diff --git a/README.md b/README.md index d694a7dbe..61b045583 100644 --- a/README.md +++ b/README.md @@ -115,15 +115,15 @@ fmodel.run() Finally, results can be analyzed via post-processing functions available within [FlorisModel](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel) such as -- [FlorisModel.get_turbine_layout](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel.get_turbine_layout) -- [FlorisModel.get_turbine_powers](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel.get_turbine_powers) -- [FlorisModel.get_farm_AEP](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel.get_farm_AEP) +- [FlorisModel.get_turbine_layout](https://nrel.github.io/floris/_autosummary/floris.floris_model.html#floris.floris_model.FlorisModel.get_turbine_layout) +- [FlorisModel.get_turbine_powers](https://nrel.github.io/floris/_autosummary/floris.floris_model.html#floris.floris_model.FlorisModel.get_turbine_powers) +- [FlorisModel.get_farm_AEP](https://nrel.github.io/floris/_autosummary/floris.floris_model.html#floris.floris_model.FlorisModel.get_farm_AEP) and in two visualization packages: [layoutviz](https://nrel.github.io/floris/_autosummary/floris.layout_visualization.html) and [flowviz](https://nrel.github.io/floris/_autosummary/floris.flow_visualization.html). A collection of examples describing the creation of simulations as well as analysis and post processing are included in the -[repository](https://github.com/NREL/floris/tree/main/examples) -and described in [Examples Index](https://github.nrel.io/floris/examples). +[repository](https://github.com/NREL/floris/tree/main/examples). Examples are also listed +in the [online documentation](https://nrel.github.io/floris/examples/001_opening_floris_computing_power.html). ## Engaging on GitHub diff --git a/docs/empirical_gauss_model.md b/docs/empirical_gauss_model.md index 5edb7f4af..1f9091482 100644 --- a/docs/empirical_gauss_model.md +++ b/docs/empirical_gauss_model.md @@ -172,10 +172,10 @@ The effect of AWC is represented by updating the wake-induced mixing term as follows: $$ \text{WIM}_j = \sum_{i \in T^{\text{up}}(j)} \frac{A_{ij} a_i} {(x_j - x_i)/D_i} + -\frac{A_{\text{AWC},j}^{p_\text{AWC}}}{d_\text{AWC}}$$ +\frac{\beta_{j}^{p}{d}$$ -where $A_{\text{AWC},j}$ is the AWC amplitude of turbine $j$, and the exponent $p_\text{AWC}$ and -denominator $d_\text{AWC}$ are tuning parameters that can be set in the `emgauss.yaml` file with +where $\beta_{j}$ is the AWC amplitude of turbine $j$, and the exponent $p$ and +denominator $d$ are tuning parameters that can be set in the `emgauss.yaml` file with the fields `awc_wake_exp` and `awc_wake_denominator`, respectively. Note that, in contrast to the yaw added mixing case, a turbine currently affects _only_ its own wake by applying AWC. diff --git a/docs/operation_models_user.ipynb b/docs/operation_models_user.ipynb index aaaae3f87..d2ccbc973 100644 --- a/docs/operation_models_user.ipynb +++ b/docs/operation_models_user.ipynb @@ -48,41 +48,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2275840e-48a3-41d2-ace9-fad05da0dc02", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "simple operation model powers [kW]: [[1753.95445918 436.4427005 506.66815478]]\n", - "cosine-loss operation model powers [kW]: [[1561.31837381 778.04338242 651.77709894]]\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from floris import FlorisModel\n", "from floris import layout_visualization as layoutviz\n", @@ -178,33 +149,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b9a5f00a-0ead-4759-b911-3a1161e55791", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Power [kW]')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RVFaBF9Ydx+6zGWLHIjI4FkREFmbPuQxMXXcCJWoN+rdyww9TusLJRiF2LBKRvbUc30zsjKHtPKCuEDBjw0lsOXVT7FhEBsWCiMiCbD2VipfXn0RZhQZD23lg9XPBsJZz5mkCFFZSrBzbHmM6NkGFRsCcn09jQ2yK2LGIDIYFEZGF+OlYCmb/HI8KjYAxHZtg5dj2UFjxI4D+ZiWTYsWTgZgQ4gNBABZsOYOvD10VOxaRQfDTkMgCrI+9jvmbz0AQgAkhPljxZCDnGKL7kkolWDI8ANP7aCdwfG/nBfz3IIsiMn/8RCQyc5uOp2DhlrMAgKm9fLFkeACkUonIqciYSSQSzBvsj1cHtgAAvL/rAr79K1nkVET1iwURkRn7Ne4m3tx8BgDwfI+mWDCkNSQSFkP0cBKJBK8ObImZ/f0AAEt3nMf3MdfEDUVUj1gQEZmpLaduYu6vp3WnyRY90YbFENXanEEtdeufLdp2Dutjr4uciKh+sCAiMkM7EtLw2s/aYujZrt5YMjyAxRA9EolEgjfC/DGtdzMAwMItZ/Hz8RsipyKqeyyIiMxMdGIWXt0YD40AjO3shXdHtGUxRI9FIpFgfngrTO7hCwB4c3MCdp9NFzkVUd1iQURkRuKuZ2P6j3Eo1wgYFuSJf49qxwHUVCckEgnefqI1xnb2gkYAZv0Uj78u3xY7FlGdYUFEZCYupOfh+e+Oo0StQZ+WjfDRU0EshqhOSSQSvD+qHcLbqlBWocG0H04g/kaO2LGI6gQLIiIzcP1OISZ8ewx5JeUI9nHG6ueCOeki1QuZVIJPx7ZHTz/t2meTvjuGy5n5Ysciemz8xCQycbfySzH+m2O4lV+KVip7fDuxMxoouBwH1R+llQxfjQ9GkJcTcorUGP/NMaTmFIsdi+ixsCAiMmFFZeWYsu44UrKL4O1ig+8nd4GjjVzsWGQBbJVWWDupM1q42SEjrwTPf3cMucVqsWMRPTIWREQmqrxCg5kbTiHhZi6cbeRYN7kL3BysxY5FFsTZVoF1k7vA3UGJS5kFeOnHOJSVa8SORfRIWBARmSBBELB4+znsu5gFpZUUX0/sDF9XW7FjkQXydGqAbyd1hq1ChiNX7uDN3xIgCILYsYhqjQURkQlac/AqfjyaAokE+PTp9gj2cRY7ElmwAE9HfPlcMGRSCTafSsUnUZfEjkRUayyIiEzMjoQ0LPvjIgDgraFtEN7OQ+RERECflo3w/si2AIDP/kzCpuMpIiciqh0WREQmJP5GDl77+TQA7WKtU3r6ipyI6G9ju3jrFoNduOUsjl69I3IioppjQURkIjJySzDt+xMoLddgQCs3vDW0jdiRiO4xZ1BLDAvyRLlGwEs/xiHlTpHYkYhqhAURkQkoLqvAtB9OICu/FC3d7fDp2PaQcRZqMkISiQQrngxEYBNH3C1S44XvjyO/hJfjk/FjQURk5ARBwNxfT+sur/96QmfYW3OuITJe1nIZ1ozvBDd77eX4r26MR4WGV56RcWNBRGTkvjyQjB0J6bCSSvCf54Lh3dBG7EhED6VytMZ/J3SC0kqKfRezELHnotiRiB6IBRGREUvIluDTfUkAgHdHtkW3Zg1FTkRUc0FeTljxVBAA4KsDV7E1Pk3kRETVY0FEZKQuZxbgx8vaX9FJ3ZvimS7eIiciqr3hQZ54pV/llWfbziOlQORARNVgQURkhHKKyjB9wymUaiTo5uuMhUNbix2J6JHNGdQSA1u7oaxcg28SZbhdUCp2JKJ7sCAiMjLlFRrM/OkUUrKL4aIUsPLpIMhl/FUl0yWVSvDJ0+3RzNUWOWUSvPLTaa55RkZH1E/ZgwcPYtiwYfD09IREIsHWrVv19kskkvv+rFixQtcmOzsbzz77LBwcHODk5IQpU6agoED/mGxCQgJ69eoFa2treHl5ISIiwhAvj+iRROxJxKHLt9FALsUL/hVwsVWIHYnosdlby7H62fawlgmIS8nB4u3nxI5EpEfUgqiwsBBBQUFYtWrVffenp6fr/Xz77beQSCQYM2aMrs2zzz6Lc+fOISoqCjt27MDBgwcxbdo03f68vDyEhobCx8cHcXFxWLFiBRYvXow1a9bU++sjqq1t8alYc/AqAGD5qLZozPVayYz4utpiQgsNJBJgQ2wK1sdeFzsSkY6VmE8eHh6O8PDwaverVCq929u2bUO/fv3QrFkzAMCFCxewe/duHD9+HJ06dQIAfP755xgyZAg+/PBDeHp6Yv369SgrK8O3334LhUKBgIAAxMfH4+OPP9YrnP6ptLQUpaV/n+fOy8sDAKjVaqjVlj3JWNXrt/R+qGuJGfmY91sCAGB6b18MatUQUTfYz4bA97RhqNVqBDgL+Fe/Zvj0z6tY/Ps5tGxkg/ZeTmJHMyt8P+uraT+IWhDVRmZmJnbu3Il169bptsXExMDJyUlXDAHAwIEDIZVKERsbi1GjRiEmJga9e/eGQvH3aYewsDB88MEHuHv3Lpyd779K+LJly7BkyZJ7tkdGRsLGhvPAAEBUVJTYEcxGcTnw0RkZStQStHLUwL/sMqKiLgNgPxsS+9owmhZdQpCLFKezpZi6NhZzAytgx7lG6xzfz1pFRTVbPsZkCqJ169bB3t4eo0eP1m3LyMiAm5ubXjsrKyu4uLggIyND18bXV38BTHd3d92+6gqi+fPnY86cObrbeXl58PLyQmhoKBwcHOrkNZkqtVqNqKgoDBo0CHI5P8UelyAIeGXjadwqyYKHozXWvdQNLrYK9rMBsa8No6qfQ0MHofcACUavPoprd4qwO8cd/x3fkcvR1BG+n/VVneF5GJMpiL799ls8++yzsLa2NsjzKZVKKJXKe7bL5XK+wSqxL+rG14euIvJ8FuQy7UzU7k76A4fYz4bDvjYMuVwOGxs5Vo8PxshVh3Eo6Q5WH7qGVwe2FDuaWeH7WaumfWAS1/IeOnQIiYmJeOGFF/S2q1QqZGVl6W0rLy9Hdna2bvyRSqVCZmamXpuq2/8co0RkaMeSs7HsD+2SBm8/0YZjKciitFI54N+j2gEAVu67jOjErIfcg6j+mERB9M033yA4OBhBQUF620NCQpCTk4O4uDjdtj///BMajQZdu3bVtTl48KDeoKqoqCj4+/tXe7qMyBBu5ZfilQ0nUaERMDzIE+O7+YgdicjgRndsgnFdvSEIwKub4pGaUyx2JLJQohZEBQUFiI+PR3x8PAAgOTkZ8fHxSElJ0bXJy8vDL7/8cs/RIQBo3bo1Bg8ejKlTp+LYsWM4fPgwXnnlFYwdOxaenp4AgHHjxkGhUGDKlCk4d+4cNm3ahJUrV+qNDyIyNI1GwJyf45GVXwo/NzssG90OEgnHT5BlWvREG7Rr7IicIjVm/XQK5RWctJEMT9SC6MSJE+jQoQM6dOgAAJgzZw46dOiARYsW6dps3LgRgiDgmWeeue9jrF+/Hq1atcKAAQMwZMgQ9OzZU2+OIUdHR0RGRiI5ORnBwcF47bXXsGjRogdeck9U3746eBWHLt+GtVyK/zzbEbZKkxnOR1TnrOUyfPlsR9grrRB3/S4+3XtZ7EhkgUT9FO7bty8EQXhgm2nTpj2weHFxccGGDRse+BiBgYE4dOjQI2Ukqmtx1+/iw8hEAMCS4QFo4W4vciIi8Xm52GD5mEDM2HASq6KTENK8IXr4uYodiyyISYwhIjIXucXaUwIVGgHDgjzxf528xI5EZDSGBnrgmS5/jyfiIrBkSCyIiAxEEAS8+VsCUnOK4e1ig/dHteW4IaJ/WPREG7R0t8Ot/FK89vNpaDQPPotAVFdYEBEZyIZjKfjjbAbkMgk+f6YDHKw5PwjRPzVQyPDFuI6wlktx4NItfP3XVbEjkYVgQURkAElZ+Vi6/TwAYN7gVgjifENE1Wrpbo93hgUAACJ2J+LMzVyRE5ElYEFEVM/KyjX418Z4lJZr0LtlI0zu4fvwOxFZuLGdvTCknQrlGgH/2nQKxWUVYkciM8eCiKiefbL3Es6l5cHZRo4PnwyElOs1ET2URCLB+yPbwd1Biau3CvHvXRfEjkRmjgURUT2KvXoHqw9cAQAsGx0INwfDrMVHZA6cbRX48CntCgU/HL2O/Re5tAfVHxZERPUkr0SNOT+fhiAAT3fywuC2XDuPqLZ6tfj7NPPcX0/zUnyqNyyIiOrJO9vOITWnGD4NbbBoWBux4xCZrDcG+6Olux1uF5Thzd/OPHRCX6JHwYKIqB5sP52GLadSIZNK8MnT7bk0B9FjsJbL8OnTHaCQSbH3QiY2Hr8hdiQyQyyIiOpYVn4J3t52FgAwo58fOno7i5yIyPS18XTA62EtAQDv7TiPG9lFIicic8OCiKgOCYKABZvPIqdIjQBPB8zs7yd2JCKzMaVnM3Ru6ozCsgrM+y2Bs1hTnWJBRFSHtpxKxd4LmZDLJPjo/4Igl/FXjKiuyKQSrHgyCNZyKY5cuYMfY6+LHYnMCD+tiepIRm4J3vn9HADg1YEt0UrlIHIiIvPT1NUWbw5uBQBYtusirt8pFDkRmQsWRER1QBAEvLk5Afkl5Qhq4ogXezcTOxKR2ZoQ0hTdmrmgWF2Bub/w1BnVDRZERHXg5xM3EJ14CworKT76vyBY8VQZUb2RVp46s1XIcOxaNr47ck3sSGQG+KlN9JjSc4vx7g7tsgKvh7aEn5u9yImIzJ+Xiw0WDG0NAIjYfRHJt3nqjB4PCyKixyAIAhZuOYuC0nJ08HbClJ48VUZkKOO6eKOnnytKyzV4k1ed0WNiQUT0GH4/nYY/L2ZBIZMiYkwgZFy4lchgJBIJlo1uhwZyGWKTszlhIz0WFkREjyi7sAxLtp8HALzS3w8t3HmqjMjQvFxs8HqYPwBg2a4LyMgtETkRmSoWRESPaOn2c8guLIO/uz2m92kudhwiizWpe1O093JCfmk53tp6lmud0SNhQUT0CPZfzMLW+DRIJcAHTwZCYcVfJSKxyKQSRDwZCLlMgr0XMrHzTLrYkcgE8VOcqJYKSsuxcMsZAMDkHr5o7+UkbiAiQkt3e8zop10q551t53C3sEzkRGRqWBAR1dKK3ReRllsCbxcbzAltKXYcIqr0cl8/+Lvb405hGd7deV7sOGRiWBAR1UL8jRx8f1S7ftK/R7WDjcJK5EREVEVhJcXyMe0gkQCbT6biyJXbYkciE8KCiKiGyis0WLD5DAQBGNWhMXq2cBU7EhH9QwdvZzzX1QcA8NaWsygtrxA5EZkKFkRENbT2yDWcT8+DYwM5FlbOkEtExmfuYH80slfi6u1CrI6+KnYcMhEsiIhqIC2nGB9HXQIAvBneCq52SpETEVF1HKzlWPREGwDAqugkLutBNcKCiKgGFv9+DkVlFejk44ynO3mJHYeIHuKJQA/0btkIZeUavLX1DOcmoodiQUT0EFHnMxF5PhNWUgn+PbodpFyeg8joSSQSvDeiLZRWUhxOuoNt8WliRyIjx4KI6AEKS8vxzrazAICpvZuhJZfnIDIZ3g1tMGtACwDAezvPI7dILXIiMmYsiIge4PM/k5CWW4Imzg0wq38LseMQUS1N7dUMLdzscLugDB9GJoodh4wYCyKialy5VYBv/tJeobJ4WAAaKGQiJyKi2lJYSbF0RFsAwPrY6zibmityIjJWLIiI7kMQBCz+/RzUFQL6t3LDwDbuYkciokcU0rwhhgV5QiMAi7adhUbDAdZ0LxZERPex+2wGDl2+DYWVFO8MayN2HCJ6TAuHtIatQoaTKTn49eRNseOQEWJBRPQPRWXleHeHdh2k6X2aw6ehrciJiOhxqRyt8a+B2nGAH/xxkQOs6R4siIj+4Yv/GUj9ct/mYschojryfA9f+LnZ4U5hGT6K4gBr0seCiOh/XL1VgP8e0g6kXvREG1jLOZCayFzIZVIsHR4AAPjx6HWcS+MAa/obCyKiSoIgYMn281BXCOjr3wiDOJCayOx093PF0ECPygHW5ziDNemwICKqtD8xCwcu3YJcJsE7wwIgkXBGaiJz9NbQ1mgglyHu+l38fpozWJMWCyIiAGXlGry74wIAYHJPX/i6ciA1kbnycPx7fODyPy6iqKxc5ERkDFgQEQH4PuYakm8XwtVOiVf6+Ykdh4jq2dTezdDYqQHSc0vw1YGrYschI8CCiCze7YJSrNx7GQDwxmB/2FvLRU5ERPXNWi7DwqGtAQCrD1xBak6xyIlIbCyIyOJ9FJmI/NJytGvsiCc7NhE7DhEZSHhbFbr6uqC0XINluy6IHYdExoKILNrZ1FxsPH4DAPDOsDaQSjmQmshSSCQSLBrWBhIJsCMhHceSs8WORCJiQUQWSxAELN1xHoIADA/yRKemLmJHIiIDC/B0xNjO3gCAJdvPoYLrnFksUQuigwcPYtiwYfD09IREIsHWrVvvaXPhwgUMHz4cjo6OsLW1RefOnZGSkqLb37dvX0gkEr2f6dOn6z1GSkoKhg4dChsbG7i5uWHu3LkoL+dVBZbuj7MZOJacDWu5FG+GtxI7DhGJ5PXQlrC3tsK5tDz8Fsd1ziyVqAVRYWEhgoKCsGrVqvvuv3LlCnr27IlWrVohOjoaCQkJePvtt2Ftba3XburUqUhPT9f9RERE6PZVVFRg6NChKCsrw5EjR7Bu3TqsXbsWixYtqtfXRsattLwCy/+4CAB4sXdzeDo1EDkREYmloZ0Ss/pr1zn7MDKRl+FbKCsxnzw8PBzh4eHV7l+4cCGGDBmiV+A0b37v2lI2NjZQqVT3fYzIyEicP38ee/fuhbu7O9q3b493330X8+bNw+LFi6FQKO57v9LSUpSWlupu5+XlAQDUajXUasteFLDq9ZtyP6w7ch0p2UVws1dicncvo3wt5tDPpoJ9bRjG3M/PdG6M72Ou4cbdYqzen4SZ/U13HUNj7mcx1LQfJIKRzFsukUiwZcsWjBw5EgCg0Wjg6OiIN954A3/99RdOnToFX19fzJ8/X9cG0J4yO3dOO/26SqXCsGHD8Pbbb8PGxgYAsGjRIvz++++Ij4/X3Sc5ORnNmjXDyZMn0aFDh/vmWbx4MZYsWXLP9g0bNugem0xToRp475QMRRUSPNO8At3cjOJXgIhEduqOBGsvyaCQCnirQwUc7//3MpmYoqIijBs3Drm5uXBwcKi2nahHiB4kKysLBQUFWL58Od577z188MEH2L17N0aPHo39+/ejT58+AIBx48bBx8cHnp6eSEhIwLx585CYmIjNmzcDADIyMuDurr8mVdXtjIyMap9//vz5mDNnju52Xl4evLy8EBoa+sAOtQRqtRpRUVEYNGgQ5HLTm7Pn/V0XUVSRglbudnhnQghkRnplman3sylhXxuGsfdzuCDg9H+P4dSNXJyBD/49JEDsSI/E2PvZ0KrO8DyM0RZEGo0GADBixAjMnj0bANC+fXscOXIEq1ev1hVE06ZN092nXbt28PDwwIABA3DlypX7nl6rKaVSCaVSec92uVzON1glU+yLa7cLsf6Y9jL7hU+0gbXS+P8ENMV+NlXsa8Mw5n5+64kAjPnPEfx6MhWTezZDaw/T/QPYmPvZkGraB0Z72b2rqyusrKzQpk0bve2tW7fWu8rsn7p27QoASEpKAgCoVCpkZmbqtam6Xd24IzJfH+y+CHWFgD4tG6FXi0ZixyEiIxPs44yh7TwgCMC/OVmjRTHagkihUKBz585ITEzU237p0iX4+PhUe7+qsUIeHh4AgJCQEJw5cwZZWVm6NlFRUXBwcLin2CLzdvxaNv44mwGpBFgwpLXYcYjISM0b3ApymQSHLt9GdGLWw+9AZkHUU2YFBQW6IzmAdrBzfHw8XFxc4O3tjblz5+Lpp59G79690a9fP+zevRvbt29HdHQ0AO1l+Rs2bMCQIUPQsGFDJCQkYPbs2ejduzcCAwMBAKGhoWjTpg3Gjx+PiIgIZGRk4K233sKMGTPue0qMzJMgCHh/p/avvac7e8FfZS9yIiIyVt4NbTAxpCm+/isZ/951Ab1aNDLasYZUd0Q9QnTixAl06NBBd6XXnDlz0KFDB90cQaNGjcLq1asRERGBdu3a4euvv8Zvv/2Gnj17AtAeRdq7dy9CQ0PRqlUrvPbaaxgzZgy2b9+uew6ZTIYdO3ZAJpMhJCQEzz33HCZMmIClS5ca/gWTaHadyUD8jRzYKGSYPail2HGIyMjN7N8Cjg3kuJRZwMkaLYSoR4j69u2Lh131P3nyZEyePPm++7y8vHDgwIGHPo+Pjw927dr1SBnJ9JWVaxCxRzsJ47TezeBmb/2QexCRpXO0kWNmfz+8t/MCPopKxLAgTzRQyMSORfXIaMcQEdWVDbHXcf1OEVztlJjaq5nYcYjIRIwP8UET5wbIzCvFt4eTxY5D9YwFEZm1vBI1PvtTO05t9qAWsFUa7UwTRGRklFYyzA3zBwD8J/oK7hSUPuQeZMpYEJFZWx19BdmFZWjWyBZPd/ISOw4RmZhhgZ5o29gBBaXl+GzfZbHjUD1iQURmKz23GN/8pT3M/ebgVrCS8e1ORLUjlUqwIFw7Tcf62BQk3y4UORHVlxqdP6jptNf/y9KXtyDxfRx5CaXlGnRp6oJBbdwffgciovvo7ueKfv6NsD/xFlbsuYgvnw0WOxLVgxoVRE5OTpBIaj4Hg0QiwaVLl9CsGQewkjguZuTh15PaS2XnD2lVq/cvEdE/vRneGgcu3cKuMxk4mXIXHb2dxY5EdazGI0x//fVXuLi4PLSdIAgYMmTIY4UielwRuxMhCMCQdip04AcXET0mf5U9ngxugp9P3MTyPy5i07Ru/EPLzNSoIPLx8UHv3r3RsGHDGj1os2bNuKAcieZYcjb+vJgFmVSC10P9xY5DRGbi1YEtsTU+DceSsxGdeAv9WrmJHYnqUI1GmSYnJ9e4GAKAs2fPwsuLV/SQ4QmCgOV/aJfoGNvZC80a2YmciIjMhadTAzzfvSkA7ULRFZoHTyxMpqXGl90kJ3NSKjJ+UeczcTIlBw3kMvxrQAux4xCRmXmpb3M4WFvhYkY+tsWnih2H6lCNC6LmzZvD19cXkydPxg8//ICbN7m2CxmXCo2AFXsSAQCTezaFmwOX6CCiuuVko8D0vs0BAB9FXkJpeYXIiaiu1Lgg+vPPPzFx4kRcvXoV06ZNg4+PD1q0aIEXX3wRGzduRGZmZn3mJHqo307exOWsAjjZyPFin+ZixyEiM/V8d1+4OyiRmlOM9UdTxI5DdaTGBVHfvn2xePFiREdH4+7du4iKisIzzzyDCxcuYNKkSfD09ERAQEB9ZiWqVom6Ap9GXQIAzOjrBwdrDuonovrRQCHDvwa0BAB8sT8J+SVqkRNRXXikqXutra3Rv39/vPXWW1iyZAlmzZoFOzs7XLx4sa7zEdXIDzHXkZZbAk9Ha4wP8RE7DhGZuf/r1ATNXG2RXViG/x7iGFtzUKuCqKysDAcPHsSSJUvQr18/ODk5Yfr06bh79y6++OILDrwmUeSVqLEqumoB15awlstETkRE5s5KJtUt/Pr1oau4zYVfTV6NJ2bs378/YmNj4evriz59+uDFF1/Ehg0b4OHhUZ/5iB7qm0PJyClSw8/NDqM7NhE7DhFZiMFtVQhq4ojTN3Pxn+grePuJNmJHosdQ4yNEhw4dQsOGDdG/f38MGDAAgwYNYjFEortbWKZbwPW1QS0hk3LmWCIyDIlEgtcqJ3/94eh1pOcWi5yIHkeNC6KcnBysWbMGNjY2+OCDD+Dp6Yl27drhlVdewa+//opbt27VZ06i+1p94AoKSssR4OmAsACV2HGIyML0auGKLk1dUFauwed/Jokdhx5DjQsiW1tbDB48GMuXL0dsbCxu376NiIgI2NjYICIiAk2aNEHbtm3rMyuRnqy8EqyLuQYAeD3UH1IeHSIiA5NIJHi9cizRz8dvIOVOkciJ6FE90lVmgLZAcnFxgYuLC5ydnWFlZYULFy7UZTaiB/pifxJK1BoE+zijr38jseMQkYXq4uuC3i0boVwj4NN9l8SOQ4+oxgWRRqPBsWPHEBERgfDwcDg5OaF79+748ssvoVKpsGrVKly9erU+sxLp3LxbhJ+OaSdEey20JVedJiJRvTZIOy/R1lOpSMrKFzkNPYoaX2Xm5OSEwsJCqFQq9OvXD5988gn69u2L5s05IzAZ3mf7LkNdIaCHX0N0b+4qdhwisnBBXk4IbeOOyPOZ+CTqMlY921HsSFRLNS6IVqxYgX79+qFly5b1mYfooa7eKsBvJ7WLKr5eeYUHEZHYXgv1R9SFTOw8k46XUnPRtrGj2JGoFmp8yuzFF19Ey5YtsX///mrbrFq1qk5CET3Iyn2XUaERMLC1Gzp4O4sdh4gIAOCvssfwIE8AwKd7OZbI1NR6UPXo0aMRFxd3z/aVK1di/vz5dRKKqDpJWfn4/XQaAODVgTxaSUTG5V8DWkAqAfZeyELCzRyx41At1LogWrFiBcLDw/XWLfvoo4+waNEi7Ny5s07DEf3Tyn1JEAQgtI07D0cTkdFp1sgOI9s3BgB8uveyyGmoNmo8hqjKCy+8gOzsbAwcOBB//fUXNm3ahH//+9/YtWsXevToUR8ZiQAAlzPzsSOBR4eIyLjNHNAC206n4c+LWYi/kYP2Xk5iR6IaqHVBBABvvPEG7ty5g06dOqGiogJ79uxBt27d6jobkZ6V+y5DEIDBASq08XQQOw4R0X35utpiZPvG+O3kTXy69xLWPt9F7EhUAzUqiD777LN7tjVu3Bg2Njbo3bs3jh07hmPHjgEAZs2aVbcJiQBcyszHzjPpAIB/DWwhchoiogeb2d8PW+NTEZ14C6dS7vICEBNQo4Lok08+ue92mUyGw4cP4/DhwwC0U5izIKL6UHV0KLytCq09eHSIiIxbU1dbjOrQGL/G3cSney9j3WQeJTJ2NSqIkpOT6zsHUbUSM/Kxi0eHiMjEzOzvhy2nUnHg0i3EXb+LYB8eJTJmj7yWGZGhrNx3CYIADG3ngVYqHh0iItPg09AWYzpWXXHGeYmMXY0Kojlz5qCwsLDGDzp//nxkZ2c/ciiiKtqjQxmQSIBZA3h0iIhMyyv9WsBKKsGhy7cRd/2u2HHoAWpUEK1cuRJFRUU1ftBVq1YhJyfnUTMR6XyxPwkAMKStB/xV9iKnISKqHe+GNhhdeZToiz85L5Exq9EYIkEQ0LJlzVcUr83RJKLqXLlVoJt36JX+fiKnISJ6NC/39cOvcTexP/EWztzMRbsmnFTWGNWoIPruu+9q/cDu7u61vg/R/1q1Xzsr9cDW7ryyjIhMVlNXWwwP8sTW+DR8/udlrJnQSexIdB81KogmTpxY3zmI9KTcKcK2eO3RoVkDeHSIiEzbK/39sO10GiLPZ+JCeh7/yDNCvMqMjNKX0Umo0Ajo07IRAps4iR2HiOix+LnZY0hbDwB/j40k48KCiIxOak4xfjt5EwCPDhGR+agaC7nrTDqSsvJFTkP/xIKIjM7q6CtQVwjo3rwhgn1cxI5DRFQnWns4YFAbdwgCsGr/FbHj0D+wICKjkplXgk0nbgAAZvbnvENEZF5mVX6ubYtPxfU7vCLbmNSqIFKr1bCyssLZs2frKw9ZuK8OXEVZuQadmzqjWzMeHSIi89KuiSP6+jeCRgC+5FEio1Krgkgul8Pb2xsVFRX1lYcsWHZhGX46lgIAeKV/ixrPe0VEZEpmVo4l2nzqJtJzi0VOQ1Vqfcps4cKFWLBgAZfmoDq39nAyitUVaNvYAb1buIodh4ioXgT7uKCrrwvUFQL+e5CLpxuLGs1D9L+++OILJCUlwdPTEz4+PrC1tdXbf/LkyToLR5ajoLQca49cA6Cd1ZVHh4jInL3czw+xycfw07EUvNLfDy62CrEjWbxaF0QjR46shxhk6dYfvY68knI0a2SLsACV2HGIiOpV7xauaNvYAWdT87D2cDLmhPqLHcni1bogeuedd+rsyQ8ePIgVK1YgLi4O6enp2LJlyz0F14ULFzBv3jwcOHAA5eXlaNOmDX777Td4e3sDAEpKSvDaa69h48aNKC0tRVhYGL788ku9pUNSUlLw0ksvYf/+/bCzs8PEiROxbNkyWFnV+uVTPShRV+Drv7SHjaf3aQ6ZlEeHiMi8SSQSvNzXDy+vP4m1R65hWp/msFPyO0lMj3TZfU5ODr7++mvMnz9fN5bo5MmTSE1NrdXjFBYWIigoCKtWrbrv/itXrqBnz55o1aoVoqOjkZCQgLfffhvW1ta6NrNnz8b27dvxyy+/4MCBA0hLS8Po0aN1+ysqKjB06FCUlZXhyJEjWLduHdauXYtFixY9wiun+vDbyZu4lV8KD0drjGzfWOw4REQGERagQjNXW+SVlGND7HWx41i8WpejCQkJGDhwIBwdHXHt2jVMnToVLi4u2Lx5M1JSUvD999/X+LHCw8MRHh5e7f6FCxdiyJAhiIiI0G1r3ry57t+5ubn45ptvsGHDBvTv3x+AdiHa1q1b4+jRo+jWrRsiIyNx/vx57N27F+7u7mjfvj3effddzJs3D4sXL4ZCcf/ztqWlpSgtLdXdzsvLA6CdekCtVtf4NZqjqtdfF/1QXqHBf6K1l55O6eEDiVABtZpXMQJ128/0YOxrw2A/32tqr6aYv+Uc/nvwKsZ1agylXPbYj8l+1lfTfqh1QTRnzhxMmjQJERERsLe3120fMmQIxo0bV9uHq5ZGo8HOnTvxxhtvICwsDKdOnYKvry/mz5+vO60WFxcHtVqNgQMH6u7XqlUreHt7IyYmBt26dUNMTAzatWundwotLCwML730Es6dO4cOHTrc9/mXLVuGJUuW3LM9MjISNjY2dfY6TVlUVNRjP8aJWxLcvCuDrZUAx9vnsGvXuTpIZl7qop+pZtjXhsF+/ptCAzgpZLhVUIalP0aih7tQZ4/NftYqKiqqUbtaF0THjx/HV199dc/2xo0bIyMjo7YPV62srCwUFBRg+fLleO+99/DBBx9g9+7dGD16NPbv348+ffogIyMDCoUCTk5Oevd1d3fXZcnIyNArhqr2V+2rzvz58zFnzhzd7by8PHh5eSE0NBQODpa9SrFarUZUVBQGDRoEuVz+yI+j0QhYtSoGQAGm9mmBkX2b1V1IM1BX/UwPx742DPbz/WU3vI73dyUi5q4dlkzoASvZ4y0iwX7WV3WG52FqXRAplcr7PvilS5fQqFGj2j5ctTQaDQBgxIgRmD17NgCgffv2OHLkCFavXo0+ffrU2XPdj1KphFKpvGe7XC7nG6zS4/bFnxczcSmrAHZKKzzfoxn7tRp8zxkO+9ow2M/6nu3WFP85kIwbd4sRefE2RtTRWEr2s1ZN+6DWZejw4cOxdOlS3Tk5iUSClJQUzJs3D2PGjKntw1XL1dUVVlZWaNOmjd721q1bIyVFO5uxSqVCWVkZcnJy9NpkZmZCpVLp2mRmZt6zv2ofiWf1gasAgGe7esPRhr+0RGSZbBRWmNS9KQBgzcGrEIS6O21GNVfrguijjz5CQUEB3NzcUFxcjD59+sDPzw/29vZ4//336yyYQqFA586dkZiYqLf90qVL8PHxAQAEBwdDLpdj3759uv2JiYlISUlBSEgIACAkJARnzpxBVlaWrk1UVBQcHBzuKbbIcE6m3MWx5GzIZRI838NX7DhERKIa380HDeQynEvLw+GkO2LHsUi1PmXm6OiIqKgo/PXXX0hISEBBQQE6duyoN7C5pgoKCpCUlKS7nZycjPj4eLi4uMDb2xtz587F008/jd69e6Nfv37YvXs3tm/fjujoaF2WKVOmYM6cOXBxcYGDgwNmzpyJkJAQdOvWDQAQGhqKNm3aYPz48YiIiEBGRgbeeustzJgx476nxMgw1lQeHRrZvjFUjtYPaU1EZN6cbRV4urMX1h65hq8OXkFPLl9kcLUuiEpKSmBtbY2ePXuiZ8+ej/XkJ06cQL9+/XS3qwYxT5w4EWvXrsWoUaOwevVqLFu2DLNmzYK/vz9+++03vef95JNPIJVKMWbMGL2JGavIZDLs2LEDL730EkJCQmBra4uJEydi6dKlj5WdHt3VWwXYc147oH1abw6kJiICgCk9ffHD0es4dPk2zqbmom1jR7EjWZRaF0ROTk7o0qUL+vTpg379+iEkJAQNGjR4pCfv27fvQ8+VTp48GZMnT652v7W1NVatWlXt5I4A4OPjg127dj1SRqp7/z2UDEEABrZ2Qwt3+4ffgYjIAni52OCJQA9si0/DmoNX8dkz958WhupHrccQ7d27F4MHD0ZsbCyGDx8OZ2dn9OzZEwsXLuScB/RQWfkl+O3kTQDAi32aP6Q1EZFlqTpqvvNMOm5k12z+HKobtS6IevbsiQULFiAyMhI5OTnYv38//Pz8EBERgcGDB9dHRjIj645cQ1m5Bh29ndDJx1nsOERERiXA0xG9WriiQiPgm8o1HskwHmkluUuXLiE6Olr3U1paiieeeAJ9+/at43hkTgpKy/FDjHa9nhf7NIdEwkVciYj+aXqf5jh0+TY2Hb+Bfw1oAWfb+y8xRXWr1gVR48aNUVxcjL59+6Jv376YN28eAgMD+eVGD7Xp+A3klZSjmastBrV2f/gdiIgsUPfmDdG2sQPOpubh+5jr+NfAFmJHsgi1PmXWqFEjFBUVISMjAxkZGcjMzERxcXF9ZCMzUl6hwbeVh3+n9m4GqZQFNBHR/UgkEkzrrR1j+X3MNZRwwWuDqHVBFB8fj4yMDLz55psoLS3FggUL4Orqiu7du2PhwoX1kZHMwK6zGUjNKYarnQKjOtTNtPREROZqSFsVGjs1wJ3CMmw5lSp2HIvwSCvIOTk5Yfjw4ViwYAHmz5+PJ598EsePH8fy5cvrOh+ZAUEQ8PUh7USM47s1hbVcJnIiIiLjZiWT4vkeTQEAXx+6Co2Gy3nUt1oXRJs3b8asWbMQGBgId3d3vPTSSygoKMBHH32EkydP1kdGMnHHkrORcDMXSispnuvmLXYcIiKT8HRnL9grrXDlViGiL2U9/A70WGo9qHr69Ono3bs3pk2bhj59+qBdu3b1kYvMyNeVY4dGd2yChnZcLoWIqCbsreUY28UL/z2UjK8PJaN/K16MUp9qXRD97yKpRA+TfLsQey9kAtBOS09ERDU3qYcvvj18DUeu3MG5tFwEeHI5j/rySPMQVVRUYOvWrbhw4QIAoE2bNhgxYgRkMo4NIX3f/HUVggAMaOUGPzc7seMQEZmUxk4NMLSdB34/nYavDyXjk6fbix3JbNV6DFFSUhJat26NCRMmYPPmzdi8eTPGjx+PgIAAXLlypT4ykom6W1iGX+O0y3S80IuLuBIRPYoXemmPrm8/nYb0XE5zU19qXRDNmjULzZs3x40bN3Dy5EmcPHkSKSkp8PX1xaxZs+ojI5moH49eR4lagwBPB3Rr5iJ2HCIikxTYxAldfF1QrhGw9sg1seOYrVoXRAcOHEBERARcXP7+gmvYsCGWL1+OAwcO1Gk4Ml0l6gqsq1ymY2qvZpzJnIjoMUytPMq+ITYFBaXlIqcxT7UuiJRKJfLz8+/ZXlBQAIWC662Q1u+n03C7oBQejtYYGughdhwiIpM2oJUbmrnaIr+kHD8fvyF2HLNU64LoiSeewLRp0xAbGwtBECAIAo4ePYrp06dj+PDh9ZGRTIwgCPjmkPZS+4ndm0Iue6T5P4mIqJJUKsHkyit1vzuSjApO1Fjnav1N9dlnn6F58+YICQmBtbU1rK2t0aNHD/j5+WHlypX1kZFMzF9Jt5GYmQ8bhQzPdOFEjEREdWFMxyZwspHjRnYxIs9liB3H7NT6snsnJyds27YNSUlJusvuW7duDT8/vzoPR6bp68qjQ//XyQuODeQipyEiMg8NFDI819UHX+xPwtd/JSO8HYcj1KUaF0QajQYrVqzA77//jrKyMgwYMADvvPMOGjRoUJ/5yMRcyszHgUu3IJUAk3twIkYioro0obsP1hy8irjrd3Ey5S46ejuLHcls1PiU2fvvv48FCxbAzs4OjRs3xsqVKzFjxoz6zEYmqGrsUFiACt4NbUROQ0RkXtzsrTG8vScA4JvKZZGobtS4IPr+++/x5ZdfYs+ePdi6dSu2b9+O9evXQ6PR1Gc+MiG38kuxJT4VwN8TiRERUd2qWgbpjzPpuJFdJHIa81HjgiglJQVDhgzR3R44cCAkEgnS0tLqJRiZnh+PXkdZuQbtvZx4GJeIqJ609nBArxau0AjgRI11qMYFUXl5OaytrfW2yeVyqNXqOg9FpqdEXYEfj2onYnyhly8nYiQiqkdVR4k2Hb+BvBJ+D9eFGg+qFgQBkyZNglKp1G0rKSnB9OnTYWtrq9u2efPmuk1IJmHLqVTcKSxDY6cGGBygEjsOEZFZ69OyEVq42eFyVgE2HbuBqb25XuTjqvERookTJ8LNzQ2Ojo66n+eeew6enp5628jyCIKgG9z3fI+msOJEjERE9UoikejGaq49cg3lFRzP+7hqfITou+++q88cZMIOXb6NpKwC2Cmt8H+dvcSOQ0RkEUa0b4yI3YlIzSlG5PlMDOG8RI+Ff8rTY/vusPbo0JPBTeBgzYkYiYgMwVouw7iu2tUAqj6H6dGxIKLHcuVWAfYn3oJEAkzq3lTsOEREFuW5bj6QyyQ4fu0uztzMFTuOSWNBRI9lXeUlnwNauaGpq+2DGxMRUZ1yd7DG0MpTZTxK9HhYENEjyytW49e4mwCA57lMBxGRKKo+f7cnpCErv0TkNKaLBRE9sl9OpqKorAL+7vbo3ryh2HGIiCxSkJcTgn2coa4Q8OPRFLHjmCwWRPRINAJ0v3jP92jKiRiJiET0fI+mAIANsddRWs5L8B8FCyJ6JGeyJbiZUwJnGzlGdmgsdhwiIosWFqCCh6M1bheUYeeZdLHjmCQWRPRIDqRr3zrjunrDWi4TOQ0RkWWTy6SYENIUALD2SAoEQdw8pogFEdXa+fQ8XMmXwEoqwfhuTcWOQ0REAJ7p4gVruRQXMvJxJV/sNKaHBRHV2veVY4fCAtyhcrR+SGsiIjIEJxsFRnVoAgA4mM6v99pij1Gt3C0sw/aEDADAxG7eIqchIqL/VTVB7plsCdJzeQl+bbAgolrZePwGyso1aGIroL0XF/MlIjIm/ip7dPV1hgYS/HTshthxTAoLIqqxCo2AH49eBwD0Vml4qT0RkREaX7m+2cYTN1GirhA5jelgQUQ1tvdCJlJziuFsI0eHhryEgYjIGA1o1QhOCgF3i9TYmcBL8GuKBRHVWNW6Zf8X3AQKXmlPRGSUrGRS9FRpJ2dcF3MNAq/BrxEWRFQjlzPzceTKHUglwDNdmogdh4iIHiDETYDCSoqEm7mIv5EjdhyTwIKIamRdzDUAwKA27mjs1EDcMERE9EB2cmBoOxWAv4/u04OxIKKHyitRY/PJVADAxMpLOomIyLhNqBxcvfNMOm7ll4qcxvixIKKH+vXETRSVVaClux1CmnFVeyIiU9C2sQM6eDtBXSHgp2MpYscxeiyI6IE0GgE/VF5qPyGEq9oTEZmSqoka18deh7pCI24YIydqQXTw4EEMGzYMnp6ekEgk2Lp1q97+SZMmQSKR6P0MHjxYr03Tpk3vabN8+XK9NgkJCejVqxesra3h5eWFiIiI+n5pZuOvpNtIvl0Ie6UVRnFVeyIikxLe1gOudkpk5pVi7/lMseMYNVELosLCQgQFBWHVqlXVthk8eDDS09N1Pz/99NM9bZYuXarXZubMmbp9eXl5CA0NhY+PD+Li4rBixQosXrwYa9asqZfXZG6qJmIcE9wEtkorkdMQEVFtKKykGNvZCwB0R/vp/kT9hgsPD0d4ePgD2yiVSqhUqge2sbe3r7bN+vXrUVZWhm+//RYKhQIBAQGIj4/Hxx9/jGnTplX7mKWlpSgt/XsQWl5eHgBArVZDrVY/MI+5SM8twd4L2r8ong721L3uf/6X6gf72XDY14bBfjaMf/bzUx098GV0Eo5cuYMLqXfh52YnZjyDq+n7TSIYyYxNEokEW7ZswciRI3XbJk2ahK1bt0KhUMDZ2Rn9+/fHe++9h4YN/x7Y27RpU5SUlECtVsPb2xvjxo3D7NmzYWWlrfUmTJiAvLw8vdNx+/fvR//+/ZGdnQ1nZ+f75lm8eDGWLFlyz/YNGzbAxsambl60kduZIkVkqhQtHDR4JYDnnomITNXXF6U4c1eK3ioNxvha1ud5UVERxo0bh9zcXDg4OFTbzqjPgQwePBijR4+Gr68vrly5ggULFiA8PBwxMTGQybRTJc+aNQsdO3aEi4sLjhw5gvnz5yM9PR0ff/wxACAjIwO+vr56j+vu7q7bV11BNH/+fMyZM0d3Oy8vD15eXggNDX1gh5qLsnIN3v3oIIAyzAxvj/C2fx+BU6vViIqKwqBBgyCXy8ULaebYz4bDvjYM9rNh3K+f7VvexuR1J3HyrgKfT+0NG4VRf/3XqaozPA9j1D0yduxY3b/btWuHwMBANG/eHNHR0RgwYAAA6BUtgYGBUCgUePHFF7Fs2TIolcpHfm6lUnnf+8vlcov4Rd59Pg23C8rgZq9EeGBjyGX3DjezlL4QG/vZcNjXhsF+Noz/7ee+/io0bWiDa3eKsOvcLTzTxVvkdIZT0/eaSV1236xZM7i6uiIpKanaNl27dkV5eTmuXbsGAFCpVMjM1B9ZX3X7YWOTLFnV4LtnunjftxgiIiLTIZVK8GxXHwDADzHXub7ZfZjUN93Nmzdx584deHh4VNsmPj4eUqkUbm5uAICQkBAcPHhQb1BVVFQU/P39qz1dZukSM/JxLDkbMqnEov6KICIyZ08GN4HSSorz6Xk4xfXN7iFqQVRQUID4+HjEx8cDAJKTkxEfH4+UlBQUFBRg7ty5OHr0KK5du4Z9+/ZhxIgR8PPzQ1hYGAAgJiYGn376KU6fPo2rV69i/fr1mD17Np577jldsTNu3DgoFApMmTIF586dw6ZNm7By5Uq9U22kr+pS+9A27lA5WouchoiI6oKzrQLDgjwBAD/G8BL8fxK1IDpx4gQ6dOiADh06ANCOB+rQoQMWLVoEmUyGhIQEDB8+HC1btsSUKVMQHByMQ4cO6cb2KJVKbNy4EX369EFAQADef/99zJ49W2+OIUdHR0RGRiI5ORnBwcF47bXXsGjRogdecm/JCkrLsfnkTQDA+G4+IqchIqK6VPW5viMhHdmFZSKnMS6iDqru27fvA89j7tmz54H379ixI44ePfrQ5wkMDMShQ4dqnc8SbTmVisKyCjRrZIuQ5ly3jIjInAR5OSGwiSMSbubi5xM3ML1Pc7EjGQ2TGkNE9UsQBGyI1S4A+FxXH65bRkRkhp6rHFz907EUaDQcXF2FBRHpnLqRgwvpeVBaSTGmYxOx4xARUT14IsgD9tZWuH6nCIev3BY7jtFgQUQ6VUeHngj0hKMN5wghIjJHNgorjK5crLvqc59YEFGl3CI1tp9OAwCM68pL7YmIzNm4ytNmkeczkZVXInIa48CCiAAAm0/dRGm5Bq1U9ujo7SR2HCIiqkf+Knt08nFGhUbAzyduiB3HKLAgIr3B1M929eZgaiIiC1B1NuCnYzdQwcHVLIgIOH7tLi5nFaCBXIYRleeViYjIvA1p5wHHBnKk5hTj4KVbYscRHQsiwoZY7YylI9p7wsGag6mJiCyBtVyGJ4O1VxSv5+BqFkSWLruwDLvOZgDgYGoiIktTtV7lnxczkZ5bLHIacbEgsnC/xd1EWbkGbRs7ILCJk9hxiIjIgPzc7NDV1wUaAdh4zLIHV7MgsmCCIOCnY1WDqbluGRGRJXq2cn2zTcdvoLxCI3Ia8bAgsmBHr2bj6u1C2CpkGF65AjIREVmWsAB3uNgqkJFXguhEyx1czYLIgm08rj06NLx9Y9gqRV3nl4iIRKK0kmFMR+0VxlXfC5aIBZGFyikqwx+Vg6mf6eIlchoiIhLT052rBldnISPXMmeuZkFkoTafTEVZuQZtPBzQrrGj2HGIiEhEfm526NJUO7j6FwuduZoFkQUSBEF3WPSZLl6cmZqIiDC28mzBphM3oLHAmatZEFmgkyk5uJRZAGu5lDNTExERAO3M1Q7WVrh5txh/Jd0WO47BsSCyQBsrL7Uf2o4zUxMRkZa1XIZRHSx3cDULIguTX6LGjoR0ABxMTURE+sZWzlwddT4TtwtKRU5jWCyILMy2+DQUqyvg52aHYB9nseMQEZERae3hgCAvJ6grBPwWd1PsOAbFgsjCVB0GHduZg6mJiOhez3SuHFx9/AYEwXIGV7MgsiBnU3NxNjUPCpkUozs2ETsOEREZoWFBnrBVyHD1diFik7PFjmMwLIgsSNW6ZaGV07QTERH9k63SCsMql3OqugjHErAgshBFZeX4PT4NAPBM5aA5IiKi+6kaXL3rbAZyi9QipzEMFkQWYmdCOvJLy+HtYoOQZg3FjkNEREYsqIkjWqnsUVauwZZTljG4mgWRhdh0XDsV+9OdvSCVcjA1ERFVTyKRYGzl4OqNFjK4mgWRBUjKyseJ63chk0rwZDAHUxMR0cON6tAECispLmbkI+Fmrthx6h0LIguw8Zj26FA/fze4O1iLnIaIiEyBo40cQ9qqAFjGzNUsiMxcaXkFNp9KBcCZqYmIqHaqBlf/Hp+GwtJykdPULxZEZm7v+SxkF5bB3UGJPi0biR2HiIhMSFdfFzRtaIPCsgrsrFz2yVyxIDJzVYc5nwr2gpWM/7uJiKjmJBIJnu6sPUr0k5mfNuM3pBm7kV2EQ5dvA9BeXUZERFRbY4Ibw0oqwamUHCRm5Isdp96wIDJjv5zQDqbu6ecKLxcbkdMQEZEpcrO3xoDWbgD+nsLFHLEgMlPlFRr8fEI7mdZYDqYmIqLHUDW4evOpmyhRV4icpn6wIDJTBy/fQkZeCZxt5BjUxl3sOEREZMJ6t2gET0dr5BSpEXk+U+w49YIFkZmqOqw5qkMTKK1kIqchIiJT9r8T+/5spqfNWBCZoVv5pdh3IQsAB1MTEVHdeKqT9vvkr6TbuJFdJHKauseCyAxtOXUT5RoBQV5O8FfZix2HiIjMgJeLDXr4aRcH/yXO/BZ8ZUFkZgRB+Hsh1048OkRERHXn/yq/V349cQMVGvNa8JUFkZk5mXIXV24VooFchmFBHmLHISIiMxIWoIJjAznSckvwV9JtsePUKRZEZqbq6NCQdh6wt5aLnIaIiMyJtVyGke09AZjf4GoWRGakoLQcOyrXmuFgaiIiqg//V/n9Enk+A9mFZSKnqTssiMzIzoQ0FJVVoJmrLTo3dRY7DhERmaEAT0e0bewAdYWALadSxY5TZ1gQmZGqmamf6uQFiUQichoiIjJXVRft/Hz8BgTBPAZXsyAyE0lZ+Yi7fhcyqQRjghuLHYeIiMzY8PaNobSSIjEzH6dv5oodp06wIDITVUeH+vm7wc3eWuQ0RERkzhwbyBHeVgXAfBZ8FbUgOnjwIIYNGwZPT09IJBJs3bpVb/+kSZMgkUj0fgYPHqzXJjs7G88++ywcHBzg5OSEKVOmoKCgQK9NQkICevXqBWtra3h5eSEiIqK+X5pBqSs02HxSWxBxMDURERlC1eDq7afTUFxm+gu+iloQFRYWIigoCKtWraq2zeDBg5Genq77+emnn/T2P/vsszh37hyioqKwY8cOHDx4ENOmTdPtz8vLQ2hoKHx8fBAXF4cVK1Zg8eLFWLNmTb29LkOLTryF2wVlcLVToK9/I7HjEBGRBejm2xBNnBugoLQcu8+lix3nsVmJ+eTh4eEIDw9/YBulUgmVSnXffRcuXMDu3btx/PhxdOrUCQDw+eefY8iQIfjwww/h6emJ9evXo6ysDN9++y0UCgUCAgIQHx+Pjz/+WK9w+qfS0lKUlpbqbufl5QEA1Go11Gp1bV9qvfr5eAoAYESQB6CpgFpTv5V61es3tn4wN+xnw2FfGwb72TAM2c+j23vis/1X8PPxG3iirXu9P9+jqGk/iFoQ1UR0dDTc3Nzg7OyM/v3747333kPDhtq1VGJiYuDk5KQrhgBg4MCBkEqliI2NxahRoxATE4PevXtDoVDo2oSFheGDDz7A3bt34ex8/8vTly1bhiVLltyzPTIyEjY2NnX8Kh9dvhrYd1EGQIJGBVewa9cVgz13VFSUwZ7LkrGfDYd9bRjsZ8MwRD87lQCAFWKuZuPHLbvgoqz3p6y1oqKaLURr1AXR4MGDMXr0aPj6+uLKlStYsGABwsPDERMTA5lMhoyMDLi5uendx8rKCi4uLsjIyAAAZGRkwNfXV6+Nu7u7bl91BdH8+fMxZ84c3e28vDx4eXkhNDQUDg4OdfkyH8t3R65DIyQisLEDpjzZzSDPqVarERUVhUGDBkEu52zY9YX9bDjsa8NgPxuGofs5KvcEYq5m466TP57r17zen6+2qs7wPIxRF0Rjx47V/btdu3YIDAxE8+bNER0djQEDBtTrcyuVSiiV95a6crncaH6RBUHA5lNpAICnOnsbPJcx9YU5Yz8bDvvaMNjPhmGofv6/zl6IuZqNLfFpeHWgP6RS45oHr6Z9YFKX3Tdr1gyurq5ISkoCAKhUKmRlZem1KS8vR3Z2tm7ckUqlQmZmpl6bqtvVjU0yFefS8nAxIx8KKymGB3qKHYeIiCzQ4AAP2CutcCO7GLHJ2WLHeWQmVRDdvHkTd+7cgYeHdhX3kJAQ5OTkIC4uTtfmzz//hEajQdeuXXVtDh48qDeoKioqCv7+/tWeLjMVv5zQzv0QFqCCow3/2iIiIsNroJDhiSDt9/IvcaY7J5GoBVFBQQHi4+MRHx8PAEhOTkZ8fDxSUlJQUFCAuXPn4ujRo7h27Rr27duHESNGwM/PD2FhYQCA1q1bY/DgwZg6dSqOHTuGw4cP45VXXsHYsWPh6ak9YjJu3DgoFApMmTIF586dw6ZNm7By5Uq98UGmqLS8AttOV54uC24ichoiIrJkTwZr5yT640wGCkrLRU7zaEQtiE6cOIEOHTqgQ4cOAIA5c+agQ4cOWLRoEWQyGRISEjB8+HC0bNkSU6ZMQXBwMA4dOqQ3tmf9+vVo1aoVBgwYgCFDhqBnz556cww5OjoiMjISycnJCA4OxmuvvYZFixY98JJ7U7D3fBZyitTwcLRGDz9XseMQEZEF6+jthGaNbFGsrsCuBNOck0jUQdV9+/Z94KJwe/bseehjuLi4YMOGDQ9sExgYiEOHDtU6nzGrOiw5umNjyIxsABsREVkWiUSCJ4ObIGJ3In6Ju6GbxdqUmNQYItLKzCvBwUu3APx9mJKIiEhMYzo2gVQCHL92F8m3C8WOU2ssiEzQ5pOp0AhA56bO8HW1FTsOERER3B2s0buldvmo3+Juipym9lgQmRhBEHQLuY7pyMHURERkPKq+l7acSoVGU/2QGGPEgsjEnEnNxeWsAiitpBgS6CF2HCIiIp1Bbdxhb22F1JxiHE2+I3acWmFBZGKqDkOGBajgYM25h4iIyHhYy2V4onKi4N/iUkVOUzssiExIWbkGv1fOPTSGcw8REZERejK4MQDgj7PpKDShOYlYEJmQ/YlZuFukhpu9Ej059xARERmhjt7OaNrQBkVlFdhzLkPsODXGgsiEVJ0uG9WBcw8REZFxkkgkGF05uPq3k6ZztRkLIhORXViG/YnahWxH8+oyIiIyYqM6aE+bHblyB2k5xSKnqRkWRCbi9/hUqCsEtG3sAH+VvdhxiIiIquXlYoOuvi4QBO0l+KaABZGJ+O2k9g3FuYeIiMgUVF3881vczQcu02UsWBCZgEuZ+TiTmgsrqQTDgzzFjkNERPRQQ9p5oIFchqu3CxF/I0fsOA/FgsgEVA1K69fKDQ3tlCKnISIiejg7pRUGt1UBMI3B1SyIjFyFRsDWU1WnyxqLnIaIiKjmRld+b20/nY7S8gqR0zwYCyIjF3PlDjLzSuHYQI5+rdzEjkNERFRj3Zu7wt1BidxiNfZfvCV2nAdiQWTkNp/SHmZ8ItADSiuZyGmIiIhqTiaVYGR77VGiLaeM+7QZCyIjVlRWjj1ntbN8Vs3pQEREZEpGVZ4223/xFnKKykROUz0WREYs6nwmCssq4O1ig2AfZ7HjEBER1VorlQNaqexRVqHBzjPpYsepFgsiI7a5cu6hkR0aQyLhUh1ERGSaqgZXbzlpvJM0siAyUln5JTh0WTsAjafLiIjIlI1o3xhSCXDi+l2k3CkSO859sSAyUttPp0MjAO29nODrait2HCIiokfm7mCNHn6uAICt8cZ5lIgFkZGqGo0/mnMPERGRGag627HlVKpRLuXBgsgIXc7Mx9nUPFhJJXgikEt1EBGR6QsLUKGBXIZkI13KgwWREdpcOTN1X383uNgqRE5DRET0+GyVVggLcAegPUpkbFgQGRmNRsC2yjcKB1MTEZE5GdWxCQBg++k0qCs0IqfRx4LIyMQmZyMttwT21lYY0JpLdRARkfno0bwhGtkrcbdIjQOJxrWUBwsiI1O1kOuQth6wlnOpDiIiMh9WMilGBGnHxm4xsqvNWBAZkRJ1BXad1c7iOZKny4iIyAxVfb/tPZ+J/BK1yGn+xoLIiEQnZiG/pBwejtbo6usidhwiIqI6F+DpgOaNbFFarsGec5lix9FhQWREtp5KAwAMb+8JqZRLdRARkfmRSCQY2V57lGibEZ02Y0FkJHKL1PjzYhYA6N4oRERE5mhE5ffc4aTbyMorETmNFgsiI/HH2XSUVWjg726P1h4OYschIiKqN94NbRDs4wyNAPx+Ok3sOABYEBmNqrVdRnTgzNRERGT+RrbXft9ti2dBRJXScooRm5wN4O/DiEREROZsaKAnrKQSnEnNRVJWgdhxWBAZg99Pp0EQgC6+Lmjs1EDsOERERPXOxVaB3i0bATCOwdUsiIxA1WSMHExNRESWpGpOom3xaRAEQdQsLIhElpiRj4sZ+VDIpBjazkPsOERERAYzqLU7bBUypGQX4WRKjqhZWBCJrGowdV//RnC0kYuchoiIyHAaKGQIC1ABEP+0GQsiEf3vyvZcqoOIiCzRiMrvvx0J6VBXaETLwYJIRLcKSmFnbQV7pRX6t+LK9kREZHl6NG8IVzsFnGzkSMspFi2HlWjPTHB3sMaeV3sjM6+UK9sTEZFFspJJsXNWL7jZKyGRiLdsFQsikUkkEqgcrcWOQUREJBp3B/G/B3nKjIiIiCweCyIiIiKyeCyIiIiIyOKxICIiIiKLJ2pBdPDgQQwbNgyenp6QSCTYunVrtW2nT58OiUSCTz/9VG9706ZNIZFI9H6WL1+u1yYhIQG9evWCtbU1vLy8EBERUQ+vhoiIiEyVqAVRYWEhgoKCsGrVqge227JlC44ePQpPT8/77l+6dCnS09N1PzNnztTty8vLQ2hoKHx8fBAXF4cVK1Zg8eLFWLNmTZ2+FiIiIjJdol52Hx4ejvDw8Ae2SU1NxcyZM7Fnzx4MHTr0vm3s7e2hUqnuu2/9+vUoKyvDt99+C4VCgYCAAMTHx+Pjjz/GtGnTqn3e0tJSlJaW6m7n5eUBANRqNdRq9cNemlmrev2W3g/1jf1sOOxrw2A/Gwb7WV9N+8Go5yHSaDQYP3485s6di4CAgGrbLV++HO+++y68vb0xbtw4zJ49G1ZW2pcWExOD3r17Q6FQ6NqHhYXhgw8+wN27d+Hs7Hzfx1y2bBmWLFlyz/bIyEjY2Ng85iszD1FRUWJHsAjsZ8NhXxsG+9kw2M9aRUVFNWpn1AXRBx98ACsrK8yaNavaNrNmzULHjh3h4uKCI0eOYP78+UhPT8fHH38MAMjIyICvr6/efdzd3XX7qiuI5s+fjzlz5uhu5+XlwcvLC6GhoXBwcHjcl2bS1Go1oqKiMGjQIMjlXJC2vrCfDYd9bRjsZ8NgP+urOsPzMEZbEMXFxWHlypU4efLkA6fy/t+iJTAwEAqFAi+++CKWLVsGpVL5yM+vVCrve3+5XM43WCX2hWGwnw2HfW0Y7GfDYD9r1bQPjPay+0OHDiErKwve3t6wsrKClZUVrl+/jtdeew1Nmzat9n5du3ZFeXk5rl27BgBQqVTIzMzUa1N1u7pxR0RERGRZjPYI0fjx4zFw4EC9bWFhYRg/fjyef/75au8XHx8PqVQKNzft6vEhISFYuHAh1Gq1rkqMioqCv79/tafLiIiIyLKIWhAVFBQgKSlJdzs5ORnx8fFwcXGBt7c3GjZsqNdeLpdDpVLB398fgHbAdGxsLPr16wd7e3vExMRg9uzZeO6553TFzrhx47BkyRJMmTIF8+bNw9mzZ7Fy5Up88sknhnuhREREZNRELYhOnDiBfv366W5XjQeaOHEi1q5d+9D7K5VKbNy4EYsXL0ZpaSl8fX0xe/ZsvXFFjo6OiIyMxIwZMxAcHAxXV1csWrTogZfc348gCABqPjjLnKnVahQVFSEvL4/np+sR+9lw2NeGwX42DPazvqrv7arv8epIhIe1IADAzZs34eXlJXYMIiIiegQ3btxAkyZNqt3PgqiGNBoN0tLSYG9v/8Cr3ixB1RQEN27csPgpCOoT+9lw2NeGwX42DPazPkEQkJ+fD09PT0il1V9LZrSDqo2NVCp9YGVpiRwcHPjLZgDsZ8NhXxsG+9kw2M9/c3R0fGgbo73snoiIiMhQWBARERGRxWNBRLWmVCrxzjvvPNZM4PRw7GfDYV8bBvvZMNjPj4aDqomIiMji8QgRERERWTwWRERERGTxWBARERGRxWNBRERERBaPBRHV2LVr1zBlyhT4+vqiQYMGaN68Od555x2UlZXptUtISECvXr1gbW0NLy8vREREiJTYdL3//vvo3r07bGxs4OTkdN82KSkpGDp0KGxsbODm5oa5c+eivLzcsEHNwKpVq9C0aVNYW1uja9euOHbsmNiRTNrBgwcxbNgweHp6QiKRYOvWrXr7BUHAokWL4OHhgQYNGmDgwIG4fPmyOGFN2LJly9C5c2fY29vDzc0NI0eORGJiol6bkpISzJgxAw0bNoSdnR3GjBmDzMxMkRIbPxZEVGMXL16ERqPBV199hXPnzuGTTz7B6tWrsWDBAl2bvLw8hIaGwsfHB3FxcVixYgUWL16MNWvWiJjc9JSVleGpp57CSy+9dN/9FRUVGDp0KMrKynDkyBGsW7cOa9euxaJFiwyc1LRt2rQJc+bMwTvvvIOTJ08iKCgIYWFhyMrKEjuaySosLERQUBBWrVp13/0RERH47LPPsHr1asTGxsLW1hZhYWEoKSkxcFLTduDAAcyYMQNHjx5FVFQU1Go1QkNDUVhYqGsze/ZsbN++Hb/88gsOHDiAtLQ0jB49WsTURk4gegwRERGCr6+v7vaXX34pODs7C6Wlpbpt8+bNE/z9/cWIZ/K+++47wdHR8Z7tu3btEqRSqZCRkaHb9p///EdwcHDQ63t6sC5duggzZszQ3a6oqBA8PT2FZcuWiZjKfAAQtmzZorut0WgElUolrFixQrctJydHUCqVwk8//SRCQvORlZUlABAOHDggCIK2X+VyufDLL7/o2ly4cEEAIMTExIgV06jxCBE9ltzcXLi4uOhux8TEoHfv3lAoFLptYWFhSExMxN27d8WIaJZiYmLQrl07uLu767aFhYUhLy8P586dEzGZ6SgrK0NcXBwGDhyo2yaVSjFw4EDExMSImMx8JScnIyMjQ6/PHR0d0bVrV/b5Y8rNzQUA3edxXFwc1Gq1Xl+3atUK3t7e7OtqsCCiR5aUlITPP/8cL774om5bRkaG3pc0AN3tjIwMg+YzZ+znx3f79m1UVFTctx/Zh/Wjql/Z53VLo9Hg1VdfRY8ePdC2bVsA2r5WKBT3jEFkX1ePBRHhzTffhEQieeDPxYsX9e6TmpqKwYMH46mnnsLUqVNFSm5aHqWfiYgeZsaMGTh79iw2btwodhSTZiV2ABLfa6+9hkmTJj2wTbNmzXT/TktLQ79+/dC9e/d7BkurVKp7rmKouq1SqeomsImqbT8/iEqluudqKPZz7bi6ukImk933/co+rB9V/ZqZmQkPDw/d9szMTLRv316kVKbtlVdewY4dO3Dw4EE0adJEt12lUqGsrAw5OTl6R4n4/q4eCyJCo0aN0KhRoxq1TU1NRb9+/RAcHIzvvvsOUqn+QcaQkBAsXLgQarUacrkcABAVFQV/f384OzvXeXZTUpt+fpiQkBC8//77yMrKgpubGwBtPzs4OKBNmzZ18hzmTqFQIDg4GPv27cPIkSMBaE897Nu3D6+88oq44cyUr68vVCoV9u3bpyuA8vLyEBsbW+0VlXR/giBg5syZ2LJlC6Kjo+Hr66u3Pzg4GHK5HPv27cOYMWMAAImJiUhJSUFISIgYkY2f2KO6yXTcvHlT8PPzEwYMGCDcvHlTSE9P1/1UycnJEdzd3YXx48cLZ8+eFTZu3CjY2NgIX331lYjJTc/169eFU6dOCUuWLBHs7OyEU6dOCadOnRLy8/MFQRCE8vJyoW3btkJoaKgQHx8v7N69W2jUqJEwf/58kZOblo0bNwpKpVJYu3atcP78eWHatGmCk5OT3tV7VDv5+fm69ysA4eOPPxZOnTolXL9+XRAEQVi+fLng5OQkbNu2TUhISBBGjBgh+Pr6CsXFxSInNy0vvfSS4OjoKERHR+t9FhcVFenaTJ8+XfD29hb+/PNP4cSJE0JISIgQEhIiYmrjxoKIauy7774TANz353+dPn1a6Nmzp6BUKoXGjRsLy5cvFymx6Zo4ceJ9+3n//v26NteuXRPCw8OFBg0aCK6ursJrr70mqNVq8UKbqM8//1zw9vYWFAqF0KVLF+Ho0aNiRzJp+/fvv+97d+LEiYIgaC+9f/vttwV3d3dBqVQKAwYMEBITE8UNbYKq+yz+7rvvdG2Ki4uFl19+WXB2dhZsbGyEUaNG6f0BS/okgiAIBjwgRURERGR0eJUZERERWTwWRERERGTxWBARERGRxWNBRERERBaPBRERERFZPBZEREREZPFYEBEREZHFY0FEREREFo8FERHR/5g0aZJubbNH1bRpU0gkEkgkEuTk5FTbbu3atXoLb9aVqueuj8cmMlcsiIioRgRBwMCBAxEWFnbPvi+//BJOTk64efOmCMmM09KlS5Geng5HR0eDP3d6ejo+/fRTgz8vkSljQURENSKRSPDdd98hNjYWX331lW57cnIy3njjDXz++edo0qSJiAmNi729PVQqFSQSicGfW6VSiVKIEZkyFkREVGNeXl5YuXIlXn/9dSQnJ0MQBEyZMgWDBg1CdHQ0fH190aBBA/j7+2PlypW6+509exZSqRS3bt0CAGRnZ0MqlWLs2LG6Nu+99x569uxZ7XP/8MMP6NSpk67QGDduHLKysnT7o6OjIZFIsG/fPnTq1Ak2Njbo3r07EhMT9R7nvffeg5ubG+zt7fHCCy/gzTffRPv27at9Xo1Gg2XLluleW1BQEH799dfadh0A7Skyb29v2NjYYNSoUbhz5849bbZt24aOHTvC2toazZo1w5IlS1BeXq7bf/HiRfTs2RPW1tZo06YN9u7dC4lEgq1btz5SJiLSYkFERLUyceJEDBgwAJMnT8YXX3yBs2fPYs2aNWjSpAl++eUXnD9/HosWLcKCBQvw888/AwACAgLQsGFDHDhwAABw6NAhvdsAcODAAfTt27fa51Wr1Xj33Xdx+vRpbN26FdeuXcOkSZPuabdw4UJ89NFHOHHiBKysrDB58mTdvvXr1+P999/HBx98gLi4OHh7e+M///nPA1/vsmXL8P3332P16tU4d+4cZs+ejeeee04ve03ExsZiypQpeOWVVxAfH49+/frhvffe02tz6NAhTJgwAf/6179w/vx5fPXVV1i7di3ef/99AEBFRQVGjhwJGxsbxMbGYs2aNVi4cGGtchBRNQQiolrKzMwUXF1dBalUKmzZsuW+bWbMmCGMGTNGd3v06NHCjBkzBEEQhFdffVWYO3eu4OzsLFy4cEEoKysTbGxshMjIyBpnOH78uABAyM/PFwRBEPbv3y8AEPbu3atrs3PnTgGAUFxcLAiCIHTt2lWXoUqPHj2EoKAg3e2JEycKI0aMEARBEEpKSgQbGxvhyJEjeveZMmWK8Mwzz1SbzcfHR/jkk0/0tj3zzDPCkCFD9LY9/fTTgqOjo+72gAEDhH//+996bX744QfBw8NDEARB+OOPPwQrKyshPT1dtz8qKkoAcM//h++++07vsYnowXiEiIhqzc3NDS+++CJat26tuyJr1apVCA4ORqNGjWBnZ4c1a9YgJSVFd58+ffogOjoagPZoUP/+/dG7d29ER0fj+PHjUKvV6NGjR7XPGRcXh2HDhsHb2xv29vbo06cPAOg9BwAEBgbq/u3h4QEAulNriYmJ6NKli177f97+X0lJSSgqKsKgQYNgZ2en+/n+++9x5cqVh/SSvgsXLqBr165620JCQvRunz59GkuXLtV7rqlTpyI9PR1FRUVITEyEl5cXVCpVjfITUc1ZiR2AiEyTlZUVrKy0HyEbN27E66+/jo8++gghISGwt7fHihUrEBsbq2vft29fvPrqq7h8+TLOnz+Pnj174uLFi4iOjsbdu3d1437up7CwEGFhYQgLC8P69evRqFEjpKSkICwsDGVlZXpt5XK57t9VA5o1Gs0jvcaCggIAwM6dO9G4cWO9fUql8pEe82HPt2TJEowePfqefdbW1nX+fET0NxZERPTYDh8+jO7du+Pll1/WbfvnEZR27drB2dkZ7733Htq3bw87Ozv07dsXH3zwAe7evfvA8UMXL17EnTt3sHz5cnh5eQEATpw4Ueuc/v7+OH78OCZMmKDbdvz48Wrbt2nTBkqlEikpKbojUo+qdevWegUiABw9elTvdseOHZGYmAg/P7/7Poa/vz9u3LiBzMxMuLu7PzQ/EdUcCyIiemwtWrTA999/jz179sDX1xc//PADjh8/Dl9fX10biUSC3r17Y/369Xj99dcBaE9vlZaWYt++fZgzZ061j+/t7Q2FQoHPP/8c06dPx9mzZ/Huu+/WOufMmTMxdepUdOrUCd27d8emTZuQkJCAZs2a3be9vb09Xn/9dcyePRsajQY9e/ZEbm4uDh8+DAcHB0ycOLHGzz1r1iz06NEDH374IUaMGIE9e/Zg9+7dem0WLVqEJ554At7e3njyySchlUpx+vRpnD17Fu+99x4GDRqE5s2bY+LEiYiIiEB+fj7eeustABDl8n4ic8IxRET02F588UWMHj0aTz/9NLp27Yo7d+7oHS2q0qdPH1RUVOiOBkmlUvTu3RsSieSB44caNWqEtWvX4pdffkGbNm2wfPlyfPjhh7XO+eyzz2L+/Pl4/fXX0bFjRyQnJ2PSpEkPPB317rvv4u2338ayZcvQunVrDB48GDt37tQr9mqiW7du+O9//4uVK1ciKCgIkZGRumKmSlhYGHbs2IHIyEh07twZ3bp1wyeffAIfHx8AgEwmw9atW1FQUIDOnTvjhRde0F1lxlNqRI9HIgiCIHYIIiKxDBo0CCqVCj/88EOdPWbTpk3x6quv4tVXX62zx6zO4cOH0bNnTyQlJaF58+a67WvXrsWrr776wKVDiOhvLIiIyGIUFRVh9erVCAsLg0wmw08//YSlS5ciKioKAwcOrLPnadq0KdLT0yGXy5Gamlqns0Zv2bIFdnZ2aNGiBZKSkvCvf/0Lzs7O+Ouvv3Rt7OzsUF5eDmtraxZERDXEMUREZDEkEgl27dqF999/HyUlJfD398dvv/1Wp8UQoJ1WQK1WA9COQ6pL+fn5mDdvHlJSUuDq6oqBAwfio48+0msTHx8PQHuKjYhqhkeIiIiIyOJxUDURERFZPBZEREREZPFYEBEREZHFY0FEREREFo8FEREREVk8FkRERERk8VgQERERkcVjQUREREQW7/8BAvFk3+CDT50AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from floris import TimeSeries\n", "import numpy as np\n", @@ -262,47 +212,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "722be425-9231-451a-bd84-7824db6a5098", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/msinner/floris3/floris/core/turbine/operation_models.py:367: RuntimeWarning: divide by zero encountered in divide\n", - " power_fractions = power_setpoints / base_powers\n", - "/Users/msinner/floris3/floris/core/wake_deflection/gauss.py:323: RuntimeWarning: invalid value encountered in divide\n", - " val = 2 * (avg_v - v_core) / (v_top + v_bottom)\n", - "/Users/msinner/floris3/floris/core/wake_deflection/gauss.py:158: RuntimeWarning: invalid value encountered in divide\n", - " C0 = 1 - u0 / freestream_velocity\n", - "/Users/msinner/floris3/floris/core/wake_velocity/gauss.py:80: RuntimeWarning: invalid value encountered in divide\n", - " sigma_z0 = rotor_diameter_i * 0.5 * np.sqrt(uR / (u_initial + u0))\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Power [kW]')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Set up the FlorisModel\n", "fmodel.set_operation_model(\"simple-derating\")\n", @@ -357,18 +272,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "5e3cda81", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Powers [kW]: [[3063.49046772 2000. ]]\n" - ] - } - ], + "outputs": [], "source": [ "fmodel.set_operation_model(\"mixed\")\n", "fmodel.set(layout_x=[0.0, 0.0], layout_y=[0.0, 500.0])\n", @@ -441,52 +348,29 @@ "The figure below shows the fit between the turbine power and thrust in OpenFAST helix AWC simulations (x) \n", "and FLORIS simulations (--) at different region II wind speeds for the NREL 5MW reference turbine.\n", "\n", - "" + "\n", + "![](./powerthrust_helix.png)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "40e9bcda", "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'awc'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mfmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_operation_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mawc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m fmodel\u001b[38;5;241m.\u001b[39mset(layout_x\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0.0\u001b[39m, \u001b[38;5;241m0.0\u001b[39m], layout_y\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0.0\u001b[39m, \u001b[38;5;241m500.0\u001b[39m])\n\u001b[0;32m 3\u001b[0m fmodel\u001b[38;5;241m.\u001b[39mreset_operation()\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\floris_model.py:1306\u001b[0m, in \u001b[0;36mFlorisModel.set_operation_model\u001b[1;34m(self, operation_model)\u001b[0m\n\u001b[0;32m 1304\u001b[0m turbine_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcore\u001b[38;5;241m.\u001b[39mfarm\u001b[38;5;241m.\u001b[39mturbine_definitions[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 1305\u001b[0m turbine_type[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moperation_model\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m operation_model\n\u001b[1;32m-> 1306\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mturbine_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mturbine_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\floris_model.py:347\u001b[0m, in \u001b[0;36mFlorisModel.set\u001b[1;34m(self, wind_speeds, wind_directions, wind_shear, wind_veer, reference_wind_height, turbulence_intensities, air_density, layout_x, layout_y, turbine_type, turbine_library_path, solver_settings, heterogenous_inflow_config, wind_data, yaw_angles, power_setpoints, disable_turbines)\u001b[0m\n\u001b[0;32m 345\u001b[0m _yaw_angles \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcore\u001b[38;5;241m.\u001b[39mfarm\u001b[38;5;241m.\u001b[39myaw_angles\n\u001b[0;32m 346\u001b[0m _power_setpoints \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcore\u001b[38;5;241m.\u001b[39mfarm\u001b[38;5;241m.\u001b[39mpower_setpoints\n\u001b[1;32m--> 347\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reinitialize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 348\u001b[0m \u001b[43m \u001b[49m\u001b[43mwind_speeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwind_speeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mwind_directions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwind_directions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 350\u001b[0m \u001b[43m \u001b[49m\u001b[43mwind_shear\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwind_shear\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mwind_veer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwind_veer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 352\u001b[0m \u001b[43m \u001b[49m\u001b[43mreference_wind_height\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreference_wind_height\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 353\u001b[0m \u001b[43m \u001b[49m\u001b[43mturbulence_intensities\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mturbulence_intensities\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 354\u001b[0m \u001b[43m \u001b[49m\u001b[43mair_density\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mair_density\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 355\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayout_x\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayout_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 356\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayout_y\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayout_y\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 357\u001b[0m \u001b[43m \u001b[49m\u001b[43mturbine_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mturbine_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 358\u001b[0m \u001b[43m \u001b[49m\u001b[43mturbine_library_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mturbine_library_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 359\u001b[0m \u001b[43m \u001b[49m\u001b[43msolver_settings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msolver_settings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 360\u001b[0m \u001b[43m \u001b[49m\u001b[43mheterogenous_inflow_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheterogenous_inflow_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 361\u001b[0m \u001b[43m \u001b[49m\u001b[43mwind_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwind_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 362\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 364\u001b[0m \u001b[38;5;66;03m# If the yaw angles or power setpoints are not the default, set them back to the\u001b[39;00m\n\u001b[0;32m 365\u001b[0m \u001b[38;5;66;03m# previous setting\u001b[39;00m\n\u001b[0;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (_yaw_angles \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\floris_model.py:230\u001b[0m, in \u001b[0;36mFlorisModel._reinitialize\u001b[1;34m(self, wind_speeds, wind_directions, wind_shear, wind_veer, reference_wind_height, turbulence_intensities, air_density, layout_x, layout_y, turbine_type, turbine_library_path, solver_settings, heterogenous_inflow_config, wind_data)\u001b[0m\n\u001b[0;32m 227\u001b[0m floris_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfarm\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m farm_dict\n\u001b[0;32m 229\u001b[0m \u001b[38;5;66;03m# Create a new instance of floris and attach to self\u001b[39;00m\n\u001b[1;32m--> 230\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcore \u001b[38;5;241m=\u001b[39m \u001b[43mCore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfloris_dict\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\type_dec.py:226\u001b[0m, in \u001b[0;36mFromDictMixin.from_dict\u001b[1;34m(cls, data)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m undefined:\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m 223\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe class definition for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 224\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis missing the following inputs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mundefined\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 225\u001b[0m )\n\u001b[1;32m--> 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m:13\u001b[0m, in \u001b[0;36m__init__\u001b[1;34m(self, logging, solver, wake, farm, flow_field, name, description, floris_version)\u001b[0m\n\u001b[0;32m 11\u001b[0m _setattr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdescription\u001b[39m\u001b[38;5;124m'\u001b[39m, __attr_converter_description(description))\n\u001b[0;32m 12\u001b[0m _setattr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloris_version\u001b[39m\u001b[38;5;124m'\u001b[39m, __attr_converter_floris_version(floris_version))\n\u001b[1;32m---> 13\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__attrs_post_init__\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\core\\core.py:75\u001b[0m, in \u001b[0;36mCore.__attrs_post_init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 69\u001b[0m logging_manager\u001b[38;5;241m.\u001b[39mconfigure_file_log(\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogging[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menable\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 71\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogging[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlevel\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 72\u001b[0m )\n\u001b[0;32m 74\u001b[0m \u001b[38;5;66;03m# Initialize farm quantities that depend on other objects\u001b[39;00m\n\u001b[1;32m---> 75\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfarm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconstruct_turbine_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 76\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfarm\u001b[38;5;241m.\u001b[39mconstruct_turbine_thrust_coefficient_functions()\n\u001b[0;32m 77\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfarm\u001b[38;5;241m.\u001b[39mconstruct_turbine_axial_induction_functions()\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\core\\farm.py:262\u001b[0m, in \u001b[0;36mFarm.construct_turbine_map\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 261\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconstruct_turbine_map\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 262\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mturbine_map \u001b[38;5;241m=\u001b[39m [\u001b[43mTurbine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mturb\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m turb \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mturbine_definitions]\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\type_dec.py:226\u001b[0m, in \u001b[0;36mFromDictMixin.from_dict\u001b[1;34m(cls, data)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m undefined:\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m 223\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe class definition for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 224\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis missing the following inputs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mundefined\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 225\u001b[0m )\n\u001b[1;32m--> 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m:24\u001b[0m, in \u001b[0;36m__init__\u001b[1;34m(self, turbine_type, rotor_diameter, hub_height, TSR, power_thrust_table, operation_model, correct_cp_ct_for_tilt, floating_tilt_table, multi_dimensional_cp_ct, power_thrust_data_file, turbine_library_path)\u001b[0m\n\u001b[0;32m 22\u001b[0m __attr_validator_floating_tilt_table(\u001b[38;5;28mself\u001b[39m, __attr_floating_tilt_table, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfloating_tilt_table)\n\u001b[0;32m 23\u001b[0m __attr_validator_turbine_library_path(\u001b[38;5;28mself\u001b[39m, __attr_turbine_library_path, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mturbine_library_path)\n\u001b[1;32m---> 24\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__attrs_post_init__\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\core\\turbine\\turbine.py:461\u001b[0m, in \u001b[0;36mTurbine.__attrs_post_init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__attrs_post_init__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 461\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initialize_power_thrust_functions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 462\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__post_init__()\n", - "File \u001b[1;32m~\\projects\\floris\\floris\\core\\turbine\\turbine.py:472\u001b[0m, in \u001b[0;36mTurbine._initialize_power_thrust_functions\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_initialize_power_thrust_functions\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 472\u001b[0m turbine_function_model \u001b[38;5;241m=\u001b[39m \u001b[43mTURBINE_MODEL_MAP\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moperation_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moperation_model\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 473\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mthrust_coefficient_function \u001b[38;5;241m=\u001b[39m turbine_function_model\u001b[38;5;241m.\u001b[39mthrust_coefficient\n\u001b[0;32m 474\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxial_induction_function \u001b[38;5;241m=\u001b[39m turbine_function_model\u001b[38;5;241m.\u001b[39maxial_induction\n", - "\u001b[1;31mKeyError\u001b[0m: 'awc'" - ] - } - ], + "outputs": [], "source": [ + "fmodel = FlorisModel(\"../examples/inputs/emgauss_helix.yaml\")\n", "fmodel.set_operation_model(\"awc\")\n", "fmodel.set(layout_x=[0.0, 0.0], layout_y=[0.0, 500.0])\n", "fmodel.reset_operation()\n", "fmodel.set(\n", - " wind_data=TimeSeries(\n", - " wind_speeds=np.array([10.0]),\n", - " wind_directions=np.array([270.0]),\n", - " turbulence_intensities=0.06\n", - " )\n", + " wind_speeds=np.array([8.0]),\n", + " wind_directions=np.array([270.0]),\n", + " turbulence_intensities=np.array([0.06])\n", ")\n", "fmodel.set(\n", - " awc_modes=np.array([\"helix\", \"baseline\"]),\n", - " awc_amplitudes=np.array([2.5, 0])\n", + " awc_modes=np.array([[\"helix\", \"baseline\"]]),\n", + " awc_amplitudes=np.array([[2.5, 0]])\n", ")\n", "fmodel.run()\n", "print(\"Powers [kW]: \", fmodel.get_turbine_powers()/1000)" @@ -515,7 +399,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/v3_to_v4.md b/docs/v3_to_v4.md index acb2ced0d..ae88733a1 100644 --- a/docs/v3_to_v4.md +++ b/docs/v3_to_v4.md @@ -108,12 +108,16 @@ in v3. However, there are a few changes to the fields on each that mean that exi will not run in v4 as is. #### Main FLORIS input yaml -The only change in fields on the main FLORIS input file is that the `turbulence_intensity` field, +On the main FLORIS input file, the `turbulence_intensity` field (on`flow_field`), which was specified as a scalar in FLORIS v3, has been changed to `turbulence_intensities`, and should now contain a list of turbulence intensities that is of the same length as `wind_directions` and `wind_speeds`. Additionally, the length of the lists for `wind_directions` and `wind_speeds` _must_ now be of equal length. +In addition, a new field `enable_active_wake_mixing` has been added to the `wake` field, +which users may set to `false` unless they would like to use active wake mixing strategies such +as [Helix](empirical_gauss_model.md#Added-mixing-by-active-wake-control). + #### Turbine input yaml To reflect the transition to more flexible [operation models](#operation-model), there are a number of changes to the fields on the turbine yaml. The changes are mostly regrouping and diff --git a/examples/examples_control_types/002_disable_turbines.py b/examples/examples_control_types/002_disable_turbines.py index e8cd4b94c..7489c7fb3 100644 --- a/examples/examples_control_types/002_disable_turbines.py +++ b/examples/examples_control_types/002_disable_turbines.py @@ -1,4 +1,4 @@ -"""Example 001: Disable turbines +"""Example: Disabling turbines This example is adapted from https://github.com/NREL/floris/pull/693 contributed by Elie Kadoche. diff --git a/floris/convert_floris_input_v3_to_v4.py b/floris/convert_floris_input_v3_to_v4.py index 36415e1d2..abdc7a76a 100644 --- a/floris/convert_floris_input_v3_to_v4.py +++ b/floris/convert_floris_input_v3_to_v4.py @@ -61,6 +61,9 @@ ) v4_floris_input_dict["wake"]["model_strings"]["velocity_model"] = "gauss" + # Add enable_active_wake_mixing field + v4_floris_input_dict["wake"]["enable_active_wake_mixing"] = False + yaml.dump( v4_floris_input_dict, open(output_path, "w"),