlxOwn3Ml7RX8gI3T3CrDm7XPq4OTq43P5eISBn3wgsv8Oijj960Tt26dfnrr784efJkjn2nTp3C29v7lq+zZs0a9uzZw8KFC29Zt1WrVjg5ObFv374bJt0uLi64uGjOFRERKV02m3QbhsHw4cO56667aNq0KYDl0rXrO3Zvb28OHz5sqePs7Ey1atVy1LnRpW83ujzNwI57Gua8jE7KIDs7qFTdvNXM/Y8zMlLhfPzlLS6Xn3GQHGee5C0t2byd3nvr13apcjkJvy4Zv5KoV/Q0f1lQ4fKXBo76A1FEyhZPT088PW99K1VoaChJSUls2rSJNm3aALBx40aSkpJo167dLY+fNWsWwcHBtGjR4pZ1d+7cSUZGBjVq1Lh1A0REREqRzSbdL7zwAn/99Rdr167Nse/6S9jyclnbzerc6PI0Nxcnut2py9TKDacK5snX3P1vXi/tfM6E/MIp80j6hVOQknD1cXbG1QT97ME8xuF2OQmvdnn03uNqQm71s9rV5y5VdC+6iJS6Ro0a0aNHDwYNGsTnn38OmJcM69Wrl9Ukag0bNmTcuHE88MADlrLk5GS+/fZbJk6cmOO8Bw4cYN68edx77714enqya9cuRowYQVBQEO3bty/+homIiBSCTSbdL774Ij/++COrV6+2mnH8yjIi8fHxVt98JyQkWEa/fXx8SE9PJzEx0Wq0OyEh4Ybfwt/o8jQfkytODvZF0iaxIS6VzZtnwM3rGYZ5crcLp65u1ybkF648Pg2pZyE1EYxs80h60gXzZfB5Ze9oTtJdTZe3qtc8NkGF6567VrWuo1ndRaSIzJs3j2HDhllu4+rduzdTp061qrNnzx7LLWJXLFiwAMMweOyxx3Kc09nZmd9++42PP/6YlJQU/Pz8uO+++3jnnXfyNQGqiIhIabCppNswDF588UUWLVrEypUr8fe3HpG8MqtpVFQUQUFBAKSnp7Nq1SrGjx8PQHBwME5OTkRFRdGvXz8A4uLi2LFjBxMmTMhfPGhkUW7Czs6c7FaoeusEHSA7G9KSzBPCpSZe/nnW/PPimauPU8/CxcSrzzNTITvzamJfEI4VcknMTeYR9CtfMrhUAZdK1zy/XOZ8uczZTaPtIoK7uztz5869aR3DMHKUPffcczz33HO51vfz82PVqlVFEp+IiEhJs6mk+/nnn2f+/Pn88MMPVK5c2XIPtslkokKFCtjZ2REREcHYsWMJCAggICCAsWPHUrFiRQYMGGCpO3DgQEaMGIGHhwfu7u6MHDmSZs2aWWYzzyulF1Kk7O3No9UV8jkbfkbq1UT9UtI12znr56nncu5PuzwpYGYqnE81XypfUHb24HwlGb8+Oa98zb5ry9yubk5u1s8dnJXEi4iIiIjNs6mk+7PPPgOgU6dOVuWzZ8/m6aefBuDVV18lNTWVoUOHkpiYSEhICMuXL7es0Q0wadIkHB0d6devH6mpqXTp0oXIyMj8X6KmhEDKAqcKYKpp3vIrO8uceN8wKT9/eUuGtJRrnp+H9JTL5efNl8Ubl0fq05Ju9ap5Y+94XTJe0Tyq7uwGTtc8dq6Ye9J+fTLvVNH8u3J0NX/BISIiIiJSAuyM3K7xkptKTk7GZDLx9396EDji59IOR6R0GQZkXLwmKU++Jim/SdKelmw+Lv0CpF8010+/AFlpt37NwnKsYE7ArbaK5oT8SnJu+XldmVWdK/Vyq1MB7HWv6e3sSl+RlJRElSpVSjscQe+JiIjcWnH0FTY10l3maKRbxPz/4MqocuVbr8N7S1mZ5snkrk/GM655nOfyC9ck8+lXXyMz1bylFj7cm3JwuZqcO7pe3lwuJ+XXPb9+v+PlY3Psv1kdF/MXCg5O+nwSERERKSOUdBfCrZYhE5ECcHAEh8sTuRWl7Czz/e8ZqeaEPONy4n3tc6vt+jpXyi5dV/8iZF5Tlnnp6mtmpZm3S+eKti23ZHeLBP/yTwdn82MHF3Oi7uhiXebofN2+a8ocnc11rcquPd7paplG/EVEROQ2pqS7UJR0i9gMe4fLE7xVKt7Xyc6+nIRfm8xfhMw0c7nVz9Rrnl8yJ/Q3rZN2g3qXrJN9jKuj+WWBncN1Cbmz9eOblVm+FHC8WsfBCeydrj62/Lz82P6axw7OeTvW3lFXB4iIiEixUNJdGPoDTUSuZ29/eXK3ioBHyb2uYZgvob+SjGdcn6znkrxnpl0+5tqfaZCVkbMsM/26fdeUWf1Mz3lfvpF1+QuIiyX3+yiIK4m5vWP+Enar5P2ax5eyS7tFIiIiUgYo6S4UJd0iUkbY2V2+bNyltCO5/AVAxnUJ+eUkPis9Z9n1P3NN9jPN5dkZl8+dfvlnxtVjsjOvPs669nHG5eOuOSY7I2fcV+oXlTTNUyoiIiJKugtHI90iIjnZ2Znv83Z0hjLwHUCuLF8M3CSRz74uqb82kc++Lqm/9vgrCX5yCjCptFsqIiIipUxJd2Eo6RYRsU3XfjFQXJKTUdItIiIi9qUdgC2z0+XlIiIiIiIichNKukVERERERESKiZLuwtDl5SIiIiIiInITSroLQ0m3iIiIiIiI3ISS7kLQPd0iIiIiIiJyM0q6C0Mj3SIiIiIiInITSroLQ0m3iIiIiIiI3ISS7kJR0i0iIiIiIiI3pqS7EDTQLSIiIiIiIjejpLsw7PTrExERERERkRtT1igiIiIiIiJSTJR0F4Kdri8XERERERGRm7htk+5p06bh7++Pq6srwcHBrFmzpgBnUdItIiJyrQ8++IB27dpRsWJFqlatmqdjDMNg9OjR+Pr6UqFCBTp16sTOnTut6qSlpfHiiy/i6emJm5sbvXv35tixY8XQAhERkaJ1WybdCxcuJCIigjfeeINt27bRoUMHevbsyZEjR/J3Io10i4iIWElPT+eRRx7hn//8Z56PmTBhAh999BFTp05l8+bN+Pj40K1bN86fP2+pExERwaJFi1iwYAFr164lJSWFXr16kZWVVRzNEBERKTJ2hmEYpR1ESQsJCaFVq1Z89tlnlrJGjRrRt29fxo0bd8vjk5OTMZlMHJzxBP6D5hRnqCIiYqOu9BVJSUlUqVKltMMpcZGRkURERHDu3Lmb1jMMA19fXyIiInjttdcA86i2t7c348ePZ/DgwSQlJVG9enXmzJlD//79AThx4gR+fn4sXbqU7t275ymm2/09ERGRWyuOvuK2G+lOT08nOjqasLAwq/KwsDDWrVuXz7NppFtERKQwYmNjiY+Pt+qXXVxc6Nixo6Vfjo6OJiMjw6qOr68vTZs2LUDfLSIiUrIcSzuAknb69GmysrLw9va2Kvf29iY+Pj7XY9LS0khLS7M8T0pKMv/McCQ5Obn4ghUREZt1pX+4DS8oy5crfW9u/fLhw4ctdZydnalWrVqOOjfqu+HG/bf6bhERuZHi6L9vu6T7iutnHjcM44azkY8bN44xY8bkKA96fjY8P7tY4hMRkfLhzJkzmEym0g6jUEaPHp1rP3itzZs307p16wK/Rn765bzWuVH/7efnV7AgRUTktlGU/fdtl3R7enri4OCQ45vxhISEHN+yXzFq1CiGDx9ueX7u3Dnq1KnDkSNHbP4PqRtJTk7Gz8+Po0ePlsv73tQ+21fe26j22b6kpCRq166Nu7t7aYdSaC+88AKPPvroTevUrVu3QOf28fEBzKPZNWrUsJRf2y/7+PiQnp5OYmKi1Wh3QkIC7dq1u+G51X+Xv/9bap/tK+9tLO/tg/LfxuLov2+7pNvZ2Zng4GCioqJ44IEHLOVRUVH06dMn12NcXFxwcXHJUW4ymcrlP7RrValSpVy3Ue2zfeW9jWqf7bO3t/3pUzw9PfH09CyWc/v7++Pj40NUVBRBQUGAef6VVatWMX78eACCg4NxcnIiKiqKfv36ARAXF8eOHTuYMGHCDc+t/rv8tlHts33lvY3lvX1Q/ttYlP33bZd0AwwfPpzw8HBat25NaGgoM2bM4MiRIwwZMqS0QxMREbFpR44c4ezZsxw5coSsrCxiYmIAuOOOO6hUqRIADRs2ZNy4cTzwwAPY2dkRERHB2LFjCQgIICAggLFjx1KxYkUGDBgAmJPkgQMHMmLECDw8PHB3d2fkyJE0a9aMrl27llZTRURE8uS2TLr79+/PmTNnePfdd4mLi6Np06YsXbqUOnXqlHZoIiIiNu3tt9/myy+/tDy/Mnq9YsUKOnXqBMCePXssk5oBvPrqq6SmpjJ06FASExMJCQlh+fLlVK5c2VJn0qRJODo60q9fP1JTU+nSpQuRkZE4ODiUTMNEREQK6LZMugGGDh3K0KFDC3Ssi4sL77zzTq6XrJUX5b2Nap/tK+9tVPts3+3QxtxERkYSGRl50zrXzwhrZ2fH6NGjGT169A2PcXV1ZcqUKUyZMqXAsd0O70l5b6PaZ/vKexvLe/ug/LexONpnZ2gtExEREREREZFiYfuzu4iIiIiIiIiUUUq6RURERERERIqJkm4RERERERGRYqKk+wamTZuGv78/rq6uBAcHs2bNmpvWX7VqFcHBwbi6ulKvXj2mT59eQpEWTH7a9/3339OtWzeqV69OlSpVCA0N5ZdffinBaAsmv+/hFX/88QeOjo60bNmyeAMspPy2Ly0tjTfeeIM6derg4uJC/fr1+eKLL0oo2oLJbxvnzZtHixYtqFixIjVq1OCZZ57hzJkzJRRt/qxevZr7778fX19f7OzsWLx48S2PsaXPmfy2z9Y+Zwry/l1hK58xtkr991W29v/qCvXf1tR/ly3qv63Z2udMqfXfhuSwYMECw8nJyZg5c6axa9cu46WXXjLc3NyMw4cP51r/4MGDRsWKFY2XXnrJ2LVrlzFz5kzDycnJ+L//+78Sjjxv8tu+l156yRg/fryxadMmY+/evcaoUaMMJycnY+vWrSUced7lt41XnDt3zqhXr54RFhZmtGjRomSCLYCCtK93795GSEiIERUVZcTGxhobN240/vjjjxKMOn/y28Y1a9YY9vb2xscff2wcPHjQWLNmjdGkSROjb9++JRx53ixdutR44403jO+++84AjEWLFt20vq19zuS3fbb2OZPf9l1hK58xtkr9tzVb+39lGOq/c6P+u2xR/23N1j5nSqv/VtKdizZt2hhDhgyxKmvYsKHx+uuv51r/1VdfNRo2bGhVNnjwYKNt27bFFmNh5Ld9uWncuLExZsyYog6tyBS0jf379zfefPNN45133inTnXZ+2/fzzz8bJpPJOHPmTEmEVyTy28YPP/zQqFevnlXZJ598YtSqVavYYiwqefnQt7XPmWvlp1O7Vln/nLkiP+2zlc8YW6X++9bK+v8r9d/W1H+Xbeq/c1fWP2euKMn+W5eXXyc9PZ3o6GjCwsKsysPCwli3bl2ux6xfvz5H/e7du7NlyxYyMjKKLdaCKEj7rpednc358+dxd3cvjhALraBtnD17NgcOHOCdd94p7hALpSDt+/HHH2ndujUTJkygZs2aNGjQgJEjR5KamloSIedbQdrYrl07jh07xtKlSzEMg5MnT/J///d/3HfffSURcrGzpc+ZolDWP2cKwlY+Y2yV+u9bK+v/r9R/56T+2/bZ0udMUSjrnzMFURSfMY5FGE+5cPr0abKysvD29rYq9/b2Jj4+Ptdj4uPjc62fmZnJ6dOnqVGjRrHFm18Fad/1Jk6cyIULF+jXr19xhFhoBWnjvn37eP3111mzZg2OjmX7v0VB2nfw4EHWrl2Lq6srixYt4vTp0wwdOpSzZ8+WyfvCCtLGdu3aMW/ePPr378+lS5fIzMykd+/eTJkypSRCLna29DlTFMr650x+2dJnjK1S/31rZf3/lfrvnNR/2z5b+pwpCmX9cya/iuozRiPdN2BnZ2f13DCMHGW3qp9beVmR3/Zd8fXXXzN69GgWLlyIl5dXcYVXJPLaxqysLAYMGMCYMWNo0KBBSYVXaPl5D7Ozs7Gzs2PevHm0adOGe++9l48++ojIyMgy+2055K+Nu3btYtiwYbz99ttER0ezbNkyYmNjGTJkSEmEWiJs7XOmoGzpcyYvbPUzxlap/86dLf2/Uv99lfrv8sHWPmcKypY+Z/KiKD9jyvZXgqXA09MTBweHHN/GJSQk5PiW6gofH59c6zs6OuLh4VFssRZEQdp3xcKFCxk4cCDffvstXbt2Lc4wCyW/bTx//jxbtmxh27ZtjVYUqAAAw+dJREFUvPDCC4C5kzMMA0dHR5YvX84999xTIrHnRUHewxo1alCzZk1MJpOlrFGjRhiGwbFjxwgICCjWmPOrIG0cN24c7du355VXXgGgefPmuLm50aFDB95//32b/ybZlj5nCsNWPmfyw9Y+Y2yV+u8bs5X/V+q/c1L/rf7bVtjK50x+FOVnjEa6r+Ps7ExwcDBRUVFW5VFRUbRr1y7XY0JDQ3PUX758Oa1bt8bJyanYYi2IgrQPzN9cPf3008yfP7/M32OT3zZWqVKF7du3ExMTY9mGDBlCYGAgMTExhISElFToeVKQ97B9+/acOHGClJQUS9nevXuxt7enVq1axRpvQRSkjRcvXsTe3vojzcHBAbj6jbIts6XPmYKypc+Z/LC1zxhbpf47d7b0/0r9d07qv9V/2wJb+pzJjyL9jMn31Gu3gStLHcyaNcvYtWuXERERYbi5uRmHDh0yDMMwXn/9dSM8PNxS/8pSAC+//LKxa9cuY9asWWV6KYD8tm/+/PmGo6Oj8emnnxpxcXGW7dy5c6XVhFvKbxuvV9ZnP81v+86fP2/UqlXLePjhh42dO3caq1atMgICAox//OMfpdWEW8pvG2fPnm04Ojoa06ZNMw4cOGCsXbvWaN26tdGmTZvSasJNnT9/3ti2bZuxbds2AzA++ugjY9u2bZYlVWz9cya/7bO1z5n8tu96Zf0zxlap/7bt/1eGof5b/bf679Km/rt4PmOUdN/Ap59+atSpU8dwdnY2WrVqZaxatcqy76mnnjI6duxoVX/lypVGUFCQ4ezsbNStW9f47LPPSjji/MlP+zp27GgAObannnqq5APPh/y+h9cq6522YeS/fbt37za6du1qVKhQwahVq5YxfPhw4+LFiyUcdf7kt42ffPKJ0bhxY6NChQpGjRo1jMcff9w4duxYCUedNytWrLjp/ytb/5zJb/ts7XOmIO/ftWzhM8ZWqf/uaHlua/+vrlD/3dGqvvrvskX9t21/zpRW/21nGOXgug0RERERERGRMkj3dIuIiIiIiIgUEyXdIiIiIiIiIsVESbeIiIiIiIhIMVHSLSIiIiIiIlJMlHSLiIiIiIiIFBMl3SIiIiIiIiLFREm3iIiIiIiISDFR0i0iIuXG6tWruf/++/H19cXOzo7FixcX6+tlZmby5ptv4u/vT4UKFahXrx7vvvsu2dnZxfq6IiIi5Ul577+VdItIDk8//TR9+/a1PO/UqRMRERHF+prp6enccccd/PHHH4U6z8iRIxk2bFgRRSW25sKFC7Ro0YKpU6eWyOuNHz+e6dOnM3XqVHbv3s2ECRP48MMPmTJlSom8vojItdR/i60q7/23km4RG/X0009jZ2eHnZ0djo6O1K5dm3/+858kJiYW+Wt9//33vPfee0V+3mvNmDGDOnXq0L59+0Kd59VXX2X27NnExsYWUWRiS3r27Mn777/Pgw8+mOv+9PR0Xn31VWrWrImbmxshISGsXLmywK+3fv16+vTpw3333UfdunV5+OGHCQsLY8uWLQU+p4iUb+q/c6f++/ZW3vtvJd0iNqxHjx7ExcVx6NAh/vvf/7JkyRKGDh1a5K/j7u5O5cqVi/y815oyZQr/+Mc/Cn0eLy8vwsLCmD59ehFEJeXNM888wx9//MGCBQv466+/eOSRR+jRowf79u0r0PnuuusufvvtN/bu3QvAn3/+ydq1a7n33nuLMmwRKWfUf+ek/ltuxtb7byXdIjbMxcUFHx8fatWqRVhYGP3792f58uWW/VlZWQwcONByv0pgYCAff/yx1TmysrIYPnw4VatWxcPDg1dffRXDMKzqXH95Wm732lStWpXIyEjA/G3kCy+8QI0aNXB1daVu3bqMGzfuhu3YunUr+/fv57777rOUHTp0CDs7O7755hs6dOhAhQoVuPPOO9m7dy+bN2+mdevWVKpUiR49enDq1Cmr8/Xu3Zuvv/46L79CuY0cOHCAr7/+mm+//ZYOHTpQv359Ro4cyV133cXs2bMLdM7XXnuNxx57jIYNG+Lk5ERQUBARERE89thjRRy9iJQn6r/Vf0velYf+W0m3SDlx8OBBli1bhpOTk6UsOzubWrVq8c0337Br1y7efvtt/vWvf/HNN99Y6kycOJEvvviCWbNmsXbtWs6ePcuiRYsKFcsnn3zCjz/+yDfffMOePXuYO3cudevWvWH91atX06BBA6pUqZJj3zvvvMObb77J1q1bcXR05LHHHuPVV1/l448/Zs2aNRw4cIC3337b6pg2bdpw9OhRDh8+XKh2SPmydetWDMOgQYMGVKpUybKtWrWKAwcOAFf/WLzZ9sILL1jOuXDhQubOncv8+fPZunUrX375Jf/5z3/48ssvS6uZImJj1H9fpf5bclMe+m/HYjmriJSI//3vf1SqVImsrCwuXboEwEcffWTZ7+TkxJgxYyzP/f39WbduHd988w39+vUDYPLkyYwaNYqHHnoIgOnTp/PLL78UKq4jR44QEBDAXXfdhZ2dHXXq1Llp/UOHDuHr65vrvpEjR9K9e3cAXnrpJR577DF+++03y71jAwcOtHxDf0XNmjUt573Va8vtIzs7GwcHB6Kjo3FwcLDaV6lSJcD8b2f37t03PU+1atUsj1955RVef/11Hn30UQCaNWvG4cOHGTduHE899VQRt0BEygv13+q/Je/KQ/+tpFvEhnXu3JnPPvuMixcv8t///pe9e/fy4osvWtWZPn06//3vfzl8+DCpqamkp6fTsmVLAJKSkoiLiyM0NNRS39HRkdatW+e4RC0/nn76abp160ZgYCA9evSgV69ehIWF3bB+amoqrq6uue5r3ry55bG3tzdg/mC8tiwhIcHqmAoVKgBw8eLFArdByp+goCCysrJISEigQ4cOudZxcnKiYcOGeT7nxYsXsbe3vmjMwcFBS4aJyE2p/1b/LXlXHvpvXV4uYsPc3Ny44447aN68OZ988glpaWlW34x/8803vPzyyzz77LMsX76cmJgYnnnmGdLT0wv1unZ2djk69YyMDMvjVq1aERsby3vvvUdqair9+vXj4YcfvuH5PD09bzhr67WX29nZ2eVadv0H5NmzZwGoXr16Hlsk5UVKSgoxMTHExMQAEBsbS0xMDEeOHKFBgwY8/vjjPPnkk3z//ffExsayefNmxo8fz9KlSwv0evfffz8ffPABP/30E4cOHWLRokV89NFHPPDAA0XYKhEpb9R/q/8Wa+W9/1bSLVKOvPPOO/znP//hxIkTAKxZs4Z27doxdOhQgoKCuOOOOyz3vgCYTCZq1KjBhg0bLGWZmZlER0ff9HWqV69OXFyc5fm+fftyfCtdpUoV+vfvz8yZM1m4cCHfffedpTO9XlBQEH///Xehvp2/1o4dO3BycqJJkyZFcj6xHVu2bCEoKIigoCAAhg8fTlBQkOW+wdmzZ/Pkk08yYsQIAgMD6d27Nxs3bsTPz69ArzdlyhQefvhhhg4dSqNGjRg5ciSDBw8u9iV6RKR8Uf9tpv779lXe+29dXi5SjnTq1IkmTZowduxYpk6dyh133MFXX33FL7/8gr+/P3PmzGHz5s34+/tbjnnppZf497//TUBAAI0aNeKjjz7i3LlzN32de+65h6lTp9K2bVuys7N57bXXrL69njRpEjVq1KBly5bY29vz7bff4uPjQ9WqVXM9X+fOnblw4QI7d+6kadOmhf49rFmzxjJjqtxeOnXqdNM//q7cJ3ntiFJhVK5cmcmTJzN58uQiOZ+I3J7Uf5up/759lff+WyPdIuXM8OHDmTlzJkePHmXIkCE8+OCD9O/fn5CQEM6cOZNjHdARI0bw5JNP8vTTTxMaGkrlypVveWnNxIkT8fPz4+6772bAgAGMHDmSihUrWvZXqlSJ8ePH07p1a+68804OHTrE0qVLc9w7c4WHhwcPPvgg8+bNK/wvAPj6668ZNGhQkZxLRESkJKj/Vv8t5ZedUVTXg4iIFML27dvp2rUr+/fvp3LlygU+z08//cQrr7zCX3/9haOjLuYREREpTuq/RW5NI90iUiY0a9aMCRMmcOjQoUKd58KFC8yePVsdtoiISAlQ/y1yaxrpFhERERERESkmGukWERERERERKSZKukVERERERESKiZJuERERERERkWKipFtERERERESkmCjpFhERERERESkmSrpFREREREREiomSbhEREREREZFioqRbREREREREpJgo6RYREREREREpJkq6RURERERERIqJkm4RERERERGRYqKkW0RERERERP6fvfuOb6p6Hzj+SVe6U9rSlkILLRvKKHsIlA0yRUVkyFBAQRRBUeSnoiJ8QRkKLhABQcDFUFSWQAXKLFT2LpRVyijdTdskvz9uGyizI+3teN6vV15Jbk7ufcLo6ZNzznNEAZGkWwghhBBCCCGEKCCSdAshhBBCCCGEEAVEkm4hhBBCCCGEEKKASNIthBBCCCGEEEIUkBKXdE+ePBmNRpPt5uPjY37dZDIxefJkfH19cXBwICQkhKNHj6oYsRBCCCGEEEKIkqrEJd0AtWvX5urVq+bb4cOHza/NmDGDWbNmMW/ePPbt24ePjw8dO3YkISFBxYiFEEIIIYQQQpREJTLptrGxwcfHx3wrW7YsoIxyz5kzh0mTJtGnTx+CgoJYsmQJycnJLF++XOWohRBCCCGEEEKUNDZqB1AQTp8+ja+vL1qtlqZNmzJ16lQCAwOJjIwkOjqaTp06mdtqtVratGlDWFgYI0eOfOD59Ho9er3e/NxoNHLr1i08PDzQaDQF/nmEEEIUPyaTiYSEBHx9fbGyKpHfcRc7RqORK1eu4OLiIv23EEKIByqI/rvEJd1Nmzblhx9+oFq1aly7do0pU6bQokULjh49SnR0NADe3t7Z3uPt7c2FCxcees5p06bx4YcfFmjcQgghSqaLFy9SoUIFtcMQwJUrV/Dz81M7DCGEEMWAJfvvEpd0d+3a1fy4Tp06NG/enMqVK7NkyRKaNWsGcN+32yaT6ZHfeE+cOJFx48aZn8fFxeHv78/W35fToE03AI5cvk2/+XvwcdWyeXzIwwO8eRbmtwGtK4w7lodPKIQQojiIj4/Hz88PFxcXtUMRmbL+Li6+4Yxrv/lQo5vKEQkhirIzMQn8vO8iv/93hUS9AQBbays61fKib2M/GviXkVkzJVBB9N8lLum+l5OTE3Xq1OH06dP07t0bgOjoaMqVK2duExMTc9/o9920Wi1arfa+485Ojri6ugKgSzRhpXXEZKc1H3sgTTnQakCTBC4uIP9RhRCiRJNfyIqOrL8LV60GVwdbeFR/LYQolfQZBtYfiebH3VHsPX8r86iWAF9H+jfx55mGFfBwvj8vECWPJfvvEp906/V6jh8/TqtWrQgICMDHx4dNmzYRHBwMQFpaGqGhoUyfPj1f17G1Vub7G4ymRzfUZn5jYjJCWhJonfN1XSGEEELkgSFN7QiEEEVI1M1kftx7gV/2X+JWkvLzwdpKQ4eaXgxoWpEnqnhiZSVfooq8KXFJ95tvvkmPHj3w9/cnJiaGKVOmEB8fz+DBg9FoNIwdO5apU6dStWpVqlatytSpU3F0dKR///75uq515n/CDIPx0Q1tHcHKFozpkHpbkm4hhBBCDRn6x7cRQpRoRqOJ7Wdu8EPYebacjMGUOXbm42pPvyZ+9Gvsj4/OXt0gRYlQ4pLuS5cu8fzzz3Pjxg3Kli1Ls2bN2L17NxUrVgRgwoQJpKSkMGrUKGJjY2natCkbN27M95x928zKdhmPG+nWaMDBDZKuQ8pt0ElxHSGEEKLQGdLVjkAIoZL41HR+3X+JpbsvEHkjyXy8VVVPBjarSPsaXthYy64TwnJKXNK9cuXKR76u0WiYPHkykydPtuh1ra0zR7ofl3QD2LspSXfqbYvGIIQo3QwGA+npkkgUFltbW6ytrdUOQ+SVTC8XotQ5GZ3AD7vOs/rgZZLTlMJoLlobnmlUgUHNKhJYNnczUKXfLZ7U6L9LXNKtFpvM6eWPXdMNykg3KCPdQgiRTyaTiejoaG7fvq12KKWOm5sbPj4+UiytOJKkW4hSIcNgZNOxayzZdZ7d526Zj1fzduaF5pV4Krg8TtrcpUTS7xZ/hd1/S9JtIXcn3Y/bggx7N+VeRrqFEBaQ1fF7eXnh6OgoCWAhMJlMJCcnExMTA5BtRwxRTEjSLUSJdiNRz8q9Ufy4J4qrcamAUoOpUy1vXmheiWaB7nnuL6XfLb7U6r8l6bYQG6s76z4yjCZsrR/xn09GuoUQFmIwGMwdv4eHh9rhlCoODg6Asu2kl5eXTDUvbiTpFqJEirh4mx/CzrPu0FXSMgscezjZ8XwTf/o39cfXzSFf55d+t/hTo/+WpNtCbO5KsjMMJmwf9Xdnr1PuZaRbCJFPWWvJHB0dVY6kdMr6c09PT5eku7iRpFuIEkOfYWDdf1f5Ydd5/rsUZz5ez8+Nwc0r0q1uObQ2lvkZLf1uyVDY/bck3RZifde+fRlGI/CIv7ys6eUy0i2EsBCZ2qYO+XMvxqR6uRDF3o1EPT/ujmLp7gvcSFS2AbSztqJ7vXK80LwS9f3cCuza8vO/eCvsvz9Jui3E5q6k+7HF1LKml6fGPbKZEEIIIQqI7NMtRLF19Eoci3ae5/eIK+Yp5D6u9gxqXpF+jf3wcNaqHKEQ2UnSbSF3j3SnGx6TdEshNSGEUEVISAj169dnzpw5aoci1CYj3UIUKwajiX+OX+P7nZHZqpDX93Nj2BMBdA3ywVb21i5ypN9VyL9MC9FoNDnfNkwKqQkhSrlvvvkGFxcXMjIyzMcSExOxtbWlVatW2dpu374djUbDqVOnCjtMkUP//vsvPXr0wNfXF41Gw5o1ax7aduTIkWg0mvt+AdPr9YwZMwZPT0+cnJzo2bMnly5dytYmNjaWQYMGodPp0Ol0DBo0KO9b9siabiGKhYTUdL7fEUnbz7YxYmk4u8/dwtpKQ496vqwa1YI1o1vSs56vJNyPIf2uuuRfpwVlFVNT1nQ/gox0CyFKubZt25KYmMj+/fvNx7Zv346Pjw/79u0jOTnZfHzbtm34+vpSrVo1NUIVOZCUlES9evWYN2/eI9utWbOGPXv24Ovre99rY8eOZfXq1axcuZIdO3aQmJhI9+7dMRgM5jb9+/cnIiKC9evXs379eiIiIhg0aFDegpakW4giLepmMh/9cYzm07bw0bpjRN1KRudgyyshldk+oS1znw+mgX8ZtcMsNqTfVZck3RaUtW1YxuOml8tItxCilKtevTq+vr5s27bNfGzbtm306tWLypUrExYWlu1427ZtSUtLY8KECZQvXx4nJyeaNm2a7f03b97k+eefp0KFCjg6OlKnTh1WrFjxyDjWr1+PTqfjhx9+sPRHLFW6du3KlClT6NOnz0PbXL58mVdffZUff/wRW1vbbK/FxcWxcOFCZs6cSYcOHQgODmbZsmUcPnyYzZs3A3D8+HHWr1/Pd999R/PmzWnevDkLFixg3bp1nDx5MvdBS9ItRJFjMpnYdfYmw3/YT5vPtvL9zkgS9RlU8XLmk6eC2D2xPW93qZHvbb9KI+l31SVrui3INi8j3SYTSPVDIYQFmUwmUtINj29oYQ621rmqBhoSEsLWrVt55513ANi6dSsTJkzAaDSydetWOnToQFpaGrt27WLu3LkMHTqU8+fPs3LlSnx9fVm9ejVdunTh8OHDVK1aldTUVBo2bMjbb7+Nq6srf/75J4MGDSIwMJCmTZved/2VK1cyYsQIli5dSq9evSz25yDuZzQaGTRoEG+99Ra1a9e+7/Xw8HDS09Pp1KmT+Zivry9BQUGEhYXRuXNndu3ahU6ny/Z32axZM3Q6HWFhYVSvXv2+8+r1evT6OwXT4uPj77woSbcQRUZahpF1h66wYHskx6/e+X8aUr0sw1oG0KqqZ5GtFq5WnwvS7xYnknRbUNZakrSMHI50G9IgPQXsZJ8/IYTlpKQbqPX+hkK/7rGPOuNol/NuJSQkhDfeeIOMjAxSUlI4ePAgrVu3xmAw8MUXXwCwe/duUlJSCAkJYfjw4Vy6dMk8NfnNN99k/fr1LFq0iKlTp1K+fHnefPNN8/nHjBnD+vXr+eWXX+7r/L/66iveffdd1q5dS9u2bS3w6cWjTJ8+HRsbG1577bUHvh4dHY2dnR1lymSfKurt7U10dLS5jZeX133v9fLyMre517Rp0/jwww8fHJQk3UKoLi4lnRV7o1i0M5Jr8coXZA621jzdsDxDWgRQxctZ5QgfT60+F6TfLU4k6bagrKQ73fCYkW47Z9BYg8mgjHZL0i2EKIXatm1LUlIS+/btIzY2lmrVquHl5UWbNm0YNGgQSUlJbNu2DX9/fw4cOIDJZLpvfZler8fDwwMAg8HA//73P3766ScuX75sHuV0cnLK9p7ffvuNa9eusWPHDpo0aVJon7e0Cg8P5/PPP+fAgQO5HqkymUzZ3vOg99/b5m4TJ05k3Lhx5ufx8fH4+fkpT6R6uRCquRSbzKKd51m5N4qkNGWU2MtFy+AWlRjQ1B83RzuVIyyZpN9VjyTdFmRnk8OkW6MBex2k3FLWdbveX1BGCCHyysHWmmMfdVblurlRpUoVKlSowNatW4mNjaVNmzYA+Pj4EBAQwM6dO9m6dSvt2rXDaDRibW1NeHg41tbZr+PsrIyEzJw5k9mzZzNnzhzq1KmDk5MTY8eOJS0t+4hm/fr1OXDgAIsWLaJx48ZFdspiSbF9+3ZiYmLw9/c3HzMYDIwfP545c+Zw/vx5fHx8SEtLIzY2Nttod0xMDC1atACUfxfXrl277/zXr1/H29v7gdfWarVotQ/Zr1f26Rai0B25HMf8f8/x5+Gr5t1+qnu7MLx1ID3qlUNrk7t+pChQq8/NunZuSL+rHkm6LShry7C0xyXdoEwxT7klFcyFEBan0WhyNd1MTW3btmXbtm3Exsby1ltvmY+3adOGDRs2sHv3boYOHUpwcDAGg4GYmJj7tjbJsn37dnr16sXAgQMBZR3x6dOnqVmzZrZ2lStXZubMmYSEhGBtbf3YitsifwYNGkSHDh2yHevcuTODBg1i6NChADRs2BBbW1s2bdpE3759Abh69SpHjhxhxowZADRv3py4uDj27t1rHinZs2cPcXFx5sQ8V2SkW4hCYTSaCD11nfn/nmPXuZvm409U8WR460BaF+H12jlRnPpckH5XLcXnX0gxcGd6+WPWdMOdYmpSwVwIUYq1bduW0aNHk56ebv7GHZTO/5VXXiE1NZW2bdvi5+fHgAEDeOGFF5g5cybBwcHcuHGDLVu2UKdOHZ588kmqVKnCb7/9RlhYGGXKlGHWrFlER0ff1/kDVKtWja1btxISEoKNjc19e0aL3ElMTOTMmTPm55GRkURERODu7o6/v795KmIWW1tbfHx8zMXPdDodL774IuPHj8fDwwN3d3fefPNN6tSpY07Ya9asSZcuXRg+fDjffvstACNGjKB79+4PLKL2WLKmW4gCpc8wsPbgFRZsP8fpmERAGaDqUc+Xl1oFUNtXp3KEpZP0u+qQpNuCbLOml2fkcKQbIDWu4AISQogirm3btqSkpFCjRo1sU4TbtGlDQkIClStXNq/BXbRoEVOmTGH8+PFcvnwZDw8PmjdvzpNPPgnAe++9R2RkJJ07d8bR0ZERI0bQu3dv4uIe/HO2evXqbNmyxfzN+8yZMwv+A5dQ+/fvz1YYJ2sd9eDBg1m8eHGOzjF79mxsbGzo27cvKSkptG/fnsWLF2eb1vjjjz/y2muvmauc9+zZM+8jJgaZXi5EQbidnMaPe6JYtPM8NxKV/2fOWhv6N/VnSItKst2XyqTfVYfGZDLlYFhW3C0+Ph6dTse+f9bQqN2dcvfPfhPGvvOxfD2gAV3rlHv0SX4ZCkdXQZf/QbNXCjhiIURJlZqaSmRkJAEBAdjb26sdTqnzqD//rL4iLi4OV1dXlSIUdzP/nbzjgqtneRh/XO2QhCgxrtxO4bvtkazYG2XeQquczp5hLQN4rokfrva2KkdoGdLvlgyF3X/LSLcFmbcMy+mabpDp5UIIIYQaMlLUjkCIEuFMTCLfhp5lTcRl8xLLWuVcGdE6kG51y5l/PxaiNJOk24LytKZbCqkJIYQQhU+qlwuRLxEXb/P1tjNsPHaNrHmzzQM9eCWkMq2KeXE0ISxNkm4LyvE+3QAOmVuiJN8qwIiEEEII8UAZqWAyKdt4CiFyxGQysePMDb7edpaws3cqkXeq5c3LIZVp4F/mEe8WovSSpNuC7GyUjjtHSbdjZiXX5JuPbieEEEIIyzMZlW3DbOzUjkSIIs9gNLH+SDRfh57hyOV4QKlE3ju4PC+3CaSKl4vKEQpRtEnSbUHmNd05qV7u5KncS9IthBBCqCMjVZJuIR5Bn2Fg1YHLfBt6lvM3kwFwsLWmXxM/XmoVSHmpRC5EjkjSbUFZSXeGMQdrus0j3TK9XAghhFCFrOsW4oES9Rks33OB77ZHEpOg/D9xc7RlcPNKDG5RCXcn+bJKiNyQpNuCzGu6czLS7eiu3MtItxBCCKGOjFS1IxCiSIlPTWfJzvMs3BnJ7eR0QNn266VWgfRr7IeTVlIHIfJC/udYkJ11HtZ0pydBegrYyvQcIYQQolBJ0i0EALFJaXy/M5LFYedJSM0AIMDTiVfaVKZ3cHnsbGTbLyHyQ5JuC7Ix79Odg+nlWlewsgVjujLaratQwNEJIYQQIhtJukUpdyNRz4Lt51i26wJJaQYAqno582q7KnSv64u1lVT3F8ISJOm2oFxtGabRKKPdidGSdAshhBBqkDXdopS6Fp/Kt6HnWL73Aqnpyu+ttcq5MqZdFTrX9sFKkm0hLErmilhQrqaXg2wbJoQo1WJiYhg5ciT+/v5otVp8fHzo3Lkzu3btAkCj0bBmzRp1gxQlW3qK2hEIUaguxSbzf2sO02r6Vr7fGUlqupF6fm4sHNyIP197gq51yknCXYJJv6seGem2oFyNdMOdYmpJknQLIUqfp59+mvT0dJYsWUJgYCDXrl3jn3/+4datnO/qkJ6ejq2tbQFGKUo0GekWpcT5G0l8ve0svx24ZN5lp3GlMoxpV5VWVT3RaCTRLg2k31WPjHRbkK1N1j7dOVjTDbJXtxCi1Lp9+zY7duxg+vTptG3blooVK9KkSRMmTpxIt27dqFSpEgBPPfUUGo3G/Hzy5MnUr1+f77//nsDAQLRaLSaTiaioKHr16oWzszOurq707duXa9euma+X9b6lS5dSqVIldDod/fr1IyEhwdwmISGBAQMG4OTkRLly5Zg9ezYhISGMHTu2EP9kRKGSNd2ihDt/I4lxP0fQbuY2ftp/kQyjiRaVPVgxvBk/j2xO62plJeEuJaTfVZeMdFtQ7ke6ZXq5EKIAmEyQnlz417V1VOpV5ICzszPOzs6sWbOGZs2aodVqs72+b98+vLy8WLRoEV26dMHa2tr82pkzZ/j555/57bffzMd79+6Nk5MToaGhZGRkMGrUKJ577jm2bdtmft/Zs2dZs2YN69atIzY2lr59+/K///2PTz75BIBx48axc+dOfv/9d7y9vXn//fc5cOAA9evXz9+fiyi6JOkWJVTUzWTmbjnNqoOXMWSObIdUL8uYdlVoWNFd5ehKGLX6XJB+txiRpNuCZE23EKJISE+Gqb6Ff913r4CdU46a2tjYsHjxYoYPH84333xDgwYNaNOmDf369aNu3bqULVsWADc3N3x8fLK9Ny0tjaVLl5rbbNq0iUOHDhEZGYmfnx8AS5cupXbt2uzbt4/GjRsDYDQaWbx4MS4uLgAMGjSIf/75h08++YSEhASWLFnC8uXLad++PQCLFi3C11eFP0dReCTpFiXMpdhk5m05w6/hd6aRh1QvyxsdqlHPz03d4EoqtfpckH63GJHp5RaU+5HurOnlNwooIiGEKLqefvpprly5wu+//07nzp3Ztm0bDRo0YPHixY98X8WKFc0dP8Dx48fx8/Mzd/wAtWrVws3NjePHj5uPVapUydzxA5QrV46YmBgAzp07R3p6Ok2aNDG/rtPpqF69en4/pijKJOkWJcSV2ym8u/owbT/bxsp9yjTy1tXKsmpUCxYPbSIJtwCk31WTjHRbkG1u9umGO4XUknNevEAIIR7L1lH59luN6+aSvb09HTt2pGPHjrz//vu89NJLfPDBBwwZMuSh73Fyyv6tvslkeuCaxHuP31v4RaPRYDQazW2zjt17DlGCSSE1UcxFx6Xy1bYzrNx7kbTMQZ8nqnjyRseqMo28sKjV52ZdO5ek31WHJN0WlFVILT1DppcLIVSk0eR4ullRU6tWLfN2Jba2thgMhhy9JyoqiosXL5q/dT927BhxcXHUrFkzR9etXLkytra27N2713yO+Ph4Tp8+TZs2bfL2YUTRJyPdopiKiU/lq21nWb43irTM3zubB3rwRsdqNAmQZLtQFeM+F6TfLSySdFuQrOkWQoicuXnzJs8++yzDhg2jbt26uLi4sH//fmbMmEGvXr0AZVraP//8Q8uWLdFqtZQpU+aB5+rQoQN169ZlwIABzJkzx1zQpU2bNjRq1ChH8bi4uDB48GDeeust3N3d8fLy4oMPPsDKykoq+5Zk6ZJ0i+LlRqKer7edZdnuC+gzk+0mldwZ27EqLSp7qhydKMqk31WXrOm2oFyv6b57y7ASOpVCCCEexNnZmaZNmzJ79mxat25NUFAQ7733HsOHD2fevHkAzJw5k02bNuHn50dwcPBDz6XRaFizZg1lypShdevWdOjQgcDAQH766adcxTRr1iyaN29O9+7d6dChAy1btqRmzZrY29vn67OKIkxGukUxEZ+azsyNJ2k9YysLd0SizzDSsGIZlr3YlJ9GNpOEWzyW9Lvq0phK6sT5AhQfH49Op2PfP2to1K6X+XjoqesM/n4vNcu58vfrrR5/ovRU+MRbefz2BXBwK5iAhRAlVmpqKpGRkQQEBJTITkpNSUlJlC9fnpkzZ/Liiy8+sM2j/vyz+oq4uDhcXV0LI2TxGOa/k3dccNVqoMlIeHKG2mEJ8VApaQZ+2HWer0PPcjs5HYC6FXSM71Sd1lU9S+SIYFEn/W7ByUm/aymF3X/L9HILss3t9HJbe7BzhrREZbRbkm4hhFDNwYMHOXHiBE2aNCEuLo6PPvoIwDztTpRAGSlqRyDEA6UbjPy07yJf/HOamASl4F8VL2fe7FSNzrV9JNkWJUJp6ncl6bYgu9xOLwdlinlaIiTdAI/KBRSZEEKInPjss884efIkdnZ2NGzYkO3bt+PpKdM2S5pUkw2uGGRNtyhyDEYTf/x3hVmbThF1KxmA8m4OvNGxGk8Fl8faSpJtUbKUln5Xkm4LylrTnZHTLcMAnL0h9jwkXiuYoIQQQuRIcHAw4eHhaochCkEqdkCKjHSLIsNkMrH5eAyfbTjJyWsJAHg62/Fq2yo839QfrY21yhEKYXmlqd8t0YXUpk2bhkajYezYseZjJpOJyZMn4+vri4ODAyEhIRw9etQi17uzT3cuRrqdvZR7SbqFEEIUU//++y89evTA19fXXGAnS3p6Om+//TZ16tTByckJX19fXnjhBa5cyb6vrV6vZ8yYMXh6euLk5ETPnj25dOlStjaxsbEMGjQInU6HTqdj0KBB3L59O9fxJpO5fi8tOdfvFcLSdp29ydNfhzH8h/2cvJaAi70Nb3Wuzr8T2jKkZYAk3EKUACU26d63bx/z58+nbt262Y7PmDGDWbNmMW/ePPbt24ePjw8dO3YkISEh39e0s1Gm/KTldJ9uUEa6ARJj8n19IYQQQg1JSUnUq1fPXAH3bsnJyRw4cID33nuPAwcOsGrVKk6dOkXPnj2ztRs7diyrV69m5cqV7Nixg8TERLp3755tz9j+/fsTERHB+vXrWb9+PREREQwaNCjX8aaYtMqDtKRcv1cISzl+NZ7B3+/l+QW7ORB1G3tbK14JqcyOCe0Y3bYKjnYyIVWIkqJE/m9OTExkwIABLFiwgClTppiPm0wm5syZw6RJk+jTpw8AS5Yswdvbm+XLlzNy5Mh8XdfOWvkmMm9Jt4x0CyHyzmjMxc8dYTHy567o2rUrXbt2feBrOp2OTZs2ZTs2d+5cmjRpQlRUFP7+/sTFxbFw4UKWLl1Khw4dAFi2bBl+fn5s3ryZzp07c/z4cdavX8/u3btp2rQpAAsWLKB58+acPHmS6tWr5zjeZDKT7nRJukXhu3I7hVmbTvHbgUuYTGBjpeH5Jv6MaVcFL1ephi1ESVQik+7Ro0fTrVs3OnTokC3pjoyMJDo6mk6dOpmPabVa2rRpQ1hYWL6Tbq1tfqaXy0i3ECL37OzssLKy4sqVK5QtWxY7OzupalsITCYTaWlpXL9+HSsrK+zs7NQOqViJi4tDo9Hg5uYGQHh4OOnp6dn6Z19fX4KCgggLC6Nz587s2rULnU5nTrgBmjVrhk6nIywsLFdJt7KmG5leLgpVfGo6X287y/eZ+2wDdKtbjrc6VaeSp5PK0QkhClKJS7pXrlzJgQMH2Ldv332vRUdHA+Dt7Z3tuLe3NxcuXHjoOfV6PXq93vw8Pj7+ge2yqpcbjCYyDEZsrHMwe99J1nQLIfLOysqKgIAArl69et8aWVHwHB0d8ff3x8qqxK7WsrjU1FTeeecd+vfvb97/NDo6Gjs7O8qUKZOtrbe3t7nvjo6OxsvL677zeXl5mdvc62H9d5IpczQxXZJuUfDSMows232BuVtOE5u513aTAHcmdq1BsH+Zx7xbCFESlKik++LFi7z++uts3LjxkZvV3zsKZDKZHjkyNG3aND788MPHXj9rpBtAn5HDpFvWdAsh8snOzg5/f38yMjKyrX8VBcva2hobGxuZWZAL6enp9OvXD6PRyFdfffXY9vf2zw/6s35UH/6w/jsFWdMtCp7JZGLdoat8uuGkefuvKl7OvNOlBu1resnPDiFKkRKVdIeHhxMTE0PDhg3NxwwGA//++y/z5s3j5MmTgPJtebly5cxtYmJi7hv9vtvEiRMZN26c+Xl8fDx+fn73tbO7K8lOyzDipM1B0HdXLzeZQH4ACyHyQKPRYGtri62trdqhCPFA6enp9O3bl8jISLZs2WIe5Qbw8fEhLS2N2NjYbKPdMTExtGjRwtzm2rX7Z4Vdv379oX34w/rv5LsLqUnfKwrA7nM3mfbXcf67FAdAWRct4zpW49mGFXI2KCOEKFFK1P/69u3bc/jwYSIiIsy3Ro0aMWDAACIiIggMDMTHxydbQZe0tDRCQ0PNnfqDaLVaXF1ds90exMbaChsrpePW57SYWlbSbUyHlNicvUcIIYQoRrIS7tOnT7N582Y8PDyyvd6wYUNsbW2z9c9Xr17lyJEj5v65efPmxMXFsXfvXnObPXv2EBcX99A+/GH9t3mk22QAQ5olP6oo5c5eT+SlJfvoN383/12Kw8nOmnEdqxH6VgjPN/GXhFuo7uLFi7z44ov4+vpiZ2dHxYoVef3117l586baoZVoJWqk28XFhaCgoGzHnJyc8PDwMB8fO3YsU6dOpWrVqlStWpWpU6fi6OhI//79LRKDnY0VGWkG9Bk5nOJpowV7N0i9DUnXwdHdInEIIYQQhSUxMZEzZ86Yn0dGRhIREYG7uzu+vr4888wzHDhwgHXr1mEwGMxrsN3d3bGzs0On0/Hiiy8yfvx4PDw8cHd3580336ROnTrmauY1a9akS5cuDB8+nG+//RaAESNG0L1791wVUQNI4a7Cd2lJSl8sRD7cTk7j839Os3TXBTKMJnNF8tfaV6Wsi/z7EkXDuXPnaN68OdWqVWPFihUEBARw9OhR3nrrLf7++292796Nu/v9uUhaWpoUDM2nUvd124QJExg7diyjRo2iUaNGXL58mY0bN+Li4mKR82ttMiuYy7ZhQgghSon9+/cTHBxMcHAwAOPGjSM4OJj333+fS5cu8fvvv3Pp0iXq169PuXLlzLewsDDzOWbPnk3v3r3p27cvLVu2xNHRkT/++APrzO04AX788Ufq1KlDp06d6NSpE3Xr1mXp0qW5jjcDG0zWWRXMZV23yLt0g5ElYecJ+Wwbi3aeJ8NookNNLza80ZqPewdJwi2KlNGjR2NnZ8fGjRtp06YN/v7+dO3alc2bN3P58mUmTZoEQKVKlZgyZQpDhgxBp9MxfPhwAH777Tdq166NVqulUqVKzJw503zuuXPnUqdOHfPzNWvWoNFo+PLLL83HOnfuzMSJEwE4e/YsvXr1wtvbG2dnZxo3bszmzZuzxVupUiWmTp3KsGHDcHFxwd/fn/nz5xfYn09BKlEj3Q+ybdu2bM81Gg2TJ09m8uTJBXI9rY01kJ7z6eWgTDG/cVKKqQkhhCiWQkJCMJlMD339Ua9lsbe3Z+7cucydO/ehbdzd3Vm2bFmeYryPraMytVwqmIs82nYyhil/HudMTCIA1b1d+L/uNWlVtazKkYnCZDKZSMlIUeXaDjYOOS7Id+vWLTZs2MAnn3yCg4NDttd8fHwYMGAAP/30k7nI5aeffsp7773H//3f/wFK7ay+ffsyefJknnvuOcLCwhg1ahQeHh4MGTKEkJAQXn/9dW7cuIGnpyehoaHm+9GjR5ORkUFYWBhvvPEGoMyQevLJJ5kyZQr29vYsWbKEHj16cPLkSfz9/c2xzZw5k48//ph3332XX3/9lVdeeYXWrVtTo0YNS/wRFpoSn3QXNrvMke4cTy8HGekWQgghCpudk7K0S0a6RS6dvpbAlD+PE3rqOgDuTnaM71SN5xr5yZrtUiglI4Wmy5uqcu09/ffgaOuYo7anT5/GZDJRs2bNB75es2ZNYmNjuX5d+Xfdrl073nzzTfPrAwYMoH379rz33nsAVKtWjWPHjvHpp58yZMgQgoKC8PDwIDQ0lKeffppt27Yxfvx4Zs+eDcC+fftITU3liSeeAKBevXrUq1fPfP4pU6awevVqfv/9d1599VXz8SeffJJRo0YB8PbbbzN79my2bdtW7JJu+clgYVnTy/XpMr1cCCGEKLKyflGVkW6RQ7FJaXyw9ghdPt9O6Knr2FprGNE6kK1vhjCgaUVJuEWxljUjKWvkvFGjRtleP378OC1btsx2rGXLlpw+fRqDwYBGo6F169Zs27aN27dvc/ToUV5++WUMBgPHjx9n27ZtNGjQAGdnZwCSkpKYMGECtWrVws3NDWdnZ06cOEFUVFS2a9StW9f8WKPR4OPjQ0xM8ZsdLCPdFpa1V7fekMvp5SDTy4UQQohCYAJlpBtkpFs8VrrByNJdF5iz+RTxqRkAdKrlzbtP1qSSp5PK0Qm1Odg4sKf/HtWunVNVqlRBo9Fw7Ngxevfufd/rJ06coEyZMnh6egJKMeq7mUym+6ay37t0KCQkhPnz57N9+3bq1auHm5sbrVu3JjQ0lG3bthESEmJu+9Zbb7FhwwY+++wzqlSpgoODA8888wxpadl3lLh3K1SNRoPRmIs8q4iQpNvClDXdeRzpTogugIiEEEIIcZ+skW5JusUjhJ29wQdrj3I6c912zXKuvNe9Ji0qe6ocmSgqNBpNjqd4q8nDw4OOHTvy1Vdf8cYbb2Rb1x0dHc2PP/7ICy+88NA14rVq1WLHjh3ZjoWFhVGtWjVzwcusdd2//vqrOcFu06YNmzdvJiwsjNdff9383u3btzNkyBCeeuopQFnjff78eQt+4qJF5sFYmJ11HtZ0u/go95J0CyGEEIUja6RbppeLB7hyO4XRyw/Qf8EeTsck4u5kx9Sn6rBuzBOScItia968eej1ejp37sy///7LxYsXWb9+PR07dqR8+fJ88sknD33v+PHj+eeff/j44485deoUS5YsYd68ednWfWet6/7xxx/NSXdISAhr1qwhJSXFvJ4blJH3VatWERERwX///Uf//v2L5Qh2TknSbWFZ08tztWWYa3nlPuFKAUQkhBBCiLuZTBpMMtItHkCfYeDLrWdoPzOUPw9dxUoDg5tXZOv4EPo39cfaKmeVooUoiqpWrcr+/fupXLkyzz33HJUrV2bEiBG0bduWXbt2PXCP7iwNGjTg559/ZuXKlQQFBfH+++/z0UcfMWTIEHMbjUZDmzZtAGjVqhWgrMnW6XQEBwfj6upqbjt79mzKlClDixYt6NGjB507d6ZBgwYF88GLAJlebmHmQmq5SrrLKfepcaBPBK1zAUQmhBBCCDO7zL5Wn6BuHKLI2Hoyhg9/P8r5m8rsh8aVyvBhzyBq+bo+5p1CFB8VK1Zk0aJFj2zzsGneTz/9NE8//fQj3/vrr79me67RaLh58+Z97SpVqsSWLVuyHRs9evRj44iIiHjk9YsqSbotzLymOzdJt9YFtK6gj4eEq6CtWkDRCSGEEAIA+8xESpLuUi/qZjIfrTvG5uPKLjJlXbS8+2QNetcvn+M9kIUQ4lEk6bawrH26czW9HMDVF67HQ/xl8JSkWwghhChQ2qykO17dOIRqUtMNfLXtLN+EniUtw4iNlYZhTwQwpl0VXOxtH38CIYTIIUm6LezO9PJcFFKDzKT7BMTLum4hhBCiIJnQYMpKulMl6S6Ntp2M4f21R4m6pUwlb1nFgw971qaKl4vKkQkhSiJJui0sT9PLQUm6QRnpFkIIIUSBMmkzkyuZXl6qXItP5aN1x/jz0FUAyunsea97LboG+chUciFEgZGk28LyPr08s4K5jHQLIYQQBU+ml5cqBqOJZbsv8NmGkyToM7DSwNCWAbzRsRrOWvl1WAhRsOSnjIXla3o5SNIthBBCFAZ7mV5eWhy+FMe7qw9z+HIcAPX83PikdxBB5XUqRyaEKC0k6bawrH269el5HemW6eVCCCFEQTKBsnMIyPTyEiwhNZ2ZG0/xw67zGE3gYm/DhC416N9E9tsWQhQuSbotLP9rumWkWwghhCho5kJq+jh1AxEWZzKZ+PtINB/+cZRr8XoAetbz5f+618TLxV7l6IQQpZEk3RaWry3DAJJvQnoq2EqnIIQQQhQUk91d+3SbTCBFtEqE6LhU/m/NEfOe25U8HPm4dxCtqpZVOTIhRGlmpXYAJU2e13Tbu4Gto/I4QUa7hRBCiIJiQnNnTbfJCGlJ6gYk8s1oNPHjngt0nBXK5uPXsLXW8Fq7Kqwf21oSbiHyICQkhLFjx+a4/fnz59FoNERERBRYTMWZJN0WlpV0p+Z2TbdGI1PMhRBCiMJi4wAaZUmYVDAv3iJvJPH8gt1MWn2EBH0G9f3cWDemFeM6Vcfe1lrt8IQoMoYMGYJGo+Hll1++77VRo0ah0WgYMmQIAKtWreLjjz/O8bn9/Py4evUqQUFBlgq3RJGk28IcMn+4p+Z2pBvuJN1xUkxNCCGEKFAajVQwL+YyDEa+CT1Llzn/sifyFg621rzXvRa/vdKC6j4uaocnRJHk5+fHypUrSUlJMR9LTU1lxYoV+Pv7m4+5u7vj4pLz/0fW1tb4+PhgYyOrlx9Ekm4Lc7BTku6UtDwk3brMf+i3oywYkRBCCCEeSPbqLraOXomj91c7+d/fJ9BnGHmiiicb32jNi08ESGVyIR6hQYMG+Pv7s2rVKvOxVatW4efnR3BwsPnYvdPLK1WqxNSpUxk2bBguLi74+/szf/588+v3Ti/ftm0bGo2GDRs2EBwcjIODA+3atSMmJoa///6bmjVr4urqyvPPP09ycnK268yZMydbzPXr12fy5Mnm5xqNhm+//Zbu3bvj6OhIzZo12bVrF2fOnCEkJAQnJyeaN2/O2bNnLfOHZgGSdFuYeaQ7PQ9Jd5mKyv3tCxaMSAghhBB3M6HBhAkc3JQDKbfVDEfkQmq6gRnrT9Bz3k6OXI5H52DLp8/UZemLTfBzd1Q7PFEKmUwmjMnJqtxMJlOeYh46dCiLFi0yP//+++8ZNmzYY983c+ZMGjVqxMGDBxk1ahSvvPIKJ06ceOR7Jk+ezLx58wgLC+PixYv07duXOXPmsHz5cv788082bdrE3Llzc/0ZPv74Y1544QUiIiKoUaMG/fv3Z+TIkUycOJH9+/cD8Oqrr+b6vAVFxv8tzN6cdOdyTTeAm4x0CyGEEIXGwV25T7mlbhwiRw5GxfLmL/9x9rpS+K5bnXJ80LOWbAMmVGVKSeFkg4aqXLv6gXA0jrn/smnQoEFMnDjRPDq9c+dOVq5cybZt2x75vieffJJRo0YB8PbbbzN79my2bdtGjRo1HvqeKVOm0LJlSwBefPFFJk6cyNmzZwkMDATgmWeeYevWrbz99tu5+gxDhw6lb9++5liaN2/Oe++9R+fOnQF4/fXXGTp0aK7OWZAk6bawrKQ7JS8j3ZJ0CyGEEIXHoYxynxKrbhzikfQZBj7ffJpvQs9iNEFZFy1TegfRubaP2qEJUSx5enrSrVs3lixZgslkolu3bnh6ej72fXXr1jU/1mg0+Pj4EBMTk+P3eHt74+joaE64s47t3bs315/h3vMC1KlTJ9ux1NRU4uPjcXV1zfX5LU2Sbgszr+nOT9IddwmMBrCSiptCCCGEpZknZGYl3cky0l1UHbkcx5u//MeJ6AQAetX35cOetXFztFM5MiEUGgcHqh8IV+3aeTVs2DDz9Osvv/wyR++xtbXNfn2NBqPx0bN7736PRqN57DmsrKzumzafnp7+2PM+7Njj4issqifd6enpREdHk5ycTNmyZXF3d1c7pHyxz9wyLC3DiNFowio3xTxcyoGVLRjTIeEq6CoUUJRCCCGEwDFrermMdBc16QYjX249w7wtZ8gwmvBwsuOTp4LoElRO7dCEyEaj0eRpirfaunTpQlpaGoB5SnZRULZsWa5evWp+Hh8fT2RkpIoRWYYqhdQSExP59ttvCQkJQafTUalSJWrVqkXZsmWpWLEiw4cPZ9++fWqElm9ZI92Qh23DrKzvJNoyxVwIIUQx8e+//9KjRw98fX3RaDSsWbMm2+smk4nJkyfj6+uLg4MDISEhHD16NFsbvV7PmDFj8PT0xMnJiZ49e3Lp0qVsbWJjYxk0aBA6nQ6dTsegQYO4fft2nmI2mZDp5UXUyegEnvpqJ3M2nybDaKJrkA8b32gtCbcQFmRtbc3x48c5fvw41tZFZ3Ztu3btWLp0Kdu3b+fIkSMMHjy4SMWXV4WedM+ePZtKlSqxYMEC2rVrx6pVq4iIiODkyZPs2rWLDz74gIyMDDp27EiXLl04ffp0YYeYL/Y2d/5R5GnbMFnXLYQQophJSkqiXr16zJs374Gvz5gxg1mzZjFv3jz27duHj48PHTt2JCEhwdxm7NixrF69mpUrV7Jjxw4SExPp3r07BsOdvrR///5ERESwfv161q9fT0REBIMGDcp74FJIrUjJyBzd7jF3B0cux+PmaMsXzwfz1YAGeDhr1Q5PiBLH1dW1SKx3vtvEiRNp3bo13bt358knn6R3795UrlxZ7bDyTWPKa635PHr22Wd5//33sy10fxC9Xs/ChQuxs7PjpZdeKqTociY+Ph6dTse+f9bQqF2v+16v9n9/k5ZhZMfbbalQJpfTTda+CgeXQttJ0GaChSIWQghR2LL6iri4uCL3S01B0mg0rF69mt69ewPKKLevry9jx441V6fV6/V4e3szffp0Ro4cSVxcHGXLlmXp0qU899xzAFy5cgU/Pz/++usvOnfuzPHjx6lVqxa7d++madOmAOzevZvmzZtz4sQJqlev/tjYsv5OPhw/mAnTFmJ/bhOseA58g2HEtgL58xA5E3UzmTd+jiD8gjLroENNL6b2qSOVyUWRk5qaSmRkJAEBAdjby7/P4upRf48F0X8X+pruX375JUfttFqtuSR9ceNga01ahjGP24Zl7tUdK3t1CyGEKP4iIyOJjo6mU6dO5mNarZY2bdoQFhbGyJEjCQ8PJz09PVsbX19fgoKCCAsLo3PnzuzatQudTmdOuAGaNWuGTqcjLCzsgUm3Xq9Hr9ebn8fHx2dvIIXUVGcymfjtwGUm/36URH0GLlobPuhZm6cblDcXQhJCiOJO9UJqJZGDrTVxKemk5qWCeZnMpPu2JN1CCCGKv+joaODOli5ZvL29uXDhgrmNnZ0dZcqUua9N1vujo6Px8vK67/xeXl7mNveaNm0aH3744cODMxdSu52TjyIs7HZyGpNWH+HPw0rRpMaVyjCrb3383ItfUSohhHiUIpF0Hzt2jKioKHMFvSw9e/ZUKaIcesgXsBbZNkySbiGEECXIvaOWJpPpsSOZ97Z5UPtHnWfixImMGzfO/Dw+Ph4/P787DbJGuvVxYEgHa1tE4Qg7c4NxP/9HdHwqNlYa3uhYjZfbVMY6N7u+CCFEMaFq0n3u3DmeeuopDh8+jEajMe/JltV53l08pTjRZm4blreR7krKfdwlyEgDG9mHUgghRPHl4+MDKCPV5crdqT4dExNjHv328fEhLS2N2NjYbKPdMTExtGjRwtzm2rVr953/+vXr942iZ9FqtWi19xfgMmV9a+5QBjRWYDJC0g1wlerYBU2fYeCzDSdZsF3ZAijQ04k5/epTt4KbuoEJIUQBUmXLsCyvv/46AQEBXLt2DUdHR44ePcq///5Lo0aN2LZtm5qh5Yt5pDsv1cudvcHWSfkFQEa7hRBCFHMBAQH4+PiwadMm87G0tDRCQ0PNCXXDhg2xtbXN1ubq1ascOXLE3KZ58+bExcWxd+9ec5s9e/YQFxdnbpNrVtbg6Kk8TorJ2zlEjp26lkDvL8PMCXf/pv6se+0JSbhFsVTItaiFhRX235+qI927du1iy5YtlC1bFisrK6ysrHjiiSeYNm0ar732GgcPHlQzvDxzsM3H9HKNBtwD4dphuHUOPKtaODohhBDCshITEzlz5oz5eWRkJBEREbi7u+Pv78/YsWOZOnUqVatWpWrVqkydOhVHR0f69+8PgE6n48UXX2T8+PF4eHjg7u7Om2++SZ06dejQoQMANWvWpEuXLgwfPpxvv/0WgBEjRtC9e/ccVS5/KGdvJeFOlKS7oJhMJn7ad5HJfxwlNd2Iu5Md05+uS8daD56hIERRZmurLENJTk7GwcFB5WhEXiUnJwN3/j4LmqpJt8FgwNnZGQBPT0+uXLlC9erVqVixIidPnlQztHyxz0y68zS9HMAjM+m+edaCUQkhhBAFY//+/bRt29b8PGsd9eDBg1m8eDETJkwgJSWFUaNGERsbS9OmTdm4cSMuLi7m98yePRsbGxv69u1LSkoK7du3Z/HixVhbW5vb/Pjjj7z22mvmKuc9e/Z86N7gj5JtfMO5LFxDku4CkpCazrurj/DHf1cAaFXVk5l968lWYKLYsra2xs3NjZgY5WeGo6OjVNovRkwmE8nJycTExODm5patjylIqibdQUFBHDp0iMDAQJo2bcqMGTOws7Nj/vz5BAYGqhlavjiYk+48bBkG4J65AfwtSbqFEEIUfSEhIY+cqqfRaJg8eTKTJ09+aBt7e3vmzp3L3LlzH9rG3d2dZcuW5SdUM3O4zpmjrTK93OIOX4rj1RUHuHAzGWsrDW92qs7I1oFYSbE0Ucxl1arISrxF8ePm5mb+eywMqibd//d//0dSUhIAU6ZMoXv37rRq1QoPDw9++uknNUPLF/v8TC8H8MhMumWkWwghhChYTmWVexnpthiTycTisPNM/es46QYT5d0c+OL5+jSs6K52aEJYhEajoVy5cnh5eZGenq52OCKXbG1tC22EO4uqSXfnzp3NjwMDAzl27Bi3bt2iTJkyxXqahoOdUp8uOS+F1EBZ0w0y0i2EEEIUtKyRbkm6LeJ2chpv/XqITceUSvOdankz45m6uDnKbiyi5LG2ti705E0UT6pUL09OTmb06NGUL18eLy8v+vfvz40bNwBl6lhxTrgBHO2U7zLyvKY7a3p53CXI0FsoKiGEEELAXVuGATh7KfeJ929HJnIn/EIsT36+nU3HrmFnbcXkHrX4dlBDSbiFEKWeKkn3Bx98wOLFi+nWrRv9+vVj06ZNvPLKK2qEUiAcM7cMS9Rn5O0Ezl5g56xsGxYr24YJIYQQBSYr6U66rm4cxZjJZGLxzkie+3YXV+JSqeThyKpRLRjSMqDYD6QIIYQlqDK9fNWqVSxcuJB+/foBMHDgQFq2bInBYCgRUzSctcofa3Jek+6sbcOiDylTzMtWs2B0QgghhDBl1TB3kpHu/EjSZzBx1WF+z6xO3q1uOaY/Xdf8u5AQQgiVRrovXrxIq1atzM+bNGmCjY0NV65cUSMci8uaXp6oz+P0cpBiakIIIURhyFrTnRILGWnqxlLMnIlJpPeXO/n9vyvYWGl4r3st5j0fLAm3EELcQ5Wk22AwYGeXfX2PjY0NGRl5HBm+y9dff03dunVxdXXF1dWV5s2b8/fff5tfN5lMTJ48GV9fXxwcHAgJCeHo0aP5vu7dnLTKaH1SXke6ATyqKvc3TlkgIiGEEEJkyba5mUMZ0GTOspMp5jn21+Gr9Jq3g9MxiXi5aFkxohkvPiHTyYUQ4kFU+SrSZDIxZMgQtFqt+Vhqaiovv/wyTk5O5mOrVq3K9bkrVKjA//73P6pUqQLAkiVL6NWrFwcPHqR27drMmDGDWbNmsXjxYqpVq8aUKVPo2LEjJ0+exMXFJf8fjruml6flI+kuW125v37SAhEJIYQQ4oGsrJR13QlXlSnmuvJqR1SkpRuMzFh/ggXbIwFoGuDO3P7BeLnYqxyZEEIUXaok3YMHD77v2MCBAy1y7h49emR7/sknn/D111+ze/duatWqxZw5c5g0aRJ9+vQBlKTc29ub5cuXM3LkSIvEcGd6uSWS7hNgMinrvIUQQghhea6+StIdfxnKN1A7miLrRqKeUT8eYG/kLQBGtg7krc7VsbFWZeKkEEIUG6ok3YsWLSqU6xgMBn755ReSkpJo3rw5kZGRREdH06lTJ3MbrVZLmzZtCAsLs1jSfWekOz9ruquAxgpSbyvT3bKqqwohhBAiX7JtGQagqwCXw5WtOsUDHbkcx4gf9nMlLhVnrQ2fPVuXLkHl1A5LCCGKhRJZ6eLw4cM0b96c1NRUnJ2dWb16NbVq1SIsLAwAb2/vbO29vb25cOHhW3Pp9Xr0+jv7ZcfHxz/y+o7afG4ZBmDrAGUqwa1zymi3JN1CCCGExZjuXtit81PuJel+oD/+u8Jbv/5HarqRQE8n5r/QiCpezmqHJYQQxYYqSfewYcNy1O7777/P0/mrV69OREQEt2/f5rfffmPw4MGEhoaaX7+3yIfJZHpk4Y9p06bx4Ycf5vj6d490P+7cj1S2RmbSfRICWuftHEIIIYR4NHPSfVHdOIoYg9HEzI0n+WqbspNKSPWyfN4vGJ2DrcqRCSFE8aJK0r148WIqVqxIcHAwpmxfNVuGnZ2duZBao0aN2LdvH59//jlvv/02ANHR0ZQrd2dKVExMzH2j33ebOHEi48aNMz+Pj4/Hz8/voe2dMpNug9GEPsOIvW0e9x73rAYn/1JGuoUQQggLSk9PJzo6muTkZMqWLYu7u7vaIalHV0G5l5Fus/jUdMaujGDLiRgARrYJZELnGlhbSY0ZIYTILVWS7pdffpmVK1dy7tw5hg0bxsCBAwu0szeZTOj1egICAvDx8WHTpk0EBwcDkJaWRmhoKNOnT3/o+7VabbZK64/jeFeSnajPyHvSXbaGci8VzIUQQlhAYmIiP/74IytWrGDv3r3Zlk5VqFCBTp06MWLECBo3bqxilAXvgWu6AW7LSDdA5I0kXlqyj7PXk9DaWDH96br0Dpaq7kIIkVeqlJv86quvuHr1Km+//TZ//PEHfn5+9O3blw0bNuR75Pvdd99l+/btnD9/nsOHDzNp0iS2bdvGgAED0Gg0jB07lqlTp7J69WqOHDnCkCFDcHR0pH///hb6dGBlpcHRTkm0k/X5KKYm24YJIYSwkNmzZ1OpUiUWLFhAu3btWLVqFREREZw8eZJdu3bxwQcfkJGRQceOHenSpQunT59WO+TCkzW9PCkG0lPVjUVlO8/coNe8HZy9noSPqz2/vNxcEm4hhMgn1QqpabVann/+eZ5//nkuXLjA4sWLGTVqFOnp6Rw7dgxn57wV6Lh27RqDBg3i6tWr6HQ66taty/r16+nYsSMAEyZMICUlhVGjRhEbG0vTpk3ZuHGjxfbozuJoZ0NymiF/xdQ8qyn3STGQdBOcPCwTnBBCiFInLCyMrVu3UqdOnQe+3qRJE4YNG8Y333zDwoULCQ0NpWrVqoUcZeHJ9hW/ozvYOEBGirJtmEdltcJS1c/7LvLu6sNkGE008Hfjm0ENZf9tIYSwgCJRvVyj0aDRaDCZTBiNxnyda+HChY+91uTJk5k8eXK+rvM4zlprbiRCclo+km6ts1LBPPY8XDsCgW0sFZ4QQohS5pdffslRO61Wy6hRowo4GnXdN6dOowE3P7hxSlnXXcqSbqPRxGd3FUzrVd+X6U/XzfvyOCGEENmoMr0clG24VqxYQceOHalevTqHDx9m3rx5REVF5XmUuyjJKqaWr5FuAJ/MEYlrR/IZkRBCCCEeylxMrXSt605NNzBm5UFzwv1a+6rMea6+JNxCCGFBqox0jxo1ipUrV+Lv78/QoUNZuXIlHh4la+q0k92dbcPyxbsOHP8Dog9bICohhBDijmPHjhEVFUVaWlq24z179lQpIhWVqaTc3zqnahiF6WainuE/7OdA1G1srTX8r09dnm5YQe2whBCixFEl6f7mm2/w9/cnICCA0NDQbHto323VqlWFHJnlOGmVb4gtNtItSbcQQggLOXfuHE899RSHDx82L+8CZQkWgMGQzy+MiyP3zCnlN8+qG0chOROTyLDF+4i6lYyrvQ3fDmpE88olawBECCGKClWS7hdeeMHcsZdUjpnTy5PynXQHKffXT0JGGtjY5TMyIYQQpd3rr79OQEAAmzdvJjAwkL1793Lz5k3Gjx/PZ599pnZ4hUBz/24pWeu4b5X8pDv8wi2GLd5PXEo6/u6OfD+kMVW8iv/SPiGEKKpUSboXL16sxmULlbOlppfr/MBeB6lxcP0ElKtrgeiEEEKUZrt27WLLli2ULVsWKysrrKyseOKJJ5g2bRqvvfYaBw8eVDvEwmce6T4HJpNSXK0E2nzsGqOXH0CfYSTY343vXmiEh7NW7bCEEKJEK/RCalFRUblqf/ny5QKKpGA5Wmp6uUajrOsGmWIuhBDCIgwGg7loqaenJ1euXAGgYsWKnDx5Us3Q1FOmEmisID0JEq+pHU2B+HnfRUYuC0efYaRdDS+Wv9RMEm4hhCgEhZ50N27cmOHDh7N3796HtomLi2PBggUEBQUV23XdzpnTy5Pzm3SDVDAXQghhUUFBQRw6dAiApk2bMmPGDHbu3MlHH31EYGCgytGpxMYO3PyVxzfPqBuLhZlMJr7ceoYJvx3CYDTxTMMKfDuoIQ52UqFcCCEKQ6En3cePH0en09GlSxe8vb3p1q0bw4cPZ8yYMQwcOJAGDRrg5eXF4sWL+fTTTxkzZkxhh2gRd7YMs0AxGimmJoQQwoL+7//+D6PRCMCUKVO4cOECrVq14q+//uKLL76w+PUyMjL4v//7PwICAnBwcCAwMJCPPvrIHAMoieHkyZPx9fXFwcGBkJAQjh49mu08er2eMWPG4OnpiZOTEz179uTSpUu5jsfEQ6aOl8BiakajiQ//OManG5QZDK+EVObTZ+pia63arrFCCFHqFPpPXHd3dz777DOuXLnC119/TbVq1bhx4wanT58GYMCAAYSHh7Nz5066du1a2OFZjFPmt8fJaZYY6c4sphZ9SFlnJoQQQuRD586d6dOnDwCBgYEcO3aMGzduEBMTQ7t27Sx+venTp/PNN98wb948jh8/zowZM/j000+ZO3euuc2MGTOYNWsW8+bNY9++ffj4+NCxY0cSEhLMbcaOHcvq1atZuXIlO3bsIDExke7du+ep2voDe9OsYmolZKQ7LcPI6z9FsDjsPADvda/F211qlPhitkIIUdSoUkgNwN7enj59+pg7/ZLG2V75o01ItUDSXbYGWNkqxdRiz4N7QP7PKYQQotRJTk7mrbfeYs2aNaSnp9OhQwe++OILPD09cXd3L7Dr7tq1i169etGtWzcAKlWqxIoVK9i/fz+gjHLPmTOHSZMmmX8vWLJkCd7e3ixfvpyRI0cSFxfHwoULWbp0KR06dABg2bJl+Pn5sXnzZjp37pz/QD2rKffXi/+69tR0A68sC2fryevYWmv47Nl69KpfXu2whBCiVJK5RQVE52ALQHxqev5PZqO9M9p9OTz/5xNCCFEqffDBByxevJhu3brRr18/Nm3axCuvvFLg133iiSf4559/OHXqFAD//fcfO3bs4MknnwQgMjKS6OhoOnXqZH6PVqulTZs2hIWFARAeHk56enq2Nr6+vgQFBZnb5NRD54x51VLuY47l6nxFTZI+g2GL97H15HXsba34bnBjSbiFEEJFqo10l3Su9krSHZdigaQboHwjuHJQSbrrPGOZcwohhChVVq1axcKFC+nXrx8AAwcOpGXLlhgMBqytC66o1ttvv01cXBw1atTA2toag8HAJ598wvPPPw9AdHQ0AN7e3tne5+3tzYULF8xt7OzsKFOmzH1tst5/L71ej16vNz+Pj49/dKDemUl33EVIjQd71xx/xqIiPjWdoYv2EX4hFmetDd8PaUyTgIKbxSCEEOLxZKS7gGSNdFss6a7QSLmXkW4hhBB5dPHiRVq1amV+3qRJE2xsbMxbhhWUn376iWXLlrF8+XIOHDjAkiVL+Oyzz1iyZEm2dveuNTaZTI9df/yoNtOmTUOn05lvfn5+jw7UoQy4+CqPY44/um0RdCspjf4LdhN+IRZXexuWvdRUEm4hhCgCJOkuIK5Z08tT0jFZovhZ+YbK/dX/wGChRF4IIUSpYjAYsLOzy3bMxsaGjAwL1B95hLfeeot33nmHfv36UadOHQYNGsQbb7zBtGnTAPDx8QG4b8Q6JibGPPrt4+NDWloasbGxD21zr4kTJxIXF2e+Xbx40fzaQ7vmrNHumKMPaVA0xSSk0m/+Lo5cjsfDyY6VI5pT389N7bCEEEIg08sLTNZIt9EEifoMXDKnm+eZe2Ww1ynF1K4dBd/6+Q9SCCFEqWIymRgyZAhardZ8LDU1lZdffhknJyfzsVWrVln0usnJyVhZZf+e39ra2rxlWEBAAD4+PmzatIng4GAA0tLSCA0NZfr06QA0bNgQW1tbNm3aRN++fQG4evUqR44cYcaMGQ+8rlarzfZZ73jE6LlXTTizGa4Vn3Xd0XGpPL9gN5E3kvB21fLjS82o4uWsdlhCCCEyFYmk+4033qBevXrUrVuX2rVrP6SDLF7sba2xs7EiLcNIfKoFkm4rK2W0++wWuLxfkm4hhBC5Nnjw4PuODRw4sMCv26NHDz755BP8/f2pXbs2Bw8eZNasWQwbNgxQppWPHTuWqVOnUrVqVapWrcrUqVNxdHSkf//+AOh0Ol588UXGjx+Ph4cH7u7uvPnmm9SpU8dczdwivGor99eKx0j3tfg7CXd5NweWD29KRQ+nx79RCCFEoSkSSXdISAiHDh3izz//5OjRo1hbW1O7dm3q1q1L3bp16d69u9oh5omrvS03EvXEJadT3s0h/yfMSrovhUPjl/J/PiGEEKXKokWLVLnu3Llzee+99xg1ahQxMTH4+voycuRI3n//fXObCRMmkJKSwqhRo4iNjaVp06Zs3LgRFxcXc5vZs2djY2ND3759SUlJoX379ixevNiyReDK1VPur/4HRgNYFVyBufyKuSvhrlDGgZUjmlGhjKPaYQkhhLhHkUi6e/XqRa9evczPU1JSOHLkCIcOHWLz5s3FNunWOdhwI1FvmW3DQKlgDlJMTQghRLHi4uLCnDlzmDNnzkPbaDQaJk+ezOTJkx/axt7enrlz5zJ37tx8xWN61PTystXB1hHSk+DGafCqka9rFZTrCXr6f7eHc9eVEe4VwyXhFkKIoqpIJN1ZMjIy2L59O/b29tSqVYvGjRurHVK+uBZUBfMbJyElVqmyKoQQQuRQ1nTux/n+++8LOJIi4GGF1KysldHuqF1w5UCRTLpvJOoZ8N1uzsQkUk5nz4rhzfBzl4RbCCGKqiKVdD/zzDN4eHiwZs0aXF1dMRqN1KlTh3Xr1qkdWp7o7qpgbhFOnuBRBW6egag9UL2LZc4rhBCiVFi8eDEVK1YkODjYMjtrlFS+DZSk+/IBqN9f7WiyuZWUxsDv9nDqWiLerlpWDG+Gv4ck3EIIUZQVqaQ7MjKSNWvWEB4eTkREBJ9//vl9W4MUJ672Fh7pBqjYQkm6L+yUpFsIIUSuvPzyy6xcuZJz584xbNgwBg4ciLu77ON8n/INlPsrB9SN4x7xqekMWriHE9EJeLkoCXclTymaJoQQRV2R2qfbwUEpNmZnZ0daWhqvv/46oaGhKkf1cJpHLAmDAhjpBqjYUrm/EGa5cwohhCgVvvrqK65evcrbb7/NH3/8gZ+fH3379mXDhg2lauT7sZ/UV9m2jOjDkJ5a0OHkSGq6gZcW7+folXg8ne1YPrwZgWVlWzAhhCgOilTS/eqrr3Lr1i369OnD6NGjWbRoEefPn1c7rDxzdVAmEsSnZljupBVbKPdXI0CfaLnzCiGEKBW0Wi3PP/88mzZt4tixY9SuXZtRo0ZRsWJFEhOlXwHAPRCcvcGQpmzTqbJ0g5FRPx5g7/lbuGhtWDKsiezDLYQQxUiRSrqzprm98847tGzZkmPHjrF27Vq1w8oznaULqQG4+YPOD4wZcGmv5c4rhBCi1NFoNGg0GkwmE0ajUe1wCpXxUSP7Gs2dL7lVnllmNJp485f/2HIiBntbK74f2pjavjpVYxJCCJE7RSLp/vzzzwE4efKkudMfMmQIn376KXXr1lUztHzJWtNt0enlUGR+ERBCCFH86PV6VqxYQceOHalevTqHDx9m3rx5REVF4excekZPHzvFPGs51/kdBR3KQ5lMJj74/ShrI65gY6Xh6wENaVxJ1uALIURxUyQKqQUFBQHwxhtvcObMGZydnalduzZBQUEEBQXRrVs3lSPMmwIZ6QYl6T70kyTdQgghcmXUqFGsXLkSf39/hg4dysqVK/Hw8FA7rEJn4jEj3XAn6b64FzLSwMauwOO61+xNp1i6+wIaDczsW4+2NbwKPQYhhBD5VySS7vbt2wPw3Xff4e3tTVJSEkeOHOHIkSNs2rSp2CbdWft0x6daOunO/EXg0n5ITwFbB8ueXwghRIn0zTff4O/vT0BAAKGhoQ8tVrpq1apCjqzwPTbpLlsDHNwh5ZZSR8WvSaHElWX5nii+2HIGgI96BdGrfvlCvb4QQgjLKRJJd5bOnTuzfft23NzcaNGiBTVr1uTs2bNqh5VnWSPdt5MtnHR7VAEXX0i4ouwjWrmdZc8vhBCiRHrhhRfQPG7rjVLiscXarayUmWUn1sH57YWadG89EcN7a48A8Fr7qgxqVrHQri2EEMLyilTSbWNjg5ubm/m5Tqdj5MiRhIeHqxdUPrg7KVPRYpPTMJlMlvtFR6NREu2IZXB2iyTdQgghcmTx4sVqh1AkmNBgMOZgi7SANkrSfWYLtBpf8IEBRy7HMXr5AQxGE083qMAbHaoWynWFEEIUnCJRSC1LhQoV2Llzp/m5lZUVaWlpKkaUP1lJd7rBZNltwwAqt1Xuz2617HmFEEKIUuCx08sBqnZU7qN2QWpcwQYEXIpNZujifSSnGXiiiifT+tSRmQlCCFECFKmR7i+//JJu3brRvHlzGjduzOHDh/H391c7rDyzt7XGyc6apDQDt5LSzNPNLSKwLaCBa0cgIRpcfCx3biGEECVOVFRUrvrUy5cvU758yV1HnJOcG/cA8KgKN08rX3LX7l1g8cQlpzNk0T6uJ+ip4ePCVwMbYGdTpMZGhBBC5FGR+mnu7+/PgQMH6NChA1FRUVSrVo2ffvpJ7bDyxcNZC8DNRL1lT+zkAeXqKY/PbbPsuYUQQpQ4jRs3Zvjw4ezdu/ehbeLi4liwYAFBQUElvphajka6Aap1Vu5PbyywWNINRkYtD+dMTCI+rvYsGtrYvO2oEEKI4q9IjXRnZGSwY8cO/Pz86Ny5MzqdTu2Q8s3dyY6oW8ncTCqAafKV2ykVVc9ugXr9LH9+IYQQJcbx48eZOnUqXbp0wdbWlkaNGuHr64u9vT2xsbEcO3aMo0eP0qhRIz799FO6du2qdsgFKidLugFlivmueXB6ExiNSoE1C/vkz+PsPHMTRztrvh/SmHI62ZVECCFKkiI10v3MM8+wbNkyunfvTv369alYsSLdu3dXO6x88chc132roJJuUJJuo9Hy5xdCCFFiuLu789lnn3HlyhW+/vprqlWrxo0bNzh9+jQAAwYMIDw8nJ07d5b4hBtyMdLt3wLsXCApBi7vt3gcK/ZGsTjsPACz+tanlq+rxa8hhBBCXUVqpDsyMpI1a9YQHh5OREQEn3/+ObGxsWqHlS/uBZl0+zXN/EXgOlw5ABUaWf4aQgghShR7e3v69OlDnz591A5FVaacJt02dlC9Kxz+GY6ssujWYXsjb/F+5tZg4ztWo0uQ1GcRQoiSqEiNdDs4KNOp7OzsSEtL4/XXXyc0NFTlqPLH3VlJum8mFkDSbWMHVTsoj0/8afnzCyGEECWQsmVYLt4QlPkFxbE1FptZdik2mVeWhZNuMNGtTjlebVfFIucVQghR9BSJpHv69OkAvPrqq9y6dYs+ffowevRoFi1axPnz59UNLp/uTC+3cCG1LNW7Kfcn/y6Y8wshhBAlUI6nl4OynEurg4SryvZh+ZSSZmDED+HcTEqjtq8rnz5bV7YGE0KIEqxIJN3Lli0D4LvvvsPd3Z133nmHli1bcuzYMdauXatydPnj7pRZvbwgppeDMtKtsYbrx+HWuYK5hhBCCFHC5CrpttFCjcwvuY/8lq/rmkwm3lt7hGNX4/FwsmP+C41wtCtSq/2EEEJYWJFIuhs1asSTTz7JuXPnWLt2LWfPnmXIkCF8+umn1K1bV+3w8sWjIKeXAziUgUotlccn/iqYawghhChR1q1bh7GUF+DMTc4NQJ1nlPsjv0J6Sp6v+9O+i/wafgkrDcztH0x5N6lULoQQJV2RSLoXLVrEtGnTMBqNhIaG8vLLL1O5cmWaNGnC0KFD1Q4vXwq0enmW6k8q9zLFXAghRA706tWLGzduqB2GakxocjfSDRAYAjo/SI2D4+vydN0jl+N4//ejAIzvVJ0WlT3zdB4hhBDFS5GZz1SnTh3effddRo0aZT5248YNDh8+rGJU+Xd39XKTyVQwa7aqPwnr34GoMEiMAWcvy19DCCFEiZHjyt0lWI736c5iZQ3BA2HbNDiwBOo+m6u3xyWn88qP4aRlGGlfw4tX2lTOZQBCCCGKqyIx0g1gZWXFzz//nO2Yp6cnbdu2VSkiy/DIXNOdZjCSoM8omIuUqQi+DcBkhGPFew28EEKIwhEREUFSUlK2Y5cvX8bVtXTsE53rkW6A+gMADZzfDjfP5vhtJpOJ8b/8x8VbKfi5OzCrb32srKRwmhBClBZFJukGaNq0KfPmzVM7DItysLPGRatMKLieUEAVzOHOdiZHVhXcNYQQQpQYXbt2RafTUaVKFfr06cPEiRN56623cHNzUzu0QmHM9VA34OYHVTsqj/d8k+O3/bDrApuPX8PO2oqvBzRE52ib+2sLIYQotopU0n348GFmzJhBpUqV6N+/P9OmTWPdutytm5o2bRqNGzfGxcUFLy8vevfuzcmTJ7O1MZlMTJ48GV9fXxwcHAgJCeHo0aOW/CjZeLkqo93X4lIL7BrUfkq5j9oF8VcK7jpCCCFKhFOnTrF9+3YmTJiAr68vhw8f5vbt28yfP79Arnf58mUGDhyIh4cHjo6O1K9fn/DwcPPrOemb9Xo9Y8aMwdPTEycnJ3r27MmlS5dyHYuJPEwvz9IscxncwWWQfOuxzU9Ex/PJX8cBePfJGgSV1+XxwkIIIYqrIpV0//XXX0RFRXHo0CFeffVVPDw82LRpU67OERoayujRo9m9ezebNm0iIyODTp06ZZtCN2PGDGbNmsW8efPYt28fPj4+dOzYkYSEBEt/JAB8dPYAXEsowKRbVwH8mgEmOLqm4K4jhBCiRHBxcaF58+aMGDGCefPmsW7dOv766y+6dOli8WvFxsbSsmVLbG1t+fvvvzl27BgzZ87MNqqek7557NixrF69mpUrV7Jjxw4SExPp3r07BoMh1zHlaXo5KAXVvIMgPRnCFz2yaWq6gddWHCQtw0jb6mUZ3KJS3q4phBCiWCsyhdQAbt26xXfffYednR1jx46lRYsWuT7H+vXrsz1ftGgRXl5ehIeH07p1a0wmE3PmzGHSpEn06aNMyV6yZAne3t4sX76ckSNHWuSz3M3bJTPpji/A6eUAQU/Dxd3KHqLNRz2+vRBCiFKpR48e2NoW3hTn6dOn4+fnx6JFd5LUSpUqmR/npG+Oi4tj4cKFLF26lA4dOgCwbNky/Pz82Lx5M507d85VTHlOujUaaP4qrHkZ9sxXHttoH9h06l/HOXUtEU9nLZ8+W69giqkKIYQo8orUSPczzzyDk5MTCxYsAODIkSNMmjQpX+eMi4sDwN3dHYDIyEiio6Pp1KmTuY1Wq6VNmzaEhYU98Bx6vZ74+Phst9zwclWS7uiCnF4OUKsXaKzh8n64cbpgryWEEKLYWrt2LWXKlCm06/3+++80atSIZ599Fi8vL4KDg819PeSsbw4PDyc9PT1bG19fX4KCgnLdf5vQ5H6f7rsFPQ0u5SAxWplm/gCbj13jh10XAJjZtx6ezg9OzIUQQpR8RSrpTkhIYPTo0djZKdtsBQUF8ddff+X5fCaTiXHjxvHEE08QFBQEQHR0NADe3t7Z2np7e5tfu9e0adPQ6XTmm5+fX67i8M5c0x1TkNPLAVy87xR4ecgvAUIIIURhO3fuHF9//TVVq1Zlw4YNvPzyy7z22mv88MMPQM765ujoaOzs7O77siCv/XeeR7oBbOyg5Vjl8b+fQnpKtpdvJup5+7dDALz0RABtqpXN+7WEEEIUe0Uq6fby8uLKlSvZpl+lpuY9UX311Vc5dOgQK1asuO+1e6d4PWoP7YkTJxIXF2e+Xbx4MVdx+LgW0vRyUPYQBfhvBRgKaIsyIYQQIheMRiMNGjRg6tSpBAcHM3LkSIYPH87XX3+drV1u+uactHlU/53nQmpZGg0F1wqQcBX2f5/tpffXHuVmUho1fFx4q0v1fF5ICCFEcVekku7Zs2czePBgYmJi+Omnnxg6dCg1atTI07nGjBnD77//ztatW6lQoYL5uI+PD8B934rHxMTc9w17Fq1Wi6ura7ZbbhTa9HKAqp3B0QMSr8GZzQV/PSGEEOIxypUrR61atbIdq1mzJlFRUUDO+mYfHx/S0tKIjY19aJt7Par/ztdINyjruNtMUB5vnwV6peDbn4eu8ufhq1hbafjs2Xpobazzdx0hhBDFXpFKuitWrMiff/7JrFmzOHLkCI0aNeLHH3/M1TlMJhOvvvoqq1atYsuWLQQEBGR7PSAgAB8fn2xV0dPS0ggNDc1T4bacuHt6uSm/nfzj2NhB3X7K44NLC/ZaQgghRA60bNnyvu07T506RcWKFYGc9c0NGzbE1tY2W5urV69y5MiRXPffJjR526f7XvX7g3tlSL4B/37GzUQ97609AsDokMqyPZgQQgigiFUvb9GiBeHh4fTt25e+ffsCSqdcrVq1HJ9j9OjRLF++nLVr1+Li4mL+1lyn0+Hg4IBGo2Hs2LFMnTqVqlWrUrVqVaZOnYqjoyP9+/cvkM/llVm9PN1gIjY5HXcnuwK5jlnwQNj9JZxaD4nXwVnWkgkhhFDPG2+8QYsWLZg6dSp9+/Zl7969zJ8/37wneE76Zp1Ox4svvsj48ePx8PDA3d2dN998kzp16pirmeeGJXJurG2h8yewoh/s+pJ5lxpwK8mGGj4uvNquqgUuIIQQoiQoEkn3unXrOHHiBElJSVy5cgVfX1/za88++yz//fdfjs+VtT4sJCQk2/FFixYxZMgQACZMmEBKSgqjRo0iNjaWpk2bsnHjRlxcXPL9WR7EzsYKDyc7bialcS0+teCTbu9a4NsArhxQRrtbjSvY6wkhhBCP0LhxY1avXs3EiRP56KOPCAgIYM6cOQwYMMDcJid98+zZs7GxsaFv376kpKTQvn17Fi9ejLV17qdw53t6eZZqXaBqJzi9kTbnZvKD1dt89mw97GyK1GRCIYQQKtKYCny+8+NFRkby999/83//938EBQVx6dIlKlSoQLly5Th9+jQHDhxQO8Rs4uPj0el07N+6hoYhvXL0nic/386xq/EsHNyI9jUfvPbMog7+CGtHKUVeXv8PrIvE9ytCCFFqZPUVcXFxua4FIgpG1t/Ja+NfpeuQd+kSVM4i5028cgK7+S2xI4N11abQvf8Yi5xXCCFE4SuI/rtIZGIBAQGMGjWKoKAgWrduDcDly5eJjIw0b/VV3FUo48Cxq/Fcvp3y+MaWEPQ0bHoP4i/ByT+VPbyFEEIIAVhoenmmz/Yb0KX34g3b3+h2cSYk9FW28RRCCCEoYoXUvLy8+PTTT1mwYAEXLlygbt26uLm5qR2WRVQo4wjApdhCSrpt7aHhUOXxnm8L55pCCCFEMWGp6eWHL8Xxw67zfGnoRWKZ2mhSYmHdWFB/IqEQQogiokgl3V27diUtLY3bt2/z7bffEhISQvXqJWN/ywplHAC4FJtceBdt/CJorOHCTog+XHjXFUIIIYo4S4x0G4wm3l19GKMJutX3x7nfArCyhZN/QUTudl8RQghRchWJ6eVZfHx8mDRpUrZjBoNBpWgs607SXUgj3QCuvlCrJxxdDbu/ht5fFd61hRBCiCLKUluGLdt9gcOX43Cxt2FSt5rgYg9tJ8I/H8Gfb4JvMHjXtkDEQgghirMiNdLduXNnli7Nvrd0XiqSFkWFPr08S7PRyv2hn+D2xcK9thBCCFFE5Xd6eWxSGrM2nQJgQufq5u1BaTkWKreDjBT4aRCkxuczUiGEEMVdkUq69+7dy6RJk6hSpQr9+/dn2rRprFu3Tu2wLKJ85kj3raQ0kvQZhXdhv8ZQqRUYMyBsbuFdVwghhCjC8jvQ/fk/p4lLSaeGjwv9m1a884KVNfT5Ttk95NZZWDsajMb8XUwIIUSxVqSS7r/++ouoqCgOHDjAq6++ioeHB5s3b1Y7LIvQOdjiaq/M5i+0CuZZWr+p3B9YAonXC/faQgghRBGUn5HuMzEJLN19AYD3utfC2kqTvYGTB/RdoqzvPv47bPkoP6EKIYQo5opE0v3ee+/x888/c+zYMQwGA66urrRo0YIRI0YwZ84ctcOzGD93ZYr5xVuFWEwNIKANlG8IGamw+8vCvbYQQghRBJnykXR/8udxDEYTHWp607KK54MbVWgEveYpj3fMhv2L8nw9IYQQxVuRSLrLlCnD+vXrGTJkCGXLlqVevXoMHDiQ//3vfyVmejncKaZW6Em3RgOtMke7934HybcK9/pCCCFEEfOo6eUZxgzWR65n+fHlXE68nO210FPX2XryOrbWGqV42qPU6wch7yqP/xwPx//IZ9RCCCGKI1WT7nXr1mE0Ghk3bhzff/89e/fu5datW6xevZq+fftiNBpZuXKlmiFaVEUPJwDO3yzkpBugWhfwqQNpCco37kIIIUQp9rDp5Qajgde2vMZb/77FtL3T6L66OytPrMx8zcTUP48DMLh5JQI8nR5/oTYToP5AMBngl6Fwcr3FPoMQQojiQdUtw3r16sXVq1fx8vLKdjwwMJDAwEB69uypUmQFIzCzcz53I6nwL25lBe0/gB+fgb3zoenLoCtf+HEIIYQQKnvUlmHrzq1j++XtONg4UMWtCodvHOaTPZ8A4JDaipPXEnC1t2FMu6o5u5hGAz0+h/RkOLoKfh4Ez6+AKh0s9XGEEEIUcaqOdOdnPVVxlPWNeOSNRHUCqNIB/Fsoa7tDp6sTgxBCCFEEPCjnNplM/HDsBwBG1B3Bj0/+yPA6wwGYumcq07f9DsDINpXROdrm/GLWNtBnPtTsAYY0WNFfppoLIUQpovqa7oiICJKSso/8Xr58GVdXV5UiyjkTmsc3uktgWWdA2as7Nd1QECE9mkYDHT5QHh9cBjdOF34MQgghRBHwoOnlZ2+f5VTsKbTWWp6t9iwajYYxwWPoU7UP/teMtDk+nwr2sQxpUSn3F7S2hae/hxrdwaCHn1+A8MX5/hxCCCGKPlWnlwN07doVjUZDpUqVqFu3LtWrV+fChQu4ubmpHZrFeTrb4aK1IUGfQdStZKp5uxR+EP7NoFpXOPU3bJgEA34u/BiEEEIIlT1opHvftX0ABHsFo9PqANBoNIyt+RrHx/xKmaRUmgd8gYNt/7xd1MYOnl0Cf74BB36AP16H+KvQ5m1lGZgQQogSSfWf8KdOnWL79u1MmDABX19fDh8+zO3bt5k/f77aoVmcRqMhsGzmuu7rKk0xB+j0MVjZwOkNcGqjenEIIYQQKjCZNA9c4rY/ej8AjX0aZzu+a84PlEkyAlA1MoEtX7yd94tb20CPL+7sKhL6P/jlBdCr+HuBEEKIAqX6SLeLiwuVK1emefPmaodSKAI8nfjvUpw6xdSyeFaFZq9A2FxY/w4EhijfvgshhBClxIOml0fERADQ0Luh+Vhqcgpuv/8EwK1y7rhfvYXHwnVEdemLf43G950jRzQaaP8elKkI68Yp67tvnoV+P4J7YN7OKYQQoshSdaS7R48e2NrmohBJCZC1rvtsjIpJN0DrCeDkBbfOwu6v1I1FCCGEKGT3Ti9PTEskJiUGgGplqpmP/zvvB8qkxBHroKP+HxuIquyCfTocf/NVDIaM/AXR4AUY+hc4e0PMMfg2BI6uzt85hRBCFDmqJt1r166lTJkyaoZQ6Kp5K0n3qWsJ6gZi7wodP1Qeh06HW5HqxiOEEEIUogyDMdvzCwkXAPCw98DFTqm5kpGegd2vywG43f1ZnJydqfHZPFJtwf9MPP/MfjP/gfg1gRHboEJj0MfBL0Ng7WhIU/nLeSGEEBaj+pru0qaGj1KV/dS1BAwP2SO00NTtB5VaKXuHrhsLpWwLNyGEEKWTCUgzZO/zzsedB6Cia0Xzse0Lf8Y7PoYEO0dajVO2DqtYswk3hnYFwGvxBiKPhOU/IFdfGPp35jpvjbLDyDet4IIFzi2EEEJ1knQXMn93RxxsrdFnGDl/U+Vvsa2soMfnYGMP57ZBxI/qxiOEEEIUkrSMe0a645WR7gBdAABGoxHDssUAXGvfE5cyd7Yybff6DC5U06HNgDNvvkZGelr+A7K2VdZ5D/4DXHyV5V+LuiprvlPj839+IYQQqpGku5BZWWmo5qNMWzsZrfIUcwCPytB2kvJ4w7uQEK1uPEIIIUQhuDfpPh9/HgB/V38A9vzyN+VvXCTFxo4Wb76Sra21tQ11Zn1DshYqnE9i8/9etVxgAa1g1C5lvTfA/oXwZVM4tlZmpAkhRDElSbcKamTuz33iahH55rrZKPANhtQ4+H2MdOpCCCFKNBMa0u9Z030t6RoAvk6+ANxcshSAi8064lHe675zlK9Sn7hXnlHes3I7J/dZcAtOBzfoOVcZ9S4TAAlX4OcXYEkPuHrIctcRQghRKCTpVkGNcplJd1EY6QZlz9DeX4O1Fk5vhL0lb490IYQQ4m73jnTfSr0FgIeDB2cPHqfyuf8woqHOqy899BwhIz4kso4ntga4POFt0lKSLRtkQGtl1Lv1BGUp2Pnt8G1r+P01SLhm2WsJIYQoMJJ0qyCrmNrRK0VkpBvAqyZ0/kR5vPE9iD6ibjxCCCFEAUq7Z6T7RsoNADwdPDny5UIAIivXJbB+jYeew8rKikazviPRQUO5q6lsmjzc8oHaOkC7SfDqPqjdBzDBgSXweT3YMAkSr1v+mkIIISxKkm4VBJV3RaOBy7dTuJ6gVzucOxq/BNW6gEEPv70IaRb+xl4IIYQApk2bhkajYezYseZjJpOJyZMn4+vri4ODAyEhIRw9ejTb+/R6PWPGjMHT0xMnJyd69uzJpUuX8hTD3Ul3akYqiemJAGhTbKmwezMAHoMGPfY8Xn7VSR03BIBKaw+wb933eYrnsdz84dlFMGyDsr1YRgrsmgef14VNH0DyrYK5rhBCiHyTpFsFLva2VPVS9uv+7+JtdYO5m0YDvb4EZ2+4fgLWvSHru4UQQljUvn37mD9/PnXr1s12fMaMGcyaNYt58+axb98+fHx86NixIwkJd5ZijR07ltWrV7Ny5Up27NhBYmIi3bt3x2Aw5CoGE5ps08tvpt4EwM7Kjv8WrMIxQ0+0zpsmz3TJ0flaDZrA2daBWAGGyTOJuXgyV/Hkin8zeHETDPhVqceSngw758Ds2vDnm3DrXMFdWwghRJ5I0q2SehXcAPjv0m1V47iPkyc8vRA01nBopazvFkIIYTGJiYkMGDCABQsWUKZMGfNxk8nEnDlzmDRpEn369CEoKIglS5aQnJzM8uXLAYiLi2PhwoXMnDmTDh06EBwczLJlyzh8+DCbN2/OdSx3F1IzTy2390D711oAUrv3wdrGOsfnaztzKdE+WnSJRg6MHmyZbcQeRqOBqh1h+FZ4fiWUq6ck3/sWwBcN4KeBELWn4K4vhBAiVyTpVkk9PzcAIorSSHeWgFbQ8SPl8YZ34UKYuvEIIYQoEUaPHk23bt3o0KFDtuORkZFER0fTqVMn8zGtVkubNm0IC1P6oPDwcNLT07O18fX1JSgoyNwmN7KNdKcoI91VrthRLvYKeisbmo0cmKvzObm44zd7DnpbqHgqjo0fjch1TLmm0UD1rjAiFF74Hap0BExw/A/4vhN82wbCF4O+iBRuFUKIUkqSbpXUz0y6/7t4G6OxCE7hbj4agp4GY4ayTUnsBbUjEkIIUYytXLmSAwcOMG3atPtei46OBsDb2zvbcW9vb/Nr0dHR2NnZZRshv7fNvfR6PfHx8dluACayJ91ZI931dyuvRwU1o4yXe64/Y5XgEGLH9AWg4i972PXL3FyfI080GghsAwN/hVF7IHgQWNvB1Qj443WYWUO5vxJROPEIIYTIRpJulVT3ccHOxor41AzO30xSO5z7aTTKHqHedSDpOvz4DKTEqh2VEEKIYujixYu8/vrrLFu2DHt7+4e202g02Z6bTKb7jt3rUW2mTZuGTqcz3/z8/Myv3T29PDY1Frt0Ew2OKMXIyvXv+9jP9DBtR3zI2bZVsAK0H3/F6fB/8nyuPPGqAb3mwbgT0GkKeFSBtERlxHt+G/j6Cdj5OcRdLty4hBCiFJOkWyW21lYE+Spbhx2Muq1uMA9j5wQDfgbX8nDjFKwcABlFqNq6EEKIYiE8PJyYmBgaNmyIjY0NNjY2hIaG8sUXX2BjY2Me4b53xDomJsb8mo+PD2lpacTGxj60zb0mTpxIXFyc+Xbx4kXza/q7RroT0hJodsKEY7qB6y6eNOzRLl+ft9Ocn4iq4opDGkSPGcutaBVmizl5QIsx8Op+GLwOgp5RRr+vHYZN7yuF1xZ3V5Jx+VJdCCEKlCTdKmpcSZm6tvvcTZUjeQRXXxjwC2hd4cJOWPMKGI2Pf58QQgiRqX379hw+fJiIiAjzrVGjRgwYMICIiAgCAwPx8fFh06ZN5vekpaURGhpKixYtAGjYsCG2trbZ2ly9epUjR46Y29xLq9Xi6uqa7WY+/10j3YnpibQ9pDxPaNsFK+ucF1B7EDutI40WrORmGRs8b2Ww76W+6FMS83XOPNNolFotzyyE8Seh+2zwbwGY4Px2Zdr5p1Vh2dOw/3tIuKZOnEIIUYJJ0q2i5pU9ANhVlJNuAO/a8NxSsLKBI7/BX+NlKzEhhBA55uLiQlBQULabk5MTHh4eBAUFmffsnjp1KqtXr+bIkSMMGTIER0dH+vfvD4BOp+PFF19k/Pjx/PPPPxw8eJCBAwdSp06d+wqzPZ6G1LQ724ylRF6mdhQYNNBgeO4KqD2MR7kAvOfNIcUO/M/Es/mlngVb0TwnHN2h0TAY9jeMPQwdJoNXbTCmw5nNylahM6vDwk7KFPSbZ9WNVwghSghJulXUuJI7NlYaLsWmcPFWstrhPFpgCDz1LaBRvglf/44k3kIIISxmwoQJjB07llGjRtGoUSMuX77Mxo0bcXFxMbeZPXs2vXv3pm/fvrRs2RJHR0f++OMPrPMwMh2fmmF+7LlT2dv6TIAf5apWzP+HyVS1YXsypowjwwoCw6+y/vVnMBaV2WJu/vDEGzAqDEbvg/YfQPmGgAku7lGmoM9tAJ/XV/b/Prke9CqN1gshRDGnMZkkc8qt+Ph4dDod+7aupVFIz3yd6+mvwwi/EMuMp+vSt7Hf49+gtoPLYO1o5XGL15StxR5T5EYIIUqjrL4iLi4u27RmoZ6sv5OR48ax3rYdZz7pipVGw489GtHobDJH+nbl2Y9mWfy6//4wDY+pP2AFRD7dhCc/WWLxa1hM3GU4+RecWAfndyi7mGSxsgX/ZlClA1RuC95BYJW/qfhCCFHUFET/LSPdKmuROcU87OwNlSPJoeCB0C3zF5KwL2Dj/8mItxBCiGIpITWDXedu4nddKRJaoWndArlO6xcmcmXEkwAE/LaXvz8aXiDXsQhdeWgyHF5YC2+fh34roNGL4FZRmYZ+fjts/gC+bQ0zAmB5PwibC5cPgCHjsacXQojSyEbtAEq75oEezN1yhrCzN3O0NUqR0PhF5ZvvvyfArnmQGgc9Ppdvu4UQQhQTSl8bl5LOb+GXeF6vTPl29SlXYFfsOG4mf9+OpdLPu6i0fAd/Zwyj60ffF9j1LELrAjWeVG4mE9w6p6z9PvOPUlw1NQ5O/a3cAOxclJHwSi3BrymUqw92jqp+BCGEKAok6VZZg4plcLC1JiZBz9Er8QSV16kdUs40HQm2jvDHa3BwKejjoc8CsNGqHZkQQgiRI1fiUvj78FWGpSkztpx0ZQv0el0/+p6/rV6i0sqdVPp5F38ZBtPl40VYWRWDiYcaDXhUVm5NRyqj2tH/wfmdSgJ+YRfo4+DMJuUGoLEGnyAo3wgqNFZuHpVlWZoQotSRpFtl9rbWtKrqycZj19h8/FrxSboBGgwCe1f49UU4thaSb0HfH5TqqEIIIUQR9/2O8xj0KVhnrpJy0nkW+DW7Tv6O9bYvU3FpKAG/7eWvxGfpOusnrK2L2a9k1jZK4bXyDaHla2A0wLUjShIeFQaX9kPCVbj6n3Lbv1B5n70bVGgEvsHgUxfK1VWmrksiLoQowYrZT/iSqUMtb3PSPbZDNbXDyZ1avWCAC/z0grLOa2FH6P+z8k22EEIIUQTZ2Cgjy5uPX0NniDcfd3XzKpTrd5n0DRtsxuC/aDOVNxxj/aCOtPtuLQ6OxbjgnpU1lKun3JqPUo7FXYZL+5Tb5XC4chBSb2dOUd985732OiUBz0rCfeqCZzUlsRdCiBJAfpoVAe1qeKHRwJHL8VyNS6GczkHtkHKncjt4cQMsfw5unoHv2sNzy6DSE2pHJoQQQtzHx9UeEpTHHtZJAKTagtbWvtBi6Pz2XLZ5fozHzOUEHohm+zMdaLz4V8p4+RdaDAVOV1651e6tPDekw7WjShJ+9T+IPgQxx5W14ee3K7csNvZQtjqUrQleNe7c6/yhOEzHF0KIu0jSXQR4OmsJ9nPjQNRtNh+7xqDmldQOKfe8a8NL/8DK/nB5P/zQCzp+DM1ekSljQgghipR6FXRoT1uhzzDSu6Yz/AF6beEnciEvvkd4OX/SJv4Pv3MJHOndDe8vZlOtUYdCj6VQWNuCb33lliUjDW6chKuHlCT86iGIPgxpCXempt/N1gnKVsuejJetBjo/KegqhCiySlzS/e+///Lpp58SHh7O1atXWb16Nb179za/bjKZ+PDDD5k/fz6xsbE0bdqUL7/8ktq1a6sXNNCptg8Hom6z7tDV4pl0A7h4w5B1sPZVOPIrbJgIF3dDz3nK2m8hhBCiCCjjrGXtqy2JTUrH/ux6ANK06iRsDZ8czMmy5bj+2jg8b2WQNHQM/741hNYvvK1KPIXOxg586ig3BijHjEaIjVRGwa8fh5gTyuObpyE9SZmmfuVg9vNY24F7IHhUySz4VuXOzamsDAAIIVRV4pLupKQk6tWrx9ChQ3n66afve33GjBnMmjWLxYsXU61aNaZMmULHjh05efIkLi4uKkSs6FHPl//9fYI9kbe4cjsFX7diNsU8i60DPP2dslXIhneVAmvXjsKzizM7VCGEEEJdGjTU8FG+DN79Xxz2QLq9eqOk1Rt3wnPVH+wb+TwVT8VhP3Uxfx46QMdPFmGnLYVbbllZ3amUXrP7neOGDGXbsqxEPOv+1lkwpMH1E8rtXlrXO4l4mQAoU1Ep3lamIriWlxFyIUSBK3FJd9euXenatesDXzOZTMyZM4dJkybRp08fAJYsWYK3tzfLly9n5MiRhRlqNuXdHGgS4M7eyFv8/t8VXm5TjAuRaTTQdIRSmfSXIco67wXtoN3/QfNXpXMTQghRZGSkJANgsFW3b/IoF0D7X7exceJgAv88ROC6Q4QeakW1z7+mYs0mqsZWZFjbZE4tr6YUcs1iNEDcRbh5NvN25s7tdpSyremDRscBrGyUqel3J+JuFe8k544eMkouhMi3Epd0P0pkZCTR0dF06tTJfEyr1dKmTRvCwsIemnTr9Xr0er35eXx8/APb5Vfv+uXZG3mLNQcvF++kO4tfYxj5L/z+Kpz8Cza9D6c2QO+vlY5MCCGEUFlGqpJ0G+3U/5XI1s6ebjN/4t9603GauYQKUcncfG4w58b0o+3wD9QOr+iysoYylZRblfbZX0tPhdjzd5Lw2PNw+0Lm/UUwpitT2WMjH3xuWyelGJxredBVUG6umQXiXCso93ZOBfrxhBDFn/o9TCGKjo4GwNvbO9txb29vLly48ND3TZs2jQ8//LBAYwPoVqcck38/yonoBI5cjitee3Y/jJMH9FsOB5fC3+/AhZ3wdUvoPAWCX5AKpEIIIQqd6a7HhtRUAIx2tuoE8wCtX3ibS807cey1EfhFJuIwcyV/bt1G4xnf4lWhmG0tqjZbe6XgmleN+18zGpS9xGMv3JWMX7iTlCdcVdaQ3zil3B7G3u2eZDwzQXf2Bhcf5WbvJiPmQpRipSrpzqK554eeyWS679jdJk6cyLhx48zP4+Pj8fPzs3hcOkdbOgf58Md/V1i2+wL/e7quxa+hCo0GGrygbCG2+mW4uAf+eB3+Wwk9Ple2BBFCCCFUYEhNAcBUBEa671ahajDea7ezacor+P+ym8AD0UR1783RV56hzfDJWMmX1vlnZX1n9LpSy/tfT09Vpq3HXYL4y8q+4/GXMu8zn6clKHuPp96Ga0cefi1rrVJw1tnnTiJ+d1KeddzBXQYkhCiBilYPU8B8fHwAZcS7XLly5uMxMTH3jX7fTavVotVqCzw+gEHNKvLHf1dYG3GFiU/WROdQdL55zzf3QBj6N+z5BrZ8AlG7lFHvJ96AVuOVb6OFEEKIQmTMSrq1dipHcj9bO3ue/GgRx7qs48p77+F7ORWn2b+w8a9N1Jw2h4q1mqodYslmaw+eVZXbw6TGPzghj78MCdeU0fLU22DQK+vLb0c9+ppWtkoy7uylVF13KgtOng9/bF2Cfk8UogQrVUl3QEAAPj4+bNq0ieDgYADS0tIIDQ1l+vTpKkenaFypDNW9XTh5LYHfwi8x7IkAtUOyLCtraD4aavaAv96CU+vh3xlwaCV0/Ahq9ZbpV0IIIQqNMXN6Odqim7zUatGdKn+1Y8uMsZT7aTsVT94m7tkh/NktmNbvzsHFzUvtEEsve1fl5lXz4W3SUyHxGiREQ2K0kownRivPE6IzX7sKyTeVNebxl5Rbjq7v9ujk3NEDHN2VEXRHd7ApnEEkIUR2JS7pTkxM5MyZM+bnkZGRRERE4O7ujr+/P2PHjmXq1KlUrVqVqlWrMnXqVBwdHenfv7+KUd+h0WgY2Lwi7605wuKw87zQvCI21iVwmpGbPzy/Eo7/rqz1vh2lVDr3bw5dpimVz4UQQogCoOHOl7um1MxCqXZFb6T7bnZaR7q8N5/Ip8I4OWk8FU/eJvD3gxzZ2pbU4c/Q+sX3sLYucb/WlQy29koB2ccVkc1Ig6SYO0l50g1Iuq7cJ9/1OOveZLgztf3m6RzG4pSZhLvdScQfel9GudfqZMq7EPlU4n4679+/n7Zt25qfZ63FHjx4MIsXL2bChAmkpKQwatQoYmNjadq0KRs3blR1j+57PdOgArM3nSLqVjJ/Hr5Kr/rl1Q6pYGg0ypYfVTpA2FzYMUeZcj6/LdR9DkLeVqakCyGEEAXElLk7iaaQlpHlV0BQCyqu3snuX+Zi+Pw7PG9lwKyfCV35O46jXqJJn1dkvXdxZWN3Z4354xiNSrKddP2u2437nyffhORbkBKrJOnpSRCXpKxVzymN9Z0k3aGM8thep4yy2+vuev6AY5KwCwGAxmQymR7fTNwtPj4enU7Hvq1raRTSs0CuMW/LaT7beIrq3i78/XorrKxKwZTruMvwz4dw6CfluZUN1B8Ard8CN8sXrhNCiIKU1VfExcXh6uqqdjiCO38nO379kpZPjwJg3cjuVA49S+SzTXny48XqBphL+pREtn42Dq9ftuOQphy7WMkJz9dfo0HXF9QNThQtRqOyX3nKLUiOzby/9Yj7WOU+PSmfF9aA1hUc7knK7d3uT97tXUHrctct87lMiReFrCD67xI30l1SDGpeiW9Cz3HyWgIbjkbTtU65x7+puNOVhz7zoenLsPUTOLMZDiyB/1ZAg8FKwTVdCR31F0IIoQ69kq1a2Re/Yp5aB2e6vDefmy9Fsvuzdyi/4RB+55PgjWls+OpLygx/iUbdX5SRb6GMNju4KTf3XLwvQ589GU+9DSm3ITUu85b52HzsrtfSkwET6OOUW15Z291JwO9OxrNu5mT93qQ969hdybvUDRIqkaS7iNI52DKsZSW+2HKGGRtO0qGWN7YlcW33g5RvAAN/g6jdsGUKnN8O+xZA+GKo2xdavPbg/TaFEEKInLjrF29NWjoAVvYOakWTbx7lAug28yeuvXacfTPeoeLWU/ifjocJs9j6+VfYDupLiwHjsbEt2uvWRRFkowXXcsottzL0SnV3cyJ++/7k/O7n+kRlNF6foNzSEpXzGNKUNe3JN/L3WaxsQesMdi5g53TXzVm51zpnf/7Ax/c8t5H/UyJnJOkuwka0qcyPe6KIvJHEir1RvNC8ktohFS7/ZjBkHZwLhdDpcGEnRPyo3Kp1gZZjlTbyraUQQog80qRlAGBdjJPuLN4Va9L9y7VcPhPBwS8+osLW4/heToX//cCu+ctJeaodTYe9g86jFMyeE+qz0YJzWeWWF0aDknhnJeGpWQn5XYm5+Rb/kOOZN0xKZfiUWOVmKVa29yfn2gck57YOYOuYeXNQjj/umLWt/I5bgkjSXYQ5a20Y26Eq7609yuebT9M7uDyu9kV3S5MCE9hGuV3aDzvnwPF1ylZjp9aDbwNo/BIE9VF+UAkhhBCPdecXWavMke6SkHRnKV+lPuW/WMWt6AvsnvcBZf/cqxRcW7iRcz9s5HLLKlR9cQzVG3dSO1QhHs7K+k6BtvwwGpW16VlJe3oSpCUpI+tpSUpin5Z0z+NHvKZPVPZdByWRz6ogb2ka64ck5453PX/Qscz297WzBxt75XUb+zs3WX5SKCTpLuL6NfFnUdh5zl1P4tP1J/m4d5DaIamnQiN4bhncOAO75kLECrhyANaOgg3vQvBAaDQMPCqrHakQQoiiTHN30q2MdNs6OKkVTYFx96nIk1MWk/jWTXYtnIrtms14x6RRedsZjNteZ2OAM7Z9utOk3xicXHKz0FeIYsTK6s4ab0sxpN+VjD8oOb/ruT4B0lMyb8l3bmnJ9xxLUdqbDMo1TIY7I/gFyVqbmZA73EnM707Os91r72r3sPb22dvYaO8/VykcwZeku4iztbZiSq8g+n+3h2V7LtA72JeGFUt5x+hZBXp8Du3eg4NLYd/3EBcFu+Ypt8rtlKrnNbrJ6LcQQohHsk5XfsG1cXBUOZKC46zzoOO4mRjHGonYsIyrSxdR8WA0fpGJMHMlJ+eu5EqTSvg+O4D6HftL4TUhHsfa9k5hOksymZSE/t5E/N7k3Jy05+JYRiqkp0JGChgz7lzToM8cuc9HsbvcsrHPnsBbazOfZx3X3nXsrtes7TLb2N1J9u87lvXeu45Z291/7kL+OSdJdzHQooonzzaswC/hl3j7t8P8+doTaG2s1Q5LfU6eSkXzFq8plc73fQenN8HZLcpNq4PavaF+f/BrWiq/VRNCCPFoNmlZSXfJG+m+l5WVlbKVWNcXiL5wjAPfzcDln3A8b2VQecd52PEJYWWmE9+uAdWeGUqV4BCVIxailNFoMhNFO8sn9HczZCjJd1YSnqFXkvSM1Hvu9dnbZbtPfUD71LuS+3teyxrBhzvtCjPRv5eVbfZE/O5EP93yKbIk3cXEpG412XoyhjMxiUz/+yTv96ildkhFh5U1VOus3G5FKoXW/lsJcReVLccOLAH3QKjbD2o/BWWrqR2xEEKUKtOmTWPVqlWcOHECBwcHWrRowfTp06levbq5jclk4sMPP2T+/PnExsbStGlTvvzyS2rXrm1uo9frefPNN1mxYgUpKSm0b9+er776igoVKuQ5Npt0IwB29iV3pPtBfCrW4smPF2P80Mh/m1dw+Zcf8d0TiUdsBh6/7SX9t71s87Yj+Yn6VO3zAlUbtlc7ZCGEpVjbgLWFp9w/jiE9+2j73Ql/1s2gz0zI7z12z/Fsx9KUe0Pa/e/NSL2rrR4w3YnHmA5p6ZCWcH+setP9x/JJYzKZLH/WEi5rw/R9W9fSKKRnoV13y4lrDFu8H4DvXmhEh1rehXbtYsdohAs7lHXfx9YqRTOyeNWCWr2VUfCy1R92BiGEyJesviIuLg5XV1e1w1FVly5d6NevH40bNyYjI4NJkyZx+PBhjh07hpOTMsI8ffp0PvnkExYvXky1atWYMmUK//77LydPnsTFRfnF8JVXXuGPP/5g8eLFeHh4MH78eG7dukV4eDjW1o+fAZb1d7Jj1Te0fGokAHsa1sI1yYTmhznUaNK54P4QioGkhFvs+3keKX9vpMKxm9gY77x2zcuOpJZ1qPjkM9Rs0R1raxm3EUIUI1lT9x+YoN99LI3427fQNX3eov23JN15oFbSDfDxumMs3BGJm6Mtf7z6BH7upeub+TxJS4Ljf8DhX+HcNuWbrSxlaygJeI0nwaeuTEEXQliMJN0Pd/36dby8vAgNDaV169aYTCZ8fX0ZO3Ysb7/9NqCMant7ezN9+nRGjhxJXFwcZcuWZenSpTz33HMAXLlyBT8/P/766y86d358wvygpPtA3Zo4pIH9rwsJCGpRcB+6mLl94zIHV80nZeMWKhy/ge1dM0PjnK24Uc8ft3btqdftBVzcvNQLVAghLKwg+m+plFHMTOhSnboVdNxOTuelJftJSE1//JtKOzsnqNcPBv4Kb52G3l9D1c7KWo7rJyD0f/Bta5hVC/54HU78pSTqQgghCkRcnLKOz91dKQwaGRlJdHQ0nTrd2cJKq9XSpk0bwsLCAAgPDyc9PT1bG19fX4KCgsxt7qXX64mPj892u5dtZj0hrWMhTrMsBtw8y9N2xIc8+et2Kv67hWvjn+NcsDcpdqBLNFJ553k8Pl7I+RZtWN+rOes/eZljYeswGDIef3IhhChlZG5QMaO1sebbQQ3pOW8nJ68l8PrKCOYPaoiNtXx/kiMOZZTCavX7Q8ptOPm3Mgp+biskXIHwxcrNWgsBrZTkvHJb8Kgio+BCCGEBJpOJcePG8cQTTxAUpGyDGR0dDYC3d/ZlU97e3ly4cMHcxs7OjjJlytzXJuv995o2bRoffvjhA15Rfp6np6Wap1DbO8pshIfReZQjZPj/t3fnYVLUd+LH31V9H3PfwzEMtxwqgiCg8VgkGo8YN4GoGyXRbFjUaFCJPslqTIxGXQkeEeMRfHbjATGSNdGo6G9FvBE5VJBBGO6BYe6Zvruqfn9Ud88JzAzTM9Mzn9fzfJ+6vlX9/TJ0ffpT56/gxxAO+Nn89oscXvMq6Z/uIK86Qsn2Oti+Fv5nLZ+5llA5Ph/79GmMmnMZIybOkqehCyEGPUm6U1BRhounr57GvD9+yP/7qpLbXtrCQ987BVWVpLBLXJlw6hVmiQTNe8DL3oCy16Fur/lE9K/fMuumFcPIc2Dk2VB6NqQX9WXLhRAiZd1www1s2bKF9957r90ypc3BTcMw2s1r61h17rjjDhYvXpyYbmhoYNiwYYnpUKApMe7wyJnuzrC73Jx+8Y/g4h8BsOvz9/j6n6uIrt9I4fYqvAED78bDsPFVQn98lY/SLdSML8Qx5VSGzT6f0VPOxWqz93EvhBCid0nSnaJOGZbJY1eexsI/b2D1xgM4bSr3fmfycX+ciKOwOWH0HLNc+IB52XnZ6/D127DvY/Ms+ObnzQKQO85MwEecCcPOgDR5qJ0QQhzPjTfeyCuvvMK7777b6onjhYWFgHk2u6io+aBmZWVl4ux3YWEh4XCY2traVme7KysrmTWr43uxHQ4HDofjqO0JB5pvJXI4vd3r1CA3cvKZjJx8JmBeObD1vVc4+M7rqJ99SVF5A1kNGlmfHIBPDmD88VU2O6ByVBZMGk/+GWczbvbFeDNy+rgXQgiRXJJ0p7DzJxSwbP6p3PTiRl74ZB+GAfdcNkkuNT9RigL5J5nlzJ9B2A/7PoJda80HsVVshqrtZvnkSXOd7JEwfCYMP8NMwnPHyOXoQggRYxgGN954I6tXr+add96htLS01fLS0lIKCwtZs2YNU6ZMASAcDrN27Vruv/9+AKZOnYrNZmPNmjXMmzcPgIqKCr744gseeOCBbrUrGgkCoIOcfe0BNruTU86bxynnmX8fX2MNW995mSMfrUP9ooz88jrcIRixtRa2fgirPqRc/R1HCp34RxfjnDiB4tPPZvSU87C75EGxQoiBQ5LuFHfJKcWEojpLXtrMi+v3UeML88gVU3Dajv/qFNFJdjeMOs8sAP4a2P0elK+FvR/B4S+hZpdZNj1n1nHnmMn30KlQfBoUTzEvZxdCiEHo+uuv5/nnn+d///d/SUtLS9yDnZGRgcvlQlEUbr75Zu69917GjBnDmDFjuPfee3G73Vx55ZWJutdeey233HILOTk5ZGdnc+uttzJ58mTmzJnTtQbFDopGwyEANAmZSeFJy+b0S66DS64DzDPhOz59i/0fvEVk0+dklR0iq0Gn6GAQDu6Cd3fB8n/wlQUqi90ExwzBPWkyxVPPYuTJ35BEXAiRsiTpHgC+O3UoXoeFn764iTe3Huaqpz9m+b+dRn6as6+bNjC5s2HCpWYB84Fs+9fD3g/NJPzABvBXw/ZXzRKXPQqGnAZDYol44WQzoRdCiAFu+fLlAJxzzjmt5q9YsYIFCxYAsGTJEgKBAIsWLaK2tpYZM2bw5ptvJt7RDfD73/8eq9XKvHnzCAQC/Mu//AvPPvtsp97R3ZFIKACAJheI9Qqb3cmEWRczYdbFAOi6zqHyL9j10ZvUb/4My/ZycvfU4wkaDNnnh3074P/twOBltqtQle/AV5KHdXQpmRNPpWTKNygomSAPahNC9Hvynu5u6Mv3dB/LR7uq+fF/f0pjMEp+moPl/3YaU0uy+7pZg080bF6CvvdDOPgZHPgM6va0r6dYzEvYCydDwSQonAQFk8Ej97YJMRDIe7r7n8R7ulc/yezLfszXm9cSmb8Qn1Nh2qatfd08gZmI79v+KXs+fpvGLRuxlu0hd18D7lDH9ZtcCjXFXiIjh+AcPZqscSczbNIMcotHSzIuhOiWZMRvOdM9gJwxMof/vX42C/+8gbLDTXz/yY/4+QXj+dHsUnmyeW+y2mHY6WaJ81XDwY2xJHyDmYj7KuHwF2ZpKa0ICibGEvFYQp4zGizydRVCiJ6kyeXl/Y6qqpScNJ2Sk6Yn5um6TsWuLezduI66Lzej7yzHs6eK3CNh82npOxth51ew5ivgH1QDe50KtQVuQkNysZYOJ33sRAonTGXYuGnY7HIloBCid8mv+AFmZJ6X1Ytms+SvW3h1SwX3vLqNNVsP81/fO4Vh2XIpc5/x5MCYOWYBMAxoOAAHNzUn3oe+gNpyaKwwS/x1ZQCqDXJGQd4488npebGSMxpsrj7pkhBCpKzYcWgtEkYBNIscmO7PVFVlyOhTGTL6VPhe8/yAv4Hdm9dxaMvH+L/ahrr3IN6KBrJroniCBp49Ptjjgw/2AOsIAV+pUJNjw5+fhlacj33YMNJKx5A3ejJDxp2GS97XLoRIAkm6ByCPw8pjV0xh1qgcfvvqNj4ur+GCZe+yeO44rp5Zgk2ebt73FAUyhprlpIub54ca4fBWOPy5+YC2Q1+Yw4jPfI3Zka/abgiyRsSS8bFmyRllPk3dWyBPUBdCiGOIhoPYAF2S7pTkcqdz0syLOGnmRa3m+5vq2LftEw5v20jTjm3oe/bjOlBNTmUQRwTyj0TgSA18WQN8BawhAuwC6tMtNOa5iRTlYBk6BM+IUWSNGEt+6QRyh47BIledCSG6QfYcA5SiKFw1o4QzR+dy6182s353Lb/5x1ZWrt/Lry6dyKxRuX3dRNERRxoMn2GWOF2Hhv1wZLtZqrbDkTIzAQ/WmWfHa8vN94q3ZPNAdqmZgLctaUUg97oJIQY5LRwyk27ZHw4obm8m406fy7jT57aar2lRKnZ9TsX2jdTv2k5o7244cBjXoXoyq4O4wpjvFW9ohJ2NwG7gfQBqgMMWqMu04s/2EM3PQi3Mxzl0GGnDRpJXehJFIyfjcMn73oUQ7UnSPcCV5Hh48d9nsurTfTzw+leUHW7iyqc+Zs5JBSw+fywTiuUyqn5PVSFzuFnGnN883zDAdySWjH8FVWVQ/bX56rK6vebZ8Y7uGQewusyEPL7djGHN45nDzVeeyVlyIcQApcT2b1okDIBulf3dYGCxWBk6ZgpDx0xpt0zXdaordnFw+2fU7NxGYE85+v6D2A/X4qkJkNmgYdMgrzoK1fWwox4zKf8EgACxM+VelcZsJ5HsNPTcTCz5eTgLi/EWDSNr6Gjyho8jLatAHvImxCAjSfcgYFEVrpg+nAsnFbJ0TRl//mgPb207zFvbDnPRyUX89LwxjCtMO/6GRP+iKODNN0vpWa2XRcNm4h1/f3jNzubx2j0QDUDlVrN0xOZuk4jHxjOGQ3oReAvlwW5CiJSnx5Nuue1q0FNVlbwho8kbMhrOa788HPJzeM82jpRvo2Hv1wQO7EM7eAjLkVrcVU1k1EZwRCGjSSejyQ97/cBhYHtiGxHgIBC0QWO6jUCmk0hOOkpuNtb8fFzFQ/AWDCOjqITsohFk5AyR5FyIAUJ+NQ8imW47v/72JK6eOYKH397B3zcf5NUtFby6pYJvjM3jx2eVcubo3MQZAJHCrHbIHW2WtrRILCEvh/q95njdvthwLzQdgojfvIy9anv79QEUFTz5kF7cXNKKIH1I62l5D7kQoh/TJOkWnWR3uBk2dirDxk7tcLmu69Qe3sOhXZ9Tt+dr/If2Ez50CKOqGmt1A85aP96GCJ6ggTMCzuoIVEdil7EfaLWtMHAI2GeBJo+FQLqdcIYLLSMNJScTa04ujrwCPAVDSC8YSnbxSLLyh2O12ZP+7yCE6B5Jugeh0fleHr1iCtefO4pH3t7B618c4t2yI7xbdoRxBWlcMX0Yl00ZQqZbdt4DkiX2JPScUR0vjwTNJ6vHk/C6vVAfS8rr95tPVtejZnLedMh8DdrRODNjiXiR+WA3b76ZrHvzm6e9+WY9OdgjhOhl8VeGyYPUxIlSVZWcolJyikph9tHrNdVXU7W/jJr9O2k8sJvg4YNEDleiVNViq27A2RDC0xTBHQKbFr/HPAD7A5h3lu9ptT0dqAIqFWj0qAS8NsJeB9E0F0ZGGkpGOtasLOzZObhy8vHkFpOeV0xmwXC5zF2IXiRJ9yA2vjCdx6+ayt5qPys+KGfV+n1sP9zIr/6+lXv/+RXfnFjI96YOZdaoHKxyFmDwsDmPnZTrunkveeNBaGhRGivMZL2hwpyO+MwHvQXroPLLY3+mxW4m4Z681sl4y0TdnQOeXDNBlx8JQogTYMTeGaZHIua0xDjRS7wZOXgzZjJi4sxj1gv4G6g5uIvait00HtqHv7KCcPURtKpqlNp6rHVNOBqCuBsjePwGqhG/tD0EhIAGzMvb2/PHSlQFn1sl6LYSSnOgpbnQ070oGelYMjOxZWbhyMjCmZWLJzsfT1Y+6bnFpGUVyFPchegi+cYIhue4ueuSidw8Zyx/23iAlev3sbWigb9vPsjfNx8kx2Nn7sRCLppcxBkjsyUBH+xUFdIKzFLc/mE0gPmQt1BDLAE/YCbkTZWxctgc+mLjwXrQwubZ9Pp9x/98RQVXFrhzY4l4jjlMTOeCO7v1PLnMXQjRgfjl5YbV0sctEaI1lzu9+d3kxxEJB6mpKKemYjdNRw4QqKokVFtFpKYGvb4e6hux1vuwNQVxNkVw+zWcEbDq8UQ9DJVhoBGoPOrn+GLlABBwKgRdFkJuKxG3A83rxEjzoHg9qOnpWDMysWdk4szKxZ2Vhzs7n7SsfDyZebg8mXKGXQw6knSLhAyXjWtmjeCaWSP44kA9K9fv4x9bDlLtC/PCJ3t54ZO9ZHvsnDMuj7PH5vGNMXlkeeQSdNEBRQFnhlnyxx+7biQYS8CPxBLyw83Jua+yOVn310CoHgwd/NVm6Syb20y+XZlmwu6MDVtNd7DMkS6XvQsxAMW/1UY0dqbbKgmASF02u5OCkpMoKDmp0+sE/A3UHd5LfeV+Go8cJFBzmFBVFZG6GvQ6M1G3+AJYmoLY/REcgSjugI49CirgCRp4glGojQJBoP6YnxcGqmMlqkLQoRByqoSdVqJOG5rbjuZxgduF4nGjejxY0tKwpqXjSM/Cnp6JKyMbV0YO3oxcPJn5uNOyJHkXKUOSbtGhSUMymDQkgzsvmcBHu6p57fNDvPHlIWp8YV7+7AAvf3YARYFThmZyzrg8Zo/O5eShGTjkbIHoKpuz+SnpxxMNQ6DGTLh9Vc3Jd7y0neerAj1iPhiu3t+5M+ktKRbzwEG7BL1FUu5MN9+v7siIjac3D+0eSdqF6Jdil5fH7uk2LBK7xODicqfjKp1EUemkLq0X8DfQWF1BQ1UFvtpKArVHCNZVE6mvI1pXh9bYAA0+VJ8fS1MQmy+MIxDBGdBwhsyE3aqDN2DgDWiAhnk5fOeEMe9srwE0pU3y7rCiOW3oTju6yw4uJ4rLheJ2o7pcWD1eLB4vdm8adm86jrRMnN5MXOlZuNOycWfk4HB6JZEXSSFJtzgmm0XlrDF5nDUmj998eyKf7K5hbdkR1m4/wleHGtm0r45N++pY9tYO7FaVU4dmMr00m9NLs5lakoXXIf/FRA+y2iGt0CydYRgQamxOwgN1EKg17zMP1HY8HR+PBsHQzCQ/UNO99irqURLytNbJuTPdrONIM8ftXnB4zaHdCzaXJO9CJIEejQJyebkQneVyp+Nyp5M/bFyX19W0KP7GGppqK/HVHcFfX02wvoZQQx2RxnoijQ1oTY3oTU3gC6D4AqiBIBZ/GFswij0QxRnUcYYMVMBixM+4dz15T7QJaIoVMBP5kF0h5FCIOCxEHWYyr7vs6A47hssBTgeKy4nidKI6XVhcbixuN1a3G5vLi83txe5Jw+5Ow+lJx+nNwOnJwO3JwupwSlI/SElGJDrNalGZNSqXWaNyuePCkzhUH2RtWSVry47wSXkNVU1hPtldwye7a+D/zBxhVJ6Xk4dkMHloBicPzWBCUQYuu/y4Eb1EUcwk1pkO2aVdWzcSaJ2Ed5SghxrNe9eDDeal78GG5mlDMy+FD9ab5dhX3h2nH2osAfc0Dx1pHUzHx1sk7B1N273ynnUhACP+IDVJuoVIOovFSlpmPmmZ+Se0HV3XzeS9rpKmuiMEaqsINtUTbqon4msk2tSI5veh+fwYfj9GIACBIGogjBoMYwlGsIai2EIatrCGI2TgiMbaaIA7ZOAOGZjPho90u50GEIiVOE2BsA3CdpWoTSVqV4narWh2C7rDhu6wYTjs4Iwl904HqtOF6nJhcTrNBN/pxOpwY3W5sTnd2Fwe7C4v9tjQ6U6PDdPkNXL9iPzqEt1WmOFk/unDmX/6cAzDoLzKxyflZtK9fncN+2oCfF3ZxNeVTby80XwHpaqYrywbX5jOuMI0xuR7GVuQxrBsNxZVzuSJfsTmMkt6UdfXNQwzaU8k5A2tx1sNG9sn7GFfrDQBhpm8x7fRUywO8wFztnhxmQm5zXXseUddJzaML7fY5ey86Pfi93QjSbcQKUNVVbwZuXgzcqGkZ7YZjYTxN9aYpaGGYGMdwcY6wk31hH0NRJuaiPqa0Hw+9FAQIxiEYAiCYZSQmcyroQiWsIY1HMUa1rFGdOxhHUcEVMP8HIsBrjC4wjpmUg/mRfMnRqM5wW95jD+qQsQKEZtC1Kqi2cxkX7NZ0G0WdLsVw27DsFsxbDZw2sFuR7HbzYTf4UB1OLE4XagOJ1anC5vLg8XhwOpwmcm/w4nN4cLmdGN3urE7PdhdHhwOr5zZb0GSbtEjFEVhZJ6XkXlevj/dvDe3sjHIFwfq2bK/ni8O1LN5fz1HGkOUHW6i7HATbG5e32lTGZ3vZWx+GiPzPJTkeBiR46Ek102609ZHvRKimxTFTD7t7s5fCt8RXTfvR48n4OEmCDXFphvNYYfTbeu2mNZjSYYWgkDIPGufDIqlRWIeS8StTnM6MXSA1WXe12+NFZvTnGd1HKNuB8stDnmVnOi82AEhI3Z5OVb5OSTEYGa12UnPLiQ9+wRi9lHouk40FMTvqyXoqyfYVE/Q10DY30jY10jE30Qk0ETU70fz+9ECfvRgACMYxAiGIBhCCYZQwlGUcAQ1HEWNaFgiGtaIjiWiY43q2CIGtqh5z3yiXzpYw+AKG5ipudbj/QPzeoAI5qvo2gpbIGqFqFVBs6hoVgXNqqJbVXNos2BYLeg2K4bNgmGzgs0GdnOo2G3mQQC7HcVmQ7U7UO1282CA3YHF4cTqcLU6EGBzurDaYwcDHK7mAwN2N3anC6ut9w8GSJQRSZOf5uS88U7OG1+QmHe4Icjn++spq2yk7FAjZYeb+PpIE8GIzhcHGvjiQPszedkeOyU5bjMJz3EzLMtNcaaL4kwnhRlOeXibGLhU1bw03OEFCo5bvVOi4VgS7jPPxkdiw7DfTPDjJexvszxe/2jLY/PjSb2hxQ4ENJrvmOkNFsfxk3aro7lY2o7bzfoWe2x+fNxpLmtXPz7eYj1J/FNK/PJyOdMthEgWVVWxu9zYXW7IHZL0z4uEg4QCPkL+BkKBJsKBJkL+RiJBP5GAj0jARzQYIBrwo4UCaMEAWjBonsEPhTFCQYxwBEJhCIVRwhGUcBQ1EkUNR7FENNSomexbojoWzcAajRXNPJvfkl0zC6F44p98Bs0HAo4mYoGoBTSL0lysCrpFpRHjGGt2jyTdolcVpDspmOBkzoTmBELTDfZU+yg73MSOw43srvazp9rH7mo/VU0hanxhanxhNu6t63CbuV4HQzKdFGW4Esl4UYaL/HQHeV4HeWkOPPJANyFMVjtYs813mSeDFmmRlMdLLGmPBs0SCUI0EBvG5wWOszw2LxpqrhsJmMl94rNDZjmhG+hPkGprTtKjcpVOf2dE5Ey3EGJgsdmd2OxOvBk5ffL50UiYULCJcNBHJOgnHPQnxqOhAJFQAC0UIhoKEA0F0MJB9FAILRRCD4fQQyH0SBgjHMEIhzEiYQhHIBKFcAQlEjVL1Dzjr0R188x/VE8UNXYgwKIZWOJJfxs2zSxmit46yfZoPX9wQKKM6HMWtfnS9Asmtb6spykUZU+1jz3VfvZU+9ld5eNAXYCDdQEO1AUIRXWqmkJUNYXYvP/oP7Tddgt5ac1JeNvxLI+dLLedbLedNKcVVe4vF6J7LDawxN7R3hu0aBcS+IB5pl8LxZaHzaEWNpP5aCxp73A83LxO2zot6RHzx0GY2FF90a/FLi9X5JVhQgjRI6w2O1ZbNp60JB3c7wZd19GiYcIhP5FY4p8o4QDRUBAtHCQaCqGFgzTW1sBVP+vRNkjSLfo1r8PKxOIMJha3/wFvGAa1/ggHY0n4wboAFfVBDsSGRxpDHGkMEYho+MNaInE/HlWBLLedTLeNbI+dTLedLLctkZhnuW1kxpLzdKfNLC4rXocVq0UuLRWiV1msYEkzn97eFwzj6El7XTX87ht90y5xTEbbe7ptclWCEEIMVKqqosauAKATPxcaGhoASbqFAMyHt2V77GR77Ewa0vFZNcMw8IW1RAJuliBHmlpMN4Wo9UWo84fxhTV0A6p9Yap9YXYe6drNqB67hbRYEp7mtJHutLaZtuF1WvE6LLjtVjx2K26HxRzaLXgd5rTdoqLIk5+F6P8Upfne7rZcPfi0eZEc8TPdNvk5JIQQInkkyogBTVEUvA7zLHRprue49UNRjTp/hFq/eR95nT8SG4ap9Ueo9YWp9YepC0RoDEZpiA0DEfPeD19YwxfWOHSCv7WtqoLbbsHjsCaGHrsVTzxZd1hw2aw4bSoumwWnzYLTpsaGlsQ8l13FYbXgssfqWFVz3GqRS+iFECJq7rsVeW+9EEKIJJIoI0QLDquFgnQLBenOLq0X0fRWSXhDMEJjMEJDwBxvCEYT043BCIGIhi8UxR/W8IWj+ELmdChqvuchqhs0BKM0BKPJ6CYAdqvanITbzETcabfgsKo4rCp2i4rdGisWFYdNxW6xJOY5Ws1vW9eSmOdot43mujZVleRfCNEHYvsdOdMthBCiF0iUEaIH2Cxq4lL3ExHVdPwRDX/ITMb9IY2mUBR/OIovrOEPmUNfyDy7HghrhKIawYhOIKwRjGqxoU4wNh2MNM8LR5tf3hiOTSczse8MVTH//WwWFatFMcdVBatFxRabbp7fYtyiYFVVbNZ4fSWxHZsltr4aX7/NtlQVm1XBoqpYVQWLqrQYqubQYk5bFCUxbdbpeB1VpXldVZGDCUL0Y4lvZ/xMt1Xu6RZCCJE8gzbpfvzxx3nwwQepqKhg4sSJLFu2jLPOOquvmyUGOatFJd2iku5Mzg9ATTcItUzMWyTugbA5HYrqhDUtkZSHYiUc1Qlreov5Wqt5oWjr4dHqRrTWT3TWDRKfMZAoCu0SeUu7ZP34ib6qNM9TVQVVMZ/4H5+vKvFx2kw3z1dj61paDhXM8di0Ettu623T/PmqgqLE20brz09smzaf3fG249tVY8vUFp+ltFimKuYtIu3qK63ry/MPBp+eiuFKPOm2n9gBUyGEEOJYBmXSvXLlSm6++WYef/xxZs+ezR//+EcuvPBCtm7dyvDhw/u6eUIkjUVVcNutuO1999XXdcNMyDWdqGYQbTmu64Sj5jASS9CjmhEb14nqRmJ+RNOJthzXW9TTjBbzm7cZ1YzYZ8W2retoupEo0XZDHU0zp3UjNl9rvVw/yluhDINYGwxgYB1Q6I+Ol5iraucSeaXD9VvWP8b6auv60WDXHsQoOqcnY3g86VblPd1CCCGSaFBGmaVLl3Lttddy3XXXAbBs2TLeeOMNli9fzn333dfHrRNiYFNVBadq3kc+EOi6gWa0TtYTCbtuHjRom8jrOs3LWyX6eqv6uhFb3zDQdQPdIDGuxZbrhoGmY47rzXU1w6wfr3v0bRDbhlnHiI/Ht2k0f1aiftvt6a3rxedpemx7LdoYr4NBoq5uGBhG8+cd7UDGMf8OsfWh/7wbWw8d/xWFout6JoabV0coWizptsmZbiGEEMkz6JLucDjMhg0buP3221vNnzt3Lh988EEftUoIkapUVUFFYYAcQ+g3jBYJeeukPJak662T9OPWT8w7dp34QYXOblNvMc+ItyfWNl9jIz9c1tf/kgNLT8XwhrrDACix21pUeU+3EEKIJBp0SXdVVRWaplFQUNBqfkFBAYcOHepwnVAoRCgUSkzX19cD0OTzx16eLoQQojcpgCVWWs3s9O3dSpthz2toMLdtGP3n7Huq62oMP1r8PrB9Kw0NDfiDEZo0jUBEl3guhBACIBEPejJ+D7qkO67tg3cMwzjqw3juu+8+7r777nbzz734iqS0TQghxMBRXV1NRkZGXzdjQOlsDD9a/L7unme57p5nm2dc/xuzCCGEEDE9Gb8HXdKdm5uLxWJpd0S8srKy3ZHzuDvuuIPFixcnpuvq6igpKWHv3r0D9odUQ0MDw4YNY9++faSnp/d1c3qc9C/1DfQ+Sv9SX319PcOHDyc7O7uvmzJgdDWGS/weeN8t6V/qG+h9HOj9g4Hfx2TE70GXdNvtdqZOncqaNWv4zne+k5i/Zs0avv3tb3e4jsPhwOFwtJufkZExIP+jtZSenj6g+yj9S30DvY/Sv9SnqmpfN2HA6GoMl/g9cPso/Ut9A72PA71/MPD72JPxe9Al3QCLFy/mBz/4AdOmTWPmzJk8+eST7N27l4ULF/Z104QQQghxDBLDhRBCpJpBmXTPnz+f6upqfv3rX1NRUcGkSZN47bXXKCkp6eumCSGEEOIYJIYLIYRINYMy6QZYtGgRixYt6ta6DoeDu+66q8NL1gaKgd5H6V/qG+h9lP6lvsHQx77S3Rg+GP4mA72P0r/UN9D7OND7BwO/j8non2LIu0yEEEIIIYQQQoikkKe7CCGEEEIIIYQQSSJJtxBCCCGEEEIIkSSSdAshhBBCCCGEEEkiSfdRPP7445SWluJ0Opk6dSrr1q07Zv21a9cydepUnE4nI0eO5IknnuillnZPV/r38ssvc/7555OXl0d6ejozZ87kjTfe6MXWdk9X/4Zx77//PlarlVNPPTW5DTxBXe1fKBTiF7/4BSUlJTgcDkaNGsWf/vSnXmpt93S1j8899xynnHIKbreboqIifvjDH1JdXd1Lre2ad999l0suuYTi4mIUReFvf/vbcddJpf1MV/uXavuZ7vz94lJlH5OqJH43S7XvVZzE79YkfvcvEr9bS7X9TJ/Fb0O08+KLLxo2m8146qmnjK1btxo33XST4fF4jD179nRYf9euXYbb7TZuuukmY+vWrcZTTz1l2Gw246WXXurllndOV/t30003Gffff7/xySefGGVlZcYdd9xh2Gw247PPPuvllndeV/sYV1dXZ4wcOdKYO3euccopp/ROY7uhO/279NJLjRkzZhhr1qwxysvLjY8//th4//33e7HVXdPVPq5bt85QVdV4+OGHjV27dhnr1q0zJk6caFx22WW93PLOee2114xf/OIXxl//+lcDMFavXn3M+qm2n+lq/1JtP9PV/sWlyj4mVUn8bi3VvleGIfG7IxK/+xeJ362l2n6mr+K3JN0dmD59urFw4cJW88aPH2/cfvvtHdZfsmSJMX78+FbzfvKTnxhnnHFG0tp4Irrav45MmDDBuPvuu3u6aT2mu32cP3++8ctf/tK46667+nXQ7mr//vnPfxoZGRlGdXV1bzSvR3S1jw8++KAxcuTIVvMeeeQRY+jQoUlrY0/pzE4/1fYzLXUlqLXU3/czcV3pX6rsY1KVxO/j6+/fK4nfrUn87t8kfnesv+9n4nozfsvl5W2Ew2E2bNjA3LlzW82fO3cuH3zwQYfrfPjhh+3qf/Ob3+TTTz8lEokkra3d0Z3+taXrOo2NjWRnZyejiSesu31csWIFO3fu5K677kp2E09Id/r3yiuvMG3aNB544AGGDBnC2LFjufXWWwkEAr3R5C7rTh9nzZrF/v37ee211zAMg8OHD/PSSy9x0UUX9UaTky6V9jM9ob/vZ7ojVfYxqUri9/H19++VxO/2JH6nvlTaz/SE/r6f6Y6e2MdYe7A9A0JVVRWaplFQUNBqfkFBAYcOHepwnUOHDnVYPxqNUlVVRVFRUdLa21Xd6V9bDz30ED6fj3nz5iWjiSesO33csWMHt99+O+vWrcNq7d9fi+70b9euXbz33ns4nU5Wr15NVVUVixYtoqampl/eF9adPs6aNYvnnnuO+fPnEwwGiUajXHrppTz66KO90eSkS6X9TE/o7/uZrkqlfUyqkvh9fP39eyXxuz2J36kvlfYzPaG/72e6qqf2MXKm+ygURWk1bRhGu3nHq9/R/P6iq/2Le+GFF/jVr37FypUryc/PT1bzekRn+6hpGldeeSV33303Y8eO7a3mnbCu/A11XUdRFJ577jmmT5/Ot771LZYuXcqzzz7bb4+WQ9f6uHXrVn76059y5513smHDBl5//XXKy8tZuHBhbzS1V6Tafqa7Umk/0xmpuo9JVRK/O5ZK3yuJ380kfg8Mqbaf6a5U2s90Rk/uY/r3IcE+kJubi8ViaXc0rrKyst1RqrjCwsIO61utVnJycpLW1u7oTv/iVq5cybXXXstf/vIX5syZk8xmnpCu9rGxsZFPP/2UjRs3csMNNwBmkDMMA6vVyptvvsl5553XK23vjO78DYuKihgyZAgZGRmJeSeddBKGYbB//37GjBmT1DZ3VXf6eN999zF79mxuu+02AE4++WQ8Hg9nnXUW99xzT8ofSU6l/cyJSJX9TFek2j4mVUn8PrpU+V5J/G5P4rfE71SRKvuZrujJfYyc6W7DbrczdepU1qxZ02r+mjVrmDVrVofrzJw5s139N998k2nTpmGz2ZLW1u7oTv/APHK1YMECnn/++X5/j01X+5iens7nn3/Opk2bEmXhwoWMGzeOTZs2MWPGjN5qeqd05284e/ZsDh48SFNTU2JeWVkZqqoydOjQpLa3O7rTR7/fj6q23qVZLBag+YhyKkul/Ux3pdJ+pitSbR+TqiR+dyyVvlcSv9uT+C3xOxWk0n6mK3p0H9PlR68NAvFXHTzzzDPG1q1bjZtvvtnweDzG7t27DcMwjNtvv934wQ9+kKgffxXAz372M2Pr1q3GM888069fBdDV/j3//POG1Wo1/vCHPxgVFRWJUldX11ddOK6u9rGt/v700672r7Gx0Rg6dKjx3e9+1/jyyy+NtWvXGmPGjDGuu+66vurCcXW1jytWrDCsVqvx+OOPGzt37jTee+89Y9q0acb06dP7qgvH1NjYaGzcuNHYuHGjARhLly41Nm7cmHilSqrvZ7rav1Tbz3S1f231931MqpL4ndrfK8OQ+C3xW+J3X5P4nZx9jCTdR/GHP/zBKCkpMex2u3HaaacZa9euTSy75pprjLPPPrtV/XfeeceYMmWKYbfbjREjRhjLly/v5RZ3TVf6d/bZZxtAu3LNNdf0fsO7oKt/w5b6e9A2jK73b9u2bcacOXMMl8tlDB061Fi8eLHh9/t7udVd09U+PvLII8aECRMMl8tlFBUVGVdddZWxf//+Xm515/zf//3fMb9Xqb6f6Wr/Um0/052/X0upsI9JVRK/z05Mp9r3Kk7i99mt6kv87l8kfqf2fqav4rdiGAPgug0hhBBCCCGEEKIfknu6hRBCCCGEEEKIJJGkWwghhBBCCCGESBJJuoUQQgghhBBCiCSRpFsIIYQQQgghhEgSSbqFEEIIIYQQQogkkaRbCCGEEEIIIYRIEkm6hRBCCCGEEEKIJJGkWwghhBBCCCGESBJJuoUQ7SxYsIDLLrssMX3OOedw8803J/Uzw+Ewo0eP5v333z+h7dx666389Kc/7aFWiVTz7rvvcskll1BcXIyiKPztb39L6udFo1F++ctfUlpaisvlYuTIkfz6179G1/Wkfq4QQnRE4rdIVQM9fkvSLUSKWrBgAYqioCgKVquV4cOH8x//8R/U1tb2+Ge9/PLL/OY3v+nx7bb05JNPUlJSwuzZs09oO0uWLGHFihWUl5f3UMtEKvH5fJxyyik89thjvfJ5999/P0888QSPPfYY27Zt44EHHuDBBx/k0Ucf7ZXPF0KkHonfHZP4PbgN9PgtSbcQKeyCCy6goqKC3bt38/TTT/P3v/+dRYsW9fjnZGdnk5aW1uPbbenRRx/luuuuO+Ht5OfnM3fuXJ544okeaJVINRdeeCH33HMPl19+eYfLw+EwS5YsYciQIXg8HmbMmME777zT7c/78MMP+fa3v81FF13EiBEj+O53v8vcuXP59NNPu71NIcTAJ/G7PYnfg9tAj9+SdAuRwhwOB4WFhQwdOpS5c+cyf/583nzzzcRyTdO49tprE5fOjBs3jocffrjVNjRNY/HixWRmZpKTk8OSJUswDKNVnbaXp3V02U9mZibPPvssYO4Yb7jhBoqKinA6nYwYMYL77rvvqP347LPP+Prrr7nooosS83bv3o2iKKxatYqzzjoLl8vF6aefTllZGevXr2fatGl4vV4uuOACjhw50mp7l156KS+88EJn/gnFIPPDH/6Q999/nxdffJEtW7bwve99jwsuuIAdO3Z0a3tnnnkmb7/9NmVlZQBs3ryZ9957j29961s92WwhxAAj8Vvit+iaVI/f1qRsVQjR63bt2sXrr7+OzWZLzNN1naFDh7Jq1Spyc3P54IMP+Pd//3eKioqYN28eAA899BB/+tOfeOaZZ5gwYQIPPfQQq1ev5rzzzut2Wx555BFeeeUVVq1axfDhw9m3bx/79u07av13332XsWPHkp6e3m7ZXXfdxbJlyxg+fDg/+tGPuOKKK0hPT+fhhx/G7XYzb9487rzzTpYvX55YZ/r06ezbt489e/ZQUlLS7X6IgWXnzp288MIL7N+/n+LiYsC8h/D1119nxYoV3HvvvV3e5s9//nPq6+sZP348FosFTdP47W9/yxVXXNHTzRdCDFASvyV+i2MbCPFbkm4hUtg//vEPvF4vmqYRDAYBWLp0aWK5zWbj7rvvTkyXlpbywQcfsGrVqkTQXrZsGXfccQf/+q//CsATTzzBG2+8cULt2rt3L2PGjOHMM89EUZTjBs7du3cndqJt3XrrrXzzm98E4KabbuKKK67g7bffTtw7du211yaO0McNGTIksV0J2iLus88+wzAMxo4d22p+KBQiJycHMP/PlJaWHnM7119/feKes5UrV/LnP/+Z559/nokTJ7Jp0yZuvvlmiouLueaaa5LTESFEypP4LfFbdN5AiN+SdAuRws4991yWL1+O3+/n6aefpqysjBtvvLFVnSeeeIKnn36aPXv2EAgECIfDnHrqqQDU19dTUVHBzJkzE/WtVivTpk1rd4laVyxYsIDzzz+fcePGccEFF3DxxRczd+7co9YPBAI4nc4Ol5188smJ8YKCAgAmT57cal5lZWWrdVwuFwB+v7/bfRADj67rWCwWNmzYgMViabXM6/UC5g++bdu2HXM7WVlZifHbbruN22+/ne9///uA+X9zz5493HfffZJ0CyGOSuK3xG/ReQMhfkvSLUQK83g8jB49GjAvCTv33HO5++67E08qXbVqFT/72c946KGHmDlzJmlpaTz44IN8/PHHJ/S5iqK0C+qRSCQxftppp1FeXs4///lP3nrrLebNm8ecOXN46aWXOtxebm4un3/+eYfLWl5upyhKh/Pavt6hpqYGgLy8vC70Sgx0U6ZMQdM0KisrOeusszqsY7PZGD9+fKe36ff7UdXWj0exWCzyyjAhxDFJ/Jb4LTpvIMRveZCaEAPIXXfdxX/9139x8OBBANatW8esWbNYtGgRU6ZMYfTo0ezcuTNRPyMjg6KiIj766KPEvGg0yoYNG475OXl5eVRUVCSmd+zY0e6odHp6OvPnz+epp55i5cqV/PWvf00E07amTJnCV199dUJH51v64osvsNlsTJw4sUe2J1JHU1MTmzZtYtOmTQCUl5ezadMm9u7dy9ixY7nqqqu4+uqrefnllykvL2f9+vXcf//9vPbaa936vEsuuYTf/va3vPrqq+zevZvVq1ezdOlSvvOd7/Rgr4QQA53Eb5PE78FroMdvOdMtxAByzjnnMHHiRO69914ee+wxRo8ezX//93/zxhtvUFpayv/8z/+wfv36Vve83HTTTfzud79jzJgxnHTSSSxdupS6urpjfs55553HY489xhlnnIGu6/z85z9vdfT697//PUVFRZx66qmoqspf/vIXCgsLyczM7HB75557Lj6fjy+//JJJkyad8L/DunXrEk9MFYPLp59+yrnnnpuYXrx4MQDXXHMNzz77LCtWrOCee+7hlltu4cCBA+Tk5DBz5sxuP6300Ucf5T//8z9ZtGgRlZWVFBcX85Of/IQ777yzR/ojhBgcJH6bJH4PXgM9fsuZbiEGmMWLF/PUU0+xb98+Fi5cyOWXX878+fOZMWMG1dXV7d4Desstt3D11VezYMGCxCVsxzvK99BDDzFs2DC+8Y1vcOWVV3LrrbfidrsTy71eL/fffz/Tpk3j9NNPZ/fu3bz22mvtLuOJy8nJ4fLLL+e555478X8A4IUXXuDHP/5xj2xLpJZzzjkHwzDalfjDeuIPJyovLyccDlNRUcHLL7/c6j7DrkhLS2PZsmWJey537tzJPffcg91u78FeCSEGA4nfEr8Hs4EevxWjp64HEUKIE/D5558zZ84cvv76a9LS0rq9nVdffZXbbruNLVu2YLXKxTxCCCFEMkn8FuL45Ey3EKJfmDx5Mg888AC7d+8+oe34fD5WrFghAVsIIYToBRK/hTg+OdMthBBCCCGEEEIkiZzpFkIIIYQQQgghkkSSbiGEEEIIIYQQIkkk6RZCCCGEEEIIIZJEkm4hhBBCCCGEECJJJOkWQgghhBBCCCGSRJJuIYQQQgghhBAiSSTpFkIIIYQQQgghkkSSbiGEEEIIIYQQIkkk6RZCCCGEEEIIIZJEkm4hhBBCCCGEECJJ/j+zh/J7PcoBvAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "fig, ax = plt.subplots(3,2,figsize=(10,10))\n", - "rs = np.linspace(0, 14e-9, 1000)\n", - "ls = rs * (np.sqrt(3*np.pi/4/0.0075) - np.pi/2)\n", - "sm.plotPrecipitateStrengthOverR(ax, rs, ls, strengthUnits='MPa', plotContributions=True)\n", - "ax[2,0].set_ylim([0, 50])\n", - "ax[2,1].set_ylim([0, 1500])\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model can now be solved." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nucleation density not set.\n", - "Setting nucleation density assuming grain size of 100 um and dislocation density of 5e+12 #/m2\n", - "N\tTime (s)\tTemperature (K)\tMatrix Comp\n", - "5000\t9.0e+02\t\t673\t\t0.0737\n", - "\n", - "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", - "\tbeta\t2.507e+20\t\t0.5066\t\t1.7305e-08\t2.2067e+03\n", - "\n", - "Finished in 54.470 seconds.\n" - ] - } - ], - "source": [ - "model.solve(verbose=True, vIt=5000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the strength contributions are done through the StrengthModel object. In plotContributions is set to False, then the overall strength contribution will be plotting. If True, then the strength contributions from the precipitate hardening mechanisms, solid solution strengthening and the base strength will be plotted. Since the solid solution strengthening and base strength was not included in the model, only the precipitate hardening mechanisms contributed to the overall strength." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\nury\\desktop\\projects\\precipitation model\\kawin\\kawin\\Strength.py:435: RuntimeWarning: divide by zero encountered in divide\n", - " return self.J * self.G * self.b / (2 * np.pi * np.sqrt(1 - self.nu) * Ls) * np.log(2 * r / self.ri)\n" - ] - }, - { - "data": { - "image/png": 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t6xyjbY4QBAFvv/02oqKiEBsbW6+EGwCSkpLg4eFR7XGZTAaZTFbfMImMliAImPfjGRTKK9C7dTOsfdkP1pYch0u6kUgk6rHdE/xaQRAEXM0uwh/nsrAz8SbS7pZgW0Iqth9Lw0Q/L4QO6wB3RxuxwyYiIiMhCAJ+OpmBf+8+jyJ5BQBgQHsXvPKkN57q3IK/VcgsGW3S/eabb+L777/Hzz//DHt7e2RlZQEAHB0dYWtrCwBYsGABMjIysG3bNgDAqlWr0KZNG3Tt2hVlZWX49ttvERkZicjISNG+B5FY9p6/jfirObCxssCn/+jJSowMQiKRoIObPTq42ePNoe1x6FoOvoy7jvirOdh+LA27T2XgnaBOmBLgDUtOvkZE1KjJKxRY8NNZ/HQyAwDQs5UjFj3nC/82j58omciUGW3SvW7dOgDAkCFDNPZv2bIF06ZNAwBkZmYiLS1NfaysrAxz585FRkYGbG1t0bVrV/z2228YNWpUQ4VNZBQqFEr8d+9FAMDrA9qirWtTkSOixsDCQoKBHVwxsIMrjqXcRfjvyUhKy8OSXy/g59O3sObF3uxyTkTUSBWWluOf2xKRcD0XUgsJ5jzdAf8a0h5SLlVJjYDRj+luaBwLRubgl9O38Pb2JDSzs8KB94ZyPBSJQqkUsON4Ov7v92QUlFbA3sYS/53YAyO6VT/kp7FhnaN/vKdExqe0XIEpm47h2I27aCqzxLpX+mBgB1exwyLSWW3rHPb1IzJDXx++AQCYGtCGCTeJxsJCgpf6tcae0IHo07oZCksrEPLtSayLvVarpeKIiMj0KZQC3vr+JI7duAt7mSV2/PNJJtzU6DDpJjIz5zLycSL1HiwtJHi5X2uxwyFCq+Z2iHgjAK/2bwMAWPHHRSzcdQ5KJRNvIiJzt/rPK9iXnA2ZpQU2TXsC3Vo6ih0SUYNj0k1kZr7YfxUA8GwPD7Rw4KzRZByspBb49+iu+PdoX0gkwHdH0/B+1Fkm3kZs7dq18PHxgY2NDfz8/HDw4MEay8fFxcHPzw82NjZo27Yt1q9fr1UmMjISvr6+kMlk8PX1RVRUlMbxNm3aQCKRaG1vvvmmXr8bETWMA5fvYPVfVwAA/zehO/r6cMI0apyYdBOZkQu3CvD7uSxIJMDMIe3FDodIy6v9fbAquBcsJMCO4+n4cPc5djU3QhEREZg9ezY++OADJCUlYeDAgRg5cqTG5KUPS0lJwahRozBw4EAkJSXh/fffx6xZszRWD0lISEBwcDAmT56M06dPY/LkyZg0aRKOHj2qLnP8+HFkZmaqt5iYGADAP/7xD8N+YSLSu4LScrz742kIAvByv9YY37uV2CERiYYTqT2CE7CQKXvjmxPYe/42nuvhgTUv9RE7HKJqRSXdRNgPqh9jc4M64q2nOogdkiiMtc7p168f+vTpo15JBAC6dOmCcePGITw8XKv8vHnzsHv3biQnJ6v3hYSE4PTp00hISAAABAcHo6CgAL///ru6zIgRI9C8eXNs3769yjhmz56NX3/9FVeuXIFEUrsZjo31nhI1Nu9HncX3R9Pg49IEv4cOhI2VVOyQiPSOE6kRNTIXswqw9/xtSCRA6LDGmcCQ6RjfuxWWjO0GAPgk+jJ+OX1L5IioUllZGRITExEUFKSxPygoCIcPH67yMwkJCVrlhw8fjhMnTqC8vLzGMtWds6ysDN9++y2mT59eY8Itl8tRUFCgsRGRuBJT7+L7o6qeMeHPd2fCTY0ek24iM7F2/zUAwKhuHujgZi9yNESPN/lJb7w2wAcA8M7O0ziXkS9yRAQAOTk5UCgUcHNz09jv5uaGrKysKj+TlZVVZfmKigrk5OTUWKa6c+7atQt5eXmYNm1ajfGGh4fD0dFRvXl5edVYnogMSxAEfPybqtfLJP9WeLKts8gREYmPSTeRGcjIu49fz6haCmcObSdyNES19/6oLniqcwuUVSjx1vcnUVhaLnZI9MCjrcuCINTY4lxV+Uf31+WcmzZtwsiRI+Hp6VljnAsWLEB+fr56S09Pr7E8ERnW3vNZOJmWB1srKd4J6iR2OERGgUk3kRnYeSIdSgHo5+OErp5cioNMh9RCgpWTeqJlM1vcyC3B+1GcWE1sLi4ukEqlWi3Q2dnZWi3Vldzd3assb2lpCWdn5xrLVHXO1NRU7Nu3D6+//vpj45XJZHBwcNDYiEgcCqWA//xxCQAwY6AP3LiKChEAJt1EJk+hFLDzxE0AwIt9uS43mZ5mdtZY/WJvWFpI8MvpW/jpZIbYITVq1tbW8PPzU88cXikmJgaBgYFVfiYgIECrfHR0NPz9/WFlZVVjmarOuWXLFrRo0QLPPvusLl+FiBrYH+eycD2nGM3srDBjUFuxwyEyGky6iUxcwrVcZOTdh6OtFUZ0cxc7HKJ68fNujjnPdAQALP7lPLILSkWOqHELCwvDV199hc2bNyM5ORlz5sxBWloaQkJCAKi6dE+ZMkVdPiQkBKmpqQgLC0NycjI2b96MTZs2Ye7cueoyoaGhiI6OxooVK3Dx4kWsWLEC+/btw+zZszWurVQqsWXLFkydOhWWlpYN8n2JSHeCIGBd3FUAwNSANrC3sRI5IiLjwaSbyMTtOZcJABjV3Z2zg5JJe2NQW3Rv6YiC0gos3MVu5mIKDg7GqlWrsGTJEvTq1QsHDhzAnj174O3tDQDIzMzUWLPbx8cHe/bsQWxsLHr16oWlS5di9erVmDBhgrpMYGAgduzYgS1btqBHjx7YunUrIiIi0K9fP41r79u3D2lpaZg+fXrDfFki0ov4qzk4l1EAWysppga2ETscIqPCdbofwfU9yZQolAL6Lf8TOUVyfD29LwZ3dBU7JCKdXMwqwOjP41GuELD25T4Y1d1D7JAMinWO/vGeEolj6uZjiLt8B9MC2+CjMV3FDoeoQXCdbqJG4GTaPeQUyWFvY4kALslBZqCzuwP+NaQ9AGDZrxdwv0whckRERPQ46XdLcODKHQDAq/3biBsMkRFi0k1kwvaeU80E/EwXN1hb8p8zmYeZQ9qhZTNb3Movxbq4a2KHQ0REj7H9WBoEARjYwQXezk3EDofI6PBXOpEJi7useqo8rEvVy/gQmSIbKykWPdcFALA+7hrS75aIHBEREVWnrEKJHx6sovJyP66iQlQVJt1EJiorvxRXsosgkQCB7di1nMzL8K7u6N/eGWUVSqz446LY4RARUTX2Jd9GTpEcrvYyNgIQVYNJN5GJOnQ1BwDQvaUjmjexFjkaIv2SSCRY+KwvJBLg1zOZOJeRL3ZIRERUhaikDADARL9WsJIytSCqCv9lEJmo+AdJ94D2LiJHQmQYXTwcMKanJwDg0+hLIkdDRESPyi8pR+ylbADA+N4tRY6GyHgx6SYyQYIg/J10d2DSTeZrztMdYWkhwf5Ld3D8xl2xwyEioof8cT4T5QoBnd3t0dHNXuxwiIwWk24iE3TtTjHuFMohs7SAn3dzscMhMpg2Lk0w6QkvAMB/97K1m4jImPx86hYAYEwvT5EjITJuTLqJTNDJ1HsAgJ6tmkFmKRU5GiLDevup9rCWWuBYyl2cYGs3EZFRuF1QioTruQCA0T2YdBPVhEk3kQlKfJB0+7VhKzeZPw9HWzzfRzVWcG0s1+0mIjIG0eezIAhAn9bN4OVkJ3Y4REaNSTeRCUpMe5B0t2bSTY3DG4PbwUIC/HUxG8mZBWKHQ0TU6EVfuA0ACOrqLnIkRMaPSTeRickrKcPV7CIAQB+O56ZGwselCUZ29wAArGNrNxGRqApKy3HkQdfyZ3y5NjfR4zDpJjIxJx+0crd1aQInrs9Njci/BrcDAPx65hbS75aIHA0RUeMVd+kOyhUC2ro2QTvXpmKHQ2T0mHQTmRj1eG62clMj062lIwZ2cIFSALYl3BA7HCKiRivmQddytnIT1Q6TbiITczo9HwDQm+O5qRGa3t8HALDjeDqK5RUiR0NE1PiUK5TYfykbABDEpJuoVph0E5kQQRBw7pYq6e7e0lHkaIga3uCOrmjjbIfC0gpEJWWIHQ4RUaNz4sY9FJZWwLmJNXp5sQGAqDaYdBOZkIy8+8grKYelhQQd3TmGihofCwsJpga2AQB8ffgGBEEQNyAiokbm4JU7AIBBHV0htZCIHA2RaWDSTWRCzmWoWrk7utlDZikVORoicUz0a4Um1lJcyS7C4Wu5YodDRNSoHLySAwAY0N5F5EiITIdeku7y8nKkp6fj0qVLuHv3rj5OSURVOJehWp+YXcupMbO3scJEv1YAVK3dRETUMO4Wl6mHuQ3swKSbqLbqnXQXFRXhyy+/xJAhQ+Do6Ig2bdrA19cXrq6u8Pb2xowZM3D8+HF9xkrU6FVWdN1aOogcCZG4XnnSGwDw58VsZBeWihwNEVHjcOhqDgQB6OxujxYONmKHQ2Qy6pV0f/bZZ2jTpg02btyIp556Cj/99BNOnTqFS5cuISEhAf/+979RUVGBZ555BiNGjMCVK1f0HTdRoyMIgrp7eVe2dFMj18HNHn7ezaFQCohM5IRqREQNoXI8N1u5ierGsj4fOnz4MPbv34/u3btXebxv376YPn061q9fj02bNiEuLg4dOnTQKVCixu52gRw5RWWwkABd3NnSTRT8hBcSU+8h4ngaQga3hUTCCX2IiAxFEATEV47n7uAqcjREpqVeSffOnTtrVU4mk2HmzJn1uQQRPaKylbtDC3vYWnMSNaLnenhgyS8XcCO3BEeu30VAO2exQyIiMlvX7hTjVn4prC0t0LeNk9jhEJkUzl5OZCIuZqkmUeviYS9yJETGwc7aEmN6eQIAdhxPEzkaIiLzduS6arUIf+/mfPhPVEd1Trrv3bunnqH8zp07iIyMxLlz5/QeGBFpunS7CADQiV3LidReeMILAPD7uSzklZSJHA0Rkfk6lqL6/d/Ph72KiOqqTkn3V199BX9/f/j5+WHdunUYP348/vzzT7zwwgvYsGGDoWIkIgCXswoBAJ3cm4ocCZHx6N7SEV08HFBWocSvZzLFDoeIyCwJgqBOuvv6sGs5UV3VaUz3559/jvPnz6OkpAStW7dGSkoKXF1dUVBQgEGDBuGf//ynoeIkatTKKpS4dkfV0t3Rjd3LiSpJJBJM6NMSy34rQFRShnopMSIi0p/0u/eRVVAKK6kEvVs3EzscIpNTp5ZuqVQKGxsbODk5oX379nB1Vc1c6ODgwFljiQzoRm4xKpQCmlhL0bKZrdjhEBmV0T09YSEBElPvIS23ROxwiIjMztEU1Xjunq2awcaK47mJ6qpOSbelpSVKS0sBAHFxcer9hYWF+o2KiDRcetC1vKO7PR9wET3CzcEG/dur1oyNSuKa3fqydu1a+Pj4wMbGBn5+fjh48GCN5ePi4uDn5wcbGxu0bdsW69ev1yoTGRkJX19fyGQy+Pr6IioqSqtMRkYGXnnlFTg7O8POzg69evVCYmKi3r4XEdXdUXYtJ9JJnZLuv/76CzKZDADg6Oio3n///n1s2rRJv5ERkdrl26qku7M7u5YTVWV875YAgF2nMiAIgsjRmL6IiAjMnj0bH3zwAZKSkjBw4ECMHDkSaWlVzxKfkpKCUaNGYeDAgUhKSsL777+PWbNmITIyUl0mISEBwcHBmDx5Mk6fPo3Jkydj0qRJOHr0qLrMvXv30L9/f1hZWeH333/HhQsX8Omnn6JZs2aG/spEVAOO5ybSjUTQw6+T0tJSnDlzBtnZ2VAqlRrHxowZo+vpG1RBQQEcHR2Rn58PBwfOEk3G4Z/bTiD6wm38e7QvXu3vI3Y4REanWF4B/2X7cL9cgV1v9kcvr2Zih1Qrxlrn9OvXD3369MG6devU+7p06YJx48YhPDxcq/y8efOwe/duJCcnq/eFhITg9OnTSEhIAAAEBwejoKAAv//+u7rMiBEj0Lx5c2zfvh0AMH/+fBw6dOixreo1MdZ7SmSqMvPvIyD8L1hIgNP/DoK9jZXYIREZjdrWOTqv0/3HH3+gdevWePLJJzFmzBiMGzdOvY0fP17X0xMR/m7p7sRJ1Iiq1ERmiaCubgCAqJM3RY7GtJWVlSExMRFBQUEa+4OCgnD48OEqP5OQkKBVfvjw4Thx4gTKy8trLPPwOXfv3g1/f3/84x//QIsWLdC7d29s3LixxnjlcjkKCgo0NiLSn8pW7q6ejky4iepJ56T7rbfewj/+8Q9kZmZCqVRqbAqFQh8xEjVq98sUSL2rmhyqI7uXE1Wrsov5L2cyUa5QPqY0VScnJwcKhQJubm4a+93c3JCVlVXlZ7KysqosX1FRgZycnBrLPHzO69evY926dejQoQP27t2LkJAQzJo1C9u2bas23vDwcDg6Oqo3Ly+vOn1fIqrZydR7AAD/Ns1FjoTIdOmcdGdnZyMsLEyrItVVeHg4nnjiCdjb26NFixYYN24cLl269NjP1WYiFyJTcjW7CIIAODexhktTmdjhEBmtAe1d4NzEGneLy5BwLVfscEzeo5M2CoJQ40SOVZV/dP/jzqlUKtGnTx8sX74cvXv3xhtvvIEZM2ZodHN/1IIFC5Cfn6/e0tPTH//liKjWktLzAAB9WjPpJqovnZPuiRMnIjY2Vg+haIqLi8Obb76JI0eOICYmBhUVFQgKCkJxcXG1n6nNRC5EpubSg67lHdyaihwJkXGzlFpgRDd3AMCes5kiR2O6XFxcIJVKtVq1s7Ozq33A7u7uXmV5S0tLODs711jm4XN6eHjA19dXo0yXLl2qncANAGQyGRwcHDQ2ItKP0nIFLtxSDdng+txE9Wep6wnWrFmDf/zjHzh48CC6d+8OKyvNsR6zZs2q13n/+OMPjfdbtmxBixYtkJiYiEGDBlX5mfXr16N169ZYtWoVAFVFfeLECXzyySeYMGFCveIgEtu1O0UAgA4t2LWc6HGe7e6B746m4Y/zWVg6rhuspDo/W250rK2t4efnh5iYGI25WWJiYjB27NgqPxMQEIBffvlFY190dDT8/f3VvwsCAgIQExODOXPmaJQJDAxUv+/fv79Wr7bLly/D29tb5+9FRHV3LiMfFUoBrvYytGxmK3Y4RCZL56T7+++/x969e2Fra4vY2FitbmT1TboflZ+fDwBwcqp+qYLqJmnZtGkTysvLtR4IEJmCa9mqpLudaxORIyEyfn19nODS1Bo5RWU4fC0Xgzu6ih2SSQoLC8PkyZPh7++PgIAAbNiwAWlpaQgJCQGg6tKdkZGhHmsdEhKCNWvWICwsDDNmzEBCQgI2bdqknpUcAEJDQzFo0CCsWLECY8eOxc8//4x9+/YhPj5eXWbOnDkIDAzE8uXLMWnSJBw7dgwbNmzAhg0bGvYGEBEA4NSDruW9vJrVOLyEiGqmc9K9cOFCLFmyBPPnz4eFhWFaFARBQFhYGAYMGIBu3bpVW+5xE7l4eHhofUYul0Mul6vfc9ZTMjbXc1RDKtq6sns50eNYSi0wvKs7vjuahj1nMpl011NwcDByc3OxZMkSZGZmolu3btizZ4+6xTkzM1Ojy7ePjw/27NmDOXPm4IsvvoCnpydWr16t0cssMDAQO3bswMKFC7Fo0SK0a9cOERER6Nevn7rME088gaioKCxYsABLliyBj48PVq1ahZdffrnhvjwRqSWl5QFg13IiXemcdJeVlSE4ONhgCTegmiH9zJkzGk/Dq1ObiVweFh4ejsWLF+seJJEBVCiUSM2tTLrZ0k1UG8/2UHUx33shC8sU7GJeXzNnzsTMmTOrPLZ161atfYMHD8bJkydrPOfEiRMxceLEGss899xzeO6552odJxEZTlKaauby3l6cRI1IFzr/Epk6dSoiIiL0EUuV3n77bezevRv79+9Hq1ataixbm4lcHsVZT8mYpd+7j3KFABsrC3g6ciwVUW3083GGS1Nr5JWU4zBnMSciqpes/FLcyi+FhQTo0cpR7HCITJrOLd0KhQL/+c9/sHfvXvTo0UNr3PTKlSvrdV5BEPD2228jKioKsbGx8PHxeexnajORy6NkMhlkMi7DRMbp+oNJ1HxcmsLCgmOpiGpDaiHBiG7u+PZIGn47c4tdzImI6uFUuqqVu5O7A5rIdE4ZiBo1nVu6z549i969e8PCwgLnzp1DUlKSejt16lS9z/vmm2/i22+/xffffw97e3tkZWUhKysL9+/fV5dZsGABpkyZon4fEhKC1NRUhIWFITk5GZs3b8amTZswd+5cXb4ikWgqZy5n13Kiunm2uycAYO/52yirUIocDRGR6eF4biL90fmx1f79+/URh5Z169YBAIYMGaKxf8uWLZg2bRqA+k3kQmRKrt9Rjedux0nUiOpENYu5DDlFchy5notBbO0mIqoTddLt1UzUOIjMQb2T7vfffx/jxo1D37599RmPWuUEaDWp70QuRKbi76SbLd1EdSG1kOAZXzdsP5aGveezmHQTEdWBQingbIZqud5eTLqJdFbv7uWZmZl47rnn4OHhgX/+85/47bffNJbeIiLdqbuXu7Clm6iuhndVLSEZc+E2lMrHP8glIiKVlJwi3C9XwM5ayiVLifSg3kn3li1bcPv2bfzwww9o1qwZ3nnnHbi4uOD555/H1q1bkZOTo884iRqdvJIy5BaXAeCYbqL6CGznAnuZJbIL5UhKzxM7HCIik1HZyu3r4QApJ3Il0plOE6lJJBIMHDgQ//nPf3Dx4kUcO3YMTz75JDZu3IiWLVti0KBB+OSTT5CRkaGveIkajWsPupa7O9hw1lCierC2tMCQzi0AANHnsx5TmoiIKp29WQAA6NaSS4UR6YPOs5c/rEuXLnjvvfdw6NAh3Lx5E1OnTsXBgwexfft2fV6GqFGoXC6sXQu2chPVV2UX873ns2o1VwgREQHnbqlaupl0E+mHwZrPXF1d8dprr+G1114z1CWIzNr1HFVLN8dzE9XfkE4tYG1pgRu5Jbh8uwid3O3FDomIyKgplQIu3FK1dHdn0k2kF3pp6X7rrbdw9+5dfZyKiB64ls01uol01VRmiQHtXQCoWruJiKhmKbnFKJJXwMbKgqunEOlJvZPumzdvql9///33KCpSJQjdu3dHenq67pERNXKVLd1co5tINw93MSciopqdezCJWhcPB1hK9ToSlajRqve/pM6dO8Pb2xsvvfQSSktL1Yn2jRs3UF5errcAiRojhVJAWm4JAMDHhU+ZiXTxdBc3WEiA87cKkH63ROxwiIiMWmXSza7lRPpT76Q7Pz8fO3fuhJ+fH5RKJUaNGoWOHTtCLpdj7969yMpiiwJRfWXm30eZQglrqQU8m9mKHQ6RSXNuKoN/GycAQPSF2yJHQ0Rk3CqXC+MkakT6U++ku7y8HH379sU777wDW1tbJCUlYcuWLZBKpdi8eTPatWuHTp066TNWokYj9UErdysnW66PSaQHwx4sHZaYyvlHiIiqo1QKOJ/xYLkwTybdRPpS79nLHRwc0Lt3b/Tv3x9lZWUoKSlB//79YWlpiYiICLRq1QrHjh3TZ6xEjcaNXNV47jbO7FpOpA9tHgzT2HPWfHth5eXl4dixY8jOzoZSqdQ4NmXKFJGiIiJTkna3BIXyClhbWqCDG+eUIdKXeifdt27dQkJCAg4fPoyKigr4+/vjiSeeQFlZGU6ePAkvLy8MGDBAn7ESNRqVLd3eznYiR0JkHrya//1vSRAESCTm1YPkl19+wcsvv4zi4mLY29trfD+JRMKkm4hq5exDk6hZcRI1Ir2p978mFxcXjB49GuHh4bCzs8Px48fx9ttvQyKRYO7cuXBwcMDgwYP1GStRo3Ejhy3dRPr0cIvNqfQ88QIxkHfeeQfTp09HYWEh8vLycO/ePfXGJT2JqLbO3XowntvTQeRIiMyL3h5hOTo6YtKkSbCyssJff/2FlJQUzJw5U1+nJ2pU2NJNpF9WUgs42Kg6d/186pbI0ehfRkYGZs2aBTs7/jeDiOrvwi3VeO6uHM9NpFd6SbrPnDmDVq1aAQC8vb1hZWUFd3d3BAcH6+P0RI2KIAhIvcuWbiJ9e8bXHQAQbYbrdQ8fPhwnTpwQOwwiMnHJmYUAgC4e9iJHQmRe6j2m+2FeXl7q1+fOndPHKYkarexCOUrLlZBaSNCyOZcLI9KXyQHeiDx5E7fyS3G/TAFba6nYIenNs88+i3fffRcXLlxA9+7dYWVlpXF8zJgxIkVGRKbiTqEcOUVySCRAJ3cm3UT6VK+kOy0tDa1bt651+YyMDLRs2bI+lyJqdCrHc7dsZstJTIj0qGerv7tLRl/Iwthe5lMvzZgxAwCwZMkSrWMSiQQKhaKhQyIiE3MxS9W1vI1zE9hZ66VdjogeqNcv+ieeeAIzZsyocUmw/Px8bNy4Ed26dcNPP/1U7wCJGhuO5yYyDIlEgifaNAcAfHskVeRo9EupVFa7MeEmotpIzlQl3exaTqR/9XqMlZycjOXLl2PEiBGwsrKCv78/PD09YWNjg3v37uHChQs4f/48/P398d///hcjR47Ud9xEZotrdBMZzqjuHjh+4x6O37gndihEREZFPZ7bnTOXE+lbvVq6nZyc8Mknn+DWrVtYt24dOnbsiJycHFy5cgUA8PLLLyMxMRGHDh1iwk1UR2zpJjKcMT091a+v3C4UMRL9i4uLw+jRo9G+fXt06NABY8aMwcGDB8UOi4hMRGVLd2cPJt1E+qbTgA0bGxs8//zzeP755/UVD1Gjx5nLiQzHuakMMksLyCuU+PHkTSwY2UXskPTi22+/xauvvornn38es2bNgiAIOHz4MIYNG4atW7fipZdeEjtEIjJiZRVKXLtTBIDdy4kMgbM0ERkRQRCQmsOWbiJDerqLGwAg+vxtkSPRn48//hj/+c9/EBERgVmzZiE0NBQRERH4v//7PyxdulTs8IjIyF3NLkK5QoC9jSVaNuPKKUT6xqSbyIjcLS5DobwCEgng5cSkm8gQXuqnWn0jJacYZRVKkaPRj+vXr2P06NFa+8eMGYOUlBQRIiIiU6KeRM3dARKJRORoiMwPk24iI3LjwXhuDwcb2FiZzxrCRMbkybbO6teHruaIGIn+eHl54c8//9Ta/+eff8LLy6te51y7di18fHxgY2MDPz+/x44Pj4uLg5+fH2xsbNC2bVusX79eq0xkZCR8fX0hk8ng6+uLqKgojeMfffQRJBKJxubu7l6v+Imo9iqXC2PXciLD4CJ8REYk9cHM5d4cz01kMFILCTq52ePS7UJ8fywNQzu3EDsknb3zzjuYNWsWTp06hcDAQEgkEsTHx2Pr1q343//+V+fzRUREYPbs2Vi7di369++PL7/8EiNHjsSFCxfQunVrrfIpKSkYNWoUZsyYgW+//RaHDh3CzJkz4erqigkTJgAAEhISEBwcjKVLl2L8+PGIiorCpEmTEB8fj379+qnP1bVrV+zbt0/9XirlA0giQ1PPXM5J1IgMgkk3kRGpnLm8jQu7lhMZ0phenvjv3kuIuWAe47r/9a9/wd3dHZ9++il++OEHAECXLl0QERGBsWPH1vl8K1euxGuvvYbXX38dALBq1Srs3bsX69atQ3h4uFb59evXo3Xr1li1apX62idOnMAnn3yiTrpXrVqFZ555BgsWLAAALFiwAHFxcVi1ahW2b9+uPpelpSVbt4kakCAInLmcyMB07l4+bdo0HDhwQB+xEDV6bOkmahgPLx2WXVgqYiT6M378eMTHxyM3Nxe5ubmIj4+vV8JdVlaGxMREBAUFaewPCgrC4cOHq/xMQkKCVvnhw4fjxIkTKC8vr7HMo+e8cuUKPD094ePjgxdeeAHXr1+vMV65XI6CggKNjYhq706RHLnFZbCQAJ3c2L2cyBB0TroLCwsRFBSEDh06YPny5cjIyNBHXESNUuWYbm9OokZkUA9PVLjzxE0RIzE+OTk5UCgUcHNz09jv5uaGrKysKj+TlZVVZfmKigrk5OTUWObhc/br1w/btm3D3r17sXHjRmRlZSEwMBC5ubnVxhseHg5HR0f1Vt8x7ESNVWXX8jYuTWBrzeEcRIagc9IdGRmJjIwMvPXWW9i5cyfatGmDkSNH4scff1Q/3Sai2mFLN1HDGdzRFQDw+7lMkSOpHycnJ3VC27x5czg5OVW71cejMxgLglDjrMZVlX90/+POOXLkSEyYMAHdu3fH008/jd9++w0A8PXXX1d73QULFiA/P1+9paenP+abEdHDHp65nIgMQy9jup2dnREaGorQ0FAkJSVh8+bNmDx5Mpo2bYpXXnkFM2fORIcOHfRxKSKzlV9SjnslqgdVXKObyPDG9PRE3OU7OJdR8NiE0hh99tlnsLe3V7/WV/wuLi6QSqVardrZ2dlaLdWV3N3dqyxvaWkJZ2fnGstUd04AaNKkCbp3744rV65UW0Ymk0Emk9X4nYioeuqkmzOXExmMXidSy8zMRHR0NKKjoyGVSjFq1CicP38evr6++M9//oM5c+bo83JEZiX1rqqV29VehiYyznFIZGjP9vDAOztPAwBOpuXBz7u5yBHVzdSpU9Wvp02bprfzWltbw8/PDzExMRg/frx6f0xMTLVjxAMCAvDLL79o7IuOjoa/vz+srKzUZWJiYjR+C0RHRyMwMLDaWORyOZKTkzFw4EBdvhIR1eDig+7lndnSTWQwOncvLy8vR2RkJJ577jl4e3tj586dmDNnDjIzM/H1118jOjoa33zzDZYsWaKPeInMlnrmcrZyEzUIGysp7G1UD7h+OX1L5Gh0I5VKkZ2drbU/Nze3XktuhYWF4auvvsLmzZuRnJyMOXPmIC0tDSEhIQBUXbqnTJmiLh8SEoLU1FSEhYUhOTkZmzdvxqZNmzB37lx1mdDQUERHR2PFihW4ePEiVqxYgX379mH27NnqMnPnzkVcXBxSUlJw9OhRTJw4EQUFBRoPGIhIf8oVSlzPKQIAdHJnSzeRoejcnObh4QGlUokXX3wRx44dQ69evbTKDB8+HM2aNdP1UkRmrXI8d2snjucmaijje7fEtoRU/HrmFj4a01XscOqtcvz0o+RyOaytret8vuDgYOTm5mLJkiXIzMxEt27dsGfPHnh7ewNQ9WxLS0tTl/fx8cGePXswZ84cfPHFF/D09MTq1avVy4UBQGBgIHbs2IGFCxdi0aJFaNeuHSIiIjTW6L558yZefPFF5OTkwNXVFU8++SSOHDmivi4R6deNnGKUKwTYWUvRspmt2OEQmS2dk+7Q0FC88847sLPTbJ0TBAHp6elo3bo1mjdvjpSUFF0vRWTWbrClm6jBTfRrhW0JqcgpKsP9MoXJzdy7evVqAKoJyr766is0bdpUfUyhUODAgQPo3Llzvc49c+ZMzJw5s8pjW7du1do3ePBgnDx5ssZzTpw4ERMnTqz2+I4dO+oUIxHp5vJtVSt3Bzd7WFiY1rwWRKZE56T7o48+whtvvKGVdN+9exc+Pj5QKBS6XoKoUVDPXO7Clm6ihtLN01H9Oib5tsb63abgs88+A6B60L1+/XqNruTW1tZo06YN1q9fL1Z4RGTkLt1Wjefu2KLpY0oSkS50Trqr69JWVFQEGxsbXU9P1GiwpZuo4VlYSNDLqxlOpefhh+PpJpd0V/YiGzp0KH766Sc0b25ak8ERkbiuPEi6OZ6byLDqnXSHhYUBUHVp+/DDDzVauhUKBY4ePVrl+G4i0lZSVoE7hXIAgDfHdBM1qOFd3XEqPQ/xV3PEDqXe9u/fL3YIRGSCKlu6O7gx6SYypHon3UlJSQBULd1nz57VmKjF2toaPXv21Ji1lIiqVzlzeXM7KzjaWYkcDVHjMq63J1b8cREAcPNeCVo1N73eJhMnToS/vz/mz5+vsf+///0vjh07hp07d4oUGREZq9Jyhfr3Rycm3UQGVe+ku/Kp+quvvor//e9/cHDg2n5E9aWeudyZrdxEDc3D8e8Ze386mYFZwzqIGE39xMXF4d///rfW/hEjRuCTTz4RISIiMnbX7xRDoRRgb2MJNweZ2OEQmTWd1+nesmULE24iHXE8N5G4Bnd0BQBEX8gSOZL6KSoqqnJpMCsrKxQUFIgQEREZuyvZD8Zzu9lDIuHM5USGVK+W7rCwMCxduhRNmjRRj+2uzsqVK+sVGFFjop65nC3dRKKY5O+FuMt3cC6jAEqlYHJL53Tr1g0RERH48MMPNfbv2LEDvr6+IkVFRMbsUhbHcxM1lHol3UlJSSgvL1e/rg6fmhHVzo0ctnQTiWlYlxbq10npefDzNq1ZwBctWoQJEybg2rVreOqppwAAf/75J7Zv387x3ERUpco1uju5cbkwIkOrV9L98CypnDGVSHdpd1VJN1u6icRhYyWFcxNr5BaXYfepDJNLuseMGYNdu3Zh+fLl+PHHH2Fra4sePXpg3759GDx4sNjhEZERuly5RjeXCyMyOJ3HdN+/fx8lJSXq96mpqVi1ahWio6N1PTVRo1BarsCt/PsAAG+2dBOJZlzvlgCAXaduiRxJ/Tz77LM4dOgQiouLkZOTg7/++osJNxFVqaSsAun3VL/fO7J7OZHB6Zx0jx07Ftu2bQMA5OXloW/fvvj0008xduxYrFu3TucAiczdzXslEASgqcwSzk20J0IiooYxuqcnACD/fjnulylEjoaIyHCuZhdBEADnJtZwacqZy4kMTeek++TJkxg4cCAA4Mcff4S7uztSU1Oxbds2rF69WucAicxd5RqZ3s52nAeBSEQ9WzmqX5vaLOYKhQKffPIJ+vbtC3d3dzg5OWlsREQPqxzP3YHjuYkahM5Jd0lJCeztVd1SoqOj8fzzz8PCwgJPPvkkUlNTdTr3gQMHMHr0aHh6ekIikWDXrl01lo+NjYVEItHaLl68qFMcRIZ046Gkm4jEI5FI0ONB4v1j4k2Ro6mbxYsXY+XKlZg0aRLy8/MRFhamro8/+ugjscMjIiNTOZ67E7uWEzUInZPu9u3bY9euXUhPT8fevXsRFBQEAMjOztZ5/e7i4mL07NkTa9asqdPnLl26hMzMTPXWoUMHneIgMqQ0LhdGZDSCfN0AAAev5IgcSd1899132LhxI+bOnQtLS0u8+OKL+Oqrr/Dhhx/iyJEjYodHREamMunmcmFEDUPnpPvDDz/E3Llz0aZNG/Tr1w8BAQEAVK3evXv31uncI0eOxLJly/D888/X6XMtWrSAu7u7epNKpTrFQWRI6pZuJ7Z0E4ltgl8r9euMvPsiRlI3WVlZ6N69OwCgadOmyM/PBwA899xz+O2338QMjYiM0JXK5cI4czlRg9A56Z44cSLS0tJw4sQJ/PHHH+r9w4YNw2effabr6euld+/e8PDwwLBhw7ikGRm9VLZ0ExkND0db9etdSRkiRlI3rVq1QmZmJgBVD7TKFUSOHz8OmYyTJBHR3wpLy9UPFTu2YNJN1BB0TroBwN3dHb1794aFxd+n69u3Lzp37qyP09eah4cHNmzYgMjISPz000/o1KkThg0bhgMHDlT7GblcjoKCAo2NqKFUKJS4eY/LhREZk4EdXAAA0edNZzK18ePH488//wQAhIaGYtGiRejQoQOmTJmC6dOnixwdERmTK9mqVm43Bxkc7axEjoaocbDUx0n+/PNP/Pnnn8jOzoZSqdQ4tnnzZn1colY6deqETp06qd8HBAQgPT0dn3zyCQYNGlTlZ8LDw7F48eKGCpFIw628UlQoBVhbWsDdwUbscIgIQPATXjh4JQenb+ZDqRRgYWH8qwr83//9n/r1xIkT4eXlhUOHDqF9+/YYM2aMiJERkbG5nKUaz831uYkajs4t3YsXL0ZQUBD+/PNP5OTk4N69exqb2J588klcuXKl2uMLFixAfn6+ektPT2/A6KixS737oGu5k51J/LAnagye7uKmfn02I1/ESGqnvLwcr776Kq5fv67e169fP4SFhTHhJiItlcuFMekmajg6t3SvX78eW7duxeTJk/URj94lJSXBw8Oj2uMymYzj3Ug0XC6MyPjYWEnh0tQaOUVliErKQE+vZmKHVCMrKytERUVh0aJFYodCRCagcubyjlyjm6jB6NzSXVZWhsDAQH3EoqWoqAinTp3CqVOnAAApKSk4deoU0tLSAKhaqadMmaIuv2rVKuzatQtXrlzB+fPnsWDBAkRGRuKtt94ySHxEuuJyYUTG6bkengCAKBOZTG38+PHYtWuX2GEQkQn4O+lmSzdRQ9G5pfv111/H999/b5An7CdOnMDQoUPV78PCwgAAU6dOxdatW5GZmalOwAHVA4C5c+ciIyMDtra26Nq1K3777TeMGjVK77ER6QNbuomM0+ienth6+Aby75ejtFwBGyvjXnqyffv2WLp0KQ4fPgw/Pz80aaL5IG/WrFkiRUZExiSvpAzZhXIAXKObqCHpnHSXlpZiw4YN2LdvH3r06AErK81ZEFeuXFnvcw8ZMgSCIFR7fOvWrRrv33vvPbz33nv1vh5RQ+NyYUTGqU/rZurX0RduY0xPT/GCqYWvvvoKzZo1Q2JiIhITEzWOSSQSJt1EBODv8dwtm9miqUwv8ykTUS3o/K/tzJkz6NWrFwDg3LlzGsckEk4MRVQdpVJA2t0HLd1ObOkmMiYSiQS+Hg64kFmAqJM3jTbpViqVsLCwQEpKitihEJEJuMTx3ESi0Dnp3r9/vz7iIGp0sgvlKC1XQmohQcvmtmKHQ0SPCOrqhguZBdh/6Y7YoVTLysoKmZmZaNGiBQDg3XffxYIFC+Dk5CRyZERkjK5UJt3u7FpO1JB0nkiNiOqnsmt5y2a2sJLynyKRsQl+wkv9OjP/voiRVO/RIVhffvkl8vLyxAmGiIzepco1ulsw6SZqSHr5pX/w4EG88sorCAgIQEaGaqbXb775BvHx8fo4PZFZSuUkakRGzcPx7x4ou0/dEjGS2qtpHhQiatwEQVDPXN6JLd1EDUrnpDsyMhLDhw+Hra0tkpKSIJerZkQsLCzE8uXLdQ6QyFyl3lW1dLfhJGpERmtgBxcAwB/ns0SOpOGtXbsWPj4+sLGxgZ+fHw4ePFhj+bi4OPj5+cHGxgZt27bF+vXrtcpERkbC19cXMpkMvr6+iIqKqvZ84eHhkEgkmD17tq5fhYgA5BSV4V5JOSQSoJ0rx3QTNSSdx3QvW7YM69evx5QpU7Bjxw71/sDAQCxZskTX0xOZLS4XRmT8XniiNQ5eyUFSWh4EQTDKCUI//PBD2Nmp/jtSVlaGjz/+GI6Ojhpl6rqSSEREBGbPno21a9eif//++PLLLzFy5EhcuHABrVu31iqfkpKCUaNGYcaMGfj2229x6NAhzJw5E66urpgwYQIAICEhAcHBwVi6dCnGjx+PqKgoTJo0CfHx8ejXr5/G+Y4fP44NGzagR48edYqbiKpXOZ67tZMdbK2NexlEInOjc9J96dIlDBo0SGu/g4MDx5UR1YDLhREZv2FdWqhfn83IR49WzcQLpgqDBg3CpUuX1O8DAwNx/fp1jTL1eVCwcuVKvPbaa3j99dcBAKtWrcLevXuxbt06hIeHa5Vfv349WrdujVWrVgEAunTpghMnTuCTTz5RJ92rVq3CM888gwULFgAAFixYgLi4OKxatQrbt29Xn6uoqAgvv/wyNm7ciGXLltU5diKq2pVs1XJhHTiem6jB6Zx0e3h44OrVq2jTpo3G/vj4eLRt21bX0xOZJUEQOKabyATYWEnh5iDD7QI5th9LN7qkOzY2Vu/nLCsrQ2JiIubPn6+xPygoCIcPH67yMwkJCQgKCtLYN3z4cGzatAnl5eWwsrJCQkIC5syZo1WmMlGv9Oabb+LZZ5/F008/zaSbSI8uc7kwItHoPKb7jTfeQGhoKI4ePQqJRIJbt27hu+++w9y5czFz5kx9xEhkdu6VlKOwtAKAqpsXERmvkd08AAB7zmaKHEnDyMnJgUKhgJubm8Z+Nzc3ZGVVPbY9KyuryvIVFRXIycmpsczD59yxYwdOnjxZZWt6deRyOQoKCjQ2ItJ25faDlm4m3UQNTueW7vfeew/5+fkYOnQoSktLMWjQIMhkMsydOxdvvfWWPmIkMjuVXcvdHWxgY8VxVUTGbHRPD2w9fAP598tRWq5oNP9mH+2W/rgx7VWVf3R/TedMT09HaGgooqOjYWNjU+s4w8PDsXjx4lqXJ2qMBEHA5WxVSze7lxM1PL0sGfbxxx8jJycHx44dw5EjR3Dnzh0sXbpUH6cmMkvsWk5kOvq0bq5+/esZ82/tdnFxgVQq1WrVzs7O1mqpruTu7l5leUtLSzg7O9dYpvKciYmJyM7Ohp+fHywtLWFpaYm4uDisXr0alpaWUCgUVV57wYIFyM/PV2/p6en1+t5E5uxOkRx5JeWwkADtW7Clm6ih6ZR0K5VKbN68Gc899xz69u2LV199FcuWLcNPP/3EtUKJalCZdHO5MCLjJ5FI0NXTAQDw6xnTWK9bF9bW1vDz80NMTIzG/piYGAQGBlb5mYCAAK3y0dHR8Pf3h5WVVY1lKs85bNgwnD17FqdOnVJv/v7+ePnll3Hq1ClIpVX3MJDJZHBwcNDYiEjT1Qddy1s72TWa3jpExqTe3csFQcCYMWOwZ88e9OzZE927d4cgCEhOTsa0adPw008/YdeuXXoMlch8VHYvb82WbiKTMKq7B87fKkDspTtih9IgwsLCMHnyZPj7+yMgIAAbNmxAWloaQkJCAKhalzMyMrBt2zYAQEhICNasWYOwsDDMmDEDCQkJ2LRpk8as5KGhoRg0aBBWrFiBsWPH4ueff8a+ffsQHx8PALC3t0e3bt004mjSpAmcnZ219hNR3VROotbBjV3LicRQ75burVu34sCBA/jzzz+RlJSE7du3Y8eOHTh9+jT27duHv/76S10ZE5GmGw+SbrZ0E5mG4Ce81K/T75aIGEn1Dh48iFdeeQUBAQHIyMgAAHzzzTfqpLYugoODsWrVKixZsgS9evXCgQMHsGfPHnh7ewMAMjMzkZaWpi7v4+ODPXv2IDY2Fr169cLSpUuxevVq9XJhgGo5sx07dmDLli3o0aMHtm7dioiICK01uolI/y6rlwtj13IiMdS7pXv79u14//33MXToUK1jTz31FObPn4/vvvsOU6ZM0SlAInOUdpdjuolMiUtTmfr17tO38ObQ9iJGoy0yMhKTJ0/Gyy+/jKSkJMjlcgBAYWEhli9fjj179tT5nDNnzqx2FZKtW7dq7Rs8eDBOnjxZ4zknTpyIiRMn1joGQyyJRtQYXVEvF8aWbiIx1Lul+8yZMxgxYkS1x0eOHInTp0/X9/REZqtIXoGcojIA7F5OZEqe7aFaOiwy8abIkWhbtmwZ1q9fj40bN6rHUAOq1uXHJcJEZN4EQcBlLhdGJKp6J913796tdhZTQLX25r179+p7eiKzdSNH1bXcqYk1HGysHlOaiIzF871bAgCu5xRDoTSuyUIvXbqEQYMGae13cHBAXl5ewwdEREbjTpEc+fdVM5e3c2XSTSSGeifdCoUClpbV906XSqWoqKio7+mJzFbleG4fF47nJjIlgzq6ql8fupojYiTaPDw8cPXqVa398fHxaNu2rQgREZGxuPKgldvbuQlnLicSiU6zl0+bNg0ymazK45XjyYhIU8odTqJGZIqspBZo42yHG7kliDiRrpGEi+2NN95AaGgoNm/eDIlEglu3biEhIQFz587Fhx9+KHZ4RCQi9czlnESNSDT1TrqnTp362DKcRI1IW8qDlu62rky6iUxNUFd3bDhwHX8m3xY7FA3vvfce8vPzMXToUJSWlmLQoEGQyWSYO3cu3nrrLbHDIyIRcTw3kfjqnXRv2bJFn3EQNRopOexeTmSqgp/wwoYD11FarkRukRzOTavu7SWGjz/+GB988AEuXLgApVIJX19fNG3KH9lEjd3VbM5cTiS2eifdRFQ/lUk3u5cTmZ6HJyH69Uwmpga2ES+YKtjZ2cHf31/sMIjISGjMXN6CSTeRWJh0EzWge8VlyCspBwC0ceFyYUSmqJ+PE46m3MXPpzKMJukuLS3F559/jv379yM7OxtKpVLjOJcNI2qc7hT+PXM5h7URiYdJN1EDqhzP7eFoAztr/vMjMkWvPOmNoyl3cTItD4IgQCKRiB0Spk+fjpiYGEycOBF9+/Y1ipiISHyVrdxtOHM5kaj4q5+oAXHmciLT93QXN/Xrk2l58PNuLmI0Kr/99hv27NmD/v37ix0KERmRypnL23PmciJR1XudbiKqO/Ua3eziRWSybK2laNnMFgDw3ZFUkaNRadmyJeztOV6TiDRdyVa1dHMSNSJx6SXpPnjwIF555RUEBAQgIyMDAPDNN98gPj5eH6cnMhvXH0yi1pYzlxOZtKe7tAAAxFwwjqXDPv30U8ybNw+pqcbxEICIjMOVyjW6uVwYkah0TrojIyMxfPhw2NraIikpCXK5HABQWFiI5cuX6xwgkTlh93Ii8xD8RGsAQKG8AnklZSJHA/j7+6O0tBRt27aFvb09nJycNDYianxUM5dzuTAiY6DzmO5ly5Zh/fr1mDJlCnbs2KHeHxgYiCVLluh6eiKzIQgCu5cTmQlfTwf161/OZGLyk94iRgO8+OKLyMjIwPLly+Hm5saJ1IgI2YVyFJRWQGoh4czlRCLTOem+dOkSBg0apLXfwcEBeXl5up6eyGxkF8pRUqaA1EICr+ZcLozI1PVt44RjN+5i54l00ZPuw4cPIyEhAT179hQ1DiIyHpWt3N7OdpBZcuZyIjHp3L3cw8MDV69e1dofHx+Ptm3b6np6IrNx/UHXcq/mtrC25ByGRKbulQBVon3mZj4EQRA1ls6dO+P+/fuixkBExuXKg+XCOnDmciLR6fzL/4033kBoaCiOHj0KiUSCW7du4bvvvsPcuXMxc+ZMfcRIZBYqu5a34SRqRGYhyPfvpcOOXL8rYiTA//3f/+Gdd95BbGwscnNzUVBQoLERUeNzJZvjuYmMhc7dy9977z3k5+dj6NChKC0txaBBgyCTyTB37ly89dZb+oiRyCykPJi53IdJN5FZsLFSLR2WkXcfO46nIaCds2ixjBgxAgAwbNgwjf2CIEAikUChUIgRFhGJ6HJlSzeTbiLR6Zx0A8DHH3+MDz74ABcuXIBSqYSvry+aNmVXFqKHVXYv53JhROYjqKsbthy6IfrSYfv37xf1+kRkXDRnLudvciKx6Zx0p6WlwcvLC3Z2dvD399c61rp1a10vQWQW2L2cyPxMC2yDLYduoKRMgezCUrSwtxEljsGDB4tyXSIyTrcL5Ch8MHM5e9gRiU/npNvHxweZmZlo0aKFxv7c3Fz4+PiwSxsRAIVSQGouu5cTmRtv57//Pe88cRNvDm0vShwHDhyo8XhVq4wQkfmqHM/NmcuJjIPOSXfleLFHFRUVwcZGnCf+RMYm4959lCsEWFtawNPRVuxwiEiPnu3hgd/OZOL7o2miJd1DhgzR2vdw3cwH4ESNS+V47o4tOJ6byBjUO+kOCwsDoKrUFy1aBDu7v9cdVigUOHr0KHr16qVzgETm4FqOqvLzcW4CCwvth1REZLrG92qJ385kIiPvPsoVSlhJG35JwHv37mm8Ly8vR1JSEhYtWoSPP/64weMhInFd4XhuIqNS76Q7KSkJgKql++zZs7C2tlYfs7a2Rs+ePTF37lzdIyQyA9eyVUl3e66VSWR2hnb+e3jVH+eyMLqnZ4PH4OjoqLXvmWeegUwmw5w5c5CYmNjgMRGReConUePM5UTGod5Jd+VMqa+++ir+97//wcHBQW9BEZmbqw+S7nauHM9NZG6kFhK0dWmC6znFiErKECXpro6rqysuXbokdhhE1IAEQcCVB93LO7kz6SYyBjqP6d6yZQsA4MKFC0hLS0NZWZnG8TFjxuh6CSKTd+3Og6SbLd1EZumVJ72x5NcL+OtitijXP3PmjMZ7QRCQmZmJ//u//0PPnj1FiYmIxJGRdx+F8gpYSTlzOZGx0DnpTklJwbhx43D27FlIJBIIggDg7wlcOHkL0cMt3Uy6iczRBL9WWPLrBQDA6fQ89PRq1qDX79Wrl0YdXOnJJ5/E5s2bGzQWIhLXpSxV1/J2rk1FmWOCiLTpnHTPmjULPj4+2LdvH9q2bYtjx44hNzcX77zzDj755BN9xEhk0u4Wl+FeSTkkEibdRObK0dYKLk1lyCmS45sjqQ2edKekpGi8t7CwgKurK1cRIWqELj5Iutm1nMh46Pz4KyEhAUuWLIGrqyssLCxgYWGBAQMGIDw8HLNmzdJHjEQmrbKVu2UzW9hac61MInM1sps7ANVkag3N29tbY/Py8tI54V67di18fHxgY2MDPz8/HDx4sMbycXFx8PPzg42NDdq2bYv169drlYmMjISvry9kMhl8fX0RFRWlcXzdunXo0aMHHBwc4ODggICAAPz+++86fQ+ixqZyEjUm3UTGQ+eWboVCgaZNVa13Li4uuHXrFjp16gRvb29O3kIEdi0naixeG+CDb46kokhegeyCUrRwMGwr8+rVq2tdtq4PwSMiIjB79mysXbsW/fv3x5dffomRI0fiwoULaN26tVb5lJQUjBo1CjNmzMC3336LQ4cOYebMmXB1dcWECRMAqB7SBwcHY+nSpRg/fjyioqIwadIkxMfHo1+/fgCAVq1a4f/+7//Qvr1qvfOvv/4aY8eORVJSErp27Vqn70DUWFV2L+/EmcuJjIZEeHQAWB0NHDgQ77zzDsaNG4eXXnoJ9+7dw8KFC7FhwwYkJibi3Llz+oq1QRQUFMDR0RH5+fmckZ30YumvF7ApPgWvDfDBoud8xQ6HiAyozfzfAABvDG6LBSO7PLa8LnWOj49PrcpJJBJcv369Tufu168f+vTpg3Xr1qn3denSBePGjUN4eLhW+Xnz5mH37t1ITk5W7wsJCcHp06eRkJAAAAgODkZBQYFGy/WIESPQvHlzbN++vdpYnJyc8N///hevvfZarWJnPU6NWblCCd8P/0C5QkD8vKFo1dxO7JCIzFpt6xydu5cvXLgQSqUSALBs2TKkpqZi4MCB2LNnT52ewlflwIEDGD16NDw9PSGRSLBr167HfqY23duIGtJVrtFN1GiM66VaLmz3qVsGv1ZKSkqttrom3GVlZUhMTERQUJDG/qCgIBw+fLjKzyQkJGiVHz58OE6cOIHy8vIay1R3ToVCgR07dqC4uBgBAQF1+g5EjdX1O8UoVwiwl1miZTNbscMhogd07l4+fPhw9eu2bdviwoULuHv3Lpo3b66ewby+iouL0bNnT7z66qvq7mk1qU33NqKGxu7lRI3HpCe8sOvULWTml6JIXoGmMp2r2Tp7dBWRusrJyYFCoYCbm5vGfjc3N2RlVT1ePSsrq8ryFRUVyMnJgYeHR7VlHj3n2bNnERAQgNLSUjRt2hRRUVHw9a2+l5BcLodcLle/LygoqNX3JDJHF7NUf/8d3e11/h1ORPqjc0t3Wlqa1hIlTk5OkEgkSEtL0+ncI0eOxLJly/D888/Xqvz69evRunVrrFq1Cl26dMHrr7+O6dOncxZ1Es39MgUy8u4DYEs3UWMQ0NZZ/XrnifQGvfa2bdvQvXt32NrawtbWFj169MA333xT7/M9+oNdEIQaf8RXVf7R/bU5Z6dOnXDq1CkcOXIE//rXvzB16lRcuHCh2uuGh4fD0dFRvXl5edX8xYjMWOUkah05npvIqOicdPv4+ODOnTta+3Nzc2s93kxfatO9jaghXbujauV2amINpybWIkdDRIYmkUgwsIMLAGBbQmqDXXflypX417/+hVGjRuGHH35AREQERowYgZCQEHz22Wd1OpeLiwukUqlWC3R2drZWS3Uld3f3KstbWlrC2dm5xjKPntPa2hrt27eHv78/wsPD0bNnT/zvf/+rNt4FCxYgPz9fvaWnN+zDDiJjUjmJWmfOXE5kVHROuqt78l1UVNTg64M+rntbVeRyOQoKCjQ2In2pTLrbuTYRORIiaigv9VXN7p2SU4yyCmWDXPPzzz/HunXrsGLFCowZMwZjx47Ff/7zH6xdu7bO86tYW1vDz88PMTExGvtjYmIQGBhY5WcCAgK0ykdHR8Pf3x9WVlY1lqnunJUEQdDoPv4omUymXmKsciNqrLhGN5Fxqvdgs7CwMACqp/qLFi2Cnd3fsyMqFAocPXoUvXr10jnAuqpN97aHhYeHY/HixQaPixqna5xEjajRCerqrn79x/ksjOnpafBrZmZmVpm8BgYGIjMzs87nCwsLw+TJk+Hv74+AgABs2LABaWlpCAkJAaBqXc7IyMC2bdsAqGYqX7NmDcLCwjBjxgwkJCRg06ZNGrOSh4aGYtCgQVixYgXGjh2Ln3/+Gfv27UN8fLy6zPvvv4+RI0fCy8sLhYWF2LFjB2JjY/HHH3/U+TsQNTZF8grcvKca0sblwoiMS72T7qSkJACqpPbs2bOwtv6766y1tTV69uyJuXPn6h5hHdSme9ujFixYoH6AAKgmYOF4MNKXq3c4iRpRYyO1kKCTmz0u3S7E2v1XGyTpbt++PX744Qe8//77GvsjIiLQoUOHOp8vODgYubm5WLJkCTIzM9GtWzfs2bMH3t7eAFRJ/sPztvj4+GDPnj2YM2cOvvjiC3h6emL16tUak5gGBgZix44dWLhwIRYtWoR27dohIiJCvUY3ANy+fRuTJ09GZmYmHB0d0aNHD/zxxx945pln6vwdiBqbyvHcLexlaM4hbURGpd5J9/79+wEAr776Kv73v/8ZRXeugIAA/PLLLxr7Hu3e9iiZTAaZTNYQ4VEjpJ65nC3dRI3KG4PbIuyH07iYVQiFUoDUwrCzCC9evBjBwcE4cOAA+vfvD4lEgvj4ePz555/44Ycf6nXOmTNnYubMmVUe27p1q9a+wYMH4+TJkzWec+LEiZg4cWK1xzdt2lSnGInob5fYtZzIaOk8pnvLli0GS7iLiopw6tQpnDp1CoBqSbBTp06pn64vWLAAU6ZMUZcPCQlBamoqwsLCkJycjM2bN2PTpk0N3uJOBABlFUpcv1MMgLOIEjU2z/bwUL/+62K2wa5TWT9OmDABR48ehYuLC3bt2oWffvoJLi4uOHbsGMaPH2+w6xOR8eAkakTGq14t3WFhYVi6dCmaNGmi0TW7KitXrqxXYABw4sQJDB06VOO6ADB16lRs3bq1Xt3biBpKSk4xKpQC7GWW8HRs2EkFiUhcMkspvJxskX73PtbGXsUzvlXP+q2rPn36oHfv3nj99dfx0ksv4dtvvzXIdYjI+FWu0d3JXfzep0SkqV5Jd1JSknoJrsqx3VWpaT3P2hgyZIjWGuAPq2/3NqKGcKlyrUx3e53/LRCR6Zk5pD0W/HQWSWl5j13jur4OHTqEzZs3Y/78+XjnnXcwYcIETJ8+XeOBNRGZP0EQ/u5ezt51REanXkl35XjuR18/bqZwosbk8oPKj13LiRqncb1aYsFPZwEAsZfvYGinFnq/RkBAAAICArB69Wr88MMP2LJlC55++mm0adMG06dPx9SpU9GqVSu9X5eIjMudIjnulZTDQgJ0cOM8MkTGRucx3YBq4pNu3brBxsYGNjY26NatG7766it9nJrIZKnXymTlR9Qo2VpL1UNLPv/zimGvZWuLqVOnIjY2FpcvX8aLL76IL7/8Ej4+Phg1apRBr01E4qts5W7j3AQ2VlKRoyGiR+mcdC9atAihoaEYPXo0du7ciZ07d2L06NGYM2cOFi5cqI8YiUzS5Ye6lxNR4/Svoe0BACcfdDFvCO3atcP8+fPxwQcfwMHBAXv37m2Q6xKReC5mcuZyImNW7yXDKq1btw4bN27Eiy++qN43ZswY9OjRA2+//TaWLVum6yWITE5JWQXS7pYA4NgqosZsQp+WWLTrHABg/6VsPNXZMBOqVYqLi8PmzZsRGRkJqVSKSZMm4bXXXjPoNYlIfBcyVZOo+XpwEjUiY6Rz0q1QKODv76+138/PDxUVFbqensgkXbmtWp/bpakMzk25DjxRY2VnbYmWzWyRkXcfq/ZdMUjSnZ6ejq1bt2Lr1q1ISUlBYGAgPv/8c0yaNAlNmjTR+/WIyPhcuPUg6fZk0k1kjHTuXv7KK69g3bp1Wvs3bNiAl19+WdfTE5kk9Qyi7hzPTdTYvf2Uqov5mZv5UCj128X8mWeegY+PD9auXYuJEyciOTkZ8fHxePXVV5lwEzUSpeUKXL2jetjPpJvIOOnc0g2oJlKLjo7Gk08+CQA4cuQI0tPTMWXKFI11vHVZs5vIlKiXC2PXcqJGb1zvlpj/YBbzPWczMbqnp97ObWtri8jISDz33HOQSjl5ElFjdDW7CAqlgGZ2VnB3sBE7HCKqgs5J97lz59CnTx8AwLVr1wAArq6ucHV1xblz59TluIwYNSaVk6h15oQmRI2ejZUUHVo0xZXsIqyNvabXpHv37t16OxcRmSZ113IPB/7eJjJSOifdD6/TTUQql7hGNxE95M2h7TE74hSSMwsgr1BAZslWaSLSD06iRmT89LJONxH97V5xGbIL5QCADky6iQjQaN3+/miaiJEQkbnhJGpExq9eLd1hYWFYunQpmjRpojFmuyocx02NTeV47lbNbdFUppdpE4jIxEktJAhs54zD13LxzZFUvNrfR+yQiMgMCIKA5Ewm3UTGrl4ZQVJSEsrLy9Wvq8NxJdQYVT5x7uzOyo+I/jZjYFscvpaL63eKkX+/HI62VmKHREQm7ua9+yiUV8BaaoF2rlwxhchY1SvpfngcN8d0E2mqHFvVlU+cieghQzq5ql9/dfA63gnqJGI0RGQOzj940N/BrSmspBw1SmSs+K+TSM/Oc2wVEVVBIpFgVHd3AMC62GsiR0NE5oCTqBGZBp2T7vDwcGzevFlr/+bNm7FixQpdT09kUsoqlLiarRrTzZZuInpU6LCOAIAKpYDrd4pEjoaITB0nUSMyDTon3V9++SU6d+6stb9r165Yv369rqcnMimXbxeiXCHA0dYKLZvZih0OERmZTu72kFmqqt6VMZdFjoaITF3lJGpd2NJNZNR0TrqzsrLg4eGhtd/V1RWZmZm6np7IpDzczYsTCRJRVSY/6Q0A+PVMJgRBEDkaIjJV+SXlyMi7D4BJN5Gx0znp9vLywqFDh7T2Hzp0CJ6enlV8gsh8VXbzYtdyIqrOm0Pbq1/vv3RHxEiIyJRVPuhv1dyWqyEQGTmdFxF+/fXXMXv2bJSXl+Opp54CAPz5559477338M477+gcIJEpOX8rHwDHVhFR9Zo3sUbLZrbIyLuPL+M4oRoR1Y/6NwdbuYmMns5J93vvvYe7d+9i5syZKCsrAwDY2Nhg3rx5mD9/vs4BEpkKpVJAcmblJGqOIkdDRMZs7vCOmBNxWr3aARFRXZ25qUq6e7Tibw4iY6dz93KJRIIVK1bgzp07OHLkCE6fPo27d+/iww8/5JhWalTS7pagSF4Ba0sLtHVtInY4RGTExvZsKXYIRGTizmWoku5uLZl0Exk7vazTffDgQYSEhCA0NBTNmzeHTCbDN998g/j4eH2cnsgkVI6t6uxuDyupXv5pEZGZsrCQYHhXN7HDICITVVBajus5xQCA7ky6iYyezplBZGQkhg8fDltbW5w8eRJyuRwAUFhYiOXLl+scIJGp4NgqIqqLeSO0l9skIqqN8xmqB/0tm9nCualM5GiI6HF0TrqXLVuG9evXY+PGjbCy+nvmxMDAQJw8eVLX0xOZjLMPKsCufOJMRLXQ1rUprCzZK4aI6u5sRh4AtnITmQqda/tLly5h0KBBWvsdHByQl5en6+mJTIIgCDhzMw8A0JMTmhBRLX30nK/YIRCRCap80N+dvzmITILOSbeHhweuXr2qtT8+Ph5t27bV9fREJiHtbgnySsphLbVAZ3d2Lyei2hnb23gnVFu7di18fHxgY2MDPz8/HDx4sMbycXFx8PPzg42NDdq2bYv169drlYmMjISvry9kMhl8fX0RFRWlcTw8PBxPPPEE7O3t0aJFC4wbNw6XLl3S6/ciMgdnHzzoZ0s3kWnQOel+4403EBoaiqNHj0IikeDWrVv47rvvMHfuXMycOVMfMRIZvdMPlu3o4ukAa3YXJSITFxERgdmzZ+ODDz5AUlISBg4ciJEjRyItLa3K8ikpKRg1ahQGDhyIpKQkvP/++5g1axYiIyPVZRISEhAcHIzJkyfj9OnTmDx5MiZNmoSjR4+qy8TFxeHNN9/EkSNHEBMTg4qKCgQFBaG4uNjg35nIVBSUluNGbgkAJt1EpkIiCIKg60k++OADfPbZZygtLQUAyGQyzJ07F0uXLtU5wIZWUFAAR0dH5Ofnw8GBLZZUO0t/vYBN8SmYEuCNJWO7iR0OEZkIY61z+vXrhz59+mDdunXqfV26dMG4ceMQHh6uVX7evHnYvXs3kpOT1ftCQkJw+vRpJCQkAACCg4NRUFCA33//XV1mxIgRaN68ObZv315lHHfu3EGLFi0QFxdX5VC2qhjrPSXSl8PXcvDSxqNo1dwW8fOeEjscokattnWOXprkPv74Y+Tk5ODYsWM4cuQI7ty5Y5IJN1F9VY7n7tGqmahxEBHpqqysDImJiQgKCtLYHxQUhMOHD1f5mYSEBK3yw4cPx4kTJ1BeXl5jmerOCQD5+apeRE5OTnX+HkTm6uyD3nVs5SYyHTol3eXl5Rg6dCguX74MOzs7+Pv7o2/fvmjatKm+4iMyehUKJc5mqCrAXl6sAInItOXk5EChUMDNTXMdcTc3N2RlZVX5maysrCrLV1RUICcnp8Yy1Z1TEASEhYVhwIAB6Nat+h5EcrkcBQUFGhuROav8zcFJ1IhMh05Jt5WVFc6dOweJRKKveIhMzpXsIpSWK9FUZom2LnzgRETm4dG6XRCEGuv7qso/ur8u53zrrbdw5syZarueVwoPD4ejo6N68/LyqrE8kalTJ91s6SYyGTp3L58yZQo2bdqkj1iITFJl1/JuLR1gYcEHUERk2lxcXCCVSrVaoLOzs7Vaqiu5u7tXWd7S0hLOzs41lqnqnG+//TZ2796N/fv3o1WrVjXGu2DBAuTn56u39PT0x35HIlN1r7gMqZxEjcjkWOp6grKyMnz11VeIiYmBv78/mjRponF85cqVul6CyKidSlc9ce7p1UzcQIiI9MDa2hp+fn6IiYnB+PHj1ftjYmIwduzYKj8TEBCAX375RWNfdHQ0/P39YWVlpS4TExODOXPmaJQJDAxUvxcEAW+//TaioqIQGxsLHx+fx8Yrk8kgk8nq9B2JTNWpBw/627o0QTM7a3GDIaJa0znpPnfuHPr06QMAuHz5ssYxdjunxuB0eh4AoCcnUSMiMxEWFobJkyfD398fAQEB2LBhA9LS0hASEgJA1bqckZGBbdu2AVDNVL5mzRqEhYVhxowZSEhIwKZNmzS6hoeGhmLQoEFYsWIFxo4di59//hn79u1DfHy8usybb76J77//Hj///DPs7e3VLeOOjo6wtbVtwDtAZJyS0vIAAL1aNxM1DiKqG52T7v379+sjDiKTVCyvwMUs1aQ9vVkBEpGZCA4ORm5uLpYsWYLMzEx069YNe/bsgbe3NwAgMzNTY81uHx8f7NmzB3PmzMEXX3wBT09PrF69GhMmTFCXCQwMxI4dO7Bw4UIsWrQI7dq1Q0REBPr166cuU7lE2ZAhQzTi2bJlC6ZNm2a4L0xkIpLS7gEAerduLnIkRFQX9V6nu6SkBO+++y527dqF8vJyPP3001i9ejVcXFz0HWOD4vqeVBeHr+bgpa+OomUzWxyaz7UyiahuWOfoH+8pmSulUkDPJdEoLK3Ar28PQDeO6SYSncHX6f73v/+NrVu34tlnn8ULL7yAmJgY/Otf/6rv6YhMUmKq6olzH28+cSYiIiLDuXanCIWlFbC1kqKzu73Y4RBRHdS7e/lPP/2ETZs24YUXXgAAvPLKK+jfvz8UCgWkUqneAiQyZokPunn5sWs5ERERGVDleO4erRxhKdV5ASIiakD1/hebnp6OgQMHqt/37dsXlpaWuHXrll4CIzJ2SqWAkw9auv28nUSOhoiIiMxZUjrHcxOZqnon3QqFAtbWmksVWFpaoqKiQuegiEzBtTtFKKjs5uXBbl5ERERkOJUt3Zy4lcj01Lt7uSAImDZtmsbamKWlpQgJCdFYq/unn37SLUIiI1U5nrtHK0dYsZsXERERGUiRvAKXbhcCAHp7NRM3GCKqs3on3VOnTtXa98orr+gUDJEpSVR3LWc3LyIiIjKcM+l5EASgZTNbtHCwETscIqqjeifdW7Zs0WccRCZHPYkak24iIiIyoMoH/exaTmSa2CeWqB7uFZfh+p1iAJzQhIiIiAzr2I27AIAn2nDiViJTxKSbqB5OPHji3Na1CZyaWD+mNBEREVH9VCiU6tVS+vow6SYyRUy6ierh6PVcAEA/H2eRIyEiIiJzdiGzAMVlCjjYWKKTG1dLITJFRp90r127Fj4+PrCxsYGfnx8OHjxYbdnY2FhIJBKt7eLFiw0YMTUGR1JUSfeTbfnEmYiIiAznWMrfXcstLCQiR0NE9WHUSXdERARmz56NDz74AElJSRg4cCBGjhyJtLS0Gj936dIlZGZmqrcOHTo0UMTUGOTfL8f5WwUAgCfbsqWbiIiIDEeddLNrOZHJMuqke+XKlXjttdfw+uuvo0uXLli1ahW8vLywbt26Gj/XokULuLu7qzepVNpAEVNjcDzlLgQBaOvSBG5ctoOIiIgMRBAEHOckakQmz2iT7rKyMiQmJiIoKEhjf1BQEA4fPlzjZ3v37g0PDw8MGzYM+/fvN2SY1AgdqRzPzVZuIiIiMqCr2UW4V1IOGysLdG/pKHY4RFRP9V6n29BycnKgUCjg5uamsd/NzQ1ZWVlVfsbDwwMbNmyAn58f5HI5vvnmGwwbNgyxsbEYNGhQlZ+Ry+WQy+Xq9wUFBfr7EmSWOJ6biIiIGkLlUmG9vZrD2tJo28qI6DGMNumuJJFoThghCILWvkqdOnVCp06d1O8DAgKQnp6OTz75pNqkOzw8HIsXL9ZfwGTWOJ6biIiIGsrR6xzPTWQOjPaRmYuLC6RSqVardnZ2tlbrd02efPJJXLlypdrjCxYsQH5+vnpLT0+vd8xk/o5ez4UgAD4cz01EREQGpFQKOHQ1BwDQvx0f9BOZMqNNuq2treHn54eYmBiN/TExMQgMDKz1eZKSkuDh4VHtcZlMBgcHB42NqDoHrtwBAAxo7yJyJERERGTOLt0uRG5xGWytpOjdurnY4RCRDoy6e3lYWBgmT54Mf39/BAQEYMOGDUhLS0NISAgAVSt1RkYGtm3bBgBYtWoV2rRpg65du6KsrAzffvstIiMjERkZKebXIDNy4LLqifPgjq4iR0JERETmrLKVu19bJ47nJjJxRp10BwcHIzc3F0uWLEFmZia6deuGPXv2wNvbGwCQmZmpsWZ3WVkZ5s6di4yMDNja2qJr16747bffMGrUKLG+ApmRGznFSLtbAiupBAHs5kVEREQGFP8g6WbvOiLTJxEEQRA7CGNSUFAAR0dH5Ofns6s5afj68A38e/d5PNnWCTv+GSB2OERkBljn6B/vKZmDsgolei6Oxv1yBX4PHYguHvxbJjJGta1z2FeFqJYOXFaN5x7csYXIkRAREZE5S0q7h/vlCrg0tUYnN3uxwyEiHTHpJqqFsgolEq6r1uce1JHdvIiIiMhwKsdzB7ZzgYVF1UvlEpHpYNJNVAsnUu+ipEwBl6YydHFnFy8iIiIynANXHiwV1p5zyBCZAybdRLWw70I2AGBIJ1c+cSYiIiKDyS2S4/TNPAAc0kZkLph0Ez2GIAiIvpAFAAjydRM5GiIiIjJnsZfuQBCArp4OcHe0ETscItIDJt1Ej5GcWYib9+7DxsoCAztwfW4iIiIynL8uqnrXDevMVm4ic8Gkm+gxKlu5B3Zwha21VORoiIiIyFyVK5Tq1VKGMukmMhtMuokeI/r8bQDsWk5EjcvatWvh4+MDGxsb+Pn54eDBgzWWj4uLg5+fH2xsbNC2bVusX79eq0xkZCR8fX0hk8ng6+uLqKgojeMHDhzA6NGj4enpCYlEgl27dunzKxEZveM37qJQXgHnJtbo2aqZ2OEQkZ4w6Saqwc17JbiQWQALCTCsC5NuImocIiIiMHv2bHzwwQdISkrCwIEDMXLkSKSlpVVZPiUlBaNGjcLAgQORlJSE999/H7NmzUJkZKS6TEJCAoKDgzF58mScPn0akydPxqRJk3D06FF1meLiYvTs2RNr1qwx+HckMkZ/JVdO3NqCE7cSmRGJIAiC2EEYk4KCAjg6OiI/Px8ODlwaqrHbeOA6Pt6TjL4+TvjhjQCxwyEiM2OsdU6/fv3Qp08frFu3Tr2vS5cuGDduHMLDw7XKz5s3D7t370ZycrJ6X0hICE6fPo2EhAQAQHBwMAoKCvD777+ry4wYMQLNmzfH9u3btc4pkUgQFRWFcePG1Sl2Y72nRI8jCAKe+jQOKTnF+OKlPni2h4fYIRHRY9S2zmFLN1ENdp++BQAY3dNT5EiIiBpGWVkZEhMTERQUpLE/KCgIhw8frvIzCQkJWuWHDx+OEydOoLy8vMYy1Z2TqLG5mFWIlJxiWFtaYHAnTtxKZE4sxQ6AyFhdv1OEsxn5kFpIMKqbu9jhEBE1iJycHCgUCri5aQ6pcXNzQ1ZWVpWfycrKqrJ8RUUFcnJy4OHhUW2Z6s5ZW3K5HHK5XP2+oKBAp/MRieX3s5kAgCEdXdFUxp/oROaELd1E1ahs5R7Q3gXOTWUiR0NE1LAkEs3xpIIgaO17XPlH99f1nLURHh4OR0dH9ebl5aXT+YjEsuec6gHUqO7sVk5kbph0E1VBEAR10j22F7uWE1Hj4eLiAqlUqtUCnZ2drdVSXcnd3b3K8paWlnB2dq6xTHXnrK0FCxYgPz9fvaWnp+t0PiIxXL5diKvZRbCWWuCpLlwqjMjcMOkmqsL5WwW4fqcYMksLBHVl13Iiajysra3h5+eHmJgYjf0xMTEIDAys8jMBAQFa5aOjo+Hv7w8rK6say1R3ztqSyWRwcHDQ2IhMzZ4HXcsHdXSBg42VyNEQkb5xwAhRFSKOq1pKnvF147gqImp0wsLCMHnyZPj7+yMgIAAbNmxAWloaQkJCAKhalzMyMrBt2zYAqpnK16xZg7CwMMyYMQMJCQnYtGmTxqzkoaGhGDRoEFasWIGxY8fi559/xr59+xAfH68uU1RUhKtXr6rfp6Sk4NSpU3ByckLr1q0b6NsTNSxBEPDrGVXSPaIbu5YTmSNmE0SPuF+mwK5TGQCAF57gjzwianyCg4ORm5uLJUuWIDMzE926dcOePXvg7e0NAMjMzNRYs9vHxwd79uzBnDlz8MUXX8DT0xOrV6/GhAkT1GUCAwOxY8cOLFy4EIsWLUK7du0QERGBfv36qcucOHECQ4cOVb8PCwsDAEydOhVbt2418LcmEsfZjHxczS560LtOt+EWRGScuE73I7i+J0Um3sQ7O0/Dy8kWcXOHwsJCt0l+iIiqwzpH/3hPydT8++dz+DohFWN6emL1i73FDoeI6oDrdBPVU2XX8mB/LybcREREZDBlFUr1xK3P92kpcjREZChMuokecjW7EMdu3IWFBJjox2VniIiIyHD2X8rGvZJytLCXYUB7F7HDISIDYdJN9JBN8TcAAMO6uMHd0UbcYIiIiMis/Zh4EwAwrndLWEr5s5zIXPFfN9EDd4vL8NNJVeX3+gAfkaMhIiIic5aRdx9/Jt8GAEzybyVyNERkSEy6iR7YfiwN8golurV0QF8fJ7HDISIiIjO2/WgalAIQ0NYZ7VvYix0OERkQk24iqCYy+frwDQDAawN8IJFwAjUiIiIyDHmFAjuOq5bdmxLgLXI0RGRoTLqJAOxMTEd2oRxuDjI8291T7HCIiIjIjP1xLgs5RWVwc5DhaV+uzU1k7ph0U6NXVqHEF39dBQD8a3A7WFvynwUREREZhiAI2HjwOgDgpb7esOIEakRmj//KqdHbmZiOW/mlaGEvwwt9W4sdDhEREZmxg1dycC6jALZWUnYtJ2okmHRTo1Zarvi7lXtIO9hYSUWOiIiIiMzZ2ljV744X+7ZG8ybWIkdDRA2BSTc1apsPpeBWfincHGR4ka3cREREZECJqfdw5PpdWEkleH0glyclaiyYdFOjlV1YirX7rwEA5o3ozFZuIiIiMhhBEPCfPy4CAMb3bgnPZrYiR0REDYVJNzVaK6Mvo0hegZ6tHDGuV0uxwyEiIiIzFnv5Do6m3IW1pQVCn+4odjhE1ICYdFOjdPzGXUScSAcALHrOFxYWXJebiIiIDEOhFLDid1Ur97TANmjJVm6iRoVJNzU6peUKzIs8A0EAJvm3gn8bJ7FDIiIiIjP2/bE0XMwqhL2NJWYOaSd2OETUwJh0U6Pz+V9XcP1OMVztZfhglK/Y4RAREZEZyy4sVY/lfnd4JzSz44zlRI0Nk25qVBKu5WJdrGrytCVjusLRzkrkiIiIiMicLfnlAgpLK9CjlSNe7sd1uYkaIybd1GjkFMkRuiMJSgH4h18rjOzuIXZIREREZMZ+PpWBX89kwkICLB/fHVLOIUPUKDHppkahXKFE6I4kZBfK0aFFUywe21XskIiIiMiMpd8twcKocwCAt57qgG4tHUWOiIjEwqSbzJ4gCFi06xwOXc2FnbUUa17qAztrS7HDIiIiIjNVWq7AW9uTUCivQJ/WzTDrqfZih0REImLSTWZvbew17DieDgsJ8PmLvdHJ3V7skIiIiMhMCYKAd388g9PpeWhmZ4X/vdAbllL+5CZqzPhfADJr6+Ou4b97LwFQrcc9rIubyBERERGRuRIEAf/Zewm/nL4FSwsJ1r3sBy8nO7HDIiKRsY8tmSVBELDmr6v4NOYyAGDO0x3xan8fkaMiIiIic/bZvivqVVKWjeuGgHbOIkdERMaASTeZnbIKJd6POosfE28CUCXcoU93EDkqIiIiMlcKpYCPf0vG5kMpAFS9617o21rkqIjIWDDpJrOSfrcEoTuScDItDxYSYPGYrpgc0EbssIiIiMhM5d8vxzs/nMa+5NsAgIXPdsFrA9i7joj+xqSbzIIgCPgx8SaW/HIBhfIK2Mss8flLvTGkUwuxQyMiIiIzdSzlLuZEnEJG3n1YW1pg5aSeeK6Hp9hhEZGRYdJNJu9cRj4+2n0eJ1LvAQD8vZvjs+BenLiEiIiIDOJOoRz/3XsROxNvQhAAb2c7/O+F3ujl1Uzs0IjICDHpJpN1Oj0Pa/ZfRcwFVXcuO2sp3n6qA2YM9OHSHERERKR32QWl2HQoBd8dSUORvAIAMMm/FT4c3RVNZfxZTURVM/rMZO3atfDx8YGNjQ38/Pxw8ODBGsvHxcXBz88PNjY2aNu2LdavX99AkVJDyCspwzdHUjFmTTzGfnEIMRduQyIBxvbyxJ/vDMa/hrRjwk1EpAeGqH8jIyPh6+sLmUwGX19fREVF6XxdIkMrq1Bi34XbePP7kxiwYj++jLuOInkFurd0ROS/AvGfiT2ZcBNRjYz6vxARERGYPXs21q5di/79++PLL7/EyJEjceHCBbRurT0jZEpKCkaNGoUZM2bg22+/xaFDhzBz5ky4urpiwoQJInwD0lVpuQIXMgtw9Ppd7L+YjROpd6EUVMespBKM7uGJmUPbo32LpuIGSkRkRgxR/yYkJCA4OBhLly7F+PHjERUVhUmTJiE+Ph79+vWr13WJDKFCocS1O8U4duMuDl6+g4RruSh80KoNqIaxhQxuh6c6t4CFhUTESInIVEgEQRDEDqI6/fr1Q58+fbBu3Tr1vi5dumDcuHEIDw/XKj9v3jzs3r0bycnJ6n0hISE4ffo0EhISanXNgoICODo6Ij8/Hw4ODrp/CXqscoUS94rLkF0oR2puCW7kFuNGTjEu3S5EcmYByhWaf6Kd3e3xD38vjOvlCeemMpGiJiLSnbHWOYaof4ODg1FQUIDff/9dXWbEiBFo3rw5tm/fXq/rVsVY7ykZD0EQUCSvQF5JObIKSnHzXglu3r2P9HsluJRViItZhZBXKDU+49JUhjE9PfF8n5bo1tJRpMiJyNjUts4x2pbusrIyJCYmYv78+Rr7g4KCcPjw4So/k5CQgKCgII19w4cPx6ZNm1BeXg4rK6taX//7o6mwsfu79fThtO/hxxSa+6t+flG5W3iodPXneHh/1eU1zy3ofD6NUz90QKiybN2+g1IQcL9MgfvlCpQ+2O6XK3C/TIH8++XILS5DXkl51V/uAecm1ujduhkGd3TF0M4t0Ko5J0gjIjIUQ9W/CQkJmDNnjlaZVatW1fu6Nfn+aCpsm9gD0K6fH61Sq6pjtcs8vo3i0SLCI2ep3XVqPkd159E8/vjP1O4e1Bx/lWHU617X/TpV3aeyCiXKKpSQV27lCsgf7CutUKDgfjny75cjr6QcFcqab2JTmSW6tXTAgPYuGNDBFd1bOkLKVm0iqiejTbpzcnKgUCjg5uamsd/NzQ1ZWVlVfiYrK6vK8hUVFcjJyYGHh4fWZ+RyOeRyufp9fn4+AGBZ1ElYyJjcNRQLCdDczgotm9vB28kO3s5N0MbFDt08HdGyuS0kksqKrgIFBQWixkpEpC+V/z0zpk5nhqp/qytTec76XBdgPU71Z2VpgRb21mjpaIeWzW3Qspkd2rjYobOHA1o3t9PoOl5cVChipERkrGpbjxtt0l3p72RLRRAErX2PK1/V/krh4eFYvHix1v6MddPqGCnpKhXAKbGDICISQWFhIRwdjavLqiHq39qcs67XZT1OurgudgBEZBYeV48bbdLt4uICqVSq9XQ7Oztb6yl4JXd39yrLW1pawtnZucrPLFiwAGFhYer3SqUSfn5+OHnyJAoLC+Hl5YX09HS9jgt74okncPz4cb2Wr65MbffX9n1BQQHvySPvTeWe1HSc96R2x2qzj/dE3HtS1/tRm88Y8p4IggA/Pz94enrWKWZDMlT9W12ZynPW57pAw9fjpvY39uhr/rszznuiy++a6o7V956YSn31uDK8J7U71pjviT5+/9a2HjfapNva2hp+fn6IiYnB+PHj1ftjYmIwduzYKj8TEBCAX375RWNfdHQ0/P39qx3PLZPJIJPJtPY5Ojqqn6w7ODjo9Y9JKpXW6Xy1KV9dmdrur+t73hPTuyc1Hec9qd2x2uzjPRH3ntT1ftTmM4a+J9bW1rCwMJ6lDg1V/wYEBCAmJkZjXHd0dDQCAwPrfV2g4etxU/wbq6o874lx3RNdftdUd0zXe2Ls9dXjyvCe1O5YY74n+vr9W5t63GiTbgAICwvD5MmT4e/vj4CAAGzYsAFpaWkICQkBoHq6nZGRgW3btgFQzZS6Zs0ahIWFYcaMGUhISMCmTZvUs6LW1ptvvqn376LL+WtTvroytd1f1/f6xnvy+Hh0LV/Tcd6T2h2rzT7eE3HvSX3OLfY9MfTfSH0Yov4NDQ3FoEGDsGLFCowdOxY///wz9u3bh/j4+Fpft7b4N9awf2O8J9oa8ndNdcd4T3hPanOsMd+TBv39Kxi5L774QvD29hasra2FPn36CHFxcepjU6dOFQYPHqxRPjY2Vujdu7dgbW0ttGnTRli3bl29r52fny8AEPLz8+t9DnPDe6KN90Qb74k23hNtvCfGzRD1786dO4VOnToJVlZWQufOnYXIyMg6Xbeu+DemjfdEG++JJt4Pbbwn2nhP6saoW7oBYObMmZg5c2aVx7Zu3aq1b/DgwTh58qReri2TyfDvf/9bq9taY8Z7oo33RBvviTbeE228J8bNEPXvxIkTMXHixHpft674N6aN90Qb74km3g9tvCfaeE/qRiIIRrROCREREREREZEZMZ6ZW4iIiIiIiIjMDJNuIiIiIiIiIgNh0k1ERERERERkIEy6iYiIiIiIiAyESXc9/frrr+jUqRM6dOiAr776SuxwjMb48ePRvHnzx85O2xikp6djyJAh8PX1RY8ePbBz506xQxJdYWEhnnjiCfTq1Qvdu3fHxo0bxQ7JaJSUlMDb2xtz584VOxSjYGlpiV69eqFXr154/fXXxQ6HzBDrcW2swzWxHtfGerx6rMc1sR7XxNnL66GiogK+vr7Yv38/HBwc0KdPHxw9ehROTk5ihya6/fv3o6ioCF9//TV+/PFHscMRVWZmJm7fvo1evXohOzsbffr0waVLl9CkSROxQxONQqGAXC6HnZ0dSkpK0K1bNxw/fhzOzs5ihya6Dz74AFeuXEHr1q3xySefiB2O6FxcXJCTkyN2GGSmWI9XjXW4Jtbj2liPV4/1uCbW45rY0l0Px44dQ9euXdGyZUvY29tj1KhR2Lt3r9hhGYWhQ4fC3t5e7DCMgoeHB3r16gUAaNGiBZycnHD37l1xgxKZVCqFnZ0dAKC0tBQKhQJ87gdcuXIFFy9exKhRo8QOhahRYD1eNdbhmliPa2M9XjXW4/Q4jTLpPnDgAEaPHg1PT09IJBLs2rVLq8zatWvh4+MDGxsb+Pn54eDBg+pjt27dQsuWLdXvW7VqhYyMjIYI3aB0vS/mRp/348SJE1AqlfDy8jJw1Ialj3uSl5eHnj17olWrVnjvvffg4uLSQNEbhj7uydy5cxEeHt5AERuePu5JQUEB/Pz8MGDAAMTFxTVQ5GQqWI9rYx2ujfW4Ntbj2liPa2M9rn+NMukuLi5Gz549sWbNmiqPR0REYPbs2fjggw+QlJSEgQMHYuTIkUhLSwOAKp/oSSQSg8bcEHS9L+ZGX/cjNzcXU6ZMwYYNGxoibIPSxz1p1qwZTp8+jZSUFHz//fe4fft2Q4VvELrek59//hkdO3ZEx44dGzJsg9LH38mNGzeQmJiI9evXY8qUKSgoKGio8MkEsB7XxjpcG+txbazHtbEe18Z63ACERg6AEBUVpbGvb9++QkhIiMa+zp07C/PnzxcEQRAOHTokjBs3Tn1s1qxZwnfffWfwWBtSfe5Lpf379wsTJkwwdIgNqr73o7S0VBg4cKCwbdu2hgizQenyN1IpJCRE+OGHHwwVYoOrzz2ZP3++0KpVK8Hb21twdnYWHBwchMWLFzdUyAanj7+TESNGCMePHzdUiGTiWI9rYx2ujfW4Ntbj2liPa2M9rh+NsqW7JmVlZUhMTERQUJDG/qCgIBw+fBgA0LdvX5w7dw4ZGRkoLCzEnj17MHz4cDHCbTC1uS+NSW3uhyAImDZtGp566ilMnjxZjDAbVG3uye3bt9VPOgsKCnDgwAF06tSpwWNtKLW5J+Hh4UhPT8eNGzfwySefYMaMGfjwww/FCLdB1Oae3Lt3D3K5HABw8+ZNXLhwAW3btm3wWMk0sR7XxjpcG+txbazHtbEe18Z6vH4sxQ7A2OTk5EChUMDNzU1jv5ubG7KysgCopsD/9NNPMXToUCiVSrz33ntmP2tjbe4LAAwfPhwnT55EcXExWrVqhaioKDzxxBMNHa7B1eZ+HDp0CBEREejRo4d6LMw333yD7t27N3S4DaI29+TmzZt47bXXIAgCBEHAW2+9hR49eogRboOo7b+bxqQ29yQ5ORlvvPEGLCwsIJFI8L///a/RzypNtcd6XBvrcG2sx7WxHtfGelwb6/H6YdJdjUfHdgmCoLFvzJgxGDNmTEOHJbrH3ZfGNvtrTfdjwIABUCqVYoQlqpruiZ+fH06dOiVCVOJ63L+bStOmTWugiMRX0z0JDAzE2bNnxQiLzAjrcW2sw7WxHtfGelwb63FtrMfrht3LH+Hi4gKpVKr19Co7O1vriU5jwvuiifdDG++JNt4TbbwnZGj8G9PGe6KN90Qb74k23hNtvCf1w6T7EdbW1vDz80NMTIzG/piYGAQGBooUlfh4XzTxfmjjPdHGe6KN94QMjX9j2nhPtPGeaOM90cZ7oo33pH4aZffyoqIiXL16Vf0+JSUFp06dgpOTE1q3bo2wsDBMnjwZ/v7+CAgIwIYNG5CWloaQkBARozY83hdNvB/aeE+08Z5o4z0hQ+PfmDbeE228J9p4T7TxnmjjPTGAhp4u3Rjs379fAKC1TZ06VV3miy++ELy9vQVra2uhT58+QlxcnHgBNxDeF028H9p4T7TxnmjjPSFD49+YNt4Tbbwn2nhPtPGeaOM90T+JIAiCftJ3IiIiIiIiInoYx3QTERERERERGQiTbiIiIiIiIiIDYdJNREREREREZCBMuomIiIiIiIgMhEk3ERERERERkYEw6SYiIiIiIiIyECbdRERERERERAbCpJuIiIiIiIjIQJh0ExERERERERkIk26iRuyjjz5Cr169RLv+okWL8M9//rNWZefOnYtZs2YZOCIiIiLTwXqcyDRIBEEQxA6CiPRPIpHUeHzq1KlYs2YN5HI5nJ2dGyiqv92+fRsdOnTAmTNn0KZNm8eWz87ORrt27XDmzBn4+PgYPkAiIiIRsR4nMh9MuonMVFZWlvp1REQEPvzwQ1y6dEm9z9bWFo6OjmKEBgBYvnw54uLisHfv3lp/ZsKECWjfvj1WrFhhwMiIiIjEx3qcyHywezmRmXJ3d1dvjo6OkEgkWvse7ZY2bdo0jBs3DsuXL4ebmxuaNWuGxYsXo6KiAu+++y6cnJzQqlUrbN68WeNaGRkZCA4ORvPmzeHs7IyxY8fixo0bNca3Y8cOjBkzRmPfjz/+iO7du8PW1hbOzs54+umnUVxcrD4+ZswYbN++Xed7Q0REZOxYjxOZDybdRKThr7/+wq1bt3DgwAGsXLkSH330EZ577jk0b94cR48eRUhICEJCQpCeng4AKCkpwdChQ9G0aVMcOHAA8fHxaNq0KUaMGIGysrIqr3Hv3j2cO3cO/v7+6n2ZmZl48cUXMX36dCQnJyM2NhbPP/88Hu6M07dvX6SnpyM1NdWwN4GIiMhEsR4nMj5MuolIg5OTE1avXo1OnTph+vTp6NSpE0pKSvD++++jQ4cOWLBgAaytrXHo0CEAqifdFhYW+Oqrr9C9e3d06dIFW7ZsQVpaGmJjY6u8RmpqKgRBgKenp3pfZmYmKioq8Pzzz6NNmzbo3r07Zs6ciaZNm6rLtGzZEgAe+/SdiIiosWI9TmR8LMUOgIiMS9euXWFh8ffzODc3N3Tr1k39XiqVwtnZGdnZ2QCAxMREXL16Ffb29hrnKS0txbVr16q8xv379wEANjY26n09e/bEsGHD0L17dwwfPhxBQUGYOHEimjdvri5ja2sLQPVUnoiIiLSxHicyPky6iUiDlZWVxnuJRFLlPqVSCQBQKpXw8/PDd999p3UuV1fXKq/h4uICQNU9rbKMVCpFTEwMDh8+jOjoaHz++ef44IMPcPToUfUsp3fv3q3xvERERI0d63Ei48Pu5USkkz59+uDKlSto0aIF2rdvr7FVN6tqu3bt4ODggAsXLmjsl0gk6N+/PxYvXoykpCRYW1sjKipKffzcuXOwsrJC165dDfqdiIiIGgvW40SGx6SbiHTy8ssvw8XFBWPHjsXBgweRkpKCuLg4hIaG4ubNm1V+xsLCAk8//TTi4+PV+44ePYrly5fjxIkTSEtLw08//YQ7d+6gS5cu6jIHDx7EwIED1d3TiIiISDesx4kMj0k3EenEzs4OBw4cQOvWrfH888+jS5cumD59Ou7fvw8HB4dqP/fPf/4TO3bsUHdvc3BwwIEDBzBq1Ch07NgRCxcuxKeffoqRI0eqP7N9+3bMmDHD4N+JiIiosWA9TmR4EuHhefyJiBqIIAh48sknMXv2bLz44ouPLf/bb7/h3XffxZkzZ2BpyekoiIiIxMR6nKj22NJNRKKQSCTYsGEDKioqalW+uLgYW7ZsYUVNRERkBFiPE9UeW7qJiIiIiIiIDIQt3UREREREREQGwqSbiIiIiIiIyECYdBMREREREREZCJNuIiIiIiIiIgNh0k1ERERERERkIEy6iYiIiIiIiAyESTcRERERERGRgTDpJiIiIiIiov9n777jm6r+P46/knTTBaUTCi17QxkiCAKyh4Di14UCbkRFQEURB+DAAYiIihNcuH6AExCUvffeUHZLWW2hu0l+fwQildVC2tvxfj4eeSS59+aedyo1/eSce47kExXdIiIiIiIiIvlERbeIiIiIiIhIPlHRLSIiIiIiIpJPVHSLiIiIiIiI5BMV3SIiIiIiIiL5REW3iIiIiIiISD5R0S0iIiIiIiKST1R0i4iIiIiIiOQTFd1XsGjRIm699VYiIiIwmUz88ssv+dpednY2L730EtHR0Xh7e1OpUiVGjRqFzWbL13ZFREREREQkf7gZHaAwS0lJoX79+jzwwAP06tUr39t7++23mTRpEl999RW1a9dmzZo1PPDAAwQEBPD000/ne/siIiIiIiLiWiq6r6Bz58507tz5svszMzN56aWX+O6770hMTKROnTq8/fbbtG7d+praW758OT169KBr164AREVF8f3337NmzZprOp+IiIiIiIgYS8PLr8MDDzzA0qVL+eGHH9i0aRP/+9//6NSpE7t3776m87Vo0YJ//vmHXbt2AbBx40aWLFlCly5dXBlbRERERERECoh6uq/R3r17+f777zl8+DAREREAPPvss8yePZvJkyfz5ptv5vmczz//PElJSdSoUQOLxYLVauWNN97gnnvucXV8ERERERERKQAquq/RunXrsNvtVKtWLcf2jIwMgoKCANi/fz/R0dFXPM8TTzzBxIkTAfjxxx/59ttvmTp1KrVr12bDhg0MGjSIiIgI+vbtmz9vRERERERERPKNiu5rZLPZsFgsrF27FovFkmOfr68vAOXKlWP79u1XPE/p0qWdj5977jleeOEF7r77bgDq1q3LgQMHGD16tIpuERERERGRIkhF9zWKiYnBarWSkJBAy5YtL3mMu7s7NWrUyPU5U1NTMZtzXmZvsVi0ZJiIiIiIiEgRpaL7Cs6ePcuePXucz2NjY9mwYQNlypShWrVq9O7dmz59+jB27FhiYmI4ceIE8+bNo27dutc0+dmtt97KG2+8QYUKFahduzbr169n3LhxPPjgg658WyIiIiIiIlJATHa73W50iMJqwYIFtGnT5qLtffv2ZcqUKWRlZfH666/z9ddfc+TIEYKCgmjWrBkjR46kbt26eW7vzJkzvPzyy8yYMYOEhAQiIiK45557eOWVV/Dw8HDFWxIREREREZECpKJbREREREREJJ9onW4RERERERGRfKKiW0RERERERCSfaCK1/7DZbBw9ehQ/Pz9MJpPRcUREpBiz2+2cOXOGiIiIi1avkGujz3ERESkouf0cV9H9H0ePHiUyMtLoGCIiUoIcOnSI8uXLGx2jWNDnuIiIFLSrfY6r6P4PPz8/wPGD8/f3NziNiIgUZ8nJyURGRjo/e+T66XNcREQKSm4/x1V0/8f5oWj+/v76sBYRkQKhYdCuo89xEREpaFf7HC+0F5B9/PHH1KtXz/mh2axZM2bNmnXF1yxcuJBGjRrh5eVFpUqVmDRpUgGlFREREREREblYoS26y5cvz1tvvcWaNWtYs2YNt9xyCz169GDr1q2XPD42NpYuXbrQsmVL1q9fz4svvsjAgQOZNm1aAScXERERERERcTDZ7Xa70SFyq0yZMrz77rs89NBDF+17/vnn+e2339i+fbtzW//+/dm4cSPLly/PdRvJyckEBASQlJSkYWkiIpKv9JnjevqZiohIQcntZ06RuKbbarXy888/k5KSQrNmzS55zPLly+nQoUOObR07duSLL74gKysLd3d3l+Wx2+1kZ2djtVpddk6RS7FYLLi5uel6TxERERGRIqpQF92bN2+mWbNmpKen4+vry4wZM6hVq9Ylj42Pjyc0NDTHttDQULKzszlx4gTh4eGXfF1GRgYZGRnO58nJyVfMlJmZSVxcHKmpqXl8NyLXxsfHh/DwcDw8PIyOIiIiIiIieVSoi+7q1auzYcMGEhMTmTZtGn379mXhwoWXLbz/2xt4fuT8lXoJR48ezciRI3OVx2azERsbi8ViISIiAg8PD/VASr6x2+1kZmZy/PhxYmNjqVq1KmZzoZ2GQURERERELqFQF90eHh5UqVIFgMaNG7N69Wref/99Pvnkk4uODQsLIz4+Pse2hIQE3NzcCAoKumwbw4YNY8iQIc7n59dau5TMzExsNhuRkZH4+Phcy1sSyRNvb2/c3d05cOAAmZmZeHl5GR1JRERERETyoFAX3f9lt9tzDAW/ULNmzfj9999zbJszZw6NGze+4vXcnp6eeHp65imHehulIOnfm4iIiIhI0VVo/5p/8cUXWbx4Mfv372fz5s0MHz6cBQsW0Lt3b8DRQ92nTx/n8f379+fAgQMMGTKE7du38+WXX/LFF1/w7LPPGvUWREREREREpIQrtEX3sWPHuP/++6levTpt27Zl5cqVzJ49m/bt2wMQFxfHwYMHncdHR0czc+ZMFixYQIMGDXjttdeYMGECvXr1MuotSDG1f/9+TCYTGzZsAGDBggWYTCYSExMNzSUiIiIiIoVPoR1e/sUXX1xx/5QpUy7a1qpVK9atW5dPiYq+ZcuW0bJlS9q3b8/s2bONjpOv9u/fT3R0tPO5v78/NWvWZPjw4dx6660ubat58+bExcUREBDg0vOKiIiIlDQpSWdJSjhFyulEUpPOkJ54hsyUVGzZWdiyrdizs/+9v2D5XpPJBOcmOL7w8YX3zgmQTf++xmQ2Y7JYHPc5HpswuVkwmy2YzCbMFxxjdrM4780XbrOYMZvNmC1uztdYzh9rMePm4Y6HlyfuXp54eHng7umhywhLiEJbdIvrffnllzz11FN8/vnnHDx4kAoVKuRbW1arFZPJZPj/SP7++29q165NYmIiH330Eb169WLdunXUqVPHZW14eHgQFhbmsvOJiIiIFGfWbCt71mzm8KoNpGzfiflgLF6JJwk4cwrfzH+X5fU4dytKbOduuZVtMpNtdiPbbMFqccNqtjhuFjds525Wixs2Dw9sHl7YvLyxe3mBtw8mb2/MPt5YfHxwK1UKdz9ffMqWwS8kiMDwYEqHB+Pl451fb1XyQEV3CZGSksJPP/3E6tWriY+PZ8qUKbzyyiuAYxK6Vq1a8dZbbzmPP378OBEREcyZM4c2bdqQmZnJSy+9xHfffUdiYiJ16tTh7bffpnXr1oBj5MGgQYP49ttvGTp0KLt27WL37t2cOHGCF198kfXr15OVlUWDBg147733aNiwobOtHTt28PDDD7NmzRoqVarEhAkTaN++PTNmzKBnz54AHDlyhCFDhjBnzhzMZjMtWrTg/fffJyoq6orvOygoiLCwMMLCwnjjjTf44IMPmD9/vrPonj17Nq+//jpbtmzBYrHQrFkz3n//fSpXruw8x6pVq3jsscfYvn07derUYfjw4TnaWLBgAW3atOH06dMEBgYyYsQIfvnlF+fwc4Dx48czfvx49u/f73zN0KFD2bp1K+7u7tSuXZupU6dSsWLFvPxnFRERESkSUpLOsvLbGZydv4CQPZsJSD9DxGWOtZrMpLl7ke7uRaanN9nuHtjNFuwWC3azBZvFAmYLdrPZ0WttB9O5pYKx2x0b+PcO7Oe2Ozu5Hc/tdsfr7DZMdjsmm82xzWbDdH6b3bHNbLM5jzNfcLwJe45t5gteZz53/vOP3e3/9syf52a34WbNBCuQ5YqftEPiuVuamycpXr6klgogo0ww9pBQ3MPDKBVZnqBKFYiKqYlvoL/rGpZLUtF9nex2O2lZF/8C5Tdvd0ue1gj/8ccfqV69OtWrV+e+++7jqaee4uWXX8ZkMtG7d2/effddRo8e7Tznjz/+SGhoKK1atQLggQceYP/+/fzwww9EREQwY8YMOnXqxObNm6latSoAqampjB49ms8//5ygoCBCQkKIjY2lb9++TJgwAYCxY8fSpUsXdu/ejZ+fHzabjZ49e1KhQgVWrlzJmTNneOaZZ3JkT01NpU2bNrRs2ZJFixbh5ubG66+/TqdOndi0aRMeHlf/DjQrK4vPPvsMIMds9ikpKQwZMoS6deuSkpLCK6+8wm233caGDRswm82kpKTQrVs3brnlFr799ltiY2N5+umnc/1zv5Ts7Gx69uzJI488wvfff09mZiarVq3Smu8iIiJS7OxauZHt708ictNywrP/XYUo3eLBsdCKZFSshGfVagRUqkiZqHKEVIokoGxpw0dL5gebzYY1K5vM9EwyMzLIzsgkKz2LrIyMc7dMrBlZZGVmYk3PxJqZRXZGJtbMTLLT0shOSSU7JRVbquNmT0+DtDRM6WmY09NxS0/FI/UMPukp+GakYLHb8M7OwPtsBpw9Ccf2wfacmQ4BJ3yDSAotj61CNL61a1K5dTMia1Uulv8NjKKi+zqlZVmp9cpfBd7utlEd8fHI/X++L774gvvuuw+ATp06cfbsWf755x/atWvHXXfdxeDBg1myZAktW7YEYOrUqdx7772YzWb27t3L999/z+HDh4mIcHwn+eyzzzJ79mwmT57Mm2++CTgK248++oj69es7273lllty5Pjkk08oXbo0CxcupFu3bsyZM4e9e/eyYMEC5xDtN954wzlhHsAPP/yA2Wzm888/dxamkydPJjAwkAULFtChQ4fLvu/mzZtjNptJS0vDZrMRFRXFnXfe6dz/34n2vvjiC0JCQti2bRt16tThu+++w2q18uWXX+Lj40Pt2rU5fPgwjz/+eK5/9v+VnJxMUlIS3bp1c/ao16xZ85rPJyIiIlLY7Fm7le0jR1Nl11qqndt23K8siTe2olyHW6jTrjkx3l6GZixoZrMZs6fjWu5S+OZrW9ZsK8knEjkdf5ykuASSjx4j5dBhso7GYTp+DM9TxwlMTMA/I4WyZ09S9uxJ2LsR5v9C6kRY6eXHiQpVsdRvSJVu7ajSpK6K8OugorsE2LlzJ6tWrWL69OkAuLm5cdddd/Hll1/Srl07goODad++Pd999x0tW7YkNjaW5cuX8/HHHwOwbt067HY71apVy3HejIwMgoKCnM89PDyoV69ejmMSEhJ45ZVXmDdvHseOHcNqtZKamuqceX7nzp1ERkbmuCb6hhtuyHGOtWvXsmfPHvz8/HJsT09PZ+/evVd87z/++CM1atRg165dDBo0iEmTJlGmTBnn/r179/Lyyy+zYsUKTpw4gc3muArn4MGD1KlTh+3bt1O/fn18fHycr2nWrNkV27yaMmXK0K9fPzp27Ej79u1p164dd955J+Hh4dd1XhERERGjpZ5J4e8X3iRq3q9UsVuxYWJf9cZEPPIALbq0UuFWQCxuFkqHBVE6LAga1LjscccPxXFg3TZObN1Bxu7deO3bTXjCAQLTzxC4ax3sWof1589ZVqo0J2s1IqRbJxr3bI+7Z1G72t5YKrqvk7e7hW2jOhrSbm598cUXZGdnU65cOec2u92Ou7s7p0+fpnTp0vTu3Zunn36aDz74gKlTp1K7dm1nj7XNZsNisbB27Voslpzt+vr++y2dt7f3RUOk+/Xrx/Hjxxk/fjwVK1bE09OTZs2akZmZ6cxxtWHVNpuNRo0a8d133120Lzg4+IqvjYyMpGrVqlStWhVfX1969erFtm3bCAkJAeDWW28lMjKSzz77jIiICGw2G3Xq1MmRL6/MZvNFr8vKynmRzuTJkxk4cCCzZ8/mxx9/5KWXXmLu3LnceOONeW5PREREpDDYsWwDR559hqqnjgKwt1J9qo94kVtvqHeVV4pRgiPDCY4Mhx5tndtSz6ayc9Fqji5bBWtXU/7AdoJSThO0+m9Y/TdrR/txrOFNRN/7P+q1a25g+qJDRfd1MplMeRrmXdCys7P5+uuvGTt27EXDsHv16sV3333Hk08+Sc+ePXnssceYPXs2U6dO5f7773ceFxMTg9VqJSEhwTn8PLcWL17MRx99RJcuXQA4dOgQJ06ccO6vUaMGBw8e5NixY4SGhgKwevXqHOdo2LAhP/74IyEhIfj7X/tED61ataJOnTq88cYbvP/++5w8eZLt27fzySefON/XkiVLcrymVq1afPPNN6SlpeHt7Zj9ccWKFVdsJzg4mPj4+BxfKFw4qdp5MTExxMTEMGzYMJo1a8bUqVNVdIuIiEiRNH/SVEpPGE2ELZtELz+yB71At363Gx1LroGPrw8xXVoR08Uxt1NK0lk2zpzP8b/+Jmz9UgLSzxCwbDYsm82ckCjMt99Ji0fv1kzpV6DxHcXcH3/8wenTp3nooYeoU6dOjtsdd9zhXA+9VKlS9OjRg5dffpnt27dz7733Os9RrVo1evfuTZ8+fZg+fTqxsbGsXr2at99+m5kzZ16x/SpVqvDNN9+wfft2Vq5cSe/evZ3FK0D79u2pXLkyffv2ZdOmTSxdutQ5O/j5grV3796ULVuWHj16sHjxYmJjY1m4cCFPP/00hw8fztPP45lnnuGTTz7hyJEjlC5dmqCgID799FP27NnDvHnzGDJkSI7jz1/X/tBDD7Ft2zZmzpzJmDFjrthG69atOX78OO+88w579+7lww8/ZNasWc79sbGxDBs2jOXLl3PgwAHmzJnDrl27dF23iIiIFDk2m40/X3ybsPGv4WnLZl90XSr98gstVXAXG6UCfGl+z630mPI+MSuXcuqVt9ld9yayzBYiE/ZTbtI7bGjeipkvjeFsYrLRcQslFd3F3BdffEG7du0ICAi4aF+vXr3YsGED69atAxzF7caNG2nZsuVFa3hPnjyZPn368Mwzz1C9enW6d+/OypUriYyMvGL7X375JadPnyYmJob777+fgQMHOod2A1gsFn755RfOnj1LkyZNePjhh3nppZcA8PJyTK7h4+PDokWLqFChArfffjs1a9bkwQcfJC0tLc893926dSMqKoo33ngDs9nMDz/8wNq1a6lTpw6DBw/m3XffzXG8r68vv//+O9u2bSMmJobhw4fz9ttvX7GNmjVr8tFHH/Hhhx9Sv359Vq1axbPPPuvc7+Pjw44dO+jVqxfVqlXj0Ucf5cknn+Sxxx7L03sRERERMdofg0ZQafoUAHa16ErH36YSGnW5xcCkqPPw8uSme7vT/efPCf9rLnu7388pn0AC0s8Q/X9fsKV1O2a+NIbUs6lXP1kJYrJfy0WrxVhycjIBAQEkJSVdVNClp6cTGxtLdHS0syAU11u6dCktWrRgz549OdbLLqn0706k+LrSZ45cG/1MRQrO78++RpU/pgIQe8dDdHn92au8QoqjzPQMFn74DZ4/fEXwGcdlpAl+wZifGMRNfXoW68nzcvuZU3x/AlJkzJgxg7lz57J//37+/vtvHn30UW666SYV3CIiIiKF1Ow3P1LBLYCj97v9Mw9z4+K/OfzwEE57BxBy5jhl3xrOrK53s3/TLqMjGk5FtxjuzJkzDBgwgBo1atCvXz+aNGnCr7/+anQsEZF8l5SWdfWDREQKmVXT/qL8Nx8CsKfz3Sq4BThXfD/7CHXnzWF3u15kmS1Uit3MqXv/x+w3P8SabTU6omFUdIvh+vTpw+7du0lPT+fw4cNMmTIlx/rfIiLFVcdx842OICKSJ7Ebd2Ia+SIWu43ddZvTdezLRkeSQsavtD/dJ76O79T/Y3+5anhnZ1Lx64n81eV/HN190Oh4hlDRLSIiYgC73U5X6z9GxxARybXM9Ax2DRyMb2YqB0Mr0W7yB8X6el25PpUa1KDDX9PZf89jZFjciT64nYN39GLFT1de/ag40m+JiIiIAb5dcYBulhVGxxARybXZz4yiwrFYzrp7U+uTD/Dx9TE6khRyFjcLnV8dhN+3P3KkbCQBGWfxe+VZfh88skQNN1fRLSIiYoDPfptHjHmv0TFERHJl9a9/U/mfGQCkDnyeyBqVDE4kRUnlmJo0nzWDXU3aYsZOlVk/MLNXvxKzrreKbhERkQKWbbVxm3mJ0TFERHIl9UwKqW+MwoydXY1vodUjdxkdSYogH79S9PhmIkf6DyXLbKHKzjUsv/VO4vYW/+u8VXSLiIgUsEe/XsNtFhXdIlI0/D38bUKSj3PaO4BW771mdBwp4toNeoCsdz4g2bMU5Y8fYN//7mLXyo1Gx8pXKrpFREQK2Oldy4gyHyPV7mF0FBGRK9qxbAPRc6cBkPXEYAKDyxicSIqDRt3aEPL1d8QHhlEmNZHTjz7ElvkrjY6Vb1R0yxUtWLAAk8lEYmLiFY+Liopi/PjxLmu3devWDBo0yGXny4spU6YQGBjofD5ixAgaNGhgSBYRKX4SktO53bIYgN2BLQxOIyJyeTabjX2vjsTNbmNPtYa0fPB/RkeSYiS6fnXqTfuRQyEV8c9IIWNgf9b9ucDoWPlCRXcJER8fz1NPPUWlSpXw9PQkMjKSW2+9lX/+ufJyNc2bNycuLo6AgADg4oL0vNWrV/Poo4/mR/RLmjJlCiaTyXkLDQ3l1ltvZevWrS5v69lnn73qz0lEJLdavjmL7pZlANTr9LDBaURELm/ZN78SfWgHmWY36o0eqeXBxOWCyoXQdPr37C9XDZ+sdExDn2b1jLlGx3I5/eaUAPv376dRo0bMmzePd955h82bNzN79mzatGnDE088cdnXZWVl4eHhQVhYGCaT6YptBAcH4+NTsMtG+Pv7ExcXx9GjR/nzzz9JSUmha9euZGZmurQdX19fgoKCXHpOESm52pvXEmBK5Yg9CFOUerpFpHDKTM8g66P3ATjQ+lYq1q5icCIprgLKlubm6d+xL7ouXtZMLC8/y/q/Fhsdy6VUdJcAAwYMwGQysWrVKu644w6qVatG7dq1GTJkCCtW/LtGrMlkYtKkSfTo0YNSpUrx+uuv5xhevmDBAh544AGSkpKcPcwjRowALh5enpiYyKOPPkpoaCheXl7UqVOHP/74A4CTJ09yzz33UL58eXx8fKhbty7ff/99nt+XyWQiLCyM8PBwGjduzODBgzlw4AA7d+50HjNu3Djq1q1LqVKliIyMZMCAAZw9ezbHeaZMmUKFChXw8fHhtttu4+TJkzn2/3d4+aWGvvfs2ZN+/fo5n3/00UdUrVoVLy8vQkNDueOOO/L8/kSk+PlzUxz/sywEILvO3aBeIxEppOaN/YywpGMke5bi5pHPGh1HirlSAb60+XkK+yrWxjs7E+uzA4vVNd76tL9edjtkphT8zW7PVbxTp04xe/ZsnnjiCUqVKnXR/v8OFX/11Vfp0aMHmzdv5sEHH8yxr3nz5owfP97ZwxwXF8ezz178P2GbzUbnzp1ZtmwZ3377Ldu2beOtt97CYrEAkJ6eTqNGjfjjjz/YsmULjz76KPfffz8rV177L1ZiYiJTp04FwN3d3bndbDYzYcIEtmzZwldffcW8efMYOnSoc//KlSt58MEHGTBgABs2bKBNmza8/vrr15wDYM2aNQwcOJBRo0axc+dOZs+ezc0333xd5xSR4uG1qXNpad4MQMW2GlouIoVTStJZAn7+GoDTd/bT5GlSIHx8fWj145cciKhKqax0Ugc9UWxmNXczOkCRl5UKb0YUfLsvHgWPi4vo/9qzZw92u50aNWrk6rT33ntvjmI7NjbW+djDw4OAgABnD/Pl/P3336xatYrt27dTrVo1ACpVquTcX65cuRzF+lNPPcXs2bP5+eefadq0aa5yAiQlJeHr64vdbic1NRWA7t2753ivF/ZIR0dH89prr/H444/z0UcfAfD+++/TsWNHXnjhBQCqVavGsmXLmD17dq5z/NfBgwcpVaoU3bp1w8/Pj4oVKxITE3PN5xOR4iHLauN2y2LMJjsrbTVoWqYSJCcbHUtE5CILx35CdPoZTvgGccszjxgdR0oQ30B/mv30FStvv4fIhAMkDHgcv59+ILxyBaOjXRf1dBdz9nM94le7Jvu8xo0bX3ebGzZsoHz58s6C+7+sVitvvPEG9erVIygoCF9fX+bMmcPBgwfz1I6fnx8bNmxg7dq1TJo0icqVKzNp0qQcx8yfP5/27dtTrlw5/Pz86NOnDydPniQlJQWA7du306xZsxyv+e/zvGrfvj0VK1akUqVK3H///Xz33XfOLwVEpOTqOXGJc2h5vW6Xn09DRMRIqWdSKP37TwCk3Xk/Hl6eBieSkiagbGkaff818QGhBKWcZmvfh0k+lWR0rOuinu7r5e7j6HU2ot1cqFq1KiaTie3bt9OzZ8+rHn+pIeh55e3tfcX9Y8eO5b333mP8+PHO660HDRqU5wnQzGYzVao4JvWoUaMG8fHx3HXXXSxatAiAAwcO0KVLF/r3789rr71GmTJlWLJkCQ899BBZWVnAv19K5LXd/77u/PnA8WXAunXrWLBgAXPmzOGVV15hxIgRrF69+pIzv4tIyeAdv5poz2Ok2D0pVf92o+OIiFzSgjGfEp2WzAnfIFo91dfoOFJCBZULIfqzT4nvcx/lThxice9HaD/jmyL7JZB6uq+XyeQY5l3Qt1z2XJcpU4aOHTvy4YcfOnt3L3S19bf/y8PDA6vVesVj6tWrx+HDh9m1a9cl9y9evJgePXpw3333Ub9+fSpVqsTu3bvzlONSBg8ezMaNG5kxYwbguLY6OzubsWPHcuONN1KtWjWOHs35BUmtWrVyTCYHXPT8v4KDg4mLi3M+t1qtbNmyJccxbm5utGvXjnfeeYdNmzaxf/9+5s2bdz1vT0SKsC1HkrjD4vhC8HB4B/D0NTiRiMjFUs+mUvq3HxyP/3cfnt5eBieSkiyqXjV8xown3eJBpdjNzHrkGaMjXTMV3SXARx99hNVq5YYbbmDatGns3r2b7du3M2HChDwPpY6KiuLs2bP8888/nDhx4pLDplu1asXNN99Mr169mDt3LrGxscyaNct5nXSVKlWYO3cuy5YtY/v27Tz22GPEx8df9/v09/fn4Ycf5tVXX8Vut1O5cmWys7P54IMP2LdvH998881Fw88HDhzI7Nmzeeedd9i1axcTJ0686vXct9xyC3/++Sd//vknO3bsYMCAATm+vPjjjz+YMGECGzZs4MCBA3z99dfYbDaqV69+3e9RRIqmOz/4m24Wxxd61Ts9bnAaEZFLW/LRNwSmJXOyVGlaD+xndBwR6rVrTsrzI7Biotrqf5g79nOjI10TFd0lQHR0NOvWraNNmzY888wz1KlTh/bt2/PPP//w8ccf5+lczZs3p3///tx1110EBwfzzjvvXPK4adOm0aRJE+655x5q1arF0KFDnT3kL7/8Mg0bNqRjx460bt2asLCwXA19z42nn36a7du38/PPP9OgQQPGjRvH22+/TZ06dfjuu+8YPXp0juNvvPFGPv/8cz744AMaNGjAnDlzeOmll67YxoMPPkjfvn3p06cPrVq1Ijo6mjZt2jj3BwYGMn36dG655RZq1qzJpEmT+P7776ldu7ZL3qOIFC02m53ulmX4mtLZZwuDis2NjiQichFrthW36Y5e7uQuvdTLLYVGiz63sb/H/QAEf/E+25etMzhR3pns13JRazGWnJxMQEAASUlJ+Pv759iXnp5ObGws0dHReHnpf0RSMPTvTqRou/ezFbxwqD/1zLGcvflVfG8Z4tx3pc8cuTb6mYpcm6VTf6PMqOdJdfOkyoL5BJQtbXQkESdrtpVZ3e+l8r5NxAeG0XjWr/iVNv7/8bn9zFFPt4iISD5K3reaeuZYMuxu+DbVpEQiUjglTfkKgMPNO6jglkLH4mah6SfjOe0dQFhiPPMef87oSHmioltERCSfrD1wmnst/wBwILQtlAoyOJGIyMW2LV5D9MFtWE1mYgY9ZnQckUsKjgzHY8Qbjuu7Nyxi7ntfGh0p11R0i4iI5JO+H/9ND8syAKp1GWhwGhGRS9v98RcA7Kt1AxVqVTY4jcjlNe7Rlv3d7gWg7Ofvs2vlRoMT5Y6KbhERkXyQkW2lh2UZpUwZ7LFFQMWbjI4kInKR0wknqbhxKQDlHuhjcBqRq+s0+gX2VayNlzWTQ4OHkJJ01uhIV6WiW0REJB80GDnHObS8fLvHwWQyOJGIyMVWfPIdntYsjpaJIKZLK6PjiFyVm7sbjSaNJ9HLj4hTR/n7yReMjnRVKrqvgSZ8l4Kkf28iRVO17F3UNh8gw+6OV+P7jI4jInIRm82G58xfAcjs3AOzWaWBFA1h0eUxvzQK27n1u5d++4vRka5Iv1l54O7uDkBqaqrBSaQkOf/v7fy/PxEp/KYsjaWv2xwATkZ1BZ8yBicSEbnYhlmLCD99lHSLB83668tBKVqa3tGJPTd3BcA8djQnjyQYnOjy3IwOUJRYLBYCAwNJSHD8B/Xx8cGk4YKST+x2O6mpqSQkJBAYGIjFYjE6kojk0oe/L2Op53IAIjoOMjaMiMhlHP7qO6oCBxvcREywvhyUoqf92BGsaL+G8MR4lj41lO7Tpxgd6ZJUdOdRWFgYgLPwFslvgYGBzn93IlL47T+Rwr2Wf/AwWdntUYuqETFGRxIRucipuONU3LICgOgH1MstRZOPXymC33gT65MPU3XbSuZ99C23DCh8/54LbdE9evRopk+fzo4dO/D29qZ58+a8/fbbVK9e/bKvWbBgAW3atLlo+/bt26lRo4ZLcplMJsLDwwkJCSErK8sl5xS5HHd3d/VwixQx7cf8zTLPvwGocuuzBqcREbm0VV/+SEVbNkfKRnLLLTcaHUfkmtVt24zfO91JlVk/4DfpPeI63kx45QpGx8qh0BbdCxcu5IknnqBJkyZkZ2czfPhwOnTowLZt2yhVqtQVX7tz5078/f2dz4ODg12ez2KxqBgSEZEcMrKtdDGvINiURLy9NGG1uhsdSUTkkkxzZgKQ2a6zJlCTIq/j6BdYuGY55Y8fYO2Tz9Llzx8K1b/rQlt0z549O8fzyZMnExISwtq1a7n55puv+NqQkBACAwPzMZ2IiMjFqr80m188/gIg8ObHwaIJEEWk8Nm9egsVjsWSbTLT+MG7jY4jct08vDypOOZt0h64j8qxm5n7zqd0fKG/0bGcCk/5fxVJSUkAlClz9UkeYmJiCA8Pp23btsyfPz+/o4mIiGC324kx7aaBea9jmbAbHzI6UqGSnZ3NSy+9RHR0NN7e3lSqVIlRo0Zhs9mcx9jtdkaMGEFERATe3t60bt2arVu3GphapHja/tUPAByoXJ+QCuEGpxFxjWpN63Pkjn4AlP12Ekd27Tc0z4WKRNFtt9sZMmQILVq0oE6dOpc9Ljw8nE8//ZRp06Yxffp0qlevTtu2bVm0aNFlX5ORkUFycnKOm4iISF498vUa+rk5erlTqvWEUmWNDVTIvP3220yaNImJEyeyfft23nnnHd59910++OAD5zHvvPMO48aNY+LEiaxevZqwsDDat2/PmTNnDEwuUrxYs60ELfsHAN/uugRGipcOLz/NgbDK+GRnsH7wsBxf7Bqp0A4vv9CTTz7Jpk2bWLJkyRWPq169eo6J1po1a8ahQ4cYM2bMZYekjx49mpEjR7o0r4iIlDzbt29lkqdjJuAytzxlcJrCZ/ny5fTo0YOuXR1rqkZFRfH999+zZs0awPEF+/jx4xk+fDi33347AF999RWhoaFMnTqVxx57zLDsIsXJml/mUiY1kbPu3txwr4puKV7c3N2Ifut1Mh64j8p7N7Dwk+9p83hvo2MV/p7up556it9++4358+dTvnz5PL/+xhtvZPfu3ZfdP2zYMJKSkpy3Q4cOXU9cEREpgb5evp8H3WbjZrJxtExTCK9vdKRCp0WLFvzzzz/s2rULgI0bN7JkyRK6dOkCQGxsLPHx8XTo0MH5Gk9PT1q1asWyZcsue16NWBPJm/j/mw7A0YYt8PH1MTiNiOtVv7EBBzrfCYD3pPGcPGL8Us+Ftui22+08+eSTTJ8+nXnz5hEdHX1N51m/fj3h4Ze/VsXT0xN/f/8cNxERkbwY++tK7rbMAyCiy/MGpymcnn/+ee655x5q1KiBu7s7MTExDBo0iHvuuQeA+Ph4AEJDQ3O8LjQ01LnvUkaPHk1AQIDzFhkZmX9vQqSIS0k6S+SWlQBUuPsOg9OI5J8ObwwlrnQEARlnWfLMS0bHKbxF9xNPPMG3337L1KlT8fPzIz4+nvj4eNLS0pzHDBs2jD59+jifjx8/nl9++YXdu3ezdetWhg0bxrRp03jyySeNeAsiIlICLNx1nPssf1PKlMERzypQ+RajIxVKP/74o/Nzfd26dXz11VeMGTOGr776KsdxJpMpx3O73X7RtgtpxJpI7q3+4Xe8szM57leW+h1bGB1HJN94entResQIbJiotmExK3+effUX5aNCe033xx9/DEDr1q1zbJ88eTL9+vUDIC4ujoMHDzr3ZWZm8uyzz3LkyBG8vb2pXbs2f/75p3PomoiIiKs9+uUSlng6Pswjuj4PVygQS7LnnnuOF154gbvvdixPVLduXQ4cOMDo0aPp27cvYWFhgKPH+8IRagkJCRf1fl/I09MTT0/P/A0vUkycmTWLUCDxhpsL1RrGIvkhpmNLfr2pE9WWziJtzGjSu7bCy8fbkCyF9rfNbrdf8na+4AaYMmUKCxYscD4fOnQoe/bsIS0tjVOnTrF48WIV3CIikm82H06il2UxwaZk4k0hmGrfZnSkQis1NfWiP/ItFotzZtno6GjCwsKYO3euc39mZiYLFy6kefPmBZpVpDhKPpVE5K71AFT6Xw+D04gUjFZvvUSitz+hSQn8/eo4w3IU2qJbRESksOsxcRGPWP4AILTjELC4G5yo8Lr11lt54403+PPPP9m/fz8zZsxg3Lhx3Hab44sKk8nEoEGDePPNN5kxYwZbtmyhX79++Pj4cO+99xqcXqToWzP1Vzxt2RzzD6HWzY2NjiNSIAKDy5D20BMAlP/zBw5sufwE2/mp0A4vFxERKcx2HztDN/Nyos3HOG33pXTM/UZHKtQ++OADXn75ZQYMGEBCQgIRERE89thjvPLKK85jhg4dSlpaGgMGDOD06dM0bdqUOXPm4OfnZ2BykeIh5a+/AEhu1kpDy6VEaT3gPmb/OoPoQzvY/PzLRP4+tcB/B0x2u91eoC0WcsnJyQQEBJCUlKSZzEVE5LIqvfA7czyGUsV8FFublzC3ei7P59BnjuvpZypysdMJJznUuhXuNiumL7+nRvMGRkcSKVC7V20ire+9uNutHH/+NW5+wDWz9+f2M0dfc4mIiOTR7mNn6GpeQRXzURLtpTA3fczoSCIil7V26m+426zElQ5XwS0lUtUb6rG/bU8AzBPHcTYxuUDbV9EtIiKSRx3eW8BTbjMA8G/zNHipR1VECq/0OY6h5SnN2xicRMQ4bd94gROlyhCUcpp5w98u0LZVdIuIiOTB5sNJdDGvopr5CEl2H8w39jc6kojIZZ2OP0nF2M0A1LhbKyxIyVUqwBeeegaA6Hm/sGvlxgJrW0W3iIhIHnSfuIiBbtMB8Gs9ELwCDE4kInJ5637+Eze7jbjSEVRtUsfoOCKGatnvdvZWicHNbmPP8BHOZSvzm4puERGRXJq34xhdzKuobj7MGXww3/i40ZFERK4o9Z+/ATjb5CaDk4gUDnXfGkmGxZ3owztY8NG3BdKmim4REZFcenTKCp5x+wkA31YDwTvQ2EAiIleQeiaFcnscQ2ije3Y1OI1I4VCxTlUOd7kLAO8vPiTpxOl8b1NFt4iISC58OH8Pd1oWUskczxlLIKbmTxodSUTkitbNmIN3dianfAKp3bqJ0XFECo12I4eQ4B9MYFoyC19+J9/bU9EtIiJyFXa7nYl/bWSQ2zQA/Dq8CJ5+BqcSEbmyk7PnAHCiwY2YzfqzX+Q8Lx9v3AYOASBqwe/sWbslX9vTb5+IiMhVtBmzgAcsswkxJZLsXQ4aPWB0JBGRK8rOyiZs62oAwrt2MjiNSOFz03092VupPu52KzteGpWvk6qp6BYREbmCzGwbiSeP0d/tdwD8O48ENw+DU4mIXNmGmQvxz0jhrIcPDbq2NjqOSKFU47WXyTJZqBy7mWXf/ppv7ajoFhERuYJqL81iiNv/4W9KIy2oNtTpZXQkEZGrOvLnbADiajbCw8vT4DQihVOVRrXZ36Y7ANYP3iM9NS1f2lHRLSIichmbDydRw3SQ3hbHkjvet74Dui5SRAo5m81G6XXLAQho39bgNCKFW+vXn+e0tz8hZ47z96jx+dKG/nIQERG5jFsnLuZVt6+xmOxYa/aAqBZGRxIRuaodS9cRfPYkGWY3Gt3R2eg4IoWaf5kA0h54HICIP37g6O6DLm9DRbeIiMglPDB5FZ3Nq2hm2UYGHlg6vm50JBGRXNn76ywADleqi2+gv8FpRAq/Nk/24UBYZbyzM1nz4kiXn19Ft4iIyH+kZ1lZvvMww92/A8Cz1WAIrGBwKhGR3PFYvQIA9xY3G5xEpGgwm82Ue+VlbJiounkZ62cudO35XXo2ERGRYqDGy7MZ5Dad8qYTZJaKgJsGGR1JRCRXjh+Ko/yxWADq9upicBqRoqPuLU3Z06g1AMffGo012+qyc6voFhERucBni/ZRy7Sfhy1/AuDRfRx4+BicSkQkdzZOm40ZO0fKRhJRVSN0RPLixjdeItXNk8iEA8ybMMVl51XRLSIico7VZmf0zK2Mdv8cN5MNe60eUF2TEIlI0ZG6yDEsNrXhjQYnESl6QqMiiO/ZGwDfrz8h6cRpl5xXRbeIiMg5lV+cSR/LHOqb95Hp5oep8ztGRxIRybXM9Awidm8EILJzO4PTiBRN7YY/xTH/EALTz7DwlXddck4V3SIiIsC4ubuoaIpnqNuPAHh0GgV+YQanEhHJvY1/LaZUVjrJnqWo26650XFEiiRPby/cnhwEQNSC39i3Ycd1n1NFt4iIlHgpGdlM/GcnY90n4WPKwF6xBTTsZ3QsEZE8iZv9DwDHqsfg5u5mcBqRoqv5fT3YF10Xd5uVra+8dt3nU9EtIiIlXu1X/+JRyx80Nu8i260Upts+BrM+IkWkaPHdsNJx37qVwUlEijaz2UzVEcOxmsxU2bWOlT/Pvr7zuSiXiIhIkVR1+ExqmA4yxO1nANy6vqs1uUWkyDmwdQ/hp+OwmszE3K4JIEWuV7Wm9dnbvBMAKePeJSsj85rPpaJbRERKrNlb4rBY03nffSIeJivU6AYN7jU6lohInm2fMQuAQxFVKB0WZHAakeLh5jde5IxnKcJPH+Wfdz+55vOo6BYRkRIpLdNK/2/XMcptCtXNh7GVCoFu48FkMjqaiEieZS9bAoD1Bk2gJuIqpcOCOHXXAwAE/TSFk0cSruk8KrpFRKREqvnKbHqZF3Gn20JsmDHf8SX4BhsdS0Qkz1LPpFB+/zYAqtza0eA0IsVL22ce4WiZcvhmprLk5dHXdA6XT2u4f/9+Fi9ezP79+0lNTSU4OJiYmBiaNWuGl5eXq5sTERHJs6gX/qSq6TCvu38JgLnNixDd0uBUIiLXZuOfCwi0ZXPKJ5BmN9YzOo5IseLu6YHfM8/B8EFUXj6HHcs2UKN5gzydw2VF99SpU5kwYQKrVq0iJCSEcuXK4e3tzalTp9i7dy9eXl707t2b559/nooVK7qqWRERkTzp8N5C/DnLJPf38DZlQqU20PIZo2OJiFyzhHkLCARO1IzBrJUXRFzuhl4d+f3rxlTZuYZ9I16j2uyf8/S75pLfyoYNGzJu3Djuu+8+9u/fT3x8PGvXrmXJkiVs27aN5ORkfv31V2w2G40bN+bnn392RbMiIiJ58snCvew7lsjH7u9T2RyH3b883P6ZlgcTkSLNb/M6AAJu1ogdkfxS97WXyTJbiD64jSVfTc/Ta13yV8Zrr73GmjVrePLJJ6lQ4eJlVjw9PWndujWTJk1i+/btREVFuaJZERGRXFt74DSjZ21nlNsUbrJsxeZeCtO9P+o6bhEp0o7s2k/46aNYMVGvezuj44gUW1H1qrH/lp4A2D+aQHpqWq5f65Kiu2vXrrk+tmzZsjRp0sQVzYqIiOSK3W6n18fLeMgyi3vd5mHH5Jg4LayO0dFERK7Ltt//BuBIaDRlwvUlokh+aj3qOU57+xNy5jj/vD4h169z+URq5yUkJJCQkIDNZsuxvV49Te4gIiIFa+PhJG4xr2O423cAmDq+AdU7GZxKROT6pS1bCkBmg8YGJxEp/vzLBJDatz+lJ71D+G/fc+zu7rl6ncuL7rVr19K3b1+2b9+O3W4HwGQyYbfbMZlMWK1WVzcpIiJyWXa7nTHfz+ID90mYTXZo1A9uHGB0LBGR65adlU3Y7k0AlOvQxuA0IiVDmyf78PeMn6lwLJY1r43J1WtcXnQ/8MADVKtWjS+++ILQ0FBMJpOrmxAREcmVzGwbY76fyTspL1LadJaTfjUI6jIG9NkkIsXAtoWr8MtMJdXdi7ptmxkdR6REsLhZCHnhBRj8GFU2LM7Va1xedMfGxjJ9+nSqVKni6lOLiIjkWnJ6FiO+/JXnjj1HuOkUyb6VCHr0N7C4Gx1NRMQlDv41n8rA0ejaNPLyNDqOSIkR0/lmfpvcgvD1C3N1vMvXSGnbti0bN2509WlFRERy7cDJFF764CuGHxtEuOkUKQFV8e//F/iFGh1NRMRlLOtWAeB+Y3ODk4iUPI3feJl0i0eujnV5T/fnn39O37592bJlC3Xq1MHdPWePQvfuubvYXERE5FrM2RrPLz9PZoz9PXxMGaQF16NUvxlQqqzR0UREXCbpxGnKH90DQI1uWipMpKBFVK3A2keehmcfveqxLi+6ly1bxpIlS5g1a9ZF+/Iykdro0aOZPn06O3bswNvbm+bNm/P2229TvXr1K75u4cKFDBkyhK1btxIREcHQoUPp37//Nb0XEREpOjKyrYz7aydZyz5igtt3uJlsZETdgvc934Cnr9HxRERcauPv/xBst5HgH0yretWMjiNSIrV65K5cFd0uH14+cOBA7r//fuLi4rDZbDlueZm5fOHChTzxxBOsWLGCuXPnkp2dTYcOHUhJSbnsa2JjY+nSpQstW7Zk/fr1vPjiiwwcOJBp06a54q2JiEghteVIEv+b8A+1VjzDK+7f4GayYat/L573/6SCW0SKpdMLlwCQWKuhwUlE5Gpc3tN98uRJBg8eTGjo9V03N3v27BzPJ0+eTEhICGvXruXmm2++5GsmTZpEhQoVGD9+PAA1a9ZkzZo1jBkzhl69el1XHhERKXxSMrL5cP4eFi+exxjLh1S3HMZmcsPc6U3MNzyqWcqvk91uZ+HChSxevJj9+/eTmppKcHAwMTExtGvXjsjISKMjipRYgdvWAVCm9aX/LhaRwsPlPd2333478+fPd/VpSUpKAqBMmTKXPWb58uV06NAhx7aOHTuyZs0asrKyXJ5JRESMYbPZmbH+MO3H/AOLxzHN7SWqmw9j8wnG3O93aPqYCu7rkJaWxptvvklkZCSdO3fmzz//JDExEYvFwp49e3j11VeJjo6mS5curFixwui4IiXOgS27CUk+TrbJTL1uWp9bpLBzeU93tWrVGDZsGEuWLKFu3boXTaQ2cODAPJ/TbrczZMgQWrRoQZ06dS57XHx8/EU97KGhoWRnZ3PixAnCw8Mvek1GRgYZGRnO58nJyXnOJyIiBcNmszNrSzwfzNuNz7G1fOI+hbru+wGw1+iKudv74BtsbMhioFq1ajRt2pRJkybRsWPHiz7LAQ4cOMDUqVO56667eOmll3jkkUcMSCpSMu2cNZ9I4EhYJeqWLW10HBG5inyZvdzX15eFCxeycGHOdctMJtM1Fd1PPvkkmzZtYsmSJVc91vSfng273X7J7eeNHj2akSNH5jmTiIgUnOT0LH5Zf4Svlx8gNWE/z7j/TC/PxQDYPf0xdX4bU/171LvtIrNmzbril9wAFStWZNiwYTzzzDMcOHCggJKJCED6ypUAZNXT9dwiRYHLi+7Y2FiXnu+pp57it99+Y9GiRZQvX/6Kx4aFhREfH59jW0JCAm5ubgQFBV3yNcOGDWPIkCHO58nJybpGTUSkEMjMtrF07wlmborjj01xlM46xgC3X7nLawHunJuYM+Y+TG1HqHfbxa5WcF/Iw8ODqlWr5mMaEbmQzWYjeM9mAMJuaWlwGhHJDZcX3a5it9t56qmnmDFjBgsWLCA6Ovqqr2nWrBm///57jm1z5syhcePGlxwaB+Dp6Ymnp6dLMouIsTKyrSSmZpGaaSU1M/vcvZWsbBtWux2bzY7NzgWPHc8BTIDZDCZMzs5Sk8nk2G5ybDNxviPV8dx8br/JdO6G4yCzyYTFZMJsBovJhJvF5Nhm/vf+wsduZhNmc87XWC7Ydv54x/7i25Nrt9uJPZHCythTLNt7kgU7EjiTkUUz8zbGWubS0WsNFmyOg6NaQruRUL6RsaFLkNTUVA4ePEhmZmaO7fXq1TMokUjJtHvVJgLTz5Bhcadu+5uMjiMiueCSovutt95i4MCB+Pj4XPXYlStXcuLECbp27XrF45544gmmTp3Kr7/+ip+fn7MHOyAgAG9vb8DRS33kyBG+/vprAPr378/EiRMZMmQIjzzyCMuXL+eLL77g+++/v853KCKFRVJqFhsPJ7I74Sx7j59l/4kUEs5kcPxMBklpxX/CRJMJPCxmPNzMeLqZ8bCYcT937+F27nbh/guee7tb8PZww8fDcu7meOztfP7fbW74uFtcXuhnW20cO5NBXGIaB06msvPYGXbEn2Hb0WROnM0A7NQxxfK4ZRW3eq0ikgtGMEXfDK1egCj9oVlQjh8/zgMPPMCsWbMuuT8vy4GKyPXbN2chUcCR8tVo4ONtdBwRyQWXFN3btm2jQoUK/O9//6N79+40btyY4GDHUL/s7Gy2bdvGkiVL+Pbbb4mLi3MWyVfy8ccfA9C6desc2ydPnky/fv0AiIuL4+DBg8590dHRzJw5k8GDB/Phhx8SERHBhAkTtFyYSBGWmW1j+b6T/L3tGCtjT7Lr2NkrHm82QSkPN2ch6e3hhoebGYsJLGYTpgt6oR092I6C8vz8Dza7HbsdvKwplM+KpYz1BAG203ja0vCwZ+JuzwS7nXMd5NjP3+wmwLHdihmr3Uy23YwVE9l2M9l2E1a76aJt2XYzWXYT2TYz2ZjIspmwYXacA7Pz8fn7LNzItJ67Zbg7ntvdSMWNTNzJxI0s3MjGgqNv/vqdL9i93M14uVvwdrfg6W7By82Mt4cFLzfHPrPJdO5nYT93D9k2G2fSszmbkc3Z9GyS07M4fibDOcIAwIyNaFMct5h3cZPHdlq47SDIduKCAL5Q7y5o8hCE1nbJe5LcGzRoEKdPn2bFihW0adOGGTNmcOzYMV5//XXGjh1rdDyREid7zSoA7DEa6SNSVLik6P7666/ZtGkTH374Ib179yYpKQmLxYKnpyepqakAxMTE8Oijj9K3b99cDec+/wfwlUyZMuWiba1atWLdunV5fg8iUrhsj0vm6+UH+GPTUc6kZ+fYF122FNVD/agcUopKZX0JD/Ai2M+TYD9PArzdLztx4lWlnoJ1X8OW/4P4LcDV/z903Uznbi5ewNGOCZvZHavZA6vJnWyzB5kmLzLMXmSYvEjDkzQ8SbGfu9k8OGP1INnmQVK2O0lWD1LtnqThQbrNk/R0D9LTPUjDg9N2D9Jx3DJw50rFvRkbpUjHj1R8TWlUMSVys/kk5c2nqOJxiurmw1TIPoCH/d9VJLABbt5QrQPU6glVO4Cnr2t/QJJr8+bN49dff6VJkyaYzWYqVqxI+/bt8ff3Z/To0VcduSYirpOdlU147DYAKrRrZXAaEcktl13TXa9ePT755BMmTZrEpk2b2L9/P2lpaZQtW5YGDRpQtmxZVzUlIsXYqthTjJu7kxX7Tjm3lfX1pGPtUFpWDaZxVGnK+ubDPAwbpsJfL0La6X+3+ZeHwArgFwYePo5C0M0TTOcr5HNF+X+/JLRZwW49d28799h2wbYL789tt9suft2ljrVm/nvLzgBrFlgzHI8v+JLAhB2LLROLLef1t7liJtdfAtgxYTV7YjU75s0w2Z19/5jtNtxs6Zd/sfXcDRw/2/D6EN0SolpA+RscP3MxXEpKCiEhIQCUKVOG48ePU61aNerWrasvuUUK2LaFqyiVlU6KuxcNWt1gdBwRySWXT6RmMpmoX78+9evXd/WpRaQYiz2Rwsjft7Jg53HAMRS8U+0wejetQNNKQVjyawIxux3mvgzLPnA8D64JN/aHap3BLzR/2swv1mxHAW7NhOz/FOfZ6ZCVCpmpkJVy7j4VMlP+c/+f/VmpkJUOWWmQnXbucarjSwAcxb2bLf3KxTWA2R28/MGnLASUh4ByEBAJwdUhtA6UjgKzJf9/RpJn1atXZ+fOnURFRdGgQQM++eQToqKimDRpEuHh4UbHEylRDv6zmMpAXHQtGrsX2vmQReQ/9NsqIobKstr4bPE+xv+9m8xsG25mE3c1ieTJW6oQHlAAE8Qsff/fgrv1i9DyGbAU0f81WtzOZS+V/21ZsxyF+PliPDvj3AgA07/TuWNyXI/t6QfuXvmfSfLFoEGDOHr0KACvvvoqHTt25LvvvsPDw+OSl3mJSP4xrVsNgFtj9XKLFCVF9C9LESkOjiWn8+TUdaze7xjS3bJqWV7rUYeosgVQNAIcWQv/jHQ87vQW3Ph4wbRbHFjcHTcvf6OTSD7r3bu383FMTAz79+9nx44dVKhQQZeOiRSg9NQ0yh3eBUClDrqeW6QocfHUPSIiubMq9hRdJyxh9f7T+Hm6MfZ/9fn6wRsKruC2WeHXpxzXTte5QwW3yH+kpqbyxBNPUK5cOUJCQrj33ns5ceIEPj4+NGzY8JoK7iNHjnDfffcRFBSEj48PDRo0YO3atc79drudESNGEBERgbe3N61bt2br1q2ufFsiRdbmuUvxtGaR6OVH1RvqGR1HRPJARbeIFLhZm+O47/OVnDibQY0wP357qgW9GpW/9lnHr8XWGZCwFbwCoPM7BdeuSBHx6quvMmXKFLp27crdd9/N3Llzefzxa/9y6vTp09x00024u7sza9Ystm3bxtixYwkMDHQe88477zBu3DgmTpzI6tWrCQsLo3379pw5c8YF70ikaIubvwSA41XqYjbrT3iRoiTfh5cnJyczb948qlevTs2aNfO7OREp5L5beYCXftmC3Q4da4cy/q4YvD0KeAItux0Wvet43OxJKBVUsO2LFAHTp0/niy++4O677wbgvvvu46abbsJqtWKx5P139u233yYyMpLJkyc7t0VFRTkf2+12xo8fz/Dhw7n99tsB+OqrrwgNDWXq1Kk89thj1/eGRIo4j42OUSFeTZsanERE8srlX5PdeeedTJw4EYC0tDQaN27MnXfeSb169Zg2bZqrmxORIuSn1YcYPsNRcN9zQwU+6t2o4AtugIPL4fgOcC8FTfWHvMilHDp0iJYtWzqf33DDDbi5uTknVcur3377jcaNG/O///2PkJAQYmJi+Oyzz5z7Y2NjiY+Pp0OHDs5tnp6etGrVimXLll37GxEpBs6cTqZc/D4AqnduY3AaEckrlxfdixYtcn5Iz5gxA7vdTmJiIhMmTOD11193dXMiUkT8sekoL0zfBMBDLaJ587Y6+bcM2NWs+8ZxX+d2x/ByEbmI1WrFw8MjxzY3Nzeys7Ov6Xz79u3j448/pmrVqvz111/079+fgQMH8vXXXwMQHx8PQGhozqX6QkNDnfsuJSMjg+Tk5Bw3keJm06wFuNltHPcNomKdqkbHEZE8cvnw8qSkJMqUKQPA7Nmz6dWrFz4+PnTt2pXnnnvO1c2JSBGwYt9JBv2wAdu5Hu6XutYs2Ou3L5SVBtt+cTxu2MeYDCJFgN1up1+/fnh6ejq3paen079/f0qV+nfCw+nTp+fqfDabjcaNG/Pmm28CjpnQt27dyscff0yfPv/+Lv73/w12u/2K/78YPXo0I0eOzFUGkaLqxJLllAFOV6trdBQRuQYu7+mOjIxk+fLlpKSkMHv2bOcwsdOnT+PlpXVaRUqaQ6dSefzbtWTb7HStG87rPesYV3AD7FsIWakQEAnlmxiXQ6SQ69u3LyEhIQQEBDhv9913HxERETm25VZ4eDi1atXKsa1mzZocPHgQgLCwMICLerUTEhIu6v2+0LBhw0hKSnLeDh06lOtMIkWF1zbHSLFSN2h9bpGiyOU93YMGDaJ37974+vpSsWJFWrduDTiGndetq2/nREqSsxnZPPTVak6nZlGvfABj76xv3JDy83bOdNxX6wRGFv8ihdyFE565wk033cTOnTtzbNu1axcVK1YEIDo6mrCwMObOnUtMTAwAmZmZLFy4kLfffvuy5/X09MzRGy9S3JxNTCbi2H4AqrZveeWDRaRQcnnRPWDAAG644QYOHTpE+/btnUsaVKpUSdd0i5Qgdrud4TM2s+vYWUL8PPn0/sZ4uRswadqFbDbYNdvxuHpnY7OIlDCDBw+mefPmvPnmm9x5552sWrWKTz/9lE8//RRwDCsfNGgQb775JlWrVqVq1aq8+eab+Pj4cO+99xqcXsQ4W/9ehr/dxslSpales5LRcUTkGuTLkmGNGzemcePGObZ17do1P5oSkUJq2roj/LrhKBaziY/va0hYQCG4vOTETjh7DNx9IKqF0WlECrUHH3wwV8d9+eWXuTquSZMmzJgxg2HDhjFq1Ciio6MZP348vXv3dh4zdOhQ0tLSGDBgAKdPn6Zp06bMmTMHPz+/a3oPIsXBsSUr8AdOVaql9blFiiiXF91X+5DO7YeziBRd+46f5ZVftwAwuF1VGlUsY3Cicw4sddyXbwJuGo4qciVTpkyhYsWKxMTEYLfbXXLObt260a1bt8vuN5lMjBgxghEjRrikPZHiwG3LRgA8GzW+ypEiUli5vOg+ffp0judZWVls2bKFxMREbrnlFlc3JyKFjNVmZ/BPG0nNtHJjpTI83rqK0ZH+deDcWr8VbzI2h0gR0L9/f3744Qf27dvHgw8+yH333edcnURECkZ6ahoRR/cAUKmtRmiJFFUuL7pnzJhx0TabzcaAAQOoVEnXoYgUd5OXxrLxUCJ+nm68d1cD4ydOO89uv6Dobm5sFpEi4KOPPuK9995j+vTpfPnllwwbNoyuXbvy0EMP0aFDB2NXIRApIbYvWIWXLZskT19uaFTr6i8QkUKpQC4MMZvNDB48mPfee68gmhMRgxw8mcqYOY7ZiV/sWpPwAG+DE10g8QCciQOzO5TXED2R3PD09OSee+5h7ty5bNu2jdq1azNgwAAqVqzI2bNnjY4nUuwdWeT4svh4dE1dzy1ShBXYb+/evXvJzs4uqOZEpIDZ7XaGzdhEepaNZpWCuLtJpNGRcjq63nEfVgfcC9GXASJFhMlkwmQyYbfbsdlsRscRKRk2bQDALaahsTlE5Lq4fHj5kCFDcjy32+3ExcXx559/0rdvX1c3JyKFxG8bj7J0z0k83cyMvr1u4Rt6GrfJcR9e39gcIkVIRkaGc3j5kiVL6NatGxMnTqRTp07qdRPJZ9lZ2YQfdIweq9BGc5GIFGUuL7rXr1+f47nZbCY4OJixY8fmevkRESlaUjOzeWvWDgCebFOFqLKlDE50CXGO2V9VdIvkzoABA/jhhx+oUKECDzzwAD/88ANBQUFGxxIpMXYsWYtPdgYp7l40aBZjdBwRuQ4uL7rnz5/v6lOKSCE3acFe4pLSKV/am0duLoQTJtrt/xbdYSq6RXJj0qRJVKhQgejoaBYuXMjChQsvedz06dMLOJlIyXBwwTKigfgK1XFzd/mf7CJSgPQbLCLX5dCpVD5ZtA+A4V1q4uVuMTjRJSQfhdQTYLJAqGZ/FcmNPn36FL7LRERKEOuGdQCY6quXW6Soc0nR3bBhQ/755x9Kly5NTEzMFT+k161b54omRaSQeHv2DjKybdxYqQyd6oQZHefS4s9dzx1cQ5OoieTSlClTjI4gUmLZbDZC928HIKKVlrkUKepcUnT36NEDT09PAHr27OmKU4pIEbDlSBJ/bIoD4JVutQtvr1jCNsd9aG1jc4iIiOTCntWb8ctIId3iQa2bmxgdR0Suk0uK7ldfffWSj0WkeBt7bk3u7vUjqBXhb3CaKzix23EfXM3YHCJFRP/+/Rk+fDiRkVdf+u/HH38kOzub3r17F0AykZJh3z9LqQjElatCjLeX0XFE5Drpmm4RuSar959i/s7jWMwmhrQv5MXscceXA5Qt5DlFCong4GDq1KlD8+bN6d69O40bNyYiIgIvLy9Onz7Ntm3bWLJkCT/88APlypXj008/NTqySLGSuW4tANa6DYwNIiIu4ZKiu3Tp0rkeVnrq1ClXNCkiBrLb7bw721HI3tk4snAuEXae3f5vT3fZ6sZmESkiXnvtNZ566im++OILJk2axJYtW3Ls9/Pzo127dnz++ed06NDBoJQixZPNZiNon+OyqLAWNxqcRkRcwSVF9/jx452PT548yeuvv07Hjh1p1qwZAMuXL+evv/7i5ZdfdkVzImKwxbtPsGr/KTzczAxsW8XoOFd2Jg4yzzhmLi9TCJczEymkQkJCGDZsGMOGDSMxMZEDBw6QlpZG2bJlqVy5cuGdw0GkiDu4ZQ9lUhPJMlmo3U6TqIkUBy4puvv27et83KtXL0aNGsWTTz7p3DZw4EAmTpzI33//zeDBg13RpIgYaOK8PQDc17Qi4QGFfDbw80PLy0SDm4exWUSKqMDAQAIDA42OIVIi7P57MeWBo2HR1PMrxCPJRCTXzK4+4V9//UWnTp0u2t6xY0f+/vtvVzcnIgVsVewpRy+3xcxjrYpAz/GJXY57DS0XEZEiIHXNGgAya9c3OImIuIrLi+6goCBmzJhx0fZffvmFoKAgVzcnIgXsw/mOXu47Gpcn1L8IzKjqLLqrGptDREQkFwJ3bwWg7E1NDU4iIq7i8tnLR44cyUMPPcSCBQuc13SvWLGC2bNn8/nnn7u6OREpQJsPJ7Fwl2PG8v43VzY6Tu6cdHxJoJnLRUSksDu6+yAhZ45jxUTt9i2MjiMiLuLyortfv37UrFmTCRMmMH36dOx2O7Vq1WLp0qU0bapv7ESKso8WOArY7vUjqBDkY3CaXDq933FfJtrQGCIiIlezY+4iwoG44ArUKVva6Dgi4iL5sk5306ZN+e677/Lj1CJikD0JZ5m9NR6Ax1sXkV5uazYkHnI8Lh1laBQREZGrObNqNeFAas26RkcRERdy+TXdF0pLSyM5OTnHTUSKpslLY7HboV3NUKqF+hkdJ3eSD4PdChZP8A0zOo1IkXTs2DHuv/9+IiIicHNzw2Kx5LiJiOv47dwCQOkbNTpUpDhxeU93amoqQ4cO5aeffuLkyZMX7bdara5uUkTyWWJqJtPWHQbg4ZZFaJj2+aHlpSuCOV+/YxQptvr168fBgwd5+eWXCQ8P1/rcIvnkxOFjhJ8+CkCtDi0NTiMiruTyovu5555j/vz5fPTRR/Tp04cPP/yQI0eO8Mknn/DWW2+5ujkRKQBTVx0kPctGrXB/mkaXMTpO7p2KddxraLnINVuyZAmLFy+mQYMGRkcRKda2zVlMMBBXOoKa5UONjiMiLuTyovv333/n66+/pnXr1jz44IO0bNmSKlWqULFiRb777jt69+7t6iZFJB9lWW18vewAAA+2iC5avVzOnu4oI1OIFGmRkZHY7XajY4gUe6dXrCQYOFO9jtFRRMTFXD7e8tSpU0RHO4af+vv7c+rUKQBatGjBokWLXN2ciOSzWVviiU9Op6yvB7fWDzc6Tt6o6Ba5buPHj+eFF15g//79RkcRKdZ8tm8GwO+GJgYnERFXc3nRXalSJecHc61atfjpp58ARw94YGBgns61aNEibr31ViIiIjCZTPzyyy9XPH7BggWYTKaLbjt27LiGdyIiAF8ucQzRvu/Gini6FbFJk1R0i1yT0qVLU6ZMGcqUKcPdd9/NggULqFy5Mn5+fs7t528icv2STpwm/PhBAGq0v9ngNCLiai4fXv7AAw+wceNGWrVqxbBhw+jatSsffPAB2dnZjBs3Lk/nSklJoX79+jzwwAP06tUr16/buXMn/v7+zufBwcF5aldEHNYdPM2GQ4l4WMz0blrR6Dh5p6Jb5JqMHz/e6AgiJcrWuUsojZ0Ev2BqVq1gdBwRcTGXF92DBw92Pm7Tpg07duxgzZo1VK5cmfr16+fpXJ07d6Zz5855zhASEpLnXnURudi3KxzXcnerH06wn6fBafIo7TSkJzoeBxbBLwxEDNS3b1+jI4iUKCeWrqQ0kFi1ttFRRCQf5PsaOhUqVOD222+nfv36/N///V9+NwdATEwM4eHhtG3blvnz5xdImyLFTWJqJn9sigPg/huLYNGa6Bimh09Z8PQ1NotIEWaxWEhISLho+8mTJ7VOt4iLeGzdCIBP48YGJxGR/ODSojs7O5utW7eya9euHNt//fVX6tevn+8zl4eHh/Ppp58ybdo0pk+fTvXq1Wnbtu0VJ3DLyMggOTk5x01E4P/WHiYz20bNcH8aRAYaHSfvkh1rnRJQ3tgcIkXc5WYuz8jIwMPDo4DTiBQ/qWdSiIh3zJ9StZ3W5xYpjlw2vHzbtm1069aNAwccw1F79OjBxx9/zJ133snGjRt5+OGH+eOPP1zV3CVVr16d6tWrO583a9aMQ4cOMWbMGG6++dKTUowePZqRI0fmay6RosZutzN1laOnuHfTCkVrmbDzzhfd/hHG5hApoiZMmACAyWTi888/x9f33xEjVquVRYsWUaNGDaPiiRQbW/9ehq/dyimfQJrVqWJ0HBHJBy4rul944QWio6OZMGEC3333HT/++CNbtmzhvvvu448//sDPz89VTeXJjTfeyLfffnvZ/cOGDWPIkCHO58nJyURGRhZENJFCa8W+U+w7nkIpDws9Y8oZHefaqOgWuS7vvfce4PgSbtKkSTmGknt4eBAVFcWkSZOMiidSbMQvWUEV4GSlWpjN+X7lp4gYwGVF96pVq5g5cyYNGzakRYsW/Pjjjzz33HM88sgjrmrimqxfv57w8MuvLezp6YmnZxGbIEokn3230jFipXuDcvh6uny+xYKholvkusTGOoa7tmnThunTp1O6dGmDE4kUT5bNGwDwaNjI2CAikm9c9td0QkIC5co5esQCAwPx8fGhVatW13XOs2fPsmfPHufz2NhYNmzYQJkyZahQoQLDhg3jyJEjfP3114BjiZOoqChq165NZmYm3377LdOmTWPatGnXlUOkJDlxNoO/tsYDjqHlRdaZc0W3n4pukeuhCUlF8k9GWjoRR3YDUKntTQanEZH84rKi22Qy5RgSYzabcXd3v65zrlmzhjZt2jifnx8G3rdvX6ZMmUJcXBwHDx507s/MzOTZZ5/lyJEjeHt7U7t2bf7880+6dOlyXTlESpKf1xwmy2qnfmQgdcoFGB3n2qmnW8QlLrwE60ImkwkvLy+qVKlCjx49KFOmTAEnEyn6ti1ajZc1i2TPUjRpUtfoOCKST1xWdNvtdqpVq+accOns2bPExMRcdG3KqVOncn3O1q1bX3bWVIApU6bkeD506FCGDh2a+9AikoPdbuenNYcA6H1DEe7lhguK7iJ6TbpIIbF+/XrWrVuH1WqlevXq2O12du/ejcVioUaNGnz00Uc888wzLFmyhFq1ahkdV6RIObpwGZWAhKiaup5bpBhzWdE9efJkV51KRAyy7uBpYk+k4ONhoWu9y8+FUOilJ0PmWcdj/yL8PkQKgfO92JMnT8bf3x9wTDr60EMP0aJFCx555BHuvfdeBg8ezF9//WVwWpGixb5xPQCWBg0NTiIi+cllRXffvn1ddSoRMcjPaw4D0LlOOKWK6gRq8G8vt1cAeJQyNotIEffuu+8yd+5cZ8EN4O/vz4gRI+jQoQNPP/00r7zyCh06dDAwpUjRk52VTdjBnQBUaN3c4DQikp80jkVEAEjLtPLHpjgA/te4vMFprlPyEce9hpaLXLekpCQSEhIu2n78+HGSk5MBxwSqmZmZBR1NpEjbuXw9pbLSSXXzpEYLzVwuUpyp6BYRAGZvjeNsRjaRZby5IaqIT4h0xvHlAX4aWi5yvXr06MGDDz7IjBkzOHz4MEeOHGHGjBk89NBD9OzZE3AsG1qtWjVjg4oUMQfnLwUgrkJ13NyL8OgyEbkq/YaLCAD/t9YxtPyOhpGYzSaD01wnzVwu4jKffPIJgwcP5u677yY7OxsANzc3+vbty3vvvQdAjRo1+Pzzz42MKVLkZK9f53hQr4GhOUQk/6noFhEOn05l2d6TANzesBgMydbwchGX8fX15bPPPuO9995j37592O12KleujK+vr/OYBg0aGBdQpAiy2WwEx24HIKJlM4PTiEh+U9EtIkxfdwS7HZpXDiKyjI/Rca5f8rnh5Zq5XMRlfH19qVevntExRIqFvWu3EZBxlgyzG7XaNDU6jojkM5cU3UOGDMn1sePGjXNFkyLiIjab/d+h5Y2K+ARq52mNbhGXSUlJ4a233uKff/4hISEBm82WY/++ffsMSiZSdO37ZwkVgKMRVWjg4210HBHJZy4putevX5+r40ymIn6dqEgxtObAaQ6eSsXX043OdYpJz7BzeLmu6Ra5Xg8//DALFy7k/vvvJzw8XJ/lIi6QsXYNANl16hucREQKgkuK7vnz57viNCJigF82OArUznXC8PawGJzGBbLSIe2U47FmLxe5brNmzeLPP//kpptuMjqKSLFgs9kI2rsVgNAWNxqcRkQKgpYMEynBsqw2Zm12XP/co0ExGYp95tzQcjdv8C5tbBaRYqB06dKUKVPElxEUKUQObd9HmdREsk1mardrbnQcESkA+TKR2urVq/n55585ePAgmZmZOfZNnz49P5oUkWuwZPcJTqdmUdbXk2aVg4yO4xoXLhemYbAi1+21117jlVde4auvvsLHpxhMtChisN1zF1MOOBoaRd1Af6PjiEgBcHnR/cMPP9CnTx86dOjA3Llz6dChA7t37yY+Pp7bbrvN1c2JyHX49dzQ8m71wrEU9bW5z3POXK7ruUVcYezYsezdu5fQ0FCioqJwd3fPsX/dunUGJRMpmlJWrwYgvZZWAxApKVxedL/55pu89957PPHEE/j5+fH+++8THR3NY489Rni4rq8UKSzSMq3M2XYMgO4NilGBqknURFyqZ8+eRkcQKVYCdjuu5y7bXEuFiZQULi+69+7dS9euXQHw9PQkJSUFk8nE4MGDueWWWxg5cqSrmxSRa/DPjmOkZlqJLONNTGSg0XFc58Lh5SJy3V599VWjI4gUG/H7DhGanIANE7U6tDQ6jogUEJdPpFamTBnOnDkDQLly5diyZQsAiYmJpKamuro5EblGv25wFKe31osoXksAnZ9IzU9Ft4irJCYm8vnnnzNs2DBOnXKsDrBu3TqOHDlicDKRomX7nMUAxJUtT+mQYjKXiohclcuL7pYtWzJ37lwA7rzzTp5++mkeeeQR7rnnHtq2bevq5kTkGiSlZbFw53GgGM1afp56ukVcatOmTVSrVo23336bMWPGkJiYCMCMGTMYNmzYNZ939OjRmEwmBg0a5Nxmt9sZMWIEEREReHt707p1a7Zu3Xqd70Ck8EheuQqAlOp1DU4iIgXJ5UX3xIkTufvuuwEYNmwYzz77LMeOHeP222/niy++cHVzInIN/toST6bVRvVQP6qH+Rkdx7VUdIu41JAhQ+jXrx+7d+/Gy8vLub1z584sWrToms65evVqPv30U+rVyzmR1DvvvMO4ceOYOHEiq1evJiwsjPbt2ztH0IkUdb47HSNAA2+8weAkIlKQ8mV4eUSE449ds9nM0KFD+e233xg3bhylS2vNXJHC4NeNjiGhxWoCNQBrNpx1TA6nolvENVavXs1jjz120fZy5coRHx+f5/OdPXuW3r1789lnn+X4u8ButzN+/HiGDx/O7bffTp06dfjqq69ITU1l6tSp1/UeRAqDU3HHiTjl+Pyt2fFmg9OISEFyedE9c+ZM/vrrr4u2z5kzh1mzZrm6ORHJo4TkdJbvPQlA9/rFrDA9ewzsNjC7Qalgo9OIFAteXl4kJydftH3nzp0EB+f99+yJJ56ga9eutGvXLsf22NhY4uPj6dChg3Obp6cnrVq1YtmyZXkPLlLIbJm9EID4wDBCKmhFH5GSxOVF9wsvvIDVar1ou81m44UXXnB1cyKSR39sisNmh5gKgUSW8TE6jmudObdGt184mC3GZhEpJnr06MGoUaPIysoCwGQycfDgQV544QV69eqVp3P98MMPrFu3jtGjR1+073yveWhoaI7toaGhV+xRz8jIIDk5OcdNpDA6vXwlAMnV6hicREQKmsuL7t27d1OrVq2LtteoUYM9e/a4ujkRyaPfNjquee5R3Hq54d81uv3UgyDiKmPGjOH48eOEhISQlpZGq1atqFKlCn5+frzxxhu5Ps+hQ4d4+umn+fbbb3NcG/5f/11NwW63X3GFhdGjRxMQEOC8RUZG5jqTSEHy2b4ZAL+mup5bpKRx+TrdAQEB7Nu3j6ioqBzb9+zZQ6lSpVzdnIjkwcGTqWw4lIjZBF3rFceiW5Ooibiav78/S5YsYd68eaxbtw6bzUbDhg0vGh5+NWvXriUhIYFGjRo5t1mtVhYtWsTEiRPZuXMn4OjxDg//94uzhISEi3q/LzRs2DCGDBnifJ6cnKzCWwqdxOOnCD9+EIAaHVoZnEZECprLi+7u3bszaNAgZsyYQeXKlQFHwf3MM8/QvXt3VzcnInnw27kJ1JpXLkuwn6fBafKBs+guZsugiRgkOzsbLy8vNmzYwC233MItt9xyzedq27YtmzdvzrHtgQceoEaNGjz//PNUqlSJsLAw5s6dS0xMDACZmZksXLiQt99++7Ln9fT0xNOzGP7/TIqVrX8toQx2EvyCqVm1gtFxRKSAubzofvfdd+nUqRM1atSgfPnyABw+fJiWLVsyZswYVzcnInlwfmh5sZu1/Dxn0a3h5SKu4ObmRsWKFS85V0te+fn5UadOzmtZS5UqRVBQkHP7oEGDePPNN6latSpVq1blzTffxMfHh3vvvfe62xcx0sllKygDJFatbXQUETFAvgwvX7ZsGXPnzmXjxo14e3tTr149br5ZSyOIGGlHfDK7jp3Fw2KmY+0wo+PkDw0vF3G5l156iWHDhvHtt99SpkyZfG1r6NChpKWlMWDAAE6fPk3Tpk2ZM2cOfn5++dquSH7z3LYRgFI36HpukZLI5UU3OCZB6dChQ45lP0TEWL9ucBSkrasHE+DtbnCafHJGw8tFXG3ChAns2bOHiIgIKlaseNH8LOvWrbvmcy9YsCDHc5PJxIgRIxgxYsQ1n1OksDmbmEzEsf0AVG3f0tgwImIIlxTdEyZM4NFHH8XLy4sJEyZc8diBAwe6okkRyQO73c5v54ruHg2KaUFqt//b063Zy0VcpkePHlecPVxErmzLnCUE2G2cLFWamrWrGB1HRAzgkqL7vffeo3fv3nh5efHee+9d9jiTyaSiW8QA6w6e5khiGqU8LLStGWJ0nPyRehKsmY7HKrpFXEa9ziLXJ2HpCgKAU1W0PrdISeWSojs2NvaSj0WkcDjfy92hdhhe7haD0+ST873cpULAzcPYLCLFSKVKlVi9ejVBQUE5ticmJtKwYUP27dtnUDKRosF9i+N6bq8LlssTkZLF7OoTjho1itTU1Iu2p6WlMWrUKFc3JyJXkWW18cemOAC61y/GE4xp5nKRfLF///5Lzl6ekZHB4cOHDUgkUnSknkmhXNxeACq316TCIiWVyydSGzlyJP3798fHxyfH9tTUVEaOHMkrr7zi6iZF5AoW7TrOyZRMyvp60LJqWaPj5J9kxxrkmkRNxDV+++035+O//vqLgIAA53Or1co///xDdHS0EdFEioyt85bja7Ny2tufG+tXNzqOiBjE5UW33W6/5IQrGzduzPelRkTkYtPXOYrRHg3K4WZx+eCWwuOMozdfy4WJuEbPnj0Bx3wsffv2zbHP3d2dqKgoxo4da0AykaIjftFyqgAnKtfGbC7Gn8EickUuK7pLly6NyWTCZDJRrVq1HIW31Wrl7Nmz9O/f31XNiUguJKVmMXf7MQBub1jMe4A1c7mIS9lsNgCio6NZvXo1ZcsW45EyIvnEsnk9AB4NdT23SEnmsqJ7/Pjx2O12HnzwQUaOHJljGJqHhwdRUVE0a9bMVc2JSC78uTmOzGwbNcL8qBXub3Sc/KXh5SL5QhOkilybjLR0Io7sAaBS2xYGpxERI7ms6D4/9Cw6OprmzZvj7u7uqlOLyDWavs4xydHtDcsV/3V2kzW8XMSVVq5cyalTp+jcubNz29dff82rr75KSkoKPXv25IMPPsDT09PAlCKF17b5K/GyZpHsWYomTeoaHUdEDOSSi0uSk5Odj2NiYkhLSyM5OfmSNxEpGAdOprDmwGnMJsf13MWec/ZyFd0irjBixAg2bdrkfL5582Yeeugh2rVrxwsvvMDvv//O6NGjDUwoUrgdWbgMgISoWrqeW6SEc0lPd+nSpYmLiyMkJITAwMBL9qidn2DtUsuOiIjrnZ9ArUXVYEL9vQxOk8/SkyHzjOOxrukWcYkNGzbw2muvOZ//8MMPNG3alM8++wyAyMhIXn31VUaMGGFQQpHCzbRhLQBuWp9bpMRzSdE9b94858zk8+fPd8UpReQ62O12pq93DC3vVdwnUIN/Zy73CgBPX2OziBQTp0+fJjQ01Pl84cKFdOrUyfm8SZMmHDp0yIhoIoVeemoa5Q7tBKByx9bGhhERw7mk6G7VqhUA2dnZLFiwgAcffJDIyEhXnFpErsGaA6c5dCqNUh4WOtQKMzpO/js/iZqfhpaLuEpoaCixsbFERkaSmZnJunXrGDlypHP/mTNnNH+LyGVsnrsUX1s2iV5+NNX13CIlnksvMHFzc2PMmDEuG0K+aNEibr31ViIiIjCZTPzyyy9Xfc3ChQtp1KgRXl5eVKpUiUmTJrkki0hRcn4CtS51w/H2sBicpgDoem4Rl+vUqRMvvPACixcvZtiwYfj4+NCyZUvn/k2bNlG5cmUDE4oUXnHzlwBwvEpdXc8tIq4tugHatm3LggULXHKulJQU6tevz8SJE3N1fGxsLF26dKFly5asX7+eF198kYEDBzJt2jSX5BEpCtKzrPyxyTHc+vaG5Q1OU0CcM5frem4RV3n99dexWCy0atWKzz77jM8++wwPDw/n/i+//JIOHToYmFCk8PLY6Lie26tpU4OTiEhh4LIlw87r3Lkzw4YNY8uWLTRq1IhSpUrl2N+9e/c8nevCpUquZtKkSVSoUIHx48cDULNmTdasWcOYMWPo1atXrs8jUpT9vf0YZ9KzKRfoTdPoMkbHKRjONbpLyJcMIgUgODiYxYsXk5SUhK+vLxZLzlEzP//8M76+mkNB5L/OnE6mXPw+AKp3bmNwGhEpDFxedD/++OMAjBs37qJ9+T17+fLlyy/61r1jx4588cUXZGVl6dozKRHOz1p+W0w5zOZivjb3ec6iW8PLRVwtICDgktvPT6AqIjltnr2Q0nYbJ3yDaFmnqtFxRKQQcHnRbbPZXH3KXIuPj88x0yo4JoLJzs7mxIkThIdfPPQ0IyODjIwM53OtJS5F2fEzGSzcdRyA20rCrOXnOa/pLkHvWURECqUTi5ZRGjhVTROoiYhDsZvZ4b9rhNvt9ktuP2/06NEEBAQ4b5p1XYqy3zYexWqz0yAykMrBJWjY5/me7gAV3SIiYizvLesA8GvezOAkIlJYuKzonjdvHrVq1bpkT3FSUhK1a9dm0aJFrmruksLCwoiPj8+xLSEhATc3N4KCgi75mmHDhpGUlOS8ac1RKcrOz1peItbmPi8zFdJOOx5reLmIiBjoVNxxIo47/pas1UXXc4uIg8uK7vHjx/PII4/g7+9/0b6AgAAee+wx3nvvPVc1d0nNmjVj7ty5ObbNmTOHxo0bX/Z6bk9PT/z9/XPcRIqiHfHJbD2ajLvFRLd6Jaj4PD+03MMXPPX7KyIixtk8cwFm7MQFhhFWSaMnRcTBZUX3xo0b6dSp02X3d+jQgbVr1+bpnGfPnmXDhg1s2LABcCwJtmHDBg4ePAg4eqn79OnjPL5///4cOHCAIUOGsH37dr788ku++OILnn322by/IZEiZsa5CdRuqRFC6VIeVzm6GHFOolYOLnMZiYiISEFIXLoMgDM16xucREQKE5dNpHbs2LErzg7u5ubG8ePH83TONWvW0KbNv0NzhgwZAkDfvn2ZMmUKcXFxzgIcIDo6mpkzZzJ48GA+/PBDIiIimDBhgpYLk2LParMzY72j+Cwxa3Ofp5nLRUSkkPDbtgGAwBY3GRtERAoVlxXd5cqVY/PmzVSpUuWS+zdt2nTJ2cOvpHXr1s6J0C5lypQpF21r1aoV69aty1M7IkXd0j0nSDiTQaCPO22qhxgdp2Bd2NMtIiJikLi9BwlPjMeGibpdWhsdp0BYrVaysrKMjiGSb9zd3bFYLNd9HpcV3V26dOGVV16hc+fOeHl55diXlpbGq6++Srdu3VzVnIhc4PwEarfWi8DDrdgtSnBlzuXC1NMtIiLG2fLrXMoDh0OjqB0ebHScfGW324mPjycxMdHoKCL5LjAwkLCwsMuuhpUbLiu6X3rpJaZPn061atV48sknqV69OiaTie3bt/Phhx9itVoZPny4q5oTkXOS07OYvdUxa//tJWnW8vOStFyYiIgYL23ZUgAyGjQ2OEn+O19wh4SE4OPjc13FiEhhZbfbSU1NJSEhASDPo7Yv5LKiOzQ0lGXLlvH4448zbNiwHOtjd+zYkY8++ojQ0FBXNSci5/y+8SjpWTaqhPjSIDLQ6DgFz9nTraJbRESMkZ2VTdjuTQCUa1+8lwqzWq3OgvtyS/KKFBfe3t6AYxnqkJCQax5q7rKiG6BixYrMnDmT06dPs2fPHux2O1WrVqV06dKubEZELvDTasd6oHc1jiyZ3zRrIjURETHYtoWr8MtIIdXdi7rtmhsdJ1+dv4bbx8fH4CQiBeP8v/WsrKzCUXSfV7p0aZo0aZIfpxaRC2yPS2bj4STcLSZuK4lDyzNTIe2U47F6ukVExCAHZ8+jMnA0ujaNvDyNjlMgSuQX/VIiueLfegmbcUmkePnxXC93u5qhlPUtGR/yOZyJc9y7lwKvAGOziIhIiWVZvxoA9xuLdy+35E1UVBTjx483OkaBWLBgASaTSZPrXYaKbpEiKiPbyi8bHEOr72wSaXAag1w4tFzfuIuIiAGSTpym/NE9ANTs3t7gNHIpJpPpird+/fpd9fW//PKLy3OlpKTw/PPPU6lSJby8vAgODqZ169b88ccfzmMKY+HeunVrBg0aZHSMIiVfhpeLSP6bs/UYialZhAd4cXPV4r00yWUl6XpuEREx1sbf/iHYbuOYfwit61Q1Oo5cQlxcnPPxjz/+yCuvvMLOnTud285PllXQ+vfvz6pVq5g4cSK1atXi5MmTLFu2jJMnT+bpPFarFZPJhNms/tTCSv9lRIqo71cdBOCORuWxmEtoL+/5nu6A8sbmEBGREuv0wkUAJNWOMTiJXE5YWJjzFhAQgMlkyrFt6tSpVK5cGQ8PD6pXr84333zjfG1UVBQAt912GyaTyfl879699OjRg9DQUHx9fWnSpAl///13nnL9/vvvvPjii3Tp0oWoqCgaNWrEU089Rd++fQFHj/KBAwcYPHiws1ceYMqUKQQGBvLHH39Qq1YtPD09OXDgAJmZmQwdOpRy5cpRqlQpmjZtyoIFC5ztnX/dX3/9Rc2aNfH19aVTp045vpTIzs5m4MCBBAYGEhQUxPPPP0/fvn3p2bMnAP369WPhwoW8//77zkz79+93vn7t2rU0btwYHx8fmjdvnuPLjZJMRbdIEbT72BmW7T2J2QR3ldSh5QBJhx336ukWERED2Gw2Sm9dB0BQ61YGp5FrMWPGDJ5++mmeeeYZtmzZwmOPPcYDDzzA/PnzAVi92nG9/uTJk4mLi3M+P3v2LF26dOHvv/9m/fr1dOzYkVtvvZWDBw/muu2wsDBmzpzJmTNnLrl/+vTplC9fnlGjRhEXF5ejOE5NTWX06NF8/vnnbN26lZCQEB544AGWLl3KDz/8wKZNm/jf//5Hp06d2L17d47XjRkzhm+++YZFixZx8OBBnn32Wef+t99+m++++47JkyezdOlSkpOTcwytf//992nWrBmPPPKIM1Nk5L9/iw4fPpyxY8eyZs0a3NzcePDBB3P98yjONLxcpAj6evkBwDGBWvnSJXjJjiTHRHIEVjA2h4iIlEixG3cSfPYkWSYL9bu3NTqOIex2O2lZVkPa9na3XPfM0mPGjKFfv34MGDAAgCFDhrBixQrGjBlDmzZtCA52XMIXGBhIWFiY83X169enfv36zuevv/46M2bM4LfffuPJJ5/MVduffvopvXv3JigoiPr169OiRQvuuOMObrrpJgDKlCmDxWLBz88vR9vgWL7qo48+cmbYu3cv33//PYcPHyYiwtEZ8eyzzzJ79mwmT57Mm2++6XzdpEmTqFy5MgBPPvkko0aNcp73gw8+YNiwYdx2220ATJw4kZkzZzr3BwQE4OHhgY+Pz0WZAN544w1atXJ8AfXCCy/QtWtX0tPT8fLyytXPpLhS0S1SxCSnZzFtnaOHt2/zKGPDGC3x3LfJKrpFRMQAO2fMJBo4HFmdeqX9jY5jiLQsK7Ve+cuQtreN6oiPx/WVM9u3b+fRRx/Nse2mm27i/fffv+LrUlJSGDlyJH/88QdHjx4lOzubtLS0PPV033zzzezbt48VK1awdOlS5s2bx/vvv8/IkSN5+eWXr/haDw8P6tWr53y+bt067HY71apVy3FcRkYGQUFBzuc+Pj7OghsgPDychIQEAJKSkjh27Bg33HCDc7/FYqFRo0bYbLZcvacLM4WHhwOQkJBAhQol+281Fd0iRcz0tYdJzbRSJcSX5pWDrv6C4spuV9EtIiLGWrHUcd+shbE55Lr8t7fcbrdftQf9ueee46+//mLMmDFUqVIFb29v7rjjDjIzM/PUtru7Oy1btqRly5a88MILvP7664waNYrnn38eDw+Py77O29s7R0abzYbFYmHt2rVYLJYcx/r6+uZo70Imkwm73X7Rtgv9d//V3s9/z5Pbgr04U9EtUoTYbHbn0PK+zSpe95CqIi3lOGSnAybw10RqIiJSsBKPnyLykGOSqBq3dTY4jXG83S1sG9XRsLavV82aNVmyZAl9+vRxblu2bBk1a9Z0Pnd3d8dqzTmEfvHixfTr1885DPvs2bM5JhS7VrVq1SI7O5v09HQ8PDzw8PC4qO1LiYmJwWq1kpCQQMuWLa+p7YCAAEJDQ1m1apXzHFarlfXr19OgQQPncbnNJP9S0S1ShCzafZx9J1Lw9XTjtoYlvNBMPHc9t184uF3+m2AREZH8sGH6X4TabcQHhtGmQQ2j4xjGZDJd9xBvIz333HPceeedNGzYkLZt2/L7778zffr0HDORR0VF8c8//3DTTTfh6elJ6dKlqVKlCtOnT+fWW2/FZDLx8ssv57lHt3Xr1txzzz00btyYoKAgtm3bxosvvkibNm3w9/d3tr1o0SLuvvtuPD09KVu27CXPVa1aNXr37k2fPn0YO3YsMTExnDhxgnnz5lG3bl26dOmSq0xPPfUUo0ePpkqVKtSoUYMPPviA06dP5+joiYqKYuXKlezfvx9fX1/KlCmTp/ddEmn2cpEiZNLCvYBjxnJfz6L7AecSiY4efw0tFxERIyTPc8xufaZBU4OTyPXo2bMn77//Pu+++y61a9fmk08+YfLkybRu3dp5zNixY5k7dy6RkZHExDiWhnvvvfcoXbo0zZs359Zbb6Vjx440bNgwT2137NiRr776ig4dOlCzZk2eeuopOnbsyE8//eQ8ZtSoUezfv5/KlSs7J3W7nMmTJ9OnTx+eeeYZqlevTvfu3Vm5cmWO2cWv5vnnn+eee+6hT58+NGvWDF9fXzp27JhjIrRnn30Wi8VCrVq1CA4OztN17CWVyZ6XQfolQHJyMgEBASQlJTm/YRIpDDYcSqTnh0txM5tYNLQNEYHeRkcy1pLx8PerUPdO6PWZ0WlErok+c1xPP1MpCNlZ2axrfCN+GSmcffsDmvRoZ3SkApOenk5sbCzR0dElfkbqksBms1GzZk3uvPNOXnvtNaPjGOJK/+Zz+5lTwrvKRIqOT871cvdoUE4FN1ywXFgJXqdcREQMsXnuUkfB7e5N/Y7Xdv2sSGF04MAB5syZQ6tWrcjIyGDixInExsZy7733Gh2tSNPwcpEiIPZECrO3xgPwWKtKBqcpJDRzuYiIGOTQzLkAxFWPwcPL0+A0Iq5jNpuZMmUKTZo04aabbmLz5s38/fffOSaWk7xTT7dIEfDpon3Y7dC2RgjVQv2MjlM4qOgWERGDlFq3AgDf1q0MTiLiWpGRkSxdutToGMWOerpFCrlDp1L5v7WOodSPt65scJpCIsca3RWNzSIiIiXK/k27iDh1BKvJTINeJXepMBHJPRXdIoXcxHl7yLLaaVm1LI2jtCQDAKmnICvV8di/nLFZRESkRNn6068AHIysTpnwK88mLSICKrpFCrX9J1L4v3WHARjcvprBaQqRxP2Oe98wcNfMqSIiUnAsSxYCYG51i8FJRKSoUNEtUohNmLcbq81Om+rBNKxQ2ug4hcepWMd9GU0qJyIiBefo7oNUjHesJlL/7u4GpxGRokJFt0ghtSfhDL+sPwKol/sip/Y57lV0i4hIAdr0o2No+YHwKoRX1kSeIpI7KrpFCqnRM3dgs0P7WqHUKx9oekM3GwAAO1FJREFUdJzCxVl0RxubQ0REShTbwnmO+5s0a7mI5J6KbpFCaMnuE/yzIwE3s4kXOtcwOk7hc9IxtI8gzeYuIiIF48ThY1Q4tBOA2nf3MDiNiBQlKrpFChmrzc7rf24D4L4bK1I52NfgRIWQhpeLiEgBW/f9r1iwc7hsJBXrVDU6jogUISq6RQqZ/1t7iB3xZ/D3cuPptvpQv0h6EqSecDwureHlIkXF6NGjadKkCX5+foSEhNCzZ0927tyZ4xi73c6IESOIiIjA29ub1q1bs3XrVoMSi+SUOf8fADKaaWi5iOSNim6RQiQpNYt3/3L8ETqwbVVKl/IwOFEhdH7m8lLB4OVvbBYRybWFCxfyxBNPsGLFCubOnUt2djYdOnQgJSXFecw777zDuHHjmDhxIqtXryYsLIz27dtz5swZA5OLwMkjCVSM3QJA9Ts1tLwoW7FiBW3btqVs2bKYTKYct8TERKPjSTHlZnQAEfnXO3/t4MTZTCoHl6JPsyij4xROp85dz11G13OLFCWzZ8/O8Xzy5MmEhISwdu1abr75Zux2O+PHj2f48OHcfvvtAHz11VeEhoYydepUHnvsMSNiiwCw5pv/o4LdxuHgCrRvUsfoOIWL3Q5Zqca07e4DJlOuD9+4cSOtW7dmwIABfPDBBxw6dIh7772X+vXr079/fwIDA/Mvq5RoKrpFCol1B08zddVBAN64rS4ebhqIckm6nlukWEhKSgKgTJkyAMTGxhIfH0+HDh2cx3h6etKqVSuWLVumolsMZZv7FwCZrdoZnKQQykqFNyOMafvFo+BRKteHDxw4kB49ejBu3DgAatWqxT333MPKlSu58847+eOPP3jmmWew2Ww8//zzPPzww/mVXEoYFd0ihUC21caL0zdjt8MdjcpzY6UgoyMVXueHl6voFimy7HY7Q4YMoUWLFtSp4+g1jI+PByA0NDTHsaGhoRw4cOCy58rIyCAjI8P5PDk5OR8SS0l2eGcsUUd2YcNEgz7/MzqOXKNjx46xZMkS5s2bl2N7qVKlMJlMZGdnM2TIEObPn4+/vz8NGzbk9ttvd34xKHI9VHSLFAKfL4llR/wZAn3cebFLTaPjFG4ndjnutVyYSJH15JNPsmnTJpYsWXLRPtN/hora7faLtl1o9OjRjBw50uUZRc7b+NXPVAIOlq9G7WpRRscpfNx9HD3ORrWdS2vXrsVms1G/fv2Ltjdu3JhVq1ZRu3ZtypUrB0CXLl3466+/uOeee1waWUomFd0iBtt17Azj5jgKyRe71KSMJk+7PLsdjp+b7ThEX06IFEVPPfUUv/32G4sWLaJ8+fLO7WFhYYCjxzs8PNy5PSEh4aLe7wsNGzaMIUOGOJ8nJycTGRmZD8mlpPJY9DcA5nadDE5SSJlMeRribRSbzQZAWlqa89rtzZs3s2jRIkaNGsXRo0edBTdA+fLlOXLkiBFRpRjSRaMiBsqy2hjy0wYyrTZuqRHC/xqVv/qLSrIzcZCRDCaLJlITKWLsdjtPPvkk06dPZ968eURH51zyLzo6mrCwMObOnevclpmZycKFC2nevPllz+vp6Ym/v3+Om4ir7F61ifInDpFtMtP4/tuNjiPXoWnTpnh7ezN06FB27NjBn3/+SY8ePejfvz/NmzfHbrdf9JorjbIRyQv1dIsYaOK8PWw5kkygjztv3V5X/3O/muM7HPdBlcFNIwJEipInnniCqVOn8uuvv+Ln5+e8hjsgIABvb29MJhODBg3izTffpGrVqlStWpU333wTHx8f7r33XoPTS0m1/ZufqAocqFSXuuVCjI4j1yE4OJiffvqJZ555hnr16hEZGUn//v159tlnAShXrlyOnu3Dhw/TtGlTo+JKMaOiW8QgGw8lMnH+HgBe61GHEH8vgxMVAeeHlgdXNzaHiOTZxx9/DEDr1q1zbJ88eTL9+vUDYOjQoaSlpTFgwABOnz5N06ZNmTNnDn5+fgWcVgSys7IJWvoPAL49ehobRlyiW7dudOvW7ZL7brjhBrZs2cKRI0fw9/dn5syZvPLKKwWcUIorFd0iBkhKy+LJ79dhtdnpWi+cW+sbtNRGUXO+pzu4hrE5RCTPLjV0879MJhMjRoxgxIgR+R9I5CpW/d8syqQmcsazFDfe18PoOJLP3NzcGDt2LG3atMFmszF06FCCgrSajLiGim6RAma323n+/zZx6FQakWW8efO2ukZHKjqcPd0qukVEJH+d+GkapYG4xq24wcfb6DhSALp370737t2NjiHFkCZSEylgXy8/wOyt8bhbTEy8pyEB3u5GRyoa7HZI2O54rOHlIiKSj04cPkbUjjUAVO2nJaNE5PoU+qL7o48+Ijo6Gi8vLxo1asTixYsve+yCBQswmUwX3Xbs2FGAiUUub9PhRN7401E4Dutck/qRgcYGKkrOHoP0RDCZIaiK0WlERKQYW/3F97jbrRwOrkCtlo2NjiMiRVyhLrp//PFHBg0axPDhw1m/fj0tW7akc+fOHDx48Iqv27lzJ3Fxcc5b1apVCyixyOUlnEnn0a/Xkmm10aFWKA/cFGV0pKIlbpPjPqgquGuYn4iI5A+bzYb7nD8BsHa89KRbIiJ5UaiL7nHjxvHQQw/x8MMPU7NmTcaPH09kZKRzBtTLCQkJISwszHmzWCwFlFjk0jKyrfT/Zi3xyelUDi7FmDvra3mwvIrf6LgPr2dsDhERKdY2zV1GuZOHyTS7ccNDdxsdR0SKgUJbdGdmZrJ27Vo6dOiQY3uHDh1YtmzZFV8bExNDeHg4bdu2Zf78+Vc8NiMjg+Tk5Bw3EVey2+28NGML6w4m4u/lxud9m+Dvpeu48+x8T3eYim4REck/B7/8GoD99ZpRJjzY4DQiUhwU2qL7xIkTWK1WQkNDc2wPDQ0lPj7+kq8JDw/n008/Zdq0aUyfPp3q1avTtm1bFi1adNl2Ro8eTUBAgPMWGRnp0vch8sWSWH5eexizCSbe25DosqWMjlQ0xZ8rutXTLSIi+eT4oTiiNjs6d6Ie6mtwGhEpLgr9kmH/HYJrt9svOyy3evXqVK/+76zGzZo149ChQ4wZM4abb775kq8ZNmwYQ4YMcT5PTk5W4S0uM3NzHG/MdEyc9mKXmtxcTd+YX5P0JDi93/FYPd0iIpJPVn44hco2K4dCoujQ/iaj44hIMVFoe7rLli2LxWK5qFc7ISHhot7vK7nxxhvZvXv3Zfd7enri7++f4ybiCiv3nWTQjxuw26FPs4o81CLa6EhFV/xmx31AJPiUMTaLiIgUS9lZ2QTM/QMA023/MziNiBQnhbbo9vDwoFGjRsydOzfH9rlz59K8efNcn2f9+vWEh4e7Op7IFe2MP8PDX68hM9tGx9qhvHprbU2cdj3izk2ipl5uERHJJ8u/+42yKac441mKmx7RBGoi4jqFenj5kCFDuP/++2ncuDHNmjXj008/5eDBg/Tv3x9wDA0/cuQIX3/tmPBi/PjxREVFUbt2bTIzM/n222+ZNm0a06ZNM/JtSAlzNDGNfpNXcSY9m8YVS/P+3TFYzCq4r8uhVY778o2MzSEiIsXWme++oSwQd1N7bvD1MTqOSIEbMWIEv/zyCxs2bDA6SrFTaHu6Ae666y7Gjx/PqFGjaNCgAYsWLWLmzJlU/P/27js8qjL9//h70hPSA2kkpNCrkCCIgNJRUEBYv6BIUQnyU9e2yKKuKKzI6oIFFRFQwM6CwrKRVTECoQlI1QVBIJEACQECqaTO/P4IDIRJIAmZTMrndV1zZeac55y5czPhmfs8zzknLAyA5OTkEvfszs/PZ/LkyXTo0IGePXuyadMmvvnmG4YPH26rX0HqmdSMXEYv2kZyevGtwRaN64yLo25Zd8OO7yj+GdLFtnGIiEid9EvcViKSfqPQYEf0kxNtHY5YQWpqKo888ghNmjTB2dmZwMBABg4cyNatW81tdu/ezV133YW/vz8uLi6Eh4czcuRIzpw5U+Z+jx49yn333UdwcDAuLi6EhIQwdOhQDh06BEBiYiIGg6HGFbIGg4FVq1bZOox6o0aPdAM8+uijPProo6WuW7JkSYnXU6ZMYcqUKdUQlYils1l5jF60jYQz2TT2duXjh7vi7eZk67Bqv/TjkHECDPbQOMrW0YiISB2U8N4HNAcSOnSnfUtdg6UuGjFiBAUFBSxdupTIyEhOnTpFXFwcaWlpQHFR3q9fP+6++26+++47vL29SUhIYPXq1eTk5JS6z/z8fPr370+rVq34+uuvCQoK4vjx46xZs4b09PQKxZefn4+Tk7431lU1eqRbpLY4n5PPAx9u5/fULAI9Xfgi5hYae7vaOqy64dLU8sB24KTbrYmISNVK3HeIyP3FfU3zJyfZOBqxhvPnz7Np0yZee+01evfuTVhYGF26dOG5555j8ODBAGzZsoWMjAwWLVpEp06diIiIoE+fPrz11ls0adKk1P3u37+fo0ePMm/ePG655RbCwsLo3r07M2fO5OabbwYgIqL4IE6nTp0wGAz06tULgPHjxzNs2DBmzZpFcHAwLVq0AODEiROMHDkSHx8f/Pz8GDp0KImJieb3vLTd7NmzCQoKws/Pj8cee4yCggJzm+TkZAYPHoyrqysRERF8/vnnhIeH89ZbbwEQHh4OwD333IPBYDC/vuSTTz4hPDwcLy8vRo0aRWZm5o2kX1DRLXLDMnILGPvRdg4kZ9DQ3ZnPY7rSxE/nglWZS0V3aFfbxiEiInXS3jfnYY+JIxHtaX2rZlRVlMlkIqcgxyYPk8lUrhjd3d1xd3dn1apV5OXlldomMDCQwsJCVq5cWe79NmrUCDs7O1asWEFRUVGpbbZvL/4e88MPP5CcnMzXX39tXhcXF8eBAwdYu3YtsbGx5OTk0Lt3b9zd3YmPj2fTpk24u7tzxx13kJ+fb95u3bp1HDlyhHXr1rF06VKWLFlSYgbw2LFjOXnyJOvXr+err75iwYIFpKammtfv2FF82t7ixYtJTk42vwY4cuQIq1atIjY2ltjYWDZs2MA//vGPcuVDylbjp5eL1GSZuQU8uHgH+46n49vAic9juhLZyN3WYdUtxy6ea6XzuUVEpIqdTkombPuPAPhPeNjG0dROFwov0PVz2xwY33b/Ntwcrz/Q4eDgwJIlS4iJiWH+/PlERUVx++23M2rUKDp0KL4zyi233MLzzz/P/fffz6RJk+jSpQt9+vRh7NixZd6uuHHjxsydO5cpU6Ywffp0OnfuTO/evRk9ejSRkZFAcWEO4OfnR2BgYIntGzRowKJFi8zTyj/66CPs7OxYtGiR+a43ixcvxtvbm/Xr1zNgwAAAfHx8ePfdd7G3t6dVq1YMHjyYuLg4YmJi+O233/jhhx/YsWMHnTt3BmDRokU0b97c/L6XYvL29raIyWg0smTJEjw8PAAYM2YMcXFxzJw587p5lrJppFukklIzcxm14Cd2/nEOTxcHPnm4Cy0CPGwdVt2Sk3b5dmHhPWwbi4iI1DlbX52Lc1EBSf5hdL6nv63DESsaMWIEJ0+eZPXq1QwcOJD169cTFRVVYoR45syZpKSkMH/+fNq0acP8+fNp1aoVv/zyS5n7feyxx0hJSeHTTz+lW7duLF++nLZt21rc9rg07du3L3Ee986dOzl8+DAeHh7m0XlfX19yc3M5cuSIuV3btm2xt798od6goCDzSPbBgwdxcHAgKuryrI1mzZrh4+NTrjyFh4ebC+6r9y2Vp5FukUpIOJPN2I+2kZR2Ab8GTix5sAttg71sHVbdk7gRMEGjVuAZZOtoRESkDkk9lkxo/BoA3CY8gp2dxqIqw9XBlW33b7PZe1eEi4sL/fv3p3///kybNo0JEybw0ksvMX78eHMbPz8/7r33Xu69915mzZpFp06dmD17NkuXLi1zvx4eHgwZMoQhQ4bwyiuvMHDgQF555RX697/2gZwGDUpeq8ZoNBIdHc1nn31m0fbS6DSAo6NjiXUGgwGj0QhQ5tT48k6Zv9a+pfJUdItU0N6k8zy4ZAdp2fmE+bmx9MEuhDfUBb6s4uj64p+RvWwZhYiI1EE/vfo2zYvySfIPp98DQ20dTq1lMBjKNcW7JmrTps01b5vl5ORE06ZNyc7OLvc+DQYDrVq1YsuWLeZ9AGWe832lqKgoli1bhr+/P56enuV+zyu1atWKwsJCdu/eTXR0NACHDx/m/PnzJdo5OjqWKyapGjqkJ1IB3/0vhfsW/kRadj7tGnuyYtKtKritSUW3iIhYwanEkzTZWDzK3WDi/9Modx139uxZ+vTpw6effsq+fftISEhg+fLlvP766wwdWnzAJTY2lgceeIDY2FgOHTrEwYMHmT17NmvWrDG3udqePXsYOnQoK1asYP/+/Rw+fJgPP/yQjz76yLyNv78/rq6ufPvtt5w6deqatxIbPXo0DRs2ZOjQoWzcuJGEhAQ2bNjAk08+yfHjx8v1u7Zq1Yp+/foxceJEtm/fzu7du5k4cSKurq7m88SheBp5XFwcKSkpnDt3rryplErSSLdIOZhMJt5bd5jZ3x8CoGfzhrz/QDTuzvoTspq0o8UPgz2Edbd1NCIiUof8NPNNWhQVcCwgkv7332XrcMTK3N3d6dq1K2+++SZHjhyhoKCA0NBQYmJieP7554HiUW83Nzf+8pe/kJSUhLOzM82bN2fRokWMGTOm1P2GhIQQHh7O9OnTSUxMNN9+a/r06Tz99NNA8UXc5s6dy4wZM5g2bRo9e/Zk/fr1pe7Pzc2N+Ph4/vrXvzJ8+HAyMzNp3Lgxffv2rdDI98cff8zDDz/MbbfdRmBgILNmzeJ///sfLi4u5jZz5szhmWeeYeHChTRu3LjEbcmk6hlM5Z3gX09kZGTg5eVFenp6pad1SN2SW1DEsyv28Z+9JwEY1y2Mv93VBkd7HRW3qi3vwvcvQMRtMO4/to5GxCrU51Q95VSuJ3HfITJGDsfRVMT56bPpNnKwrUOqVXJzc0lISCAiIqJEESc11/HjxwkNDeWHH36gb9++tg6n1rnWZ768fY6G6USu4eT5C/y/T3ey93g6DnYGpg9ty+iuYbYOq374Lbb4Z6u7bRuHiIjUKftefpXmpiKOhrdjsApuqYN+/PFHsrKyaN++PcnJyUyZMoXw8HBuu+02W4dWb6noFinDut9SefpfezifU4CPmyPzRkfTramfrcOqH7JOw7Gfip+3GmTbWEREpM7Y/d94mu/fhhEDkX+bautwRKyioKCA559/nqNHj+Lh4cGtt97KZ599ZnFlcqk+KrpFrlJYZOSNtYeYt774fogdQrx47/4oQn1r55U5a6XfYgETBHUErxBbRyMiInWA0Wjk1Ov/JAw4HN2LoT2ibR2SiFUMHDiQgQMH2joMuYKKbpErnMrI5YkvdrMtIQ0oPn/7+cGtcXawt3Fk9czeL4t/tr3HtnGIiEid8ePcpYQlHybX3okuMzTKLSLVR0W3yEWx+07ywspfSb9QgLuzA/8Y0Z67OgTbOqz65+wRSPoJDHbQYaStoxERkTogLfk0HovfA+Dk0Pvp1LSJjSMSkfpERbfUe+kXCnjp37+yak/x1ck7hHjx1siORDZyt3Fk9dSlUe7I3uAZZNtYRESkTtg4ZTot8rJJ9gmm/7SnbB2OiNQzKrqlXtv4+2mmrNhHcnou9nYGHuvdjD/3aabbgdlKYT7s+rj4ecf7bRuLiIjUCbvXbKDFjjgAvF74G04uzjaOSETqGxXdUi+dzcpj5jcH+Hr3CQAiGjbgjf+7iU5NfGwcWT33v5WQlQLugdB6iK2jERGRWi47PYv0l6fhAhyK6sXQu3rbOiQRqYdUdEu9YjKZ+HrXCV75Zj/ncgowGGBct3Cm3NESNyf9OdiUyQQ/FZ9vR5cJ4OBk23hERKTW++HpF2mRkUqamze3vfGKrcMRkXpKc2il3jh0KpPRi7bxl+V7OZdTQKtAD1Y+2p2Xh7RVwV0THP4BkveCgwtEP2TraEREpJbbtvxbWmz5FgC7v/4Nn0A/G0ck9VF4eDhvvfVWudsnJiZiMBjYs2eP1WKqz5YsWYK3t3e1v6+Kbqnz0rLzeXHVr9z59ka2HDmLs4MdU+9sxX/+3IOOod62Dk8AjEaIm1H8/OYJ0EBfjEREpPLOHD9F4azpABy6ZQDdRg62cURia+PHj8dgMGAwGHB0dCQyMpLJkyeTnZ1t1ffdsWMHEydOLHf70NBQkpOTadeuHQDr16/HYDBw/vz5Cr1vVRbvqampPPLIIzRp0gRnZ2cCAwMZOHAgW7duNbcxGAysWrXqht+rKlX0gIc1aXhP6qz8QiOf/vQHb/1wiIzcQgDuaBvI84Na08TPzcbRSQn/+xpS9oGTB/R4xtbRiIhILVZYUMi2CY8TmXOeU17+9H3r77YOSWqIO+64g8WLF1NQUMDGjRuZMGEC2dnZvP/++xZtCwoKcHR0vOH3bNSoUYXa29vbExgYeMPvW5VGjBhBQUEBS5cuJTIyklOnThEXF0daWlqF9lNVOa2NNNItdU5hkZEVO4/T9431zIjdT0ZuIa2DPPki5hbmj4lWwV3T5KbDdy8UP7/1zxrlFhGRG/LfqbOITPyVPHtHAt58E3dvT1uHJDXEpVHa0NBQ7r//fkaPHm0enX355Zfp2LEjH330EZGRkTg7O2MymUhPT2fixIn4+/vj6elJnz592Lt3b4n9rl69ms6dO+Pi4kLDhg0ZPny4ed3Vo60Gg4H333+fO++8E1dXVyIiIli+fLl5/ZUj1ImJifTuXXzxPx8fHwwGA+PHjwfg22+/pUePHnh7e+Pn58ddd93FkSNHzPuJiIgAoFOnThgMBnr16mVet3jxYlq3bo2LiwutWrVi3rx5Zebs/PnzbNq0iddee43evXsTFhZGly5deO655xg8eLD5dwS45557MBgM5teVzeml7T755BPCw8Px8vJi1KhRZGZmmttkZmYyevRoGjRoQFBQEG+++Sa9evXiqaeeAqBXr1788ccfPP300+YZDlf67rvvaN26Ne7u7txxxx0kJyeXmYOqoKJb6gyj0cTqvScZ8FY8k5fvJSntAg3dnXn1nvbE/rkH3ZqqmKuR4mYUX7Hctyl0f9LW0YiISC225Yv/EPnNFwCciXma1rdG2Tiius9kMmHMybHJw2Qy3VDsrq6uFBQUmF8fPnyYf/3rX3z11VfmadmDBw8mJSWFNWvWsHPnTqKioujbt695lPebb75h+PDhDB48mN27dxMXF0fnzp2v+b4vvvgiI0aMYO/evTzwwAPcd999HDhwwKJdaGgoX331FQAHDx4kOTmZt99+G4Ds7GyeeeYZduzYQVxcHHZ2dtxzzz0YjUYAtm/fDsAPP/xAcnIyX3/9NQALFy7khRdeYObMmRw4cIBXX32VF198kaVLl5Yaq7u7O+7u7qxatYq8vLxS2+zYsQMoLuaTk5PNryubU4AjR46watUqYmNjiY2NZcOGDfzjH/8wr3/mmWfYvHkzq1evZu3atWzcuJFdu3aZ13/99deEhIQwY8YMkpOTSxTVOTk5zJ49m08++YT4+HiOHTvG5MmTS/3dqoqml0utl19o5N97TrAg/ii/p2YB4OPmyKTbmzK2WziuTvY2jlDKdPBb2LGo+Pldb4Kji23jERGRWuvAll04z3wRO0wcurkvQ5960NYh1QumCxc4GBVtk/duuWsnBrfKzWDcvn07n3/+OX379jUvy8/P55NPPjFPCf/xxx/55ZdfSE1Nxdm5+P7us2fPZtWqVaxYsYKJEycyc+ZMRo0axfTp0837uemmm6753vfeey8TJkwA4O9//ztr167lnXfesRhxtre3x9fXFwB/f/8SFwAbMWJEibYffvgh/v7+7N+/n3bt2pl/Bz8/vxLT1f/+978zZ84c82h8REQE+/fv54MPPmDcuHEWsTo4OLBkyRJiYmKYP38+UVFR3H777YwaNYoOHToAl6fQe3t7W0yNr0xOAYxGI0uWLMHDwwOAMWPGEBcXx8yZM8nMzGTp0qUl/v0WL15McHCw+X19fX2xt7fHw8PDIqaCggLmz59P06ZNAXj88ceZMWOGxe9elVR0S62VmVvAF9uP8eGmBE5lFB9583B2IOa2SB7sHo6HS/08Z6TWOH8MVk0qft51EkTebtt4RESk1ko5msTZPz+OX2EeCSGtuGPBbFuHJDVQbGws7u7uFBYWUlBQwNChQ3nnnXfM68PCwkqcg71z506ysrLw8ys5W/LChQvmqdx79uwhJiamQnF069bN4nVFL3h25MgRXnzxRX766SfOnDljHuE+duyY+SJsVzt9+jRJSUk8/PDDJWIuLCzEy8urzPcaMWIEgwcPZuPGjWzdupVvv/2W119/nUWLFpmnu5elMjmF4inrlwpugKCgIFJTUwE4evQoBQUFdOnSxbzey8uLli1bXjOWS9zc3MwF99X7thYV3VKrmEwmdh07x5fbk/jml2Ry8osACPB05sHuEdzftQmeKrZrvpw0+PRPcOEcBHeC/tY9uigiInXXudSz/DJuAiHZ5zjl5c8tnyzA2VUzp6qLwdWVlrt22uy9K6J37968//77ODo6EhwcbHFRrwYNGpR4bTQaCQoKYv369Rb7ujTq7FrBGMpy9TnH13P33XcTGhrKwoULCQ4Oxmg00q5dO/Lz88vc5lJhvnDhQrp27Vpinb39tWeGuri40L9/f/r378+0adOYMGECL7300nWL7srkFLD4tzEYDOb4L51WcHXOynu6QWn7vtFTFa5HRbfUCmey8li56wRf7jjGkdOXb+3Q3N+dmNsiGdoxGGcHTSOvFXLT4YtRcOYgeATDyE/BwdnWUYmISC2UkZbO9v8bS5PTx0h3dqfJBx/gG1Sxq0XLjTEYDJWe4l3dGjRoQLNmzcrdPioqipSUFBwcHMwXB7tahw4diIuL48EHy386w08//cTYsWNLvO7UqVOpbZ2cnAAoKioyLzt79iwHDhzggw8+oGfPngBs2rTputsFBATQuHFjjh49yujRo8sdb2natGlT4hZhjo6OJd6rLOXJ6fU0bdoUR0dHtm/fTmhoKAAZGRn8/vvv3H775ZmTTk5O5YqpOqjolhorr7CIDQdPs3L3CdbuP0WhsfgIlKujPYM7BDHy5lA6h/lU+Mig2FBWKnw6HFJ+ARcveOAr8AqxdVQiIlILZZ3PYPO9YwlPOUqWkxue775PZMdWtg5L6pB+/frRrVs3hg0bxmuvvUbLli05efIka9asYdiwYXTu3JmXXnqJvn370rRpU0aNGkVhYSH//e9/mTJlSpn7Xb58OZ07d6ZHjx589tlnbN++nQ8//LDUtmFhYRgMBmJjYxk0aBCurq74+Pjg5+fHggULCAoK4tixY0ydOrXEdv7+/ri6uvLtt98SEhKCi4sLXl5evPzyyzzxxBN4enpy5513kpeXx88//8y5c+d45hnL27aePXuWe++9l4ceeogOHTrg4eHBzz//zOuvv87QoUPN7cLDw4mLi6N79+44Ozvj4+NT6Zxej4eHB+PGjePZZ5/F19cXf39/XnrpJezs7ErUBeHh4cTHxzNq1CicnZ1p2LDhdfdtLbp6udQohUVGNv5+mmeX76XzKz8w8ZOd/PfXFAqNJm4K9ebVe9qz/YW+zL73Jm4O91XBXZsc+wk+uL244G7QCMbFQkAbW0clIiK1UFryaTYPu4/wE4fIcXTB5c13adPz+l/WRSrCYDCwZs0abrvtNh566CFatGjBqFGjSExMJCAgACi+NdXy5ctZvXo1HTt2pE+fPmzbtu2a+50+fTpffvklHTp0YOnSpXz22We0aVP6d6LGjRszffp0pk6dSkBAAI8//jh2dnZ8+eWX7Ny5k3bt2vH000/zz3/+s8R2Dg4OzJ07lw8++IDg4GBzgTxhwgQWLVrEkiVLaN++PbfffjtLliwx32Lsau7u7nTt2pU333yT2267jXbt2vHiiy8SExPDu+++a243Z84c1q5dS2hoaJmj9uXNaXm88cYbdOvWjbvuuot+/frRvXt3823QLpkxYwaJiYk0bdq0wvdLr2oGk7UnsNcyGRkZeHl5kZ6ejqen7utYHbLyCtn0+2niDqTy42+pnM2+fC5KgKczd3UI5k/RIbQO0r9HrVSQC5vegPjZYCoCv+Zw/zLwa3r9bUXqOPU5VU85rftOHErk4LiHCDqXTJaTGw7/fItOA3vaOqx6Izc3l4SEBCIiIkoUOFI+BoOBlStXMmzYMFuHUqdkZ2fTuHFj5syZw8MPP1yl+77WZ768fY6ml0u1MxpNHDyVyZYjZ1l/MJWfjp6loOjysR8fN0cGtQ/i7puC6RLui52dRrNrJZMJfvsG1k6DtItXo2w3Au5+G5w9rr2tiIhIKfau3UzWXycTlHOeNDdvGs57n5a3dLR1WCJSzXbv3s1vv/1Gly5dSE9PN9/y68op7zWJim6xuoIiIwdTMtmekMZPR8+yPTGN8zkFJdqE+7nRt3UAfVr50yXCF0d7nflQaxXmwYH/wOa3iqeSA7gHwp3/gDbDQKcEiIhIJaydswj/D9/G11hIincgTT9aRJM2mjUlUl/Nnj2bgwcP4uTkRHR0NBs3brTpedvXoqJbqlReYREJZ7L59UQGvxw/z97j6exPziC/0FiinZuTPTeH+9KjWUP6tvYnspG7jSKWKmE0womdsH8V7PkcLqQVL3dyh66PQPcniy+cJiIiUkEZaemse3wqLXatB+Bw8yhuW/wuXg1Lv1CTSE2mM3urRqdOndi50za3qqsMFd1SIfmFRk5n5ZGakUtqZh6pmXkcT8vhcGoWR05ncSwtB2Mp/5d4uDjQqYkP3SL9uCXSl3aNvTSaXZuZTHD+GCRtg8SNcPBbyE69vN4jGKLGFhfcbr62i1NERGq1Xd+sJ+ulv9Ei6yxGDBwdNIpBr7+AvW4TKiK1iIrueiqvsIjsvCKy8wrJyiskJ7+QrCteZ+UWciaruKg+lZHL6YsFdtoVFzkri4eLA60DPekQ4kX7EC9uCvGmia+bzs2urYoK4OwRSN0PqQeKf57YBZknS7Zz9oRm/aDDyOKf9vrvRUREKufsiVQ2Tf07zXbE4YqJMw18cZ02g7uH9rV1aHKRRmylvqiKz7q+FVcDk8lEkdFEkcmEycTl50YourjOeKmN8XLbEq8vLjMaTRQai38WmUzk5BeRmVtoLpaz8y49Ly6gs/MvLSsqXn/x9ZUXLqsoR3sDjdydaeTpgr+HM8FeLjT1d6dZI3ea+bvTyMNZt/KqDYoK4cI5yDl7+ZF9GtKPX3wkFf/MOFl81fGr2TlA0E0Qegs07wdhPcDBqfp/DxERqTPyLuSy7s0P8f3yI1rk5wBwqNPt3D73VbwbaeZUTeDo6AhATk4Orq6uNo5GxPpycor/L7r02a8MFd1lOXcMiq68wnIZRWoZRz7m/niIVbtOFhfXF5cZythH2csr0vb6RbTLxUdDTOadOzvY4eZkj4ujA25Odrg52uPqZI+bkz3ero74uTvh6+aEr7szfg0c8XVzwtPVEctB6+ziR04K5Fy9rmK5K7V9RdqW2b6stmXsutrjKGt5ERTlFxfJxoLikeeigiue54Ox8PLzogIoyIb8Uh4FOZCfBRfOQ+75Mn6XUji5Q6NW4N+6+BHYARpHg5Nb+fchIiJShvzcPNa/sxTXL5cSll18XZCTvo3x+utzDNXodo1ib2+Pt7c3qanFp5W5ublpsEXqJJPJRE5ODqmpqXh7e2NvX/nTWlR0l2X+reBc+f9AngCeqC2DfkUXH7m2DkRswtUH3PwuPzwbg3coeIWA18Wf7oFgp3PwRUSkap1OSmbbO4vx+eE/hOacB+CcqycZIx6g77OP4OhcW75M1S+BgYEA5sJbpC7z9vY2f+YrS0X3tThePYpXRhFeytG97PxCjCYDrk722Bmu2rJEe8MV68raf1kBlrKizCON5Y/d+u1rUixltK9Izq0eix3YOxWfI23neNXziw+7K346OIFTA3BsUPzTqUHxSLWT2+Xlrt7FBbaLt869FhGRapV3IZcd//ovaf+JJWz/dpoaCwFId/Eg7e6R9JoyCTePBjaOUq7FYDAQFBSEv78/BQUF199ApJZydHS8oRHuS/RtuyxP7oPA8Epv3u3l78jIKyTu8dtpqtthiYiISD12LuUse//9Pekb4gn+dTt++Tn4XVyX5B+G3fCR3DphJG7uOm2pNrG3t6+SgkSkrqvx80XnzZtHREQELi4u5pueX8uGDRuIjo7GxcWFyMhI5s+fX7k3vsFzU4ou3jfLUVNyRUREKqSifb/ULEajkeMHE1i/4EtWT5rKd70Gc6JXTwLenEGLXetxz8/hvKsnh3oMpuDdD+m3fg39nnpQBbeI1Fk1eqR72bJlPPXUU8ybN4/u3bvzwQcfcOedd7J//36aNGli0T4hIYFBgwYRExPDp59+yubNm3n00Udp1KgRI0aMqOC731jRXXCx6La314UlREREyquifb/YTk5mNilHkjj9eyJpvx0i/+hRHE8k4XP6ON4XMggAAq5on+wTRGb7zgQN7MvNQ/ri4Fijv4aKiFQZg6kG32Sva9euREVF8f7775uXtW7dmmHDhjFr1iyL9n/9619ZvXo1Bw4cMC+bNGkSe/fuZevWreV6z4yMDLy8vEg/lYSnf0ilY2/6/BqKjCa2P98Xf0+XSu9HRETqLnOfk56Op6enrcOpESra91+tLubUaDSan5uMl7+2XfoKZ7py/aVlJst2xfsyXVpIQV4+eTm55OVcIC/7AgUXcsm/kEtBTi75OTnkpp0n/9x5Cs6dx5iejiEzA/uM87ikn8Uz6zzu+Ra3KzErMtiR4teY7MiWuHXsSOvBfQhpGXFjiRARqWHK2+fU2EOM+fn57Ny5k6lTp5ZYPmDAALZs2VLqNlu3bmXAgAEllg0cOJAPP/yQgoKCUu+tlpeXR15envl1eno6AH1e+54Cl8rfD7LgQvFFJXKyMskgv9L7ERGRuisjIwMoWRTVZ5Xp+8vqx+Nv6YT7leeaXkxxiflnV6T90nJDGf8U5u1K2aas7a63rLR91pST0hwvPq4lC8hzhHPukOoDp3zhlI+BU74GkhsVUeCYBCQBP0DcbIizetgiItWq8EIRcP1+vMYW3WfOnKGoqIiAgIASywMCAkhJSSl1m5SUlFLbFxYWcubMGYKCgiy2mTVrFtOnT7dYvvONh28g+ssi3qqS3YiISB2WmZmJl5eXrcOwucr0/WX143cfOGqVGEVERK52vX68xhbdlxiuuqCZyWSyWHa99qUtv+S5557jmWeeMb82Go1ER0eza9cuMjMzCQ0NJSkpqUqnqN18883s2LGjStuX1aa8y8v7OiMjQzm56nVtycm11isn5VtXnmXKiW1zUtF8lGcba+bEZDIRHR1NcHBwhWKu6yrS91d3P17bPmNXP9ffXc3MyY18rylrXWVzUlv6q+u1UU7Kt64+56Qqvv+Wtx+vsUV3w4YNsbe3tziynZqaanEE/JLAwMBS2zs4OODn51fqNs7Ozjg7O1ss8/LyMnfwnp6eVfphsre3r9D+ytO+rDblXV7R18pJ7cvJtdYrJ+VbV55lyoltc1LRfJRnG2vnxMnJCTvd6QKoXN9f3f14bfyMldZeOalZObmR7zVlrbvRnNT0/up6bZST8q2rzzmpqu+/5enHa2wv7+TkRHR0NGvXri2xfO3atdx6662lbtOtWzeL9t9//z2dO3cu9Xzusjz22GMVD7gCKrr/8rQvq015l1f0dVVTTq4fz422v9Z65aR868qzTDmxbU4qs29b58Tan5HapDJ9f2n0Gavez5hyYqk6v9eUtU45UU7Ks64+56Q6v//W6KuXL1u2jDFjxjB//ny6devGggULWLhwIf/73/8ICwvjueee48SJE3z88cdA8S3D2rVrxyOPPEJMTAxbt25l0qRJfPHFF5W4ZVjdvALqjVJOLCknlpQTS8qJJeVESnO9vr8i9BmzpJxYUk5KUj4sKSeWlJOKqbHTywFGjhzJ2bNnmTFjBsnJybRr1441a9aYO93k5GSOHTtmbh8REcGaNWt4+umnee+99wgODmbu3LmVKriheHraSy+9ZDFtrT5TTiwpJ5aUE0vKiSXlREpzvb6/IvQZs6ScWFJOSlI+LCknlpSTiqnRI90iIiIiIiIitVmNPadbREREREREpLZT0S0iIiIiIiJiJSq6RURERERERKxERbeIiIiIiIiIlajorqTY2FhatmxJ8+bNWbRoka3DqTHuuecefHx8+NOf/mTrUGwuKSmJXr160aZNGzp06MDy5cttHZLNZWZmcvPNN9OxY0fat2/PwoULbR1SjZGTk0NYWBiTJ0+2dSg1goODAx07dqRjx45MmDDB1uFIHaR+3JL68JLUj1tSP1429eMlqR8vSVcvr4TCwkLatGnDunXr8PT0JCoqim3btuHr62vr0Gxu3bp1ZGVlsXTpUlasWGHrcGwqOTmZU6dO0bFjR1JTU4mKiuLgwYM0aNDA1qHZTFFREXl5ebi5uZGTk0O7du3YsWMHfn5+tg7N5l544QV+//13mjRpwuzZs20djs01bNiQM2fO2DoMqaPUj5dOfXhJ6sctqR8vm/rxktSPl6SR7krYvn07bdu2pXHjxnh4eDBo0CC+++47W4dVI/Tu3RsPDw9bh1EjBAUF0bFjRwD8/f3x9fUlLS3NtkHZmL29PW5ubgDk5uZSVFSEjvvB77//zm+//cagQYNsHYpIvaB+vHTqw0tSP25J/Xjp1I/L9dTLojs+Pp67776b4OBgDAYDq1atsmgzb948IiIicHFxITo6mo0bN5rXnTx5ksaNG5tfh4SEcOLEieoI3apuNC91TVXm4+eff8ZoNBIaGmrlqK2rKnJy/vx5brrpJkJCQpgyZQoNGzaspuitoypyMnnyZGbNmlVNEVtfVeQkIyOD6OhoevTowYYNG6opcqkt1I9bUh9uSf24JfXjltSPW1I/XvXqZdGdnZ3NTTfdxLvvvlvq+mXLlvHUU0/xwgsvsHv3bnr27Mmdd97JsWPHAEo9omcwGKwac3W40bzUNVWVj7NnzzJ27FgWLFhQHWFbVVXkxNvbm71795KQkMDnn3/OqVOnqit8q7jRnPz73/+mRYsWtGjRojrDtqqq+JwkJiayc+dO5s+fz9ixY8nIyKiu8KUWUD9uSX24JfXjltSPW1I/bkn9uBWY6jnAtHLlyhLLunTpYpo0aVKJZa1atTJNnTrVZDKZTJs3bzYNGzbMvO6JJ54wffbZZ1aPtTpVJi+XrFu3zjRixAhrh1itKpuP3NxcU8+ePU0ff/xxdYRZrW7kM3LJpEmTTP/617+sFWK1q0xOpk6dagoJCTGFhYWZ/Pz8TJ6enqbp06dXV8hWVxWfkzvuuMO0Y8cOa4UotZz6cUvqwy2pH7ekftyS+nFL6serRr0c6b6W/Px8du7cyYABA0osHzBgAFu2bAGgS5cu/Prrr5w4cYLMzEzWrFnDwIEDbRFutSlPXuqT8uTDZDIxfvx4+vTpw5gxY2wRZrUqT05OnTplPtKZkZFBfHw8LVu2rPZYq0t5cjJr1iySkpJITExk9uzZxMTEMG3aNFuEWy3Kk5Nz586Rl5cHwPHjx9m/fz+RkZHVHqvUTurHLakPt6R+3JL6cUvqxy2pH68cB1sHUNOcOXOGoqIiAgICSiwPCAggJSUFKL4E/pw5c+jduzdGo5EpU6bU+as2licvAAMHDmTXrl1kZ2cTEhLCypUrufnmm6s7XKsrTz42b97MsmXL6NChg/lcmE8++YT27dtXd7jVojw5OX78OA8//DAmkwmTycTjjz9Ohw4dbBFutSjv3019Up6cHDhwgEceeQQ7OzsMBgNvv/12vb+qtJSf+nFL6sMtqR+3pH7ckvpxS+rHK0dFdxmuPrfLZDKVWDZkyBCGDBlS3WHZ3PXyUt+u/nqtfPTo0QOj0WiLsGzqWjmJjo5mz549NojKtq73d3PJ+PHjqyki27tWTm699VZ++eUXW4QldYj6cUvqwy2pH7ekftyS+nFL6scrRtPLr9KwYUPs7e0tjl6lpqZaHNGpT5SXkpQPS8qJJeXEknIi1qbPmCXlxJJyYkk5saScWFJOKkdF91WcnJyIjo5m7dq1JZavXbuWW2+91UZR2Z7yUpLyYUk5saScWFJOxNr0GbOknFhSTiwpJ5aUE0vKSeXUy+nlWVlZHD582Pw6ISGBPXv24OvrS5MmTXjmmWcYM2YMnTt3plu3bixYsIBjx44xadIkG0ZtfcpLScqHJeXEknJiSTkRa9NnzJJyYkk5saScWFJOLCknVlDdl0uvCdatW2cCLB7jxo0zt3nvvfdMYWFhJicnJ1NUVJRpw4YNtgu4migvJSkflpQTS8qJJeVErE2fMUvKiSXlxJJyYkk5saScVD2DyWQyVU35LiIiIiIiIiJX0jndIiIiIiIiIlaioltERERERETESlR0i4iIiIiIiFiJim4RERERERERK1HRLSIiIiIiImIlKrpFRERERERErERFt4iIiIiIiIiVqOgWERERERERsRIV3SIiIiIiIiJWoqJbpB57+eWX6dixo83e/8UXX2TixInlajt58mSeeOIJK0ckIiJSe6gfF6kdDCaTyWTrIESk6hkMhmuuHzduHO+++y55eXn4+flVU1SXnTp1iubNm7Nv3z7Cw8Ov2z41NZWmTZuyb98+IiIirB+giIiIDakfF6k7VHSL1FEpKSnm58uWLWPatGkcPHjQvMzV1RUvLy9bhAbAq6++yoYNG/juu+/Kvc2IESNo1qwZr732mhUjExERsT314yJ1h6aXi9RRgYGB5oeXlxcGg8Fi2dXT0saPH8+wYcN49dVXCQgIwNvbm+nTp1NYWMizzz6Lr68vISEhfPTRRyXe68SJE4wcORIfHx/8/PwYOnQoiYmJ14zvyy+/ZMiQISWWrVixgvbt2+Pq6oqfnx/9+vUjOzvbvH7IkCF88cUXN5wbERGRmk79uEjdoaJbREr48ccfOXnyJPHx8bzxxhu8/PLL3HXXXfj4+LBt2zYmTZrEpEmTSEpKAiAnJ4fevXvj7u5OfHw8mzZtwt3dnTvuuIP8/PxS3+PcuXP8+uuvdO7c2bwsOTmZ++67j4ceeogDBw6wfv16hg8fzpWTcbp06UJSUhJ//PGHdZMgIiJSS6kfF6l5VHSLSAm+vr7MnTuXli1b8tBDD9GyZUtycnJ4/vnnad68Oc899xxOTk5s3rwZKD7SbWdnx6JFi2jfvj2tW7dm8eLFHDt2jPXr15f6Hn/88Qcmk4ng4GDzsuTkZAoLCxk+fDjh4eG0b9+eRx99FHd3d3Obxo0bA1z36LuIiEh9pX5cpOZxsHUAIlKztG3bFju7y8fjAgICaNeunfm1vb09fn5+pKamArBz504OHz6Mh4dHif3k5uZy5MiRUt/jwoULALi4uJiX3XTTTfTt25f27dszcOBABgwYwJ/+9Cd8fHzMbVxdXYHio/IiIiJiSf24SM2joltESnB0dCzx2mAwlLrMaDQCYDQaiY6O5rPPPrPYV6NGjUp9j4YNGwLF09MutbG3t2ft2rVs2bKF77//nnfeeYcXXniBbdu2ma9ympaWds39ioiI1Hfqx0VqHk0vF5EbEhUVxe+//46/vz/NmjUr8SjrqqpNmzbF09OT/fv3l1huMBjo3r0706dPZ/fu3Tg5ObFy5Urz+l9//RVHR0fatm1r1d9JRESkvlA/LmJ9KrpF5IaMHj2ahg0bMnToUDZu3EhCQgIbNmzgySef5Pjx46VuY2dnR79+/di0aZN52bZt23j11Vf5+eefOXbsGF9//TWnT5+mdevW5jYbN26kZ8+e5ulpIiIicmPUj4tYn4puEbkhbm5uxMfH06RJE4YPH07r1q156KGHuHDhAp6enmVuN3HiRL788kvz9DZPT0/i4+MZNGgQLVq04G9/+xtz5szhzjvvNG/zxRdfEBMTY/XfSUREpL5QPy5ifQbTldfxFxGpJiaTiVtuuYWnnnqK++6777rtv/nmG5599ln27duHg4MuRyEiImJL6sdFyk8j3SJiEwaDgQULFlBYWFiu9tnZ2SxevFgdtYiISA2gflyk/DTSLSIiIiIiImIlGukWERERERERsRIV3SIiIiIiIiJWoqJbRERERERExEpUdIuIiIiIiIhYiYpuEREREREREStR0S0iIiIiIiJiJSq6RURERERERKxERbeIiIiIiIiIlajoFhEREREREbESFd0iIiIiIiIiVvL/AUT5tIdRHcEiAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", - "\n", - "model.plot(axes[0,0], 'Precipitate Density')\n", - "model.plot(axes[0,1], 'Volume Fraction')\n", - "model.plot(axes[1,0], 'Average Radius', label='Average Radius')\n", - "model.plot(axes[1,0], 'Critical Radius', label='Critical Radius')\n", - "axes[1,0].legend()\n", - "sm.plotStrength(axes[1,1], model, plotContributions=True)\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The individual strengthening mechanisms can be plotted as a function of time as well as the precipitate radius. Rather than including the mean projected radius and inter-particle distance, the solved precipitate model is inserted into the plotting function.\n", - "\n", - "Here, we can see that the interfacial energy had very little contribution to the strength compared to the other three mechanisms." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(3,2,figsize=(10,10))\n", - "sm.plotPrecipitateStrengthOverTime(ax, model, plotContributions=True)\n", - "ax[2,1].set_ylim([0,400])\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. A. T. Dinsdale, \"SGTE Data for Pure Elements\" *Calphad* 15 (1991) p. 317\n", - "2. H. Bo et al, \"Experimental study and thermodynamic modeling of the Al-Sc-Zr system\" *Computational Materials Science* 133 (2017) p. 82\n", - "3. M. R. Ahmadi et al, \"A model for precipitate strengthening in multi-particle systems\" *Computational Materials Science* 91 (2014) p. 173\n", - "4. D. Seidman et al, \"Precipitation strengthening at ambient and elevated temperatures of heat-treatable Al(Sc) alloys\" *Acta Materialia* 50 (2002) p. 4021\n", - "5. K. Deane et al, \"Utilization of bayesian optimization and KWN modeling for increased efficiency of Al-Sc precipitation strengthening\" *Metals* 12 (2022) p. 975\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "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": "3.9.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Surrogates.ipynb b/examples/Surrogates.ipynb deleted file mode 100644 index b3972fc..0000000 --- a/examples/Surrogates.ipynb +++ /dev/null @@ -1,427 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Surrogates\n", - "\n", - "Surrogates can be contructed in place of thermodynamic functions to reduce computational time of the KWN model. This is useful for sensitivity analysis where certain parameters need to be pertubated often.\n", - "\n", - "As with the Thermodynamics module, the Surrogates module are split into two classes for binary and multicomponent systems." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Binary Systems\n", - "\n", - "Surrogates for driving force, interfacial composition and diffusivity can be created for binary systems.\n", - "\n", - "Both the Binary and Multicomponent surrogates require the thermodynamic functions for the various terms. While these can be user-defined, it is easiest to use a Thermodynamics object." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import BinaryThermodynamics\n", - "from kawin.Surrogate import BinarySurrogate\n", - "\n", - "#Load TDB file into a Thermodynamics object\n", - "binaryTherm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'])\n", - "binaryTherm.setGuessComposition(0.24)\n", - "\n", - "#Create Surrogate object\n", - "binarySurr = BinarySurrogate(binaryTherm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Driving force\n", - "\n", - "Training a surrogate model for driving forces in a binary system requires a set of compositions and temperatures (or a single temperature for isothermal systems). An additional parameter called 'scale' will convert the set of training compositions into linear or logarithmic spacing. This will allow for training on both dilute (logarithmic spacing) and non-dilute (linear spacing) systems." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "#Train driving forces\n", - "T = 723.15\n", - "xtrain = np.logspace(-5, -2, 20)\n", - "binarySurr.trainDrivingForce(xtrain, [T], scale='log')\n", - "\n", - "#Compare surrogate and thermodynamics modules\n", - "xTest = np.linspace(1e-7, 1.5e-2, 100)\n", - "binaryTherm.clearCache()\n", - "dgTherm, _ = binaryTherm.getDrivingForce(xTest, np.ones(100)*T)\n", - "dgSurr, _ = binarySurr.getDrivingForce(xTest, np.ones(100)*T)\n", - "\n", - "fig1 = plt.figure(1, figsize=(6, 5))\n", - "ax1 = fig1.add_subplot(111)\n", - "ax1.plot(xTest, dgTherm, label='Thermodynamics')\n", - "ax1.plot(xTest, dgSurr, label='Surrogate', linestyle='--')\n", - "ax1.set_xlim([0, 0.014])\n", - "ax1.set_ylim([-10000, 10000])\n", - "ax1.set_xlabel('xZr (mole fraction)')\n", - "ax1.set_ylabel('Driving Force (J/mol)')\n", - "ax1.legend()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interfacial composition\n", - "\n", - "Training a surrogate for interfacial compositions requires a set of temperatures and free energy contributions. For the free energy contributions, it may be useful to setup the KWN model first, then calling $ KWNBase.particleGibbs(R) $ where R is a set of radii. In practice, R should encompass a larger domain than what is set for the particle size distribution in the KWN model in case the particle size distribution is updated to include large size classes during a simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Create training points\n", - "T = 723.15\n", - "gamma = 0.1\n", - "Vm = 1e-5\n", - "R = np.linspace(1e-10, 1e-8, 100)\n", - "G = 2 * gamma * Vm / R\n", - "\n", - "#Train surrogate\n", - "binarySurr.trainInterfacialComposition([T], G, scale='log')\n", - "\n", - "#Compare surrogate and thermodynamics modules\n", - "Gtest = np.linspace(1000, 25000, 100)\n", - "Rtest = 2 * gamma * Vm / Gtest\n", - "binaryTherm.clearCache()\n", - "xMTherm, _ = binaryTherm.getInterfacialComposition(T, Gtest)\n", - "xMSurr, _ = binarySurr.getInterfacialComposition(T, Gtest)\n", - "\n", - "fig2 = plt.figure(2, figsize=(6, 5))\n", - "ax2 = fig2.add_subplot(111)\n", - "ax2.plot(Rtest[xMTherm != -1], xMTherm[xMTherm != -1], label='Thermodynamics')\n", - "ax2.plot(Rtest[xMSurr != -1], xMSurr[xMSurr != -1], label='Surrogate', linestyle='--')\n", - "ax2.set_xlim([0, 1e-9])\n", - "ax2.set_ylim([0, 0.2])\n", - "ax2.set_xlabel('Radius (m)')\n", - "ax2.set_ylabel('Matrix Composition of Zr (mole fraction)')\n", - "ax2.legend()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interdiffusivity\n", - "\n", - "Training a surrogate on the interdiffusivity requires a set of compositions and temperatures. If the interdiffusivity only depends on temperature, then only a single value for the composition is required." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Train interdiffusivity\n", - "Ttrain = np.linspace(500, 1000, 10)\n", - "binarySurr.trainInterdiffusivity([0.01], Ttrain, scale='log')\n", - "\n", - "#Compare surrogate and thermodynamics modules\n", - "Ttest = np.linspace(500, 1000, 100)\n", - "binaryTherm.clearCache()\n", - "dTherm = binaryTherm.getInterdiffusivity(np.ones(100)*0.01, Ttest)\n", - "dSurr = binarySurr.getInterdiffusivity(np.ones(100)*0.01, Ttest)\n", - "\n", - "fig3 = plt.figure(3, figsize=(6, 5))\n", - "ax3 = fig3.add_subplot(111)\n", - "ax3.plot(1/Ttest, np.log(dTherm), label='Thermodynamics')\n", - "ax3.plot(1/Ttest, np.log(dSurr), label='Surrogate', linestyle='--')\n", - "ax3.set_xlim([1/1000, 1/500])\n", - "ax3.set_xlabel('1/T ($K^{-1}$)')\n", - "ax3.set_ylabel('$ln(D (m^2/s))$')\n", - "ax3.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Multicomponent Systems\n", - "\n", - "Surrogates for driving force, interfacial composition, growth rate and impingement factor can be created for multicomponent systems. Note that as the interfacial composition, growth rate and impingement factor can all be determined by a single equilibrium calculation, these terms are grouped into 'curvature factors'. This is similar to how these terms are handled in the Thermodynamics module.\n", - "\n", - "As with the Binary surrogates, the multicomponent surrogate object only requires a MulticomponentThermodynamics object." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import MulticomponentThermodynamics\n", - "from kawin.Surrogate import MulticomponentSurrogate\n", - "\n", - "multiTherm = MulticomponentThermodynamics('NiCrAl.tdb', ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'])\n", - "multiSurr = MulticomponentSurrogate(multiTherm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Driving force\n", - "\n", - "Training a surrogate for driving force calculations requires a set of compositions and temperatures. The difference between the Binary and Multicomponent surrogate objects is that the set of compositions for a multicomponent systems is a 2D array of size m x n, where m is the number of training points and n is the number of solutes.\n", - "\n", - "A utility function is provided to create a cartesian product of multiple arrays for each solute." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\nury\\Anaconda3\\lib\\site-packages\\pycalphad\\core\\utils.py:54: RuntimeWarning: invalid value encountered in divide\n", - " pts[:, cur_idx:end_idx] /= pts[:, cur_idx:end_idx].sum(axis=1)[:, None]\n" - ] - } - ], - "source": [ - "from kawin.Surrogate import generateTrainingPoints\n", - "\n", - "#Create training points\n", - "T = 1273.15\n", - "xCr = np.linspace(0.01, 0.05, 8)\n", - "xAl = np.linspace(0.1, 0.2, 8)\n", - "xTrain = generateTrainingPoints(xCr, xAl)\n", - "\n", - "#Train driving force\n", - "multiSurr.trainDrivingForce(xTrain, [T])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Curvature factors\n", - "\n", - "The growth rate, interfacial composition, interdiffusivity and impingement rate can all be determined from the curvature of the Gibbs free energy surface. Thus, these terms are lumped into a single group that will be referred to as 'curvature factors'. Training the curvature factors only requires a set of compositions and temperatures." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Create training points\n", - "T = 1273.15\n", - "xCr = np.linspace(0.01, 0.05, 16)\n", - "xAl = np.linspace(0.1, 0.2, 16)\n", - "xTrain = generateTrainingPoints(xCr, xAl)\n", - "\n", - "#Train curvature surrogate\n", - "multiSurr.trainCurvature(xTrain, T)\n", - "\n", - "#Compare growth rate from surrogate and thermodynamics modules\n", - "xTest = [0.03, 0.175] #Ni-3Cr-17.5Al\n", - "\n", - "gamma = 0.023 #Interfacial energy between FCC-Ni and Ni3Al\n", - "Vm = 1e-5 #Molar volume\n", - "Rtest = np.linspace(1e-10, 3e-8, 300)\n", - "Gtest = 2 * gamma * Vm / Rtest\n", - "\n", - "multiTherm.clearCache()\n", - "dgTherm, _ = multiTherm.getDrivingForce(xTest, T)\n", - "grTherm, caTherm, cbTherm, _, _ = multiTherm.getGrowthAndInterfacialComposition(xTest, T, dgTherm, Rtest, Gtest)\n", - "\n", - "dgSurr, _ = multiSurr.getDrivingForce(xTest, T)\n", - "grSurr, caSurr, cbSurr, _, _ = multiSurr.getGrowthAndInterfacialComposition(xTest, T, dgSurr, Rtest, Gtest)\n", - "\n", - "fig4 = plt.figure(4, figsize=(6, 5))\n", - "ax4 = fig4.add_subplot(111)\n", - "ax4.plot(Rtest, grTherm, label='Thermodynamics')\n", - "ax4.plot(Rtest, grSurr, label='Surrogate', linestyle='--')\n", - "ax4.set_xlim([0, 3e-8])\n", - "ax4.set_ylim([-1e-6, 1e-6])\n", - "ax4.set_xlabel('Radius (m)')\n", - "ax4.set_ylabel('Growth Rate (m/s)')\n", - "ax4.legend()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hyperparameters\n", - "\n", - "The surrogates are created through scipy's radial basis functions. The same hyperparameters used in the scipy's implementation can be used for these surrogates. These include: 'function', 'epsilon', 'smooth'. 'function' is the basis function to use, 'epsilon' is the scale between training points (the surrogates will automatically scale the training points such that the optimal value for 'epsilon' should be near 1), and 'smooth' allows for smoothing the interpolation (a value of 0 means that the surrogate will cross all training points). When training the surrogates, these are set as additional parameters. For example:\n", - "\n", - "$ Surrogate.trainDrivingForce(x, T, function='linear', epsilon=1, smooth=0) $\n", - "\n", - "If a surrogate is already trained, the hyperparameters can be changed without the need for re-training.\n", - "\n", - "$ Surrogate.changeDrivingForceHyperparameters(function, epsilon, smooth) $" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Saving and loading\n", - "\n", - "The surrogates can be saved and loaded for later usage. These will not retain the thermodynamic functions used for the training, so re-training of the surrogate cannot be done after saving/loading; however, the hyperparameters can still be changed.\n", - "\n", - "$ Surrogate.save(filename) $\n", - "\n", - "$ surr = Surrogate.load(filename) $" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Usage in the KWN Model\n", - "\n", - "As with the Thermodynamics module, the Surrogate objects can be easily used in the KWN model by:\n", - "\n", - "$ KWNModel.setSurrogate(Surrogate) $\n", - "\n", - "For binary systems, the interdiffusivity also has to be inputted separately.\n", - "\n", - "$ KWNModel.setDiffusivity(BinarySurrogate.getInterdiffusivity) $" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "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": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Thermodynamics.ipynb b/examples/Thermodynamics.ipynb deleted file mode 100644 index 4867032..0000000 --- a/examples/Thermodynamics.ipynb +++ /dev/null @@ -1,560 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Thermodynamics\n", - "\n", - "The thermodynamic modules interfaces with pycalphad to perform important calculations for the KWN model. They are split into two classes to handle binary and multicomponent systems.\n", - "\n", - "Setting up a Thermodynamics object requires the database, the elements involved (where first element will be the reference element) and the phases involved (where the first phase will be the matrix phase). For systems where the parent and precipitate phases are handled by an order/disorder model (ex. $\\gamma$ and $\\gamma$' in nickel-based alloys), the matrix phase is assumed to be the disordered part of the model.\n", - "\n", - "For multicomponent systems, any compositions that is used as a parameter or as a return value will be in the same order of solutes that was used when creating the Thermodynamics object. In the example below, all compositions will be in the order [xCr, xAl]. If the solutes were ordered as ['Ni', 'Al', 'Cr'] in the constructor, then all compositions will be in the order [xAl, xCr]." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from kawin.Thermodynamics import BinaryThermodynamics, MulticomponentThermodynamics\n", - "\n", - "binaryTherm = BinaryThermodynamics('AlScZr.tdb', ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'])\n", - "multiTherm = MulticomponentThermodynamics('NiCrAl.tdb', ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hyperparameters\n", - "\n", - "### Sampling density\n", - "\n", - "When calculating equilibrium, pycalphad samples the free energy surfaces of each phase to find a suitable starting point for the free energy minimization procedure. The sampling density (defined as the number of samples to create per degree of freedom in the free energy model) can influence the accuracy of the equilibrium results and the computation time. A low sampling density may lead to inaccurate results while a high sampling density may result in slow calculations. By default, the Thermodynamics object sets the sampling density to 500.\n", - "\n", - "There is a second sampling density parameter that is used when calculating the driving force using the sampling method. By default, it is set to 2000. This sampling density is set to be higher than for the sampling density used for solving equilibrium because the samples themselves are used in the driving force calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#Change sampling density\n", - "multiTherm.setEQSamplingDensity(500)\n", - "\n", - "#Change driving force sampling density\n", - "multiTherm.setDFSamplingDensity(2000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Moblity correction factors\n", - "\n", - "For mobility terms, a correction factor can be applied to each element. This may be useful in parameter assessment, sensitivity analysis or in cases where the mobility will be known to be higher (e.g. higher vacancy concentrations from a solutionizing/quenching treatment)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#Change mobility factor for Cr\n", - "multiTherm.setMobilityCorrection('Cr', 1)\n", - "\n", - "#Change mobility factor for all components\n", - "multiTherm.setMobilityCorrection('all', 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Starting conditions for Binary Systems\n", - "\n", - "For BinaryThermodynamics, the interfacial composition is independent of the composition of the system and is calculated by solving equilibrium at several compositions until a 2-phase region is found. By default, it samples the composition in intervals of 0.1. The starting compositions can be manually set to always be inside the 2-phase region to improve computation time." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#Change starting conditions for BinaryThermodynamics\n", - "\n", - "#Compositions between 0 and 0.5 at intervals of 0.015\n", - "binaryTherm.setGuessComposition((0, 0.5, 0.015))\n", - "\n", - "#Single composition at 0.24\n", - "binaryTherm.setGuessComposition(0.24)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Driving Force Calculations\n", - "\n", - "### Nucleation\n", - "\n", - "Nucleation of a precipitate results in a reduction in Gibbs free energy that scales with the precipitate volume and an increase in the free energy that scales with the surface, creating a barrier for nucleation.\n", - "\n", - "$$\\Delta G = -\\frac{4}{3}\\pi R^3 \\Delta G_{vol} + 4\\pi R^2 \\gamma$$\n", - "\n", - "The height of this barrier, $\\Delta G^{*}$, can be used to find the nucleation rate.\n", - "\n", - "$$J_N = N_0 Z \\beta exp\\left(-\\frac{\\Delta G^{*}}{k_B T}\\right) exp\\left(-\\frac{\\tau}{t}\\right)$$\n", - "\n", - "The driving force is defined as the maximum difference in Gibbs free energy between the chemical potential hyperplane computed for the matrix ($\\alpha$) and precipitate ($\\beta$) phase separately. This can also be defined as the difference in the Gibbs free energy when the chemical potential hyperplanes of each phase are parallel. The chemical potential of the $\\alpha$ is computed at the matrix composition while the chemical potential of the $\\beta$ phase is computed at the composition which maximizes the driving force (Rheingans and Mittemeijer, 2015).\n", - "\n", - "$$\\Delta G_m = \\sum{x_A^\\beta \\, \\mu_A^\\alpha (\\boldsymbol{x}^\\alpha) - x_A^\\beta \\, \\mu_A^\\beta (\\boldsymbol{x}^\\beta)} = \\left(\\frac{2 \\gamma}{R^*} + \\Delta G_{el}\\right) V_m^\\beta$$\n", - "\n", - "Three different methods are available for driving force calculations: approximate, sampling and curvature.\n", - "\n", - "### Approximate method\n", - "\n", - "The approximate method assumes that the composition of a newly nucleated precipitate is near the equilibrium composition. This is the default method when the Thermodynamics object is created.\n", - "\n", - "$$ \\Delta G_M = \\sum_{A}{x_{eq}^\\beta \\, \\mu_A^\\alpha \\left(\\boldsymbol{x^\\alpha}\\right) - x_{eq}^\\beta \\, \\mu_A^\\beta \\left(\\boldsymbol{x_{eq}^\\beta}\\right)} $$\n", - "\n", - "### Sampling method\n", - "\n", - "The sampling method approximates calculating the driving force by the parallel tangent method. Rather than finding the composition of the precipitate phase that gives the same chemical potential as for the parent phase, the maximum difference between Gibbs free energy of the precipitate phase and the chemical potential hyperplane of the parent phase is found. This is the only method of the three that can calculate negative driving forces and is used by the other two methods if the precipitate phase is unstable.\n", - "$$ \\Delta G_M = argmax \\left(\\sum_{A}{x_A^\\beta \\, \\mu_A^\\alpha \\left(\\boldsymbol{x^\\alpha}\\right)} - G_M^\\beta \\left(\\boldsymbol{x^\\beta}\\right) \\right) $$\n", - "\n", - "### Curvature method\n", - "\n", - "The curvature method determines the local curvature of the free energy surface of the parent phase at the given composition and calculates driving force based off the equilibrium composition of the parent and precipitate phase. This is only valid for small supersaturations and non-dilute systems and thus, is not recommended.\n", - "$$ \\Delta G_M = \\boldsymbol{\\left(x^\\alpha - x_{eq}^\\alpha\\right)} \\boldsymbol{\\nabla^2} G_M^\\alpha \\boldsymbol{\\left(x_{eq}^\\beta - x_{eq}^\\alpha\\right)} $$\n", - "\n", - "### Binary System\n", - "\n", - "For a binary system, the driving force method is defined as:\n", - "\n", - "$ \\Delta G_M, x^\\beta = BinaryThermodynamics.getDrivingForce(x, T, returnComp) $\n", - "\n", - "The example below compares the three methods on the Al-Zr system. The approximate and sampling method gives the same values for the driving force. This is due to the $Al_3Zr$ having zero degrees of freedom for the composition, so the calculation of the driving force ends up being the same. The curvature method is only accurate near the equilibrium composition (where the driving force is 0), but at higher concentrations, it greatly over predicts the driving force. This is due to the high curvature at low concentrations." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "#Plot comparison of driving force methods for Al-Zr system\n", - "\n", - "#Driving force methods\n", - "DGmethods = ['approximate', 'sampling', 'curvature']\n", - "\n", - "x = np.linspace(1e-5, 1e-2, 100)\n", - "\n", - "fig1 = plt.figure(1, figsize=(6, 5))\n", - "ax1 = fig1.add_subplot(111)\n", - "\n", - "for m in DGmethods:\n", - " #Clear cache before using a different method\n", - " binaryTherm.clearCache()\n", - " binaryTherm.setDrivingForceMethod(m)\n", - "\n", - " #Calculate driving force (x and T must be same shape)\n", - " dg, _ = binaryTherm.getDrivingForce(x, np.ones(100) * 673.15)\n", - " ax1.plot(x, dg, label=m)\n", - "\n", - "ax1.set_xlim([0, 0.01])\n", - "ax1.set_ylim([-1000, 10000])\n", - "ax1.set_xlabel('xZr (mole fraction)')\n", - "ax1.set_ylabel('Driving Force (J/mol)')\n", - "ax1.legend(DGmethods)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multicomponent systems\n", - "\n", - "For multicomponent systems, the driving force method is defined as:\n", - "\n", - "$ \\Delta G_M, \\boldsymbol{x^\\beta} = MulticomponentThermodynamics.getDrivingForce(\\boldsymbol{x}, T, returnComp) $\n", - "\n", - "This is similar to the method for binary systems except that the composition must be an array of the solute components. Below is an example of the different driving force methods in the Ni-Cr-Al system. Because the equilibrium composition is non-dilute, the curvature method gives similar values to the other two methods. Once the driving force becomes negative (no driving force for nucleation), the three methods converge since the sampling method is used if the precipitate is unstable." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\nury\\Anaconda3\\lib\\site-packages\\pycalphad\\core\\utils.py:54: RuntimeWarning: invalid value encountered in divide\n", - " pts[:, cur_idx:end_idx] /= pts[:, cur_idx:end_idx].sum(axis=1)[:, None]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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hhNBzDg4OWFtb67qMAkeCTh4obluclS1XUsSqCOEJ4Xy862Puxt/VdVlCCJH7NBpIS9TN1ytsw7N+/Xr8/PwwNzfH0dGRpk2bkpiYyJkzZ2jWrBlOTk7Y2trSsGFDzp07l+2zCoWCxYsX06ZNGywsLChfvjwnTpzg5s2bNGrUCEtLS2rXrs2tW7eyPjNx4kQqV67M4sWL8fT0xMLCgvfee4/Y2NgX1vjvR1fFixdn6tSp9OzZE2tra4oVK8ZPP/2U7TMBAQFUrlwZMzMzqlevzubNm1EoFFy4cCHHfzYFnZGuCyioiloXZUXLFfTZ04c78Xf4eNfH/Nz8Z0raldR1aUIIkXvSk2Cqh26uPSYCTCz/s1lkZCQffvgh3377LR06dCAhIYGjR4+i0WhISEjg448/Zv78+QDMmjWL1q1bExISkq13ZcqUKcyePZvZs2czcuRIunTpQsmSJRk9ejTFihWjZ8+eDBgwgJ07d2Z95ubNm6xbt46tW7cSHx9Pr169+Pzzz1m9enWOb3HWrFlMmTKFMWPGsH79evr370+DBg0oV64cCQkJtG3bltatW7NmzRrCwsJkjM9zSI9OHnKzdGN5y+V423nzMPkhn+z+hBtPbui6LCGEKFQiIyPJyMigY8eOFC9eHD8/Pz777DOsrKx466236NatG+XLl6d8+fIsXryYpKQkDh8+nO0cn3zyCe+//z5lypRh5MiR3Llzh65du9KiRQvKly/PF198waFDh7J9JiUlhZUrV1K5cmUaNGjAggULWLt2LVFRUTmuvXXr1nz22Wd4e3szcuRInJycsq6zevVqFAoFS5YswcfHh1atWjF8+PA3/eMqcKRHJ485mTuxvMVy+u7tS9CTID7Z/Qk/Nv2Ris4VdV2aEEK8OWMLbc+Krq6dA5UqVaJJkyb4+fnRokULmjdvzrvvvou9vT3R0dFMmDCBAwcO8ODBAzIzM0lKSiI8PDzbOSpW/P/PbFdXVwD8/PyyvZaSkkJ8fDw2NjYAFCtWjKJFi2a1qV27Nmq1mhs3buDm5paj2v95XYVCgZubG9HR0QDcuHGDihUrYmZmltWmZs2aOTpvYSI9OvnAzsyOn1v8TGXnyiSkJdBnTx/ORJ3RdVlCCPHmFArt4yNdfCkUOSpRpVKxd+9edu7ciY+PDwsWLKBs2bKEhobSo0cPAgMDmTt3LgEBAVy4cAFHR0fS0rLvX2hsbPyPW1a88DW1Wv2SPypFtv/mxD+v8fdn/76GRqN55lyaVxi3VFhI0MknNiY2LG62GH83f5Iykui/rz9H7x3VdVlCCFEoKBQK6taty6RJkzh//jwmJiZs2rSJo0ePMmjQIFq3bk2FChUwNTXl0aNHuXLN8PBwIiL+39t14sQJlEolZcqUyZXzlytXjkuXLpGampr12tmzZ3Pl3AWJBJ18ZGFswQ9Nf6Bh0YakZqYy6OAg9obt1XVZQghRoJ06dYqpU6dy9uxZwsPD2bhxIw8fPqR8+fJ4e3uzatUqgoKCOHXqFF27dsXc3DxXrmtmZsbHH3/MxYsXswLV+++/n+PHVv+lS5cuqNVq+vbtS1BQELt372bmzJnAq/UaFXQSdPKZqcqUOY3n0KJ4CzLUGQw7PIwtt7bouiwhhCiwbGxsOHLkCK1bt6ZMmTKMGzeOWbNm0apVK5YtW0ZMTAxVqlShe/fuDBo0CBcXl1y5rre3Nx07dqR169Y0b94cX19fFi5cmCvnBu19bd26lQsXLlC5cmXGjh3LhAkTALKN2ynsFBp5oPfK4uPjsbW1JS4uLmvQ2avKVGcy6cQkNt3cBMAY/zF8WO7D3CxTCCFyVUpKCqGhoZQoUUJ+kf6HiRMnsnnz5nxfz2b16tV88sknxMXF5VrP1Ot42d+V3Pgd+ipk1pWOqJQqJtaZiKWxJb8G/crUU1NJTE+kt19vXZcmhBDCQPzyyy+ULFmSIkWKcPHiRUaOHMn777+v05CjbyTo6JBSoWREjRFYmVjx48UfmXduHglpCQyuOlierwohhPhPUVFRTJgwgaioKNzd3Xnvvff45ptvdF2WXpFHV68hL7rdVl5dycyz2kFknct2Zoz/GJQKGUIlhNAf8uhK5JQ+PbqS36R64uMKHzOh9gQUKPj9xu+MOTaGdHW6rssSQgghDJoEHT3yXpn3mNFgBkYKI7bf3s7Qg0NJyUjRdVlCCCGEwZKg8ybu5v7CTK1KtGLeW/MwVZly6N4hPtv/GYnpibl+HSGEEKIwkKDzJn7tBEe+A3Vmrp62QdEG/Nj0RyyNLTkTdYZeu3sRkxKTq9cQQgghCgMJOm8kEw58Db+8A/G5u6lddbfqLG2xFDtTO64+vkqPXT2ISsz5jrdCCCGEkKDzZt6eA8aWcOcoLKoL13fk6ukrOFZgZcuVuFi4cDvuNh/v/Jiw+LBcvYYQQghRkEnQeRMV34NPj4J7ZUh+Ams/hB3DIT33BhCXtCvJqlar8LLxIiIxgo92fsT1J9dz7fxCCCFEQSZB5005loJee6H2AO3x6Z9gyVsQnXthxMPKg5UtV1LOoRxPUp7wya5PCHwQmGvnF0IIoTuNGjVi8ODBui6jwJKgkxuMTKDFN9B1A1g6Q/RV+KkRnF0OubQeo6O5I8taLKOqS1Wepj+l395+HLl3JFfOLYQQ4tVkZmaiVqt1XUY26emy9trzSNDJTaWbwqfHoWRjyEiGbYNh3UeQnDszpqxNrFncbDENizYkNTOVQQcGsfXW1lw5txBCvA6NRkNSepJOvl51YX+1Ws2MGTPw9vbG1NSUYsWK8c0333Do0CEUCgWxsbFZbS9cuIBCoeDOnTsArFixAjs7O7Zt24aPjw+mpqYsWbIEMzOzbJ8DGDRoEA0bNgTg8ePHfPjhhxQtWhQLCwv8/Pz47bffstr26NGDw4cPM2/ePBQKRdY1/77eP23evDnb9kATJ06kcuXKLFu2jJIlS2JqaopGoyEuLo6+ffvi4uKCjY0Nb731FhcvXnylP6uCRPa6ym3WrtBtI5xYAPsnQ9AWuH8OOv0MXrXf+PRmRmbMaTyHr45/xdbbWxlzbAyxqbF09+meC8ULIcSrSc5Ixn+Nv06ufarLKSyMLXLcfvTo0SxZsoQ5c+ZQr149IiMjuX4958MMkpKSmDZtGj///DOOjo4ULVqUr776ig0bNtCrVy9A29Ozbt06Jk+eDGi3QqhWrRojR47ExsaG7du30717d0qWLIm/vz/z5s0jODgYX1/frM84OzvnuKabN2+ybt06NmzYgEqlAuDtt9/GwcGBHTt2YGtry+LFi2nSpAnBwcE4ODjk+NwFhV706EybNo0aNWpgbW2Ni4sL7du358aNG9naaDQaJk6ciIeHB+bm5jRq1IirV69ma5OamsrAgQNxcnLC0tKSdu3ace/evWxtYmJi6N69O7a2ttja2tK9e/dn0vgbUyqh7hfQaw/Yl4D4e7CiNRyakStr7hgrjfm63td0K98NgG/PfMv8c/Nf+V83QghRWCQkJDBv3jy+/fZbPv74Y0qVKkW9evXo3bt3js+Rnp7OwoULqVOnDmXLlsXS0pLOnTuzZs2arDb79+8nJiaG9957D4AiRYowbNgwKleuTMmSJRk4cCAtWrTgjz/+AMDW1hYTExMsLCxwc3PDzc0tK7DkRFpaGqtWraJKlSpUrFiRgwcPcvnyZf744w+qV69O6dKlmTlzJnZ2dqxfvz7H5y1I9KJH5/Dhw3z++efUqFGDjIwMxo4dS/Pmzbl27RqWlpYAfPvtt8yePZsVK1ZQpkwZvv76a5o1a8aNGzewtrYGYPDgwWzdupW1a9fi6OjIl19+SZs2bQgMDMz6i9OlSxfu3bvHrl27AOjbty/du3dn69Y8eARUpJp2Vtb2YXBpLRyaCrcPQaclYFv0jU79987nDmYOzD8/nyWXlxCTGsM4/3GolDn/n0QIId6EuZE5p7qc0tm1cyooKIjU1FSaNGny2tczMTGhYsWK2V7r2rUrtWvXJiIiAg8PD1avXk3r1q2xt7cHtD0806dP5/fff+f+/fukpqaSmpqa9bvtTXl5eWXrAQoMDOTp06c4Ojpma5ecnMytW7dy5ZqGRi+Czt+h42/Lly/HxcWFwMBAGjRogEajYe7cuYwdO5aOHTsCsHLlSlxdXVmzZg39+vUjLi6OpUuXsmrVKpo2bQrAr7/+iqenJ/v27aNFixYEBQWxa9cuTp48ib+/tqt1yZIl1K5dmxs3blC2bNncvzlTa+i4GEq9BduHQniAds2dd76H8m3f6NQKhYI+Fftga2rL1ye/Zn3wemJTYpneYDqmKtNcugEhhHgxhULxSo+PdMXc/MWhSKnUPtz4Z6/48wb2mpubZxsjA1CzZk1KlSrF2rVr6d+/P5s2bWL58uVZ78+aNYs5c+Ywd+5c/Pz8sLS0ZPDgwaSlpb20XqVS+Uwv/fNq+ndgUqvVuLu7c+jQoWfa/nvMT2GhF4+u/i0uLg4g61liaGgoUVFRNG/ePKuNqakpDRs2JCAgANCm2PT09GxtPDw88PX1zWpz4sQJbG1ts0IOQK1atbC1tc1q8zypqanEx8dn+3pllTpDvyPgUQVSYuH3brBtKKQnv/q5/uX9su8zs+FMjJXG7AvfR/99/Xma9vSNzyuEEAVF6dKlMTc3Z//+/c+893ePSGRkZNZrFy5cyPG5u3TpwurVq9m6dStKpZK33347672jR4/yzjvv0K1bNypVqkTJkiUJCQnJ9nkTExMyM7MPa3B2diYhIYHExP/vdZiTmqpWrUpUVBRGRkZ4e3tn+3JycsrxPRUkehd0NBoNQ4cOpV69evj6+gIQFaXd+sDV1TVbW1dX16z3oqKiMDExyeoufFEbFxeXZ67p4uKS1eZ5pk2bljWmx9bWFk9Pz9e7OcdS0HOPdvwOwNml8FNjeHDt9c73D82LN2dR00VZ+2P13N2TR8mP3vi8QghREJiZmTFy5EhGjBjBL7/8wq1btzh58iRLly7F29sbT09PJk6cSHBwMNu3b2fWrFk5PnfXrl05d+4c33zzDe+++y5mZmZZ73l7e7N3714CAgIICgqiX79+z/y+KV68OKdOneLOnTs8evQItVqNv78/FhYWjBkzhps3b7JmzRpWrFjxn7U0bdqU2rVr0759e3bv3s2dO3cICAhg3LhxnD2b+xtRGwK9CzoDBgzg0qVL2abf/e3fXYYajeaZ1/7t322e1/6/zjN69Gji4uKyvu7evftft/FiRibQbLJ2ZpalCzwMgiWN4czPb7zmjr+7P8taLMPBzIGgJ0F8tPMj7ia8Qa1CCFGAjB8/ni+//JIJEyZQvnx5OnfuTHR0NMbGxvz2229cv36dSpUqMWPGDL7++uscn7d06dLUqFGDS5cu0bVr12euWbVqVVq0aEGjRo1wc3Ojffv22doMGzYMlUqFj48Pzs7OhIeH4+DgwK+//sqOHTuypqRPnDjxP2tRKBTs2LGDBg0a0LNnT8qUKcMHH3zAnTt3nuksKCwUGj2aqjNw4EA2b97MkSNHKFGiRNbrt2/fplSpUpw7d44qVapkvf7OO+9gZ2fHypUrOXDgAE2aNOHJkyfZenUqVapE+/btmTRpEsuWLWPo0KHPzLKys7Njzpw5fPLJJzmqMz4+HltbW+Li4rCxsXn9G376EDZ/Cjf3aY/LtYF2C8Dizab/hceH03dvX+4/vY+jmSOLmi6ivGP5NzqnEEKkpKQQGhpKiRIlsvVaCPFvL/u7kmu/Q3NIL3p0NBoNAwYMYOPGjRw4cCBbyAEoUaIEbm5u7N27N+u1tLQ0Dh8+TJ06dQCoVq0axsbG2dpERkZy5cqVrDa1a9cmLi6O06dPZ7U5deoUcXFxWW3ylZUzdPkDWkwFpTFc3wY/1oM7x97otMVsirGq1SrK2pflccpjPtn9CacidTMrQgghhNAlvQg6n3/+Ob/++itr1qzB2tqaqKgooqKiSE7WDtRVKBQMHjyYqVOnsmnTJq5cuUKPHj2wsLCgS5cugHYtgl69evHll1+yf/9+zp8/T7du3fDz88uahVW+fHlatmxJnz59OHnyJCdPnqRPnz60adMmb2Zc5YRSCbU/h977wNEb4u/DyrZw4BvIzHjt0zpbOLO85XJquNUgMT2R/vv6s+vOrv/+oBBCCFGA6EXQWbRoEXFxcTRq1Ah3d/esr99//z2rzYgRIxg8eDCfffYZ1atX5/79++zZsydrDR2AOXPm0L59e95//33q1q2LhYUFW7duzbb40urVq/Hz86N58+Y0b96cihUrsmrVqny93+fyqAx9D0PlrqBRw5FvtYsMxoa/9imtTaxZ1HQRzbyaka5OZ8ThEawJWvPfHxRCCCEKCL0ao2Mo8vz54uX1sG0IpMaDqS20mwcVOrz26TLVmUw7PY3fb2iDYx+/PgysMvA/B3ILIcQ/yRgdkVMyRke8nN+72hWVi9aA1Dj4owdsGQhpif/50edRKVWM9R/L55U/B2DJ5SWMPz6edLXsdCuEeHXy72PxX/Tp74gEHX1lXxw+2Qn1vwQUcO4X+KkRRF1+rdMpFAo+rfQpE2tPRKlQ8uetPxl0YBBJ6Um5WbUQogAzNjYGtJtbCvEyf/8d+fvvjC7Jo6vXkN/dbtw+DJv6QUIkqEyg2RTw7wev+ejp0N1DDD88nJTMFPyc/Pi+yfc4mBW+HW2FEK8uMjKS2NhYXFxcsLCwkEfgIhuNRkNSUhLR0dHY2dnh7u7+TJv8/h0qQec15HvQAUh8DH9+DsE7tcelW0D7hWD5ekt6X3x4kQH7BxCbGouXjReLmi7C0/o1V3wWQhQaGo2GqKioZ9YjE+Kf7OzscHNze24QlqBjAHQSdEC7cvLpJbBnHGSmgpWbdsPQko1e63ShcaF8uvdTIhIjcDBzYGHThVRwrJC7NQshCqTMzMznbjIphLGxcbbZzv8mQccA6Czo/C3qCmzoBQ+vAwrt3llvjQPVqz8LfZj0kM/2f8b1J9cxNzJndqPZ1CtSL/drFkIIIZBZVyIn3Hyhz0Go9gmggeNzYVkLeHL7lU/lbOHM8hbLqeVei+SMZAbsH8Dmm5tzu2IhhBBCJyToGCoTC2g7F97/Bcxs4X4g/NgALq175VNZmVixsMlC2pRsQ6Ymk/HHx/PjxR/1anqgEEII8Tok6Bg6n3fg0+NQrDakJcDGPrDpU0hNeKXTGKuMmVpvKr18ewHww4UfmHRiEhnq19+GQgghhNA1CToFgZ0nfLwNGo0GhRIu/gaLG8D9c690GoVCweBqgxnrPxalQsmGkA2y1o4QQgiDJkGnoFAZQaNR0GM72BTVjtdZ2hyOzwe1+pVO9UG5D5jTaA5mKjOO3j9Kz909eZT8KI8KF0IIIfKOBJ2CxqsO9D8G5duBOh32jofVnSDhwSud5q1ib/Fzi5+xN7Xn6uOrdNvRjdC40DwqWgghhMgbEnQKInN77SDlNnPAyBxuHYAf60LI3lc6TSXnSqxqvQpPa0/uP71P953dOffg1R6HCSGEELokQaegUiigek/oewhcKkDiQ1j9LuwaDRmpOT6Nl40Xq1qtws/Jj7jUOPrs6cPuO7vzrm4hhBAiF0nQKehcykGfA1Czn/b45EL4uQk8CsnxKRzNHVnaYimNPRuTpk5j2OFhrLy6UqafCyGE0HsSdAoDYzNo/S18uBbMHbQ7oC9uAOdWabeVyAFzI3PmNJpDl3JdAJh5dibTTk8jU52Zl5ULIYQQb0SCTmFSthX0D4ASDSA9CbYMgPU9ITk2Rx9XKVWMqjmK4dWHo0DBb9d/Y/DBwTL9XAghhN6SoFPY2LhD983Q5CtQqODqRvixPoSfytHHFQoFH1X4iJkNZ2KiNOHQvUN8svsTmX4uhBBCL0nQKYyUKqg/FHrtAfviEBcOy1vB4e8gh4+imhdvztIWS7E3tefa42t03d6VmzE387ZuIYQQ4hVJ0CnMilaHfkfB7z3QZMLBr2FlO4i7n6OPV3apzK+tf8XLxouIxAg+2vkRpyJz1jMkhBBC5AcJOoWdmQ10XALtfwQTKwg7BovqQNDWHH28mE0xfm31K1VcqpCQnsCnez/lz5t/5nHRQgghRM5I0BHaNXcqfwj9joBHFUiJhd+7wbYhkJ78nx+3M7NjSfMltCzekgxNBuOOj+P789/L9HMhhBA6J0FH/J9jKei5B+oM0h6fXQY/NYYH1/7zo6YqU2Y0mEEfvz4ALL60mFFHR5GWmZaXFQshhBAvJUFHZGdkAs2nQLeNYOkCD4NgSWM4veQ/19xRKpQMqjqISXUmYaQwYkfoDvrs6UNsSmz+1C6EEEL8iwQd8XzeTbRr7ng3g4wU2DEM1naFpCf/+dGOpTuysOlCrIytOBd9jm47uxEWH5YPRQshhBDZSdB5A5nqAj4GxcoZuqyDFtNAZQI3tmsHKoce+c+P1vaozapWq/Cw9CAsPoyuO7pyNupsPhQthBBC/J8EnTfQfekpbkYn6LqMvKVUQu3PoPc+cCwNCZHaKej7J0Nm+ks/6m3vzeq3V1PRqaJ2Q9C9fdhya0s+FS6EEEJI0Hkjl+7F0XreMX44eJP0TLWuy8lb7pWg32Go+hGggaOztIsMxtx56ceczJ1Y2mIpzb2ak6HOYOyxsSw4vwC1poD/eQkhhNALEnTeQP3SjqRlqvlu9w3e+f44VyPidF1S3jKxhHYL4L0VYGoL985ot4+4vP6lHzMzMuO7ht/R2683AD9d+omRR0aSkpGSD0ULIYQozBQaWezklcXHx2Nra0tsbCwHbicwaes1YpPSMVIq+KxRKQa8VRoTowKeIWPDYUMfuHtSe1ypi3aHdFPrl35sU8gmJp+cTIY6g4rOFZnXeB5O5k75ULAQQgh98Pfv0Li4OGxsbPL8ehJ0XsO/v0kPE1KZ8OcVdl6JAqCsqzXfvVeRikXtdFtoXsvMgCPfwZFvQaMGh5LQaSkUqfrSj52JOsPgg4OJT4vHw9KD75t8T2n70vlUtBBCCF2SoGMAXvRN2n4pkgl/XuFxYhpKBfRtUIrBTUtjZqzSYbX5ICxA27sTfw+UxtBkPNQeqB3I/AJ34u4w4MAAwuLDsDS25LsG31G/aP18LFoIIYQuSNAxAC/7Jj1+msqkrdfYcjECgJLOlnz3bkWqeTnootT8kxwDWwZB0F+zqko2hg4/grXbCz8SlxrH4IODOfvgLEqFkhE1RtClXBcUCkU+FS2EECK/SdAxADn5Ju25GsW4zVeITkhFoYAedYozvEVZLEyM8rnafKTRwLmVsHMUZCSDhRO0XwRlmr/wI+mZ6Uw5OYVNNzcB0LlsZ0bWHImx0ji/qhZCCJGPJOgYgJx+k+KS0vl6+zX+CLwHgKeDOTM6VqSOdwEffPvwBqzvBQ8ua4/9+0OzSWBk+tzmGo2GFVdXMCdwDho01HKvxaxGs7Axyfv/AYQQQuQvCToG4FW/SYeDHzJm42Xux2p3Av+ghidj3i6PjVkB7rVIT4F9E+HUIu2xmx90WgbOZV74kQPhBxh1dBTJGckUtynOD01+oJhNsfypVwghRL6QoGMAXueb9DQ1gxk7r7PqpHbPJ1cbU75p70dTH9e8LFX3gnfD5v6Q9BiMLaDldO2igy8Yh3P9yXUG7B/Ag6QH2JraMqfRHGq41cjnooUQQuQVCToG4E2+SaduP2bUxsuEPkoEoF0lD75q64Oj1fMf6xQICVGwqR/cPqQ99nkH2s4Dc/vnNn+Y9JAvDn7B5UeXMVIYMbbWWN4t827+1SuEECLPSNAxAG/6TUpJz2TOvmCWHLmNWgP2FsZ81bYC71T2KLgzjtRqCJgPB6aAOgNsPaHjEvCq/dzmKRkpTDg+gZ13dgLQrXw3vqz+JUbKAjyYWwghCgEJOgYgt75Jl+7FMmL9Ja5HaTcGbVzWmW86+OFhZ55bpeqf+4HagcoxoaBQQsORUH8YqJ4NMBqNhsWXFvPDhR8AqFukLt81+A5rk5evviyEEEJ/SdAxALn5TUrPVLP48C3m779JWqYaSxMVI1uVo5u/F0plAe3dSU2AHcPh4m/a42K1tb07dp7Pbb7nzh7GHhtLSmYKJW1LsuCtBTJIWQghDJQEHQOQF9+km9FPGbnhEoFhMQBU97JneqeKeLtY5cr59dKlP2DbEEhLADNb7bidCh2e2/Tq46sMOjCI6KRobE1tmd1wNjXda+ZzwUIIId6UBB0DkFffJLVaw6qTYXy76zqJaZmYqJQMfMubfg1LFdxNQp+EwobecP+s9rhKd2g1Q7tT+r/8e5DyqJqj6Fyucz4XLIQQ4k1I0DEAef1Nuh+bzLhNlzl44yEA5dysmd6pIpU97XL9WnohMx0OTYOjswENOJaGd5eCe6VnmqZkpDDxxES2394OyErKQghhaCToGID8+CZpNBq2XIxg0tZrPElMy9pGYljzsliaFtCZR6FHYGNfSIgElQk0nQS1+j+z5o5Go2HplaXMPzcfDRpqutVkVsNZ2JnZ6aZuIYQQOSZBxwDk5zfpSWIaU7ZdY9P5+wAUsTPn6w6+NC7rkqfX1ZnEx7BlANzYoT32bqbdL8vK+ZmmB8MPMuroKJIykihqVZQFby3A2947nwsWQgjxKiToGID8/iaBdhuJsZsucy9Gu41Eu0oeTGjrg1NBXGhQo4EzP8PusZCZCpYu0GEReDd9pmlITAgDDwzk/tP7WBhZMKPBDBp5Nsr/moUQQuSIBB0DoIugA5CUlsHsPcEsOx6KWgN2FsaMbV2ed6sVLZgLDT64But7wsMg7XHtAdBkwjObg8akxPDl4S85E3UGBQoGVR1EL99eBfPPRAghDJwEHQOgq6Dzt0v3Yhm14TLXIuMBqFPKkakd/Cju9OxMJYOXngx7xml7eEA7QLnTMnDK/ogqXZ3OjNMz+P3G7wC0Kt6KSXUnYW5UgBdfFEIIAyRBxwDoOuiAdqHBZcdCmbMvmJR0NaZGSgY1KU3fBiUxVhXAqejXt8OfAyD5CRhbQutvoXLXZwYqr7uxjmmnppGhyaC8Q3nmNZ6Hu5W7jooWQgjxbxJ0DIA+BJ2/hT1OZNzmKxwNeQRop6JP7ehH1WLP3zDToMVHaGdl3TmqPa7QEdrMAXO7bM3ORp1l6KGhxKTG4GDmwJxGc6jqWjX/6xVCCPEMCToGQJ+CDminW286f5+vtwdlTUXv5u/F8JZlsTErYOvLqDPh+Dw4+M1fm4MWg04/QzH/bM0inkYw6MAgbsTcwEhpxFh/2QFdCCH0gQQdA6BvQedvTxLTmLojiPWB9wBwtTFlYtsKtPR1K3gDc+8FwoaeEHMHFCrt5qANhoFSldUkKT2JCQET2H1nN/DX4oI1RmKsKmDhTwghDEh+/w59pcEcGo2GQ4cOMWXKFHr16sWHH37IoEGDWL58OXfv3n2jQo4cOULbtm3x8PBAoVCwefPmbO/36NEDhUKR7atWrVrZ2qSmpjJw4ECcnJywtLSkXbt23Lt3L1ubmJgYunfvjq2tLba2tnTv3p3Y2Ng3ql1fOFiaMPO9Sqzp7U9xRwsexKfSf/U5+vxylvuxybouL3cVrQb9jkLFzqDJhENTYUUbiP3/30MLYwu+a/Adg6oMQoGC32/8Tu89vXmc/FiHhQshhMhPOQo6ycnJTJ06FU9PT1q1asX27duJjY1FpVJx8+ZNvvrqK0qUKEHr1q05efLkaxWSmJhIpUqV+P7771/YpmXLlkRGRmZ97dixI9v7gwcPZtOmTaxdu5Zjx47x9OlT2rRpQ2ZmZlabLl26cOHCBXbt2sWuXbu4cOEC3bt3f62a9VUdbyd2DW7AgMbeGKsU7AuKptnsw/x89DYZmWpdl5d7zGyg40/QYTGYWEF4APxYF65uzmqiUCjoU7EPC95agKWxJeeiz/HB9g+49via7uoWQgiRb3L06MrT0xN/f3969OhBixYtMDZ+tus/LCyMNWvW8OOPPzJu3Dj69Onz+kUpFGzatIn27dtnvdajRw9iY2Of6en5W1xcHM7OzqxatYrOnbUbPUZERODp6cmOHTto0aIFQUFB+Pj4cPLkSfz9tWM6Tp48Se3atbl+/Tply5bNUX36+ujqeYIfJDBm42XO/rUregUPG6Z19KNiUTvdFpbbHt/Sbg4acU57XPVjaDkt2+agt+Nu88WBL7gTfwdTlSkT60ykTck2OipYCCEKJ718dLVz507Wr19PmzZtnhtyALy8vBg9ejQhISE0atQoN2vMcujQIVxcXChTpgx9+vQhOjo6673AwEDS09Np3rx51mseHh74+voSEBAAwIkTJ7C1tc0KOQC1atXC1tY2q83zpKamEh8fn+3LUJRxtWZdv9pM7+iHjZkRVyPiaf/DcSZuuUpCSrquy8s9jqWg526oNwRQwLmV8FMjiLyU1aSkbUlWv72a+kXqk5qZyuijo5l5ZiYZ6gydlS2EECJv5Sjo+Pr65viEJiYmlC5d+rULepFWrVqxevVqDhw4wKxZszhz5gxvvfUWqampAERFRWFiYoK9ffZp1a6urkRFRWW1cXF5do8oFxeXrDbPM23atKwxPba2tnh6eubineU9pVLBBzWLsf/LRrSv7IFaAysC7tB09mF2XI6kwIxHNzKBphPho81g5QaPguHnJnBioXZbCcDGxIYFby2gj5+2x3HltZX039ef2JRYnZUthBAi7+RoG+xLly79d6O/VKxY8bWLeZm/H0eBNnhVr14dLy8vtm/fTseOHV/4OY1Gk23G0fNmH/27zb+NHj2aoUOHZh3Hx8cbXNgBcLY2Ze4HVehUrSjjN1/hzuMkPlt9jrfKuTCpXQU8HSx0XWLuKNkI+gf8f3PQ3aPh1oGszUFVShWDqg6inEM5xh0fx8nIk3yw/QPmNZ5HWYecPb4UQghhGHIUdCpXroxCoXjhv/z/fk+hUGQb+JuX3N3d8fLyIiQkBAA3NzfS0tKIiYnJ1qsTHR1NnTp1sto8ePDgmXM9fPgQV1fXF17L1NQUU9OCs3lm/dLO7BrcgIUHb7Lo8C0OXI8m4NYjvmhSht71SxSMlZUtHeGDNf/fHPTmXlhUJ9vmoM2LN6e4bXG+OPAF957eo/vO7kyuM5mWJVrquHghhBC5JUe/0UJDQ7l9+zahoaHP/fr7vdu3b+d1vVkeP37M3bt3cXfXLu9frVo1jI2N2bt3b1abyMhIrly5khV0ateuTVxcHKdPn85qc+rUKeLi4rLaFBZmxiqGNi/Lzi8aUKukAynpambsus7b849y5s4TXZeXOxQKqNkH+h4C5/KQGA2/dtIGnwztI88y9mVY22YtdTzqkJyRzPAjw5l9draM2xFCiAJCbxYMfPr0KTdv3gSgSpUqzJ49m8aNG+Pg4ICDgwMTJ06kU6dOuLu7c+fOHcaMGUN4eDhBQUFYW1sD0L9/f7Zt28aKFStwcHBg2LBhPH78mMDAQFQq7UJyrVq1IiIigsWLFwPQt29fvLy82Lp1a45rNaRZVzmh0WjYeO4+3+zQrqwM8H71ooxqVR4HSxMdV5dLnrs56FJw0o4ny1RnMv/8fJZdWQZALfdafNfgO+zM7HRUsBBCFEwGsTLyrVu3mDt3LkFBQSgUCsqXL88XX3xBqVKlXruQQ4cO0bhx42de//jjj1m0aBHt27fn/PnzxMbG4u7uTuPGjZkyZUq2sTIpKSkMHz6cNWvWkJycTJMmTVi4cGG2Nk+ePGHQoEFs2bIFgHbt2vH9999jZ2eX41oLWtD5W2xSGjN2Xee309pF9+wtjBnVqhzvVfNEqSwgKytf3w5/fg7JMWBsAa2+hSrdsjYH3XVnFxOOTyA5I5kiVkWY23gu5RzK6bhoIYQoOPQ+6OzevZt27dpRuXJl6tati0ajISAggIsXL7J161aaNWuWV7XqjYIadP529s4Txm2+wvWoBACqe9nzdQdfyrkVkHuNj4BN/SD0iPa4QgdoMzdrc9DgmOCscTtmKjO+qvOVrLcjhBC5RO+DTpUqVWjRogXTp0/P9vqoUaPYs2cP586dy9UC9VFBDzoA6ZlqVhy/w5x9wSSlZaJSKuhVrwRfNCmNpWmOxrDrt2c2B/WEjkvAqzYAcalxjDw6kuP3jwPQrXw3hlYfirFS9skSQog3ofdBx8zMjMuXLz+zVk5wcDAVK1YkJSUlVwvUR4Uh6PwtIjaZSVuvsvuqdraau60ZE9r4FJyNQrNtDqrUbg5afxiojMhUZ7Lw4kJ+uvQTANVcqzGz4UyczJ10W7MQQhgwvVwZ+Z+cnZ25cOHCM69fuHDhuYvxCcPmYWfO4u7VWdajOp4O5kTGpdB/9Tl6LD/DnUeJui7vzWVtDvoBaNRwaBqs1G4OqlKqGFhlIHMbz8XS2JLAB4F03taZiw8v6rpqIYQQOfTKQadPnz707duXGTNmcPToUY4dO8b06dPp168fffv2zYsahR54q5wre4c0ZNBb3piolBwOfkjzuUeYszeYlPT8WTspz5jZQMfF2kdXJtYQfiLb5qBNijXht7d/o6RtSaKToumxqwfrbqwrOCtKCyFEAfbKj640Gg1z585l1qxZREREANo9pYYPH86gQYMKxuOM/1CYHl09T+ijRCb8eYWjIY8AKOZgwaR3KtC4bAHo0XsSqt0c9P5Z7XGV7tBqBphYkpieyPjj49kbpl2r6Z1S7zCu1jjMjMx0WLAQQhgWvR+j808JCdpZOX+vY1NYFPagA9rAu+NyFFO2XSMqXjsuq0UFVya0rUARO3MdV/eGMtPh0HQ4OgvQgKO3ds0dj8poNBqWX13OvHPzUGvUlHcoz5zGcyhiVUTXVQshhEEwqKBTWEnQ+b+nqRnM3x/C0mOhZKo1mBurGNjEm971SmJiZOBbSYQehY19ISEClMbaDUNrfQZKJScjTzLi8AhiUmOwNbVlRv0Z1C1SV9cVCyGE3tP7oPP48WMmTJjAwYMHiY6ORq1WZ3v/yZMCsn3AS0jQedaNqATG/3mF06Ha739JZ0smtatA/dLOOq7sDSU9gS0D4fo27XGpJtrNQa1diXwaydBDQ7ny+AoKFHxW+TP6VuyLUmHgAU8IIfKQ3gedVq1acevWLXr16oWrq+szY3I+/vjjXC1QH0nQeT6NRsPmC/f5Zvt1Hj3V7iXV2s+NcW/74GHIj7M0GghcDrvGQEYyWDhpw06Z5qRmpjL99HTWB68HoEHRBkytNxVbU1sdFy2EEPpJ74OOtbU1x44do1KlSnlVk96ToPNy8SnpzNkbzMqAO6g1ZD3O6lWvBKZGKl2X9/qir8OGXvDgivbYvz80mwRGpmwK2cTXJ78mTZ1GUauizG08l7IOZXVbrxBC6CG9X0enXLlyJCcn50UtooCwMTPmq7YV2D6oPjWK25Ocnsm3u27Qau5RjgQ/1HV5r8+lHPTeD/6fao9PLYIlTeDhDTqU7sCq1qsoYlWEe0/v0XVHV/68+adu6xVCCPHqPTpnzpxh1KhRTJgwAV9fX4yNsy+JXxh6OKRHJ+c0Gg2bzt9n6o7/P85qWcGNcW3KU9TeQsfVvYHg3bC5PyQ9BiNzaDkNqvUgLi2eUUdHcez+MQDeK/Meo2qOwkRVQHaBF0KIN6T3j65CQkL48MMPOX/+fLbXNRoNCoWCzEwDXzwuByTovLr4lHTm7g1h5Yk7ZKo1mBkr+ayRN30blMTM2EAfZyVEwaZP4fZB7XH5ttB2PmpzOxZfWsyiC4vQoKGCYwVmN5qNh5WHbusVQgg9oPdBp2bNmhgZGfHFF188dzByw4YNc7VAfSRB5/Vdj4pnwp9Xs2ZnFXOwYEIbH5r6uOq4stekVsOJ72H/ZFCng00R6PgTFK/H8fvHGXl0JHGpcdia2jK9/nTqFamn64qFEEKn9D7oWFhYcP78ecqWLbwDLSXovBmNRsOWixFM3RHEg3jt46zGZZ2Z0LYCJZwsdVzda4o4D+t7wZNbgAIaDIOGI4lIfsjQQ0O5+vgqChT0q9SPTyt+ikppoL1YQgjxhvR+MHL16tW5e/duXtQiCgmFQsE7lYuw/8tG9GtYEmOVgoM3HtJizhG+3XWdpLQMXZf46jyqQL8jUKUboIEj38HyVnikpfJLq194v8z7aNDw48Uf6b+vP09SCv56U0IIoQ9euUfnjz/+YOLEiQwfPhw/P79nBiNXrFgxVwvUR9Kjk7tuPXzKpK3XsmZkuduaMaZ1edpUdDfMvdOubIStgyE1TrtJaJs5UPE9tt7ayuQTk0nJTMHVwpWZDWdS2aWyrqsVQoh8pfePrpTKZzuBFAqFDEYWb0Sj0bD32gOmbL/G3Sfa5Qv8SzgwsV0Fyrsb4J9xbDhs6AN3T2qPK34Arb8jJPkBQw8N5U78HYwURgyrMYwu5boYZqATQojXoPdBJyws7KXve3l5vVFBhkCCTt5JSc9k8eHbLDx0k9QMNUoFdK/lxZBmZbCzMLAp2pkZcHQmHJ4BGjXYF4dOy3jqUpavAr5iT9geAJp7NWdSnUlYmVjptl4hhMgHeht0xowZQ/v27alZs2Ze16T3JOjkvXsxSUzdEcSOy1EA2FsYM6xFWT6oUQyV0sB6P8JOwMY+EHcXlEbQeAyaOl+wJvh3Zp6dSYY6Ay8bL2Y3mk0Z+zK6rlYIIfKU3g5GjoyMpE2bNri7u9O3b1+2b99OampqXtYmCrGi9hYs7FqNNb39KeNqRUxSOmM3XaHtgmNZU9MNhldt+PQYVOgA6gzYPxnFqvZ0LdKYFS1X4GbpRlh8GF23d2Xzzc26rlYIIQqUV3p0pdFoOHbsGFu3bmXLli3cv3+fZs2a0a5dO9q0aYOTk1Ne1qo3pEcnf2Vkqvn1ZBiz9wYTn6KdkdW2kgejW5UzrM1CNRo4/yvsHAHpSWBuD+/8QEzxOow+OprjEccB6ODdgdH+ozE3MqB7E0KIHNLbR1fPExQUxNatW/nzzz85e/Ys/v7+tGvXjg8//JAiRYrkZp16RYKObjx+msqsvcH8djoczV+bhfZvVMrwVld+FKLdHDTyova4ei/UzSbz0/VfWXRxEWqNmtL2pZnVcBYlbEvotlYhhMhlBhV0/ik6Ojqrp6d+/foMGzYsN06rlyTo6NaV+3FM2nqVM3diAChqb87Y1uVp6etmOLOXMlLhwBQIWKA9di4HnZZySpPIiCMjeJLyBAsjCybVmUTLEi11W6sQQuQigw06hYkEHd3TaDRsvRTJtB1BRMalAFCrpAMT2lTAx8OAvie3Dmj3y3r6AFSm0GwyD/06MvzoCAIfBALQuWxnhtcYjqnKVMfFCiHEm9PboNOxY8f/bGNkZISbmxvNmjWjbdu2b1ycvpKgoz+S0jL48fBtFh++lTUd/cOaxRjarAyOVgYSDBIfwebPIGS39rh0CzLaLeCHkLX8fPlnAMo7lGdWw1l42njqsFAhhHhzeht0Pvnkk/9so1ariY6O5vDhwwwbNozJkye/cYH6SIKO/rkXk8S0ndfZfikSAGszIwY3LcNHtb0wVr3yTif5T6OB0z/BnvGQmQpWrtB+EUfNTBhzbAyxqbFYGVsxue5kmnk103W1Qgjx2vQ26LyK7du3079/f8LDw3P71HpBgo7+Onn7MZO3XuNaZDwAJZ0tGf+2D43Luei4shyKuqIdqPzwuva49gCiavVl+PFxXHh4AYCu5bsytNpQTFQGtoCiEEJQQIJObGwsPXv2ZOPGjbl9ar0gQUe/Zao1rDt7l5m7b/A4MQ2AhmWcGd+mPN4u1jquLgfSk2H3WDi7VHvsXon0DotZEL6T5VeXA1DBsQLfNfwOT2t5lCWEMCx6GXQ+/fRTxo4di6fnf/9Q/f3338nIyKBr1665UqA+kqBjGOJT0lmwP4QVAXdIz9SgUiroXsuLwU1LG8Z2Ete3w5+fQ3IMGFtAq2857OzJ2OPjiEuNw9rYmsl1J9PUq6muKxVCiBzTy6Azfvx45s+fT506dWjXrh3Vq1fHw8MDMzMzYmJiuHbtGseOHWPt2rUUKVKEn376CT8/vzwvXlck6BiW0EeJfLM9iH1BDwCwszBmSNMydPEvpv/jd+IjYFM/CD2iPfZpT1ST0Qw/9U3Wo6wu5brwZfUv5VGWEMIg6GXQAe06OUuXLmXt2rVcuXIl23vW1tY0bdqUvn370rx58zwpVJ9I0DFMx0IeMWXbNW48SADA28WKcW+Xp1FZPR+/o1ZDwHztujvqDLD1JL39IhY8OcPyK9pHWT6OPsxsMFNmZQkh9J7eBp1/io2NJSwsjOTkZJycnChVqpThLNSWCyToGK6MTDW/nbnLnL3BPPnH+J1xb5entKuej9+5HwgbesOT26BQQoPhHClVm7EBE4hNjcXS2JKJdSbSsrgsMCiE0F8GEXQKOwk6hi8uOZ3vD2Qfv9PVvxiDm5bBwVKPHwGlJsDOkXBhtfa4aE2iWk9j5MUFnIs+B8B7Zd5jRI0RmBmZ6bBQIYR4Pgk6BkCCTsFx51EiU3cEseeadvyOjZkRg5qU5qPaxTEx0uPxO5fXw7YhkBoPpjZkvD2LhRlR/Hz5ZzRoKG1fmpkNZ1LStqSuKxVCiGwk6BgACToFT8DNR0zZHkTQX+vvFHe0YHTr8jT3cdXfx7IxYbCxD9w9pT2u9CEBVd9j9MnJPEl5grmROWP9x/KO9zu6rVMIIf5Bgo4BkKBTMGWqNawPvMt3u4N59DQV0O6fNe5tH3yL2Oq4uhfIzIAj38KR70CjBvsSPGwzi9E3f+NUlDYAtS3ZlrG1xmJpbKnjYoUQQoKOQZCgU7A9Tc1g0aGbLDkaSlqGGoUCOlUtyvAWZXG10dNxL2EntL07cXdBaURmo9H8bGPFwkuLUGvUeNl4MbPhTMo5lNN1pUKIQs4ggk5GRgaHDh3i1q1bdOnSBWtrayIiIrCxscHKyiov6tQrEnQKh3sxSXy3+wZ/XogAwNxYRb+GJenboCQWJkY6ru45kmNh22C4ukl7XLw+gQ0GMjLwOx4kPcBYacyw6sP4sNyH+vs4TghR4Ol90AkLC6Nly5aEh4eTmppKcHAwJUuWZPDgwaSkpPDjjz/mVa16Q4JO4XIuPIavt13jXHgsAK42pnzZvCydqhZFpdSzwKDRaGdk7RgB6Ylgbk9sqxmMf3iMQ/cOAdDYszFT6k7B1lRPH8cJIQq0/P4d+srTSr744guqV69OTEwM5ubmWa936NCB/fv352pxQuiDqsXs2dC/Dt93qYKngzkP4lMZsf4SbRccI+DmI12Xl51CAVW6Qb8j4F4ZkmOw29iX+UkqRlUdirHSmIN3D9JpSycCHwTqulohhMhzr9yj4+TkxPHjxylbtizW1tZcvHiRkiVLcufOHXx8fEhKSsqrWvWG9OgUXqkZmawMuMOCAzdJSMkAoEk5F0a3Lqd/G4ZmpGlXUw6Yrz12Ksu15uMYcfUnwuLDUCqUfFrpU/r69UWlVOm2ViFEoaH3PTpqtZrMzMxnXr937x7W1nr2g16IXGZqpKJvg1IcHt6Yj2t7oVIq2H89mhZzjzJu8+Ws2Vp6wcgEmk+B7pvByg0e3cDn91787tqMtiXboNaoWXhhIb339CYqMUrX1QohRJ545aDTrFkz5s6dm3WsUCh4+vQpX331Fa1bt87N2oTQWw6WJkx6x5c9QxrQtLwrmWoNv54Mp9F3h/jh4E1S0p/9x4DOlGoM/Y9DmVaQmYblnglMDQthavVRmBuZc/bBWd7d+i4Hwg/oulIhhMh1r/zoKiIigsaNG6NSqQgJCaF69eqEhITg5OTEkSNHcHHR8w0Sc4E8uhL/duLWY77ZcY0r97ULDnrYmjGsRVnaVy6CUl8GLGs0cOZn2DMOMlLA0pmwFlMYHraJoCdBAHxY7kO+rP4lpipTHRcrhCio9H7WFUBycjJr164lMDAQtVpN1apV6dq1a7bByQWZBB3xPGq1hj8v3ue7XTeIiEsBwLeIDWNal6dOKScdV/cPD67Bhl4QfQ2AdP/+zHO0Z2XQrwCUsS/Dtw2+pZRdKV1WKYQooAwi6BR2EnTEy6SkZ7LseCgLD97iaer/ByyPalVOf3ZIT0+GvRPg9E/aY1c/jjUcwNjLi3iS8gQzlRnDawznvTLvyZo7QohcpfdBZ9q0abi6utKzZ89sry9btoyHDx8ycuTIXC1QH0nQETnx6Gkq8/eHsPpUOJlqDUoFdK5RjCHNSuNirScrLN/YBX9+BkmPwcicR03GMTbhMgGRAQA0LdaUiXUmypo7Qohco/dBp3jx4qxZs4Y6depke/3UqVN88MEHhIaG5mqB+kiCjngVtx4+ZcbO61k7pFuYqOjboCR96pfE0lQPVlhOiIJNn8LtgwCoy7VhVdl6zL3yExnqDFwtXJlWfxo13GrouFAhREGg90HHzMyMoKAgSpQoke3127dv4+PjQ0pKSq4WqI8k6IjXcTr0CVN3BHHhbiwAztamDG1WhveqFcVI9coTIHOXWg0nf4B9k0CdDtYeXG02jpG3fiMsPgwFCnr79aZ/5f4YK411W6sQwqDp/To6np6eHD9+/JnXjx8/joeHR64UJURBVLOEA5s+066wXMzBgocJqYzeeJlW846yP+gBOh0up1RCnYHQex84ekNCBBU2fs46q6p0KPUOGjQsubyEHjt7cDfhru7qFEKIV/TKQad3794MHjyY5cuXExYWRlhYGMuWLWPIkCH06dMnL2oUosBQKBS0qejBvqENmdDGBzsLY0Kin9Jr5Vk++OkkF//q7dEZj8rQ9zBU6Q5osDg+j8lBAXxXbTjWxtZcenSJ97a+x9ZbW3VbpxBC5NArP7rSaDSMGjWK+fPnk5aWBmgfZ40cOZIJEybkSZH6Rh5didwSl5zOokO3WHY8lLQMNQBtKrozvEVZvBwtdVvc1U2w9QtIiQMTKyKaTWD0o+Ociz4PQOsSrRlXaxzWJnoyk0wIYRD0eoxOZmYmx44dw8/PDxMTE4KCgjA3N6d06dKYmhaeBcYk6Ijcdj82mVl7brDp/H00GjBWKejq78WgJqVxsDTRXWGxd2FjXwjXzsLK8H2XJSX8WHx1BZmaTDwsPZjeYDpVXKrorkYhhEHR6zE6KpWKFi1aEBcXh5WVFTVq1MDX1zdXQs6RI0do27YtHh4eKBQKNm/enO19jUbDxIkT8fDwwNzcnEaNGnH16tVsbVJTUxk4cCBOTk5YWlrSrl077t27l61NTEwM3bt3x9bWFltbW7p3705sbOwb1y/EmyhiZ87s9yuzfWB9GpRxJj1Tw4qAOzT89iA/HLxJcpqOtpSw84Qe26DxOFCoMLqynv5Hl7KiygiKWBUhIjGCHrt68MOFH8hQZ+imRiGEeIlXHqPj5+fH7du3c72QxMREKlWqxPfff//c97/99ltmz57N999/z5kzZ3Bzc6NZs2YkJCRktRk8eDCbNm1i7dq1HDt2jKdPn9KmTZtsm5B26dKFCxcusGvXLnbt2sWFCxfo3r17rt+PEK/Dx8OGX3rW5Nde/lTwsCEhNYPvdt+g0cyDrD0dTkamOv+LUqqg4XDouQvsvCA2nMobB7DeoV7W5qA/XvyRj3d9LAOVhRB655XH6OzZs4eRI0cyZcoUqlWrhqVl9nEEudENpVAo2LRpE+3btwe0vTkeHh4MHjw4a0HC1NRUXF1dmTFjBv369SMuLg5nZ2dWrVpF586dAe2+XJ6enuzYsYMWLVoQFBSEj48PJ0+exN/fH4CTJ09Su3Ztrl+/TtmyZZ9bT2pqKqmp/9+VOj4+Hk9PT3l0JfKUWq1h66UIvtt9g3sxyQB4u1gxokVZmvm46mbF4pR42P4lXF6nPS5Whx01u/D1pUUkpCdgYWTBGP8xtCvVTlZUFkI8l14/ugJo2bIlFy9epF27dhQtWhR7e3vs7e2xs7PD3t4+L2okNDSUqKgomjdvnvWaqakpDRs2JCBAO3YgMDCQ9PT0bG08PDzw9fXNanPixAlsbW2zQg5ArVq1sLW1zWrzPNOmTct61GVra4unp2du36IQz1AqFbxTuQj7v2zI+DY+2FsYczP6KX1XBfL+4hMEhj3J/6LMbKDTEujwE5hYQ3gArbeOZX3Z3lR1qUpSRhLjjo9j2OFhxKXG5X99QgjxL6+8LOvBgwfzoo6XioqKAsDV1TXb666uroSFhWW1MTExeSZsubq6Zn0+Kirquburu7i4ZLV5ntGjRzN06NCs4797dITID6ZGKnrVK8F71Yvy418ztM7ciaHTohM093FlRMtyeLtY5W9RlTqDZw3Y0Afun8Vj6xCWVerKMr9+LLyylD1he7j48CJT602lpnvN/K1NCCH+4ZWDTsOGDfOijhz5d1e4RqP5z+7xf7d5Xvv/Oo+pqWmhmlUm9JONmTEjWpbjo9rFmbsvmHVn77Ln2gP2BT2gcw1PvmhSBjfbfNxDy6GkdtzOoelwdBaqi6vpc7cUtZuOZdSNFYTFh9F7T296VOjBgCoDMFHpcPaYEKLQeq1152NjY5k1axa9e/emT58+zJkzh7i4vOumdnNzA3im1yU6Ojqrl8fNzY20tDRiYmJe2ubBgwfPnP/hw4fP9BYJoa/cbM2Y3qkie4Y0oJmPK2oN/Hb6Lo1mHmTGruvEJafnXzEqY2gyXjszy6YIPLmF7/p+rHNsyLulO6FBw/Kry+myvQs3Y27mX11CCPGXVw46Z8+epVSpUsyZM4cnT57w6NEjZs+eTalSpTh37lxe1EiJEiVwc3Nj7969Wa+lpaVx+PDhrM1Fq1WrhrGxcbY2kZGRXLlyJatN7dq1iYuL4/Tp01ltTp06RVxc3DOblAqh77xdrFnyUXXWf1qb6l72pKSrWXToFg2+PciSI7dJSc/HKenF68Gnx6B8O1BnYHHga74KOcf8WpOwN7XnRswNOm/rzOqg1ag1Opg5JoQotF551lX9+vXx9vZmyZIlGBlpn3xlZGTQu3dvbt++zZEjR16rkKdPn3LzpvZffFWqVGH27Nk0btwYBwcHihUrxowZM5g2bRrLly+ndOnSTJ06lUOHDnHjxg2srbUrs/bv359t27axYsUKHBwcGDZsGI8fPyYwMBCVSgVAq1atiIiIYPHixQD07dsXLy8vtm7N+ZL2smCg0DcajYZ9QdF8u+s6IdFPAfCwNWNIszJ0rFoUlTKfZkBpNHB+FewcCelJYO7Ao1bTGB99mGP3jwFQ2702U+pOwdVSelGFKIz0emVkAHNzc86fP0+5cuWyvX7t2jWqV69OUlLSaxVy6NAhGjdu/MzrH3/8MStWrECj0TBp0iQWL15MTEwM/v7+/PDDD/j6+ma1TUlJYfjw4axZs4bk5GSaNGnCwoULsw0cfvLkCYMGDWLLli0AtGvXju+//x47O7sc1ypBR+irjEw1G8/dZ/beYKLiUwAo42rF8BblaFreJf+mfD8KgfU9IeoSAJpqPVlbojKzLiwgNTMVGxMbJtSeQIviLfKnHiGE3tD7oOPq6sqqVauyTeMG2L17Nx999NFzx8AUNBJ0hL5LSc9kZcAdFh66lTVmp7qXPSNblaNGcYf8KSIjFQ5MgYAF2mPnctxuMZnR15dx7fE1ANqWbMto/9GyX5YQhYjeB51BgwaxadMmZs6cSZ06dVAoFBw7dozhw4fTqVMn5s6dm0el6g8JOsJQxCWn8+PhWyw/HkpKunZsTJNyLgxvWZZybvn0d/fWAdj0KTx9ACoT0pt+xSJTNUuvLEWtUeNh6cHX9b6mhluN/KlHCKFTeh900tLSGD58OD/++CMZGdq9bYyNjenfvz/Tp08vFNOwJegIQ/MgPoV5+0P4/cxdMtUaFAroULkIQ5qVwdPBIu8LSHwEf34Owbu0x97NOF/vM0YHfsv9p/dRoJBp6EIUEnofdP6WlJTErVu30Gg0eHt7Y2GRDz8s9YQEHWGobj98yqw9wWy/HAn8f5f0AW9542SVx/9I0WjgzM+weyxkpoKlM4lt5/LtkzNsDNkIQGn70kyrN42yDs/fjkUIYfj0Nuh89NFH/PDDD1kznC5evIiPjw/GxsZ5WqA+kqAjDN2le7F8u+sGx24+AsDSREWv+iXpU78E1mZ5/P/0g2uwoRdEa8fp4N+fA+UaM+n0VJ6kPMFYaczAKgP5yOcjVEpV3tYihMh3eht0VCoVkZGRWVso2NjYcOHCBUqWLJmnBeojCTqioDh+8xEzdl3n0j3tgp/2FsZ83tibbrW8MDPOw5CRngx7v4LT2mUecPXjUZuZTApezaF7hwCo5lqNr+t+TVHronlXhxAi3+ntpp7/zkOv+cRLCKFH6no78efndVnUtSolnS2JSUrn6+1BvDXzEOvO3iUjM48W9zM2h9bfQpd1YOEEDy7jtPId5ltXYlLtiVgYWRD4IJBOWzqxKWST/LwRQry219oCQghRcCgUClr5ubNncAOmd/TD3daMiLgURqy/RIu5R9h1JTLvgkaZFtA/AEq9BRkpKHZ8ScfADaxv+nPWbugTAibwxcEveJz8OG9qEEIUaDl+dKVUKjlw4AAODto1OOrUqcO6desoWjR7t3LFihVzv0o9I4+uREGWkp7JqhNh/HDoJrFJ2jV4KhW1ZXiLctQr7ZQ3F1Wr4dQi2DcRMtPA2p3M9gtZmXyH789/T7o6HXtTe76q/RVNvJrkTQ1CiHyht2N0lEolCoXiuf+y+/t1hUJBZmY+7q+jIxJ0RGEQn5LOkiO3WXoslKQ07f/XdUo5MqJlOSp72uXNRSMvaQcqPwoGFFB3EDcqd2bMiQkExwQD2kUGR/mPwsZE/t8TwhDpbdAJCwvL0Qm9vLzeqCBDIEFHFCYPE1L54eBN1pwKJ+2vMTvNfVwZ1qIsZVzzYEXjtCTYPQYCl2uP3SuT1v5HFt3fw7Iry1Br1LhauDKl7hRqe9TO/esLIfKU3gYd8X8SdERhdC8mibn7Qth47h5qDXm/6GDQVtgyEJJjwNgSWs3gQlE/xh4bS3hCOAAflP2AIdWGYGFceNbxEsLQSdAxABJ0RGEW8iCBWXuC2XU1CtAuOvhhzWIMaOyNi41Z7l4sPgI29oU7R7XHPu1JajWN2VeX8fuN3wEoZl2Mb+p9Q2WXyrl7bSFEnpCgYwAk6AgBF+/GMnPPDY6GaBcdNDNW8kndEvRrUBI7i1zcxkGdCQHz4cDXoM4Am6LQ8ScCTBRMOD6BB0kPUCqUfFzhYz6v/DmmqoK/DY0QhkyCjgGQoCPE/wXcesR3u29wPjwWAGszI/o1KMkndUtgaWqUexe6HwgbesOT26BQQv0via/9GTMCZ7Hl1hYAvO28+bre11RwrJB71xVC5CoJOgZAgo4Q2Wk0GvYHRTNzzw2uRyUA4GRlwmeNvOniXyz3VllOfQo7R8KFX7XHRWtAxyUceBrKpBOTeJLyBJVCRd+KfelTsQ/GysK3RY0Q+k6CjgGQoCPE86nVGrZeimD23mDCHicB4GFrxhdNS9OpalGMVLm0RumVDbB1CKTGgYk1tJlNTJlmfH3ya/aE7QGgvEN5vq73NWXsy+TONYUQuULvg06VKlVQKBTPnkihwMzMDG9vb3r06EHjxo1zrUh9I0FHiJdLz1Tzx9l7zN8fQlR8CgAlnCwZ0qwMbfzcUSqf/RnyymLDtQOVw09oj/3eh7dnsSsygK9PfU1cahzGSmM+q/wZPSr0wEiZi4/RhBCvTW/3uvpby5YtuX37NpaWljRu3JhGjRphZWXFrVu3qFGjBpGRkTRt2pQ///wzL+oVQhgAY5WSLv7FODS8EePeLo+DpQmhjxIZ9Nt5Ws8/yr5rD958Wwm7YvDxNmg8FhQquLwOfqxHSyMHNrXbRKOijUhXpzPv3Dw+2vkRt2Nv587NCSEMyiv36PTp04dixYoxfvz4bK9//fXXhIWFsWTJEr766iu2b9/O2bNnc7VYfSE9OkK8mqepGSw7FsqSI7dJSM0AoLKnHSNalKWOdy5sKxF+Cjb21vbyKFTQaBSaekPZErqdGadnkJCegInShEFVB9GtfDdUyjzcmV0I8VJ6/+jK1taWwMBAvL29s71+8+ZNqlWrRlxcHNevX6dGjRokJCTkarH6QoKOEK8nNimNHw/fZkVAKCnp2lWW65Ry5MvmZanmZf9mJ0+Jg+3DtD07AMVqQ8efiDI2YWLARI5HHAegsnNlptSdQnHb4m92PSHEa9H7R1dmZmYEBAQ883pAQABmZtrFwtRqNaamspaFECI7OwsTRrUqx5Hhjfm4thfGKgUBtx7TaVEAvVac4WpE3Ouf3MwWOi2BDj9pByiHn4BF9XALPc6ipouYWHsilsaWXHh4gXe3vssvV38hU13w9+YTorB75R6dr7/+mqlTp9KnTx9q1KiBQqHg9OnT/Pzzz4wZM4axY8cyZ84cduzYwd69e/Oqbp2SHh0hcse9mCTm7w9hw7n7ZKq1P4rerujO0GZlKOVs9fonfhIKG/vAvTPa48pdodUMItIT+CrgK05GngSgqktVptSdQjGbYm96K0KIHNL7R1cAq1ev5vvvv+fGjRsAlC1bloEDB9KlSxcAkpOTs2ZhFUQSdITIXbcfPmXOvhC2XowAQKmAjlWL8kWT0q+/j1ZmOhz+Fo7OBI0a7EtAp6VoilTlj+A/mHV2FkkZSZipzBhUdRBdy3dFqcil6e9CiBcyiKBT2EnQESJvBEXGM3tvMHuvPQDASKmgcw1PBrzljbut+eud9M5x7TT0+HugNILGY6DuYO4nRfFVwFecijwFaHt3JtedjJeNV27djhDiOQwm6KSlpREdHY1arc72erFiBb8LWIKOEHnrwt1YZv1jHy0TIyXda3nRv1EpnKxeY/xfcgxsHQzXNmuPi9eHDovR2Hhk690xVZkyqIq2d0dmZgmRN/Q+6ISEhNCzZ89nBiRrNBoUCgWZmQV/cJ8EHSHyx6nbj5m1J5jTd54AYGGioked4vR9nY1DNRo4/6t2C4n0RDCzg3YLwKcdEU8jso3dqeRciSl1p1DCtkQu35EQQu+DTt26dTEyMmLUqFG4u7s/s0pypUqVcrVAfSRBR4j8o9FoOBryiFl7bnDxnnZWlrWpEX0alOSTusWxNnvF/awe34INvSDivPa46kfQcjoaYws2hGxg5tmZJKYnYqI04fMqn/ORz0eyqrIQuUjvg46lpSWBgYGUK1cur2rSexJ0hMh/Go2GvdceMHtvcNbGofYWxnzasBQf1S6OuckrPGrKSIOD38DxeYAGHL2h01LwqEzk00gmnphIQIS219rX0Zcpdafgbe/98nMKIXJE74NOjRo1mDNnDvXq1curmvSeBB0hdEet1rDtciRz9wZz+1EiAE5WpgxoXIoP/YthavQKgef2YdjUDxIiQWkMTSZA7QFoFAo239zMd2e+IyE9AWOlMZ9W+pRPfD+RHdGFeEN6H3QOHDjAuHHjmDp1Kn5+fhgbZ/+fvjD84pegI4TuZWSq2Xwhgrn7grkXkwxod0of2KQ071YrinFOd0pPegJbBsL1bdrjko2g/Y9g486DxAdMOTmFw/cOA1DOoRxT6k6hnEPh7dEW4k3pfdBRKrU/PP49NkcGIwshdCEtQ80fgXdZsP9m1k7pxRws+KJJadpXKYIqJzulazQQuAJ2jYaMZDB3gHd+gHKt0Wg0bLu9jRlnZhCXGoeRwoiefj3pV7EfJqpXHBAthND/oHP48OGXvt+wYcM3KsgQSNARQv+kpGey5lQ4Cw/d5NHTNABKOlsyuGkZ2vi5o8xJ4Hl4QztQOeqy9rh6L2j+NZhY8Cj5EVNPTWVvmHbF91K2pZhcdzIVnSvm1S0JUSDpfdAREnSE0GdJaRmsDAhj8ZFbxCalA1DOzZohzcrQ3Mf1md7oZ2Skwv7JcOJ77bFzOe1AZTdfAPbc2cM3p77hScoTlAol3cp3Y0CVAZgbveaChkIUMnoZdC5duoSvry9KpZJLly69tG3FigX/XzcSdITQfwkp6Sw7doefj94mITUDAL8itgxtVoZGZZ3/O/Dc3A+b+8PTB6AygWaTwf9TUCiITYllxpkZbLutHdfjae3JpDqTqOFWI69vSwiDp5dBR6lUEhUVhYuLC0qlEoVCwfM+JmN0hBD6JjYpjSVHb7P8+B2S0rQ/n6oWs2Nos7LU9XZ8eeBJfAR/fg7Bu7TH3k2h/SKwcgHgyL0jTD4xmQdJ2i0r3i3zLkOrDcXaxDpP70kIQ6aXQScsLIxixYqhUCgICwt7aVsvr4K/T4wEHSEMz+OnqSw+cptfTtwhJV27dY1/CQe+bF6WmiUcXvxBjQbO/Ax7xkFGClg6wzsLoUxzAJ6mPWXuubn8fuN3AFwsXBhfazyNPBvl9S0JYZD0Muj8U1JSEhYWr7mbcAEhQUcIwxUdn8LCQ7dYcyqctExt4Klf2okhzcpQtZj9Sz4YBOt7QfRV7bH/p9B0EhibAXAm6gwTAyYSnhAOQMviLRlVcxSO5o55ej9CGBq9DzpWVla0b9+e7t2706xZs6zp5oWJBB0hDF9kXDLfH7jJurN3Sc/U/hhsXNaZoc3K4lfU9vkfSk+BfV/BqR+1xy4VoNPP4OoDQEpGCgsvLuSXq7+QqcnE1tSWkTVG0qZkm/8eEyREIaH3QWfjxo389ttvbN++HRsbGzp37ky3bt2oUaPwDMKToCNEwXH3SRILDoSw4dx9MtXaH4fNfVwZ0qwM5d1f8P93yF7tQOXEh2Bkpp2CXqM3/BVmrj6+ylfHv+JGzA0A6hapy4RaE/Cw8siXexJCn+l90PlbQkIC69ev57fffuPgwYOUKFGCbt26MWHChNyuUe9I0BGi4LnzKJF5+0PYfOE+f/9UfNvPncFNS1Pa9TmDi59Gw+bP4KZ2XR3KtNQuMmjpBEC6Op2VV1ey6MIi0tRpmBuZM6jKID4s9yEq5StsUyFEAWMwQeefrl27RteuXbl06ZLMuhJCGLSb0QnM2RfC9kuRgLaT5p1KHgxqUpqSzlbZG2s0cGox7J0Amalg6QIdfgTvJllNQuNCmRgwkXPR5wCo6FSRiXUmUtq+dL7dkxD6xGCCTkpKClu2bGHNmjXs2rULFxcXPvzwQ2bMmJHbNeodCTpCFHzXo+KZszeY3Ve1U8dVSgUdqhRh0FulKeb4rwkZD65qByo/DNIe1/ocmn4FRqYAqDVq1gevZ07gHJ6mP8VIaUQv3170qdgHU5Vpft6WEDqn90Fnz549rF69ms2bN6NSqXj33Xfp2rVrodj64W8SdIQoPK7cj2PO3mD2X48GwEip4L3qRfm8sTdF7f8ReNKTtT07p3/SHrv6aQcqu/x/A9AHiQ/45tQ3HLx7EIDiNsWZWGci1Vyr5dv9CKFreh90LCwsePvtt+natStvv/32M7uXFwYSdIQofC7cjWX23mCOBD8EwFiloHMNTz5v7I277T+2f7ixC/78DJIeawcqt5gK1XtmDVTWaDTsC9/H1FNTeZT8CID3yrzHkGpDZKFBUSjofdCJj48v9L/cJegIUXidvfOEOfuCOX7zMQAmRkq61CzGZ41K4WKjXVOHhCjtrKxbB7THZd+GdgvA8v9r6sSlxjEncA4bQjYA4GzuzBj/MTQp1kSmoosCTS+Dzj/DTXx8/EvbFoZf/BJ0hBAnbz9m9p5gTt95AoCpkZLutbz4tFEpnKxMQa2Gkwth30RQp4OVm3agcqnG2c5zJuoMk05MIixeu+p8Y8/GjPEfg5ulW37fkhD5Qi+DjkqlIjIyMtteV/+m0WhkryshRKGi0WgIuPWYWXtucC48FgBzYxUf1ylO3wYlcbA0gciLsKE3PArWfqjOIHhrPBiZZJ0nNTOVny79xLLLy8jQZGBpbMmgKoPoXLazTEUXBY5eBp3Dhw9Tt25djIyMOHz48EvbFoZByRJ0hBD/pNFoOBLyiNl7bnDxXhwAliYqetQtTp/6JbEzyoDdYyBwufYD7pWg01Jwyj7FPCQmhEknJnHx4UVAOxV9Qu0JlHUom6/3I0Re0sug87eMjAy++eYbevbsiaenZ17Wpdck6Aghnkej0XDgejSz9wZzNUL7mN/a1IhP6pWgV70S2N7ZDVsGQHIMGFtAy+lQ9aOsgcqgnYr++43fmXduHonpiRgpjPiowkd8WulTzI3MX3RpIQyGXgcdAGtray5fvkzx4sXzqCT9J0FHCPEyGo2GPdceMGdvMNejEgCwNjOiT/2S9KxoitWOzyH0iLZx+bbQdj5YZN9B/UHiA6afns6+8H0AFLUqyvha46lTpE6+3osQuU3vg0779u1p3749PXr0yKOS9J8EHSFETqjVGnZdjWLuvmCCHzwFwM7CmD71itNbtR3Tw99oBypbe0DHxVCiwTPnOBB+gKmnpvIgSbtwYesSrRlRY4Tsii4Mlt4HncWLFzNx4kS6du1KtWrVsLS0zPZ+u3btcrVAfSRBRwjxKtRqDdsvRzJ3XzC3HiYC4GBpwpgqKXS8/RXKJ7cABdQbDI3GZBuoDJCYnsiC8wtYE7QGDRpsTGwYWm0oHUp3QKlQ5v8NCfEG9D7oKJUv/p9KZl0JIcSLZao1bLl4n/n7bxL6SBt4ilqq+dl1I+UiNmobeVTRDlR2LPXM5688usLkE5MJeqLdaqKqS1Um1J5AKbtn2wqhr/L7d+gr/1NArVa/8CsvQ87EiRNRKBTZvtzc/r/OhEajYeLEiXh4eGBubk6jRo24evVqtnOkpqYycOBAnJycsLS0pF27dty7dy/PahZCiH/S7pdVlL1DGjDzvUoUc7DgXqKSlrffZYRqOKlGNhBxHn6sD+dWwb/+Herr5Muat9cwvPpwzI3MORd9jne3vsv8c/NJyUjR0V0Jod8Mqs+zQoUKREZGZn1dvnw5671vv/2W2bNn8/3333PmzBnc3Nxo1qwZCQkJWW0GDx7Mpk2bWLt2LceOHePp06e0adOmUPRCCSH0h5FKybvVirL/y4Z826kiRe3NWZdYhYZPv+GswhfSE7Wzs/74WDtD65+fVWpnYf35zp808mxEhjqDJZeX0OHPDhy7f0xHdySE/nqlR1dqtZoVK1awceNG7ty5g0KhoESJErz77rt07949T5ctnzhxIps3b+bChQvPvKfRaPDw8GDw4MGMHDkS0PbeuLq6MmPGDPr160dcXBzOzs6sWrWKzp07AxAREYGnpyc7duygRYsWL7x2amoqqampWcfx8fF4enrKoyshRK5Iy1Cz4dw9vj9wk8jYRPqqtjHM+A+MyERj7YGi0xIoXu+5n90fvp9pp6ZlDVZuUbwFI2uMxNnCOT9vQYgc09tHVxqNhnbt2tG7d2/u37+Pn58fFSpUICwsjB49etChQ4e8rBOAkJAQPDw8KFGiBB988AG3b98GIDQ0lKioKJo3b57V1tTUlIYNGxIQEABAYGAg6enp2dp4eHjg6+ub1eZFpk2bhq2tbdZXYV5DSAiR+0yMlHxYsxgHhjVkUvuKbLZ8n46pE7mtdkOREIFmRRsy906CzPRnPtukWBP+bP8n3X26o1Qo2X1nN+02t2NN0Boy1dJbLUSOg86KFSs4cuQI+/fv5/z58/z222+sXbuWixcvsm/fPg4cOMAvv/ySZ4X6+/vzyy+/sHv3bpYsWUJUVBR16tTh8ePHREVFAeDq6prtM66urlnvRUVFYWJigr29/QvbvMjo0aOJi4vL+rp7924u3pkQQmiZGqnoXsuLQ8Mb0altOz4xmcnvGY1QoEF1fDaPFzQm/eHNZz5naWzJiBoj+O3t3/B19OVp+lOmnZ5Glx1duPr46nOuJEThkeOg89tvvzFmzBgaN278zHtvvfUWo0aNYvXq1bla3D+1atWKTp064efnR9OmTdm+fTsAK1euzGrz70dnf++/9TI5aWNqaoqNjU22LyGEyCtmf+2XtXtkaxJbzmWkchhxGgscYy+T/kNdzmxeQEbGs701Po4+/Nr6V8b6j8Xa2Jprj6/RZXsXpp2aRkJawnOuJETBl+Ogc+nSJVq2bPnC91u1asXFixdzpaicsLS0xM/Pj5CQkKzZV//umYmOjs7q5XFzcyMtLY2YmJgXthFCCH1iZqyiZ70STBw1hu11N3AWHyxIocaFcRyd3patJ6+Sqc4+zFKlVPFBuQ/Y0mELrUu0Rq1Rs+b6Gt7Z/A47Q3fyiiuKCGHwchx0njx58tJA4Orq+kyIyEupqakEBQXh7u5OiRIlcHNzY+/evVnvp6WlcfjwYerU0S6XXq1aNYyNjbO1iYyM5MqVK1lthBBCH5mbqOjSvA4+ow5xqsTnpKOiccZxqu5sy5ffLeTPC/efCTxO5k7MaDCDn5r9RHGb4jxMfsiIIyPou7cvYfFhOroTIfJfjoNOZmYmRkZGL3xfpVKRkZGRK0U9z7Bhwzh8+DChoaGcOnWKd999l/j4eD7++GMUCgWDBw9m6tSpbNq0iStXrtCjRw8sLCzo0qULALa2tvTq1Ysvv/wya5xRt27dsh6FCSGEvrMwM8X/46lkfLyTOLOiFFE8ZlbSWMLXj6H1nANsvRiB+l+Bp7ZHbTa028DnlT/HRGnCyciTdPizAz9c+EHW3hGFQo6nlyuVSlq1aoWpqelz309NTWXXrl15tibNBx98wJEjR3j06BHOzs7UqlWLKVOm4OPjA2jH2kyaNInFixcTExODv78/P/zwA76+vlnnSElJYfjw4axZs4bk5GSaNGnCwoULX3kWlayMLITQudQE0rcNx/jybwCcV3vzRfrnmLt480XT0rSs4IZSmX384d34u3xz+huO3z8OaDcKHeM/hvpF6+d7+aLw0tstID755JMcnXD58uVvVJAhkKAjhNAbVzag2ToYRWo8iZgxPq0HG9X1Kedmw+CmZWhRwTXbhAuNRsPesL3MODOD6KRoAJoWa8rImiNxs3R70VWEyDV6G3TE/0nQEULoldhw2NgPwrVrgu3Q1GFU6ifEY4mPuw2Dm5ammU/2wJOYnsiiC4v4NehXMjWZmBuZ069iPz7y+QhjlbGu7kQUAhJ0DIAEHSGE3lFnwtHZcGgaaDKJM3VnQEp/jqZ6A+BbxIbBTcrQpLxLtsATHBPMNye/4Vz0OQBK2pZkrP9YarrX1MltiIJPgo4BkKAjhNBbd8/Axt4QcweNQskJjx70u9uUhDTt2xWL2jKkaRkalXXOCjwajYatt7cy6+wsnqQ8AaBViVYMrz5ctpIQuU6CjgGQoCOE0Gsp8bBzBFzUDlRO96jBz86jmX8uneR07YSRSp52DGlamoZl/h944tPiWXBuAeuC16HWqLE0tuSzSp/xYfkPMVbK4yyROyToGAAJOkIIg3B5PWwbCqlxYGJNQtPpLHhUjV9O3CElXQ1A1WJ2DGlWhnreTlmB59rja3xz8hsuPboEgLedN2P9x1LdrbrObkUUHBJ0DIAEHSGEwYgNh419IfyE9tjvPR42nMqPJx/x68kwUjO0gadGcXsGNy1DnVKOKBQK1Bo1m29uZk7gHGJTYwFoXaI1w6oPk8dZ4o1I0DEAEnSEEAYlMwOOzYZD00GTCXbFoOMSou0qs+jwLVafCiftr8BTs4QDQ5qWoXYpRwDiUuNYcH4B626sQ4MGS2NL+lfqT5fyXeRxlngtEnQMgAQdIYRBunsGNvSC2DBQKKHBCGgwnAeJGSw6dIs1p8JJy9QGnloltYHHv6Q28Fx9dJVvTn3D5UeXAe3srDH+Y/B399fZ7QjDJEHHAEjQEUIYrJR42DEcLq3VHnv6Q8efwL44kXHJLDx4i7VnwknP1P5qqOvtyJCmZahe3CHrcdbcwLnEpGr3NmxRvAXDqg+TxQZFjknQMQASdIQQBu/yetg2BFLjwdQG3p4FFd8H4H5sMj8cvMkfZ+9mBZ76pZ0Y0qwMVYvZE5cax/fnv8+anWVuZE7fin35yOcjTFQmurwrYQAk6BgACTpCiAIhJkw7UPnuSe2x33vawGNmC8DdJ0n8cPAm6wPvkfHXZqENyzgzpFkZKnvacf3Jdaaemsr56PMAFLMuxsiaI2lQtIFObkcYBgk6BkCCjhCiwMjMgKOz4PCMbAOVKVYrq8ndJ0ksOBDChnP3yfwr8LxVzoUhTcvgW8SGbbe3MTtwNo+SHwHQqGgjRtQYgafNq22YLAoHCToGQIKOEKLAuXsaNvR+ZqAyKqOsJmGPE1lw4CYbz93jr7xD0/IuDG5ahuLOKhZfWsyv134lQ5OBidKEjyt8TG+/3lgYW+jopoQ+kqBjACToCCEKpJR42DEMLv2uPS5aEzotAfvi2ZqFPkpkwf4QNl+4nxV4mvu4MrhpGcwsHjH99HRORGrX7XG1cOXL6l/SsnjLbHtsicJLgo4BkKAjhCjQLv0B24c+d6DyP916+JT5+0PYcjGCv3+TtKzgxhdNvYlIP8t3Z77j/tP7AFRzrcbomqMp61A2P+9E6CEJOgZAgo4QosCLCYONfeDuKe2x3/vw9sysgcr/dDM6gXn7b7Lt0v8Dz9t+7nzauBjHH65n6eWlpGSmoFQoebf0uwyoMgB7M/t8vBmhTyToGAAJOkKIQiEzA47O/Gugsvqvgco/Q7HnLxIY/CCBeftD2H4pEgCFAtpU9KBLHSs23FnM7ju7AbAxseHzyp/zftn3MVIaPfdcouCSoGMAJOgIIQqV8FOwsbd23yyFChqOgPrDsg1U/qfrUfHM2xfCzitRgDbwtKvkwVuVE/gleB7BMcGAdrPQkTVHUsu91nPPIwomCToGQIKOEKLQSYmD7cPg8jrtsWetv1ZU9nrhR65FxDN3XzB7rj0AQKmAdpXcKFP6KmtCfiIuNQ6AJsWaMKz6MIpaF83z2xC6J0HHAEjQEUIUWpfWwbahkJbw10Dl2VDxvZd+5Mr9OObuC2ZfUDQAKqWCtyvbYuV6gB1hG8jUZMp09EJEgo4BkKAjhCjUYu7Ahj5w77T2uGJnaD0TzF7+8/Di3Vjm7gvm4I2HABgpFbSoDPGWG7jwSHsuZ3NnBlcbTJuSbVAqlHl4E0JXJOgYAAk6QohC75mByl5/raj837uZnw+PYc6+EI4EawOPsQrqV4rmnnIdkYn3APBz8mNEjRFUdqmcl3chdECCjgGQoCOEEH95xYHK/xQY9oQ5e0M4dlO7dYSJkZqqfpcJzdhCUkYiAK1LtGZItSGyO3oBIkHHAEjQEUKIf3hmoLL/XwOVi+fo46dDnzB77w1O3n4CgIlpImXLHSMs7RAaNJipzPjE9xN6VOgh43cKAAk6BkCCjhBCPMeldbD9y/9cUflFAm49Yu7eEE7f0QYeM8sI3Evu4VHGdQBcLFwYUm0IrUu0lvE7BkyCjgGQoCOEEC8QEwYb+8Ldk9rjl6yo/DwajYaAW4+ZszeYs2ExgAZzu6vYFtlNolo7pkfG7xg2CToGQIKOEEK8xCuuqPw8Go2GoyGPmL03mAt3Y0GRjqVzACZOB8nQpADQsnhLhlQbgoeVRx7diMgLEnQMgAQdIYTIgTcYqPw3jUbDoeCHzNkbzKV7cShUCVi67UVhcwbQYKI04aMKH9HbrzeWxpZ5dy8i10jQMQASdIQQIodS4mDHcLj0u/bY0187Df0lKyo/j0ajYX9QNLP3BnMtMh6laQSW7tvB/BYAjmaODKgygA7eHVApVbl9FyIXSdAxABJ0hBDiFV36A7YPfe2Byn/TaDTsvvqAufuCuR4Vj5HVNczddoKxdop6afvSDKs+jDoedXL7DkQukaBjACToCCHEa4i589dA5VPa41ccqPxParWGnVeimLsvmJDoWIztT2LmvB9UyQDUK1KPL6t9ibe9dy7egMgNEnQMgAQdIYR4TZkZcHTWXwOVM19roHK206k1bLsUwbz9Idx+/BBTp/2YOJwAhRqlQkmn0p34rPJnOJk75fKNiNclQccASNARQog3FH4KNvaB2DBQKKHBCGgw/JUGKv9TplrDlov3mbcvhLCEMEydd2FscxUASyNLevn1ortPd8yMzHLzLsRrkKBjACToCCFELkiJhx3D/j9QuWhN6LQkxysqP09GppqN5+4z/0AIkSlXMXXdgcpcu3+Wq4UrX1T9grdLvi0LDuqQBB0DIEFHCCFyUS4NVP6ntAw1G87dY/7+GzzUnMbUZRdK41gAytqXY0SN4dR0r5kLxYtXJUHHAEjQEUKIXPbMisrvaQPPawxU/qfUjEzWnb3H9weuEWN8ABPHgyhUqQDUL9KAL6sPpZRdqTetXrwCCToGQIKOEELkgcwMODYbDk3XDlS2LaZ9lFWs1hufOiU9k99Oh/PDkYskmO3A2P4UCoUaBUo6eHdgQJXPcbZwzoWbEP9Fgo4BkKAjhBB56O5p2NA71wYq/1NyWiarT4Wx8NgJkqy2Zg1YNlGa0dO3B5/4fiI7pOcxCToGQIKOEELksZR42DkCLv6mPS5aEzr+BA4lcuX0SWkZrAwI48dTe0m32YrKIhwAKyN7vqj2OZ3KdMRYaZwr1xLZSdAxABJ0hBAin1xeD9uGQmocmFhrFxis2BkUilw5fUJKOsuPhbLk3GbU9jtQmjwGwMXMk9G1htKkWBMUuXQtoSVBxwBI0BFCiHwUG64dqBx+Qnvs+652oLK5Xa5dIi45nZ+O3GDlld/R2O1FaZQIQAmrCkyqN5IqrlVy7VqFnQQdAyBBRwgh8pk6E47OhkPT/j9QueNP4FU7Vy8Tk5jG94evsPbGLyjsjqBQpgNQyaEek+sPp6RdyVy9XmEkQccASNARQggduXsGNvbW7pulUEL9YdBwZK4MVP6nR09TmXPgDJvuLEdpcwaFQgMaBQ3c3+ar+kNwsXDJ1esVJhJ0DIAEHSGE0KGUeNg5Ei6u0R4XrfHXQOXc722Jjk9h+v7D7IpYjspKO0NLoTGhbfHOjKrTH2sT61y/ZkEnQccASNARQgg9cGUDbB3y10BlK2g9Eyp9kGsDlf8pIjaZSXu3cfTxSlTmYQCoNJZ8UKYHQ/w/wVRlmuvXLKgk6BgACTpCCKEnYsNhYz8ID9AeV+gIbebk6kDlfwp/nMi4PWsJTFiD0jQaABMc6OPbnz5V3kOlVOXJdQsSCToGQIKOEELoEXWmdkXlg38PVPaEDouheN08u+TN6DjG7F3O1eT1KI3jALBQFOGLqoP4sEIrmZL+EhJ0DIAEHSGE0EP3zmpXVI4J1Q5UrjcUGo0CVd4t/Hcl4hFj9v/I7fQtKFTJANgqSzPG/0tal8m7oGXIJOgYAAk6Qgihp1ITtAOVL6zWHhepBh2XgGPebtx5Ouwe4w/9wH317qwp6c6qSkyqP5z6XpXy9NqGRoKOAZCgI4QQeu7KRtg2GFL+Gqjc6luo3CVPBir/06GbN5l0dB4PFUdQKNQAFDWuzTeNh1HVvUyeXttQSNAxABJ0hBDCAMTehU39IOy49tinPbSdC+b2eX7prdcuMf3kPOJVpwHQaJR4mzdm+ltfUs7ZM8+vr88k6BgACTpCCGEg1JlwfC4cnArqDLApol1zp3i9PL+0RqPh94unmBM4nySjy9rX1Eb4WrdiRtPBeNkVzkUHJegYAAk6QghhYO4HagcqP7kNKKDeEGg8Jk8HKv9No9Hw89n9LL70A6lGN7Uvqk2pZteeb5sNwMXKLs9r0Cf5/TtUmedX0FMLFy6kRIkSmJmZUa1aNY4eParrkoQQQuSVItWg31Go0g3QaKejL20Gj2/l+aUVCgV9ajTl9Ccb6OX9DUYZRUGZSmD87zRZ15IDty7meQ2FWaEMOr///juDBw9m7NixnD9/nvr169OqVSvCw8N1XZoQQoi8YmoF7/wA760EMzuIOA8/1odzqyAfHm4olUoG123H6R7b+MBrLMoMJ1AlMu/0L3l+7cKsUAad2bNn06tXL3r37k358uWZO3cunp6eLFq0SNelCSGEyGsV2kP/ACheH9ITYcsAWPcRJD3Jl8sbq1SMbfQB7bw+BiAq5U6+XLewKnRBJy0tjcDAQJo3b57t9ebNmxMQEPDcz6SmphIfH5/tSwghhAGzLQIf/QlNJ4LSCIK2wKK6EHok30qoWaQCAEmae6jV6ny7bmFT6ILOo0ePyMzMxNXVNdvrrq6uREVFPfcz06ZNw9bWNuvL07NwTw0UQogCQanSDkruvQ8cvSEhAla2g70TICMtzy9fv7gPGo0CVEnceBSR59crrApd0Pnbv/ch0Wg0L9ybZPTo0cTFxWV93b17Nz9KFEIIkR88qkC/I1D1I0ADx+dpByo/CsnTy9qZW2KU6QzA0bDLeXqtwqzQBR0nJydUKtUzvTfR0dHP9PL8zdTUFBsbm2xfQgghChATS2i3AN5fpV1QMPICLG4AgSvydKCynVExAC5EXc+zaxR2hS7omJiYUK1aNfbu3Zvt9b1791KnTh0dVSWEEEIv+LTTDlQu0RDSk2DrF/B7tzwbqOxpVRKA2/E38+T8ohAGHYChQ4fy888/s2zZMoKCghgyZAjh4eF8+umnui5NCCGErtl4QPfN0GwKKI3h+jZYVAduHcz1S1Vw1u5/9Sg1LNfPLbSMdF2ALnTu3JnHjx8zefJkIiMj8fX1ZceOHXh5eem6NCGEEPpAqYS6g6BkQ+2Kyo+CYVV7qDMQ3hoPRqa5cpnaRX1ZHQopiggyMjMxUqly5bzi/2QLiNcgW0AIIUQhkpYEe8bC2WXaYzc/6LQUnMu+8alT0tOo/qs/CmUGPzfegH+xgr/D+f/au/O4qOr9f+CvMwMzwzoiKMMmitZVv2CFdpEWxTQxt/x6yxRFSUP7pWKa5S6g16V7zeXateuWuIvl0k1xATPFJCEFv5ommiiYkIWACzIMzOf3B9dzG0EUhRkZXs/HYx4P55zPOfM+b1JenfM5Z/gVEERERE8SlT3QexEwcBNg1xjIO1UxUTlt1WNPVNbYqqAy6gAA32Wfro1q6R4MOkRERA+jdS/gvRTArwtQVgLs/gDYPAi4/ftj7dZVVTFt4vTv52qjSroHgw4REdHDctIBQ7YDoXMBpQrI3FMxUfnCgUfeZQttSwDApRt1/wWjDRGDDhERUU0oFEDwaCDyG6BJa+DWr8CG/sDeKYChpMa7a9e0NQCgwMAvlq4LDDpERESPQhcAjPwWeD6y4v33y4BVXYFrZ2u0m5d8AwAABuWvuK3X13KRxKBDRET0qGztgF4LgEHxgL0b8OtpYEUIkLryoScqt3NvBhg1kCQjjmSfqdt6GyAGHSIiosf1px4VT1Ru1a1ionLCRGDTW8Ct3x64qUKhgEZ4AQCOXfmxrittcBh0iIiIaoOTOzD4S6DHx4BSDZzfB3wWDJxPfOCm7prmAICz+Zl1XGTDw6BDRERUWyQJ6Phuxdydpm2B278BG98AEj4CDHfuu1nLRq0AAFduZ5mp0IaDQYeIiKi2ubcFIg8CQf/5DsXU5cCKLkBe1Q8FfM694s6rG2U55qqwwWDQISIiqgu2GuC1jysuZzk0BX47C6x8Bfj+M8BoNBnaqUXFnVdGm3xcu1VkiWqtFoMOERFRXXrq1YqJyk/3AMr1wN7JFZezbubJQ/wauwPlTgCAw1n8KojaxKBDRERU1xybAIO2AL0+AWw0wM8HKp6ofG7Pf4dIPgCAH3J5i3ltYtAhIiIyB0kCnn8HGHkIcA8AivOBzQOBXROA0mJ42rcAAJy7ft7ChVoXBh0iIiJzatoaiDwABI+peP/DamBFZzyrcQAA5N25ZLnarBCDDhERkbnZqIHQOUD4DsBRB/yeidfPLgMA3BJXLFycdWHQISIispSWr1RMVP5TL7TS/+c5O8qbSP0p2bJ1WREGHSIiIktycAUGboR9r0VorTcAAMYffRcrvpph4cKsA4MOERGRpUkS0OFtTOy4GH6lwA2lAksLd2L0ys7IL8x78PZ0Xww6RERET4igdj2wafBRhJZ5QxICh1XXMfjLbtjz3XpLl1ZvMegQERE9QRzsnbBgxB5M9RwBtzIjfrGVMPX8x5i5dgBKS/WWLq/eYdAhIiJ6Ag3sPh7reu5EhxJ7lEkSduAswuM64v/Of2/p0uoVBh0iIqInlI/HU1j9zlG8rekEO6MRZ9RleDd5BD7bMcXSpdUbDDpERERPMIVSiQlv/RP/6LAIrfQSbioVWHZjF95d8TJ+zf/F0uU98Rh0iIiI6oGOAd2xcWgKXiv3hSQEvlMXInxHKL4+/LmlS3uiMegQERHVE/YaB/xt+C7M9Pl/aFJmRK6thBkXF2L6mr9wovJ9MOgQERHVM290HY11fXYjSO+IcknCV4pMDI4Lwgk+UbkSBh0iIqJ6yLtpc6wYcQSR9l1hbzTiJ3U53kt5F59um2jp0p4oDDpERET1lEKpRNSbi/FZ0DI8rVfgtkKB5bf2YeSKF3H1t8uWLu+JwKBDRERUzwW27YyNw1LQx9gKCiGQor6BoV/1xM6Dyy1dmsUx6BAREVkBjdoec9/egVnNo6AzCPxqq0D05aWYsuZ1lOiLLV2exTDoEBERWZHXQ0Zifb89CNY7wyhJ2KW4iMFrg5H24wFLl2YRDDpERERWRufmgxUjv8Mox1A4lhuRqTZiTGoUFm8dC2N5uaXLMysGHSIiIis15i8L8NmLy9FGr0SxQoHVd77FO6teRE7eRUuXZjYMOkRERFbs2T+9hA0Rx/C/aA0bIZCmuY1hu/vgi6RPLV2aWTDoEBERWTmVSo1Zw77AnFYfwdMg8JuNArOv/Asfre6F28U3LV1enWLQISIiaiB6vjQUG/6yHy/rXSAkCXtssjF444tI+b+9li6tzjDoEBERNSBNXDyxbORhjNH2gVO5ET+rBKKOT8SCLe9a5URlBh0iIqIGaFS/uVjZeS3+R2+DEoWEtfrvMHxVMC5fzbR0abWKQYeIiKiB+p+WHbBheCrekPxhIwSOa+5g2J7/xaZ9n1i6tFrDoENERNSA2djYInroZnz8p2nwNgjk2ygwLy8OH6wKxc3bhZYu77Ex6BARERG6Bw/CpjcPonOpGwBgv+1VhG16Gckn/m3hyh4Pgw4REREBAFy0TfBp5EGMb/wmtOVGXFIB409OxfyNw+vtRGUGHSIiIjIxvM9MfN51I9qVqKBXSNhYloZhq4JwIfu0pUurMQYdIiIiquRp32exdsT3GKgMhMookKHRY3jSW1iXMNfSpdUIgw4RERFVycbGFtOGrMUC/1j4lgIFSgX+/ttmjFvZDYU3f7d0eQ+FQYeIiIiq1eX5v2DjoEPoWuoOAPhG9SsGb+mCb1K/tHBlD8agQ0RERA+kdWyMxZFJ+LDJILiUGZGtAiaeicFf14ejrMxg6fLui0GHiIiIHtrQnlPx+avxeE6vgUGSEG/MwNDVQTh78bilS6sSgw4RERHVSKtm/ogb8T2G2AZBbRQ4pTEg8tuhWPXvmZYurRIGHSIiIqoxhVKJSWGrsPjZ+WhRChQpFVhSsAMjV7yInNzzli5PxqBDREREj+yl53pjY1gyQsu8oRACKeobGJLQD59/HWvp0gAw6BAREdFjcnJohAUj9mC233h4GwSu2yiw6PqXiFzxAi7m/GjR2upN0GnevDkkSTJ5TZ482WRMdnY2+vTpAwcHB7i5uSEqKgqlpaUmY06dOoXOnTvDzs4OXl5emDVrFoQQ5jwUIiIiq9S30whsGXhYPrvzvfom3jjwFt5eHoRV/47G7eKbZq/Jxuyf+BhmzZqFyMhI+b2jo6P85/LycvTq1QtNmjTBkSNHkJ+fj2HDhkEIgaVLlwIAbty4gVdffRVdunRBWloaMjMzERERAQcHB3zwwQdmPx4iIiJro3VsjAUj9uDrw59j1blFuKiS8IOmGD8UbEfcli/hf8vVrPXUq6Dj5OQEnU5X5br9+/fjzJkzyMnJgaenJwDgk08+QUREBObMmQNnZ2ds3LgRJSUliIuLg1qthr+/PzIzM7Fw4UJMmDABkiSZ83CIiIisVp9Ow9Gn03AkHfsCe06twnHpCvJtFDisMu8TlSVRT67bNG/eHHq9HqWlpfDx8cGbb76JDz/8ECqVCgAwc+ZMfPXVVzh58qS8TUFBARo3boxvvvkGXbp0wdChQ1FUVISvvvpKHpOeno7AwEBcvHgRLVq0qPKz9Xo99Hq9/L6oqAjNmjVDTk4OnJ2d6+iIiYiIrEdpqR47Dn2Ggxf+jc2Tj6OwsBBarbbOP7fenNEZN24cAgMD4eLigtTUVEyZMgVZWVlYtWoVACAvLw/u7u4m27i4uEClUiEvL08e07x5c5Mxd7fJy8u7b9CZN28eYmMrzx738fF53MMiIiJqkPLz860/6MTExFQZIP4oLS0NHTp0wPjx4+Vl7dq1g4uLC9544w18/PHHcHWtuN5X1aUnIYTJ8nvH3D2hVd1lqylTpmDChAny+8LCQvj6+iI7O9ssPySqmF/l4+PDs2hmxJ6bH3tufuy5+d29KtK4cWOzfJ5Fg86YMWMwcODAasfcewbmro4dOwIALly4AFdXV+h0Ohw7dsxkTEFBAQwGg3zWRqfTyWd37rp27RoAVDob9EdqtRpqtbrScq1Wy78YZubs7Myemxl7bn7sufmx5+anUJjnxm+LBh03Nze4ubk90rbp6ekAAA8PDwBAcHAw5syZg9zcXHnZ/v37oVar0b59e3nM1KlTUVpaKs/t2b9/Pzw9Pe8bqIiIiKj+qhfP0UlJScGiRYuQkZGBrKwsbN26FaNGjULfvn3RrFkzAED37t3Rtm1bhIeHIz09HQcOHMDEiRMRGRkpp/SwsDCo1WpERETg9OnT2LFjB+bOncs7roiIiKxUvZiMrFarER8fj9jYWOj1evj6+iIyMhIfffSRPEapVGL37t1477338OKLL8LOzg5hYWFYsGCBPEar1SIxMRGjR49Ghw4d4OLiggkTJpjMv3nYeqKjo6u8nEV1gz03P/bc/Nhz82PPzc/cPa83t5cTERER1VS9uHRFRERE9CgYdIiIiMhqMegQERGR1WLQISIiIqvFoIOKhxJKklTpNXr0aAAVT0+OiYmBp6cn7OzsEBISgh9//NFkH3q9HmPHjoWbmxscHBzQt29fXLlyxRKHUy+UlZVh+vTpaNGiBezs7ODn54dZs2bBaDTKY9j32nfz5k28//778PX1hZ2dHV544QWkpaXJ69nzx3P48GH06dMHnp6ekCQJO3fuNFlfW/0tKChAeHg4tFottFotwsPDUVhYWMdH92R6UM+3b9+O0NBQuLm5QZIkZGRkVNoHe14z1fXcYDBg0qRJCAgIgIODAzw9PTF06FBcvXrVZB9m7bkgce3aNZGbmyu/EhMTBQBx8OBBIYQQ8+fPF05OTmLbtm3i1KlT4q233hIeHh7ixo0b8j7effdd4eXlJRITE8WJEydEly5dxDPPPCPKysosdFRPtr/+9a/C1dVV7Nq1S2RlZYkvvvhCODo6isWLF8tj2PfaN2DAANG2bVtx6NAhcf78eREdHS2cnZ3FlStXhBDs+eNKSEgQ06ZNE9u2bRMAxI4dO0zW11Z/e/ToIfz9/cXRo0fF0aNHhb+/v+jdu7e5DvOJ8qCer1u3TsTGxoqVK1cKACI9Pb3SPtjzmqmu54WFhaJbt24iPj5e/PTTTyIlJUUEBQWJ9u3bm+zDnD1n0KnCuHHjRMuWLYXRaBRGo1HodDoxf/58eX1JSYnQarXiX//6lxCi4gdra2srtmzZIo/55ZdfhEKhEHv37jV7/fVBr169xPDhw02W9e/fXwwZMkQIIdj3OlBcXCyUSqXYtWuXyfJnnnlGTJs2jT2vZff+Aqit/p45c0YAEN9//708JiUlRQAQP/30Ux0f1ZOtqqBzV1ZWVpVBhz1/PNX1/K7U1FQBQFy+fFkIYf6e89LVPUpLS7FhwwYMHz4ckiQhKysLeXl56N69uzxGrVajc+fOOHr0KADg+PHjMBgMJmM8PT3h7+8vjyFTL730Eg4cOIDMzEwAwMmTJ3HkyBH07NkTANj3OlBWVoby8nJoNBqT5XZ2djhy5Ah7Xsdqq78pKSnQarUICgqSx3Ts2BFarZY/g0fAnte9oqIiSJKERo0aATB/z+vFk5HNaefOnSgsLERERAQAyF8Ceu+Xfrq7u+Py5cvyGJVKBRcXl0pj7v0SUaowadIkFBUVoXXr1lAqlSgvL8ecOXMwaNAgAOx7XXByckJwcDBmz56NNm3awN3dHZs3b8axY8fw1FNPsed1rLb6m5eXh6ZNm1baf9OmTfkzeATsed0qKSnB5MmTERYWJn8dk7l7zjM691i9ejVee+01eHp6miy/97uwhBAP/H6shxnTUMXHx2PDhg3YtGkTTpw4gbVr12LBggVYu3atyTj2vXatX78eQgh4eXlBrVbjH//4B8LCwqBUKuUx7Hndqo3+VjWeP4PaxZ4/PoPBgIEDB8JoNGLZsmUPHF9XPWfQ+YPLly8jKSkJ77zzjrxMp9MBQKUEee3aNfn/zHQ6HUpLS1FQUHDfMWTqww8/xOTJkzFw4EAEBAQgPDwc48ePx7x58wCw73WlZcuWOHToEG7duoWcnBykpqbCYDCgRYsW7Hkdq63+6nQ6/Prrr5X2/9tvv/Fn8AjY87phMBgwYMAAZGVlITExUT6bA5i/5ww6f7BmzRo0bdoUvXr1kpfd/QWQmJgoLystLcWhQ4fwwgsvAADat28PW1tbkzG5ubk4ffq0PIZMFRcXQ6Ew/c9PqVTKt5ez73XLwcEBHh4eKCgowL59+/D666+z53WstvobHByMoqIipKamymOOHTuGoqIi/gweAXte++6GnPPnzyMpKQmurq4m683e8xpNXbZi5eXlolmzZmLSpEmV1s2fP19otVqxfft2cerUKTFo0KAqbwn19vYWSUlJ4sSJE+KVV17hLbfVGDZsmPDy8pJvL9++fbtwc3MTH330kTyGfa99e/fuFXv27BEXL14U+/fvF88884z485//LEpLS4UQ7PnjunnzpkhPTxfp6ekCgFi4cKFIT0+X7zaprf726NFDtGvXTqSkpIiUlBQREBDQYG91flDP8/PzRXp6uti9e7cAILZs2SLS09NFbm6uvA/2vGaq67nBYBB9+/YV3t7eIiMjw+TRLXq9Xt6HOXvOoPMf+/btEwDEuXPnKq0zGo0iOjpa6HQ6oVarRadOncSpU6dMxty5c0eMGTNGNG7cWNjZ2YnevXuL7Oxsc5Vf79y4cUOMGzdONGvWTGg0GuHn5yemTZtm8heBfa998fHxws/PT6hUKqHT6cTo0aNFYWGhvJ49fzwHDx4UACq9hg0bJoSovf7m5+eLwYMHCycnJ+Hk5CQGDx4sCgoKzHSUT5YH9XzNmjVVro+Ojpb3wZ7XTHU9v3sbf1Wvu8+mE8K8PZeEEKJm54CIiIiI6gfO0SEiIiKrxaBDREREVotBh4iIiKwWgw4RERFZLQYdIiIisloMOkRERGS1GHSIiIjIajHoEBERkdVi0CEiMqPVq1eje/fuNdrm008/Rd++feuoIiLrxqBD1ABJklTtKyIiwtIl1rqQkBC8//77Fq1Br9dj5syZmDFjhrwsJiYGzz77rMm45ORkNGrUCGPHjoUQApGRkUhLS8ORI0fMXDFR/cegQ9QA5ebmyq/FixfD2dnZZNmSJUssXeJDMxgM9ebztm3bBkdHR7z88sv3HbN7926EhoZi3LhxWLp0KSRJglqtRlhYGJYuXfrIn03UUDHoEDVAOp1Ofmm1WkiSZLLs8OHDaN++PTQaDfz8/BAbG4uysjJ5e0mSsHz5cvTu3Rv29vZo06YNUlJScOHCBYSEhMDBwQHBwcH4+eef5W3unrlYvnw5fHx8YG9vjzfffBOFhYUmta1ZswZt2rSBRqNB69atsWzZMnndpUuXIEkStm7dipCQEGg0GmzYsAH5+fkYNGgQvL29YW9vj4CAAGzevFneLiIiAocOHcKSJUvks1aXLl1CXFwcGjVqZPL5O3fuhCRJler+/PPP4efnB7VaDSEEioqKMHLkSDRt2hTOzs545ZVXcPLkyWr7vmXLlmovQW3atAn9+/fH/PnzERsba7Kub9++2LlzJ+7cuVPtZxCRKQYdIjKxb98+DBkyBFFRUThz5gyWL1+OuLg4zJkzx2Tc7NmzMXToUGRkZKB169YICwvDqFGjMGXKFPzwww8AgDFjxphsc+HCBWzduhVff/019u7di4yMDIwePVpev3LlSkybNg1z5szB2bNnMXfuXMyYMQNr16412c+kSZMQFRWFs2fPIjQ0FCUlJWjfvj127dqF06dPY+TIkQgPD8exY8cAAEuWLEFwcDAiIyPls1Y+Pj4P3ZO7dW/btg0ZGRkAgF69eiEvLw8JCQk4fvw4AgMD0bVrV1y/fv2++0lOTkaHDh2qXPfPf/4Tb7/9NlavXo2oqKhK6zt06ACDwYDU1NSHrpuIADzGN7UTkRVYs2aN0Gq18vuXX35ZzJ0712TM+vXrhYeHh/wegJg+fbr8PiUlRQAQq1evlpdt3rxZaDQa+X10dLRQKpUiJydHXrZnzx6hUChEbm6uEEIIHx8fsWnTJpPPnj17tggODhZCCJGVlSUAiMWLFz/wuHr27Ck++OAD+X3nzp3FuHHjqj12IYTYsWOH+OM/jdHR0cLW1lZcu3ZNXnbgwAHh7OwsSkpKTLZt2bKlWL58eZX1FBQUCADi8OHDJsujo6OFSqWq1L+quLi4iLi4uGrHEJEpG0uGLCJ68hw/fhxpaWkmZ3DKy8tRUlKC4uJi2NvbAwDatWsnr3d3dwcABAQEmCwrKSnBjRs34OzsDABo1qwZvL295THBwcEwGo04d+4clEolcnJyMGLECERGRspjysrKoNVqTWq896xIeXk55s+fj/j4ePzyyy/Q6/XQ6/VwcHB43HYAAHx9fdGkSRP5/fHjx3Hr1i24urqajLtz547J5bp71wGARqOptM7b2xuNGjXC3/72N7z22mvw8PCoch92dnYoLi5+1MMgapAYdIjIhNFoRGxsLPr3719p3R9/Sdva2sp/vjunpaplRqPxvp91d4wkSfK4lStXIigoyGScUqk0eX9vgPnkk0+waNEiLF68GAEBAXBwcMD777+P0tLS+x8oAIVCASGEybKqJhvf+3lGoxEeHh749ttvK429d87PXa6urpAkCQUFBZXWOTk5ISkpCd27d0dISAgOHjwIT0/PSuOuX79uEriI6MEYdIjIRGBgIM6dO4dWrVrV+r6zs7Nx9epV+Zd4SkoKFAoFnn76abi7u8PLywsXL17E4MGDa7Tf5ORkvP766xgyZAiAiiBy/vx5tGnTRh6jUqlQXl5usl2TJk1w8+ZN3L59Ww4zd+fgVCcwMBB5eXmwsbFB8+bNH6pGlUqFtm3b4syZM1U+R8fFxQVJSUkIDQ2Vw46Xl5e8/ueff0ZJSQmee+65h/o8IqrAychEZGLmzJlYt24dYmJi8OOPP+Ls2bOIj4/H9OnTH3vfGo0Gw4YNw8mTJ5GcnIyoqCgMGDAAOp0OQMUdTvPmzcOSJUuQmZmJU6dOYc2aNVi4cGG1+23VqhUSExNx9OhRnD17FqNGjUJeXp7JmObNm+PYsWO4dOkSfv/9dxiNRgQFBcHe3h5Tp07FhQsXsGnTJsTFxT3wOLp164bg4GD069cP+/btw6VLl3D06FFMnz5dnohdldDQ0GqfhaPVarF//364ubkhJCQEV65ckdclJyfDz88PLVu2fGB9RPRfDDpEZCI0NBS7du1CYmIinn/+eXTs2BELFy6Er6/vY++7VatW6N+/P3r27Inu3bvD39/f5Pbxd955B6tWrUJcXBwCAgLQuXNnxMXFoUWLFtXud8aMGQgMDJTPhuh0OvTr189kzMSJE6FUKtG2bVs0adIE2dnZaNy4MTZs2ICEhAT5lvSYmJgHHockSUhISECnTp0wfPhwPP300xg4cCAuXbokz1eqSmRkJBISElBUVHTfMc7Ozti3bx/c3d0REhKCnJwcAMDmzZtN5i4R0cORxL0XqImI6kBMTAx27tz5UJeGrNmAAQPw3HPPYcqUKQ+9zenTp9G1a1dkZmZWmphNRNXjGR0iIjP6+9//DkdHxxptc/XqVaxbt44hh+gRcDIyEZEZ+fr6YuzYsTXapqZfAkpE/8VLV0RERGS1eOmKiIiIrBaDDhEREVktBh0iIiKyWgw6REREZLUYdIiIiMhqMegQERGR1WLQISIiIqvFoENERERW6/8DB0ojF/uM2IgAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Driving force for NiCrAl system\n", - "\n", - "#Create points to calculate driving force at\n", - "# x and T must be same length\n", - "comps = np.array([[0.08, 0.1] for i in range(100)])\n", - "T = np.linspace(700, 1200, 100)\n", - "\n", - "fig2 = plt.figure(2, figsize=(6, 5))\n", - "ax2 = fig2.add_subplot(111)\n", - "\n", - "for m in DGmethods:\n", - " #Clear cache before switching method\n", - " multiTherm.clearCache()\n", - " multiTherm.setDrivingForceMethod(m)\n", - "\n", - " #Calculate driving force\n", - " dg, xP = multiTherm.getDrivingForce(comps, T)\n", - " ax2.plot(T, dg, label=m)\n", - "\n", - "ax2.set_xlim([700, 1200])\n", - "ax2.set_ylim([-500, 2500])\n", - "ax2.set_xlabel('Temperature (K)')\n", - "ax2.set_ylabel('Driving Force (J/mol)')\n", - "ax2.legend(DGmethods)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interfacial Composition and Precipitate Growth Rates\n", - "\n", - "### Binary Systems\n", - "\n", - "Assuming diffusion controlled growth, the growth rate of a spherical preciptiate in a binary system can be written as:\n", - "\n", - "$$ \\frac{dR}{dt} = \\frac{D}{R} \\frac{x - x_R^\\alpha}{x_R^\\beta - x_R^\\alpha} $$\n", - "\n", - "Where $x_R^\\alpha$ and $x_R^\\beta$ is the interfacial composition of the matrix and precipitate phase respectively. For binary systems, the interfacial composition is independent of the composition of the system. This becomes useful in the KWN model as these values can be calculated beforehand and used for determining the growth rate, rather than calculating them at every iteration.\n", - "\n", - "Determining the interfacial composition requires solving for equilibrium while accounting for the Gibbs-Thompson effect (proportional to 1/r). Elastic energy can also be accounted for.\n", - "\n", - "$$\\mu_i^\\alpha (\\boldsymbol{x_R^\\alpha}) = \\mu_i^\\beta (\\boldsymbol{x_R^\\beta}) + \\left(\\frac{2 \\gamma}{R} + \\Delta G_{el}\\right) V_m^\\beta$$\n", - "\n", - "For a binary system, the interfacial composition can be calculated from the curvature of the Gibbs free energy surfaces similar to the curvature method for calculating driving force:\n", - "$$ \\left(\\frac{2 \\gamma}{R} + \\Delta G_{el}\\right) V_m^\\beta = \\boldsymbol{\\left(x^\\alpha - x_{eq}^\\alpha\\right)} \\boldsymbol{\\nabla^2} G_M^\\alpha \\boldsymbol{\\left(x_{eq}^\\beta - x_{eq}^\\alpha\\right)} $$\n", - "\n", - "For composition of the precipitate:\n", - "$$ \\boldsymbol{\\nabla^2} G_M^\\beta \\boldsymbol{\\left(x^\\beta - x_{eq}^\\beta\\right)} = \\boldsymbol{\\nabla^2} G_M^\\alpha \\boldsymbol{\\left(x^\\alpha - x_{eq}^\\alpha\\right)} $$\n", - "\n", - "As with the curvature method for calculating driving force, the curvature method for calculating interfacial composition is only valid for small supersaturations and non-dilute systems. Additionally, while these two equations can be generalized to multicomponent systems, they are generally indeterminate and the interfacial compositions in multicomponent systems cannot be determined by the free energy curvature alone.\n", - "\n", - "The interfacial composition method for binary systems is defined as:\n", - "\n", - "$ x^\\alpha, x^\\beta = BinaryThermodynamics.getInterfacialComposition(T, G_{TH}) $\n", - "\n", - "Where $G_{TH}$ is the free energy contribution from the Gibbs-Thomson effect. The example below compares the two methods for calculating the interfacial composition in the matrix phase. If $G_{TH}$ is too large such that the precipitate phase becomes unstable, then the function will return -1 for both the matrix and precipitate compostion." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Interfacial composition for Al-Zr system\n", - "\n", - "#Different methods for calculating interfacial composition\n", - "ICmethods = ['equilibrium', 'curvature']\n", - "\n", - "#Get Gibbs-Thomson contribution from radius\n", - "gamma = 0.1 #Interfacial energy between FCC-Al and Al3Zr\n", - "Vm = 1e-5 #Molar volume\n", - "R = np.linspace(1e-10, 5e-9, 100) #Radius\n", - "G = 2 * gamma * Vm / R #Contribution from Gibbs-Thomson effect\n", - "\n", - "fig3 = plt.figure(3, figsize=(6, 5))\n", - "ax3 = fig3.add_subplot(111)\n", - "\n", - "for m in ICmethods:\n", - " binaryTherm.clearCache()\n", - " binaryTherm.setInterfacialMethod(m)\n", - "\n", - " #Calculate interfacial composition\n", - " xM, xP = binaryTherm.getInterfacialComposition(673.15, G)\n", - " ax3.plot(R[xM != -1], xM[xM != -1], label=m)\n", - "\n", - "ax3.set_xlim([0, 5e-9])\n", - "ax3.set_ylim([0, 0.001])\n", - "ax3.set_xlabel('Radius (m)')\n", - "ax3.set_ylabel('Matrix composition of Zr (mole fraction)')\n", - "ax3.legend(ICmethods)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Multicomponent Systems\n", - "\n", - "The governing equations for solving multicomponent precipitate is the same as for binary precipitation. However, calculating interfacial composition through solving for equilibrium requires the solution to the following equations for each component.\n", - "\n", - "$$\\frac{dR}{dt} = \\sum_{j}{\\frac{D_{ij}}{R} \\frac{x_i - x_{R,i}^{\\alpha}}{x_{R,j}^{\\beta} - x_{R,j}^{\\alpha}}}$$\n", - "\n", - "$$\\mu_i^\\alpha (\\boldsymbol{x_R^\\alpha}) = \\mu_i^\\beta (\\boldsymbol{x_R^\\beta}) + \\left(\\frac{2 \\gamma}{R} + \\Delta G_{el}\\right) V_m^\\beta$$\n", - "\n", - "This gives 2N-1 equations to solve, which can be time consuming and, in worst cases, a solution may not be found. At small saturations, the growth rate can be determined through local expansion of the chemical potential at equilibrium (Philippe and Voorhees, 2013). The growth rate (assuming $\\Delta G_{el} = 0$ for simplicity) then becomes:\n", - "\n", - "$$\\frac{dR}{dt}=\\frac{1}{R (\\boldsymbol{\\Delta \\overline{x}})^T M^{-1} \\boldsymbol{\\Delta \\overline{x}}}\\left(\\Delta G_m - \\frac{2 \\gamma V_m^\\beta}{R}\\right)$$\n", - "\n", - "\n", - "$$\\boldsymbol{\\Delta \\overline{x} = x_{\\infty}^{\\beta} - x_{\\infty}^{\\alpha}}$$\n", - "\n", - "Where $\\boldsymbol{x_{\\infty}^{\\alpha}}$ and $\\boldsymbol{x_{\\infty}^{\\beta}}$ are the equilibrium compositions of $\\alpha$ and $\\beta$ on a planar interface and $M^{-1} = \\boldsymbol{\\nabla^2} G^{\\alpha} * D^{-1}$, where $\\boldsymbol{\\nabla^2} G^{\\alpha}$ is the curvature of the free energy surface of phase $\\alpha$ and $D^{-1}$ is the inverse of the interdiffusivity matrix.\n", - "\n", - "Interfacial compositions can be determined by the following equations, which are needed for solving mass balance.\n", - "\n", - "$$\\boldsymbol{x^{\\alpha}} = \\boldsymbol{x} - \\frac{D^{-1} \\boldsymbol{\\Delta \\overline{x}}}{(\\boldsymbol{\\Delta \\overline{x}})^T M^{-1} \\boldsymbol{\\Delta \\overline{x}}} \\left(\\Delta G_m - \\frac{2 \\gamma V_m^\\beta}{R}\\right)$$\n", - "\n", - "\n", - "$$\\boldsymbol{x^\\beta} = \\boldsymbol{x_{\\infty}^{\\beta}} + \\left(\\boldsymbol{\\nabla^2} G^\\beta \\right)^{-1} \\boldsymbol{\\nabla^2} G^{\\alpha}\\left(\\boldsymbol{x-x_{\\infty}^{\\alpha}}\\right)$$\n", - "\n", - "The growth rate and interfacial composition method for multicomponent systems is defined as:\n", - "\n", - "$ \\frac{dR}{dt}, \\boldsymbol{x^\\alpha}, \\boldsymbol{x^\\beta}, \\boldsymbol{x_{\\infty}^{\\alpha}}, \\boldsymbol{x_{\\infty}^{\\beta}} = MulticomponentThermodynamics.getGrowthAndInterfacialComposition(\\boldsymbol{x}, T, \\Delta G_M, R, G_{TH}) $\n", - "\n", - "Where $\\Delta G_M$ is the driving force at composition $\\boldsymbol{x}$ and temperature $T$, $R$ is the precipitate radius and $G_{TH}$ is the free energy contribution from the Gibbs-Thomson effect corresponding to $R$." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Set driving force method since driving forces are required for \n", - "# calculating growth rate and interfacial compositions\n", - "multiTherm.setDrivingForceMethod('approximate')\n", - "\n", - "#Gibbs-Thomson contribution from radius\n", - "gamma = 0.023 #Interfacial energy between FCC-Ni and Ni3Al\n", - "Vm = 1e-5 #Molar volume\n", - "R = np.linspace(1e-10, 3e-8, 300)\n", - "G = 2 * gamma * Vm / R\n", - "\n", - "fig4 = plt.figure(4, figsize=(6, 5))\n", - "ax4 = fig4.add_subplot(111)\n", - "\n", - "#Calculate growth rate for different sets of compositions\n", - "xset = {'Ni-3Cr-15Al': [0.03, 0.15], 'Ni-3Cr-17.5Al': [0.03, 0.175], 'Ni-3Cr-20Al': [0.03, 0.2]}\n", - "T = 1273\n", - "for x in xset:\n", - " #Clear cache since the compositions are quite different in values\n", - " multiTherm.clearCache()\n", - "\n", - " #Calculate driving force and growth rate\n", - " dg, _ = multiTherm.getDrivingForce(xset[x], T)\n", - " gr, ca, cb, _, _ = multiTherm.getGrowthAndInterfacialComposition(xset[x], T, dg, R, G)\n", - " ax4.plot(R, gr, label=x)\n", - "\n", - "ax4.set_xlim([0, 3e-8])\n", - "ax4.set_ylim([-1.4e-6, 1.4e-6])\n", - "ax4.set_xlabel('Radius (m)')\n", - "ax4.set_ylabel('Growth Rate (m/s)')\n", - "ax4.legend(xset.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interdiffusivity\n", - "\n", - "For binary systems, the interdiffusivity (as used in the growth rate equation) must be defined separately from the other thermodynamic/kinetic terms. To be used in the Thermodynamics module, parameters for the diffusivity/mobility must be defined in the TDB database file (either as 'MF'/'MQ' for mobility or 'DF'/'DQ' for diffusivity). The method is defined as:\n", - "\n", - "$ D = BinaryThermodynamics.getInterdiffusivity(x, T) $\n", - "\n", - "The reference element for the interdiffusivity will be the first element in the list of elements used to define the Thermodynamics modules.\n", - "\n", - "This method is also available for multicomponent systems, where $x$ must be defined as an array of the solute components and the method returns the interdiffusivity matrix of the solute components; however, it is not need for the KWN model as it is already accounted for when calculating the growth rate and interfacial compositions. The example below shows usage of this method for the Al-Zr system." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$ln(D (m/s^2))$')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Calculate interdiffusivity for various temperatures\n", - "T = np.linspace(500, 1000, 100)\n", - "d = binaryTherm.getInterdiffusivity(np.ones(100)*0.01, T)\n", - "\n", - "fig5 = plt.figure(5, figsize=(6, 5))\n", - "ax5 = fig5.add_subplot(111)\n", - "\n", - "#Arrhennius plot of diffusivities\n", - "ax5.plot(1/T, np.log(d))\n", - "\n", - "ax5.set_xlim([1/1000, 1/500])\n", - "ax5.set_xlabel('1/T ($K^{-1}$)')\n", - "ax5.set_ylabel('$ln(D (m/s^2))$')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Usage in the KWN model\n", - "\n", - "The thermodynamics modules can be easily used in the KWN model as:\n", - "\n", - "$ KWNModel.setThermodynamics(Thermodynamics) $\n", - "\n", - "For binary systems, the interdiffusivity must be defined separately. This is to allow for user-defined functions. The interdiffusivity method can be inputted by:\n", - "\n", - "$ KWNModel.setDiffusivity(BinaryThermodynamics.getInterdiffusivity) $" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.13 ('base')", - "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": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "0273dda5b9fff289b5eb7a13f97dc7960051b95b09ad9bf692ef3217ee21f064" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/kawin/Diffusion.py b/kawin/Diffusion.py deleted file mode 100644 index 8a9b2dc..0000000 --- a/kawin/Diffusion.py +++ /dev/null @@ -1,971 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from kawin.Mobility import mobility_from_composition_set -import time -import csv -import copy -from itertools import zip_longest - -class DiffusionModel: - #Boundary conditions - FLUX = 0 - COMPOSITION = 1 - - def __init__(self, zlim, N, elements = ['A', 'B'], phases = ['alpha']): - ''' - Class for defining a 1-dimensional mesh - - Parameters - ---------- - zlim : tuple - Z-bounds of mesh (lower, upper) - N : int - Number of nodes - elements : list of str - Elements in system (first element will be assumed as the reference element) - phases : list of str - Number of phases in the system - ''' - if isinstance(phases, str): - phases = [phases] - self.zlim, self.N = zlim, N - self.allElements, self.elements = elements, elements[1:] - self.phases = phases - self.therm = None - - self.z = np.linspace(zlim[0], zlim[1], N) - self.dz = self.z[1] - self.z[0] - - self.reset() - - self.LBC, self.RBC = self.FLUX*np.ones(len(self.elements)), self.FLUX*np.ones(len(self.elements)) - self.LBCvalue, self.RBCvalue = np.zeros(len(self.elements)), np.zeros(len(self.elements)) - - self.cache = True - self.setHashSensitivity(4) - self.minComposition = 1e-8 - - self.maxCompositionChange = 0.002 - - def reset(self): - ''' - Resets model - - This involves clearing any caches in the Thermodynamics object and this model - as well as resetting the composition and phase profiles - ''' - if self.therm is not None: - self.therm.clearCache() - - self.x = np.zeros((len(self.elements), self.N)) - self.p = np.ones((1,self.N)) if len(self.phases) == 1 else np.zeros((len(self.phases), self.N)) - self.hashTable = {} - self.isSetup = False - - def setThermodynamics(self, thermodynamics): - ''' - Defines thermodynamics object for the diffusion model - - Parameters - ---------- - thermodynamics : Thermodynamics object - Requires the elements in the Thermodynamics and DiffusionModel objects to have the same order - ''' - self.therm = thermodynamics - - def setTemperature(self, T): - ''' - Sets iso-thermal temperature - - Parameters - ---------- - T : float - Temperature in Kelvin - ''' - self.T = T - - def save(self, filename, compressed = False, toCSV = False): - ''' - Saves mesh, composition and phases - - Parameters - ---------- - filename : str - File to save to - compressed : bool - Whether to compress data if saving to numpy binary format (toCSV = False) - toCSV : bool - Whether to output data to a .CSV file format - ''' - if toCSV: - headers = ['Distance(m)'] - arrays = [self.z] - for i in range(len(self.allElements)): - headers.append('x(' + self.allElements[i] + ')') - if i == 0: - arrays.append(1 - np.sum(self.x, axis=0)) - else: - arrays.append(self.x[i-1,:]) - for i in range(len(self.phases)): - headers.append('f(' + self.phases[i] + ')') - arrays.append(self.p[i,:]) - rows = zip_longest(*arrays, fillvalue='') - if '.csv' not in filename.lower(): - filename = filename + '.csv' - with open(filename, 'w', newline='') as f: - csv.writer(f).writerow(headers) - csv.writer(f).writerows(rows) - else: - variables = ['zlim', 'N', 'allElements', 'phases', 'z', 'x', 'p'] - vDict = {v: getattr(self, v) for v in variables} - if compressed: - np.savez_compressed(filename, **vDict, allow_pickle=True) - else: - np.savez(filename, **vDict, allow_pickle=True) - - def load(filename): - ''' - Loads a previously saved model - - filename : str - File name to load model from, must include file extension - ''' - if '.np' in filename.lower(): - data = np.load(filename, allow_pickle=True) - model = DiffusionModel(data['zlim'], data['N'], data['allElements'], data['phases']) - model.z = data['z'] - model.x = data['x'] - model.p = data['p'] - else: - with open(filename, 'r') as csvFile: - data = csv.reader(csvFile, delimiter=',') - i = 0 - headers = [] - columns = {} - for row in data: - if i == 0: - headers = row - columns = {h: [] for h in headers} - else: - for j in range(len(row)): - if row[j] != '': - columns[headers[j]].append(float(row[j])) - i += 1 - - elements, phases = [], [] - x, p = [], [] - for h in headers: - if 'Distance' in h: - z = columns[h] - elif 'x' in h: - elements.append(h[2:-1]) - x.append(columns[h]) - elif 'f' in h: - phases.append(h[2:-1]) - p.append(columns[h]) - model = DiffusionModel([z[0], z[-1]], len(z), elements, phases) - model.z = np.array(z) - model.x = np.array(x)[1:,:] - model.p = np.array(p) - return model - - def setHashSensitivity(self, s): - ''' - Sets sensitivity of the hash table by significant digits - - For example, if a composition set is (0.5693, 0.2937) and s = 3, then - the hash will be stored as (0.569, 0.294) - - Lower s values will give faster simulation times at the expense of accuracy - - Parameters - ---------- - s : int - Number of significant digits to keep for the hash table - ''' - self.hashSensitivity = np.power(10, int(s)) - - def _getHash(self, x): - ''' - Gets hash value for a composition set - - Parameters - ---------- - x : list of floats - Composition set to create hash - ''' - return hash(tuple((x*self.hashSensitivity).astype(np.int32))) - #return int(np.sum(np.power(self.hashSensitivity, 1+np.arange(len(x))) * x)) - - def useCache(self, use): - ''' - Whether to use the hash table - - Parameters - ---------- - use : bool - If True, then the hash table will be used - ''' - self.cache = use - - def clearCache(self): - ''' - Clears hash table - ''' - self.hashTable = {} - - def _getElementIndex(self, element = None): - ''' - Gets index of element in self.elements - - Parameters - ---------- - element : str - Specified element, will return first element if None - ''' - if element is None: - return 0 - else: - return self.elements.index(element) - - def _getPhaseIndex(self, phase = None): - ''' - Gets index of phase in self.phases - - Parameters - ---------- - phase : str - Specified phase, will return first phase if None - ''' - if phase is None: - return 0 - else: - return self.phases.index(phase) - - def setBC(self, LBCtype = 0, LBCvalue = 0, RBCtype = 0, RBCvalue = 0, element = None): - ''' - Set boundary conditions - - Parameters - ---------- - LBCtype : int - Left boundary condition type - Mesh1D.FLUX - constant flux - Mesh1D.COMPOSITION - constant composition - LBCvalue : float - Value of left boundary condition - RBCtype : int - Right boundary condition type - Mesh1D.FLUX - constant flux - Mesh1D.COMPOSITION - constant composition - RBCvalue : float - Value of right boundary condition - element : str - Specified element to apply boundary conditions on - ''' - eIndex = self._getElementIndex(element) - self.LBC[eIndex] = LBCtype - self.LBCvalue[eIndex] = LBCvalue - if LBCtype == self.COMPOSITION: - self.x[eIndex,0] = LBCvalue - - self.RBC[eIndex] = RBCtype - self.RBCvalue[eIndex] = RBCvalue - if RBCtype == self.COMPOSITION: - self.x[eIndex,-1] = RBCvalue - - def setCompositionLinear(self, Lvalue, Rvalue, element = None): - ''' - Sets composition as a linear function between ends of the mesh - - Parameters - ---------- - Lvalue : float - Value at left boundary - Rvalue : float - Value at right boundary - element : str - Element to apply composition profile to - ''' - eIndex = self._getElementIndex(element) - self.x[eIndex] = np.linspace(Lvalue, Rvalue, self.N) - - def setCompositionStep(self, Lvalue, Rvalue, z, element = None): - ''' - Sets composition as a step-wise function - - Parameters - ---------- - Lvalue : float - Value on left side of mesh - Rvalue : float - Value on right side of mesh - z : float - Position on mesh where composition switches from Lvalue to Rvalue - element : str - Element to apply composition profile to - ''' - eIndex = self._getElementIndex(element) - Lindices = self.z <= z - self.x[eIndex,Lindices] = Lvalue - self.x[eIndex,~Lindices] = Rvalue - - def setCompositionSingle(self, value, z, element = None): - ''' - Sets single node to specified composition - - Parameters - ---------- - value : float - Composition - z : float - Position to set value to (will use closest node to z) - element : str - Element to apply composition profile to - ''' - eIndex = self._getElementIndex(element) - zIndex = np.argmin(np.abs(self.z-z)) - self.x[eIndex,zIndex] = value - - def setCompositionInBounds(self, value, Lbound, Rbound, element = None): - ''' - Sets single node to specified composition - - Parameters - ---------- - value : float - Composition - Lbound : float - Position of left bound - Rbound : float - Position of right bound - element : str - Element to apply composition profile to - ''' - eIndex = self._getElementIndex(element) - indices = (self.z >= Lbound) & (self.z <= Rbound) - self.x[eIndex,indices] = value - - def setCompositionFunction(self, func, element = None): - ''' - Sets composition as a function of z - - Parameters - ---------- - func : function - Function taking in z and returning composition - element : str - Element to apply composition profile to - ''' - eIndex = self._getElementIndex(element) - self.x[eIndex,:] = func(self.z) - - def setCompositionProfile(self, z, x, element = None): - ''' - Sets composition profile by linear interpolation - - Parameters - ---------- - z : array - z-coords of composition profile - x : array - Composition profile - element : str - Element to apply composition profile to - ''' - eIndex = self._getElementIndex(element) - z = np.array(z) - x = np.array(x) - sortIndices = np.argsort(z) - z = z[sortIndices] - x = x[sortIndices] - self.x[eIndex,:] = np.interp(self.z, z, x) - - def setup(self): - ''' - General setup function for all diffusio models - - This will clear any cached values in the thermodynamics function and check if all compositions add up to 1 - - This will also make sure that all compositions are not 0 or 1 to speed up equilibrium calculations - ''' - if self.therm is not None: - self.therm.clearCache() - xsum = np.sum(self.x, axis=0) - if any(xsum > 1): - print('Compositions add up to above 1 between z = [{:.3e}, {:.3e}]'.format(np.amin(self.z[xsum>1]), np.amax(self.z[xsum>1]))) - raise Exception('Some compositions sum up to above 1') - self.x[self.x > self.minComposition] = self.x[self.x > self.minComposition] - len(self.allElements) * self.minComposition - self.x[self.x < self.minComposition] = self.minComposition - self.isSetup = True - - def getFluxes(self): - ''' - "Virtual" function to be implemented by child objects - ''' - return [], [] - - def updateMesh(self): - ''' - "Virtual" function to be implemented by child objects - ''' - pass - - def update(self): - ''' - Updates the mesh by a given dt that is calculated for numerical stability - ''' - #Get fluxes - fluxes, dt = self.getFluxes() - - if self.t + dt > self.tf: - dt = self.tf - self.t - - #Update mesh - self.updateMesh(fluxes, dt) - self.x[self.x < self.minComposition] = self.minComposition - self.t += dt - - def solve(self, simTime, verbose=False, vIt=10): - ''' - Solves the model by updated the mesh until the final simulation time is met - ''' - self.setup() - - self.t = 0 - self.tf = simTime - i = 0 - t0 = time.time() - if verbose: - print('Iteration\tSim Time (h)\tRun time (s)') - while self.t < self.tf: - if verbose and i % vIt == 0: - tf = time.time() - print(str(i) + '\t\t{:.3f}\t\t{:.3f}'.format(self.t/3600, tf-t0)) - self.update() - i += 1 - - tf = time.time() - print(str(i) + '\t\t{:.3f}\t\t{:.3f}'.format(self.t/3600, tf-t0)) - - def getX(self, element): - ''' - Gets composition profile of element - - Parameters - ---------- - element : str - Element to get profile of - ''' - if element in self.allElements and element not in self.elements: - return 1 - np.sum(self.x, axis=0) - else: - e = self._getElementIndex(element) - return self.x[e] - - def getP(self, phase): - ''' - Gets phase profile - - Parameters - ---------- - phase : str - Phase to get profile of - ''' - p = self._getPhaseIndex(phase) - return self.p[p] - - def plot(self, ax, plotReference = True, plotElement = None, zScale = 1, *args, **kwargs): - ''' - Plots composition profile - - Parameters - ---------- - ax : matplotlib Axes object - Axis to plot on - plotReference : bool - Whether to plot reference element (composition = 1 - sum(composition of rest of elements)) - plotElement : None or str - Plots single element if it is defined, otherwise, all elements are plotted - zScale : float - Scale factor for z-coordinates - ''' - if not self.isSetup: - self.setup() - - if plotElement is not None: - if plotElement not in self.elements and plotElement in self.allElements: - x = 1 - np.sum(self.x, axis=0) - else: - e = self._getElementIndex(plotElement) - x = self.x[e] - ax.plot(self.z/zScale, x, *args, **kwargs) - else: - if plotReference: - refE = 1 - np.sum(self.x, axis=0) - ax.plot(self.z/zScale, refE, label=self.allElements[0], *args, **kwargs) - for e in range(len(self.elements)): - ax.plot(self.z/zScale, self.x[e], label=self.elements[e], *args, **kwargs) - - ax.set_xlim([self.zlim[0]/zScale, self.zlim[1]/zScale]) - ax.legend() - ax.set_xlabel('Distance (m)') - ax.set_ylabel('Composition (at.%)') - - def plotTwoAxis(self, axL, Lelements, Relements, zScale = 1, *args, **kwargs): - ''' - Plots composition profile with two y-axes - - Parameters - ---------- - axL : matplotlib Axes object - Left axis to plot on - Lelements : list of str - Elements to plot on left axis - Relements : list of str - Elements to plot on right axis - zScale : float - Scale factor for z-coordinates - ''' - if not self.isSetup: - self.setup() - - if type(Lelements) is str: - Lelements = [Lelements] - if type(Relements) is str: - Relements = [Relements] - - ci = 0 - refE = 1 - np.sum(self.x, axis=0) - axR = axL.twinx() - for e in range(len(Lelements)): - if Lelements[e] in self.elements: - eIndex = self._getElementIndex(Lelements[e]) - axL.plot(self.z/zScale, self.x[eIndex], label=self.elements[eIndex], color = 'C' + str(ci), *args, **kwargs) - ci = ci+1 if ci <= 9 else 0 - elif Lelements[e] in self.allElements: - axL.plot(self.z/zScale, refE, label=self.allElements[0], color = 'C' + str(ci), *args, **kwargs) - ci = ci+1 if ci <= 9 else 0 - for e in range(len(Relements)): - if Relements[e] in self.elements: - eIndex = self._getElementIndex(Relements[e]) - axR.plot(self.z/zScale, self.x[eIndex], label=self.elements[eIndex], color = 'C' + str(ci), *args, **kwargs) - ci = ci+1 if ci <= 9 else 0 - elif Relements[e] in self.allElements: - axR.plot(self.z/zScale, refE, label=self.allElements[0], color = 'C' + str(ci), *args, **kwargs) - ci = ci+1 if ci <= 9 else 0 - - - axL.set_xlim([self.zlim[0]/zScale, self.zlim[1]/zScale]) - axL.set_xlabel('Distance (m)') - axL.set_ylabel('Composition (at.%) ' + str(Lelements)) - axR.set_ylabel('Composition (at.%) ' + str(Relements)) - - lines, labels = axL.get_legend_handles_labels() - lines2, labels2 = axR.get_legend_handles_labels() - axR.legend(lines+lines2, labels+labels2, framealpha=1) - - return axL, axR - - def plotPhases(self, ax, plotPhase = None, zScale = 1, *args, **kwargs): - ''' - Plots phase fractions over z - - Parameters - ---------- - ax : matplotlib Axes object - Axis to plot on - plotPhase : None or str - Plots single phase if it is defined, otherwise, all phases are plotted - zScale : float - Scale factor for z-coordinates - ''' - if not self.isSetup: - self.setup() - - if plotPhase is not None: - p = self._getPhaseIndex(plotPhase) - ax.plot(self.z/zScale, self.p[p], *args, **kwargs) - else: - for p in range(len(self.phases)): - ax.plot(self.z/zScale, self.p[p], label=self.phases[p], *args, **kwargs) - ax.set_xlim([self.zlim[0]/zScale, self.zlim[1]/zScale]) - ax.set_ylim([0, 1]) - ax.set_xlabel('Distance (m)') - ax.set_ylabel('Phase Fraction') - ax.legend() - -class SinglePhaseModel(DiffusionModel): - def getFluxes(self): - ''' - Gets fluxes at the boundary of each nodes - - Returns - ------- - fluxes : (e-1, n+1) array of floats - e - number of elements including reference element - n - number of nodes - dt : float - Maximum calculated time interval for numerical stability - ''' - xMid = (self.x[:,1:] + self.x[:,:-1]) / 2 - - if len(self.elements) == 1: - d = np.zeros(self.N-1) - else: - d = np.zeros((self.N-1, len(self.elements), len(self.elements))) - if self.cache: - for i in range(self.N-1): - hashValue = self._getHash(xMid[:,i]) - if hashValue not in self.hashTable: - self.hashTable[hashValue] = self.therm.getInterdiffusivity(xMid[:,i], self.T, phase=self.phases[0]) - d[i] = self.hashTable[hashValue] - else: - d = self.therm.getInterdiffusivity(xMid.T, self.T*np.ones(self.N-1), phase=self.phases[0]) - - dxdz = (self.x[:,1:] - self.x[:,:-1]) / self.dz - fluxes = np.zeros((len(self.elements), self.N+1)) - if len(self.elements) == 1: - fluxes[0,1:-1] = -d * dxdz - else: - dxdz = np.expand_dims(dxdz, axis=0) - fluxes[:,1:-1] = -np.matmul(d, np.transpose(dxdz, (2,1,0)))[:,:,0].T - for e in range(len(self.elements)): - fluxes[e,0] = self.LBCvalue[e] if self.LBC[e] == self.FLUX else fluxes[e,1] - fluxes[e,-1] = self.RBCvalue[e] if self.RBC[e] == self.FLUX else fluxes[e,-2] - - dt = 0.4 * self.dz**2 / np.amax(np.abs(d)) - - return fluxes, dt - - def updateMesh(self, fluxes, dt): - ''' - Updates mesh using fluxes by time increment dt - - Parameters - ---------- - fluxes : 2D array - Fluxes for each element between each node. Size must be (E, N-1) - E - number of elements (NOT including reference element) - N - number of nodes - Boundary conditions will automatically be applied - dt : float - Time increment - ''' - for e in range(len(self.elements)): - self.x[e] += -(fluxes[e,1:] - fluxes[e,:-1]) * dt / self.dz - -class HomogenizationModel(DiffusionModel): - def __init__(self, zlim, N, elements = ['A', 'B'], phases = ['alpha']): - super().__init__(zlim, N, elements, phases) - - self.mobilityFunction = self.wienerUpper - self.defaultMob = 0 - self.eps = 0.05 - - self.sortIndices = np.argsort(self.allElements) - self.unsortIndices = np.argsort(self.sortIndices) - self.labFactor = 1 - - def reset(self): - ''' - Resets model - - This also includes chemical potential and pycalphad CompositionSets for each node - ''' - super().reset() - self.mu = np.zeros((len(self.elements)+1, self.N)) - self.compSets = [None for _ in range(self.N)] - - def setMobilityFunction(self, function): - ''' - Sets averaging function to use for mobility - - Default mobility value should be that a phase of unknown mobility will be ignored for average mobility calcs - - Parameters - ---------- - function : str - Options - 'upper wiener', 'lower wiener', 'upper hashin-shtrikman', 'lower hashin-strikman', 'labyrinth' - ''' - #np.finfo(dtype).max - largest representable value - #np.finfo(dtype).tiny - smallest positive usable value - if 'upper' in function and 'wiener' in function: - self.mobilityFunction = self.wienerUpper - self.defaultMob = np.finfo(np.float64).tiny - elif 'lower' in function and 'wiener' in function: - self.mobilityFunction = self.wienerLower - self.defaultMob = np.finfo(np.float64).max - elif 'upper' in function and 'hashin' in function: - self.mobilityFunction = self.hashin_shtrikmanUpper - self.defaultMob = np.finfo(np.float64).tiny - elif 'lower' in function and 'hashin' in function: - self.mobilityFunction = self.hashin_shtrikmanLower - self.defaultMob = np.finfo(np.float64).max - elif 'lab' in function: - self.mobilityFunction = self.labyrinth - self.defaultMob = np.finfo(np.float64).tiny - - def setLabyrinthFactor(self, n): - ''' - Labyrinth factor - - Parameters - ---------- - n : int - Either 1 or 2 - Note: n = 1 will the same as the weiner upper bounds - ''' - if n < 1: - n = 1 - if n > 2: - n = 2 - self.labFactor = n - - def setup(self): - ''' - Sets up model - - This also includes getting the CompositionSets for each node - ''' - super().setup() - #self.midX = 0.5 * (self.x[:,1:] + self.x[:,:-1]) - self.p = self.updateCompSets(self.x) - - def _newEqCalc(self, x): - ''' - Calculates equilibrium and returns a CompositionSet - ''' - eq = self.therm.getEq(x, self.T, 0, self.phases) - state_variables = np.array([0, 1, 101325, self.T], dtype=np.float64) - stable_phases = eq.Phase.values.ravel() - phase_amounts = eq.NP.values.ravel() - comp = [] - for p in stable_phases: - if p != '': - idx = np.where(stable_phases == p)[0] - cs, misc = self.therm._createCompositionSet(eq, state_variables, p, phase_amounts, idx) - comp.append(cs) - - if len(comp) == 0: - comp = None - - return self.therm.getLocalEq(x, self.T, 0, self.phases, comp) - - def updateCompSets(self, xarray): - ''' - Updates the array of CompositionSets - - If an equilibrium calculation is already done for a given composition, - the CompositionSet will be taken out of the hash table - - Otherwise, a new equilibrium calculation will be performed - - Parameters - ---------- - xarray : (e-1, N) array - Composition for each node - e is number of elements - N is number of nodes - - Returns - ------- - parray : (p, N) array - Phase fractions for each node - p is number of phases - ''' - parray = np.zeros((len(self.phases), xarray.shape[1])) - for i in range(parray.shape[1]): - if self.cache: - hashValue = self._getHash(xarray[:,i]) - if hashValue not in self.hashTable: - result, comp = self._newEqCalc(xarray[:,i]) - #result, comp = self.therm.getLocalEq(xarray[:,i], self.T, 0, self.phases, self.compSets[i]) - self.hashTable[hashValue] = (result, comp, None) - else: - result, comp, _ = self.hashTable[hashValue] - results, self.compSets[i] = copy.copy(result), copy.copy(comp) - else: - if self.compSets[i] is None: - results, self.compSets[i] = self._newEqCalc(xarray[:,i]) - else: - results, self.compSets[i] = self.therm.getLocalEq(xarray[:,i], self.T, 0, self.phases, self.compSets[i]) - self.mu[:,i] = results.chemical_potentials[self.unsortIndices] - cs_phases = [cs.phase_record.phase_name for cs in self.compSets[i]] - for p in range(len(cs_phases)): - parray[self._getPhaseIndex(cs_phases[p]), i] = self.compSets[i][p].NP - - return parray - - def getMobility(self, xarray): - ''' - Gets mobility of all phases - - Returns - ------- - (p, e+1, N) array - p is number of phases, e is number of elements, N is number of nodes - ''' - mob = self.defaultMob * np.ones((len(self.phases), len(self.elements)+1, xarray.shape[1])) - for i in range(xarray.shape[1]): - if self.cache: - hashValue = self._getHash(xarray[:,i]) - _, _, mTemp = self.hashTable[hashValue] - else: - mTemp = None - if mTemp is None or not self.cache: - maxPhaseAmount = 0 - maxPhaseIndex = 0 - for p in range(len(self.phases)): - if self.p[p,i] > 0: - if self.p[p,i] > maxPhaseAmount: - maxPhaseAmount = self.p[p,i] - maxPhaseIndex = p - if self.phases[p] in self.therm.mobCallables and self.therm.mobCallables[self.phases[p]] is not None: - #print(self.phases, self.phases[p], xarray[:,i], self.p[:,i], i, self.compSets[i]) - compset = [cs for cs in self.compSets[i] if cs.phase_record.phase_name == self.phases[p]][0] - mob[p,:,i] = mobility_from_composition_set(compset, self.therm.mobCallables[self.phases[p]], self.therm.mobility_correction)[self.unsortIndices] - mob[p,:,i] *= np.concatenate(([1-np.sum(xarray[:,i])], xarray[:,i])) - else: - mob[p,:,i] = -1 - for p in range(len(self.phases)): - if any(mob[p,:,i] == -1) and not all(mob[p,:,i] == -1): - mob[p,:,i] = mob[maxPhaseIndex,:,i] - if all(mob[p,:,i] == -1): - mob[p,:,i] = self.defaultMob - if self.cache: - self.hashTable[hashValue] = (self.hashTable[hashValue][0], self.hashTable[hashValue][1], copy.copy(mob[:,:,i])) - else: - mob[:,:,i] = mTemp - - return mob - - def wienerUpper(self, xarray): - ''' - Upper wiener bounds for average mobility - - Returns - ------- - (e+1, N) mobility array - e is number of elements, N is number of nodes - ''' - mob = self.getMobility(xarray) - avgMob = np.sum(np.multiply(self.p[:,np.newaxis], mob), axis=0) - return avgMob - - def wienerLower(self, xarray): - ''' - Lower wiener bounds for average mobility - - Returns - ------- - (e+1, N) mobility array - e is number of elements, N is number of nodes - ''' - #(p, e, N) - mob = self.getMobility(xarray) - avgMob = 1/np.sum(np.multiply(self.p[:,np.newaxis], 1/mob), axis=0) - return avgMob - - def labyrinth(self, xarray): - ''' - Labyrinth mobility - - Returns - ------- - (e+1, N) mobility array - e is number of elements, N is number of nodes - ''' - mob = self.getMobility(xarray) - avgMob = np.sum(np.multiply(np.power(self.p[:,np.newaxis], self.labFactor), mob), axis=0) - return avgMob - - def hashin_shtrikmanUpper(self, xarray): - ''' - Upper hashin shtrikman bounds for average mobility - - Returns - ------- - (e+1, N) mobility array - e is number of elements, N is number of nodes - ''' - #self.p #(p,N) - mob = self.getMobility(xarray) #(p,e+1,N) - maxMob = np.amax(mob, axis=0) #(e+1,N) - - # 1 / ((1 / mPhi - mAlpha) + 1 / (3mAlpha)) = 3mAlpha * (mPhi - mAlpha) / (2mAlpha + mPhi) - Ak = 3 * maxMob * (mob - maxMob) / (2*maxMob + mob) - Ak = Ak * self.p[:,np.newaxis] - Ak = np.sum(Ak, axis=0) - avgMob = maxMob + Ak / (1 - Ak / (3*maxMob)) - return avgMob - - def hashin_shtrikmanLower(self, xarray): - ''' - Lower hashin shtrikman bounds for average mobility - - Returns - ------- - (e, N) mobility array - e is number of elements, N is number of nodes - ''' - #self.p #(p,N) - mob = self.getMobility(xarray) #(p,e+1,N) - minMob = np.amin(mob, axis=0) #(e+1,N) - - #This prevents an infinite mobility which could cause the time interval to be 0 - minMob[minMob == np.inf] = 0 - - # 1 / ((1 / mPhi - mAlpha) + 1 / (3mAlpha)) = 3mAlpha * (mPhi - mAlpha) / (2mAlpha + mPhi) - Ak = 3 * minMob * (mob - minMob) / (2*minMob + mob) - - Ak = Ak * self.p[:,np.newaxis] - Ak = np.sum(Ak, axis=0) - avgMob = minMob + Ak / (1 - Ak / (3*minMob)) - return avgMob - - def getFluxes(self): - ''' - Return fluxes and time interval for the current iteration - ''' - self.p = self.updateCompSets(self.x) - - #Get average mobility between nodes - avgMob = self.mobilityFunction(self.x) - avgMob = 0.5 * (avgMob[:,1:] + avgMob[:,:-1]) - - #Composition between nodes - avgX = 0.5 * (self.x[:,1:] + self.x[:,:-1]) - avgX = np.concatenate(([1-np.sum(avgX, axis=0)], avgX), axis=0) - - #Chemical potential gradient - dmudz = (self.mu[:,1:] - self.mu[:,:-1]) / self.dz - - #Composition gradient (we need to calculate gradient for reference element) - dxdz = (self.x[:,1:] - self.x[:,:-1]) / self.dz - dxdz = np.concatenate(([0-np.sum(dxdz, axis=0)], dxdz), axis=0) - - # J = -M * dmu/dz - # Ideal contribution: J_id = -eps * M*R*T / x * dx/dz - fluxes = np.zeros((len(self.elements)+1, self.N-1)) - fluxes = -avgMob * dmudz - nonzeroComp = avgX != 0 - fluxes[nonzeroComp] += -self.eps * avgMob[nonzeroComp] * 8.314 * self.T * dxdz[nonzeroComp] / avgX[nonzeroComp] - - #Flux in a volume fixed frame: J_vi = J_i - x_i * sum(J_j) - vfluxes = np.zeros((len(self.elements), self.N+1)) - vfluxes[:,1:-1] = fluxes[1:,:] - avgX[1:,:] * np.sum(fluxes, axis=0) - - #Boundary conditions - for e in range(len(self.elements)): - vfluxes[e,0] = self.LBCvalue[e] if self.LBC[e] == self.FLUX else vfluxes[e,1] - vfluxes[e,-1] = self.RBCvalue[e] if self.RBC[e] == self.FLUX else vfluxes[e,-2] - - #Time increment - #This is done by finding the time interval such that the composition - # change caused by the fluxes will be lower than self.maxCompositionChange - dJ = np.abs(vfluxes[:,1:] - vfluxes[:,:-1]) / self.dz - dt = self.maxCompositionChange / np.amax(dJ[dJ!=0]) - - return vfluxes, dt - - def updateMesh(self, fluxes, dt): - ''' - Updates the mesh based off the fluxes and time interval - ''' - for e in range(len(self.elements)): - self.x[e] += -(fluxes[e,1:] - fluxes[e,:-1]) * dt / self.dz \ No newline at end of file diff --git a/kawin/GenericModel.py b/kawin/GenericModel.py new file mode 100644 index 0000000..af103ba --- /dev/null +++ b/kawin/GenericModel.py @@ -0,0 +1,526 @@ +from kawin.solver.Solver import SolverType, DESolver +import numpy as np +from typing import List +import copy + +class GenericModel: + ''' + Abstract model that new models can inherit from to interface with the Solver + + The model is intended to be defined by an ordinary differential equation or a set of them + The differential equations are defined by dX/dt = f(t,X) + Where t is time and X is the set of time-dependent variables at time t + + Required functions to be implemented: + getCurrentX(self) - should return time and all time-dependent variables + getdXdt(self, t, x) - should return all time-dependent derivatives + getDt(self, dXdt) - should return a suitable time step + + + Functions that can be implemented but not necessary: + _getVarDict(self) - returns a dictionary of {variable name : member name} + _addExtraSaveVariables(self, saveDict) - adds to saveDict additional variables to save + _loadExtraVariables(self, data) - loads additional data to model + + setup(self) - ran before solver is called + correctdXdt(self, dt, x, dXdt) - does not need to return anything, but should modify dXdt + preProcess(self) - preprocessing before each iteration + postProcess(self, time, x) - postprocessing after each iteration + printHeader(self) - initial output statements before solver is called + printStatus(self, iteration, modelTime, simTimeElapsed) - output states made after n iterations + ''' + def __init__(self): + self.clearCouplingModels() + + def _getVarDict(self): + ''' + Returns variable dictionary mapping variable name to internal member name + + This is used to when saving the model into a npz format, where the member names + will be replaced with the variable names defined by this dictionary + ''' + return {} + + def _addExtraSaveVariables(self, saveDict): + ''' + Adds extra variables to the save dictionary that are not covered by the variable dictionary + The variable dictionary only cover members that can be retrieved from getattr, so + this function is used to save data if it is from another class that itself is an attribute + + Parameters + ---------- + saveDict : dictionary { str : np.ndarray } + Dictionary to add data to + ''' + return + + def _loadExtraVariables(self, data): + ''' + Loads extra variables in data not covered by the variable dictionary + + Parameters + ---------- + data : dictionary { str : np.ndarray } + Dictionary to read data from + ''' + return + + def save(self, filename, compressed = True): + ''' + Saves model data into file + + 1. Store model attributes into saveDict using mapping defined from _getVarDict + 2. Add extra variables to saveDict if needed + 3. Save data into .npz format + + Parameters + ---------- + filename : str + File name to save to + compressed : bool (defaults to True) + Whether to save in compressed format + ''' + varDict = self._getVarDict() + saveDict = {} + for var in varDict: + saveDict[var] = getattr(self, varDict[var]) + self._addExtraSaveVariables(saveDict) + if compressed: + np.savez_compressed(filename, **saveDict) + else: + np.savez(filename, **saveDict) + + def _loadData(self, data): + ''' + Loads data taken from .npz file into model + + 1. Sets attributes using mapping defined from _getVarDict + 2. Loads extra variables using _loadExtraVariables + + Parameters + ---------- + data : dictionary { str : np.ndarray } + Data to load from + ''' + varDict = self._getVarDict() + for var in varDict: + setattr(self, varDict[var], data[var]) + self._loadExtraVariables(data) + + def addCouplingModel(self, model): + ''' + Adds a coupling model to the KWN model + + These will be updated after each iteration with the new values of the model + + Parameters + ---------- + model : object + Must have a function called updateCoupledModel that takes in a KWNBase or KWNEuler object + ''' + self.couplingModels.append(model) + + def clearCouplingModels(self): + ''' + Clears list of coupling models + + Note - this will not reset the coupling models, just removes them from the list + ''' + self.couplingModels = [] + + def updateCoupledModels(self): + ''' + Updates coupled models with current values + ''' + for cm in self.couplingModels: + cm.updateCoupledModel(self) + + def setup(self): + ''' + Sets up model before being solved + + This is the first thing that is called when the solve function is called + + Note: this will be called each time the solve function called, so if setup only needs to + be called once, then make sure there's a check in the model implementation to prevent + setup from being called more than once + ''' + pass + + def getCurrentX(self): + ''' + Gets values of time-dependent variables at current time + + The required format of X is not strict as long as it matches dXdt + Example: if X is a nested list of [[a, b], c], then dXdt should be [[da/dt, db/dt], dc/dt] + + Note: X should only be for variables that are solved by dX/dt = f(t,X) + Variables that can be computed directly from X should be calculated in the preProcess or postProcess functions + + Returns + ------- + t : current time of model + X : unformatted list of floats + ''' + raise NotImplementedError() + + def getDt(self, dXdt): + ''' + Gets suitable time step based off dXdt + + Parameters + ---------- + dXdt : unformated list of floats + Time derivatives that may be used to find dt + + Returns + ------- + dt : float + ''' + raise NotImplementedError() + + def getdXdt(self, t, x): + ''' + Gets dXdt from current time and X + + Parameters + ---------- + t : float + Current time + x : unformated list of floats + Current values of time-dependent variables + + Returns + ------- + dXdt : unformated list of floats + Must be in same format as x + ''' + raise NotImplementedError() + + def correctdXdt(self, dt, x, dXdt): + ''' + Intended for cases where dXdt can only be corrected once dt is known + For example, the time derivatives in the population balance model in PrecipitateModel needs to be + adjusted to avoid negative bins, but this can only be done once dt is known + + If dXdt can be corrected without knowing dt, then it is recommended to be done during the getdXdt function + + No return value, dXdt is to be modified directly + ''' + pass + + def preProcess(self): + ''' + Performs any pre-processing before an iteration. This may include some calculations or storing temporary variables + ''' + pass + + def postProcess(self, time, x): + ''' + Post processing done after an iteration + + This should at least involve storing the new values of time and X + But this can also include additional calculations or return a signal to stop simulations + + Parameters + ---------- + time : float + New time + x : unformatted list of floats + New values of X + + Returns + ------- + x : unformatted list of floats + This is in case X was modified in postProcess + stop : bool + If the simulation needs to end early (ex. a stopping condition is met), then return True to stop solving + ''' + return x, False + + def printHeader(self): + ''' + First output to be printed when solve is called + + verbose must be True when calling solve + ''' + print('Iteration\tSim Time(s)\tRun Time(s)') + + def printStatus(self, iteration, modelTime, simTimeElapsed): + ''' + Output to be printed after n iterations (defined by vIt in solve) + + verbose must be True when calling solve + ''' + print('{}\t\t{:.1e}\t\t{:.1f}'.format(iteration, modelTime, simTimeElapsed)) + + def setTimeInfo(self, currTime, simTime): + ''' + Store time variables for starting, final and delta time + + This is sometimes useful for determining the time step + ''' + self.deltaTime = simTime + self.startTime = currTime + self.finalTime = currTime+simTime + + def flattenX(self, X): + ''' + Since X can be a nested list of values or arrays (or anything), + we want some instructions for the solver and Iterator for how to convert X + to a 1D array + + By default, we'll assume X is a list of either floats or 1D arrays + + For more complex nesting, this function should be overloaded + + Parameters + ---------- + X : list of arrays + + Returns + ------- + X_flat : 1D numpy array + ''' + return np.hstack(X) + + def unflattenX(self, X_flat, X_ref): + ''' + Converts flattened X array to original nested X + + Parameters + ---------- + X_flat : 1D numpy array + Flattened array + X_ref : list of arrays + Template to convert X_flat to + + Returns + ------- + X_new : unflattened list in the same format as X_ref + ''' + #Not sure if this is the most efficient way, but we can't assume how the nested list in X_ref is structured + #This should be a shallow copy though, so maybe it's fine + X_new = copy.copy(X_ref) + n = 0 + for i in range(len(X_new)): + #We can't be sure that X_new[i] a python scalar or numpy scalar, so we'll convert to an np.ndarray first + if len(np.array(X_new[i]).shape) == 0: + X_new[i] = X_flat[n] + n += 1 + else: + arrLen = np.prod(np.array(X_new[i]).shape) + X_new[i] = np.reshape(X_flat[n:n+arrLen], np.array(X_new[i]).shape) + n += arrLen + return X_new + + def solve(self, simTime, solverType = SolverType.RK4, verbose=False, vIt=10, minDtFrac = 1e-8, maxDtFrac = 1): + ''' + Solves model using the DESolver + + Steps: + 1. Call setup + 2. Create DESolver object and set necessary functions + 3. Get current values of t and X + 4. Solve from current t to t+simTime + + Parameters + ---------- + simTime : float + Simulation time (as a delta from current time) + solverType : SolverType or Iterator (defaults to SolverType.RK4) + Defines what iteration scheme to use + verbose : bool (defaults to False) + Outputs status if true + vIt : integer (defaults to 10) + Number of iterations before printing status + minDtFrac : float (defaults to 1e-8) + Minimum dt as fraction of simulation time + maxDtFrac : float (defaults to 1) + Maximum dt as fraction of simulation time + ''' + self.setup() + + solver = DESolver(solverType, minDtFrac = minDtFrac, maxDtFrac = maxDtFrac) + solver.setFunctions(preProcess=self.preProcess, postProcess=self.postProcess, printHeader=self.printHeader, printStatus=self.printStatus) + solver.setdXdtFunctions(self.getdXdt, self.correctdXdt, self.getDt, self.flattenX, self.unflattenX) + + t, X0 = self.getCurrentX() + self.setTimeInfo(t, simTime) + solver.solve(self.startTime, X0, self.finalTime, verbose, vIt) + #solver.solve(self.getdXdt, self.startTime, X0, self.finalTime, verbose, vIt, self.correctdXdt, self.flattenX, self.unflattenX) + +class Coupler(GenericModel): + ''' + Class for coupling multiple GenericModel objects together + + Note: + coupleddXdt, coupledPreProcess and coupledPostProcess aren't really necessary since + tighter coupling can also be done by overloading the getdXdt, preProcess and/or postProcess + functions and calling the method of the Coupler before anything else + Ex. tighter coupling can be done by + a) Overloading coupleddXdt as + def coupleddXdt(self, dXdt): + === + Modify dXdt here + === + + b) Overriding getdXdt as + def getdXdt(self, t, x): + dXdt = super().getdXdt(t, x) + --- + modify dXdt here + --- + return dXdt + + Parameters + ---------- + models : List[GenericModel] + List of models to be solved + ''' + def __init__(self, models : List[GenericModel]): + self.models = models + + #Internal time to record + #We have the option to solve a model for a given amount of time before coupling it + # to another model, which would make each model have a different internal time + # Thus, we'll record time here as well representing the time during the coupling + self.time = np.zeros(1) + + def setup(self): + ''' + Sets up each model + ''' + super().setup() + for m in self.models: + m.setup() + + def setTimeInfo(self, currTime, simTime): + ''' + Sets time info for the CoupledModel class and each model + ''' + super().setTimeInfo(currTime, simTime) + for m in self.models: + m.setTimeInfo(currTime, simTime) + + def flattenX(self, X): + ''' + Instructions for converting X to 1D array + + We grab the flattened x array of each model and concatenate them + Thus we don't have to care about the structure of x in each model as + long as the model itself has the instructions to flatten its x array + + Also record the length of each flattened x in each model so we know what + indices to used for unflattening + ''' + X_new = [] + for m, xsub in zip(self.models, X): + xsub_new = m.flattenX(xsub) + X_new.append(xsub_new) + self._sizeRef = [len(xi) for xi in X_new] + return np.concatenate(X_new) + + def unflattenX(self, X_flat, X_ref): + ''' + Instructions for converting X_flat to list of x of each model + + We take the subset of X_flat corresponding to each model and unflatten it + based off the instructions in the model. Then we just return a list containing + each unflattened x + ''' + X_new = [] + ind = 0 + for m, s, x_refsub in zip(self.models, self._sizeRef, X_ref): + xi_new = m.unflattenX(X_flat[ind:ind+s], x_refsub) + X_new.append(xi_new) + ind += s + return X_new + + def getCurrentX(self): + ''' + Get current time and x for each model + ''' + xs = [] + for m in self.models: + _, x = m.getCurrentX() + xs.append(x) + + return self.time[-1], xs + + def getDt(self, dXdt): + ''' + Get the minimum dt out of all models + ''' + dts = [] + for m, dxdtsub in zip(self.models, dXdt): + dts.append(m.getDt(dxdtsub)) + return np.amin(dts) + + def getdXdt(self, t, x): + ''' + Get dXdt for each model + ''' + dxdts = [] + for m, xsub in zip(self.models, x): + dxdts.append(m.getdXdt(t, xsub)) + self.coupledXdt(t, x, dxdts) + return dxdts + + def correctdXdt(self, dt, x, dXdt): + ''' + Corrects dXdt for each model + + Note - dXdt has to be modified since we don't return dXdt in this function + Since dXdt here is composed of a nested list of dXdts of each model, these + will be passed by reference + ''' + for m, xsub, dxdtsub in zip(self.models, x, dXdt): + m.correctdXdt(dt, xsub, dxdtsub) + + def preProcess(self): + ''' + Pre process on each model + ''' + for m in self.models: + m.preProcess() + self.couplePreProcess() + + def postProcess(self, time, x): + ''' + Post process on each model and records new time + ''' + xNew = [] + stop = False + for m, xsub in zip(self.models, x): + xnew_sub, s = m.postProcess(time, xsub) + stop = stop or s + xNew.append(xnew_sub) + self.time = np.append(self.time, time) + self.couplePostProcess() + return xNew, stop + + def coupledXdt(self, t, x, dXdt): + ''' + Empty function where inherited classes can do extra operations on + the time derivatives each models or between models + ''' + return + + def couplePreProcess(self): + ''' + Empty function where inherited classes can do extra operations on + each models or between models for an iteration + ''' + return + + def couplePostProcess(self): + ''' + Empty function where inherited classes can do extra operations on + each models or between models after an iteration + ''' + return + + + + \ No newline at end of file diff --git a/kawin/KWNBase.py b/kawin/KWNBase.py deleted file mode 100644 index 1ddea75..0000000 --- a/kawin/KWNBase.py +++ /dev/null @@ -1,1689 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from kawin.EffectiveDiffusion import EffectiveDiffusionFunctions -from kawin.ShapeFactors import ShapeFactor -from kawin.ElasticFactors import StrainEnergy -from kawin.GrainBoundaries import GBFactors -import copy -import time -import csv -from itertools import zip_longest - -class PrecipitateBase: - ''' - Base class for precipitation models - Note: currently only the Euler implementation is available, but - other implementations are planned to be added - - Parameters - ---------- - t0 : float - Initial time in seconds - tf : float - Final time in seconds - steps : int - Number of time steps - phases : list (optional) - Precipitate phases (array of str) - If only one phase is considered, the default is ['beta'] - linearTimeSpacing : bool (optional) - Whether to have time increment spaced linearly or logarithimically - Defaults to False - elements : list (optional) - Solute elements in system - Note: order of elements must correspond to order of elements set in Thermodynamics module - If binary system, then defualt is ['solute'] - ''' - def __init__(self, t0, tf, steps, phases = ['beta'], linearTimeSpacing = False, elements = ['solute']): - #Store input parameters - self.initialSteps = int(steps) #Initial number of steps for when model is reset - self.steps = int(steps) #This includes the number of steps added when adaptive time stepping is enabled - self.t0 = t0 - self.tf = tf - self.phases = np.array(phases) - self.linearTimeSpacing = linearTimeSpacing - - #Change t0 to finite value if logarithmic time spacing - #New t0 will be tf / 1e6 - if self.t0 <= 0 and self.linearTimeSpacing == False: - self.t0 = self.tf / 1e6 - print('Warning: Cannot use 0 as an initial time when using logarithmic time spacing') - print('\tSetting t0 to {:.3e}'.format(self.t0)) - - #Time variables - self.adaptiveTimeStepping(True) - - #Predefined constraints, these can be set if they make the simulation unstable - self._defaultConstraints() - - #Stopping conditions - self.clearStoppingConditions() - - #Composition array - self.elements = elements - self.numberOfElements = len(elements) - - #All other arrays - self._resetArrays() - - #Constants - self.Rg = 8.314 #J/mol-K - self.avo = 6.022e23 #/mol - self.kB = self.Rg / self.avo #J/K - - #Default variables, these terms won't have to be set before simulation - self.strainEnergy = [StrainEnergy() for i in self.phases] - self.calculateAspectRatio = [False for i in self.phases] - self.RdrivingForceLimit = np.zeros(len(self.phases), dtype=np.float32) - self.shapeFactors = [ShapeFactor() for i in self.phases] - self.theta = 2 * np.ones(len(self.phases), dtype=np.float32) - self.effDiffFuncs = EffectiveDiffusionFunctions() - self.effDiffDistance = self.effDiffFuncs.effectiveDiffusionDistance - self.infinitePrecipitateDiffusion = [True for i in self.phases] - self.dTemp = 0 - self.iterationSinceTempChange = 0 - self.GBenergy = 0.3 #J/m2 - self.parentPhases = [[] for i in self.phases] - self.GB = [GBFactors() for p in self.phases] - - #Set other variables to None to throw errors if not set - self.xInit = None - self.T = None - self.Tparameters = None - - self._isNucleationSetup = False - self.GBareaN0 = None - self.GBedgeN0 = None - self.GBcornerN0 = None - self.dislocationN0 = None - self.bulkN0 = None - - #Unit cell parameters - self.aAlpha = None - self.VaAlpha = None - self.VmAlpha = None - self.atomsPerCellBeta = np.empty(len(self.phases), dtype=np.float32) - self.VaBeta = np.empty(len(self.phases), dtype=np.float32) - self.VmBeta = np.empty(len(self.phases), dtype=np.float32) - self.Rmin = np.empty(len(self.phases), dtype=np.float32) - - #Free energy parameters - self.gamma = np.empty(len(self.phases), dtype=np.float32) - self.dG = [None for i in self.phases] - self.interfacialComposition = [None for i in self.phases] - - if self.numberOfElements == 1: - self._Beta = self._BetaBinary1 - else: - self._Beta = self._BetaMulti - self._betaFuncs = [None for p in phases] - self._defaultBeta = 20 - - def phaseIndex(self, phase = None): - ''' - Returns index of phase in list - - Parameters - ---------- - phase : str (optional) - Precipitate phase (defaults to None, which will return 0) - ''' - return 0 if phase is None else np.where(self.phases == phase)[0][0] - - def reset(self): - ''' - Resets simulation results - This does not reset the model parameters, however, it will clear any stopping conditions - ''' - self._resetArrays() - self.xComp[0] = self.xInit - self.dTemp = 0 - - #Reset temperature array - if np.isscalar(self.Tparameters): - self.setTemperature(self.Tparameters) - elif len(self.Tparameters) == 2: - self.setTemperatureArray(*self.Tparameters) - elif self.Tparameters is not None: - self.setNonIsothermalTemperature(self.Tparameters) - - def _resetArrays(self): - ''' - Resets and initializes arrays for all variables - time - matrix composition, equilibrium composition - critial radius and nucleation barrier - average radius and aspect ratio - volume fraction - nucleation rate - precipitate density - driving force - beta - incubation time - ''' - self.steps = self.initialSteps - self.time = np.linspace(self.t0, self.tf, self.steps) if self.linearTimeSpacing else np.logspace(np.log10(self.t0), np.log10(self.tf), self.steps) - - if self.numberOfElements == 1: - self.xComp = np.zeros(self.steps) #Current composition of matrix phase - self.xEqAlpha = np.zeros((len(self.phases), self.steps)) #Equilibrium composition of matrix phase with respect to each precipitate phase - self.xEqBeta = np.zeros((len(self.phases), self.steps)) #Equilibrium composition of precipitate phases - else: - self.xComp = np.zeros((self.steps, self.numberOfElements)) - self.xEqAlpha = np.zeros((len(self.phases), self.steps, self.numberOfElements)) - self.xEqBeta = np.zeros((len(self.phases), self.steps, self.numberOfElements)) - - self.Rcrit = np.zeros((len(self.phases), self.steps)) #Critical radius - self.Gcrit = np.zeros((len(self.phases), self.steps)) #Height of nucleation barrier - self.Rad = np.zeros((len(self.phases), self.steps)) #Radius of particles formed at each time step - self.avgR = np.zeros((len(self.phases), self.steps)) #Average radius - self.avgAR = np.zeros((len(self.phases), self.steps)) #Mean aspect ratio - self.betaFrac = np.zeros((len(self.phases), self.steps)) #Fraction of precipitate - - self.nucRate = np.zeros((len(self.phases), self.steps)) #Nucleation rate - self.precipitateDensity = np.zeros((len(self.phases), self.steps)) #Number of nucleates - - self.dGs = np.zeros((len(self.phases), self.steps)) #Driving force - self.betas = np.zeros((len(self.phases), self.steps)) #Impingement rates (used for non-isothermal) - self.incubationOffset = np.zeros(len(self.phases)) #Offset for incubation time (for non-isothermal precipitation) - self.incubationSum = np.zeros(len(self.phases)) #Sum of incubation time - - self.prevFConc = np.zeros((2, len(self.phases), self.numberOfElements)) #Sum of precipitate composition for mass balance - - def save(self, filename, compressed = False, toCSV = False): - ''' - Save results into a numpy .npz or .csv format - - Parameters - ---------- - filename : str - compressed : bool - If true, will save compressed .npz format - toCSV : bool - If true, will save to .csv - ''' - variables = ['t0', 'tf', 'steps', 'phases', 'linearTimeSpacing', 'elements', \ - 'time', 'xComp', 'Rcrit', 'Gcrit', 'Rad', 'avgR', 'avgAR', 'betaFrac', 'nucRate', 'precipitateDensity', 'dGs', 'xEqAlpha', 'xEqBeta'] - vDict = {v: getattr(self, v) for v in variables} - - if toCSV: - vDict['t0'] = np.array([vDict['t0']]) - vDict['tf'] = np.array([vDict['tf']]) - vDict['steps'] = np.array([vDict['steps']]) - vDict['linearTimeSpacing'] = np.array([vDict['linearTimeSpacing']]) - if self.numberOfElements == 2: - vDict['xComp'] = vDict['xComp'].T - arrays = [] - headers = [] - for v in vDict: - vDict[v] = np.array(vDict[v]) - if len(vDict[v].shape) == 2: - for i in range(len(vDict[v])): - arrays.append(vDict[v][i]) - headers.append(v + str(i)) - if v == 'xComp': - headers.append(v + '_' + self.elements[i]) - else: - headers.append(v + '_' + self.phases[i]) - elif v == 'xEqAlpha' or v == 'xEqBeta': - for i in range(len(self.phases)): - for j in range(self.numberOfElements): - arrays.append(vDict[v][i,:,j]) - headers.append(v + '_' + self.phases[i] + '_' + self.elements[j]) - else: - arrays.append(vDict[v]) - headers.append(v) - rows = zip_longest(*arrays, fillvalue='') - if '.csv' not in filename.lower(): - filename = filename + '.csv' - with open(filename, 'w', newline='') as f: - csv.writer(f).writerow(headers) - csv.writer(f).writerows(rows) - else: - if compressed: - np.savez_compressed(filename, **vDict) - #np.savez_compressed(filename, **vDict, allow_pickle=True) - else: - np.savez(filename, **vDict) - #np.savez(filename, **vDict, allow_pickle=True) - - def load(filename): - ''' - Loads data - - Parameters - ---------- - filename : str - - Returns - ------- - PrecipitateBase object - Note: this will only contain model outputs which can be used for plotting - ''' - setupVars = ['t0', 'tf', 'steps', 'phases', 'linearTimeSpacing', 'elements'] - if '.np' in filename.lower(): - data = np.load(filename, allow_pickle=True) - model = PrecipitateBase(data['t0'], data['tf'], data['steps'], data['phases'], data['linearTimeSpacing'], data['elements']) - for d in data: - if d not in setupVars: - setattr(model, d, data[d]) - elif '.csv' in filename.lower(): - with open(filename, 'r') as csvFile: - data = csv.reader(csvFile, delimiter=',') - i = 0 - headers = [] - columns = {} - #Grab all columns - for row in data: - if i == 0: - headers = row - columns = {h: [] for h in headers} - else: - for j in range(len(row)): - if row[j] != '': - columns[headers[j]].append(row[j]) - i += 1 - - t0, tf, steps, phases, elements = float(columns['t0'][0]), float(columns['tf'][0]), int(columns['steps'][0]), columns['phases'], columns['elements'] - linearTimeSpacing = True if columns['linearTimeSpacing'][0] == 'True' else False - model = PrecipitateBase(t0, tf, steps, phases, linearTimeSpacing, elements) - - restOfVariables = ['time', 'xComp', 'Rcrit', 'Gcrit', 'Rad', 'avgR', 'avgAR', 'betaFrac', 'nucRate', 'precipitateDensity', 'dGs', 'xEqAlpha', 'xEqBeta'] - restOfColumns = {v: [] for v in restOfVariables} - for d in columns: - if d not in setupVars: - if d == 'time': - restOfColumns[d] = np.array(columns[d], dtype='float') - elif d == 'xComp': - if model.numberOfElements == 1: - restOfColumns[d] = np.array(columns[d], dtype='float') - else: - restOfColumns['xComp'].append(columns[d], dtype='float') - else: - selectedVar = '' - for r in restOfVariables: - if r in d: - selectedVar = r - restOfColumns[selectedVar].append(np.array(columns[d], dtype='float')) - for d in restOfColumns: - restOfColumns[d] = np.array(restOfColumns[d]) - setattr(model, d, restOfColumns[d]) - - #For multicomponent systems, adjust as necessary such that number of elements will be the last axis - if model.numberOfElements > 1: - model.xComp = model.xComp.T - if len(model.phases) == 1: - model.xEqAlpha = np.expand_dims(model.xEqAlpha, 0) - model.xEqBeta = np.expand_dims(model.xEqBeta, 0) - else: - model.xEqAlpha = np.reshape(model.xEqAlpha, ((len(model.phases), model.numberOfElements, len(model.time)))) - model.xEqBeta = np.reshape(model.xEqBeta, ((len(model.phases), model.numberOfElements, len(model.time)))) - model.xEqAlpha = np.transpose(model.xEqAlpha, (0, 2, 1)) - model.xEqBeta = np.transpose(model.xEqBeta, (0, 2, 1)) - return model - - def _divideTimestep(self, i, dt): - ''' - Adds a new time step between t_i-1 and t_i, with new time being t_i-1 + dt - - Parameters - ---------- - i : int - dt : float - Note: this must be smaller than t_i - t_i-1 - ''' - self.steps += 1 - - if self.numberOfElements == 1: - self.xComp = np.append(self.xComp, 0) - self.xEqAlpha = np.append(self.xEqAlpha, np.zeros((len(self.phases), 1)), axis=1) - self.xEqBeta = np.append(self.xEqBeta, np.zeros((len(self.phases), 1)), axis=1) - else: - self.xComp = np.append(self.xComp, np.zeros((1, self.numberOfElements)), axis=0) - self.xEqAlpha = np.append(self.xEqAlpha, np.zeros((len(self.phases), 1, self.numberOfElements)), axis=1) - self.xEqBeta = np.append(self.xEqBeta, np.zeros((len(self.phases), 1, self.numberOfElements)), axis=1) - - #Add new element to each variable - self.Rcrit = np.append(self.Rcrit, np.zeros((len(self.phases), 1)), axis=1) - self.Gcrit = np.append(self.Gcrit, np.zeros((len(self.phases), 1)), axis=1) - self.Rad = np.append(self.Rad, np.zeros((len(self.phases), 1)), axis=1) - self.avgR = np.append(self.avgR, np.zeros((len(self.phases), 1)), axis=1) - self.avgAR = np.append(self.avgAR, np.zeros((len(self.phases), 1)), axis=1) - self.betaFrac = np.append(self.betaFrac, np.zeros((len(self.phases), 1)), axis=1) - self.nucRate = np.append(self.nucRate, np.zeros((len(self.phases), 1)), axis=1) - self.precipitateDensity = np.append(self.precipitateDensity, np.zeros((len(self.phases), 1)), axis=1) - self.dGs = np.append(self.dGs, np.zeros((len(self.phases), 1)), axis=1) - self.betas = np.append(self.betas, np.zeros((len(self.phases), 1)), axis=1) - - prevDT = self.time[i] - self.time[i-1] - self.time = np.insert(self.time, i, self.time[i-1] + dt) - - ratio = dt / prevDT - self.T = np.insert(self.T, i, ratio * self.T[i-1] + (1-ratio) * self.T[i]) - - def adaptiveTimeStepping(self, adaptive = True): - ''' - Sets if adaptive time stepping is used - - Parameters - ---------- - adaptive : bool (optional) - Defaults to True - ''' - if adaptive: - self._timeIncrementCheck = self._checkDT - #self._postTimeIncrementCheck = self._postCheckDT - self._postTimeIncrementCheck = self._noPostCheckDT - else: - self._timeIncrementCheck = self._noCheckDT - self._postTimeIncrementCheck = self._noPostCheckDT - - def _calculateDT(self, i, fraction): - ''' - Calculates DT as a fraction of the total simulation time - ''' - if self.linearTimeSpacing: - dt = fraction*(self.tf - self.t0) - else: - dt = self.time[i] * (np.exp(fraction*np.log(self.tf / self.t0)) - 1) - return dt - - def _defaultConstraints(self): - ''' - Default values for contraints - ''' - self.minRadius = 3e-10 - self.maxTempChange = 1 - - self.maxDTFraction = 1e-2 - self.minDTFraction = 1e-5 - - self.checkTemperature = True - self.maxNonIsothermalDT = 1 - - self.checkPSD = True - self.maxDissolution = 0.01 - - self.checkRcrit = True - self.maxRcritChange = 0.01 - - self.checkNucleation = True - self.maxNucleationRateChange = 0.5 - self.minNucleationRate = 1e-5 - - self.checkVolumePre = True - self.checkVolumePost = False - self.maxVolumeChange = 0.001 - - self.checkComposition = False - self.checkCompositionPre = False - self.maxCompositionChange = 0.001 - self.minComposition = 0 - - self.minNucleateDensity = 1e-5 - - def setConstraints(self, **kwargs): - ''' - Sets constraints - - TODO: the following constraints are not implemented - maxDTFraction - maxRcritChange - this is somewhat implemented but disabled by default - - Possible constraints: - --------------------- - minRadius - minimum radius to be considered a precipitate (1e-10 m) - maxTempChange - maximum temperature change before lookup table is updated (only for Euler in binary case) (1 K) - - maxDTFraction - maximum time increment allowed as a fraction of total simulation time (0.1) - minDTFraction - minimum time increment allowed as a fraction of total simulation time (1e-5) - - checkTemperature - checks max temperature change (True) - maxNonIsothermalDT - maximum time step when temperature is changing (1 second) - - checkPSD - checks maximum growth rate for particle size distribution (True) - maxDissolution - maximum relative volume fraction of precipitates allowed to dissolve in a single time step (0.01) - - checkRcrit - checks maximum change in critical radius (False) - maxRcritChange - maximum change in critical radius (as a fraction) per single time step (0.01) - - checkNucleation - checks maximum change in nucleation rate (True) - maxNucleationRateChange - maximum change in nucleation rate (on log scale) per single time step (0.5) - minNucleationRate - minimum nucleation rate to be considered for checking time intervals (1e-5) - - checkVolumePre - estimates maximum volume change (True) - checkVolumePost - checks maximum calculated volume change (True) - maxVolumeChange - maximum absolute value that volume fraction can change per single time step (0.001) - - checkComposition - checks maximum change in composition (True) - chekcCompositionPre - estimates maximum change in composition (False) - maxCompositionChange - maximum change in composition in single time step (0.01) - - minNucleateDensity - minimum nucleate density to consider nucleation to have occurred (1e-5) - ''' - for key, value in kwargs.items(): - setattr(self, key, value) - - def setBetaBinary(self, functionType = 1): - ''' - Sets function for beta calculation in binary systems - - If using a multicomponent system, this function will not do anything - - Parameters - ---------- - functionType : int - ID for function - 1 for implementation seen in Perez et al, 2008 (default) - 2 for implementation similar to multicomponent systems - ''' - if self.numberOfElements == 1: - if functionType == 2: - self.beta = self._BetaBinary2 - else: - self.beta = self._BetaBinary1 - - def setInitialComposition(self, xInit): - ''' - Parameters - - xInit : float or array - Initial composition of parent matrix phase in atomic fraction - Use float for binary system and array of solutes for multicomponent systems - ''' - self.xInit = xInit - self.xComp[0] = xInit - - def setInterfacialEnergy(self, gamma, phase = None): - ''' - Parameters - ---------- - gamma : float - Interfacial energy between precipitate and matrix in J/m2 - phase : str (optional) - Phase to input interfacial energy (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.gamma[index] = gamma - - def resetAspectRatio(self, phase = None): - ''' - Resets aspect ratio variables of defined phase to default - - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.shapeFactors[index].setSpherical() - - def setSpherical(self, phase = None): - ''' - Sets precipitate shape to spherical for defined phase - - Parameters - ---------- - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.shapeFactors[index].setSpherical() - - def setAspectRatioNeedle(self, ratio=1, phase = None): - ''' - Consider specified precipitate phase as needle-shaped - with specified aspect ratio - - Parameters - ---------- - ratio : float or function - Aspect ratio of needle-shaped precipitate - If float, must be greater than 1 - If function, must take in radius as input and output float greater than 1 - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.shapeFactors[index].setNeedleShape(ratio) - - def setAspectRatioPlate(self, ratio=1, phase = None): - ''' - Consider specified precipitate phase as plate-shaped - with specified aspect ratio - - Parameters - ---------- - ratio : float or function - Aspect ratio of needle-shaped precipitate - If float, must be greater than 1 - If function, must take in radius as input and output float greater than 1 - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.shapeFactors[index].setPlateShape(ratio) - - def setAspectRatioCuboidal(self, ratio=1, phase = None): - ''' - Consider specified precipitate phase as cuboidal-shaped - with specified aspect ratio - - TODO: add cuboidal factor - Currently, I think this considers that the cuboidal factor is 1 - - Parameters - ---------- - ratio : float or function - Aspect ratio of needle-shaped precipitate - If float, must be greater than 1 - If function, must take in radius as input and output float greater than 1 - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.shapeFactors[index].setCuboidalShape(ratio) - - def setVmAlpha(self, Vm, atomsPerCell): - ''' - Molar volume for parent phase - - Parameters - ---------- - Vm : float - Molar volume (m3 / mol) - atomsPerCell : int - Number of atoms in a unit cell - ''' - self.VmAlpha = Vm - self.VaAlpha = atomsPerCell * self.VmAlpha / self.avo - self.aAlpha = np.cbrt(self.VaAlpha) - self.atomsPerCellAlpha = atomsPerCell - - def setVaAlpha(self, Va, atomsPerCell): - ''' - Unit cell volume for parent phase - - Parameters - ---------- - Va : float - Unit cell volume (m3 / unit cell) - atomsPerCell : int - Number of atoms in a unit cell - ''' - self.VaAlpha = Va - self.VmAlpha = self.VaAlpha * self.avo / atomsPerCell - self.aAlpha = np.cbrt(Va) - self.atomsPerCellAlpha = atomsPerCell - - def setUnitCellAlpha(self, a, atomsPerCell): - ''' - Lattice parameter for parent phase (assuming cubic unit cell) - - Parameters - ---------- - a : float - Lattice constant (m) - atomsPerCell : int - Number of atoms in a unit cell - ''' - self.aAlpha = a - self.VaAlpha = a**3 - self.VmAlpha = self.VaAlpha * self.avo / atomsPerCell - self.atomsPerCellAlpha = atomsPerCell - - def setVmBeta(self, Vm, atomsPerCell, phase = None): - ''' - Molar volume for precipitate phase - - Parameters - ---------- - Vm : float - Molar volume (m3 / mol) - atomsPerCell : int - Number of atoms in a unit cell - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.VmBeta[index] = Vm - self.VaBeta[index] = atomsPerCell * self.VmBeta[index] / self.avo - self.atomsPerCellBeta[index] = atomsPerCell - - def setVaBeta(self, Va, atomsPerCell, phase = None): - ''' - Unit cell volume for precipitate phase - - Parameters - ---------- - Va : float - Unit cell volume (m3 / unit cell) - atomsPerCell : int - Number of atoms in a unit cell - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.VaBeta[index] = Va - self.VmBeta[index] = self.VaBeta[index] * self.avo / atomsPerCell - self.atomsPerCellBeta[index] = atomsPerCell - - def setUnitCellBeta(self, a, atomsPerCell, phase = None): - ''' - Lattice parameter for precipitate phase (assuming cubic unit cell) - - Parameters - ---------- - a : float - Latice parameter (m) - atomsPerCell : int - Number of atoms in a unit cell - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.VaBeta[index] = a**3 - self.VmBeta[index] = self.VaBeta[index] * self.avo / atomsPerCell - self.atomsPerCellBeta[index] = atomsPerCell - - def setNucleationDensity(self, grainSize = 100, aspectRatio = 1, dislocationDensity = 5e12, bulkN0 = None): - ''' - Sets grain size and dislocation density which determines the available nucleation sites - - Parameters - ---------- - grainSize : float (optional) - Average grain size in microns (default at 100um if this function is not called) - aspectRatio : float (optional) - Aspect ratio of grains (default at 1) - dislocationDensity : float (optional) - Dislocation density (m/m3) (default at 5e12) - bulkN0 : float (optional) - This allows for the use to override the nucleation site density for bulk precipitation - By default (None), this is calculated by the number of lattice sites containing a solute atom - However, for calibration purposes, it may be better to set the nucleation site density manually - ''' - self.grainSize = grainSize * 1e-6 - self.grainAspectRatio = aspectRatio - self.dislocationDensity = dislocationDensity - - self.bulkN0 = bulkN0 - self._isNucleationSetup = True - - def _getNucleationDensity(self): - ''' - Calculates nucleation density - This is separated from setting nucleation density to - allow it to be called right before the simulation starts - ''' - #Set bulk nucleation site to the number of solutes per unit volume - #NOTE: some texts will state the bulk nucleation sites to just be the number - # of lattice sites per unit volume. The justification for this would be - # the solutes can diffuse around to any lattice site and nucleate there - if self.bulkN0 is None: - if self.numberOfElements == 1: - self.bulkN0 = self.xComp[0] * (self.avo / self.VmAlpha) - else: - self.bulkN0 = np.amin(self.xComp[0,:]) * (self.avo / self.VmAlpha) - - self.dislocationN0 = self.dislocationDensity * (self.avo / self.VmAlpha)**(1/3) - - if self.grainSize != np.inf: - if self.GBareaN0 is None: - self.GBareaN0 = (6 * np.sqrt(1 + 2 * self.grainAspectRatio**2) + 1 + 2 * self.grainAspectRatio) / (4 * self.grainAspectRatio * self.grainSize) - self.GBareaN0 *= (self.avo / self.VmAlpha)**(2/3) - if self.GBedgeN0 is None: - self.GBedgeN0 = 2 * (np.sqrt(2) + 2 * np.sqrt(1 + self.grainAspectRatio**2)) / (self.grainAspectRatio * self.grainSize**2) - self.GBedgeN0 *= (self.avo / self.VmAlpha)**(1/3) - if self.GBcornerN0 is None: - self.GBcornerN0 = 12 / (self.grainAspectRatio * self.grainSize**3) - else: - self.GBareaN0 = 0 - self.GBedgeN0 = 0 - self.GBcornerN0 = 0 - - def setNucleationSite(self, site, phase = None): - ''' - Sets nucleation site type for specified phase - If site type is grain boundaries, edges or corners, the phase morphology will be set to spherical and precipitate shape will depend on wetting angle - - Parameters - ---------- - site : str - Type of nucleation site - Options are 'bulk', 'dislocations', 'grain_boundaries', 'grain_edges' and 'grain_corners' - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - - self.GB[index].setNucleationType(site) - - if self.GB[index].nucleationSiteType != GBFactors.BULK and self.GB[index].nucleationSiteType != GBFactors.DISLOCATION: - self.shapeFactors[index].setSpherical() - - def _setGBfactors(self): - ''' - Calcualtes factors for bulk or grain boundary nucleation - This is separated from setting the nucleation sites to allow - it to be called right before simulation - ''' - for p in range(len(self.phases)): - self.GB[p].setFactors(self.GBenergy, self.gamma[p]) - - def _GBareaRemoval(self, p): - ''' - Returns factor to multiply radius by to give the equivalent radius of circles representing the area of grain boundary removal - - Parameters - ---------- - p : int - Index for phase - ''' - if self.GB[p].nucleationSiteType == GBFactors.BULK or self.GB[p].nucleationSiteType == GBFactors.DISLOCATION: - return 1 - else: - return np.sqrt(self.GB[p].gbRemoval / np.pi) - - def setParentPhases(self, phase, parentPhases): - ''' - Sets parent precipitates at which a precipitate can nucleate on the surface of - - Parameters - ---------- - phase : str - Precipitate phase of interest that will nucleate - parentPhases : list - Phases that the precipitate of interest can nucleate on the surface of - ''' - index = self.phaseIndex(phase) - for p in parentPhases: - self.parentPhases[index].append(self.phaseIndex(p)) - - def setGrainBoundaryEnergy(self, energy): - ''' - Grain boundary energy - this will decrease the critical radius as some grain boundaries will be removed upon nucleation - - Parameters - ---------- - energy : float - GB energy in J/m2 - - Default upon initialization is 0.3 - Note: GBenergy of 0 is equivalent to bulk precipitation - ''' - self.GBenergy = energy - - def setTheta(self, theta, phase = None): - ''' - This is a scaling factor for the incubation time calculation, default is 2 - - Incubation time is defined as 1 / \theta * \beta * Z^2 - \theta differs by derivation. By default, this is set to 2 following the - Feder derivation. In the Wakeshima derivation, \theta is 4pi - - Parameters - ---------- - theta : float - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.theta[index] = theta - - def setTemperature(self, temperature): - ''' - Sets isothermal temperature - - Parameters - ---------- - temperature : float - Temperature in Kelvin - ''' - #Store parameter in case model is reset - self.Tparameters = temperature - - self.T = np.full(self.steps, temperature, dtype=np.float32) - self._incubation = self._incubationIsothermal - - def setNonIsothermalTemperature(self, temperatureFunction): - ''' - Sets temperature as a function of time - - Parameters - ---------- - temperatureFunction : function - Takes in time and returns temperature in K - ''' - #Store parameter in case model is reset - self.Tparameters = temperatureFunction - - self.T = np.array([temperatureFunction(t) for t in self.time]) - - if len(np.unique(self.T)) == 1: - self._incubation = self._incubationIsothermal - else: - self._incubation = self._incubationNonIsothermal - - def setTemperatureArray(self, times, temperatures): - ''' - Sets temperature as a function of time interpolating between the inputted times and temperatures - - Parameters - ---------- - times : list - Time in hours for when the corresponding temperature is reached - temperatures : list - Temperatures in K to be reached at corresponding times - ''' - #Store parameter in case model is reset - self.Tparameters = (times, temperatures) - - self.T = np.full(self.steps, temperatures[0]) - for i in range(1, len(times)): - self.T[(self.time < 3600*times[i]) & (self.time >= 3600*times[i-1])] = (temperatures[i] - temperatures[i-1]) / (3600 * (times[i] - times[i-1])) * (self.time[(self.time < 3600*times[i]) & (self.time >= 3600*times[i-1])] - 3600 * times[i-1]) + temperatures[i-1] - self.T[self.time >= 3600*times[-1]] = temperatures[-1] - - if len(np.unique(self.T)) == 1: - self._incubation = self._incubationIsothermal - else: - self._incubation = self._incubationNonIsothermal - - def setStrainEnergy(self, strainEnergy, phase = None, calculateAspectRatio = False): - ''' - Sets strain energy class to precipitate - ''' - index = self.phaseIndex(phase) - self.strainEnergy[index] = strainEnergy - self.calculateAspectRatio[index] = calculateAspectRatio - - def _setupStrainEnergyFactors(self): - #For each phase, the strain energy calculation will be set to assume - # a spherical, cubic or ellipsoidal shape depending on the defined shape factors - for i in range(len(self.phases)): - self.strainEnergy[i].setup() - if self.strainEnergy[i].type != StrainEnergy.CONSTANT: - if self.shapeFactors[i].particleType == ShapeFactor.SPHERE: - self.strainEnergy[i].setSpherical() - elif self.shapeFactors[i].particleType == ShapeFactor.CUBIC: - self.strainEnergy[i].setCuboidal() - else: - self.strainEnergy[i].setEllipsoidal() - - def setDiffusivity(self, diffusivity): - ''' - Parameters - ---------- - diffusivity : function taking - Composition and temperature (K) and returning diffusivity (m2/s) - Function must have inputs in this order: f(x, T) - For multicomponent systems, x is an array - ''' - self.Diffusivity = diffusivity - - def setInfinitePrecipitateDiffusivity(self, infinite, phase = None): - ''' - Sets whether to assuming infinitely fast or no diffusion in phase - - Parameters - ---------- - infinite : bool - True will assume infinitely fast diffusion - False will assume no diffusion - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - Use 'all' to apply to all phases - ''' - if phase == 'all': - self.infinitePrecipitateDiffusion = [infinite for i in range(len(self.phases))] - else: - index = self.phaseIndex(phase) - self.infinitePrecipitateDiffusion[index] = infinite - - def setDrivingForce(self, drivingForce, phase = None): - ''' - Parameters - ---------- - drivingForce : function - Taking in composition (at. fraction) and temperature (K) and return driving force (J/mol) - f(x, T) = dg, where x is float for binary and array for multicomponent - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.dG[index] = drivingForce - - def setInterfacialComposition(self, composition, phase = None): - ''' - Parameters - ---------- - composition : function - Takes in temperature (K) and excess free energy (J/mol) and - returns a tuple of (matrix composition, precipitate composition) - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - - The excess free energy term will be taken as the interfacial curvature and elastic energy contributions. - This will be a positive value, so the function should ensure that the excess free energy to reduce the driving force - - If equilibrium cannot be solved, then the function should return (None, None) or (-1, -1) - ''' - index = self.phaseIndex(phase) - self.interfacialComposition[index] = composition - - def setThermodynamics(self, therm, phase = None, removeCache = False, addDiffusivity = True): - ''' - Parameters - ---------- - therm : Thermodynamics class - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - removeCache : bool (optional) - Will not cache equilibrium results if True (defaults to False) - addDiffusivity : bool (optional) - For binary systems, will add diffusivity functions from the database if present - Defaults to True - ''' - index = self.phaseIndex(phase) - self.dG[index] = lambda x, T, removeCache = removeCache: therm.getDrivingForce(x, T, precPhase=phase, training = removeCache) - - if self.numberOfElements == 1: - self.interfacialComposition[index] = lambda x, T: therm.getInterfacialComposition(x, T, precPhase=phase) - if (therm.mobCallables is not None or therm.diffCallables is not None) and addDiffusivity: - self.Diffusivity = lambda x, T, removeCache = removeCache: therm.getInterdiffusivity(x, T, removeCache = removeCache) - else: - self.interfacialComposition[index] = lambda x, T, dG, R, gExtra, removeCache = removeCache: therm.getGrowthAndInterfacialComposition(x, T, dG, R, gExtra, precPhase=phase, training = False) - self._betaFuncs[index] = lambda x, T, removeCache = removeCache: therm.impingementFactor(x, T, precPhase=phase, training = False) - - def setSurrogate(self, surr, phase = None): - ''' - Parameters - ---------- - surr : Surrogate class - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.dG[index] = surr.getDrivingForce - - if self.numberOfElements == 1: - self.interfacialComposition[index] = surr.getInterfacialComposition - else: - self.interfacialComposition[index] = surr.getGrowthAndInterfacialComposition - self._betaFuncs[index] = surr.impingementFactor - - def particleGibbs(self, radius, phase = None): - ''' - Returns Gibbs Thomson contribution of a particle given its radius - - Parameters - ---------- - radius : float or array - Precipitate radius - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - return self.VmBeta[index] * (self.strainEnergy[index].strainEnergy(self.shapeFactors[index].normalRadii(radius)) + 2 * self.shapeFactors[index].thermoFactor(radius) * self.gamma[index] / radius) - - def neglectEffectiveDiffusionDistance(self, neglect = True): - ''' - Whether or not to account for effective diffusion distance dependency on the supersaturation - By default, effective diffusion distance is considered - - Parameters - ---------- - neglect : bool (optional) - If True (default), will assume effective diffusion distance is particle radius - If False, will calculate correction factor from Chen, Jeppson and Agren (2008) - ''' - if neglect: - self.effDiffDistance = self.effDiffFuncs.noDiffusionDistance - else: - self.effDiffDistance = self.effDiffFuncs.effectiveDiffusionDistance - - def addStoppingCondition(self, variable, condition, value, phase = None, element = None, mode = 'or'): - ''' - Adds condition to stop simulation when condition is met - - Parameters - ---------- - variable : str - Variable to set condition for, options are - 'Volume Fraction' - 'Average Radius' - 'Driving Force' - 'Nucleation Rate' - 'Precipitate Density' - condition : str - Operator for condition, options are - 'greater than' or '>' - 'less than' or '<' - value : float - Value for condition - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - element : str (optional) - For 'Composition', element to consider for condition (defaults to first element in list) - mode : str (optional) - How the condition will be handled - 'or' (default) - at least one condition in this mode needs to be met before stopping - 'and' - all conditions in this mode need to be met before stopping - This will also record the times each condition is met - - Example - model.addStoppingCondition('Volume Fraction', '>', 0.002, 'beta') - will add a condition to stop simulation when the volume fraction of the 'beta' - phase becomes greater than 0.002 - ''' - index = self.phaseIndex(phase) - - if self._stoppingConditions is None: - self._stoppingConditions = [] - self.stopConditionTimes = [] - self._stopConditionMode = [] - - standardLabels = { - 'Volume Fraction': 'betaFrac', - 'Average Radius': 'avgR', - 'Driving Force': 'dGs', - 'Nucleation Rate': 'nucRate', - 'Precipitate Density': 'precipitateDensity', - } - otherLabels = ['Composition'] - - if variable in standardLabels: - if 'greater' in condition or '>' in condition: - cond = lambda self, i, p = index, var=standardLabels[variable] : getattr(self, var)[p,i] > value - elif 'less' in condition or '<' in condition: - cond = lambda self, i, p = index, var=standardLabels[variable] : getattr(self, var)[p,i] < value - else: - if variable == 'Composition': - eIndex = 0 if element is None else self.elements.index(element) - if 'greater' in condition or '>' in condition: - if self.numberOfElements > 1: - cond = lambda self, i, e = eIndex, var='xComp' : getattr(self, var)[i, e] > value - else: - cond = lambda self, i, var='xComp' : getattr(self, var)[i] > value - elif 'less' in condition or '<' in condition: - if self.numberOfElements > 1: - cond = lambda self, i, e = eIndex, var='xComp' : getattr(self, var)[i, e] < value - else: - cond = lambda self, i, var='xComp' : getattr(self, var)[i] < value - - self._stoppingConditions.append(cond) - self.stopConditionTimes.append(-1) - if mode == 'and': - self._stopConditionMode.append(False) - else: - self._stopConditionMode.append(True) - - def clearStoppingConditions(self): - ''' - Clears all stopping conditions - ''' - self._stoppingConditions = None - self.stopConditionTimes = None - self._stopConditionMode = None - - def printModelParameters(self): - ''' - Prints the model parameters - ''' - print('Temperature (K): {:.3f}'.format(self.T[0])) - print('Initial Composition (at%): {:.3f}'.format(100*self.xInit)) - print('Molar Volume (m3): {:.3e}'.format(self.VmAlpha)) - - for p in range(len(self.phases)): - print('Phase: {}'.format(self.phases[p])) - print('\tMolar Volume (m3): {:.3e}'.format(self.VmBeta[p])) - print('\tInterfacial Energy (J/m2): {:.3f}'.format(self.gamma[p])) - print('\tMinimum Radius (m): {:.3e}'.format(self.Rmin[p])) - - def setup(self): - ''' - Sets up hidden parameters before solving - Here it's just the nucleation density and the grain boundary nucleation factors - ''' - if not self._isNucleationSetup: - #Set nucleation density assuming grain size of 100 um and dislocation density of 5e12 m/m3 (Thermocalc default) - print('Nucleation density not set.\nSetting nucleation density assuming grain size of {:.0f} um and dislocation density of {:.0e} #/m2'.format(100, 5e12)) - self.setNucleationDensity(100, 1, 5e12) - for p in range(len(self.phases)): - self.Rmin[p] = self.minRadius - self._getNucleationDensity() - self._setGBfactors() - self._setupStrainEnergyFactors() - - def _printOutput(self, i): - ''' - Prints various terms at step i - ''' - if self.numberOfElements == 1: - print('N\tTime (s)\tTemperature (K)\tMatrix Comp') - print('{:.0f}\t{:.1e}\t\t{:.0f}\t\t{:.4f}\n'.format(i, self.time[i], self.T[i], 100*self.xComp[i])) - else: - compStr = 'N\tTime (s)\tTemperature (K)\t' - compValStr = '{:.0f}\t{:.1e}\t\t{:.0f}\t\t'.format(i, self.time[i], self.T[i]) - for a in range(self.numberOfElements): - compStr += self.elements[a] + '\t' - compValStr += '{:.4f}\t'.format(100*self.xComp[i,a]) - compValStr += '\n' - print(compStr) - print(compValStr) - print('\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)') - for p in range(len(self.phases)): - print('\t{}\t{:.3e}\t\t{:.4f}\t\t{:.4e}\t{:.4e}'.format(self.phases[p], self.precipitateDensity[p,i], 100*self.betaFrac[p,i], self.avgR[p,i], self.dGs[p,i]*self.VmBeta[p])) - print('') - - def solve(self, verbose = False, vIt = 1000): - ''' - Solves the KWN model between initial and final time - - Note: _calculateNucleationRate, _calculatePSD and _printOutput will need to be implemented in the child classes - - Parameters - ---------- - verbose : bool (optional) - Whether to print current simulation terms every couple iterations - Defaults to False - vIt : int (optional) - If verbose is True, vIt is how many iterations will pass before printing an output - Defaults to 1000 - ''' - self.setup() - - t0 = time.time() - - #While loop since number of steps may change with adaptive time stepping - i = 1 - stopCondition = False - while i < self.steps and not stopCondition: - self._iterate(i) - - #Apply stopping condition - if self._stoppingConditions is not None: - andConditions = True - numberOfAndConditions = 0 - orConditions = False - for s in range(len(self._stoppingConditions)): - #Record time if stopping condition is met - conditionResult = self._stoppingConditions[s](self, i) - if conditionResult and self.stopConditionTimes[s] == -1: - self.stopConditionTimes[s] = self.time[i] - - #If condition mode is 'or' - if self._stopConditionMode[s]: - orConditions = orConditions or conditionResult - #If condition mode is 'and' - else: - andConditions = andConditions and conditionResult - numberOfAndConditions += 1 - - #If there are no 'and' conditions, andConditions will be True - #Set to False so andConditions will not stop the model unneccesarily - if numberOfAndConditions == 0: - andConditions = False - - stopCondition = andConditions or orConditions - - #Print current variables - if i % vIt == 0 and verbose: - self._printOutput(i) - - i += 1 - - t1 = time.time() - if verbose: - print('Finished in {:.3f} seconds.'.format(t1 - t0)) - - def _iterate(self, i): - ''' - Blank iteration function to be implemented in other classes - ''' - pass - - def _nucleationRate(self, p, i): - ''' - Calculates nucleation rate at current timestep (normalized to number of nucleation sites) - This step is general to all systems except for how self._Beta is defined - - Parameters - ---------- - p : int - Phase index (int) - i : int - Current iteration - dt : float - Current time increment - ''' - #Most equations in literature take the driving force to be positive - #Although this really only changes the calculation of Rcrit since Z and beta is dG^2 - self.dGs[p, i], _ = self.dG[p](self.xComp[i-1], self.T[i]) - self.dGs[p, i] /= self.VmBeta[p] - - #Add strain energy for spherical shape, use previous critical radius - #This should still be correct even if the interfacial energy dominates at small radii since the aspect ratio may be near 1 - self.dGs[p, i] -= self.strainEnergy[p].strainEnergy(self.shapeFactors[p].normalRadii(self.Rcrit[p, i-1])) - - if self.dGs[p, i] < 0: - return self._noDrivingForce(p, i) - - #Only do this if there is some parent phase left (brute force solution for to avoid numerical errors) - if self.betaFrac[p, i-1] < 1: - - #Calculate critical radius - #For bulk or dislocation nucleation sites, use previous critical radius to get aspect ratio - if self.GB[p].nucleationSiteType == GBFactors.BULK or self.GB[p].nucleationSiteType == GBFactors.DISLOCATION: - self.Rcrit[p, i] = 2 * self.shapeFactors[p].thermoFactor(self.Rcrit[p, i-1]) * self.gamma[p] / self.dGs[p, i] - #self.Rcrit[p, i] = 2 * self.gamma[p] / self.dGs[p, i] - if self.Rcrit[p, i] < self.Rmin[p]: - self.Rcrit[p, i] = self.Rmin[p] - - self.Gcrit[p, i] = (4 * np.pi / 3) * self.gamma[p] * self.Rcrit[p, i]**2 - - #If nucleation is on a grain boundary, then use the critical radius as defined by the grain boundary type - else: - self.Rcrit[p, i] = self.GB[p].Rcrit(self.dGs[p, i]) - if self.Rcrit[p, i] < self.Rmin[p]: - self.Rcrit[p, i] = self.Rmin[p] - - self.Gcrit[p, i] = self.GB[p].Gcrit(self.dGs[p, i], self.Rcrit[p, i]) - - #Calculate nucleation rate - Z = self._Zeldovich(p, i) - self.betas[p,i] = self._Beta(p, i) - if self.betas[p,i] == 0: - return self._noDrivingForce(p, i) - - #Incubation time, either isothermal or nonisothermal - self.incubationSum[p] = self._incubation(Z, p, i) - if self.incubationSum[p] > 1: - self.incubationSum[p] = 1 - - return Z * self.betas[p,i] * np.exp(-self.Gcrit[p, i] / (self.kB * self.T[i])) * self.incubationSum[p] - - else: - return self._noDrivingForce(p, i) - - def _noCheckDT(self, i): - ''' - Default for no adaptive time stepping - ''' - pass - - def _noPostCheckDT(self, i): - ''' - Default for no adaptive time stepping - ''' - pass - - def _checkDT(self, i): - ''' - Default time increment function if implement (which is no implementation) - ''' - pass - - def _postCheckDT(self, i): - ''' - Default time increment function if implement (which is no implementation) - ''' - pass - - def _noDrivingForce(self, p, i): - ''' - Set everything to 0 if there is no driving force for precipitation - ''' - self.Rcrit[p, i] = 0 - self.incubationOffset[p] = np.amax([i-1, 0]) - return 0 - - def _nucleateFreeEnergy(self, Rsph, p, i): - ''' - Free energy change for a nucleate with radius of Rsph - ''' - volContribution = 4/3 * np.pi * Rsph**3 * (self.dGs[p,i] + self.strainEnergy[p].strainEnergy(self.shapeFactors[p].normalRadii(Rsph))) - areaContribution = 4 * np.pi * self.gamma[p] * Rsph**2 * self.shapeFactors[p].thermoFactor(Rsph) - return -volContribution + areaContribution - - def _Zeldovich(self, p, i): - ''' - Zeldovich factor - probability that cluster at height of nucleation barrier will continue to grow - ''' - return np.sqrt(3 * self.GB[p].volumeFactor / (4 * np.pi)) * self.VmBeta[p] * np.sqrt(self.gamma[p] / (self.kB * self.T[i])) / (2 * np.pi * self.avo * self.Rcrit[p,i]**2) - - def _BetaBinary1(self, p, i): - ''' - Impingement rate for binary systems using Perez et al - ''' - return self.GB[p].areaFactor * self.Rcrit[p,i]**2 * self.xComp[0] * self.Diffusivity(self.xComp[i-1], self.T[i]) / self.aAlpha**4 - - def _BetaBinary2(self, p, i): - ''' - Impingement rate for binary systems taken from Thermocalc prisma documentation - This will follow the same equation as with _BetaMulti; however, some simplications can be made based off the summation contraint - ''' - D = self.Diffusivity(self.xComp[i-1], self.T[i]) - Dfactor = (self.xEqBeta[p,i-1] - self.xEqAlpha[p,i-1])**2 / (self.xEqAlpha[p,i-1]*D) + (self.xEqBeta[p,i-1] - self.xEqAlpha[p,i-1])**2 / ((1 - self.xEqAlpha[p,i-1])*D) - return self.GB[p].areaFactor * self.Rcrit[p,i]**2 * (1/Dfactor) / self.aAlpha**4 - - def _BetaMulti(self, p, i): - ''' - Impingement rate for multicomponent systems - ''' - if self._betaFuncs[p] is None: - return self._defaultBeta - else: - beta = self._betaFuncs[p](self.xComp[i-1], self.T[i-1]) - if beta is None: - return self.betas[p,i-1] - else: - return (self.GB[p].areaFactor * self.Rcrit[p, i]**2 / self.aAlpha**4) * beta - - def _incubationIsothermal(self, Z, p, i): - ''' - Incubation time for isothermal conditions - ''' - tau = 1 / (self.theta[p] * (self.betas[p,i] * Z**2)) - return np.exp(-tau / self.time[i]) - - def _incubationNonIsothermal(self, Z, p, i): - ''' - Incubation time for non-isothermal conditions - This must match isothermal conditions if the temperature is constant - - Solve for integral(beta(t-t0)) from 0 to tau = 1/theta*Z(tau)^2 - ''' - LHS = 1 / (self.theta[p] * Z**2 * (self.T[i] / self.T[int(self.incubationOffset[p]):])) - - RHS = np.cumsum(self.betas[p,int(self.incubationOffset[p])+1:i] * (self.time[int(self.incubationOffset[p])+1:i] - self.time[int(self.incubationOffset[p]):i-1])) - if len(RHS) == 0: - RHS = self.betas[p,i] * (self.time[int(self.incubationOffset[p]):] - self.time[int(self.incubationOffset[p])]) - else: - RHS = np.concatenate((RHS, RHS[-1] + self.betas[p,i] * (self.time[i-1:] - self.time[int(self.incubationOffset[p])]))) - - #Test for intersection - diff = RHS - LHS - signChange = np.sign(diff[:-1]) != np.sign(diff[1:]) - - #If no intersection - if not any(signChange): - #If RHS > LHS, then intersection is at t = 0 - if diff[0] > 0: - tau = 0 - #Else, RHS intersects LHS beyond simulation time - #Extrapolate integral of RHS from last point to intersect LHS - #integral(beta(t-t0)) from t0 to ti + beta_i * (tau - (ti - t0)) = 1 / theta * Z(tau+t0)^2 - else: - tau = LHS[-1] / self.betas[p,i] - RHS[-1] / self.betas[p,i] + (self.time[i] - self.time[int(self.incubationOffset[p])]) - else: - tau = self.time[int(self.incubationOffset[p]):-1][signChange][0] - self.time[int(self.incubationOffset[p])] - - return np.exp(-tau / (self.time[i] - self.time[int(self.incubationOffset[p])])) - - def _setNucleateRadius(self, i): - ''' - Adds 1/2 * sqrt(kb T / pi gamma) to critical radius to ensure they grow when growth rates are calculated - ''' - for p in range(len(self.phases)): - #If nucleates form, then calculate radius of precipitate - #Radius is set slightly larger so precipitate - if self.nucRate[p,i]*(self.time[i]-self.time[i-1]) >= self.minNucleateDensity and self.Rcrit[p, i] >= self.Rmin[p]: - self.Rad[p, i] = self.Rcrit[p, i] + 0.5 * np.sqrt(self.kB * self.T[i] / (np.pi * self.gamma[p])) - else: - self.Rad[p, i] = 0 - - def getTimeAxis(self, timeUnits='s', bounds=None): - ''' - Returns scaling factor, label and x-limits depending on units of time - - Parameters - ---------- - timeUnits : str - 's' / 'sec' / 'seconds' - seconds - 'min' / 'minutes' - minutes - 'h' / 'hrs' / 'hours' - hours - ''' - timeScale = 1 - timeLabel = 'Time (s)' - if 'min' in timeUnits: - timeScale = 1/60 - timeLabel = 'Time (min)' - if 'h' in timeUnits: - timeScale = 1/3600 - timeLabel = 'Time (hrs)' - - if bounds is None: - if self.t0 == 0: - bounds = [timeScale * 1e-5 * self.tf, timeScale * self.tf] - else: - bounds = [timeScale * self.t0, timeScale * self.tf] - - return timeScale, timeLabel, bounds - - - def plot(self, axes, variable, bounds = None, timeUnits = 's', radius='spherical', *args, **kwargs): - ''' - Plots model outputs - - Parameters - ---------- - axes : Axis - variable : str - Specified variable to plot - Options are 'Volume Fraction', 'Total Volume Fraction', 'Critical Radius', - 'Average Radius', 'Volume Average Radius', 'Total Average Radius', - 'Total Volume Average Radius', 'Aspect Ratio', 'Total Aspect Ratio' - 'Driving Force', 'Nucleation Rate', 'Total Nucleation Rate', - 'Precipitate Density', 'Total Precipitate Density', - 'Temperature' and 'Composition' - - Note: for multi-phase simulations, adding the word 'Total' will - sum the variable for all phases. Without the word 'Total', the variable - for each phase will be plotted separately - - bounds : tuple (optional) - Limits on the x-axis (float, float) or None (default, this will set bounds to (initial time, final time)) - timeUnits : str (optional) - Plot time dependent variables per seconds ('s'), minutes ('min') or hours ('h') - radius : str (optional) - For non-spherical precipitates, plot the Average Radius by the - - Equivalent spherical radius ('spherical') - Short axis ('short') - Long axis ('long') - Note: Total Average Radius and Volume Average Radius will still use the equivalent spherical radius - *args, **kwargs - extra arguments for plotting - ''' - timeScale, timeLabel, bounds = self.getTimeAxis(timeUnits, bounds) - - axes.set_xlabel(timeLabel) - axes.set_xlim(bounds) - - labels = { - 'Volume Fraction': 'Volume Fraction', - 'Total Volume Fraction': 'Volume Fraction', - 'Critical Radius': 'Critical Radius (m)', - 'Average Radius': 'Average Radius (m)', - 'Volume Average Radius': 'Volume Average Radius (m)', - 'Total Average Radius': 'Average Radius (m)', - 'Total Volume Average Radius': 'Volume Average Radius (m)', - 'Aspect Ratio': 'Mean Aspect Ratio', - 'Total Aspect Ratio': 'Mean Aspect Ratio', - 'Driving Force': 'Driving Force (J/m$^3$)', - 'Nucleation Rate': 'Nucleation Rate (#/m$^3$-s)', - 'Total Nucleation Rate': 'Nucleation Rate (#/m$^3$-s)', - 'Precipitate Density': 'Precipitate Density (#/m$^3$)', - 'Total Precipitate Density': 'Precipitate Density (#/m$^3$)', - 'Temperature': 'Temperature (K)', - 'Composition': 'Matrix Composition (at.%)', - 'Eq Composition Alpha': 'Matrix Composition (at.%)', - 'Eq Composition Beta': 'Matrix Composition (at.%)', - 'Supersaturation': 'Supersaturation', - 'Eq Volume Fraction': 'Volume Fraction' - } - - totalVariables = ['Total Volume Fraction', 'Total Average Radius', 'Total Aspect Ratio', \ - 'Total Nucleation Rate', 'Total Precipitate Density'] - singleVariables = ['Volume Fraction', 'Critical Radius', 'Average Radius', 'Aspect Ratio', \ - 'Driving Force', 'Nucleation Rate', 'Precipitate Density'] - eqCompositions = ['Eq Composition Alpha', 'Eq Composition Beta'] - saturations = ['Supersaturation', 'Eq Volume Fraction'] - - if variable == 'Temperature': - axes.semilogx(timeScale * self.time, self.T, *args, **kwargs) - axes.set_ylabel(labels[variable]) - - elif variable == 'Composition': - if self.numberOfElements == 1: - axes.semilogx(timeScale * self.time, self.xComp, *args, **kwargs) - axes.set_ylabel('Matrix Composition (at.% ' + self.elements[0] + ')') - else: - for i in range(self.numberOfElements): - #Keep color consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement - if 'color' in kwargs: - axes.semilogx(timeScale * self.time, self.xComp[:,i], label=self.elements[i], *args, **kwargs) - else: - axes.semilogx(timeScale * self.time, self.xComp[:,i], label=self.elements[i], color='C'+str(i), *args, **kwargs) - axes.legend(self.elements) - axes.set_ylabel(labels[variable]) - yRange = [np.amin(self.xComp), np.amax(self.xComp)] - axes.set_ylim([yRange[0] - 0.1 * (yRange[1] - yRange[0]), yRange[1] + 0.1 * (yRange[1] - yRange[0])]) - - elif variable in eqCompositions: - if variable == 'Eq Composition Alpha': - plotVariable = self.xEqAlpha - elif variable == 'Eq Composition Beta': - plotVariable = self.xEqBeta - - if len(self.phases) == 1: - if self.numberOfElements == 1: - axes.semilogx(timeScale * self.time, plotVariable[0], *args, **kwargs) - axes.set_ylabel('Matrix Composition (at.% ' + self.elements[0] + ')') - else: - for i in range(self.numberOfElements): - #Keep color consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement - if 'color' in kwargs: - axes.semilogx(timeScale * self.time, plotVariable[0,:,i], label=self.elements[i]+'_Eq', *args, **kwargs) - else: - axes.semilogx(timeScale * self.time, plotVariable[0,:,i], label=self.elements[i]+'_Eq', color='C'+str(i), *args, **kwargs) - axes.legend() - axes.set_ylabel(labels[variable]) - else: - if self.numberOfElements == 1: - for p in range(len(self.phases)): - #Keep color somewhat consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement - if 'color' in kwargs: - axes.semilogx(timeScale * self.time, plotVariable[p], label=self.phases[p]+'_Eq', *args, **kwargs) - else: - axes.semilogx(timeScale * self.time, plotVariable[p], label=self.phases[p]+'_Eq', color='C'+str(p), *args, **kwargs) - axes.legend() - axes.set_ylabel('Matrix Composition (at.% ' + self.elements[0] + ')') - else: - cIndex = 0 - for p in range(len(self.phases)): - for i in range(self.numberOfElements): - #Keep color somewhat consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement - if 'color' in kwargs: - axes.semilogx(timeScale * self.time, plotVariable[p,:,i], label=self.phases[p]+'_'+self.elements[i]+'_Eq', *args, **kwargs) - else: - axes.semilogx(timeScale * self.time, plotVariable[p,:,i], label=self.phases[p]+'_'+self.elements[i]+'_Eq', color='C'+str(cIndex), *args, **kwargs) - cIndex += 1 - axes.legend() - axes.set_ylabel(labels[variable]) - - elif variable in saturations: - #Since supersaturation is calculated in respect to the tie-line, it is the same for each element - #Thus only a single element is needed - plotVariable = np.zeros(self.betaFrac.shape) - for p in range(len(self.phases)): - if self.numberOfElements == 1: - if variable == 'Eq Volume Fraction': - num = self.xComp[0] - self.xEqAlpha[p] - else: - num = self.xComp - self.xEqAlpha[p] - den = self.xEqBeta[p] - self.xEqAlpha[p] - else: - if variable == 'Eq Volume Fraction': - num = self.xComp[0,0] - self.xEqAlpha[p,:,0] - else: - num = self.xComp[:,0] - self.xEqAlpha[p,:,0] - den = self.xEqBeta[p,:,0] - self.xEqAlpha[p,:,0] - #If precipitate is unstable, both xEqAlpha and xEqBeta are set to 0 - #For these cases, change the values of numerator and denominator so that supersaturation is 0 instead of undefined - num[den == 0] = 0 - den[den == 0] = 1 - plotVariable[p] = num / den - - if len(self.phases) == 1: - axes.semilogx(timeScale * self.time, plotVariable[0], *args, **kwargs) - else: - for p in range(len(self.phases)): - if 'color' in kwargs: - axes.semilogx(timeScale * self.time, plotVariable[p], label=self.phases[p], *args, **kwargs) - else: - axes.semilogx(timeScale * self.time, plotVariable[p], label=self.phases[p], color='C'+str(p), *args, **kwargs) - axes.legend() - axes.set_ylabel(labels[variable]) - - elif variable in singleVariables: - if variable == 'Volume Fraction': - plotVariable = self.betaFrac - elif variable == 'Critical Radius': - plotVariable = self.Rcrit - elif variable == 'Average Radius': - plotVariable = self.avgR - for p in range(len(self.phases)): - if self.GB[p].nucleationSiteType == self.GB[p].BULK or self.GB[p].nucleationSiteType == self.GB[p].DISLOCATION: - if radius != 'spherical': - plotVariable[p] /= self.shapeFactors[p].eqRadiusFactor(self.avgR[p]) - if radius == 'long': - plotVariable[p] *= self.avgAR[p] - else: - plotVariable[p] *= self._GBareaRemoval(p) - - elif variable == 'Volume Average Radius': - plotVariable = np.cbrt(self.betaFrac / self.precipitateDensity / (4/3*np.pi)) - elif variable == 'Aspect Ratio': - plotVariable = self.avgAR - elif variable == 'Driving Force': - plotVariable = self.dGs - elif variable == 'Nucleation Rate': - plotVariable = self.nucRate - elif variable == 'Precipitate Density': - plotVariable = self.precipitateDensity - - if (len(self.phases)) == 1: - axes.semilogx(timeScale * self.time, plotVariable[0], *args, **kwargs) - else: - for p in range(len(self.phases)): - axes.semilogx(timeScale * self.time, plotVariable[p], label=self.phases[p], color='C'+str(p), *args, **kwargs) - axes.legend() - axes.set_ylabel(labels[variable]) - yb = 1 if variable == 'Aspect Ratio' else 0 - axes.set_ylim([yb, 1.1 * np.amax(plotVariable)]) - - elif variable in totalVariables: - if variable == 'Total Volume Fraction': - plotVariable = np.sum(self.betaFrac, axis=0) - elif variable == 'Total Average Radius': - totalN = np.sum(self.precipitateDensity, axis=0) - totalN[totalN == 0] = 1 - totalR = np.sum(self.avgR * self.precipitateDensity, axis=0) - plotVariable = totalR / totalN - elif variable == 'Total Volume Average Radius': - totalN = np.sum(self.precipitateDensity, axis=0) - totalN[totalN == 0] = 1 - totalVol = np.sum(self.betaFrac, axis=0) - plotVariable = np.cbrt(totalVol / totalN) - elif variable == 'Total Aspect Ratio': - totalN = np.sum(self.precipitateDensity, axis=0) - totalN[totalN == 0] = 1 - totalAR = np.sum(self.avgAR * self.precipitateDensity, axis=0) - plotVariable = totalAR / totalN - elif variable == 'Total Nucleation Rate': - plotVariable = np.sum(self.nucRate, axis=0) - elif variable == 'Total Precipitate Density': - plotVariable = np.sum(self.precipitateDensity, axis=0) - - axes.semilogx(timeScale * self.time, plotVariable, *args, **kwargs) - axes.set_ylabel(labels[variable]) - yb = 1 if variable == 'Total Aspect Ratio' else 0 - axes.set_ylim(bottom=yb) diff --git a/kawin/KWNEuler.py b/kawin/KWNEuler.py deleted file mode 100644 index f143c6b..0000000 --- a/kawin/KWNEuler.py +++ /dev/null @@ -1,1061 +0,0 @@ -import numpy as np -from kawin.KWNBase import PrecipitateBase -from kawin.PopulationBalance import PopulationBalanceModel -from kawin.GrainBoundaries import GBFactors -import copy -import csv -from itertools import zip_longest -import time - -class PrecipitateModel (PrecipitateBase): - ''' - Euler implementation of the KWN model designed for binary systems - - Parameters - ---------- - t0 : float - Initial time in seconds - tf : float - Final time in seconds - steps : int - Number of time steps - phases : list (optional) - Precipitate phases (array of str) - If only one phase is considered, the default is ['beta'] - linearTimeSpacing : bool (optional) - Whether to have time increment spaced linearly or logarithimically - Defaults to False - elements : list (optional) - Solute elements in system - Note: order of elements must correspond to order of elements set in Thermodynamics module - If binary system, then defualt is ['solute'] - ''' - def __init__(self, t0, tf, steps, phases = ['beta'], linearTimeSpacing = False, elements = ['solute']): - #Initialize base class - super().__init__(t0, tf, steps, phases, linearTimeSpacing, elements) - - if self.numberOfElements == 1: - self._growthRate = self._growthRateBinary - self._Beta = self._BetaBinary1 - else: - self._growthRate = self._growthRateMulti - self._Beta = self._BetaMulti - - #Additional outputs - self.additionalFunctions = [] - self.additionalFunctionNames = [] - self.additionalOutputs = None - - def _resetArrays(self): - ''' - Resets and initializes arrays for all variables - - In addition to PrecipitateBase, the equilibrium aspect ratio area and population balance models are created here - ''' - super()._resetArrays() - self.PBM = [PopulationBalanceModel() for p in self.phases] - - #Index of particle size classes which below, precipitates are unstable - self.RdrivingForceIndex = np.zeros(len(self.phases), dtype=np.int32) - - #Aspect ratio - self.eqAspectRatio = [[] for p in self.phases] - - def reset(self): - ''' - Resets model results - ''' - super().reset() - - #Bounds of the bins in PSD - for i in range(len(self.phases)): - self.PBM[i].reset() - - #Resets PSD outputs - self._setupAdditionalOutputs() - - def save(self, filename, compressed = False, toCSV = False): - ''' - Save results into a numpy .npz format - - Parameters - ---------- - filename : str - compressed : bool - If true, will save compressed .npz format - toCSV : bool - If true, wil save to .csv - ''' - variables = ['t0', 'tf', 'steps', 'phases', 'linearTimeSpacing', 'elements', \ - 'time', 'xComp', 'Rcrit', 'Gcrit', 'Rad', 'avgR', 'avgAR', 'betaFrac', 'nucRate', 'precipitateDensity', 'dGs', 'xEqAlpha', 'xEqBeta'] - vDict = {v: getattr(self, v) for v in variables} - if self.additionalOutputs is not None: - vDict['additionalOutputs'] = self.additionalOutputs - if not toCSV: - vDict['additionalFunctionNames'] = self.additionalFunctionNames - for p in range(len(self.phases)): - vDict['PSDdata_'+self.phases[p]] = [self.PBM[p].min, self.PBM[p].max, self.PBM[p].bins] - vDict['PSDsize_' + self.phases[p]] = self.PBM[p].PSDsize - vDict['PSD_' + self.phases[p]] = self.PBM[p].PSD - vDict['PSDbounds_' + self.phases[p]] = self.PBM[p].PSDbounds - vDict['eqAspectRatio_' + self.phases[p]] = self.eqAspectRatio[p] - - if toCSV: - vDict['t0'] = np.array([vDict['t0']]) - vDict['tf'] = np.array([vDict['tf']]) - vDict['steps'] = np.array([vDict['steps']]) - vDict['linearTimeSpacing'] = np.array([vDict['linearTimeSpacing']]) - if self.numberOfElements == 2: - vDict['xComp'] = vDict['xComp'].T - arrays = [] - headers = [] - for v in vDict: - vDict[v] = np.array(vDict[v]) - if len(vDict[v].shape) == 2: - for i in range(len(vDict[v])): - arrays.append(vDict[v][i]) - if v == 'xComp': - headers.append(v + '_' + self.elements[i]) - else: - headers.append(v + '_' + self.phases[i]) - elif v == 'xEqAlpha' or v == 'xEqBeta': - for i in range(len(self.phases)): - for j in range(self.numberOfElements): - arrays.append(vDict[v][i,:,j]) - headers.append(v + '_' + self.phases[i] + '_' + self.elements[j]) - elif v == 'additionalOutputs': - for i in range(len(self.phases)): - for j in range(len(self.additionalFunctionNames)): - arrays.append(vDict[v][i,:,j]) - headers.append(v + '_' + self.phases[i] + '_' + self.additionalFunctionNames[j]) - else: - arrays.append(vDict[v]) - headers.append(v) - rows = zip_longest(*arrays, fillvalue='') - if '.csv' not in filename.lower(): - filename = filename + '.csv' - with open(filename, 'w', newline='') as f: - csv.writer(f).writerow(headers) - csv.writer(f).writerows(rows) - else: - if compressed: - np.savez_compressed(filename, **vDict) - #np.savez_compressed(filename, **vDict, allow_pickle=True) - else: - np.savez(filename, **vDict) - #np.savez(filename, **vDict, allow_pickle=True) - - def load(filename): - ''' - Loads data - - Parameters - ---------- - filename : str - - Returns - ------- - PrecipitateModel object - Note: this will only contain model outputs which can be used for plotting - ''' - setupVars = ['t0', 'tf', 'steps', 'phases', 'linearTimeSpacing', 'elements'] - if '.np' in filename.lower(): - data = np.load(filename, allow_pickle=True) - - #Input arbitrary values for PSD parameters (rMin, rMax, bins) since this will be changed shortly after - model = PrecipitateModel(data['t0'], data['tf'], data['steps'], data['phases'], data['linearTimeSpacing'], data['elements']) - for p in range(len(model.phases)): - PSDvars = ['PSDdata_' + model.phases[p], 'PSD_' + model.phases[p], 'PSDsize_' + model.phases[p], 'eqAspectRatio_' + model.phases[p], 'PSDbounds_' + model.phases[p]] - #For back compatibility - if PSDvars[0] not in data: - PSDvars = ['PSDdata' + str(p), 'PSD' + str(p), 'PSDsize' + str(p), 'eqAspectRatio' + str(p), 'PSDbounds' + str(p)] - setupVars = np.concatenate((setupVars, PSDvars)) - model.PBM[p] = PopulationBalanceModel(data[PSDvars[0]][0], data[PSDvars[0]][1], int(data[PSDvars[0]][2]), True) - model.PBM[p].PSD = data[PSDvars[1]] - model.PBM[p].PSDsize = data[PSDvars[2]] - model.eqAspectRatio[p] = data[PSDvars[3]] - model.PBM[p].PSDbounds = data[PSDvars[4]] - for d in data: - if d not in setupVars: - setattr(model, d, data[d]) - if 'additionalOutputs' not in data: - model.additionalOutputs = None - model.additionalFunctions = [] - model.additionalFunctionNames = [] - elif '.csv' in filename.lower(): - with open(filename, 'r') as csvFile: - data = csv.reader(csvFile, delimiter=',') - i = 0 - headers = [] - columns = {} - #Grab all columns - for row in data: - if i == 0: - headers = row - columns = {h: [] for h in headers} - else: - for j in range(len(row)): - if row[j] != '': - columns[headers[j]].append(row[j]) - i += 1 - - t0, tf, steps, phases, elements = float(columns['t0'][0]), float(columns['tf'][0]), int(columns['steps'][0]), columns['phases'], columns['elements'] - linearTimeSpacing = True if columns['linearTimeSpacing'][0] == 'True' else False - model = PrecipitateModel(t0, tf, steps, phases, linearTimeSpacing, elements) - - for p in range(len(model.phases)): - PSDvars = ['PSDdata_' + model.phases[p], 'PSD_' + model.phases[p], 'PSDsize_' + model.phases[p], 'eqAspectRatio_' + model.phases[p], 'PSDbounds_' + model.phases[p]] - #For back compatibility - if PSDvars[0] not in columns: - PSDvars = ['PSDdata' + str(p), 'PSD' + str(p), 'PSDsize' + str(p), 'eqAspectRatio' + str(p), 'PSDbounds' + str(p)] - setupVars = np.concatenate((setupVars, PSDvars)) - model.PBM[p] = PopulationBalanceModel(float(columns[PSDvars[0]][0]), float(columns[PSDvars[0]][1]), int(float(columns[PSDvars[0]][2])), True) - model.PBM[p].PSD = np.array(columns[PSDvars[1]], dtype='float') - model.PBM[p].PSDsize = np.array(columns[PSDvars[2]], dtype='float') - model.eqAspectRatio[p] = np.array(columns[PSDvars[3]], dtype='float') - model.PBM[p].PSDbounds = np.array(columns[PSDvars[4]], dtype='float') - - restOfVariables = ['time', 'xComp', 'Rcrit', 'Gcrit', 'Rad', 'avgR', 'avgAR', 'betaFrac', 'nucRate', 'precipitateDensity', 'dGs', 'xEqAlpha', 'xEqBeta', 'additionalOutputs'] - restOfColumns = {v: [] for v in restOfVariables} - additionalFunctionNames = [] - for d in columns: - if d not in setupVars: - if d == 'time': - restOfColumns[d] = np.array(columns[d], dtype='float') - elif d == 'xComp': - if model.numberOfElements == 1: - restOfColumns[d] = np.array(columns[d], dtype='float') - else: - restOfColumns['xComp'].append(columns[d], dtype='float') - else: - selectedVar = '' - for r in restOfVariables: - if r in d: - selectedVar = r - if selectedVar == 'additionalOutputs': - additionalFunctionNames.append(d[18:]) - restOfColumns[selectedVar].append(np.array(columns[d], dtype='float')) - for d in restOfColumns: - restOfColumns[d] = np.array(restOfColumns[d]) - setattr(model, d, restOfColumns[d]) - - #For multicomponent systems, adjust as necessary such that number of elements will be the last axis - if model.numberOfElements > 1: - model.xComp = model.xComp.T - if len(model.phases) == 1: - model.xEqAlpha = np.expand_dims(model.xEqAlpha, 0) - model.xEqBeta = np.expand_dims(model.xEqBeta, 0) - else: - model.xEqAlpha = np.reshape(model.xEqAlpha, ((len(model.phases), model.numberOfElements, len(model.time)))) - model.xEqBeta = np.reshape(model.xEqBeta, ((len(model.phases), model.numberOfElements, len(model.time)))) - model.xEqAlpha = np.transpose(model.xEqAlpha, (0, 2, 1)) - model.xEqBeta = np.transpose(model.xEqBeta, (0, 2, 1)) - - #If additional outputs exists, then reshape array to (phase, iterations, functions) - if len(additionalFunctionNames) > 0: - numberOfFunctions = int(len(additionalFunctionNames) / len(model.phases)) - model.additionalOutputs = np.reshape(model.additionalOutputs, (len(model.phases), numberOfFunctions, len(model.time))) - model.additionalOutputs = np.transpose(model.additionalOutputs, (0, 2, 1)) - model.additionalFunctionNames = [] - for i in range(numberOfFunctions): - model.additionalFunctionNames.append(additionalFunctionNames[i][len(model.phases[0])+1:]) - model.additionalFunctionNames = np.array(model.additionalFunctionNames) - - return model - - def _divideTimestep(self, i, dt): - ''' - Divides timestep at iteration i - ''' - super()._divideTimestep(i, dt) - - if len(self.additionalFunctions) > 0: - self.additionalOutputs = np.append(self.additionalOutputs, np.zeros((len(self.phases), 1, len(self.additionalFunctions))), axis=1) - - def setPBMParameters(self, cMin = 1e-10, cMax = 1e-9, bins = 150, minBins = 100, maxBins = 200, adaptive = True, phase = None): - ''' - Sets population balance model parameters for each phase - - Parameters - ---------- - cMin : float - Minimum bin size - cMax : float - Maximum bin size - bins : int - Initial number of bins - minBins : int - Minimum number of bins - will not be used if adaptive = False - maxBins : int - Maximum number of bins - will not be used if adaptive = False - adaptive : bool - Sets adaptive bin sizes - bins may still change upon nucleation - phase : str - Phase to consider (will set all phases if phase = None or 'all') - ''' - if phase is None or phase == 'all': - for p in range(len(self.phases)): - self.PBM[p] = PopulationBalanceModel(cMin, cMax, bins, minBins, maxBins) - self.PBM[p].setAdaptiveBinSize(adaptive) - else: - index = self.phaseIndex(phase) - self.PBM[index] = PopulationBalanceModel(cMin, cMax, bins, minBins, maxBins) - self.PBM[index].setAdaptiveBinSize(adaptive) - - def loadParticleSizeDistribution(self, data, phase = None): - ''' - Loads particle size distribution for specified phase - - Parameters - ---------- - data : array - Array of data containing precipitate sizes - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - self.PBM[index].LoadDistribution(data) - - def addAdditionalOutput(self, name, f): - ''' - Creates output based off PSD - - Parameters - ---------- - name : str - Name of the function - f : function - Takes in model, phase index and iteration index and returns a value - ''' - if name in self.additionalFunctionNames: - i = 1 - name = name + '_{}'.format(i) - while name in self.additionalFunctionNames: - i += 1 - name = name[:-2] - name = name + '_{}'.format(i) - print('Warning: Function \'{}\' has already been set, this function will be stored as \'{}\''.format(name[:-2], name)) - - self.additionalFunctions.append(f) - self.additionalFunctionNames = np.append(self.additionalFunctionNames, name) - - def _setupAdditionalOutputs(self): - ''' - Function to setup PSD output arrays, will be used in setup and reset functions - ''' - #Resets PSD outputs - if len(self.additionalFunctions) > 0: - self.additionalOutputs = np.zeros((len(self.phases), self.steps, len(self.additionalFunctions))) - - def _calculateAdditionalOutputs(self, i): - ''' - Calculates additional PSD functions - ''' - for f in range(len(self.additionalFunctions)): - for p in range(len(self.phases)): - self.additionalOutputs[p, i, f] = self.additionalFunctions[f](self, p, i) - - def getAdditionalOutput(self, name): - ''' - Gets additional output by name - - Parameters - ---------- - name : str - Name of function used for the additional output - - Returns - ------- - (p, N) array for the output for each phase - ''' - if name in self.additionalFunctionNames: - index, = np.where(self.additionalFunctionNames == name) - return self.additionalOutputs[:, :, index[0]] - - def particleRadius(self, phase = None): - ''' - Returns PSD bounds of given phase - - Parameters - ---------- - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - return self.PBM[index].PSDbounds - - def particleGibbs(self, radius = None, phase = None): - ''' - Returns Gibbs Thomson contribution of a particle given its radius - - Parameters - ---------- - radius : array (optional) - Precipitate radaii (defaults to None, which will use boundaries - of the size classes of the precipitate PSD) - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - if radius is None: - index = self.phaseIndex(phase) - radius = self.PBM[index].PSDbounds - return super().particleGibbs(radius, phase) - - def PSD(self, phase = None): - ''' - Returns frequency of particle size distribution of given phase - - Parameters - ---------- - phase : str (optional) - Phase to consider (defaults to first precipitate in list) - ''' - index = self.phaseIndex(phase) - return self.PBM[index].PSD - - def createLookup(self, i = 0): - ''' - This creates a lookup table mapping the particle size classes to the interfacial composition - ''' - #RdrivingForceIndex will find the index of the largest particle size class where the precipitate is unstable - #This is determined by the interfacial composition function, where it should return -1 or None - #All compositions from the PSD bounds will be set to the compositions just above RdrivingForceLimit - #This is just to allow for particles to dissolve instead of pile up in the smallest bin - self.RdrivingForceIndex = np.zeros(len(self.phases), dtype=np.int32) - - #Keep as separate arrays so that number of PSD classes can change within precipitate phases - self.PSDXalpha = [] - self.PSDXbeta = [] - - for p in range(len(self.phases)): - #Interfacial compositions at equilibrium (planar interface) - self.xEqAlpha[p,i], self.xEqBeta[p,i] = self.interfacialComposition[p](self.T[i], 0) - if self.xEqAlpha[p,i] == -1 or self.xEqAlpha[p,i] is None: - self.xEqAlpha[p,i] = 0 - self.xEqBeta[p,i] = 0 - - #Interfacial compositions at each size class in PSD - self.PSDXalpha.append(np.zeros(self.PBM[p].bins + 1)) - self.PSDXbeta.append(np.zeros(self.PBM[p].bins + 1)) - - self.PSDXalpha[p], self.PSDXbeta[p] = self.interfacialComposition[p](self.T[i], self.particleGibbs(self.PBM[p].PSDbounds, self.phases[p])) - self.RdrivingForceIndex[p] = np.argmax(self.PSDXalpha[p] != -1)-1 - self.RdrivingForceIndex[p] = 0 if self.RdrivingForceIndex[p] < 0 else self.RdrivingForceIndex[p] - self.RdrivingForceLimit[p] = self.PBM[p].PSDbounds[self.RdrivingForceIndex[p]] - - #Sets particle radii smaller than driving force limit to driving force limit composition - #If RdrivingForceIndex is at the end of the PSDX arrays, then no precipitate in the size classes of the PSD is stable - #This can occur in non-isothermal situations where the temperature gets too high - if self.RdrivingForceIndex[p]+1 < len(self.PSDXalpha[p]): - self.PSDXalpha[p][:self.RdrivingForceIndex[p]+1] = self.PSDXalpha[p][self.RdrivingForceIndex[p]+1] - self.PSDXbeta[p][:self.RdrivingForceIndex[p]+1] = self.PSDXbeta[p][self.RdrivingForceIndex[p]+1] - else: - self.PSDXalpha[p] = np.zeros(self.PBM[p].bins + 1) - self.PSDXbeta[p] = np.zeros(self.PBM[p].bins + 1) - - def setup(self): - ''' - Sets up additional variables in addition to PrecipitateBase - - Sets up additional outputs, population balance models, equilibrium aspect ratio and equilibrium compositions - ''' - super().setup() - - self._setupAdditionalOutputs() - - #Equilibrium aspect ratio and PBM setup - #If calculateAspectRatio is True, then use strain energy to calculate aspect ratio for each size class in PSD - #Else, then use aspect ratio defined in shape factors - self.eqAspectRatio = [None for p in range(len(self.phases))] - for p in range(len(self.phases)): - self.PBM[p].reset() - - if self.calculateAspectRatio[p]: - self.eqAspectRatio[p] = self.strainEnergy[p].eqAR_bySearch(self.PBM[p].PSDbounds, self.gamma[p], self.shapeFactors[p]) - arFunc = lambda R, p1=p : self._interpolateAspectRatio(R, p1) - self.shapeFactors[p].setAspectRatio(arFunc) - else: - self.eqAspectRatio[p] = self.shapeFactors[p].aspectRatio(self.PBM[p].PSDbounds) - - #Only create lookup table for binary system - if self.numberOfElements == 1: - self.createLookup(0) - else: - self.PSDXalpha = [None for p in range(len(self.phases))] - self.PSDXbeta = [None for p in range(len(self.phases))] - - #Set first index of eq composition - for p in range(len(self.phases)): - #Use arbitrary dg, R and gE since only the eq compositions are needed here - _, _, _, xEqAlpha, xEqBeta = self.interfacialComposition[p](self.xComp[0], self.T[0], 0, 1, 0) - if xEqAlpha is not None: - self.xEqAlpha[p,0] = xEqAlpha - self.xEqBeta[p,0] = xEqBeta - - def _interpolateAspectRatio(self, R, p): - ''' - Linear interpolation between self.eqAspectRatio and self.PBM[p].PSDbounds - - Parameters - ---------- - R : float - Equivalent spherical radius - p : int - Phase index - ''' - return np.interp(R, self.PBM[p].PSDbounds, self.eqAspectRatio[p]) - - def _iterate(self, i): - ''' - Iteration function - ''' - #Nucleation and growth rate are independent of time increment - #They can be calculated first and used to determine the time increment for numerical stability - self._nucleate(i) - self._setNucleateRadius(i) - self._growthRate(i) - self._timeIncrementCheck(i) - - #Backup variables in case size classes on PSD changes - self.growthBackup = copy.copy(self.growth) - self.PSDXalphaBackup = copy.copy(self.PSDXalpha) - self.PSDXbetaBackup = copy.copy(self.PSDXbeta) - self.eqAspectRatioBackup = copy.copy(self.eqAspectRatio) - self.RdrivingForceIndexBackup = copy.copy(self.RdrivingForceIndex) - self.RdrivingForceLimitBackup = copy.copy(self.RdrivingForceLimit) - - postDTCheck = False - while not postDTCheck: - dt = self.time[i] - self.time[i-1] - self._calculatePSD(i, dt) - self._massBalance(i) - - if i < self.steps - 1: - postDTCheck = self._postTimeIncrementCheck(i) - else: - postDTCheck = True - - #Calculate additional PSD function - self._calculateAdditionalOutputs(i) - - def _noCheckDT(self, i): - ''' - Function if adaptive time stepping is not used - Will calculated growth rate since it is done in the _checkDT function (not a good way of doing this, but works for now) - ''' - return - - def _checkDT(self, i): - ''' - Checks max growth rate and updates dt correspondingly - ''' - dt = self._calculateDT(i-1, self.maxDTFraction) - dtAll = [dt] - - if self.checkPSD: - if self.T[i] == self.T[i-1]: - dtPBM = [self.PBM[p].getDTEuler(dt, self.growth[p], self.maxDissolution, self.RdrivingForceIndex[p]) for p in range(len(self.phases))] - else: - dtPBM = [dt] - dt = np.amin(np.concatenate(([dt], dtPBM))) - dtAll.append(dt) - - if i > 1: - dtPrev = self.time[i-1] - self.time[i-2] - else: - dtPrev = dt - - #Nucleation rate constraint - if self.checkNucleation: - dtNuc = dt * np.ones(len(self.phases)+1) - for p in range(len(self.phases)): - if self.nucRate[p,i] > self.minNucleationRate and self.nucRate[p,i-1] > self.minNucleationRate and self.nucRate[p,i-1] != self.nucRate[p,i]: - dtNuc[p] = self.maxNucleationRateChange * dtPrev / np.abs(np.log10(self.nucRate[p,i-1] / self.nucRate[p,i])) - dt = np.amin(dtNuc) - dtAll.append(dt) - - #Temperature change constraint - if self.checkTemperature: - Tchange = self.T[i] - self.T[i-1] - dtTemp = dt - if Tchange > self.maxNonIsothermalDT: - dtTemp = self.maxNonIsothermalDT * (self.time[i] - self.time[i-1]) / Tchange - dt = np.amin([dt, dtTemp]) - - if self.checkRcrit: - dtRad = dt * np.ones(len(self.phases)+1) - if not all((self.Rcrit[:,i-1] == 0) & (self.Rcrit[:,i] - self.Rcrit[:,i-1] == 0) & (self.dGs[:,i] <= 0)): - indices = (self.Rcrit[:,i-1] > 0) & (self.Rcrit[:,i] - self.Rcrit[:,i-1] != 0) & (self.dGs[:,i] > 0) - dtRad[:-1][indices] = self.maxRcritChange * dtPrev / np.abs((self.Rcrit[:,i][indices] - self.Rcrit[:,i-1][indices]) / self.Rcrit[:,i-1][indices]) - dt = np.amin(dtRad) - dtAll.append(dt) - - if self.checkVolumePre or self.checkCompositionPre: - dV = np.zeros(len(self.phases)) - for p in range(len(self.phases)): - #Calculate estimate volume change based off growth rate and nucleated particles - #TODO: account for non-spherical precipitates - dVi = self.PBM[p].PSD * self.PBM[p].PSDsize**2 * 0.5 * (self.growth[p][1:] + self.growth[p][:-1]) - dVi[dVi < 0] = 0 - dV = self.VmAlpha / self.VmBeta[p] * (self.GB[p].areaFactor * np.sum(dVi) + self.GB[p].volumeFactor * self.nucRate[p,i] * self.Rad[p,i]**3) - - if self.checkVolumePre: - dtVol = dt * np.ones(len(self.phases) + 1) - for p in range(len(self.phases)): - if dV != 0: - dtVol[p] = self.maxVolumeChange / (2 * np.abs(dV)) - #if not all((self.Rad[:,i]**3*self.nucRate[:,i] > 1e-30)): - # indices = (self.Rad[:,i]**3*self.nucRate[:,i] > 1e-30) - # dtVol[:-1][indices] = self.maxVolumeChange / (10 * (4*np.pi*self.Rad[:,i][indices]**3*self.nucRate[:,i][indices]/3)) - dt = np.amin(dtVol) - dtAll.append(dt) - - if self.checkCompositionPre: - dtComp = dt * np.ones(self.numberOfElements + 1) - fvsum = np.sum(self.betaFrac[:,i-1]) - xbavg = np.zeros(self.numberOfElements) - if self.numberOfElements == 1: - xbavg[0] = 0 if fvsum == 0 else (self.xComp[0] - self.xComp[i-1] * (1 - fvsum)) / fvsum - dxadt = (self.xComp[i-1] - xbavg) * np.sum(dV) / (1 - fvsum) - else: - for e in range(self.numberOfElements): - xbavg[e] = 0 if fvsum == 0 else (self.xComp[0,e] - self.xComp[i-1,e] * (1 - fvsum)) / fvsum - dxadt = (self.xComp[i-1,:] - xbavg) * np.sum(dV) / (1 - fvsum) - dxadt[dxadt == 0] = self.maxCompositionChange / (2 * dt) - dtComp[:self.numberOfElements] = self.maxCompositionChange / (2 * dxadt) - - dt = np.amin(dtComp) - dtAll.append(dt) - - #Minimum dt is the lower of the minimum allowed time increment or the time to the next pre-defined increment - minDT = self._calculateDT(i-1, self.minDTFraction) - dt = np.amax([dt, minDT]) - - #Override time increment with the predefined time steps - #This prevents the next time increment from becoming 0 or negative - dt = np.amin([dt, self.time[i] - self.time[i-1]]) - - if dt < self.time[i] - self.time[i-1]: - #print(dtAll) - self._divideTimestep(i, dt) - - def _noPostCheckDT(self, i): - ''' - Function if no adaptive time stepping is used, no need to do anything in this function - ''' - return True - - def _postCheckDT(self, i): - ''' - CURRENTLY UNUSED AND MAY BE REMOVED LATER - - If adaptive time step is used, this checks new values at iteration i - and compares with simulation contraints - - If contraints are not met, then remove current values and divide time step - ''' - #Only perform checks in non-isothermal situations - if np.abs(self.T[i] - self.T[i-1]) > 1: - return True - - #Composition and volume change are checks in absolute changes - #This prevents any unneccessary reduction in time increments for dilute solutions, or - #if there is a long incubation time until nucleations starts occuring - - if self.checkVolumePost: - volChange = np.abs(self.betaFrac[:,i] - self.betaFrac[:,i-1]) - #If current volume fraction is 0, then ignore (either precipitation has not occured or precipitates has dissolved) - volChange[self.betaFrac[:,i] == 0] = 0 - volCheck = np.amax(volChange) < self.maxVolumeChange - else: - volCheck = True - - if self.checkComposition: - if self.numberOfElements == 1: - compCheck = (np.abs(self.xComp[i] - self.xComp[i-1]) < self.maxCompositionChange) & (self.xComp[i] > 0) - else: - compCheck = (np.amax(np.abs(self.xComp[i,:] - self.xComp[i-1,:])) < self.maxCompositionChange) & (np.amin(self.xComp[i,:] > self.minComposition)) - else: - compCheck = True - - checks = [volCheck, compCheck] - - #If any test fails, then reset iteration and divide time increment - if not all(checks): - dt = (self.time[i] - self.time[i-1]) / 2 - minDT = self._calculateDT(i-1, self.minDTFraction) - - #If proposed time increment is smaller than the minimum allowed increment, then skip the checks - if dt < minDT: - return True - - #Only revert changes to variables that aren't stored per iteration - #Variables related to nucleation are not dependent on the time increment - #Variables related to the particle size distribution (composition, volume fraction, etc) - # will be overridden if the time increment changes - self.prevFConc[0] = copy.copy(self.prevFConc[1]) - - for p in range(len(self.phases)): - self.PBM[p].revert() - self.growth = copy.copy(self.growthBackup) - self.PSDXalpha = copy.copy(self.PSDXalphaBackup) - self.PSDXbeta = copy.copy(self.PSDXbetaBackup) - self.eqAspectRatio = copy.copy(self.eqAspectRatioBackup) - self.RdrivingForceIndex = copy.copy(self.RdrivingForceIndexBackup) - self.RdrivingForceLimit = copy.copy(self.RdrivingForceLimitBackup) - - self._divideTimestep(i, dt) - - return False - else: - return True - - def _nucleate(self, i): - ''' - Calculates the nucleation rate at current timestep - This can be done before the initial time increment checks are performed - ''' - for p in range(len(self.phases)): - #If parent phases exists, then calculate the number of potential nucleation sites on the parent phase - #This is the number of lattice sites on the total surface area of the parent precipitate - nucleationSites = np.sum([4 * np.pi * self.PBM[p2].SecondMoment() * (self.avo / self.VmBeta[p2])**(2/3) for p2 in self.parentPhases[p]]) - - if self.GB[p].nucleationSiteType == GBFactors.BULK: - #bulkPrec = np.sum([self.GB[p2].volumeFactor * self.PBM[p2].ThirdMoment() for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.BULK]) - #nucleationSites += self.bulkN0 - bulkPrec * (self.avo / self.VmAlpha) - bulkPrec = np.sum([self.PBM[p2].ZeroMoment() for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.BULK]) - nucleationSites += self.bulkN0 - bulkPrec - elif self.GB[p].nucleationSiteType == GBFactors.DISLOCATION: - bulkPrec = np.sum([self.PBM[p2].FirstMoment() for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.DISLOCATION]) - nucleationSites += self.dislocationN0 - bulkPrec * (self.avo / self.VmAlpha)**(1/3) - elif self.GB[p].nucleationSiteType == GBFactors.GRAIN_BOUNDARIES: - boundPrec = np.sum([self.GB[p2].gbRemoval * self.PBM[p2].SecondMoment() for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.GRAIN_BOUNDARIES]) - nucleationSites += self.GBareaN0 - boundPrec * (self.avo / self.VmAlpha)**(2/3) - elif self.GB[p].nucleationSiteType == GBFactors.GRAIN_EDGES: - edgePrec = np.sum([np.sqrt(1 - self.GB[p2].GBk**2) * self.PBM[p2].FirstMoment() for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.GRAIN_EDGES]) - nucleationSites += self.GBedgeN0 - edgePrec * (self.avo / self.VmAlpha)**(1/3) - elif self.GB[p].nucleationSiteType == GBFactors.GRAIN_CORNERS: - cornerPrec = np.sum([self.PBM[p2].ZeroMoment() for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.GRAIN_CORNERS]) - nucleationSites += self.GBcornerN0 - cornerPrec - - if nucleationSites < 0: - nucleationSites = 0 - self.nucRate[p, i] = nucleationSites * self._nucleationRate(p, i) - - def _calculatePSD(self, i, dt): - ''' - Updates the PSD using the population balance model from coarsening and nucleation rate - This also updates the fraction of precipitates, matrix composition and average radius - ''' - for p in range(len(self.phases)): - #Backup PSD for time increment checks - #Also backup PSDXbeta for precipitate composition with no diffusion - self.PBM[p].createBackup() - self._prevPSDXbeta = copy.copy(self.PSDXbeta) - - change1, newIndices = self.PBM[p].UpdateEuler(dt, self.growth[p]) - change2 = self.PBM[p].Nucleate(self.nucRate[p, i] * dt, self.Rad[p, i]) - if change1 or change2: - #Add aspect ratio, do this before growth rate and interfacial composition since those are dependent on this - if self.calculateAspectRatio[p]: - self.eqAspectRatio[p] = self.strainEnergy[p].eqAR_bySearch(self.PBM[p].PSDbounds, self.gamma[p], self.shapeFactors[p]) - else: - self.eqAspectRatio[p] = self.shapeFactors[p].aspectRatio(self.PBM[p].PSDbounds) - - self.growth[p] = np.zeros(len(self.PBM[p].PSDbounds)) - if self.numberOfElements == 1: - if newIndices is None: - #This is very slow to do - self.createLookup(i) - else: - self.PSDXalpha[p] = np.concatenate((self.PSDXalpha[p], np.zeros(self.PBM[p].bins+1 - len(self.PSDXalpha[p])))) - self.PSDXbeta[p] = np.concatenate((self.PSDXbeta[p], np.zeros(self.PBM[p].bins+1 - len(self.PSDXbeta[p])))) - self.PSDXalpha[p][newIndices:], self.PSDXbeta[p][newIndices:] = self.interfacialComposition[p](self.T[i-1], self.particleGibbs(self.PBM[p].PSDbounds[newIndices:], self.phases[p])) - self.growth[p] = self._singleGrowthBinary(i, p) - else: - self.growth[p] = self._singleGrowthMulti(i, p) - - #Set negative frequencies in PSD to 0 - #Also set any less than the minimum possible radius to be 0 - self.PBM[p].PSD[:self.RdrivingForceIndex[p]] = 0 - self.PBM[p].PSD[self.PBM[p].PSDsize < self.minRadius] = 0 - - def _massBalance(self, i): - ''' - Updates matrix composition and volume fraction of precipitates - ''' - fBeta = np.zeros(len(self.phases)) - if self.numberOfElements == 1: - fConc = np.zeros(len(self.phases)) - else: - fConc = np.zeros((len(self.phases), self.numberOfElements)) - - for p in range(len(self.phases)): - #Sum up particles and average for particles - Ntot = self.PBM[p].ZeroMoment() - RadSum = self.PBM[p].Moment(order=1) - ARsum = self.PBM[p].WeightedMoment(0, self.shapeFactors[p].aspectRatio(self.PBM[p].PSDsize)) - fBeta[p] = self.VmAlpha / self.VmBeta[p] * self.GB[p].volumeFactor * self.PBM[p].ThirdMoment() - - if self.numberOfElements == 1: - if self.infinitePrecipitateDiffusion[p]: - fConc[p] = self.VmAlpha / self.VmBeta[p] * self.GB[p].volumeFactor * self.PBM[p].WeightedMoment(3, 0.5 * (self.PSDXbeta[p][:-1] + self.PSDXbeta[p][1:])) - else: - y = self.VmAlpha / self.VmBeta[p] * self.GB[p].areaFactor * np.sum(self.PBM[p]._prevPSDbounds[1:]**2 * self.PBM[p]._fv[1:] * self._prevPSDXbeta[p][1:] * (self.PBM[p]._prevPSDbounds[1:] - self.PBM[p]._prevPSDbounds[:-1])) - fConc[p] = self.prevFConc[0,p,0] + y - self.prevFConc[1,p,0] = copy.copy(self.prevFConc[0,p,0]) - self.prevFConc[0,p,0] = fConc[p] - else: - if self.infinitePrecipitateDiffusion[p]: - for a in range(self.numberOfElements): - fConc[p,a] = self.VmAlpha / self.VmBeta[p] * self.GB[p].volumeFactor * self.PBM[p].WeightedMoment(3, 0.5 * (self.PSDXbeta[p][:-1,a] + self.PSDXbeta[p][1:,a])) - else: - for a in range(self.numberOfElements): - y = self.VmAlpha / self.VmBeta[p] * self.GB[p].areaFactor * np.sum(self.PBM[p]._prevPSDbounds[1:]**2 * self.PBM[p]._fv[1:] * self._prevPSDXbeta[p][1:,a] * (self.PBM[p]._prevPSDbounds[1:] - self.PBM[p]._prevPSDbounds[:-1])) - fConc[p,a] = self.prevFConc[0,p,a] + y - self.prevFConc[1,p] = copy.copy(self.prevFConc[0,p]) - self.prevFConc[0,p] = fConc[p] - - #Average radius and precipitate density - if Ntot > 0: - self.avgR[p, i] = RadSum / Ntot - self.precipitateDensity[p, i] = Ntot - self.avgAR[p, i] = ARsum / Ntot - else: - self.avgR[p, i] = 0 - self.precipitateDensity[p, i] = 0 - self.avgAR[p, i] = 0 - - #Volume fraction (max at 1) - if fBeta[p] > 1: - fBeta[p] = 1 - if self.betaFrac[p, i-1] == 1: - fBeta[p] = 1 - - self.betaFrac[p, i] = fBeta[p] - - #Composition (min at 0) - if self.numberOfElements == 1: - if np.sum(fBeta) < 1: - self.xComp[i] = (self.xComp[0] - np.sum(fConc)) / (1 - np.sum(fBeta)) - else: - self.xComp[i] = 0 - else: - if np.sum(fBeta) < 1: - self.xComp[i] = (self.xComp[0] - np.sum(fConc, axis=0)) / (1 - np.sum(fBeta)) - self.xComp[i][self.xComp[i] < 0] = self.minComposition - else: - self.xComp[i] = np.zeros(self.numberOfElements) - - def _singleGrowthBinary(self, i, p): - ''' - Calculates growth rate for a single phase - This is separated from _growthRateBinary since it's used in _calculatePSD - - Matrix/precipitate composition are not calculated here since it's - already calculated in createLookup - ''' - growthRate = np.zeros(self.PBM[p].bins + 1) - #If no precipitates are stable, don't calculate growth rate and set PSD to 0 - #This should represent dissolution of the precipitates - if self.RdrivingForceIndex[p]+1 < len(self.PSDXalpha[p]): - superSaturation = (self.xComp[i-1] - self.PSDXalpha[p]) / (self.VmAlpha * self.PSDXbeta[p] / self.VmBeta[p] - self.PSDXalpha[p]) - growthRate = self.shapeFactors[p].kineticFactor(self.PBM[p].PSDbounds) * self.Diffusivity(self.xComp[i-1], self.T[i]) * superSaturation / (self.effDiffDistance(superSaturation) * self.PBM[p].PSDbounds) - else: - self.PBM[p].PSD = np.zeros(self.PBM[p].bins) - - return growthRate - - - def _growthRateBinary(self, i): - ''' - Determines current growth rate of all particle size classes in a binary system - ''' - #Update equilibrium interfacial compositions - #This will be override if createLookup is called - self.xEqAlpha[:,i] = self.xEqAlpha[:,i-1] - self.xEqBeta[:,i] = self.xEqBeta[:,i-1] - - #Update lookup table if temperature changes too much - self.dTemp += self.T[i] - self.T[i-1] - if np.abs(self.dTemp) > self.maxTempChange: - self.createLookup(i) - self.dTemp = 0 - - #growthRate = np.zeros((len(self.phases), self.bins + 1)) - growthRate = [] - for p in range(len(self.phases)): - growthRate.append(self._singleGrowthBinary(i, p)) - - self.growth = growthRate - - def _singleGrowthMulti(self, i, p): - ''' - Calculates growth rate for a single phase - This is separated from _growthRateMulti since it's used in _calculatePSD - - This will also calculate the matrix/precipitate composition - for the radius in the PSD as well as equilibrium (infinite radius) - ''' - growth, xAlpha, xBeta, xEqAlpha, xEqBeta = self.interfacialComposition[p](self.xComp[i-1], self.T[i], self.dGs[p,i-1] * self.VmBeta[p], self.PBM[p].PSDbounds, self.particleGibbs(phase=self.phases[p])) - - #If two-phase equilibrium not found, two possibilities - precipitates are unstable or equilibrium calculations didn't converge - if growth is None: - #If driving force is negative, then precipitates are unstable - if self.dGs[p,i] < 0: - #Completely reset the PBM, including bounds and number of bins - #In case nucleation occurs again, the PBM will be at a good length scale - self.PBM[p].reset() - self.PSDXalpha[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) - self.PSDXbeta[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) - self.xEqAlpha[p,i] = np.zeros(self.numberOfElements) - self.xEqBeta[p,i] = np.zeros(self.numberOfElements) - return np.zeros(self.PBM[p].bins + 1) - #Else, equilibrium did not converge and just use previous values - #Only the growth rate needs to be updated, since all other terms are previous - #Also revert the PSD in case this function was called to adjust for the new PSD bins - else: - self.PBM[p].revert() - return self.growth[p] - else: - #Update interfacial composition for each precipitate size - self.PSDXalpha[p] = xAlpha - self.PSDXbeta[p] = xBeta - self.xEqAlpha[p,i] = xEqAlpha - self.xEqBeta[p,i] = xEqBeta - - #Add shape factor to growth rate - will need to add effective diffusion distance as well - return self.shapeFactors[p].kineticFactor(self.PBM[p].PSDbounds) * growth - - def _growthRateMulti(self, i): - ''' - Determines current growth rate of all particle size classes in a multicomponent system - ''' - growthRate = [] - for p in range(len(self.phases)): - growthRate.append(self._singleGrowthMulti(i, p)) - self.growth = growthRate - - def plot(self, axes, variable, bounds = None, timeUnits = 's', radius='spherical', *args, **kwargs): - ''' - Plots model outputs - - Parameters - ---------- - axes : Axis - variable : str - Specified variable to plot - Options are 'Volume Fraction', 'Total Volume Fraction', 'Critical Radius', - 'Average Radius', 'Volume Average Radius', 'Total Average Radius', - 'Total Volume Average Radius', 'Aspect Ratio', 'Total Aspect Ratio' - 'Driving Force', 'Nucleation Rate', 'Total Nucleation Rate', - 'Precipitate Density', 'Total Precipitate Density', - 'Temperature', 'Composition', - 'Size Distribution', 'Size Distribution Curve', - 'Size Distribution KDE', 'Size Distribution Density - 'Interfacial Composition Alpha', 'Interfacial Composition Beta' - - Note: for multi-phase simulations, adding the word 'Total' will - sum the variable for all phases. Without the word 'Total', the variable - for each phase will be plotted separately - - Interfacial composition terms are more relavent for binary systems than - for multicomponent systems - - bounds : tuple (optional) - Limits on the x-axis (float, float) or None (default, this will set bounds to (initial time, final time)) - radius : str (optional) - For non-spherical precipitates, plot the Average Radius by the - - Equivalent spherical radius ('spherical') - Short axis ('short') - Long axis ('long') - Note: Total Average Radius and Volume Average Radius will still use the equivalent spherical radius - *args, **kwargs - extra arguments for plotting - ''' - sizeDistributionVariables = ['Size Distribution', 'Size Distribution Curve', 'Size Distribution KDE', 'Size Distribution Density'] - compositionVariables = ['Interfacial Composition Alpha', 'Interfacial Composition Beta'] - - scale = [] - for p in range(len(self.phases)): - if self.GB[p].nucleationSiteType == self.GB[p].BULK or self.GB[p].nucleationSiteType == self.GB[p].DISLOCATION: - if radius == 'spherical': - scale.append(self._GBareaRemoval(p) * np.ones(len(self.PBM[p].PSDbounds))) - else: - scale.append(1/self.shapeFactors[p].eqRadiusFactor(self.PBM[p].PSDbounds)) - if radius == 'long': - scale.append(self.shapeFactors[p].aspectRatio(self.PBM[p].PSDbounds) / self.shapeFactors[p].eqRadiusFactor(self.PBM[p].PSDbounds)) - else: - scale.append(self._GBareaRemoval(p) * np.ones(len(self.PBM[p].PSDbounds))) - - if variable in compositionVariables: - if variable == 'Interfacial Composition Alpha': - yVar = self.PSDXalpha - ylabel = 'Composition in Alpha phase' - else: - yVar = self.PSDXbeta - ylabel = 'Composition in Beta Phase' - - if (len(self.phases)) == 1: - axes.semilogx(self.PBM[0].PSDbounds, yVar[0], *args, **kwargs) - else: - for p in range(len(self.phases)): - axes.plot(self.PBM[p].PSDbounds, yVar[p], label=self.phases[p], *args, **kwargs) - axes.legend() - axes.set_xlim([self.PBM[0].PSDbounds[0], self.PBM[0].PSDbounds[-1]]) - axes.set_xlabel('Radius (m)') - axes.set_ylabel(ylabel) - - elif variable in sizeDistributionVariables: - ylabel = 'Frequency (#/$m^3$)' - if variable == 'Size Distribution': - functionName = 'PlotHistogram' - elif variable == 'Size Distribution KDE': - functionName = 'PlotKDE' - elif variable == 'Size Distribution Density': - functionName = 'PlotDistributionDensity' - ylabel = 'Distribution Density (#/$m^4$)' - else: - functionName = 'PlotCurve' - - if len(self.phases) == 1: - getattr(self.PBM[0], functionName)(axes, scale=scale[0], *args, **kwargs) - else: - for p in range(len(self.phases)): - getattr(self.PBM[p], functionName)(axes, label=self.phases[p], scale=scale[p], *args, **kwargs) - axes.legend() - axes.set_xlabel('Radius (m)') - axes.set_ylabel(ylabel) - axes.set_xlim([0, np.amax([pb.max for pb in self.PBM])]) - if variable == 'Size Distribution Density': - axes.set_ylim([0, 1.1*np.amax(np.concatenate(([np.amax(pb.PSD/(pb.PSDbounds[1:] - pb.PSDbounds[:-1])) for pb in self.PBM], [1])))]) - else: - axes.set_ylim([0, 1.1*np.amax(np.concatenate(([np.amax(pb.PSD) for pb in self.PBM], [1])))]) - - elif variable == 'Cumulative Size Distribution': - ylabel = 'CDF' - if len(self.phases) == 1: - self.PBM[0].PlotCDF(axes, scale=scale[0], *args, **kwargs) - else: - for p in range(len(self.phases)): - self.PBM[p].PlotCDF(axes, label=self.phases[p], scale=scale[p], *args, **kwargs) - axes.legend() - axes.set_xlabel('Radius (m)') - axes.set_ylabel(ylabel) - axes.set_xlim([0, np.amax([pb.max for pb in self.PBM])]) - - elif variable == 'Aspect Ratio Distribution': - if len(self.phases) == 1: - axes.plot(self.PBM[0].PSDbounds * np.interp(self.PBM[p].PSDbounds, self.PBM[0].PSDbounds, scale[0]), self.eqAspectRatio[0], *args, **kwargs) - else: - for p in range(len(self.phases)): - axes.plot(self.PBM[p].PSDbounds * np.interp(self.PBM[p].PSDbounds, self.PBM[p].PSDbounds, scale[p]), self.eqAspectRatio[p], label=self.phases[p], *args, **kwargs) - axes.legend() - axes.set_xlim([0, np.amax(self.PBM[p].PSDbounds * np.interp(self.PBM[p].PSDbounds, self.PBM[p].PSDbounds, scale[p]))]) - axes.set_ylim(bottom=1) - axes.set_xlabel('Radius (m)') - axes.set_ylabel('Aspect ratio distribution') - - else: - super().plot(axes, variable, bounds, timeUnits, radius, *args, **kwargs) - - \ No newline at end of file diff --git a/kawin/Thermodynamics.py b/kawin/Thermodynamics.py deleted file mode 100644 index df98955..0000000 --- a/kawin/Thermodynamics.py +++ /dev/null @@ -1,1735 +0,0 @@ -import numpy as np -from numpy.lib.function_base import diff -import scipy.spatial as sps -from pycalphad import Model, Database, calculate, equilibrium, variables as v -from pycalphad.codegen.callables import build_callables, build_phase_records -from pycalphad.core.composition_set import CompositionSet -from pycalphad.core.utils import get_state_variables -from pycalphad.plot.utils import phase_legend -from kawin.Mobility import MobilityModel, interdiffusivity, interdiffusivity_from_diff, inverseMobility, inverseMobility_from_diffusivity, tracer_diffusivity, tracer_diffusivity_from_diff -from kawin.FreeEnergyHessian import dMudX -from kawin.LocalEquilibrium import local_equilibrium -import matplotlib.pyplot as plt -import copy -from tinydb import where - -setattr(v, 'GE', v.StateVariable('GE')) - -class ExtraGibbsModel(Model): - ''' - Child of pycalphad Model with extra variable GE - GE represents any extra contribution to the Gibbs free energy - such as the Gibbs-Thomson contribution - ''' - energy = GM = property(lambda self: self.ast + v.GE) - formulaenergy = G = property(lambda self: (self.ast + v.GE) * self._site_ratio_normalization) - orderingContribution = OCM = property(lambda self: self.models['ord']) - -class GeneralThermodynamics: - ''' - Class for defining driving force and essential functions for - binary and multicomponent systems using pycalphad for equilibrium - calculations - - Parameters - ---------- - database : Database or str - pycalphad Database or file name for database - elements : list - Elements to consider - Note: reference element must be the first index in the list - phases : list - Phases involved - Note: matrix phase must be first index in the list - drivingForceMethod : str (optional) - Method used to calculate driving force - Options are 'approximate' (default), 'sampling' and 'curvature' (not recommended) - ''' - - gOffset = 1 #Small value to add to precipitate phase for when order/disorder models are used - - def __init__(self, database, elements, phases, drivingForceMethod = 'approximate'): - if isinstance(database, str): - database = Database(database) - self.db = database - self.elements = copy.copy(elements) - - if 'VA' not in self.elements: - self.elements.append('VA') - - if type(phases) == str: # check if a single phase was passed as a string instead of a list of phases. - phases = [phases] - self.phases = phases - self.orderedPhase = {phases[i]: False for i in range(1, len(phases))} - for i in range(1, len(phases)): - if 'disordered_phase' in self.db.phases[phases[i]].model_hints: - if self.db.phases[phases[i]].model_hints['disordered_phase'] == self.phases[0]: - self.orderedPhase[phases[i]] = True - self._forceDisorder(self.phases[0]) - - #Build phase models assuming first phase is parent phase and rest of precipitate phases - #If the same phase is used for matrix and precipitate phase, then force the matrix phase to remove the ordering contribution - #This may be unnecessary as already disordered phase models will not be affected, but I guess just in case the matrix phase happens to be an ordered solution - self.models = {self.phases[0]: Model(self.db, self.elements, self.phases[0])} - self.models[self.phases[0]].state_variables = sorted([v.T, v.P, v.N, v.GE], key=str) - - for i in range(1, len(phases)): - self.models[self.phases[i]] = ExtraGibbsModel(self.db, self.elements, self.phases[i]) - self.models[self.phases[i]].state_variables = sorted([v.T, v.P, v.N, v.GE], key=str) - - self.phase_records = build_phase_records(self.db, self.elements, self.phases, - self.models[self.phases[0]].state_variables, - self.models, build_gradients=True, build_hessians=True) - - self.OCMphase_records = {} - for i in range(1, len(self.phases)): - if self.orderedPhase[self.phases[i]]: - self.OCMphase_records[self.phases[i]] = build_phase_records(self.db, self.elements, [self.phases[i]], - self.models[self.phases[0]].state_variables, - {self.phases[i]: self.models[self.phases[i]]}, - output='OCM', build_gradients=False, build_hessians=False) - - - #Amount of points to sample per degree of freedom - # sampling_pDens is for when using sampling method in driving force calculations - # pDens is for equilibrium calculations - self.sampling_pDens = 2000 - self.pDens = 500 - - #Stored variables of last time the class was used - #This is so that these can be used again if the temperature has not changed since last usage - self._prevTemperature = None - - #Pertains to parent phase (composition, sampled points, equilibrium calculations) - self._prevX = None - self._parentEq = None - - #Pertains to precipitate phases (sampled points) - self._pointsPrec = {self.phases[i]: None for i in range(1, len(self.phases))} - self._orderingPoints = {self.phases[i]: None for i in range(1, len(self.phases))} - - self.setDrivingForceMethod(drivingForceMethod) - - self.mobModels = {p: None for p in self.phases} - self.mobCallables = {p: None for p in self.phases} - self.diffCallables = {p: None for p in self.phases} - for p in self.phases: - #Get mobility/diffusivity of phase p if exists - param_search = self.db.search - param_query_mob = ( - (where('phase_name') == p) & \ - (where('parameter_type') == 'MQ') | \ - (where('parameter_type') == 'MF') - ) - - param_query_diff = ( - (where('phase_name') == p) & \ - (where('parameter_type') == 'DQ') | \ - (where('parameter_type') == 'DF') - ) - - pMob = param_search(param_query_mob) - pDiff = param_search(param_query_diff) - - if len(pMob) > 0 or len(pDiff) > 0: - self.mobModels[p] = MobilityModel(self.db, self.elements, p) - if len(pMob) > 0: - self.mobCallables[p] = {} - for c in self.phase_records[p].nonvacant_elements: - bcp = build_callables(self.db, self.elements, [p], {p: self.mobModels[p]}, - parameter_symbols=None, output='mob_'+c, build_gradients=False, build_hessians=False, - additional_statevars=[v.T, v.P, v.N, v.GE]) - self.mobCallables[p][c] = bcp['mob_'+c]['callables'][p] - else: - self.diffCallables[p] = {} - for c in self.phase_records[p].nonvacant_elements: - bcp = build_callables(self.db, self.elements, [p], {p: self.mobModels[p]}, - parameter_symbols=None, output='diff_'+c, build_gradients=False, build_hessians=False, - additional_statevars=[v.T, v.P, v.N, v.GE]) - self.diffCallables[p][c] = bcp['diff_'+c]['callables'][p] - - #This applies to all phases since this is typically reflective of quenched-in vacancies - self.mobility_correction = {A: 1 for A in self.elements} - - #Cached results - self._compset_cache = {} - self._compset_cache_df = {} - self._matrix_cs = None - - def _forceDisorder(self, phase): - ''' - For phases using an order/disorder model, pycalphad will neglect the disordered phase unless - it is the only phase set active, so the order and disordered portion of the phase will use the same model - - For the Gibbs-Thomson effect to be applied, the ordered and disordered parts of the model will need to be kept separate - As a fix, a new phase is added to the database that uses only the disordered part of the model - ''' - newPhase = 'DIS_' + phase - self.phases[0] = newPhase - self.db.phases[newPhase] = copy.deepcopy(self.db.phases[phase]) - self.db.phases[newPhase].name = newPhase - del self.db.phases[newPhase].model_hints['ordered_phase'] - del self.db.phases[newPhase].model_hints['disordered_phase'] - - #Copy database parameters with new name - param_query = where('phase_name') == phase - params = self.db.search(param_query) - for p in params: - #We have to create a new dictionary since p is a TinyDB.Document - newP = {} - for entry in p: - newP[entry] = p[entry] - newP['phase_name'] = newPhase - self.db._parameters.insert(newP) - - def clearCache(self): - ''' - Removes any cached data - This is intended for surrogate training, where the cached data - will be removed incase - ''' - self._compset_cache = {} - self._compset_cache_df = {} - self._matrix_cs = None - - def setDrivingForceMethod(self, drivingForceMethod): - ''' - Sets method for calculating driving force - - Parameters - ---------- - drivingForceMethod - str - Options are ['approximate', 'sampling', 'curvature'] - ''' - if drivingForceMethod == 'approximate': - self._drivingForce = self._getDrivingForceApprox - elif drivingForceMethod == 'sampling': - self._drivingForce = self._getDrivingForceSampling - elif drivingForceMethod == 'curvature': - self._drivingForce = self._getDrivingForceCurvature - else: - raise Exception('Driving force method must be either \'approximate\', \'sampling\' or \'curvature\'') - - def setDFSamplingDensity(self, density): - ''' - Sets sampling density for sampling method in driving - force calculations - - Default upon initialization is 2000 - - Parameters - ---------- - density : int - Number of samples to take per degree of freedom in the phase - ''' - self._pointsPrec = {self.phases[i]: None for i in range(1, len(self.phases))} - self.sampling_pDens = density - - def setEQSamplingDensity(self, density): - ''' - Sets sampling density for equilibrium calculations - - Default upon initialization is 500 - - Parameters - ---------- - density : int - Number of samples to take per degree of freedom in the phase - ''' - self.pDens = density - - def setMobility(self, mobility): - ''' - Allows user to define mobility functions - - mobility : dict - Dictionary of functions for each element (including reference) - Each function takes in (v.T, v.P, v.N, v.GE, site fractions) and returns mobility - - Optional - only required for multicomponent systems where - mobility terms are not defined in the TDB database - ''' - self.mobCallables = mobility - - def setDiffusivity(self, diffusivity): - ''' - Allows user to define diffusivity functions - - diffusivity : dict - Dictionary of functions for each element (including reference) - Each function takes in (v.T, v.P, v.N, v.GE, site fractions) and returns diffusivity - - Optional - only required for multicomponent systems where - diffusivity terms are not defined in the TDB database - and if mobility terms are not defined - ''' - self.diffCallables = diffusivity - - def setMobilityCorrection(self, element, factor): - ''' - Factor to multiply mobility by for each element - - Parameters - ---------- - element : str - Element to set factor for - If 'all', factor will be set to all elements - factor : float - Scaling factor - ''' - if element == 'all': - for e in self.mobility_correction: - self.mobility_correction[e] = factor - else: - self.mobility_correction[element] = factor - - def _getConditions(self, x, T, gExtra = 0): - ''' - Creates dictionary of conditions from composition, temperature and gExtra - - Parameters - ---------- - x : list - Composition (excluding reference element) - T : float - Temperature - gExtra : float - Gibbs free energy to add to phase - ''' - cond = {v.X(self.elements[i+1]): x[i] for i in range(len(x))} - cond[v.P] = 101325 - cond[v.T] = T - cond[v.GE] = gExtra - cond[v.N] = 1 - return cond - - def _createCompositionSet(self, eq, state_variables, phase, phase_amounts, idx): - ''' - Creates a pycalphad CompositionSet from equilibrium results - - Parameters - ---------- - eq : pycalphad equilibrium result - state_variables : list - List of state variables - phase : str - Phase to create CompositionSet for - phase_amounts : list - Array of floats for phase fraction of each phase - idx : ndarray - Index array for the index of phase - ''' - miscibility = False - cs = CompositionSet(self.phase_records[phase]) - #If there's a miscibility gap in the matrix phase, then take the largest value - if len(idx) > 1: - idx = [idx[np.argmax(phase_amounts[idx])]] - miscibility = True - cs.update(eq.Y.isel(vertex=idx).values.ravel()[:cs.phase_record.phase_dof], - phase_amounts[idx], state_variables) - - return cs, miscibility - - def getEq(self, x, T, gExtra = 0, precPhase = None): - ''' - Calculates equilibrium at specified x, T, gExtra - - This is separated from the interfacial composition function so that this can be used for getting curvature for interfacial composition from mobility - - Parameters - ---------- - x : float or array - Composition - Needs to be array for multicomponent systems - T : float - Temperature - gExtra : float - Gibbs-Thomson contribution (if applicable) - precPhase : str - Precipitate phase (default is first precipitate) - - Returns - ------- - Dataset from pycalphad equilibrium results - ''' - phases = [self.phases[0]] - if precPhase != -1: - if precPhase is None: - precPhase = self.phases[1] - if isinstance(precPhase, str): - phases.append(precPhase) - else: - phases = [p for p in precPhase] - phaseRec = {p: self.phase_records[p] for p in phases} - - if not hasattr(x, '__len__'): - x = [x] - - #Remove first element if x lists composition of all elements - if len(x) == len(self.elements) - 1: - x = x[1:] - - cond = self._getConditions(x, T, gExtra+self.gOffset) - - eq = equilibrium(self.db, self.elements, phases, cond, model=self.models, - phase_records=phaseRec, - calc_opts={'pdens': self.pDens}) - return eq - - def getLocalEq(self, x, T, gExtra = 0, precPhase = None, composition_sets = None): - phases = [self.phases[0]] - if precPhase != -1: - if precPhase is None: - precPhase = self.phases[1] - if isinstance(precPhase, str): - phases.append(precPhase) - else: - phases = [p for p in precPhase] - - if not hasattr(x, '__len__'): - x = [x] - - #Remove first element if x lists composition of all elements - if len(x) == len(self.elements) - 1: - x = x[1:] - - cond = self._getConditions(x, T, gExtra) - result, composition_sets = local_equilibrium(self.db, self.elements, phases, cond, - self.models, self.phase_records, - composition_sets=composition_sets) - return result, composition_sets - - def getInterdiffusivity(self, x, T, removeCache = True, phase = None): - ''' - Gets interdiffusivity at specified x and T - Requires TDB database to have mobility or diffusivity parameters - - Parameters - ---------- - x : float, array or 2D array - Composition - Float or array for binary systems - Array or 2D array for multicomponent systems - T : float or array - Temperature - If array, must be same length as x - For multicomponent systems, must be same length as 0th axis - removeCache : boolean - If True, recalculates equilibrium to get interdiffusivity (default) - If False, will use calculation from driving force calcs (if available) to compute diffusivity - phase : str - Phase to compute diffusivity for (defaults to first or matrix phase) - This only needs to be used for multiphase diffusion simulations - - Returns - ------- - interdiffusivity - will return array if T is an array - For binary case - float or array of floats - For multicomponent - matrix or array of matrices - ''' - dnkj = [] - - if hasattr(T, '__len__'): - for i in range(len(T)): - dnkj.append(self._interdiffusivitySingle(x[i], T[i], removeCache, phase)) - return np.array(dnkj) - else: - return self._interdiffusivitySingle(x, T, removeCache, phase) - - def _interdiffusivitySingle(self, x, T, removeCache = True, phase = None): - ''' - Gets interdiffusivity at unique composition and temperature - - Parameters - ---------- - x : float or array - Composition - T : float - Temperature - removeCache : boolean - phase : str - - Returns - ------- - Interdiffusivity as a matrix (will return float in binary case) - ''' - if phase is None: - phase = self.phases[0] - - if not hasattr(x, '__len__'): - x = [x] - - #Remove first element if x lists composition of all elements - if len(x) == len(self.elements) - 1: - x = x[1:] - - cond = self._getConditions(x, T, 0) - - if removeCache: - self._matrix_cs = None - self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [phase], cond, - self.models, self.phase_records, - composition_sets=self._matrix_cs) - - cs_matrix = [cs for cs in self._matrix_cs if cs.phase_record.phase_name == phase][0] - chemical_potentials = self._parentEq.chemical_potentials - - if self.mobCallables[phase] is None: - Dnkj, _, _ = inverseMobility_from_diffusivity(chemical_potentials, cs_matrix, - self.elements[0], self.diffCallables[phase], - diffusivity_correction=self.mobility_correction) - else: - Dnkj, _, _ = inverseMobility(chemical_potentials, cs_matrix, self.elements[0], - self.mobCallables[phase], - mobility_correction=self.mobility_correction) - - if len(x) == 1: - return Dnkj.ravel()[0] - else: - sortIndices = np.argsort(self.elements[1:-1]) - unsortIndices = np.argsort(sortIndices) - Dnkj = Dnkj[unsortIndices,:] - Dnkj = Dnkj[:,unsortIndices] - return Dnkj - - - def getTracerDiffusivity(self, x, T, removeCache = True, phase = None): - ''' - Gets tracer diffusivity for element el at specified x and T - Requires TDB database to have mobility or diffusivity parameters - - Parameters - ---------- - x : float, array or 2D array - Composition - Float or array for binary systems - Array or 2D array for multicomponent systems - T : float or array - Temperature - If array, must be same length as x - For multicomponent systems, must be same length as 0th axis - removeCache : boolean - phase : str - - Returns - ------- - tracer diffusivity - will return array if T is an array - ''' - td = [] - - if hasattr(T, '__len__'): - for i in range(len(T)): - td.append(self._tracerDiffusivitySingle(x[i], T[i], removeCache, phase)) - return np.array(td) - else: - return self._tracerDiffusivitySingle(x, T, removeCache, phase) - - def _tracerDiffusivitySingle(self, x, T, removeCache = True, phase = None): - ''' - Gets tracer diffusivity at unique composition and temperature - - Parameters - ---------- - x : float or array - Composition - T : float - Temperature - el : str - Element to calculate diffusivity - - Returns - ------- - Tracer diffusivity as a float - ''' - if phase is None: - phase = self.phases[0] - - if not hasattr(x, '__len__'): - x = [x] - - #Remove first element if x lists composition of all elements - if len(x) == len(self.elements) - 1: - x = x[1:] - - cond = self._getConditions(x, T, 0) - - if removeCache: - self._matrix_cs = None - self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [phase], cond, - self.models, self.phase_records, - composition_sets=self._matrix_cs) - - cs_matrix = [cs for cs in self._matrix_cs if cs.phase_record.phase_name == phase][0] - - if self.mobCallables[phase] is None: - #NOTE: This is note tested yet - Dtrace = tracer_diffusivity_from_diff(cs_matrix, self.diffCallables[phase], diffusivity_correction=self.mobility_correction) - else: - Dtrace = tracer_diffusivity(cs_matrix, self.mobCallables[phase], mobility_correction=self.mobility_correction) - - sortIndices = np.argsort(self.elements[:-1]) - unsortIndices = np.argsort(sortIndices) - - Dtrace = Dtrace[unsortIndices] - - return Dtrace - - def getDrivingForce(self, x, T, precPhase = None, returnComp = False, training = False): - ''' - Gets driving force using method defined upon initialization - - Parameters - ---------- - x : float, array or 2D array - Composition of minor element in bulk matrix phase - For binary system, use an array for multiple compositions - For multicomponent systems, use a 2D array for multiple compositions - Where 0th axis is for indices of each composition - T : float or array - Temperature in K - Must be same length as x if x is array or 2D array - precPhase : str (optional) - Precipitate phase to consider (default is first precipitate phase in list) - returnComp : bool (optional) - Whether to return composition of precipitate (defaults to False) - - Returns - ------- - (driving force, precipitate composition) - Driving force is positive if precipitate can form - Precipitate composition will be None if driving force is negative or returnComp is False - ''' - if hasattr(T, '__len__'): - dgArray = [] - compArray = [] - for i in range(len(T)): - dg, comp = self._drivingForce(x[i], T[i], precPhase, returnComp, training) - dgArray.append(dg) - compArray.append(comp) - dgArray = np.array(dgArray) - compArray = np.array(compArray) - return dgArray, compArray - else: - return self._drivingForce(x, T, precPhase, returnComp, training) - - def _getDrivingForceSampling(self, x, T, precPhase = None, returnComp = False, training = False): - ''' - Gets driving force for nucleation by sampling - - Parameters - ---------- - x : float or array - Composition of minor element in bulk matrix phase - Use float for binary systems - Use array for multicomponent systems - T : float - Temperature in K - precPhase : str (optional) - Precipitate phase to consider (default is first precipitate phase in list) - returnComp : bool (optional) - Whether to return composition of precipitate (defaults to False) - - Returns - ------- - (driving force, precipitate composition) - Driving force is positive if precipitate can form - Precipitate composition will be None if driving force is negative or returnComp is False - ''' - precPhase = self.phases[1] if precPhase is None else precPhase - - #Calculate equilibrium with only the parent phase ------------------------------------------------------------------------------------------- - if not hasattr(x, '__len__'): - x = [x] - cond = self._getConditions(x, T, 0) - self._prevX = x - - #Equilibrium at matrix composition for only the parent phase - self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [self.phases[0]], cond, - self.models, self.phase_records, - composition_sets = self._matrix_cs) - - #Remove cache when training - if training: - self._matrix_cs = None - - #Check if equilibrium has converged and chemical potential can be obtained - #If not, then return None for driving force - if any(np.isnan(self._parentEq.chemical_potentials)): - return None, None - - #Sample precipitate phase and get driving force differences at all points ------------------------------------------------------------------- - #Sample points of precipitate phase - if self._pointsPrec[precPhase] is None or self._prevTemperature != T: - self._pointsPrec[precPhase] = calculate(self.db, self.elements, precPhase, P = 101325, T = T, GE=self.gOffset, pdens = self.sampling_pDens, model=self.models, output='GM', phase_records=self.phase_records) - if self.orderedPhase[precPhase]: - self._orderingPoints[precPhase] = calculate(self.db, self.elements, precPhase, P = 101325, T = T, GE=self.gOffset, pdens = self.sampling_pDens, model=self.models, output='OCM', phase_records=self.OCMphase_records[precPhase]) - self._prevTemperature = T - - #Get value of chemical potential hyperplane at composition of sampled points - precComp = self._pointsPrec[precPhase].X.values.ravel() - precComp = precComp.reshape((int(len(precComp) / (len(self.elements) - 1)), len(self.elements) - 1)) - mu = np.array([self._parentEq.chemical_potentials]) - mult = precComp * mu - - #Difference between the chemical potential hyperplane and the samples points - #The max driving force is the same as when the chemical potentials of the two phases are parallel - diff = np.sum(mult, axis=1) - self._pointsPrec[precPhase].GM.values.ravel() - - #Find maximum driving force and corresponding composition ----------------------------------------------------------------------------------- - #For phases with order/disorder transition, a filter is applied such that it will only use points that are below the disordered energy surface - if self.orderedPhase[precPhase]: - diff = diff[self._orderingPoints[precPhase].OCM.values.ravel() < -1e-8] - - if returnComp: - g = np.amax(diff) - - if g < 0: - return g, None - else: - #Get all compositions for each point and grab the composition corresponding to max driving force - #For ordered compounds, the composition needs to be filtered to remove any disordered points (corresponding to matrix phase) - #This only has to be done for composition since 'diff' is already filtered - if len(x) == 1: - betaX = self._pointsPrec[precPhase].X.sel(component=self.elements[1]).values.ravel() - if self.orderedPhase[precPhase]: - betaX = betaX[self._orderingPoints[precPhase].OCM.values < -1e-8] - comp = betaX[np.argmax(diff)] - else: - betaX = [self._pointsPrec[precPhase].X.sel(component=self.elements[i+1]).values for i in range(len(x))] - if self.orderedPhase[precPhase]: - for i in range(len(x)): - betaX[i] = betaX[i][self._orderingPoints[precPhase].OCM.values < -1e-8] - comp = [betaX[i][np.argmax(diff)] for i in range(len(x))] - - return g, comp - else: - return np.amax(diff), None - - def _getDrivingForceApprox(self, x, T, precPhase = None, returnComp = False, training = False): - ''' - Approximate method of driving force calculation - Assumes equilibrium composition of precipitate phase - - Sampling method is used if driving force is negative - - Parameters - ---------- - x : float or array - Composition of minor element in bulk matrix phase - Use float for binary systems - Use array for multicomponent systems - T : float - Temperature in K - precPhase : str (optional) - Precipitate phase to consider (default is first precipitate phase in list) - returnComp : bool (optional) - Whether to return composition of precipitate (defaults to False) - - Returns - ------- - (driving force, precipitate composition) - Driving force is positive if precipitate can form - Precipitate composition will be None if driving force is negative or returnComp is False - ''' - if precPhase is None: - precPhase = self.phases[1] - - if not hasattr(x, '__len__'): - x = [x] - cond = self._getConditions(x, T, 0) - self._prevX = x - - #Create cache of composition set if not done so already or if training a surrogate - #Training points for surrogates may be far apart, so starting from a previous - # composition set could give a bad starting position for the minimizer - if self._compset_cache_df.get(precPhase, None) is None or training: - #Calculate equilibrium ---------------------------------------------------------------------------------------------------------------------- - eq = self.getEq(x, T, 0, precPhase) - #Cast values in state_variables to double for updating composition sets - state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) - stable_phases = eq.Phase.values.ravel() - phase_amounts = eq.NP.values.ravel() - matrix_idx = np.where(stable_phases == self.phases[0])[0] - precip_idx = np.where(stable_phases == precPhase)[0] - chemical_potentials = eq.MU.values.ravel() - x_precip = eq.isel(vertex=precip_idx).X.values.ravel() - - #If matrix phase is not stable, then use sampling method - # This may occur during surrogate training of interfacial composition, - # where we're trying to calculate the driving force at the precipitate composition - # In this case, the conditions will be at th precipitate composition which can result in - # only that phase being stable - if len(matrix_idx) == 0: - return self._getDrivingForceSampling(x, T, precPhase, returnComp) - - #Test that precipitate phase is stable and that we're not training a surrogate - #If not, then there's no composition set to cache - if len(precip_idx) > 0: - cs_matrix, miscMatrix = self._createCompositionSet(eq, state_variables, self.phases[0], phase_amounts, matrix_idx) - cs_precip, miscPrec = self._createCompositionSet(eq, state_variables, precPhase, phase_amounts, precip_idx) - x_precip = np.array(cs_precip.X) - - composition_sets = [cs_matrix, cs_precip] - self._compset_cache_df[precPhase] = composition_sets - - if miscMatrix or miscPrec: - result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, - self.models, self.phase_records, - composition_sets=self._compset_cache_df[precPhase]) - self._compset_cache_df[precPhase] = composition_sets - chemical_potentials = result.chemical_potentials - cs_precip = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] - x_precip = np.array(cs_precip.X) - - ph = np.unique(stable_phases[stable_phases != '']) - ele = eq.component.values.ravel() - else: - result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, - self.models, self.phase_records, - composition_sets=self._compset_cache_df[precPhase]) - self._compset_cache_df[precPhase] = composition_sets - chemical_potentials = result.chemical_potentials - ph = [cs.phase_record.phase_name for cs in composition_sets if cs.NP > 0] - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - cs_precip = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] - x_precip = np.array(cs_precip.X) - ele = list(cs_precip.phase_record.nonvacant_elements) - - #Check that equilibrium has converged - #If not, then return None, None since driving force can't be obtained - if any(np.isnan(chemical_potentials)): - return None, None - - #If in two phase region, then calculate equilibrium using only parent phase and find free energy difference between chemical potential and free energy of preciptiate - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - for i in range(len(ele)): - if ele[i] == self.elements[0]: - refIndex = i - break - - #Equilibrium at matrix composition for only the parent phase - self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [self.phases[0]], cond, - self.models, self.phase_records, - composition_sets=self._matrix_cs) - - - #Remove caching if training surrogate in case training points are far apart - if training: - self._matrix_cs = None - - #Check if equilibrium has converged and chemical potential can be obtained - #If not, then return None for driving force - if any(np.isnan(self._parentEq.chemical_potentials)): - return None, None - - sortIndices = np.argsort(self.elements[1:-1]) - unsortIndices = np.argsort(sortIndices) - - xP = x_precip - - dg = np.sum(xP * self._parentEq.chemical_potentials) - np.sum(xP * chemical_potentials) - - #Remove reference element - xP = np.delete(xP, refIndex) - - if returnComp: - if len(x) == 1: - return dg.ravel()[0], xP[unsortIndices][0] - else: - return dg.ravel()[0], xP[unsortIndices] - else: - return dg.ravel()[0], None - else: - #If driving force is negative, then use sampling method --------------------------------------------------------------------------------- - return self._getDrivingForceSampling(x, T, precPhase, returnComp) - - def _getDrivingForceCurvature(self, x, T, precPhase = None, returnComp = False, training = False): - ''' - Gets driving force from curvature of free energy function - Assumes small saturation - - Sampling method is used if driving force is negative - - Parameters - ---------- - x : float or array - Composition of minor element in bulk matrix phase - Use float for binary systems - Use array for multicomponent systems - T : float - Temperature in K - precPhase : str (optional) - Precipitate phase to consider (default is first precipitate phase in list) - returnComp : bool (optional) - Whether to return composition of precipitate (defaults to False) - - Returns - ------- - (driving force, precipitate composition) - Driving force is positive if precipitate can form - Precipitate composition will be None if driving force is negative or returnComp is False - ''' - if precPhase is None: - precPhase = self.phases[1] - - if not hasattr(x, '__len__'): - x = [x] - cond = self._getConditions(x, T, 0) - self._prevX = x - - #Create cache of composition set if not done so already or if training a surrogate - #Training points for surrogates may be far apart, so starting from a previous - # composition set could give a bad starting position for the minimizer - if self._compset_cache_df.get(precPhase, None) is None or training: - #Calculate equilibrium ---------------------------------------------------------------------------------------------------------------------- - eq = self.getEq(x, T, 0, precPhase) - #Cast values in state_variables to double for updating composition sets - state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) - stable_phases = eq.Phase.values.ravel() - phase_amounts = eq.NP.values.ravel() - matrix_idx = np.where(stable_phases == self.phases[0])[0] - precip_idx = np.where(stable_phases == precPhase)[0] - chemical_potentials = eq.MU.values.ravel() - x_precip = eq.isel(vertex=precip_idx).X.values.ravel() - x_matrix = eq.isel(vertex=matrix_idx).X.values.ravel() - - #If matrix phase is not stable, then use sampling method - # This may occur during surrogate training of interfacial composition, - # where we're trying to calculate the driving force at the precipitate composition - # In this case, the conditions will be at th precipitate composition which can result in - # only that phase being stable - if len(matrix_idx) == 0: - return self._getDrivingForceSampling(x, T, precPhase, returnComp) - - #Test that precipitate phase is stable and that we're not training a surrogate - #If not, then there's no composition set to cache - if len(precip_idx) > 0: - cs_matrix, miscMatrix = self._createCompositionSet(eq, state_variables, self.phases[0], phase_amounts, matrix_idx) - cs_precip, miscPrec = self._createCompositionSet(eq, state_variables, precPhase, phase_amounts, precip_idx) - x_matrix = np.array(cs_matrix.X) - x_precip = np.array(cs_precip.X) - - composition_sets = [cs_matrix, cs_precip] - self._compset_cache_df[precPhase] = composition_sets - - if miscMatrix or miscPrec: - result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, - self.models, self.phase_records, - composition_sets=self._compset_cache_df[precPhase]) - self._compset_cache_df[precPhase] = composition_sets - chemical_potentials = result.chemical_potentials - cs_precip = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] - x_precip = np.array(cs_precip.X) - - cs_matrix = [cs for cs in composition_sets if cs.phase_record.phase_name == self.phases[0]][0] - x_matrix = np.array(cs_matrix.X) - - ph = np.unique(stable_phases[stable_phases != '']) - ele = eq.component.values.ravel() - else: - result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, - self.models, self.phase_records, - composition_sets=self._compset_cache_df[precPhase]) - self._compset_cache_df[precPhase] = composition_sets - chemical_potentials = result.chemical_potentials - ph = [cs.phase_record.phase_name for cs in composition_sets if cs.NP > 0] - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - cs_precip = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] - x_precip = np.array(cs_precip.X) - - cs_matrix = [cs for cs in composition_sets if cs.phase_record.phase_name == self.phases[0]][0] - x_matrix = np.array(cs_matrix.X) - - ele = list(cs_precip.phase_record.nonvacant_elements) - - #Check that equilibrium has converged - #If not, then return None, None since driving force can't be obtained - if any(np.isnan(chemical_potentials)): - return None, None - - if not hasattr(x, '__len__'): - x = [x] - - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - for i in range(len(ele)): - if ele[i] == self.elements[0]: - refIndex = i - break - - #If in two phase region, then get curvature of parent phase and use it to calculate driving force --------------------------------------- - sortIndices = np.argsort(self.elements[1:-1]) - unsortIndices = np.argsort(sortIndices) - - dMudxParent = dMudX(chemical_potentials, composition_sets[0], self.elements[0]) - xM = np.delete(x_matrix, refIndex) - - xP = np.delete(x_precip, refIndex) - xBar = np.array([xP - xM]) - - x = np.array(x)[sortIndices] - xD = np.array([x - xM]) - - dg = np.matmul(xD, np.matmul(dMudxParent, xBar.T)) - - if returnComp: - if len(x) == 1: - return dg.ravel()[0], xP[unsortIndices][0] - else: - return dg.ravel()[0], xP[unsortIndices] - else: - return dg.ravel()[0], None - else: - #If driving force is negative, then use sampling method --------------------------------------------------------------------------------- - return self._getDrivingForceSampling(x, T, precPhase, returnComp) - -class BinaryThermodynamics (GeneralThermodynamics): - ''' - Class for defining driving force and interfacial composition functions - for a binary system using pyCalphad and thermodynamic databases - - Parameters - ---------- - database : str - File name for database - elements : list - Elements to consider - Note: reference element must be the first index in the list - phases : list - Phases involved - Note: matrix phase must be first index in the list - drivingForceMethod : str (optional) - Method used to calculate driving force - Options are 'approximate' (default), 'sampling' and 'curvature' (not recommended) - interfacialCompMethod: str (optional) - Method used to calculate interfacial composition - Options are 'eq' (default) and 'curvature' (not recommended) - ''' - def __init__(self, database, elements, phases, drivingForceMethod = 'approximate', interfacialCompMethod = 'equilibrium'): - super().__init__(database, elements, phases, drivingForceMethod) - - if self.elements[1] < self.elements[0]: - self.reverse = True - else: - self.reverse = False - - #Guess composition for when finding tieline - self._guessComposition = {self.phases[i]: (0, 1, 0.1) for i in range(1, len(self.phases))} - - self.setInterfacialMethod(interfacialCompMethod) - - - def setInterfacialMethod(self, interfacialCompMethod): - ''' - Changes method for caluclating interfacial composition - - Parameters - ---------- - interfacialCompMethod - str - Options are ['equilibrium', 'curvature'] - ''' - if interfacialCompMethod == 'equilibrium': - self._interfacialComposition = self._interfacialCompositionFromEq - elif interfacialCompMethod == 'curvature': - self._interfacialComposition = self._interfacialCompositionFromCurvature - else: - raise Exception('Interfacial composition method must be either \'equilibrium\' or \'curvature\'') - - def setGuessComposition(self, conditions): - ''' - Sets initial composition when calculating equilibrium for interfacial energy - - Parameters - ---------- - conditions : float, tuple or dict - Guess composition(s) to solve equilibrium for - This should encompass the region where a tieline can be found - between the matrix and precipitate phases - Options: float - will set to all precipitate phases - tuple - (min, max dx) will set to all precipitate phases - dictionary {phase name: scalar or tuple} - ''' - if isinstance(conditions, dict): - #Iterating over conditions dictionary in case not all precipitate phases are listed - for p in conditions: - self._guessComposition[p] = conditions[p] - #If not dictionary, then set to all phases - else: - for i in range(1, len(self.phases)): - self._guessComposition[self.phases[i]] = conditions - - def getInterfacialComposition(self, T, gExtra = 0, precPhase = None): - ''' - Gets interfacial composition accounting for Gibbs-Thomson effect - - Parameters - ---------- - T : float or array - Temperature in K - gExtra : float or array (optional) - Extra contributions to the precipitate Gibbs free energy - Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) - Defaults to 0 - precPhase : str - Precipitate phase to consider (default is first precipitate in list) - - Note: for multiple conditions, only gExtra has to be an array - This will calculate compositions for multiple gExtra at the input Temperature - - If T is also an array, then T and gExtra must be the same length - where each index will pertain to a single condition - - Returns - ------- - (parent composition, precipitate composition) - Both will be either float or array based off shape of gExtra - Will return (None, None) if precipitate is unstable - ''' - if hasattr(gExtra, '__len__'): - if not hasattr(T, '__len__'): - caArray, cbArray = self._interfacialComposition(T, gExtra, precPhase) - else: - #If T is also an array, then iterate through T and gExtra - #Otherwise, pycalphad will create a cartesian product of the two - caArray = [] - cbArray = [] - for i in range(len(gExtra)): - ca, cb = self._interfacialComposition(T[i], gExtra[i], precPhase) - caArray.append(ca) - cbArray.append(cb) - caArray = np.array(caArray) - cbArray = np.array(cbArray) - - return caArray, cbArray - else: - return self._interfacialComposition(T, gExtra, precPhase) - - - def _interfacialCompositionFromEq(self, T, gExtra = 0, precPhase = None): - ''' - Gets interfacial composition by calculating equilibrum with Gibbs-Thomson effect - - Parameters - ---------- - T : float - Temperature in K - gExtra : float (optional) - Extra contributions to the precipitate Gibbs free energy - Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) - Defaults to 0 - precPhase : str - Precipitate phase to consider (default is first precipitate in list) - - Returns - ------- - (parent composition, precipitate composition) - Both will be either float or array based off shape of gExtra - Will return (None, None) if precipitate is unstable - ''' - if precPhase is None: - precPhase = self.phases[1] - - if hasattr(gExtra, '__len__'): - gExtra = np.array(gExtra) - else: - gExtra = np.array([gExtra]) - gExtra += self.gOffset - - #Compute equilibrium at guess composition - cond = {v.X(self.elements[1]): self._guessComposition[precPhase], v.T: T, v.P: 101325, v.GE: gExtra} - eq = equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, model=self.models, - phase_records={self.phases[0]: self.phase_records[self.phases[0]], precPhase: self.phase_records[precPhase]}, - calc_opts = {'pdens': self.pDens}) - - xParentArray = np.zeros(len(gExtra)) - xPrecArray = np.zeros(len(gExtra)) - for g in range(len(gExtra)): - eqG = eq.where(eq.GE == gExtra[g], drop=True) - gm = eqG.GM.values.ravel() - for i in range(len(gm)): - eqSub = eqG.where(eqG.GM == gm[i], drop=True) - - ph = eqSub.Phase.values.ravel() - ph = ph[ph != ''] - - #Check if matrix and precipitate phase are stable, and check if there's no miscibility gaps - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - #Get indices for each phase - eqPa = eqSub.where(eqSub.Phase == self.phases[0], drop=True) - eqPr = eqSub.where(eqSub.Phase == precPhase, drop=True) - - cParent = eqPa.X.values.ravel() - cPrec = eqPr.X.values.ravel() - - #Get composition of element, use element index of 1 is the parent index is first alphabetically - if self.reverse: - xParent = cParent[0] - xPrec = cPrec[0] - else: - xParent = cParent[1] - xPrec = cPrec[1] - - xParentArray[g] = xParent - xPrecArray[g] = xPrec - break - if xParentArray[g] == 0: - xParentArray[g] = -1 - xPrecArray[g] = -1 - - if len(gExtra) == 1: - return xParentArray[0], xPrecArray[0] - else: - return xParentArray, xPrecArray - - - def _interfacialCompositionFromCurvature(self, T, gExtra = 0, precPhase = None): - ''' - Gets interfacial composition using free energy curvature - G''(x - xM)(xP-xM) = 2*y*V/R - - Parameters - ---------- - T : float - Temperature in K - gExtra : float (optional) - Extra contributions to the precipitate Gibbs free energy - Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) - Defaults to 0 - precPhase : str - Precipitate phase to consider (default is first precipitate in list) - - Returns - ------- - (parent composition, precipitate composition) - Both will be either float or array based off shape of gExtra - Will return (None, None) if precipitate is unstable - ''' - if precPhase is None: - precPhase = self.phases[1] - - if hasattr(gExtra, '__len__'): - gExtra = np.array(gExtra) - else: - gExtra = np.array([gExtra]) - - #Compute equilibrium at guess composition - cond = {v.X(self.elements[1]): self._guessComposition[precPhase], v.T: T, v.P: 101325, v.GE: self.gOffset} - eq = equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, model=self.models, - phase_records={self.phases[0]: self.phase_records[self.phases[0]], precPhase: self.phase_records[precPhase]}, - calc_opts = {'pdens': self.pDens}) - - gm = eq.GM.values.ravel() - for g in gm: - eqSub = eq.where(eq.GM == g, drop=True) - - ph = eqSub.Phase.values.ravel() - ph = ph[ph != ''] - - #Check if matrix and precipitate phase are stable, and check if there's no miscibility gaps - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - #Cast values in state_variables to double for updating composition sets - state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) - stable_phases = eqSub.Phase.values.ravel() - phase_amounts = eqSub.NP.values.ravel() - matrix_idx = np.where(stable_phases == self.phases[0])[0] - precip_idx = np.where(stable_phases == precPhase)[0] - - cs_matrix = CompositionSet(self.phase_records[self.phases[0]]) - if len(matrix_idx) > 1: - matrix_idx = [matrix_idx[np.argmax(phase_amounts[matrix_idx])]] - cs_matrix.update(eqSub.Y.isel(vertex=matrix_idx).values.ravel()[:cs_matrix.phase_record.phase_dof], - phase_amounts[matrix_idx], state_variables) - cs_precip = CompositionSet(self.phase_records[precPhase]) - if len(precip_idx) > 1: - precip_idx = [precip_idx[np.argmax(phase_amounts[precip_idx])]] - cs_precip.update(eqSub.Y.isel(vertex=precip_idx).values.ravel()[:cs_precip.phase_record.phase_dof], - phase_amounts[precip_idx], state_variables) - - chemical_potentials = eqSub.MU.values.ravel() - cPrec = eqSub.isel(vertex=precip_idx).X.values.ravel() - cParent = eqSub.isel(vertex=matrix_idx).X.values.ravel() - - dMudxParent = dMudX(chemical_potentials, cs_matrix, self.elements[0]) - dMudxPrec = dMudX(chemical_potentials, cs_precip, self.elements[0]) - - #Get composition of element, use element index of 1 is the parent index is first alphabetically - if self.reverse: - xParentEq = cParent[0] - xPrecEq = cPrec[0] - else: - xParentEq = cParent[1] - xPrecEq = cPrec[1] - - dMudxParent = dMudxParent[0,0] - dMudxPrec = dMudxPrec[0,0] - - if dMudxParent != 0: - xParent = gExtra / dMudxParent / (xPrecEq - xParentEq) + xParentEq - else: - xParent = xParentEq - - if dMudxPrec != 0: - xPrec = dMudxParent * (xParent - xParentEq) / dMudxPrec + xPrecEq - else: - xPrec = xPrecEq - - xParent[xParent < 0] = 0 - xParent[xParent > 1] = 1 - xPrec[xPrec < 0] = 0 - xPrec[xPrec > 1] = 1 - - if len(gExtra) == 1: - return xParent[0], xPrec[0] - else: - return xParent, xPrec - - if len(gExtra) == 1: - return -1, -1 - else: - return -1*np.ones(len(gExtra)), -1*np.ones(len(gExtra)) - - - def plotPhases(self, ax, T, gExtra = 0, plotGibbsOffset = False, *args, **kwargs): - ''' - Plots sampled points from the parent and precipitate phase - - Parameters - ---------- - ax : Axis - T : float - Temperature in K - gExtra : float (optional) - Extra contributions to the Gibbs free energy of precipitate - Defaults to 0 - plotGibbsOffset : bool (optional) - If True and gExtra is not 0, the sampled points of the - precipitate phase will be plotted twice with gExtra and - with no extra Gibbs free energy contributions - Defualts to False - ''' - points = calculate(self.db, self.elements, self.phases[0], P=101325, T=T, GE=0, model=self.models, phase_records=self.phase_records, output='GM') - ax.scatter(points.X.sel(component=self.elements[1]), points.GM / 1000, label=self.phases[0], *args, **kwargs) - - #Add gExtra to precipitate phase - for i in range(1, len(self.phases)): - points = calculate(self.db, self.elements, self.phases[i], P=101325, T=T, GE=0, model=self.models, phase_records=self.phase_records, output='GM') - ax.scatter(points.X.sel(component=self.elements[1]), (points.GM + gExtra) / 1000, label=self.phases[i], *args, **kwargs) - - #Plot non-offset precipitate phase - if plotGibbsOffset and gExtra != 0: - ax.scatter(points.X.sel(component=self.elements[1]), points.GM / 1000, color='silver', alpha=0.3, *args, **kwargs) - - ax.legend() - ax.set_xlim([0, 1]) - ax.set_xlabel('Composition ' + self.elements[1]) - ax.set_ylabel('Gibbs Free Energy (kJ/mol)') - - -class MulticomponentThermodynamics (GeneralThermodynamics): - ''' - Class for defining driving force and (possibly) interfacial composition functions - for a multicomponent system using pyCalphad and thermodynamic databases - - Parameters - ---------- - database : str - File name for database - elements : list - Elements to consider - Note: reference element must be the first index in the list - phases : list - Phases involved - Note: matrix phase must be first index in the list - drivingForceMethod : str (optional) - Method used to calculate driving force - Options are 'approximate' (default), 'sampling' and 'curvature' (not recommended) - ''' - def __init__(self, database, elements, phases, drivingForceMethod = 'approximate'): - super().__init__(database, elements, phases, drivingForceMethod) - - #Previous variables for curvature terms - #Near saturation, pycalphad may detect only a single phase (if sampling density is too low) - #When this occurs, this will assume that the system is on the same tie-line and - #use the previously calculated values - self._prevDc = {p: None for p in phases[1:]} - self._prevMc = {p: None for p in phases[1:]} - self._prevGba = {p: None for p in phases[1:]} - self._prevBeta = {p: None for p in phases[1:]} - self._prevCa = {p: None for p in phases[1:]} - self._prevCb = {p: None for p in phases[1:]} - - def getInterfacialComposition(self, x, T, gExtra = 0, precPhase = None): - ''' - Gets interfacial composition by calculating equilibrum with Gibbs-Thomson effect - - Parameters - ---------- - T : float or array - Temperature in K - gExtra : float or array (optional) - Extra contributions to the precipitate Gibbs free energy - Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) - Defaults to 0 - precPhase : str - Precipitate phase to consider (default is first precipitate in list) - - Note: for multiple conditions, only gExtra has to be an array - This will calculate compositions for multiple gExtra at the input Temperature - - If T is also an array, then T and gExtra must be the same length - where each index will pertain to a single condition - - Returns - ------- - (parent composition, precipitate composition) - Both will be either float or array based off shape of gExtra - Will return (None, None) if precipitate is unstable - ''' - if hasattr(gExtra, '__len__'): - if not hasattr(T, '__len__'): - T = T * np.ones(len(gExtra)) - - caArray = [] - cbArray = [] - for i in range(len(gExtra)): - ca, cb = self._interfacialComposition(x, T[i], gExtra[i], precPhase) - caArray.append(ca) - cbArray.append(cb) - caArray = np.array(caArray) - cbArray = np.array(cbArray) - return caArray, cbArray - else: - return self._interfacialComposition(x, T, gExtra, precPhase) - - - def _interfacialComposition(self, x, T, gExtra = 0, precPhase = None): - ''' - Gets interfacial composition, will return None, None if composition is in single phase region - - Parameters - ---------- - T : float - Temperature in K - gExtra : float (optional) - Extra contributions to the precipitate Gibbs free energy - Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) - Defaults to 0 - precPhase : str - Precipitate phase to consider (default is first precipitate in list) - - Returns - ------- - (parent composition, precipitate composition) - Both will be either float or array based off shape of gExtra - Will return (None, None) if precipitate is unstable - ''' - if precPhase is None: - precPhase = self.phases[1] - - eq = self.getEq(x, T, gExtra, precPhase) - - #Check for convergence, return None if not converged - if np.any(np.isnan(eq.MU.values.ravel())): - return None, None - - ph = eq.Phase.values.ravel() - ph = ph[ph != ''] - - #Check if matrix and precipitate phase are stable, and check if there's no miscibility gaps - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - sortIndices = np.argsort(self.elements[:-1]) - unsortIndices = np.argsort(sortIndices) - - mu = eq.MU.values.ravel() - mu = mu[unsortIndices] - - eqPh = eq.where(eq.Phase == self.phases[0], drop=True) - xM = eqPh.X.values.ravel() - xM = xM[unsortIndices] - - eqPh = eq.where(eq.Phase == precPhase, drop=True) - xP = eqPh.X.values.ravel() - xP = xP[unsortIndices] - - return xM, xP - - return None, None - - def _curvatureFactorFromEq(self, chemical_potentials, composition_sets, precPhase=None, training = False): - ''' - Curvature factor (from Phillipes and Voorhees - 2013) - - Parameters - ---------- - chemical_potentials : 1-D float64 array - composition_sets : List[pycalphad.composition_set.CompositionSet] - precPhase : str (optional) - Precipitate phase (defaults to first precipitate in list) - training : bool (optional) - For surrogate training, will return None rather than previous results - if 2-phase region is not detected in equilibrium calculation - - Returns - ------- - {D-1 dCbar / dCbar^T M-1 dCbar} - for calculating interfacial composition of matrix - {1 / dCbar^T M-1 dCbar} - for calculating growth rate - {Gb^-1 Ga} - for calculating precipitate composition - beta - Impingement rate - Ca - interfacial composition of matrix phase - Cb - interfacial composition of precipitate phase - - Will return (None, None, None, None, None, None) if single phase - ''' - if precPhase is None: - precPhase = self.phases[1] - - #Check if input equilibrium has converged - if np.any(np.isnan(chemical_potentials)): - if training: - return None, None, None, None, None, None - else: - print('Warning: equilibrum was not able to be solved for, using results of previous calculation') - return self._prevDc[precPhase], self._prevMc[precPhase], self._prevGba[precPhase], self._prevBeta[precPhase], self._prevCa[precPhase], self._prevCb[precPhase] - - ele = list(composition_sets[0].phase_record.nonvacant_elements) - refIndex = ele.index(self.elements[0]) - - ph = [cs.phase_record.phase_name for cs in composition_sets] - - if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: - sortIndices = np.argsort(self.elements[1:-1]) - unsortIndices = np.argsort(sortIndices) - - matrix_cs = [cs for cs in composition_sets if cs.phase_record.phase_name == self.phases[0]][0] - - if self.mobCallables[self.phases[0]] is None: - Dnkj, dMudxParent, invMob = inverseMobility_from_diffusivity(chemical_potentials, matrix_cs, - self.elements[0], self.diffCallables[self.phases[0]], - diffusivity_correction=self.mobility_correction) - - #NOTE: This is note tested yet - Dtrace = tracer_diffusivity_from_diff(matrix_cs, self.diffCallables[self.phases[0]], diffusivity_correction=self.mobility_correction) - else: - Dnkj, dMudxParent, invMob = inverseMobility(chemical_potentials, matrix_cs, self.elements[0], - self.mobCallables[self.phases[0]], - mobility_correction=self.mobility_correction) - Dtrace = tracer_diffusivity(matrix_cs, self.mobCallables[self.phases[0]], mobility_correction=self.mobility_correction) - - xMFull = np.array(matrix_cs.X) - xM = np.delete(xMFull, refIndex) - - precip_cs = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] - dMudxPrec = dMudX(chemical_potentials, precip_cs, self.elements[0]) - xPFull = np.array(precip_cs.X) - xP = np.delete(xPFull, refIndex) - xBarFull = np.array([xPFull - xMFull]) - xBar = np.array([xP - xM]) - - num = np.matmul(np.linalg.inv(Dnkj), xBar.T).flatten() - - #Denominator should be a scalar since its V * M * V^T - den = np.matmul(xBar, np.matmul(invMob, xBar.T)).flatten()[0] - - if np.linalg.matrix_rank(dMudxPrec) == dMudxPrec.shape[0]: - Gba = np.matmul(np.linalg.inv(dMudxPrec), dMudxParent) - Gba = Gba[unsortIndices,:] - Gba = Gba[:,unsortIndices] - else: - Gba = np.zeros(dMudxPrec.shape) - - betaNum = xBarFull**2 - betaDen = Dtrace * xMFull.flatten() - bsum = np.sum(betaNum / betaDen) - if bsum == 0: - beta = self._prevBeta[precPhase] - else: - beta = 1 / bsum - - self._prevDc[precPhase] = num[unsortIndices] / den - self._prevMc[precPhase] = 1 / den - self._prevGba[precPhase] = Gba - self._prevBeta[precPhase] = beta - self._prevCa[precPhase] = xM[unsortIndices] - self._prevCb[precPhase] = xP[unsortIndices] - - return self._prevDc[precPhase], self._prevMc[precPhase], self._prevGba[precPhase], self._prevBeta[precPhase], self._prevCa[precPhase], self._prevCb[precPhase] - else: - if training: - return None, None, None, None, None, None - else: - #print('Warning: only a single phase detected in equilibrium, using results of previous calculation') - #return self._prevDc[precPhase], self._prevMc[precPhase], self._prevGba[precPhase], self._prevBeta[precPhase], self._prevCa[precPhase], self._prevCb[precPhase] - - #If two-phase equilibrium is not found, then the temperature may have changed to where the precipitate is unstable - #Return None in this case - return None, None, None, self._prevBeta[precPhase], None, None - - - def curvatureFactor(self, x, T, precPhase = None, training = False): - ''' - Curvature factor (from Phillipes and Voorhees - 2013) from composition and temperature - This is the same as curvatureFactorEq, but will calculate equilibrium from x and T first - - Parameters - ---------- - x : array - Composition of solutes - T : float - Temperature - precPhase : str (optional) - Precipitate phase (defaults to first precipitate in list) - - Returns - ------- - {D-1 dCbar / dCbar^T M-1 dCbar} - for calculating interfacial composition of matrix - {1 / dCbar^T M-1 dCbar} - for calculating growth rate - {Gb^-1 Ga} - for calculating precipitate composition - beta - Impingement rate - Ca - interfacial composition of matrix phase - Cb - interfacial composition of precipitate phase - - Will return (None, None, None, None, None, None) if single phase - ''' - if precPhase is None: - precPhase = self.phases[1] - if not hasattr(x, '__len__'): - x = [x] - - #Remove first element if x lists composition of all elements - if len(x) == len(self.elements) - 1: - x = x[1:] - cond = self._getConditions(x, T, 0) - - #Perform equilibrium from scratch if cache not set or when training surrogate - if self._compset_cache.get(precPhase, None) is None or training: - eq = self.getEq(x, T, 0, precPhase) - state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) - stable_phases = eq.Phase.values.ravel() - phase_amounts = eq.NP.values.ravel() - matrix_idx = np.where(stable_phases == self.phases[0])[0] - precip_idx = np.where(stable_phases == precPhase)[0] - - #If matrix phase is not stable (why?), then return previous values - #Curvature can't be calculated if matrix phase isn't present - if len(matrix_idx) == 0: - if training: - return None, None, None, None, None, None - else: - print('Warning: matrix phase not detected, using results of previous calculation') - return self._prevDc, self._prevMc, self._prevGba, self._prevBeta, self._prevCa, self._prevCb - - cs_matrix, miscMatrix = self._createCompositionSet(eq, state_variables, self.phases[0], phase_amounts, matrix_idx) - - chemical_potentials = eq.MU.values.ravel() - - #If precipitate phase is not stable, then only store matrix phase in composition sets - #Checks for single phase regions are done in _curvatureFactorFromEq, - # so this will allow to fail there - if len(precip_idx) == 0: - composition_sets = [cs_matrix] - self._compset_cache[precPhase] = None - else: - cs_precip, miscPrec = self._createCompositionSet(eq, state_variables, precPhase, phase_amounts, precip_idx) - - composition_sets = [cs_matrix, cs_precip] - self._compset_cache[precPhase] = composition_sets - - if miscMatrix or miscPrec: - result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, - self.models, self.phase_records, - composition_sets=self._compset_cache[precPhase]) - self._compset_cache[precPhase] = composition_sets - chemical_potentials = result.chemical_potentials - - else: - result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, - self.models, self.phase_records, - composition_sets=self._compset_cache[precPhase]) - self._compset_cache[precPhase] = composition_sets - chemical_potentials = result.chemical_potentials - - result = self._curvatureFactorFromEq(chemical_potentials, composition_sets, precPhase, training) - return result - - def getGrowthAndInterfacialComposition(self, x, T, dG, R, gExtra, precPhase = None, training = False): - ''' - Returns growth rate and interfacial compostion given Gibbs-Thomson contribution - - Parameters - ---------- - x : array - Composition of solutes - T : float - Temperature - dG : float - Driving force at given x and T - R : float or array - Precipitate radius - gExtra : float or array - Gibbs-Thomson contribution (must be same shape as R) - precPhase : str (optional) - Precipitate phase (defaults to first precipitate in list) - - Returns - ------- - (growth rate, matrix composition, precipitate composition, equilibrium matrix comp, equilibrium precipitate comp) - growth rate will be float or array based off shape of R - matrix and precipitate composition will be array or 2D array based - off shape of R - ''' - if hasattr(R, '__len__'): - R = np.array(R) - if hasattr(gExtra, '__len__'): - gExtra = np.array(gExtra) - - dc, mc, gba, beta, ca, cb = self.curvatureFactor(x, T, precPhase, training) - if dc is None: - return None, None, None, None, None - - Rdiff = (dG - gExtra) - - gr = (mc / R) * Rdiff - - if hasattr(Rdiff, '__len__'): - calpha = np.zeros((len(Rdiff), len(self.elements[1:-1]))) - dca = np.zeros((len(Rdiff), len(self.elements[1:-1]))) - cbeta = np.zeros((len(Rdiff), len(self.elements[1:-1]))) - for i in range(len(self.elements[1:-1])): - calpha[:,i] = x[i] - dc[i] * Rdiff - dca[:,i] = calpha[:,i] - ca[i] - - dcb = np.matmul(gba, dca.T) - for i in range(len(self.elements[1:-1])): - cbeta[:,i] = cb[i] + dcb[i,:] - - calpha[calpha < 0] = 0 - calpha[calpha > 1] = 1 - cbeta[cbeta < 0] = 0 - cbeta[cbeta > 1] = 1 - - return gr, calpha, cbeta, ca, cb - else: - calpha = x - dc * Rdiff - cbeta = cb + np.matmul(gba, (calpha - ca)).flatten() - - calpha[calpha < 0] = 0 - calpha[calpha > 1] = 1 - cbeta[cbeta < 0] = 0 - cbeta[cbeta > 1] = 1 - - return gr, calpha, cbeta, ca, cb - - def impingementFactor(self, x, T, precPhase = None, training = False): - ''' - Returns impingement factor for nucleation rate calculations - - Parameters - ---------- - x : array - Composition of solutes - T : float - Temperature - precPhase : str (optional) - Precipitate phase (defaults to first precipitate in list) - ''' - dc, mc, gba, beta, ca, cb = self.curvatureFactor(x, T, precPhase, training) - return beta diff --git a/kawin/diffusion/Diffusion.py b/kawin/diffusion/Diffusion.py new file mode 100644 index 0000000..d5dc1ab --- /dev/null +++ b/kawin/diffusion/Diffusion.py @@ -0,0 +1,574 @@ +import numpy as np +import time +import csv +from itertools import zip_longest +from kawin.solver.Solver import DESolver, SolverType +from kawin.GenericModel import GenericModel +import kawin.diffusion.Plot as diffPlot + +class DiffusionModel(GenericModel): + #Boundary conditions + FLUX = 0 + COMPOSITION = 1 + + def __init__(self, zlim, N, elements = ['A', 'B'], phases = ['alpha'], record = True): + ''' + Class for defining a 1-dimensional mesh + + Parameters + ---------- + zlim : tuple + Z-bounds of mesh (lower, upper) + N : int + Number of nodes + elements : list of str + Elements in system (first element will be assumed as the reference element) + phases : list of str + Number of phases in the system + ''' + super().__init__() + if isinstance(phases, str): + phases = [phases] + self.zlim, self.N = zlim, N + self.allElements, self.elements = elements, elements[1:] + self.phases = phases + self.therm = None + + self.z = np.linspace(zlim[0], zlim[1], N) + self.dz = self.z[1] - self.z[0] + self.t = 0 + + self.reset() + + self.LBC, self.RBC = self.FLUX*np.ones(len(self.elements)), self.FLUX*np.ones(len(self.elements)) + self.LBCvalue, self.RBCvalue = np.zeros(len(self.elements)), np.zeros(len(self.elements)) + + self.cache = True + self.setHashSensitivity(4) + self.minComposition = 1e-8 + + self.maxCompositionChange = 0.002 + + if record: + self.enableRecording() + else: + self.disableRecording() + self._recordedX = None + self._recordedP = None + self._recordedZ = None + self._recordedTime = None + + def reset(self): + ''' + Resets model + + This involves clearing any caches in the Thermodynamics object and this model + as well as resetting the composition and phase profiles + ''' + if self.therm is not None: + self.therm.clearCache() + + self.x = np.zeros((len(self.elements), self.N)) + self.p = np.ones((1,self.N)) if len(self.phases) == 1 else np.zeros((len(self.phases), self.N)) + self.hashTable = {} + self.isSetup = False + self.t = 0 + + def setThermodynamics(self, thermodynamics): + ''' + Defines thermodynamics object for the diffusion model + + Parameters + ---------- + thermodynamics : Thermodynamics object + Requires the elements in the Thermodynamics and DiffusionModel objects to have the same order + ''' + self.therm = thermodynamics + + def setTemperature(self, T): + ''' + Sets iso-thermal temperature + + Parameters + ---------- + T : float + Temperature in Kelvin + ''' + self.Tparam = T + self.T = T + self.Tfunc = lambda z, t: self.Tparam * np.ones(len(z)) + + def setTemperatureArray(self, times, temperatures): + self.Tparam = (times, temperatures) + self.T = temperatures[0] + self.Tfunc = lambda z, t: np.interp(t/3600, self.Tparam[0], self.Tparam[1], self.Tparam[1][0], self.Tparam[1][-1]) * np.ones(len(z)) + + def setTemperatureFunction(self, func): + ''' + Function should be T = (x, t) + ''' + self.Tparam = func + self.Tfunc = lambda z, t: self.Tparam(z, t) + + def _getVarDict(self): + ''' + Returns mapping of { variable name : attribute name } for saving + The variable name will be the name in the .npz file + ''' + saveDict = { + 'elements': 'elements', + 'phases': 'phases', + 'z': 'z', + 'zLim': 'zLim', + 'N': 'N', + 'finalTime': 't', + 'finalX': 'x', + 'finalP': 'p', + 'recordX': '_recordedX', + 'recordP': '_recordedP', + 'recordZ': '_recordedZ', + 'recordTime': '_recordedTime', + } + return saveDict + + def load(filename): + ''' + Loads data from filename and returns a PrecipitateModel + ''' + data = np.load(filename) + model = DiffusionModel(data['zLim'], data['N'], data['elements'], data['phases']) + model._loadData(data) + model.isSetup = True + return model + + def setHashSensitivity(self, s): + ''' + Sets sensitivity of the hash table by significant digits + + For example, if a composition set is (0.5693, 0.2937) and s = 3, then + the hash will be stored as (0.569, 0.294) + + Lower s values will give faster simulation times at the expense of accuracy + + Parameters + ---------- + s : int + Number of significant digits to keep for the hash table + ''' + self.hashSensitivity = np.power(10, int(s)) + + def _getHash(self, x, T): + ''' + Gets hash value for a composition set + + Parameters + ---------- + x : list of floats + Composition set to create hash + ''' + return hash(tuple((np.concatenate((x, [T]))*self.hashSensitivity).astype(np.int32))) + + def useCache(self, use): + ''' + Whether to use the hash table + + Parameters + ---------- + use : bool + If True, then the hash table will be used + ''' + self.cache = use + + def clearCache(self): + ''' + Clears hash table + ''' + self.hashTable = {} + + def enableRecording(self, dtype = np.float32): + ''' + Enables recording of composition and phase + + Parameters + ---------- + dtype : numpy data type (optional) + Data type to record particle size distribution in + Defaults to np.float32 + ''' + self._record = True + self._recordedX = np.zeros((1, len(self.elements), self.N)) + self._recordedP = np.zeros((1, 1,self.N)) if len(self.phases) == 1 else np.zeros((1, len(self.phases), self.N)) + self._recordedZ = self.z + self._recordedTime = np.zeros(1) + + def disableRecording(self): + ''' + Disables recording + ''' + self._record = False + + def removeRecordedData(self): + ''' + Removes recorded data + ''' + self._recordedX = None + self._recordedP = None + self._recordedZ = None + self._recordedTime = None + + def record(self, time): + ''' + Adds current mesh data to recorded arrays + ''' + if self._record: + if time > 0: + self._recordedX = np.pad(self._recordedX, ((0, 1), (0, 0), (0, 0))) + self._recordedP = np.pad(self._recordedP, ((0, 1), (0, 0), (0, 0))) + self._recordedTime = np.pad(self._recordedTime, (0, 1)) + + self._recordedX[-1] = self.x + self._recordedP[-1] = self.p + self._recordedTime[-1] = time + + def setMeshtoRecordedTime(self, time): + ''' + From recorded values, interpolated at time to get composition and phase fraction + ''' + if self._record: + if time < self._recordedTime[0]: + print('Input time is lower than smallest recorded time, setting PSD to t = {:.3e}'.format(self._recordedTime[0])) + self.x, self.p = self._recordedX[0], self._recordedP[0] + elif time > self._recordedTime[-1]: + print('Input time is larger than longest recorded time, setting PSD to t = {:.3e}'.format(self._recordedTime[-1])) + self.x, self.p = self._recordedX[-1], self._recordedP[-1] + else: + uind = np.argmax(self._recordedTime > time) + lind = uind - 1 + + ux, up, utime = self._recordedX[uind], self._recordedP[uind], self._recordedTime[uind] + lx, lp, ltime = self._recordedX[lind], self._recordedP[lind], self._recordedTime[lind] + + self.x = (ux - lx) * (time - ltime) / (utime - ltime) + lx + self.p = (up - lp) * (time - ltime) / (utime - ltime) + lp + + self.z = self._recordedZ + + def _getElementIndex(self, element = None): + ''' + Gets index of element in self.elements + + Parameters + ---------- + element : str + Specified element, will return first element if None + ''' + if element is None: + return 0 + else: + return self.elements.index(element) + + def _getPhaseIndex(self, phase = None): + ''' + Gets index of phase in self.phases + + Parameters + ---------- + phase : str + Specified phase, will return first phase if None + ''' + if phase is None: + return 0 + else: + return self.phases.index(phase) + + def setBC(self, LBCtype = 0, LBCvalue = 0, RBCtype = 0, RBCvalue = 0, element = None): + ''' + Set boundary conditions + + Parameters + ---------- + LBCtype : int + Left boundary condition type + Mesh1D.FLUX - constant flux + Mesh1D.COMPOSITION - constant composition + LBCvalue : float + Value of left boundary condition + RBCtype : int + Right boundary condition type + Mesh1D.FLUX - constant flux + Mesh1D.COMPOSITION - constant composition + RBCvalue : float + Value of right boundary condition + element : str + Specified element to apply boundary conditions on + ''' + eIndex = self._getElementIndex(element) + self.LBC[eIndex] = LBCtype + self.LBCvalue[eIndex] = LBCvalue + if LBCtype == self.COMPOSITION: + self.x[eIndex,0] = LBCvalue + + self.RBC[eIndex] = RBCtype + self.RBCvalue[eIndex] = RBCvalue + if RBCtype == self.COMPOSITION: + self.x[eIndex,-1] = RBCvalue + + def setCompositionLinear(self, Lvalue, Rvalue, element = None): + ''' + Sets composition as a linear function between ends of the mesh + + Parameters + ---------- + Lvalue : float + Value at left boundary + Rvalue : float + Value at right boundary + element : str + Element to apply composition profile to + ''' + eIndex = self._getElementIndex(element) + self.x[eIndex] = np.linspace(Lvalue, Rvalue, self.N) + + def setCompositionStep(self, Lvalue, Rvalue, z, element = None): + ''' + Sets composition as a step-wise function + + Parameters + ---------- + Lvalue : float + Value on left side of mesh + Rvalue : float + Value on right side of mesh + z : float + Position on mesh where composition switches from Lvalue to Rvalue + element : str + Element to apply composition profile to + ''' + eIndex = self._getElementIndex(element) + Lindices = self.z <= z + self.x[eIndex,Lindices] = Lvalue + self.x[eIndex,~Lindices] = Rvalue + + def setCompositionSingle(self, value, z, element = None): + ''' + Sets single node to specified composition + + Parameters + ---------- + value : float + Composition + z : float + Position to set value to (will use closest node to z) + element : str + Element to apply composition profile to + ''' + eIndex = self._getElementIndex(element) + zIndex = np.argmin(np.abs(self.z-z)) + self.x[eIndex,zIndex] = value + + def setCompositionInBounds(self, value, Lbound, Rbound, element = None): + ''' + Sets single node to specified composition + + Parameters + ---------- + value : float + Composition + Lbound : float + Position of left bound + Rbound : float + Position of right bound + element : str + Element to apply composition profile to + ''' + eIndex = self._getElementIndex(element) + indices = (self.z >= Lbound) & (self.z <= Rbound) + self.x[eIndex,indices] = value + + def setCompositionFunction(self, func, element = None): + ''' + Sets composition as a function of z + + Parameters + ---------- + func : function + Function taking in z and returning composition + element : str + Element to apply composition profile to + ''' + eIndex = self._getElementIndex(element) + self.x[eIndex,:] = func(self.z) + + def setCompositionProfile(self, z, x, element = None): + ''' + Sets composition profile by linear interpolation + + Parameters + ---------- + z : array + z-coords of composition profile + x : array + Composition profile + element : str + Element to apply composition profile to + ''' + eIndex = self._getElementIndex(element) + z = np.array(z) + x = np.array(x) + sortIndices = np.argsort(z) + z = z[sortIndices] + x = x[sortIndices] + self.x[eIndex,:] = np.interp(self.z, z, x) + + def setup(self): + ''' + General setup function for all diffusio models + + This will clear any cached values in the thermodynamics function and check if all compositions add up to 1 + + This will also make sure that all compositions are not 0 or 1 to speed up equilibrium calculations + ''' + if self.therm is not None: + self.therm.clearCache() + xsum = np.sum(self.x, axis=0) + if any(xsum > 1): + print('Compositions add up to above 1 between z = [{:.3e}, {:.3e}]'.format(np.amin(self.z[xsum>1]), np.amax(self.z[xsum>1]))) + raise Exception('Some compositions sum up to above 1') + self.x[self.x > self.minComposition] = self.x[self.x > self.minComposition] - len(self.allElements) * self.minComposition + self.x[self.x < self.minComposition] = self.minComposition + self.T = self.Tfunc(self.z, 0) + self.isSetup = True + self.record(self.t) #Record at t = 0 + + def _getFluxes(self): + ''' + "Virtual" function to be implemented by child objects + + Should return (fluxes (list), dt (float)) + ''' + raise NotImplementedError() + + def printHeader(self): + print('Iteration\tSim Time (h)\tRun time (s)') + + def printStatus(self, iteration, modelTime, simTimeElapsed): + super().printStatus(iteration, modelTime/3600, simTimeElapsed) + + def getCurrentX(self): + return self.t, [self.x] + + def getdXdt(self, t, x): + ''' + dXdt is defined as -dJ/dz + ''' + fluxes = self._getFluxes(t, x) + return [-(fluxes[:,1:] - fluxes[:,:-1])/self.dz] + + def preProcess(self): + return + + def postProcess(self, time, x): + ''' + Stores new x and t + Records new values if recording is enabled + ''' + self.t = time + self.x = x[0] + self.record(self.t) + self.updateCoupledModels() + return self.getCurrentX()[1], False + + def flattenX(self, X): + ''' + np.hstack does not flatten a 2D array, so we have to overload this function + By itself, this doesn't actually affect the solver/iterator, but when coupled with other models, + it becomes an issue + + This will convert the 2D array X to a 1D array by reshaping to 1D array of len(# elements * # nodes) + ''' + return np.reshape(X[0], (np.prod(X[0].shape))) + + def unflattenX(self, X_flat, X_ref): + ''' + Reshape X_flat to original shape + ''' + return [np.reshape(X_flat, X_ref[0].shape)] + + def getX(self, element): + ''' + Gets composition profile of element + + Parameters + ---------- + element : str + Element to get profile of + ''' + if element in self.allElements and element not in self.elements: + return 1 - np.sum(self.x, axis=0) + else: + e = self._getElementIndex(element) + return self.x[e] + + def getP(self, phase): + ''' + Gets phase profile + + Parameters + ---------- + phase : str + Phase to get profile of + ''' + p = self._getPhaseIndex(phase) + return self.p[p] + + def plot(self, ax = None, plotReference = True, plotElement = None, zScale = 1, *args, **kwargs): + ''' + Plots composition profile + + Parameters + ---------- + ax : matplotlib Axes object + Axis to plot on + plotReference : bool + Whether to plot reference element (composition = 1 - sum(composition of rest of elements)) + plotElement : None or str + Plots single element if it is defined, otherwise, all elements are plotted + zScale : float + Scale factor for z-coordinates + ''' + return diffPlot.plot(self, ax, plotReference, plotElement, zScale, *args, **kwargs) + + def plotTwoAxis(self, Lelements, Relements, zScale = 1, axL = None, axR = None, *args, **kwargs): + ''' + Plots composition profile with two y-axes + + Parameters + ---------- + axL : matplotlib Axes object + Left axis to plot on + Lelements : list of str + Elements to plot on left axis + Relements : list of str + Elements to plot on right axis + axR : matplotlib Axes object (optional) + Right axis to plot on + If None, then the right axis will be created + zScale : float + Scale factor for z-coordinates + ''' + return diffPlot.plotTwoAxis(self, Lelements, Relements, zScale, axL, axR, *args, **kwargs) + + def plotPhases(self, ax = None, plotPhase = None, zScale = 1, *args, **kwargs): + ''' + Plots phase fractions over z + + Parameters + ---------- + ax : matplotlib Axes object + Axis to plot on + plotPhase : None or str + Plots single phase if it is defined, otherwise, all phases are plotted + zScale : float + Scale factor for z-coordinates + ''' + return diffPlot.plotPhases(self, ax, plotPhase, zScale, *args, **kwargs) diff --git a/kawin/diffusion/Homogenization.py b/kawin/diffusion/Homogenization.py new file mode 100644 index 0000000..a4b610d --- /dev/null +++ b/kawin/diffusion/Homogenization.py @@ -0,0 +1,342 @@ +import numpy as np +from kawin.diffusion.Diffusion import DiffusionModel +from kawin.thermo.Mobility import mobility_from_composition_set +import copy + +class HomogenizationModel(DiffusionModel): + def __init__(self, zlim, N, elements = ['A', 'B'], phases = ['alpha'], record = True): + super().__init__(zlim, N, elements, phases, record) + + self.mobilityFunction = self.wienerUpper + self.defaultMob = 0 + self.eps = 0.05 + + self.sortIndices = np.argsort(self.allElements) + self.unsortIndices = np.argsort(self.sortIndices) + self.labFactor = 1 + + def reset(self): + ''' + Resets model + + This also includes chemical potential and pycalphad CompositionSets for each node + ''' + super().reset() + self.mu = np.zeros((len(self.elements)+1, self.N)) + self.compSets = [None for _ in range(self.N)] + + def setMobilityFunction(self, function): + ''' + Sets averaging function to use for mobility + + Default mobility value should be that a phase of unknown mobility will be ignored for average mobility calcs + + Parameters + ---------- + function : str + Options - 'upper wiener', 'lower wiener', 'upper hashin-shtrikman', 'lower hashin-strikman', 'labyrinth' + ''' + #np.finfo(dtype).max - largest representable value + #np.finfo(dtype).tiny - smallest positive usable value + if 'upper' in function and 'wiener' in function: + self.mobilityFunction = self.wienerUpper + self.defaultMob = np.finfo(np.float64).tiny + elif 'lower' in function and 'wiener' in function: + self.mobilityFunction = self.wienerLower + self.defaultMob = np.finfo(np.float64).max + elif 'upper' in function and 'hashin' in function: + self.mobilityFunction = self.hashin_shtrikmanUpper + self.defaultMob = np.finfo(np.float64).tiny + elif 'lower' in function and 'hashin' in function: + self.mobilityFunction = self.hashin_shtrikmanLower + self.defaultMob = np.finfo(np.float64).max + elif 'lab' in function: + self.mobilityFunction = self.labyrinth + self.defaultMob = np.finfo(np.float64).tiny + + def setLabyrinthFactor(self, n): + ''' + Labyrinth factor + + Parameters + ---------- + n : int + Either 1 or 2 + Note: n = 1 will the same as the weiner upper bounds + ''' + if n < 1: + n = 1 + if n > 2: + n = 2 + self.labFactor = n + + def setup(self): + ''' + Sets up model + + This also includes getting the CompositionSets for each node + ''' + super().setup() + #self.midX = 0.5 * (self.x[:,1:] + self.x[:,:-1]) + self.p = self.updateCompSets(self.x) + + def _newEqCalc(self, x, T): + ''' + Calculates equilibrium and returns a CompositionSet + ''' + eq = self.therm.getEq(x, T, 0, self.phases) + state_variables = np.array([0, 1, 101325, T], dtype=np.float64) + stable_phases = eq.Phase.values.ravel() + phase_amounts = eq.NP.values.ravel() + comp = [] + for p in stable_phases: + if p != '': + idx = np.where(stable_phases == p)[0] + cs, misc = self.therm._createCompositionSet(eq, state_variables, p, phase_amounts, idx) + comp.append(cs) + + if len(comp) == 0: + comp = None + + return self.therm.getLocalEq(x, T, 0, self.phases, comp) + + def updateCompSets(self, xarray): + ''' + Updates the array of CompositionSets + + If an equilibrium calculation is already done for a given composition, + the CompositionSet will be taken out of the hash table + + Otherwise, a new equilibrium calculation will be performed + + Parameters + ---------- + xarray : (e-1, N) array + Composition for each node + e is number of elements + N is number of nodes + + Returns + ------- + parray : (p, N) array + Phase fractions for each node + p is number of phases + ''' + parray = np.zeros((len(self.phases), xarray.shape[1])) + for i in range(parray.shape[1]): + if self.cache: + hashValue = self._getHash(xarray[:,i], self.T[i]) + if hashValue not in self.hashTable: + result, comp = self._newEqCalc(xarray[:,i], self.T[i]) + #result, comp = self.therm.getLocalEq(xarray[:,i], self.T, 0, self.phases, self.compSets[i]) + self.hashTable[hashValue] = (result, comp, None) + else: + result, comp, _ = self.hashTable[hashValue] + results, self.compSets[i] = copy.copy(result), copy.copy(comp) + else: + if self.compSets[i] is None: + results, self.compSets[i] = self._newEqCalc(xarray[:,i], self.T[i]) + else: + results, self.compSets[i] = self.therm.getLocalEq(xarray[:,i], self.T[i], 0, self.phases, self.compSets[i]) + self.mu[:,i] = results.chemical_potentials[self.unsortIndices] + cs_phases = [cs.phase_record.phase_name for cs in self.compSets[i]] + for p in range(len(cs_phases)): + parray[self._getPhaseIndex(cs_phases[p]), i] = self.compSets[i][p].NP + + return parray + + def getMobility(self, xarray): + ''' + Gets mobility of all phases + + Returns + ------- + (p, e+1, N) array - p is number of phases, e is number of elements, N is number of nodes + ''' + mob = self.defaultMob * np.ones((len(self.phases), len(self.elements)+1, xarray.shape[1])) + for i in range(xarray.shape[1]): + if self.cache: + hashValue = self._getHash(xarray[:,i], self.T[i]) + _, _, mTemp = self.hashTable[hashValue] + else: + mTemp = None + if mTemp is None or not self.cache: + maxPhaseAmount = 0 + maxPhaseIndex = 0 + for p in range(len(self.phases)): + if self.p[p,i] > 0: + if self.p[p,i] > maxPhaseAmount: + maxPhaseAmount = self.p[p,i] + maxPhaseIndex = p + if self.phases[p] in self.therm.mobCallables and self.therm.mobCallables[self.phases[p]] is not None: + #print(self.phases, self.phases[p], xarray[:,i], self.p[:,i], i, self.compSets[i]) + compset = [cs for cs in self.compSets[i] if cs.phase_record.phase_name == self.phases[p]][0] + mob[p,:,i] = mobility_from_composition_set(compset, self.therm.mobCallables[self.phases[p]], self.therm.mobility_correction)[self.unsortIndices] + mob[p,:,i] *= np.concatenate(([1-np.sum(xarray[:,i])], xarray[:,i])) + else: + mob[p,:,i] = -1 + for p in range(len(self.phases)): + if any(mob[p,:,i] == -1) and not all(mob[p,:,i] == -1): + mob[p,:,i] = mob[maxPhaseIndex,:,i] + if all(mob[p,:,i] == -1): + mob[p,:,i] = self.defaultMob + if self.cache: + self.hashTable[hashValue] = (self.hashTable[hashValue][0], self.hashTable[hashValue][1], copy.copy(mob[:,:,i])) + else: + mob[:,:,i] = mTemp + + return mob + + def wienerUpper(self, xarray): + ''' + Upper wiener bounds for average mobility + + Returns + ------- + (e+1, N) mobility array - e is number of elements, N is number of nodes + ''' + mob = self.getMobility(xarray) + avgMob = np.sum(np.multiply(self.p[:,np.newaxis], mob), axis=0) + return avgMob + + def wienerLower(self, xarray): + ''' + Lower wiener bounds for average mobility + + Returns + ------- + (e+1, N) mobility array - e is number of elements, N is number of nodes + ''' + #(p, e, N) + mob = self.getMobility(xarray) + avgMob = 1/np.sum(np.multiply(self.p[:,np.newaxis], 1/mob), axis=0) + return avgMob + + def labyrinth(self, xarray): + ''' + Labyrinth mobility + + Returns + ------- + (e+1, N) mobility array - e is number of elements, N is number of nodes + ''' + mob = self.getMobility(xarray) + avgMob = np.sum(np.multiply(np.power(self.p[:,np.newaxis], self.labFactor), mob), axis=0) + return avgMob + + def hashin_shtrikmanUpper(self, xarray): + ''' + Upper hashin shtrikman bounds for average mobility + + Returns + ------- + (e+1, N) mobility array - e is number of elements, N is number of nodes + ''' + #self.p #(p,N) + mob = self.getMobility(xarray) #(p,e+1,N) + maxMob = np.amax(mob, axis=0) #(e+1,N) + + # 1 / ((1 / mPhi - mAlpha) + 1 / (3mAlpha)) = 3mAlpha * (mPhi - mAlpha) / (2mAlpha + mPhi) + Ak = 3 * maxMob * (mob - maxMob) / (2*maxMob + mob) + Ak = Ak * self.p[:,np.newaxis] + Ak = np.sum(Ak, axis=0) + avgMob = maxMob + Ak / (1 - Ak / (3*maxMob)) + return avgMob + + def hashin_shtrikmanLower(self, xarray): + ''' + Lower hashin shtrikman bounds for average mobility + + Returns + ------- + (e, N) mobility array - e is number of elements, N is number of nodes + ''' + #self.p #(p,N) + mob = self.getMobility(xarray) #(p,e+1,N) + minMob = np.amin(mob, axis=0) #(e+1,N) + + #This prevents an infinite mobility which could cause the time interval to be 0 + minMob[minMob == np.inf] = 0 + + # 1 / ((1 / mPhi - mAlpha) + 1 / (3mAlpha)) = 3mAlpha * (mPhi - mAlpha) / (2mAlpha + mPhi) + Ak = 3 * minMob * (mob - minMob) / (2*minMob + mob) + + Ak = Ak * self.p[:,np.newaxis] + Ak = np.sum(Ak, axis=0) + avgMob = minMob + Ak / (1 - Ak / (3*minMob)) + return avgMob + + def _getFluxes(self, t, x_curr): + ''' + Return fluxes and time interval for the current iteration + + Steps: + 1. Get average mobility from homogenization function. Interpolate to get mobility (M) at cell boundaries + 2. Interpolate composition to get composition (x) at cell boundaries + 3. Calculate chemical potential gradient (dmu/dz) at cell boundaries + 4. Calculate composition gradient (dx/dz) at cell boundaries + 5. Calculate homogenization flux = -M / dmu/dz + 6. Calculate ideal contribution = -eps * M*R*T / x * dx/dz + 7. Apply boundary conditions for fluxes at ends of mesh + If fixed flux condition (Neumann) - then use the flux defined in the condition + If fixed composition condition (Dirichlet) - then use nearby flux (this will keep the composition fixed after apply the fluxes) + + TODO: If using RK4, I believe the phase fraction will be from the last step of the RK4 iteration. May not make sense to do that + ''' + x = x_curr[0] + self.T = self.Tfunc(self.z, t) + self.p = self.updateCompSets(x) + + #Get average mobility between nodes + avgMob = self.mobilityFunction(x) + avgMob = 0.5 * (avgMob[:,1:] + avgMob[:,:-1]) + + #Composition between nodes + avgX = 0.5 * (x[:,1:] + x[:,:-1]) + avgX = np.concatenate(([1-np.sum(avgX, axis=0)], avgX), axis=0) + + #Chemical potential gradient + dmudz = (self.mu[:,1:] - self.mu[:,:-1]) / self.dz + + #Composition gradient (we need to calculate gradient for reference element) + dxdz = (x[:,1:] - x[:,:-1]) / self.dz + dxdz = np.concatenate(([0-np.sum(dxdz, axis=0)], dxdz), axis=0) + + # J = -M * dmu/dz + # Ideal contribution: J_id = -eps * M*R*T / x * dx/dz + fluxes = np.zeros((len(self.elements)+1, self.N-1)) + fluxes = -avgMob * dmudz + nonzeroComp = avgX != 0 + Tmid = (self.T[1:] + self.T[:-1]) / 2 + Tmidfull = Tmid[np.newaxis,:] + for i in range(fluxes.shape[0]-1): + Tmidfull = np.concatenate((Tmidfull, Tmid[np.newaxis,:]), axis=0) + fluxes[nonzeroComp] += -self.eps * avgMob[nonzeroComp] * 8.314 * Tmidfull[nonzeroComp] * dxdz[nonzeroComp] / avgX[nonzeroComp] + + #Flux in a volume fixed frame: J_vi = J_i - x_i * sum(J_j) + vfluxes = np.zeros((len(self.elements), self.N+1)) + vfluxes[:,1:-1] = fluxes[1:,:] - avgX[1:,:] * np.sum(fluxes, axis=0) + + #Boundary conditions + for e in range(len(self.elements)): + vfluxes[e,0] = self.LBCvalue[e] if self.LBC[e] == self.FLUX else vfluxes[e,1] + vfluxes[e,-1] = self.RBCvalue[e] if self.RBC[e] == self.FLUX else vfluxes[e,-2] + + return vfluxes + + def getFluxes(self): + ''' + Return fluxes and time interval for the current iteration + ''' + vfluxes = self._getFluxes(self.t, [self.x]) + dJ = np.abs(vfluxes[:,1:] - vfluxes[:,:-1]) / self.dz + dt = self.maxCompositionChange / np.amax(dJ[dJ!=0]) + return vfluxes, dt + + def getDt(self, dXdt): + ''' + Time increment + This is done by finding the time interval such that the composition + change caused by the fluxes will be lower than self.maxCompositionChange + ''' + return self.maxCompositionChange / np.amax(np.abs(dXdt[0][dXdt[0]!=0])) \ No newline at end of file diff --git a/kawin/diffusion/Plot.py b/kawin/diffusion/Plot.py new file mode 100644 index 0000000..7adba76 --- /dev/null +++ b/kawin/diffusion/Plot.py @@ -0,0 +1,142 @@ +import matplotlib.pyplot as plt +import numpy as np + +def plot(diffModel, ax = None, plotReference = True, plotElement = None, zScale = 1, *args, **kwargs): + ''' + Plots composition profile + + Parameters + ---------- + ax : matplotlib Axes object + Axis to plot on + plotReference : bool + Whether to plot reference element (composition = 1 - sum(composition of rest of elements)) + plotElement : None or str + Plots single element if it is defined, otherwise, all elements are plotted + zScale : float + Scale factor for z-coordinates + ''' + if ax is None: + fig, ax = plt.subplots(1,1) + + if not diffModel.isSetup: + diffModel.setup() + + if plotElement is not None: + if plotElement not in diffModel.elements and plotElement in diffModel.allElements: + x = 1 - np.sum(diffModel.x, axis=0) + else: + e = diffModel._getElementIndex(plotElement) + x = diffModel.x[e] + ax.plot(diffModel.z/zScale, x, *args, **kwargs) + else: + if plotReference: + refE = 1 - np.sum(diffModel.x, axis=0) + ax.plot(diffModel.z/zScale, refE, label=diffModel.allElements[0], *args, **kwargs) + for e in range(len(diffModel.elements)): + ax.plot(diffModel.z/zScale, diffModel.x[e], label=diffModel.elements[e], *args, **kwargs) + + ax.set_xlim([diffModel.zlim[0]/zScale, diffModel.zlim[1]/zScale]) + if plotElement is None: + ax.legend() + ax.set_xlabel('Distance (m)') + ax.set_ylabel('Composition (at.%)') + + return ax + +def plotTwoAxis(diffModel, Lelements, Relements, zScale = 1, axL = None, axR = None, *args, **kwargs): + ''' + Plots composition profile with two y-axes + + Parameters + ---------- + axL : matplotlib Axes object + Left axis to plot on + Lelements : list of str + Elements to plot on left axis + Relements : list of str + Elements to plot on right axis + axR : matplotlib Axes object (optional) + Right axis to plot on + If None, then the right axis will be created + zScale : float + Scale factor for z-coordinates + ''' + if axL is None: + fig, axL = plt.subplots(1,1) + + if not diffModel.isSetup: + diffModel.setup() + + if type(Lelements) is str: + Lelements = [Lelements] + if type(Relements) is str: + Relements = [Relements] + + ci = 0 + refE = 1 - np.sum(diffModel.x, axis=0) + if axR is None: + axR = axL.twinx() + for e in range(len(Lelements)): + if Lelements[e] in diffModel.elements: + eIndex = diffModel._getElementIndex(Lelements[e]) + axL.plot(diffModel.z/zScale, diffModel.x[eIndex], label=diffModel.elements[eIndex], color = 'C' + str(ci), *args, **kwargs) + ci = ci+1 if ci <= 9 else 0 + elif Lelements[e] in diffModel.allElements: + axL.plot(diffModel.z/zScale, refE, label=diffModel.allElements[0], color = 'C' + str(ci), *args, **kwargs) + ci = ci+1 if ci <= 9 else 0 + for e in range(len(Relements)): + if Relements[e] in diffModel.elements: + eIndex = diffModel._getElementIndex(Relements[e]) + axR.plot(diffModel.z/zScale, diffModel.x[eIndex], label=diffModel.elements[eIndex], color = 'C' + str(ci), *args, **kwargs) + ci = ci+1 if ci <= 9 else 0 + elif Relements[e] in diffModel.allElements: + axR.plot(diffModel.z/zScale, refE, label=diffModel.allElements[0], color = 'C' + str(ci), *args, **kwargs) + ci = ci+1 if ci <= 9 else 0 + + + axL.set_xlim([diffModel.zlim[0]/zScale, diffModel.zlim[1]/zScale]) + axL.set_xlabel('Distance (m)') + axL.set_ylabel('Composition (at.%) ' + str(Lelements)) + axR.set_ylabel('Composition (at.%) ' + str(Relements)) + + lines, labels = axL.get_legend_handles_labels() + lines2, labels2 = axR.get_legend_handles_labels() + axR.legend(lines+lines2, labels+labels2, framealpha=1) + + return axL, axR + +def plotPhases(diffModel, ax = None, plotPhase = None, zScale = 1, *args, **kwargs): + ''' + Plots phase fractions over z + + Parameters + ---------- + ax : matplotlib Axes object + Axis to plot on + plotPhase : None or str + Plots single phase if it is defined, otherwise, all phases are plotted + zScale : float + Scale factor for z-coordinates + ''' + if ax is None: + fig, ax = plt.subplots(1,1) + + if not diffModel.isSetup: + diffModel.setup() + + if plotPhase is not None: + p = diffModel._getPhaseIndex(plotPhase) + ax.plot(diffModel.z/zScale, diffModel.p[p], *args, **kwargs) + else: + for p in range(len(diffModel.phases)): + ax.plot(diffModel.z/zScale, diffModel.p[p], label=diffModel.phases[p], *args, **kwargs) + ax.set_xlim([diffModel.zlim[0]/zScale, diffModel.zlim[1]/zScale]) + ax.set_ylim([0, 1]) + ax.set_xlabel('Distance (m)') + ax.set_ylabel('Phase Fraction') + + if plotPhase is None: + ax.legend() + + return ax \ No newline at end of file diff --git a/kawin/diffusion/SinglePhase.py b/kawin/diffusion/SinglePhase.py new file mode 100644 index 0000000..2e6769f --- /dev/null +++ b/kawin/diffusion/SinglePhase.py @@ -0,0 +1,87 @@ +import numpy as np +from kawin.diffusion.Diffusion import DiffusionModel + +class SinglePhaseModel(DiffusionModel): + def _getFluxes(self, t, x_curr): + ''' + Private function that gets fluxes at the boundary of each nodes given an array of compositions and current time + + Steps: + 1. Get diffusivity from cell centers using cell compositions + 2. Interpolate diffusivity to get diffusivity (D) at cell boundaries + 3. Calculate fluxes from concentration gradient (dx/dz) and interpolated diffusivity = -D * dx/dz + 4. Apply boundary conditions for fluxes at ends of mesh + If fixed flux condition (Neumann) - then use the flux defined in the condition + If fixed composition condition (Dirichlet) - then use nearby flux (this will keep the composition fixed after apply the fluxes) + 5. Store dt (from von Neumann analysis) for later + + Returns + ------- + fluxes : (e-1, n+1) array of floats + e - number of elements including reference element + n - number of nodes + dt : float + Maximum calculated time interval for numerical stability + ''' + #Calculate diffusivity at cell centers + x = x_curr[0] + T = self.Tfunc(self.z, t) + if len(self.elements) == 1: + d = np.zeros(self.N) + else: + d = np.zeros((self.N, len(self.elements), len(self.elements))) + if self.cache: + for i in range(self.N): + hashValue = self._getHash(x[:,i], T[i]) + if hashValue not in self.hashTable: + self.hashTable[hashValue] = self.therm.getInterdiffusivity(x[:,i], T[i], phase=self.phases[0]) + d[i] = self.hashTable[hashValue] + else: + d = self.therm.getInterdiffusivity(x.T, T, phase=self.phases[0]) + + #Get diffusivity and composition gradient at cell boundaries + dmid = (d[1:] + d[:-1]) / 2 + dxdz = (x[:,1:] - x[:,:-1]) / self.dz + + #Fluxes = -D * dx/dz + fluxes = np.zeros((len(self.elements), self.N+1)) + if len(self.elements) == 1: + fluxes[0,1:-1] = -dmid * dxdz + else: + dxdz = np.expand_dims(dxdz, axis=0) + fluxes[:,1:-1] = -np.matmul(dmid, np.transpose(dxdz, (2,1,0)))[:,:,0].T + + #Boundary condition + for e in range(len(self.elements)): + fluxes[e,0] = self.LBCvalue[e] if self.LBC[e] == self.FLUX else fluxes[e,1] + fluxes[e,-1] = self.RBCvalue[e] if self.RBC[e] == self.FLUX else fluxes[e,-2] + + #Time step from von Neumann analysis (using 0.4 instead of 0.5 to be safe) + self._currdt = 0.4 * self.dz**2 / np.amax(np.abs(dmid)) + + return fluxes + + def getFluxes(self): + ''' + Gets fluxes at the boundary of each nodes + + This calls the private _getFluxes method with the internal current x and t + + Returns + ------- + fluxes : (e-1, n+1) array of floats + e - number of elements including reference element + n - number of nodes + dt : float + Maximum calculated time interval for numerical stability + ''' + fluxes = self._getFluxes(self.t, [self.x]) + dt = self._currdt + return fluxes, dt + + def getDt(self, dXdt): + ''' + Returns dt that was calculated from _getFluxes + This prevents double calculation of the diffusivity just to get a time step + ''' + return self._currdt \ No newline at end of file diff --git a/kawin/diffusion/__init__.py b/kawin/diffusion/__init__.py new file mode 100644 index 0000000..0623f82 --- /dev/null +++ b/kawin/diffusion/__init__.py @@ -0,0 +1,2 @@ +from .SinglePhase import SinglePhaseModel +from .Homogenization import HomogenizationModel \ No newline at end of file diff --git a/kawin/precipitation/KWNBase.py b/kawin/precipitation/KWNBase.py new file mode 100644 index 0000000..2077b40 --- /dev/null +++ b/kawin/precipitation/KWNBase.py @@ -0,0 +1,1220 @@ +import numpy as np +from kawin.precipitation.non_ideal.EffectiveDiffusion import EffectiveDiffusionFunctions +from kawin.precipitation.non_ideal.ShapeFactors import ShapeFactor +from kawin.precipitation.non_ideal.ElasticFactors import StrainEnergy +from kawin.precipitation.non_ideal.GrainBoundaries import GBFactors +from kawin.GenericModel import GenericModel +from enum import Enum + +class VolumeParameter(Enum): + MOLAR_VOLUME = 0 + ATOMIC_VOLUME = 1 + LATTICE_PARAMETER = 2 + +class PrecipitateBase(GenericModel): + ''' + Base class for precipitation models + + Parameters + ---------- + phases : list (optional) + Precipitate phases (array of str) + If only one phase is considered, the default is ['beta'] + elements : list (optional) + Solute elements in system + Note: order of elements must correspond to order of elements set in Thermodynamics module + Also, the list here should just be the solutes while the Thermodynamics module needs also the parent element + If binary system, then defualt is ['solute'] + ''' + def __init__(self, phases = ['beta'], elements = ['solute']): + super().__init__() + self.elements = elements + self.numberOfElements = len(elements) + self.phases = np.array(phases) + + self._resetArrays() + self.resetConstraints() + self._isSetup = False + self._currY = None + + #Constants + self.Rg = 8.314 #Gas constant - J/mol-K + self.avo = 6.022e23 #Avogadro's number (/mol) + self.kB = self.Rg / self.avo #Boltzmann constant (J/K) + + #Default variables, these terms won't have to be set before simulation + self.strainEnergy = [StrainEnergy() for i in self.phases] + self.calculateAspectRatio = [False for i in self.phases] + self.RdrivingForceLimit = np.zeros(len(self.phases), dtype=np.float32) + self.shapeFactors = [ShapeFactor() for i in self.phases] + self.theta = 2 * np.ones(len(self.phases), dtype=np.float32) + self.effDiffFuncs = EffectiveDiffusionFunctions() + self.effDiffDistance = self.effDiffFuncs.effectiveDiffusionDistance + self.infinitePrecipitateDiffusion = [True for i in self.phases] + self.dTemp = 0 + self.iterationSinceTempChange = 0 + self.GBenergy = 0.3 #J/m2 + self.parentPhases = [[] for i in self.phases] + self.GB = [GBFactors() for p in self.phases] + + #Set other variables to None to throw errors if not set + self.xInit = None + self.Tparameters = None + + #Nucleation site density, it will default to dislocations with 5e12 /m2 density + self._isNucleationSetup = False + self.GBareaN0 = None + self.GBedgeN0 = None + self.GBcornerN0 = None + self.dislocationN0 = None + self.bulkN0 = None + + #Unit cell parameters + self.aAlpha = None + self.VaAlpha = None + self.VmAlpha = None + self.atomsPerCellAlpha = None + self.atomsPerCellBeta = np.empty(len(self.phases), dtype=np.float32) + self.VaBeta = np.empty(len(self.phases), dtype=np.float32) + self.VmBeta = np.empty(len(self.phases), dtype=np.float32) + self.Rmin = np.empty(len(self.phases), dtype=np.float32) + + #Free energy parameters + self.gamma = np.empty(len(self.phases), dtype=np.float32) + self.dG = [None for i in self.phases] + self.interfacialComposition = [None for i in self.phases] + + #Beta function for nucleation rate + if self.numberOfElements == 1: + self._Beta = self._BetaBinary1 + else: + self._Beta = self._BetaMulti + self._betaFuncs = [None for p in phases] + self._defaultBeta = 20 + + #Stopping conditions + self.clearStoppingConditions() + + #Coupling models + self.clearCouplingModels() + + def phaseIndex(self, phase = None): + ''' + Returns index of phase in list + + Parameters + ---------- + phase : str (optional) + Precipitate phase (defaults to None, which will return 0) + ''' + return 0 if phase is None else np.where(self.phases == phase)[0][0] + + def reset(self): + ''' + Resets simulation results + This does not reset the model parameters, however, it will clear any stopping conditions + ''' + self._resetArrays() + self.xComp[0] = self.xInit + self.dTemp = 0 + + self._isSetup = False + self._currY = None + + #Reset temperature array + if np.isscalar(self.Tparameters): + self.setTemperature(self.Tparameters) + elif len(self.Tparameters) == 2: + self.setTemperatureArray(*self.Tparameters) + elif self.Tparameters is not None: + self.setNonIsothermalTemperature(self.Tparameters) + + #Reset stopping conditions + for sc in self._stoppingConditions: + sc.reset() + + def _resetArrays(self): + ''' + Resets and initializes arrays for all variables + time, temperature + matrix composition, equilibrium composition (alpha and beta) + driving force, impingement factor, nucleation barrier, critical radius, nucleation radius + nucleation rate, precipitate density + average radius, average aspect ratio, volume fraction + + Extra variables include incubation offset and incubation sum + + Time dependent variables will be set up as either + (iterations) time, temperature + (iterations, elements) composition + (iterations, phases, elements) eq composition, total precipitate composition + (iterations, phases) Everything else + This is intended for appending arrays to always be on the first axis + ''' + self.n = 0 + + #Time + self.time = np.zeros(1) + + #Temperature + self.temperature = np.zeros(1) + + #Composition + self.xComp = np.zeros((1, self.numberOfElements)) #Matrix composition + self.xEqAlpha = np.zeros((1, len(self.phases), self.numberOfElements)) #Equilibrium matrix composition + self.xEqBeta = np.zeros((1, len(self.phases), self.numberOfElements)) #Equilibrium beta compostion + + #Nucleation + self.dGs = np.zeros((1, len(self.phases))) #Driving force + self.betas = np.zeros((1, len(self.phases))) #Impingement rates (used for non-isothermal) + self.incubationOffset = np.zeros(len(self.phases)) #Offset for incubation time (for non-isothermal precipitation) + self.Gcrit = np.zeros((1, len(self.phases))) #Height of nucleation barrier + self.Rcrit = np.zeros((1, len(self.phases))) #Critical radius + self.Rad = np.zeros((1, len(self.phases))) #Radius of particles formed at each time step + + self.nucRate = np.zeros((1, len(self.phases))) #Nucleation rate + self.precipitateDensity = np.zeros((1, len(self.phases))) #Number of nucleates + + #Average radius and precipitate fraction + self.avgR = np.zeros((1, len(self.phases))) #Average radius + self.avgAR = np.zeros((1, len(self.phases))) #Mean aspect ratio + self.betaFrac = np.zeros((1, len(self.phases))) #Fraction of precipitate + + #Fconc - auxiliary array to store total solute composition of precipitates + self.fConc = np.zeros((1, len(self.phases), self.numberOfElements)) + + self._setEnum() + self._packArrays() + + #Temporary storage variables + self._precBetaTemp = [None for _ in range(len(self.phases))] #Composition of nucleate (found from driving force) + + def _setEnum(self): + ''' + Pseudo-enumeration + + This is just to keep a consistent list of IDs for each variable + so we can grab the current values from varList + ''' + self.TIME = 0 + self.TEMPERATURE = 1 + self.COMPOSITION = 2 + self.EQ_COMP_ALPHA = 3 + self.EQ_COMP_BETA = 4 + self.DRIVING_FORCE = 5 + self.IMPINGEMENT = 6 + self.G_CRIT = 7 + self.R_CRIT = 8 + self.R_NUC = 9 + self.NUC_RATE = 10 + self.PREC_DENS = 11 + self.R_AVG = 12 + self.AR_AVG = 13 + self.VOL_FRAC = 14 + self.FCONC = 15 + self.NUM_TERMS = 16 + + def _packArrays(self): + ''' + Create internal list of variables to solve for + The "enumerators" in getEnum will serve as the indexing for this list + Make sure the arrays in here and getEnum correspond to the same values + ''' + self.varList = [ + self.time, + self.temperature, + self.xComp, + self.xEqAlpha, + self.xEqBeta, + self.dGs, + self.betas, + self.Gcrit, + self.Rcrit, + self.Rad, + self.nucRate, + self.precipitateDensity, + self.avgR, + self.avgAR, + self.betaFrac, + self.fConc + ] + + def _getVarDict(self): + ''' + Returns mapping of { variable name : attribute name } for saving + The variable name will be the name in the .npz file + ''' + saveDict = { + 'elements': 'elements', + 'phases': 'phases', + 'time': 'time', + 'temperature': 'temperature', + 'composition': 'xComp', + 'xEqAlpha': 'xEqAlpha', + 'xEqBeta': 'xEqBeta', + 'drivingForce': 'dGs', + 'impingement': 'betas', + 'Gcrit': 'Gcrit', + 'Rcrit': 'Rcrit', + 'nucRadius': 'Rad', + 'nucRate': 'nucRate', + 'precipitateDensity': 'precipitateDensity', + 'avgRadius': 'avgR', + 'avgAspectRatio': 'avgAR', + 'volFrac': 'betaFrac', + 'fConc': 'fConc', + } + return saveDict + + def load(filename): + ''' + Loads data from filename and returns a PrecipitateModel + ''' + data = np.load(filename) + model = PrecipitateBase(data['phases'], data['elements']) + model._loadData(data) + return model + + def _appendArrays(self, newVals): + ''' + Appends new values to the variable list + NOTE: newVals must correspond to the same order as _packArrays with first axis as 1 + Ex rCrit is (n, phases) so corresponding new value should be (1, phases) + Since np append creates a new variable in memory, we have to reassign each term, then pack them into varList again + TODO: it would be nice to reduce the number of times it copies, perhaps by preallocating some amount (say 1000) + for each array and if we have not reached the end of the array, just stick the values at the latest index + but once we reach the end of the array, we would append another 1000 + The after solving, we could clean up the arrays, or just use self.n to state where the end of the simulation is + I suppose we could make a list of str for each variable and call setattr + ''' + self.time = np.append(self.time, newVals[self.TIME], axis=0) + self.temperature = np.append(self.temperature, newVals[self.TEMPERATURE], axis=0) + self.xComp = np.append(self.xComp, newVals[self.COMPOSITION], axis=0) + self.xEqAlpha = np.append(self.xEqAlpha, newVals[self.EQ_COMP_ALPHA], axis=0) + self.xEqBeta = np.append(self.xEqBeta, newVals[self.EQ_COMP_BETA], axis=0) + self.dGs = np.append(self.dGs, newVals[self.DRIVING_FORCE], axis=0) + self.betas = np.append(self.betas, newVals[self.IMPINGEMENT], axis=0) + self.Gcrit = np.append(self.Gcrit, newVals[self.G_CRIT], axis=0) + self.Rcrit = np.append(self.Rcrit, newVals[self.R_CRIT], axis=0) + self.Rad = np.append(self.Rad, newVals[self.R_NUC], axis=0) + self.nucRate = np.append(self.nucRate, newVals[self.NUC_RATE], axis=0) + self.precipitateDensity = np.append(self.precipitateDensity, newVals[self.PREC_DENS], axis=0) + self.avgR = np.append(self.avgR, newVals[self.R_AVG], axis=0) + self.avgAR = np.append(self.avgAR, newVals[self.AR_AVG], axis=0) + self.betaFrac = np.append(self.betaFrac, newVals[self.VOL_FRAC], axis=0) + self.fConc = np.append(self.fConc, newVals[self.FCONC], axis=0) + self._packArrays() + self.n += 1 + + def resetConstraints(self): + ''' + Default values for contraints + ''' + self.minRadius = 3e-10 + self.maxTempChange = 1 + + self.maxDTFraction = 1e-2 + self.minDTFraction = 1e-5 + + #Constraints on maximum time step + self.checkTemperature = True + self.maxNonIsothermalDT = 1 + + self.checkPSD = True + self.maxDissolution = 1e-3 + + self.checkRcrit = True + self.maxRcritChange = 0.01 + + self.checkNucleation = True + self.maxNucleationRateChange = 0.5 + self.minNucleationRate = 1e-5 + + self.checkVolumePre = True + self.maxVolumeChange = 0.001 + + self.minComposition = 0 + + self.minNucleateDensity = 1e-10 + + #TODO: may want to test more to see if this value should be lower or higher + #This will attempt to increase the time by 0.1% + #This also only affects the sim if the calculated dt is extremely large + #So probably only when nucleation rate is 0 will this matter + #This roughly corresponds to 1e4 steps over 5-7 orders of magnitude on a log time scale + self.dtScale = 1e-3 + + def setConstraints(self, **kwargs): + ''' + Sets constraints + + Possible constraints: + --------------------- + minRadius - minimum radius to be considered a precipitate (1e-10 m) + maxTempChange - maximum temperature change before lookup table is updated (only for Euler in binary case) (1 K) + + maxDTFraction - maximum time increment allowed as a fraction of total simulation time (0.1) + minDTFraction - minimum time increment allowed as a fraction of total simulation time (1e-5) + + checkTemperature - checks max temperature change (True) + maxNonIsothermalDT - maximum time step when temperature is changing (1 second) + + checkPSD - checks maximum growth rate for particle size distribution (True) + maxDissolution - maximum relative volume fraction of precipitates allowed to dissolve in a single time step (0.01) + + checkRcrit - checks maximum change in critical radius (False) + maxRcritChange - maximum change in critical radius (as a fraction) per single time step (0.01) + + checkNucleation - checks maximum change in nucleation rate (True) + maxNucleationRateChange - maximum change in nucleation rate (on log scale) per single time step (0.5) + minNucleationRate - minimum nucleation rate to be considered for checking time intervals (1e-5) + + checkVolumePre - estimates maximum volume change (True) + checkVolumePost - checks maximum calculated volume change (True) + maxVolumeChange - maximum absolute value that volume fraction can change per single time step (0.001) + + minNucleateDensity - minimum nucleate density to consider nucleation to have occurred (1e-5) + dtScale - scaling factor to attempt to progressively increase dt over time + ''' + for key, value in kwargs.items(): + setattr(self, key, value) + + def setBetaBinary(self, functionType = 1): + ''' + Sets function for beta calculation in binary systems + 1 for implementation seen in Perez et al, 2008 (default) + 2 for implementation similar to multicomponent systems + + If using a multicomponent system, the beta function defaults to the 2nd + So this function will not do anything + + Parameters + ---------- + functionType : int + ID for function + 1 for implementation seen in Perez et al, 2008 (default) + 2 for implementation similar to multicomponent systems + ''' + if self.numberOfElements == 1: + if functionType == 2: + self.beta = self._BetaBinary2 + else: + self.beta = self._BetaBinary1 + + def setInitialComposition(self, xInit): + ''' + Parameters + + xInit : float or array + Initial composition of parent matrix phase in atomic fraction + Use float for binary system and array of solutes for multicomponent systems + ''' + self.xInit = xInit + self.xComp[0] = xInit + + def setInterfacialEnergy(self, gamma, phase = None): + ''' + Parameters + ---------- + gamma : float + Interfacial energy between precipitate and matrix in J/m2 + phase : str (optional) + Phase to input interfacial energy (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + self.gamma[index] = gamma + + def resetAspectRatio(self, phase = None): + ''' + Resets aspect ratio variables of defined phase to default + + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + self.shapeFactors[index].setSpherical() + + def setPrecipitateShape(self, precipitateShape, phase = None, ratio = 1): + ''' + Sets precipitate shape to user-defined shape + + Parameters + ---------- + precipitateShape : int + Precipitate shape (ShapeFactor.SPHERE, NEEDLE, PLATE or CUBIC) + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ratio : float (optional) + Aspect ratio of precipitate (long axis / short axis) + If float, must be greater than 1 + If function, must take in radius as input and output float greater than 1 + ''' + index = self.phaseIndex(phase) + self.shapeFactors[index].setPrecipitateShape(precipitateShape, ratio) + + def _setVolume(self, value, valueType: VolumeParameter, atomsPerCell): + ''' + Private function that returns Vm, Va, a, atomsPerCell given a VolumeParameter and atomsPerCell + + Parameters + ---------- + value : float + Value for volume parameters (lattice parameter, atomic (unit cell) volume or molar volume) + valueType : VolumeParameter + States what volume term that value is + atomsPerCell : int + Number of atoms in the unit cell + ''' + if valueType == VolumeParameter.MOLAR_VOLUME: + Vm = value + Va = atomsPerCell * Vm / self.avo + a = np.cbrt(Va) + elif valueType == VolumeParameter.ATOMIC_VOLUME: + Va = value + Vm = Va * self.avo / atomsPerCell + a = np.cbrt(Va) + elif valueType == VolumeParameter.LATTICE_PARAMETER: + a = value + Va = a**3 + Vm = Va * self.avo / atomsPerCell + return Vm, Va, a, atomsPerCell + + def setVolumeAlpha(self, value, valueType: VolumeParameter, atomsPerCell): + ''' + Sets volume parameters for parent phase + + Parameters + ---------- + value : float + Value for volume parameters (lattice parameter, atomic (unit cell) volume or molar volume) + valueType : VolumeParameter + States what volume term that value is + atomsPerCell : int + Number of atoms in the unit cell + ''' + self.VmAlpha, self.VaAlpha, self.aAlpha, self.atomsPerCellAlpha = self._setVolume(value, valueType, atomsPerCell) + + def setVolumeBeta(self, value, valueType: VolumeParameter, atomsPerCell, phase = None): + ''' + Sets volume parameters for precipitate phase + + Parameters + ---------- + value : float + Value for volume parameters (lattice parameter, atomic (unit cell) volume or molar volume) + valueType : VolumeParameter + States what volume term that value is + atomsPerCell : int + Number of atoms in the unit cell + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + self.VmBeta[index], self.VaBeta[index], _, self.atomsPerCellBeta[index] = self._setVolume(value, valueType, atomsPerCell) + + def setNucleationDensity(self, grainSize = 100, aspectRatio = 1, dislocationDensity = 5e12, bulkN0 = None): + ''' + Sets grain size and dislocation density which determines the available nucleation sites + + Parameters + ---------- + grainSize : float (optional) + Average grain size in microns (default at 100um if this function is not called) + aspectRatio : float (optional) + Aspect ratio of grains (default at 1) + dislocationDensity : float (optional) + Dislocation density (m/m3) (default at 5e12) + bulkN0 : float (optional) + This allows for the use to override the nucleation site density for bulk precipitation + By default (None), this is calculated by the number of lattice sites containing a solute atom + However, for calibration purposes, it may be better to set the nucleation site density manually + ''' + self.grainSize = grainSize * 1e-6 + self.grainAspectRatio = aspectRatio + self.dislocationDensity = dislocationDensity + + self.bulkN0 = bulkN0 + self._isNucleationSetup = True + + def _getNucleationDensity(self): + ''' + Calculates nucleation density + This is separated from setting nucleation density to + allow it to be called right before the simulation starts + ''' + #Set bulk nucleation site to the number of solutes per unit volume + # This is the represent that any solute atom can be a nucleation site + #NOTE: some texts will state the bulk nucleation sites to just be the number + # of lattice sites per unit volume. The justification for this would be + # the solutes can diffuse around to any lattice site and nucleate there + if self.bulkN0 is None: + if self.numberOfElements == 1: + self.bulkN0 = self.xComp[0] * (self.avo / self.VmAlpha) + else: + self.bulkN0 = np.amin(self.xComp[0,:]) * (self.avo / self.VmAlpha) + + self.dislocationN0 = self.dislocationDensity * (self.avo / self.VmAlpha)**(1/3) + + if self.grainSize != np.inf: + #Number of lattice sites on grain boundaries (#/m3) + if self.GBareaN0 is None: + self.GBareaN0 = (6 * np.sqrt(1 + 2 * self.grainAspectRatio**2) + 1 + 2 * self.grainAspectRatio) / (4 * self.grainAspectRatio * self.grainSize) + self.GBareaN0 *= (self.avo / self.VmAlpha)**(2/3) + #Number of lattice sites on grain edges (#/m3) + if self.GBedgeN0 is None: + self.GBedgeN0 = 2 * (np.sqrt(2) + 2 * np.sqrt(1 + self.grainAspectRatio**2)) / (self.grainAspectRatio * self.grainSize**2) + self.GBedgeN0 *= (self.avo / self.VmAlpha)**(1/3) + #Number of lattice sites on grain corners (which is just the number of corners) (#/m3) + if self.GBcornerN0 is None: + self.GBcornerN0 = 12 / (self.grainAspectRatio * self.grainSize**3) + else: + self.GBareaN0 = 0 + self.GBedgeN0 = 0 + self.GBcornerN0 = 0 + + def setNucleationSite(self, site, phase = None): + ''' + Sets nucleation site type for specified phase + If site type is grain boundaries, edges or corners, the phase morphology will be set to spherical and precipitate shape will depend on wetting angle + + Parameters + ---------- + site : str + Type of nucleation site + Options are 'bulk', 'dislocations', 'grain_boundaries', 'grain_edges' and 'grain_corners' + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + + self.GB[index].setNucleationType(site) + + if self.GB[index].nucleationSiteType != GBFactors.BULK and self.GB[index].nucleationSiteType != GBFactors.DISLOCATION: + self.shapeFactors[index].setSpherical() + + def _setGBfactors(self): + ''' + Calcualtes factors for bulk or grain boundary nucleation + This is separated from setting the nucleation sites to allow + it to be called right before simulation + ''' + for p in range(len(self.phases)): + self.GB[p].setFactors(self.GBenergy, self.gamma[p]) + + def _GBareaRemoval(self, p): + ''' + Returns factor to multiply radius by to give the equivalent radius of circles representing the area of grain boundary removal + + Parameters + ---------- + p : int + Index for phase + ''' + if self.GB[p].nucleationSiteType == GBFactors.BULK or self.GB[p].nucleationSiteType == GBFactors.DISLOCATION: + return 1 + else: + return np.sqrt(self.GB[p].gbRemoval / np.pi) + + def setParentPhases(self, phase, parentPhases): + ''' + Sets parent precipitates at which a precipitate can nucleate on the surface of + + Parameters + ---------- + phase : str + Precipitate phase of interest that will nucleate + parentPhases : list + Phases that the precipitate of interest can nucleate on the surface of + ''' + index = self.phaseIndex(phase) + for p in parentPhases: + self.parentPhases[index].append(self.phaseIndex(p)) + + def setGrainBoundaryEnergy(self, energy): + ''' + Grain boundary energy - this will decrease the critical radius as some grain boundaries will be removed upon nucleation + + Parameters + ---------- + energy : float + GB energy in J/m2 + + Default upon initialization is 0.3 + Note: GBenergy of 0 is equivalent to bulk precipitation + ''' + self.GBenergy = energy + + def setTheta(self, theta, phase = None): + ''' + This is a scaling factor for the incubation time calculation, default is 2 + + Incubation time is defined as 1 / \theta * \beta * Z^2 + \theta differs by derivation. By default, this is set to 2 following the + Feder derivation. In the Wakeshima derivation, \theta is 4pi + + Parameters + ---------- + theta : float + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + self.theta[index] = theta + + def setTemperature(self, temperature): + ''' + Sets temperature parameter + + Options: + temperature : float + Isothermal temperature + temperature : function + Function takes in time in seconds and returns temperature + temperature : [times, temps] + Temperature will be interpolated between the times and temps list + Each index in the lists will correspond to the time that temperature is reached + Ex. [0, 15, 20], [100, 500, 400] + Temperature starts at 100 and ramps to 500, reaching it at 15 hours + Then temperature will drop to 400, reaching it at 20 hours + ''' + self.Tparameters = temperature + self.temperature[0] = self.getTemperature(0) + if np.isscalar(temperature): + self._incubation = self._incubationIsothermal + else: + self._incubation = self._incubationNonIsothermal + + def getTemperature(self, t): + ''' + Gets temperature at time t + + Options: + Options: + temperature : float + Returns temperature + temperature : function + Returns evaluated temperature function at time t + temperature : [times, temps] + If t < time[0] -> return first temperature + If t > time[-1] -> return last temperature + Else, find the two times that t is between and interpolate + ''' + if np.isscalar(self.Tparameters): + return self.Tparameters + elif len(self.Tparameters) == 2: + if t/3600 < self.Tparameters[0][0]: + return self.Tparameters[1][0] + for i in range(len(self.Tparameters[0])-1): + if t/3600 >= self.Tparameters[0][i] and t/3600 < self.Tparameters[0][i+1]: + t0, tf, T0, Tf = self.Tparameters[0][i], self.Tparameters[0][i+1], self.Tparameters[1][i], self.Tparameters[1][i+1] + return (Tf - T0) / (tf - t0) * (t/3600 - t0) + T0 + return self.Tparameters[1][-1] + elif self.Tparameters is not None: + return self.Tparameters(t) + else: + return None + + def setStrainEnergy(self, strainEnergy, phase = None, calculateAspectRatio = False): + ''' + Sets strain energy class to precipitate + + Parameters + ---------- + strainEnergy : StrainEnergy object + phase : str + Precipitate phase of interest that will nucleate + calculateAspectRatio : bool + Will use strain energy to get aspect ratio if True + ''' + index = self.phaseIndex(phase) + self.strainEnergy[index] = strainEnergy + self.calculateAspectRatio[index] = calculateAspectRatio + + def _setupStrainEnergyFactors(self): + '''' + For each phase, the strain energy calculation will be set to assume + a spherical, cubic or ellipsoidal shape depending on the defined shape factors + ''' + for i in range(len(self.phases)): + self.strainEnergy[i].setup() + if self.strainEnergy[i].type != StrainEnergy.CONSTANT: + if self.shapeFactors[i].particleType == ShapeFactor.SPHERE: + self.strainEnergy[i].setSpherical() + elif self.shapeFactors[i].particleType == ShapeFactor.CUBIC: + self.strainEnergy[i].setCuboidal() + else: + self.strainEnergy[i].setEllipsoidal() + + def setDiffusivity(self, diffusivity): + ''' + For binary systems only + + Parameters + ---------- + diffusivity : function taking + Composition and temperature (K) and returning diffusivity (m2/s) + Function must have inputs in this order: f(x, T) + ''' + self.Diffusivity = diffusivity + + def setInfinitePrecipitateDiffusivity(self, infinite, phase = None): + ''' + Sets whether to assuming infinitely fast or no diffusion in phase + + Parameters + ---------- + infinite : bool + True will assume infinitely fast diffusion + False will assume no diffusion + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + Use 'all' to apply to all phases + ''' + if phase == 'all': + self.infinitePrecipitateDiffusion = [infinite for i in range(len(self.phases))] + else: + index = self.phaseIndex(phase) + self.infinitePrecipitateDiffusion[index] = infinite + + def setThermodynamics(self, therm, phase = None, removeCache = False, addDiffusivity = True): + ''' + Parameters + ---------- + therm : Thermodynamics class + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + removeCache : bool (optional) + Will not cache equilibrium results if True (defaults to False) + addDiffusivity : bool (optional) + For binary systems, will add diffusivity functions from the database if present + Defaults to True + ''' + index = self.phaseIndex(phase) + self.dG[index] = lambda x, T, removeCache = removeCache: therm.getDrivingForce(x, T, precPhase=phase, training = removeCache, returnComp = True) + + if self.numberOfElements == 1: + self.interfacialComposition[index] = lambda x, T: therm.getInterfacialComposition(x, T, precPhase=phase) + if (therm.mobCallables is not None or therm.diffCallables is not None) and addDiffusivity: + self.Diffusivity = lambda x, T, removeCache = removeCache: therm.getInterdiffusivity(x, T, removeCache = removeCache) + else: + self.interfacialComposition[index] = lambda x, T, dG, R, gExtra, removeCache = removeCache, searchDir = None: therm.getGrowthAndInterfacialComposition(x, T, dG, R, gExtra, precPhase=phase, training = False, searchDir = searchDir) + self._betaFuncs[index] = lambda x, T, removeCache = removeCache: therm.impingementFactor(x, T, precPhase=phase, training = False) + + def setSurrogate(self, surr, phase = None): + ''' + Parameters + ---------- + surr : Surrogate class + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + self.dG[index] = surr.getDrivingForce + + if self.numberOfElements == 1: + self.interfacialComposition[index] = surr.getInterfacialComposition + else: + self.interfacialComposition[index] = surr.getGrowthAndInterfacialComposition + self._betaFuncs[index] = surr.impingementFactor + + def particleGibbs(self, radius, phase = None): + ''' + Returns Gibbs Thomson contribution of a particle given its radius + + Parameters + ---------- + radius : float or array + Precipitate radius + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + return self.VmBeta[index] * (self.strainEnergy[index].strainEnergy(self.shapeFactors[index].normalRadii(radius)) + 2 * self.shapeFactors[index].thermoFactor(radius) * self.gamma[index] / radius) + + def neglectEffectiveDiffusionDistance(self, neglect = True): + ''' + Whether or not to account for effective diffusion distance dependency on the supersaturation + By default, effective diffusion distance is considered + + Parameters + ---------- + neglect : bool (optional) + If True (default), will assume effective diffusion distance is particle radius + If False, will calculate correction factor from Chen, Jeppson and Agren (2008) + ''' + self.effDiffDistance = self.effDiffFuncs.noDiffusionDistance if neglect else self.effDiffFuncs.effectiveDiffusionDistance + + def addStoppingCondition(self, condition, mode = 'or'): + ''' + Adds condition to stop simulation when condition is met + + Parameters + ---------- + condition: PrecipitateStoppingCondition + mode: str + 'or' or 'and + Conditions with 'or' will stop the simulation when at least one condition is met + Conditions with 'and' will stop the simulation when all conditions are met + ''' + self._stoppingConditions.append(condition) + if mode == 'or': + self._stopConditionMode.append(True) + else: + self._stopConditionMode.append(False) + + def clearStoppingConditions(self): + ''' + Clears all stopping conditions + ''' + self._stoppingConditions = [] + self._stopConditionMode = [] + + def setup(self): + ''' + Sets up hidden parameters before solving + Nucleation site density + Grain boundary factors + Strain energy + ''' + if self._isSetup: + return + + if not self._isNucleationSetup: + #Set nucleation density assuming grain size of 100 um and dislocation density of 5e12 m/m3 (Thermocalc default) + print('Nucleation density not set.\nSetting nucleation density assuming grain size of {:.0f} um and dislocation density of {:.0e} #/m2'.format(100, 5e12)) + self.setNucleationDensity(100, 1, 5e12) + for p in range(len(self.phases)): + self.Rmin[p] = self.minRadius + self._getNucleationDensity() + self._setGBfactors() + self._setupStrainEnergyFactors() + self._isSetup = True + + def printHeader(self): + ''' + Overloads printHeader from GenericModel to do nothing + since status displays the necessary outputs + ''' + return + + def printStatus(self, iteration, modelTime, simTimeElapsed): + ''' + Prints various terms at latest step + + Will print: + Model time, simulation time, temperature, matrix composition + For each phase + Phase name, precipitate density, volume fraction, avg radius and driving force + ''' + i = len(self.time)-1 + #For single element, we just print the composition as matrix comp in terms of the solute + if self.numberOfElements == 1: + print('N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp') + print('{:.0f}\t{:.1e}\t\t{:.1f}\t\t{:.0f}\t\t{:.4f}\n'.format(i, modelTime, simTimeElapsed, self.temperature[i], 100*self.xComp[i,0])) + #For multicomponent systems, print each element + else: + compStr = 'N\tTime (s)\tSim Time (s)\tTemperature (K)\t' + compValStr = '{:.0f}\t{:.1e}\t\t{:.1f}\t\t{:.0f}\t\t'.format(i, modelTime, simTimeElapsed, self.temperature[i]) + for a in range(self.numberOfElements): + compStr += self.elements[a] + '\t' + compValStr += '{:.4f}\t'.format(100*self.xComp[i,a]) + compValStr += '\n' + print(compStr) + print(compValStr) + + #Print status of each phase + print('\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)') + for p in range(len(self.phases)): + print('\t{}\t{:.3e}\t\t{:.4f}\t\t{:.4e}\t{:.4e}'.format(self.phases[p], self.precipitateDensity[i,p], 100*self.betaFrac[i,p], self.avgR[i,p], self.dGs[i,p]*self.VmBeta[p])) + print('') + + def preProcess(self): + ''' + Store array for non-derivative terms (which is everything except for the PBM models) + + We use these terms for the first step of the iterators (for Euler, this is all the steps) + For RK4, these terms will be recalculated in dXdt + ''' + self._currY = None + return + + def _calculateDependentTerms(self, t, x): + ''' + Gets all dependent terms (everything but PBM variables) that are needed to find dXdt + + Steps: + 1. Mass balance + 2. Driving force - must be done after mass balance to get the current matrix composition + 3. Growth rate - must be done after driving force since dG is needed in multicomponent systems + 4. Nucleation rate + 5. Nucleate radius - must be done after nucleation rate since derived classes can change nucleation rate + + For the first iteration, self._currY will be None from the preProcess function, in this case, we want + to just grab the latest values to avoid double calculations + ''' + self._processX(x) + if self._currY is None: + #print('start iteration') + self._currY = [np.array([self.varList[i][self.n]]) for i in range(self.NUM_TERMS)] + else: + self._currY[self.TIME] = np.array([t]) + self._currY[self.TEMPERATURE] = np.array([self.getTemperature(t)]) + self._calcMassBalance(t, x) + self._calcDrivingForce(t, x) + self._growthRate() + self._calcNucleationRate(t, x) + self._setNucleateRadius(t) #Must be done afterwards since derived classes can change nucRate + #print(self._currY) + + def getdXdt(self, t, x): + ''' + Gets dXdt as a list for each phase + + For the eulerian implementation, this is dn_i/dt for the bins in PBM for each phase + ''' + self._calculateDependentTerms(t, x) + dXdt = self._getdXdt(t, x) + return dXdt + + def postProcess(self, t, x): + ''' + 1) Updates internal arrays with new values of t and x + 2) Updates particle size distribution + 3) Updates coupled models + 4) Check stopping conditions + 5) Return new values and whether to stop the model + ''' + self._calculateDependentTerms(t, x) + self._appendArrays(self._currY) + + #Update particle size distribution (this includes adding bins, resizing bins, etc) + #Should be agnostic of eulerian or lagrangian implementations + self._updateParticleSizeDistribution(t, x) + + #Update coupled models + self.updateCoupledModels() + + #Check stopping conditions + orCondition = False + andCondition = True + numAndCondition = 0 + for i in range(len(self._stoppingConditions)): + self._stoppingConditions[i].testCondition(self) + if self._stopConditionMode[i]: + orCondition = orCondition or self._stoppingConditions[i].isSatisfied() + else: + andCondition = andCondition and self._stoppingConditions[i].isSatisfied() + numAndCondition += 1 + + #If no and conditions, then andCondition will still be True, so set to False + if numAndCondition == 0: + andCondition = False + + stop = orCondition or andCondition + + return self.getCurrentX()[1], stop + + def _processX(self, x): + return NotImplementedError() + + def _calcMassBalance(self, t, x): + return NotImplementedError() + + def _getdXdt(self, t, x): + return NotImplementedError() + + def _updateParticleSizeDistribution(self, t, x): + return NotImplementedError() + + def _calcDrivingForce(self, t, x): + ''' + Driving force is defined in terms of J/m3 + + Calculation is dG_ch / V_m - dG_el + dG_ch - chemical driving force + V_m - molar volume + dG_el - elastic strain energy (always reduces driving force) + I guess there could be a case where it increases the driving force if + the matrix is prestrained and the precipitate releases stress, but this should + be handled in the ElasticFactors module + + If driving force is positive (precipitation is favorable) + Calculate Rcrit and Gcrit based off the nucleation site type + + This will also calculate critical radius (Rcrit) and nucleation barrier (Gcrit) + ''' + #Get variables + dGs = np.zeros((1,len(self.phases))) + Rcrit = np.zeros((1,len(self.phases))) + Gcrit = np.zeros((1,len(self.phases))) + if self.numberOfElements == 1: + xComp = self._currY[self.COMPOSITION][0,0] + else: + xComp = self._currY[self.COMPOSITION][0] + T = self._currY[self.TEMPERATURE][0] + + for p in range(len(self.phases)): + dGs[0,p], self._precBetaTemp[p] = self.dG[p](xComp, T) + dGs[0,p] /= self.VmBeta[p] + dGs[0,p] -= self.strainEnergy[p].strainEnergy(self.shapeFactors[p].normalRadii(self.Rcrit[self.n, p])) + if self.betaFrac[self.n, p] < 1 and dGs[0,p] >= 0: + #Calculate critical radius + #For bulk or dislocation nucleation sites, use previous critical radius to get aspect ratio + if self.GB[p].nucleationSiteType == GBFactors.BULK or self.GB[p].nucleationSiteType == GBFactors.DISLOCATION: + Rcrit[0,p] = np.amax((2 * self.shapeFactors[p].thermoFactor(self.Rcrit[self.n, p]) * self.gamma[p] / dGs[0,p], self.Rmin[p])) + Gcrit[0,p] = (4 * np.pi / 3) * self.gamma[p] * Rcrit[0,p]**2 + + #If nucleation is on a grain boundary, then use the critical radius as defined by the grain boundary type + else: + Rcrit[0,p] = np.amax((self.GB[p].Rcrit(dGs[0,p]), self.Rmin[p])) + Gcrit[0,p] = self.GB[p].Gcrit(dGs[0,p], Rcrit[0,p]) + + self._currY[self.DRIVING_FORCE] = dGs + self._currY[self.R_CRIT] = Rcrit + self._currY[self.G_CRIT] = Gcrit + + def _calcNucleationRate(self, t, x): + ''' + nucleation rate is defined as dn_nuc/dt = N_0 Z beta exp(-G/kBt) * exp(-tau/t) + ''' + gCrit = self._currY[self.G_CRIT][0] + T = self._currY[self.TEMPERATURE][0] + dg = self._currY[self.DRIVING_FORCE][0] + + betas = np.zeros((1,len(self.phases))) + nucRate = np.zeros((1,len(self.phases))) + for p in range(len(self.phases)): + #If driving force is negative, then nucleation rate is 0 + if dg[p] < 0: + continue + + Z = self._Zeldovich(p) + betas[0,p] = self._Beta(p) + + #If beta is 0, then nucRate is 0 and no need to do anymore calculation + if betas[0,p] == 0: + continue + + #Incubation time, either isothermal or nonisothermal + incubation = self._incubation(t, p, Z, betas[0]) + if incubation > 1: + incubation = 1 + + nucRate[0,p] = Z * betas[0,p] * np.exp(-gCrit[p] / (self.kB * T)) * incubation + + self._currY[self.IMPINGEMENT] = betas + self._currY[self.NUC_RATE] = nucRate + + def _Zeldovich(self, p): + ''' + Zeldovich factor - probability that cluster at height of nucleation barrier will continue to grow + ''' + rCrit = self._currY[self.R_CRIT][0] + T = self._currY[self.TEMPERATURE][0] + if rCrit[p] == 0: + return 0 + return np.sqrt(3 * self.GB[p].volumeFactor / (4 * np.pi)) * self.VmBeta[p] * np.sqrt(self.gamma[p] / (self.kB * T)) / (2 * np.pi * self.avo * rCrit[p]**2) + + def _BetaBinary1(self, p): + ''' + Impingement rate for binary systems using Perez et al + ''' + rCrit = self._currY[self.R_CRIT][0] + xComp = self._currY[self.COMPOSITION][0][0] + T = self._currY[self.TEMPERATURE][0] + return self.GB[p].areaFactor * rCrit[p]**2 * self.xComp[0] * self.Diffusivity(xComp, T) / self.aAlpha**4 + + def _BetaBinary2(self, p): + ''' + Impingement rate for binary systems taken from Thermocalc prisma documentation + This will follow the same equation as with _BetaMulti; however, some simplications can be made based off the summation contraint + ''' + xComp = self._currY[self.COMPOSITION][0][0] + xEqAlpha = self._currY[self.EQ_COMP_ALPHA][0] + xEqBeta = self._currY[self.EQ_COMP_BETA][0] + rCrit = self._currY[self.R_CRIT][0] + T = self._currY[self.TEMPERATURE][0] + D = self.Diffusivity(xComp, T) + Dfactor = (xEqBeta[p] - xEqAlpha[p])**2 / (xEqAlpha[p]*D) + (xEqBeta[p] - xEqAlpha[p])**2 / ((1 - xEqAlpha[p])*D) + return self.GB[p].areaFactor * rCrit[p]**2 * (1/Dfactor) / self.aAlpha**4 + + def _BetaMulti(self, p): + ''' + Impingement rate for multicomponent systems + ''' + if self._betaFuncs[p] is None: + return self._defaultBeta + else: + xComp = self._currY[self.COMPOSITION][0] + T = self._currY[self.TEMPERATURE][0] + beta = self._betaFuncs[p](xComp, T) + if beta is None: + return self.betas[p] + else: + rCrit = self._currY[self.R_CRIT][0] + return (self.GB[p].areaFactor * rCrit[p]**2 / self.aAlpha**4) * beta + + def _incubationIsothermal(self, t, p, Z, betas): + ''' + Incubation time for isothermal conditions + ''' + tau = 1 / (self.theta[p] * (betas[p] * Z**2)) + return np.exp(-tau / t) + + def _incubationNonIsothermal(self, t, p, Z, betas): + ''' + Incubation time for non-isothermal conditions + This must match isothermal conditions if the temperature is constant + + Solve for tau by: integral(beta(t-t0)) from 0 to tau = 1/theta*Z(tau)^2 + + Then it's exp(-tau/t) like the isothermal behavior + ''' + T = self._currY[self.TEMPERATURE][0] + startIndex = int(self.incubationOffset[p]) + LHS = 1 / (self.theta[p] * Z**2 * (T / self.temperature[startIndex:self.n+1])) + + RHS = np.cumsum(self.betas[startIndex+1:self.n+1,p] * (self.time[startIndex+1:self.n+1] - self.time[startIndex:self.n])) + if len(RHS) == 0: + RHS = self.betas[self.n,p] * (self.time[startIndex:] - self.time[startIndex]) + else: + RHS = np.concatenate((RHS, [RHS[-1] + betas[p] * (t - self.time[startIndex])])) + + #Test for intersection + diff = RHS - LHS + signChange = np.sign(diff[:-1]) != np.sign(diff[1:]) + + #If no intersection + if not any(signChange): + #If RHS > LHS, then intersection is at t = 0 + if diff[0] > 0: + tau = 0 + #Else, RHS intersects LHS beyond simulation time + #Extrapolate integral of RHS from last point to intersect LHS + #integral(beta(t-t0)) from t0 to ti + beta_i * (tau - (ti - t0)) = 1 / theta * Z(tau+t0)^2 + else: + tau = LHS[-1] / betas[p] - RHS[-1] / betas[p] + (t - self.time[startIndex]) + else: + tau = self.time[startIndex:-1][signChange][0] - self.time[startIndex] + + return np.exp(-tau / (t - self.time[startIndex])) + + def _setNucleateRadius(self, t): + ''' + Adds 1/2 * sqrt(kb T / pi gamma) to critical radius to ensure they grow when growth rates are calculated + ''' + nucRate = self._currY[self.NUC_RATE][0] + T = self._currY[self.TEMPERATURE][0] + dt = t - self.time[self.n] + Rcrit = self._currY[self.R_CRIT][0] + Rad = np.zeros((1,len(self.phases))) + for p in range(len(self.phases)): + #If nucleates form, then calculate radius of precipitate + #Radius is set slightly larger so precipitate + dt = 0.01 if self.n == 0 else self.time[self.n] - self.time[self.n-1] + if nucRate[p]*dt >= self.minNucleateDensity and Rcrit[p] >= self.Rmin[p]: + Rad[0,p] = Rcrit[p] + 0.5 * np.sqrt(self.kB * T / (np.pi * self.gamma[p])) + else: + Rad[0,p] = 0 + + self._currY[self.R_NUC] = Rad diff --git a/kawin/precipitation/KWNEuler.py b/kawin/precipitation/KWNEuler.py new file mode 100644 index 0000000..974ffda --- /dev/null +++ b/kawin/precipitation/KWNEuler.py @@ -0,0 +1,766 @@ +import numpy as np +from kawin.precipitation.KWNBase import PrecipitateBase +from kawin.precipitation.PopulationBalance import PopulationBalanceModel +from kawin.precipitation.non_ideal.GrainBoundaries import GBFactors +from kawin.precipitation.Plot import plotEuler + +class PrecipitateModel (PrecipitateBase): + ''' + Euler implementation of KWN model + + Parameters + ---------- + phases : list (optional) + Precipitate phases (array of str) + If only one phase is considered, the default is ['beta'] + elements : list (optional) + Solute elements in system + Note: order of elements must correspond to order of elements set in Thermodynamics module + If binary system, then defualt is ['solute'] + ''' + def __init__(self, phases = ['beta'], elements = ['solute']): + super().__init__(phases, elements) + + if self.numberOfElements == 1: + self._growthRate = self._growthRateBinary + self._Beta = self._BetaBinary1 + else: + self._growthRate = self._growthRateMulti + self._Beta = self._BetaMulti + + self.eqAspectRatio = [None for p in range(len(self.phases))] + + def _resetArrays(self): + ''' + Resets and initializes arrays for all variables + + In addition to PrecipitateBase, the equilibrium aspect ratio area and population balance models are created here + ''' + super()._resetArrays() + self.PBM = [PopulationBalanceModel() for p in self.phases] + + self.RdrivingForceIndex = np.zeros(len(self.phases), dtype=np.int32) + self.dissolutionIndex = np.zeros(len(self.phases), dtype=np.int32) + + def reset(self): + ''' + Resets model results + ''' + super().reset() + + for i in range(len(self.phases)): + self.PBM[i].reset() + self.PBM[i].resetRecordedData() + + def _addExtraSaveVariables(self, saveDict): + for p in range(len(self.phases)): + saveDict['PBM_data_' + self.phases[p]] = [self.PBM[p].min, self.PBM[p].max, self.PBM[p].bins] + saveDict['PBM_PSD_' + self.phases[p]] = self.PBM[p].PSD + saveDict['PBM_bounds_' + self.phases[p]] = self.PBM[p].PSDbounds + saveDict['PBM_size_' + self.phases[p]] = self.PBM[p].PSDsize + saveDict['eqAspectRatio_' + self.phases[p]] = self.eqAspectRatio[p] + + def load(filename): + data = np.load(filename) + model = PrecipitateModel(data['phases'], data['elements']) + model._loadData(data) + return model + + def _loadExtraVariables(self, data): + for p in range(len(self.phases)): + PBMdata = data['PBM_data_' + self.phases[p]] + psd = data['PBM_PSD_' + self.phases[p]] + bounds = data['PBM_bounds_' + self.phases[p]] + size = data['PBM_size_' + self.phases[p]] + eqAR = data['eqAspectRatio_' + self.phases[p]] + self.PBM[p] = PopulationBalanceModel(PBMdata[0], PBMdata[1], int(PBMdata[2])) + self.PBM[p].PSD = psd + self.PBM[p].PSDsize = size + self.PBM[p].PSDbounds = bounds + self.eqAspectRatio[p] = eqAR + + def setPBMParameters(self, cMin = 1e-10, cMax = 1e-9, bins = 150, minBins = 100, maxBins = 200, adaptive = True, phase = None): + ''' + Sets population balance model parameters for each phase + + Parameters + ---------- + cMin : float + Minimum bin size + cMax : float + Maximum bin size + bins : int + Initial number of bins + minBins : int + Minimum number of bins - will not be used if adaptive = False + maxBins : int + Maximum number of bins - will not be used if adaptive = False + adaptive : bool + Sets adaptive bin sizes - bins may still change upon nucleation + phase : str + Phase to consider (will set all phases if phase = None or 'all') + ''' + if phase is None or phase == 'all': + for p in range(len(self.phases)): + self.PBM[p] = PopulationBalanceModel(cMin, cMax, bins, minBins, maxBins) + self.PBM[p].setAdaptiveBinSize(adaptive) + else: + index = self.phaseIndex(phase) + self.PBM[index] = PopulationBalanceModel(cMin, cMax, bins, minBins, maxBins) + self.PBM[index].setAdaptiveBinSize(adaptive) + + def setPSDrecording(self, record = True, phase = 'all'): + ''' + Sets recording parameters for PSD of specified phase + + Parameters + ---------- + record : bool (optional) + Whether to record PSD, defaults to True + phase : str (optional) + Precipitate phase to record for + Defaults to 'all', which will apply to all precipitate phases + ''' + if phase is None or phase == 'all': + for p in self.phases: + index = self.phaseIndex(p) + self.PBM[index].setRecording(record) + else: + index = self.phaseIndex(phase) + self.PBM[index].setRecording(record) + + def saveRecordedPSD(self, filename, compressed = True, phase = 'all'): + ''' + Saves recorded PSD in npz format + + Parameters + ---------- + filename : str + File name to save to + Note: the phase name will be added to the filename if all phases are being saved + compressed : bool (optional) + Whether to save in compressed npz format + Defualts to True + phase : str (optional) + Phase to save PSD for + Defaults to 'all', which will save a file for each phase + ''' + if phase is None or phase == 'all': + for p in self.phases: + index = self.phaseIndex(p) + self.PBM[index].saveRecordedPSD(filename + '_' + p, compressed) + else: + index = self.phaseIndex(phase) + self.PBM[index].saveRecordedPSD(filename, compressed) + + def loadParticleSizeDistribution(self, data, phase = None): + ''' + Loads particle size distribution for specified phase + + Parameters + ---------- + data : array + Array of data containing precipitate sizes + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + self.PBM[index].LoadDistribution(data) + + def particleRadius(self, phase = None): + ''' + Returns PSD bounds of given phase + + Parameters + ---------- + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + return self.PBM[index].PSDbounds + + def particleGibbs(self, radius = None, phase = None): + ''' + Returns Gibbs Thomson contribution of a particle given its radius + + Parameters + ---------- + radius : array (optional) + Precipitate radaii (defaults to None, which will use boundaries + of the size classes of the precipitate PSD) + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + if radius is None: + index = self.phaseIndex(phase) + radius = self.PBM[index].PSDbounds + return super().particleGibbs(radius, phase) + + def PSD(self, phase = None): + ''' + Returns frequency of particle size distribution of given phase + + Parameters + ---------- + phase : str (optional) + Phase to consider (defaults to first precipitate in list) + ''' + index = self.phaseIndex(phase) + return self.PBM[index].PSD + + def createLookup(self): + ''' + This creates a lookup table mapping the particle size classes to the interfacial composition + ''' + #RdrivingForceIndex will find the index of the largest particle size class where the precipitate is unstable + #This is determined by the interfacial composition function, where it should return -1 or None + #All compositions from the PSD bounds will be set to the compositions just above RdrivingForceLimit + #This is just to allow for particles to dissolve instead of pile up in the smallest bin + self.RdrivingForceIndex = np.zeros(len(self.phases), dtype=np.int32) + + #Keep as separate arrays so that number of PSD classes can change within precipitate phases + self.PSDXalpha = [] + self.PSDXbeta = [] + + xEqAlpha = np.zeros((1, len(self.phases), self.numberOfElements)) + xEqBeta = np.zeros((1, len(self.phases), self.numberOfElements)) + T = self._currY[self.TEMPERATURE][0] + for p in range(len(self.phases)): + #Interfacial compositions at equilibrium (planar interface) + xAResult, xBResult = self.interfacialComposition[p](T, 0) + if xAResult == -1 or xAResult is None: + xEqAlpha[0,p,0] = 0 + xEqBeta[0,p,0] = 0 + else: + xEqAlpha[0,p,0] = xAResult + xEqBeta[0,p,0] = xBResult + + #Interfacial compositions at each size class in PSD + self.PSDXalpha.append(np.zeros((self.PBM[p].bins + 1, 1))) + self.PSDXbeta.append(np.zeros((self.PBM[p].bins + 1, 1))) + + self.PSDXalpha[p][:,0], self.PSDXbeta[p][:,0] = self.interfacialComposition[p](T, self.particleGibbs(self.PBM[p].PSDbounds, self.phases[p])) + self.RdrivingForceIndex[p] = np.argmax(self.PSDXalpha[p][:,0] != -1)-1 + self.RdrivingForceIndex[p] = 0 if self.RdrivingForceIndex[p] < 0 else self.RdrivingForceIndex[p] + self.RdrivingForceLimit[p] = self.PBM[p].PSDbounds[self.RdrivingForceIndex[p]] + + #Sets particle radii smaller than driving force limit to driving force limit composition + #If RdrivingForceIndex is at the end of the PSDX arrays, then no precipitate in the size classes of the PSD is stable + #This can occur in non-isothermal situations where the temperature gets too high + if self.RdrivingForceIndex[p]+1 < len(self.PSDXalpha[p][:,0]): + self.PSDXalpha[p][:self.RdrivingForceIndex[p]+1,0] = self.PSDXalpha[p][self.RdrivingForceIndex[p]+1,0] + self.PSDXbeta[p][:self.RdrivingForceIndex[p]+1,0] = self.PSDXbeta[p][self.RdrivingForceIndex[p]+1,0] + else: + self.PSDXalpha[p] = np.zeros((self.PBM[p].bins + 1,1)) + self.PSDXbeta[p] = np.zeros((self.PBM[p].bins + 1,1)) + + return xEqAlpha, xEqBeta + + def setup(self): + ''' + Sets up additional variables in addition to PrecipitateBase + + Sets up additional outputs, population balance models, equilibrium aspect ratio and equilibrium compositions + ''' + if self._isSetup: + return + + super().setup() + + #Equilibrium aspect ratio and PBM setup + #If calculateAspectRatio is True, then use strain energy to calculate aspect ratio for each size class in PSD + #Else, then use aspect ratio defined in shape factors + self.eqAspectRatio = [None for p in range(len(self.phases))] + for p in range(len(self.phases)): + self.PBM[p].reset() + + if self.calculateAspectRatio[p]: + self.eqAspectRatio[p] = self.strainEnergy[p].eqAR_bySearch(self.PBM[p].PSDbounds, self.gamma[p], self.shapeFactors[p]) + arFunc = lambda R, p1=p : self._interpolateAspectRatio(R, p1) + self.shapeFactors[p].setAspectRatio(arFunc) + else: + self.eqAspectRatio[p] = self.shapeFactors[p].aspectRatio(self.PBM[p].PSDbounds) + + self._currY = [np.array([self.varList[i][self.n]]) for i in range(self.NUM_TERMS)] + self._currY[self.TIME] = np.array([self.time[self.n]]) + self._currY[self.TEMPERATURE] = np.array([self.getTemperature(self.time[self.n])]) + + #Setup interfacial composition + if self.numberOfElements == 1: + self.xEqAlpha[self.n], self.xEqBeta[self.n] = self.createLookup() + else: + self.PSDXalpha = [None for p in range(len(self.phases))] + self.PSDXbeta = [None for p in range(len(self.phases))] + + #Set first index of eq composition + for p in range(len(self.phases)): + #Use arbitrary dg, R and gE since only the eq compositions are needed here + _, _, _, xEqAlpha, xEqBeta = self.interfacialComposition[p](self.xComp[self.n], self.temperature[self.n], 0, 1, 0) + if xEqAlpha is not None: + self.xEqAlpha[self.n,p] = xEqAlpha + self.xEqBeta[self.n,p] = xEqBeta + + x = [self.PBM[p].PSD for p in range(len(self.phases))] + self._calcDrivingForce(self.time[self.n], x) + self._growthRate() + self._calcNucleationRate(self.time[self.n], x) + for i in range(self.NUM_TERMS): + self.varList[i][self.n] = self._currY[i][0] + + def _interpolateAspectRatio(self, R, p): + ''' + Linear interpolation between self.eqAspectRatio and self.PBM[p].PSDbounds + + Parameters + ---------- + R : float + Equivalent spherical radius + p : int + Phase index + ''' + return np.interp(R, self.PBM[p].PSDbounds, self.eqAspectRatio[p]) + + def getDt(self, dXdt): + ''' + The following checks are made + 1) change in number of particles moving between bins + This is controlled by the implementation in PopulationBalanceModel, + but essentially limits the number of particles moving between bins + 2) change in nucleation rate + Time will be proportional to the 1/log(previous nuc rate / new nuc rate) + 3) change in temperature + Limits how fast temperature can change + 4) change in critical radius + Proportional to a percent change in critical radius + 5) estimated change in volume fraction + Estimates the change in volume fraction from the nucleation rate and nucleation radius + ''' + #Start test dt at 0.01 or previous dt + i = self.n + dtPrev = 0.01 if self.n == 0 else self.time[i] - self.time[i-1] + #Try to slowly increase the time step + # Precipitation kinetics is more on a log scale than linear (unless temperature changes are involve) + # Thus, we can get away with increasing the time step over time assuming that kinetics are slowing down + # Plus, unlike the single phase diffusion module, there's no form way to define a good time step apart from the checks here + dtPropose = (1 + self.dtScale) * dtPrev + dtMax = self.finalTime - self.time[i] + + dtAll = [dtMax] + if self.checkPSD: + dtPBM = [dtMax] + if i > 0 and self.temperature[i] == self.temperature[i-1]: + dtPBM += [self.PBM[p].getDTEuler(dtMax, self.growth[p], self.dissolutionIndex[p]) for p in range(len(self.phases))] + dtPBM = np.amin(dtPBM) + dtAll.append(dtPBM) + + if self.checkNucleation: + dtNuc = dtMax * np.ones(len(self.phases)) + if i > 0: + nRateCurr = self.nucRate[i] + nRatePrev = self.nucRate[i-1] + for p in range(len(self.phases)): + if nRateCurr[p] > self.minNucleationRate and nRatePrev[p] > self.minNucleationRate and nRatePrev[p] != nRateCurr[p]: + dtNuc[p] = self.maxNucleationRateChange * dtPrev / np.abs(np.log10(nRatePrev[p] / nRateCurr[p])) + else: + for p in range(len(self.phases)): + if self.nucRate[i,p] * dtPrev > 1e5: + dtNuc[p] = 1e5 / self.nucRate[i,p] + dtNuc = np.amin(dtNuc) + dtAll.append(dtNuc) + + #Temperature change constraint + if self.checkTemperature and i > 0: + Tchange = self.temperature[i] - self.temperature[i-1] + dtTemp = dtMax + if Tchange > self.maxNonIsothermalDT: + dtTemp = self.maxNonIsothermalDT * dtPrev / Tchange + dtAll.append(dtTemp) + + if self.checkRcrit and i > 0: + dtRad = dtMax * np.ones(len(self.phases)) + if not all((self.Rcrit[i-1,:] == 0) & (self.Rcrit[i,:] - self.Rcrit[i-1,:] == 0) & (self.dGs[i,:] <= 0)): + indices = (self.Rcrit[i-1,:] > 0) & (self.Rcrit[i,:] - self.Rcrit[i-1,:] != 0) & (self.dGs[i,:] > 0) + dtRad[indices] = self.maxRcritChange * dtPrev / np.abs((self.Rcrit[i,:][indices] - self.Rcrit[i-1,:][indices]) / self.Rcrit[i-1,:][indices]) + dtRad = np.amin(dtRad) + dtAll.append(dtRad) + + if self.checkVolumePre: + dV = np.zeros(len(self.phases)) + for p in range(len(self.phases)): + #Calculate estimate volume change based off growth rate and nucleated particles + #TODO: account for non-spherical precipitates + dVi = self.PBM[p].PSD * self.PBM[p].PSDsize**2 * 0.5 * (self.growth[p][1:] + self.growth[p][:-1]) + dVi[dVi < 0] = 0 + dV = self.VmAlpha / self.VmBeta[p] * (self.GB[p].areaFactor * np.sum(dVi) + self.GB[p].volumeFactor * self.nucRate[i,p] * self.Rad[i,p]**3) + + dtVol = dtMax * np.ones(len(self.phases)) + for p in range(len(self.phases)): + if dV != 0: + dtVol[p] = self.maxVolumeChange / (2 * np.abs(dV)) + dtVol = np.amin(dtVol) + dtAll.append(dtVol) + + dt = np.amin(dtAll) + #If all time checks pass, then go back to previous time step and increase it slowly + # This is so we don't step at the maximum possible time + if dt == dtMax: + dt = dtPropose + + return dt + + def _processX(self, x): + ''' + Quick check to make sure particles below the thresholds are 0 + RdrivingForceIndex - only for binary, where energy from the Gibbs-Thompson effect is high enough + that the free energy of the precipitate is above the free energy surface of the matrix phase + and equilibrium cannot be calculated + minRadius - minimum radius to be considered a precipitate + ''' + for p in range(len(self.phases)): + x[p][:self.RdrivingForceIndex[p]+1] = 0 + x[p][self.PBM[p].PSDsize < self.minRadius] = 0 + return + + def _calcNucleationRate(self, t, x): + ''' + The _calcNucleationRate function in KWNBase calculates the nucleation rate as the + probability that a site can form a nucleate that will continue to grow + + To convert this probability to an actual nucleation rate, we multiply by the amount + of available nucleation sites + + The number of available sites is determined by: + Available sites = All sites - used up sites + sites on parent precipitates + The used up sites depends on the type of nucleation + Bulk and grain corners - used sites = number of current precipitates + Dislocation and grain edges - number of sites filled along the edges (assumes average radius of precipitates) + Grain boundaries - number of sites filled along the faces (assumes average cross sectional area of precipitates) + ''' + super()._calcNucleationRate(t, x) + for p in range(len(self.phases)): + #If parent phases exists, then calculate the number of potential nucleation sites on the parent phase + #This is the number of lattice sites on the total surface area of the parent precipitate + nucleationSites = np.sum([4 * np.pi * self.PBM[p2].SecondMomentFromN(x[p2]) * (self.avo / self.VmBeta[p2])**(2/3) for p2 in self.parentPhases[p]]) + + if self.GB[p].nucleationSiteType == GBFactors.BULK: + #bulkPrec = np.sum([self.GB[p2].volumeFactor * self.PBM[p2].ThirdMoment() for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.BULK]) + #nucleationSites += self.bulkN0 - bulkPrec * (self.avo / self.VmAlpha) + bulkPrec = np.sum([self.PBM[p2].ZeroMomentFromN(x[p2]) for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.BULK]) + nucleationSites += self.bulkN0 - bulkPrec + elif self.GB[p].nucleationSiteType == GBFactors.DISLOCATION: + bulkPrec = np.sum([self.PBM[p2].FirstMomentFromN(x[p2]) for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.DISLOCATION]) + nucleationSites += self.dislocationN0 - bulkPrec * (self.avo / self.VmAlpha)**(1/3) + elif self.GB[p].nucleationSiteType == GBFactors.GRAIN_BOUNDARIES: + boundPrec = np.sum([self.GB[p2].gbRemoval * self.PBM[p2].SecondMomentFromN(x[p2]) for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.GRAIN_BOUNDARIES]) + nucleationSites += self.GBareaN0 - boundPrec * (self.avo / self.VmAlpha)**(2/3) + elif self.GB[p].nucleationSiteType == GBFactors.GRAIN_EDGES: + edgePrec = np.sum([np.sqrt(1 - self.GB[p2].GBk**2) * self.PBM[p2].FirstMomentFromN(x[p2]) for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.GRAIN_EDGES]) + nucleationSites += self.GBedgeN0 - edgePrec * (self.avo / self.VmAlpha)**(1/3) + elif self.GB[p].nucleationSiteType == GBFactors.GRAIN_CORNERS: + cornerPrec = np.sum([self.PBM[p2].ZeroMomentFromN(x[p2]) for p2 in range(len(self.phases)) if self.GB[p2].nucleationSiteType == GBFactors.GRAIN_CORNERS]) + nucleationSites += self.GBcornerN0 - cornerPrec + + if nucleationSites < 0: + nucleationSites = 0 + self._currY[self.NUC_RATE][0,p] *= nucleationSites + + def _calcMassBalance(self, t, x): + ''' + Mass balance to find matrix composition with new particle size distribution + + This also includes: volume fraction, precipitate density, average radius, average aspect ratio and sum of precipitate composition + ''' + fBeta = np.zeros((1,len(self.phases))) + fConc = np.zeros((1, len(self.phases),self.numberOfElements)) + precDens = np.zeros((1,len(self.phases))) + avgR = np.zeros((1,len(self.phases))) + avgAR = np.zeros((1,len(self.phases))) + xComp = np.zeros((1,self.numberOfElements)) + + for p in range(len(self.phases)): + volRatio = self.VmAlpha / self.VmBeta[p] + Ntot = self.PBM[p].ZeroMomentFromN(x[p]) + #If no precipitates, then avgR, avgAR, precDens, fConc and fBeta for phase p is all 0 + if Ntot == 0: + continue + RadSum = self.PBM[p].MomentFromN(x[p], 1) + ARsum = self.PBM[p].WeightedMomentFromN(x[p], 0, self.shapeFactors[p].aspectRatio(self.PBM[p].PSDsize)) + fBeta[0,p] = np.amin([volRatio * self.GB[p].volumeFactor * self.PBM[p].ThirdMomentFromN(x[p]), 1]) + + ''' + Concentration of the precipitates - needed to get matrix composition + + For a line compound with composition x^beta, this boils down to: + x_0 = (1-f_v) * x^inf + f_v * x^beta + Where x_0 is initial composition, f_v is volume fraction and x^inf is matrix composition + + For non-stoichiometric compounds, we want to integrate the precipitate composition as a function of radius + We'll call this term f_conc (fraction + concentration of precipitates), so: + x_0 = (1-f_v) * x^inf + f_conc + + For infinite precipitate diffusion, the concentration of a single precipitate is assumed to be homogenous + f_conc = r_vol * vol_factor * sum(n_i * R_i^3 * x_i^beta) + Where r_vol is V^alpha / V^beta and vol_factor is a factor for converting R^3 to volume (for sphere, this is 4*pi/3) + + For no diffusion in precipitate, the concentration depends on the history of the precipitate compositions and growth rate + We just have to convert the summation to an integral of the time derivative of the terms inside + f_conc = r_vol * vol_factor * sum(int(d(n_i * R_i^3 * x_i^beta)/dt, dt)) + We'll assume x_i^beta is constant with time (for 3 or more components, this is not true, but assume it doesn't change significantly per iteration - it'll also be a lot harder to account for) + d(f_conc)/dt = r_vol * vol_factor * sum(d(n_i)/dt * R_i^3 * x_i^beta + 3 * R_i^3 * d(R_i)/dt * n_i * x_i^beta) + d(n_i)/dt is the change in precipitates, since we don't record this, this is just (x[p] - self.PBM[p].PSD) / dt - with x[p] being the new number density for phase p + d(R_i)/dt is the growth rate, however, since we use a eulerian solver, this corresponds to the growth rate of the bins themselves, which is 0 + If we were to use a langrangian solver, then d(n_i)/dt would be 0 (since the density in each bin would be constant) and d(R_i)/dt would be the growth rate at R_i + Then we can calculate f_conc per iteration as a time integration like we do with some of the other variables + ''' + if self.infinitePrecipitateDiffusion[p]: + compAvg = 0.5 * (self.PSDXbeta[p][:-1] + self.PSDXbeta[p][1:]) + for e in range(self.numberOfElements): + fConc[0,p,e] = volRatio * self.GB[p].volumeFactor * self.PBM[p].WeightedMomentFromN(x[p], 3, compAvg[:,e]) + else: + midX = (self.PSDXbeta[p][1:] + self.PSDXbeta[p][:-1]) / 2 + for e in range(self.numberOfElements): + #y = volRatio * self.GB[p].volumeFactor * np.sum((3*midG*self.PBM[p].PSDsize**2*self.PBM[p].PSD*dt + self.PBM[p].PSDsize**3*(x[p]-self.PBM[p].PSD))*midX[:,e]) + y = volRatio * self.GB[p].volumeFactor * np.sum((self.PBM[p].PSDsize**3*(x[p]-self.PBM[p].PSD))*midX[:,e]) + fConc[0,p,e] = self.fConc[self.n,p,e] + y + + #Only record these terms if there are non-zero number of precipitates + #Otherwise we will be dividing by 0 for avgR and avgAR + # Argueably, RadSum and ARsum would be 0 if Ntot is 0, so it should be fine to do this + if Ntot > self.minNucleateDensity: + avgR[0,p] = RadSum / Ntot + precDens[0,p] = Ntot + avgAR[0,p] = ARsum / Ntot + else: + avgR[0,p] = 0 + precDens[0,p] = 0 + avgAR[0,p] = 0 + + #Not sure if needed, but just in case + if self.betaFrac[self.n,p] == 1: + fBeta[0,p] = 1 + + if np.sum(fBeta[0]) < 1: + xComp[0] = (self.xComp[0] - np.sum(fConc[0], axis=0)) / (1 - np.sum(fBeta[0])) + xComp[0,xComp[0] < 0] = self.minComposition + + self._currY[self.VOL_FRAC] = fBeta + self._currY[self.FCONC] = fConc + self._currY[self.PREC_DENS] = precDens + self._currY[self.R_AVG] = avgR + self._currY[self.AR_AVG] = avgAR + self._currY[self.COMPOSITION] = xComp + + def getCurrentX(self): + ''' + Returns current value of time and X + In this case, X is the particle size distribution for each phase + ''' + return self.time[self.n], [self.PBM[p].PSD for p in range(len(self.phases))] + + def _getdXdt(self, t, x): + ''' + Returns dn_i/dt for each PBM of each phase + ''' + return [self.PBM[p].getdXdtEuler(self.growth[p], self._currY[self.NUC_RATE][0,p], self._currY[self.R_NUC][0,p], x[p]) for p in range(len(self.phases))] + + def correctdXdt(self, dt, x, dXdt): + ''' + Corrects dXdt with the newly found dt, this adjusts the fluxes at the ends of the PBM so that we don't get negative bins + ''' + for p in range(len(self.phases)): + dXdt[p] = self.PBM[p].correctdXdtEuler(dt, self.growth[p], self._currY[self.NUC_RATE][0,p], self._currY[self.R_NUC][0,p], x[p]) + + def _singleGrowthBinary(self, p): + ''' + Calculates growth rate for a single phase + This is separated from _growthRateBinary since it's used in _calculatePSD + + Matrix/precipitate composition are not calculated here since it's + already calculated in createLookup + ''' + xComp = self._currY[self.COMPOSITION][0] + T = self._currY[self.TEMPERATURE][0] + growthRate = np.zeros(self.PBM[p].bins + 1) + #If no precipitates are stable, don't calculate growth rate and set PSD to 0 + #This should represent dissolution of the precipitates + if self.RdrivingForceIndex[p]+1 < len(self.PSDXalpha[p][:,0]): + superSaturation = (xComp[0] - self.PSDXalpha[p][:,0]) / (self.VmAlpha * self.PSDXbeta[p][:,0] / self.VmBeta[p] - self.PSDXalpha[p][:,0]) + growthRate = self.shapeFactors[p].kineticFactor(self.PBM[p].PSDbounds) * self.Diffusivity(xComp[0], T) * superSaturation / (self.effDiffDistance(superSaturation) * self.PBM[p].PSDbounds) + + return growthRate + + def _growthRateBinary(self): + ''' + Determines current growth rate of all particle size classes in a binary system + ''' + #Update equilibrium interfacial compositions + #This will be override if createLookup is called + T = self._currY[self.TEMPERATURE] + self.dTemp += T - self.temperature[self.n] + if np.abs(self.dTemp) > self.maxTempChange: + xEqAlpha, xEqBeta = self.createLookup() + else: + xEqAlpha, xEqBeta = np.array([self.xEqAlpha[self.n]]), np.array([self.xEqBeta[self.n]]) + self.dTemp = 0 + self._currY[self.EQ_COMP_ALPHA] = xEqAlpha + self._currY[self.EQ_COMP_BETA] = xEqBeta + + #growthRate = np.zeros((len(self.phases), self.bins + 1)) + growthRate = [] + for p in range(len(self.phases)): + growthRate.append(self._singleGrowthBinary(p)) + + self.growth = growthRate + + def _singleGrowthMulti(self, p): + ''' + Calculates growth rate for a single phase + This is separated from _growthRateMulti since it's used in _calculatePSD + + This will also calculate the matrix/precipitate composition + for the radius in the PSD as well as equilibrium (infinite radius) + ''' + xComp = self._currY[self.COMPOSITION][0] + dGs = self._currY[self.DRIVING_FORCE][0] + T = self._currY[self.TEMPERATURE][0] + precDens = self._currY[self.PREC_DENS][0] + if dGs[p] < 0 and precDens[p] <= 0: + xEqAlpha = np.zeros(self.numberOfElements) + xEqBeta = np.zeros(self.numberOfElements) + growthRate = np.zeros(self.PBM[p].bins + 1) + return growthRate, xEqAlpha, xEqBeta + + + growth, xAlpha, xBeta, xEqAlpha, xEqBeta = self.interfacialComposition[p](xComp, T, dGs[p] * self.VmBeta[p], self.PBM[p].PSDbounds, self.particleGibbs(phase=self.phases[p]), searchDir = self._precBetaTemp[p]) + + #If two-phase equilibrium not found, two possibilities - precipitates are unstable or equilibrium calculations didn't converge + #We try to avoid this as much as possible to where if precipitates are unstable, then attempt to get a growth rate from the nearest composition on the phase boundary + #And if equilibrium calculations didn't converge, try to use the previous calculations assuming the new composition is close to the previous + if growth is None: + #If driving force is negative, then precipitates are unstable + if dGs[p] < 0: + #Completely reset the PBM, including bounds and number of bins + #In case nucleation occurs again, the PBM will be at a good length scale + self.PSDXalpha[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) + self.PSDXbeta[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) + xEqAlpha = np.zeros(self.numberOfElements) + xEqBeta = np.zeros(self.numberOfElements) + growthRate = np.zeros(self.PBM[p].bins + 1) + #Else, equilibrium did not converge and just use previous values + #Only the growth rate needs to be updated, since all other terms are previous + #Also revert the PSD in case this function was called to adjust for the new PSD bins + else: + growthRate = self.growth[p] + else: + #Update interfacial composition for each precipitate size + self.PSDXalpha[p] = xAlpha + self.PSDXbeta[p] = xBeta + + #Add shape factor to growth rate - will need to add effective diffusion distance as well + growthRate = self.shapeFactors[p].kineticFactor(self.PBM[p].PSDbounds) * growth + + return growthRate, xEqAlpha, xEqBeta + + def _growthRateMulti(self): + ''' + Determines current growth rate of all particle size classes in a multicomponent system + ''' + xEqAlpha = np.zeros((1,len(self.phases), self.numberOfElements)) + xEqBeta = np.zeros((1,len(self.phases), self.numberOfElements)) + growthRate = [] + for p in range(len(self.phases)): + growthRate_p, xEqAlpha_p, xEqBeta_p = self._singleGrowthMulti(p) + growthRate.append(growthRate_p) + xEqAlpha[0,p] = xEqAlpha_p + xEqBeta[0,p] = xEqBeta_p + self._currY[self.EQ_COMP_ALPHA] = xEqAlpha + self._currY[self.EQ_COMP_BETA] = xEqBeta + self.growth = growthRate + + def _updateParticleSizeDistribution(self, t, x): + ''' + Updates particle size distribution with new x + + Steps: + 1. Check if growth rate calculation failed with negative driving force + We'll reset the PBM since we can't do much from here, but the chances of this happening should be pretty low + 2. Update the PBM with new x + 3. Check if the PBM needs to adjust the size class + If so, then update the cached aspect ratio and precipitate composition with the new size classes + 4. Remove precipitates below a certain threshold (RdrivingForceIndex and minRadius) + 5. Calculate the dissolution index (index at which below are not considered when calculating dt) + This is to prevent very small dt as the growth rate increases rapidly when R->0 + ''' + for p in range(len(self.phases)): + if self.dGs[self.n,p] < 0 and np.all(self.xEqAlpha[self.n,p,:] == 0): + self.PBM[p].reset() + self.PSDXalpha[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) + self.PSDXbeta[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) + self.growth[p] = np.zeros(self.PBM[p].bins+1) + continue + self.PBM[p].UpdatePBMEuler(t, x[p]) + change, addedIndices = self.PBM[p].adjustSizeClassesEuler(all(self.growth[p] < 0)) + if change: + if self.calculateAspectRatio[p]: + self.eqAspectRatio[p] = self.strainEnergy[p].eqAR_bySearch(self.PBM[p].PSDbounds, self.gamma[p], self.shapeFactors[p]) + else: + self.eqAspectRatio[p] = self.shapeFactors[p].aspectRatio(self.PBM[p].PSDbounds) + + self.growth[p] = np.zeros(len(self.PBM[p].PSDbounds)) + if self.numberOfElements == 1: + if addedIndices is None: + #This is very slow to do + self.createLookup() + else: + self.PSDXalpha[p] = np.concatenate((self.PSDXalpha[p], np.zeros((self.PBM[p].bins+1 - len(self.PSDXalpha[p]),1)))) + self.PSDXbeta[p] = np.concatenate((self.PSDXbeta[p], np.zeros((self.PBM[p].bins+1 - len(self.PSDXbeta[p]),1)))) + self.PSDXalpha[p][addedIndices:,0], self.PSDXbeta[p][addedIndices:,0] = self.interfacialComposition[p](self.temperature[self.n], self.particleGibbs(self.PBM[p].PSDbounds[addedIndices:], self.phases[p])) + else: + self.PSDXalpha[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) + self.PSDXbeta[p] = np.zeros((self.PBM[p].bins + 1, self.numberOfElements)) + self._growthRate() + self.PBM[p].PSD[:self.RdrivingForceIndex[p]+1] = 0 + self.PBM[p].PSD[self.PBM[p].PSDsize < self.minRadius] = 0 + self.dissolutionIndex[p] = self.PBM[p].getDissolutionIndex(self.maxDissolution, self.RdrivingForceIndex[p]) + #self.PBM[p].PSD[:self.dissolutionIndex[p]] = 0 + + def plot(self, axes, variable, bounds = None, timeUnits = 's', radius='spherical', *args, **kwargs): + ''' + Plots model outputs + + Parameters + ---------- + axes : Axis + variable : str + Specified variable to plot + Options are 'Volume Fraction', 'Total Volume Fraction', 'Critical Radius', + 'Average Radius', 'Volume Average Radius', 'Total Average Radius', + 'Total Volume Average Radius', 'Aspect Ratio', 'Total Aspect Ratio' + 'Driving Force', 'Nucleation Rate', 'Total Nucleation Rate', + 'Precipitate Density', 'Total Precipitate Density', + 'Temperature', 'Composition', + 'Size Distribution', 'Size Distribution Curve', + 'Size Distribution KDE', 'Size Distribution Density + 'Interfacial Composition Alpha', 'Interfacial Composition Beta' + + Note: for multi-phase simulations, adding the word 'Total' will + sum the variable for all phases. Without the word 'Total', the variable + for each phase will be plotted separately + + Interfacial composition terms are more relavent for binary systems than + for multicomponent systems + + bounds : tuple (optional) + Limits on the x-axis (float, float) or None (default, this will set bounds to (initial time, final time)) + radius : str (optional) + For non-spherical precipitates, plot the Average Radius by the - + Equivalent spherical radius ('spherical') + Short axis ('short') + Long axis ('long') + Note: Total Average Radius and Volume Average Radius will still use the equivalent spherical radius + *args, **kwargs - extra arguments for plotting + ''' + plotEuler(self, axes, variable, bounds, timeUnits, radius, *args, **kwargs) + + + \ No newline at end of file diff --git a/kawin/precipitation/Plot.py b/kawin/precipitation/Plot.py new file mode 100644 index 0000000..1674096 --- /dev/null +++ b/kawin/precipitation/Plot.py @@ -0,0 +1,400 @@ +import numpy as np +import matplotlib.pyplot as plt + +def getTimeAxis(precModel, timeUnits='s', bounds=None): + ''' + Returns scaling factor, label and x-limits depending on units of time + + Parameters + ---------- + timeUnits : str + 's' / 'sec' / 'seconds' - seconds + 'min' / 'minutes' - minutes + 'h' / 'hrs' / 'hours' - hours + ''' + timeScale = 1 + timeLabel = 'Time (s)' + if 'min' in timeUnits: + timeScale = 1/60 + timeLabel = 'Time (min)' + if 'h' in timeUnits: + timeScale = 1/3600 + timeLabel = 'Time (hrs)' + + if bounds is None: + bounds = [timeScale*1e-5*precModel.time[-1], timeScale * precModel.time[-1]] + + return timeScale, timeLabel, bounds + +def plotBase(precModel, axes, variable, bounds = None, timeUnits = 's', radius='spherical', *args, **kwargs): + ''' + Plots model outputs + + Parameters + ---------- + axes : Axis + variable : str + Specified variable to plot + Options are 'Volume Fraction', 'Total Volume Fraction', 'Critical Radius', + 'Average Radius', 'Volume Average Radius', 'Total Average Radius', + 'Total Volume Average Radius', 'Aspect Ratio', 'Total Aspect Ratio' + 'Driving Force', 'Nucleation Rate', 'Total Nucleation Rate', + 'Precipitate Density', 'Total Precipitate Density', + 'Temperature' and 'Composition' + + Note: for multi-phase simulations, adding the word 'Total' will + sum the variable for all phases. Without the word 'Total', the variable + for each phase will be plotted separately + + bounds : tuple (optional) + Limits on the x-axis (float, float) or None (default, this will set bounds to (initial time, final time)) + timeUnits : str (optional) + Plot time dependent variables per seconds ('s'), minutes ('min') or hours ('h') + radius : str (optional) + For non-spherical precipitates, plot the Average Radius by the - + Equivalent spherical radius ('spherical') + Short axis ('short') + Long axis ('long') + Note: Total Average Radius and Volume Average Radius will still use the equivalent spherical radius + *args, **kwargs - extra arguments for plotting + ''' + timeScale, timeLabel, bounds = getTimeAxis(precModel, timeUnits, bounds) + + axes.set_xlabel(timeLabel) + axes.set_xlim(bounds) + + labels = { + 'Volume Fraction': 'Volume Fraction', + 'Total Volume Fraction': 'Volume Fraction', + 'Critical Radius': 'Critical Radius (m)', + 'Average Radius': 'Average Radius (m)', + 'Volume Average Radius': 'Volume Average Radius (m)', + 'Total Average Radius': 'Average Radius (m)', + 'Total Volume Average Radius': 'Volume Average Radius (m)', + 'Aspect Ratio': 'Mean Aspect Ratio', + 'Total Aspect Ratio': 'Mean Aspect Ratio', + 'Driving Force': 'Driving Force (J/m$^3$)', + 'Nucleation Rate': 'Nucleation Rate (#/m$^3$-s)', + 'Total Nucleation Rate': 'Nucleation Rate (#/m$^3$-s)', + 'Precipitate Density': 'Precipitate Density (#/m$^3$)', + 'Total Precipitate Density': 'Precipitate Density (#/m$^3$)', + 'Temperature': 'Temperature (K)', + 'Composition': 'Matrix Composition (at.%)', + 'Eq Composition Alpha': 'Matrix Composition (at.%)', + 'Eq Composition Beta': 'Matrix Composition (at.%)', + 'Supersaturation': 'Supersaturation', + 'Eq Volume Fraction': 'Volume Fraction' + } + + totalVariables = ['Total Volume Fraction', 'Total Average Radius', 'Total Aspect Ratio', \ + 'Total Nucleation Rate', 'Total Precipitate Density', 'Total Volume Average Radius'] + singleVariables = ['Volume Fraction', 'Critical Radius', 'Average Radius', 'Aspect Ratio', \ + 'Driving Force', 'Nucleation Rate', 'Precipitate Density', 'Volume Average Radius'] + eqCompositions = ['Eq Composition Alpha', 'Eq Composition Beta'] + saturations = ['Supersaturation', 'Eq Volume Fraction'] + + if variable == 'Temperature': + plotTemperature(precModel, timeScale, labels, variable, axes, *args, **kwargs) + elif variable == 'Composition': + plotCompositions(precModel, timeScale, labels, variable, axes, *args, **kwargs) + elif variable in eqCompositions: + plotEqCompositions(precModel, timeScale, labels, variable, axes, *args, **kwargs) + elif variable in saturations: + plotSaurations(precModel, timeScale, labels, variable, axes, *args, **kwargs) + elif variable in singleVariables: + plotSingleVariables(precModel, timeScale, radius, labels, variable, axes, *args, **kwargs) + elif variable in totalVariables: + plotTotalVariables(precModel, timeScale, labels, variable, axes, *args, **kwargs) + +def plotEuler(precModel, axes, variable, bounds = None, timeUnits = 's', radius='spherical', *args, **kwargs): + ''' + Plots model outputs + + Parameters + ---------- + axes : Axis + variable : str + Specified variable to plot + Options are 'Volume Fraction', 'Total Volume Fraction', 'Critical Radius', + 'Average Radius', 'Volume Average Radius', 'Total Average Radius', + 'Total Volume Average Radius', 'Aspect Ratio', 'Total Aspect Ratio' + 'Driving Force', 'Nucleation Rate', 'Total Nucleation Rate', + 'Precipitate Density', 'Total Precipitate Density', + 'Temperature', 'Composition', + 'Size Distribution', 'Size Distribution Curve', + 'Size Distribution KDE', 'Size Distribution Density + 'Interfacial Composition Alpha', 'Interfacial Composition Beta' + + Note: for multi-phase simulations, adding the word 'Total' will + sum the variable for all phases. Without the word 'Total', the variable + for each phase will be plotted separately + + Interfacial composition terms are more relavent for binary systems than + for multicomponent systems + + bounds : tuple (optional) + Limits on the x-axis (float, float) or None (default, this will set bounds to (initial time, final time)) + radius : str (optional) + For non-spherical precipitates, plot the Average Radius by the - + Equivalent spherical radius ('spherical') + Short axis ('short') + Long axis ('long') + Note: Total Average Radius and Volume Average Radius will still use the equivalent spherical radius + *args, **kwargs - extra arguments for plotting + ''' + sizeDistributionVariables = ['Size Distribution', 'Size Distribution Curve', 'Size Distribution KDE', 'Size Distribution Density'] + compositionVariables = ['Interfacial Composition Alpha', 'Interfacial Composition Beta'] + + scale = [] + for p in range(len(precModel.phases)): + if precModel.GB[p].nucleationSiteType == precModel.GB[p].BULK or precModel.GB[p].nucleationSiteType == precModel.GB[p].DISLOCATION: + if radius == 'spherical': + scale.append(precModel._GBareaRemoval(p) * np.ones(len(precModel.PBM[p].PSDbounds))) + else: + scale.append(1/precModel.shapeFactors[p].eqRadiusFactor(precModel.PBM[p].PSDbounds)) + if radius == 'long': + scale.append(precModel.shapeFactors[p].aspectRatio(precModel.PBM[p].PSDbounds) / precModel.shapeFactors[p].eqRadiusFactor(precModel.PBM[p].PSDbounds)) + else: + scale.append(precModel._GBareaRemoval(p) * np.ones(len(precModel.PBM[p].PSDbounds))) + + if variable in compositionVariables: + plotEulerComposition(precModel, variable, axes, *args, **kwargs) + elif variable in sizeDistributionVariables: + plotEulerSizeDistribution(precModel, scale, variable, axes, *args, **kwargs) + elif variable == 'Cumulative Size Distribution': + plotEulerCumulativeSizeDistribution(precModel, scale, variable, axes, *args, **kwargs) + elif variable == 'Aspect Ratio Distribution': + plotEulerAspectRatioDistribution(precModel, scale, variable, axes, *args, **kwargs) + else: + plotBase(precModel, axes, variable, bounds, timeUnits, radius, *args, **kwargs) + +def plotTemperature(precModel, timeScale, labels, variable, axes, *args, **kwargs): + axes.semilogx(timeScale * precModel.time, precModel.temperature, *args, **kwargs) + axes.set_ylabel(labels[variable]) + +def plotCompositions(precModel, timeScale, labels, variable, axes, *args, **kwargs): + if precModel.numberOfElements == 1: + axes.semilogx(timeScale * precModel.time, precModel.xComp[:,0], *args, **kwargs) + axes.set_ylabel('Matrix Composition (at.% ' + precModel.elements[0] + ')') + else: + #If kwargs has label, add it as an extension to the label we add + #And also pop label from kwargs so we don't have double arguments + label_ext = '' + if 'label' in kwargs: + label_ext = '_' + kwargs['label'] + kwargs.pop('label') + for i in range(precModel.numberOfElements): + #Keep color consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement + if 'color' in kwargs: + axes.semilogx(timeScale * precModel.time, precModel.xComp[:,i], label=precModel.elements[i] + label_ext, *args, **kwargs) + else: + axes.semilogx(timeScale * precModel.time, precModel.xComp[:,i], label=precModel.elements[i] + label_ext, color='C'+str(i), *args, **kwargs) + axes.legend() + axes.set_ylabel(labels[variable]) + yRange = [np.amin(precModel.xComp), np.amax(precModel.xComp)] + axes.set_ylim([yRange[0] - 0.1 * (yRange[1] - yRange[0]), yRange[1] + 0.1 * (yRange[1] - yRange[0])]) + + +def plotEqCompositions(precModel, timeScale, labels, variable, axes, *args, **kwargs): + if variable == 'Eq Composition Alpha': + plotVariable = precModel.xEqAlpha + elif variable == 'Eq Composition Beta': + plotVariable = precModel.xEqBeta + + if len(precModel.phases) == 1: + if precModel.numberOfElements == 1: + axes.semilogx(timeScale * precModel.time, plotVariable[:,0,0], *args, **kwargs) + axes.set_ylabel('Matrix Composition (at.% ' + precModel.elements[0] + ')') + else: + for i in range(precModel.numberOfElements): + #Keep color consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement + if 'color' in kwargs: + axes.semilogx(timeScale * precModel.time, plotVariable[:,0,i], label=precModel.elements[i]+'_Eq', *args, **kwargs) + else: + axes.semilogx(timeScale * precModel.time, plotVariable[:,0,i], label=precModel.elements[i]+'_Eq', color='C'+str(i), *args, **kwargs) + axes.legend() + axes.set_ylabel(labels[variable]) + else: + if precModel.numberOfElements == 1: + for p in range(len(precModel.phases)): + #Keep color somewhat consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement + if 'color' in kwargs: + axes.semilogx(timeScale * precModel.time, plotVariable[:,p,0], label=precModel.phases[p]+'_Eq', *args, **kwargs) + else: + axes.semilogx(timeScale * precModel.time, plotVariable[:,p,0], label=precModel.phases[p]+'_Eq', color='C'+str(p), *args, **kwargs) + axes.legend() + axes.set_ylabel('Matrix Composition (at.% ' + precModel.elements[0] + ')') + else: + cIndex = 0 + for p in range(len(precModel.phases)): + for i in range(precModel.numberOfElements): + #Keep color somewhat consistent between Composition, Eq Composition Alpha and Eq Composition Beta if color isn't passed as an arguement + if 'color' in kwargs: + axes.semilogx(timeScale * precModel.time, plotVariable[:,p,i], label=precModel.phases[p]+'_'+precModel.elements[i]+'_Eq', *args, **kwargs) + else: + axes.semilogx(timeScale * precModel.time, plotVariable[:,p,i], label=precModel.phases[p]+'_'+precModel.elements[i]+'_Eq', color='C'+str(cIndex), *args, **kwargs) + cIndex += 1 + axes.legend() + axes.set_ylabel(labels[variable]) + +def plotSaurations(precModel, timeScale, labels, variable, axes, *args, **kwargs): + #Since supersaturation is calculated in respect to the tie-line, it is the same for each element + #Thus only a single element is needed + plotVariable = np.zeros(precModel.betaFrac.shape) + for p in range(len(precModel.phases)): + if variable == 'Eq Volume Fraction': + num = precModel.xComp[0,0] - precModel.xEqAlpha[:,p,0] + else: + num = precModel.xComp[:,0] - precModel.xEqAlpha[:,p,0] + den = precModel.xEqBeta[:,p,0] - precModel.xEqAlpha[:,p,0] + #If precipitate is unstable, both xEqAlpha and xEqBeta are set to 0 + #For these cases, change the values of numerator and denominator so that supersaturation is 0 instead of undefined + num[den == 0] = 0 + den[den == 0] = 1 + plotVariable[:,p] = num / den + + if len(precModel.phases) == 1: + axes.semilogx(timeScale * precModel.time, plotVariable[:,0], *args, **kwargs) + else: + for p in range(len(precModel.phases)): + if 'color' in kwargs: + axes.semilogx(timeScale * precModel.time, plotVariable[:,p], label=precModel.phases[p], *args, **kwargs) + else: + axes.semilogx(timeScale * precModel.time, plotVariable[:,p], label=precModel.phases[p], color='C'+str(p), *args, **kwargs) + axes.legend() + axes.set_ylabel(labels[variable]) + +def plotSingleVariables(precModel, timeScale, radius, labels, variable, axes, *args, **kwargs): + if variable == 'Volume Fraction': + plotVariable = precModel.betaFrac + elif variable == 'Critical Radius': + plotVariable = precModel.Rcrit + elif variable == 'Average Radius': + plotVariable = precModel.avgR + for p in range(len(precModel.phases)): + if precModel.GB[p].nucleationSiteType == precModel.GB[p].BULK or precModel.GB[p].nucleationSiteType == precModel.GB[p].DISLOCATION: + if radius != 'spherical': + plotVariable[p] /= precModel.shapeFactors[p].eqRadiusFactor(precModel.avgR[p]) + if radius == 'long': + plotVariable[p] *= precModel.avgAR[p] + else: + plotVariable[p] *= precModel._GBareaRemoval(p) + elif variable == 'Volume Average Radius': + plotVariable = np.zeros(precModel.betaFrac.shape) + indices = precModel.precipitateDensity > 0 + plotVariable[indices] = np.cbrt(precModel.betaFrac[indices] / precModel.precipitateDensity[indices] / (4/3*np.pi)) + elif variable == 'Aspect Ratio': + plotVariable = precModel.avgAR + elif variable == 'Driving Force': + plotVariable = precModel.dGs + elif variable == 'Nucleation Rate': + plotVariable = precModel.nucRate + elif variable == 'Precipitate Density': + plotVariable = precModel.precipitateDensity + + if (len(precModel.phases)) == 1: + axes.semilogx(timeScale * precModel.time, plotVariable[:,0], *args, **kwargs) + else: + for p in range(len(precModel.phases)): + axes.semilogx(timeScale * precModel.time, plotVariable[:,p], label=precModel.phases[p], color='C'+str(p), *args, **kwargs) + axes.legend() + axes.set_ylabel(labels[variable]) + yb = 1 if variable == 'Aspect Ratio' else 0 + axes.set_ylim([yb, 1.1 * np.amax(plotVariable)]) + +def plotTotalVariables(precModel, timeScale, labels, variable, axes, *args, **kwargs): + if variable == 'Total Volume Fraction': + plotVariable = np.sum(precModel.betaFrac, axis=1) + elif variable == 'Total Average Radius': + totalN = np.sum(precModel.precipitateDensity, axis=1) + totalN[totalN == 0] = 1 + totalR = np.sum(precModel.avgR * precModel.precipitateDensity, axis=1) + plotVariable = totalR / totalN + elif variable == 'Total Volume Average Radius': + totalN = np.sum(precModel.precipitateDensity, axis=1) + totalN[totalN == 0] = 1 + totalVol = np.sum(precModel.betaFrac, axis=1) + plotVariable = np.cbrt(totalVol / totalN) + elif variable == 'Total Aspect Ratio': + totalN = np.sum(precModel.precipitateDensity, axis=1) + totalN[totalN == 0] = 1 + totalAR = np.sum(precModel.avgAR * precModel.precipitateDensity, axis=1) + plotVariable = totalAR / totalN + elif variable == 'Total Nucleation Rate': + plotVariable = np.sum(precModel.nucRate, axis=1) + elif variable == 'Total Precipitate Density': + plotVariable = np.sum(precModel.precipitateDensity, axis=1) + + axes.semilogx(timeScale * precModel.time, plotVariable, *args, **kwargs) + axes.set_ylabel(labels[variable]) + yb = 1 if variable == 'Total Aspect Ratio' else 0 + axes.set_ylim(bottom=yb) + +def plotEulerComposition(precModel, variable, axes, *args, **kwargs): + if variable == 'Interfacial Composition Alpha': + yVar = precModel.PSDXalpha + ylabel = 'Composition in Alpha phase' + else: + yVar = precModel.PSDXbeta + ylabel = 'Composition in Beta Phase' + + if (len(precModel.phases)) == 1: + axes.semilogx(precModel.PBM[0].PSDbounds, yVar[0], *args, **kwargs) + else: + for p in range(len(precModel.phases)): + axes.plot(precModel.PBM[p].PSDbounds, yVar[p], label=precModel.phases[p], *args, **kwargs) + axes.legend() + axes.set_xlim([precModel.PBM[0].PSDbounds[0], precModel.PBM[0].PSDbounds[-1]]) + axes.set_xlabel('Radius (m)') + axes.set_ylabel(ylabel) + +def plotEulerSizeDistribution(precModel, scale, variable, axes, *args, **kwargs): + ylabel = 'Frequency (#/$m^3$)' + if variable == 'Size Distribution': + functionName = 'PlotHistogram' + elif variable == 'Size Distribution KDE': + functionName = 'PlotKDE' + elif variable == 'Size Distribution Density': + functionName = 'PlotDistributionDensity' + ylabel = 'Distribution Density (#/$m^4$)' + else: + functionName = 'PlotCurve' + + if len(precModel.phases) == 1: + getattr(precModel.PBM[0], functionName)(axes, scale=scale[0], *args, **kwargs) + else: + for p in range(len(precModel.phases)): + getattr(precModel.PBM[p], functionName)(axes, label=precModel.phases[p], scale=scale[p], *args, **kwargs) + axes.legend() + axes.set_xlabel('Radius (m)') + axes.set_ylabel(ylabel) + axes.set_xlim([0, np.amax([pb.max for pb in precModel.PBM])]) + if variable == 'Size Distribution Density': + axes.set_ylim([0, 1.1*np.amax(np.concatenate(([np.amax(pb.PSD/(pb.PSDbounds[1:] - pb.PSDbounds[:-1])) for pb in precModel.PBM], [1])))]) + else: + axes.set_ylim([0, 1.1*np.amax(np.concatenate(([np.amax(pb.PSD) for pb in precModel.PBM], [1])))]) + +def plotEulerCumulativeSizeDistribution(precModel, scale, variable, axes, *args, **kwargs): + ylabel = 'CDF' + if len(precModel.phases) == 1: + precModel.PBM[0].PlotCDF(axes, scale=scale[0], *args, **kwargs) + else: + for p in range(len(precModel.phases)): + precModel.PBM[p].PlotCDF(axes, label=precModel.phases[p], scale=scale[p], *args, **kwargs) + axes.legend() + axes.set_xlabel('Radius (m)') + axes.set_ylabel(ylabel) + axes.set_xlim([0, np.amax([pb.max for pb in precModel.PBM])]) + +def plotEulerAspectRatioDistribution(precModel, scale, variable, axes, *args, **kwargs): + if len(precModel.phases) == 1: + axes.plot(precModel.PBM[0].PSDbounds * np.interp(precModel.PBM[0].PSDbounds, precModel.PBM[0].PSDbounds, scale[0]), precModel.eqAspectRatio[0], *args, **kwargs) + else: + for p in range(len(precModel.phases)): + axes.plot(precModel.PBM[p].PSDbounds * np.interp(precModel.PBM[p].PSDbounds, precModel.PBM[p].PSDbounds, scale[p]), precModel.eqAspectRatio[p], label=precModel.phases[p], *args, **kwargs) + axes.legend() + axes.set_xlim([0, np.amax([precModel.PBM[p].PSDbounds * np.interp(precModel.PBM[p].PSDbounds, precModel.PBM[p].PSDbounds, scale[p]) for p in range(len(precModel.phases))])]) + axes.set_ylim(bottom=1) + axes.set_xlabel('Radius (m)') + axes.set_ylabel('Aspect ratio distribution') + diff --git a/kawin/PopulationBalance.py b/kawin/precipitation/PopulationBalance.py similarity index 52% rename from kawin/PopulationBalance.py rename to kawin/precipitation/PopulationBalance.py index 5b36935..4a4dbd8 100644 --- a/kawin/PopulationBalance.py +++ b/kawin/precipitation/PopulationBalance.py @@ -62,14 +62,229 @@ def __init__(self, cMin = 1e-10, cMax = 1e-9, bins = 150, minBins = 100, maxBins self.reset() self._adaptiveBinSize = True + + self._record = False + self._recordedBins = None + self._recordedPSD = None + self._recordedTime = None + + def reset(self, resetBounds = True): + ''' + Resets the PSD to 0 and resets bin size and number of bins to original values + This will remove any size classes that were added since initialization + ''' + if resetBounds: + self.min = self.originalMin + self.max = self.originalMax + self.bins = self.originalBins + self.PSDbounds = np.linspace(self.min, self.max, self.bins+1) + self.PSDsize = 0.5 * (self.PSDbounds[:-1] + self.PSDbounds[1:]) + + self.PSD = np.zeros(self.bins) + + #Hidden variable for use in KWNEuler when adaptive time stepping is enabled + #This allows for PSD to revert to its previous value if a time constraint is not met + self._prevPSD = np.zeros(self.bins) + self._prevPSDbounds = np.zeros(self.bins+1) + + #Temporary storage for net flux + #This is used to correct the fluxes once the time step is known + self._netFlux = None + + def enableRecording(self): + ''' + Enables recording of particle size distribution per iteration + + The initial data in the recorded bin is t = 0, N_i = 0 + + The size of the recorded particle size distribution will be (n x max bins) + Where n in the number of iterations + max bins is the maximum number of bins, if the current number is smaller, the rest of the array will be 0 + ''' + self._record = True + self._recordedBins = np.zeros((1, self.maxBins + 1)) + self._recordedPSD = np.zeros((1, self.maxBins)) + self._recordedTime = np.zeros(1) + + def resetRecordedData(self): + ''' + If recording, then reset the recorded bins to the original size (starting with t = 0, N_i = 0) + If not recording, then clear the recorded data + ''' + if self._record: + self._recordedBins = np.zeros((1, self.maxBins + 1)) + self._recordedPSD = np.zeros((1, self.maxBins)) + self._recordedTime = np.zeros(1) + else: + self._recordedBins = None + self._recordedPSD = None + self._recordedTime = None + + def disableRecording(self): + ''' + Disables recording + + We won't clear the recorded bins here in case the user still wants to grab recorded data + ''' + self._record = False + + def setRecording(self, record = True): + ''' + Wrapper around enable and disable recording + ''' + if record: + self.enableRecording() + else: + self.disableRecording() + + def removeRecordedData(self): + ''' + Removes recorded data + ''' + self._recordedBins = None + self._recordedPSD = None + self._recordedTime = None + + def record(self, time): + ''' + Adds current PSD data to recorded arrays + + TODO: Make sure this works when adaptive bins is False + ''' + if self._record: + maxBins = self.maxBins if self._adaptiveBinSize else self.bins + self._recordedBins = np.pad(self._recordedBins, ((0, 1), (0, maxBins+1 - self._recordedBins.shape[1]))) + self._recordedPSD = np.pad(self._recordedPSD, ((0, 1), (0, maxBins - self._recordedPSD.shape[1]))) + self._recordedTime = np.pad(self._recordedTime, (0,1)) + self._recordedBins[-1][:self.PSDbounds.shape[0]] = self.PSDbounds + self._recordedPSD[-1][:self.PSD.shape[0]] = self.PSD + self._recordedTime[-1] = time + + def saveRecordedPSD(self, filename, compressed = True): + ''' + Saves recorded data into npz format + + Note: If recording is disabled, then this function will do nothing since + there is nothing to save anyways + + Parameters + ---------- + filename : str + File name to save to + compressed : bool (optional) + Whether to save as in compressed format (defaults to True) + ''' + if self._record: + if compressed: + np.savez_compressed(filename, time = self._recordedTime, bins = self._recordedBins, PSD = self._recordedPSD) + else: + np.savez(filename, time = self._recordedTime, bins = self._recordedBins, PSD = self._recordedPSD) + + def loadRecordedPSD(self, filename): + ''' + Loads recorded PSD + ''' + data = np.load(filename) + self._record = True + self._recordedTime = data['time'] + self._recordedBins = data['bins'] + self._recordedPSD = data['PSD'] + + def _grabPSDfromIndex(self, index): + ''' + Returns PSD bounds, PSD bins and PSD from recorded data based off index + + Since the number of bins is likely less than the max, we want to grab only the non-zero indices + TODO: two concerns + 1) this may remove the last 1 bins (this may be okay since we add new bins once the + list bins has at least 1 particle), so the last bin would be 0 anyways + ''' + nonzero = len(np.nonzero(self._recordedBins[index])[0]) + if nonzero == 0: + PSDbounds = np.linspace(self.originalMin, self.originalMax, self.originalBins+1) + PSDsize = 0.5 * (PSDbounds[1:] + PSDbounds[:-1]) + PSD = np.zeros(self.originalBins) + else: + PSDbounds = self._recordedBins[index,:nonzero] + PSD = self._recordedPSD[index,:nonzero-1] + PSDsize = 0.5 * (PSDbounds[1:] + PSDbounds[:-1]) + bins = len(PSD) + minBound, maxBound = np.amin(PSDbounds), np.amax(PSDbounds) + return PSDbounds, PSD, PSDsize, bins, minBound, maxBound + + def setPSDtoRecordedTime(self, time): + ''' + Sets particle size distribution to specific time if recorded + + Parameter + --------- + time : float + Time to load PSD from, will load to nearest time available + ''' + if self._record: + if time <= self._recordedTime[0]: + print('Input time is lower than smallest recorded time, setting PSD to t = {:.3e}'.format(self._recordedTime[0])) + self.PSDbounds, self.PSD, self.PSDsize, self.bins, self.min, self.max = self._grabPSDfromIndex(0) + elif time >= self._recordedTime[-1]: + print('Input time is larger than longest recorded time, setting PSD to t = {:.3e}'.format(self._recordedTime[-1])) + self.PSDbounds, self.PSD, self.PSDsize, self.bins, self.min, self.max = self._grabPSDfromIndex(-1) + else: + #Upper and lower PSD + #Note: horrible naming convention here + # Upper PSD refers to the PSD just after time + # Lower PSD refers to the PSD just before time + #This does NOT refer to the PSD with the larger or smaller number of bins + uind = np.argmax(self._recordedTime > time) + lind = uind - 1 + + utime, ltime = self._recordedTime[uind], self._recordedTime[lind] + uPSDbounds, uPSD, uPSDsize, ubins, umin, umax = self._grabPSDfromIndex(uind) + lPSDbounds, lPSD, lPSDsize, lbins, lmin, lmax = self._grabPSDfromIndex(lind) + + #Interpolate from lower PSD to upper PSD using bounds of larger PSD + #This will account for all possible cases if the PSD size classes change + #This is done by pretending we're calling changeSizeClasses + # Where we resize the PSD with the smaller number of bins to have the same bins as the larger PSD + # And correct for the possible change in number density + if ubins >= lbins: + #Resize lower PSD to upper PSD + oldV = np.sum(lPSD * lPSDsize**3) + distDen = lPSD / (lPSDbounds[1:] - lPSDbounds[:-1]) + rOld = 0.5 * (lPSDbounds[1:] + lPSDbounds[:-1]) + lPSD = np.interp(uPSDsize, rOld, distDen, left=0, right=0) * (uPSDbounds[1:] - uPSDbounds[:-1]) + newV = np.sum(lPSD * uPSDsize**3) + if newV != 0: + lPSD *= oldV / newV + else: + lPSD = np.zeros(ubins) + + else: + #Resize upper PSD to lower PSD + oldV = np.sum(uPSD * uPSDsize**3) + distDen = uPSD / (uPSDbounds[1:] - uPSDbounds[:-1]) + rOld = 0.5 * (uPSDbounds[1:] + uPSDbounds[:-1]) + uPSD = np.interp(lPSDsize, rOld, distDen, left=0, right=0) * (lPSDbounds[1:] - lPSDbounds[:-1]) + uPSDbounds = lPSDbounds + newV = np.sum(uPSD * lPSDsize**3) + if newV != 0: + uPSD *= oldV / newV + else: + uPSD = np.zeros(lbins) + + #Now that the bin sizes are the same, we can just interpolate the PSD + self.PSDbounds = uPSDbounds + self.PSDsize = 0.5 * (self.PSDbounds[1:] + self.PSDbounds[:-1]) + self.PSD = (uPSD - lPSD) * (time - ltime) / (utime - ltime) + lPSD + self.bins = len(self.PSDsize) + self.min, self.max = np.amin(self.PSDbounds), np.amax(self.PSDbounds) def setAdaptiveBinSize(self, adaptive): ''' For Euler implementation, sets whether to change the bin size when the number of filled bins > maxBins or < minBins - If False, the bins will still change if nucleated particles are greater than the max bin size - and bins will still be added when the last bins starts to fill (although this will not change the bin size) + If False, the bins will still be if nucleated particles are greater than the max bin size + and bins will still be added when the last bins starts to fill (but this will not change the bin size) ''' self._adaptiveBinSize = adaptive @@ -118,6 +333,10 @@ def createBackup(self): def revert(self): ''' Reverts to previous PSD and PSDbounds + + NOTE: this appears to be unused + (this was used in the previous KWNEuler implementation when the PSD could change within an iteration) + (now it changes between iterations, so we don't need to revert back if something goes wrong) ''' self.PSD = copy.copy(self._prevPSD) self.PSDbounds = copy.copy(self._prevPSDbounds) @@ -125,33 +344,15 @@ def revert(self): self.bins = len(self.PSD) self.min, self.max = self.PSDbounds[0], self.PSDbounds[-1] - def reset(self, resetBounds = True): - ''' - Resets the PSD to 0 - This will remove any size classes that were added since initialization - ''' - if resetBounds: - self.min = self.originalMin - self.max = self.originalMax - self.bins = self.originalBins - self.PSDbounds = np.linspace(self.min, self.max, self.bins+1) - self.PSDsize = 0.5 * (self.PSDbounds[:-1] + self.PSDbounds[1:]) - - self.PSD = np.zeros(self.bins) - - #Hidden variable for use in KWNEuler when determining composition assuming no diffusion in precipitate - #Represents d(PSD)/dr * growth rate * dt - #I would like this variable to be in KWNEuler, but this way is much easier - self._fv = np.zeros(self.bins + 1) - - #Hidden variable for use in KWNEuler when adaptive time stepping is enabled - #This allows for PSD to revert to its previous value if a time constraint is not met - self._prevPSD = np.zeros(self.bins) - self._prevPSDbounds = np.zeros(self.bins+1) - def changeSizeClasses(self, cMin, cMax, bins = None, resetPSD = False): ''' Changes the size classes and resets the PSD + + This is done by linear interpolation of the previous bins and PSD + And interpolating to the new bins and PSD + Due to differences in bin size (thus resolution of the PSD), the number density + could be a little different. To correct for this, we get the 3rd moment of the + previous PSD and the new PSD, and correct the new PSD to have the same 3rd moment Parameters ---------- @@ -184,7 +385,7 @@ def changeSizeClasses(self, cMin, cMax, bins = None, resetPSD = False): def addSizeClasses(self, bins = 1): ''' - Adds an additional size class to end of distribution + Adds an additional number of size classes to end of distribution Parameters ---------- @@ -198,39 +399,27 @@ def addSizeClasses(self, bins = 1): self.PSDbounds = np.linspace(self.min, self.max, self.bins+1) self.PSDsize = 0.5 * (self.PSDbounds[:-1] + self.PSDbounds[1:]) - def getDTEuler(self, currDT, growth, maxDissolution, startIndex): + def adjustSizeClassesEuler(self, checkDissolution = False): ''' - Calculates time interval for Euler implementation - dt < dR / (2 * growth rate) - This ensures that at most, only half of particles in one size class can go to another + 1) adds some bins to the end of the PSD if the last bin has at least 1 precipitate + Number of bins is 1/4 of the original number of bins + 2) If adaptive bin size is enabled, then two checks + 2a) if number of bins > max bins, then resize to have the number of bins be the minimum + 2b) if checking dissolution and number of filled bins < 1/2 min bins, + then resize to last filled bin with the number of bins being the maximum Parameters ---------- - currDT : float - Current time interval, will be returned if it's smaller than what's given by the contraint - growth : array of floats - Growth rate, must have lenth of bins+1 (or length of PSDbounds) - maxDissolution : float - Maximum volume allowed to dissolve - startIndex : int - First index to look at for growth rate, all indices below startIndex will be ignored - ''' - dissFrac = maxDissolution * self.ThirdMoment() - dissIndex = np.amax([np.argmax(self.CumulativeMoment(3) > dissFrac), startIndex]) - growthFilter = growth[dissIndex:-1][self.PSD[dissIndex:] > 0] - #if len(growthFilter) == 0 or np.amax(growthFilter) < 0: - if len(growthFilter) == 0: - return currDT - else: - if np.amax(np.abs(growthFilter)) == 0: - return currDT - else: - return np.amin([currDT, (self.PSDbounds[1] - self.PSDbounds[0]) / (2 * np.amax(np.abs(growthFilter)))]) + checkDissolution : bool + Whether to check if the PSD is getting smaller and resize accordingly - def adjustSizeClassesEuler(self, checkDissolution = False): - ''' - Adds a size class if last class in PBM is filled - Changes length of size classes based off number of allowed bins + Returns + ------- + change : bool + When the number of bins changed + newIndices : int or None + The number of bins added to the PSD + If the size of the bins changed, then this is None to indicate that resizing occured ''' change = False newIndices = None @@ -274,95 +463,256 @@ def NormalizeToMoment(self, order = 0): total = self.Moment(order) self.PSD /= total - def Nucleate(self, amount, radius): + def getDissolutionIndex(self, maxDissolution, minIndex = 0): ''' - Adds nucleated particles to PSD given radius and amount of particles + Finds indices when the volume fraction of particles below this index is + within the maximum amount (fraction-wise) that the PSD is allowed to dissolve + + So find R_max where int(0, R_max, R^3 * dr) < maxDissolution * int(0, infinity, R^3 * dr) + The index is the correspoinding index to R_max Parameters ---------- - amount : float - Amount of nucleated particles - radius : float - Radius of nucleated particles - ''' - if amount < 1: - return False - - change = False - - #Find size class for nucleated particles - nRad = np.argmax(self.PSDbounds > radius) - 1 + maxDissolution : float + Max fraction allowed to dissolve + minIndex : int + Minimum index which below, all particles are allowed to dissolve + Upper limit on dissolution index - #If radius is larger than length scale of PBM, adjust PBM such that radius is towards the beginning - if nRad == -1 and radius > 0: - #print('adding nucleated bins') - self.changeSizeClasses(self.PSDbounds[0], 5 * radius, self.originalBins) - nRad = np.argmax(self.PSDbounds > radius) - change = True - self.PSD[nRad] += amount - return change + Returns + ------- + max of [dissolution index, minIndex] + ''' + dissFrac = maxDissolution * self.ThirdMoment() + dissIndex = np.argmax(self.CumulativeMoment(3) > dissFrac) - 1 + if dissIndex < 0: + dissIndex = 0 + return np.amax([np.argmax(self.CumulativeMoment(3) > dissFrac), minIndex]) - def UpdateEuler(self, dt, flux): + + def getDTEuler(self, currDT, growth, dissolutionIndex, maxBinRatio = 0.4): ''' - Updates PSD given the flux and any external contributions + Calculates time interval for Euler implementation + dt < dR / (2 * growth rate) + This ensures that at most, only half of particles in one size class can go to another + + Also finds dt such that the max delta in growth rate is 0.4 dR + We could use 0.5 dR which is the upper limit + (for a given bin, the max change in density would remove all particles, with 0.5 getting smaller and 0.5 getting bigger) + But 0.4 dR should be slightly more stable + + TODO: allow variable ratio - this will make it more flexible for testing different time step constraints - Change in the amount of particles in a given size class = d(G*n)/dx - Where G is flux of size class, n is number of particles in size class and dx is range of size class - Parameters ---------- - dt : float - Time increment - flux : array - Growth rate of each particle size class - Array size must be (bins + 1) since this operates on bounds of size classes + currDT : float + Current time interval, will be returned if it's smaller than what's given by the contraint + growth : array of floats + Growth rate, must have lenth of bins+1 (or length of PSDbounds) + maxDissolution : float + Maximum volume allowed to dissolve + startIndex : int + First index to look at for growth rate, all indices below startIndex will be ignored + maxBinRatio : float (optional) + Max ratio of particles in bin allowed to move to a nearby bin + Default is 0.4 ''' - netFlux = np.zeros(self.bins + 1) + self.maxRatio = maxBinRatio + growthFilter = growth[dissolutionIndex:-1][self.PSD[dissolutionIndex:] > 0] - #Array of 0 (flux <= 0), 1 (flux > 0) + if len(growthFilter) == 0: + return currDT + else: + if np.amax(np.abs(growthFilter)) == 0: + return currDT + else: + return self.maxRatio * (self.PSDbounds[1] - self.PSDbounds[0]) / np.amax(np.abs(growthFilter)) + + def getdXdtEuler(self, flux, nucRate, nucRadius, psd): + ''' + dn_i/dt = d(G*n)/dr + nucRate + + d(G*n)/dr is calculated from two conditions + For positive growth rates - d(G*n)/dr|_i = n_i * flux_i / dr + For negative growth rates - d(G*n)/dr|_i = n_(i-1) * flux_i / dr + TODO : check that the two equations above represent the implementation + + Parameters + ---------- + flux : numpy array (bins+1) + Growth rate of particles in m/s + nucRate : float + Nucleation rate in #/m^3/s + nucRadius : float + Nucleation radius in m + psd : numpy array (bins) + Particle size distribution with number density #/m3 + + Returns + ------- + dXdt (bins) - corresponds to dn_i/dt + ''' + self._netFlux = np.zeros(self.bins+1) fluxSign = np.sign(flux) fluxSign[fluxSign == -1] = 0 + dR = self.PSDbounds[1:] - self.PSDbounds[:-1] + self._netFlux[:-1] += flux[:-1] * psd * (1-fluxSign[:-1]) / dR + self._netFlux[1:] += flux[1:] * psd * fluxSign[1:] / dR - #If flux is negative (from class n to n-1), then take from size class n - #If flux is positive, then take from size class n-1 - netFlux[:-1] += dt * flux[:-1] * self.PSD * (1-fluxSign[:-1]) / (self.PSDbounds[1:] - self.PSDbounds[:-1]) - netFlux[1:] += dt * flux[1:] * self.PSD * fluxSign[1:] / (self.PSDbounds[1:] - self.PSDbounds[:-1]) + dXdt = (self._netFlux[:-1] - self._netFlux[1:]) - #Brute force stability so PSD is never negative - #If the time step is determined from getDTEuler, this should be unnecessary - netFlux[1:-1][netFlux[1:-1] < -self.PSD[1:]] = -self.PSD[1:][netFlux[1:-1] < -self.PSD[1:]] - netFlux[1:-1][netFlux[1:-1] > self.PSD[:-1]] = self.PSD[:-1][netFlux[1:-1] > self.PSD[:-1]] + #Find size class for nucleated particles + nRad = np.argmax(self.PSDbounds > nucRadius) - 1 + dXdt[nRad] += nucRate - self._fv = netFlux - - self.PSD += (netFlux[:-1] - netFlux[1:]) - - #Adjust size classes and return True if the size classes had changed - change, newIndices = self.adjustSizeClassesEuler(all(flux<0)) - - #Set negative frequencies to 0 + return dXdt + + def correctdXdtEuler(self, dt, flux, nucRate, nucRadius, psd): + ''' + Given dt, correct the net flux so PSD will not be negative + Essentially, the total number of particles leaving a bin should be less than or equal to the number of particles in the bin + + Size of fluxes is bins+1 while for PSD, it is bins + + For fluxes on the right (positive) side of the PSD + We limit fluxes so that J_i+1 * dt < PSD_i + + For fluxes on the left (negative) side of the PSD + We limit fluxes so that -J_i * dt < PSD_i + + Normally, this wouldn't be an issue since the time step from getDtEuler would limit J_i*dt to less than half the number of particles in a bin + But because we set a dissolution threshold to ignore (to prevent extremely small dt since growth rate scales by 1/r), the bins below + the dissolution threshold will not follow the constraint we apply in getDtEuler + + Parameters + ---------- + dt : float + time step + flux : numpy array (bins+1) + Growth rate of particles in m/s + nucRate : float + Nucleation rate in #/m^3/s + nucRadius : float + Nucleation radius in m + psd : numpy array (bins) + Particle size distribution with number density #/m3 + + Returns + ------- + dXdt (bins) - corresponds to dn_i/dt corrected to avoid negative bins + ''' + #indBelow = self._netFlux[1:-1]*dt < -psd[1:] + #self._netFlux[1:-1][indBelow] = -psd[1:][indBelow] / dt + #indAbove = self._netFlux[1:-1]*dt > psd[:-1] + #self._netFlux[1:-1][indAbove] = psd[:-1][indAbove] / dt + + indBelow = self._netFlux[:-1]*dt < -psd + self._netFlux[:-1][indBelow] = -psd[indBelow] / dt + indAbove = self._netFlux[1:]*dt > psd + self._netFlux[1:][indAbove] = psd[indAbove] / dt + + dXdt = (self._netFlux[:-1] - self._netFlux[1:]) + + #Find size class for nucleated particles + nRad = np.argmax(self.PSDbounds > nucRadius) - 1 + dXdt[nRad] += nucRate + + return dXdt + + def UpdatePBMEuler(self, time, newN): + ''' + Updates PBM with new values + + Parameters + ---------- + time : float + New time + newN : numpy array + New number density + ''' + self.PSD = newN self.PSD[self.PSD < 1] = 0 + self.record(time) - return change, newIndices + def MomentFromN(self, N, order): + ''' + Given arbtrary PSD, return moment - def UpdateLagrange(self, dt, flux): + Parameters + ---------- + N : numpy array + PSD / number density + order : float + Moment order ''' - Updates bounds of size classes with given growth rate - Fluxes of particles between size classes is d(Gn)/dx, - however, keeping the number of particles in each size class the same, - the bounds of the size classes can be updated by r_i = v_i * dt + return np.sum(N * self.PSDsize**order) + + def CumulativeMomentFromN(self, N, order): + ''' + Given arbtrary PSD, return cumulative moment (from 0 to max) Parameters ---------- - dt : float - Time increment - flux : array - Growth rate of each particle size class - Array size must be (bins + 1) since this operates on bounds of size classes + N : numpy array + PSD / number density + order : float + Moment order ''' - self._prevPSDbounds = copy.copy(self.PSDbounds) - self.PSDbounds += flux * dt - self.PSDsize = 0.5 * (self.PSDbounds[1:] + self.PSDbounds[:-1]) + return np.cumsum(N * self.PSDsize**order) + + def WeightedMomentFromN(self, N, order, weights): + ''' + Given arbtrary PSD, return weighted moment + + Parameters + ---------- + N : numpy array + PSD / number density + order : float + Moment order + weights : numpy array + Weights for each bin + ''' + return np.sum(N * self.PSDsize**order * weights) + + def CumulativeWeightedMomentFromN(self, N, order, weights): + ''' + Given arbtrary PSD, return cumulative weighted moment (from 0 to max) + + Parameters + ---------- + N : numpy array + PSD / number density + order : float + Moment order + weights : numpy array + Weights for each bin + ''' + return np.cumsum(self.PSD * self.PSDsize**order * weights) + + def ZeroMomentFromN(self, N): + ''' + Sum of N + ''' + return self.MomentFromN(N, 0) + + def FirstMomentFromN(self, N): + ''' + Length weighted moment of N + ''' + return self.MomentFromN(N, 1) + + def SecondMomentFromN(self, N): + ''' + Area weighted moment of N + ''' + return self.MomentFromN(N, 2) + + def ThirdMomentFromN(self, N): + ''' + Volume weighted moment of N + ''' + return self.MomentFromN(N, 3) def Moment(self, order): ''' @@ -373,7 +723,7 @@ def Moment(self, order): order : int Order of moment ''' - return np.sum(self.PSD * self.PSDsize**order) + return self.MomentFromN(self.PSD, order) def CumulativeMoment(self, order): ''' @@ -384,7 +734,7 @@ def CumulativeMoment(self, order): order : int Order of moment ''' - return np.cumsum(self.PSD * self.PSDsize**order) + return self.CumulativeMomentFromN(self.PSD, order) def WeightedMoment(self, order, weights): ''' @@ -398,7 +748,7 @@ def WeightedMoment(self, order, weights): Weights to apply to each size class Array size of (bins) ''' - return np.sum(self.PSD * self.PSDsize**order * weights) + return self.WeightedMomentFromN(self.PSD, order, weights) def CumulativeWeightedMoment(self, order, weights): ''' @@ -412,7 +762,7 @@ def CumulativeWeightedMoment(self, order, weights): Weights to apply to each size class Array size of (bins) ''' - return np.cumsum(self.PSD * self.PSDsize**order * weights) + return self.CumulativeWeightedMomentFromN(self.PSD, order, weights) def ZeroMoment(self): ''' @@ -462,7 +812,7 @@ def PlotCurve(self, axes, fill = False, logX = False, logY = False, scale = 1, * if hasattr(scale, '__len__'): scale = np.interp(self.PSDsize, self.PSDbounds, scale) else: - scale = scale * np.ones(self.PSDsize) + scale = scale * np.ones(len(self.PSDsize)) if fill: axes.fill_between(self.PSDsize * scale, self.PSD, np.zeros(len(self.PSD)), *args, **kwargs) @@ -495,7 +845,7 @@ def PlotDistributionDensity(self, axes, fill = False, logX = False, logY = False if hasattr(scale, '__len__'): scale = np.interp(self.PSDsize, self.PSDbounds, scale) else: - scale = scale * np.ones(self.PSDsize) + scale = scale * np.ones(len(self.PSDsize)) if fill: axes.fill_between(self.PSDsize * scale, self.PSD, np.zeros(len(self.PSD)), *args, **kwargs) @@ -543,14 +893,17 @@ def PlotKDE(self, axes, bw_method = None, fill = False, logX = False, logY = Fal determined by precipitate curvature *args, **kwargs - extra arguments for plotting ''' - kernel = sts.gaussian_kde(self.PSDsize, bw_method = bw_method, weights = self.PSD) - x = np.linspace(self.min, self.max, 1000) - y = kernel(x) * self.ZeroMoment() * (self.PSDbounds[1] - self.PSDbounds[0]) + x = np.linspace(self.min, self.max, 1000) + if np.all(self.PSD == 0): + y = np.zeros(x.shape) + else: + kernel = sts.gaussian_kde(self.PSDsize, bw_method = bw_method, weights = self.PSD) + y = kernel(x) * self.ZeroMoment() * (self.PSDbounds[1] - self.PSDbounds[0]) if hasattr(scale, '__len__'): scale = np.interp(x, self.PSDbounds, scale) else: - scale = scale * np.ones(x) + scale = scale * np.ones(len(x)) if fill: axes.fill_between(x * scale, y, np.zeros(len(y)), *args, **kwargs) @@ -594,7 +947,7 @@ def PlotHistogram(self, axes, outline = 'outline bins', fill = True, logX = Fals if hasattr(scale, '__len__'): scale = np.interp(xCoord, self.PSDbounds, scale) else: - scale = scale * np.ones(xCoord) + scale = scale * np.ones(len(xCoord)) if outline != 'no outline': axes.plot(xCoord * scale, yCoord, *args, **kwargs) @@ -626,7 +979,7 @@ def PlotCDF(self, axes, logX = False, scale = 1, order = 0, *args, **kwargs): if hasattr(scale, '__len__'): scale = np.interp(self.PSDsize, self.PSDbounds, scale) else: - scale = scale * np.ones(self.PSDsize) + scale = scale * np.ones(len(self.PSDsize)) axes.plot(self.PSDsize * scale, self.CumulativeMoment(order) / self.Moment(order), *args, **kwargs) self.setAxes(axes, scale, logX, False) diff --git a/kawin/precipitation/StoppingConditions.py b/kawin/precipitation/StoppingConditions.py new file mode 100644 index 0000000..18202e9 --- /dev/null +++ b/kawin/precipitation/StoppingConditions.py @@ -0,0 +1,137 @@ +from enum import Enum + +''' +Defines class to handle a single stopping conditions + +Per iteration, these will take in a model, and check with internal members to see if stopping condition has been satisfied +If it has, then it will be set to True and the time will be recorded +These can also be checked if they were satisfied already if we want to use them to stop a simulation + +TODO: Abstract out the stopping condition so it can be used in GenericModel +''' + +class Inequality (Enum): + GREATER_THAN = 0 + LESSER_THAN = 1 + +class PrecipitationStoppingCondition: + ''' + Parameters + ---------- + condition : Inequality enum + GREATER_THAN -> result > value + LESS_THAN -> result < value + value : double + phase : str + element : el + ''' + def __init__(self, condition, value, phase = None, element = None): + self._condition = condition + self._value = value + self._isSatisfied = False + self._satisfiedTime = -1 + self._phase = phase + self._element = element + self._modelVar = None + + def reset(self): + ''' + Resets condition to being not yet satisfied + ''' + self._isSatisfied = False + self._satisfiedTime = -1 + + def _poll(self, model, n): + ''' + Gets current value of attribute at iteration n for phase p + + Parameters + ---------- + model : PrecipitateModel + n : int + Iteration number + + Returns value (float) of attribute at n,p + ''' + p = model.phaseIndex(self._phase) + return getattr(model, self._modelVar)[n,p] + + def _testCondition(self, model): + ''' + Private function only testing if stopping condition is satisfied based off current state of model + + Parameters + ---------- + model : PrecipitateModel + + Returns bool for whether condition is satisfied or not + ''' + if self._condition == Inequality.GREATER_THAN: + return self._poll(model, model.n) > self._value + else: + return self._poll(model, model.n) < self._value + + def testCondition(self, model): + ''' + Tests if condition is satisfied, if so, then interpolate to find time when it was satisfied + + Parameters + ---------- + model : PrecipitateModel + ''' + if not self._isSatisfied: + self._isSatisfied = self._testCondition(model) + + if self._isSatisfied: + if model.n > 0: + currVal, currTime = self._poll(model, model.n), model.time[model.n] + prevVal, prevTime = self._poll(model, model.n-1), model.time[model.n-1] + self._satisfiedTime = (currTime - prevTime) * (self._value - prevVal) / (currVal - prevVal) + prevTime + else: + self._satisfiedTime = model.time[model.n] + + def isSatisfied(self): + ''' + Returns whether condition is satisfied + ''' + return self._isSatisfied + + def satisfiedTime(self): + ''' + Returns time when condition was satisfied + ''' + return self._satisfiedTime + +class VolumeFractionCondition (PrecipitationStoppingCondition): + def __init__(self, condition, value, phase = None): + super().__init__(condition, value, phase = phase) + self._modelVar = 'betaFrac' + +class AverageRadiusCondition (PrecipitationStoppingCondition): + def __init__(self, condition, value, phase = None): + super().__init__(condition, value, phase = phase) + self._modelVar = 'avgR' + +class DrivingForceCondition (PrecipitationStoppingCondition): + def __init__(self, condition, value, phase = None): + super().__init__(condition, value, phase = phase) + self._modelVar = 'dGs' + +class NucleationRateCondition (PrecipitationStoppingCondition): + def __init__(self, condition, value, phase = None): + super().__init__(condition, value, phase = phase) + self._modelVar = 'nucRate' + +class PrecipitateDensityCondition (PrecipitationStoppingCondition): + def __init__(self, condition, value, phase = None): + super().__init__(condition, value, phase = phase) + self._modelVar = 'precipitateDensity' + +class CompositionCondition (PrecipitationStoppingCondition): + def __init__(self, condition, value, element = None): + super().__init__(condition, value, element = element) + self._modelVar = 'xComp' + + def _poll(self, model, n): + e = 0 if self._element is None else model.elements.index(self._element) + return getattr(model, self._modelVar)[n,e] diff --git a/kawin/precipitation/TimeTemperaturePrecipitation.py b/kawin/precipitation/TimeTemperaturePrecipitation.py new file mode 100644 index 0000000..5ee5869 --- /dev/null +++ b/kawin/precipitation/TimeTemperaturePrecipitation.py @@ -0,0 +1,108 @@ +import numpy as np +import matplotlib.pyplot as plt +from kawin.precipitation import PrecipitateModel +from kawin.precipitation.StoppingConditions import PrecipitationStoppingCondition +from typing import List + +class TTPCalculator: + ''' + Time-temperature-precipitation + + Parameters + ---------- + model : PrecipitateModel + stopConds : list of PrecipitateStoppingConditions + Stopping conditions to store times when these conditions are reached + Model will continue to solve until the max time is reached or all conditions are satisfied + ''' + def __init__(self, model : PrecipitateModel, stopConds : List[PrecipitationStoppingCondition]): + self.model = model + self.stopConds = stopConds + self._maxTime = 0 + self.transformationTimes = None + + #Add stopping conditions to model + #NOTE: this clears any previous stopping conditions + self.model.clearStoppingConditions() + for j in range(len(stopConds)): + self.model.addStoppingCondition(self.stopConds[j], 'and') + + def _getStopTime(self, T): + ''' + Internal function to get times for each stopping conditions at a single temperature + + Parameters + ---------- + T : float + Temperature + ''' + self.model.reset() + self.model.setTemperature(T) + self.model.solve(self._maxTime, verbose = True, vIt = 1000) + + values = np.zeros(len(self.stopConds)) + for j in range(len(self.stopConds)): + values[j] = self.stopConds[j].satisfiedTime() + + return values + + def calculateTTP(self, Tlow, Thigh, Tsteps, maxTime, pool = None): + ''' + Calculates TTP diagram between Tlow and Thigh + + Parameters + ---------- + Tlow : float + Lower temperature range + Thigh : float + Upper temperature range + Tsteps : int + Number of temperatures between Tlow and Thigh to evaluate + maxTime : float + Maximum simulation time + If the model reaches the max time before all stopping conditions are met, it will stop prematurely + and any unsatisfied stopping conditions will be recorded as -1 + pool : None or multiprocessing pool + If None, each temperature will be evaluated in serial + If a pool, must have a map function + Possible options: + multiprocessing.Pool - (mac and unix only) + pathos.multiprocessing.ProcessingPool - (windows, mac and unix) + dask.Client - (windows, mac and unix) + ''' + self.transformationTimes = np.zeros((Tsteps, len(self.stopConds))) + self._maxTime = maxTime + self.temperatures = np.linspace(Tlow, Thigh, Tsteps) + + if pool is None: + outputs = list(map(self._getStopTime, self.temperatures)) + else: + outputs = list(pool.map(self._getStopTime, self.temperatures)) + + for i in range(len(self.temperatures)): + for j in range(len(self.stopConds)): + self.transformationTimes[i,j] = outputs[i][j] + + def plot(self, ax, labels, xlim = [1, 1e6], *args, **kwargs): + ''' + Plots TTP diagram + + Parameters + ---------- + ax : Matplotlib axes object + labels : list of str + Labels for each stopping condition + xlim : list of float + x-axis limits + Plotting will be set on log scale, so lower limits will be set to be non-zero + ''' + for i in range(len(self.stopConds)): + indices = self.transformationTimes[:,i] != -1 + ax.plot(self.transformationTimes[indices,i], self.temperatures[indices], label=labels[i], *args, **kwargs) + ax.legend() + if xlim[0] == 0: + xlim[0] = 1e-3 + ax.set_xlim(xlim) + ax.set_xlabel('Time (s)') + ax.set_xscale('log') + ax.set_ylabel('Temperature (K)') \ No newline at end of file diff --git a/kawin/precipitation/__init__.py b/kawin/precipitation/__init__.py new file mode 100644 index 0000000..276c048 --- /dev/null +++ b/kawin/precipitation/__init__.py @@ -0,0 +1,6 @@ +from .KWNBase import PrecipitateBase, VolumeParameter +from .KWNEuler import PrecipitateModel +from .PopulationBalance import PopulationBalanceModel +from .non_ideal.ElasticFactors import StrainEnergy +from .non_ideal.ShapeFactors import ShapeFactor +from .TimeTemperaturePrecipitation import TTPCalculator \ No newline at end of file diff --git a/kawin/precipitation/coupling/GrainGrowth.py b/kawin/precipitation/coupling/GrainGrowth.py new file mode 100644 index 0000000..d84e746 --- /dev/null +++ b/kawin/precipitation/coupling/GrainGrowth.py @@ -0,0 +1,372 @@ +import numpy as np +import matplotlib.pyplot as plt +from kawin.precipitation import PopulationBalanceModel +from kawin.solver import SolverType +from kawin.GenericModel import GenericModel +from kawin.precipitation.Plot import getTimeAxis + +class GrainGrowthModel(GenericModel): + ''' + Model for grain growth that can be coupled with the KWN model to account for Zener pinning + + Following implentation described in + K. W, J. Jeppsson and P. Mason, J. Phase Equilib. Diffus. 43 (2022) 866-875 + + Parameters + ---------- + cMin : float (optional) + Minimum grain size (default is 1e-10) + cMax : float (optional) + Maximum grain size (default is 1e-8) + bins : int (optional) + Initial bins (default is 150) + minBins : int (optional) + Minimum number of bins (default is 100) + maxBins : int (optional) + Maximum number of bins (default is 200) + ''' + def __init__(self, cMin = 1e-10, cMax = 1e-8, bins = 150, minBins = 100, maxBins = 200, solverType = SolverType.RK4): + super().__init__() + self.pbm = PopulationBalanceModel(cMin, cMax, bins, minBins, maxBins) + self._oldPSD, self._oldPSDbounds = np.array(self.pbm.PSD), np.array(self.pbm.PSDbounds) + + #Model parameters - these are values taken from the paper as general default values + self.gbe = 0.5 #Grain boundary energy (J/m2) + self.M = 1e-14 #Grain boundary mobility (m4/J-s) + self.alpha = 1 #Correction factor (for when fitting data to the model) + self.m, self.K = {'all': 1}, {'all': 4/3} #Factors related to spatial distribution of precipitates + + self.solverType = solverType + + self.maxDissolution = 1e-6 + + self.reset() + + def setGrainBoundaryEnergy(self, gbe): + ''' + Parameters + ---------- + gbe : float + Grain boundary energy + ''' + self.gbe = gbe + + def setGrainBoundaryMobility(self, M): + ''' + Parameters + ---------- + M : float + Grain boundary mobility + ''' + self.M = M + + def setAlpha(self, alpha): + ''' + Correction factor + + Parameters + ---------- + alpha : float + ''' + self.alpha = alpha + + def setZenerParameters(self, m, K, phase='all'): + ''' + Parameters for defining zener radius + + Zener radius is defined as + Rz = K * r / f^m + + Parameters + ---------- + m : float + Exponential factor for volume fraction + K : float + Scaling factor + phase : str (optional) + Precipitate phase to apply parameters to + Default is 'all' + ''' + self.m[phase] = m + self.K[phase] = K + + def LoadDistribution(self, data): + ''' + Creates a particle size distribution from a set of data + + Parameters + ---------- + data : array of floats + Array of data to be inserted into PSD + ''' + self.pbm.reset() + self.pbm.PSD, self.pbm.PSDbounds = np.histogram(data, self.pbm.PSDbounds) + self.pbm.PSD = self.pbm.PSD.astype('float') + self.Normalize() + self.avgR[0] = self.Rm(self.pbm.PSD) + self._oldPSD, self._oldPSDbounds = np.array(self.pbm.PSD), np.array(self.pbm.PSDbounds) + self.dissolutionIndex = self.pbm.getDissolutionIndex(self.maxDissolution, 0) + + def LoadDistributionFunction(self, function): + ''' + Creates a particle size distribution from a function + + Parameters + ---------- + function : function + Takes in R and returns density + ''' + self.pbm.reset() + self.pbm.PSD = function(self.pbm.PSDsize) + self.Normalize() + self.avgR[0] = self.Rm(self.pbm.PSD) + self._oldPSD, self._oldPSDbounds = np.array(self.pbm.PSD), np.array(self.pbm.PSDbounds) + self.dissolutionIndex = self.pbm.getDissolutionIndex(self.maxDissolution, 0) + + def reset(self): + ''' + Resets model with initially loaded grain size distribution + ''' + self.time = np.zeros(1) + self.avgR = np.zeros(1) + self._z = 0 + self._growthRate = np.zeros(len(self.pbm.PSDbounds)) + self.pbm.reset() + self.pbm.PSD, self.pbm.PSDbounds = np.array(self._oldPSD), np.array(self._oldPSDbounds) + self.dissolutionIndex = 0 + + def Rcr(self, x): + ''' + Critical radius, grains larger than Rcr will growth while smaller grains will shrink + + Critical radius is defined so that the volume will be constant when applying the growth rate + + Parameters + ---------- + x : np.array + Grain size distribution corresponding to GrainGrowthModel.pbm.PSDbounds + ''' + return self.pbm.SecondMomentFromN(x) / self.pbm.FirstMomentFromN(x) + + def Rm(self, x): + ''' + Mean radius + + Parameters + ---------- + x : np.array + Grain size distribution corresponding to GrainGrowthModel.pbm.PSDbounds + ''' + return np.cbrt(self.pbm.ThirdMomentFromN(x) / self.pbm.ZeroMomentFromN(x)) + + def grainGrowth(self, x): + ''' + Grain growth model + dRi/dt = alpha * M * gbe * (1/Rcr - 1/Ri) + + Parameters + ---------- + x : np.array + Grain size distribution corresponding to GrainGrowthModel.pbm.PSDbounds + ''' + return self.alpha * self.M * self.gbe * (1 / self.Rcr(x) - 1 / self.pbm.PSDbounds) + + def Normalize(self): + ''' + Normalize PSD to have a third moment of 1 + + Ideally, this isn't needed since the grain growth model accounts for constant volume + But numerical errors will lead to small changes in volume over time + ''' + self.pbm.PSD *= 1 / self.pbm.ThirdMoment() + + def constrainedGrowth(self, growthRate, z = 0): + ''' + Constrain growth rate due to zener pinning + + The growth rate given the zener radius is defined by: + dR/dt = alpha * M * gbe * ((1/Rcr - 1/Ri) +/- 1/Rz) + Where 1/Rz is added if (1/Rcr - 1/Ri) + 1/Rz < 0 (inhibits grain dissolution) + And 1/Rz is subtracted in (1/Rcr - 1/Ri) - 1/Rz) > 0 (inhibits grain growth) + And dR/dt is 0 for Ri between these two limits + + Note: Rather than Rz (zener radius), we use z here which represents the drag force + But these are related by z = 1/Rz + + Parameters + ---------- + growthRate : array + Growth rate for grain sizes + z : float (optional) + Zener radius, default is 0, which will not change the growth rate + ''' + upper = growthRate + self.alpha * self.M * self.gbe * z + lower = growthRate - self.alpha * self.M * self.gbe * z + growIndices = lower > 0 + dissolveIndices = upper < 0 + cG = np.zeros(len(growthRate)) + cG[growIndices] = lower[growIndices] + cG[dissolveIndices] = upper[dissolveIndices] + return cG + + def getCurrentX(self): + ''' + Returns current time and grain size distribution + ''' + return self.time[-1], [self.pbm.PSD] + + def getdXdt(self, t, x): + ''' + Returns dn_i/dt for the grain size distribution + + Steps: + 1. Get grain growth rate and corrected it with zener drag force + 2. Get dn_i/dt from the PBM given the Eulerian implementation + ''' + self._growthRate = self.grainGrowth(x[0]) + self._growthRate = self.constrainedGrowth(self._growthRate, self._z) + return [self.pbm.getdXdtEuler(self._growthRate, 0, 0, x[0])] + + def correctdXdt(self, dt, x, dXdt): + ''' + Corrects dn_i/dt with the new time step + ''' + dXdt[0] = self.pbm.correctdXdtEuler(dt, self._growthRate, 0, 0, x[0]) + + def getDt(self, dXdt): + ''' + Calculated a suitable dt with the growth rate and new time step + We'll limit the max time step to the remaining time for solving + ''' + return self.pbm.getDTEuler(self.finalTime - self.time[-1], self._growthRate, self.dissolutionIndex) + + def postProcess(self, time, x): + ''' + Sets grain size distribution to x and record time and average grain size + + Steps: + 1. Set grain size distribution + 2. Adjust PSD size classes + 3. Remove grains below the dissolution threshold + 4. Normalize grain size distribution to 1 (should be a tiny correction factor due to step 3) + 5. Record time and average grain size + ''' + self.pbm.UpdatePBMEuler(time, x[0]) + self.pbm.adjustSizeClassesEuler(True) + self.dissolutionIndex = self.pbm.getDissolutionIndex(self.maxDissolution, 0) + #self.pbm.PSD[:self.dissolutionIndex] = 0 + self.Normalize() + self.time = np.append(self.time, time) + self.avgR = np.append(self.avgR, self.Rm(self.pbm.PSD)) + self.updateCoupledModels() + return [self.pbm.PSD], False + + def printHeader(self): + ''' + Header string before solving + ''' + print('Iteration\tTime(s)\t\tSim Time(s)\tGrain Size (um)') + + def printStatus(self, iteration, modelTime, simTimeElapsed): + ''' + Status string that prints every n iteration + ''' + print('{}\t\t{:.1e}\t\t{:.1f}\t\t{:.3e}'.format(iteration, modelTime, simTimeElapsed, self.avgR[-1]*1e6)) + + def computeZenerRadius(self, model): + ''' + Gets zener radius/drag force from PrecipitateModel + + Drag force is defined as z_j = f_j^m_j / (K_j * avgR_j) + Where f_j is volume fraction for phase j + And avgR_j is average radius for phase j + The total drag force is the sum of z_j over all the phases + + Parameters + ---------- + model : PrecpitateModel + ''' + z = np.zeros(len(model.phases)) + for p in range(len(model.phases)): + phaseName = model.phases[p] if model.phases[p] in self.m else 'all' + if model.avgR[model.n, p] > 0: + z[p] += np.power(model.betaFrac[model.n, p], self.m[phaseName]) / (self.K[phaseName] * model.avgR[model.n, p]) + self._z = np.sum(z) + + def computeZenerRadiusByN(self, model, x): + ''' + Gets zener radius/drag force from PrecipitateModel and PSD defined by x + + Drag force is defined as z_j = f_j^m_j / (K_j * avgR_j) + Where f_j is volume fraction for phase j + And avgR_j is average radius for phase j + The total drag force is the sum of z_j over all the phases + + Parameters + ---------- + model : PrecpitateModel + x : list[np.array] + List of particle size distributions in model + ''' + z = np.zeros(len(model.phases)) + for p in range(len(model.phases)): + volRatio = model.VmAlpha / model.VmBeta[p] + phaseName = model.phases[p] if model.phases[p] in self.m else 'all' + Ntot = model.PBM[p].ZeroMomentFromN(x[p]) + RadSum = model.PBM[p].MomentFromN(x[p], 1) + fBeta = np.amin([volRatio * model.GB[p].volumeFactor * model.PBM[p].ThirdMomentFromN(x[p]), 1]) + avgR = 0 if Ntot == 0 else RadSum / Ntot + + if avgR > 0: + z[p] += np.power(fBeta, self.m[phaseName]) / (self.K[phaseName] * avgR) + + self._z = np.sum(z) + + def updateCoupledModel(self, model): + ''' + Computes zener radius/drag force from the PrecipitateModel, + Then solves the grain growth model with the time step of the PrecipitateModel + + Parameters + ---------- + model : PrecpitateModel + ''' + self.computeZenerRadius(model) + self.solve(model.time[model.n] - model.time[model.n-1], solverType=self.solverType) + + def plotDistribution(self, ax, *args, **kwargs): + ''' + Plots particle size distribution + + Parameters + ---------- + ax : matplotlib axes + ''' + self.pbm.PlotCurve(ax, *args, **kwargs) + ax.set_xlabel('Grain Radius (m)') + + def plotDistributionDensity(self, ax, *args, **kwargs): + ''' + Plots particle size distribution density + + Parameters + ---------- + ax : matplotlib axes + ''' + self.pbm.PlotDistributionDensity(ax, *args, **kwargs) + ax.set_xlabel('Grain Radius (m)') + + def plotRadiusvsTime(self, ax, bounds = None, timeUnits = 's', *args, **kwargs): + ''' + Plots average grain radius over time + + Parameters + ---------- + ax : matplotlib axes + ''' + timeScale, timeLabel, bounds = getTimeAxis(self, timeUnits, bounds) + ax.plot(self.time*timeScale, self.avgR, *args, **kwargs) + ax.set_xlabel(timeLabel) + ax.set_ylabel('Grain Radius (m)') + ax.set_ylim([0, 1.1*np.amax(self.avgR)]) + ax.set_xlim(bounds) \ No newline at end of file diff --git a/kawin/Strength.py b/kawin/precipitation/coupling/Strength.py similarity index 80% rename from kawin/Strength.py rename to kawin/precipitation/coupling/Strength.py index 9a222cf..5074643 100644 --- a/kawin/Strength.py +++ b/kawin/precipitation/coupling/Strength.py @@ -1,9 +1,14 @@ import numpy as np +from kawin.precipitation.Plot import getTimeAxis class StrengthModel: ''' Defines strength model + Following implementation described in + M.R. Ahmadi, E. Povoden-Karadeni, K.I. Oksuz, A. Falahati and E. Kozeschnik + Computational Materials Science 91 (2014) 173-186 + 6 contributions are accounted for For dislocation cutting, contributions are coherency, modulus, anti-phase boundary, stacking fault energy and interfacial energy For dislocation bowing, contribution is orowan @@ -65,19 +70,69 @@ def __init__(self): #Superposition exponent for total strength self.totalStrengthExp = 1.8 - def _getStrengthFunctions(self): + #Strength terms + self.rss = None + self.ls = None + self.solidStrength = None + + def save(self, filename, compressed = True): + ''' + Saves strength model data + + Note, this only saves solid solution strength, the rss and ls terms + Parameters should be free so user can load model and evaluate different parameters + ''' + if compressed: + np.savez_compressed(filename, ssStrength=self.solidStrength, rss = self.rss, ls = self.ls) + else: + np.savez(filename, ssStrength=self.solidStrength, rss = self.rss, ls = self.ls) + + def load(self, filename): + data = np.load(filename) + self.solidStrength = data['ssStrength'] + self.rss = data['rss'] + self.ls = data['ls'] + + def _getStrengthFunctions(self, selectedContributions = None): ''' Internal function that creates arrays for dislocation cutting mechanisms wfuncs, sfuncs - list of functions for each contribution for weak and strong effects contributions - each contribution has a dictionary of str : boolean to say whether a phase has that contribution labels - labels for plotting + + Parameters + ---------- + selectedContributions : None or List[str] + If None, will return weak/strong functions and labels for all contributions + If List[str], will return weak/strong functions and labels for only the contributions defined in list + Options are: Coherency, Modulus, APB, SFE and/or Interfacial + + Returns + ------- + wfuncs - List of functions for weak contributions + sfuncs - List of functions for strong contributions + contributions - List of {phase str:boolean} for whether the contribution is enabled + labels - List of labels for plotting ''' wfuncs = [self.coherencyWeak, self.modulusWeak, self.APBweak, self.SFEweak, self.interfacialWeak] sfuncs = [self.coherencyStrong, self.modulusStrong, self.APBstrong, self.SFEstrong, self.interfacialStrong] contributions = [self.coherencyEffect, self.modulusEffect, self.APBEffect, self.SFEffect, self.IFEffect] labels = ['Coherency', 'Modulus', 'APB', 'SFE', 'Interfacial'] - return wfuncs, sfuncs, contributions, labels + if selectedContributions is None: + return wfuncs, sfuncs, contributions, labels + else: + wfuncsSub, sfuncsSub, contributionsSub, labelsSub = [], [], [], [] + lowerLabels = [l.lower() for l in labels] + for c in selectedContributions: + if c.lower() in lowerLabels: + index = lowerLabels.index(c.lower()) + wfuncsSub.append(wfuncs[index]) + sfuncsSub.append(sfuncs[index]) + contributionsSub.append(contributions[index]) + labelsSub.append(labels[index]) + return wfuncsSub, sfuncsSub, contributionsSub, labelsSub + def setBaseStrength(self, sigma0): ''' @@ -434,10 +489,10 @@ def orowan(self, r, Ls): ''' return self.J * self.G * self.b / (2 * np.pi * np.sqrt(1 - self.nu) * Ls) * np.log(2 * r / self.ri) - def ssStrength(self, model): + def ssStrength(self, model, n): ''' Solid solution strength model - \sigma_ss = \sum{k_i * c_i^n} + sigma_ss = sum(k_i * c_i^n) Parameters ---------- @@ -449,16 +504,19 @@ def ssStrength(self, model): strength : array of floats Solid solution strength contribution over time ''' - if len(model.xComp.shape) == 1: - return self.ssweights[model.elements[0]] * model.xComp**self.ssexp - else: - return np.sum([self.ssweights[model.elements[i]]*model.xComp[:,i]**self.ssexp for i in range(len(model.elements))], axis=0) + val = 0 + for i in range(len(model.elements)): + if model.elements[i] in self.ssweights: + val += self.ssweights[model.elements[i]]*model.xComp[n,i]**self.ssexp + return val - def rssterm(self, model, p, i): + def rssterm(self, model, p): ''' Mean projected radius of particles - This function is inserted into a PrecipitateModel object as an additional output + r1 = first ordered moment of particle size distribution + r2 = second ordered moment of particle size distribution + rss = sqrt(2/3) * r2 / r1 Parameters ---------- @@ -476,11 +534,14 @@ def rssterm(self, model, p, i): rss = np.sqrt(2/3) * r2 / r1 return rss - def Lsterm(self, model, p, i): + def Lsterm(self, model, p): ''' Mean surface to surface distance between particles - This function is inserted into a PrecipitateModel object as an additional output + r1 = first ordered moment of particle size distribution + r2 = second ordered moment of particle size distribution + rss = sqrt(2/3) * r2 / r1 + ls = sqrt(ln(3)/(2*pi*r1) + (2*rss)^2) - 2*rss Parameters ---------- @@ -498,32 +559,25 @@ def Lsterm(self, model, p, i): rss = np.sqrt(2/3) * r2 / r1 Ls = np.sqrt(np.log(3) / (2*np.pi*r1) + (2*rss)**2) - 2*rss return Ls - - def insertStrength(self, model): + + def updateCoupledModel(self, model): ''' - Inserts Fterm into the KWNmodel to be solved for + Computes rss, ls and solid solution strengthening terms + from current state of the PrecipitateModel Parameters ---------- - model : KWNEuler object + model : PrecpitateModel ''' - model.addAdditionalOutput(self.rssName, self.rssterm) - model.addAdditionalOutput(self.LsName, self.Lsterm) + if self.rss is None: + self.rss = np.zeros((1, len(model.phases))) + self.ls = np.zeros((1, len(model.phases))) + self.solidStrength = np.zeros(1) + self.solidStrength[0] = self.ssStrength(model, 0) - def getParticleSpacing(self, model, phase = None): - ''' - Grabs mean projected radius and surface to surface distance from a PrecipitateModel - - Parameters - ---------- - model : PrecipitateModel - phase : str (optional) - Phase name, will default to first phase in the model - ''' - index = model.phaseIndex(phase) - rss = model.getAdditionalOutput(self.rssName)[index] - Ls = model.getAdditionalOutput(self.LsName)[index] - return rss, Ls + self.rss = np.append(self.rss, [[self.rssterm(model, p) for p in range(len(model.phases))]], axis=0) + self.ls = np.append(self.ls, [[self.Lsterm(model, p) for p in range(len(model.phases))]], axis=0) + self.solidStrength = np.append(self.solidStrength, [self.ssStrength(model, model.n)], axis=0) def precStrength(self, model): ''' @@ -533,14 +587,16 @@ def precStrength(self, model): ---------- model : PrecipitateModel ''' - rss = model.getAdditionalOutput(self.rssName) - Ls = model.getAdditionalOutput(self.LsName) + #rss = model.getAdditionalOutput(self.rssName) + #Ls = model.getAdditionalOutput(self.LsName) + rss = self.rss + Ls = self.ls ps = [] - totalCompare = np.zeros(len(rss[0])) + totalCompare = np.zeros(len(rss[:,0])) for i in range(len(model.phases)): - weakContributions, strongContributions, orowan, _ = self.getStrengthContributions(rss[i], Ls[i], model.phases[i]) - strength, compare = self.combineStrengthContributions(weakContributions, strongContributions, orowan, returnComparison=True) + weakContributions, strongContributions, orowan, _ = self.getStrengthContributions(rss[:,i], Ls[:,i], model.phases[i]) + strength, compare, _ = self.combineStrengthContributions(weakContributions, strongContributions, orowan, returnComparison=True) compare[~np.isfinite(strength)] = 0 strength[~np.isfinite(strength)] = 0 ps.append(strength) @@ -552,7 +608,7 @@ def precStrength(self, model): totalStrength[~indices] = np.power(np.sum(np.power(ps[:,~indices], self.multiphaseMixedExp), axis=0), 1/self.multiphaseMixedExp) return totalStrength - def getStrengthContributions(self, rss, Ls, phase = 'all'): + def getStrengthContributions(self, rss, Ls, phase = 'all', selectedContributions=None): ''' Gets strength contributions from a model @@ -565,13 +621,17 @@ def getStrengthContributions(self, rss, Ls, phase = 'all'): phase : str (optional) Phase name Defaults to 'all' + selectedContributions : None or List[str] + If None, will return weak/strong functions and labels for all contributions + If List[str], will return weak/strong functions and labels for only the contributions defined in list + Options are: Coherency, Modulus, APB, SFE and/or Interfacial ''' r0Weak = Ls / np.sqrt(np.cos(self.psi / 2)) r0Strong = Ls weakContributions = [] strongContributions = [] contributionsList = [] - wfuncs, sfuncs, contributions, ylabel = self._getStrengthFunctions() + wfuncs, sfuncs, contributions, ylabel = self._getStrengthFunctions(selectedContributions) for i in range(len(wfuncs)): if contributions[i]['all'] or (phase in contributions[i] and contributions[i][phase]): with np.errstate(divide='ignore', invalid='ignore'): @@ -589,7 +649,7 @@ def getStrengthContributions(self, rss, Ls, phase = 'all'): tauowo = np.array(self.orowan(rss, Ls)) tauowo[~np.isfinite(tauowo)] = 0 return weakContributions, strongContributions, tauowo, contributionsList - + def combineStrengthContributions(self, weakContributions, strongContributions, orowan, returnComparison = False): ''' Combines weak, strong and orowan contributions @@ -614,7 +674,7 @@ def combineStrengthContributions(self, weakContributions, strongContributions, o orowan[~np.isfinite(orowan)] = 0 taumin = np.amin(np.array([tausumweak, tausumstrong, orowan]), axis=0) if returnComparison: - return self.M * taumin, (tausumweak > tausumstrong) & (tausumweak > orowan) + return self.M * taumin, (tausumweak > tausumstrong) & (tausumweak > orowan), (self.M * tausumweak, self.M * tausumstrong, self.M * orowan) else: return self.M * taumin @@ -649,7 +709,7 @@ def getStrengthUnits(self, strengthUnits = 'Pa'): ylabel = 'Strength (GPa)' return yscale, ylabel - def plotPrecipitateStrengthOverR(self, ax, r, Ls, phase=None, strengthUnits = 'MPa', plotContributions = False, *args, **kwargs): + def plotPrecipitateStrengthOverR(self, ax, r, Ls, phase=None, strengthUnits = 'MPa', contribution = None, *args, **kwargs): ''' Plots precipitate strength contribution as a function of radius @@ -662,15 +722,18 @@ def plotPrecipitateStrengthOverR(self, ax, r, Ls, phase=None, strengthUnits = 'M Surface to surface particle distance strengthUnits : str Units for strength, options are 'Pa', 'kPa', 'MPa' or 'GPa' - plotContributions : bool - Whether to plot all contributions + contribution : None or str + If None, will plot overall strength + If str, will plot selected contribution or all contributions + Options are: Coherency, Modulus, APB, SFE or Interfacial ''' if phase is None: phase = 'all' - self.plotPrecipitateStrengthOverX(ax, r, r, Ls, phase, strengthUnits, plotContributions, *args, **kwargs) + self.plotPrecipitateStrengthOverX(ax, r, r, Ls, phase, strengthUnits, contribution, *args, **kwargs) + ax.set_xlabel('Radius (m)') - def plotPrecipitateStrengthOverTime(self, ax, model, phase = None, bounds = None, timeUnits = 's', strengthUnits = 'MPa', plotContributions = False, *args, **kwargs): + def plotPrecipitateStrengthOverTime(self, ax, model, phase = None, bounds = None, timeUnits = 's', strengthUnits = 'MPa', contribution = None, *args, **kwargs): ''' Plots precipitate strength contribution as a function of time @@ -683,27 +746,25 @@ def plotPrecipitateStrengthOverTime(self, ax, model, phase = None, bounds = None Surface to surface particle distance strengthUnits : str Units for strength, options are 'Pa', 'kPa', 'MPa' or 'GPa' - plotContributions : bool - Whether to plot all contributions + contribution : None or str + If None, will plot overall strength + If str, will plot selected contribution or all contributions + Options are: Coherency, Modulus, APB, SFE or Interfacial ''' - r, Ls = self.getParticleSpacing(model, phase) + timeScale, timeLabel, bounds = getTimeAxis(model, timeUnits, bounds) - timeScale, timeLabel, bounds = model.getTimeAxis(timeUnits, bounds) - - self.plotPrecipitateStrengthOverX(ax, model.time*timeScale, r, Ls, phase, strengthUnits, plotContributions, *args, **kwargs) - if plotContributions: - row, col = [0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1] - for i in range(len(row)): - ax[row[i], col[i]].set_xlabel(timeLabel) - ax[row[i], col[i]].set_xscale('log') - else: - ax.set_xlabel(timeLabel) - ax.set_xscale('log') + self.plotPrecipitateStrengthOverX(ax, model.time*timeScale, self.rss, self.ls, phase, strengthUnits, contribution, *args, **kwargs) + ax.set_xlabel(timeLabel) + ax.set_xscale('log') + ax.set_xlim(bounds) - def plotPrecipitateStrengthOverX(self, ax, x, r, Ls, phase = None, strengthUnits = 'MPa', plotContributions = False, *args, **kwargs): + def plotPrecipitateStrengthOverX(self, ax, x, r, Ls, phase = None, strengthUnits = 'MPa', contribution = None, *args, **kwargs): ''' Plots precipitate strength contribution as a function of x + TODO: make this a bit more generalized where you can set the contribution you want to plot + This should also remove the restriction that axes subplot must be 3x2 + Parameters ---------- ax : Axis @@ -715,40 +776,49 @@ def plotPrecipitateStrengthOverX(self, ax, x, r, Ls, phase = None, strengthUnits Surface to surface particle distance, must correspond to x strengthUnits : str Units for strength, options are 'Pa', 'kPa', 'MPa' or 'GPa' - plotContributions : bool - Whether to plot all contributions + contribution : None or str + If None, will plot overall strength + If str, will plot selected contribution or all contributions + Options are: Coherency, Modulus, APB, SFE, Interfacial, Orowan or All ''' yscale, ylabel = self.getStrengthUnits(strengthUnits) - if plotContributions: - _, _, _, ylabel = self._getStrengthFunctions() - row, col = [0, 0, 1, 1, 2], [0, 1, 0, 1, 0] - weak, strong, oro, contributionList = self.getStrengthContributions(r, Ls, phase) - for i in range(len(row)): - if ylabel[i] in contributionList: - index = contributionList.index(ylabel[i]) - ax[row[i], col[i]].plot(x, self.M * weak[index] / yscale, x, self.M * strong[index] / yscale, *args, **kwargs) - ax[row[i], col[i]].legend(['Weak', 'Strong']) - ax[row[i], col[i]].set_ylim(bottom=0) + if contribution is not None: + if contribution.lower() == 'orowan': + tauowo = np.array(self.orowan(r, Ls)) + tauowo[~np.isfinite(tauowo)] = 0 + ax.plot(x, self.M * tauowo / yscale, *args, **kwargs) + ax.set_ylabel(r'$\tau_{orowan}$ (' + strengthUnits + ')') + ax.set_ylim(bottom=0) + ax.set_xlim([x[0], x[-1]]) + + elif contribution.lower() != 'all': + _, _, _, ylabel = self._getStrengthFunctions([contribution]) + weak, strong, oro, contributionList = self.getStrengthContributions(r, Ls, phase, [contribution]) + if ylabel[0] in contributionList: + ax.plot(x, self.M * weak[0] / yscale, x, self.M * strong[0] / yscale, *args, **kwargs) + ax.set_ylim(bottom=0) + ax.legend(['Weak', 'Strong']) else: - ax[row[i], col[i]].plot(x, np.zeros(len(x)), *args, **kwargs) - ax[row[i], col[i]].set_ylim([-1, 1]) - ax[row[i], col[i]].set_xlabel('Radius (m)') - ax[row[i], col[i]].set_ylabel(r'$\tau_{' + ylabel[i] + '}$ (' + strengthUnits + ')') - ax[row[i], col[i]].set_xlim([x[0], x[-1]]) - - #If no contributions exists for shearable precipitates, then wtot and stot is 0 - wtot = np.zeros(len(x)) if len(weak) == 0 else np.array(np.power(np.sum(np.power(weak, self.singlePhaseExp), axis=0), 1/self.singlePhaseExp)) - stot = np.zeros(len(x)) if len(strong) == 0 else np.array(np.power(np.sum(np.power(strong, self.singlePhaseExp), axis=0), 1/self.singlePhaseExp)) - smin = np.amin([wtot, stot, oro], axis=0) - ax[2,1].plot(x, self.M * wtot/yscale, x, self.M * stot/yscale, x, self.M * oro/yscale, x, self.M * smin/yscale, *args, **kwargs) - ax[2,1].set_ylim(bottom=0) - ax[2,1].set_ylabel(r'$\tau$ (' + strengthUnits + ')') - ax[2,1].set_xlabel('Radius (m)') - ax[2,1].legend(['Weak', 'Strong', 'Orowan', 'Minimum']) - ax[2,1].set_xlim([x[0], x[-1]]) + ax.plot(x, np.zeros(len(x)), *args, **kwargs) + ax.set_ylim([-1, 1]) + ax.set_ylabel(r'$\tau_{' + ylabel[0] + '}$ (' + strengthUnits + ')') + ax.set_xlim([x[0], x[-1]]) + + else: + _, _, _, ylabel = self._getStrengthFunctions() + weak, strong, oro, contributionList = self.getStrengthContributions(r, Ls, phase) + strength, _, summedContributions = self.combineStrengthContributions(weak, strong, oro, returnComparison=True) + wtot, stot, oro = summedContributions + + ax.plot(x, wtot/yscale, x, stot/yscale, x, oro/yscale, x, strength/yscale, *args, **kwargs) + ax.set_ylim(bottom=0) + ax.set_ylabel(r'$\tau$ (' + strengthUnits + ')') + ax.legend(['Weak', 'Strong', 'Orowan', 'Minimum']) + ax.set_xlim([x[0], x[-1]]) + else: - weak, strong, oro, contributionList = self.getStrengthContributions(r, Ls, Leff, Ls, phase) + weak, strong, oro, contributionList = self.getStrengthContributions(r, Ls, phase) strength = self.combineStrengthContributions(weak, strong, oro) ax.plot(x, strength / yscale, *args, **kwargs) ax.set_ylabel('Yield ' + ylabel) @@ -772,11 +842,12 @@ def plotStrength(self, ax, model, plotContributions = False, bounds = None, time strengthUnits : str Units for strength, options are 'Pa', 'kPa', 'MPa' or 'GPa' ''' - timeScale, timeLabel, bounds = model.getTimeAxis(timeUnits, bounds) + timeScale, timeLabel, bounds = getTimeAxis(model, timeUnits, bounds) yscale, ylabel = self.getStrengthUnits(strengthUnits) sigma0 = self.sigma0 * np.ones(len(model.time)) - ssStrength = self.ssStrength(model) if len(self.ssweights) > 0 else np.zeros(len(model.time)) + #ssStrength = self.ssStrength(model) if len(self.ssweights) > 0 else np.zeros(len(model.time)) + ssStrength = self.solidStrength precStrength = self.precStrength(model) total = self.totalStrength(ssStrength, precStrength) diff --git a/kawin/precipitation/coupling/__init__.py b/kawin/precipitation/coupling/__init__.py new file mode 100644 index 0000000..7727dae --- /dev/null +++ b/kawin/precipitation/coupling/__init__.py @@ -0,0 +1,2 @@ +from .GrainGrowth import GrainGrowthModel +from .Strength import StrengthModel \ No newline at end of file diff --git a/kawin/EffectiveDiffusion.py b/kawin/precipitation/non_ideal/EffectiveDiffusion.py similarity index 100% rename from kawin/EffectiveDiffusion.py rename to kawin/precipitation/non_ideal/EffectiveDiffusion.py diff --git a/kawin/ElasticFactors.py b/kawin/precipitation/non_ideal/ElasticFactors.py similarity index 91% rename from kawin/ElasticFactors.py rename to kawin/precipitation/non_ideal/ElasticFactors.py index 67264ee..75527b7 100644 --- a/kawin/ElasticFactors.py +++ b/kawin/precipitation/non_ideal/ElasticFactors.py @@ -1,7 +1,7 @@ import numpy as np import itertools import matplotlib.pyplot as plt -from kawin.LebedevNodes import loadPoints +from kawin.precipitation.non_ideal.LebedevNodes import loadPoints import copy class StrainEnergy: @@ -45,6 +45,24 @@ def __init__(self): self._cachedRange = 5 self._cachedIntervals = 100 + self._ohm_inverse = self._ohm_quickInverse + + def setOhmInverseFunction(self, method = 'quick'): + ''' + Sets method to invert the ohm term in calculating eshelby's tensor + + Parameters + ---------- + method : str + 'numpy' - uses np.linalg.inv, which can be slower for batch, but runs through + multiple checks for whether values are real/complex or if inverse exists + 'quick' - quick inverse using Cramer's rule assuming that values are real and inverse exists - recommended method + ''' + if method == 'numpy': + self._ohm_inverse = self._ohm_npinv + else: + self._ohm_inverse = self._ohm_quickInverse + def setAspectRatioResolution(self, resolution = 0.01, cachedRange = 5): ''' Sets resolution to which equilibrium aspect ratios are calculated @@ -278,7 +296,7 @@ def _setModuli(self, E = None, nu = None, G = None, lam = None, K = None, M = No nu = (3*K - E) / (6*K) G = 3*K*E / (9*K - E) elif M: - S = -np.sqrt(E**2 + 9*M**2 - 10*E*M) + S = np.sqrt(E**2 + 9*M**2 - 10*E*M) nu = (E - M + S) / (4*M) G = (3*M + E - S) / 8 elif nu: @@ -634,6 +652,54 @@ def _OhmGeneral(self, n): ''' invOhm = np.tensordot(self._c4, np.tensordot(n, n, axes=0), axes=[[1,2], [0,1]]) return np.linalg.inv(invOhm) + + def _ohm_quickInverse(self, m): + ''' + Hard coded inverse of m which is of shape (3,3,n) + + numpy inv is more optimized for larger matrices, but can be slower for small + matrices such as a 2x2 or 3x3. We can take advantage of 3x3 matrices having a computable + inverse to make it faster + + NOTE: this only works since we know that m has a shape of (3,3,n) and is only composed of real numbers + + This function can probably be a bit more efficient, but quick + profiling on sphInt gives around a 35x speedup compared to doing + np.transpose(np.linalg.inv(np.transpose(m, (2,0,1))), (1,2,0)) + where the slowdown was in np.linalg.inv + + For matrix: + | a b c | + | d e f | + | g h i | + + Inverse is defined as: + | ei-fh fg-di dh-eg | | A B C | + | ch-bi ai-cg bg-ah | / det -> | D E F | / det + | bf-ce cd-af ae-bd | | G H I | + Where det = aA + bB + cC + ''' + a, b, c, d, e, f, g, h, i = m[0,0], m[0,1], m[0,2], m[1,0], m[1,1], m[1,2], m[2,0], m[2,1], m[2,2] + A = e*i - f*h + B = f*g - d*i + C = d*h - e*g + D = c*h - b*i + E = a*i - c*g + F = b*g - a*h + G = b*f - c*e + H = c*d - a*f + I = a*e - b*d + det = a*A + b*B + c*C + return np.array([[A, B, C], [D, E, F], [G, H, I]]) / det + + def _ohm_npinv(self, m): + ''' + Inverts ohm term using np.linalg.inv + + numpy inverse function takes in an array of shape (m,n,n) and inverts each nxn matrix + So we have to transpose m from (3,3,n) -> (n,3,3), then invert, then transpose (n,3,3) ->(3,3,n) + ''' + return np.transpose(np.linalg.inv(np.transpose(m, (2,0,1))), (1,2,0)) def sphInt(self): ''' @@ -654,7 +720,8 @@ def sphInt(self): #Ohm term (Ohm_ij = inverse(C_iklj * n_k * n_l)) #For all grid points (Ohm_ijn = inverse(C_iklj) * nProd_kln) invOhm = np.tensordot(self._c4, nProd, axes=[[1,2], [0,1]]) - ohm = np.transpose(np.linalg.inv(np.transpose(invOhm, (2,0,1))), (1,2,0)) + + ohm = self._ohm_inverse(invOhm) #Tensor product (D_ijkl = intergral(ohm_ij * n_k * n_l * endTerm)) #For summing over grid points (D_ijkl = ohm_ij * nProd_kln * endTerm_n) @@ -667,8 +734,8 @@ def Dijkl(self): ''' Dijkl term for Eshelby's theory ''' - #return -np.product(self.r)/(4*np.pi) * self.sphericalIntegral(self.Dfunc) - return -np.product(self.r)/(4*np.pi) * self.sphInt() + #return -np.prod(self.r)/(4*np.pi) * self.sphericalIntegral(self.Dfunc) + return -np.prod(self.r)/(4*np.pi) * self.sphInt() def Sijmn(self, D): ''' @@ -696,7 +763,7 @@ def _strainEnergyEllipsoidWithStress(self): ''' Strain energy of ellipsoidal particle with applied stress ''' - V = 4*np.pi/3 * np.product(self.r) + V = 4*np.pi/3 * np.prod(self.r) S = self.Sijmn(self.Dijkl()) stress = self._multiply(self._c4, self._multiply(S, self.eigstrain) - self.eigstrain) stress0 = self._multiply(self._c4, self._multiply(S, self.appstrain) - self.appstrain) @@ -706,7 +773,7 @@ def _strainEnergyEllipsoid(self): ''' Strain energy of ellipsoidal particle ''' - V = 4*np.pi/3 * np.product(self.r) + V = 4*np.pi/3 * np.prod(self.r) S = self.Sijmn(self.Dijkl()) stress = self._multiply(self._c4, self._multiply(S, self.eigstrain) - self.eigstrain) return self._strainEnergy(stress, self.eigstrain, V) @@ -715,7 +782,7 @@ def _strainEnergyEllipsoid2(self): ''' Alternative method of strain energy on ellipsoidal particle using 2nd rank tensors ''' - V = 4*np.pi/3 * np.product(self.r) + V = 4*np.pi/3 * np.prod(self.r) S = self._convert4To2rankTensor(self.Sijmn(self.Dijkl())) eigFlat = self._convert2rankToVec(self.eigstrain) multTerm = np.matmul(self.c, S - np.eye(6)) @@ -725,7 +792,7 @@ def _strainEnergyBohm(self): ''' Strain energy of particle for when matrix and precipitate phases have different elastic tensors ''' - V = 4*np.pi/3 * np.product(self.r) + V = 4*np.pi/3 * np.prod(self.r) S = self.Sijmn(self.Dijkl()) #invTerm = np.linalg.tensorinv(self._multiply(self._c4Prec - self._c4, S) + self._c4) invTerm = self._invert4rankTensor(self._multiply(self._c4Prec - self._c4, S) + self._c4) @@ -738,7 +805,7 @@ def _strainEnergyBohm2(self): ''' Strain energy of particle for when matrix and precipitate phases have different elastic tensors using 2nd rank tensors ''' - V = 4*np.pi/3 * np.product(self.r) + V = 4*np.pi/3 * np.prod(self.r) S = self._convert4To2rankTensor(self.Sijmn(self.Dijkl())) eigFlat = self._convert2rankToVec(self.eigstrain) invTerm = np.linalg.inv(np.matmul(self.cPrec - self.c, S) + self.c) @@ -751,7 +818,7 @@ def _Khachaturyan(self, I1, I2): ''' Khachaturyan's approximation for strain energy of spherical and cuboidal precipitates ''' - V = 4*np.pi/3 * np.product(self.r) + V = 4*np.pi/3 * np.prod(self.r) A1 = 2 * (self.c[0,0] - self.c[0,1]) / self.c[0,0] A1 -= 12 * (self.c[0,0] + 2 * self.c[0,1]) * (self.c[0,0] - self.c[0,1] - 2 * self.c[3,3]) / (self.c[0,0] * (self.c[0,0] + self.c[0,1] + 2*self.c[3,3])) * I1 A2 = -54 * (self.c[0,0] + 2 * self.c[0,1]) * (self.c[0,0] - self.c[0,1] - 2 * self.c[3,3])**2 / (self.c[0,0] * (self.c[0,0] + self.c[0,1] + 2 * self.c[3,3]) * (self.c[0,0] + 2 * self.c[0,1] + 4 * self.c[3,3])) * I2 @@ -779,7 +846,7 @@ def _strainEnergyCuboidal(self, eta = 1): return (sC - sS) * (eta - 1) / (np.sqrt(2) - 1) + sS def _strainEnergyConstant(self): - return 4 * np.pi / 3 * np.product(self.r) * self._gElasticConstant + return 4 * np.pi / 3 * np.prod(self.r) * self._gElasticConstant def _strainEnergySingle(self, rsingle): ''' diff --git a/kawin/GrainBoundaries.py b/kawin/precipitation/non_ideal/GrainBoundaries.py similarity index 100% rename from kawin/GrainBoundaries.py rename to kawin/precipitation/non_ideal/GrainBoundaries.py diff --git a/kawin/LebedevNodes.py b/kawin/precipitation/non_ideal/LebedevNodes.py similarity index 100% rename from kawin/LebedevNodes.py rename to kawin/precipitation/non_ideal/LebedevNodes.py diff --git a/kawin/ShapeFactors.py b/kawin/precipitation/non_ideal/ShapeFactors.py similarity index 96% rename from kawin/ShapeFactors.py rename to kawin/precipitation/non_ideal/ShapeFactors.py index 53ec02c..46c9d85 100644 --- a/kawin/ShapeFactors.py +++ b/kawin/precipitation/non_ideal/ShapeFactors.py @@ -64,6 +64,21 @@ def _scalarAspectRatioEquation(self, R): return self._aspectRatioScalar * np.ones(len(R)) else: return self._aspectRatioScalar + + def setPrecipitateShape(self, precipitateShape, ar = 1): + ''' + General shape setting function + + Defualts to spherical + ''' + if precipitateShape == ShapeFactor.NEEDLE: + self.setNeedleShape(ar) + elif precipitateShape == ShapeFactor.PLATE: + self.setPlateShape(ar) + elif precipitateShape == ShapeFactor.CUBIC: + self.setCuboidalShape(ar) + else: + self.setSpherical(ar) def setSpherical(self, ar = 1): ''' diff --git a/kawin/precipitation/non_ideal/__init__.py b/kawin/precipitation/non_ideal/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/kawin/solver/Iterators.py b/kawin/solver/Iterators.py new file mode 100644 index 0000000..5697a2b --- /dev/null +++ b/kawin/solver/Iterators.py @@ -0,0 +1,83 @@ +''' +Built-in iterators + +Currently, this is explicit-euler and 4th order runga kutta +''' +def ExplicitEulerIterator(f, t, X_old, updateX): + ''' + Explicit euler iteration scheme + + Defined by: + dXdt = f(t, X_n) + X_n+1 = X_n + f(t, X_n) * dt + + Parameters + ---------- + f : function + dX/dt - function taking in time and X and returning dX/dt + t : float + Current time + X_old : list of arrays + X at time t + updateX : function + Helper function to handle any correction to dxdt + Takes in X_old, dxdt, dt and returns X_new + + Returns + ------- + X_new : unformatted list of floats + New values of X in format of X_old + dt : float + Time step + ''' + dxdt, dt = f(t, X_old, True) + return updateX(X_old, dxdt, dt), dt + +def RK4Iterator(f, t, X_old, updateX): + ''' + 4th order Runga Kutta iteration scheme + + Defined by: + k1 = f(t, X_n) + k2 = f(t + dt/2, X_n + k1 * dt/2) + k3 = f(t + dt/2, X_n + k2 * dt/2) + k4 = f(t + dt, X_n, k3 * dt) + X_n+1 = X_n + 1/6 * (k1 + 2*k2 + 2*k3 + k4) * dt + + Parameters + ---------- + f : function + dX/dt - function taking in time and X and returning dX/dt + t : float + Current time + X_old : list of arrays + X at time t + updateX : function + Helper function to handle any correction to dxdt + Takes in X_old, dxdt, dt and returns X_new + + Returns + ------- + X_new : unformatted list of floats + New values of X in format of X_old + dt : float + Time step, important if modified from dtfunc + ''' + dxdt, dt = f(t, X_old, True) + + k1 = dxdt + dxdtsum = k1 + X_k1 = updateX(X_old, k1, dt/2) + + k2 = f(t, X_k1) + dxdtsum += 2*k2 + X_k2 = updateX(X_old, k2, dt/2) + + k3 = f(t, X_k2) + dxdtsum += 2*k3 + X_k3 = updateX(X_old, k3, dt) + + k4 = f(t, X_k3) + dxdtsum += k4 + + return updateX(X_old, dxdtsum/6, dt), dt \ No newline at end of file diff --git a/kawin/solver/Solver.py b/kawin/solver/Solver.py new file mode 100644 index 0000000..87e57a7 --- /dev/null +++ b/kawin/solver/Solver.py @@ -0,0 +1,225 @@ +from kawin.solver.Iterators import ExplicitEulerIterator, RK4Iterator +from enum import Enum +import time + +class SolverType(Enum): + EXPLICITEULER = 0 + RK4 = 1 + +class DESolver: + ''' + Generic class for ODE/PDE solvers + + Generalization - coupled ODEs or PDEs (bunch of coupled ODEs) can be stated as dX/dt = f(X, t) + + Parameters + ---------- + iterator : SolverType or Iterator + Defines what iteration scheme to use + defaultDt : float (defaults to 0.1) + Default time increment if no function is implement to estimate a good time increment + minDtFrac : float (defaults to 1e-8) + Minimum time step as a fraction of simulation time + maxDtFrac : float (defaults to 1) + Maximum time step as a fraction of simulation time + ''' + def __init__(self, iterator = SolverType.RK4, defaultDT = 0.1, minDtFrac = 1e-8, maxDtFrac = 1): + self.dtmin = minDtFrac #Min and max dt fraction of simulation time + self.dtmax = maxDtFrac + self.dt = defaultDT + + self.setFunctions(self.defaultPreProcess, self.defaultPostProcess, self.defaultPrintHeader, self.defaultPrintStatus) + + self.setIterator(iterator) + + def setIterator(self, iterator): + ''' + Parameters + ---------- + iterator : SolverType or Iterator + Defines what iteration scheme to use + ''' + if iterator == SolverType.EXPLICITEULER: + self.iterator = ExplicitEulerIterator + elif iterator == SolverType.RK4: + self.iterator = RK4Iterator + else: + self.iterator = iterator + + def setFunctions(self, preProcess = None, postProcess = None, printHeader = None, printStatus = None): + ''' + Sets functions before solving + + If any of these are not defined, then the corresponding function will be the default defined here + Except for getDt (which returns defaultDt), the other functions will do nothing + ''' + self.preProcess = self.preProcess if preProcess is None else preProcess + self.postProcess = self.postProcess if postProcess is None else postProcess + self.printHeader = self.printHeader if printHeader is None else printHeader + self.printStatus = self.printStatus if printStatus is None else printStatus + + def setdXdtFunctions(self, f, correctdXdt, getDt, flattenX, unflattenX): + self._f = f + self._correctdXdt = correctdXdt + self._getDt = getDt + self._flattenX = flattenX + self._unflattenX = unflattenX + + def defaultDtFunc(self, dXdt): + ''' + Returns the default time increment + ''' + return self.dt + + def defaultPreProcess(self): + ''' + Default pre-processing function before an iteration + ''' + return + + def defaultPostProcess(self, currTime, X_new): + ''' + Default post-processing function after an iteration + ''' + return X_new, False + + def defaultPrintHeader(self): + ''' + Default print function before solving + ''' + return + + def defaultPrintStatus(self, iteration, modeltime, simTimeElapsed): + ''' + Default print function for when n iterations passed and verbose is true + ''' + return + + def correctdXdtNotImplemented(self, dt, x, dXdt): + ''' + Default function to correct dXdt + ''' + pass + + def flattenXNotImplemented(self, X): + ''' + Default flattenX function, which assumes X is in the correct format + ''' + return X + + def unflattenXNotImplemented(self, X_flat, X_ref): + ''' + Default unflattenX function which assumes X is in the correct format + ''' + return X_flat + + def _getdXdt(self, t, x, getDt = False): + ''' + Wrapper around getdXdt which will handle the following: + Handle flattening/unfalttening the x and dx/dt arrays + Calculate dt if not supplied + + The API for the iterator will be that all arrays are 1D np.arrays where operators will be trivial + + Parameters + ---------- + t : float + Time + x : 1D np.array + Model values + getDt : bool + Will calculate dt if True + ''' + unflatX = self._unflattenX(x, self._X0) + dXdt = self._f(t, unflatX) + if getDt: + dt = self._getDt(dXdt) + dt = dt if dt > self._dtmin else self._dtmin + dt = dt if dt < self._dtmax else self._dtmax + return self._flattenX(dXdt), dt + else: + return self._flattenX(dXdt) + + def _updateX(self, x, dxdt, dt): + ''' + Helper function that hides the correctdXdt function + + The API for the iterator will be that all arrays are 1D np.arrays where operators will be trivial + + Parameters + ---------- + x : 1D np.array + Model values + dxdt : 1D np.array + Derivatives at x + dt : float + Time step + ''' + unflatdxdt = self._unflattenX(dxdt, self._X0) + self._correctdXdt(dt, self._X0, unflatdxdt) + return x + self._flattenX(unflatdxdt)*dt + + def solve(self, t0, X0, tf, verbose = False, vIt = 10): + ''' + Solves dX/dt over a time increment + This will be the main function that a model will use + + Steps during each iteration + 1. Print status if vIt iterations passed + 2. preProcess + 3. Iterate + 4. Update current time + 5. postProcess + + Parameters + ---------- + f : function + dX/dt - function taking in time and returning dX/dt + t0 : float + Starting time + X0 : list of arrays + X at time t + tf : float + Final time + verbose: bool (defaults to False) + Whether to print status + vIt : integer (defaults to 10) + Number of iterations to print status + ''' + if verbose: + self.printHeader() + + self._dtmin = self.dtmin * (tf - t0) + self._dtmax = self.dtmax * (tf - t0) + currTime = t0 + i = 0 + timeStart = time.time() + stop = False + while currTime < tf and not stop: + if verbose and i % vIt == 0: + timeFinish = time.time() + self.printStatus(i, currTime, timeFinish - timeStart) + + self.preProcess() + #Limit dtmax to remaining time if it's larger + if self._dtmax > tf - currTime: + self._dtmax = tf - currTime + + #Store X0 as a reference variable for _unflattenX + #We have to do this per iteration since the shape of X0 can change during postProcess + # This is especially true for the population balance model with adaptive bins + #The iterator also returns the flat array of X, so we need to unflatten it afterwards here + self._X0 = X0 + X0_flat, dt = self.iterator(self._getdXdt, currTime, self._flattenX(X0), self._updateX) + X0 = self._unflattenX(X0_flat, self._X0) + + currTime += dt + X0, stop = self.postProcess(currTime, X0) + i += 1 + + if verbose: + if stop: + print('Stopping condition met. Ending simulation early.') + + timeFinish = time.time() + self.printStatus(i, currTime, timeFinish - timeStart) diff --git a/kawin/solver/__init__.py b/kawin/solver/__init__.py new file mode 100644 index 0000000..df1348b --- /dev/null +++ b/kawin/solver/__init__.py @@ -0,0 +1 @@ +from .Solver import DESolver, SolverType \ No newline at end of file diff --git a/kawin/tests/datasets.py b/kawin/tests/datasets.py index baddfcf..5f259dc 100644 --- a/kawin/tests/datasets.py +++ b/kawin/tests/datasets.py @@ -1678,4 +1678,704 @@ PARAMETER DQ(FCC_A1&CR,*;0) 298.15 -40800+R*T*LN(3e-6); 6000 N ! PARAMETER DQ(FCC_A1&NI,*;0) 298.15 -271960+R*T*LN(1.27E-4); 6000 N ! $ +""" + +FECRNI_DB = """ +$ FeCrNi database using parameters taken from MatCalc open steel database mc_fe_v2.060.tdb +$ +$ The mc_fe_v2.059.tdb database is made available under the +$ Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. +$ Any rights in individual contents of the database are licensed under the +$ Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/. +$ +$ ########################################################################## + +ELEMENT VA VACUUM 0.0 0.00 0.00 ! +ELEMENT CR BCC_A2 51.996 4050.0 23.5429 ! +ELEMENT FE BCC_A2 55.847 4489.0 27.2797 ! +ELEMENT NI FCC_A1 58.69 4787.0 29.7955 ! + +FUNCTION GHSERCR + 273.00 -8856.94+157.48*T-26.908*T*LN(T) + +0.00189435*T**2-1.47721E-6*T**3+139250*T**(-1); 2180.00 Y + -34869.344+344.18*T-50*T*LN(T)-2.88526E+32*T**(-9); 6000.00 N +REF:0 ! +FUNCTION GCRFCC + 273.00 +7284+0.163*T+GHSERCR#; 6000.00 N +REF:0 ! +FUNCTION GHSERFE + 273.00 +1225.7+124.134*T-23.5143*T*LN(T)-0.00439752*T**2 + -5.89269E-8*T**3+77358.5*T**(-1); 1811.00 Y + -25383.581+299.31255*T-46*T*LN(T)+2.2960305E+31*T**(-9); 6000.00 N +REF:0 ! +FUNCTION GFEFCC + 273.00 -1462.4+8.282*T-1.15*T*LN(T)+6.4E-04*T**2+GHSERFE#; 1811.00 Y + -27098.266+300.25256*T-46*T*LN(T)+2.78854E+31*T**(-9); 6000.00 N +REF:0 ! +FUNCTION GHSERNI + 273.00 -5179.159+117.854*T-22.096*T*LN(T)-4.8407E-3*T**2; 1728.00 Y + -27840.655+279.135*T-43.10*T*LN(T)+1.12754E+31*T**(-9); 6000.00 N +REF:0 ! +FUNCTION GNIBCC + 273.00 +8715.084-3.556*T+GHSERNI#; 6000.00 N +REF:0 ! + +$FCC_A1 phase + +TYPE_DEFINITION ' GES A_P_D FCC_A1 MAGNETIC -3.0 0.28 ! +TYPE_DEFINITION % SEQ *! +PHASE FCC_A1 %' 2 1 1 ! +CONSTITUENT FCC_A1 : CR,FE%,NI : VA% : ! + +PARAMETER G(FCC_A1,CR:VA;0) 273.00 +7284+0.163*T+GHSERCR#; 6000.00 N +REF:0 ! +PARAMETER G(FCC_A1,FE:VA;0) 273.00 -1462.4+8.282*T-1.15*T*LN(T) + +0.00064*T**2+GHSERFE#; 1811.00 Y + -1713.815+0.94001*T+0.4925095E+31*T**(-9)+GHSERFE#; 6000.00 N +REF:0 ! +PARAMETER G(FCC_A1,NI:VA;0) 273.00 +GHSERNI#; 3000.00 N +REF:0 ! +PARAMETER L(FCC_A1,CR,FE:VA;0) 273.00 +10833-7.477*T; 6000.00 N +REF:11 ! +PARAMETER L(FCC_A1,CR,FE:VA;1) 273.00 +1410; 6000.00 N +REF:11 ! +PARAMETER L(FCC_A1,CR,NI:VA;0) 273.00 +8030-12.8801*T; 6000.00 N +REF:11 ! +PARAMETER L(FCC_A1,CR,NI:VA;1) 273.00 +33080-16.0362*T; 6000.00 N +REF:11 ! +PARAMETER L(FCC_A1,FE,NI:VA;0) 273.00 -12054.355+3.27413*T; 6000.00 N +REF:20 ! +PARAMETER L(FCC_A1,FE,NI:VA;1) 273.00 +11082.1315-4.45077*T; 6000.00 N +REF:20 ! +PARAMETER L(FCC_A1,FE,NI:VA;2) 273.00 -725.805174; 6000.00 N +REF:20 ! +PARAMETER L(FCC_A1,CR,FE,NI:VA;0) 273.00 +8000-8*T; 6000.00 N +REF:jac17 ! +PARAMETER L(FCC_A1,CR,FE,NI:VA;1) 273.00 -6500; 6000.00 N +REF:jac17 ! +PARAMETER L(FCC_A1,CR,FE,NI:VA;2) 273.00 +30000; 6000.00 N +REF:jac17 ! +PARAMETER TC(FCC_A1,CR:VA;0) 273.00 -1109; 6000.00 N +REF:11 ! +PARAMETER BMAGN(FCC_A1,CR:VA;0) 273.00 -2.46; 6000.00 N +REF:11 ! +PARAMETER TC(FCC_A1,CR,NI:VA;0) 273.00 -3605; 6000.00 N +REF:11 ! +PARAMETER BMAGN(FCC_A1,CR,NI:VA;0) 273.00 -1.91; 6000.00 N +REF:11 ! +PARAMETER TC(FCC_A1,FE:VA;0) 273.00 -201; 6000.00 N +REF:20 ! +PARAMETER BMAGN(FCC_A1,FE:VA;0) 273.00 -2.1; 6000.00 N +REF:20 ! +PARAMETER TC(FCC_A1,FE,NI:VA;0) 273.00 +2133; 6000.00 N +REF:20 ! +PARAMETER TC(FCC_A1,FE,NI:VA;1) 273.00 -682; 6000.00 N +REF:20 ! +PARAMETER BMAGN(FCC_A1,FE,NI:VA;0) 273.00 +9.55; 6000.00 N +REF:20 ! +PARAMETER BMAGN(FCC_A1,FE,NI:VA;1) 273.00 +7.23; 6000.00 N +REF:20 ! +PARAMETER BMAGN(FCC_A1,FE,NI:VA;2) 273.00 +5.93; 6000.00 N +REF:20 ! +PARAMETER BMAGN(FCC_A1,FE,NI:VA;3) 273.00 +6.18; 6000.00 N +REF:20 ! +PARAMETER TC(FCC_A1,NI:VA;0) 273.00 +633; 6000.00 N +REF:0 ! +PARAMETER BMAGN(FCC_A1,NI:VA;0) 273.00 +0.52; 6000.00 N +REF:0 ! + +$BCC_A2 phase + +TYPE_DEFINITION & GES A_P_D BCC_A2 MAGNETIC -1.0 0.4 ! +PHASE BCC_A2 %& 2 1 3 ! +CONSTITUENT BCC_A2 : CR,FE%,NI : VA% : ! + +PARAMETER G(BCC_A2,CR:VA;0) 273.00 +GHSERCR#; 6000.00 N +REF:0 ! +PARAMETER G(BCC_A2,FE:VA;0) 273.00 +GHSERFE#; 6000.00 N +REF:0 ! +PARAMETER G(BCC_A2,NI:VA;0) 273.00 +8715.084-3.556*T+GHSERNI#; 3000.00 N +REF:0 ! +PARAMETER L(BCC_A2,CR,FE:VA;0) 273.00 +20500-9.68*T; 6000.00 N +REF:11 ! +PARAMETER L(BCC_A2,CR,NI:VA;0) 273.00 +17170-11.8199*T; 6000.00 N +REF:11 ! +PARAMETER L(BCC_A2,CR,NI:VA;1) 273.00 +34418-11.8577*T; 6000.00 N +REF:11 ! +PARAMETER L(BCC_A2,CR,NI:VA;2) 273.00 +1e-8; 6000.00 N +REF:11 ! +PARAMETER L(BCC_A2,FE,NI:VA;0) 273.00 -956.63-1.28726*T; 6000.00 N +REF:20 ! +PARAMETER L(BCC_A2,FE,NI:VA;1) 273.00 +5000-5*T; 6000.00 N +REF:pov12 ! +PARAMETER L(BCC_A2,CR,FE,NI:VA;0) 273.00 +3000+5*T; 6000.00 N +REF:jac17 ! +PARAMETER L(BCC_A2,CR,FE,NI:VA;1) 273.00 +9000-6*T; 6000.00 N +REF:jac17 ! +PARAMETER L(BCC_A2,CR,FE,NI:VA;2) 273.00 -30000+20*T; 6000.00 N +REF:jac17 ! +PARAMETER TC(BCC_A2,CR:VA;0) 273.00 -311.5; 6000.00 N +REF:22 ! +PARAMETER BMAGN(BCC_A2,CR:VA;0) 273.00 -0.008; 6000.00 N +REF:0 ! +PARAMETER TC(BCC_A2,FE:VA;0) 273.00 +1043; 6000.00 N +REF:0 ! +PARAMETER BMAGN(BCC_A2,FE:VA;0) 273.00 +2.22; 6000.00 N +REF:0 ! +PARAMETER TC(BCC_A2,NI:VA;0) 273.00 +575; 6000.00 N +REF:0 ! +PARAMETER BMAGN(BCC_A2,NI:VA;0) 273.00 +0.85; 6000.00 N +REF:0 ! +PARAMETER TC(BCC_A2,CR,FE:VA;0) 273.00 +1650; 6000.00 N +REF:11 ! +PARAMETER TC(BCC_A2,CR,FE:VA;1) 273.00 +550; 6000.00 N +REF:11 ! +PARAMETER BMAGN(BCC_A2,CR,FE:VA;0) 273.00 -0.85; 6000.00 N +REF:pov09 ! +PARAMETER TC(BCC_A2,CR,NI:VA;0) 273.00 +2373; 6000.00 N +REF:11 ! +PARAMETER TC(BCC_A2,CR,NI:VA;1) 273.00 +617; 6000.00 N +REF:11 ! +PARAMETER BMAGN(BCC_A2,CR,NI:VA;0) 273.00 +4; 6000.00 N +REF:11 ! + +$SIGMA phase + +PHASE SIGMA % 3 8 4 18 ! +CONSTITUENT SIGMA : FE%,NI : CR% : CR,FE,NI :! + +PARAMETER G(SIGMA,FE:CR:CR;0) 273.00 +8*GFEFCC#+22*GHSERCR# + +92300-95.96*T; 6000.00 N +REF:62 ! +PARAMETER G(SIGMA,FE:CR:FE;0) 273.00 +117300-95.96*T+8*GFEFCC# + +4*GHSERCR#+18*GHSERFE#; 6000.00 N +REF:62 ! +PARAMETER G(SIGMA,FE:CR:NI;0) 273.00 -50000+32*T+8*GFEFCC#+4*GHSERCR# + +18*GNIBCC#; 6000.00 N +REF:pov13 ! +PARAMETER G(SIGMA,NI:CR:CR;0) 273.00 +8*GHSERNI#+22*GHSERCR# + +180000-170*T; 6000.00 N +REF:13 ! +PARAMETER G(SIGMA,NI:CR:FE;0) 273.00 +8*GHSERNI#+4*GHSERCR# + +18*GHSERFE#-50000+32*T; 6000.00 N +REF:pov13 ! +PARAMETER G(SIGMA,NI:CR:NI;0) 273.00 +8*GHSERNI#+4*GHSERCR# + +18*GNIBCC#+175400; 6000.00 N +REF:13 ! +PARAMETER L(SIGMA,FE:CR:CR,NI;0) 273.00 +1e-8; 6000.00 N +REF:pov12 ! +PARAMETER L(SIGMA,FE:CR:FE,NI;0) 273.00 -200000; 6000.00 N +REF:pov13 ! + +$FCC mobility +$CR + +PARAMETER MQ(FCC_A1&CR,CR:*) 273.00 -235000-82.0*T; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&CR,FE:*) 273.00 -286000-71.9*T; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&CR,NI:*) 273.00 -287000-64.4*T; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&CR,CR,FE:*;0) 273.00 -105000; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&CR,CR,NI:*;0) 273.00 -68000; 6000.00 N +Ref:41 ! +PARAMETER MQ(FCC_A1&CR,FE,NI:*;0) 273.00 +16100; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&CR,CR,FE,NI:*;0) 273.00 +310000; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&CR,CR,FE,NI:*;1) 273.00 +320000; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&CR,CR,FE,NI:*;2) 273.00 +120000; 6000.00 N +Ref:17 ! + +$FE + +PARAMETER MQ(FCC_A1&FE,CR:*) 273.00 -235000-82.0*T; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&FE,FE:*) 273.00 -286000+R*T*LN(7.0E-5); 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&FE,NI:*) 273.00 -287000-67.5*T; 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&FE,CR,FE:*;0) 273.00 +15900; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&FE,CR,NI:*;0) 273.00 -77500; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&FE,FE,NI:*;0) 273.00 -115000+104*T; 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&FE,FE,NI:*;1) 273.00 +78800-73.3*T; 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&FE,CR,FE,NI:*;0) 273.00 -740000; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&FE,CR,FE,NI:*;1) 273.00 -540000; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&FE,CR,FE,NI:*;2) 273.00 +750000; 6000.00 N +Ref:17 ! + +$NI + +PARAMETER MQ(FCC_A1&NI,CR:*) 273.00 -235000-82.0*T; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&NI,FE:*) 273.00 -286000-86.0*T; 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&NI,NI:*) 273.00 -287000-69.8*T; 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&NI,CR,FE:*;0) 273.00 -119000; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&NI,CR,NI:*;0) 273.00 -81000; 6000.00 N +Ref:19 ! +PARAMETER MQ(FCC_A1&NI,FE,NI:*;0) 273.00 +124000-51.4*T; 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&NI,FE,NI:*;1) 273.00 -300000+213*T; 6000.00 N +Ref:18 ! +PARAMETER MQ(FCC_A1&NI,CR,FE,NI:*;0) 273.00 +1840000; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&NI,CR,FE,NI:*;1) 273.00 +670000; 6000.00 N +Ref:17 ! +PARAMETER MQ(FCC_A1&NI,CR,FE,NI:*;2) 273.00 -1120000; 6000.00 N +Ref:17 ! + +$BCC Mobility + +$CR + +PARAMETER MQ(BCC_A2&CR,CR:*) 273.00 -407000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,CR:*) 273.00 -35.6*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,FE:*) 273.00 -218000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,FE:*) 273.00 +R*T*LN(8.5E-5); 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,NI:*) 273.00 -218000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,NI:*) 273.00 +R*T*LN(8.5E-5); 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,CR,FE:*;0) 273.00 +361000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,CR,FE:*;0) 273.00 -116*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,CR,FE:*;1) 273.00 +2820; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,CR,FE:*;1) 273.00 +37.5*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,CR,NI:*;0) 273.00 +350000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,CR,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,FE,NI:*;0) 273.00 +150000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,FE,NI:*;1) 273.00 +150000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,FE,NI:*;1) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,CR,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,CR,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,CR,FE,NI:*;1) 273.00 -2400000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,CR,FE,NI:*;1) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&CR,CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&CR,CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! + +$FE + +PARAMETER MQ(BCC_A2&FE,CR:*) 273.00 -407000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,CR:*) 273.00 -17.2*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,FE:*) 273.00 -218000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,FE:*) 273.00 +R*T*LN(4.6E-5); 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,NI:*) 273.00 -218000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,NI:*) 273.00 +R*T*LN(4.6E-5); 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,CR,FE:*;0) 273.00 +267000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,CR,FE:*;0) 273.00 -117*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,CR,FE:*;1) 273.00 -416000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,CR,FE:*;1) 273.00 +348*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,CR,NI:*;0) 273.00 +350000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,CR,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,FE,NI:*;0) 273.00 +150000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,CR,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,CR,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,CR,FE,NI:*;1) 273.00 +1400000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,CR,FE,NI:*;1) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&FE,CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&FE,CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! + +$NI + +PARAMETER MQ(BCC_A2&NI,CR:*) 273.00 -407000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,CR:*) 273.00 -17.2*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,FE:*) 273.00 -204000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,FE:*) 273.00 +R*T*LN(1.8E-5); 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,NI:*) 273.00 -204000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,NI:*) 273.00 +R*T*LN(1.8E-5); 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,CR,FE:*;0) 273.00 +88000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,CR,FE:*;0) 273.00 +10*T; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,CR,NI:*;0) 273.00 +350000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,CR,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,FE,NI:*;0) 273.00 +150000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,CR,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,CR,FE,NI:*;0) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,CR,FE,NI:*;1) 273.00 -500000; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,CR,FE,NI:*;1) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MQ(BCC_A2&NI,CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! +PARAMETER MF(BCC_A2&NI,CR,FE,NI:*;2) 273.00 +1e-8; 6000.00 N +Ref:14 ! + +""" + +ALMGSI_DB = """ + + +$ AL-MG-SI system with metastable phases +$ +$ Parameters for metastable phases and mobility +$ taken from E. Povoden-Karadeniz et al, CALPHAD 43 (2011) p. 94 +$ + +$Element Standard state mass [g/mol] H_298 S_298 +ELEMENT VA VACUUM 0.0 0.00 0.00 ! +ELEMENT AL FCC_A1 26.98154 4540 28.30 ! +ELEMENT MG HCP_A3 24.305 4998.0 32.671 ! +ELEMENT SI DIA_A4 28.0855 3217. 18.81 ! + + +$ +$FUNCTIONS FOR PURE ELEMENT +$ +FUNCTION GHSERAL + 273.00 -7976.15+137.093038*T-24.3671976*T*LN(T) + -1.884662E-3*T**2-0.877664E-6*T**3+74092*T**(-1); 700.00 Y + -11276.24+223.048446*T-38.5844296*T*LN(T) + +18.531982E-3*T**2-5.764227E-6*T**3+74092*T**(-1); 933.47 Y + -11278.378+188.684153*T-31.748192*T*LN(T)-1.231E+28*T**(-9); 2900.00 N +REF:0 ! +FUNCTION GHSERMG + 273.00 -8367.34+143.675547*T-26.1849782*T*LN(T)+0.4858E-3*T**2 + -1.393669E-6*T**3+78950*T**(-1); 923.00 Y + -14130.185+204.716215*T-34.3088*T*LN(T)+1038.192E25*T**(-9); 3000.00 N +REF:0 ! +FUNCTION GHSERSI + 273.00 -8162.609+137.236859*T-22.8317533*T*LN(T) + -1.912904E-3*T**2-0.003552E-6*T**3+176667*T**(-1); 1687.00 Y + -9457.642+167.271767*T-27.196*T*LN(T)-4.2037E+30*T**(-9); 3600.00 N +REF:0 ! + +$ +$ OTHER FUNCTIONS +$ +FUNCTION R + 273.00 +8.31451; 6000.00 N ! +FUNCTION GMG2SI 273.00 -92250.0+440.4*T-75.9*T*LN(T) + -0.0018*T**2+630000*T**(-1); 6000.00 N +REF:31 ! + +$ +$ LIQUID +$ + TYPE-DEF % SEQ * ! + PHASE LIQUID % 1 1.0 > +Random substitutional model. +>> 6 ! + CONSTITUENT LIQUID : AL,MG,SI: ! + +PARAMETER G(LIQUID,AL;0) + 273.00 +11005.029-11.841867*T+7.934E-20*T**7+GHSERAL#; 700.00 Y + +11005.03-11.841867*T+7.9337E-20*T**7+GHSERAL#; 933.47 Y + +10482.382-11.253974*T+1.231E+28*T**(-9)+GHSERAL#; 2900.00 N +REF:0 ! +PARAMETER G(LIQUID,MG;0) + 273.00 +8202.243-8.83693*T+GHSERMG#-8.0176E-20*T**7; 923.00 Y + +8690.316-9.392158*T+GHSERMG#-1.038192E+28*T**(-9); 6000.00 N +REF:31 ! +PARAMETER G(LIQUID,SI;0) + 273.00 +50696.36-30.099439*T+2.0931E-21*T**7+GHSERSI#; 1687.00 Y + +49828.165-29.559068*T+4.2037E+30*T**(-9)+GHSERSI#; 3600.00 N +REF:0 ! + +PARAMETER L(LIQUID,AL,MG;0) 273.00 -12000.0+8.566*T; 6000.00 N +REF:31 ! +PARAMETER L(LIQUID,AL,MG;1) 273.00 +1894.0-3.000*T; 6000.00 N +REF:31 ! +PARAMETER L(LIQUID,AL,MG;2) 273.00 +2000.0; 6000.00 N +REF:31 ! +PARAMETER L(LIQUID,AL,SI;0) 273.00 -11655.93-0.92934*T; 6000.00 N +REF:37 ! +PARAMETER L(LIQUID,AL,SI;1) 273.00 -2873.45+0.2945*T; 6000.00 N +REF:37 ! +PARAMETER L(LIQUID,AL,SI;2) 273.00 +2520; 6000.00 N +REF:37 ! +PARAMETER L(LIQUID,MG,SI;0) 273.00 -70055+24.98*T; 6000.00 N +REF:39 ! +PARAMETER L(LIQUID,MG,SI;1) 273.00 -1300; 6000.00 N +REF:39 ! +PARAMETER L(LIQUID,MG,SI;2) 273.00 +6272; 6000.00 N +REF:39 ! + +PARAMETER L(LIQUID,AL,MG,SI;0) 273.00 +11882; 6000.00 N +REF:34 ! +PARAMETER L(LIQUID,AL,MG,SI;1) 273.00 -24207; 6000.00 N +REF:34 ! +PARAMETER L(LIQUID,AL,MG,SI;2) 273.00 -38223; 6000.00 N +REF:34 ! + +$ +$ FCC_A1 +$ + PHASE FCC_A1 % 2 1 1 > Al-Matrix phase, face-centered cubic >> 6 ! + CONSTITUENT FCC_A1 : AL%,MG,SI : VA : ! + +PARAMETER G(FCC_A1,AL:VA;0) 273.00 +GHSERAL#; 2900.00 N +REF:0 ! +PARAMETER G(FCC_A1,MG:VA;0) 273.00 +2600-0.90*T+GHSERMG#; 6000.00 N +REF:0 ! +PARAMETER G(FCC_A1,SI:VA;0) 273.00 +51000-21.8*T+GHSERSI#; 3600.00 N +REF:0 ! + +PARAMETER L(FCC_A1,AL,MG:VA;0) 273.00 +1*LDF0ALMG#; 6000.00 N +REF:41 ! +PARAMETER L(FCC_A1,AL,MG:VA;1) 273.00 +1*LDF1ALMG#; 6000.00 N +REF:41 ! +PARAMETER L(FCC_A1,AL,MG:VA;2) 273.00 +1*LDF2ALMG#; 6000.00 N +REF:41 ! +PARAMETER L(FCC_A1,AL,SI:VA;0) 273.00 +1*LDF0ALSI#; 6000.00 N +REF:41 ! +PARAMETER L(FCC_A1,AL,SI:VA;1) 273.00 +1*LDF1ALSI#; 6000.00 N +REF:37 ! +PARAMETER L(FCC_A1,AL,SI:VA;2) 273.00 +1*LDF2ALSI#; 6000.00 N +REF:37 ! +PARAMETER L(FCC_A1,MG,SI:VA;0) 273.00 +1*LDF0SIMG#; 6000.00 N +REF:41 ! +PARAMETER L(FCC_A1,MG,SI:VA;1) 273.00 +1*LDF1SIMG#; 6000.00 N +REF:31 ! +PARAMETER L(FCC_A1,MG,SI:VA;2) 273.00 +1*LDF2SIMG#; 6000.00 N +REF:31 ! + +$ +$ SI_DIAMOND_A4 +$ + PHASE SI_DIAMOND_A4 % 1 1 > +Silicon precipitate. Space group Fd3m, prototype: C(diamond) +>> 4 ! + CONSTITUENT SI_DIAMOND_A4 : AL,MG,SI% : ! +PARAMETER G(SI_DIAMOND_A4,AL;0) 273.00 +30.0*T+GHSERAL#; 6000.00 N +REF:31 ! +PARAMETER G(SI_DIAMOND_A4,MG;0) 273.00 +GHSERMG#; 6000.00 N +REF:41 ! +PARAMETER G(SI_DIAMOND_A4,SI;0) 273.00 +GHSERSI#; 6000.00 N +REF:31 ! +PARAMETER L(SI_DIAMOND_A4,AL,SI;0) 273.00 +111417.7-46.1392*T; 6000.00 N +REF:37 ! +PARAMETER L(SI_DIAMOND_A4,MG,SI;0) 273.00 +65000; 6000.00 N +REF:41 ! + +$ +$ MG2SI_B +$ + PHASE MG2SI_B % 2 2 1 > +Face-centered cubic equilibrium phase. +Incoherent precipitates (plates or cubes) in the overaging regime, 6xxx alloys. [REF:C31,C32] +>> 5 ! + CONSTITUENT MG2SI_B : MG : SI : ! +PARAMETER G(MG2SI_B,MG:SI;0) 273.00 GMG2SI; 6000.00 N +REF:31 ! + +$ +$ BETA_AL3MG2 +$ + PHASE BETA_AL3MG2 % 2 89 140 > +Cubic (Al,Zn)3Mg2 equilibrium phase, prototype: Al3Mg2. +>> 2 ! + CONSTITUENT BETA_AL3MG2 : MG : AL : ! +PARAMETER G(BETA_AL3MG2,MG:AL;0) 273.00 -246175.0-675.5500*T + +89*GHSERMG#+140*GHSERAL#; 6000.00 N +REF:31 ! + +$ +$ E_AL30MG23 +$ + PHASE E_AL30MG23 % 2 23 30 > +Epsilon equilibrium phase, prototype: Co5Cr2Mo3 +>> 1 ! + CONSTITUENT E_AL30MG23 : MG : AL : ! +PARAMETER G(E_AL30MG23,MG:AL;0) 273.00 -52565.4-173.1775*T + +23*GHSERMG#+30*GHSERAL#; 6000.00 N +REF:31 ! + +$ +$ G_AL12MG17 +$ + PHASE G_AL12MG17 % 3 10 24 24 > +Gamma equilibrium phase, space group: I43m, prototype: Alpha-Mn. +Precipitate in Al-Mg-Zn alloy +>> 2 ! + CONSTITUENT G_AL12MG17 : MG : AL,MG% : AL : ! +PARAMETER G(G_AL12MG17,MG:MG:MG;0) 273.00 +266939.2-174.638*T+58*GHSERMG#; 6000.00 N +REF:40 ! +PARAMETER G(G_AL12MG17,MG:AL:AL;0) 273.00 +195750-203*T + +10*GHSERMG#+48*GHSERAL#; 6000.00 N +REF:40 ! +PARAMETER G(G_AL12MG17,MG:MG:AL;0) 273.00 -105560-101.5*T + +34*GHSERMG#+24*GHSERAL#; 6000.00 N +REF:40 ! +PARAMETER G(G_AL12MG17,MG:AL:MG;0) 273.00 +568249.2-276.138*T + +34*GHSERMG#+24*GHSERAL#; 6000.00 N +REF:40 ! +PARAMETER L(G_AL12MG17,MG:AL:AL,MG;0) 273.00 +226200-29*T; 6000.00 N +REF:40 ! +PARAMETER L(G_AL12MG17,MG:MG:AL,MG;0) 273.00 +226200-29*T; 6000.00 N +REF:40 ! + +$ +$ B_PRIME_L +$ + PHASE B_PRIME_L % 3 3 9 7 > +Metastable B´ phase - low-T type reflecting lowest energy modification from 1st principles. +Structure can be related to the hexagonal structure of Q-Phase, with empty Cu-sites. [REF:C11,C37] +>> 2 ! + CONSTITUENT B_PRIME_L : AL : MG : SI : ! +PARAMETER G(B_PRIME_L,AL:MG:SI;0) 273.00 -140000-10*T+3*GHSERAL#+9*GHSERMG#+7*GHSERSI#; 6000.00 N +REF:41 ! + +$ +$ MGSI_B_P +$ + PHASE MGSI_B_P % 2 1.8 1 > +Beta´, metastable hexagonal close-packed rod-like Mg-Si precipitates in 6xxx alloys. [REF:C31,C32,C34] +>> 5 ! + CONSTITUENT MGSI_B_P : MG : SI : ! + +PARAMETER G(MGSI_B_P,MG:SI;0) 273.00 GMG2SI + 24250 - 40.4*T + 5.9*T*LN(T) - 0.0042*T**2 - 130000*T**(-1); 6000.00 N +REF:41 ! + +$ +$ MG5SI6_B_DP +$ + PHASE MG5SI6_B_DP % 2 5 6 > +Main metastable hardening phase in 6xxx, monoclinic with space group C2/m. Al-free Mg5Si6. +Semicoherent needles. [REF:C31,C32,C35]. +>> 5 ! + CONSTITUENT MG5SI6_B_DP : MG : SI : ! +PARAMETER G(MG5SI6_B_DP,MG:SI;0) 273.00 -5000-30*T-0.0096*T**2 + -1e-7*T**3+5*GHSERMG#+6*GHSERSI#; 6000.00 N +REF:41 ! + +$ +$ U1_PHASE +$ + PHASE U1_PHASE % 3 2 1 2 > +Needle-like precipitate with trigonal structure observed in 6xxx in the beta´ precipitation regime. [REF:C37] +>> 3 ! + CONSTITUENT U1_PHASE : AL : MG : SI : ! +PARAMETER G(U1_PHASE,AL:MG:SI;0) 273.00 -5000-10*T-0.0055*T**2 + +3e-6*T**3+150000*T**(-1)+2*GHSERAL#+GHSERMG#+2*GHSERSI#; 6000.00 N +REF:41 ! + +$ +$ U2_PHASE +$ + PHASE U2_PHASE % 3 1 1 1 > +Needle-like precipitate with orthorhombic structure in 6xxx in the beta' precipitation regime. [REF:C37] +>> 3 ! + CONSTITUENT U2_PHASE : AL : MG : SI : ! +PARAMETER G(U2_PHASE,AL:MG:SI;0) 273.00 -14000-3.75*T-0.0015*T**2 + +7.5e-7*T**3+62500*T**(-1)+1*GHSERAL#+1*GHSERMG#+1*GHSERSI#; 6000.00 N +REF:41 ! + +$ +$ Mobility terms +$ +PARAMETER MQ(FCC_A1&AL,*) 273.00 -127200+R*T*LN(1.39e-5); 6000.00 N +Ref:41 ! + +PARAMETER MQ(FCC_A1&MG,AL:*) 273.00 -119000+R*T*LN(3.7e-5); 6000.00 N +Ref:41! +PARAMETER MQ(FCC_A1&MG,MG:*) 273.00 -112499+R*T*LN(5.7e-5); 6000.00 N +Ref:41! +PARAMETER MQ(FCC_A1&MG,MG,AL:*) 273.00 54511; 6000.00 N +Ref:41! +PARAMETER MQ(FCC_A1&MG,SI:*) 273.00 -119000+R*T*LN(3.7e-5); 6000.00 N +Ref:41! + +PARAMETER MQ(FCC_A1&SI,AL:*) 273.00 -136400+R*T*LN(2.31e-4); 6000.00 N +Ref:41 ! +PARAMETER MQ(FCC_A1&SI,MG:*) 273.00 -136400+R*T*LN(2.31e-4); 6000.00 N +Ref:41 ! +PARAMETER MQ(FCC_A1&SI,SI:*) 273.00 -448400+R*T*LN(154e-4); 6000.00 N +Ref:41 ! + +$ +$ References +$ + +LIST_OF_REFERENCES + +A00201-0 unary A.T. Dinsdale, SGTE Data of pure elements, CALPHAD, Vol. 15, No. 4, pp 317-425, 1991. + 31 bin, tern N. Saunders, COST 507: Thermochemical database for light metal alloys, Vol. 2, pp 23-27, 1998. +A00166-34 Al-Mg-Si J. Lacaze and R. Valdes, CALPHAD-type assessment of the Al-Mg-Si system, Monatshefte f. Chemie, Vol. 136, A00169-37 Al-Fe-Si Z.-K. Liu, Y.A. Chang, Thermodynamic assessment of the Al-Fe-Si system, Metall. Mater. Trans A, Vol. 30A, pp 1081-1095, 1999. +A00172-39 Mg-Si-Li D. Kevorkov, R. Schmid-Fetzer, F. Zhang, Phase equilibria and thermodynamics of the Mg-Si-Li system and remodeling of the Mg-Si system, J. Phase Equilib. Diffusion, Vol. 25, pp 140-151, 2004. +A00173-40 Al-Mg-Zn P. Liang et al., Experimental investigation and thermodynamic calculation of the Al-Mg-Zn system, Thermochim. Acta, Vol. 314, pp 87-110, 1998. + 41 E. Povoden-Karadeniz et al, Calphad modeling of metastable phases in the Al-Mg-Si system, CALPHAD, Vol. 42 pp 94-104, 1991 +$ ################################################################################################################################################################################################################################################################################ + +$ References of phase descriptions + + C31 Al-Mg-Si J.P. Lynch, L.M. Brown, M.H.Jacobs, Microanalysis of age-hardening precipitates in Aluminium-alloys, Acta metall. mater. 30 (1982) 1389. +C00025-C32 Al-Mg-Si G.A. Edwards, K. Stiller, G.L. Dunlop, M.J. Couper, The precipitaton sequence in Al-Mg-Si alloys, Acta mater. 46 (1998) 3893-3904. +C00026-C33 Al-Mg-Si, GP-zones K. Matsuda, H. Gamada, K. Fujii, Y. Uetani, T. Sato, A. Kamio, S. Ikeno, High-resolution electron microscopy on the structure of Guinier-Preston zones in an Al-1.6mass% Mg2Si alloy, Metall. mater. trans. A 29 (1998) 1161-1167. +C00027-C34 Mg-Si beta´ R. Vissers, M.A. van Huis, J. Jansen, H.W. Zandbergen, C.D. Marioara, S.J. Andersen, The structure of the beta´ phase in Al-Mg-Si alloys, Acta mater. 55 (2007) 3815-3823. +C00028-C35 Mg-Si beta´´ H.W. Zandbergen, S.J. Andersen, J. Jansen, Structure determination of Mg5Si6 particles in Al by dynamic electron diffraction studies, Science 277 (1997) 1221-1225. + C36 Al-Mg-Si H.S. Hasting, A.G. Froseth, S.J. Andersen, R. Vissers, J.C. Walmsley, C.D. Marioara, F. Danoix, W. Lefebvre, R. Holmestad, Composition of beta´´ precipitates in Al-Mg-Si alloys by atom probe tomography and first principles calculations, J. appl. phys. 106 (2009) 123527. +C00029-C37 Al-Mg-Si, U-phases S.J. Andersen, C.D. Marioara, R. Vissers, A. Froseth, H.W. Zandbergen, The structural relation between precipitates in Al-Mg-Si alloys, the Al-matrix and diamond silicon, with emphasis on the trigonal U1-MgAl2Si2, Mater. sci. eng. A 444 (2007) 157-169. + + 11 Smithells Metals Reference Book, Seventh Edition, Butterworth-Heinemann, Oxford, 1999. + 32 J. Yao, Y.W. Cui, H. Liu, H. Kou, J. Li, L. Zhou, Computer Coupling of Phase Diagrams and Thermochemistry Vol.32, 602-607, 2008. """ \ No newline at end of file diff --git a/kawin/tests/test_PBM.py b/kawin/tests/test_PBM.py index a4087a4..54f1c82 100644 --- a/kawin/tests/test_PBM.py +++ b/kawin/tests/test_PBM.py @@ -1,6 +1,6 @@ import numpy as np from numpy.testing import assert_allclose -from kawin.PopulationBalance import PopulationBalanceModel +from kawin.precipitation import PopulationBalanceModel #Set parameters for pbm. Default bins are increased here so that added bins should be 50 bins = 200 @@ -72,39 +72,17 @@ def test_decreaseBinSize(): assert_allclose(pbm.PSDsize[0], 0.5*(pbm.PSDbounds[0] + pbm.PSDbounds[1]), atol=0, rtol=1e-6) pbm.reset() -def test_nucleateSmall(): - ''' - If nucleate radius is smaller than PSD length, then no change - ''' - pbm.Nucleate(10, 1e-9) - assert(len(pbm.PSD) == bins and pbm.bins == len(pbm.PSD)) - assert(len(pbm.PSDbounds) == bins+1) - assert(len(pbm.PSDsize) == bins) - assert(pbm.Moment(0) == 10) - assert(pbm.PSDbounds[-1] == 1e-8) - -def test_nucleateBig(): - ''' - If nucleate radius is larger than PSD length, then increase bin size - such that number of bins is the same, but max if 5*radius - ''' - r = 1e-7 - pbm.Nucleate(10, r) - assert(len(pbm.PSD) == bins and pbm.bins == len(pbm.PSD)) - assert(len(pbm.PSDbounds) == bins+1) - assert(len(pbm.PSDsize) == bins) - assert(pbm.Moment(0) == 10) - assert_allclose(pbm.PSDbounds[-1], 5*r, rtol=1e-6) - assert_allclose(pbm.PSDsize[0], 0.5*(pbm.PSDbounds[0] + pbm.PSDbounds[1]), atol=0, rtol=1e-6) - pbm.reset() - def test_DT(): ''' Calculated DT with constant growth rate - DT = binSize / (2*max(growth rate)) + DT = ratio * binSize / (max(growth rate)) + + Previous version had ratio of 0.5, but this was decreased slightly to 0.4 for numerical stability ''' growth = 5*np.ones(pbm.bins+1) pbm.PSD = 2*np.ones(pbm.bins) - trueDT = (pbm.PSDbounds[1] - pbm.PSDbounds[0]) / (2*growth[0]) - calcDT = pbm.getDTEuler(5, growth, 1e-3, 0) + ratio = 0.4 + trueDT = ratio * (pbm.PSDbounds[1] - pbm.PSDbounds[0]) / (growth[0]) + dissIndex = pbm.getDissolutionIndex(1e-3, 0) + calcDT = pbm.getDTEuler(5, growth, dissIndex, maxBinRatio=ratio) assert_allclose(trueDT, calcDT, rtol=1e-6) diff --git a/kawin/tests/test_diffusion.py b/kawin/tests/test_diffusion.py index b72c2dd..e6ec525 100644 --- a/kawin/tests/test_diffusion.py +++ b/kawin/tests/test_diffusion.py @@ -1,7 +1,7 @@ from numpy.testing import assert_allclose import numpy as np -from kawin.Diffusion import SinglePhaseModel, HomogenizationModel -from kawin.Thermodynamics import GeneralThermodynamics +from kawin.diffusion import SinglePhaseModel, HomogenizationModel +from kawin.thermo import GeneralThermodynamics from kawin.tests.datasets import * N = 100 @@ -11,6 +11,7 @@ homogenizationTernary = HomogenizationModel([-1e-3, 1e-3], N, ['NI', 'CR', 'AL'], ['FCC_A1', 'BCC_A2']) NiCrTherm = GeneralThermodynamics(NICRAL_TDB, ['NI', 'CR'], ['FCC_A1', 'BCC_A2']) NiCrAlTherm = GeneralThermodynamics(NICRAL_TDB, ['NI', 'CR', 'AL'], ['FCC_A1', 'BCC_A2']) +FeCrNiTherm = GeneralThermodynamics(FECRNI_DB, ['FE', 'CR', 'NI'], ['FCC_A1', 'BCC_A2']) def test_CompositionInput(): ''' @@ -26,6 +27,7 @@ def test_CompositionInput(): singleModelTernary.setCompositionStep(0.2, 1, 0, 'CR') singleModelTernary.setCompositionStep(0.8, 0, 0, 'AL') singleModelTernary.setThermodynamics(NiCrAlTherm) + singleModelTernary.setTemperature(1200+273.15) singleModelTernary.setup() assert(singleModelTernary.x[0,25] + singleModelTernary.x[1,25] < 1) @@ -34,7 +36,7 @@ def test_CompositionInput(): assert(1 - (singleModelTernary.x[0,75] + singleModelTernary.x[1,75]) >= singleModelTernary.minComposition) assert(singleModelTernary.x[1,75] >= singleModelTernary.minComposition) -def test_SinglePhaseFluxes(): +def test_SinglePhaseFluxes_shape(): ''' Tests the dimensions of the single phase fluxes function @@ -203,5 +205,122 @@ def test_homogenization_lab(): assert(np.allclose(mob[:,0], [3.927302e-22, 2.323337e-23, 6.206029e-23], atol=0, rtol=1e-3)) assert(np.allclose(mob[:,-1], [2.025338e-22, 5.106062e-22, 8.524977e-23], atol=0, rtol=1e-3)) +def test_single_phase_dxdt(): + ''' + Check dxdt values of arbitrary single phase model problem + + We spot check a few points on dxdt rather than checking the entire array + + This uses the parameters from 06_Single_Phase_Diffusion example with the composition + being linear rather then step functions + ''' + #Define mesh spanning between -1mm to 1mm with 50 volume elements + #Since we defined L12, the disordered phase as DIS_ attached to the front + m = SinglePhaseModel([-1e-3, 1e-3], 20, ['NI', 'CR', 'AL'], ['FCC_A1']) + + #Define Cr and Al composition, with step-wise change at z=0 + m.setCompositionLinear(0.077, 0.359, 'CR') + m.setCompositionLinear(0.054, 0.062, 'AL') + + m.setThermodynamics(NiCrAlTherm) + m.setTemperature(1200 + 273.15) + + m.setup() + t, x = m.getCurrentX() + dxdt = m.getdXdt(t, x) + dt = m.getDt(dxdt) + + #Index 5 + ind5, vals5 = 5, np.array([1.640437e-9, 5.669268e-10]) + + #Index 10 + ind10, vals10 = 10, np.array([1.542640e-9, 1.091229e-9]) + + #Index 15 + ind15, vals15 = 15, np.array([1.596203e-9, 1.842238e-9]) + + assert_allclose(dxdt[0][:,ind5], vals5, atol=0, rtol=1e-3) + assert_allclose(dxdt[0][:,ind10], vals10, atol=0, rtol=1e-3) + assert_allclose(dxdt[0][:,ind15], vals15, atol=0, rtol=1e-3) + assert_allclose(dt, 28721.530474, rtol=1e-3) + +def test_diffusion_x_shape(): + ''' + Check the flatten and unflatten behavior for Diffusion model + + SinglePhaseModel and Homogenization model follows the same path for these functions + since we just deal with fluxes for elements + + For this setup: + getCurrentX will return a single element array with the element having a shape of (2,20) + flattenX will return a 1D array of length 40 (2x20) + unflattenX should take the output of flattenX and getCurrentX to bring the (40,) array to [(2,20)] + ''' + #Define mesh spanning between -1mm to 1mm with 50 volume elements + #Since we defined L12, the disordered phase as DIS_ attached to the front + m = SinglePhaseModel([-1e-3, 1e-3], 20, ['NI', 'CR', 'AL'], ['DIS_FCC_A1']) + + #Define Cr and Al composition, with step-wise change at z=0 + m.setCompositionLinear(0.077, 0.359, 'CR') + m.setCompositionLinear(0.054, 0.062, 'AL') + + m.setThermodynamics(NiCrAlTherm) + m.setTemperature(1200 + 273.15) + + m.setup() + t, x = m.getCurrentX() + origShape = x[0].shape + + x_flat = m.flattenX(x) + flatShape = x_flat.shape + + x_restore = m.unflattenX(x_flat, x) + unflatShape = x_restore[0].shape + + assert(len(x) == 1) + assert(origShape == unflatShape) + assert(flatShape == (np.prod(origShape),)) + assert(len(x_restore) == 1) + +def test_homogenization_dxdt(): + ''' + Check flux values of arbitrary homogenization model problem + + We spot check a few points on dxdt rather than checking the entire array + + We'll only test using the hashin lower homogenization function since there's already tests for + the output of each homogenization function + + This uses the parameters from 07_Homogenization_Model example with the compositions + being linear rather than stepwise functions + ''' + m = HomogenizationModel([-5e-4, 5e-4], 20, ['FE', 'CR', 'NI'], ['FCC_A1', 'BCC_A2']) + m.setCompositionLinear(0.257, 0.423, 'CR') + m.setCompositionLinear(0.065, 0.276, 'NI') + m.setTemperature(1100+273.15) + m.setThermodynamics(FeCrNiTherm) + m.eps = 0.01 + + m.setMobilityFunction('hashin lower') + + m.setup() + t, x = m.getCurrentX() + dxdt = m.getdXdt(t, x) + dt = m.getDt(dxdt) + + #Index 5 + ind5, vals5 = 5, np.array([-1.592463e-9, 1.211067e-9]) + + #Index 10 + ind10, vals10 = 10, np.array([-9.751858e-10, 1.702190e-9]) + + #Index 15 + ind15, vals15 = 15, np.array([-4.728854e-10, 8.590127e-10]) + + assert_allclose(dxdt[0][:,ind5], vals5, atol=0, rtol=1e-3) + assert_allclose(dxdt[0][:,ind10], vals10, atol=0, rtol=1e-3) + assert_allclose(dxdt[0][:,ind15], vals15, atol=0, rtol=1e-3) + assert_allclose(dt, 61865.352193, rtol=1e-3) + diff --git a/kawin/tests/test_extraFactors.py b/kawin/tests/test_extraFactors.py index 3c7b681..ea58659 100644 --- a/kawin/tests/test_extraFactors.py +++ b/kawin/tests/test_extraFactors.py @@ -1,7 +1,10 @@ from numpy.testing import assert_allclose import numpy as np -from kawin.ShapeFactors import ShapeFactor -from kawin.ElasticFactors import StrainEnergy + +from kawin.precipitation import ShapeFactor +from kawin.precipitation import StrainEnergy + +import itertools Rsingle = 2 Rarray = np.linspace(1, 2, 10) @@ -199,4 +202,98 @@ def test_StrainOutput(): elArray = se.strainEnergy(rArray) assert np.isscalar(elSingle) or (type(elSingle) == np.ndarray and elSingle.ndim == 0) - assert elArray.shape == (10,) \ No newline at end of file + assert elArray.shape == (10,) + +def test_StrainValues(): + ''' + Test strain energy calculation of arbitrary system + + Parameters are taken from 11_Extra_Factors for the Cu-Ti system + ''' + se = StrainEnergy() + se.setEllipsoidal() + se.setElasticConstants(168.4e9, 121.4e9, 75.4e9) + se.setEigenstrain([0.022, 0.022, 0.003]) + se.setup() + + aspect = 1.5 + rSph = 4e-9 / np.cbrt(aspect) + r = np.array([rSph, rSph, aspect*rSph]) + E = se.strainEnergy(r) + + assert_allclose(E, 1.22956765e-17, rtol=1e-3) + +def test_AspectRatio(): + ''' + Test eq aspect ratio calculation of arbitrary system + + Parameters are taken from 11_Extra_Factors for the IN718 system + ''' + se = StrainEnergy() + se.setEigenstrain([6.67e-3, 6.67e-3, 2.86e-2]) + se.setModuli(G=57.1e9, nu=0.33) + se.setEllipsoidal() + se.setup() + + sf = ShapeFactor() + sf.setPlateShape() + + gamma = 0.02375 + Rsph = np.array([5e-10]) + eqAR = se.eqAR_bySearch(Rsph, gamma, sf) + R = 2*Rsph*eqAR / np.cbrt(eqAR**2) + + assert_allclose(R, [1.13444719e-9], rtol=1e-3) + assert_allclose(eqAR, [1.46], rtol=1e-3) + +def test_different_strain_energy_inputs(): + ''' + Make sure the elastic tensor is the same for different types of inputs + + Following options in kawin are: + 2nd rank elastic tensor (6x6 matrix) + Elastic constants c11, c12 and c44 + 2 different moduli (from E, nu, G, lambda, K, or M) + + This will use the G and nu parameters from 11_Extra_Factors for the IN718 example + Any values should work though since we're just checking that the elastic tensor is the same + ''' + G = 57.1e9 #Shear modulus + nu = 0.33 #Poisson ratio + E = 2*G*(1+nu) #Elastic modulus + lam = 2*G*nu / (1-2*nu) #Lame's first parameter + K = 2*G*(1+nu)/(3*(1-2*nu)) #Bulk modulus + M = 2*G*(1-nu)/(1-2*nu) #P-wave modulus + + c11 = E*(1-nu)/((1+nu)*(1-2*nu)) + c12 = E*nu/((1+nu)*(1-2*nu)) + c44 = G + + se = StrainEnergy() + + r2Tensor = np.array([[c11, c12, c12, 0, 0, 0], [c12, c11, c12, 0, 0, 0], [c12, c12, c11, 0, 0, 0], [0, 0, 0, c44, 0, 0], [0, 0, 0, 0, c44, 0], [0, 0, 0, 0, 0, c44]]) + r4Tensor = se._convert2To4rankTensor(r2Tensor) + + #Test 2nd rank tensor input + se.setElasticTensor(r2Tensor) + assert_allclose(se._c4, r4Tensor, rtol=1e-3) + + #Test elastic constants input + se.setElasticConstants(c11, c12, c44) + assert_allclose(se._c4, r4Tensor, rtol=1e-3) + + #This is in the order of the if statements in StrainEnergy._setModuli so it's easier to debug + moduli = {'E': E, 'nu': nu, 'G': G, 'lam': lam, 'K': K, 'M': M} + moduli_names = moduli.keys() + + #Test each pair of moduli as inputs + for pair in itertools.combinations(moduli_names, 2): + moduli_input = {m: moduli[m] for m in pair} + se.setModuli(**moduli_input) + assert_allclose(se._c4, r4Tensor, rtol=1e-3) + + + + + + diff --git a/kawin/tests/test_plotting.py b/kawin/tests/test_plotting.py new file mode 100644 index 0000000..5e9ecdd --- /dev/null +++ b/kawin/tests/test_plotting.py @@ -0,0 +1,117 @@ +from kawin.precipitation import PrecipitateModel +from kawin.diffusion.Diffusion import DiffusionModel +import matplotlib.pyplot as plt +import numpy as np + +def test_precipitate_plotting(): + binary_single = PrecipitateModel(phases=['beta'], elements=['A']) + binary_multi = PrecipitateModel(phases=['beta', 'gamma', 'zeta'], elements=['A']) + ternary_single = PrecipitateModel(phases=['beta'], elements=['A', 'B']) + ternary_multi = PrecipitateModel(phases=['beta', 'gamma', 'zeta'], elements=['A', 'B']) + + models = [ + (binary_single, 1, 1), + (binary_multi, 1, 3), + (ternary_single, 2, 1), + (ternary_multi, 2, 3), + ] + + varTypes = [ + ('Volume Fraction', [2]), + ('Total Volume Fraction', None), + ('Critical Radius', [2]), + ('Average Radius', [2]), + ('Volume Average Radius', [2]), + ('Total Average Radius', None), + ('Total Volume Average Radius', None), + ('Aspect Ratio', [2]), + ('Total Aspect Ratio', None), + ('Driving Force', [2]), + ('Nucleation Rate', [2]), + ('Total Nucleation Rate', None), + ('Precipitate Density', [2]), + ('Total Precipitate Density', None), + ('Temperature', None), + ('Composition', [1]), + ('Eq Composition Alpha', [1,2]), + ('Eq Composition Beta', [1,2]), + ('Supersaturation', [2]), + ('Eq Volume Fraction', [2]), + ('Size Distribution', [2]), + ('Size Distribution Curve', [2]), + ('Size Distribution KDE', [2]), + ('Size Distribution Density', [2]), + ] + + for m in models: + for v in varTypes: + fig, ax = plt.subplots(1,1) + m[0].plot(ax, v[0]) + numLines = len(ax.lines) + plt.close(fig) + + #Check that the number of lines on the plot correspond to the right amount + # Number of lines should either be 1, elements, phases or elements*phases depending on variable + desiredNumber = 1 + if v[1] is not None: + desiredNumber = np.prod([m[vi] for vi in v[1]], dtype=np.int32) + assert numLines == desiredNumber + +def test_diffusion_plotting(): + #Single phase and Homogenizaton model goes through the same path for plotting + binary_single = DiffusionModel(zlim=[-1,1], N=100, elements=['A', 'B'], phases=['alpha']) + binary_multi = DiffusionModel(zlim=[-1,1], N=100, elements=['A', 'B'], phases=['alpha', 'beta', 'gamma']) + ternary_single = DiffusionModel(zlim=[-1,1], N=100, elements=['A', 'B', 'C'], phases=['alpha']) + ternary_multi = DiffusionModel(zlim=[-1,1], N=100, elements=['A', 'B', 'C'], phases=['alpha', 'beta', 'gamma']) + + models = [ + (binary_single, 2, 1), + (binary_multi, 2, 3), + (ternary_single, 3, 1), + (ternary_multi, 3, 3), + ] + + for m in models: + m[0].setTemperature(900) + + #For each plot, check that the number of lines correspond to number of elements or phases + #For 'plot', number of lines should be elements (with or without reference) or a single element + #For 'plotTwoAxis', number of lines for each axis should be length of input array + #For 'plotPhases', number of lines is number of phases or single phase + fig, ax = plt.subplots(1,1) + m[0].plot(ax, plotReference = False) + assert len(ax.lines) == m[1]-1 + plt.close(fig) + + fig, ax = plt.subplots(1,1) + m[0].plot(ax, plotReference = True) + assert len(ax.lines) == m[1] + plt.close(fig) + + fig, ax = plt.subplots(1,1) + m[0].plot(ax, plotElement = m[0].allElements[0]) + assert len(ax.lines) == 1 + plt.close(fig) + + fig, ax = plt.subplots(1,1) + m[0].plot(ax, plotElement = m[0].allElements[1]) + assert len(ax.lines) == 1 + plt.close(fig) + + + fig, axL = plt.subplots(1,1) + axR = ax.twinx() + m[0].plotTwoAxis(Lelements=[m[0].allElements[0]], Relements = m[0].allElements[1:], axL=axL, axR=axR) + assert len(axL.lines) == 1 + assert len(axR.lines) == len(m[0].allElements)-1 + plt.close(fig) + + fig, ax = plt.subplots(1,1) + m[0].plotPhases(ax) + assert len(ax.lines) == m[2] + plt.close(fig) + + fig, ax = plt.subplots(1,1) + m[0].plotPhases(ax, plotPhase=m[0].phases[0]) + assert len(ax.lines) == 1 + plt.close(fig) \ No newline at end of file diff --git a/kawin/tests/test_precipitation.py b/kawin/tests/test_precipitation.py new file mode 100644 index 0000000..b42c33b --- /dev/null +++ b/kawin/tests/test_precipitation.py @@ -0,0 +1,197 @@ +from kawin.tests.datasets import ALZR_TDB, NICRAL_TDB, ALMGSI_DB +from kawin.precipitation import PrecipitateModel, VolumeParameter +from kawin.thermo import BinaryThermodynamics, MulticomponentThermodynamics +import numpy as np +from numpy.testing import assert_allclose + +AlZrTherm = BinaryThermodynamics(ALZR_TDB, ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'], drivingForceMethod='tangent') +NiAlCrTherm = MulticomponentThermodynamics(NICRAL_TDB, ['NI', 'AL', 'CR'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='tangent') +AlMgSitherm = MulticomponentThermodynamics(ALMGSI_DB, ['AL', 'MG', 'SI'], ['FCC_A1', 'MGSI_B_P', 'MG5SI6_B_DP', 'B_PRIME_L', 'U1_PHASE', 'U2_PHASE'], drivingForceMethod='tangent') + +AlZrTherm.setDFSamplingDensity(2000) +AlZrTherm.setEQSamplingDensity(500) +NiAlCrTherm.setDFSamplingDensity(2000) +NiAlCrTherm.setEQSamplingDensity(500) +AlMgSitherm.setDFSamplingDensity(2000) +AlMgSitherm.setEQSamplingDensity(500) + +def test_binary_precipitation_dxdt(): + ''' + Check flux values of arbitrary binary precipitation problem + + We spot check a few points on dxdt rather than checking the entire array + + This uses the parameters from 01_Binary_Precipitation example + ''' + #Create model + model = PrecipitateModel() + bins = 75 + minBins = 50 + maxBins = 100 + model.setPBMParameters(cMin=1e-10, cMax=1e-8, bins=bins, minBins=minBins, maxBins=maxBins) + + xInit = 4e-3 #Initial composition (mole fraction) + model.setInitialComposition(xInit) + + T = 450 + 273.15 #Temperature (K) + model.setTemperature(T) + + gamma = 0.1 #Interfacial energy (J/m2) + model.setInterfacialEnergy(gamma) + + D0 = 0.0768 #Diffusivity pre-factor (m2/s) + Q = 242000 #Activation energy (J/mol) + Diff = lambda x, T: D0 * np.exp(-Q / (8.314 * T)) + model.setDiffusivity(Diff) + + a = 0.405e-9 #Lattice parameter + Va = a**3 #Atomic volume of FCC-Al + Vb = a**3 #Assume Al3Zr has same unit volume as FCC-Al + atomsPerCell = 4 #Atoms in an FCC unit cell + model.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, atomsPerCell) + model.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, atomsPerCell) + + #Average grain size (um) and dislocation density (1e15) + model.setNucleationDensity(grainSize = 1, dislocationDensity = 1e15) + model.setNucleationSite('dislocations') + + #Set thermodynamic functions + model.setThermodynamics(AlZrTherm, addDiffusivity=False) + + #This roughly follows the steps in model.solve so we can get dxdt + model.setup() + + #Replace x (which is just all 0 right now) with an arbitrary lognormal distribution + r = model.PBM[0].PSDsize + sigma = 0.25 + r0 = 0.1e-8 + n = 1/(r*sigma*np.sqrt(2*np.pi)) * np.exp(-np.log(r/r0)**2/(2*sigma**2)) + model.PBM[0].PSD = n + + t, x = model.getCurrentX() + #Call calculateDependentTerms so it can recognize that we changed PSD, otherwise, it'll use the initial values + model._calculateDependentTerms(t, x) + dxdt = model.getdXdt(t, x) + + #Set arbitrary final time, this is done during the solve function, but we do it here since we're not using the solve function + # the initial guess for the time steo will be 0.01*(1.001) regardless of finalTime + model.finalTime = 1 + dt = model.getDt(dxdt) + + indices = [10, 20, 30] + vals = [6773393.32259, 1919.5404124, 0.4106318] + assert_allclose(vals, [dxdt[0][i] for i in indices], rtol=1e-3) + assert_allclose(dt, 0.01001, rtol=1e-3) + +def test_multi_precipitation_dxdt(): + ''' + Check flux values of arbitrary binary precipitation problem + + We spot check a few points on dxdt rather than checking the entire array + + This uses the parameters from 02_Multicomponent_Precipitation example + ''' + model = PrecipitateModel(elements=['Al', 'Cr']) + bins = 75 + minBins = 50 + maxBins = 100 + model.setPBMParameters(cMin=1e-10, cMax=1e-8, bins=bins, minBins=minBins, maxBins=maxBins) + + model.setInitialComposition([0.098, 0.083]) + model.setInterfacialEnergy(0.023) + + T = 1073 + model.setTemperature(T) + + a = 0.352e-9 #Lattice parameter + Va = a**3 #Atomic volume of FCC-Ni + Vb = Va #Assume Ni3Al has same unit volume as FCC-Ni + atomsPerCell = 4 #Atoms in an FCC unit cell + model.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, atomsPerCell) + model.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, atomsPerCell) + + #Set nucleation sites to dislocations and use defualt value of 5e12 m/m3 + #model.setNucleationSite('dislocations') + #model.setNucleationDensity(dislocationDensity=5e12) + model.setNucleationSite('bulk') + model.setNucleationDensity(bulkN0=1e30) + + model.setThermodynamics(NiAlCrTherm) + + #This roughly follows the steps in model.solve so we can get dxdt + model.setup() + + #Replace x (which is just all 0 right now) with an arbitrary lognormal distribution + r = model.PBM[0].PSDsize + sigma = 0.25 + r0 = 0.1e-8 + n = 1/(r*sigma*np.sqrt(2*np.pi)) * np.exp(-np.log(r/r0)**2/(2*sigma**2)) + model.PBM[0].PSD = n + + t, x = model.getCurrentX() + #Call calculateDependentTerms so it can recognize that we changed PSD, otherwise, it'll use the initial values + model._calculateDependentTerms(t, x) + dxdt = model.getdXdt(t, x) + + #Set arbitrary final time, this is done during the solve function, but we do it here since we're not using the solve function + # the initial guess for the time steo will be 0.01*(1.001) regardless of finalTime + model.finalTime = 1 + dt = model.getDt(dxdt) + + indices = [10, 20, 30] + vals = [2.837811e+08, 8.424854e+05, 2.312587e+02] + assert_allclose(vals, [dxdt[0][i] for i in indices], rtol=1e-3) + assert_allclose(dt, 0.01001, rtol=1e-3) + +def test_multiphase_precipitation_x_shape(): + ''' + Check the flatten and unflatten behavior for Precipitate model + + For this setup: + getCurrentX will return a array of length p with each element being an array of length bins + flattenX will return a 1D array of length p*bins + unflattenX should take the output of flattenX and getCurrentX to bring the (p*bins,) to [(bins,), (bins,), ...] + + This uses the parameters from 07_Homogenization_Model example + ''' + phases = ['FCC_A1', 'MGSI_B_P', 'MG5SI6_B_DP', 'B_PRIME_L', 'U1_PHASE', 'U2_PHASE'] + model = PrecipitateModel(phases=phases[1:], elements=['MG', 'SI']) + bins = 75 + minBins = 50 + maxBins = 100 + model.setPBMParameters(cMin=1e-10, cMax=1e-8, bins=bins, minBins=minBins, maxBins=maxBins) + + model.setInitialComposition([0.0072, 0.0057]) + model.setVolumeAlpha(1e-5, VolumeParameter.MOLAR_VOLUME, 4) + + lowTemp = 175+273.15 + highTemp = 250+273.15 + model.setTemperature(([0, 16, 17], [lowTemp, lowTemp, highTemp])) + + gamma = { + 'MGSI_B_P': 0.18, + 'MG5SI6_B_DP': 0.084, + 'B_PRIME_L': 0.18, + 'U1_PHASE': 0.18, + 'U2_PHASE': 0.18 + } + + for i in range(len(phases)-1): + model.setInterfacialEnergy(gamma[phases[i+1]], phase=phases[i+1]) + model.setVolumeBeta(1e-5, VolumeParameter.MOLAR_VOLUME, 4, phase=phases[i+1]) + model.setThermodynamics(AlMgSitherm, phase=phases[i+1]) + + model.setup() + t, x = model.getCurrentX() + origLen = 5 + + x_flat = model.flattenX(x) + flatShape = x_flat.shape + + x_restore = model.unflattenX(x_flat, x) + + assert(len(x) == origLen) + assert(np.all(psd.shape == (bins,) for psd in x)) + assert(flatShape == (origLen*bins,)) + assert(len(x_restore) == origLen) + assert(np.all(psd.shape == (bins,) for psd in x_restore)) \ No newline at end of file diff --git a/kawin/tests/test_solver.py b/kawin/tests/test_solver.py new file mode 100644 index 0000000..bd36af9 --- /dev/null +++ b/kawin/tests/test_solver.py @@ -0,0 +1,124 @@ +from kawin.precipitation import PrecipitateModel, VolumeParameter +from kawin.diffusion import SinglePhaseModel +from kawin.thermo import BinaryThermodynamics, MulticomponentThermodynamics +from kawin.GenericModel import GenericModel, Coupler +from kawin.solver import SolverType +import numpy as np +from numpy.testing import assert_allclose +from kawin.tests.datasets import * + +AlZrTherm = BinaryThermodynamics(ALZR_TDB, ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'], drivingForceMethod='tangent') +NiAlCrTherm = MulticomponentThermodynamics(NICRAL_TDB, ['NI', 'AL', 'CR'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='tangent') + +AlZrTherm.setDFSamplingDensity(2000) +AlZrTherm.setEQSamplingDensity(500) +NiAlCrTherm.setDFSamplingDensity(2000) +NiAlCrTherm.setEQSamplingDensity(500) + +def test_iterators(): + ''' + Tests explicit euler and RK4 iterators + ''' + class TestModel(GenericModel): + def __init__(self): + self.reset() + + def reset(self): + self.x = np.array([0]) + self.time = np.zeros(1) + + def getCurrentX(self): + return self.time[-1], [self.x[-1]] + + def getdXdt(self, t, x): + return [np.cos(t)] + + def getDt(self, dXdt): + return 0.001 + + def postProcess(self, time, x): + self.time = np.append(self.time, time) + self.x = np.append(self.x, x[0]) + return x, False + + m = TestModel() + m.solve(10, solverType=SolverType.EXPLICITEULER) + eulerX = m.x[-1] + + m.reset() + m.solve(10, solverType=SolverType.RK4) + rkX = m.x[-1] + + assert_allclose(eulerX, np.sin(10), rtol=1e-2) + assert_allclose(rkX, np.sin(10), rtol=1e-2) + +def test_coupler_shape(): + ''' + Test that coupler returns correct shape when flattening and unflattening arrays + + Here we use a precipitate model and diffusion model where the shape of x is: + Precipitate model: [(bins,)] + Diffusion model: [(elements,cells,)] + Flattening the arrays will result in a 1D array of [bins + elements*cells] + ''' + #Create model + p_model = PrecipitateModel() + bins = 75 + minBins = 50 + maxBins = 100 + p_model.setPBMParameters(cMin=1e-10, cMax=1e-8, bins=bins, minBins=minBins, maxBins=maxBins) + + xInit = 4e-3 #Initial composition (mole fraction) + p_model.setInitialComposition(xInit) + + T = 450 + 273.15 #Temperature (K) + p_model.setTemperature(T) + + gamma = 0.1 #Interfacial energy (J/m2) + p_model.setInterfacialEnergy(gamma) + + D0 = 0.0768 #Diffusivity pre-factor (m2/s) + Q = 242000 #Activation energy (J/mol) + Diff = lambda x, T: D0 * np.exp(-Q / (8.314 * T)) + p_model.setDiffusivity(Diff) + + a = 0.405e-9 #Lattice parameter + Va = a**3 #Atomic volume of FCC-Al + Vb = a**3 #Assume Al3Zr has same unit volume as FCC-Al + atomsPerCell = 4 #Atoms in an FCC unit cell + p_model.setVolumeAlpha(Va, VolumeParameter.ATOMIC_VOLUME, atomsPerCell) + p_model.setVolumeBeta(Vb, VolumeParameter.ATOMIC_VOLUME, atomsPerCell) + + #Average grain size (um) and dislocation density (1e15) + p_model.setNucleationDensity(grainSize = 1, dislocationDensity = 1e15) + p_model.setNucleationSite('dislocations') + + #Set thermodynamic functions + p_model.setThermodynamics(AlZrTherm, addDiffusivity=False) + + #Define mesh spanning between -1mm to 1mm with 50 volume elements + #Since we defined L12, the disordered phase as DIS_ attached to the front + N = 20 + d_model = SinglePhaseModel([-1e-3, 1e-3], N, ['NI', 'AL', 'CR'], ['DIS_FCC_A1']) + + #Define Cr and Al composition, with step-wise change at z=0 + d_model.setCompositionLinear(0.077, 0.359, 'CR') + d_model.setCompositionLinear(0.054, 0.062, 'AL') + + d_model.setThermodynamics(NiAlCrTherm) + d_model.setTemperature(1200 + 273.15) + + coupled_model = Coupler([p_model, d_model]) + coupled_model.setup() + + t, x = coupled_model.getCurrentX() + x_flat = coupled_model.flattenX(x) + x_restore = coupled_model.unflattenX(x_flat, x) + + assert(len(x) == 2) + assert(len(x[0]) == 1 and x[0][0].shape == (bins,)) + assert(len(x[1]) == 1 and x[1][0].shape == (2,N)) + assert(x_flat.shape == (bins+2*N,)) + assert(len(x_restore) == len(x)) + assert(len(x_restore[0]) == len(x[0]) and x_restore[0][0].shape == x[0][0].shape) + assert(len(x_restore[1]) == 1 and x_restore[1][0].shape == x[1][0].shape) \ No newline at end of file diff --git a/kawin/tests/test_strength.py b/kawin/tests/test_strength.py index 0640e1b..09af006 100644 --- a/kawin/tests/test_strength.py +++ b/kawin/tests/test_strength.py @@ -1,4 +1,4 @@ -from kawin.Strength import StrengthModel +from kawin.precipitation.coupling import StrengthModel import numpy as np sm = StrengthModel() diff --git a/kawin/tests/test_surrogate.py b/kawin/tests/test_surrogate.py index 03d5045..670dacf 100644 --- a/kawin/tests/test_surrogate.py +++ b/kawin/tests/test_surrogate.py @@ -1,8 +1,7 @@ from numpy.testing import assert_allclose import numpy as np import os -from kawin.Thermodynamics import BinaryThermodynamics, MulticomponentThermodynamics -from kawin.Surrogate import BinarySurrogate, MulticomponentSurrogate +from kawin.thermo import BinaryThermodynamics, MulticomponentThermodynamics, BinarySurrogate, MulticomponentSurrogate from kawin.tests.datasets import * AlZrTherm = BinaryThermodynamics(ALZR_TDB, ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'], drivingForceMethod='approximate') diff --git a/kawin/tests/test_thermodynamics.py b/kawin/tests/test_thermodynamics.py index 1db1914..6bd180c 100644 --- a/kawin/tests/test_thermodynamics.py +++ b/kawin/tests/test_thermodynamics.py @@ -1,15 +1,16 @@ import numpy as np from numpy.testing import assert_allclose -from kawin.Thermodynamics import BinaryThermodynamics, GeneralThermodynamics, MulticomponentThermodynamics +from kawin.thermo import GeneralThermodynamics, BinaryThermodynamics, MulticomponentThermodynamics from kawin.tests.datasets import * from pycalphad import Database -AlZrTherm = BinaryThermodynamics(ALZR_TDB, ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'], drivingForceMethod='approximate') -NiCrAlTherm = MulticomponentThermodynamics(NICRAL_TDB, ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='approximate') -NiCrAlThermDiff = MulticomponentThermodynamics(NICRAL_TDB_DIFF, ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='approximate') -NiAlCrTherm = MulticomponentThermodynamics(NICRAL_TDB, ['NI', 'AL', 'CR'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='approximate') -NiAlCrThermDiff = MulticomponentThermodynamics(NICRAL_TDB_DIFF, ['NI', 'AL', 'CR'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='approximate') -AlCrNiTherm = MulticomponentThermodynamics(NICRAL_TDB, ['AL', 'CR', 'NI'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='approximate') +#Default driving force method will be 'tangent' +AlZrTherm = BinaryThermodynamics(ALZR_TDB, ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'], drivingForceMethod='tangent') +NiCrAlTherm = MulticomponentThermodynamics(NICRAL_TDB, ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='tangent') +NiCrAlThermDiff = MulticomponentThermodynamics(NICRAL_TDB_DIFF, ['NI', 'CR', 'AL'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='tangent') +NiAlCrTherm = MulticomponentThermodynamics(NICRAL_TDB, ['NI', 'AL', 'CR'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='tangent') +NiAlCrThermDiff = MulticomponentThermodynamics(NICRAL_TDB_DIFF, ['NI', 'AL', 'CR'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='tangent') +AlCrNiTherm = MulticomponentThermodynamics(NICRAL_TDB, ['AL', 'CR', 'NI'], ['FCC_A1', 'FCC_L12'], drivingForceMethod='tangent') #Set constant sampling densities for each Thermodynamics object #pycalphad equilibrium results may change based off sampling density, so this is to make sure @@ -30,9 +31,11 @@ def test_DG_binary(): ''' Checks value of binary driving force calculation + + Driving force value was updated due to switch from approximate to tangent method ''' dg, _ = AlZrTherm.getDrivingForce(0.004, 673.15, training = True) - assert_allclose(dg, 6346.929428, atol=0, rtol=1e-3) + assert_allclose(dg, 6346.930428, atol=0, rtol=1e-3) def test_DG_binary_output(): ''' @@ -41,20 +44,27 @@ def test_DG_binary_output(): (scalar, scalar) input -> scalar (array, array) input -> array ''' - dg, xP = AlZrTherm.getDrivingForce(0.004, 673.15, returnComp=True, training = True) - dgarray, xParray = AlZrTherm.getDrivingForce([0.004, 0.005], [673.15, 683.15], returnComp=True, training = True) + methods = ['sampling', 'approximate', 'curvature', 'tangent'] + for m in methods: + AlZrTherm.setDrivingForceMethod(m) + dg, xP = AlZrTherm.getDrivingForce(0.004, 673.15, returnComp=True, training = True) + dgarray, xParray = AlZrTherm.getDrivingForce([0.004, 0.005], [673.15, 683.15], returnComp=True, training = True) + + assert np.isscalar(dg) or (type(dg) == np.ndarray and dg.ndim == 0) + assert np.isscalar(xP) or (type(xP) == np.ndarray and xP.ndim == 0) + assert hasattr(dgarray, '__len__') and len(dgarray) == 2 + assert hasattr(xParray, '__len__') and len(xParray) == 2 - assert np.isscalar(dg) or (type(dg) == np.ndarray and dg.ndim == 0) - assert np.isscalar(xP) or (type(xP) == np.ndarray and xP.ndim == 0) - assert hasattr(dgarray, '__len__') and len(dgarray) == 2 - assert hasattr(xParray, '__len__') and len(xParray) == 2 + AlZrTherm.setDrivingForceMethod('tangent') def test_DG_ternary(): ''' Checks value of ternary driving force calculation + + Driving force value was updated due to switch from approximate to tangent method ''' dg, _ = NiCrAlTherm.getDrivingForce([0.08, 0.1], 1073.15, training = True) - assert_allclose(dg, 244.012027, atol=0, rtol=1e-3) + assert_allclose(dg, 265.779087, atol=0, rtol=1e-3) def test_DG_ternary_output(): ''' @@ -63,12 +73,17 @@ def test_DG_ternary_output(): (array, scalar) -> scalar (2D array, array) -> array ''' - dg, xP = NiCrAlTherm.getDrivingForce([0.08, 0.1], 1073.15, returnComp=True, training = True) - dgarray, xParray = NiCrAlTherm.getDrivingForce([[0.08, 0.1], [0.085, 0.1], [0.09, 0.1]], [1073.15, 1078.15, 1083.15], returnComp=True, training = True) - assert np.isscalar(dg) or (type(dg) == np.ndarray and dg.ndim == 0) - assert xP.ndim == 1 and len(xP) == 2 - assert hasattr(dgarray, '__len__') - assert xParray.shape == (3, 2) + methods = ['sampling', 'approximate', 'curvature', 'tangent'] + for m in methods: + NiCrAlTherm.setDrivingForceMethod(m) + dg, xP = NiCrAlTherm.getDrivingForce([0.08, 0.1], 1073.15, returnComp=True, training = True) + dgarray, xParray = NiCrAlTherm.getDrivingForce([[0.08, 0.1], [0.085, 0.1], [0.09, 0.1]], [1073.15, 1078.15, 1083.15], returnComp=True, training = True) + assert np.isscalar(dg) or (type(dg) == np.ndarray and dg.ndim == 0) + assert xP.ndim == 1 and len(xP) == 2 + assert hasattr(dgarray, '__len__') + assert xParray.shape == (3, 2) + + NiCrAlTherm.setDrivingForceMethod('tangent') def test_DG_ternary_order(): ''' @@ -105,16 +120,21 @@ def test_IC_binary_output(): (array, array) -> (array, array) (scalar, array) -> (array, array) Special case where T is scalar ''' - xm, xp = AlZrTherm.getInterfacialComposition(673.15, 5000) - xmarray, xparray = AlZrTherm.getInterfacialComposition([673.15, 683.15], [5000, 50000]) - xmarray2, xparray2 = AlZrTherm.getInterfacialComposition(673.15, [5000, 50000]) - - assert np.isscalar(xm) or (type(xm) == np.ndarray and xm.ndim == 0) - assert np.isscalar(xp) or (type(xp) == np.ndarray and xp.ndim == 0) - assert hasattr(xmarray, '__len__') and len(xmarray) == 2 - assert hasattr(xparray, '__len__') and len(xparray) == 2 - assert hasattr(xmarray2, '__len__') and len(xmarray2) == 2 - assert hasattr(xparray2, '__len__') and len(xparray2) == 2 + methods = ['curvature', 'equilibrium'] + for m in methods: + AlZrTherm.setInterfacialMethod(m) + xm, xp = AlZrTherm.getInterfacialComposition(673.15, 5000) + xmarray, xparray = AlZrTherm.getInterfacialComposition([673.15, 683.15], [5000, 50000]) + xmarray2, xparray2 = AlZrTherm.getInterfacialComposition(673.15, [5000, 50000]) + + assert np.isscalar(xm) or (type(xm) == np.ndarray and xm.ndim == 0) + assert np.isscalar(xp) or (type(xp) == np.ndarray and xp.ndim == 0) + assert hasattr(xmarray, '__len__') and len(xmarray) == 2 + assert hasattr(xparray, '__len__') and len(xparray) == 2 + assert hasattr(xmarray2, '__len__') and len(xmarray2) == 2 + assert hasattr(xparray2, '__len__') and len(xparray2) == 2 + + AlZrTherm.setInterfacialMethod('equilibrium') def test_Mob_binary(): ''' diff --git a/kawin/thermo/BinTherm.py b/kawin/thermo/BinTherm.py new file mode 100644 index 0000000..ea9f6e2 --- /dev/null +++ b/kawin/thermo/BinTherm.py @@ -0,0 +1,349 @@ +from kawin.thermo.Thermodynamics import GeneralThermodynamics +import numpy as np +from pycalphad import equilibrium, calculate, variables as v +from pycalphad.core.composition_set import CompositionSet +from kawin.thermo.FreeEnergyHessian import dMudX + +class BinaryThermodynamics (GeneralThermodynamics): + ''' + Class for defining driving force and interfacial composition functions + for a binary system using pyCalphad and thermodynamic databases + + Parameters + ---------- + database : str + File name for database + elements : list + Elements to consider + Note: reference element must be the first index in the list + phases : list + Phases involved + Note: matrix phase must be first index in the list + drivingForceMethod : str (optional) + Method used to calculate driving force + Options are 'tangent' (default), 'approximate', 'sampling', and 'curvature' (not recommended) + interfacialCompMethod: str (optional) + Method used to calculate interfacial composition + Options are 'equilibrium' (default) and 'curvature' (not recommended) + parameters : list [str] or dict {str : float} + List of parameters to keep symbolic in the thermodynamic or mobility models + ''' + def __init__(self, database, elements, phases, drivingForceMethod = 'tangent', interfacialCompMethod = 'equilibrium', parameters = None): + super().__init__(database, elements, phases, drivingForceMethod, parameters) + + if self.elements[1] < self.elements[0]: + self.reverse = True + else: + self.reverse = False + + #Guess composition for when finding tieline + self._guessComposition = {self.phases[i]: (0, 1, 0.1) for i in range(1, len(self.phases))} + + self.setInterfacialMethod(interfacialCompMethod) + + + def setInterfacialMethod(self, interfacialCompMethod): + ''' + Changes method for caluclating interfacial composition + + Parameters + ---------- + interfacialCompMethod - str + Options are ['equilibrium', 'curvature'] + ''' + if interfacialCompMethod == 'equilibrium': + self._interfacialComposition = self._interfacialCompositionFromEq + elif interfacialCompMethod == 'curvature': + self._interfacialComposition = self._interfacialCompositionFromCurvature + else: + raise Exception('Interfacial composition method must be either \'equilibrium\' or \'curvature\'') + + def setGuessComposition(self, conditions): + ''' + Sets initial composition when calculating equilibrium for interfacial energy + + Parameters + ---------- + conditions : float, tuple or dict + Guess composition(s) to solve equilibrium for + This should encompass the region where a tieline can be found + between the matrix and precipitate phases + Options: float - will set to all precipitate phases + tuple - (min, max dx) will set to all precipitate phases + dictionary {phase name: scalar or tuple} + ''' + if isinstance(conditions, dict): + #Iterating over conditions dictionary in case not all precipitate phases are listed + for p in conditions: + self._guessComposition[p] = conditions[p] + #If not dictionary, then set to all phases + else: + for i in range(1, len(self.phases)): + self._guessComposition[self.phases[i]] = conditions + + def getInterfacialComposition(self, T, gExtra = 0, precPhase = None): + ''' + Gets interfacial composition accounting for Gibbs-Thomson effect + + Parameters + ---------- + T : float or array + Temperature in K + gExtra : float or array (optional) + Extra contributions to the precipitate Gibbs free energy + Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) + Defaults to 0 + precPhase : str + Precipitate phase to consider (default is first precipitate in list) + + Note: for multiple conditions, only gExtra has to be an array + This will calculate compositions for multiple gExtra at the input Temperature + + If T is also an array, then T and gExtra must be the same length + where each index will pertain to a single condition + + Returns + ------- + (parent composition, precipitate composition) + Both will be either float or array based off shape of gExtra + Will return (None, None) if precipitate is unstable + ''' + if hasattr(gExtra, '__len__'): + if not hasattr(T, '__len__'): + caArray, cbArray = self._interfacialComposition(T, gExtra, precPhase) + else: + #If T is also an array, then iterate through T and gExtra + #Otherwise, pycalphad will create a cartesian product of the two + caArray = [] + cbArray = [] + for i in range(len(gExtra)): + ca, cb = self._interfacialComposition(T[i], gExtra[i], precPhase) + caArray.append(ca) + cbArray.append(cb) + caArray = np.array(caArray) + cbArray = np.array(cbArray) + + return caArray, cbArray + else: + return self._interfacialComposition(T, gExtra, precPhase) + + + def _interfacialCompositionFromEq(self, T, gExtra = 0, precPhase = None): + ''' + Gets interfacial composition by calculating equilibrum with Gibbs-Thomson effect + + Parameters + ---------- + T : float + Temperature in K + gExtra : float (optional) + Extra contributions to the precipitate Gibbs free energy + Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) + Defaults to 0 + precPhase : str + Precipitate phase to consider (default is first precipitate in list) + + Returns + ------- + (parent composition, precipitate composition) + Both will be either float or array based off shape of gExtra + Will return (None, None) if precipitate is unstable + ''' + if precPhase is None: + precPhase = self.phases[1] + + if hasattr(gExtra, '__len__'): + gExtra = np.array(gExtra) + else: + gExtra = np.array([gExtra]) + gExtra += self.gOffset + + #Compute equilibrium at guess composition + cond = {v.X(self.elements[1]): self._guessComposition[precPhase], v.T: T, v.P: 101325, v.GE: gExtra} + eq = equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, model=self.models, + phase_records={self.phases[0]: self.phase_records[self.phases[0]], precPhase: self.phase_records[precPhase]}, + calc_opts = {'pdens': self.pDens}) + + xParentArray = np.zeros(len(gExtra)) + xPrecArray = np.zeros(len(gExtra)) + for g in range(len(gExtra)): + eqG = eq.where(eq.GE == gExtra[g], drop=True) + gm = eqG.GM.values.ravel() + for i in range(len(gm)): + eqSub = eqG.where(eqG.GM == gm[i], drop=True) + + ph = eqSub.Phase.values.ravel() + ph = ph[ph != ''] + + #Check if matrix and precipitate phase are stable, and check if there's no miscibility gaps + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + #Get indices for each phase + eqPa = eqSub.where(eqSub.Phase == self.phases[0], drop=True) + eqPr = eqSub.where(eqSub.Phase == precPhase, drop=True) + + cParent = eqPa.X.values.ravel() + cPrec = eqPr.X.values.ravel() + + #Get composition of element, use element index of 1 is the parent index is first alphabetically + if self.reverse: + xParent = cParent[0] + xPrec = cPrec[0] + else: + xParent = cParent[1] + xPrec = cPrec[1] + + xParentArray[g] = xParent + xPrecArray[g] = xPrec + break + if xParentArray[g] == 0: + xParentArray[g] = -1 + xPrecArray[g] = -1 + + if len(gExtra) == 1: + return xParentArray[0], xPrecArray[0] + else: + return xParentArray, xPrecArray + + + def _interfacialCompositionFromCurvature(self, T, gExtra = 0, precPhase = None): + ''' + Gets interfacial composition using free energy curvature + G''(x - xM)(xP-xM) = 2*y*V/R + + Parameters + ---------- + T : float + Temperature in K + gExtra : float (optional) + Extra contributions to the precipitate Gibbs free energy + Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) + Defaults to 0 + precPhase : str + Precipitate phase to consider (default is first precipitate in list) + + Returns + ------- + (parent composition, precipitate composition) + Both will be either float or array based off shape of gExtra + Will return (None, None) if precipitate is unstable + ''' + if precPhase is None: + precPhase = self.phases[1] + + if hasattr(gExtra, '__len__'): + gExtra = np.array(gExtra) + else: + gExtra = np.array([gExtra]) + + #Compute equilibrium at guess composition + cond = {v.X(self.elements[1]): self._guessComposition[precPhase], v.T: T, v.P: 101325, v.GE: self.gOffset} + eq = equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, model=self.models, + phase_records={self.phases[0]: self.phase_records[self.phases[0]], precPhase: self.phase_records[precPhase]}, + calc_opts = {'pdens': self.pDens}) + + gm = eq.GM.values.ravel() + for g in gm: + eqSub = eq.where(eq.GM == g, drop=True) + + ph = eqSub.Phase.values.ravel() + ph = ph[ph != ''] + + #Check if matrix and precipitate phase are stable, and check if there's no miscibility gaps + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + #Cast values in state_variables to double for updating composition sets + state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) + stable_phases = eqSub.Phase.values.ravel() + phase_amounts = eqSub.NP.values.ravel() + matrix_idx = np.where(stable_phases == self.phases[0])[0] + precip_idx = np.where(stable_phases == precPhase)[0] + + cs_matrix = CompositionSet(self.phase_records[self.phases[0]]) + if len(matrix_idx) > 1: + matrix_idx = [matrix_idx[np.argmax(phase_amounts[matrix_idx])]] + cs_matrix.update(eqSub.Y.isel(vertex=matrix_idx).values.ravel()[:cs_matrix.phase_record.phase_dof], + phase_amounts[matrix_idx], state_variables) + cs_precip = CompositionSet(self.phase_records[precPhase]) + if len(precip_idx) > 1: + precip_idx = [precip_idx[np.argmax(phase_amounts[precip_idx])]] + cs_precip.update(eqSub.Y.isel(vertex=precip_idx).values.ravel()[:cs_precip.phase_record.phase_dof], + phase_amounts[precip_idx], state_variables) + + chemical_potentials = eqSub.MU.values.ravel() + cPrec = eqSub.isel(vertex=precip_idx).X.values.ravel() + cParent = eqSub.isel(vertex=matrix_idx).X.values.ravel() + + dMudxParent = dMudX(chemical_potentials, cs_matrix, self.elements[0]) + dMudxPrec = dMudX(chemical_potentials, cs_precip, self.elements[0]) + + #Get composition of element, use element index of 1 is the parent index is first alphabetically + if self.reverse: + xParentEq = cParent[0] + xPrecEq = cPrec[0] + else: + xParentEq = cParent[1] + xPrecEq = cPrec[1] + + #dmudx are scalars here + dMudxParent = dMudxParent[0,0] + dMudxPrec = dMudxPrec[0,0] + + if dMudxParent != 0: + xParent = gExtra / dMudxParent / (xPrecEq - xParentEq) + xParentEq + else: + xParent = xParentEq*np.ones(len(gExtra)) + + if dMudxPrec != 0: + xPrec = dMudxParent * (xParent - xParentEq) / dMudxPrec + xPrecEq + else: + xPrec = xPrecEq*np.ones(len(gExtra)) + + xParent[xParent < 0] = 0 + xParent[xParent > 1] = 1 + xPrec[xPrec < 0] = 0 + xPrec[xPrec > 1] = 1 + + if len(gExtra) == 1: + return xParent[0], xPrec[0] + else: + return xParent, xPrec + + if len(gExtra) == 1: + return -1, -1 + else: + return -1*np.ones(len(gExtra)), -1*np.ones(len(gExtra)) + + + def plotPhases(self, ax, T, gExtra = 0, plotGibbsOffset = False, *args, **kwargs): + ''' + Plots sampled points from the parent and precipitate phase + + Parameters + ---------- + ax : Axis + T : float + Temperature in K + gExtra : float (optional) + Extra contributions to the Gibbs free energy of precipitate + Defaults to 0 + plotGibbsOffset : bool (optional) + If True and gExtra is not 0, the sampled points of the + precipitate phase will be plotted twice with gExtra and + with no extra Gibbs free energy contributions + Defualts to False + ''' + points = calculate(self.db, self.elements, self.phases[0], P=101325, T=T, GE=0, model=self.models, phase_records=self.phase_records, output='GM') + ax.scatter(points.X.sel(component=self.elements[1]), points.GM / 1000, label=self.phases[0], *args, **kwargs) + + #Add gExtra to precipitate phase + for i in range(1, len(self.phases)): + points = calculate(self.db, self.elements, self.phases[i], P=101325, T=T, GE=0, model=self.models, phase_records=self.phase_records, output='GM') + ax.scatter(points.X.sel(component=self.elements[1]), (points.GM + gExtra) / 1000, label=self.phases[i], *args, **kwargs) + + #Plot non-offset precipitate phase + if plotGibbsOffset and gExtra != 0: + ax.scatter(points.X.sel(component=self.elements[1]), points.GM / 1000, color='silver', alpha=0.3, *args, **kwargs) + + ax.legend() + ax.set_xlim([0, 1]) + ax.set_xlabel('Composition ' + self.elements[1]) + ax.set_ylabel('Gibbs Free Energy (kJ/mol)') \ No newline at end of file diff --git a/kawin/FreeEnergyHessian.py b/kawin/thermo/FreeEnergyHessian.py similarity index 80% rename from kawin/FreeEnergyHessian.py rename to kawin/thermo/FreeEnergyHessian.py index b91f1ea..e6adfca 100644 --- a/kawin/FreeEnergyHessian.py +++ b/kawin/thermo/FreeEnergyHessian.py @@ -8,6 +8,18 @@ def hessian(chemical_potentials, composition_set): ''' Returns the hessian of the objective function for a single phase + For the Lagrangian function + L = N * G + sum(mu_A * (N_A - N * dM_A/dy_i)) + sum(lambda_s * (1 - sum(y_i))) + We have 5 derivatives + d2L/dyi2 = N * d2G/dyi2 + d2L/dyidlambda_s = -1 if y_i in s else 0 + d2L/dyidN = dG/dy - sum(mu_A * dM_A/dy_i) + d2L/dyidmu_A = -N dM_A/dy_i + d2L/dmu_AdN = -M_A + + Everything is per mole of formula unit, so N has to be corrected for phases where the + total moles of atoms could be off from 1 + Parameters ---------- chemical_potentials : 1-D ndarray @@ -20,11 +32,23 @@ def hessian(chemical_potentials, composition_set): site fractions, phase amount, lagrangian multipliers, chemical potential ''' elements = list(composition_set.phase_record.nonvacant_elements) - x = np.array(composition_set.X) mu = np.asarray(chemical_potentials) + + #dM_A / dy_i dxdy = np.zeros((len(elements), len(composition_set.dof))) + #M_A + moleA = np.zeros((len(elements),1)) for comp_idx in range(len(elements)): composition_set.phase_record.formulamole_grad(dxdy[comp_idx, :], composition_set.dof, comp_idx) + composition_set.phase_record.formulamole_obj(moleA[comp_idx,:], composition_set.dof, comp_idx) + + #Moles of phase per formula unit + #We assume 1 mole of phase, but this is per mole of atoms + #This is generally okay, but for interstitials or vacancies in the main sublattice + #We need to use moles of formula units when constructing the hessian + formulaPhAmt = 1 / np.sum(moleA) + + #dG/dy_i and d2G/dy2 dg = np.zeros(len(composition_set.dof)) composition_set.phase_record.formulagrad(dg, composition_set.dof) d2g = np.zeros((len(composition_set.dof), len(composition_set.dof))) @@ -36,26 +60,30 @@ def hessian(chemical_potentials, composition_set): #Create hessian matrix hess = np.zeros((phase_dof + num_internal_cons + len(elements) + 1, phase_dof + num_internal_cons + len(elements) + 1)) - # wrt phase dof + # wrt phase dof - d2L / dyi dyj hess[:phase_dof, :phase_dof] = d2g[composition_set.phase_record.num_statevars:, - composition_set.phase_record.num_statevars:] - cons_jac_tmp = np.zeros((num_internal_cons, len(composition_set.dof))) - composition_set.phase_record.internal_cons_jac(cons_jac_tmp, composition_set.dof) - - # wrt phase amount + composition_set.phase_record.num_statevars:] * formulaPhAmt + + # wrt phase amount - d2L / dyi dN for i in range(phase_dof): hess[i, phase_dof] = dg[num_statevars + i] - np.sum(mu * dxdy[:, num_statevars+i]) hess[phase_dof, i] = hess[i, phase_dof] + # d2L / dyi dlambda + cons_jac_tmp = np.zeros((num_internal_cons, len(composition_set.dof))) + composition_set.phase_record.internal_cons_jac(cons_jac_tmp, composition_set.dof) hess[:phase_dof, phase_dof+1:phase_dof+1+num_internal_cons] = -cons_jac_tmp[:, num_statevars:].T hess[phase_dof+1:phase_dof+1+num_internal_cons, :phase_dof] = hess[:phase_dof, phase_dof+1:phase_dof+1+num_internal_cons].T + + # d2L / dyi dmuA index = phase_dof + num_internal_cons + 1 - hess[:phase_dof, index:] = -1 * dxdy[:, num_statevars:].T - hess[index:, :phase_dof] = -1 * dxdy[:, num_statevars:] + hess[:phase_dof, index:] = -1 * dxdy[:, num_statevars:].T * formulaPhAmt + hess[index:, :phase_dof] = -1 * dxdy[:, num_statevars:] * formulaPhAmt + # d2L / dmuA dN for A in range(len(elements)): - hess[phase_dof, index + A] = -x[A] - hess[index + A, phase_dof] = -x[A] + hess[phase_dof, index + A] = -moleA[A,0] + hess[index + A, phase_dof] = -moleA[A,0] return hess @@ -139,6 +167,10 @@ def dMudX(chemical_potentials, composition_set, refElement): This more or less represents the curvature of the free energy surface with reference element R + Rows correspond to mu_A and columns correspond to X_A so for ternary system with (A,B,R), its + | dmu_A/dX_A dmu_A/dX_B | + | dmu_B/dX_A dmu_B/dX_B | + Parameters ---------- chemical_potentials : 1-D ndarray @@ -172,6 +204,10 @@ def partialdMudX(chemical_potentials, composition_set): ''' Partial derivative of chemical potential with respect to system composition + Rows correspond to mu_A and columns correspond to X_A so for binary system, its + | dmu_A/dX_A dmu_A/dX_B | + | dmu_B/dX_A dmu_B/dX_B | + Parameters ---------- composition_set : pycalphad.core.composition_set.CompositionSet diff --git a/kawin/LocalEquilibrium.py b/kawin/thermo/LocalEquilibrium.py similarity index 90% rename from kawin/LocalEquilibrium.py rename to kawin/thermo/LocalEquilibrium.py index 2e90a91..cb7c08b 100644 --- a/kawin/LocalEquilibrium.py +++ b/kawin/thermo/LocalEquilibrium.py @@ -28,7 +28,10 @@ def local_equilibrium(dbf, comps, phases, conds, models, phase_records, composit ''' # Broadcasting conditions not supported cur_conds = {str(k): float(v) for k, v in conds.items()} - state_variables = np.array([cur_conds['GE'], cur_conds['N'], cur_conds['P'], cur_conds['T']], dtype=np.float64) + if 'GE' in cur_conds: + state_variables = np.array([cur_conds['GE'], cur_conds['N'], cur_conds['P'], cur_conds['T']], dtype=np.float64) + else: + state_variables = np.array([0, cur_conds['N'], cur_conds['P'], cur_conds['T']], dtype=np.float64) if composition_sets is None: # Note: filter_phases() not called, so all specified phases must be valid composition_sets = [] diff --git a/kawin/Mobility.py b/kawin/thermo/Mobility.py similarity index 51% rename from kawin/Mobility.py rename to kawin/thermo/Mobility.py index 2e8c118..cc9f424 100644 --- a/kawin/Mobility.py +++ b/kawin/thermo/Mobility.py @@ -1,11 +1,18 @@ from tinydb import where import numpy as np from pycalphad import Model, variables as v -from symengine import exp, Symbol -from kawin.FreeEnergyHessian import partialdMudX, dMudX +from pycalphad.core.utils import wrap_symbol, extract_parameters +from symengine import exp, Symbol, Add +from kawin.thermo.FreeEnergyHessian import partialdMudX, dMudX setattr(v, 'GE', v.StateVariable('GE')) +#List of interstitial elements +# When calculating interdiffusivity, we do not require reference element +# When calculating the mobility factor, we have an additional vacancy term to multiply by +#As a list here, hopefully this should be editable by a user outside of this module - may have to edit __init__.py +interstitials = ['C', 'N', 'O', 'H', 'B'] + class MobilityModel(Model): ''' Handles mobility and diffusivity data from .tdb files @@ -30,11 +37,25 @@ def __init__(self, dbe, comps, phase_name, parameters=None): super().__init__(dbe, comps, phase_name, parameters) symbols = {Symbol(s): val for s, val in dbe.symbols.items()} + if self._parameters_arg is not None: + if isinstance(self._parameters_arg, dict): + symbols.update([(wrap_symbol(s), val) for s, val in self._parameters_arg.items()]) + else: + # Lists of symbols that should remain symbolic + for s in self._parameters_arg: + symbols.pop(wrap_symbol(s)) + + #Replace symbols with database symbols for mobility and exponential term + #Also store a copy of the mobility/diffusivity as MOB_A or DIFF_A for name, value in self.mobility.items(): - self.mobility[name] = self.symbol_replace(value, symbols) + self.mobility[name] = self.symbol_replace(value, symbols).xreplace(v.supported_variables_in_databases) + setattr(self, 'MOB_'+name, self.mobility[name]) + setattr(self, 'MQ_'+name, self.symbol_replace(getattr(self, 'MQ_'+name), symbols).xreplace(v.supported_variables_in_databases)) for name, value in self.diffusivity.items(): - self.diffusivity[name] = self.symbol_replace(value, symbols) + self.diffusivity[name] = self.symbol_replace(value, symbols).xreplace(v.supported_variables_in_databases) + setattr(self, 'DIFF_'+name, self.diffusivity[name]) + setattr(self, 'DQ_'+name, self.symbol_replace(getattr(self, 'DQ_'+name), symbols).xreplace(v.supported_variables_in_databases)) self.mob_site_fractions = {c: sorted([x for x in self.mobility_variables[c] if isinstance(x, v.SiteFraction)], key=str) for c in self.mobility} self.diff_site_fractions = {c: sorted([x for x in self.diffusivity_variables[c] if isinstance(x, v.SiteFraction)], key=str) for c in self.diffusivity} @@ -116,15 +137,132 @@ def build_mobility(self, dbe): if name not in self.mob_models: self.mob_models[name] = {} self.mob_models[name][c.name] = rk - - mob[c.name] = (1 / (v.R * v.T)) * exp((self.mob_models['MF'][c.name] + self.mob_models['MQ'][c.name]) / (v.R * v.T)) - setattr(self, 'mob_'+str(c.name).upper(), mob[c.name]) - diff[c.name] = exp((self.mob_models['DF'][c.name] + self.mob_models['DQ'][c.name]) / (v.R * v.T)) - setattr(self, 'diff_' + str(c.name).upper(), diff[c.name]) + + #Additional parameters search if diffusing species are not included + # This is mainly intended to help with parameter fitting + # Parameters will be in the format for MOB_A, MOB_B (or the respective keyword) + # This will reflect how the models are stored in MobilityModel + # Ex. MOB_A is the entire mobility model of A while MQ_A is the redlich kister polynomial used for A mobility + #Additional parameters will be in tuples of (database keyword, mob_models keyword) + additional_params = [('MOB', 'MQ'), ('MQ', 'MQ'), ('DIFF', 'DQ'), ('DQ', 'DQ')] + for p in additional_params: + fit_name = p[0] + '_' + c.name + param_query = ( + (where('phase_name') == phase.name) & \ + (where('parameter_type') == fit_name) & \ + (where('constituent_array').test(self._mobility_validity)) + ) + rk = self.redlich_kister_sum(phase, param_search, param_query) + self.mob_models[p[1]][c.name] += rk + + self.checkOrderingContribution(dbe) + for c in self.components: + if c.name != 'VA': + #In thermo-calc, the mobility model is defined as exp(sum(MF)/RT) * exp(sum(MQ)/RT) / RT + #The diffusivity model is defined either as dilute - exp(sum(DF)/RT) * exp(sum(DQ)/RT) + # or simple - sum(DF) + sum(DQ) + # We use the dilute assumption here + #In summary, there's no difference between MF and MQ, or between DF and DQ + # For papers using Q and theta (pre-exponential term), corrections must be made to theta have it fit the definitions above + mqsum = self.mob_models['MF'][c.name] + self.mob_models['MQ'][c.name] + dqsum = self.mob_models['DF'][c.name] + self.mob_models['DQ'][c.name] + mob[c.name] = (1 / (v.R * v.T)) * exp(mqsum / (v.R * v.T)) + diff[c.name] = exp(dqsum / (v.R * v.T)) + + #Also store the exponential term in case we want to grab the activation energy or pre-exp term + setattr(self, 'MQ_'+str(c.name).upper(), mqsum) + setattr(self, 'DQ_'+str(c.name).upper(), dqsum) return mob, diff + + def checkOrderingContribution(self, dbe): + ''' + Checks if phase is an ordered part of a order-disorder model + + The ordered part of the phase double counts the disordered contribution, so the model is + G = G_dis + G_ord(y) - G_ord(y=x) + + This is straight up copied from Model.atomic_ordering_energy in pycalphad with + the minor difference that we replace the symbols in mob and diff + ''' + phase = dbe.phases[self.phase_name] + ordered_phase_name = phase.model_hints.get('ordered_phase', None) + disordered_phase_name = phase.model_hints.get('disordered_phase', None) -def mobility_from_composition_set(composition_set, mobility_callables = None, mobility_correction = None): + #If not order-disorder model, then return as unchanged + if phase.name != ordered_phase_name: + return + + ordered_phase = dbe.phases[ordered_phase_name] + constituents = [sorted(set(c).intersection(self.components)) for c in ordered_phase.constituents] + disordered_phase = dbe.phases[disordered_phase_name] + disordered_model = self.__class__(dbe, sorted(self.components), disordered_phase_name) + + disordered_subl_constituents = disordered_phase.constituents[0] + ordered_constituents = ordered_phase.constituents + substitutional_sublattice_idxs = [] + for idx, subl_constituents in enumerate(ordered_constituents): + if len(disordered_subl_constituents.symmetric_difference(subl_constituents)) == 0: + substitutional_sublattice_idxs.append(idx) + + num_substitutional_sublattice_idxs = len(substitutional_sublattice_idxs) + num_ordered_interstitial_subls = len(ordered_phase.sublattices) - num_substitutional_sublattice_idxs + num_disordered_interstitial_subls = len(disordered_phase.sublattices) - 1 + if num_ordered_interstitial_subls != num_disordered_interstitial_subls: + raise ValueError( + f'Number of interstitial sublattices for the disordered phase ' + f'({num_disordered_interstitial_subls}) and the ordered phase ' + f'({num_ordered_interstitial_subls}) do not match. Got ' + f'substitutional sublattice indices of {substitutional_sublattice_idxs}.' + ) + + for c in self.mob_models['MF']: + ordered_mobQ = Add(self.mob_models['MQ'][c]) + ordered_mobF = Add(self.mob_models['MF'][c]) + ordered_diffQ = Add(self.mob_models['DQ'][c]) + ordered_diffF = Add(self.mob_models['DF'][c]) + + # Compute the molefraction_dict, which will map ordered phase site + # fractions to the quasi mole fractions representing the disordered state + molefraction_dict = {} + ordered_sitefracs = [x for x in ordered_mobQ.free_symbols if isinstance(x, v.SiteFraction)] + for sitefrac in ordered_sitefracs: + if sitefrac.sublattice_index in substitutional_sublattice_idxs: + molefraction_dict[sitefrac] = \ + self._quasi_mole_fraction(sitefrac.species, + ordered_phase_name, + constituents, + ordered_phase.sublattices, + substitutional_sublattice_idxs, + ) + + # Compute the variable_rename_dict, which will map disordered phase site + # fractions to the quasi mole fractions representing the disordered state + variable_rename_dict = {} + disordered_sitefracs = [x for x in disordered_model.energy.free_symbols if isinstance(x, v.SiteFraction)] + for atom in disordered_sitefracs: + if atom.sublattice_index == 0: # only the first sublattice is substitutional + variable_rename_dict[atom] = \ + self._quasi_mole_fraction(atom.species, + ordered_phase_name, + constituents, + ordered_phase.sublattices, + substitutional_sublattice_idxs, + ) + + else: + shifted_subl_index = atom.sublattice_index + num_substitutional_sublattice_idxs - 1 + variable_rename_dict[atom] = \ + v.SiteFraction(ordered_phase_name, shifted_subl_index, atom.species) + + self.mob_models['MQ'][c] = self._partitioned_expr(disordered_model.mob_models['MQ'][c], ordered_mobQ, variable_rename_dict, molefraction_dict) + self.mob_models['MF'][c] = self._partitioned_expr(disordered_model.mob_models['MF'][c], ordered_mobF, variable_rename_dict, molefraction_dict) + self.mob_models['DQ'][c] = self._partitioned_expr(disordered_model.mob_models['DQ'][c], ordered_diffQ, variable_rename_dict, molefraction_dict) + self.mob_models['DF'][c] = self._partitioned_expr(disordered_model.mob_models['DF'][c], ordered_diffF, variable_rename_dict, molefraction_dict) + + return + +def mobility_from_composition_set(composition_set, mobility_callables = None, mobility_correction = None, parameters = {}): ''' Computes mobility from equilibrium results @@ -135,6 +273,8 @@ def mobility_from_composition_set(composition_set, mobility_callables = None, mo Pre-computed mobility callables for each element mobility_correction : dict (optional) Factor to multiply mobility by for each given element (defaults to 1) + parameters : dict {str : float} + List of parameters to override free symbols in the model Returns ------- @@ -153,10 +293,15 @@ def mobility_from_composition_set(composition_set, mobility_callables = None, mo if A not in mobility_correction: mobility_correction[A] = 1 - return np.array([mobility_correction[elements[A]] * mobility_callables[elements[A]](composition_set.dof) for A in range(len(elements))]) + #return np.array([mobility_correction[elements[A]] * mobility_callables[elements[A]](composition_set.dof) for A in range(len(elements))]) + param_keys, param_values = extract_parameters(parameters) + if len(param_values) > 0: + callableInput = np.concatenate((composition_set.dof, param_values[0]), dtype=np.float_) + else: + callableInput = composition_set.dof + return np.array([mobility_correction[elements[A]] * mobility_callables[elements[A]](callableInput) for A in range(len(elements))]) - -def tracer_diffusivity(composition_set, mobility_callables = None, mobility_correction = None): +def tracer_diffusivity(composition_set, mobility_callables = None, mobility_correction = None, parameters = {}): ''' Computes tracer diffusivity for given equilibrium results D = MRT @@ -178,42 +323,9 @@ def tracer_diffusivity(composition_set, mobility_callables = None, mobility_corr R = 8.314 T = composition_set.dof[composition_set.phase_record.state_variables.index(v.T)] - return R * T * mobility_from_composition_set(composition_set, mobility_callables, mobility_correction) + return R * T * mobility_from_composition_set(composition_set, mobility_callables, mobility_correction, parameters) -def tracer_diffusivity_from_diff(composition_set, diffusivity_callables = None, diffusivity_correction = None): - ''' - Tracer diffusivity from diffusivity callables - - This will just return the Da as an array - - Parameters - ---------- - composition_set : pycalphad.core.composition_set.CompositionSet - diffusivity_callables : dict - Pre-computed diffusivity callables for each element - diffusivity_correction : dict (optional) - Factor to multiply diffusivity by for each given element (defaults to 1) - - Returns - ------- - Array of floats of diffusivity for each element (alphabetical order) - ''' - if diffusivity_callables is None: - raise ValueError('diffusivity_callables is required') - - elements = list(composition_set.phase_record.nonvacant_elements) - - #Set diffusivity correction if not set - if diffusivity_correction is None: - diffusivity_correction = {A: 1 for A in elements} - else: - for A in elements: - if A not in diffusivity_correction: - diffusivity_correction[A] = 1 - - return np.array([diffusivity_correction[elements[A]] * diffusivity_callables[elements[A]](composition_set.dof) for A in range(len(elements))]) - -def mobility_matrix(composition_set, mobility_callables = None, mobility_correction = None): +def mobility_matrix(composition_set, mobility_callables = None, mobility_correction = None, parameters = {}): ''' Mobility matrix Used to obtain diffusivity when multipled with free energy hessian @@ -234,22 +346,65 @@ def mobility_matrix(composition_set, mobility_callables = None, mobility_correct elements = list(composition_set.phase_record.nonvacant_elements) X = composition_set.X - computedMob = mobility_from_composition_set(composition_set, mobility_callables, mobility_correction) - mob = np.array([X[A] * computedMob[A] for A in range(len(elements))]) - + #U-fraction - defined as U_a = X_a / sum(substitutionals) + Usum = np.sum([X[A] for A in range(len(elements)) if elements[A] not in interstitials]) + U = X / Usum + + #Multiply mobility by U-fraction for ease of use when constructing the mobility matrix + computedMob = mobility_from_composition_set(composition_set, mobility_callables, mobility_correction, parameters) + mob = np.array([U[A] * computedMob[A] for A in range(len(elements))]) + + #Find vacancy site fractions for multiplying with interstitials when making the mobility matrix + #If vacancies are not found on the same sublattice, we'll defualt to 1 so there's at least some mobility and not 0 + # A mobility of 0 would be quite unrealistic + # In addition, as we're working with interstitals, the vacancies are going to be close to 1, so this assumption wouldn't hurt + vaTerms = {} #Maps sublattice index to site fraction index for vacancies + interstitialTerms = {} #Maps interstitial to sublattice index + index = len(composition_set.phase_record.state_variables) + for i in range(len(composition_set.phase_record.variables)): + if composition_set.phase_record.variables[i].species.name == 'VA': + vaTerms[composition_set.phase_record.variables[i].sublattice_index] = composition_set.dof[index+i] + if composition_set.phase_record.variables[i].species.name in interstitials: + interstitialTerms[composition_set.phase_record.variables[i].species.name] = composition_set.phase_record.variables[i].sublattice_index + + #For interstitials + # M_aa = y_Va * M_a + # y_Va is taken from the same sublattice that a is on, where more vacancies on the sublattice implies faster diffusion + #For substitutionals + # M_aa = (1-U_a) * U_a * M_a + # M_ab = -U_a * U_b * M_b + #There are no entries for M_ab if one index is interstitial and the other is substitutional mobMatrix = np.zeros((len(elements), len(elements))) for a in range(len(elements)): - for b in range(len(elements)): - if a == b: - mobMatrix[a, b] = (1 - X[a]) * mob[b] - else: - mobMatrix[a, b] = -X[a] * mob[b] + if elements[a] in interstitials: + mobMatrix[a, a] = vaTerms.get(interstitialTerms[elements[a]], 1) * mob[a] + else: + for b in range(len(elements)): + if elements[b] not in interstitials: + if a == b: + mobMatrix[a, b] = (1 - U[a]) * mob[b] + else: + mobMatrix[a, b] = -U[a] * mob[b] + #Diffusivity requires dmu_a/dU_b; however, the free energy curvature gives dmu_a/dX_b + #Assuming that Usum is constant and using chain-rule derivatives, + # the conversion from dmu_a/dX_b to dmu_a/dU_b can be done by multiplying the sum(substitutionals) + mobMatrix *= Usum + + #Old way of computing mobility assuming only substitutional elements (so much simpler...) + #mob = np.array([X[A] * computedMob[A] for A in range(len(elements))]) + #for a in range(len(elements)): + # for b in range(len(elements)): + # if a == b: + # mobMatrix[a, b] = (1 - X[a]) * mob[b] + # else: + # mobMatrix[a, b] = -X[a] * mob[b] return mobMatrix -def chemical_diffusivity(chemical_potentials, composition_set, mobility_callables, mobility_correction = None, returnHessian = False): +def chemical_diffusivity(chemical_potentials, composition_set, mobility_callables, mobility_correction = None, returnHessian = False, parameters = {}): ''' - Chemical diffusivity (D_ab) + Chemical diffusivity (D_kj) + D_kj = sum((delta_ik - U_k) * U_i * M_i) * dmu_i/dU_j D_ab = mobility matrix * free energy hessian Parameters @@ -271,7 +426,7 @@ def chemical_diffusivity(chemical_potentials, composition_set, mobility_callable ''' dmudx = partialdMudX(chemical_potentials, composition_set) #print('dmudx', dmudx) - mobMatrix = mobility_matrix(composition_set, mobility_callables, mobility_correction) + mobMatrix = mobility_matrix(composition_set, mobility_callables, mobility_correction, parameters) #print('mobMatrix', mobMatrix) Dkj = np.matmul(mobMatrix, dmudx) @@ -280,7 +435,7 @@ def chemical_diffusivity(chemical_potentials, composition_set, mobility_callable else: return Dkj, None -def interdiffusivity(chemical_potentials, composition_set, refElement, mobility_callables = None, mobility_correction = None, returnHessian = False): +def interdiffusivity(chemical_potentials, composition_set, refElement, mobility_callables = None, mobility_correction = None, returnHessian = False, parameters = {}): ''' Interdiffusivity (D^n_ab) @@ -307,19 +462,18 @@ def interdiffusivity(chemical_potentials, composition_set, refElement, mobility_ alphabetical order excluding reference element free energy hessian will be None if returnHessian is False ''' - #List of interstitial elements - do not require reference element when calculating interdiffusivity - interstitials = ['C', 'N', 'O', 'H', 'B'] - - Dkj, hessian = chemical_diffusivity(chemical_potentials, composition_set, mobility_callables, mobility_correction, returnHessian) + Dkj, hessian = chemical_diffusivity(chemical_potentials, composition_set, mobility_callables, mobility_correction, returnHessian, parameters) #print('Dkj', Dkj) elements = list(composition_set.phase_record.nonvacant_elements) + #Find index of reference element refIndex = 0 for a in range(len(elements)): if elements[a] == refElement: refIndex = a break + #Build Dnkj, skipping the reference element Dnkj = np.zeros((len(elements) - 1, len(elements) - 1)) c = 0 d = 0 @@ -337,8 +491,80 @@ def interdiffusivity(chemical_potentials, composition_set, refElement, mobility_ return Dnkj, hessian +def inverseMobility(chemical_potentials, composition_set, refElement, mobility_callables, mobility_correction = None, returnOther = True, parameters = {}): + ''' + Inverse mobility matrix for determining interfacial composition from + Philippe and P. W. Voorhees, Acta Materialia 61 (2013) p. 4237 + + M^-1 = (free energy hessian) * Dnkj^-1 -def interdiffusivity_from_diff(composition_set, refElement, diffusivity_callables, diffusivity_correction = None): + Parameters + ---------- + chemical_potentials : 1-D ndarray + composition_set : pycalphad.core.composition_set.CompositionSet + refElement : str + Reference element n + mobility_callables : dict + Pre-computed mobility callables for each element + mobility_correction : dict (optional) + Factor to multiply mobility by for each given element (defaults to 1) + returnOther : bool (optional) + Whether to return interdiffusivity and hessian (defaults to False) + + Returns + ------- + (interdiffusivity, hessian, inverse mobility) + Interdiffusivity and hessian will be None if returnOther is False + ''' + Dnkj, _ = interdiffusivity(chemical_potentials, composition_set, refElement, mobility_callables, mobility_correction, False, parameters) + totalH = dMudX(chemical_potentials, composition_set, refElement) + #print('totalH', totalH) + if returnOther: + return Dnkj, totalH, np.matmul(totalH, np.linalg.inv(Dnkj)) + else: + return None, None, np.matmul(totalH, np.linalg.inv(Dnkj)) + +def tracer_diffusivity_from_diff(composition_set, diffusivity_callables = None, diffusivity_correction = None, parameters = {}): + ''' + Tracer diffusivity from diffusivity callables + + This will just return the Da as an array + + Parameters + ---------- + composition_set : pycalphad.core.composition_set.CompositionSet + diffusivity_callables : dict + Pre-computed diffusivity callables for each element + diffusivity_correction : dict (optional) + Factor to multiply diffusivity by for each given element (defaults to 1) + + Returns + ------- + Array of floats of diffusivity for each element (alphabetical order) + ''' + if diffusivity_callables is None: + raise ValueError('diffusivity_callables is required') + + elements = list(composition_set.phase_record.nonvacant_elements) + + #Set diffusivity correction if not set + if diffusivity_correction is None: + diffusivity_correction = {A: 1 for A in elements} + else: + for A in elements: + if A not in diffusivity_correction: + diffusivity_correction[A] = 1 + + #return np.array([diffusivity_correction[elements[A]] * diffusivity_callables[elements[A]](composition_set.dof) for A in range(len(elements))]) + + param_keys, param_values = extract_parameters(parameters) + if len(param_values) > 0: + callableInput = np.concatenate((composition_set.dof, param_values[0]), dtype=np.float_) + else: + callableInput = composition_set.dof + return np.array([diffusivity_correction[elements[A]] * diffusivity_callables[elements[A]](callableInput) for A in range(len(elements))]) + +def interdiffusivity_from_diff(composition_set, refElement, diffusivity_callables, diffusivity_correction = None, parameters = {}): ''' Interdiffusivity (D^n_ab) calculated from diffusivity callables This is if the TDB database only has diffusivity data and no mobility data @@ -371,54 +597,26 @@ def interdiffusivity_from_diff(composition_set, refElement, diffusivity_callable if A not in diffusivity_correction: diffusivity_correction[A] = 1 + param_keys, param_values = extract_parameters(parameters) + if len(param_values) > 0: + callableInput = np.concatenate((composition_set.dof, param_values[0]), dtype=np.float_) + else: + callableInput = composition_set.dof Dnkj = np.zeros((len(elements) - 1, len(elements) - 1)) eleIndex = 0 for a in range(len(elements) - 1): if elements[eleIndex] == refElement: eleIndex += 1 - Daa = diffusivity_correction[elements[eleIndex]] * diffusivity_callables[elements[eleIndex]](composition_set.dof) + #Daa = diffusivity_correction[elements[eleIndex]] * diffusivity_callables[elements[eleIndex]](composition_set.dof) + Daa = diffusivity_correction[elements[eleIndex]] * diffusivity_callables[elements[eleIndex]](callableInput) Dnkj[a, a] = Daa eleIndex += 1 return Dnkj - -def inverseMobility(chemical_potentials, composition_set, refElement, mobility_callables, mobility_correction = None, returnOther = True): - ''' - Inverse mobility matrix for determining interfacial composition - - M^-1 = (free energy hessian) * Dnkj^-1 - - Parameters - ---------- - chemical_potentials : 1-D ndarray - composition_set : pycalphad.core.composition_set.CompositionSet - refElement : str - Reference element n - mobility_callables : dict - Pre-computed mobility callables for each element - mobility_correction : dict (optional) - Factor to multiply mobility by for each given element (defaults to 1) - returnOther : bool (optional) - Whether to return interdiffusivity and hessian (defaults to False) - - Returns - ------- - (interdiffusivity, hessian, inverse mobility) - Interdiffusivity and hessian will be None if returnOther is False - ''' - Dnkj, _ = interdiffusivity(chemical_potentials, composition_set, refElement, mobility_callables, mobility_correction, False) - totalH = dMudX(chemical_potentials, composition_set, refElement) - #print('totalH', totalH) - if returnOther: - return Dnkj, totalH, np.matmul(totalH, np.linalg.inv(Dnkj)) - else: - return None, None, np.matmul(totalH, np.linalg.inv(Dnkj)) - - -def inverseMobility_from_diffusivity(chemical_potentials, composition_set, refElement, diffusivity_callables, diffusivity_correction = None, returnOther = True): +def inverseMobility_from_diffusivity(chemical_potentials, composition_set, refElement, diffusivity_callables, diffusivity_correction = None, returnOther = True, parameters = {}): ''' Inverse mobility matrix for determining interfacial composition @@ -442,10 +640,12 @@ def inverseMobility_from_diffusivity(chemical_potentials, composition_set, refEl (interdiffusivity, hessian, inverse mobility) Interdiffusivity and hessian will be None if returnOther is False ''' - Dnkj = interdiffusivity_from_diff(composition_set, refElement, diffusivity_callables, diffusivity_correction) + Dnkj = interdiffusivity_from_diff(composition_set, refElement, diffusivity_callables, diffusivity_correction, parameters) totalH = dMudX(chemical_potentials, composition_set, refElement) if returnOther: return Dnkj, totalH, np.matmul(totalH, np.linalg.inv(Dnkj)) else: return None, None, np.matmul(totalH, np.linalg.inv(Dnkj)) + + diff --git a/kawin/thermo/MultiTherm.py b/kawin/thermo/MultiTherm.py new file mode 100644 index 0000000..e1657c6 --- /dev/null +++ b/kawin/thermo/MultiTherm.py @@ -0,0 +1,526 @@ +from kawin.thermo.Thermodynamics import GeneralThermodynamics +import numpy as np +from pycalphad import variables as v +from kawin.thermo.Mobility import inverseMobility, inverseMobility_from_diffusivity, tracer_diffusivity, tracer_diffusivity_from_diff +from kawin.thermo.FreeEnergyHessian import dMudX +from kawin.thermo.LocalEquilibrium import local_equilibrium + +class MulticomponentThermodynamics (GeneralThermodynamics): + ''' + Class for defining driving force and (possibly) interfacial composition functions + for a multicomponent system using pyCalphad and thermodynamic databases + + Parameters + ---------- + database : str + File name for database + elements : list + Elements to consider + Note: reference element must be the first index in the list + phases : list + Phases involved + Note: matrix phase must be first index in the list + drivingForceMethod : str (optional) + Method used to calculate driving force + Options are 'tangent' (default), 'approximate', 'sampling' and 'curvature' (not recommended) + parameters : list [str] or dict {str : float} + List of parameters to keep symbolic in the thermodynamic or mobility models + ''' + def __init__(self, database, elements, phases, drivingForceMethod = 'tangent', parameters = None): + super().__init__(database, elements, phases, drivingForceMethod, parameters) + + #Previous variables for curvature terms + #Near saturation, pycalphad may detect only a single phase (if sampling density is too low) + #When this occurs, this will assume that the system is on the same tie-line and + #use the previously calculated values + self._prevDc = {p: None for p in phases[1:]} + self._prevMc = {p: None for p in phases[1:]} + self._prevGba = {p: None for p in phases[1:]} + self._prevBeta = {p: None for p in phases[1:]} + self._prevCa = {p: None for p in phases[1:]} + self._prevCb = {p: None for p in phases[1:]} + + def getInterfacialComposition(self, x, T, gExtra = 0, precPhase = None): + ''' + Gets interfacial composition by calculating equilibrum with Gibbs-Thomson effect + + Parameters + ---------- + T : float or array + Temperature in K + gExtra : float or array (optional) + Extra contributions to the precipitate Gibbs free energy + Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) + Defaults to 0 + precPhase : str + Precipitate phase to consider (default is first precipitate in list) + + Note: for multiple conditions, only gExtra has to be an array + This will calculate compositions for multiple gExtra at the input Temperature + + If T is also an array, then T and gExtra must be the same length + where each index will pertain to a single condition + + Returns + ------- + (parent composition, precipitate composition) + Both will be either float or array based off shape of gExtra + Will return (None, None) if precipitate is unstable + ''' + if hasattr(gExtra, '__len__'): + if not hasattr(T, '__len__'): + T = T * np.ones(len(gExtra)) + + caArray = [] + cbArray = [] + for i in range(len(gExtra)): + ca, cb = self._interfacialComposition(x, T[i], gExtra[i], precPhase) + caArray.append(ca) + cbArray.append(cb) + caArray = np.array(caArray) + cbArray = np.array(cbArray) + return caArray, cbArray + else: + return self._interfacialComposition(x, T, gExtra, precPhase) + + + def _interfacialComposition(self, x, T, gExtra = 0, precPhase = None): + ''' + Gets interfacial composition, will return None, None if composition is in single phase region + + Parameters + ---------- + T : float + Temperature in K + gExtra : float (optional) + Extra contributions to the precipitate Gibbs free energy + Gibbs Thomson contribution defined as Vm * (2*gamma/R + g_Elastic) + Defaults to 0 + precPhase : str + Precipitate phase to consider (default is first precipitate in list) + + Returns + ------- + (parent composition, precipitate composition) + Both will be either float or array based off shape of gExtra + Will return (None, None) if precipitate is unstable + ''' + if precPhase is None: + precPhase = self.phases[1] + + eq = self.getEq(x, T, gExtra, precPhase) + + #Check for convergence, return None if not converged + if np.any(np.isnan(eq.MU.values.ravel())): + return None, None + + ph = eq.Phase.values.ravel() + ph = ph[ph != ''] + + #Check if matrix and precipitate phase are stable, and check if there's no miscibility gaps + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + sortIndices = np.argsort(self.elements[:-1]) + unsortIndices = np.argsort(sortIndices) + + mu = eq.MU.values.ravel() + mu = mu[unsortIndices] + + eqPh = eq.where(eq.Phase == self.phases[0], drop=True) + xM = eqPh.X.values.ravel() + xM = xM[unsortIndices] + + eqPh = eq.where(eq.Phase == precPhase, drop=True) + xP = eqPh.X.values.ravel() + xP = xP[unsortIndices] + + return xM, xP + + return None, None + + def _curvatureFactorFromEq(self, chemical_potentials, composition_sets, precPhase=None): + ''' + Curvature factor (from Phillipes and Voorhees - 2013) + + Steps + 1. Check that there is 2 phases in equilibrium, one being the matrix and the other being precipitate + 2. Get Dnkj, dmu/dx and inverse mobility term from composition set of matrix phase + 3. Get dmu/dx of precipitate phase + 4. Get difference in matrix and precipitate phase composition (we use a second order approximation to get precipitate composition as function of R) + 5. Compute numerator, denominator, Gba and beta term + Denominator (X_bar^T * invMob * X_bar) is used for growth rate (eq 28) + Numerator (D^-1 * X_bar), denominator is used for matrix interfacial composition (eq 31) + Gba and matrix interfacial composition is used for precipitate interfacial composition (eq 36) + Gba here is (dmu/dx_beta)^-1 * dmu/dx_alpha + Note: these equations have a term X_bar^T * dmu/dx_alpha * X_bar_infty, but this is just the driving force so we don't need to calculate it here + + Parameters + ---------- + chemical_potentials : 1-D float64 array + composition_sets : List[pycalphad.composition_set.CompositionSet] + precPhase : str (optional) + Precipitate phase (defaults to first precipitate in list) + + Returns + ------- + {D-1 dCbar / dCbar^T M-1 dCbar} - for calculating interfacial composition of matrix + {1 / dCbar^T M-1 dCbar} - for calculating growth rate + {Gb^-1 Ga} - for calculating precipitate composition + beta - Impingement rate + Ca - interfacial composition of matrix phase + Cb - interfacial composition of precipitate phase + + Will return (None, None, None, None, None, None) if single phase + ''' + if precPhase is None: + precPhase = self.phases[1] + + ele = list(composition_sets[0].phase_record.nonvacant_elements) + refIndex = ele.index(self.elements[0]) + + ph = [cs.phase_record.phase_name for cs in composition_sets] + + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + sortIndices = np.argsort(self.elements[1:-1]) + unsortIndices = np.argsort(sortIndices) + + matrix_cs = [cs for cs in composition_sets if cs.phase_record.phase_name == self.phases[0]][0] + + if self.mobCallables[self.phases[0]] is None: + Dnkj, dMudxParent, invMob = inverseMobility_from_diffusivity(chemical_potentials, matrix_cs, + self.elements[0], self.diffCallables[self.phases[0]], + diffusivity_correction=self.mobility_correction, parameters=self._parameters) + + #NOTE: This is note tested yet + Dtrace = tracer_diffusivity_from_diff(matrix_cs, self.diffCallables[self.phases[0]], diffusivity_correction=self.mobility_correction, parameters=self._parameters) + else: + Dnkj, dMudxParent, invMob = inverseMobility(chemical_potentials, matrix_cs, self.elements[0], + self.mobCallables[self.phases[0]], + mobility_correction=self.mobility_correction, parameters=self._parameters) + Dtrace = tracer_diffusivity(matrix_cs, self.mobCallables[self.phases[0]], mobility_correction=self.mobility_correction, parameters=self._parameters) + + xMFull = np.array(matrix_cs.X) + xM = np.delete(xMFull, refIndex) + + precip_cs = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] + dMudxPrec = dMudX(chemical_potentials, precip_cs, self.elements[0]) + xPFull = np.array(precip_cs.X) + xP = np.delete(xPFull, refIndex) + xBarFull = np.array([xPFull - xMFull]) + xBar = np.array([xP - xM]) + + num = np.matmul(np.linalg.inv(Dnkj), xBar.T).flatten() + + #Denominator should be a scalar since its V * M * V^T + den = np.matmul(xBar, np.matmul(invMob, xBar.T)).flatten()[0] + + if np.linalg.matrix_rank(dMudxPrec) == dMudxPrec.shape[0]: + Gba = np.matmul(np.linalg.inv(dMudxPrec), dMudxParent) + Gba = Gba[unsortIndices,:] + Gba = Gba[:,unsortIndices] + else: + Gba = np.zeros(dMudxPrec.shape) + + betaNum = xBarFull**2 + betaDen = Dtrace * xMFull.flatten() + bsum = np.sum(betaNum / betaDen) + if bsum == 0: + beta = self._prevBeta[precPhase] + else: + beta = 1 / bsum + + self._prevDc[precPhase] = num[unsortIndices] / den + self._prevMc[precPhase] = 1 / den + self._prevGba[precPhase] = Gba + self._prevBeta[precPhase] = beta + self._prevCa[precPhase] = xM[unsortIndices] + self._prevCb[precPhase] = xP[unsortIndices] + + return self._prevDc[precPhase], self._prevMc[precPhase], self._prevGba[precPhase], self._prevBeta[precPhase], self._prevCa[precPhase], self._prevCb[precPhase] + else: + return None + # if training: + # return None + # else: + # #print('Warning: only a single phase detected in equilibrium, using results of previous calculation') + # #return self._prevDc[precPhase], self._prevMc[precPhase], self._prevGba[precPhase], self._prevBeta[precPhase], self._prevCa[precPhase], self._prevCb[precPhase] + + # #If two-phase equilibrium is not found, then the temperature may have changed to where the precipitate is unstable + # #Return None in this case + # return None + + + def curvatureFactor(self, x, T, precPhase = None, training = False, searchDir = None): + ''' + Curvature factor (from Phillipes and Voorhees - 2013) from composition and temperature + This is the same as curvatureFactorEq, but will calculate equilibrium from x and T first + + Parameters + ---------- + x : array + Composition of solutes + T : float + Temperature + precPhase : str (optional) + Precipitate phase (defaults to first precipitate in list) + searchDir : None or array + If two-phase equilibrium is not present, then move x towards this composition to find two-phase equilibria + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + {D-1 dCbar / dCbar^T M-1 dCbar} - for calculating interfacial composition of matrix + {1 / dCbar^T M-1 dCbar} - for calculating growth rate + {Gb^-1 Ga} - for calculating precipitate composition + beta - Impingement rate + Ca - interfacial composition of matrix phase + Cb - interfacial composition of precipitate phase + + Will return (None, None, None, None, None, None) if single phase + ''' + if precPhase is None: + precPhase = self.phases[1] + if not hasattr(x, '__len__'): + x = [x] + + #Remove first element if x lists composition of all elements + if len(x) == len(self.elements) - 1: + x = x[1:] + cond = self._getConditions(x, T, 0) + + #Perform equilibrium from scratch if cache not set or when training surrogate + if self._compset_cache.get(precPhase, None) is None or training: + cs_results = self._getCompositionSetsForCurvature(x, T, precPhase) + if cs_results is None: + return None + + chemical_potentials, composition_sets = cs_results + else: + result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, + self.models, self.phase_records, + composition_sets=self._compset_cache[precPhase]) + self._compset_cache[precPhase] = composition_sets + chemical_potentials = result.chemical_potentials + + #Check if input equilibrium has converged + if np.any(np.isnan(chemical_potentials)): + if training: + return None + else: + print('Warning: equilibrum was not able to be solved for, using results of previous calculation') + return self._prevDc[precPhase], self._prevMc[precPhase], self._prevGba[precPhase], self._prevBeta[precPhase], self._prevCa[precPhase], self._prevCb[precPhase] + + ph = [cs.phase_record.phase_name for cs in composition_sets] + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + return self._curvatureFactorFromEq(chemical_potentials, composition_sets, precPhase) + #If in a singl phase region, we want to go along a search direction to find the nearest two phase region + # We then use this two-phase region to calculate growth rate (which should all be negative for dissolution) + # In PrecipitateModel, searchDir is the previous precipitate nucleate composition + # We performe a rouch search + elif searchDir is not None: + currX = np.array(x) + searchDir = np.array(searchDir) + currX = 0.5 * currX + 0.5 * searchDir + foundTwoPhases = False + maxIt = 15 + currIt = 0 + while not foundTwoPhases: + cs_results = self._getCompositionSetsForCurvature(currX, T, precPhase) + if cs_results is None: + return None + chemical_potentials, composition_sets = cs_results + ph = [cs.phase_record.phase_name for cs in composition_sets] + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + foundTwoPhases = True + elif len(ph) == 1 and self.phases[0] in ph: + #Only matrix is stable, move closer to searchDir + currX = 0.5*currX + 0.5*searchDir + elif len(ph) == 1 and precPhase in ph: + #Only precipitate is stable, move closer to original x + currX = 0.5*currX + 0.5*np.array(x) + + #More than likely, this is not needed, but just in case + #MaxIt is 15, which refers to a 6e-5 difference in test composition between the 14th and 15th iteration + # Which is probably more than enough to find a two-phase region + currIt += 1 + if currIt > maxIt: + return None + + chemical_potentials, composition_sets = cs_results + return self._curvatureFactorFromEq(chemical_potentials, composition_sets, precPhase) + else: + return None + + def getGrowthAndInterfacialComposition(self, x, T, dG, R, gExtra, precPhase = None, training = False, searchDir = None): + ''' + Returns growth rate and interfacial compostion given Gibbs-Thomson contribution + + Parameters + ---------- + x : array + Composition of solutes + T : float + Temperature + dG : float + Driving force at given x and T + R : float or array + Precipitate radius + gExtra : float or array + Gibbs-Thomson contribution (must be same shape as R) + precPhase : str (optional) + Precipitate phase (defaults to first precipitate in list) + searchDir : None or array + If two-phase equilibrium is not present, then move x towards this composition to find a two-phase region + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + (growth rate, matrix composition, precipitate composition, equilibrium matrix comp, equilibrium precipitate comp) + growth rate will be float or array based off shape of R + matrix and precipitate composition will be array or 2D array based + off shape of R + ''' + if hasattr(R, '__len__'): + R = np.array(R) + if hasattr(gExtra, '__len__'): + gExtra = np.array(gExtra) + + curv_results = self.curvatureFactor(x, T, precPhase, training, searchDir) + if curv_results is None: + return None, None, None, None, None + + dc, mc, gba, beta, ca, cb = curv_results + + #dc, mc, gba, beta, ca, cb = self.curvatureFactor(x, T, precPhase, training, searchDir) + #if dc is None: + # return None, None, None, None, None + + Rdiff = (dG - gExtra) + + gr = (mc / R) * Rdiff + + if hasattr(Rdiff, '__len__'): + calpha = np.zeros((len(Rdiff), len(self.elements[1:-1]))) + dca = np.zeros((len(Rdiff), len(self.elements[1:-1]))) + cbeta = np.zeros((len(Rdiff), len(self.elements[1:-1]))) + for i in range(len(self.elements[1:-1])): + calpha[:,i] = x[i] - dc[i] * Rdiff + dca[:,i] = calpha[:,i] - ca[i] + + dcb = np.matmul(gba, dca.T) + for i in range(len(self.elements[1:-1])): + cbeta[:,i] = cb[i] + dcb[i,:] + + calpha[calpha < 0] = 0 + calpha[calpha > 1] = 1 + cbeta[cbeta < 0] = 0 + cbeta[cbeta > 1] = 1 + + return gr, calpha, cbeta, ca, cb + else: + calpha = x - dc * Rdiff + cbeta = cb + np.matmul(gba, (calpha - ca)).flatten() + + calpha[calpha < 0] = 0 + calpha[calpha > 1] = 1 + cbeta[cbeta < 0] = 0 + cbeta[cbeta > 1] = 1 + + return gr, calpha, cbeta, ca, cb + + def impingementFactor(self, x, T, precPhase = None, training = False): + ''' + Returns impingement factor for nucleation rate calculations + + Parameters + ---------- + x : array + Composition of solutes + T : float + Temperature + precPhase : str (optional) + Precipitate phase (defaults to first precipitate in list) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + ''' + curv_results = self.curvatureFactor(x, T, precPhase, training) + if curv_results is None: + return self._prevBeta[precPhase] + dc, mc, gba, beta, ca, cb = curv_results + return beta + + def _getCompositionSetsForCurvature(self, x, T, precPhase): + ''' + Create composition sets from equilibrium to be used for curvature factor + + Parameters + ---------- + x : array + Composition of solutes + T : float + Temperature + precPhase : str (optional) + Precipitate phase (defaults to first precipitate in list) + ''' + cond = self._getConditions(x, T, 0) + eq = self.getEq(x, T, 0, precPhase) + state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) + stable_phases = eq.Phase.values.ravel() + phase_amounts = eq.NP.values.ravel() + matrix_idx = np.where(stable_phases == self.phases[0])[0] + precip_idx = np.where(stable_phases == precPhase)[0] + + #If matrix phase is not stable (why?), then return previous values + #Curvature can't be calculated if matrix phase isn't present + if len(matrix_idx) == 0: + return None + + cs_matrix, miscMatrix = self._createCompositionSet(eq, state_variables, self.phases[0], phase_amounts, matrix_idx) + + chemical_potentials = eq.MU.values.ravel() + + #If precipitate phase is not stable, then only store matrix phase in composition sets + #Checks for single phase regions are done in _curvatureFactorFromEq, + # so this will allow to fail there + if len(precip_idx) == 0: + composition_sets = [cs_matrix] + self._compset_cache[precPhase] = None + else: + cs_precip, miscPrec = self._createCompositionSet(eq, state_variables, precPhase, phase_amounts, precip_idx) + + composition_sets = [cs_matrix, cs_precip] + self._compset_cache[precPhase] = composition_sets + + if miscMatrix or miscPrec: + result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, + self.models, self.phase_records, + composition_sets=self._compset_cache[precPhase]) + self._compset_cache[precPhase] = composition_sets + chemical_potentials = result.chemical_potentials + + return chemical_potentials, composition_sets + + def _curvatureWithSearch(self, x, T, precPhase = None, training = True): + ''' + Performs driving force calculation to get xb, which can be used to find + curvature factors when driving force is negative. Main use is for the surrogate model + to train on all points + + Parameters + ---------- + x : array + Composition of solutes + T : float + Temperature + precPhase : str (optional) + Precipitate phase (defaults to first precipitate in list) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + ''' + dg, xb = self.getDrivingForce(x, T, precPhase, returnComp = True, training = training) + return self.curvatureFactor(x, T, precPhase, training = training, searchDir=xb) \ No newline at end of file diff --git a/kawin/Surrogate.py b/kawin/thermo/Surrogate.py similarity index 99% rename from kawin/Surrogate.py rename to kawin/thermo/Surrogate.py index 3db680e..2c16674 100644 --- a/kawin/Surrogate.py +++ b/kawin/thermo/Surrogate.py @@ -922,9 +922,12 @@ def __init__(self, thermodynamics = None, drivingForce = None, interfacialCompos else: self.interfacialCompositionFunction = interfacialComposition + #TODO: curvatureFactor should take in searchDir from drivingForceFunction + # but this needs to be compatible with the same parameters if curvature is None: #self.curvature = self.therm.curvatureFactor - self.curvature = lambda x, T, training = True: self.therm.curvatureFactor(x, T, self.precPhase, training) + #self.curvature = lambda x, T, training = True: self.therm.curvatureFactor(x, T, self.precPhase, training) + self.curvature = lambda x, T, training = True: self.therm._curvatureWithSearch(x, T, self.precPhase, training) else: self.curvature = curvature @@ -1248,9 +1251,11 @@ def trainCurvature(self, comps, temperature, function='linear', epsilon=1, smoot for t in temperature: for x in comps: - dc, mc, gba, beta, ca, cb = self.curvature(x, t) + results = self.curvature(x, t) + #dc, mc, gba, beta, ca, cb = self.curvature(x, t) - if dc is not None: + if results is not None: + dc, mc, gba, beta, ca, cb = results #Since Dc, Mc and Gba is constant for a given tie-line, add 3 training data points (at bulk compostion and phase boundaries) #This should give more accurate values at very small or very large supersaturations without having to calculate a lot of training data compCoords = [x, ca, cb] @@ -1418,7 +1423,7 @@ def getCurvature(self, x, T): return dc, mc, gba, beta, ca, cb - def getGrowthAndInterfacialComposition(self, x, T, dG, R, gExtra): + def getGrowthAndInterfacialComposition(self, x, T, dG, R, gExtra, searchDir = None): ''' Returns growth rate and interfacial compostion given Gibbs-Thomson contribution diff --git a/kawin/thermo/Thermodynamics.py b/kawin/thermo/Thermodynamics.py new file mode 100644 index 0000000..9360e84 --- /dev/null +++ b/kawin/thermo/Thermodynamics.py @@ -0,0 +1,1334 @@ +import numpy as np +from pycalphad import Model, Database, calculate, equilibrium, variables as v +from pycalphad.codegen.callables import build_callables, build_phase_records +from pycalphad.core.composition_set import CompositionSet +from pycalphad.core.utils import extract_parameters +from kawin.thermo.Mobility import MobilityModel, inverseMobility, inverseMobility_from_diffusivity, tracer_diffusivity, tracer_diffusivity_from_diff +from kawin.thermo.FreeEnergyHessian import dMudX +from kawin.thermo.LocalEquilibrium import local_equilibrium +import matplotlib.pyplot as plt +import copy +from tinydb import where + +setattr(v, 'GE', v.StateVariable('GE')) + +class ExtraGibbsModel(Model): + ''' + Child of pycalphad Model with extra variable GE + GE represents any extra contribution to the Gibbs free energy + such as the Gibbs-Thomson contribution + ''' + energy = GM = property(lambda self: self.ast + v.GE) + formulaenergy = G = property(lambda self: (self.ast + v.GE) * self._site_ratio_normalization) + orderingContribution = OCM = property(lambda self: self.models['ord']) + +class GeneralThermodynamics: + ''' + Class for defining driving force and essential functions for + binary and multicomponent systems using pycalphad for equilibrium + calculations + + Parameters + ---------- + database : Database or str + pycalphad Database or file name for database + elements : list + Elements to consider + Note: reference element must be the first index in the list + phases : list + Phases involved + Note: matrix phase must be first index in the list + drivingForceMethod : str (optional) + Method used to calculate driving force + Options are 'tangent' (default), 'approximate', 'sampling' and 'curvature' (not recommended) + parameters : list [str] or dict {str : float} or None + List of parameters to keep symbolic in the thermodynamic or mobility models + If None, then parameters are fixed + ''' + + gOffset = 1 #Small value to add to precipitate phase for when order/disorder models are used + + def __init__(self, database, elements, phases, drivingForceMethod = 'tangent', parameters = None): + if isinstance(database, str): + database = Database(database) + self.db = database + self.elements = copy.copy(elements) + if parameters is None: + self._parameters = {} + else: + if isinstance(parameters, list): + self._parameters = {p: 0 for p in parameters} + else: + self._parameters = parameters + + if 'VA' not in self.elements: + self.elements.append('VA') + + if type(phases) == str: # check if a single phase was passed as a string instead of a list of phases. + phases = [phases] + self.phases = phases + + self._buildThermoModels() + + #Amount of points to sample per degree of freedom + # sampling_pDens is for when using sampling method in driving force calculations + # pDens is for equilibrium calculations + self.sampling_pDens = 2000 + self.pDens = 500 + + #Stored variables of last time the class was used + #This is so that these can be used again if the temperature has not changed since last usage + self._prevTemperature = None + + #Pertains to parent phase (composition, sampled points, equilibrium calculations) + self._prevX = None + self._parentEq = None + + #Pertains to precipitate phases (sampled points) + self._pointsPrec = {self.phases[i]: None for i in range(1, len(self.phases))} + self._orderingPoints = {self.phases[i]: None for i in range(1, len(self.phases))} + + self.setDrivingForceMethod(drivingForceMethod) + + self._buildMobilityModels() + + #Cached results + self._compset_cache = {} + self._compset_cache_df = {} + self._matrix_cs = None + + def _buildThermoModels(self): + ''' + Builds thermodynamic models for each phase + + This assumes that the first phase is the parent phase and the rest of the phases are precipitate phases + For usage in a diffusion model, this won't affect anything + + For each precipitate phase, it checks whether the phase has an order/disorder contribution + If so, then it checks if the disorder contribution comes from the parent phase (ex. gamma and gamma prime in Ni alloys) + An ordering contribution phase record will be created to allow separated between the parent and the ordered phase + ''' + self.orderedPhase = {self.phases[i]: False for i in range(1, len(self.phases))} + for i in range(1, len(self.phases)): + if 'disordered_phase' in self.db.phases[self.phases[i]].model_hints: + if self.db.phases[self.phases[i]].model_hints['disordered_phase'] == self.phases[0]: + self.orderedPhase[self.phases[i]] = True + self._forceDisorder(self.phases[0]) + + #Build phase models assuming first phase is parent phase and rest of precipitate phases + #If the same phase is used for matrix and precipitate phase, then force the matrix phase to remove the ordering contribution + #This may be unnecessary as already disordered phase models will not be affected, but I guess just in case the matrix phase happens to be an ordered solution + param_keys, _ = extract_parameters(self._parameters) + self.models = {self.phases[0]: Model(self.db, self.elements, self.phases[0], parameters=param_keys)} + self.models[self.phases[0]].state_variables = sorted([v.T, v.P, v.N, v.GE], key=str) + + for i in range(1, len(self.phases)): + self.models[self.phases[i]] = ExtraGibbsModel(self.db, self.elements, self.phases[i], parameters=param_keys) + self.models[self.phases[i]].state_variables = sorted([v.T, v.P, v.N, v.GE], key=str) + + self.phase_records = build_phase_records(self.db, self.elements, self.phases, + self.models[self.phases[0]].state_variables, + self.models, build_gradients=True, build_hessians=True, + parameters=self._parameters) + + self.OCMphase_records = {} + for i in range(1, len(self.phases)): + if self.orderedPhase[self.phases[i]]: + self.OCMphase_records[self.phases[i]] = build_phase_records(self.db, self.elements, [self.phases[i]], + self.models[self.phases[0]].state_variables, + {self.phases[i]: self.models[self.phases[i]]}, + output='OCM', build_gradients=False, build_hessians=False, + parameters=self._parameters) + + def _buildMobilityModels(self): + ''' + Builds mobility models for phases that have model parameters + ''' + self.mobModels = {p: None for p in self.phases} + self.mobCallables = {p: None for p in self.phases} + self.diffCallables = {p: None for p in self.phases} + param_keys, _ = extract_parameters(self._parameters) + for p in self.phases: + #Get mobility/diffusivity of phase p if exists + param_search = self.db.search + param_query_mob = ( + (where('phase_name') == p) & \ + (where('parameter_type') == 'MQ') | \ + (where('parameter_type') == 'MF') + ) + + param_query_diff = ( + (where('phase_name') == p) & \ + (where('parameter_type') == 'DQ') | \ + (where('parameter_type') == 'DF') + ) + + pMob = param_search(param_query_mob) + pDiff = param_search(param_query_diff) + + if len(pMob) > 0 or len(pDiff) > 0: + self.mobModels[p] = MobilityModel(self.db, self.elements, p, parameters=param_keys) + if len(pMob) > 0: + self.mobCallables[p] = {} + for c in self.phase_records[p].nonvacant_elements: + bcp = build_callables(self.db, self.elements, [p], {p: self.mobModels[p]}, + parameter_symbols=self._parameters, output='MOB_'+c, build_gradients=False, build_hessians=False, + additional_statevars=[v.T, v.P, v.N, v.GE]) + self.mobCallables[p][c] = bcp['MOB_'+c]['callables'][p] + else: + self.diffCallables[p] = {} + for c in self.phase_records[p].nonvacant_elements: + bcp = build_callables(self.db, self.elements, [p], {p: self.mobModels[p]}, + parameter_symbols=self._parameters, output='DIFF_'+c, build_gradients=False, build_hessians=False, + additional_statevars=[v.T, v.P, v.N, v.GE]) + self.diffCallables[p][c] = bcp['DIFF_'+c]['callables'][p] + + #This applies to all phases since this is typically reflective of quenched-in vacancies + self.mobility_correction = {A: 1 for A in self.elements} + + def updateParameters(self, parameters): + ''' + Update parameter dictionary with new values + + Parameters + ---------- + parameters : dict {str : float} + Dictionary of parameters + NOTE: this does not have to be the full list and can also have other parameters in it + Only the parameters that are stored upon initialization will be changed + ''' + for pm in parameters: + if pm in self._parameters: + self._parameters[pm] = parameters[pm] + + param_keys, param_values = extract_parameters(self._parameters) + for p in self.phases: + self.phase_records[p].parameters[:] = np.asarray(param_values, dtype=np.float_) + + def _forceDisorder(self, phase): + ''' + For phases using an order/disorder model, pycalphad will neglect the disordered phase unless + it is the only phase set active, so the order and disordered portion of the phase will use the same model + + For the Gibbs-Thomson effect to be applied, the ordered and disordered parts of the model will need to be kept separate + As a fix, a new phase is added to the database that uses only the disordered part of the model + ''' + newPhase = 'DIS_' + phase + self.phases[0] = newPhase + self.db.phases[newPhase] = copy.deepcopy(self.db.phases[phase]) + self.db.phases[newPhase].name = newPhase + del self.db.phases[newPhase].model_hints['ordered_phase'] + del self.db.phases[newPhase].model_hints['disordered_phase'] + + #Copy database parameters with new name + param_query = where('phase_name') == phase + params = self.db.search(param_query) + for p in params: + #We have to create a new dictionary since p is a TinyDB.Document + newP = {} + for entry in p: + newP[entry] = p[entry] + newP['phase_name'] = newPhase + self.db._parameters.insert(newP) + + def clearCache(self): + ''' + Removes any cached data + This is intended for surrogate training, where the cached data + will be removed incase + ''' + self._compset_cache = {} + self._compset_cache_df = {} + self._matrix_cs = None + + def setDrivingForceMethod(self, drivingForceMethod): + ''' + Sets method for calculating driving force + + Parameters + ---------- + drivingForceMethod - str + Options are ['approximate', 'sampling', 'curvature'] + ''' + if drivingForceMethod == 'approximate': + self._drivingForce = self._getDrivingForceApprox + elif drivingForceMethod == 'sampling': + self._drivingForce = self._getDrivingForceSampling + elif drivingForceMethod == 'curvature': + self._drivingForce = self._getDrivingForceCurvature + elif drivingForceMethod == 'tangent': + self._drivingForce = self._getDrivingForceTangent + else: + raise Exception('Driving force method must be either \'approximate\', \'sampling\', \'tangent\' or \'curvature\'') + + def setDFSamplingDensity(self, density): + ''' + Sets sampling density for sampling method in driving + force calculations + + Default upon initialization is 2000 + + Parameters + ---------- + density : int + Number of samples to take per degree of freedom in the phase + ''' + self._pointsPrec = {self.phases[i]: None for i in range(1, len(self.phases))} + self.sampling_pDens = density + + def setEQSamplingDensity(self, density): + ''' + Sets sampling density for equilibrium calculations + + Default upon initialization is 500 + + Parameters + ---------- + density : int + Number of samples to take per degree of freedom in the phase + ''' + self.pDens = density + + def setMobility(self, mobility): + ''' + Allows user to define mobility functions + + mobility : dict + Dictionary of functions for each element (including reference) + Each function takes in (v.T, v.P, v.N, v.GE, site fractions) and returns mobility + + Optional - only required for multicomponent systems where + mobility terms are not defined in the TDB database + ''' + self.mobCallables = mobility + + def setDiffusivity(self, diffusivity): + ''' + Allows user to define diffusivity functions + + diffusivity : dict + Dictionary of functions for each element (including reference) + Each function takes in (v.T, v.P, v.N, v.GE, site fractions) and returns diffusivity + + Optional - only required for multicomponent systems where + diffusivity terms are not defined in the TDB database + and if mobility terms are not defined + ''' + self.diffCallables = diffusivity + + def setMobilityCorrection(self, element, factor): + ''' + Factor to multiply mobility by for each element + + Parameters + ---------- + element : str + Element to set factor for + If 'all', factor will be set to all elements + factor : float + Scaling factor + ''' + if element == 'all': + for e in self.mobility_correction: + self.mobility_correction[e] = factor + else: + self.mobility_correction[element] = factor + + def _getConditions(self, x, T, gExtra = 0): + ''' + Creates dictionary of conditions from composition, temperature and gExtra + + Parameters + ---------- + x : list + Composition (excluding reference element) + T : float + Temperature + gExtra : float + Gibbs free energy to add to phase + ''' + cond = {v.X(self.elements[i+1]): x[i] for i in range(len(x))} + cond[v.P] = 101325 + cond[v.T] = T + cond[v.GE] = gExtra + cond[v.N] = 1 + return cond + + def _createCompositionSet(self, eq, state_variables, phase, phase_amounts, idx): + ''' + Creates a pycalphad CompositionSet from equilibrium results + + Parameters + ---------- + eq : pycalphad equilibrium result + state_variables : list + List of state variables + phase : str + Phase to create CompositionSet for + phase_amounts : list + Array of floats for phase fraction of each phase + idx : ndarray + Index array for the index of phase + ''' + miscibility = False + cs = CompositionSet(self.phase_records[phase]) + #If there's a miscibility gap in the matrix phase, then take the largest value + if len(idx) > 1: + idx = [idx[np.argmax(phase_amounts[idx])]] + miscibility = True + cs.update(eq.Y.isel(vertex=idx[0]).values.ravel()[:cs.phase_record.phase_dof], + phase_amounts[idx[0]], state_variables) + + return cs, miscibility + + def getEq(self, x, T, gExtra = 0, precPhase = None): + ''' + Calculates equilibrium at specified x, T, gExtra + + This is separated from the interfacial composition function so that this can be used for getting curvature for interfacial composition from mobility + + Parameters + ---------- + x : float or array + Composition + Needs to be array for multicomponent systems + T : float + Temperature + gExtra : float + Gibbs-Thomson contribution (if applicable) + precPhase : str, int, list or None + Precipitate phase (default is first precipitate) + Options: + None - first precipitate phase in phase list + str - specific precipitate phase by name + list - all phases by name in list + -1 - no precipitate phase + + Returns + ------- + Dataset from pycalphad equilibrium results + ''' + phases = [self.phases[0]] + if precPhase != -1: + if precPhase is None: + precPhase = self.phases[1] + if isinstance(precPhase, str): + phases.append(precPhase) + else: + phases = [p for p in precPhase] + phaseRec = {p: self.phase_records[p] for p in phases} + + if not hasattr(x, '__len__'): + x = [x] + + #Remove first element if x lists composition of all elements + if len(x) == len(self.elements) - 1: + x = x[1:] + + cond = self._getConditions(x, T, gExtra+self.gOffset) + + eq = equilibrium(self.db, self.elements, phases, cond, model=self.models, + phase_records=phaseRec, + calc_opts={'pdens': self.pDens}) + return eq + + def getLocalEq(self, x, T, gExtra = 0, precPhase = None, composition_sets = None): + ''' + Calculates local equilibrium at specified x, T, gExtra + + Parameters + ---------- + x : float or array + Composition + Needs to be array for multicomponent systems + T : float + Temperature + gExtra : float + Gibbs-Thomson contribution (if applicable) + precPhase : str, int, list or None + Precipitate phase (default is first precipitate) + Options: + None - first precipitate phase in phase list + str - specific precipitate phase by name + list - all phases by name in list + -1 - no precipitate phase + + Returns + ------- + result - equilibrium convergence and chemical potentials + composition_sets - list of CompositionSet for phases in "equilibrium" + Note - "equilibrium" in terms of the matrix and singled out precipitate phase (or just matrix if precPhase is -1) + ''' + phases = [self.phases[0]] + if precPhase != -1: + if precPhase is None: + precPhase = self.phases[1] + if isinstance(precPhase, str): + phases.append(precPhase) + else: + phases = [p for p in precPhase] + + if not hasattr(x, '__len__'): + x = [x] + + #Remove first element if x lists composition of all elements + if len(x) == len(self.elements) - 1: + x = x[1:] + + cond = self._getConditions(x, T, gExtra) + result, composition_sets = local_equilibrium(self.db, self.elements, phases, cond, + self.models, self.phase_records, + composition_sets=composition_sets) + return result, composition_sets + + def getInterdiffusivity(self, x, T, removeCache = True, phase = None): + ''' + Gets interdiffusivity at specified x and T + Requires TDB database to have mobility or diffusivity parameters + + Parameters + ---------- + x : float, array or 2D array + Composition + Float or array for binary systems + Array or 2D array for multicomponent systems + T : float or array + Temperature + If array, must be same length as x + For multicomponent systems, must be same length as 0th axis + removeCache : boolean + If True, recalculates equilibrium to get interdiffusivity (default) + If False, will use calculation from driving force calcs (if available) to compute diffusivity + phase : str + Phase to compute diffusivity for (defaults to first or matrix phase) + This only needs to be used for multiphase diffusion simulations + + Returns + ------- + interdiffusivity - will return array if T is an array + For binary case - float or array of floats + For multicomponent - matrix or array of matrices + ''' + dnkj = [] + + if hasattr(T, '__len__'): + for i in range(len(T)): + dnkj.append(self._interdiffusivitySingle(x[i], T[i], removeCache, phase)) + return np.array(dnkj) + else: + return self._interdiffusivitySingle(x, T, removeCache, phase) + + def _interdiffusivitySingle(self, x, T, removeCache = True, phase = None): + ''' + Gets interdiffusivity at unique composition and temperature + + Parameters + ---------- + x : float or array + Composition + T : float + Temperature + removeCache : boolean + phase : str + + Returns + ------- + Interdiffusivity as a matrix (will return float in binary case) + ''' + if phase is None: + phase = self.phases[0] + + if not hasattr(x, '__len__'): + x = [x] + + #Remove first element if x lists composition of all elements + if len(x) == len(self.elements) - 1: + x = x[1:] + + cond = self._getConditions(x, T, 0) + + if removeCache: + self._matrix_cs = None + self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [phase], cond, + self.models, self.phase_records, + composition_sets=self._matrix_cs) + + cs_matrix = [cs for cs in self._matrix_cs if cs.phase_record.phase_name == phase][0] + chemical_potentials = self._parentEq.chemical_potentials + + if self.mobCallables[phase] is None: + Dnkj, _, _ = inverseMobility_from_diffusivity(chemical_potentials, cs_matrix, + self.elements[0], self.diffCallables[phase], + diffusivity_correction=self.mobility_correction, + parameters = self._parameters) + else: + Dnkj, _, _ = inverseMobility(chemical_potentials, cs_matrix, self.elements[0], + self.mobCallables[phase], + mobility_correction=self.mobility_correction, + parameters=self._parameters) + + if len(x) == 1: + return Dnkj.ravel()[0] + else: + sortIndices = np.argsort(self.elements[1:-1]) + unsortIndices = np.argsort(sortIndices) + Dnkj = Dnkj[unsortIndices,:] + Dnkj = Dnkj[:,unsortIndices] + return Dnkj + + + def getTracerDiffusivity(self, x, T, removeCache = True, phase = None): + ''' + Gets tracer diffusivity for element el at specified x and T + Requires TDB database to have mobility or diffusivity parameters + + Parameters + ---------- + x : float, array or 2D array + Composition + Float or array for binary systems + Array or 2D array for multicomponent systems + T : float or array + Temperature + If array, must be same length as x + For multicomponent systems, must be same length as 0th axis + removeCache : boolean + phase : str + + Returns + ------- + tracer diffusivity - will return array if T is an array + ''' + td = [] + + if hasattr(T, '__len__'): + for i in range(len(T)): + td.append(self._tracerDiffusivitySingle(x[i], T[i], removeCache, phase)) + return np.array(td) + else: + return self._tracerDiffusivitySingle(x, T, removeCache, phase) + + def _tracerDiffusivitySingle(self, x, T, removeCache = True, phase = None): + ''' + Gets tracer diffusivity at unique composition and temperature + + Parameters + ---------- + x : float or array + Composition + T : float + Temperature + el : str + Element to calculate diffusivity + + Returns + ------- + Tracer diffusivity as a float + ''' + if phase is None: + phase = self.phases[0] + + if not hasattr(x, '__len__'): + x = [x] + + #Remove first element if x lists composition of all elements + if len(x) == len(self.elements) - 1: + x = x[1:] + + cond = self._getConditions(x, T, 0) + + if removeCache: + self._matrix_cs = None + self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [phase], cond, + self.models, self.phase_records, + composition_sets=self._matrix_cs) + + cs_matrix = [cs for cs in self._matrix_cs if cs.phase_record.phase_name == phase][0] + + if self.mobCallables[phase] is None: + #NOTE: This is not tested yet + Dtrace = tracer_diffusivity_from_diff(cs_matrix, self.diffCallables[phase], diffusivity_correction=self.mobility_correction, parameters=self._parameters) + else: + Dtrace = tracer_diffusivity(cs_matrix, self.mobCallables[phase], mobility_correction=self.mobility_correction, parameters=self._parameters) + + sortIndices = np.argsort(self.elements[:-1]) + unsortIndices = np.argsort(sortIndices) + + Dtrace = Dtrace[unsortIndices] + + return Dtrace + + def getDrivingForce(self, x, T, precPhase = None, returnComp = False, training = False): + ''' + Gets driving force using method defined upon initialization + + Parameters + ---------- + x : float, array or 2D array + Composition of minor element in bulk matrix phase + For binary system, use an array for multiple compositions + For multicomponent systems, use a 2D array for multiple compositions + Where 0th axis is for indices of each composition + T : float or array + Temperature in K + Must be same length as x if x is array or 2D array + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + (driving force, precipitate composition) + Driving force is positive if precipitate can form + Precipitate composition will be None if driving force is negative or returnComp is False + ''' + if hasattr(T, '__len__'): + dgArray = [] + compArray = [] + for i in range(len(T)): + dg, comp = self._drivingForce(x[i], T[i], precPhase, returnComp, training) + dgArray.append(dg) + compArray.append(comp) + dgArray = np.array(dgArray) + compArray = np.array(compArray) + return dgArray, compArray + else: + return self._drivingForce(x, T, precPhase, returnComp, training) + + def _getDrivingForceSampling(self, x, T, precPhase = None, returnComp = False, training = False): + ''' + Gets driving force for nucleation by sampling + + Steps + 1. Compute local equilibrium at x and T of only the matrix phase + 2. Sample precipitate phase + If ordered contribution to matrix phase, then sample ordering contribution + and remove points on the matrix free energy surface + 3. Compute energy difference between precipitate samples and chemical potential hyperplane + 4. Find sample that maximizes energy difference and return sample composition and driving force + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + (driving force, precipitate composition) + Driving force is positive if precipitate can form + Precipitate composition will be None if driving force is negative or returnComp is False + ''' + precPhase = self.phases[1] if precPhase is None else precPhase + + #Calculate equilibrium with only the parent phase ------------------------------------------------------------------------------------------- + if not hasattr(x, '__len__'): + x = [x] + cond = self._getConditions(x, T, 0) + self._prevX = x + + cs_results = self._getPrecCompositionSetSamplingDF(x, T, cond, precPhase, training) + if cs_results is None: + return None, None + + dg, prec_cs = cs_results + + #Remove cache when training + if training: + self._matrix_cs = None + + if returnComp: + sortIndices = np.argsort(self.elements[:-1]) + unsortIndices = np.argsort(sortIndices) + beta_x = np.array(prec_cs.X) + beta_x = beta_x[unsortIndices] + if len(x) == 1: + return dg, beta_x[1:][0] + else: + return dg, beta_x[1:] + else: + return dg, None + + def _getDrivingForceApprox(self, x, T, precPhase = None, returnComp = False, training = False): + ''' + Approximate method of driving force calculation + Assumes equilibrium composition of precipitate phase + + Sampling method is used if driving force is negative + + Steps: + 1. Compute equilibrium and get composition sets for matrix and precipitate phase + 2. Check for 2 phases and that one phase is the matrix and other phase is precipitate + If not, then resort to sampling method + 3. Compute equilibrium at matrix composition and get chemical potential hyperplane + 4. Driving force is the difference between the free energy of the precipitate (from step 1) + and the free energy on the chemical potential hyperplane (from step 3) at the precipitate composition + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + (driving force, precipitate composition) + Driving force is positive if precipitate can form + Precipitate composition will be None if driving force is negative or returnComp is False + ''' + if precPhase is None: + precPhase = self.phases[1] + + if not hasattr(x, '__len__'): + x = [x] + cond = self._getConditions(x, T, 0) + self._prevX = x + + cs_results = self._getCompositionSetsForDF(x, T, cond, precPhase, training=training) + if cs_results is None: + return self._getDrivingForceSampling(x, T, precPhase, returnComp, training=training) + else: + ph, ele, chemical_potentials, composition_sets, cs_matrix, x_matrix, cs_precip, x_precip = cs_results + + #Check that equilibrium has converged + #If not, then return None, None since driving force can't be obtained + if any(np.isnan(chemical_potentials)): + return None, None + + #If in two phase region, then calculate equilibrium using only parent phase and find free energy difference between chemical potential and free energy of preciptiate + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + for i in range(len(ele)): + if ele[i] == self.elements[0]: + refIndex = i + break + + #Equilibrium at matrix composition for only the parent phase + self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [self.phases[0]], cond, + self.models, self.phase_records, + composition_sets=self._matrix_cs) + + + #Remove caching if training surrogate in case training points are far apart + if training: + self._matrix_cs = None + + #Check if equilibrium has converged and chemical potential can be obtained + #If not, then return None for driving force + if any(np.isnan(self._parentEq.chemical_potentials)): + return None, None + + sortIndices = np.argsort(self.elements[1:-1]) + unsortIndices = np.argsort(sortIndices) + + xP = x_precip + + dg = np.sum(xP * self._parentEq.chemical_potentials) - np.sum(xP * chemical_potentials) + + #Remove reference element + xP = np.delete(xP, refIndex) + + if returnComp: + if len(x) == 1: + return dg.ravel()[0], xP[unsortIndices][0] + else: + return dg.ravel()[0], xP[unsortIndices] + else: + return dg.ravel()[0], None + else: + #If driving force is negative, then use sampling method --------------------------------------------------------------------------------- + return self._getDrivingForceSampling(x, T, precPhase, returnComp, training=training) + + def _getDrivingForceCurvature(self, x, T, precPhase = None, returnComp = False, training = False): + ''' + Gets driving force from curvature of free energy function + Assumes small saturation + + Steps: + 1. Compute equilibrium and get composition sets for matrix and precipitate phase + 2. Check for 2 phases and that one phase is the matrix and other phase is precipitate + If not, then resort to sampling method + 3. Get dmu/dx (free energy curvature) + 4. Compute (x_infty - x_matrix) * dmu/dx * (x_prec - x_matrix)^T + This does a first (or second?) order approximation of the driving force based off the curvature at x_infty + + Sampling method is used if driving force is negative + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + (driving force, precipitate composition) + Driving force is positive if precipitate can form + Precipitate composition will be None if driving force is negative or returnComp is False + ''' + if precPhase is None: + precPhase = self.phases[1] + + if not hasattr(x, '__len__'): + x = [x] + cond = self._getConditions(x, T, 0) + self._prevX = x + + cs_results = self._getCompositionSetsForDF(x, T, cond, precPhase, training=training) + if cs_results is None: + return self._getDrivingForceSampling(x, T, precPhase, returnComp, training=training) + else: + ph, ele, chemical_potentials, composition_sets, cs_matrix, x_matrix, cs_precip, x_precip = cs_results + + #Check that equilibrium has converged + #If not, then return None, None since driving force can't be obtained + if any(np.isnan(chemical_potentials)): + return None, None + + if not hasattr(x, '__len__'): + x = [x] + + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + for i in range(len(ele)): + if ele[i] == self.elements[0]: + refIndex = i + break + + #If in two phase region, then get curvature of parent phase and use it to calculate driving force --------------------------------------- + sortIndices = np.argsort(self.elements[1:-1]) + unsortIndices = np.argsort(sortIndices) + + dMudxParent = dMudX(chemical_potentials, composition_sets[0], self.elements[0]) + xM = np.delete(x_matrix, refIndex) + + xP = np.delete(x_precip, refIndex) + xBar = np.array([xP - xM]) + + x = np.array(x)[sortIndices] + xD = np.array([x - xM]) + + dg = np.matmul(xD, np.matmul(dMudxParent, xBar.T)) + + if returnComp: + if len(x) == 1: + return dg.ravel()[0], xP[unsortIndices][0] + else: + return dg.ravel()[0], xP[unsortIndices] + else: + return dg.ravel()[0], None + else: + #If driving force is negative, then use sampling method --------------------------------------------------------------------------------- + return self._getDrivingForceSampling(x, T, precPhase, returnComp, training=training) + + def _getDrivingForceTangent(self, x, T, precPhase = None, returnComp = False, training = False): + ''' + Gets driving force from parallel tangent calculation + + Steps + 1. Compute equilibrium to get composition sets (or used previous cached CS) + 2. Compute equilibrium of matrix phase at matrix composition + 3. Remove composition and extra free energy from conditions + 4. Add chemical potential for each component to conditions + 5. Compute equilibrium of precipitate phase with new conditions + The calculated v.GE is the driving force + + This will work for positive and negative driving forces + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + (driving force, precipitate composition) + Driving force is positive if precipitate can form + Precipitate composition will be None if driving force is negative or returnComp is False + ''' + if precPhase is None: + precPhase = self.phases[1] + + if not hasattr(x, '__len__'): + x = [x] + cond = self._getConditions(x, T, self.gOffset) + self._prevX = x + + if self._compset_cache_df.get(precPhase, None) is None or training: + #This will calculate local equilibrium for the matrix phase and get the composition set for the precipitate phase + cs_results = self._getPrecCompositionSetSamplingDF(x, T, cond, precPhase, training=training) + if cs_results is None: + return None, None + + dg, _prec_cs = cs_results + self._compset_cache_df[precPhase] = [_prec_cs] + else: + #If we already have a cache, then we just need equilibrium at the matrix phase + self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [self.phases[0]], cond, + self.models, self.phase_records, composition_sets=self._matrix_cs) + + #Check that equilibrium has converged + #If not, then return None, None since driving force can't be obtained + if any(np.isnan(self._parentEq.chemical_potentials)): + return None, None + + #Remove element conditions and free extra Gibbs energy conditions + for e in self.elements: + if v.X(e) in cond: + cond.pop(v.X(e)) + if v.GE in cond: + cond.pop(v.GE) + + #Add chemical potential conditions + sortedEl = sorted(list(set(self.elements) - set(['VA']))) + for i in range(len(sortedEl)): + cond[v.MU(sortedEl[i])] = self._parentEq.chemical_potentials[i] + + #Solving for local equilibrium on precipitate + #The fixed conditions are T, P and MU, so this should solve for precipitate composition and GE + # Rather than solving for parallel tangent where the driving force is the difference between the chemical potentials of matrix and precipitate phase + # This instead solves for the offset in the precipitate energy surface to make the precipitate lie on the chemical potential hyperplane of the matrix phase + prev_dof = np.array(self._compset_cache_df[precPhase][0].dof) + _precEq, _prec_cs = local_equilibrium(self.db, self.elements, [precPhase], cond, + self.models, self.phase_records, composition_sets=self._compset_cache_df[precPhase]) + + #Check if precipitate composition at equilibrium is the matrix composition + #This can occur in order/disordered models where the miscibility gap is small enough that the parallel tangent can only be found at the matrix composition + #In this case, switch to sampling for the driving force + #This still seems to be an improvement over approximate and curvature methods since this occurs after the driving force becomes negative + prec_comps = np.array(_prec_cs[0].X) + mat_comps = np.array(self._matrix_cs[0].X) + if np.allclose(prec_comps, mat_comps, 1e-6): + self._compset_cache_df[precPhase] = None + return self._getDrivingForceSampling(x, T, precPhase, returnComp, training=training) + + self._compset_cache_df[precPhase] = _prec_cs + + #Check that equilibrium has converged + #If not, then return None, None since driving force can't be obtained + if any(np.isnan(_precEq.chemical_potentials)): + return None, None + + dg = _precEq.x[0] + xb = np.array(_prec_cs[0].X) + + sortIndices = np.argsort(self.elements[:-1]) + unsortIndices = np.argsort(sortIndices) + xb = xb[unsortIndices] + + if len(x) == 1: + return dg, xb[1:][0] + else: + return dg, xb[1:] + + def _getCompositionSetsForDF(self, x, T, cond, precPhase = None, training = False): + ''' + Wrapper for getting composition set from x and T by either global equilibrium or local from a cached composition set + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + phases - set of stable phases + elements - set of elements + chemical_potentials + composition_sets - all composition sets at equilibrium + cs_matrix - composition set of matrix phase + x_matrix - composition of matrix phase + cs_precip - composition set of precipitate phase + x_precip - composition of precipitate phase + ''' + if self._compset_cache_df.get(precPhase, None) is None or training: + return self._getCompositionSetsEq(x, T, cond, precPhase) + else: + return self._getCompositionSetsCache(x, T, cond, precPhase) + + def _getCompositionSetsEq(self, x, T, cond, precPhase = None): + ''' + Gets composition set from x and T by global equilibrium + + Steps + 1. Compute equilibrium at x and T + If equilibrium did not converge or matrix phase is not stable, then return None + 2. Get composition sets and add to cache + If precipitate is not stable, the return None + 3. Resolve possible issues with miscibility gaps + 4. Return values + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + + Returns + ------- + phases - set of stable phases + elements - set of elements + chemical_potentials + composition_sets - all composition sets at equilibrium + cs_matrix - composition set of matrix phase + x_matrix - composition of matrix phase + cs_precip - composition set of precipitate phase + x_precip - composition of precipitate phase + ''' + #Create cache of composition set if not done so already or if training a surrogate + #Training points for surrogates may be far apart, so starting from a previous + # composition set could give a bad starting position for the minimizer + #Calculate equilibrium ---------------------------------------------------------------------------------------------------------------------- + eq = self.getEq(x, T, 0, precPhase) + #Cast values in state_variables to double for updating composition sets + state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) + stable_phases = eq.Phase.values.ravel() + phase_amounts = eq.NP.values.ravel() + matrix_idx = np.where(stable_phases == self.phases[0])[0] + precip_idx = np.where(stable_phases == precPhase)[0] + chemical_potentials = eq.MU.values.ravel() + x_precip = eq.isel(vertex=precip_idx).X.values.ravel() + x_matrix = eq.isel(vertex=matrix_idx).X.values.ravel() + + #If matrix phase is not stable, then use sampling method + # This may occur during surrogate training of interfacial composition, + # where we're trying to calculate the driving force at the precipitate composition + # In this case, the conditions will be at th precipitate composition which can result in + # only that phase being stable + if len(matrix_idx) == 0: + return None + + if any(np.isnan(chemical_potentials)): + return None + + #Test that precipitate phase is stable and that we're not training a surrogate + #If not, then there's no composition set to cache + if len(precip_idx) > 0: + cs_matrix, miscMatrix = self._createCompositionSet(eq, state_variables, self.phases[0], phase_amounts, matrix_idx) + cs_precip, miscPrec = self._createCompositionSet(eq, state_variables, precPhase, phase_amounts, precip_idx) + x_matrix = np.array(cs_matrix.X) + x_precip = np.array(cs_precip.X) + + composition_sets = [cs_matrix, cs_precip] + self._compset_cache_df[precPhase] = composition_sets + + #If there's a miscibility gap in the matrix or precipitate phase, then calculate local equilibrium with the singled out comp sets + if miscMatrix or miscPrec: + result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, + self.models, self.phase_records, + composition_sets=self._compset_cache_df[precPhase]) + self._compset_cache_df[precPhase] = composition_sets + chemical_potentials = result.chemical_potentials + cs_precip = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] + x_precip = np.array(cs_precip.X) + + cs_matrix = [cs for cs in composition_sets if cs.phase_record.phase_name == self.phases[0]][0] + x_matrix = np.array(cs_matrix.X) + else: + return None + + ph = np.unique(stable_phases[stable_phases != '']) + ele = eq.component.values.ravel() + + return ph, ele, chemical_potentials, composition_sets, cs_matrix, x_matrix, cs_precip, x_precip + + def _getCompositionSetsCache(self, x, T, cond, precPhase = None): + ''' + Gets composition set from x and T by global equilibrium + + Steps + 1. Compute local equilibrium at x and T using previous composition sets + 2. Get composition sets and update cache + If equilibrium did not converge, then return None + 3. Return values + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + + Returns + ------- + phases - set of stable phases + elements - set of elements + chemical_potentials + composition_sets - all composition sets at equilibrium + cs_matrix - composition set of matrix phase + x_matrix - composition of matrix phase + cs_precip - composition set of precipitate phase + x_precip - composition of precipitate phase + ''' + result, composition_sets = local_equilibrium(self.db, self.elements, [self.phases[0], precPhase], cond, + self.models, self.phase_records, + composition_sets=self._compset_cache_df[precPhase]) + self._compset_cache_df[precPhase] = composition_sets + chemical_potentials = result.chemical_potentials + if any(np.isnan(chemical_potentials)): + return None + + ph = [cs.phase_record.phase_name for cs in composition_sets if cs.NP > 0] + if len(ph) == 2 and self.phases[0] in ph and precPhase in ph: + cs_precip = [cs for cs in composition_sets if cs.phase_record.phase_name == precPhase][0] + x_precip = np.array(cs_precip.X) + + cs_matrix = [cs for cs in composition_sets if cs.phase_record.phase_name == self.phases[0]][0] + x_matrix = np.array(cs_matrix.X) + + ele = list(cs_precip.phase_record.nonvacant_elements) + else: + return None + + return ph, ele, chemical_potentials, composition_sets, cs_matrix, x_matrix, cs_precip, x_precip + + def _getPrecCompositionSetSamplingDF(self, x, T, cond, precPhase = None, training = False): + ''' + Gets samples for precipitate phase for use in sampling driving force method and returns driving force and precipitate composition + + This is also use in tangent driving force method for when equilibrium is not (yet) cached + + Steps + 1. Compute local equilibrium at x and T of only the matrix phase + 2. Sample precipitate phase + If ordered contribution to matrix phase, then sample ordering contribution + and remove points on the matrix free energy surface + 3. Compute energy difference between precipitate samples and chemical potential hyperplane + 4. Find sample that maximizes energy difference and return sample composition and driving force + + Parameters + ---------- + x : float or array + Composition of minor element in bulk matrix phase + Use float for binary systems + Use array for multicomponent systems + T : float + Temperature in K + precPhase : str (optional) + Precipitate phase to consider (default is first precipitate phase in list) + returnComp : bool (optional) + Whether to return composition of precipitate (defaults to False) + training : bool (optional) + If True, this will not cache any equilibrium + This is used for training since training points may not be near each other + + Returns + ------- + driving force - max free energy difference + precipitate composition - corresponds to max driving force + ''' + orderTol = -1e-8 + + #Equilibrium at matrix composition for only the parent phase + self._parentEq, self._matrix_cs = local_equilibrium(self.db, self.elements, [self.phases[0]], cond, + self.models, self.phase_records, + composition_sets = self._matrix_cs) + + if any(np.isnan(self._parentEq.chemical_potentials)): + return None + + #Sample precipitate phase and get driving force differences at all points ------------------------------------------------------------------- + #Sample points of precipitate phase + if self._pointsPrec[precPhase] is None or self._prevTemperature != T: + self._pointsPrec[precPhase] = calculate(self.db, self.elements, precPhase, P = 101325, T = T, GE=self.gOffset, pdens = self.sampling_pDens, model=self.models, output='GM', phase_records=self.phase_records) + if self.orderedPhase[precPhase]: + self._orderingPoints[precPhase] = calculate(self.db, self.elements, precPhase, P = 101325, T = T, GE=self.gOffset, pdens = self.sampling_pDens, model=self.models, output='OCM', phase_records=self.OCMphase_records[precPhase]) + self._prevTemperature = T + + #Get value of chemical potential hyperplane at composition of sampled points + precComp = self._pointsPrec[precPhase].X.values.ravel() + precComp = precComp.reshape((int(len(precComp) / (len(self.elements) - 1)), len(self.elements) - 1)) + mu = np.array([self._parentEq.chemical_potentials]) + mult = precComp * mu + + #Difference between the chemical potential hyperplane and the samples points + #The max driving force is the same as when the chemical potentials of the two phases are parallel + diff = np.sum(mult, axis=1) - self._pointsPrec[precPhase].GM.values.ravel() + + #Find maximum driving force and corresponding composition ----------------------------------------------------------------------------------- + #For phases with order/disorder transition, a filter is applied such that it will only use points that are below the disordered energy surface + if self.orderedPhase[precPhase]: + indices = self._orderingPoints[precPhase].OCM.values.ravel() < orderTol + diff = diff[indices] + + dg = np.amax(diff) + idx = np.argmax(diff) + + prec_cs = CompositionSet(self.phase_records[precPhase]) + state_variables = np.array([cond[v.GE], cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) + #state_variables = np.array([-dg, cond[v.N], cond[v.P], cond[v.T]], dtype=np.float64) + if self.orderedPhase[precPhase]: + y = np.squeeze(self._pointsPrec[precPhase].Y.values) + y = y[indices][idx] + else: + y = np.array(self._pointsPrec[precPhase].Y.isel(points=idx).values.ravel()) + prec_cs.update(y, 1, state_variables) + + return dg, prec_cs diff --git a/kawin/thermo/__init__.py b/kawin/thermo/__init__.py new file mode 100644 index 0000000..c639353 --- /dev/null +++ b/kawin/thermo/__init__.py @@ -0,0 +1,4 @@ +from .Thermodynamics import GeneralThermodynamics +from .BinTherm import BinaryThermodynamics +from .MultiTherm import MulticomponentThermodynamics +from .Surrogate import BinarySurrogate, MulticomponentSurrogate, generateTrainingPoints \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 285367a..7b4326f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,3 +5,18 @@ requires = [ ] build-backend = "setuptools.build_meta" + +[tool.coverage.paths] +# The first path is the path to the modules to report coverage against. +# All following paths are patterns to match against the collected data. +# Any matches will be combined with the first path for coverage. +source = [ + "./kawin", + "*/lib/*/site-packages/kawin", # allows testing against site-packages for a local virtual environment +] + +[tool.coverage.run] +# Only consider coverage for these packages: +source_pkgs = [ + "kawin" +] diff --git a/setup.py b/setup.py index a0af210..c2d166f 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ def read(fname): author='Nicholas Ury', author_email='nury12n@gmail.com', description='Tool for simulating precipitation using the KWN model coupled with Calphad.', - packages=['kawin', 'kawin.tests'], + packages=['kawin', 'kawin.tests', 'kawin.diffusion', 'kawin.precipitation', 'kawin.precipitation.coupling', 'kawin.precipitation.non_ideal', 'kawin.solver', 'kawin.thermo'], license='MIT', long_description=read('README.md'), long_description_content_type='text/markdown',