diff --git a/README.md b/README.md index 013209c7f..d2b851194 100644 --- a/README.md +++ b/README.md @@ -86,32 +86,36 @@ FLORIS is a Python package run on the command line typically by providing an input file with an initial configuration. It can be installed with ```pip install floris``` (see [installation](https://github.nrel.io/floris/installation)). The typical entry point is -[FlorisInterface](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.floris_interface.FlorisInterface) +[FlorisModel](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel) which accepts the path to the input file as an argument. From there, changes can be made to the initial configuration through the -[FlorisInterface.reinitialize](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.floris_interface.FlorisInterface.reinitialize) +[FlorisModel.set](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel.set) routine, and the simulation is executed with -[FlorisInterface.calculate_wake](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.floris_interface.FlorisInterface.calculate_wake). +[FlorisModel.run](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel.run). ```python -from floris.tools import FlorisInterface -fi = FlorisInterface("path/to/input.yaml") -fi.reinitialize(wind_directions=[i for i in range(10)]) -fi.calculate_wake() +from floris import FlorisModel +fmodel = FlorisModel("path/to/input.yaml") +fmodel.set( + wind_directions=[i for i in range(10)], + wind_speeds=[i for i in range(10)], + turbulence_intensities=[0.1 for i in range(10)], +) +fmodel.run() ``` Finally, results can be analyzed via post-processing functions available within -[FlorisInterface](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.floris_interface.FlorisInterface) +[FlorisModel](https://nrel.github.io/floris/_autosummary/floris.floris_model.FlorisModel.html#floris.floris_model.FlorisModel) such as -- [FlorisInterface.get_turbine_layout](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.floris_interface.FlorisInterface.get_turbine_layout) -- [FlorisInterface.get_turbine_powers](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.floris_interface.FlorisInterface.get_turbine_powers) -- [FlorisInterface.get_farm_AEP](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.floris_interface.FlorisInterface.get_farm_AEP) +- [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) -and in a visualization package at [floris.tools.visualization](https://nrel.github.io/floris/_autosummary/floris.tools.floris_interface.FlorisInterface.html#floris.tools.visualization). +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 detail in [Examples Index](https://github.nrel.io/floris/examples). +and described in [Examples Index](https://github.nrel.io/floris/examples). ## Engaging on GitHub diff --git a/docs/api_docs.rst b/docs/api_docs.rst index add2940c1..d35ca905a 100644 --- a/docs/api_docs.rst +++ b/docs/api_docs.rst @@ -13,9 +13,18 @@ more users will interface with the software. :template: custom-module-template.rst :recursive: - floris.logging_manager - floris.simulation - floris.tools - floris.type_dec + floris.flow_visualization + floris.floris_model + floris.wind_data + floris.uncertainty_interface floris.turbine_library + floris.parallel_computing_interface + floris.optimization + floris.layout_visualization + floris.cut_plane + floris.core + floris.convert_turbine_v3_to_v4 + floris.convert_floris_input_v3_to_v4 floris.utilities + floris.type_dec + floris.logging_manager diff --git a/docs/examples.md b/docs/examples.md index 73fcbda00..74108924e 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -186,7 +186,7 @@ and thrust coefficients or as absolute values. ## Optimization These examples demonstrate use of the optimization routines -included in FLORIS through {py:mod}`floris.tools.optimization`. These +included in FLORIS through {py:mod}`floris.optimization`. These focus on yaw settings and wind farm layout, but the concepts are general and can be used for other optimizations. diff --git a/docs/floris_101.ipynb b/docs/floris_101.ipynb index 5b73de57f..5e6553ec8 100644 --- a/docs/floris_101.ipynb +++ b/docs/floris_101.ipynb @@ -10,9 +10,13 @@ "\n", "FLORIS is a Python-based software library for calculating wind farm performance considering\n", "the effect of turbine-turbine interactions through their wakes.\n", - "There are two primary packages that make up the software:\n", - "- `floris.simulation`: simulation framework including wake model definitions\n", - "- `floris.tools`: utilities for pre and post processing as well as driving the simulation\n", + "There are two primary packages to understand when using FLORIS:\n", + "- `floris.core`: This package contains the core functionality for calculating the wind farm wake\n", + " and turbine-turbine interactions. This package is the computational engine of FLORIS.\n", + " All of the mathematical models and algorithms are implemented here.\n", + "- `floris`: This is the top-level package that provides most of the functionality that the\n", + " majority of users will need. The main entry point is `FlorisModel` which is a high-level\n", + " interface to the computational engine.\n", "\n", "\n", "\n", @@ -22,9 +26,9 @@ "2. Run the wind farm wake calculation\n", "3. Extract data and postprocess results\n", "\n", - "Generally, users will only interact with `floris.tools` and most often through\n", - "the `FlorisInterface` class. Additionally, `floris.tools` contains functionality\n", - "for comparing results, creating visualizations, and developing optimization cases. \n", + "Generally, users will only interact with `floris` and most often through the `FlorisModel` class.\n", + "Additionally, `floris` contains functionality for comparing results, creating visualizations,\n", + "and developing optimization cases. \n", "\n", "This notebook steps through the basic ideas and operations of FLORIS while showing\n", "realistic uses and expected behavior." @@ -35,9 +39,9 @@ "id": "699c51dd", "metadata": {}, "source": [ - "## Initialize FlorisInterface\n", + "## Initialize Floris\n", "\n", - "The `FlorisInterface` provides functionality to build a wind farm representation and drive\n", + "The `FlorisModel` class provides functionality to build a wind farm representation and drive\n", "the simulation. This object is created (instantiated) by passing the path to a FLORIS input\n", "file as the only argument. After this object is created, it can immediately be used to\n", "inspect the data." @@ -64,10 +68,10 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "from floris.tools import FlorisInterface\n", + "from floris import FlorisModel\n", "\n", - "fi = FlorisInterface(\"gch.yaml\")\n", - "x, y = fi.get_turbine_layout()\n", + "fmodel = FlorisModel(\"gch.yaml\")\n", + "x, y = fmodel.get_turbine_layout()\n", "\n", "print(\" x y\")\n", "for _x, _y in zip(x, y):\n", @@ -85,7 +89,7 @@ "However, it is often simplest to define a basic configuration in the input file as\n", "a starting point and then make modifications in the Python script. This allows for\n", "generating data algorithmically or loading data from a data file. Modifications to\n", - "the wind farm representation are handled through the `FlorisInterface.reinitialize()`\n", + "the wind farm representation are handled through the `FlorisModel.set()`\n", "function with keyword arguments. Another way to think of this function is that it\n", "changes the value of inputs specified in the input file.\n", "\n", @@ -114,9 +118,9 @@ "source": [ "x_2x2 = [0, 0, 800, 800]\n", "y_2x2 = [0, 400, 0, 400]\n", - "fi.reinitialize(layout_x=x_2x2, layout_y=y_2x2)\n", + "fmodel.set(layout_x=x_2x2, layout_y=y_2x2)\n", "\n", - "x, y = fi.get_turbine_layout()\n", + "x, y = fmodel.get_turbine_layout()\n", "\n", "print(\" x y\")\n", "for _x, _y in zip(x, y):\n", @@ -128,14 +132,13 @@ "id": "63f45e11", "metadata": {}, "source": [ - "Additionally, we can change the wind speeds and wind directions.\n", - "The set of wind conditions is given as arrays of wind speeds and\n", - "wind directions that combined describe the atmospheric conditions\n", - "to compute. This requires that the wind speed and wind direction\n", - "arrays be the same length.\n", + "Additionally, we can change the wind speeds, wind directions, and turbulence intensity.\n", + "The set of wind conditions is given as arrays of wind speeds, wind directions, and turbulence\n", + "intensity combinations that describe the atmospheric conditions to compute.\n", + "This requires that all arrays be the same length.\n", "\n", - "Notice that we can give `FlorisInterface.reinitialize()` multiple keyword arguments at once.\n", - "Note that there is no expected output from the `FlorisInterface.reinitialize()` function." + "Notice that we can give `FlorisModel.set()` multiple keyword arguments at once.\n", + "There is no expected output from the `FlorisModel.set()` function." ] }, { @@ -145,17 +148,19 @@ "metadata": {}, "outputs": [], "source": [ - "# One wind direction and one speed\n", - "# -> one atmospheric condition (270 degrees at 8 m/s)\n", - "fi.reinitialize(wind_directions=[270.0], wind_speeds=[8.0])\n", + "fmodel.set(wind_directions=[270.0], wind_speeds=[8.0], turbulence_intensities=[0.1])\n", "\n", - "# Two wind directions and one speed (repeated)\n", - "# -> two atmospheric conditions (270 degrees at 8 m/s and 280 degrees at 8 m/s)\n", - "fi.reinitialize(wind_directions=[270.0, 280.0], wind_speeds=[8.0, 8.0])\n", + "fmodel.set(\n", + " wind_directions=[270.0, 280.0],\n", + " wind_speeds=[8.0, 8.0],\n", + " turbulence_intensities=[0.1, 0.1],\n", + ")\n", "\n", - "# Two wind directions and two speeds combined\n", - "# -> four atmospheric conditions (270 degrees at 8 m/s and 9 m/s, 280 degrees at 8 m/s and 9 m/s)\n", - "fi.reinitialize(wind_directions=[270.0, 280.0, 270.0, 280.0], wind_speeds=[8.0, 8.0, 9.0, 9.0])" + "fmodel.set(\n", + " wind_directions=[270.0, 280.0, 270.0, 280.0],\n", + " wind_speeds=[8.0, 8.0, 9.0, 9.0],\n", + " turbulence_intensities=[0.1, 0.1, 0.1, 0.1],\n", + ")" ] }, { @@ -163,7 +168,7 @@ "id": "da4f3309", "metadata": {}, "source": [ - "`FlorisInterface.reinitialize()` creates all of the basic data structures required\n", + "`FlorisModel.set()` creates all of the basic data structures required\n", "for the simulation but it does not do any aerodynamic calculations. The low level\n", "data structures have a complex shape that enables faster computations. Specifically,\n", "most data is structured as a 4-dimensional Numpy array with the following dimensions:\n", @@ -210,28 +215,26 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Dimensions of grid x-components\")\n", - "print(np.shape(fi.floris.grid.x_sorted))\n", + "print(np.shape(fmodel.core.grid.x_sorted))\n", "\n", "print()\n", "print(\"3rd turbine x-components for first wind condition (at findex=0)\")\n", - "print(fi.floris.grid.x_sorted[0, 2, :, :])\n", + "print(fmodel.core.grid.x_sorted[0, 2, :, :])\n", "\n", - "x = fi.floris.grid.x_sorted[0, :, :, :]\n", - "y = fi.floris.grid.y_sorted[0, :, :, :]\n", - "z = fi.floris.grid.z_sorted[0, :, :, :]\n", + "x = fmodel.core.grid.x_sorted[0, :, :, :]\n", + "y = fmodel.core.grid.y_sorted[0, :, :, :]\n", + "z = fmodel.core.grid.z_sorted[0, :, :, :]\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111, projection=\"3d\")\n", @@ -245,12 +248,12 @@ "id": "ebfdc746", "metadata": {}, "source": [ - "## Execute wake calculation\n", + "## Run the Floris wake calculation\n", "\n", "Running the wake calculation is a one-liner. This will calculate the velocities\n", "at each turbine given the wake of other turbines for every wind speed and wind\n", - "direction combination. Since we have not explicitly specified yaw control settings,\n", - "all turbines are aligned with the inflow." + "direction combination. Since we have not explicitly specified yaw control settings\n", + "when creating the `FlorisModel` settings, all turbines are aligned with the inflow." ] }, { @@ -260,7 +263,7 @@ "metadata": {}, "outputs": [], "source": [ - "fi.calculate_wake()" + "fmodel.run()" ] }, { @@ -270,10 +273,9 @@ "source": [ "## Get turbine power\n", "\n", - "At this point, the simulation has completed and we can use the `FlorisInterface` to\n", + "At this point, the simulation has completed and we can use `FlorisModel` to\n", "extract useful information such as the power produced at each turbine. Remember that\n", - "we have configured the simulation with two wind directions, two wind speeds, and four\n", - "turbines." + "we have configured the simulation with four wind conditions and four turbines." ] }, { @@ -291,32 +293,32 @@ "\n", "Turbine powers for 8 m/s\n", "Wind condition 0\n", - " Turbine 0 - 1,691.33 kW\n", - " Turbine 1 - 1,691.33 kW\n", - " Turbine 2 - 592.65 kW\n", - " Turbine 3 - 592.98 kW\n", + " Turbine 0 - 1,753.95 kW\n", + " Turbine 1 - 1,753.95 kW\n", + " Turbine 2 - 904.68 kW\n", + " Turbine 3 - 904.85 kW\n", "\n", "Wind condition 1\n", - " Turbine 0 - 1,691.33 kW\n", - " Turbine 1 - 1,691.33 kW\n", - " Turbine 2 - 1,631.07 kW\n", - " Turbine 3 - 1,629.76 kW\n", + " Turbine 0 - 1,753.95 kW\n", + " Turbine 1 - 1,753.95 kW\n", + " Turbine 2 - 1,644.86 kW\n", + " Turbine 3 - 1,643.39 kW\n", "\n", "Turbine powers for all turbines at all wind conditions\n", - "[[1691.32664838 1691.32664838 592.6531181 592.97842923]\n", - " [1691.32664838 1691.32664838 1631.06554071 1629.75543674]\n", - " [2407.84167188 2407.84167188 861.30649817 861.73255027]\n", - " [2407.84167188 2407.84167188 2321.40975418 2319.53218301]]\n" + "[[1753.95445918 1753.95445918 904.68478734 904.84672946]\n", + " [1753.95445918 1753.95445918 1644.85720431 1643.39012544]\n", + " [2496.42786184 2496.42786184 1276.4580679 1276.67310219]\n", + " [2496.42786184 2496.42786184 2354.40522998 2352.47398836]]\n" ] } ], "source": [ - "powers = fi.get_turbine_powers() / 1000.0 # calculated in Watts, so convert to kW\n", + "powers = fmodel.get_turbine_powers() / 1000.0 # calculated in Watts, so convert to kW\n", "\n", "print(\"Dimensions of `powers`\")\n", "print( np.shape(powers) )\n", "\n", - "N_TURBINES = fi.floris.farm.n_turbines\n", + "N_TURBINES = fmodel.core.farm.n_turbines\n", "\n", "print()\n", "print(\"Turbine powers for 8 m/s\")\n", @@ -337,16 +339,12 @@ "source": [ "## Applying yaw angles\n", "\n", - "Yaw angles are applied to turbines through the `FlorisInterface.calculate_wake` function.\n", + "Yaw angles are another configuration option through `FlorisModel.set`.\n", "In order to fit into the vectorized framework, the yaw settings must be represented as\n", "a `Numpy.array` with dimensions equal to:\n", "- 0: findex\n", "- 1: number of turbines\n", "\n", - "**Unlike the data configured in `FlorisInterface.reinitialize()`, yaw angles are not retained**\n", - "**in memory and must be provided each time `FlorisInterface.calculate_wake` is used.**\n", - "**If no yaw angles are given, all turbines will be aligned with the inflow.**\n", - "\n", "It is typically easiest to start with an array of 0's and modify individual\n", "turbine yaw settings, as shown below." ] @@ -375,7 +373,7 @@ } ], "source": [ - "# Recall that the previous `fi.reinitialize()` command set up four atmospheric conditions\n", + "# Recall that the previous `fmodel.set()` command set up four atmospheric conditions\n", "# and there are 4 turbines in the farm. So, the yaw angles array must be 4x4.\n", "yaw_angles = np.zeros((4, 4))\n", "print(\"Yaw angle array initialized with 0's\")\n", @@ -385,7 +383,7 @@ "yaw_angles[:, 0] = 25\n", "print(yaw_angles)\n", "\n", - "fi.calculate_wake(yaw_angles=yaw_angles)" + "fmodel.set(yaw_angles=yaw_angles)" ] }, { @@ -417,16 +415,14 @@ "output_type": "stream", "text": [ "Power % difference with yaw\n", - " 270 degrees: 6.43%\n", - " 280 degrees: 0.05%\n" + " 270 degrees: 0.16%\n", + " 280 degrees: 0.17%\n" ] } ], "source": [ "# 1. Load an input file\n", - "fi = FlorisInterface(\"gch.yaml\")\n", - "\n", - "fi.floris.solver\n", + "fmodel = FlorisModel(\"gch.yaml\")\n", "\n", "# 2. Modify the inputs with a more complex wind turbine layout\n", "D = 126.0 # Design the layout based on turbine diameter\n", @@ -434,21 +430,23 @@ "y = [0, 3 * D, 0, 3 * D]\n", "wind_directions = [270.0, 280.0]\n", "wind_speeds = [8.0, 8.0]\n", + "turbulence_intensities = [0.1, 0.1]\n", "\n", "# Pass the new data to FlorisInterface\n", - "fi.reinitialize(\n", + "fmodel.set(\n", " layout_x=x,\n", " layout_y=y,\n", " wind_directions=wind_directions,\n", - " wind_speeds=wind_speeds\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=turbulence_intensities,\n", ")\n", "\n", "# 3. Calculate the velocities at each turbine for all atmospheric conditions\n", "# All turbines have 0 degrees yaw\n", - "fi.calculate_wake()\n", + "fmodel.run()\n", "\n", "# 4. Get the total farm power\n", - "turbine_powers = fi.get_turbine_powers() / 1000.0 # Given in W, so convert to kW\n", + "turbine_powers = fmodel.get_turbine_powers() / 1000.0 # Given in W, so convert to kW\n", "farm_power_baseline = np.sum(turbine_powers, 1) # Sum over the second dimension\n", "\n", "# 5. Develop the yaw control settings\n", @@ -457,12 +455,13 @@ "yaw_angles[0, 1] = 15 # At 270 degrees, yaw the second turbine 15 degrees\n", "yaw_angles[1, 0] = 10 # At 280 degrees, yaw the first turbine 10 degrees\n", "yaw_angles[1, 1] = 0 # At 280 degrees, yaw the second turbine 0 degrees\n", + "fmodel.set(yaw_angles=yaw_angles)\n", "\n", "# 6. Calculate the velocities at each turbine for all atmospheric conditions with the new yaw settings\n", - "fi.calculate_wake(yaw_angles=yaw_angles)\n", + "fmodel.run()\n", "\n", "# 7. Get the total farm power\n", - "turbine_powers = fi.get_turbine_powers() / 1000.0\n", + "turbine_powers = fmodel.get_turbine_powers() / 1000.0\n", "farm_power_yaw = np.sum(turbine_powers, 1)\n", "\n", "# 8. Compare farm power with and without wake steering\n", @@ -480,7 +479,7 @@ "## Visualization\n", "\n", "While comparing turbine and farm powers is meaningful, a picture is worth at least\n", - "1000 Watts, and the `FlorisInterface` provides powerful routines for visualization.\n", + "1000 Watts, and `FlorisModel` provides powerful routines for visualization.\n", "\n", "The visualization functions require that the user select a single atmospheric condition\n", "to plot. The internal data structures still have the same shape but the wind speed and\n", @@ -489,9 +488,7 @@ "be selected.\n", "\n", "Let's create a horizontal slice of each atmospheric condition from above with and without\n", - "yaw settings included. Notice that although we are plotting the conditions for two\n", - "different wind directions, the farm is rotated so that the wind is coming from the\n", - "left (West) in both cases." + "yaw settings included." ] }, { @@ -502,45 +499,50 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from floris.tools.visualization import visualize_cut_plane, add_turbine_id_labels\n", + "from floris.flow_visualization import visualize_cut_plane\n", + "from floris.layout_visualization import plot_turbine_labels\n", "\n", "fig, axarr = plt.subplots(2, 2, figsize=(15,8))\n", "\n", "# Plot the first wind condition\n", "wd = wind_directions[0]\n", "ws = wind_speeds[0]\n", + "ti = turbulence_intensities[0]\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wd], ws=[ws], height=90.0)\n", + "fmodel.reset_operation()\n", + "horizontal_plane = fmodel.calculate_horizontal_plane(wd=[wd], ws=[ws], ti=[ti], height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[0,0], title=\"270 - Aligned\")\n", - "add_turbine_id_labels(fi, axarr[0,0], color=\"w\", backgroundcolor=\"k\")\n", + "plot_turbine_labels(fmodel, axarr[0,0])\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wd], ws=[ws], yaw_angles=yaw_angles[0:1] , height=90.0)\n", + "fmodel.set(yaw_angles=yaw_angles[0:1])\n", + "horizontal_plane = fmodel.calculate_horizontal_plane(wd=[wd], ws=[ws], ti=[ti], height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[0,1], title=\"270 - Yawed\")\n", - "add_turbine_id_labels(fi, axarr[0,1], color=\"w\", backgroundcolor=\"k\")\n", + "plot_turbine_labels(fmodel, axarr[0,1])\n", "\n", "# Plot the second wind condition\n", "wd = wind_directions[1]\n", "ws = wind_speeds[1]\n", + "ti = turbulence_intensities[1]\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wd], ws=[ws], height=90.0)\n", + "fmodel.reset_operation()\n", + "horizontal_plane = fmodel.calculate_horizontal_plane(wd=[wd], ws=[ws], ti=[ti], height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[1,0], title=\"280 - Aligned\")\n", - "add_turbine_id_labels(fi, axarr[1,0], color=\"w\", backgroundcolor=\"k\")\n", + "plot_turbine_labels(fmodel, axarr[1,0])\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wd], ws=[ws], yaw_angles=yaw_angles[1:2] , height=90.0)\n", + "fmodel.set(yaw_angles=yaw_angles[1:2])\n", + "horizontal_plane = fmodel.calculate_horizontal_plane(wd=[wd], ws=[ws], ti=[ti], height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[1,1], title=\"280 - Yawed\")\n", - "add_turbine_id_labels(fi, axarr[1,1], color=\"w\", backgroundcolor=\"k\")\n", + "plot_turbine_labels(fmodel, axarr[1,1])\n", "\n", "plt.show()" ] @@ -564,36 +566,32 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from floris.tools.visualization import plot_rotor_values\n", + "from floris.flow_visualization import plot_rotor_values\n", "\n", - "fig, _, _ , _ = plot_rotor_values(fi.floris.flow_field.u, findex=0, n_rows=1, n_cols=4, return_fig_objects=True)\n", + "fig, _, _ , _ = plot_rotor_values(fmodel.core.flow_field.u, findex=0, n_rows=1, n_cols=4, return_fig_objects=True)\n", "fig.suptitle(\"Wind direction 270\")\n", "\n", - "fig, _, _ , _ = plot_rotor_values(fi.floris.flow_field.u, findex=1, n_rows=1, n_cols=4, return_fig_objects=True)\n", + "fig, _, _ , _ = plot_rotor_values(fmodel.core.flow_field.u, findex=1, n_rows=1, n_cols=4, return_fig_objects=True)\n", "fig.suptitle(\"Wind direction 280\")\n", "\n", "plt.show()" @@ -640,22 +638,20 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get the grid points\n", - "xs = fi.floris.grid.x_sorted\n", - "ys = fi.floris.grid.y_sorted\n", - "zs = fi.floris.grid.z_sorted\n", + "xs = fmodel.core.grid.x_sorted\n", + "ys = fmodel.core.grid.y_sorted\n", + "zs = fmodel.core.grid.z_sorted\n", "\n", "# Consider the shape\n", "print(f\"shape of xs: {xs.shape}\")\n", @@ -710,9 +706,9 @@ "Calculating AEP for 1440 wind direction and speed combinations...\n", "Number of turbines = 25\n", "Model AEP (GWh) Compute Time (s)\n", - "Jensen 643.122 1.179 \n", - "GCH 646.972 3.742 \n", - "CC 633.776 6.833 \n" + "Jensen 661.838 0.285 \n", + "GCH 683.869 1.415 \n", + "CC 661.315 2.859 \n" ] } ], @@ -731,6 +727,7 @@ "# meshgrid returns arrays with shape (len(wind_speeds), len(wind_directions)), so we \"flatten\" them\n", "wind_directions = wind_directions.flatten()\n", "wind_speeds = wind_speeds.flatten()\n", + "turbulence_intensities = 0.1 * np.ones_like(wind_speeds)\n", "\n", "n_findex = len(wind_directions)\n", "print(f\"Calculating AEP for {n_findex} wind direction and speed combinations...\")\n", @@ -749,23 +746,41 @@ "print(f\"Number of turbines = {len(x)}\")\n", "\n", "# Define several models\n", - "fi_jensen = FlorisInterface(\"jensen.yaml\")\n", - "fi_gch = FlorisInterface(\"gch.yaml\")\n", - "fi_cc = FlorisInterface(\"cc.yaml\")\n", + "fmodel_jensen = FlorisModel(\"jensen.yaml\")\n", + "fmodel_gch = FlorisModel(\"gch.yaml\")\n", + "fmodel_cc = FlorisModel(\"cc.yaml\")\n", "\n", "# Assign the layouts, wind speeds and directions\n", - "fi_jensen.reinitialize(layout_x=x, layout_y=y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", - "fi_gch.reinitialize(layout_x=x, layout_y=y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", - "fi_cc.reinitialize(layout_x=x, layout_y=y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", + "fmodel_jensen.set(\n", + " layout_x=x,\n", + " layout_y=y,\n", + " wind_directions=wind_directions,\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=turbulence_intensities\n", + ")\n", + "fmodel_gch.set(\n", + " layout_x=x,\n", + " layout_y=y,\n", + " wind_directions=wind_directions,\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=turbulence_intensities,\n", + ")\n", + "fmodel_cc.set(\n", + " layout_x=x,\n", + " layout_y=y,\n", + " wind_directions=wind_directions,\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=turbulence_intensities,\n", + ")\n", "\n", - "def time_model_calculation(model_fi: FlorisInterface) -> Tuple[float, float]:\n", + "def time_model_calculation(model_fmodel: FlorisModel) -> Tuple[float, float]:\n", " \"\"\"\n", " This function performs the wake calculation for a given\n", - " FlorisInterface object and computes the AEP while\n", + " FlorisModel object and computes the AEP while\n", " tracking the amount of wall-time required for both steps.\n", "\n", " Args:\n", - " model_fi (FlorisInterface): _description_\n", + " model_fmodel (FlorisModel): _description_\n", " float (_type_): _description_\n", "\n", " Returns:\n", @@ -774,14 +789,14 @@ " 1: Wall-time for the computation\n", " \"\"\"\n", " start = time.perf_counter()\n", - " model_fi.calculate_wake()\n", - " aep = model_fi.get_farm_power().sum() / n_findex / 1E9 * 365 * 24\n", + " model_fmodel.run()\n", + " aep = model_fmodel.get_farm_power().sum() / n_findex / 1E9 * 365 * 24\n", " end = time.perf_counter()\n", " return aep, end - start\n", "\n", - "jensen_aep, jensen_compute_time = time_model_calculation(fi_jensen)\n", - "gch_aep, gch_compute_time = time_model_calculation(fi_gch)\n", - "cc_aep, cc_compute_time = time_model_calculation(fi_cc)\n", + "jensen_aep, jensen_compute_time = time_model_calculation(fmodel_jensen)\n", + "gch_aep, gch_compute_time = time_model_calculation(fmodel_gch)\n", + "cc_aep, cc_compute_time = time_model_calculation(fmodel_cc)\n", "\n", "print('Model AEP (GWh) Compute Time (s)')\n", "print('{:8s} {:<10.3f} {:<6.3f}'.format(\"Jensen\", jensen_aep, jensen_compute_time))\n", @@ -818,7 +833,14 @@ "y = np.zeros_like(x)\n", "wind_directions = np.arange(0.0, 360.0, 2.0)\n", "wind_speeds = 8.0 * np.ones_like(wind_directions)\n", - "fi_gch.reinitialize(layout_x=x, layout_y=y, wind_directions=wind_directions, wind_speeds=wind_speeds)" + "turbulence_intensities = 0.1 * np.ones_like(wind_directions)\n", + "fmodel_gch.set(\n", + " layout_x=x,\n", + " layout_y=y,\n", + " wind_directions=wind_directions,\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=turbulence_intensities,\n", + ")" ] }, { @@ -826,42 +848,48 @@ "execution_count": 14, "id": "7d773cdc", "metadata": {}, - "outputs": [ - { - "ename": "UserWarning", - "evalue": "Variable input must have shape (n_wind_directions, n_wind_speeds, nturbs)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUserWarning\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfloris\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtools\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptimization\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myaw_optimization\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myaw_optimizer_sr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m YawOptimizationSR\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Define the SerialRefine optimization\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m yaw_opt \u001b[38;5;241m=\u001b[39m \u001b[43mYawOptimizationSR\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mfi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfi_gch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mminimum_yaw_angle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Allowable yaw angles lower bound\u001b[39;49;00m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximum_yaw_angle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m25.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Allowable yaw angles upper bound\u001b[39;49;00m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mNy_passes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude_downstream_turbines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mexploit_layout_symmetry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Development/floris/floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py:49\u001b[0m, in \u001b[0;36mYawOptimizationSR.__init__\u001b[0;34m(self, fi, minimum_yaw_angle, maximum_yaw_angle, yaw_angles_baseline, x0, Ny_passes, turbine_weights, exclude_downstream_turbines, exploit_layout_symmetry, verify_convergence)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mInstantiate YawOptimizationSR object with a FlorisInterface object\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;124;03mand assign parameter values.\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;66;03m# Initialize base class\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 50\u001b[0m \u001b[43m \u001b[49m\u001b[43mfi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mminimum_yaw_angle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminimum_yaw_angle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximum_yaw_angle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximum_yaw_angle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43myaw_angles_baseline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myaw_angles_baseline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43mturbine_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mturbine_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[43mcalc_baseline_power\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude_downstream_turbines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude_downstream_turbines\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m \u001b[49m\u001b[43mexploit_layout_symmetry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexploit_layout_symmetry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_convergence\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_convergence\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# Start a timer for FLORIS computations\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_spent_in_floris \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[0;32m~/Development/floris/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py:135\u001b[0m, in \u001b[0;36mYawOptimization.__init__\u001b[0;34m(self, fi, minimum_yaw_angle, maximum_yaw_angle, yaw_angles_baseline, x0, turbine_weights, normalize_control_variables, calc_baseline_power, exclude_downstream_turbines, exploit_layout_symmetry, verify_convergence)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfi\u001b[38;5;241m.\u001b[39mfloris\u001b[38;5;241m.\u001b[39mfarm\u001b[38;5;241m.\u001b[39myaw_angles\n\u001b[0;32m--> 135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myaw_angles_baseline \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_unpack_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(np\u001b[38;5;241m.\u001b[39mabs(b) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.0\u001b[39m):\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m 138\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mINFO: Baseline yaw angles were not specified and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwere derived from the floris object.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 140\u001b[0m )\n", - "File \u001b[0;32m~/Development/floris/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py:245\u001b[0m, in \u001b[0;36mYawOptimization._unpack_variable\u001b[0;34m(self, variable, subset)\u001b[0m\n\u001b[1;32m 235\u001b[0m variable \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mtile(\n\u001b[1;32m 236\u001b[0m variable,\n\u001b[1;32m 237\u001b[0m (\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 241\u001b[0m )\n\u001b[1;32m 242\u001b[0m )\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(np\u001b[38;5;241m.\u001b[39mshape(variable)) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 245\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mUserWarning\u001b[39;00m(\n\u001b[1;32m 246\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVariable input must have shape (n_wind_directions, n_wind_speeds, nturbs)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 247\u001b[0m )\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m variable\n", - "\u001b[0;31mUserWarning\u001b[0m: Variable input must have shape (n_wind_directions, n_wind_speeds, nturbs)" - ] - } - ], + "outputs": [], "source": [ - "from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR\n", + "from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR\n", "\n", "# Define the SerialRefine optimization\n", "yaw_opt = YawOptimizationSR(\n", - " fi=fi_gch,\n", + " fmodel=fmodel_gch,\n", " minimum_yaw_angle=0.0, # Allowable yaw angles lower bound\n", " maximum_yaw_angle=25.0, # Allowable yaw angles upper bound\n", " Ny_passes=[5, 4],\n", " exclude_downstream_turbines=True,\n", - " exploit_layout_symmetry=True,\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "1ccb9ab7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Serial Refine] Processing pass=0, turbine_depth=0 (0.0%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=1 (7.1%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=2 (14.3%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=3 (21.4%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=4 (28.6%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=5 (35.7%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=6 (42.9%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=0 (50.0%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=1 (57.1%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=2 (64.3%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=3 (71.4%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=4 (78.6%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=5 (85.7%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=6 (92.9%)\n", + "Optimization wall time: 2.581 s\n" + ] + } + ], "source": [ "start = time.perf_counter()\n", "\n", @@ -884,10 +912,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "686548be", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Show the results\n", "yaw_angles_opt = np.vstack(df_opt[\"yaw_angles_opt\"])\n", @@ -901,6 +940,14 @@ "\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c654c6d8", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -925,7 +972,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.12.1" }, "vscode": { "interpreter": { diff --git a/docs/turbine_interaction.ipynb b/docs/turbine_interaction.ipynb index a9123df1d..2b7507765 100644 --- a/docs/turbine_interaction.ipynb +++ b/docs/turbine_interaction.ipynb @@ -89,14 +89,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -114,14 +112,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -171,14 +167,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -199,14 +193,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -250,10 +242,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "nrel_5MW\n", - "iea_10MW\n", + "iea_15MW_floating\n", "iea_15MW_multi_dim_cp_ct\n", - "iea_15MW_floating\n" + "nrel_5MW\n", + "iea_10MW\n" ] } ], @@ -294,10 +286,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "iea_15MW_floating\n", + "iea_15MW_multi_dim_cp_ct\n", "nrel_5MW\n", "iea_10MW\n", - "iea_15MW_multi_dim_cp_ct\n", - "iea_15MW_floating\n", "iea_15MW\n" ] } @@ -336,14 +328,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -387,20 +377,19 @@ "name": "stdout", "output_type": "stream", "text": [ - " Turbine | Efficiency | Rotor Diameter (m) | Hub Height (m) | TSR | Air Density (ρ) | Tilt (º)\n", - "------------------------------------------------------------------------------------------------------------------\n", - " nrel_5MW | 0.94 | 125.88 | 90.0 | 8.0 | 1.225 | 5.000\n", - " iea_10MW | 0.94 | 198.00 | 119.0 | 8.0 | 1.225 | 6.000\n", - " iea_15MW_multi_dim_cp_ct | 1.00 | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n", - " iea_15MW_floating | 1.00 | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n", - " iea_15MW | 1.00 | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n" + " Turbine | Rotor Diameter (m) | Hub Height (m) | TSR | Air Density (ρ) | Tilt (º)\n", + "-----------------------------------------------------------------------------------------------------\n", + " iea_15MW_floating | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n", + " iea_15MW_multi_dim_cp_ct | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n", + " nrel_5MW | 125.88 | 90.0 | 8.0 | 1.225 | 5.000\n", + " iea_10MW | 198.00 | 119.0 | 8.0 | 1.225 | 6.000\n", + " iea_15MW | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n" ] } ], "source": [ "header = f\"\\\n", "{'Turbine':>25} | \\\n", - "{'Efficiency':>10} | \\\n", "{'Rotor Diameter (m)':>18} | \\\n", "{'Hub Height (m)':>14} | \\\n", "{'TSR':>6} | \\\n", @@ -411,7 +400,6 @@ "print(\"-\" * len(header))\n", "for name, t in tl.turbine_map.items():\n", " print(f\"{name:>25}\", end=\" | \")\n", - " print(f\"{t.turbine.generator_efficiency:>10,.2f}\", end=\" | \")\n", " print(f\"{t.turbine.rotor_diameter:>18,.2f}\", end=\" | \")\n", " print(f\"{t.turbine.hub_height:>14,.1f}\", end=\" | \")\n", " print(f\"{t.turbine.TSR:>6,.1f}\", end=\" | \")\n", @@ -423,6 +411,14 @@ " print(f\"{t.turbine.power_thrust_table['ref_air_density']:>15,.3f}\", end=\" | \")\n", " print(f\"{t.turbine.power_thrust_table['ref_tilt']:>8,.3f}\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8bb4fa6", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -441,7 +437,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.12.1" } }, "nbformat": 4, diff --git a/docs/wake_models.ipynb b/docs/wake_models.ipynb index 5252f3f55..e1f37de4b 100644 --- a/docs/wake_models.ipynb +++ b/docs/wake_models.ipynb @@ -58,23 +58,25 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from floris.tools import FlorisInterface\n", - "import floris.tools.visualization as wakeviz\n", + "from floris import FlorisModel\n", + "import floris.flow_visualization as flowviz\n", + "import floris.layout_visualization as layoutviz\n", "\n", "NREL5MW_D = 126.0\n", "\n", "def model_plot(inputfile):\n", " fig, axes = plt.subplots(1, 1, figsize=(10, 10))\n", - " fi = FlorisInterface(inputfile)\n", - " fi.reinitialize(layout_x=np.array([0.0, 2*NREL5MW_D]), layout_y=np.array([0.0, 2*NREL5MW_D]))\n", " yaw_angles = np.zeros((1, 2))\n", " yaw_angles[:,0] = 20.0\n", - " horizontal_plane = fi.calculate_horizontal_plane(\n", - " height=90.0,\n", - " yaw_angles=yaw_angles\n", + " fmodel = FlorisModel(inputfile)\n", + " fmodel.set(\n", + " layout_x=np.array([0.0, 2*NREL5MW_D]),\n", + " layout_y=np.array([0.0, 2*NREL5MW_D]),\n", + " yaw_angles=yaw_angles,\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes)\n", - " wakeviz.plot_turbines_with_fi(fi, ax=axes, yaw_angles=yaw_angles)" + " horizontal_plane = fmodel.calculate_horizontal_plane(height=90.0)\n", + " flowviz.visualize_cut_plane(horizontal_plane, ax=axes)\n", + " layoutviz.plot_turbine_rotors(fmodel, ax=axes,yaw_angles=yaw_angles)" ] }, { @@ -99,14 +101,12 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAERCAYAAACqx6miAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAzDklEQVR4nO3deXRc533m+e+vFuwrN3ABxRUUTWolKWq1YovaLZt24kWxO1HcmqOZbmc6Pelz0nJ8ZnrmnEl33D0nTjzxJNGx0i2n7ciKbLdoW7YsyZIlS6EkitRCihtEiiYp7gsIEsRW9Zs/6gKoKtzCQhSqgKrncw6Ee9/73lvvRYmoB+9933vN3RERERGRyRcpdgNEREREyoWCl4iIiEiBKHiJiIiIFIiCl4iIiEiBKHiJiIiIFIiCl4iIiEiB5CV4mdkHZvaumb1lZluCshlm9qyZ7Q2+NwflZmbfNLN2M3vHzNbkow0iIiIiU10+e7w+7u7XuPu6YP1h4Hl3bwOeD9YB7gHagq+HgL/JYxtEREREpqzJvNS4EXgsWH4M+HRa+Xc8ZTPQZGbzJrEdIiIiIlNCLE/HceAXZubA37n7I0CLux8Jth8FWoLlBcDBtH0PBWVHyKHRoj6HeJ6aKiIiIjJ52uk56e6zw7blK3jd4u6HzWwO8KyZ7Urf6O4ehLIxM7OHSF2KZDYx/jK2KE9NFREREZk89/XvOZBrW14uNbr74eD7ceBHwHrg2MAlxOD78aD6YWBh2u6tQVn2MR9x93Xuvq6RaD6aKSIiIlJUEw5eZlZrZvUDy8CdwHZgE/BAUO0B4KlgeRPw+8HsxhuAjrRLkiIiIiIlKx+XGluAH5nZwPG+5+4/N7M3gCfM7EHgAPD5oP7TwL1AO9AFfDkPbRARERGZ8iYcvNx9H3B1SPkpYENIuQNfmejrioiIiEw3unO9iIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIEoeImIiIgUiIKXiIiISIHkLXiZWdTMtpnZT4L1JWb2mpm1m9n3zawiKK8M1tuD7Yvz1QYRERGRqSyfPV5/BOxMW/868A13Xw6cAR4Myh8EzgTl3wjqiYiIiJS8vAQvM2sFPgF8O1g34DbgyaDKY8Cng+WNwTrB9g1BfREREZGSlq8er78E/gRIBuszgbPu3h+sHwIWBMsLgIMAwfaOoL6IiIhISZtw8DKz+4Dj7v5mHtqTftyHzGyLmW3pIJHPQ4uIiIgURSwPx7gZ+JSZ3QtUAQ3AXwFNZhYLerVagcNB/cPAQuCQmcWARuBU9kHd/RHgEYA2q/I8tFNERESkqCbc4+XuX3X3VndfDNwP/NLdvwS8AHw2qPYA8FSwvClYJ9j+S3dXsBIREZGSN5n38fr3wB+bWTupMVyPBuWPAjOD8j8GHp7ENoiIiIhMGfm41DjI3V8EXgyW9wHrQ+p0A5/L5+uKiIiITAe6c72IiIhIgeS1x0tERERkutjtV9LjVfRQFbp9rDcZNbKHqu/JWVfBS0RERKa0pBtnmM1pn00i5GLdaAHJ8OHhyIyzPosr76oj6VHq4l3D9ss188/d0uqEvPpPcrdFwUtEREQm5Jw30UnjpBw74VE+ZDE9VPKR2xqIRfrDK3ru+JXM2jZwL4Ul1ee47p7ZNNX2ALV5ajEKXiIiIgLnvZ4+KkatN/zSWbhuajju8znNbJbcOpPI4ANswoX2Do3aFlhfd4zrPjGXhupe8htdZgE9eTze6BS8REREpoA+j19SMBmLs8zkgC/nPE1EGN5jlDtoDbQn50U3Vm+o46qqTj7+O2N5ysyl3rZzNtB7iftOLQpeIiIiReKeCkUf+ApOewtGP5aVvcba+5SrrmPE6WXZx2dyz2/XUFvZN3KbhrUxPAym3/q8IhY+OF2GU/ASEREZhTt0MIP9vnLCY5nSw1GCGFV0sfhjc5i37BoWzOjgqsuODb0u4WOTcrYzR4+ZO0TMaartvvSGS14oeImIyLSVdGMvV3DeG7NCx6U/iS4sunRRi2MsuHkuratXEI8OXVYLfaURBnqn1z+7vR2AxqsWURG7QH11bzDQW0qVgpeIiEyKHq/kqLfSS+WwbSMNwh7PpTXHOOVzmHXLIrr6q4gRNs4oRy9Qeo2giruF3HbAqQKikSSXXTePqz85m+bGHDPrxm3m4NL5rigHntudp+PKVKXgJSJS5s57A905biAZZqzDv895E0d9AbU3fIQL/VVESGJ4Ws9U5pHMMgNPql5mWdhr10Z7uW5DNUvvWEFN9ciz6sJkX74Lu5yXXhaN5it0STlS8BIRmQYSHg3tORrJWHuO9vnlnPQWTtMSsnXYUOuMVxi+NFTHgWXra7nljioW37GCyopkRoAJG7Q9UggKOxt3o7oyQTQKjHIrg1yGDWafnImFIoCCl4jIlHbRa3g/uZJdfk1o8MoOV4YTsWTG+vAAlrmeIMaVN8IXP1HNzJuvyKw5pt6g9Lt4Z2qq7ycWcwi9BHjp47BEpisFLxGRCTrmC3jPr+Wsz8TTHmeSCjwjh4vhwSmz16bTm+ilgpu/1MqC6xYMlg+EHWcoDLnD+Qsxrru6g3jMB8uG9sncd0BFRZK5s3qJRICQezyJSP4oeIlISftNcin7/HL6QnuLxi47EA0EKgMuUkvDtQtZvGwRddU9VMZS4SWZHP5MuXTJsBZ4xjdm1l7kmvvmc3HpqjFdAjuzeR8LWnqorFBvkshUpOAlIlNGn8c5kFxGJ02hl9ByD7TOKk8LKEd9IfVrF1PZejnVFb0Z25IeuvvwPirPHAyevb3B4SMLTtL8hXXMmJX/wNPN+EKiiExdCl4iMiYJj3DC5+V8ztvwHqHcskNVkggnmc/25FpOMYfm5fXD67phNjQjLh7ppzbWPXSkwRlxmY84iZiz/nqY/aVrmDFzsnuB1MskIiNT8BKZxnq9YoQglJ8QkCDGgeRS3vCPcZK5WHCBLLy3KXPGW2b/0ECPVXjfTZR+1m1s4dZr59K5fBWxkN9OAz1UnoT63nOsuW5oPFKu2W8Rg1g8u1REpDgUvESmsd1+FduSN3GSufRSAcF9j1LBaHgP1EAYiwyOLhoeRnKVr//UTG68ex3dVfUZ5QOvsrLlLMm0l/RgOeNInhbDkmnRzYe+qmucigqCgd65JY4MBCoRkelDwUukABIeZW/iI7zCXZxjxmC5ZfUWZY9jyu4bSg9TBvRRwbLVSS7/+FWcrpxPc7OnjpAcOsxAT9BAb1FvrzF7TpIZM5KZrx68dCKZdUemIBDF47CsrZ9DOR711knTiD+DseoERslcIiLTloKXSIidyWt4J3kd/8zTAFzD5yErFAFEc45rGprxBqlQcoE6Ftx0GXPXr8ys6ZBMQFPv0aE9BsJS2uGTua6UOVRV9jF3dS2HT54d9dzO9xktsxuJzxu16jC5QpeIiIyNgpdIiNPMoeHK+Zw91E0yCd1rPj2sjmO4D82gG3y4SfrlNIfmhj6ao87iugSLrp/Jvot9w44VAc6lPbNtvLoAxhC6RESkuCYcvMysCngJqAyO96S7/wczWwI8TuoJoG8Cv+fuvWZWCXwHWAucAr7g7h9MtB0i+VYR6aWiJkqfwanqPZkbc/Q+efrFQYf5bddQMTNJVY2TAPZdDLt7t4iIlIt8DKXoAW5z96uBa4C7zewG4OvAN9x9OXAGeDCo/yBwJij/RlBPZEqLRDzzKxr+FY0mh75iSWJxzaQTEZEhEw5ennI+WI0HXw7cBjwZlD8GfDpY3hisE2zfYKZHkoqIiEjpy8vkITOLmtlbwHHgWeB94Ky7D9xk5xAw8JCxBcBBgGB7B0xgcIuIiIjINJGX4OXuCXe/BmgF1gMrR95jdGb2kJltMbMtHaFPtRcRERGZXvJ6uxx3Pwu8ANwINJnZwOD9VuBwsHwYWAgQbG8kNcg++1iPuPs6d1/XSDSfzRQREREpigkHLzObbWZNwXI1cAewk1QA+2xQ7QHgqWB5U7BOsP2X7q4RyCIiIlLy8nEfr3nAY2YWJRXknnD3n5jZe8DjZvZ/A9uAR4P6jwL/YGbtwGng/jy0QURERGTKm3Dwcvd3gGtDyveRGu+VXd4NfG6irysiIiIy3eiRaCIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFMuHgZWYLzewFM3vPzHaY2R8F5TPM7Fkz2xt8bw7Kzcy+aWbtZvaOma2ZaBtEREREpoN89Hj1A//O3VcBNwBfMbNVwMPA8+7eBjwfrAPcA7QFXw8Bf5OHNoiIiIhMeRMOXu5+xN23BsudwE5gAbAReCyo9hjw6WB5I/AdT9kMNJnZvIm2Q0RERGSqy+sYLzNbDFwLvAa0uPuRYNNRoCVYXgAcTNvtUFAmIiIiUtLyFrzMrA74AfBv3f1c+jZ3d8DHebyHzGyLmW3pIJGvZoqIiIgUTV6Cl5nFSYWu77r7D4PiYwOXEIPvx4Pyw8DCtN1bg7IM7v6Iu69z93WNRPPRTJExS2K4R3DAHRKJCIlEBB/Xnw8iIiKZ8jGr0YBHgZ3u/hdpmzYBDwTLDwBPpZX/fjC78QagI+2SpMiUMJ8D7H4nyrnD50mc6aH1dCNXLV9LX2+82E0TEZFpLJaHY9wM/B7wrpm9FZT9KfDnwBNm9iBwAPh8sO1p4F6gHegCvpyHNojk1bLILv5n+490JPo51tXK0dcOcPLgBVquv5ZI2p8rDtT2nB7sCRvWI7bzPPWze4hFhzYkk5n7A8SjEFm9lDPdZ4kp24mIlKwJBy93/zVgOTZvCKnvwFcm+roiky1m/fxpDNwPcTT5DV7+8G4O/+hQ8D/7QGRyunGM9H8ESdKHNHaQ+Q/ESKYtp1yklobF+5h/UxsNy2cSTb+67qmjJdLDXdqoSXcbvCQK4MHhk8H6ZYsT7DiepKL6En4IIiKSV/no8RIpaWYwL3qYz/PopL3GW8kb2PbBjez8oJN+4hCEuZShEGcZc1Q8KMsMfZZWnsR4jWpWf2wmMzesooM6agxOHIG+3sw2DAS3FbPPDiuDoSDnWMZUGU9CXUOSkzRn9AaKiMhwCl4iU8AVtoWV0bdHqTX+kf3naOafk7dz+MVF7H7xBEkMw4mSyApxAz1xxi+HvU52veHtSGLcsrGZ5V9aw2kaB3cb7IULO5ugZy6jzlBHIk4q9DbPhph+U4lIidCvM5EpIGb9xOjP+3Hjfpwb7VmSNnDtcvTwFhasRuJEeNtv5PT7V/CThzfjbpgNfzXDB4/tWaMTbHDVs74brStquOkT9RyZfy3R4DeWO0Tz/+MSEZl0Cl4iJSxqCebY0Ul/ne7kW3TtaGdxdqDK7i3LNRo0w9A+p7wFb7yal3/8Ebp7NxONOE4q2MUjSY79+ELWbsNfIOPirNuwCRAOLJrVwQ2faaFn2UfG2EYRkUuj4CUiE7Y40j4pxz3pLezZliDOLrInexrOhVEui6bXzbWeJMLzvpRN3+5k9ZqdLLhqTugxUmPbLLtw8FuufkL31Ji4mXVdrPnUPLqXrlK4EyljCl4iMmXNsmPMih6b9Nfp9xi/SS5l57a1nNx2nJECXHpoG2t+cqDL69jx+jkW3DeDWU2pmQ3JZNoUisHDpnrl0nvmBmatRj7Yk3lch4pYkpV3tNK/YmXmbFgRmZIUvESk7MWsn6XRPSxlz+iVL0HCI7zmt3FyWwsfbv05ybR7V+fqjbOQfrTUXpkzW/uo5PVfd7P8mlMsvnk+Z85V0Do3RmVFMJ4u+9Kqh82Xza6Te31GYz+xmB7hIHKpFLxERCZZ1JJcx6/wCV5jDJv4cIAVHHp3MVvenssrj30AwHPWPcpxRj5mmCj93P6Hy1h9dyuzZ/RlbMvoncvqqQuTHv4q4kkq4gpyUj4UvERECiBufaNXugSt/j4z7SgjPdJ2vDNVs/UT47jP58W/7uPnf32A1rVNocf3kFcyG1aSsRazBDfeVs3Ke5bQWJ8YV7tyBb7sMjN0jzmZMhS8RESmsSrrpoqRe7jyYZYdp813cMwXkNg69sFko4W+gyxjV2Urr79wnqilglf27UYypB1ueAfi0G1ISAt8EZx1H2+g7a5l4w53Ivmm4CUiImMStz5a7YO8HtM9wrlXzmDEBx+mNdYeurBaYft2Uc0Tmxfi/+kIV9wSY+aVS4cd4+z29Jm5IbclCetRw7j8phZW3LWUGU26sZyMjYKXiIgUzSJrH/v00Ano8lp+48s48soCPnzlSEhAG//NhZ0Iu37dxZwXLjKn6jQLr7sMgKRnXtfMHPdmw7YNbE94hIbqSk6cq8moU1PZR23l5FyqlsJT8BIRkZJXYxdYae+wknfyetwur+XgliW0+zL2vrwdCO91y5xLOvyWJEP7OL/MCqJJolyzIc7lzQdYcedHUrWy7ylHeK9c6oi5k232PpXxBNGIJjtMJgUvERGRS1RjF7jcttPmO0imzXCY6ISGdOdp4DfPL+Mt5hN58vQIxx8p8I1u9W11tNSe4qOfmXdpDZUxUfASERGZoIg5kUl43ipAI2e40rbQ45X0UD2ufccaAC9Sw+FfLuQd5rD3TDxkNmqacWRKx5hdfYYb75vNrPqLY9+xhCl4iYiITAOV1kMlPZNy7Ho6mGNHuOB1dL7SlLfj9lHBPp/J6Z5GqmL9GbceCZPrculoopakItrPjZ+YxZyGrkttbkEoeImIiAgAtXaeWs7n7Xh9HqfPKuh76XV608JWrp64sd1jePi+Z30WcXro6Y8zs7ojqDXC2LawspBxc7FIgqvuvCyvvXUKXiIiIjIp4tbHUnZN+sxVZzcdzODwCwkOUDdYnjPg5X6uQsZaH5V09p6kPxljft2J0MQ24n3nQih4iYiIyLRmBk2cpslO5/W4x30++569jF6q2EU8L5MmFLxEREREQsyxD5ljH45/x2TuTXp6lYiIiEiBKHiJiIiIFIiCl4iIiEiB5CV4mdnfm9lxM9ueVjbDzJ41s73B9+ag3Mzsm2bWbmbvmNmafLRBREREZKrLV4/XfwPuzip7GHje3duA54N1gHuAtuDrIeBv8tQGERERkSktL8HL3V8CsudwbgQeC5YfAz6dVv4dT9kMNJmZHgwlIiIiJW8yx3i1uPuRYPko0BIsLwAOptU7FJRlMLOHzGyLmW3pIDGJzRQREREpjIIMrnd3Z1yP1QR3f8Td17n7usa0J76LiIiITFeTGbyODVxCDL4fD8oPAwvT6rUGZSIiIiIlbTKD1ybggWD5AeCptPLfD2Y33gB0pF2SFBERESlZeXlkkJn9I/AxYJaZHQL+A/DnwBNm9iBwAPh8UP1p4F6gHegCvpyPNoiIiIhMdXkJXu7+uzk2bQip68BX8vG6IiIiItOJ7lwvIiIiUiAKXiIiIiIFouAlIiIiUiAKXiIiIiIFouAlIiIiUiB5mdUoU1PCI3R4M8lR7vxvozxUoMlOEzU9tklERGSiFLxKwDHv4zU/zyetCTMbLO+kiV/6pznoS+mkAcdIbc0MWkPdnpnlhpPEaOQ01/MCV0S2UGk9k3IOUfpJa7qIiEhJUvAqAVv9Ao8kT7AqWs1yqjK2VdDDHb9bx5aGO7lsKbinvgAIlgdWk8mh8gHNsU46X9rGaz/fwK+Td6cCUkgPWXpZdrjLrO9Y2vLA9gbOsM5eZllkJ3HrG9f5i4hIaev1Co6zgLPMLHZTxmhPzi0KXiXgo1bP33GCXybPsTxaNWx7NOLMaoEuOwvj7FVqWtxEMrmGtlVGMrjaOJjb0h593tx7FIC+fmPV8vNDB3BwT71oMi2CuQPJoNxh7+bT/OpnDWxObKCa1P7ZTTWSZMsdAj0kAIZJEsGpsF4W2V5WRbaF1hIRmaq6vJZDLM3rMUcbglKoYwAkiHDBG5jHAWZ+rI26+MUJHa/fo9w07528tC2Xhx/LvU3BqwTUWZT1VstL3smDPptoHq/Z7f3gLFQAc4dvS3+Vgb9CGuJNcFV4vZFGmq2rf5sZdfO50BMHhsLaAA8pS5dMRrLqDq8zEMXSfzyehJ5ElMqTu9m9rYpz3jRCKyGSM/yldSNmbAurG/ILyS79l5ThGftW0cUi20uVdV/S8USmoi6v5RQtxW7GoHyFinwc7yK1uDstt7fRnagkbv0TaouP9y/0sGPk98dDSzRBxOZQGz/D7Z+dWK/Xpn+8kKdWXRoFrxJxmzXwqp9nq1/gOqsrdnPGrfvqq5l/df6PO/CPf9gvgWA96TDHzrD3uWY6e97lTGRJsHnoF09Gjh04kGUcZvg+oY3J3iObpVcMf/2sA2ZeuI3QED/PqW27MJwaPz9sj8yQmKsVubaHl4c1L/sYI32o5OfvhDz/lh9Fvj90C/Mahf0ZDZjo29tHBWeYzWmfReut84jlcaJPPsPBeMJK+P/zw/8YC9035H2sdmNW9Rmuu3cmM+suMvKfudNP5vtUU6xm5I2CV4lYa7U0EOEF7+Q6pl/wmiwDv+ByfbhHgNM0U3F9M3d+bC5LlmX2aKX/gw/tRQsLdp6jTo7jZqznKh9pOfg+5+R7vPPjQ/y67xp2JdcP722z8EZa1kLc+qmK9WbsH/b5lHm8YUcL1obquA9/H8J+poP7hLxnx7eeyNmiXL2LuT4Qs8cejiRzDOPwn91YPr9Hez3Lsexp/x0QJUGNDQ/Wly5XaLj0sD10BAvZluu4w8sdo5ZOPrKhkd/+YmIKT8IZe4rL+e9/2BFz/KSzM5pBNNIMTOwS3FQ1dd/zS6PgVSLiZnzU6nnWz9HlCWqstP7iKZb0f/BT/R9/V90qlv+vq5j3ZTh9KnXpdWHXzsHtnpYp08Og+9Av8qTDidMVXH/1uRE/HHL2iaVdDh7Lh8vwMhul7orw9oRcmh7ra4ddwh7W9hGPM/ZzHqhbseuNrJ9nVvuHvX7maxw42cRtq/cPb0yeje09S9sWEhRGrB/2sw+t10LEHLPhl/qno+zfJSP1akvpUfAqIR+PNPDTRAe/9vPcaY3Fbo4USW0d1NYNfEBdPlieq0clXTIJFa/up6qyND7gpqz5V05o9yP/9Js8NWRkYYFg5D9AxhsUFCyk/OjO9SXkcqqYT5wX/FyxmyIiIiIhFLxKiJnx8UgD7/pFjrvuhSUiIjLVKHiVmI9bPQAvemeRWyIiIiLZFLxKzFyrYBXVvJA8h+f7RioiIiIyIQpeJei2SD0H6eUDinuTOBEREcmk4FWCbrF6Yhgv+8liN0VERETSKHiVoIFHCG32kyQ9f3d5FhERkYlR8CpRt1kD5+jnEO3FboqIiIgEiha8zOxuM9ttZu1m9nCx2lGq1lotdcTYw9vFboqIiIgEihK8zCwKfAu4B1gF/K6ZrSpGW0pV3IwbbSYH2MXFXg2yFxERmQqK1eO1Hmh3933u3gs8DmwsUltK1s02iwT9vHvwlWI3RURERChe8FoAHExbPxSUSR4to45GZvLmvueL3RQRERFhCg+uN7OHzGyLmW3pQDPzLoWZ0cbV7Dv+Lp1nDxS7OSIiImWvWMHrMLAwbb01KBvk7o+4+zp3X9dItKCNKyXLuRqAve9+t8gtERERkWIFrzeANjNbYmYVwP3ApiK1paQ1WDNLZq9mz7v/oEcIiYiIFFlRgpe79wN/CDwD7ASecPcdxWhLOVi7dANnT+7ixKG3it0UERGRsla0MV7u/rS7r3D3Ze7+Z8VqRzm46rJbiEYr2bP18WI3RUREpKxN2cH1kj/VFXUsWvFJ2t/6IYlEX7GbIyIiUrYUvMrEiqt+j+4LJzm0W7eWEBERKRYFrzKxcPndVNXOZM/WJ4rdFBERkbKl4FUmotEKll/923zw3tP0XOwodnNERETKkoJXGWlb8wUS/T3se1d37hARESkGBa8yMmfhGhpnLWfPm5rdKCIiUgwKXmXEzFix9gsc2f8q507/ptjNERERKTsKXmWm7drPA7B3mwbZi4iIFJqCV5lpmHEZ85bcxN6tT+gRQiIiIgWm4FWGVqz5AmdP7OXEoW3FboqIiEhZUfAqQ0uv2kg0Vsmerd8vdlNERETKioJXGaqsbmTRqntof+sHeoSQiIhIASl4lakVaz5P94VTHNz9XLGbIiIiUjYUvMrUwstv1yOERERECixW7AbI5DGci9SyY+tFWurauVi5nIGJjLEkXH/b/87c1priNlJERKSMKHiVsAbOcCs/5tWdd/CrnTUk2Q84YCxcmuTG2++je+EyPjya+exGd6ipg+p63W5CREQknxS8SpgZzIt+yO/wGBeST9JLJQB7uJpTtbfyzjOVdPcdB4NUIEv1koFxNpJk/SfnULFmBfsOdgwer7LaMSvO+YiIiEx3Cl5lojZygVouAHB58i1ObD/GUqKhdQ2n3Vfx0v+3noscZMGaliCcweIra6lZs4QTnWeJRAbiGoMLrlGDIiIiOSl4laGmyBmaODNincvZzseTP2GnX8v5bY0AnPQWOrvaOHUqjgOR6EDsslQwczhxei8zjjaw8Ka59DQ2Tup5iIiITDcKXpJTTaSLtbwyuH4guYy3d/XSvfMgjpFk4NJkSoI4CaLEFl7G3hcjzJ9zCABPDh2zsiLBqcVX0Ty7UGchIiIydSh4yZgtirzPIt7Puf2C1/FS8h5e/W6Mbs6QdiFyUB9VzF3xAWs/t5yala1EIml1PLVHMgmRCCxYmORof3P+T0RERKRIFLwkb2rtPHdGfsDggLAMqYD1pn+UvXtW8/Sf9dDHboxkVm3DSHDjZ5o5dev1MDP7CKkFd4jGYMYciIYPVRMREZlyFLwkr6KWHHH7lbzB5ZG3B9ezZ0gmiHIwuYQ3f3QrJ390nhlLaoOKQ5c1I+bMqupg9Wev4ExkFTW1w19n4H5l8QqoqLzk0xEREcmrCQUvM/sc8H8CHwHWu/uWtG1fBR4EEsC/cfdngvK7gb8CosC33f3PJ9IGmV6qrYtq6xqxTnP0FKt9G0d8Ib0HhqemPVxFw7WX8cb3d9OT+IBIetgLAldlpJdlV9XRsvEGki0NGduc1KVM3RZDREQKbaI9XtuB3wb+Lr3QzFYB9wOrgfnAc2a2Itj8LeAO4BDwhpltcvf3JtgOKTFRS9BqH4RuSySjnNp6kgU5boeRJMqHLOaN3U3MP7ODqnhiMGQN9IQ11XSz5P7r6K5vJBafhBMQEREJMaHg5e47AWx418FG4HF37wH2m1k7sD7Y1u7u+4L9Hg/qKnjJmLVF3qNtlP9l+jzO/mQbu547SGfa/+aGkyTCYZo4e7GS6oXzqalJkExCvLkP9vemKnpqpFpTQx8N1y0novuTiYhIHkzWGK8FwOa09UNBGcDBrPLrJ6kNUsbi1seK6HusCAlovV7Bq34Hx56+SBeZA8QGxpElibJyrbPo2hZWxqAiPnQ5c6DXzB3cjUjE6V++InSsmYiISLpRg5eZPQfMDdn0NXd/Kv9NGnzdh4CHAGZrDoDkUYX1citP49Hcg7x2+1Xs3no1u96M8NNvnwnuWhbOiTB71WFu+aizduNcjjWvztyeFtTicWho1DMwRUTK1aiJxt1vv4TjHgYWpq23BmWMUJ79uo8AjwC0WZU+qSSvIuaE3WdswFJ2sdD2j+lYCY/QvnMVP3tvLT/8u/PMXnkss4JBNJJgds1Zrvji9VRuWJix2dObkdWkyipNAhARKSWT1ZW0Cfiemf0FqcH1bcDrpIbNtJnZElKB637gi5PUBpFLVmXdVNE9tsoG63iFq/11jvl8Ensy/1kliPEBl1OzfiEvP/oev350x9COIeHPzKmLX2TV9Y0s/PLt6iETESkhE72dxGeA/xeYDfzUzN5y97vcfYeZPUFq0Hw/8BV3TwT7/CHwDKnbSfy9u+/IcXiRaSVufbTagWHlSTcSHqPz9ZPMy8pQZsND1QXqOeYL6Oqr5uCpN4hFh+oM9I4tbzlF4xfuYcZMhTIRkelkorMafwT8KMe2PwP+LKT8aeDpibyuyHQSMafNxvf3xYVkLXu3r+bY9suGbeuhglPXXEbzkTdoqummMt4HpAb6e9rTM4fGlhnxWIJZa5Yw/7cWa4amiEgRadS6yBRUG7nANbxO6gp9phM+lx1vr2XfW0fo89jgI5fSH1ieutM/QRAzll9fRTIZwZOpWZjpA/4hdcHT3WioS9DTq2cwiYhMFgUvkWlmth3lVp4OfyRmiDM+k52vXcuLm5MkOYTheLBzRlgjSR+VXH1DH9f09VJ13brMgf9kTgSIxZyKuC51ioiMh4KXyDQUCRkblstMO8ktPEu3v0w/FSPW3e7rwFfxrf86k+h/ez+kRtqEAHPaGg9y65/ezuwZfWNvvIhIGVPwEikTVdYNo8zUXM52et9oZw6W1RuWHfScLhrY6lfywgtvsPy6qswHpHvqMueCuuMKZiIiaRS8RGTQLDs+5kuYAEt9Jyd8Hse3zB+2rZsadno9vuwk0UiSqorE4DYPMpr70IvNaTzPiruX0lifyD6UiEjJUPASkUsWMafFPqSFD4dtO+/17OYq9nz7V4NjytJ7zizte5IIXdSx9OVTLL1uHvXVqWdmZk8CYPAoqUkCM+susvTulfk9KRGRSaTgJSKTos46WWuvjKluv8fY5jdx8JVe2l85n7HNQm4y6xiX3dDM4rVzqXh2e6osuLw5WMcz6w/0rhlOLOYs2LBaTwUQkYJT8BKRootZP+t4acyXObup4cBrbby8eRXPp00YCAtp6Qxn+Y01LL++hejJisGb02aENB/eiBmNfcQ1g1NE8kDBS0SmhPH0PlXTxUp7m+W+g26qx7xfF3V8sHkFP//nC7z58oWsW2sE7QjCm+HUxi+y+uZZLPjoclpm9WQca9h0gyCwVcaTCmkikpOCl4hMWzHrp47OMdevo5M5doTzXk/XtvoR6/ZQxYd+GU9vbqHlpU6ikf7Bbe6WFRSNimgvMyvPsfLWBaz+1LJxnomIlAsFLxEpO3XWOabA1so+zjCL41vmMfw66FCvVj8VdNLIEa/n2MUuXn9uS1o9GzY5YGhvY9XNs2m7aymzmnXLDZFyoOAlIpKDGcywk8zg5Kh1Ex7lOPM4+FoHXQx/7FLYldReKjjx6m/Y+eoJZl25mIpY+K00PG0hFk1SW6kHbopMVwpeIiJ5ELUE8+wQ8zg05n06vJl2VvPhq0f48NUjo78G/Sz7rRms/0wtXT1Dv749x6yEgZ62moo+PRxdZIpQ8BIRKZJGO8Na+/WY63d4Mwd+1cY3fzWXCImMuBV2040UI04vGz7Vy42fbp1Yg0VkwhS8RESmiUY7w1X2Or1eQT/xMe2TIMZeX83zm2r5xaZTaVvCe8ni9LBhYy9rPrGEeCwZWkdELp2Cl4jINFNhvVTQO+b6l9FOMnTc2fB+sk5v5GdPLeNnT53i8o81hh4vbLJAxJyaeDc33TeLWfUXx9w2kXKj4CUiUuJm2fEx151tR1nk7ZxkLhdfrMldMavDrJMm+ryCjt465taeDJ0DGnZz2ppYD2vuvYyayv5h20RKkYKXiIhkiFqCFg6P64HpAF3UcvhXZ3mH8J6y8H3qOXGxk7qKLhorhh4XlR3SwiYQOMaMyg4uv3v1+BoqUkQKXiIikhc1doE22zGuffb6Fex/rp4u6mEcTyHoJ84sjrDkzoXMODfUMxf2zIBhIc6hrqqXuirdO00KT8FLRESKps22j7tnDaDHKznqrWz9RQ/bfvHBiHUzZ38aa++qwDFu+0zzYFku2Q9bj0cTVGjSgUyAgpeIiEw7ldbDInufVt9PL1Vj3u8itex/Zj4nmcfWZz4EwnNf2MSDa++qJonxiS80XGqzRRS8RERk+opakmq6xly/mi5m2Am6vJ0uase834e+mNnV3bx7ajn//bHxXaKsi18kFkly3/1149pPSpOCl4iIlJ0au0ANF8ZcP0qCHU81EeEo4Q92CnfKW7j2cz1sPb6S//G98ICYfalz4PJmLJIkHu3nns+PfbKCTH0TCl5m9l+ATwK9wPvAl939bLDtq8CDQAL4N+7+TFB+N/BXQBT4trv/+UTaICIiMtma7RTNnBq9YpYquvjVky24v8e5EeqlLm0OXd7sppZ666DptlW88IMTGXXHMoEgVS9VVhXt4ZbPzB9322VyTLTH61ngq+7eb2ZfB74K/HszWwXcD6wG5gPPmdmKYJ9vAXcAh4A3zGyTu783wXaIiIhMOXPsCHM4Mu4JBO7QwQw+fL6C/dSTHrfGeqheKvmdz5/krRMreX3TgVFv0ZG9HrUE6z+1aHwNl1FNKHi5+y/SVjcDnw2WNwKPu3sPsN/M2oH1wbZ2d98HYGaPB3UVvERERAJm0MRpmuz0JR9jr1/Bi/8U4bwf5EDIkwsgO8Q5hpMkykc/5fzm/DwWp9+qI6xXLaz7DWio7tFNcXPI5xivfwl8P1heQCqIDTgUlAEczCq/PuxgZvYQ8BDAbA1FExERGZel7KSf2CXcCLeOt3/cSqcn2f/80YxtYbM9w8r6iXH1XXWsvWc+tZWZj7cKvRlu1iEi5iUb3EZNNGb2HDA3ZNPX3P2poM7XgH7gu/lqmLs/AjwC0GZVOTK1iIiIhIlagui4pgKkVNJDM6e4SA3djPDYqBF00My7z8zlnWc6WXxby7DtlpW9jFT4iliSxsoLnO5u5LNfjBGJXNLLT2mjBi93v32k7Wb2B8B9wAb3wcx6GFiYVq01KGOEchEREZkiqq1rXLfqSNfMSRayj1O00PPCwdF3CJxlJifoYd6Gan7wvWRob9pIHKM2fpGF9ce44p4VwwLeVDDRWY13A38C/Ja7p787m4DvmdlfkBpc3wa8TirUtpnZElKB637gixNpg4iIiEw9UUswhw/HtU8r++nyWo4930svFYPlY81PjvGhV1F3XwM//F4P8cjwy5V9yeIOX5roq/81UAk8a6lYudnd/xd332FmT5AaNN8PfMXdEwBm9ofAM6RuJ/H37j6+B3uJiIhIyaqxCyxh9yXvv5NrefunHUBHzjr/lPUgd8eo5Ryf+BeT/zgo81xTEqaQNqvyv4xpSquIiIhMjneS66mwnkve33Bmc4Sb/0UTyx776Zvuvi6snqYLioiISNlbbW+SZPyj+QfGoSWIssPXceG/HxmxvoKXiIiIlL1LnQU6IEY/bWwfNbwpeImIiIjkQb3lHlc2oATvkCEiIiIyNSl4iYiIiBSIgpeIiIhIgSh4iYiIiBSIgpeIiIhIgSh4iYiIiBSIgpeIiIhIgSh4iYiIiBTItHhWo5mdAA5M8DCzgJN5aM50pHMvX+V8/jr38lTO5w7lff5T6dwXufvssA3TInjlg5ltyfXAylKncy/Pc4fyPn+du869HJXz+U+Xc9elRhEREZECUfASERERKZByCl6PFLsBRaRzL1/lfP469/JUzucO5X3+0+Lcy2aMl4iIiEixlVOPl4iIiEhRlVzwMrP/Yma7zOwdM/uRmTWlbfuqmbWb2W4zuyut/O6grN3MHi5KwydJKZ8bgJktNLMXzOw9M9thZn8UlM8ws2fNbG/wvTkoNzP7ZvDzeMfM1hT3DCbOzKJmts3MfhKsLzGz14Jz/L6ZVQTllcF6e7B9cVEbPkFm1mRmTwb/3nea2Y3l8r6b2f8W/P++3cz+0cyqSvl9N7O/N7PjZrY9rWzc77WZPRDU32tmDxTjXMYrx7mXzedc2Pmnbft3ZuZmNitYnx7vvbuX1BdwJxALlr8OfD1YXgW8DVQCS4D3gWjw9T6wFKgI6qwq9nnk6WdRsueWdo7zgDXBcj2wJ3iv/zPwcFD+cNr/B/cCPwMMuAF4rdjnkIefwR8D3wN+Eqw/AdwfLP8t8K+C5X8N/G2wfD/w/WK3fYLn/RjwPwXLFUBTObzvwAJgP1Cd9n7/QSm/78CtwBpge1rZuN5rYAawL/jeHCw3F/vcLvHcy+ZzLuz8g/KFwDOk7vE5azq99yXX4+Xuv3D3/mB1M9AaLG8EHnf3HnffD7QD64Ovdnff5+69wONB3VJQyucGgLsfcfetwXInsJPUB9NGUh/MBN8/HSxvBL7jKZuBJjObV9hW54+ZtQKfAL4drBtwG/BkUCX73Ad+Jk8CG4L6046ZNZL6hfwogLv3uvtZyuR9B2JAtZnFgBrgCCX8vrv7S8DprOLxvtd3Ac+6+2l3PwM8C9w96Y2foLBzL6fPuRzvPcA3gD8B0geqT4v3vuSCV5Z/SSr9QurD+GDatkNBWa7yUlDK5zZMcAnlWuA1oMXdjwSbjgItwXKp/Uz+ktQvn2SwPhM4m/ZLOf38Bs892N4R1J+OlgAngP8aXGb9tpnVUgbvu7sfBv4f4DekAlcH8Cbl8b6nG+97XTL/D2Qpu885M9sIHHb3t7M2TYvzn5bBy8yeC8Y2ZH9tTKvzNaAf+G7xWiqFYmZ1wA+Af+vu59K3eaqvueSm75rZfcBxd3+z2G0pghipyw9/4+7XAhdIXW4aVMLvezOpv+yXAPOBWqZBz81kKtX3ejTl+DlnZjXAnwL/R7HbcqlixW7ApXD320fabmZ/ANwHbAj+QQIcJnVNeEBrUMYI5dPdSOdcMswsTip0fdfdfxgUHzOzee5+JOhqPh6Ul9LP5GbgU2Z2L1AFNAB/Rap7PRb0bqSf38C5HwouUTUCpwrf7Lw4BBxy99eC9SdJBa9yeN9vB/a7+wkAM/shqf8XyuF9Tzfe9/ow8LGs8hcL0M5JUcafc8tI/dHxdnDFvBXYambrmSbv/bTs8RqJmd1N6tLLp9y9K23TJuD+YIbPEqANeB14A2gLZgRVkBp8uqnQ7Z4kpXxuwOCYpkeBne7+F2mbNgEDM1ceAJ5KK//9YPbLDUBH2uWKacXdv+rure6+mNR7+0t3/xLwAvDZoFr2uQ/8TD4b1J+WvQTufhQ4aGaXB0UbgPcog/ed1CXGG8ysJvj/f+DcS/59zzLe9/oZ4E4zaw56De8Myqadcv6cc/d33X2Ouy8OfvcdIjXB6ijT5b0vxoj+yfwiNZjwIPBW8PW3adu+Rmpmx27gnrTye0nNhnsf+FqxzyHPP4+SPbfg/G4hdYnhnbT3/F5SY1ieB/YCzwEzgvoGfCv4ebwLrCv2OeTp5/AxhmY1LiX1y7Yd+CegMiivCtbbg+1Li93uCZ7zNcCW4L3/H6RmK5XF+w78X8AuYDvwD6RmsZXs+w78I6nxbH2kPmgfvJT3mtR4qPbg68vFPq8JnHvZfM6FnX/W9g8YmtU4Ld573bleREREpEBK7lKjiIiIyFSl4CUiIiJSIApeIiIiIgWi4CUiIiJSIApeIiIiIgWi4CUiIiJSIApeIiIiIgWi4CUiIiJSIP8/vmE9otLIWh4AAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -139,14 +139,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -175,14 +173,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -209,14 +205,12 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAERCAYAAACqx6miAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAABh7UlEQVR4nO29ebwk2VXf+T2x5Pbeq7Wrl1p6k0ot9aqWhBYQMkiy1BIaiTEgZPszCEbzkbHx2B7jsQWSmBnADB77w2KjAdrAjOTBCIwNkjFIai0gBOqlWuquai3dXb0v1V171VtyiYh75497Y8l8+bZ6+fJt5/v5xIuIe29E3ojMl/HLc849V6y1KIqiKIqiKGtPsN4dUBRFURRF2S6o8FIURVEURRkTKrwURVEURVHGhAovRVEURVGUMaHCS1EURVEUZUyo8FIURVEURRkTIxFeIvKkiBwTkQdE5Igv2yMid4nIo36925eLiPxbETkuIkdF5FWj6IOiKIqiKMpGZ5QWr++11r7SWvsav/8h4AvW2sPAF/w+wDuAw375IPDrI+yDoiiKoijKhmUtXY3vAT7utz8OfH+l/BPWcTewS0SuWsN+KIqiKIqibAiiEZ3HAp8TEQv8prX2TuAKa+0JX/8CcIXfPgA8Uzn2WV92ggXYKaG9nHhEXVUURVEURVk7jtM9ba3dN6xuVMLrjdba50TkcuAuEfl2tdJaa70oWzYi8kGcK5J9RPxKdM2IuqooiqIoirJ2vCt95KmF6kbiarTWPufXJ4E/Al4LvJi7EP36pG/+HHCocvhBXzZ4zjutta+x1r5mJ+EouqkoiqIoirKurFp4iciEiEzl28DbgIeATwPv983eD3zKb38a+BE/uvH1wIWKS1JRFEVRFGXLMgpX4xXAH4lIfr7/aK39jIjcB/yBiHwAeAp4r2//p8A7gePAHPBjI+iDoiiKoijKhmfVwsta+zhw25DyM8BbhpRb4CdW+7qKoiiKoiibDc1cryiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjYmTCS0RCEfm6iPyJ379ORO4RkeMi8vsiUvPldb9/3NdfO6o+KIqiKIqibGRGafH6x8C3Kvv/Cvhla+1LgXPAB3z5B4BzvvyXfTtFURRFUZQtz0iEl4gcBL4P+C2/L8CbgT/0TT4OfL/ffo/fx9e/xbdXFEVRFEXZ0ozK4vUrwD8HjN/fC5y31qZ+/1nggN8+ADwD4Osv+PaKoiiKoihbmlULLxF5F3DSWnv/CPpTPe8HReSIiBy5QDbKUyuKoiiKoqwL0QjO8V3Au0XknUAD2AH8KrBLRCJv1ToIPOfbPwccAp4VkQjYCZwZPKm19k7gToDD0rAj6KeiKIqiKMq6smqLl7X2p6y1B6211wLvA75orf27wJeAH/TN3g98ym9/2u/j679orVVhpSiKoijKlmct83j9C+CfishxXAzXb/vy3wb2+vJ/CnxoDfugKIqiKIqyYRiFq7HAWvvnwJ/77ceB1w5p0wF+aJSvqyiKoiiKshnQzPWKoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiiKoijKmFDhpSiKoiiKMiZUeCmKoiiKoowJFV6KoiiKoihjQoWXoiyBtevdA0VRFGWrEK13BxRlI3PR7uK4vYka3YEaS4AlICPA+LXbDsmQeXVmoMwgfl3dFmy5LetyyYqiKMoaosJLURbhWa7n7/3y1UzUk75yayEzAcYKmREyE5BZvzaCMUFR/9cf/TNSYi+nQixCRuilm2AIMcW2k3OGAJZhaXNCrRRrYBGobLslLy+3q3X0bTOkbvD18nbV+vnlLFFv+849/DWWd9xCrzHYfqHjyu3h1wyoEFYUZSSo8FKUBZi2O6nTnie6wD2Eo9As6zzv/dh3jrprgBN/1grGOmlgjJcYFlfmy/vWlqJNsfb1WKcsquVuP3896dv+6s/8GQCGfkVikaFrFiyff1x/Xf9+VdYNP5cM6UNZt5Lj+l5vy7qch1/YYjpzmGBeDaM+31pT/XzkfR/8PFdZ7PoGj1vofGtzjy7tnCvty/D2K//cLXS++edf6NzDf4ANO2awH/3HLP5Dzq0fGdoHUOGlKAtykV3ssGc5O9sEIAwMoViCwBKK8Wu3vx6IgIizm7kOjvf1r/7YG8b7gsrIWSh+MRfdyzrHil9TELF9+5sRETuv79XrGqR63QttV9tWz1d9nWFll4KlFBer+QZbqh/DPmOLidTFjht27HI/w/Ok2SL18+pstd3wur4faxb4Xz8zvGOo8FKUBcmIuO1nvp9vPDfFnok5jA363IvGuxbNEl88uUgLpFxEnJATcfadwAu4wTZu3V8m4mxMQVAeW21brRNxv77cPkW9+GOU7c1Cn4HFBMTqWZ51YnOwkr7Pt5gsfI7l3KPNfN+2Nyq8FGUBOrToJBHXv/0Grrisd0nncC47yDLBWOcOLNbGuworZc5NWG6nVggf+CqpDQr34aBr0VjnBjM2KFyIxpbuQjPE1biUWFyKqqjLLW/DhF1eX4pCC0PLmScaS6Hpy8UWdQyK04q4HC5W+/cVRVHWCxVeirIAPWr8m8/8MXsf/Qv+9b/4+5d0DvfQp+KOvIRfqVfdfkmvvZbkgrKIEbN4cVfZL8pykenN8ZXjzMA5THXbuPbhA1/14lG82KzEsPXFs1WEqS+vxroZI8V5lsMwa+JwsTe/blj7QUG4kHgMKkK0sIAOiEoVj4qyeVHhpSgLIjz64nO8yJn17siGIxeUjjV2Ha2T8CzEn51vkewTk4VQrFgih1gugwfu7hOKZlAQ9onL+QLSGHfDVyIeh1kDqxbFqnCcJzBhSFuGHNcvLgdd3vPKVDwq25xVCy8RaQBfBur+fH9orf3fROQ64JPAXuB+4H+w1vZEpA58Ang1cAb4YWvtk6vth6KMmjxo0hjh3IWIXhKQpEKaunVmhDQT50Y0uTvRb5ut/1TpdzWWwf7ugeva5Ja+3OpXPtwpH+RBbt0BCSxBkD/cy3L3APfHVOqLdaU+GFFaaBEIQ6iM61zdCa965eqOvwRycVgVhc6SWN0WrOm3QObu71xgGgs8cHchCActj/2u7X6Xd2lpLF3fK3F1Dwq6YVZEGdJmMWtj1eK4VLtBwakoq2UUFq8u8GZr7YyIxMBXROTPgH8K/LK19pMi8hvAB4Bf9+tz1tqXisj7gH8F/PAI+qEoI8NYl18LYHom5PmTdWqxJY4NT+24hTCEKIIgtAQhhAHEIV485GJinS9ijel3N1K6EPM6Q8WVWC7G5DFpQ9oZJxZe0j5KmvbHwxk7sDbSJxJy4bvYTAO5UAuDimALyrKwsi0D+0WbcOCY0Im+MNx4VpxchIbhCMTjOlgeq5+ZhURhIfgWabNQrGR1sQMislqeVeMpl2ChwTKlwDOluBtoEwbzhZ9U2oWBmXfOfqviGN4UZdWsWnhZay0w43djv1jgzcDf8eUfB/53nPB6j98G+EPg10RE/HkUZUPgHSxYC3FsCW67kRRIgct1NBEw6G5cCUvfvzluGf6auKwZl5o5wxgwWe4GhCyD1Lq1NUJmnPjL19fNHiVLhE7Fopll+CS5bsm8CMzMwqJvUKAFA6IuDC1h4IT8oAAs2vs2Zdkl3oRNxPwYSbhk8Tgm4Zi7qPsG0tj5FsZh9ZmB4IG7Sf0I6tyqmI+ethULYt+yjPjFQcFWiLjAEki5vVDKHPFt3OfT9InF6r6Kv6UZSYyXiIQ4d+JLgY8BjwHnrbWpb/IscMBvHwCeAbDWpiJyAeeOPD2KvijKaChT4230LxJrIUshTb2QSP0v9gzSzAuN/Iveiw6K+KVybU0ZDF/kplnEUGLtwL3x21JZ565Higeo36a0CjrXoSvPLUl527wuCPN1bqlydWG+Dpf3PuXHLnAn55UME4ABK5/kNssqgi+D1Iu+wpqXlULw+tljdCpirir0sqwsG2Z9yV1mYWiJ/L0KK2KuKvCi0D0ooygXevn29hJ3oyZ3UV+ylXGNBOKgGMx/MBTl/vOWW5WzDKiIwKxP9AWuvV8XM3jYhT8woRj3OfOiz33mTLlfKS/qC/HXX17NqbgZGYnwstZmwCtFZBfwR8DLV3tOEfkg8EGAfToGQBkzucUL1jqn0cKkCXS70J4Tej3o9YReF3pdIakk0y++6COIotL1GYROZESR5ejTU30u0MJaVRVEOBGTn3PY/lIUbkdK9yOVMmMG2lVcjeVoR7jtmmmSBIxPw5G7IMsHRSkkc+vUMGtTfm+CkEJIuIdiXm7L+sDdwyCwhJGrj8LyPq6G/PWG3LF5JW1u7ttfidDLhXQu9BID3czdx6wQ4M6S1+0GGEsRp5jlwm4Jcdcn4IJS5EWhLw9LkRflVrrQ+PtZttvoP2i2Grlr3bHM77QRicDic1mxErvYWClS7eSfO75+N7007JuGzVn1gnnTsw398UFphYsKoZb1CTdX7uqDvnZuOwrNmlrvRqporLXnReRLwBuAXSISeavXQeA53+w54BDwrIhEwE6YP2zMWnsncCfAYWlsTlmrbFpKk73QCSbX/PU6bbh4Qbh4UZidEbIUohgaDUujAbW65fiZSeIaRDUIW0uIIYvziwL0YPe+Nb+EkfL03NTwihWam4ypijYwSVnm3Itw69XT9LpSiJXMi5Qsc+9Dls0XdYWAi7xFqRC+fj9yoi0Xw2VM4OpF3GKU1pbBmv4LmB2w5AnuYbCcB4IxzsKaGWdR7VWEXebrrps9Si8JSAcGoWSZ3x7ilnWWOrylzlnfoj5RZ/rKCsEXldY9ZWPSbwVc4nF+x+rEXv6jzH3OGPgx4cXd1+6mm0bMeSGXmgDj1+7zGgy13uWWtyjMxVpGFDrrWxQa4jBz5UtMJzeKUY37gMSLribwN3EB818CfhA3svH9wKf8IZ/2+1/19V/U+C5lo9EXK7FGv3rOnxNOvShMXxTqDdix0/L0zCSNlvuSSoA2kAeXTe1am35sZQIv1BaLCZsn8nJxFy98TNU9aHpURBvcesgJuXbmrJaF4Ejmi7iqgKsKtDCyRF7IRbGvy/fX2QEQBBDUBm9P/1d4e0DYLUfU5fc09Us39VYRf1+vnTlKu+PEXJqVYi7NxA/E6P9HdQ9I7z4NLXFVyEWGyJeFkSWOTFGvAm5zk1v2omgRWXGJ4i53++efweLz5z+Dva/fw5yJSbPFP0Sj+Be+Cvi4j/MKgD+w1v6JiHwT+KSI/DzwdeC3ffvfBv6DiBwHzgLvG0EfFGVNsIxed50+KTz7dEBrwvJse5LWLkgEZnowsYChZ5wY4wRD0vPCIaWw/Ji0tB7lFiO7vLnCL43cLRqU8VlF/FdYlgUV12q+HUa5i9GtR+k2WCxWrE/IVU1J9fltc7GRZWA65XaWwi2Hpul0nGBLU/fFntdV+xHloi0uhVkUWeJ8v1Zub2RRkd/TqE/RlQ/ParydUI7kWojM38s0cVa52UyKOMhrpo/S7QUkaVA8NJN0voDLY+DiKF+bYjsKLbXY9JXHkbpQtzK51a4WLyDqqiljfnbh84xiVONRYJ58tNY+Drx2SHkH+KHVvq6irDWF1WtEX6RZBse/HSABzLYm6EbrK7R6XejOQbcDvY5bZ5kTKHHNLVHsRMtDDz7vc2y5PFrFSDNZ24f5vFQVPn9UnneqyD9l4JWv3k+aVIRhWhE1GX1GGQko4rgKgVa4CiHIt/2yVg/T4WLD8WzHfzgWURmFcEsh65UC+ZaD08zOOKGRJEKauLhAY8r4vjiGKHaibN52rdzerEIid7nWanlJ+QFoX1mKuHyU7BBdXHyO0hTm0lwAw7XTR+l0A6ZnQyfeUnGu1ax0oQZSCrZa7IVZbKjFpWCrRW5/UeuMsuXQqHVFWQBb/BnBuSx8+xshey8zPDU7tS7/eFkG0+dh5ryLKYtr0GjBN44+TxQbotgF5g+jufZhbkOZn7Ji4bQCjz76zLLPW8Z+CSYrh/ff+qr9dL3lKU0h8+7B/KXEi6Qo9kItzl2B/eXjYiHh9ky7ouiHiDZjYDZ3hbbdtd5ycJr2nHAxcWIt6TmRkQuJKIJazRaiPN8u15tXpC1E4VYdEG+zly8t3Ixx9zVN4GLiLJdJAtdOH2N6Nixi4HLBBl4QezFWrxnqsaFWM9Rit9RrTrRtZMulsjQqvBRlMUb0IDn1otBsWp6aHb+Jq9uBU89Dtw2TO+Hxx58lrpcukdYGcG+OmzL2qz/Y9/gS4s1ZmHzcUSq88jUH6HVgLsG7BJ07FkqRllsO47qzIMV1b0lcx4fnMMHW5yIdItbaXkQkF9365gPTtNtOoOUjbXORVqu5ASH1ul/7ASL12nDr3lYkCPx9qEH1M9bxo1ZzD3Sjcoy1TpwlPZjtuXt6zYVjzM6FdJOAXi+gmwTFfY4jJ9Aa9cyta4ZG3S31mtlyQniroMJLURZglN9ZJ54PmK5PVH45rz3GwIvPQnsGLj8ADx19hlNnodZY+lhlOMWQ/BjA8vC3FxZqLr+aFMutt++nPVPGzuVuv8KCVPdLw4mzjWbVyN2u9abbL1yhIdAq21kLswkkbUgvOoF24ZzQ7QjdnhQxanEMdT9qt960NBqWZpOx/o9sNERKsTbhxVr7in6hVrnVLuVMD2a7LtXMoQvHOH2+RrdbCjQRaNQMzUZGs+7XjYxWw6iLc51Q4aUoa0zqc26N84FiLTz7GDQn4OSZZzip83yPHREXNxX5QNzjx+eLtKo4u/X2A8zNwvkzLv7OWid0ckHWaEK95bY3MlUxCRVLWs0vnrkEkg70zsMrrprh4nmh03Y564IA6g2XTqXZsjRbMDFht421bLnk7u2JCfcZ6111c6GDc4FmjPs8XegIJ7uw/+xDvHC67kaIZkIQQKsQYxmTLbfECwWQK6tGhZeirDHtNrRals4YX/PMi84y8dhjy497UsZPVZwNs56ZDNJESJOAm27dz7nT7iEaBKUQa7ScwN5oFrKlyEVDcwKe7026MfETbjEGphMnym5sTnPqpPDUbECaOGtQa8LSmrBMTFomJjfftY+T/LPSaDohlV55U58GNsaFI5zpCPUzR3n+ZJ3ZdkQvEeLIMtlKmWhlTLUydkymKshGgAovRVljkp64X//Z+F7z/Gln6doMDyRjIPMj71zeJukftejbCfgRldanl7B+uiBL4HM1jTptxHoThFALLbVGxhNPlMLMGEh7ws23HeTCaXjhaXfdrSmYmHTrcQb5j5ogKC19T89NuTffD/BoJ/DiRXhFc5oXng+YmRaCEKamLFM7LTt2utgyZXkEATRb0GxZ2ntuIQDyaL80gelZYceZBzl5tsajT7VIM2FqImPnVMKuqZQdk+mm+J7ZSGzif01F2RxkxmcSH5Pw6rbdA2sjfxkaA3PTIZ2ZEGOFODaEsRNPp556EgnyOR5tMWWTtQJWyvkkjXDl9VfTS4JCsGWpeCuSIa4bag3TN5BgqxAEUGtYHnm4IsYy6D0d8PKbDnDqhGuzYw/s2L21AtqjGCZjP3IzBHa5lA+nL8DhaIYXnw9IEti9x7LnMsPUjq0lxsdJFMPOXZaZXbcCbpoZa2FuFsIXj/LMCw0uzkTUY8Nle3rs29NjormWif22Biq8FGWNmTeZ9BrT65YB0BuR9kzI9LmI5mTG7LlHCEJLt1K/kuD/cyeOzytzcVMhl19zHbMXI5JeQBBYJnakNCa27kivIITGhOHJJ50YSxPh5TsP8vSjLt7q8oMubmorEkZuxO6JdNK5KjN49Axcm8xw/GFh7z7LVQdMJaeXcqmIOKvqzOStRMAeXHqa4PljPPz4JO1uwP59XQ5c2Vk40eg2R4WXooyBcU60nWULTci8/nTbAbMXQjoXv013hgXzhq0GZ/HKOPt8KcqyNCCuvZTpczE79ybUW1v/V3kU2yKgv9sOSHoHaE3BlYfWuWNjIAidpe8sk5id0GhM840HQ3bttlxzvebBGjWNJsy+5BaaL4E4heDJYxx5aCc7JlNuuHZW48IG0I+fomw11mKeoxHRmQ25eOpxZMzfPGFkuHjqEdoXHubCmZikt0Fv0BpRbxpOnnkGa12Kke1EEMCTM1N0d7p0Lt88GrqkuMqaEEUw99Jb2P1dh9m7q8c9R3dy/qLaeKqo8FIUZWxkqRBE6/fUC0LLxVOP05ndoCbBNebJJ59h+ryLsdtuiMBz3Ul27ra88Pz2Et7rxflrbmPiOw7z0KOTpKne8xwVXoqijBE7smmYLplt/P0v4gZe5LnltiNPzUxy/qw++sZFowGX7+lx+twWGuGxSvTTpyhbDWH9xc0CRLHl8muvX7fXN0bYue866s3t6Wuy1g2+iLax5ydN0IztYyaKLMZu4188A6jwUpQxYMf4pRMEG9eVNLEzZfpcRNob75PfZMLuq15Kc8cNTO5KqTW234PXGDh04BC79q7NoIbNQJbBXjvLlfu33/u/XqQpnDhVZ8/ObWxmHWAb/+5RlPEgUk4ePA6i2M0HuBEJI9h7ZY8weglhaDn7/BNEtdTNfzgirAWTBWRpxL5rriHpBF6IZuy9qrupE4teCtbCK246xJkTMLEDdu9b7x6tD4f3TPP0EwFXXm3YuVuF1zhoz0Hy4CNcd7BDo75Bfw2uA9vsK0hRxk8YMNZRVI0WtGfH93orJapZLtvfo9sOCKNr6XUCjBGi2BDFljC2nHjsaZehvpKGo+pBzROognDFdVeTJX4y6sxZFqPYEtUMcc3QmsqK+RK3C9ZCrxPwshsOMDvtkuoeOrzx53kcNWkCL9nlMtyfNcINN2Y0W0sfp6yO2VloHP8WvdmQG66bZffOdL27tKFQ4aUoa0xcsy59wZjcO0EArUl4xVWH+NY3N+5cjfWmoe6zXFvrEn5miZCmwhXXXlNOG1TRTC75qfVTB7lpg4LQUm9mhNHWmzJouRgDSTfgxpsPMHvRZXJvTsLkDrjy6u1zT4yBzhy8ZNcM584KEkCaCjfemmny1DVmdhZ2P/MQL56p06gZ9l3R4+bDG9T0vs6o8FKUNabZhLk5KSdAGwOXH4AnH4ZeV6jVN761R8QJ1Li28fu63qSJkPaEm249QGcOeh03h2Wj6TLUH7jerbc61jpL3iuumGb6ojAzLRgDl01ZmhOW/QfNlpoqaSNhLczOwL4TD3H2QsxcJ2CyldHYY/iOmy9owtQlUOGlKGtM/uWf9Mb3QIxiuPowCAfZuRceffSZbWP12OxYW04afsvtB+h1IfGLxX2GGi3nNtyx202xtJXfW5O5kZjdNtxwxQztOei0nSV0V8vS6wmX7XMZ6bfzaM21IunB7Kxw1aljXJwNmZmLsBamJlLCHYYbrp/R+RlXiH5MFWUM7D9ouHB+hjNMju01a3W47kY48wLs23OIHbvhkW8/S6RWpXXDGDA+Fi1LhVtv30+SQNpzDzhjylxbsZ/ofGqX369tTYFlMnftSQ9u2j9Ntyt02tDtCmnipr/a27I0d0JrwrLvckujubEngd9spAl0Ok7QHjh3jNl2yFw7JEmFWmzZNZEStgzX7O8x2co27JRkmwUVXooyBvZdbjl9MuDQrmmeaY/P5xgEsG8/7L0CLp6DgwcPkqUwMQXf+sbzxHWz7QLPR4kbQenyg5lMMBnc9uoDpImLs0pTJ6pM5qxVQeAEVFRzubSiGBoTriyOt1aaBzdZuXuoJz23vmn/DL2eE1W9rhOaYQj1hqW+y+1PTlou22ep11FX4QiwFno9bzXsCIcuHKXTDel0A9qdgMwIcWRpNjL2NDOazYw9uxImGpm6DNcIFV6KshQj+u552SsyHn80YFc2w2mZHOtDJQhh12VuyTKYm4abb9tPe9Z9IbuHH9SacOzrzxFGdtsEqxuDC+Q34rfd2hjhtlfvJ0udgMgyyBI/QjX/TIhLkRFFEMZlYtJGqyyPYrZECotcZKb5/Ujc9s0HZ0h67uGe9ISkkq6pVnOxe7UpqNUstRrs2AX1uqFWV6vVajAGEi9qez0h6cHVF4/R6QZ0ewGdrhNVQQD12NCqG/bWMzeqeHePRt3QamT6HqwDW+DrQFHWBhlx+vcwhMMvN5w+KbSfnmVi0vJse5LmxHjFTRg699XUrrIsS6HbcXE0N916oLBQpAmFyAhygRG67TB0gu7B+593qR/8SEMJrFsDCC4lhFRm6lnqWvNZhfzrWit9ZdZWyir71sBtr9mPNd6lZ3JrlBNWWeb2B99WCdx1hJETAmF1O4J6vXLtftnMYjS/L1nm3nfj17ccmiZNnHBKEjcaMPX1OVHkhFTU8pa7yBLXYHLKC6zYCc3NfH/Wiyxz9z1LIUmEXg+uuXiUJAnoJgFJInR7AWkm7v9LnKBq1Aw7Y0MtttTqht07Euo1Q71m1CW4QVm18BKRQ8AngCtwX2l3Wmt/VUT2AL8PXAs8CbzXWntORAT4VeCdwBzwo9bar622H4oySgSL9Qph1Mb2yy63XHZ5xvlzgjkxw+x5YWLCsmOX5ZHTU9Sb439whZFLQdFaIARt0NqRP6yNgVtftb8QOCbzQshURZHfL062cEJZcam5KqKtXKr7QSHy/HZQlgU1t52Xh2H/ejP/ws/vc1a5//l7cevV02ReLGX+vUoTcYKzEvssklviLGHTuThzMdlsWXbEPg9aXAptZXkYk993J57y9+Gai0dJ04AkFZJUSNOAXiJkpvxHDwNLHFlqsWEydusotrQaKbXYEMeGemx1uqMtwCgsXinwk9bar4nIFHC/iNwF/CjwBWvtL4rIh4APAf8CeAdw2C+vA37drxVlQyFeejWymTU5/67dll27LdbC3CxcvCBcGTkhFgTuIVhvQKNh+eaJKeJ1DLAWKa09ytJY229pq65zsXTr1dMuyD7rt0BlRgrX5qBADUKIQggjZ3UKw9xCZwlD585zLk7rxFVUWu+UhckFUvk+eNGUwXUzR731z4umLCDLhNQv1fcoECeeosjSiAxx5PdjS7OeEoWWOK6Uq4jalqz6a9RaewI44benReRbwAHgPcD3+GYfB/4cJ7zeA3zCWmuBu0Vkl4hc5c+jKBsC52Z0X4prPd2PCExMwsSk5aoD7sWMgXYbum2h04Frd8zQbgvJRVcXBN7tE1vv8nEP32PPTDmB5N1luYUnCEuL0HaliOXKRZEthVDVPZmX33r1tA+aL61MhYUpL7ella+KiHdf5m7Mmi1Ek3NhWj960RJ6l2YYujZhIa629/s1iLWD70EulNx7kXoBe93sUS9onUUpzbcr+4PvVxhYotAJoTj0234hgmYjK8si27et75GyUkb6+1VErgVuB+4BrqiIqRdwrkhwoqyaTvtZX6bCS9kwCLlvpnQ5jpMggIkJmJgoJsnpq3eZ3qGX+ISaPtD51qtdnE7qR9QZ43+994YLhPy1gj6Xne1351WXvIyKKPBrKdYLK9XqZOG21LZO7FTK7EKLAeOz2ReiiXJ7qXsqAQSFKLJI5EWp9K/D0N2DKLaFeHLxX7ZPyOYxYdv14VvE0dlSDDnrnlRGfJbC9frZY04wGSeCssy9n1kmvoxCIA0i4ixKUWgJQ0vs348otIVwCnxdo2YIQy9qQ+PFrG+nYklZZ0YmvERkEvjPwD+x1l4UqX7BWiuLfRsPP98HgQ8C7NMxAMo6EOQWrw2YG9Bles8Tsg7+a63MRFe4wCruMVMVNpXgdmPoE0bVV+sTdcO6IGVF9cGXi7jBOK48zisXgoMCMPAiCilF0Hagz1pXfc+KRfpcndX38rrZo27Upi3Fj/FiNj/WVMpyYbQQgTghGgZO8ESCFzxleRDk4tXNw1n3+/kxIk4MBZVjNa5MGQemIvQL0Z/vZ+WPA3ngHuditkJmgsoipCYgzYIV/UAfiaIRkRgnun7XWvtffPGLuQtRRK4CTvry54BDlcMP+rI+rLV3AncCHJaGOsKVsRLgFIYIJKlw9iuP+rgMQxRZ6n4U0ZNTtxRuvTB08TWlRaW0jGxkwpAl5pHc+v9+VfcjthQ0VMpzYZLXV8VP1YV5/VwpbqwdEDY2F0Xlelj9YuSWHyc8/TqwhF6k5qKnWhf40aZB4MRP33GB9ZbAfrGUC6GN/vlVtgb5/1duETXWu5Gtz5FnS5FkDMjX73Y/DkwpiNxxThAVdUbI7PAPcSCWMDCE+TqwBIEhFOOtqOV2PUp9G+PLfb0/PgiW/z05ilGNAvw28C1r7S9Vqj4NvB/4Rb/+VKX8H4rIJ3FB9Rc0vkvZaIhAiBtHv3NHynfefr4Irk1SoZcE9JKAa2eOloG2PvDWGBcD1DNLWwyqrxdIadmpbgPFP7X49AzVOmFgP29TOXf/uvyCKCxLA30pd1YouoZca1VI2MH93G1oq/XS716kFCO5WMm3+9pSChprl3ffi8us3PMgcPfOuVpLQdMneCptcpFCXhdaAi9uiom883MEDC0Pqq+hQkdZQ6o/IvIfB5np/5Fgc8spFbext4CGD95NZtzQI2OEzHorqRV3Dhs4sWSdSLJ+bawsahUKxBZLGBi/tgRi/P/IQHlgicQQBmnRJhdJ1Xa5oNpIjMLi9V3A/wAcE5EHfNlP4wTXH4jIB4CngPf6uj/FpZI4jksn8WMj6IOijBzBcv2+Q1z1sh3OtRfbSibnbNFjV0qfRQWKL8RccJhBkeEFiTu4IlzyNswvq75WsU1/3BV9x13SlcyLn+lzK3ohVxWFuVAs9plf5lJMVGPPbJ+LshCjqIBRVk/1/ywXIEsJfYt4y6cMrS+tna5t+MBXnXixUqkv98s6Kc5prPS3y1/Dt10OQm7xdP9DYWDKHxXihI5I+cMhDEzxfxWIRQI3AKE4hxc7VeGU11XFUn4+ZTSjGr/CwikR3zKkvQV+YrWvqyhrjUX4J2/7AV7+7hugCLZfG0Ty1ABLBUop24E+K6AtxXG/ZXD+YIX8ge/2+y2FRRtknlgv6/pfs/+4BV7PtwkfvHu+hRIpz1XtV6V/Rb68at+QBeur5xt2rr4fJZdIYUXOxb7Yitgvy6BaZys/AEphUz2uEDNewOQiKK+j0qaso69NMPAagdhCGCmbA41aV5QFiEiYqPWY/vJ9NN/26mUd0/cr2Q6xVFE+vIog6SEPwqUegjD8QThs3de/ygMpfOCrC1/HAg+u5VrBBo8fPG6elW1IuR3SvvpAn7c/r06Gtp937BBRsJzj15K8F/kDttjucyH3b+dt8uMHjy2PsXmjol31mL7zDXmd+a/hX6dw09r5x1f6QkWIVK+xbz3kOqvnGWwLFAKk2kdF2Yio8FKUBdjBeeIo5eEX9vH4f3xiWcdUHwrVX7zBwEMD+n8pM/CQHHwI9j2oKMvz9vlrF/2oPhArZX19XeQX8mLTJS01QHmhZ97gccP67QqGX+fgMf0jIxcWDYPnWvC4gQf54PGD91tRFOVSUOGlKAsQkvK1n/1v/PDH3rDeXVEURVG2CBp+qigLsJcXOcVVlxhkriiKoijzUYuXoixALAlNO8up6Ql2T7RdnpjK0Ol8iHQ50igo6owV/vpnPgOAIcASYPzvHLfvHIGmcAjmS0AZhVOWU6zpq9+YzO9d9Yr629h5begrW/ocS9cNP//gaw9zry7V78WOEyyv/9l3Lun6dOv5MVDD6gfds4vFQw2ed1i8WLVfC7ljFUUZLSq8FGURDvAk//mn6gAEZF4WGQKMl0lZsd1fXsqtfJ9iTVE+WJbLquoDvf/hXqnfoA/HwYD0/m1ZpG7h4/rbXMo5Fj5mWLD8/DqZVz5MHpZC2ZXf/TN/OvR1+s8z/zUW6+d8Ib7QsdK3P7zdQte1Vh+uitgrXm3wcz7/Coa1qZ4nP/fg/8389fz/qcH1G372HcuK86sK5cXqlxLLLPIag8cNO09xjsq5hp57g35fbEdUeCnKIjSkzSt4YL27sakYHjSv/lplYFTpPAE5TAT2i8OlReh80brwOXL6BepiYnmYIB98zaXaDnvtwXbD78f8uuo9GF4+zP68WVi454NW5vlW5+HfN4sOGlr0O2r531/LudMqvBRFUZSx0GcpUjGuLEB/kueFLdIly2lzaalgLuWYpVDhpSiKoijKhmEw5ct8Nrdo11GNiqIoiqIoY0KFl6IoiqIoyphQ4aUoiqIoijImVHgpiqIoiqKMCRVeiqIoiqIoY0KFl6IoiqIoyphQ4aUoiqIoijImVHgpiqIoiqKMCRVeiqIoiqIoY0KFl6IoiqIoyphQ4aUoiqIoijImVHgpiqIoiqKMCRVeiqIoiqIoY0KFl6IoiqIoypgYifASkd8RkZMi8lClbI+I3CUij/r1bl8uIvJvReS4iBwVkVeNog+KoiiKoigbnVFZvP5f4I6Bsg8BX7DWHga+4PcB3gEc9ssHgV8fUR8URVEURVE2NCMRXtbaLwNnB4rfA3zcb38c+P5K+Ses425gl4hcNYp+KIqiKIqibGTWMsbrCmvtCb/9AnCF3z4APFNp96wv60NEPigiR0TkyAWyNeymoiiKoijKeBhLcL211gJ2hcfcaa19jbX2NTsJ16hniqIoiqIo42MthdeLuQvRr0/68ueAQ5V2B32ZoiiKoijKlmYthdengff77fcDn6qU/4gf3fh64ELFJakoiqIoirJliUZxEhH5PeB7gMtE5FngfwN+EfgDEfkA8BTwXt/8T4F3AseBOeDHRtEHRVEURVGUjc5IhJe19m8vUPWWIW0t8BOjeF1FURRFUZTNhGauVwqMlfXugqIoiqJsaUZi8VI2Py/Yg7xgDxKRApYaPWp0qNOhRtevO8T0ENVniqIoinJJqPBSADhp9/OPfu0QQWCxFjpJRDuJ6fRivvSRL3CR3fSok1DDWiEipeZFWUyvEGY1usSSrPflKIqiKMqGRIWXwoydoilzBIFLtSYCzVpKs5bCRJu/+7HvmHdMkgV0kohOEvGlD93FHJOcYy89GqQ2xiJEJNToFuKs3Hb7gawotZuiKIqibHpUeCmcYx/v/oXXADPLPiYODXHYY6rR470f+66hbarirJvE/MVHP88F9pB4RyZWqLo1+8VZzy1qPVMURVG2ECq8tjnGCjPsYK4X8+WHryMOMxpxSj1OacQpjSilHic04pRmnBZWseVQFWfAUMuZMUInzcVZxF98+C4u0vLirF5YzwKMF2WlQCvEGT0iEo09UxRFUTY8Kry2AC/ahHvsDP+d7EKWqT4SG/O8vZonuYGMiLOzLW77geupx5ZuInS7IZ1eQOfIvZxvN+gkMZ0kIjNCIJZ6lNGIk0Kg1SMv1OKUWrT8uTWDwNKqJbRqzrL1vo+9fmi7zEi/9ewjzr2ZUCOhRkrsJ6WyhRirFevSvakuTkVRFGU9UeG1BfianeVOc4obwyYvpbGsYx7n5cwxyXs/cjXXvPNWarEFDABRZJloum3eedu8Y42BXhLQ6QZ0ewHJffdydrZFN4nopBFJ5ubWjALjRVlSiLKqJW0l1rMwsEzUEybqCdDmb3/sdUPbWQvdqgXtpz/HHJP02EuPGqkfHAAQYLwY67ee5WItIlGRpiiKoowUFV5bgO+WKX6TU3zRXOSl4fKElyHiO//Z6zjwthuoxWZFrxcE0KgbGnV/3DteObRdmgptL8463YDZI/dxxgu0bhoVecNya1mx9q7NepRRj9IVuRBFKEQewHs/9p0Ltk2zgG4a0k1LN+csO+h5K1pSxKFBSFq4NGOSeYItIiGUld1HRVEUZfuhwmsLMCkhr5UJvmyn+YDdR7gMpZIRkGRhKZ7WgCiyTEUZUxPe9fiuW+e1sRa6vYBuT+h0Q7Ij93BhrsnJ3GqVuo+oc296i5lfX6p7s+hfaIhC461oC7s5wQ0U6CYRvSwkSUP+/KdLkZZ6kWYLkZZVRFpv6HYk6Yr7qyiKomx+VHhtEd4sO/hrO8PX7CzfIZPLOEIAIVjnuQtEcusZ7JzK4I7bh7YzJhdoznqWHrmXc3NNOr2YbuoEkbXOJZm7N10cWmlBa8QpcXhpQjMfKJDzwx97w4Jtkyyg5y1pSRryFz/9OdpMcJHdpMQk1Mhs6Ftbb0HrFpa0iB4xSSHW1OWpKIqydVDhtUV4tUywg4Av2Wm+g+UIL4cxMNcJmZ1zwfRJEtBLBGtdQLsxzopjLVgrfXFZQWARgSi0hIElDP0SWKLIEkf52hD7/UsdeRgE0GwYmo3F3ZtZVhFoPv5spjsxL/4sFEM9zvriz2oVC9qlCjTIRVppSVvM3ZnHpHXTkG7i+vflj9xFuxg4EBdxaRYprGmDS+SFWr7oCE9FUZSNiQqvLUIswnfLFHfZi8zZjJaESx5jjfCXR3azcyplopnRqGe8cNnNRDGIWMKQwiIm4hbj9Yi1kFq3bzIhM2AyJ3yumz1KuxNwMRWSNCBNhcRvW1ueLwqdKIsiSz324iw21GJLLTbEsaEeO/G2XMIQWk1Dqxgc8Mqh7bIMOr2Abjekmwi9++7lYrtON43ppmGfQBsUZVV352oEWn4fipi0ZhdgwYEDUI7u7KUhvbQcPOAEWrngb1kem9bv7uy3rIWycjetoiiKcmmo8NpCfG+wg/+WXeArdoa3yc559dbCWbuPJ3kZ0+wis0KrmVF/9ctJcelT93IpLq3+Y+a4pdgO/VIf0pcshTSFdgIXEyFN4ZoLx5hrO6HWSwJ6SUCa+VGIYqnXLFFkqNecKKvVDLXYLfWaE23LsfaEIUw0TWX05iuHtssFWs9b0KoCrZNEpMaJyTg0ZQyaF2Y1v27EKdEqBVrR777RnUtb05Is9G5PJ9S+/OHPMctUkYIjJe5ze0akfS7OeDA2TePTFEVRVoVYu/FjRw5Lw/5KdM16d2PDY63l72VPslci/s/w0Lz64/ZGnrHXc+h9b2Sy0aVVS3nJW6/H3HTTOvR25RgDvR6kCSSJkPTg6gvH6KUuML+XOLEGzpJUjw21minWjZobiZmLtHBpo+CySVPpy3+WHrnXW6aieQKtz4IWpTRqSTFQIFxBio1RYy3OkubFWi7UqiItoUZGeeMCjBdk3npGSkTq16WAC0l11KeiKNuGd6WP3G+tfc2wOrV4bSFEhO8NdvC75gwnbcLlEvfVp8R8zz+6keCmQ+x542Egz9y1OQgCaDTApSpzAqVz5c0A1PySY60TaZ0eXOwJ11x4kNl2yJnzMb3ExYBlPn4tCi11L8oaNUO9nrm1L1vOAITIx7Mtlv8MnGDsJgHdrrOgtY/cy7m5Rp9AE4FamBUWtHrk5s2sRSnN2A0aWEkOtOXixGpGPS5dj4sNIgDn+qyKtTQL+csPf5YOTVJ2kHn5lRFjbHkjQ7JCnFXF2qBwK/bVHaooyhZBhdcW43tlit/lDH9up3mv7Fnv7qwbIlCvuwUs03tdKosAp9uq2c7SxIm0s11nRTt04RjnLsTFCMo831huKXPiLLegZd6KtjwXZxxb4jhjsuWFxPcNF2i9ROh0gyJR7cyR++ikZQ60zIi7xhHmQLsUwsC6CdUp3Y+LuT9z0iwgyVxKkyQLSDMXV/eVj36WLk0yQjKiwtJWFW1gvXDLiOgVLcNiyYZvq8VNUZQNgAqvLcaVUuNGmnzJXOSHZPeypxDazkSxW1oTzorUvfJmhH6BZm0p0Ka7wtXnH+TiTMTJszW63YBu4lyJUWhp1EuB1mhkhYuzUTfLFkJugEEGLJ4DLRdo3V5Iel9/DrReGmIRhHKKp3KAwOpTbKyGPIdaVbDB4rnUcqyF1DjRlmYBvSwkywJSE/CXH/kcPepkRH4Ji+1+8ebcpLk4C/w6F3CBP7Iq3vKygExFnKIol4wKry3Im4Mpfs2c5DG6y55CSFkcEYhrbpmYtMx4C1rklwnfLsug14ULXeFk11nPzp6P6fZc7Je1bpBALsRWI85EoF6z1GteoC2SAy23nOU50GZm+lNsWFuZ4mnIBOn1KBvZAIHVIlKm7BhkOcItJzNC6gVbagK3nYWkJuArH/ksXeJCvJlCwLntQRFXtcIFuTgbEG99wq1YpwQYFXOKso1Q4bUFeaNM8Ruc4gsrmEJoI2ItdLvQaQtJ4kZBJomQJk7gWJ/OAguIiwGTwKWGzVNhiLiyMLSEEUShq4tiv++XURGG0GxBs1VazwKg6RfwyWA7C4szY0rLmVsymvm6Yfy8mstnJVM85QME2l0Xf3a+3XACLYnIvNjIBVotci7OepT1CbQ4zDZFHrEwsIRBRp358WNLxbYNklvh0iwg80IuM+LWWchXPvIZUuLCjWoqzlLjbW/zxRwItk+claLNLFKezSsL2BzviaJsB1R4bUEGpxCKKt+4cknpIsZHew7OnA44f1bo9aDRhEbDEtdcAPu3TkwSxhVR5ReX4LWSZ8y47bz81kPT9LpCOysFXJa5FBZZxdsVRhDHENcscQy1mqVWc67IWs1Sq7Pq0ZBBsLQ4yzInzs52hUPnnFvzxTM1ur2gGLlZi52YatYNzUYpzpY7IGCQeQMEFog/qwq0biJ9SWrzWQTAfdZqfvaAWpT6AQMZtbgUbJcy1dNGZDErHCwv7m0YJhdvRshMQGalEHf58lcf/QwJEV0v5kxl7SRXhEGKeUerlMKtFHCleBusMwP1Q+p0hgVFWRIVXluUfAqhr/sphC6YXZy0V7J3epJ9G/C7cXYGnjgeIgJ79xnOx5PEE5AA0wA9t+zce2nnf3puqr8g9ssAWeZiudIZJ8huPjDN7IzQSyDpBfS6pbgLI+fqq9Wh3rDU61CrW+oNJ95WY2EIQ2hNuLizmT23Du1y0oN2B853hEPnj3FxJqLd6Xdpumz/Toy1KuvViMf5IzhfObRddR7OPCdbbkXrpVExGhJKkeYsZin1OHNCzY/irEVufy1Gc25kgsBSCxYXpyu1zlUpBF1F2OX7Jhd3Vvirj36WnhdzFilEnanY2QxhMV9pP5YAO0TgDZZV195K51241Xrxx6lrVtmsrFseLxG5A/hVXH7N37LW/uJCbTWP18pJrOX92WPcJDt5q/wQz9truPG9N3L7912F3PxyGs2lzzEuzp4Wnn4y4EI8QaO13r1ZPlkGac8JoJsPTNPtOCtd7hq11gmoWh3qdUujaWk0vEhrjNbFuVD/uh3odoWD547S6QTMdUIX55W5eTobtdJa1mxkNBsZrYZZ0WwBoyAfKJBb9HpJQHbkHmdB8yKt6wcLgJtRoOZHbtYqwqwWp0Uqjlqk7rWNgLVg7HCBZ6yQZUFR/1cf/QwW6bPYVcVeLtfyugVesbD1BRhkwFInQwVf3q605vW38/Vq0VOWyWJ5vNZFeIlICDwC/E3gWeA+4G9ba785rL0Kr0vj17MX+Zyd5hM//is0b/1OJr/jpUwsfxrHsZD04NgDIZ2pCcItaH81GSSJF2f7p+l0hE4bOh3BZM4qVqs7d2q94YRZo2lpNFfv0lyyb8YNBOh2hIPnHqTdCQthlqTzhZmLMbu0OLNRk8/JmYu0XiJk999LLw1Jsshn6p8v1NzixFmxnYu2KFvXBLbKaDBGvJATMhtgjMyz5C0k9PqteaXAyyUYDFfygyLNCTezpNirWvWkUtZXrz8eNiUbMYHqa4Hj1trHAUTkk8B7gKHCS7k03uynELrn8SO85fY3XFLcz1rzwomA/QcNT82ud0/WhiCEegj1Bjw5492dFZ+hMTCdQHIRktPwiitnOH1K6HSELHXuzEbd0mhBs+ksZY2m9fnJVtm3wMfQNS3Tu507s5qINhdmFzpuEMCBcw9x6lyNdsfNEpDPDtBolG7Mpo8xW8nozEth3pycsGBMGlSEWhqQ+BkOuvffy3SnXkyr1EvDYgCBYIlDM0SoOVdoVazpg3FjEQTOtRmFUKRjWYDVuGmrzBN3VYveELGXVuLwSrGXr/stfMNi8yAXe4tb9sqz9Ltpg3kiTy1742S9hNcB4JnK/rPAwjMDK5fEy2hwOS2+8M27ecsP/M/r3Z2hTF8UzsgktREIic1IEDiLV379J9LJPmHWzeB8F3qn4Marppm+KHTaAb2es5Y1m5ZmCxotS6NhabXcQIBR9S0XZgDJlTcRAXm0XD47wGwxCOAY5y5EdLplnFkc2QGLmY85G/GUTUtRCLXqXA0LzC4ApeuzlwQk3qJm7r+H6U6NXtokySJvXQuGWtWiIOtzgcYVt2gUrK0oVcZPLvaWkxNvLcSesTJU/OWC76/8qNqMsJBr/Va9fmvf8Fi9+Za9QbFXddkuJfCqgnG7xettWOeOiHwQ+CDAvo3bzQ2NiPA62c9/ffYRTp45wXUcXu8uzaPXhXjHevdi4xKE0Gi55dnOlPN0tNxiDFzsQveUs5RdPC+051x8WRhBq+VEWWvC0mo5a9koH/iDswN0LnejM333ADdQoduFi23h4PmjnD5Xo9MNaHfcAyHPadZslGkzcqEWr6M7c16ONFgwT1pOllG4PZPUx6ndfw8XejFJFtDzOdN6Pm+aSL9Yi8OqUOu3sqlYUwZZidi71FG1gwyKvap1rzogw1gpRttWXbaDrluTC8EhqVRgvlVvKQEXeDviwu1923X+X1ovRfMcUJ3F+aAvK7DW3gncCS7Ga3xd21q8VvbzX+1x/vyez3Dd29+03t2ZR/4AUlZOEEC96ZbnupNuPiQfw9fJ4FwHbtw1zcXzwgvPB3Q7rq7RhIkJy8SkpTXh4snW6j3IZwWYmLS0990CQN0vUOY0O9vpT5uRT3o+zJ2Zr+u19f8CrRKG0AwNzQYUYm2BnGk5uQs0SUuxlhy5h9le002n5Ed+JllpHhyMV4tDM1SsrceMBMrWZtyWvTxeL60IvUGLXnXkbUo8IPJK921V+A3LqlRa85Yj3hZvEw7MyDHvtdYpuD7CBde/BSe47gP+jrX2G8Paa3D9pWGs8CQv4zev/DYXsoSP/8nXaU1soCcV8PX7Qnq7JpZuqIwEa52VsTMHL798htlZF+yfuy1bE04kTUyNJo5sJP3tOXHW8Wkz5johnU45TVMttjTqbjRmdYTmWseZrRfDxJq5/x66qRNquVWtKtaiwIu1MCX2VrV65MWaDi5QlHmu2mJt+615f/XRz/SJuKxP1JW58/5F9nsba1QjgIi8E/gVXDqJ37HW/suF2qrwWhmZDXnGXs/D9lbmmKL7+if5/756J7/+H77EK1+zuLtk3Kjw2hgYA70OdNvwsstnmJ0Wul1vrfLWMbew4QZpJD2KNB4Hzx1jrhP05TOrCrOqQNuqwmwYaSouZq0yuCC7/97CBbrY4IJ8MEG9MrigXnGPbpd7qCgrYfLHf2HDjWrEWvunwJ+u1+tvZS6ym1Ncxd/4By+j9cqbiKOz/MGR/5cv/NnvbTjhpWwMgqCMJXsxmywCtTopnJ6Gl7emeeH5gNkZ5/5rTVgmp0oL2VrnJFuM6hya7X03V8PggIE4s3PHePFMxFy7FGb1mosxazXKdBmtRrbuKTNGSZ70dqWDCxIv1Lo9Z1Wb6dToZc15CXCh36qWuzvrUdXCprnVFAU2cHC9sjrqtJmoJ/QO38DuKyzf9eZ38IXP/Bf+8Yf+JVE8omFvypYnjGBiBzzTnnK26Z0uN9nZNrx8appTJ4UnHw+wBiannBib2uEE2UZ5wPbHmd0M9AuzXg9m23Cm44TZmfM12p2Abi9w0zvVM1rNjJZPLttqbn1rWf/gAs8SgwsKq1olt9rc/ffSm/MzFWT9udUKoVad89MLtVykaZyashVR4bVNeMe7f5gvffZT3P2VL/DG771jvbtTkM+zuJUfYluNIITWpJ+GSYAp56o8Ows3tKZ59umAuVkhimDnLsvOXZbJHXas6SNWQq3mlh07Lb0rbibEjVGYpAz+P9122f9PnnV5zNpd55Jr1JwQm2hlTDSybSHKFqKwqi0zt1o+52evV85UcLFT925PN+9nakrXZ3Xez3ohzvJJ2lMVacqmQYXXFqbqKHn9G9/Krt17+cynf39DCa+4BtM9tm0er61CEMDElE95EQO7YC6BKyemOXVKeOKxABHYuduye49lx067KcRJdULzub1uVGY+mXke+H9+TmidcaIsd2ECNOuGyVbK5ETGRDNlsrW6OTK3GvPm/FzEomaMS9WRz/vZ7QXMHrmPc3PNYnL2qkirRxmNOKEep4VYa8SlYNsMnz1l66LCa4siA+Nl41qNt77zb/Hp//QJZqYvMDm1c5161s+OHZZ9jWmemJ5aurGyqYhiePyCt4rtdKPxDkxOc/IF4bFHAloTToTtvcyOLOnrOCnzmFnmdjtRlrswrYVOG07NCfGZY5w+12R2LiQzQr1mmGxlTLZSpiYzplqpCrIlCAL8jAhQpOp4161D2xrjJ2dPArrdgPS+e7gw1+RkGnmR5tydVStaPRdmcUKzEGuLZ71XlEtFhdcWJB/O2ktCrjj5EGbfTQQB3PHu9/GHv/vv+eJnP8W7f/BH1rubAOy70vDwN0LslLobtzphCI9d8LFiu+HFOZjoznDsgYBGE/Zdbti7b3NYwpZCpLSUdS67OTcCAs5KdmFWaJ4+yjMnGszOhaSZ0GoYpiZSJidSdk6m/S47ZdkEAX6GBOOmWXjHcEuatV6g9YRuLyS97x7OzzV5MYnpJFGR6DYOjRdnCc2aE2WNOKEZJ9RjFWfKylHhtUVIbMwx+1rO2b1cZDcBGVdc8Rp29eaY+atHmWxlHL75VVx97Uv5s099csMIr0YDdu+xTCYznGaDzeCtrCmNFjyfTMIuuDgHjYszPP1kwFUHDJdfuXFjwlaLiymztHffQgTspLSQvTgrBGeOceJkg7lOQL1m2DmZssMvKsZGh8iAFW0BV2eaCu2uc292ugEzR+7j1PQE7V5ciLN6lNGsJV6QpTRrPSfSopRAc6MpA6jw2iJkhIQkvP7Hb0FecTP7didMNGd42XVzADx/ss4LRx/h7e/+Yf79v/2XnHjuKa46sDFyo119neHxRwOmOrOcCyc2pdtJWR2NFpy0k2RTkCYzHP1awEtelrFjY3jE15yqhay37+YijqzbhVPTQnDqIZ57sUG7GzDZyti9M2H3joSpCU3PsNZEkWUqypiaWNjF2UuEts8fl9x3LxcvTtFJYtq9CGOFWpTRqiW0aj23rru1DgjYnqjw2iLkMV1TzS4XsoBabDh87VxRv//yLrNzIW+5/tX8e+Cz//U/8aM//s/Wqbfzuf6w4expofP4LHv3WZ5tT6oA24aEkbOC9ZrwxPFZrr7OsHvP9rUY5DFkyWU30QKaFuZmgReO8fgzLWbmQiaaGfv29Ni3p0e9tn3v1XpSiy21OGPnVAbvfOW8+l4izHVC5tohs/fex+mZCeZ6Mb00JAwsE7UeE/Vymaz31FK2hVHhtUUQnx1nopbwitvOs3vn/LmiDl87R7NxGTe/7HY+958/wY988CcJgo3zc3nPZZZdezJOvShMnJml2bTs3Wd55OzUlnU7KcOp1WEmmOCJ47PsfE224bLlrxciMDEJcy+9pZjzcnYWes8+xIPf3kGWwRWX9ThwRUdF2AbCCbOUXVPpPItZlsFsO3TLPUc4eXGS2V7NDcSIMqYaHaYaPaaaHaZUkG0JVHhtEZzFywUmDxNdOQev7PJjf+t7+clf/CXu/d1P8eY3vITzV99CrTa+vi5GEMAVV1muuCpj+iKcOR3QmpklimD3XsMjp6dotDbetDXK6MmnK2q3YUJnlVqQiQno3nAzU7gs/dHTx/j6N3cQhZZrDrTZtydZ7y4qixCGsGMyY8dkNk+UdXvC9GxE+6v38dTpPUx3aliEiVqPqUaXna02u1oddVluMlR4bSEEizFLW7B+4I7v5qd/6d/x0KN/zPe+7p/RPvIIHYHL9/Q4c/BmWq2NMcJwagdM7TBwPXQ6cP6scLDp5hEUociS/s0Xpqg3XGJPZWthgQ1klN3wRDHMvuQWdr4E2nNw4uFv8fgzLW64bpZdOxb+QaZsTNzsAUmf+9JaZyGbng05/ddf47GTl5FkAVONHjubbXZPtNnZ7GyI73BlOCq8tgjO1WiLJIKLsWtqkne86Q388V1f4hd/8u9x7cGITjfg1NmY6Nvf5uxcSC227JpKOHXFzUzusOtuEWs04Mr9liv3OzN7msLstDA9Leyvz9CeFbLMjRhrTViaLcs3T0xRa7gkrfoltPloz0I9FRrN9e7J5qTZAm5/BcksfPvBRzhwRYdDV3XXu1vKKhHB54HL4D1uCixrYXo2ZPav7uPpM7u50K7TqiXsmZhj7+QcO5r6vm8kVHhtEQIyEJYlvAB++Pveyh9//st8/qv38Y43vYFG3XDoqm7xxdztCecvxvDiQ1x4LGK6FxCFlolWxlQr47m9t9BqWeqN9RE1UeSyoO/c3R/v0OtBe1aYm4Prd80wNyf0Lri6MPK/IBtQb1i+8dwUcb2cy0/ZGGQZHGrOcOqccMPNOmpvtbQmIHvdy3jhyMO0Goa9u9X1uNUQ8e7Kt7+Kq3zZbDvgwl/cz2Mn9zLTrXHZ5CxX7ZpmV6uzrn1VVHhtembsFHM+/1XHTmCXGXf51jd8B3t37eT3/9vneceb3jCvvl6zXHFZjysu6xVlSSLMdkJm50Iue+EhZucizvk56+qxodnIaDYMz+6+hXrdCZzamK1NeY6knbuBgez9aeqG53c7Qq8Lh/fN0O1Arysks65NEDgLWRzbYv3Qc1NEkRNuYeREmsaYjZZuBw7vmebCeWFuVggnLbe92uigihERhiA338BT33xYhdc2YaJpmLjjdvbjsvmfOlvjqb84yrFOnYO7L3BozwUijQ1bF1R4bXJOcA1t2+SUvYqGtEmzkDQVomhxBRbHEf/sA3+HVqOx7NeKY8uufGROhTwDdLsT0O6GHDx3jLlOSKcTMJsEWOtG9dRrhkY9o14zPLPzVuKaGyof1xjLAzaK3DIxkd+b+ffIGGc1SxPo9YQ0gZv2z5AmkORls2B8Sh8Rd844tk6U5eIsshx9xo3GDELmrbejFccYf1+7cNNV03Tawtyc0GnDVNPVH7rWaCD9GlGvw0xvG37wFDdo6bIeV/zAy0lT4cTnvs5XH7ua/bsucv2+s9vy+2g9EbtcE8k6clga9leijZHsc6PxmH0Fb/zI3yAKDdd8323r3Z0FSRKX/bmXuOzPeRbofN9Y958fR06g1WuGRs3w1M5biGNnxXIWqI0lWqx1gixLnUUtTYU0dftJ4uLOstS5z9JUiu3Bf7swgjCAMHIZ24PAibQwBAkgDC0PPjWFBK5OxJWLzN+vLgzuXyLG+D5btzbGiU9r3dpkkBm49dA0WSZ9QjXpufZB4NJENBqWRtO6hKFNqzFcY+LsaWH389/gxpfOrndXlA2AMfDUnx7l9PQEt1/zPI1YB1+Mkskf/4X7rbWvGVanwmuT84I9SGpDDvzP76MRJwSBy+cViEXEEgaWMDCEgSEOM2phhrz69dRiQxRaosgSR4Y4shvCfdZLxM+f5paen+y2lwi9XkAvDQrREoVOpMWRoV6z1GqGp6ZuLV2FPnZrIwm1YVjrxJgxpUjLMin2TS5uDBgjbjsvs2BNKYysdcfZfJ+yfjUEgRd2fjsI3Wcst+KFUS4UneUvjqvuWnXNrjcXL0By9FFedeNFnXZI6eP8l+7h0Rcu43XXP6M5wkbIYsJLXY2bnIgeRhp8z8sfw1jBWHEP33wxQmaFNAtIs5BeFpIcuZd2FpCkIUnmltSUVieAUAxx6MRaHGVuHRqCV7+uEGpx7NfR6CY2zjNAF9NzLEKaCt1ckCVOoO0/e8wJtiSgmwozFaEWBrbot3sdw1NTtxQCLcrFWjReoZC7KwEoRo8u9AWoX4zK8pmZhujhbxNmwi03zqjoUuax63tfx85PfYPTMy0u36HW0HGgwmuTs5NzPMKtiEAolnBED+bMCEkW0ktDEi/akiwkOXIP7Xy7EG6BS2ZhnYjIRVocZkRBRi1y+/Lq11GLLFHkhE8c2SVj0RYj8sdPLPNhkmXQSwKSVIr19XNHSfx2txfQS4XZilgD5/6sxYYostS86Hxy6hYf2+UEWx7fFel/lLKOGAMXLwi7n/sGp8/FTDQzrj3QXjSpsqJMvuFVzBy5l8tR4TUO9DGxyYklIbAZ7V5Esza6L1fnokxX7Pe31qW0yAVZVaB17znCTBaQmlzQhX3pL3IrWy3KiLxbNA4zgte8zlvWjBdBlybYwhCaocGFFC1tUcuvJ0nFLwFJ4tbXzx0lrQi4NA2YTYSsksA2kNyV2+/SjSLLk1O3Eoa5ePQWtzFb2pTNTZrA7Kyw/8xRZmZDLs5EZEbYvSNh754eh6+Z1VGhyrLo3XvfSJ8fyuKo8NoCXM7z/D//S8AU5wnICMkIMHzXz99BGFgCMUShIfKxXlHg9sM18Oc7i5e5pCksBq1sSRaRZAG9e+9jJg0XFWy5VS0OU2qRs7LJq17vRVrpWlypsBHJ3Z8WWNk1GQNpVoq2tCLgDp471refpEI76xduIhBHZSxeFLoljixPTN5SjqL0cVVOyPmA/A0e16YsTZZB0oNuV+h24dB5N1q43XHxj3FkmWql2Am48rIeL7t2blUWZGV7MtsOOHFhiu986VPr3ZVtgwbXbxFm7BQZEYaQjBBDiPEyrFrm2gTFuh/rW2aEpMXatU757p9/O2HFjZiv10LArYTMSCHIet7CNrifb1fj2OKKVS2O/MCDV72uEGlrEcO2EqylEGdp5qxquZBLMyHLt714yzLfLuvvrAiFaAtDW4i50C9PTt5aCZC35QhLX6ZWuNWTD6DIMmepShM3+jVJ4NrpY/RSodMN3SCSxN3wQCyNuh/hW/d58vxaJ8BWVou1cOqu+3ns1B5uO3RCs9uPGB3VqCwLY6UQaRmRX8K+dUpc2Y7IiAcEnCUqRFvit93+m37+bYSF2MmtVOuTmTx3ifYqIi23tvUGYth6WVjErwViS8Hm+18dKbpad+haMCjgqgItTYUsC8i8dS5N3UCMLBMyg1/Pf4NEnDs6CCxhAEGx7dNhiBN1gYAElicmbi1SXgRSjpIMfMqLILB9KTCCgXQYUM7Z2Pd5WSJNRt/Xmy3LbKXe2soo0Eq6DGul3DblCNLrZo9iKvfH2PJeGlPe28Gv1lz8Rj5mMI76rbG12Ams9RL6yvYgSYRTX/gaT5/dxZ6JOV56+Rlq0fJCL5Tls2bCS0R+CPjfgVcAr7XWHqnU/RTwAVwwzT+y1n7Wl98B/CoQAr9lrf3FpV5HhdfmwVoqoiwXam7fEJJQK/ZTYlIiXJICyEVbROIXt/3dP/82b2HLKi7F9RFsxogXZk60FSNFs4qVbRF3aC0qBxwMc4fG0ebI1u4sOIKxpTgzuRAxZbnx6S0yI1hfbowXNH3bUhE9UryGrZSXYqmsr66HIUIhmqUQbu6APDUGUAyjl4oIDMSlWCnWgROSQZinaXHlYWAJvSvYCU+3rZZCZaMw2w44+6WvcXJ6kswEXLFjmkN7LqjgWkPWMp3EQ8DfAn6zWigiNwLvA24C9gOfF5GX+eqPAX8TeBa4T0Q+ba395ir7oWwQRChk1UqxFi/GqkvEX37kcxXBFheiLcfZ35Jiienx3T//NqIwox5lhWXtUuLOBgkCSyNIaaxwbsc0C5xY88IszQXaPUe4mFX2s4DMlk/svN+1MCXyVkK5/bWFlSQP3r+U+LXV4FJgeMUTbwzLnqIozqJ1YSZi5q/u50K7wVwvplVLuHyH4bZDJzRR6gZgVcLLWvstAJlvengP8ElrbRd4QkSOA6/1dcettY/74z7p26rwUlwwOQkxK5tLLrMhyYBgy8VatSwjxHrrWi7QIv96MT3e9Atvpxal1MJs5G7QKHQDGporEKTVEaLFgIM0ovfAPcxlAee9gKvGrw2m9KiFqXeJmg0h2BRFGQ2dbsBcO6R7zz1Md+rMdmt0kog4NOxodtjVTNi/+yKtms7NudFYq1GNB4C7K/vP+jKAZwbKX7dGfVC2CaG4qDPoLPuY1EZ9wiyhxhd++otFWW5hywkwFaHWI6bH3/iFtxVCrR6nRIEZqfuzOkJ0JV+elyLYcqJ8hgPvEs1j8eTVr+tLi5GPrlTRpiijxxiKadU6vYD0vntpJzGdJGau58zt9SilVesxWRcu3zHDZL2n1qxNwpLCS0Q+D1w5pOrD1tpPjb5Lxet+EPggwD7NeqGMmEhW5g4trWo1EmokxBWh5varQi0knWdRy4VaPXYB+dEIXJ/DuFTBBs4lOhjDlmQhvXvvYzYLvct0+GwHgdhCuLl0JXkSXYO86nV9aTHyOKi8TIPJla2OMS6Bcy8RksQlazZH7qGbRm5JIrqp+yEUiKURpzTixK8zdjQ7NGspzTjR/5dNzpKKxlr71ks473PAocr+QV/GIuWDr3sncCe44PpL6IOijIyVWNXyAQZV61lCjbt++i/6yvLRoIIl8la0uBKjVotS6lFGPXK5ycbxZZu7RC8FY8TNcmCCwuKW+f3k6/fQqdSlWUhmxG8HhQs4J5Qy15zLP5cV845GgcHe/vq+YPZiHbpRlvm+WuSUUZGPDs6MVEYAu9HCfO3uclBN5cdJUvlsB2IrYQx5vkHLzlabepTRiFPqUaqiahuwVqakTwP/UUR+CRdcfxi4FzeQ6LCIXIcTXO8D/s4a9UFR1oWVDjAwVua5Pb/8kbv6RFtCDJX4tKrL800/9zeJvUireaG2HrnVgsBSDzLqy5wVYDHcw82JtMzka8Hk2w9+ldQEdI0U9ca3yUxQzE86KOjACd3ATyDv1qZINBxUyvI25pWvL0Y25ik0gnz0Y1CmzigmEK/Uq/AbD8UE8gOjbY0VzMDo2ywDeeCe8rNihMwG5WfLf/aqg1xyokoC6ijIisTUtUhoxik7mp15eQ5VSCmDrEp4ich/D/w7YB/w30TkAWvt26213xCRP8AFzafAT1hrM3/MPwQ+i0sn8TvW2m+s6goUZZMTiKVOlzpLJzCsjvzMRdlXPvrZvv3U51azCCFZIdCqsWmxF2j1aO1cnqvBWbdGI+IGydNXZN5VWn0Al5PLB0W9efCrLleXFXp+0nljqhPSB32T0hvbvyyXQCxCmcqiKBOf44yBtW9PpQzcNlC0cWW+TkpBXi2zlX7m+9W25b3zqTyGlNtKfX6fc+Gbb1t/z6CSQgQpU4cU5Su7f4HYYslFc7HtxXTkt0MvsGuhJfQJoEMxfRbVtZrZQ1FAE6gqypbGxabV+kTZ4H6GSxwm2Hki7U3/snR5rmfC261OLjzACw8oxEcuFKFf4BTrIQKnes6+b/iqMBpiDcyPy/OfVenLiTYovcSdrU/YyYAIxFkL81fNRWW53d+mKjoVZbOxlnm8FEXZwLjYtDYN2ku2zV2e1eWLH/5iIdZyS1tx7kr+tKpQy6dfqvl1oJaDJalap4J59iRFUbYSKrwURQFW5vKE4SM9v/jhLxaJbnOxVto4LDEJYTE7gVu/6effRuwHEOTTSW1E96eiKMooUOGlKMolsdL8acZKMdqzFGcxf/GRu7xIi0gHYtQES+AnmypFm5v/M5+dwCWKLWcnUAuboigbGRVeiqKMhUAswSXNTBBU5vyMvXiLB6aSKuuqsUtusva0sLLl7tE3/tzbfeqMrMg15nKQqXBTFGVtUeGlKMqGJhRDSBeW6QKtktmwmLC9FG4hX/noZ8m8DHMTuJcTu1eFm2ALK1tQWNucmHvjz72dOMwIfUqBMs2AukoVRVkYFV6KomxZSnfoykUblO7RrCLM3JKLt6gQcJmXZJl3lfZj+0RbULR2ZW/8+be7RLGhKZLHFmkNxKoVTlG2ECq8FEVRFuBS3aODlAIuxPQJNbf+y498bl6Z8dLMVCZ3L7GEGAIv3HIh5+LhUgIMb/y5t/vksKZIEhv5bRV0irJ+qPBSFEVZY0Yl4HLyaakMQd86F2qGgK989LMD9XndYoLO59IqxJzx2/PX3/VzbycskpSWmf5zkZcnKlWBpyj9qPBSFEXZZOTTUjl6Iz23sVKIt+o6Gyj7649+pm+/Wm+9RMvrFkrWWoo811oKYefKB+u/8+fuKISciOmb+mkwa72KPmWjosJLURRFKXDWueXNM7pacpFnkT4rXFWwuW23/uuPfqZoW4o7J9OGH7ew6MvJU5ZIIfysXxs3FVNlO8BA0d7ynT97RyH0pLLOt/OM/INtikz9A/vK9kCFl6IoirIuVEXeqNywK6GYK7IQcDIg5IK+urLcbd/9M3/adxwV6WYrko5F6vPzsYRAHOi5m1bJC0HpO4ufw7MiFPPy6tXm9J8jP7ase8PPvQN876QyNVTf1FELlFfnDy22pZTCg9sU2/3TUg3OQ9pXNmRO0WF3crntFmMh22nfHKXLOI8KL0VRFGVbUj7g89Gvm4OqYOyXTFKRWUuXzS+nbx+Er370zyr7DD02L6/Wl2XV8pz5xy1WPji1e3/doHyaL6eWsnrmbfIeLrS9EPPrF2+vwktRFEVRNhFVwahsPgaTzSiKoiiKoihrhAovRVEURVGUMaHCS1EURVEUZUyo8FIURVEURRkTKrwURVEURVHGhAovRVEURVGUMaHCS1EURVEUZUyo8FIURVEURRkTKrwURVEURVHGhAovRVEURVGUMbEq4SUi/1pEvi0iR0Xkj0RkV6Xup0TkuIg8LCJvr5Tf4cuOi8iHVvP6iqIoiqIom4nVWrzuAm621t4KPAL8FICI3Ai8D7gJuAP4v0UkFJEQ+BjwDuBG4G/7toqiKIqiKFueVQkva+3nrLWp370bOOi33wN80lrbtdY+ARwHXuuX49bax621PeCTvq2iKIqiKMqWZ5QxXv8j8Gd++wDwTKXuWV+2UPk8ROSDInJERI5c0BnYFUVRFEXZAkRLNRCRzwNXDqn6sLX2U77Nh4EU+N1RdcxaeydwJ8BhadhRnVdRFEVRFGW9WFJ4WWvfuli9iPwo8C7gLdbaXCA9BxyqNDvoy1ikXFEURVEUZUuz2lGNdwD/HHi3tXauUvVp4H0iUheR64DDwL3AfcBhEblORGq4APxPr6YPiqIoiqIom4UlLV5L8GtAHbhLRADuttb+uLX2GyLyB8A3cS7In7DWZgAi8g+BzwIh8DvW2m+ssg+KoiiKoiibAim9gxuXw9KwvxJds97dUBRFURRFWZJ3pY/cb619zbA6zVyvKIqiKIoyJlR4KYqiKIqijAkVXoqiKIqiKGNChZeiKIqiKMqYUOGlKIqiKIoyJlR4KYqiKIqijAkVXoqiKIqiKGNChZeiKIqiKMqYUOGlKIqiKIoyJlR4KYqiKIqijAkVXoqiKIqiKGNiU8zVKCKngKdWeZrLgNMj6M5mRK99+7Kdr1+vfXuyna8dtvf1b6Rrv8Zau29YxaYQXqNARI4sNGHlVkevfXteO2zv69dr12vfjmzn698s166uRkVRFEVRlDGhwktRFEVRFGVMbCfhded6d2Ad0Wvfvmzn69dr355s52uH7X39m+Lat02Ml6IoiqIoynqznSxeiqIoiqIo68qWE14i8q9F5NsiclRE/khEdlXqfkpEjovIwyLy9kr5Hb7suIh8aF06vkZs5WsDEJFDIvIlEfmmiHxDRP6xL98jIneJyKN+vduXi4j8W38/jorIq9b3ClaPiIQi8nUR+RO/f52I3OOv8fdFpObL637/uK+/dl07vkpEZJeI/KH/f/+WiLxhu7zvIvK/+M/7QyLyeyLS2Mrvu4j8joicFJGHKmUrfq9F5P2+/aMi8v71uJaVssC1b5vn3LDrr9T9pIhYEbnM72+O995au6UW4G1A5Lf/FfCv/PaNwINAHbgOeAwI/fIYcD1Q821uXO/rGNG92LLXVrnGq4BX+e0p4BH/Xv9fwId8+Ycqn4N3An8GCPB64J71voYR3IN/CvxH4E/8/h8A7/PbvwH8fb/9D4Df8NvvA35/vfu+yuv+OPA/+e0asGs7vO/AAeAJoFl5v390K7/vwJuAVwEPVcpW9F4De4DH/Xq339693td2ide+bZ5zw67flx8CPovL8XnZZnrvt5zFy1r7OWtt6nfvBg767fcAn7TWdq21TwDHgdf65bi19nFrbQ/4pG+7FdjK1waAtfaEtfZrfnsa+BbuwfQe3IMZv/5+v/0e4BPWcTewS0SuGm+vR4eIHAS+D/gtvy/Am4E/9E0Grz2/J38IvMW333SIyE7cF/JvA1hre9ba82yT9x2IgKaIREALOMEWft+ttV8Gzg4Ur/S9fjtwl7X2rLX2HHAXcMead36VDLv27fScW+C9B/hl4J8D1UD1TfHebznhNcD/iFO/4B7Gz1TqnvVlC5VvBbbytc3Du1BuB+4BrrDWnvBVLwBX+O2tdk9+BfflY/z+XuB85Uu5en3Ftfv6C779ZuQ64BTw/3g362+JyATb4H231j4H/BvgaZzgugDcz/Z436us9L3eMp+BAbbdc05E3gM8Z619cKBqU1z/phReIvJ5H9swuLyn0ubDQAr87vr1VBkXIjIJ/Gfgn1hrL1brrLM1b7nhuyLyLuCktfb+9e7LOhDh3A+/bq29HZjFuZsKtvD7vhv3y/46YD8wwSaw3KwlW/W9Xort+JwTkRbw08DPrHdfLpVovTtwKVhr37pYvYj8KPAu4C3+HxLgOZxPOOegL2OR8s3OYte8ZRCRGCe6ftda+1988YsicpW19oQ3NZ/05VvpnnwX8G4ReSfQAHYAv4ozr0feulG9vvzan/Uuqp3AmfF3eyQ8Czxrrb3H7/8hTnhth/f9rcAT1tpTACLyX3Cfhe3wvldZ6Xv9HPA9A+V/PoZ+rgnb+Dn3EtyPjge9x/wg8DUReS2b5L3flBavxRCRO3Cul3dba+cqVZ8G3udH+FwHHAbuBe4DDvsRQTVc8Omnx93vNWIrXxtQxDT9NvAta+0vVao+DeQjV94PfKpS/iN+9MvrgQsVd8Wmwlr7U9bag9baa3Hv7RettX8X+BLwg77Z4LXn9+QHfftNaSWw1r4APCMiN/iitwDfZBu87zgX4+tFpOU///m1b/n3fYCVvtefBd4mIru91fBtvmzTsZ2fc9baY9bay6211/rvvmdxA6xeYLO89+sR0b+WCy6Y8BngAb/8RqXuw7iRHQ8D76iUvxM3Gu4x4MPrfQ0jvh9b9tr89b0R52I4WnnP34mLYfkC8CjweWCPby/Ax/z9OAa8Zr2vYUT34XsoRzVej/uyPQ78J6Duyxt+/7ivv369+73Ka34lcMS/93+MG620Ld534P8Avg08BPwH3Ci2Lfu+A7+Hi2dLcA/aD1zKe42Lhzrulx9b7+taxbVvm+fcsOsfqH+SclTjpnjvNXO9oiiKoijKmNhyrkZFURRFUZSNigovRVEURVGUMaHCS1EURVEUZUyo8FIURVEURRkTKrwURVEURVHGhAovRVEURVGUMaHCS1EURVEUZUyo8FIURVEURRkT/z+sTKmBqqXwsgAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -286,43 +280,52 @@ " X1_BOUND = 1500\n", "\n", " # Set the combination method\n", - " fi = FlorisInterface(\"../examples/inputs/jensen.yaml\")\n", - " settings = fi.floris.as_dict()\n", + " fmodel = FlorisModel(\"../examples/inputs/jensen.yaml\")\n", + " settings = fmodel.core.as_dict()\n", " settings[\"wake\"][\"model_strings\"][\"combination_model\"] = method\n", - " fi = FlorisInterface(settings)\n", + " fmodel = FlorisModel(settings)\n", "\n", " # Plot two turbines individually\n", " fig, axes = plt.subplots(1, 2, figsize=(10, 10))\n", - " fi.reinitialize(layout_x=np.array([X_UPSTREAM]), layout_y=np.zeros(1))\n", - " horizontal_plane = fi.calculate_horizontal_plane(\n", + " fmodel.set(\n", + " layout_x=np.array([X_UPSTREAM]),\n", + " layout_y=np.zeros(1),\n", + " yaw_angles=np.array([[20.0]]),\n", + " )\n", + " horizontal_plane = fmodel.calculate_horizontal_plane(\n", " height=90.0,\n", " x_bounds=(X0_BOUND, X1_BOUND),\n", - " yaw_angles=np.array([[20.0]])\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes[0])\n", - " wakeviz.plot_turbines_with_fi(fi, ax=axes[0])\n", - " wakeviz.plot_turbines_with_fi(fi, ax=axes[1])\n", + " layoutviz.plot_turbine_rotors(fmodel, ax=axes[0])\n", + " flowviz.visualize_cut_plane(horizontal_plane, ax=axes[0])\n", + " layoutviz.plot_turbine_rotors(fmodel, ax=axes[1])\n", "\n", - " fi.reinitialize(layout_x=np.array([X_DOWNSTREAM]), layout_y=np.zeros(1))\n", - " horizontal_plane = fi.calculate_horizontal_plane(\n", + " fmodel.set(\n", + " layout_x=np.array([X_DOWNSTREAM]),\n", + " layout_y=np.zeros(1),\n", + " yaw_angles=np.array([[0.0]]),\n", + " )\n", + " horizontal_plane = fmodel.calculate_horizontal_plane(\n", " height=90.0,\n", " x_bounds=(X0_BOUND, X1_BOUND),\n", - " yaw_angles=np.array([[0.0]])\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes[1])\n", - " wakeviz.plot_turbines_with_fi(fi, ax=axes[0])\n", - " wakeviz.plot_turbines_with_fi(fi, ax=axes[1])\n", + " flowviz.visualize_cut_plane(horizontal_plane, ax=axes[1])\n", + " layoutviz.plot_turbine_rotors(fmodel, ax=axes[0])\n", + " layoutviz.plot_turbine_rotors(fmodel, ax=axes[1])\n", "\n", " # Plot the combination of turbines\n", " fig, axes = plt.subplots(1, 1, figsize=(10, 10))\n", - " fi.reinitialize(layout_x=np.array([X_UPSTREAM, X_DOWNSTREAM]), layout_y=np.zeros(2))\n", - " horizontal_plane = fi.calculate_horizontal_plane(\n", + " fmodel.set(\n", + " layout_x=np.array([X_UPSTREAM, X_DOWNSTREAM]),\n", + " layout_y=np.zeros(2),\n", + " yaw_angles=np.array([[20.0, 0.0]]),\n", + " )\n", + " horizontal_plane = fmodel.calculate_horizontal_plane(\n", " height=90.0,\n", " x_bounds=(X0_BOUND, X1_BOUND),\n", - " yaw_angles=np.array([[20.0, 0.0]])\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes)\n", - " wakeviz.plot_turbines_with_fi(fi, ax=axes)" + " flowviz.visualize_cut_plane(horizontal_plane, ax=axes)\n", + " layoutviz.plot_turbine_rotors(fmodel, ax=axes)" ] }, { @@ -345,26 +348,22 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAADECAYAAABOQy+KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAApiUlEQVR4nO3deZgc9X3n8fe3j5menvvQMZJGF4wEAgPikLEhNgYCAmOTZB0bexMbxxt2s/hJnM1ubMyuN+sn3thJHsd2TJwQ21mcmGCvj4BjAgZsr2/MIRshQCCBAN3XaCTN3V3f/aOqZ7p7ukcjzah7js/reaTu/tWvqn9VXdP17d9V5u6IiIiIyOkXq3YBREREROYLBV4iIiIiFaLAS0RERKRCFHiJiIiIVIgCLxEREZEKSVS7AJPRbHFfSLLaxRARERE5oW0MHXT3BaWWzYrAayFJPpVYUe1iiIiIiJzQDZnnXy63TE2NIiIiIhWiwEtERESkQhR4iYiIiFSIAi8RERGRClHgJSIiIlIhCrxEREREKkSBl4iIiEiFKPASERERqRAFXiIiIiIVosBLREREpEIUeImIiIhUiAIvERERkQqZcuBlZl1m9j0ze8bMtpjZH0TpbWb2kJm9ED22RulmZp8xs21m9pSZXTjVMoiIiIjMBtNR45UB/sjd1wGXArea2TrgQ8Aj7t4NPBK9BrgO6I7+3QJ8bhrKICIiIjLjTTnwcvc97v5k9PwY8CywFLgRuCvKdhfwa9HzG4EveehnQIuZdU61HCIiIiIz3bT28TKzlcB64FFgkbvviRbtBRZFz5cCr+attjNKK97WLWb2uJk93kt2OospIiIiUhXTFniZWQPwdeAD7n40f5m7O+Ansz13v9PdL3b3i5uJT1cxRURERKpmWgIvM0sSBl1fdvdvRMn7ck2I0eP+KH0X0JW3+rIoTURERGROm45RjQZ8AXjW3T+Zt+g+4D3R8/cA9+alvzsa3Xgp0JvXJCkiIiIyZyWmYRuXAb8NbDazX0RpHwY+DnzVzN4HvAy8PVp2P3A9sA3oB947DWUQERERmfGmHHi5+48AK7P4qhL5Hbh1qu8rIiIiMtto5noRERGRClHgJSIiIlIhCrxEREREKkSBl4iIiEiFKPASERERqRAFXiIiIiIVosBLREREpEIUeImIiIhUiAIvERERkQpR4CUiIiJSIQq8RERERCpEgZeIiIhIhSjwEhEREakQBV4iIiIiFaLAS0RERKRCFHiJiIiIVIgCLxEREZEKUeAlIiIiUiEKvEREREQqRIGXiIiISIUo8BIRERGpEAVeIiIiIhWiwEtERESkQhR4iYiIiFRIYjo2YmZfBG4A9rv7uVFaG/AVYCWwA3i7u/eYmQGfBq4H+oGb3f3J6SiHiIiIyOky4GlGqClIM/yktjEtgRfwf4DPAl/KS/sQ8Ii7f9zMPhS9/iBwHdAd/Xst8LnoUUREROS0cIfjNHPMm3Mp5XICYEWp/dSznXOpYZARryFmQZm1DXisbDmmJfBy9x+Y2cqi5BuBK6LndwHfJwy8bgS+5O4O/MzMWsys0933TEdZREREZG7p8Q4O+GIGSANjtUzFwVG52qdhajjMQgJirP/NrnHr5XMvvZWFyREubd/HGW88l/pUZsLyfurN5ZdNV41XKYvygqm9wKLo+VLg1bx8O6O0gsDLzG4BbgFYcFqLKSIiIqeDO+ylix2+luGiJrp8J2qu66eBN7yrnUwQp73+eNn3ci8dUiViAWvf1EV74yCxU+7dHgdWABMHXSdSkYjG3d3MTqoR1N3vBO4E6LbUyTWgioiIyDhDXkv2FC/9O3wNu1hJhiSxgkDpxJfoa29uZHHjUVouXFc2j0f1UF60udxrM2hvHCSdSp1s0SODp7je9Dqdgde+XBOimXUC+6P0XUBXXr5lUZqIiIgUyXiCI7ST8fKX7Bil+huNRTADpNnLcg6zkDjZoqUnEgZE9RzjnbcmWPkrq0nEx9YuDpTG0qP1UiNRLVMb4Zi6+e10Bl73Ae8BPh493puX/n4zu4ewU32v+neJiMhcNew17PMlHKV1wnzl+isdoZ0jtFPrg9RYqVqbsTV9gt5LG35rBWcv2sWqK9ZOtuhljExx/fltuqaT+GfCjvQdZrYT+J+EAddXzex9wMvA26Ps9xNOJbGNMPR973SUQUREZDplPcZWP5+Dvni0f9Jkpg7I5YkREFiMAepp4RCX39xVdp3irea/bqod5Owrl9OYno6AZ6pBl0zVdI1qfGeZRVeVyOvArdPxviIiMrdlPEFQNNf3ZIKfgBibfQOv+upx8y6F2yje3vhtjpDEibHhLc10rV9IPFaYJ4ha94o7dOdyHemr5bJz9rGso49EfOkJyzwx1TLNFRouKCIi02rA0/R7Gp/g5igT9UlyYuz1Lrazjh46GJtXyRnfIFeaAykG2PjOJGsvW8zgirPD9LzYKT9gati9BYpGxcVjAU2XrCFdd2rju55/4BUWtw4U9IcSUeAlIiIFMp5gb7CUY7RMonYpPygKH/exjAMs4Zg3E7dSQ+9LB0+jo9qAZnp4+8dfw5LLVlBb62U7cEOpUXCGmdNQH+RNHTA88W6ceWa5rU+8nshJUuAlIjKHHAsaeSlYy97RweOlu1sbQclJKHvoYDcrcIxVr20bDWoyHmdxYy+NtYNlt5rL2wScUX+MN7y5gZZLp9qnKDvF9UVmFgVeIiLTaKKaGZuglcwd9nsnzwQX8jLdFIZDhRuNlaiFMZwB6uijmTqOcfn71oA5Hox/08BzWx2/bHX9EL99VQesXEFt7Vj63t1Gl7/E8qUnqDkSkQkp8BIRifQHaTJlvhZL90kKZYnzSnAmT7GBnZwx2hOploESUwSMKZyC0gDj7NenufrfraGlKazpCYre1r0wYPJgLD2dDkiduZSWtomDPBGpHgVes8RnM72MUMM7YmPV9jEytNsBkqbRLiLlfC57mH5P82t2QdGSXNhjvOhreJaLo47cpfIU5g//99GmuoAYSYZ54zsXcNGb1pFKwYG9xtnnBsTzvmW9ROxW8A4OiSQsWOgk9O0sMifpT3uWeJYYA9RAcMvorMMBMQxjMS9zJs8QzwvASjdFBHnLszTaUZbZS8RO7m5OIrPKC56hhxqG/D+SJZH3d2AQ1U2tXp/i+t/s4tiS1dSlo+bC4g7b+c89r0nRwWLQ2EJBsLQk2cfiJU6y/O3pRGQeUuA1i7QuiHHzRy9g5a8sBiDIQmLnDrY83M5TPz+H3PXES4xDym+ayHqMoZEEPU+8TBfbSDIU/Xofyz26lhE1gIRpCUZot32ssc0K2GTWaGhLcsPvnUPiNStoyp88PDqFY/HwMV3xkolItQyNxMhkY2ODTKKL4Nhj4eCT0de55XlpJ3PjbQVes4zZ2AcciwGrVnLO767knN+d/DbcofcIHHt2CS/9tJugqPNtNprHZmg4zvozDo1OLRhkY7y06RCPPdDD034Jsajmrfxw88IQcGygeChNH932NEtth5pLpSLy/35EpHIO9tZy6GiKbBAbDVagMHgZF/iMC4iKA6GxNMquU3iNMguvbft76+gbSNLSMASEV6bcHG652uxx/SlH08e26A69fbW88bzd1KdKTZ0yngKvecgMWlqh5fVL6Hr9krL59j++m9ddHC9Iu+K3Ojj6JzF6H99GNjv+hMwJimdy9sK87vDLH/Xwk3/pJONJEuRO2FJBXPE8QUX7U7SO4SxhB932NK12cNzWJjPr9anI326MrGoERea5TDbG7kNpapPRj1QrHTyEj8W1KV42ECneVjaIcehoigO9KbLZwsCm0szGyg3hfmYDY2gkzoo3LKMm4QUBTPgYZc4LeGC0EadkQJR7LN5W46ubw3WDsXUKuwkYyUSWFW9aTl1qeo7Tjod3jKvAmIgCLzlpTY0BTW9aPeXtLHnDKi57f6zgj6pk2JUXtAUlMhSP+urdtIPNP17Mg/efjXlhrZuP9uspeIdx27S8//PzNHGERMGEkMXrhttv5QBn+ybiVn4k3Pj3nMyXwOS/KE4mwCyXN0ZAygYmvR2ZXYIARqLK5tFuBaMRQe7iHr50h117ahgasoL0UrUPZmMLctsNnxcGE4XPSwQkJZp+CrdXuL6VeE+K1x0XzIy9908fayRZM7m/m3I1NfnPM/0J9h5uLxxhatG3UDTJq3tuHcPwsNehEaU7eHi880sVjzldC46PbdKgrXGQNdecSTI5qeJPu4J+j4wPkmIxiMUmVyM0JUsnO29c9YJTBV5SNQ31AQ31kw9MJmtkyRJWX9nFjf8jfF3qy6BYccfpgmVR4Hfw0ZdY3dnMgubBCbf10vef5+ufHOQJ3jD2C+6Etzk58a+lk/2aOPF7TlyOwGPELOAs30Tajp8gd7mtlS/11GseJ14/t/0MPWQD41hfjP4XoecAYBCPURBcWCz6x9jrmI3lIT8wyQ86bPwFFwM/Ck8+GiMWj7YVy2vqjLYVi+W9D9H7W/jaonwxg1h0j8BYvEQZ8vdhbOdLlqk4aNm31zh+1EjX+9gR9dxFNO8HkUM2A/39XWw9Ov4iG+azvPN9bDv5n9Yi3x12QCgaLFrQ9GS5oCQKPnACjDWrB4jH87Y/+h4lmofyfsS527ggIL+GJb88LS0Z6teVv5F14f6WrnnJr7Xxs5fSWCq9oCkrf18m2B5j+3r82d0s3XBsRo18zT/fZGIz6GMTmR7JJDQnp3+265F04cSR5b5kVr9pDf/tTdP+9hXXPxhn63d3cP/nVjLkqXHLTy2wm9jktzlxvvzr+gB7SSRT7B5YwL7n+qhJBaNBwuhFN3exzpsTqzhwWL68vuiCPfY8HodlZ+StB2S9nmwGfHhsmx6MXWxHnwfFAU9hWq5GNwjy3neCCzZAV9sADgwPwRndWeobi/JFeevqYCCVhrrCo1rq6CaB8WfByTpj3LZHg4yobAGFr90hcbQPXx6QnEGjH6oTaIQHJaib/h+sUjkKvESkpHQqy/rru1h//eRqAGaqH/xhHYcGkwwlUhza/RNqak9tIMczPeWXLVx+EcmeUwj2DYifMBfABLebHm9f9Li4s5mDWchM8B615RdVxGQCmESVms9ETgeN7xERERGpEAVeIiIiIhWiwEtERESkQhR4iYiIiFSIAi8RERGRClHgJSIiIlIhCrxEREREKkSBl4iIiEiFKPASERERqRAFXiIiIiIVUrXAy8w2mtlWM9tmZh+qVjlEREREKqUqgZeZxYE7gOuAdcA7zWxdNcoiIiIiUinVqvHaAGxz9xfdfRi4B7ixSmURERERqYhqBV5LgVfzXu+M0kaZ2S1m9riZPd5LtqKFExERETkdZmznene/090vdveLm4lXuzgiIiIiU1atwGsX0JX3elmUJiIiIjJnVSvwegzoNrNVZlYD3ATcV6WyiIiIiFREohpv6u4ZM3s/8CAQB77o7luqURYRERGRSqlK4AXg7vcD91fr/UVEREQqbcZ2rhcRERGZaxR4iYiIiFSIAi8RERGRClHgJSIiIlIhCrxEREREKkSBl4iIiEiFKPASERERqRAFXiIiIiIVosBLREREpEIUeImIiIhUiAIvERERkQpR4CUiIiJSIQq8RERERCpEgZeIiIhIhSSqXQCZvMFMku9+4VUu3rKLdVd34CtWEI+PLXcPHxP6VEVGxWJOdnCEwU3bWLWgm3jCCQIIHPDw76ZleSOHyLD7hV8Qi3vpDZVIdgyItiUiMgm6RM8S3cTo78my4vF/4uePreVf/noVWZ4lTnY0T9clCznvkgQd111IS6vjDvWNUFNTxYKLVNkla5cQBMb7Nj7Bc6+2AvDET4ZIJ4cA6BtOceD4mfQdqaPBlxOPOe5hQGUTbNc9zODA0IEDHN/WT905Z3A8PUy6IYzECuIxL7F+7nmQt8CgphZicURkDlLgNUt8IFFP+M39Yy7ix2Q8wWFfQDb6CEeoYefjq/jeYxdy/G/2R2sFGM4VGwPOesd6GhvDIM0d3I1k0ulcNEJPyxlV2SeRSvjLW94RPevlnJW9ADTt6+X1K18AoKc/zZa9z3J8US34WKhlFhRsx/KisHjRsqzHePHQIn7x3aMcG0wRszJVYJb/NMqTF8A1LmwgddYKGlan2Lvv6Nj2x35fsXZN01jQFhXDPVy/phb2Hu4lqR9bIjOWAq9ZKmEZFtqegrTlbOdSf4Qh6kbTngnW89QDF/HjBx4bTXNiBMRYe1kTK85tovmsMFBraMzQsGYJLe2FFxmRuaw13c/lq1+Yhi1tYSQbZ3dvC1kf6z5bHMAVy/2pDWcTPLt/KT/d1M32HzSPC9Jyf5Obv1O8vkfvA0EAC1bWEzvrDBYsDDj0wgBLuwJy1W35ARxEyXnxX048Dm0d0GMNJ9xrETk5CrzmmJg5dfSPvr4oHtaQFesJ2tn2k3Vs+vHZBNEYi36aOE4TaY5x8bvXkUg4lySfJ3H+uRUrv8hsloxnWdF26JTXX7NgH29d9yQDIydXZWVRDdtPdnTz1O7l7P3RPl7OxsCcY7EsMcvlA0rUxhljAdxQNkHMnJYVrXRd0IJZYcAWFMWRudo3z6stXLRwhD2ta2hph5iGcIkUUOA1T7XGDnEJP+QSfjia5g5HvZkdvpY9X3qRAyzig1/Mcs51zwMQtyznXdbEiiuW09Za/NNZRKaDGaRrhk9p3au6n+Gq7mem9P4j2Tgv97TxxM5VHPh+U1gmvKAGLmY+1lQasZiPZnmst5Wewb2kE8O0rmopaMLFw0EJPvYyfMzv8+bQvradRee1c7y2gfSpHQ6RGUmBl4wyg2br5Xx+zvn8nGGvYU/QxcAD9QAMU8tX/u08jrOHuI+M9mNxYN01C9lwRR1rrlk2ur38X8A56bpAzZgiM1gynuXMjgOc2XHglLfhDj0Dabbu7+T4UGq0ls1K9XGjsLnUCEeJPv/kEn760EIyQYy4Bexo6iVmHuYsrrQr8V3jGGdd0kD3lYsYWLASs8LgLlfOCV/nPU+n1QVDpocCLymrxoZZEd9ekLY82MYINWBjX5YH6eSlh9by+e8sxT68uyC/EX5RBsS4eGMjF/3OBbQ0ZwBIpQI62rJqihCZY8ygLd3P61ZuP3HmMq7sfo7hTJzdR1sYCcaGeFrxY8mm04DeoTSbnlnFL386SOCvYLGgYD0Ig7Pi7eULgEQsy9Krz6P1nIWk68feKzcStVyNXcFzDwc+LFjoJJIT77fMfQq85KS0xHrGpbX5AVbx3LhvrvxftD3ewdYHzuOuB/aNzn00Qi1xMrzh1+tYc2nH6JeVOdTWBnQt6CN14brTti8iMrPVJLKsPMU+c0FwmDPaJlFrV24EKpANwsESmx47zDMPtxU2t1LU2a1oOxb9ywRxkvEsC9a2M3DuEurqCrtpBNmxjRbXuAVRTV5TYwZb3kVTi2rd5gIFXjJlMXNqGZowzyLbzSIKa8MynuCgL2T7N9fx1Dc7wm3hDFDPMDWsvWYJ7V/fBBjukIwHLGg4ymtuvZLGholHionI/BaLOY2pwSlvp6Wun3WLd584YxnZwNjd28qW/UvZe/8rQOFvVMOx2PigLbcMYOtgPYklvaRqstQmA44PJmjasxfLtRYU1LCNj8wCDMNpTI/Q/rozSdVqxt9qmlLgZWa/CfwJcDawwd0fz1t2G/A+IAv8vrs/GKVvBD4NxIHPu/vHp1IGmb0SlmGx7WZxUUAWuNHjHez8zir2kwrTMA6xmGO08M27vseqq5eHmR0aagdZ97oWzn9rZ5iU98WTSLiaMkWkauIxp6v1MF2th095Gwf7Gnilp52+oVpyv3EPb45q1Ypr7Eq0PBhwbChFz0AD9vAm6pNhQOrjGljHXpe6G8PqDZ0seN1K2jW4akqmWuP1NPAbwN/lJ5rZOuAm4BxgCfCwma2JFt8B/CqwE3jMzO5z96kNw5E5JWZOux2gncJmAnc45k0ctoXw3TBtiDp2sYpN/9bFP37k5TAfECfLa9/SzLmXt9B52Yq8bYRfLHW1AfX1qjUTkZmvo/44HfXHp7ydkWyc/ccbGcoUXvpLNV/mTzEC4Y/fpx41nvphDyNBnESs9PdncY2bA02pAS6+soW69WvHbnM3wUCG4ibX/LT6dFBwq7zZaEqBl7s/C2DjP7UbgXvcfQh4ycy2ARuiZdvc/cVovXuivAq85ITMoMmO0sTRgvS1bGbQ68JO/5Gtfh5bvrWSn3zrGMar47YVEOOSNzdxweVNNNeHPyENqF/fTVtrVv0oRGTOScazLG0+csrrdzYeYTg78eiA4mlGHOO5/Z388IEaBr+9mRhlbsU1ro9c4etMkGDd65uxM85k0YKRcNs+9q+UIBh7p8adm8HDeeVqLziLulT1mltPVx+vpcDP8l7vjNKAgqvgTuC1pTZgZrcAtwAsUFc0OYGUDZBiYPT1en7MBfbTsvkP+GK2fvs8vvLtBQS5Gx0TZ4B9XHBtGyvXt1OTCEdk1teO0Pn65WQDRWMiMn+lkhlSycxJr7dh+YtsWP7ilN77hYOLeGnLAo4/uWc0iMifhqSUWN5dIww4OphmzaVtDNy7j+b6wsnhRicCLkizgmXp2gytG86grSUzpS4sJ4xozOxhYHGJRbe7+72n/tYTc/c7gTsBuq2KoanMSuG99Mo3JXbaTjrZOS59h69h73eW8uSD6dH+D/000Mt2uq9exrpFh6ivGR43jVDMnLa6fs7fuEy1ZSIi06y7Yx/dHfumtI2dR9o4uKOBzEgNAxZ+w3/rHwZyt0sdFYVbeSnh8ww1DLONddctZel5CxncHgaTx4drySx7iXRyZFLlOGHg5e5XT2pLhXYBXXmvl0VpTJAuUnUr7XlW2vMFaRlPsNNX0vdIIzuitOL+D0dppZc2vv43z5Eg/OOrYZjrb13A2itXkapRZ1QRkWpa1nKYZS2Fgxw2x/o41x4vs0ahEU/S4+3sfmAlmx9oIT84+9ZJlON0teHdB9xtZp8k7FzfDfyc8HrVbWarCAOum4B3naYyiEyLhGVYadtOmC/3R+nRvS8HqOfeO5L03vEyCcaq5+voY+Mtbay7ajm1yTAgy++QGo8FJBOq5BURmUmSNsJC28tC9p4w7ycmGLs11ekkfh34a2AB8G0z+4W7X+vuW8zsq4Sd5jPAre6ejdZ5P/Ag4XQSX3T3LVMpg8hMkfujzLeSF8h6nKFoWgyAXm/j+3eu4ut3jow2Z+aHWYbRwkHecmsbnZeuGU1vSI2QTqnmTERkNpvqqMZvAt8ss+xjwMdKpN8P3D+V9xWZTeKWJU3f6Ou09dEZdQ/NH42T6xs24kl+6Zfy1TuG4I7No8uHSHHR25awrPkwtYnCvgSO4W4kYlkaaoc46+rVp2+HRETklGm4oEgVleqIn7QRLrYfjks/5s28+rXVvEL92Pr528LpIbwDwKUH+jHG30u4s/EIZ125gsb05DqBiojI9FLgJTJLNFov62zThHmyHuOIt7Pv7vZx8+AEGJvp5Kt/lyXBMHHGmi1jZLn8PUu58tebiZW4d51m/xcRmR4KvETmkLgFJWf9zzmD58h6jOM0EzAWTR2kkx/dBQ/ddaTkerUM8NZbmlj5K6uoSQTjJiysSQbUp05+fh8RkflGgZfIPBO3gGZ6CtJaOcSZPM0QqZIzT+/2Fdx3Zzcjdz477ga+AFni1HGcjb/bzqIN3WPbrR+ipWFYc5uJiEQUeIkIEPY3SzFYctlq28pqtpZdN+MJtvhFPPL3IwR/H9a2OcZxmqjzfi5991JS8UyUXigWC6hPDrN+4xJNoyEic54CLxGZsoRlON8eHZfe7/XsZRnb/nFPUU3a2PODLMZwntvfSTJeOF1GbrqN1ro+ljUf5jXXrkBEZDZT4CUip03a+lht5WvKAAI3jtFC/ze3EBTVhxlOH43sZAHf88XEP7u9xBaMGga46F2ref31LQV3CcifmBagNpmlJjnBzIYiIqeZAi8RqaqYOc300Gw9ZfME/jz91jB6VwAY62PmGM/4hfzy7uf58d3NWIl7dAbE+ZV3LSCVHGHdFZ30j5RuUhUROd0UeInIjBczp4FjZZdfzP+jcFazfE4fTbx6dxd76eKHd+0mRsAOG5vUNusJ4jbCO/64g+RoX7Sx7SViwbhmUBGRU6HAS0RmvbhN3HzYxBGa7Ahr2Vw6g8FBX8i9fz5AQHzc4gxJssS54QOdxGPl36uhZoh0zfBJlV1E5hcFXiIiQIftp4P9ZZf3eAc/+tQhgHFTbkB4U/Q+b2SB7S65fg1DXPPhc6hNaL4zkflMgZeIyCS02kFaOVh2edZj9Fo7jpUMzAap4+7/fRjHqCmYtmOsSTNLjIXs4bqPnDOdRReRGUSBl4jINIhbQFuZOwbkLOEVMp4gQ7Lk8iFS7GIVf//R/pLBG0DGk9z84XTZmjNNVisysynwEhGpoIRlSFA6aEoxMO6uAsUOsoh7/mwxmbyv71ysFRCjhkGu/cNVpJPj+5rlbvWUjGfVF02kShR4iYjMIh22jw72lV3e4+088leZktNqQBikHfRFdNmLXPIHF49bnj/3WbpmiNa6PtWiiUwjBV4iInNIqx2ilUMT5unkZY7SymOffrzkcsNxjAHqOewLabP9xAin08ifZiNOlqv+61m0pPqJxXS7J5HJUOAlIjLPpGyQFHsmlbeTlxkucfN0CKfZ+PZfvsJRbyFtx6PU8dVjATHe+98bFZyJoMBLREQmEAZp5Wf6b+VgQX8zGD/dxj6W8sU/nVx75aCn+Y0PtNKWPj6a5kXBnCa0ldlMgZeIiExJwiaem2wJr7CEVya1rQHSfP/Ty8uO/HSg3xt52x+1jb4umS/qqxaPBXTUHy+TS6TyFHiJiMiMUWf9rOa5ssvdYbet5Puf3AuUnsw2x3AO+uKCPmqlcrVwkKs+ctFUii0yaQq8RERk1jCDpeyYdP6F7OI4zQXNlfnB2hAptvs57Plo6clxc+slGKGZw1xz+7kk4hPfokpkIgq8RERkzkrayIR3HADoYG/Zps2QM0ItR2jnc3+apc76S+Qw6jk2Gpzl7h96ouEEMXNN1zHPKPASEZF5baJJbXNSDNJIL132Ysnl7tBHE0do558+1jNuQEApGU+QtuPc8N9WjC9TLEtD7dDkdkBmFQVeIiIiU2QGDRylgaOTXmeQFLtZwQN/sX3csgxJBkjTwFEu//11Bcs2febRsoHd9bev1YjPGW5KgZeZ/QXwFmAY2A68192PRMtuA94HZIHfd/cHo/SNwKeBOPB5d//4VMogIiIyG6VskNVsLbs8cOM4zWz6zLGiJV5yUMEROvinjx2mjv7RXMXy12pnP9d+5LxTKbpMwVRrvB4CbnP3jJl9ArgN+KCZrQNuAs4BlgAPm9maaJ07gF8FdgKPmdl97v7MFMshIiIyp8TMaeIITRyZVP4O30s/DYST2E482nOQNNv8HHZ+dPxUG0mGaeEQ19x2NqnkxE2wcvKmFHi5+3fyXv4MeFv0/EbgHncfAl4ys23AhmjZNnd/EcDM7onyKvASERGZgpg5DRTXjpVWz3Ha2F+yVmyYFEdo554/O3iCQQdhrVodfbRwiCs+eD5mXnC/z8K8Y1KJzLxtEp3OPl6/A3wler6UMBDL2RmlAbxalP7aUhszs1uAWwAWqCuaiIjItDIrPQ9aigEWs5PF7JzUdvq9nkMs4huf2DPBfGnRe+KMUINjbPzA8oIlxRprB+bkAIMTRjRm9jCwuMSi29393ijP7UAG+PJ0Fczd7wTuBOi2lG7wJSIiMgOlrY80pUd7lpL1GPtZyk8/dbggPT8IdML+bUOkaKSX+AlGndYyyK/eto665MjJFb4KThh4ufvVEy03s5uBG4Cr3D131HYBXXnZlkVpTJAuIiIic1zcAjoLGr/KC9zoo4mA2IT5BqjnK392gAwJkoTB12Sm9OjztlKVbafVVEc1bgT+GHiju+fPKHcfcLeZfZKwc3038HPC3es2s1WEAddNwLumUgYRERGZm2LmNNJ7wnzN9LCYnWQ9RkD8hPkNp5c2+micjmKelKl2nvosUAs8ZOHUuz9z9//k7lvM7KuEneYzwK3ungUws/cDDxJOJ/FFd98yxTKIiIiIELeAOJO7pVM7+2m3/ae5ROPZWOvgzNVtKf9UYvzMviIiIiIzzQ2Z559w94tLLZu40VREREREpo0CLxEREZEKUeAlIiIiUiEKvEREREQqRIGXiIiISIUo8BIRERGpEAVeIiIiIhWiwEtERESkQhR4iYiIiFSIAi8RERGRClHgJSIiIlIhs+JejWZ2DNha7XLMAB3AwWoXYgbQcdAxyNFxCOk46Bjk6DiEqn0cVrj7glILEpUuySnaWu5mk/OJmT2u46DjADoGOToOIR0HHYMcHYfQTD4OamoUERERqRAFXiIiIiIVMlsCrzurXYAZQschpOOgY5Cj4xDScdAxyNFxCM3Y4zArOteLiIiIzAWzpcZLREREZNZT4CUiIiJSITMu8DKzvzCz58zsKTP7ppm15C27zcy2mdlWM7s2L31jlLbNzD5UlYKfZvNhHwHMrMvMvmdmz5jZFjP7gyi9zcweMrMXosfWKN3M7DPRcXnKzC6s7h5MHzOLm9kmM/vX6PUqM3s02tevmFlNlF4bvd4WLV9Z1YJPIzNrMbOvRd8Jz5rZ6+bpufCH0d/D02b2z2aWmg/ng5l90cz2m9nTeWkn/fmb2Xui/C+Y2XuqsS+nqswxmHfXyVLHIW/ZH5mZm1lH9HpmnwvuPqP+AdcAiej5J4BPRM/XAb8EaoFVwHYgHv3bDqwGaqI866q9H9N8TOb8PubtaydwYfS8EXg++uz/HPhQlP6hvPPieuDfAAMuBR6t9j5M47H4L8DdwL9Gr78K3BQ9/1vg96Ln/xn42+j5TcBXql32aTwGdwH/IXpeA7TMt3MBWAq8BNTlnQc3z4fzAXgDcCHwdF7aSX3+QBvwYvTYGj1vrfa+TfEYzLvrZKnjEKV3AQ8CLwMds+FcmHE1Xu7+HXfPRC9/BiyLnt8I3OPuQ+7+ErAN2BD92+buL7r7MHBPlHcumQ/7CIC773H3J6Pnx4BnCS88NxJehIkefy16fiPwJQ/9DGgxs87Klnr6mdky4M3A56PXBlwJfC3KUnwMcsfma8BVUf5ZzcyaCb9svwDg7sPufoR5di5EEkCdmSWANLCHeXA+uPsPgMNFySf7+V8LPOTuh929B3gI2HjaCz9NSh2D+XidLHMuAPwV8MdA/kjBGX0uzLjAq8jvEEatEF58X81btjNKK5c+l8yHfRwnaiJZDzwKLHL3PdGivcCi6PlcPTafIvwyCaLX7cCRvC/b/P0cPQbR8t4o/2y3CjgA/EPU5Pp5M6tnnp0L7r4L+EvgFcKAqxd4gvl3PuSc7Oc/J8+LPPP2OmlmNwK73P2XRYtm9HGoSuBlZg9HfRWK/92Yl+d2IAN8uRpllOoyswbg68AH3P1o/jIP64zn7DwoZnYDsN/dn6h2WaosQdi08Dl3Xw/0ETYtjZrr5wJA1IfpRsJAdAlQzyyqsTmd5sPnP5H5fJ00szTwYeAj1S7LyarKvRrd/eqJlpvZzcANwFXRHxbALsK23JxlURoTpM8VE+37nGNmScKg68vu/o0oeZ+Zdbr7nqjKeH+UPhePzWXAW83seiAFNAGfJqwuT0S1GPn7mTsGO6OmqGbgUOWLPe12Ajvd/dHo9dcIA6/5dC4AXA285O4HAMzsG4TnyHw7H3JO9vPfBVxRlP79CpTztNJ1kjMIf4z8MmpJXwY8aWYbmOHnwoxrajSzjYRNLG919/68RfcBN0UjdlYB3cDPgceA7miETw1hZ9L7Kl3u02w+7CMw2pfpC8Cz7v7JvEX3AbkRKO8B7s1Lf3c0iuVSoDevGWJWcvfb3H2Zu68k/Ky/6+7/Hvge8LYoW/ExyB2bt0X5Z30tgLvvBV41s7VR0lXAM8yjcyHyCnCpmaWjv4/ccZhX50Oek/38HwSuMbPWqPbwmiht1tJ1Etx9s7svdPeV0XflTsKBWXuZ6edCpXvzn+gfYWfAV4FfRP/+Nm/Z7YQjM7YC1+WlX084+m07cHu19+E0HZc5v4/Rfl5O2HTwVN45cD1hH5VHgBeAh4G2KL8Bd0THZTNwcbX3YZqPxxWMjWpcTfglug34v0BtlJ6KXm+Llq+udrmncf8vAB6Pzod/IRyJNO/OBeB/Ac8BTwP/SDhqbc6fD8A/E/ZrGyG8sL7vVD5/wn5Q26J/7632fk3DMZh318lSx6Fo+Q7GRjXO6HNBtwwSERERqZAZ19QoIiIiMlcp8BIRERGpEAVeIiIiIhWiwEtERESkQhR4iYiIiFSIAi8RERGRClHgJSIiIlIh/x+uO2sus9hkzQAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -390,26 +389,22 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -435,26 +430,22 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -485,7 +476,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.12.1" }, "orig_nbformat": 4 }, diff --git a/examples/01_opening_floris_computing_power.py b/examples/01_opening_floris_computing_power.py index 59372a866..dcb1987c1 100644 --- a/examples/01_opening_floris_computing_power.py +++ b/examples/01_opening_floris_computing_power.py @@ -1,79 +1,62 @@ +"""Example 1: Opening FLORIS and Computing Power -import numpy as np - -from floris.tools import FlorisInterface +This first example illustrates several of the key concepts in FLORIS. It: - -""" -This example creates a FLORIS instance -1) Makes a two-turbine layout -2) Demonstrates single ws/wd simulations -3) Demonstrates mulitple ws/wd simulations + 1) Initializing FLORIS + 2) Changing the wind farm layout + 3) Changing the incoming wind speed, wind direction and turbulence intensity + 4) Running the FLORIS simulation + 5) Getting the power output of the turbines Main concept is introduce FLORIS and illustrate essential structure of most-used FLORIS calls """ -# Initialize FLORIS with the given input file via FlorisInterface. -# For basic usage, FlorisInterface provides a simplified and expressive -# entry point to the simulation routines. -fi = FlorisInterface("inputs/gch.yaml") -# Convert to a simple two turbine layout -fi.set(layout_x=[0, 500.0], layout_y=[0.0, 0.0]) +import numpy as np -# Single wind speed and wind direction -print("\n========================= Single Wind Direction and Wind Speed =========================") +from floris import FlorisModel -# Get the turbine powers assuming 1 wind direction and speed -# Set the yaw angles to 0 with 1 wind direction and speed -fi.set(wind_directions=[270.0], wind_speeds=[8.0], yaw_angles=np.zeros([1, 2])) -fi.run() +# Initialize FLORIS with the given input file. +# The Floris class is the entry point for most usage. +fmodel = FlorisModel("inputs/gch.yaml") -# Get the turbine powers -turbine_powers = fi.get_turbine_powers() / 1000.0 +# Changing the wind farm layout uses FLORIS' set method to a two-turbine layout +fmodel.set(layout_x=[0, 500.0], layout_y=[0.0, 0.0]) -print("The turbine power matrix should be of dimensions 1 findex X 2 Turbines") -print(turbine_powers) -print("Shape: ", turbine_powers.shape) +# Changing wind speed, wind direction, and turbulence intensity using the set method +# as well. Note that the wind_speeds, wind_directions, and turbulence_intensities +# are all specified as arrays of the same length. +fmodel.set(wind_directions=np.array([270.0]), + wind_speeds=[8.0], + turbulence_intensities=np.array([0.06])) -# Single wind speed and multiple wind directions -print("\n========================= Single Wind Direction and Multiple Wind Speeds ===============") - -wind_speeds = np.array([8.0, 9.0, 10.0]) -wind_directions = np.array([270.0, 270.0, 270.0]) -turbulence_intensities = np.array([0.06, 0.06, 0.06]) - -# 3 wind directions/ speeds -fi.set( - wind_speeds=wind_speeds, - wind_directions=wind_directions, - turbulence_intensities=turbulence_intensities, - yaw_angles=np.zeros([3, 2]) -) -fi.run() -turbine_powers = fi.get_turbine_powers() / 1000.0 -print("The turbine power matrix should be of dimensions 3 findex X 2 Turbines") -print(turbine_powers) -print("Shape: ", turbine_powers.shape) +# Note that typically all 3, wind_directions, wind_speeds and turbulence_intensities +# must be supplied to set. However, the exception is if not changing the lenght +# of the arrays, then only one or two may be supplied. +fmodel.set(turbulence_intensities=np.array([0.07])) -# Multiple wind speeds and multiple wind directions -print("\n========================= Multiple Wind Directions and Multiple Wind Speeds ============") +# The number of elements in the wind_speeds, wind_directions, and turbulence_intensities +# corresponds to the number of conditions to be simulated. In FLORIS, each of these are +# tracked by a simple index called a findex. There is no requirement that the values +# be unique. Internally in FLORIS, most data structures will have the findex as their +# 0th dimension. The value n_findex is the total number of conditions to be simulated. +# This command would simulate 4 conditions (n_findex = 4). +fmodel.set(wind_directions=np.array([270.0, 270.0, 270.0, 270.0]), + wind_speeds=[8.0, 8.0, 10.0, 10.0], + turbulence_intensities=np.array([0.06, 0.06, 0.06, 0.06])) -# To consider each combination, this needs to be broadcast out in advance +# After the set method, the run method is called to perform the simulation +fmodel.run() -wind_speeds = np.tile([8.0, 9.0, 10.0], 3) -wind_directions = np.repeat([260.0, 270.0, 280.0], 3) -turbulence_intensities = np.tile([0.06, 0.06, 0.06], 3) +# There are functions to get either the power of each turbine, or the farm power +turbine_powers = fmodel.get_turbine_powers() / 1000.0 +farm_power = fmodel.get_farm_power() / 1000.0 -fi.set( - wind_directions=wind_directions, - wind_speeds=wind_speeds, - turbulence_intensities=turbulence_intensities, - yaw_angles=np.zeros([9, 2]) -) -fi.run() -turbine_powers = fi.get_turbine_powers() / 1000.0 -print("The turbine power matrix should be of dimensions 9 WD/WS X 2 Turbines") +print("The turbine power matrix should be of dimensions 4 (n_findex) X 2 (n_turbines)") print(turbine_powers) print("Shape: ", turbine_powers.shape) + +print("The farm power should be a 1D array of length 4 (n_findex)") +print(farm_power) +print("Shape: ", farm_power.shape) diff --git a/examples/02_visualizations.py b/examples/02_visualizations.py index f7e8c8ea6..de526328f 100644 --- a/examples/02_visualizations.py +++ b/examples/02_visualizations.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np -import floris.tools.flow_visualization as flowviz -from floris.tools import FlorisInterface +import floris.flow_visualization as flowviz +from floris import FlorisModel """ @@ -12,7 +12,7 @@ we are plotting three slices of the resulting flow field: 1. Horizontal slice parallel to the ground and located at the hub height 2. Vertical slice of parallel with the direction of the wind -3. Veritical slice parallel to to the turbine disc plane +3. Vertical slice parallel to to the turbine disc plane Additionally, an alternative method of plotting a horizontal slice is shown. Rather than calculating points in the domain behind a turbine, @@ -21,36 +21,36 @@ rotor. """ -# Initialize FLORIS with the given input file via FlorisInterface. -# For basic usage, FlorisInterface provides a simplified and expressive +# Initialize FLORIS with the given input file via FlorisModel. +# For basic usage, FlorisModel provides a simplified and expressive # entry point to the simulation routines. -fi = FlorisInterface("inputs/gch.yaml") +fmodel = FlorisModel("inputs/gch.yaml") # The rotor plots show what is happening at each turbine, but we do not # see what is happening between each turbine. For this, we use a # grid that has points regularly distributed throughout the fluid domain. -# The FlorisInterface contains functions for configuring the new grid, +# The FlorisModel contains functions for configuring the new grid, # running the simulation, and generating plots of 2D slices of the # flow field. # Note this visualization grid created within the calculate_horizontal_plane function will be reset # to what existed previously at the end of the function -# Using the FlorisInterface functions, get 2D slices. -horizontal_plane = fi.calculate_horizontal_plane( +# Using the FlorisModel functions, get 2D slices. +horizontal_plane = fmodel.calculate_horizontal_plane( x_resolution=200, y_resolution=100, height=90.0, yaw_angles=np.array([[25.,0.,0.]]), ) -y_plane = fi.calculate_y_plane( +y_plane = fmodel.calculate_y_plane( x_resolution=200, z_resolution=100, crossstream_dist=0.0, yaw_angles=np.array([[25.,0.,0.]]), ) -cross_plane = fi.calculate_cross_plane( +cross_plane = fmodel.calculate_cross_plane( y_resolution=100, z_resolution=100, downstream_dist=630.0, @@ -82,7 +82,7 @@ # Some wake models may not yet have a visualization method included, for these cases can use # a slower version which scans a turbine model to produce the horizontal flow horizontal_plane_scan_turbine = flowviz.calculate_horizontal_plane_with_turbines( - fi, + fmodel, x_resolution=20, y_resolution=10, yaw_angles=np.array([[25.,0.,0.]]), @@ -101,11 +101,11 @@ # Run the wake calculation to get the turbine-turbine interfactions # on the turbine grids -fi.run() +fmodel.run() # Plot the values at each rotor fig, axes, _ , _ = flowviz.plot_rotor_values( - fi.floris.flow_field.u, + fmodel.core.flow_field.u, findex=0, n_rows=1, n_cols=3, @@ -125,15 +125,15 @@ "type": "turbine_grid", "turbine_grid_points": 10 } -fi.set(solver_settings=solver_settings) +fmodel.set(solver_settings=solver_settings) # Run the wake calculation to get the turbine-turbine interfactions # on the turbine grids -fi.run() +fmodel.run() # Plot the values at each rotor fig, axes, _ , _ = flowviz.plot_rotor_values( - fi.floris.flow_field.u, + fmodel.core.flow_field.u, findex=0, n_rows=1, n_cols=3, diff --git a/examples/03_making_adjustments.py b/examples/03_making_adjustments.py index a17eb3396..0bac6e98b 100644 --- a/examples/03_making_adjustments.py +++ b/examples/03_making_adjustments.py @@ -2,9 +2,9 @@ import matplotlib.pyplot as plt import numpy as np -import floris.tools.flow_visualization as flowviz -import floris.tools.layout_visualization as layoutviz -from floris.tools import FlorisInterface +import floris.flow_visualization as flowviz +import floris.layout_visualization as layoutviz +from floris import FlorisModel """ @@ -20,12 +20,12 @@ MIN_WS = 1.0 MAX_WS = 8.0 -# Initialize FLORIS with the given input file via FlorisInterface -fi = FlorisInterface("inputs/gch.yaml") +# Initialize FLORIS with the given input file via FlorisModel +fmodel = FlorisModel("inputs/gch.yaml") # Plot a horizatonal slice of the initial configuration -horizontal_plane = fi.calculate_horizontal_plane(height=90.0) +horizontal_plane = fmodel.calculate_horizontal_plane(height=90.0) flowviz.visualize_cut_plane( horizontal_plane, ax=axarr[0], @@ -35,7 +35,7 @@ ) # Change the wind speed -horizontal_plane = fi.calculate_horizontal_plane(ws=[7.0], height=90.0) +horizontal_plane = fmodel.calculate_horizontal_plane(ws=[7.0], height=90.0) flowviz.visualize_cut_plane( horizontal_plane, ax=axarr[1], @@ -46,8 +46,8 @@ # Change the wind shear, reset the wind speed, and plot a vertical slice -fi.set(wind_shear=0.2, wind_speeds=[8.0]) -y_plane = fi.calculate_y_plane(crossstream_dist=0.0) +fmodel.set(wind_shear=0.2, wind_speeds=[8.0]) +y_plane = fmodel.calculate_y_plane(crossstream_dist=0.0) flowviz.visualize_cut_plane( y_plane, ax=axarr[2], @@ -59,11 +59,11 @@ # # Change the farm layout N = 3 # Number of turbines per row and per column X, Y = np.meshgrid( - 5.0 * fi.floris.farm.rotor_diameters[0,0] * np.arange(0, N, 1), - 5.0 * fi.floris.farm.rotor_diameters[0,0] * np.arange(0, N, 1), + 5.0 * fmodel.core.farm.rotor_diameters[0,0] * np.arange(0, N, 1), + 5.0 * fmodel.core.farm.rotor_diameters[0,0] * np.arange(0, N, 1), ) -fi.set(layout_x=X.flatten(), layout_y=Y.flatten(), wind_directions=[270.0]) -horizontal_plane = fi.calculate_horizontal_plane(height=90.0) +fmodel.set(layout_x=X.flatten(), layout_y=Y.flatten(), wind_directions=[270.0]) +horizontal_plane = fmodel.calculate_horizontal_plane(height=90.0) flowviz.visualize_cut_plane( horizontal_plane, ax=axarr[3], @@ -71,8 +71,8 @@ min_speed=MIN_WS, max_speed=MAX_WS ) -layoutviz.plot_turbine_labels(fi, axarr[3],plotting_dict={'color':"w"})#, backgroundcolor="k") -layoutviz.plot_turbine_rotors(fi, axarr[3]) +layoutviz.plot_turbine_labels(fmodel, axarr[3], plotting_dict={'color':"w"}) #, backgroundcolor="k") +layoutviz.plot_turbine_rotors(fmodel, axarr[3]) # Change the yaw angles and configure the plot differently yaw_angles = np.zeros((1, N * N)) @@ -87,7 +87,7 @@ yaw_angles[:,4] = 30.0 yaw_angles[:,7] = -30.0 -horizontal_plane = fi.calculate_horizontal_plane(yaw_angles=yaw_angles, height=90.0) +horizontal_plane = fmodel.calculate_horizontal_plane(yaw_angles=yaw_angles, height=90.0) flowviz.visualize_cut_plane( horizontal_plane, ax=axarr[4], @@ -96,11 +96,11 @@ min_speed=MIN_WS, max_speed=MAX_WS ) -layoutviz.plot_turbine_rotors(fi, axarr[4], yaw_angles=yaw_angles, color="c") +layoutviz.plot_turbine_rotors(fmodel, axarr[4], yaw_angles=yaw_angles, color="c") # Plot the cross-plane of the 3x3 configuration -cross_plane = fi.calculate_cross_plane(yaw_angles=yaw_angles, downstream_dist=610.0) +cross_plane = fmodel.calculate_cross_plane(yaw_angles=yaw_angles, downstream_dist=610.0) flowviz.visualize_cut_plane( cross_plane, ax=axarr[5], diff --git a/examples/04_sweep_wind_directions.py b/examples/04_sweep_wind_directions.py index a06892e16..d049a0772 100644 --- a/examples/04_sweep_wind_directions.py +++ b/examples/04_sweep_wind_directions.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -16,19 +16,19 @@ """ # Instantiate FLORIS using either the GCH or CC model -fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 +fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 # Define a two turbine farm D = 126. layout_x = np.array([0, D*6]) layout_y = [0, 0] -fi.set(layout_x=layout_x, layout_y=layout_y) +fmodel.set(layout_x=layout_x, layout_y=layout_y) # Sweep wind speeds but keep wind direction fixed wd_array = np.arange(250,291,1.) ws_array = 8.0 * np.ones_like(wd_array) ti_array = 0.06 * np.ones_like(wd_array) -fi.set(wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=ti_array) +fmodel.set(wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=ti_array) # Define a matrix of yaw angles to be all 0 # Note that yaw angles is now specified as a matrix whose dimensions are @@ -38,13 +38,13 @@ n_findex = num_wd # Could be either num_wd or num_ws num_turbine = len(layout_x) # Number of turbines yaw_angles = np.zeros((n_findex, num_turbine)) -fi.set(yaw_angles=yaw_angles) +fmodel.set(yaw_angles=yaw_angles) # Calculate -fi.run() +fmodel.run() # Collect the turbine powers -turbine_powers = fi.get_turbine_powers() / 1E3 # In kW +turbine_powers = fmodel.get_turbine_powers() / 1E3 # In kW # Pull out the power values per turbine pow_t0 = turbine_powers[:,0].flatten() diff --git a/examples/05_sweep_wind_speeds.py b/examples/05_sweep_wind_speeds.py index a9dbc979c..e5cd07c3a 100644 --- a/examples/05_sweep_wind_speeds.py +++ b/examples/05_sweep_wind_speeds.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -16,19 +16,19 @@ # Instantiate FLORIS using either the GCH or CC model -fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 +fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 # Define a two turbine farm D = 126. layout_x = np.array([0, D*6]) layout_y = [0, 0] -fi.set(layout_x=layout_x, layout_y=layout_y) +fmodel.set(layout_x=layout_x, layout_y=layout_y) # Sweep wind speeds but keep wind direction fixed ws_array = np.arange(5,25,0.5) wd_array = 270.0 * np.ones_like(ws_array) ti_array = 0.06 * np.ones_like(ws_array) -fi.set(wind_directions=wd_array,wind_speeds=ws_array, turbulence_intensities=ti_array) +fmodel.set(wind_directions=wd_array,wind_speeds=ws_array, turbulence_intensities=ti_array) # Define a matrix of yaw angles to be all 0 # Note that yaw angles is now specified as a matrix whose dimensions are @@ -38,13 +38,13 @@ n_findex = num_wd # Could be either num_wd or num_ws num_turbine = len(layout_x) yaw_angles = np.zeros((n_findex, num_turbine)) -fi.set(yaw_angles=yaw_angles) +fmodel.set(yaw_angles=yaw_angles) # Calculate -fi.run() +fmodel.run() # Collect the turbine powers -turbine_powers = fi.get_turbine_powers() / 1E3 # In kW +turbine_powers = fmodel.get_turbine_powers() / 1E3 # In kW # Pull out the power values per turbine pow_t0 = turbine_powers[:,0].flatten() diff --git a/examples/06_sweep_wind_conditions.py b/examples/06_sweep_wind_conditions.py index dd1756685..e9f42487b 100644 --- a/examples/06_sweep_wind_conditions.py +++ b/examples/06_sweep_wind_conditions.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -20,14 +20,14 @@ """ # Instantiate FLORIS using either the GCH or CC model -fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 -# fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model +fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 +# fmodel = FlorisModel("inputs/cc.yaml") # New CumulativeCurl model # Define a 5 turbine farm D = 126.0 layout_x = np.array([0, D*6, D*12, D*18, D*24]) layout_y = [0, 0, 0, 0, 0] -fi.set(layout_x=layout_x, layout_y=layout_y) +fmodel.set(layout_x=layout_x, layout_y=layout_y) # In this case we want to check a grid of wind speed and direction combinations wind_speeds_to_expand = np.arange(6, 9, 1.0) @@ -47,7 +47,7 @@ turbulence_intensities = 0.06 * np.ones_like(wd_array) # Now reinitialize FLORIS -fi.set( +fmodel.set( wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=turbulence_intensities @@ -61,13 +61,13 @@ n_findex = num_wd # Could be either num_wd or num_ws num_turbine = len(layout_x) yaw_angles = np.zeros((n_findex, num_turbine)) -fi.set(yaw_angles=yaw_angles) +fmodel.set(yaw_angles=yaw_angles) # Calculate -fi.run() +fmodel.run() # Collect the turbine powers -turbine_powers = fi.get_turbine_powers() / 1e3 # In kW +turbine_powers = fmodel.get_turbine_powers() / 1e3 # In kW # Show results by ws and wd fig, axarr = plt.subplots(num_unique_ws, 1, sharex=True, sharey=True, figsize=(6, 10)) diff --git a/examples/07_calc_aep_from_rose.py b/examples/07_calc_aep_from_rose.py index 116f6f1cd..cc2de88d4 100644 --- a/examples/07_calc_aep_from_rose.py +++ b/examples/07_calc_aep_from_rose.py @@ -3,15 +3,15 @@ import pandas as pd from scipy.interpolate import NearestNDInterpolator -from floris.tools import FlorisInterface +from floris import FlorisModel """ This example demonstrates how to calculate the Annual Energy Production (AEP) of a wind farm using wind rose information stored in a .csv file. -The wind rose information is first loaded, after which we initialize our Floris -Interface. A 3 turbine farm is generated, and then the turbine wakes and powers +The wind rose information is first loaded, after which we initialize our FlorisModel. +A 3 turbine farm is generated, and then the turbine wakes and powers are calculated across all the wind directions. Finally, the farm power is converted to AEP and reported out. """ @@ -42,13 +42,13 @@ freq = freq / np.sum(freq) # Load the FLORIS object -fi = FlorisInterface("inputs/gch.yaml") # GCH model -# fi = FlorisInterface("inputs/cc.yaml") # CumulativeCurl model +fmodel = FlorisModel("inputs/gch.yaml") # GCH model +# fmodel = FlorisModel("inputs/cc.yaml") # CumulativeCurl model # Assume a three-turbine wind farm with 5D spacing. We reinitialize the # floris object and assign the layout, wind speed and wind direction arrays. -D = fi.floris.farm.rotor_diameters[0] # Rotor diameter for the NREL 5 MW -fi.set( +D = fmodel.core.farm.rotor_diameters[0] # Rotor diameter for the NREL 5 MW +fmodel.set( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wind_directions, @@ -57,7 +57,7 @@ ) # Compute the AEP using the default settings -aep = fi.get_farm_AEP(freq=freq) +aep = fmodel.get_farm_AEP(freq=freq) print("Farm AEP (default options): {:.3f} GWh".format(aep / 1.0e9)) # Compute the AEP again while specifying a cut-in and cut-out wind speed. @@ -66,7 +66,7 @@ # prevent unexpected behavior for zero/negative and very high wind speeds. # In this example, the results should not change between this and the default # call to 'get_farm_AEP()'. -aep = fi.get_farm_AEP( +aep = fmodel.get_farm_AEP( freq=freq, cut_in_wind_speed=3.0, # Wakes are not evaluated below this wind speed cut_out_wind_speed=25.0, # Wakes are not evaluated above this wind speed @@ -76,5 +76,5 @@ # Finally, we can also compute the AEP while ignoring all wake calculations. # This can be useful to quantity the annual wake losses in the farm. Such # calculations can be facilitated by enabling the 'no_wake' handle. -aep_no_wake = fi.get_farm_AEP(freq, no_wake=True) +aep_no_wake = fmodel.get_farm_AEP(freq, no_wake=True) print("Farm AEP (no_wake=True): {:.3f} GWh".format(aep_no_wake / 1.0e9)) diff --git a/examples/09_compare_farm_power_with_neighbor.py b/examples/09_compare_farm_power_with_neighbor.py index 48c02ff8d..59e16f841 100644 --- a/examples/09_compare_farm_power_with_neighbor.py +++ b/examples/09_compare_farm_power_with_neighbor.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -18,19 +18,19 @@ # Instantiate FLORIS using either the GCH or CC model -fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 +fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 # Define a 4 turbine farm turbine farm D = 126. layout_x = np.array([0, D*6, 0, D*6]) layout_y = [0, 0, D*3, D*3] -fi.set(layout_x=layout_x, layout_y=layout_y) +fmodel.set(layout_x=layout_x, layout_y=layout_y) # Define a simple wind rose with just 1 wind speed wd_array = np.arange(0,360,4.) ws_array = 8.0 * np.ones_like(wd_array) turbulence_intensities = 0.06 * np.ones_like(wd_array) -fi.set( +fmodel.set( wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=turbulence_intensities @@ -38,25 +38,25 @@ # Calculate -fi.run() +fmodel.run() # Collect the farm power -farm_power_base = fi.get_farm_power() / 1E3 # In kW +farm_power_base = fmodel.get_farm_power() / 1E3 # In kW # Add a neighbor to the east layout_x = np.array([0, D*6, 0, D*6, D*12, D*15, D*12, D*15]) layout_y = np.array([0, 0, D*3, D*3, 0, 0, D*3, D*3]) -fi.set(layout_x=layout_x, layout_y=layout_y) +fmodel.set(layout_x=layout_x, layout_y=layout_y) # Define the weights to exclude the neighboring farm from calcuations of power turbine_weights = np.zeros(len(layout_x), dtype=int) turbine_weights[0:4] = 1.0 # Calculate -fi.run() +fmodel.run() # Collect the farm power with the neightbor -farm_power_neighbor = fi.get_farm_power(turbine_weights=turbine_weights) / 1E3 # In kW +farm_power_neighbor = fmodel.get_farm_power(turbine_weights=turbine_weights) / 1E3 # In kW # Show the farms fig, ax = plt.subplots() diff --git a/examples/10_opt_yaw_single_ws.py b/examples/10_opt_yaw_single_ws.py index fb3b534b0..f33878c9e 100644 --- a/examples/10_opt_yaw_single_ws.py +++ b/examples/10_opt_yaw_single_ws.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface -from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR +from floris import FlorisModel +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR """ @@ -16,25 +16,25 @@ """ # Load the default example floris object -fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 -# fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model +fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 +# fmodel = FlorisModel("inputs/cc.yaml") # New CumulativeCurl model # Reinitialize as a 3-turbine farm with range of WDs and 1 WS wd_array = np.arange(0.0, 360.0, 3.0) ws_array = 8.0 * np.ones_like(wd_array) turbulence_intensities = 0.06 * np.ones_like(wd_array) D = 126.0 # Rotor diameter for the NREL 5 MW -fi.set( +fmodel.set( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=turbulence_intensities, ) -print(fi.floris.farm.rotor_diameters) +print(fmodel.core.farm.rotor_diameters) # Initialize optimizer object and run optimization using the Serial-Refine method -yaw_opt = YawOptimizationSR(fi) +yaw_opt = YawOptimizationSR(fmodel) df_opt = yaw_opt.optimize() print("Optimization results:") diff --git a/examples/11_opt_yaw_multiple_ws.py b/examples/11_opt_yaw_multiple_ws.py index f0ee51e14..0a7d9668a 100644 --- a/examples/11_opt_yaw_multiple_ws.py +++ b/examples/11_opt_yaw_multiple_ws.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface -from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR +from floris import FlorisModel +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR """ @@ -16,8 +16,8 @@ """ # Load the default example floris object -fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 -# fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model +fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 +# fmodel = FlorisModel("inputs/cc.yaml") # New CumulativeCurl model # Define arrays of ws/wd wind_speeds_to_expand = np.arange(2.0, 18.0, 1.0) @@ -36,7 +36,7 @@ # Reinitialize as a 3-turbine farm with range of WDs and WSs D = 126.0 # Rotor diameter for the NREL 5 MW -fi.set( +fmodel.set( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, @@ -55,7 +55,7 @@ # but has no effect on the predicted power uplift from wake steering. # Hence, it should mostly be used when actually synthesizing a practicable # wind farm controller. -yaw_opt = YawOptimizationSR(fi) +yaw_opt = YawOptimizationSR(fmodel) df_opt = yaw_opt.optimize() print("Optimization results:") @@ -74,7 +74,7 @@ figsize=(10, 8) ) jj = 0 -for ii, ws in enumerate(np.unique(fi.floris.flow_field.wind_speeds)): +for ii, ws in enumerate(np.unique(fmodel.core.flow_field.wind_speeds)): xi = np.remainder(ii, 4) if ((ii > 0) & (xi == 0)): jj += 1 @@ -104,7 +104,7 @@ figsize=(10, 8) ) jj = 0 -for ii, ws in enumerate(np.unique(fi.floris.flow_field.wind_speeds)): +for ii, ws in enumerate(np.unique(fmodel.core.flow_field.wind_speeds)): xi = np.remainder(ii, 4) if ((ii > 0) & (xi == 0)): jj += 1 diff --git a/examples/12_optimize_yaw.py b/examples/12_optimize_yaw.py index 41d7f23e2..d631d5437 100644 --- a/examples/12_optimize_yaw.py +++ b/examples/12_optimize_yaw.py @@ -5,8 +5,8 @@ import numpy as np import pandas as pd -from floris.tools import FlorisInterface -from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR +from floris import FlorisModel +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR """ @@ -26,18 +26,18 @@ def load_floris(): # Load the default example floris object - fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 - # fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model + fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 + # fmodel = FlorisModel("inputs/cc.yaml") # New CumulativeCurl model # Specify wind farm layout and update in the floris object N = 5 # number of turbines per row and per column X, Y = np.meshgrid( - 5.0 * fi.floris.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), - 5.0 * fi.floris.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), + 5.0 * fmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), + 5.0 * fmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), ) - fi.set(layout_x=X.flatten(), layout_y=Y.flatten()) + fmodel.set(layout_x=X.flatten(), layout_y=Y.flatten()) - return fi + return fmodel def load_windrose(): @@ -49,11 +49,11 @@ def load_windrose(): return df -def calculate_aep(fi, df_windrose, column_name="farm_power"): +def calculate_aep(fmodel, df_windrose, column_name="farm_power"): from scipy.interpolate import NearestNDInterpolator # Define columns - nturbs = len(fi.layout_x) + nturbs = len(fmodel.layout_x) yaw_cols = ["yaw_{:03d}".format(ti) for ti in range(nturbs)] if "yaw_000" not in df_windrose.columns: @@ -64,7 +64,7 @@ def calculate_aep(fi, df_windrose, column_name="farm_power"): ws_array = np.array(df_windrose["ws"], dtype=float) turbulence_intensities = 0.06 * np.ones_like(wd_array) yaw_angles = np.array(df_windrose[yaw_cols], dtype=float) - fi.set( + fmodel.set( wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=turbulence_intensities, @@ -72,8 +72,8 @@ def calculate_aep(fi, df_windrose, column_name="farm_power"): ) # Calculate FLORIS for every WD and WS combination and get the farm power - fi.run() - farm_power_array = fi.get_farm_power() + fmodel.run() + farm_power_array = fmodel.get_farm_power() # Now map FLORIS solutions to dataframe interpolant = NearestNDInterpolator( @@ -94,17 +94,17 @@ def calculate_aep(fi, df_windrose, column_name="farm_power"): df_windrose = load_windrose() # Load FLORIS - fi = load_floris() - ws_array = 8.0 * np.ones_like(fi.floris.flow_field.wind_directions) - fi.set(wind_speeds=ws_array) - nturbs = len(fi.layout_x) + fmodel = load_floris() + ws_array = 8.0 * np.ones_like(fmodel.core.flow_field.wind_directions) + fmodel.set(wind_speeds=ws_array) + nturbs = len(fmodel.layout_x) # First, get baseline AEP, without wake steering start_time = timerpc() print(" ") print("===========================================================") print("Calculating baseline annual energy production (AEP)...") - aep_bl = calculate_aep(fi, df_windrose, "farm_power_baseline") + aep_bl = calculate_aep(fmodel, df_windrose, "farm_power_baseline") t = timerpc() - start_time print("Baseline AEP: {:.3f} GWh. Time spent: {:.1f} s.".format(aep_bl, t)) print("===========================================================") @@ -116,13 +116,13 @@ def calculate_aep(fi, df_windrose, column_name="farm_power"): wd_array = np.arange(0.0, 360.0, 5.0) ws_array = 8.0 * np.ones_like(wd_array) turbulence_intensities = 0.06 * np.ones_like(wd_array) - fi.set( + fmodel.set( wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=turbulence_intensities, ) yaw_opt = YawOptimizationSR( - fi=fi, + fmodel=fmodel, minimum_yaw_angle=0.0, # Allowable yaw angles lower bound maximum_yaw_angle=20.0, # Allowable yaw angles upper bound Ny_passes=[5, 4], @@ -132,7 +132,7 @@ def calculate_aep(fi, df_windrose, column_name="farm_power"): df_opt = yaw_opt.optimize() end_time = timerpc() t_tot = end_time - start_time - t_fi = yaw_opt.time_spent_in_floris + t_fmodel = yaw_opt.time_spent_in_floris print("Optimization finished in {:.2f} seconds.".format(t_tot)) print(" ") @@ -171,7 +171,7 @@ def calculate_aep(fi, df_windrose, column_name="farm_power"): start_time = timerpc() print("==================================================================") print("Calculating annual energy production (AEP) with wake steering...") - aep_opt = calculate_aep(fi, df_windrose, "farm_power_opt") + aep_opt = calculate_aep(fmodel, df_windrose, "farm_power_opt") aep_uplift = 100.0 * (aep_opt / aep_bl - 1) t = timerpc() - start_time print("Optimal AEP: {:.3f} GWh. Time spent: {:.1f} s.".format(aep_opt, t)) diff --git a/examples/12_optimize_yaw_in_parallel.py b/examples/12_optimize_yaw_in_parallel.py index 74461ce94..ac57f8548 100644 --- a/examples/12_optimize_yaw_in_parallel.py +++ b/examples/12_optimize_yaw_in_parallel.py @@ -4,7 +4,7 @@ import pandas as pd from scipy.interpolate import LinearNDInterpolator -from floris.tools import FlorisInterface, ParallelComputingInterface +from floris import FlorisModel, ParallelComputingInterface """ @@ -14,18 +14,18 @@ def load_floris(): # Load the default example floris object - fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 - # fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model + fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 + # fmodel = FlorisModel("inputs/cc.yaml") # New CumulativeCurl model # Specify wind farm layout and update in the floris object N = 4 # number of turbines per row and per column X, Y = np.meshgrid( - 5.0 * fi.floris.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), - 5.0 * fi.floris.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), + 5.0 * fmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), + 5.0 * fmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), ) - fi.set(layout_x=X.flatten(), layout_y=Y.flatten()) + fmodel.set(layout_x=X.flatten(), layout_y=Y.flatten()) - return fi + return fmodel def load_windrose(): @@ -43,7 +43,7 @@ def load_windrose(): df_windrose, windrose_interpolant = load_windrose() # Load a FLORIS object for AEP calculations - fi_aep = load_floris() + fmodel_aep = load_floris() # Define arrays of wd/ws wind_directions_to_expand = np.arange(0.0, 360.0, 1.0) @@ -60,7 +60,7 @@ def load_windrose(): ws_array = wind_speeds_grid.flatten() turbulence_intensities = 0.08 * np.ones_like(wd_array) - fi_aep.set( + fmodel_aep.set( wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=turbulence_intensities, @@ -68,8 +68,8 @@ def load_windrose(): # Pour this into a parallel computing interface parallel_interface = "concurrent" - fi_aep_parallel = ParallelComputingInterface( - fi=fi_aep, + fmodel_aep_parallel = ParallelComputingInterface( + fmodel=fmodel_aep, max_workers=max_workers, n_wind_condition_splits=max_workers, interface=parallel_interface, @@ -81,11 +81,11 @@ def load_windrose(): freq_grid = freq_grid / np.sum(freq_grid) # Normalize to 1.0 # Calculate farm power baseline - farm_power_bl = fi_aep_parallel.get_farm_power() + farm_power_bl = fmodel_aep_parallel.get_farm_power() aep_bl = np.sum(24 * 365 * np.multiply(farm_power_bl, freq_grid)) # Alternatively to above code, we could calculate AEP using - # 'fi_aep_parallel.get_farm_AEP(...)' but then we would not have the + # 'fmodel_aep_parallel.get_farm_AEP(...)' but then we would not have the # farm power productions, which we use later on for plotting. # First, get baseline AEP, without wake steering @@ -97,7 +97,7 @@ def load_windrose(): print(" ") # Load a FLORIS object for yaw optimization - fi_opt = load_floris() + fmodel_opt = load_floris() # Define arrays of wd/ws wind_directions_to_expand = np.arange(0.0, 360.0, 3.0) @@ -114,15 +114,15 @@ def load_windrose(): ws_array_opt = wind_speeds_grid.flatten() turbulence_intensities = 0.08 * np.ones_like(wd_array_opt) - fi_opt.set( + fmodel_opt.set( wind_directions=wd_array_opt, wind_speeds=ws_array_opt, turbulence_intensities=turbulence_intensities, ) # Pour this into a parallel computing interface - fi_opt_parallel = ParallelComputingInterface( - fi=fi_opt, + fmodel_opt_parallel = ParallelComputingInterface( + fmodel=fmodel_opt, max_workers=max_workers, n_wind_condition_splits=max_workers, interface=parallel_interface, @@ -130,7 +130,7 @@ def load_windrose(): ) # Now optimize the yaw angles using the Serial Refine method - df_opt = fi_opt_parallel.optimize_yaw_angles( + df_opt = fmodel_opt_parallel.optimize_yaw_angles( minimum_yaw_angle=-25.0, maximum_yaw_angle=25.0, Ny_passes=[5, 4], @@ -163,12 +163,12 @@ def load_windrose(): # Get optimized AEP, with wake steering yaw_grid = yaw_angles_interpolant(wd_array, ws_array) - farm_power_opt = fi_aep_parallel.get_farm_power(yaw_angles=yaw_grid) + farm_power_opt = fmodel_aep_parallel.get_farm_power(yaw_angles=yaw_grid) aep_opt = np.sum(24 * 365 * np.multiply(farm_power_opt, freq_grid)) aep_uplift = 100.0 * (aep_opt / aep_bl - 1) # Alternatively to above code, we could calculate AEP using - # 'fi_aep_parallel.get_farm_AEP(...)' but then we would not have the + # 'fmodel_aep_parallel.get_farm_AEP(...)' but then we would not have the # farm power productions, which we use later on for plotting. print(" ") @@ -196,7 +196,7 @@ def load_windrose(): }) # Plot power and AEP uplift across wind direction - wd_step = np.diff(fi_aep.floris.flow_field.wind_directions)[0] # Useful variable for plotting + wd_step = np.diff(fmodel_aep.core.flow_field.wind_directions)[0] # Useful variable for plotting fig, ax = plt.subplots(nrows=3, sharex=True) df_8ms = df[df["ws"] == 8.0].reset_index(drop=True) @@ -276,7 +276,7 @@ def load_windrose(): # Now plot yaw angle distributions over wind direction up to first three turbines wd_plot = np.arange(0.0, 360.001, 1.0) - for ti in range(np.min([fi_aep.floris.farm.n_turbines, 3])): + for ti in range(np.min([fmodel_aep.core.farm.n_turbines, 3])): fig, ax = plt.subplots(figsize=(6, 3.5)) ws_to_plot = [6.0, 9.0, 12.0] colors = ["maroon", "dodgerblue", "grey"] diff --git a/examples/13_optimize_yaw_with_neighboring_farm.py b/examples/13_optimize_yaw_with_neighboring_farm.py index bab42aaf3..18d5e1b26 100644 --- a/examples/13_optimize_yaw_with_neighboring_farm.py +++ b/examples/13_optimize_yaw_with_neighboring_farm.py @@ -4,8 +4,8 @@ import pandas as pd from scipy.interpolate import NearestNDInterpolator -from floris.tools import FlorisInterface -from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR +from floris import FlorisModel +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR """ @@ -25,8 +25,8 @@ def load_floris(): # Load the default example floris object - fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 - # fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model + fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 + # fmodel = FlorisModel("inputs/cc.yaml") # New CumulativeCurl model # Specify the full wind farm layout: nominal and neighboring wind farms X = np.array( @@ -51,7 +51,7 @@ def load_floris(): turbine_weights[0:10] = 1.0 # Now reinitialize FLORIS layout - fi.set(layout_x = X, layout_y = Y) + fmodel.set(layout_x = X, layout_y = Y) # And visualize the floris layout fig, ax = plt.subplots() @@ -62,7 +62,7 @@ def load_floris(): ax.set_ylabel("y coordinate (m)") ax.legend() - return fi, turbine_weights + return fmodel, turbine_weights def load_windrose(): @@ -89,32 +89,32 @@ def load_windrose(): return ws_windrose, wd_windrose, freq_windrose -def optimize_yaw_angles(fi_opt): +def optimize_yaw_angles(fmodel_opt): # Specify turbines to optimize - turbs_to_opt = np.zeros(len(fi_opt.layout_x), dtype=bool) + turbs_to_opt = np.zeros(len(fmodel_opt.layout_x), dtype=bool) turbs_to_opt[0:10] = True # Specify turbine weights - turbine_weights = np.zeros(len(fi_opt.layout_x)) + turbine_weights = np.zeros(len(fmodel_opt.layout_x)) turbine_weights[turbs_to_opt] = 1.0 # Specify minimum and maximum allowable yaw angle limits minimum_yaw_angle = np.zeros( ( - fi_opt.floris.flow_field.n_findex, - fi_opt.floris.farm.n_turbines, + fmodel_opt.core.flow_field.n_findex, + fmodel_opt.core.farm.n_turbines, ) ) maximum_yaw_angle = np.zeros( ( - fi_opt.floris.flow_field.n_findex, - fi_opt.floris.farm.n_turbines, + fmodel_opt.core.flow_field.n_findex, + fmodel_opt.core.farm.n_turbines, ) ) maximum_yaw_angle[:, turbs_to_opt] = 30.0 yaw_opt = YawOptimizationSR( - fi=fi_opt, + fmodel=fmodel_opt, minimum_yaw_angle=minimum_yaw_angle, maximum_yaw_angle=maximum_yaw_angle, turbine_weights=turbine_weights, @@ -136,8 +136,8 @@ def yaw_opt_interpolant(wd, ws): ws = np.array(ws, dtype=float) # Interpolate optimal yaw angles - x = yaw_opt.fi.floris.flow_field.wind_directions - nturbs = fi_opt.floris.farm.n_turbines + x = yaw_opt.fmodel.core.flow_field.wind_directions + nturbs = fmodel_opt.core.farm.n_turbines y = np.stack( [np.interp(wd, x, yaw_angles_opt[:, ti]) for ti in range(nturbs)], axis=np.ndim(wd) @@ -171,8 +171,8 @@ def yaw_opt_interpolant(wd, ws): if __name__ == "__main__": # Load FLORIS: full farm including neighboring wind farms - fi, turbine_weights = load_floris() - nturbs = len(fi.layout_x) + fmodel, turbine_weights = load_floris() + nturbs = len(fmodel.layout_x) # Load a dataframe containing the wind rose information ws_windrose, wd_windrose, freq_windrose = load_windrose() @@ -180,19 +180,19 @@ def yaw_opt_interpolant(wd, ws): turbulence_intensities_windrose = 0.06 * np.ones_like(wd_windrose) # Create a FLORIS object for AEP calculations - fi_AEP = fi.copy() - fi_AEP.set( + fmodel_aep = fmodel.copy() + fmodel_aep.set( wind_speeds=ws_windrose, wind_directions=wd_windrose, turbulence_intensities=turbulence_intensities_windrose ) # And create a separate FLORIS object for optimization - fi_opt = fi.copy() + fmodel_opt = fmodel.copy() wd_array = np.arange(0.0, 360.0, 3.0) ws_array = 8.0 * np.ones_like(wd_array) turbulence_intensities = 0.06 * np.ones_like(wd_array) - fi_opt.set( + fmodel_opt.set( wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=turbulence_intensities, @@ -202,7 +202,7 @@ def yaw_opt_interpolant(wd, ws): print(" ") print("===========================================================") print("Calculating baseline annual energy production (AEP)...") - aep_bl_subset = 1.0e-9 * fi_AEP.get_farm_AEP( + aep_bl_subset = 1.0e-9 * fmodel_aep.get_farm_AEP( freq=freq_windrose, turbine_weights=turbine_weights ) @@ -225,19 +225,19 @@ def yaw_opt_interpolant(wd, ws): turbs_to_opt = (turbine_weights > 0.0001) # Optimize yaw angles while including neighboring farm - yaw_opt_interpolant = optimize_yaw_angles(fi_opt=fi_opt) + yaw_opt_interpolant = optimize_yaw_angles(fmodel_opt=fmodel_opt) # Optimize yaw angles while ignoring neighboring farm - fi_opt_subset = fi_opt.copy() - fi_opt_subset.set( - layout_x = fi.layout_x[turbs_to_opt], - layout_y = fi.layout_y[turbs_to_opt] + fmodel_opt_subset = fmodel_opt.copy() + fmodel_opt_subset.set( + layout_x = fmodel.layout_x[turbs_to_opt], + layout_y = fmodel.layout_y[turbs_to_opt] ) - yaw_opt_interpolant_nonb = optimize_yaw_angles(fi_opt=fi_opt_subset) + yaw_opt_interpolant_nonb = optimize_yaw_angles(fmodel_opt=fmodel_opt_subset) - # Use interpolant to get optimal yaw angles for fi_AEP object - wd = fi_AEP.floris.flow_field.wind_directions - ws = fi_AEP.floris.flow_field.wind_speeds + # Use interpolant to get optimal yaw angles for fmodel_aep object + wd = fmodel_aep.core.flow_field.wind_directions + ws = fmodel_aep.core.flow_field.wind_speeds yaw_angles_opt_AEP = yaw_opt_interpolant(wd, ws) yaw_angles_opt_nonb_AEP = np.zeros_like(yaw_angles_opt_AEP) # nonb = no neighbor yaw_angles_opt_nonb_AEP[:, turbs_to_opt] = yaw_opt_interpolant_nonb(wd, ws) @@ -246,13 +246,13 @@ def yaw_opt_interpolant(wd, ws): print(" ") print("===========================================================") print("Calculating annual energy production with wake steering (AEP)...") - fi_AEP.set(yaw_angles=yaw_angles_opt_nonb_AEP) - aep_opt_subset_nonb = 1.0e-9 * fi_AEP.get_farm_AEP( + fmodel_aep.set(yaw_angles=yaw_angles_opt_nonb_AEP) + aep_opt_subset_nonb = 1.0e-9 * fmodel_aep.get_farm_AEP( freq=freq_windrose, turbine_weights=turbine_weights, ) - fi_AEP.set(yaw_angles=yaw_angles_opt_AEP) - aep_opt_subset = 1.0e-9 * fi_AEP.get_farm_AEP( + fmodel_aep.set(yaw_angles=yaw_angles_opt_AEP) + aep_opt_subset = 1.0e-9 * fmodel_aep.get_farm_AEP( freq=freq_windrose, turbine_weights=turbine_weights, ) @@ -270,38 +270,38 @@ def yaw_opt_interpolant(wd, ws): print(" ") # Plot power and AEP uplift across wind direction at wind_speed of 8 m/s - wd = fi_opt.floris.flow_field.wind_directions - ws = fi_opt.floris.flow_field.wind_speeds + wd = fmodel_opt.core.flow_field.wind_directions + ws = fmodel_opt.core.flow_field.wind_speeds yaw_angles_opt = yaw_opt_interpolant(wd, ws) yaw_angles_opt_nonb = np.zeros_like(yaw_angles_opt) # nonb = no neighbor yaw_angles_opt_nonb[:, turbs_to_opt] = yaw_opt_interpolant_nonb(wd, ws) - fi_opt = fi_opt.copy() - fi_opt.set(yaw_angles=np.zeros_like(yaw_angles_opt)) - fi_opt.run() - farm_power_bl_subset = fi_opt.get_farm_power(turbine_weights).flatten() + fmodel_opt = fmodel_opt.copy() + fmodel_opt.set(yaw_angles=np.zeros_like(yaw_angles_opt)) + fmodel_opt.run() + farm_power_bl_subset = fmodel_opt.get_farm_power(turbine_weights).flatten() - fi_opt = fi_opt.copy() - fi_opt.set(yaw_angles=yaw_angles_opt) - fi_opt.run() - farm_power_opt_subset = fi_opt.get_farm_power(turbine_weights).flatten() + fmodel_opt = fmodel_opt.copy() + fmodel_opt.set(yaw_angles=yaw_angles_opt) + fmodel_opt.run() + farm_power_opt_subset = fmodel_opt.get_farm_power(turbine_weights).flatten() - fi_opt = fi_opt.copy() - fi_opt.set(yaw_angles=yaw_angles_opt_nonb) - fi_opt.run() - farm_power_opt_subset_nonb = fi_opt.get_farm_power(turbine_weights).flatten() + fmodel_opt = fmodel_opt.copy() + fmodel_opt.set(yaw_angles=yaw_angles_opt_nonb) + fmodel_opt.run() + farm_power_opt_subset_nonb = fmodel_opt.get_farm_power(turbine_weights).flatten() fig, ax = plt.subplots() ax.bar( - x=fi_opt.floris.flow_field.wind_directions - 0.65, + x=fmodel_opt.core.flow_field.wind_directions - 0.65, height=100.0 * (farm_power_opt_subset / farm_power_bl_subset - 1.0), edgecolor="black", width=1.3, label="Including wake effects of neighboring farms" ) ax.bar( - x=fi_opt.floris.flow_field.wind_directions + 0.65, + x=fmodel_opt.core.flow_field.wind_directions + 0.65, height=100.0 * (farm_power_opt_subset_nonb / farm_power_bl_subset - 1.0), edgecolor="black", width=1.3, diff --git a/examples/14_compare_yaw_optimizers.py b/examples/14_compare_yaw_optimizers.py index ea4e100ee..4e0fa1d99 100644 --- a/examples/14_compare_yaw_optimizers.py +++ b/examples/14_compare_yaw_optimizers.py @@ -4,18 +4,18 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface -from floris.tools.optimization.yaw_optimization.yaw_optimizer_geometric import ( +from floris import FlorisModel +from floris.optimization.yaw_optimization.yaw_optimizer_geometric import ( YawOptimizationGeometric, ) -from floris.tools.optimization.yaw_optimization.yaw_optimizer_scipy import YawOptimizationScipy -from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR +from floris.optimization.yaw_optimization.yaw_optimizer_scipy import YawOptimizationScipy +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR """ This example compares the SciPy-based yaw optimizer with the new Serial-Refine optimizer. -First, we initialize our Floris Interface, and then generate a 3 turbine wind farm. +First, we initialize Floris, and then generate a 3 turbine wind farm. Next, we create two yaw optimization objects, `yaw_opt_sr` and `yaw_opt_scipy` for the Serial-Refine and SciPy methods, respectively. We then perform the optimization using both methods. @@ -25,21 +25,21 @@ The example now also compares the Geometric Yaw optimizer, which is fast a method to find approximately optimal yaw angles based on the wind farm geometry. Its main use case is for coupled layout and yaw optimization. -see floris.tools.optimization.yaw_optimization.yaw_optimizer_geometric.py and the paper online +see floris.optimization.yaw_optimization.yaw_optimizer_geometric.py and the paper online at https://wes.copernicus.org/preprints/wes-2023-1/. See also example 16c. """ # Load the default example floris object -fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 -# fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model +fmodel = FlorisModel("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 +# fmodel = FlorisModel("inputs/cc.yaml") # New CumulativeCurl model # Reinitialize as a 3-turbine farm with range of WDs and 1 WS D = 126.0 # Rotor diameter for the NREL 5 MW wd_array = np.arange(0.0, 360.0, 3.0) ws_array = 8.0 * np.ones_like(wd_array) turbulence_intensities = 0.06 * np.ones_like(wd_array) -fi.set( +fmodel.set( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, @@ -49,19 +49,19 @@ print("Performing optimizations with SciPy...") start_time = timerpc() -yaw_opt_scipy = YawOptimizationScipy(fi) +yaw_opt_scipy = YawOptimizationScipy(fmodel) df_opt_scipy = yaw_opt_scipy.optimize() time_scipy = timerpc() - start_time print("Performing optimizations with Serial Refine...") start_time = timerpc() -yaw_opt_sr = YawOptimizationSR(fi) +yaw_opt_sr = YawOptimizationSR(fmodel) df_opt_sr = yaw_opt_sr.optimize() time_sr = timerpc() - start_time print("Performing optimizations with Geometric Yaw...") start_time = timerpc() -yaw_opt_geo = YawOptimizationGeometric(fi) +yaw_opt_geo = YawOptimizationGeometric(fmodel) df_opt_geo = yaw_opt_geo.optimize() time_geo = timerpc() - start_time @@ -94,9 +94,9 @@ # Before plotting results, need to compute values for GEOOPT since it doesn't compute # power within the optimization -fi.set(yaw_angles=yaw_angles_opt_geo) -fi.run() -geo_farm_power = fi.get_farm_power().squeeze() +fmodel.set(yaw_angles=yaw_angles_opt_geo) +fmodel.run() +geo_farm_power = fmodel.get_farm_power().squeeze() fig, ax = plt.subplots() diff --git a/examples/15_optimize_layout.py b/examples/15_optimize_layout.py index 8049b0e6c..071a62b87 100644 --- a/examples/15_optimize_layout.py +++ b/examples/15_optimize_layout.py @@ -4,8 +4,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface, WindRose -from floris.tools.optimization.layout_optimization.layout_optimization_scipy import ( +from floris import FlorisModel, WindRose +from floris.optimization.layout_optimization.layout_optimization_scipy import ( LayoutOptimizationScipy, ) @@ -22,7 +22,7 @@ # Initialize the FLORIS interface fi file_dir = os.path.dirname(os.path.abspath(__file__)) -fi = FlorisInterface('inputs/gch.yaml') +fmodel = FlorisModel('inputs/gch.yaml') # Setup 72 wind directions with a 1 wind speed and frequency distribution wind_directions = np.arange(0, 360.0, 5.0) @@ -42,7 +42,7 @@ ti_table=0.06 ) -fi.set(wind_data=wind_rose) +fmodel.set(wind_data=wind_rose) # The boundaries for the turbines, specified as vertices boundaries = [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)] @@ -51,21 +51,21 @@ D = 126.0 # rotor diameter for the NREL 5MW layout_x = [0, 0, 6 * D, 6 * D] layout_y = [0, 4 * D, 0, 4 * D] -fi.set(layout_x=layout_x, layout_y=layout_y) +fmodel.set(layout_x=layout_x, layout_y=layout_y) # Setup the optimization problem -layout_opt = LayoutOptimizationScipy(fi, boundaries, wind_data=wind_rose) +layout_opt = LayoutOptimizationScipy(fmodel, boundaries, wind_data=wind_rose) # Run the optimization sol = layout_opt.optimize() # Get the resulting improvement in AEP print('... calcuating improvement in AEP') -fi.run() -base_aep = fi.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 -fi.set(layout_x=sol[0], layout_y=sol[1]) -fi.run() -opt_aep = fi.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 +fmodel.run() +base_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 +fmodel.set(layout_x=sol[0], layout_y=sol[1]) +fmodel.run() +opt_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 percent_gain = 100 * (opt_aep - base_aep) / base_aep diff --git a/examples/16_heterogeneous_inflow.py b/examples/16_heterogeneous_inflow.py index 335a8043a..26451ffa5 100644 --- a/examples/16_heterogeneous_inflow.py +++ b/examples/16_heterogeneous_inflow.py @@ -1,8 +1,8 @@ import matplotlib.pyplot as plt -from floris.tools import FlorisInterface -from floris.tools.flow_visualization import visualize_cut_plane +from floris import FlorisModel +from floris.flow_visualization import visualize_cut_plane """ @@ -22,7 +22,7 @@ """ -# Initialize FLORIS with the given input file via FlorisInterface. +# Initialize FLORIS with the given input file via FlorisModel. # Note that the heterogeneous flow is defined in the input file. The heterogenous_inflow_config # dictionary is defined as below. The speed ups are multipliers of the ambient wind speed, # and the x and y are the locations of the speed ups. @@ -34,20 +34,20 @@ # } -fi_2d = FlorisInterface("inputs/gch_heterogeneous_inflow.yaml") +fmodel_2d = FlorisModel("inputs/gch_heterogeneous_inflow.yaml") # Set shear to 0.0 to highlight the heterogeneous inflow -fi_2d.set(wind_shear=0.0) +fmodel_2d.set(wind_shear=0.0) -# Using the FlorisInterface functions for generating plots, run FLORIS +# Using the FlorisModel functions for generating plots, run FLORIS # and extract 2D planes of data. -horizontal_plane_2d = fi_2d.calculate_horizontal_plane( +horizontal_plane_2d = fmodel_2d.calculate_horizontal_plane( x_resolution=200, y_resolution=100, height=90.0 ) -y_plane_2d = fi_2d.calculate_y_plane(x_resolution=200, z_resolution=100, crossstream_dist=0.0) -cross_plane_2d = fi_2d.calculate_cross_plane( +y_plane_2d = fmodel_2d.calculate_y_plane(x_resolution=200, z_resolution=100, crossstream_dist=0.0) +cross_plane_2d = fmodel_2d.calculate_cross_plane( y_resolution=100, z_resolution=100, downstream_dist=500.0 @@ -101,28 +101,28 @@ 'z': z_locs, } -# Initialize FLORIS with the given input file via FlorisInterface. +# Initialize FLORIS with the given input file. # Note that we initialize FLORIS with a homogenous flow input file, but # then configure the heterogeneous inflow via the reinitialize method. -fi_3d = FlorisInterface("inputs/gch.yaml") -fi_3d.set(heterogenous_inflow_config=heterogenous_inflow_config) +fmodel_3d = FlorisModel("inputs/gch.yaml") +fmodel_3d.set(heterogenous_inflow_config=heterogenous_inflow_config) # Set shear to 0.0 to highlight the heterogeneous inflow -fi_3d.set(wind_shear=0.0) +fmodel_3d.set(wind_shear=0.0) -# Using the FlorisInterface functions for generating plots, run FLORIS +# Using the FlorisModel functions for generating plots, run FLORIS # and extract 2D planes of data. -horizontal_plane_3d = fi_3d.calculate_horizontal_plane( +horizontal_plane_3d = fmodel_3d.calculate_horizontal_plane( x_resolution=200, y_resolution=100, height=90.0 ) -y_plane_3d = fi_3d.calculate_y_plane( +y_plane_3d = fmodel_3d.calculate_y_plane( x_resolution=200, z_resolution=100, crossstream_dist=0.0 ) -cross_plane_3d = fi_3d.calculate_cross_plane( +cross_plane_3d = fmodel_3d.calculate_cross_plane( y_resolution=100, z_resolution=100, downstream_dist=500.0 diff --git a/examples/16b_heterogeneity_multiple_ws_wd.py b/examples/16b_heterogeneity_multiple_ws_wd.py index 56dbd3e9b..c183c4a26 100644 --- a/examples/16b_heterogeneity_multiple_ws_wd.py +++ b/examples/16b_heterogeneity_multiple_ws_wd.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface -from floris.tools.flow_visualization import visualize_cut_plane +from floris import FlorisModel +from floris.flow_visualization import visualize_cut_plane """ @@ -19,12 +19,12 @@ x_locs = [-300.0, -300.0, 2600.0, 2600.0] y_locs = [ -300.0, 300.0, -300.0, 300.0] -# Initialize FLORIS with the given input file via FlorisInterface. +# Initialize FLORIS with the given input. # Note the heterogeneous inflow is defined in the input file. -fi = FlorisInterface("inputs/gch_heterogeneous_inflow.yaml") +fmodel = FlorisModel("inputs/gch_heterogeneous_inflow.yaml") # Set shear to 0.0 to highlight the heterogeneous inflow -fi.set( +fmodel.set( wind_shear=0.0, wind_speeds=[8.0], wind_directions=[270.], @@ -32,8 +32,8 @@ layout_x=[0, 0], layout_y=[-299., 299.], ) -fi.run() -turbine_powers = fi.get_turbine_powers().flatten() / 1000. +fmodel.run() +turbine_powers = fmodel.get_turbine_powers().flatten() / 1000. # Show the initial results print('------------------------------------------') @@ -53,14 +53,14 @@ 'x': x_locs, 'y': y_locs, } -fi.set( +fmodel.set( wind_directions=[270.0, 275.0], wind_speeds=[8.0, 8.0], turbulence_intensities=[0.06, 0.06], heterogenous_inflow_config=heterogenous_inflow_config ) -fi.run() -turbine_powers = np.round(fi.get_turbine_powers() / 1000.) +fmodel.run() +turbine_powers = np.round(fmodel.get_turbine_powers() / 1000.) print('With wind directions now set to 270 and 275 deg') print(f'T0: {turbine_powers[:, 0].flatten()} kW') print(f'T1: {turbine_powers[:, 1].flatten()} kW') @@ -71,6 +71,6 @@ # print() # print('~~ Now forcing an error by not matching wd and het_map') -# fi.set(wind_directions=[270, 275, 280], wind_speeds=3*[8.0]) -# fi.run() -# turbine_powers = np.round(fi.get_turbine_powers() / 1000.) +# fmodel.set(wind_directions=[270, 275, 280], wind_speeds=3*[8.0]) +# fmodel.run() +# turbine_powers = np.round(fmodel.get_turbine_powers() / 1000.) diff --git a/examples/16c_optimize_layout_with_heterogeneity.py b/examples/16c_optimize_layout_with_heterogeneity.py index d41ac70a0..616b60e68 100644 --- a/examples/16c_optimize_layout_with_heterogeneity.py +++ b/examples/16c_optimize_layout_with_heterogeneity.py @@ -4,8 +4,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface, WindRose -from floris.tools.optimization.layout_optimization.layout_optimization_scipy import ( +from floris import FlorisModel, WindRose +from floris.optimization.layout_optimization.layout_optimization_scipy import ( LayoutOptimizationScipy, ) @@ -22,9 +22,9 @@ show the benefits of coupled optimization when flows are heterogeneous. """ -# Initialize the FLORIS interface fi +# Initialize FLORIS file_dir = os.path.dirname(os.path.abspath(__file__)) -fi = FlorisInterface('inputs/gch.yaml') +fmodel = FlorisModel('inputs/gch.yaml') # Setup 2 wind directions (due east and due west) # and 1 wind speed with uniform probability @@ -76,7 +76,7 @@ ) -fi.set( +fmodel.set( layout_x=layout_x, layout_y=layout_y, wind_data=wind_rose, @@ -85,7 +85,7 @@ # Setup and solve the layout optimization problem without heterogeneity maxiter = 100 layout_opt = LayoutOptimizationScipy( - fi, + fmodel, boundaries, wind_data=wind_rose, min_dist=2*D, @@ -99,11 +99,11 @@ # Get the resulting improvement in AEP print('... calcuating improvement in AEP') -fi.run() -base_aep = fi.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 -fi.set(layout_x=sol[0], layout_y=sol[1]) -fi.run() -opt_aep = fi.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 +fmodel.run() +base_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 +fmodel.set(layout_x=sol[0], layout_y=sol[1]) +fmodel.run() +opt_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 percent_gain = 100 * (opt_aep - base_aep) / base_aep @@ -124,9 +124,9 @@ # Rerun the layout optimization with geometric yaw enabled print("\nReoptimizing with geometric yaw enabled.") -fi.set(layout_x=layout_x, layout_y=layout_y) +fmodel.set(layout_x=layout_x, layout_y=layout_y) layout_opt = LayoutOptimizationScipy( - fi, + fmodel, boundaries, wind_data=wind_rose, min_dist=2*D, @@ -141,11 +141,11 @@ # Get the resulting improvement in AEP print('... calcuating improvement in AEP') -fi.set(yaw_angles=np.zeros_like(layout_opt.yaw_angles)) -base_aep = fi.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 -fi.set(layout_x=sol[0], layout_y=sol[1], yaw_angles=layout_opt.yaw_angles) -fi.run() -opt_aep = fi.get_farm_AEP_with_wind_data( +fmodel.set(yaw_angles=np.zeros_like(layout_opt.yaw_angles)) +base_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6 +fmodel.set(layout_x=sol[0], layout_y=sol[1], yaw_angles=layout_opt.yaw_angles) +fmodel.run() +opt_aep = fmodel.get_farm_AEP_with_wind_data( wind_data=wind_rose ) / 1e6 diff --git a/examples/17_multiple_turbine_types.py b/examples/17_multiple_turbine_types.py index cd913b832..b7d1c4173 100644 --- a/examples/17_multiple_turbine_types.py +++ b/examples/17_multiple_turbine_types.py @@ -1,8 +1,8 @@ import matplotlib.pyplot as plt -import floris.tools.flow_visualization as flowviz -from floris.tools import FlorisInterface +import floris.flow_visualization as flowviz +from floris import FlorisModel """ @@ -10,16 +10,20 @@ The first two turbines are the NREL 5MW, and the third turbine is the IEA 10MW. """ -# Initialize FLORIS with the given input file via FlorisInterface. -# For basic usage, FlorisInterface provides a simplified and expressive +# Initialize FLORIS with the given input file. +# For basic usage, FlorisModel provides a simplified and expressive # entry point to the simulation routines. -fi = FlorisInterface("inputs/gch_multiple_turbine_types.yaml") +fmodel = FlorisModel("inputs/gch_multiple_turbine_types.yaml") -# Using the FlorisInterface functions for generating plots, run FLORIS +# Using the FlorisModel functions for generating plots, run FLORIS # and extract 2D planes of data. -horizontal_plane = fi.calculate_horizontal_plane(x_resolution=200, y_resolution=100, height=90) -y_plane = fi.calculate_y_plane(x_resolution=200, z_resolution=100, crossstream_dist=0.0) -cross_plane = fi.calculate_cross_plane(y_resolution=100, z_resolution=100, downstream_dist=500.0) +horizontal_plane = fmodel.calculate_horizontal_plane(x_resolution=200, y_resolution=100, height=90) +y_plane = fmodel.calculate_y_plane(x_resolution=200, z_resolution=100, crossstream_dist=0.0) +cross_plane = fmodel.calculate_cross_plane( + y_resolution=100, + z_resolution=100, + downstream_dist=500.0 +) # Create the plots fig, ax_list = plt.subplots(3, 1, figsize=(10, 8)) diff --git a/examples/18_check_turbine.py b/examples/18_check_turbine.py index da526e7da..258525340 100644 --- a/examples/18_check_turbine.py +++ b/examples/18_check_turbine.py @@ -4,7 +4,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -18,13 +18,13 @@ wind_speed_to_test_yaw = 11 # Grab the gch model -fi = FlorisInterface("inputs/gch.yaml") +fmodel = FlorisModel("inputs/gch.yaml") # Make one turbine simulation -fi.set(layout_x=[0], layout_y=[0]) +fmodel.set(layout_x=[0], layout_y=[0]) # Apply wind directions and wind speeds -fi.set( +fmodel.set( wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=turbulence_intensities @@ -34,7 +34,7 @@ # multi-dimensional Cp/Ct turbine definitions as they require different handling turbines = [ t.stem - for t in fi.floris.farm.internal_turbine_library.iterdir() + for t in fmodel.core.farm.internal_turbine_library.iterdir() if t.suffix == ".yaml" and ("multi_dim" not in t.stem) ] @@ -45,22 +45,22 @@ for t in turbines: # Set t as the turbine - fi.set(turbine_type=[t]) + fmodel.set(turbine_type=[t]) # Since we are changing the turbine type, make a matching change to the reference wind height - fi.assign_hub_height_to_ref_height() + fmodel.assign_hub_height_to_ref_height() # Plot power and ct onto the fig_pow_ct plot axarr_pow_ct[0].plot( - fi.floris.farm.turbine_map[0].power_thrust_table["wind_speed"], - fi.floris.farm.turbine_map[0].power_thrust_table["power"],label=t + fmodel.core.farm.turbine_map[0].power_thrust_table["wind_speed"], + fmodel.core.farm.turbine_map[0].power_thrust_table["power"],label=t ) axarr_pow_ct[0].grid(True) axarr_pow_ct[0].legend() axarr_pow_ct[0].set_ylabel('Power (kW)') axarr_pow_ct[1].plot( - fi.floris.farm.turbine_map[0].power_thrust_table["wind_speed"], - fi.floris.farm.turbine_map[0].power_thrust_table["thrust_coefficient"],label=t + fmodel.core.farm.turbine_map[0].power_thrust_table["wind_speed"], + fmodel.core.farm.turbine_map[0].power_thrust_table["thrust_coefficient"],label=t ) axarr_pow_ct[1].grid(True) axarr_pow_ct[1].legend() @@ -73,17 +73,17 @@ # Try a few density for density in [1.15,1.225,1.3]: - fi.set(air_density=density) + fmodel.set(air_density=density) # POWER CURVE ax = axarr[0] - fi.set( + fmodel.set( wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=turbulence_intensities ) - fi.run() - turbine_powers = fi.get_turbine_powers().flatten() / 1e3 + fmodel.run() + turbine_powers = fmodel.get_turbine_powers().flatten() / 1e3 if density == 1.225: ax.plot(ws_array,turbine_powers,label='Air Density = %.3f' % density, lw=2, color='k') else: @@ -96,16 +96,16 @@ # Power loss to yaw, try a range of yaw angles ax = axarr[1] - fi.set( + fmodel.set( wind_speeds=[wind_speed_to_test_yaw], wind_directions=[270.0], turbulence_intensities=[0.06] ) yaw_result = [] for yaw in yaw_angles: - fi.set(yaw_angles=np.array([[yaw]])) - fi.run() - turbine_powers = fi.get_turbine_powers().flatten() / 1e3 + fmodel.set(yaw_angles=np.array([[yaw]])) + fmodel.run() + turbine_powers = fmodel.get_turbine_powers().flatten() / 1e3 yaw_result.append(turbine_powers[0]) if density == 1.225: ax.plot(yaw_angles,yaw_result,label='Air Density = %.3f' % density, lw=2, color='k') diff --git a/examples/20_calculate_farm_power_with_uncertainty.py b/examples/20_calculate_farm_power_with_uncertainty.py index 21aa18286..96a1818d0 100644 --- a/examples/20_calculate_farm_power_with_uncertainty.py +++ b/examples/20_calculate_farm_power_with_uncertainty.py @@ -1,25 +1,25 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface, UncertaintyInterface +from floris import FlorisModel, UncertaintyInterface """ This example demonstrates how one can create an "UncertaintyInterface" object, -which adds uncertainty on the inflow wind direction on the FlorisInterface +which adds uncertainty on the inflow wind direction on the FlorisModel class. The UncertaintyInterface class is interacted with in the exact same -manner as the FlorisInterface class is. This example demonstrates how the +manner as the FlorisModel class is. This example demonstrates how the wind farm power production is calculated with and without uncertainty. Other use cases of UncertaintyInterface are, e.g., comparing FLORIS to historical SCADA data and robust optimization. """ # Instantiate FLORIS using either the GCH or CC model -fi = FlorisInterface("inputs/gch.yaml") # GCH model -fi_unc_3 = UncertaintyInterface( +fmodel = FlorisModel("inputs/gch.yaml") # GCH model +umodel_unc_3 = UncertaintyInterface( "inputs/gch.yaml", verbose=True, wd_std=3 ) -fi_unc_5 = UncertaintyInterface( +umodel_unc_5 = UncertaintyInterface( "inputs/gch.yaml", verbose=True, wd_std=5 ) @@ -29,27 +29,27 @@ layout_y = [0, 0] wd_array = np.arange(240.0, 300.0, 1.0) wind_speeds = 8.0 * np.ones_like(wd_array) -fi.set(layout_x=layout_x, layout_y=layout_y, wind_directions=wd_array, wind_speeds=wind_speeds) -fi_unc_3.set( +fmodel.set(layout_x=layout_x, layout_y=layout_y, wind_directions=wd_array, wind_speeds=wind_speeds) +umodel_unc_3.set( layout_x=layout_x, layout_y=layout_y, wind_directions=wd_array, wind_speeds=wind_speeds ) -fi_unc_5.set( +umodel_unc_5.set( layout_x=layout_x, layout_y=layout_y, wind_directions=wd_array, wind_speeds=wind_speeds ) # Run both models -fi.run() -fi_unc_3.run() -fi_unc_5.run() +fmodel.run() +umodel_unc_3.run() +umodel_unc_5.run() # Collect the nominal and uncertain farm power -turbine_powers_nom = fi.get_turbine_powers() / 1e3 -turbine_powers_unc_3 = fi_unc_3.get_turbine_powers() / 1e3 -turbine_powers_unc_5 = fi_unc_5.get_turbine_powers() / 1e3 -farm_powers_nom = fi.get_farm_power() / 1e3 -farm_powers_unc_3 = fi_unc_3.get_farm_power() / 1e3 -farm_powers_unc_5 = fi_unc_5.get_farm_power() / 1e3 +turbine_powers_nom = fmodel.get_turbine_powers() / 1e3 +turbine_powers_unc_3 = umodel_unc_3.get_turbine_powers() / 1e3 +turbine_powers_unc_5 = umodel_unc_5.get_turbine_powers() / 1e3 +farm_powers_nom = fmodel.get_farm_power() / 1e3 +farm_powers_unc_3 = umodel_unc_3.get_farm_power() / 1e3 +farm_powers_unc_5 = umodel_unc_5.get_farm_power() / 1e3 # Plot results fig, axarr = plt.subplots(1, 3, figsize=(15, 5)) @@ -108,9 +108,9 @@ freq = np.ones_like(wd_array) freq = freq / freq.sum() -aep_nom = fi.get_farm_AEP(freq=freq) -aep_unc_3 = fi_unc_3.get_farm_AEP(freq=freq) -aep_unc_5 = fi_unc_5.get_farm_AEP(freq=freq) +aep_nom = fmodel.get_farm_AEP(freq=freq) +aep_unc_3 = umodel_unc_3.get_farm_AEP(freq=freq) +aep_unc_5 = umodel_unc_5.get_farm_AEP(freq=freq) print(f"AEP without uncertainty {aep_nom}") print(f"AEP without uncertainty (3 deg) {aep_unc_3} ({100*aep_unc_3/aep_nom:.2f}%)") diff --git a/examples/21_demo_time_series.py b/examples/21_demo_time_series.py index 61f9b7995..8afa28f2f 100644 --- a/examples/21_demo_time_series.py +++ b/examples/21_demo_time_series.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -11,10 +11,10 @@ """ # Initialize FLORIS to simple 4 turbine farm -fi = FlorisInterface("inputs/gch.yaml") +fmodel = FlorisModel("inputs/gch.yaml") # Convert to a simple two turbine layout -fi.set(layout_x=[0, 500.], layout_y=[0., 0.]) +fmodel.set(layout_x=[0, 500.], layout_y=[0., 0.]) # Create a fake time history where wind speed steps in the middle while wind direction # Walks randomly @@ -29,14 +29,14 @@ # Now intiialize FLORIS object to this history using time_series flag -fi.set(wind_directions=wd, wind_speeds=ws, turbulence_intensities=turbulence_intensities) +fmodel.set(wind_directions=wd, wind_speeds=ws, turbulence_intensities=turbulence_intensities) # Collect the powers -fi.run() -turbine_powers = fi.get_turbine_powers() / 1000. +fmodel.run() +turbine_powers = fmodel.get_turbine_powers() / 1000. # Show the dimensions -num_turbines = len(fi.layout_x) +num_turbines = len(fmodel.layout_x) print( f'There are {len(time)} time samples, and {num_turbines} turbines and ' f'so the resulting turbine power matrix has the shape {turbine_powers.shape}.' diff --git a/examples/22_get_wind_speed_at_turbines.py b/examples/22_get_wind_speed_at_turbines.py index b5dfeb7d4..7f15a4100 100644 --- a/examples/22_get_wind_speed_at_turbines.py +++ b/examples/22_get_wind_speed_at_turbines.py @@ -1,33 +1,33 @@ import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel -# Initialize FLORIS with the given input file via FlorisInterface. -# For basic usage, FlorisInterface provides a simplified and expressive +# Initialize FLORIS with the given input file. +# For basic usage, FlorisModel provides a simplified and expressive # entry point to the simulation routines. -fi = FlorisInterface("inputs/gch.yaml") +fmodel = FlorisModel("inputs/gch.yaml") # Create a 4-turbine layouts -fi.set(layout_x=[0, 0., 500., 500.], layout_y=[0., 300., 0., 300.]) +fmodel.set(layout_x=[0, 0., 500., 500.], layout_y=[0., 300., 0., 300.]) # Calculate wake -fi.run() +fmodel.run() # Collect the wind speed at all the turbine points -u_points = fi.floris.flow_field.u +u_points = fmodel.core.flow_field.u print('U points is 1 findex x 4 turbines x 3 x 3 points (turbine_grid_points=3)') print(u_points.shape) print('turbine_average_velocities is 1 findex x 4 turbines') -print(fi.turbine_average_velocities) +print(fmodel.turbine_average_velocities) # Show that one is equivalent to the other following averaging print( 'turbine_average_velocities is determined by taking the cube root of mean ' 'of the cubed value across the points ' ) -print(f'turbine_average_velocities: {fi.turbine_average_velocities}') +print(f'turbine_average_velocities: {fmodel.turbine_average_velocities}') print(f'Recomputed: {np.cbrt(np.mean(u_points**3, axis=(2,3)))}') diff --git a/examples/23_layout_visualizations.py b/examples/23_layout_visualizations.py index 1b84f602a..465490e6e 100644 --- a/examples/23_layout_visualizations.py +++ b/examples/23_layout_visualizations.py @@ -2,9 +2,9 @@ import matplotlib.pyplot as plt import numpy as np -import floris.tools.layout_visualization as layoutviz -from floris.tools import FlorisInterface -from floris.tools.flow_visualization import visualize_cut_plane +import floris.layout_visualization as layoutviz +from floris import FlorisModel +from floris.flow_visualization import visualize_cut_plane """ @@ -18,18 +18,18 @@ MIN_WS = 1.0 MAX_WS = 8.0 -# Initialize FLORIS with the given input file via FlorisInterface -fi = FlorisInterface("inputs/gch.yaml") +# Initialize FLORIS with the given input file. +fmodel = FlorisModel("inputs/gch.yaml") # Change to 5-turbine layout with a wind direction from northwest -fi.set( +fmodel.set( layout_x=[0, 0, 1000, 1000, 1000], layout_y=[0, 500, 0, 500, 1000], wind_directions=[300] ) # Plot 1: Visualize the flow ax = axarr[0] # Plot a horizatonal slice of the initial configuration -horizontal_plane = fi.calculate_horizontal_plane(height=90.0) +horizontal_plane = fmodel.calculate_horizontal_plane(height=90.0) visualize_cut_plane( horizontal_plane, ax=ax, @@ -37,14 +37,14 @@ max_speed=MAX_WS, ) # Plot the turbine points, setting the color to white -layoutviz.plot_turbine_points(fi, ax=ax, plotting_dict={"color": "w"}) +layoutviz.plot_turbine_points(fmodel, ax=ax, plotting_dict={"color": "w"}) ax.set_title('Flow visualization and turbine points') # Plot 2: Show a particular flow case ax = axarr[1] turbine_names = [f"T{i}" for i in [10, 11, 12, 13, 22]] -layoutviz.plot_turbine_points(fi, ax=ax) -layoutviz.plot_turbine_labels(fi, +layoutviz.plot_turbine_points(fmodel, ax=ax) +layoutviz.plot_turbine_labels(fmodel, ax=ax, turbine_names=turbine_names, show_bbox=True, @@ -54,7 +54,7 @@ # Plot 2: Show turbine rotors on flow ax = axarr[2] -horizontal_plane = fi.calculate_horizontal_plane(height=90.0, +horizontal_plane = fmodel.calculate_horizontal_plane(height=90.0, yaw_angles=np.array([[0., 30., 0., 0., 0.]])) visualize_cut_plane( horizontal_plane, @@ -62,32 +62,32 @@ min_speed=MIN_WS, max_speed=MAX_WS ) -layoutviz.plot_turbine_rotors(fi,ax=ax,yaw_angles=np.array([[0., 30., 0., 0., 0.]])) +layoutviz.plot_turbine_rotors(fmodel,ax=ax,yaw_angles=np.array([[0., 30., 0., 0., 0.]])) ax.set_title("Flow visualization with yawed turbine") # Plot 3: Show the layout, including wake directions ax = axarr[3] -layoutviz.plot_turbine_points(fi, ax=ax) -layoutviz.plot_turbine_labels(fi, ax=ax, turbine_names=turbine_names) -layoutviz.plot_waking_directions(fi, ax=ax) +layoutviz.plot_turbine_points(fmodel, ax=ax) +layoutviz.plot_turbine_labels(fmodel, ax=ax, turbine_names=turbine_names) +layoutviz.plot_waking_directions(fmodel, ax=ax) ax.set_title("Show turbine names and wake direction") # Plot 4: Plot a subset of the layout, and limit directions less than 7D ax = axarr[4] -layoutviz.plot_turbine_points(fi, ax=ax, turbine_indices=[0,1,2,3]) -layoutviz.plot_turbine_labels(fi, ax=ax, turbine_names=turbine_names, turbine_indices=[0,1,2,3]) -layoutviz.plot_waking_directions(fi, ax=ax, turbine_indices=[0,1,2,3], limit_dist_D=7) +layoutviz.plot_turbine_points(fmodel, ax=ax, turbine_indices=[0,1,2,3]) +layoutviz.plot_turbine_labels(fmodel, ax=ax, turbine_names=turbine_names, turbine_indices=[0,1,2,3]) +layoutviz.plot_waking_directions(fmodel, ax=ax, turbine_indices=[0,1,2,3], limit_dist_D=7) ax.set_title("Plot a subset and limit wake line distance") # Plot with a shaded region ax = axarr[5] -layoutviz.plot_turbine_points(fi, ax=ax) +layoutviz.plot_turbine_points(fmodel, ax=ax) layoutviz.shade_region(np.array([[0,0],[300,0],[300,1000],[0,700]]),ax=ax) ax.set_title("Plot with a shaded region") # Change hub heights and plot as a proxy for terrain ax = axarr[6] -fi.floris.farm.hub_heights = np.array([110, 90, 100, 100, 95]) -layoutviz.plot_farm_terrain(fi, ax=ax) +fmodel.core.farm.hub_heights = np.array([110, 90, 100, 100, 95]) +layoutviz.plot_farm_terrain(fmodel, ax=ax) plt.show() diff --git a/examples/24_floating_turbine_models.py b/examples/24_floating_turbine_models.py index 63aecc4c0..76822a76f 100644 --- a/examples/24_floating_turbine_models.py +++ b/examples/24_floating_turbine_models.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -25,59 +25,61 @@ In the example below, three single-turbine simulations are run to show the different behaviors. -fi_fixed: Fixed bottom turbine (no tilt variation with wind speed) -fi_floating: Floating turbine (tilt varies with wind speed) -fi_floating_defined_floating: Floating turbine (tilt varies with wind speed, but +fmodel_fixed: Fixed bottom turbine (no tilt variation with wind speed) +fmodel_floating: Floating turbine (tilt varies with wind speed) +fmodel_floating_defined_floating: Floating turbine (tilt varies with wind speed, but tilt does not scale cp/ct) """ -# Declare the Floris Interfaces -fi_fixed = FlorisInterface("inputs_floating/gch_fixed.yaml") -fi_floating = FlorisInterface("inputs_floating/gch_floating.yaml") -fi_floating_defined_floating = FlorisInterface("inputs_floating/gch_floating_defined_floating.yaml") +# Create the Floris instances +fmodel_fixed = FlorisModel("inputs_floating/gch_fixed.yaml") +fmodel_floating = FlorisModel("inputs_floating/gch_floating.yaml") +fmodel_floating_defined_floating = FlorisModel("inputs_floating/gch_floating_defined_floating.yaml") # Calculate across wind speeds ws_array = np.arange(3., 25., 1.) wd_array = 270.0 * np.ones_like(ws_array) ti_array = 0.06 * np.ones_like(ws_array) -fi_fixed.set(wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=ti_array) -fi_floating.set(wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=ti_array) -fi_floating_defined_floating.set( +fmodel_fixed.set(wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=ti_array) +fmodel_floating.set(wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=ti_array) +fmodel_floating_defined_floating.set( wind_speeds=ws_array, wind_directions=wd_array, turbulence_intensities=ti_array ) -fi_fixed.run() -fi_floating.run() -fi_floating_defined_floating.run() +fmodel_fixed.run() +fmodel_floating.run() +fmodel_floating_defined_floating.run() # Grab power -power_fixed = fi_fixed.get_turbine_powers().flatten()/1000. -power_floating = fi_floating.get_turbine_powers().flatten()/1000. -power_floating_defined_floating = fi_floating_defined_floating.get_turbine_powers().flatten()/1000. +power_fixed = fmodel_fixed.get_turbine_powers().flatten()/1000. +power_floating = fmodel_floating.get_turbine_powers().flatten()/1000. +power_floating_defined_floating = ( + fmodel_floating_defined_floating.get_turbine_powers().flatten()/1000. +) # Grab Ct -ct_fixed = fi_fixed.get_turbine_thrust_coefficients().flatten() -ct_floating = fi_floating.get_turbine_thrust_coefficients().flatten() +ct_fixed = fmodel_fixed.get_turbine_thrust_coefficients().flatten() +ct_floating = fmodel_floating.get_turbine_thrust_coefficients().flatten() ct_floating_defined_floating = ( - fi_floating_defined_floating.get_turbine_thrust_coefficients().flatten() + fmodel_floating_defined_floating.get_turbine_thrust_coefficients().flatten() ) # Grab turbine tilt angles -eff_vels = fi_fixed.turbine_average_velocities +eff_vels = fmodel_fixed.turbine_average_velocities tilt_angles_fixed = np.squeeze( - fi_fixed.floris.farm.calculate_tilt_for_eff_velocities(eff_vels) + fmodel_fixed.core.farm.calculate_tilt_for_eff_velocities(eff_vels) ) -eff_vels = fi_floating.turbine_average_velocities +eff_vels = fmodel_floating.turbine_average_velocities tilt_angles_floating = np.squeeze( - fi_floating.floris.farm.calculate_tilt_for_eff_velocities(eff_vels) + fmodel_floating.core.farm.calculate_tilt_for_eff_velocities(eff_vels) ) -eff_vels = fi_floating_defined_floating.turbine_average_velocities +eff_vels = fmodel_floating_defined_floating.turbine_average_velocities tilt_angles_floating_defined_floating = np.squeeze( - fi_floating_defined_floating.floris.farm.calculate_tilt_for_eff_velocities(eff_vels) + fmodel_floating_defined_floating.core.farm.calculate_tilt_for_eff_velocities(eff_vels) ) # Plot results diff --git a/examples/25_tilt_driven_vertical_wake_deflection.py b/examples/25_tilt_driven_vertical_wake_deflection.py index 1efd5aa8a..b8d6ffbf5 100644 --- a/examples/25_tilt_driven_vertical_wake_deflection.py +++ b/examples/25_tilt_driven_vertical_wake_deflection.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface -from floris.tools.flow_visualization import visualize_cut_plane +from floris import FlorisModel +from floris.flow_visualization import visualize_cut_plane """ @@ -17,10 +17,10 @@ # Initialize two FLORIS objects: one with 5 degrees of tilt (fixed across all # wind speeds) and one with 15 degrees of tilt (fixed across all wind speeds). -fi_5 = FlorisInterface("inputs_floating/emgauss_floating_fixedtilt5.yaml") -fi_15 = FlorisInterface("inputs_floating/emgauss_floating_fixedtilt15.yaml") +fmodel_5 = FlorisModel("inputs_floating/emgauss_floating_fixedtilt5.yaml") +fmodel_15 = FlorisModel("inputs_floating/emgauss_floating_fixedtilt15.yaml") -D = fi_5.floris.farm.rotor_diameters[0] +D = fmodel_5.core.farm.rotor_diameters[0] num_in_row = 5 @@ -46,10 +46,10 @@ powers = np.zeros((2, num_in_row)) # Calculate wakes, powers, plot -for i, (fi, tilt) in enumerate(zip([fi_5, fi_15], [5, 15])): +for i, (fmodel, tilt) in enumerate(zip([fmodel_5, fmodel_15], [5, 15])): # Farm layout and wind conditions - fi.set( + fmodel.set( layout_x=[x * 5.0 * D for x in range(num_in_row)], layout_y=[0.0]*num_in_row, wind_speeds=[8.0], @@ -57,11 +57,11 @@ ) # Flow solve and power computation - fi.run() - powers[i,:] = fi.get_turbine_powers().flatten() + fmodel.run() + powers[i,:] = fmodel.get_turbine_powers().flatten() # Compute flow slices - y_plane = fi.calculate_y_plane( + y_plane = fmodel.calculate_y_plane( x_resolution=200, z_resolution=100, crossstream_dist=streamwise_plane_location, diff --git a/examples/26_empirical_gauss_velocity_deficit_parameters.py b/examples/26_empirical_gauss_velocity_deficit_parameters.py index 8d7d73857..a3c43343a 100644 --- a/examples/26_empirical_gauss_velocity_deficit_parameters.py +++ b/examples/26_empirical_gauss_velocity_deficit_parameters.py @@ -4,8 +4,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface -from floris.tools.flow_visualization import plot_rotor_values, visualize_cut_plane +from floris import FlorisModel +from floris.flow_visualization import plot_rotor_values, visualize_cut_plane """ @@ -13,10 +13,6 @@ velocity deficit model and their effects on the wind turbine wake. """ -# Initialize FLORIS with the given input file via FlorisInterface. -# For basic usage, FlorisInterface provides a simplified and expressive -# entry point to the simulation routines. - # Options show_flow_cuts = True num_in_row = 5 @@ -24,8 +20,8 @@ yaw_angles = np.zeros((1, num_in_row)) # Define function for visualizing wakes -def generate_wake_visualization(fi: FlorisInterface, title=None): - # Using the FlorisInterface functions, get 2D slices. +def generate_wake_visualization(fmodel: FlorisModel, title=None): + # Using the FlorisModel functions, get 2D slices. x_bounds = [-500, 3000] y_bounds = [-250, 250] z_bounds = [0.001, 500] @@ -36,7 +32,7 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): min_ws = 4 max_ws = 10 - horizontal_plane = fi.calculate_horizontal_plane( + horizontal_plane = fmodel.calculate_horizontal_plane( x_resolution=200, y_resolution=100, height=horizontal_plane_location, @@ -44,7 +40,7 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): y_bounds=y_bounds, yaw_angles=yaw_angles ) - y_plane = fi.calculate_y_plane( + y_plane = fmodel.calculate_y_plane( x_resolution=200, z_resolution=100, crossstream_dist=streamwise_plane_location, @@ -55,7 +51,7 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): cross_planes = [] for cpl in cross_plane_locations: cross_planes.append( - fi.calculate_cross_plane( + fmodel.calculate_cross_plane( y_resolution=100, z_resolution=100, downstream_dist=cpl @@ -101,9 +97,9 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): ## Main script # Load input yaml and define farm layout -fi = FlorisInterface("inputs/emgauss.yaml") -D = fi.floris.farm.rotor_diameters[0] -fi.set( +fmodel = FlorisModel("inputs/emgauss.yaml") +D = fmodel.core.farm.rotor_diameters[0] +fmodel.set( layout_x=[x*5.0*D for x in range(num_in_row)], layout_y=[0.0]*num_in_row, wind_speeds=[8.0], @@ -111,13 +107,13 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): ) # Save dictionary to modify later -fi_dict = fi.floris.as_dict() +fmodel_dict = fmodel.core.as_dict() # Run wake calculation -fi.run() +fmodel.run() # Look at the powers of each turbine -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 fig0, ax0 = plt.subplots(1,1) width = 0.1 @@ -131,20 +127,20 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): # Visualize wakes if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Increase the base recovery rate -fi_dict_mod = copy.deepcopy(fi_dict) -fi_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ +fmodel_dict_mod = copy.deepcopy(fmodel_dict) +fmodel_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ ['wake_expansion_rates'] = [0.03, 0.015] -fi = FlorisInterface(fi_dict_mod) -fi.set( +fmodel = FlorisModel(fmodel_dict_mod) +fmodel.set( wind_speeds=[8.0], wind_directions=[270.0] ) -fi.run() -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +fmodel.run() +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 x = np.array(range(num_in_row))+width*nw nw += 1 @@ -153,25 +149,25 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): ax0.bar(x, turbine_powers, width=width, label=title) if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Add new expansion rate -fi_dict_mod = copy.deepcopy(fi_dict) -fi_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ +fmodel_dict_mod = copy.deepcopy(fmodel_dict) +fmodel_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ ['wake_expansion_rates'] = \ - fi_dict['wake']['wake_velocity_parameters']['empirical_gauss']\ + fmodel_dict['wake']['wake_velocity_parameters']['empirical_gauss']\ ['wake_expansion_rates'] + [0.0] -fi_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ +fmodel_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ ['breakpoints_D'] = [5, 10] -fi = FlorisInterface(fi_dict_mod) -fi.set( +fmodel = FlorisModel(fmodel_dict_mod) +fmodel.set( wind_speeds=[8.0], wind_directions=[270.0] ) -fi.run() -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +fmodel.run() +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 x = np.array(range(num_in_row))+width*nw nw += 1 @@ -180,20 +176,20 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): ax0.bar(x, turbine_powers, width=width, label=title) if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Increase the wake-induced mixing gain -fi_dict_mod = copy.deepcopy(fi_dict) -fi_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ +fmodel_dict_mod = copy.deepcopy(fmodel_dict) +fmodel_dict_mod['wake']['wake_velocity_parameters']['empirical_gauss']\ ['mixing_gain_velocity'] = 3.0 -fi = FlorisInterface(fi_dict_mod) -fi.set( +fmodel = FlorisModel(fmodel_dict_mod) +fmodel.set( wind_speeds=[8.0], wind_directions=[270.0] ) -fi.run() -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +fmodel.run() +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 x = np.array(range(num_in_row))+width*nw nw += 1 @@ -202,7 +198,7 @@ def generate_wake_visualization(fi: FlorisInterface, title=None): ax0.bar(x, turbine_powers, width=width, label=title) if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Power plot aesthetics ax0.set_xticks(range(num_in_row)) diff --git a/examples/27_empirical_gauss_deflection_parameters.py b/examples/27_empirical_gauss_deflection_parameters.py index cb59ee821..79bdee9f8 100644 --- a/examples/27_empirical_gauss_deflection_parameters.py +++ b/examples/27_empirical_gauss_deflection_parameters.py @@ -4,8 +4,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface -from floris.tools.flow_visualization import plot_rotor_values, visualize_cut_plane +from floris import FlorisModel +from floris.flow_visualization import plot_rotor_values, visualize_cut_plane """ @@ -13,8 +13,8 @@ deflection model and their effects on the wind turbine wake. """ -# Initialize FLORIS with the given input file via FlorisInterface. -# For basic usage, FlorisInterface provides a simplified and expressive +# Initialize FLORIS with the given input file. +# For basic usage, FlorisModel provides a simplified and expressive # entry point to the simulation routines. # Options @@ -27,8 +27,8 @@ print("Turbine yaw angles (degrees): ", yaw_angles[0]) # Define function for visualizing wakes -def generate_wake_visualization(fi, title=None): - # Using the FlorisInterface functions, get 2D slices. +def generate_wake_visualization(fmodel, title=None): + # Using the FlorisModel functions, get 2D slices. x_bounds = [-500, 3000] y_bounds = [-250, 250] z_bounds = [0.001, 500] @@ -39,7 +39,7 @@ def generate_wake_visualization(fi, title=None): min_ws = 4 max_ws = 10 - horizontal_plane = fi.calculate_horizontal_plane( + horizontal_plane = fmodel.calculate_horizontal_plane( x_resolution=200, y_resolution=100, height=horizontal_plane_location, @@ -47,7 +47,7 @@ def generate_wake_visualization(fi, title=None): y_bounds=y_bounds, yaw_angles=yaw_angles ) - y_plane = fi.calculate_y_plane( + y_plane = fmodel.calculate_y_plane( x_resolution=200, z_resolution=100, crossstream_dist=streamwise_plane_location, @@ -58,7 +58,7 @@ def generate_wake_visualization(fi, title=None): cross_planes = [] for cpl in cross_plane_locations: cross_planes.append( - fi.calculate_cross_plane( + fmodel.calculate_cross_plane( y_resolution=100, z_resolution=100, downstream_dist=cpl @@ -105,9 +105,9 @@ def generate_wake_visualization(fi, title=None): ## Main script # Load input yaml and define farm layout -fi = FlorisInterface("inputs/emgauss.yaml") -D = fi.floris.farm.rotor_diameters[0] -fi.set( +fmodel = FlorisModel("inputs/emgauss.yaml") +D = fmodel.core.farm.rotor_diameters[0] +fmodel.set( layout_x=[x*5.0*D for x in range(num_in_row)], layout_y=[0.0]*num_in_row, wind_speeds=[8.0], @@ -116,13 +116,13 @@ def generate_wake_visualization(fi, title=None): ) # Save dictionary to modify later -fi_dict = fi.floris.as_dict() +fmodel_dict = fmodel.core.as_dict() # Run wake calculation -fi.run() +fmodel.run() # Look at the powers of each turbine -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 fig0, ax0 = plt.subplots(1,1) width = 0.1 @@ -136,23 +136,23 @@ def generate_wake_visualization(fi, title=None): # Visualize wakes if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Increase the maximum deflection attained -fi_dict_mod = copy.deepcopy(fi_dict) +fmodel_dict_mod = copy.deepcopy(fmodel_dict) -fi_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ +fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ ['horizontal_deflection_gain_D'] = 5.0 -fi = FlorisInterface(fi_dict_mod) -fi.set( +fmodel = FlorisModel(fmodel_dict_mod) +fmodel.set( wind_speeds=[8.0], wind_directions=[270.0], yaw_angles=yaw_angles, ) -fi.run() -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +fmodel.run() +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 x = np.array(range(num_in_row))+width*nw nw += 1 @@ -161,22 +161,22 @@ def generate_wake_visualization(fi, title=None): ax0.bar(x, turbine_powers, width=width, label=title) if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Add (increase) influence of wake added mixing -fi_dict_mod = copy.deepcopy(fi_dict) -fi_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ +fmodel_dict_mod = copy.deepcopy(fmodel_dict) +fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ ['mixing_gain_deflection'] = 100.0 -fi = FlorisInterface(fi_dict_mod) -fi.set( +fmodel = FlorisModel(fmodel_dict_mod) +fmodel.set( wind_speeds=[8.0], wind_directions=[270.0], yaw_angles=yaw_angles, ) -fi.run() -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +fmodel.run() +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 x = np.array(range(num_in_row))+width*nw nw += 1 @@ -185,25 +185,25 @@ def generate_wake_visualization(fi, title=None): ax0.bar(x, turbine_powers, width=width, label=title) if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Add (increase) the yaw-added mixing contribution -fi_dict_mod = copy.deepcopy(fi_dict) +fmodel_dict_mod = copy.deepcopy(fmodel_dict) # Include a WIM gain so that YAM is reflected in deflection as well # as deficit -fi_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ +fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ ['mixing_gain_deflection'] = 100.0 -fi_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ +fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\ ['yaw_added_mixing_gain'] = 1.0 -fi = FlorisInterface(fi_dict_mod) -fi.set( +fmodel = FlorisModel(fmodel_dict_mod) +fmodel.set( wind_speeds=[8.0], wind_directions=[270.0], yaw_angles=yaw_angles, ) -fi.run() -turbine_powers = fi.get_turbine_powers().flatten()/1e6 +fmodel.run() +turbine_powers = fmodel.get_turbine_powers().flatten()/1e6 x = np.array(range(num_in_row))+width*nw nw += 1 @@ -212,7 +212,7 @@ def generate_wake_visualization(fi, title=None): ax0.bar(x, turbine_powers, width=width, label=title) if show_flow_cuts: - generate_wake_visualization(fi, title) + generate_wake_visualization(fmodel, title) # Power plot aesthetics ax0.set_xticks(range(num_in_row)) diff --git a/examples/28_extract_wind_speed_at_points.py b/examples/28_extract_wind_speed_at_points.py index 52c28c9ca..7c9b9adbc 100644 --- a/examples/28_extract_wind_speed_at_points.py +++ b/examples/28_extract_wind_speed_at_points.py @@ -2,12 +2,12 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ This example demonstrates the use of the sample_flow_at_points method of -FlorisInterface. sample_flow_at_points extracts the wind speed +FlorisModel. sample_flow_at_points extracts the wind speed information at user-specified locations in the flow. Specifically, this example returns the wind speed at a single x, y @@ -26,21 +26,21 @@ met_mast_option = 0 # Try 0, 1, 2, 3 # Instantiate FLORIS model -fi = FlorisInterface("inputs/"+floris_model+".yaml") +fmodel = FlorisModel("inputs/"+floris_model+".yaml") # Set up a two-turbine farm D = 126 -fi.set(layout_x=[0, 3 * D], layout_y=[0, 3 * D]) +fmodel.set(layout_x=[0, 3 * D], layout_y=[0, 3 * D]) fig, ax = plt.subplots(1,2) fig.set_size_inches(10,4) -ax[0].scatter(fi.layout_x, fi.layout_y, color="black", label="Turbine") +ax[0].scatter(fmodel.layout_x, fmodel.layout_y, color="black", label="Turbine") # Set the wind direction to run 360 degrees wd_array = np.arange(0, 360, 1) ws_array = 8.0 * np.ones_like(wd_array) ti_array = 0.06 * np.ones_like(wd_array) -fi.set(wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=ti_array) +fmodel.set(wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=ti_array) # Simulate a met mast in between the turbines if met_mast_option == 0: @@ -59,7 +59,7 @@ points_z = [30, 90, 150, 250] # Collect the points -u_at_points = fi.sample_flow_at_points(points_x, points_y, points_z) +u_at_points = fmodel.sample_flow_at_points(points_x, points_y, points_z) ax[0].scatter(points_x, points_y, color="red", marker="x", label="Met mast") ax[0].grid() diff --git a/examples/29_floating_vs_fixedbottom_farm.py b/examples/29_floating_vs_fixedbottom_farm.py index 044d24342..e04ac3f98 100644 --- a/examples/29_floating_vs_fixedbottom_farm.py +++ b/examples/29_floating_vs_fixedbottom_farm.py @@ -4,8 +4,8 @@ import pandas as pd from scipy.interpolate import NearestNDInterpolator -import floris.tools.flow_visualization as flowviz -from floris.tools import FlorisInterface +import floris.flow_visualization as flowviz +from floris import FlorisModel """ @@ -27,32 +27,32 @@ the Empirical Gaussian wake model to show the effects of floating turbines on both turbine power and wake development. -fi_fixed: Fixed bottom turbine (no tilt variation with wind speed) -fi_floating: Floating turbine (tilt varies with wind speed) +fmodel_fixed: Fixed bottom turbine (no tilt variation with wind speed) +fmodel_floating: Floating turbine (tilt varies with wind speed) """ # Declare the Floris Interface for fixed bottom, provide layout -fi_fixed = FlorisInterface("inputs_floating/emgauss_fixed.yaml") -fi_floating = FlorisInterface("inputs_floating/emgauss_floating.yaml") +fmodel_fixed = FlorisModel("inputs_floating/emgauss_fixed.yaml") +fmodel_floating = FlorisModel("inputs_floating/emgauss_floating.yaml") x, y = np.meshgrid(np.linspace(0, 4*630., 5), np.linspace(0, 3*630., 4)) x = x.flatten() y = y.flatten() -for fi in [fi_fixed, fi_floating]: - fi.set(layout_x=x, layout_y=y) +for fmodel in [fmodel_fixed, fmodel_floating]: + fmodel.set(layout_x=x, layout_y=y) # Compute a single wind speed and direction, power and wakes -for fi in [fi_fixed, fi_floating]: - fi.set( +for fmodel in [fmodel_fixed, fmodel_floating]: + fmodel.set( layout_x=x, layout_y=y, wind_speeds=[10], wind_directions=[270], turbulence_intensities=[0.06], ) - fi.run() + fmodel.run() -powers_fixed = fi_fixed.get_turbine_powers() -powers_floating = fi_floating.get_turbine_powers() +powers_fixed = fmodel_fixed.get_turbine_powers() +powers_floating = fmodel_floating.get_turbine_powers() power_difference = powers_floating - powers_fixed # Show the power differences @@ -78,16 +78,16 @@ # Visualize flows (see also 02_visualizations.py) horizontal_planes = [] y_planes = [] -for fi in [fi_fixed, fi_floating]: +for fmodel in [fmodel_fixed, fmodel_floating]: horizontal_planes.append( - fi.calculate_horizontal_plane( + fmodel.calculate_horizontal_plane( x_resolution=200, y_resolution=100, height=90.0, ) ) y_planes.append( - fi.calculate_y_plane( + fmodel.calculate_y_plane( x_resolution=200, z_resolution=100, crossstream_dist=0.0, @@ -118,16 +118,16 @@ freq = freq_interp(wd_grid, ws_grid).flatten() freq = freq / np.sum(freq) -for fi in [fi_fixed, fi_floating]: - fi.set( +for fmodel in [fmodel_fixed, fmodel_floating]: + fmodel.set( wind_directions=wd_grid.flatten(), wind_speeds= ws_grid.flatten(), turbulence_intensities=0.06 * np.ones_like(wd_grid.flatten()) ) # Compute the AEP -aep_fixed = fi_fixed.get_farm_AEP(freq=freq) -aep_floating = fi_floating.get_farm_AEP(freq=freq) +aep_fixed = fmodel_fixed.get_farm_AEP(freq=freq) +aep_floating = fmodel_floating.get_farm_AEP(freq=freq) print("Farm AEP (fixed bottom): {:.3f} GWh".format(aep_fixed / 1.0e9)) print("Farm AEP (floating): {:.3f} GWh".format(aep_floating / 1.0e9)) print( diff --git a/examples/30_multi_dimensional_cp_ct.py b/examples/30_multi_dimensional_cp_ct.py index 3eebf0854..e33ca31d2 100644 --- a/examples/30_multi_dimensional_cp_ct.py +++ b/examples/30_multi_dimensional_cp_ct.py @@ -1,7 +1,7 @@ import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -36,31 +36,31 @@ and used to select the interpolant at each turbine. Also note in the example below that there is a specific method for computing powers when -using turbines with multi-dimensional Cp/Ct data under FlorisInterface, called +using turbines with multi-dimensional Cp/Ct data under FlorisModel, called 'get_turbine_powers_multidim'. The normal 'get_turbine_powers' method will not work. """ -# Initialize FLORIS with the given input file via FlorisInterface. -fi = FlorisInterface("inputs/gch_multi_dim_cp_ct.yaml") +# Initialize FLORIS with the given input file. +fmodel = FlorisModel("inputs/gch_multi_dim_cp_ct.yaml") # Convert to a simple two turbine layout -fi.set(layout_x=[0., 500.], layout_y=[0., 0.]) +fmodel.set(layout_x=[0., 500.], layout_y=[0., 0.]) # Single wind speed and wind direction print('\n========================= Single Wind Direction and Wind Speed =========================') # Get the turbine powers assuming 1 wind speed and 1 wind direction -fi.set(wind_directions=[270.0], wind_speeds=[8.0], turbulence_intensities=[0.06]) +fmodel.set(wind_directions=[270.0], wind_speeds=[8.0], turbulence_intensities=[0.06]) # Set the yaw angles to 0 yaw_angles = np.zeros([1, 2]) # 1 wind direction and wind speed, 2 turbines -fi.set(yaw_angles=yaw_angles) +fmodel.set(yaw_angles=yaw_angles) # Calculate -fi.run() +fmodel.run() # Get the turbine powers -turbine_powers = fi.get_turbine_powers() / 1000.0 +turbine_powers = fmodel.get_turbine_powers() / 1000.0 print("The turbine power matrix should be of dimensions 1 findex X 2 Turbines") print(turbine_powers) print("Shape: ",turbine_powers.shape) @@ -73,14 +73,14 @@ turbulence_intensities = np.array([0.06, 0.06, 0.06]) yaw_angles = np.zeros([3, 2]) # 3 wind directions/ speeds, 2 turbines -fi.set( +fmodel.set( wind_speeds=wind_speeds, wind_directions=wind_directions, turbulence_intensities=turbulence_intensities, yaw_angles=yaw_angles ) -fi.run() -turbine_powers = fi.get_turbine_powers() / 1000.0 +fmodel.run() +turbine_powers = fmodel.get_turbine_powers() / 1000.0 print("The turbine power matrix should be of dimensions 3 findex X 2 Turbines") print(turbine_powers) print("Shape: ",turbine_powers.shape) @@ -93,14 +93,14 @@ turbulence_intensities = 0.06 * np.ones_like(wind_speeds) yaw_angles = np.zeros([9, 2]) # 9 wind directions/ speeds, 2 turbines -fi.set( +fmodel.set( wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=turbulence_intensities, yaw_angles=yaw_angles ) -fi.run() -turbine_powers = fi.get_turbine_powers()/1000. +fmodel.run() +turbine_powers = fmodel.get_turbine_powers()/1000. print("The turbine power matrix should be of dimensions 9 WD/WS X 2 Turbines") print(turbine_powers) print("Shape: ",turbine_powers.shape) diff --git a/examples/31_multi_dimensional_cp_ct_2Hs.py b/examples/31_multi_dimensional_cp_ct_2Hs.py index df5d4d171..56bb6fc20 100644 --- a/examples/31_multi_dimensional_cp_ct_2Hs.py +++ b/examples/31_multi_dimensional_cp_ct_2Hs.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -13,46 +13,46 @@ values of the original Cp/Ct data for the IEA 15MW turbine. """ -# Initialize FLORIS with the given input file via FlorisInterface. -fi = FlorisInterface("inputs/gch_multi_dim_cp_ct.yaml") +# Initialize FLORIS with the given input file. +fmodel = FlorisModel("inputs/gch_multi_dim_cp_ct.yaml") -# Make a second FLORIS interface with a different setting for Hs. +# Make a second Floris instance with a different setting for Hs. # Note the multi-cp-ct file (iea_15MW_multi_dim_Tp_Hs.csv) # for the turbine model iea_15MW_floating_multi_dim_cp_ct.yaml # Defines Hs at 1 and 5. # The value in gch_multi_dim_cp_ct.yaml is 3.01 which will map # to 5 as the nearer value, so we set the other case to 1 # for contrast. -fi_dict_mod = fi.floris.as_dict() -fi_dict_mod['flow_field']['multidim_conditions']['Hs'] = 1.0 -fi_hs_1 = FlorisInterface(fi_dict_mod) +fmodel_dict_mod = fmodel.core.as_dict() +fmodel_dict_mod['flow_field']['multidim_conditions']['Hs'] = 1.0 +fmodel_hs_1 = FlorisModel(fmodel_dict_mod) # Set both cases to 3 turbine layout -fi.set(layout_x=[0., 500., 1000.], layout_y=[0., 0., 0.]) -fi_hs_1.set(layout_x=[0., 500., 1000.], layout_y=[0., 0., 0.]) +fmodel.set(layout_x=[0., 500., 1000.], layout_y=[0., 0., 0.]) +fmodel_hs_1.set(layout_x=[0., 500., 1000.], layout_y=[0., 0., 0.]) # Use a sweep of wind speeds wind_speeds = np.arange(5, 20, 1.0) wind_directions = 270.0 * np.ones_like(wind_speeds) turbulence_intensities = 0.06 * np.ones_like(wind_speeds) -fi.set( +fmodel.set( wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=turbulence_intensities ) -fi_hs_1.set( +fmodel_hs_1.set( wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=turbulence_intensities ) # Calculate wakes with baseline yaw -fi.run() -fi_hs_1.run() +fmodel.run() +fmodel_hs_1.run() # Collect the turbine powers in kW -turbine_powers = fi.get_turbine_powers()/1000. -turbine_powers_hs_1 = fi_hs_1.get_turbine_powers()/1000. +turbine_powers = fmodel.get_turbine_powers()/1000. +turbine_powers_hs_1 = fmodel_hs_1.get_turbine_powers()/1000. # Plot the power in each case and the difference in power fig, axarr = plt.subplots(1,3,sharex=True,figsize=(12,4)) diff --git a/examples/32_plot_velocity_deficit_profiles.py b/examples/32_plot_velocity_deficit_profiles.py index 490809571..a0b2949e0 100644 --- a/examples/32_plot_velocity_deficit_profiles.py +++ b/examples/32_plot_velocity_deficit_profiles.py @@ -3,9 +3,9 @@ import numpy as np from matplotlib import ticker -import floris.tools.flow_visualization as flowviz -from floris.tools import cut_plane, FlorisInterface -from floris.tools.flow_visualization import VelocityProfilesFigure +import floris.flow_visualization as flowviz +from floris import cut_plane, FlorisModel +from floris.flow_visualization import VelocityProfilesFigure from floris.utilities import reverse_rotate_coordinates_rel_west @@ -37,7 +37,7 @@ def annotate_coordinate_system(x_origin, y_origin, quiver_length): x2 = np.array([0.0, quiver_length + 0.35 * D]) x3 = np.array([90.0, 90.0]) x, y, _ = reverse_rotate_coordinates_rel_west( - fi.floris.flow_field.wind_directions, + fmodel.core.flow_field.wind_directions, x1[None, :], x2[None, :], x3[None, :], @@ -54,8 +54,8 @@ def annotate_coordinate_system(x_origin, y_origin, quiver_length): hub_height = 90.0 homogeneous_wind_speed = 8.0 - fi = FlorisInterface("inputs/gch.yaml") - fi.set(layout_x=[0.0], layout_y=[0.0]) + fmodel = FlorisModel("inputs/gch.yaml") + fmodel.set(layout_x=[0.0], layout_y=[0.0]) # ------------------------------ Single-turbine layout ------------------------------ # We first show how to sample and plot velocity deficit profiles on a single-turbine layout. @@ -63,13 +63,13 @@ def annotate_coordinate_system(x_origin, y_origin, quiver_length): downstream_dists = D * np.array([3, 5, 7]) # Sample three profiles along three corresponding lines that are all parallel to the y-axis # (cross-stream direction). The streamwise location of each line is given in `downstream_dists`. - profiles = fi.sample_velocity_deficit_profiles( + profiles = fmodel.sample_velocity_deficit_profiles( direction='cross-stream', downstream_dists=downstream_dists, homogeneous_wind_speed=homogeneous_wind_speed, ) - horizontal_plane = fi.calculate_horizontal_plane(height=hub_height) + horizontal_plane = fmodel.calculate_horizontal_plane(height=hub_height) fig, ax = plt.subplots(figsize=(6.4, 3)) flowviz.visualize_cut_plane(horizontal_plane, ax) colors = ['b', 'g', 'c'] @@ -95,10 +95,10 @@ def annotate_coordinate_system(x_origin, y_origin, quiver_length): # Change velocity model to jensen, get the velocity deficit profiles, # and add them to the figure. - floris_dict = fi.floris.as_dict() + floris_dict = fmodel.core.as_dict() floris_dict['wake']['model_strings']['velocity_model'] = 'jensen' - fi = FlorisInterface(floris_dict) - profiles = fi.sample_velocity_deficit_profiles( + fmodel = FlorisModel(floris_dict) + profiles = fmodel.sample_velocity_deficit_profiles( direction='cross-stream', downstream_dists=downstream_dists, homogeneous_wind_speed=homogeneous_wind_speed, @@ -125,16 +125,16 @@ def annotate_coordinate_system(x_origin, y_origin, quiver_length): # (i.e. where to start sampling the profiles). wind_direction = 315.0 # Try to change this downstream_dists = D * np.array([3, 5]) - floris_dict = fi.floris.as_dict() + floris_dict = fmodel.core.as_dict() floris_dict['wake']['model_strings']['velocity_model'] = 'gauss' - fi = FlorisInterface(floris_dict) + fmodel = FlorisModel(floris_dict) # Let (x_t1, y_t1) be the location of the second turbine x_t1 = 2 * D y_t1 = -2 * D - fi.set(wind_directions=[wind_direction], layout_x=[0.0, x_t1], layout_y=[0.0, y_t1]) + fmodel.set(wind_directions=[wind_direction], layout_x=[0.0, x_t1], layout_y=[0.0, y_t1]) # Extract profiles at a set of downstream distances from the starting point (x_start, y_start) - cross_profiles = fi.sample_velocity_deficit_profiles( + cross_profiles = fmodel.sample_velocity_deficit_profiles( direction='cross-stream', downstream_dists=downstream_dists, homogeneous_wind_speed=homogeneous_wind_speed, @@ -142,7 +142,10 @@ def annotate_coordinate_system(x_origin, y_origin, quiver_length): y_start=y_t1, ) - horizontal_plane = fi.calculate_horizontal_plane(height=hub_height, x_bounds=[-2 * D, 9 * D]) + horizontal_plane = fmodel.calculate_horizontal_plane( + height=hub_height, + x_bounds=[-2 * D, 9 * D] + ) ax = flowviz.visualize_cut_plane(horizontal_plane) colors = ['b', 'g', 'c'] for i, profile in enumerate(cross_profiles): @@ -162,7 +165,7 @@ def annotate_coordinate_system(x_origin, y_origin, quiver_length): # locations as before. We stay directly downstream of the turbine (i.e. x2 = 0). These # profiles are almost identical to the cross-stream profiles. However, we now explicitly # set the profile range. The default range is [-2 * D, 2 * D]. - vertical_profiles = fi.sample_velocity_deficit_profiles( + vertical_profiles = fmodel.sample_velocity_deficit_profiles( direction='vertical', profile_range=[-1.5 * D, 1.5 * D], downstream_dists=downstream_dists, diff --git a/examples/33_specify_turbine_power_curve.py b/examples/33_specify_turbine_power_curve.py index f10e4f7cd..420f5aeab 100644 --- a/examples/33_specify_turbine_power_curve.py +++ b/examples/33_specify_turbine_power_curve.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import FlorisInterface +from floris import FlorisModel from floris.turbine_library import build_cosine_loss_turbine_dict @@ -39,12 +39,12 @@ ref_tilt=5 ) -fi = FlorisInterface("inputs/gch.yaml") +fmodel = FlorisModel("inputs/gch.yaml") wind_speeds = np.linspace(1, 15, 100) wind_directions = 270 * np.ones_like(wind_speeds) turbulence_intensities = 0.06 * np.ones_like(wind_speeds) # Replace the turbine(s) in the FLORIS model with the created one -fi.set( +fmodel.set( layout_x=[0], layout_y=[0], wind_directions=wind_directions, @@ -52,9 +52,9 @@ turbulence_intensities=turbulence_intensities, turbine_type=[turbine_dict] ) -fi.run() +fmodel.run() -powers = fi.get_farm_power() +powers = fmodel.get_farm_power() specified_powers = ( np.array(turbine_data_dict["power_coefficient"]) diff --git a/examples/34_wind_data.py b/examples/34_wind_data.py index 79469c988..3a4d56fe5 100644 --- a/examples/34_wind_data.py +++ b/examples/34_wind_data.py @@ -1,8 +1,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import ( - FlorisInterface, +from floris import ( + FlorisModel, TimeSeries, WindRose, ) @@ -59,28 +59,28 @@ plt.tight_layout() # Now set up a FLORIS model and initialize it using the time series and wind rose -fi = FlorisInterface("inputs/gch.yaml") -fi.set(layout_x=[0, 500.0], layout_y=[0.0, 0.0]) +fmodel = FlorisModel("inputs/gch.yaml") +fmodel.set(layout_x=[0, 500.0], layout_y=[0.0, 0.0]) -fi_time_series = fi.copy() -fi_wind_rose = fi.copy() -fi_wind_ti_rose = fi.copy() +fmodel_time_series = fmodel.copy() +fmodel_wind_rose = fmodel.copy() +fmodel_wind_ti_rose = fmodel.copy() -fi_time_series.set(wind_data=time_series) -fi_wind_rose.set(wind_data=wind_rose) -fi_wind_ti_rose.set(wind_data=wind_ti_rose) +fmodel_time_series.set(wind_data=time_series) +fmodel_wind_rose.set(wind_data=wind_rose) +fmodel_wind_ti_rose.set(wind_data=wind_ti_rose) -fi_time_series.run() -fi_wind_rose.run() -fi_wind_ti_rose.run() +fmodel_time_series.run() +fmodel_wind_rose.run() +fmodel_wind_ti_rose.run() -time_series_power = fi_time_series.get_farm_power() -wind_rose_power = fi_wind_rose.get_farm_power() -wind_ti_rose_power = fi_wind_ti_rose.get_farm_power() +time_series_power = fmodel_time_series.get_farm_power() +wind_rose_power = fmodel_wind_rose.get_farm_power() +wind_ti_rose_power = fmodel_wind_ti_rose.get_farm_power() -time_series_aep = fi_time_series.get_farm_AEP_with_wind_data(time_series) -wind_rose_aep = fi_wind_rose.get_farm_AEP_with_wind_data(wind_rose) -wind_ti_rose_aep = fi_wind_ti_rose.get_farm_AEP_with_wind_data(wind_ti_rose) +time_series_aep = fmodel_time_series.get_farm_AEP_with_wind_data(time_series) +wind_rose_aep = fmodel_wind_rose.get_farm_AEP_with_wind_data(wind_rose) +wind_ti_rose_aep = fmodel_wind_ti_rose.get_farm_AEP_with_wind_data(wind_ti_rose) print(f"AEP from TimeSeries {time_series_aep / 1e9:.2f} GWh") print(f"AEP from WindRose {wind_rose_aep / 1e9:.2f} GWh") diff --git a/examples/35_sweep_ti.py b/examples/35_sweep_ti.py index 23942150e..5bf2ffa34 100644 --- a/examples/35_sweep_ti.py +++ b/examples/35_sweep_ti.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import ( - FlorisInterface, +from floris import ( + FlorisModel, TimeSeries, WindRose, ) @@ -29,10 +29,10 @@ # Now set up a FLORIS model and initialize it using the time -fi = FlorisInterface("inputs/gch.yaml") -fi.set(layout_x=[0, 500.0], layout_y=[0.0, 0.0], wind_data=time_series) -fi.run() -turbine_power = fi.get_turbine_powers() +fmodel = FlorisModel("inputs/gch.yaml") +fmodel.set(layout_x=[0, 500.0], layout_y=[0.0, 0.0], wind_data=time_series) +fmodel.run() +turbine_power = fmodel.get_turbine_powers() fig, axarr = plt.subplots(2, 1, sharex=True, figsize=(6, 6)) ax = axarr[0] diff --git a/examples/36_generate_ti.py b/examples/36_generate_ti.py index 3c6d8a9bf..317bc8dbe 100644 --- a/examples/36_generate_ti.py +++ b/examples/36_generate_ti.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np -from floris.tools import ( - FlorisInterface, +from floris import ( + FlorisModel, TimeSeries, WindRose, ) diff --git a/examples/40_test_derating.py b/examples/40_test_derating.py index 4385ff4a0..2a7260167 100644 --- a/examples/40_test_derating.py +++ b/examples/40_test_derating.py @@ -3,7 +3,7 @@ import numpy as np import yaml -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -12,39 +12,39 @@ """ # Grab model of FLORIS and update to deratable turbines -fi = FlorisInterface("inputs/gch.yaml") +fmodel = FlorisModel("inputs/gch.yaml") with open(str( - fi.floris.as_dict()["farm"]["turbine_library_path"] / - (fi.floris.as_dict()["farm"]["turbine_type"][0] + ".yaml") + fmodel.core.as_dict()["farm"]["turbine_library_path"] / + (fmodel.core.as_dict()["farm"]["turbine_type"][0] + ".yaml") )) as t: turbine_type = yaml.safe_load(t) turbine_type["power_thrust_model"] = "simple-derating" # Convert to a simple two turbine layout with derating turbines -fi.set(layout_x=[0, 1000.0], layout_y=[0.0, 0.0], turbine_type=[turbine_type]) +fmodel.set(layout_x=[0, 1000.0], layout_y=[0.0, 0.0], turbine_type=[turbine_type]) # Set the wind directions and speeds to be constant over n_findex = N time steps N = 50 -fi.set( +fmodel.set( wind_directions=270 * np.ones(N), wind_speeds=10.0 * np.ones(N), turbulence_intensities=0.06 * np.ones(N) ) -fi.run() -turbine_powers_orig = fi.get_turbine_powers() +fmodel.run() +turbine_powers_orig = fmodel.get_turbine_powers() # Add derating power_setpoints = np.tile(np.linspace(1, 6e6, N), 2).reshape(2, N).T -fi.set(power_setpoints=power_setpoints) -fi.run() -turbine_powers_derated = fi.get_turbine_powers() +fmodel.set(power_setpoints=power_setpoints) +fmodel.run() +turbine_powers_derated = fmodel.get_turbine_powers() # Compute available power at downstream turbine power_setpoints_2 = np.array([np.linspace(1, 6e6, N), np.full(N, None)]).T -fi.set(power_setpoints=power_setpoints_2) -fi.run() -turbine_powers_avail_ds = fi.get_turbine_powers()[:,1] +fmodel.set(power_setpoints=power_setpoints_2) +fmodel.run() +turbine_powers_avail_ds = fmodel.get_turbine_powers()[:,1] # Plot the results fig, ax = plt.subplots(1, 1) @@ -97,7 +97,7 @@ [2e6, None,], [None, 1e6] ]) -fi.set( +fmodel.set( wind_directions=270 * np.ones(len(yaw_angles)), wind_speeds=10.0 * np.ones(len(yaw_angles)), turbulence_intensities=0.06 * np.ones(len(yaw_angles)), @@ -105,8 +105,8 @@ yaw_angles=yaw_angles, power_setpoints=power_setpoints, ) -fi.run() -turbine_powers = fi.get_turbine_powers() +fmodel.run() +turbine_powers = fmodel.get_turbine_powers() print(turbine_powers) plt.show() diff --git a/examples/41_test_disable_turbines.py b/examples/41_test_disable_turbines.py index 717bb02e5..3dadc1e0d 100644 --- a/examples/41_test_disable_turbines.py +++ b/examples/41_test_disable_turbines.py @@ -3,7 +3,7 @@ import numpy as np import yaml -from floris.tools import FlorisInterface +from floris import FlorisModel """ @@ -12,19 +12,19 @@ during a simulation. """ -# Initialize the FLORIS interface -fi = FlorisInterface("inputs/gch.yaml") +# Initialize FLORIS +fmodel = FlorisModel("inputs/gch.yaml") # Change to the mixed model turbine with open( str( - fi.floris.as_dict()["farm"]["turbine_library_path"] - / (fi.floris.as_dict()["farm"]["turbine_type"][0] + ".yaml") + fmodel.core.as_dict()["farm"]["turbine_library_path"] + / (fmodel.core.as_dict()["farm"]["turbine_type"][0] + ".yaml") ) ) as t: turbine_type = yaml.safe_load(t) turbine_type["power_thrust_model"] = "mixed" -fi.set(turbine_type=[turbine_type]) +fmodel.set(turbine_type=[turbine_type]) # Consider a wind farm of 3 aligned wind turbines layout = np.array([[0.0, 0.0], [500.0, 0.0], [1000.0, 0.0]]) @@ -43,7 +43,7 @@ # ------------------------------------------ # Reinitialize flow field -fi.set( +fmodel.set( layout_x=layout[:, 0], layout_y=layout[:, 1], wind_directions=wind_directions, @@ -53,15 +53,15 @@ ) # # Compute wakes -fi.run() +fmodel.run() # Results # ------------------------------------------ # Get powers and effective wind speeds -turbine_powers = fi.get_turbine_powers() +turbine_powers = fmodel.get_turbine_powers() turbine_powers = np.round(turbine_powers * 1e-3, decimals=2) -effective_wind_speeds = fi.turbine_average_velocities +effective_wind_speeds = fmodel.turbine_average_velocities # Plot the results diff --git a/floris/__init__.py b/floris/__init__.py index 64c9e8c9a..0ac26a149 100644 --- a/floris/__init__.py +++ b/floris/__init__.py @@ -4,3 +4,18 @@ with open(Path(__file__).parent / "version.py") as _version_file: __version__ = _version_file.read().strip() + + +from .floris_model import FlorisModel +from .flow_visualization import ( + plot_rotor_values, + visualize_cut_plane, + visualize_quiver, +) +from .parallel_computing_interface import ParallelComputingInterface +from .uncertainty_interface import UncertaintyInterface +from .wind_data import ( + TimeSeries, + WindRose, + WindTIRose, +) diff --git a/floris/tools/convert_floris_input_v3_to_v4.py b/floris/convert_floris_input_v3_to_v4.py similarity index 100% rename from floris/tools/convert_floris_input_v3_to_v4.py rename to floris/convert_floris_input_v3_to_v4.py diff --git a/floris/tools/convert_turbine_v3_to_v4.py b/floris/convert_turbine_v3_to_v4.py similarity index 100% rename from floris/tools/convert_turbine_v3_to_v4.py rename to floris/convert_turbine_v3_to_v4.py diff --git a/floris/simulation/__init__.py b/floris/core/__init__.py similarity index 98% rename from floris/simulation/__init__.py rename to floris/core/__init__.py index 68da31838..e37f9c113 100644 --- a/floris/simulation/__init__.py +++ b/floris/core/__init__.py @@ -55,7 +55,7 @@ sequential_solver, turbopark_solver, ) -from .floris import Floris +from .core import Core # initialize the logger floris.logging_manager._setup_logger() diff --git a/floris/simulation/base.py b/floris/core/base.py similarity index 100% rename from floris/simulation/base.py rename to floris/core/base.py diff --git a/floris/simulation/floris.py b/floris/core/core.py similarity index 98% rename from floris/simulation/floris.py rename to floris/core/core.py index 5e1379dcd..a31583567 100644 --- a/floris/simulation/floris.py +++ b/floris/core/core.py @@ -9,7 +9,7 @@ from attrs import define, field from floris import logging_manager -from floris.simulation import ( +from floris.core import ( BaseClass, cc_solver, empirical_gauss_solver, @@ -38,7 +38,7 @@ @define -class Floris(BaseClass): +class Core(BaseClass): """ Top-level class that describes a Floris model and initializes the simulation. Use the :py:class:`~.simulation.farm.Farm` attribute to @@ -265,7 +265,7 @@ def solve_for_velocity_deficit_profiles( ) -> list[pd.DataFrame]: """ Extract velocity deficit profiles. See - :py:meth:`~floris.tools.floris_interface.FlorisInterface.sample_velocity_deficit_profiles` + :py:meth:`~floris.floris_model.FlorisModel.sample_velocity_deficit_profiles` for more details. """ @@ -336,7 +336,7 @@ def finalize(self): ## I/O @classmethod - def from_file(cls, input_file_path: str | Path) -> Floris: + def from_file(cls, input_file_path: str | Path) -> Core: """Creates a `Floris` instance from an input file. Must be filetype YAML. Args: @@ -348,7 +348,7 @@ def from_file(cls, input_file_path: str | Path) -> Floris: """ input_dict = load_yaml(Path(input_file_path).resolve()) check_input_file_for_v3_keys(input_dict) - return Floris.from_dict(input_dict) + return Core.from_dict(input_dict) def to_file(self, output_file_path: str) -> None: """Converts the `Floris` object to an input-ready YAML file at `output_file_path`. diff --git a/floris/simulation/farm.py b/floris/core/farm.py similarity index 98% rename from floris/simulation/farm.py rename to floris/core/farm.py index 678b47e3e..26cec1bec 100644 --- a/floris/simulation/farm.py +++ b/floris/core/farm.py @@ -15,13 +15,13 @@ from attrs import define, field from scipy.interpolate import interp1d -from floris.simulation import ( +from floris.core import ( BaseClass, State, Turbine, ) -from floris.simulation.rotor_velocity import compute_tilt_angles_for_floating_turbines_map -from floris.simulation.turbine.operation_models import POWER_SETPOINT_DEFAULT +from floris.core.rotor_velocity import compute_tilt_angles_for_floating_turbines_map +from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT from floris.type_dec import ( convert_to_path, floris_array_converter, diff --git a/floris/simulation/flow_field.py b/floris/core/flow_field.py similarity index 99% rename from floris/simulation/flow_field.py rename to floris/core/flow_field.py index ad9c54693..655f771a9 100644 --- a/floris/simulation/flow_field.py +++ b/floris/core/flow_field.py @@ -9,7 +9,7 @@ from scipy.spatial import ConvexHull from shapely.geometry import Polygon -from floris.simulation import ( +from floris.core import ( BaseClass, Grid, ) diff --git a/floris/simulation/grid.py b/floris/core/grid.py similarity index 99% rename from floris/simulation/grid.py rename to floris/core/grid.py index 926896821..3dc6280ae 100644 --- a/floris/simulation/grid.py +++ b/floris/core/grid.py @@ -8,7 +8,7 @@ import numpy as np from attrs import define, field -from floris.simulation import BaseClass +from floris.core import BaseClass from floris.type_dec import ( floris_array_converter, floris_float_type, diff --git a/floris/simulation/rotor_velocity.py b/floris/core/rotor_velocity.py similarity index 100% rename from floris/simulation/rotor_velocity.py rename to floris/core/rotor_velocity.py diff --git a/floris/simulation/solver.py b/floris/core/solver.py similarity index 99% rename from floris/simulation/solver.py rename to floris/core/solver.py index 011f41985..00abcc129 100644 --- a/floris/simulation/solver.py +++ b/floris/core/solver.py @@ -5,7 +5,7 @@ import numpy as np -from floris.simulation import ( +from floris.core import ( axial_induction, Farm, FlowField, @@ -15,10 +15,10 @@ thrust_coefficient, TurbineGrid, ) -from floris.simulation.rotor_velocity import average_velocity -from floris.simulation.wake import WakeModelManager -from floris.simulation.wake_deflection.empirical_gauss import yaw_added_wake_mixing -from floris.simulation.wake_deflection.gauss import ( +from floris.core.rotor_velocity import average_velocity +from floris.core.wake import WakeModelManager +from floris.core.wake_deflection.empirical_gauss import yaw_added_wake_mixing +from floris.core.wake_deflection.gauss import ( calculate_transverse_velocity, wake_added_yaw, yaw_added_turbulence_mixing, diff --git a/floris/simulation/turbine/__init__.py b/floris/core/turbine/__init__.py similarity index 63% rename from floris/simulation/turbine/__init__.py rename to floris/core/turbine/__init__.py index 8f447dbee..5f361f463 100644 --- a/floris/simulation/turbine/__init__.py +++ b/floris/core/turbine/__init__.py @@ -1,5 +1,5 @@ -from floris.simulation.turbine.operation_models import ( +from floris.core.turbine.operation_models import ( CosineLossTurbine, MixedOperationTurbine, SimpleDeratingTurbine, diff --git a/floris/simulation/turbine/operation_models.py b/floris/core/turbine/operation_models.py similarity index 99% rename from floris/simulation/turbine/operation_models.py rename to floris/core/turbine/operation_models.py index 3d7a2b8e6..88f0f4fac 100644 --- a/floris/simulation/turbine/operation_models.py +++ b/floris/core/turbine/operation_models.py @@ -13,8 +13,8 @@ from attrs import define, field from scipy.interpolate import interp1d -from floris.simulation import BaseClass -from floris.simulation.rotor_velocity import ( +from floris.core import BaseClass +from floris.core.rotor_velocity import ( average_velocity, compute_tilt_angles_for_floating_turbines, rotor_velocity_tilt_correction, diff --git a/floris/simulation/turbine/turbine.py b/floris/core/turbine/turbine.py similarity index 99% rename from floris/simulation/turbine/turbine.py rename to floris/core/turbine/turbine.py index 191072ce6..dbc588093 100644 --- a/floris/simulation/turbine/turbine.py +++ b/floris/core/turbine/turbine.py @@ -11,8 +11,8 @@ from attrs import define, field from scipy.interpolate import interp1d -from floris.simulation import BaseClass -from floris.simulation.turbine import ( +from floris.core import BaseClass +from floris.core.turbine import ( CosineLossTurbine, MixedOperationTurbine, SimpleDeratingTurbine, diff --git a/floris/simulation/wake.py b/floris/core/wake.py similarity index 95% rename from floris/simulation/wake.py rename to floris/core/wake.py index 28560151a..2f9907c99 100644 --- a/floris/simulation/wake.py +++ b/floris/core/wake.py @@ -2,24 +2,24 @@ import attrs from attrs import define, field -from floris.simulation import BaseClass, BaseModel -from floris.simulation.wake_combination import ( +from floris.core import BaseClass, BaseModel +from floris.core.wake_combination import ( FLS, MAX, SOSFS, ) -from floris.simulation.wake_deflection import ( +from floris.core.wake_deflection import ( EmpiricalGaussVelocityDeflection, GaussVelocityDeflection, JimenezVelocityDeflection, NoneVelocityDeflection, ) -from floris.simulation.wake_turbulence import ( +from floris.core.wake_turbulence import ( CrespoHernandez, NoneWakeTurbulence, WakeInducedMixing, ) -from floris.simulation.wake_velocity import ( +from floris.core.wake_velocity import ( CumulativeGaussCurlVelocityDeficit, EmpiricalGaussVelocityDeficit, GaussVelocityDeficit, diff --git a/floris/core/wake_combination/__init__.py b/floris/core/wake_combination/__init__.py new file mode 100644 index 000000000..246aab65c --- /dev/null +++ b/floris/core/wake_combination/__init__.py @@ -0,0 +1,4 @@ + +from floris.core.wake_combination.fls import FLS +from floris.core.wake_combination.max import MAX +from floris.core.wake_combination.sosfs import SOSFS diff --git a/floris/simulation/wake_combination/fls.py b/floris/core/wake_combination/fls.py similarity index 95% rename from floris/simulation/wake_combination/fls.py rename to floris/core/wake_combination/fls.py index fa2d88326..42e68045f 100644 --- a/floris/simulation/wake_combination/fls.py +++ b/floris/core/wake_combination/fls.py @@ -2,7 +2,7 @@ import numpy as np from attrs import define -from floris.simulation import BaseModel +from floris.core import BaseModel @define diff --git a/floris/simulation/wake_combination/max.py b/floris/core/wake_combination/max.py similarity index 96% rename from floris/simulation/wake_combination/max.py rename to floris/core/wake_combination/max.py index f4beda1c8..0898cc842 100644 --- a/floris/simulation/wake_combination/max.py +++ b/floris/core/wake_combination/max.py @@ -2,7 +2,7 @@ import numpy as np from attrs import define -from floris.simulation import BaseModel +from floris.core import BaseModel @define diff --git a/floris/simulation/wake_combination/sosfs.py b/floris/core/wake_combination/sosfs.py similarity index 95% rename from floris/simulation/wake_combination/sosfs.py rename to floris/core/wake_combination/sosfs.py index 6598faf2b..c277e21bb 100644 --- a/floris/simulation/wake_combination/sosfs.py +++ b/floris/core/wake_combination/sosfs.py @@ -2,7 +2,7 @@ import numpy as np from attrs import define -from floris.simulation import BaseModel +from floris.core import BaseModel @define diff --git a/floris/core/wake_deflection/__init__.py b/floris/core/wake_deflection/__init__.py new file mode 100644 index 000000000..ba5e63788 --- /dev/null +++ b/floris/core/wake_deflection/__init__.py @@ -0,0 +1,5 @@ + +from floris.core.wake_deflection.empirical_gauss import EmpiricalGaussVelocityDeflection +from floris.core.wake_deflection.gauss import GaussVelocityDeflection +from floris.core.wake_deflection.jimenez import JimenezVelocityDeflection +from floris.core.wake_deflection.none import NoneVelocityDeflection diff --git a/floris/simulation/wake_deflection/empirical_gauss.py b/floris/core/wake_deflection/empirical_gauss.py similarity index 99% rename from floris/simulation/wake_deflection/empirical_gauss.py rename to floris/core/wake_deflection/empirical_gauss.py index 85681544c..00a506b3c 100644 --- a/floris/simulation/wake_deflection/empirical_gauss.py +++ b/floris/core/wake_deflection/empirical_gauss.py @@ -4,7 +4,7 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/simulation/wake_deflection/gauss.py b/floris/core/wake_deflection/gauss.py similarity index 99% rename from floris/simulation/wake_deflection/gauss.py rename to floris/core/wake_deflection/gauss.py index fc1cedfc4..e19fd147b 100644 --- a/floris/simulation/wake_deflection/gauss.py +++ b/floris/core/wake_deflection/gauss.py @@ -12,7 +12,7 @@ ) from numpy import pi -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/simulation/wake_deflection/jimenez.py b/floris/core/wake_deflection/jimenez.py similarity index 93% rename from floris/simulation/wake_deflection/jimenez.py rename to floris/core/wake_deflection/jimenez.py index 6f0a8ccf6..daca6e9c5 100644 --- a/floris/simulation/wake_deflection/jimenez.py +++ b/floris/core/wake_deflection/jimenez.py @@ -5,7 +5,7 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, @@ -64,13 +64,13 @@ def function( y_locations (np.array): spanwise locations in wake z_locations (np.array): vertical locations in wake (not used in Jiménez) - turbine (:py:class:`floris.simulation.turbine.Turbine`): + turbine (:py:class:`floris.core.turbine.Turbine`): Turbine object coord - (:py:meth:`floris.simulation.turbine_map.TurbineMap.coords`): + (:py:meth:`floris.core.turbine_map.TurbineMap.coords`): Spatial coordinates of wind turbine. flow_field - (:py:class:`floris.simulation.flow_field.FlowField`): + (:py:class:`floris.core.flow_field.FlowField`): Flow field object. Returns: diff --git a/floris/simulation/wake_deflection/none.py b/floris/core/wake_deflection/none.py similarity index 97% rename from floris/simulation/wake_deflection/none.py rename to floris/core/wake_deflection/none.py index 44e466651..b428c8af9 100644 --- a/floris/simulation/wake_deflection/none.py +++ b/floris/core/wake_deflection/none.py @@ -4,7 +4,7 @@ import numpy as np from attrs import define -from floris.simulation import ( +from floris.core import ( BaseModel, FlowField, Grid, diff --git a/floris/core/wake_turbulence/__init__.py b/floris/core/wake_turbulence/__init__.py new file mode 100644 index 000000000..8bec72939 --- /dev/null +++ b/floris/core/wake_turbulence/__init__.py @@ -0,0 +1,4 @@ + +from floris.core.wake_turbulence.crespo_hernandez import CrespoHernandez +from floris.core.wake_turbulence.none import NoneWakeTurbulence +from floris.core.wake_turbulence.wake_induced_mixing import WakeInducedMixing diff --git a/floris/simulation/wake_turbulence/crespo_hernandez.py b/floris/core/wake_turbulence/crespo_hernandez.py similarity index 98% rename from floris/simulation/wake_turbulence/crespo_hernandez.py rename to floris/core/wake_turbulence/crespo_hernandez.py index 09d045986..b5c623fe0 100644 --- a/floris/simulation/wake_turbulence/crespo_hernandez.py +++ b/floris/core/wake_turbulence/crespo_hernandez.py @@ -5,7 +5,7 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/simulation/wake_turbulence/none.py b/floris/core/wake_turbulence/none.py similarity index 95% rename from floris/simulation/wake_turbulence/none.py rename to floris/core/wake_turbulence/none.py index 3975c2581..146ca970b 100644 --- a/floris/simulation/wake_turbulence/none.py +++ b/floris/core/wake_turbulence/none.py @@ -4,7 +4,7 @@ import numpy as np from attrs import define, field -from floris.simulation import BaseModel +from floris.core import BaseModel @define diff --git a/floris/simulation/wake_turbulence/wake_induced_mixing.py b/floris/core/wake_turbulence/wake_induced_mixing.py similarity index 98% rename from floris/simulation/wake_turbulence/wake_induced_mixing.py rename to floris/core/wake_turbulence/wake_induced_mixing.py index f39e6a8a6..64306ff75 100644 --- a/floris/simulation/wake_turbulence/wake_induced_mixing.py +++ b/floris/core/wake_turbulence/wake_induced_mixing.py @@ -4,7 +4,7 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/core/wake_velocity/__init__.py b/floris/core/wake_velocity/__init__.py new file mode 100644 index 000000000..dc1342f8a --- /dev/null +++ b/floris/core/wake_velocity/__init__.py @@ -0,0 +1,7 @@ + +from floris.core.wake_velocity.cumulative_gauss_curl import CumulativeGaussCurlVelocityDeficit +from floris.core.wake_velocity.empirical_gauss import EmpiricalGaussVelocityDeficit +from floris.core.wake_velocity.gauss import GaussVelocityDeficit +from floris.core.wake_velocity.jensen import JensenVelocityDeficit +from floris.core.wake_velocity.none import NoneVelocityDeficit +from floris.core.wake_velocity.turbopark import TurbOParkVelocityDeficit diff --git a/floris/simulation/wake_velocity/cumulative_gauss_curl.py b/floris/core/wake_velocity/cumulative_gauss_curl.py similarity index 99% rename from floris/simulation/wake_velocity/cumulative_gauss_curl.py rename to floris/core/wake_velocity/cumulative_gauss_curl.py index 902b085b5..86d8c982e 100644 --- a/floris/simulation/wake_velocity/cumulative_gauss_curl.py +++ b/floris/core/wake_velocity/cumulative_gauss_curl.py @@ -5,7 +5,7 @@ from attrs import define, field from scipy.special import gamma -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/simulation/wake_velocity/empirical_gauss.py b/floris/core/wake_velocity/empirical_gauss.py similarity index 99% rename from floris/simulation/wake_velocity/empirical_gauss.py rename to floris/core/wake_velocity/empirical_gauss.py index cfeb261fb..722771012 100644 --- a/floris/simulation/wake_velocity/empirical_gauss.py +++ b/floris/core/wake_velocity/empirical_gauss.py @@ -5,14 +5,14 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, Grid, Turbine, ) -from floris.simulation.wake_velocity.gauss import gaussian_function +from floris.core.wake_velocity.gauss import gaussian_function from floris.utilities import ( cosd, sind, diff --git a/floris/simulation/wake_velocity/gauss.py b/floris/core/wake_velocity/gauss.py similarity index 99% rename from floris/simulation/wake_velocity/gauss.py rename to floris/core/wake_velocity/gauss.py index 4cf5cbdf9..5c73786ae 100644 --- a/floris/simulation/wake_velocity/gauss.py +++ b/floris/core/wake_velocity/gauss.py @@ -5,7 +5,7 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/simulation/wake_velocity/jensen.py b/floris/core/wake_velocity/jensen.py similarity index 99% rename from floris/simulation/wake_velocity/jensen.py rename to floris/core/wake_velocity/jensen.py index f84461502..7d6b09c31 100644 --- a/floris/simulation/wake_velocity/jensen.py +++ b/floris/core/wake_velocity/jensen.py @@ -9,7 +9,7 @@ fields, ) -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/simulation/wake_velocity/none.py b/floris/core/wake_velocity/none.py similarity index 97% rename from floris/simulation/wake_velocity/none.py rename to floris/core/wake_velocity/none.py index 37b4e09bc..af1ea448a 100644 --- a/floris/simulation/wake_velocity/none.py +++ b/floris/core/wake_velocity/none.py @@ -4,7 +4,7 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.core import ( BaseModel, FlowField, Grid, diff --git a/floris/simulation/wake_velocity/turbopark.py b/floris/core/wake_velocity/turbopark.py similarity index 99% rename from floris/simulation/wake_velocity/turbopark.py rename to floris/core/wake_velocity/turbopark.py index 33071f9a1..63ad6e06c 100644 --- a/floris/simulation/wake_velocity/turbopark.py +++ b/floris/core/wake_velocity/turbopark.py @@ -9,7 +9,7 @@ from scipy import integrate from scipy.interpolate import RegularGridInterpolator -from floris.simulation import ( +from floris.core import ( BaseModel, Farm, FlowField, diff --git a/floris/simulation/wake_velocity/turbopark_lookup_table.mat b/floris/core/wake_velocity/turbopark_lookup_table.mat similarity index 100% rename from floris/simulation/wake_velocity/turbopark_lookup_table.mat rename to floris/core/wake_velocity/turbopark_lookup_table.mat diff --git a/floris/tools/cut_plane.py b/floris/cut_plane.py similarity index 99% rename from floris/tools/cut_plane.py rename to floris/cut_plane.py index 64c24458b..10c573353 100644 --- a/floris/tools/cut_plane.py +++ b/floris/cut_plane.py @@ -338,7 +338,7 @@ def calculate_wind_speed(cross_plane, x1_loc, x2_loc, R): Calculate effective wind speed within specified range of a point. Args: - cross_plane (:py:class:`floris.tools.cut_plane.CrossPlane`): + cross_plane (:py:class:`floris.cut_plane.CrossPlane`): plane of data. x1_loc (float): x1-coordinate of point of interest. x2_loc (float): x2-coordinate of point of interest. @@ -377,7 +377,7 @@ def calculate_power( Calculate maximum power available in a given cross plane. Args: - cross_plane (:py:class:`floris.tools.cut_plane.CrossPlane`): + cross_plane (:py:class:`floris.cut_plane.CrossPlane`): plane of data. x1_loc (float): x1-coordinate of point of interest. x2_loc (float): x2-coordinate of point of interest. diff --git a/floris/tools/floris_interface.py b/floris/floris_model.py similarity index 84% rename from floris/tools/floris_interface.py rename to floris/floris_model.py index 4cd8dc888..8ca0c1a96 100644 --- a/floris/tools/floris_interface.py +++ b/floris/floris_model.py @@ -7,30 +7,30 @@ import numpy as np import pandas as pd -from floris.logging_manager import LoggingManager -from floris.simulation import Floris, State -from floris.simulation.rotor_velocity import average_velocity -from floris.simulation.turbine.operation_models import ( +from floris.core import Core, State +from floris.core.rotor_velocity import average_velocity +from floris.core.turbine.operation_models import ( POWER_SETPOINT_DEFAULT, POWER_SETPOINT_DISABLED, ) -from floris.simulation.turbine.turbine import ( +from floris.core.turbine.turbine import ( axial_induction, power, thrust_coefficient, ) -from floris.tools.cut_plane import CutPlane -from floris.tools.wind_data import WindDataBase +from floris.cut_plane import CutPlane +from floris.logging_manager import LoggingManager from floris.type_dec import ( floris_array_converter, NDArrayBool, NDArrayFloat, ) +from floris.wind_data import WindDataBase -class FlorisInterface(LoggingManager): +class FlorisModel(LoggingManager): """ - FlorisInterface provides a high-level user interface to many of the + FlorisModel provides a high-level user interface to many of the underlying methods within the FLORIS framework. It is meant to act as a single entry-point for the majority of users, simplifying the calls to methods on objects within FLORIS. @@ -42,7 +42,7 @@ class FlorisInterface(LoggingManager): - **farm**: See `floris.simulation.farm.Farm` for more details. - **turbine**: See `floris.simulation.turbine.Turbine` for more details. - **wake**: See `floris.simulation.wake.WakeManager` for more details. - - **logging**: See `floris.simulation.floris.Floris` for more details. + - **logging**: See `floris.simulation.core.Core` for more details. """ def __init__(self, configuration: dict | str | Path): @@ -50,31 +50,31 @@ def __init__(self, configuration: dict | str | Path): if isinstance(self.configuration, (str, Path)): try: - self.floris = Floris.from_file(self.configuration) + self.core = Core.from_file(self.configuration) except FileNotFoundError: # If the file cannot be found, then attempt the configuration path relative to the - # file location from which FlorisInterface was attempted to be run. If successful, + # file location from which FlorisModel was attempted to be run. If successful, # update self.configuration to an absolute, working file path and name. base_fn = Path(inspect.stack()[-1].filename).resolve().parent config = (base_fn / self.configuration).resolve() - self.floris = Floris.from_file(config) + self.core = Core.from_file(config) self.configuration = config elif isinstance(self.configuration, dict): - self.floris = Floris.from_dict(self.configuration) + self.core = Core.from_dict(self.configuration) else: raise TypeError("The Floris `configuration` must be of type 'dict', 'str', or 'Path'.") # If ref height is -1, assign the hub height - if np.abs(self.floris.flow_field.reference_wind_height + 1.0) < 1.0e-6: + if np.abs(self.core.flow_field.reference_wind_height + 1.0) < 1.0e-6: self.assign_hub_height_to_ref_height() # Make a check on reference height and provide a helpful warning - unique_heights = np.unique(np.round(self.floris.farm.hub_heights, decimals=6)) + unique_heights = np.unique(np.round(self.core.farm.hub_heights, decimals=6)) if (( len(unique_heights) == 1) and - (np.abs(self.floris.flow_field.reference_wind_height - unique_heights[0]) > 1.0e-6 + (np.abs(self.core.flow_field.reference_wind_height - unique_heights[0]) > 1.0e-6 )): err_msg = ( "The only unique hub-height is not the equal to the specified reference " @@ -84,10 +84,10 @@ def __init__(self, configuration: dict | str | Path): self.logger.warning(err_msg, stack_info=True) # Check the turbine_grid_points is reasonable - if self.floris.solver["type"] == "turbine_grid": - if self.floris.solver["turbine_grid_points"] > 3: + if self.core.solver["type"] == "turbine_grid": + if self.core.solver["turbine_grid_points"] > 3: self.logger.error( - f"turbine_grid_points value is {self.floris.solver['turbine_grid_points']} " + f"turbine_grid_points value is {self.core.solver['turbine_grid_points']} " "which is larger than the recommended value of less than or equal to 3. " "High amounts of turbine grid points reduce the computational performance " "but have a small change on accuracy." @@ -97,7 +97,7 @@ def __init__(self, configuration: dict | str | Path): def assign_hub_height_to_ref_height(self): # Confirm can do this operation - unique_heights = np.unique(self.floris.farm.hub_heights) + unique_heights = np.unique(self.core.farm.hub_heights) if len(unique_heights) > 1: raise ValueError( "To assign hub heights to reference height, can not have more than one " @@ -105,11 +105,11 @@ def assign_hub_height_to_ref_height(self): f"Current length is {unique_heights}." ) - self.floris.flow_field.reference_wind_height = unique_heights[0] + self.core.flow_field.reference_wind_height = unique_heights[0] def copy(self): - """Create an independent copy of the current FlorisInterface object""" - return FlorisInterface(self.floris.as_dict()) + """Create an independent copy of the current FlorisModel object""" + return FlorisModel(self.core.as_dict()) def set( self, @@ -165,8 +165,8 @@ def set( and the power setpoint at that position is set to 0. Defaults to None. """ # Initialize a new Floris object after saving the setpoints - _yaw_angles = self.floris.farm.yaw_angles - _power_setpoints = self.floris.farm.power_setpoints + _yaw_angles = self.core.farm.yaw_angles + _power_setpoints = self.core.farm.power_setpoints self._reinitialize( wind_speeds=wind_speeds, wind_directions=wind_directions, @@ -187,12 +187,12 @@ def set( # If the yaw angles or power setpoints are not the default, set them back to the # previous setting if not (_yaw_angles == 0).all(): - self.floris.farm.set_yaw_angles(_yaw_angles) + self.core.farm.set_yaw_angles(_yaw_angles) if not ( (_power_setpoints == POWER_SETPOINT_DEFAULT) | (_power_setpoints == POWER_SETPOINT_DISABLED) ).all(): - self.floris.farm.set_power_setpoints(_power_setpoints) + self.core.farm.set_power_setpoints(_power_setpoints) # Set the operation self._set_operation( @@ -252,7 +252,7 @@ def _reinitialize( wind_data (type[WindDataBase] | None, optional): Wind data. Defaults to None. """ # Export the floris object recursively as a dictionary - floris_dict = self.floris.as_dict() + floris_dict = self.core.as_dict() flow_field_dict = floris_dict["flow_field"] farm_dict = floris_dict["farm"] @@ -316,7 +316,7 @@ def _reinitialize( floris_dict["farm"] = farm_dict # Create a new instance of floris and attach to self - self.floris = Floris.from_dict(floris_dict) + self.core = Core.from_dict(floris_dict) def _set_operation( self, @@ -337,7 +337,7 @@ def _set_operation( """ # Add operating conditions to the floris object if yaw_angles is not None: - self.floris.farm.set_yaw_angles(yaw_angles) + self.core.farm.set_yaw_angles(yaw_angles) if power_setpoints is not None: power_setpoints = np.array(power_setpoints) @@ -348,7 +348,7 @@ def _set_operation( ] = POWER_SETPOINT_DEFAULT power_setpoints = floris_array_converter(power_setpoints) - self.floris.farm.set_power_setpoints(power_setpoints) + self.core.farm.set_power_setpoints(power_setpoints) # Check for turbines to disable if disable_turbines is not None: @@ -357,25 +357,25 @@ def _set_operation( disable_turbines = np.array(disable_turbines) # Must have first dimension = n_findex - if disable_turbines.shape[0] != self.floris.flow_field.n_findex: + if disable_turbines.shape[0] != self.core.flow_field.n_findex: raise ValueError( f"disable_turbines has a size of {disable_turbines.shape[0]} " f"in the 0th dimension, must be equal to " - f"n_findex={self.floris.flow_field.n_findex}" + f"n_findex={self.core.flow_field.n_findex}" ) # Must have first dimension = n_turbines - if disable_turbines.shape[1] != self.floris.farm.n_turbines: + if disable_turbines.shape[1] != self.core.farm.n_turbines: raise ValueError( f"disable_turbines has a size of {disable_turbines.shape[1]} " f"in the 1th dimension, must be equal to " - f"n_turbines={self.floris.farm.n_turbines}" + f"n_turbines={self.core.farm.n_turbines}" ) # Set power setpoints to small value (non zero to avoid numerical issues) and # yaw_angles to 0 in all locations where disable_turbines is True - self.floris.farm.yaw_angles[disable_turbines] = 0.0 - self.floris.farm.power_setpoints[disable_turbines] = POWER_SETPOINT_DISABLED + self.core.farm.yaw_angles[disable_turbines] = 0.0 + self.core.farm.power_setpoints[disable_turbines] = POWER_SETPOINT_DISABLED def run(self) -> None: """ @@ -383,10 +383,10 @@ def run(self) -> None: """ # Initialize solution space - self.floris.initialize_domain() + self.core.initialize_domain() # Perform the wake calculations - self.floris.steady_state_atmospheric_condition() + self.core.steady_state_atmospheric_condition() def run_no_wake(self) -> None: """ @@ -396,10 +396,10 @@ def run_no_wake(self) -> None: """ # Initialize solution space - self.floris.initialize_domain() + self.core.initialize_domain() # Finalize values to user-supplied order - self.floris.finalize() + self.core.finalize() def get_plane_of_points( self, @@ -408,7 +408,7 @@ def get_plane_of_points( ): """ Calculates velocity values through the - :py:meth:`FlorisInterface.calculate_wake` method at points in plane + :py:meth:`FlorisModel.calculate_wake` method at points in plane specified by inputs. Args: @@ -422,16 +422,16 @@ def get_plane_of_points( """ # Get results vectors if normal_vector == "z": - x_flat = self.floris.grid.x_sorted_inertial_frame[0].flatten() - y_flat = self.floris.grid.y_sorted_inertial_frame[0].flatten() - z_flat = self.floris.grid.z_sorted_inertial_frame[0].flatten() + x_flat = self.core.grid.x_sorted_inertial_frame[0].flatten() + y_flat = self.core.grid.y_sorted_inertial_frame[0].flatten() + z_flat = self.core.grid.z_sorted_inertial_frame[0].flatten() else: - x_flat = self.floris.grid.x_sorted[0].flatten() - y_flat = self.floris.grid.y_sorted[0].flatten() - z_flat = self.floris.grid.z_sorted[0].flatten() - u_flat = self.floris.flow_field.u_sorted[0].flatten() - v_flat = self.floris.flow_field.v_sorted[0].flatten() - w_flat = self.floris.flow_field.w_sorted[0].flatten() + x_flat = self.core.grid.x_sorted[0].flatten() + y_flat = self.core.grid.y_sorted[0].flatten() + z_flat = self.core.grid.z_sorted[0].flatten() + u_flat = self.core.flow_field.u_sorted[0].flatten() + v_flat = self.core.flow_field.v_sorted[0].flatten() + w_flat = self.core.flow_field.w_sorted[0].flatten() # Create a df of these if normal_vector == "z": @@ -527,15 +527,15 @@ def calculate_horizontal_plane( """ # TODO update docstring if wd is None: - wd = self.floris.flow_field.wind_directions + wd = self.core.flow_field.wind_directions if ws is None: - ws = self.floris.flow_field.wind_speeds + ws = self.core.flow_field.wind_speeds if ti is None: - ti = self.floris.flow_field.turbulence_intensities + ti = self.core.flow_field.turbulence_intensities self.check_wind_condition_for_viz(wd=wd, ws=ws, ti=ti) # Store the current state for reinitialization - floris_dict = self.floris.as_dict() + floris_dict = self.core.as_dict() # Set the solver to a flow field planar grid solver_settings = { "type": "flow_field_planar_grid", @@ -555,7 +555,7 @@ def calculate_horizontal_plane( ) # Calculate wake - self.floris.solve_for_viz() + self.core.solve_for_viz() # Get the points of data in a dataframe # TODO this just seems to be flattening and storing the data in a df; is this necessary? @@ -568,13 +568,13 @@ def calculate_horizontal_plane( # Compute the cutplane horizontal_plane = CutPlane( df, - self.floris.grid.grid_resolution[0], - self.floris.grid.grid_resolution[1], + self.core.grid.grid_resolution[0], + self.core.grid.grid_resolution[1], "z", ) - # Reset the fi object back to the turbine grid configuration - self.floris = Floris.from_dict(floris_dict) + # Reset the fmodel object back to the turbine grid configuration + self.core = Core.from_dict(floris_dict) # Run the simulation again for futher postprocessing (i.e. now we can get farm power) self.run() @@ -617,15 +617,15 @@ def calculate_cross_plane( """ # TODO update docstring if wd is None: - wd = self.floris.flow_field.wind_directions + wd = self.core.flow_field.wind_directions if ws is None: - ws = self.floris.flow_field.wind_speeds + ws = self.core.flow_field.wind_speeds if ti is None: - ti = self.floris.flow_field.turbulence_intensities + ti = self.core.flow_field.turbulence_intensities self.check_wind_condition_for_viz(wd=wd, ws=ws, ti=ti) # Store the current state for reinitialization - floris_dict = self.floris.as_dict() + floris_dict = self.core.as_dict() # Set the solver to a flow field planar grid solver_settings = { @@ -646,7 +646,7 @@ def calculate_cross_plane( ) # Calculate wake - self.floris.solve_for_viz() + self.core.solve_for_viz() # Get the points of data in a dataframe # TODO this just seems to be flattening and storing the data in a df; is this necessary? @@ -659,8 +659,8 @@ def calculate_cross_plane( # Compute the cutplane cross_plane = CutPlane(df, y_resolution, z_resolution, "x") - # Reset the fi object back to the turbine grid configuration - self.floris = Floris.from_dict(floris_dict) + # Reset the fmodel object back to the turbine grid configuration + self.core = Core.from_dict(floris_dict) # Run the simulation again for futher postprocessing (i.e. now we can get farm power) self.run() @@ -716,15 +716,15 @@ def calculate_y_plane( """ # TODO update docstring if wd is None: - wd = self.floris.flow_field.wind_directions + wd = self.core.flow_field.wind_directions if ws is None: - ws = self.floris.flow_field.wind_speeds + ws = self.core.flow_field.wind_speeds if ti is None: - ti = self.floris.flow_field.turbulence_intensities + ti = self.core.flow_field.turbulence_intensities self.check_wind_condition_for_viz(wd=wd, ws=ws, ti=ti) # Store the current state for reinitialization - floris_dict = self.floris.as_dict() + floris_dict = self.core.as_dict() # Set the solver to a flow field planar grid solver_settings = { @@ -745,7 +745,7 @@ def calculate_y_plane( ) # Calculate wake - self.floris.solve_for_viz() + self.core.solve_for_viz() # Get the points of data in a dataframe # TODO this just seems to be flattening and storing the data in a df; is this necessary? @@ -758,8 +758,8 @@ def calculate_y_plane( # Compute the cutplane y_plane = CutPlane(df, x_resolution, z_resolution, "y") - # Reset the fi object back to the turbine grid configuration - self.floris = Floris.from_dict(floris_dict) + # Reset the fmodel object back to the turbine grid configuration + self.core = Core.from_dict(floris_dict) # Run the simulation again for futher postprocessing (i.e. now we can get farm power) self.run() @@ -793,77 +793,77 @@ def get_turbine_powers(self) -> NDArrayFloat: """ # Confirm calculate wake has been run - if self.floris.state is not State.USED: + if self.core.state is not State.USED: raise RuntimeError( - "Can't run function `FlorisInterface.get_turbine_powers` without " - "first running `FlorisInterface.run`." + "Can't run function `FlorisModel.get_turbine_powers` without " + "first running `FlorisModel.run`." ) # Check for negative velocities, which could indicate bad model # parameters or turbines very closely spaced. - if (self.floris.flow_field.u < 0.0).any(): + if (self.core.flow_field.u < 0.0).any(): self.logger.warning("Some velocities at the rotor are negative.") turbine_powers = power( - velocities=self.floris.flow_field.u, - air_density=self.floris.flow_field.air_density, - power_functions=self.floris.farm.turbine_power_functions, - yaw_angles=self.floris.farm.yaw_angles, - tilt_angles=self.floris.farm.tilt_angles, - power_setpoints=self.floris.farm.power_setpoints, - tilt_interps=self.floris.farm.turbine_tilt_interps, - turbine_type_map=self.floris.farm.turbine_type_map, - turbine_power_thrust_tables=self.floris.farm.turbine_power_thrust_tables, - correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt, - multidim_condition=self.floris.flow_field.multidim_conditions, + velocities=self.core.flow_field.u, + air_density=self.core.flow_field.air_density, + power_functions=self.core.farm.turbine_power_functions, + yaw_angles=self.core.farm.yaw_angles, + tilt_angles=self.core.farm.tilt_angles, + power_setpoints=self.core.farm.power_setpoints, + tilt_interps=self.core.farm.turbine_tilt_interps, + turbine_type_map=self.core.farm.turbine_type_map, + turbine_power_thrust_tables=self.core.farm.turbine_power_thrust_tables, + correct_cp_ct_for_tilt=self.core.farm.correct_cp_ct_for_tilt, + multidim_condition=self.core.flow_field.multidim_conditions, ) return turbine_powers def get_turbine_thrust_coefficients(self) -> NDArrayFloat: turbine_thrust_coefficients = thrust_coefficient( - velocities=self.floris.flow_field.u, - air_density=self.floris.flow_field.air_density, - yaw_angles=self.floris.farm.yaw_angles, - tilt_angles=self.floris.farm.tilt_angles, - power_setpoints=self.floris.farm.power_setpoints, - thrust_coefficient_functions=self.floris.farm.turbine_thrust_coefficient_functions, - tilt_interps=self.floris.farm.turbine_tilt_interps, - correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt, - turbine_type_map=self.floris.farm.turbine_type_map, - turbine_power_thrust_tables=self.floris.farm.turbine_power_thrust_tables, - average_method=self.floris.grid.average_method, - cubature_weights=self.floris.grid.cubature_weights, - multidim_condition=self.floris.flow_field.multidim_conditions, + velocities=self.core.flow_field.u, + air_density=self.core.flow_field.air_density, + yaw_angles=self.core.farm.yaw_angles, + tilt_angles=self.core.farm.tilt_angles, + power_setpoints=self.core.farm.power_setpoints, + thrust_coefficient_functions=self.core.farm.turbine_thrust_coefficient_functions, + tilt_interps=self.core.farm.turbine_tilt_interps, + correct_cp_ct_for_tilt=self.core.farm.correct_cp_ct_for_tilt, + turbine_type_map=self.core.farm.turbine_type_map, + turbine_power_thrust_tables=self.core.farm.turbine_power_thrust_tables, + average_method=self.core.grid.average_method, + cubature_weights=self.core.grid.cubature_weights, + multidim_condition=self.core.flow_field.multidim_conditions, ) return turbine_thrust_coefficients def get_turbine_ais(self) -> NDArrayFloat: turbine_ais = axial_induction( - velocities=self.floris.flow_field.u, - air_density=self.floris.flow_field.air_density, - yaw_angles=self.floris.farm.yaw_angles, - tilt_angles=self.floris.farm.tilt_angles, - power_setpoints=self.floris.farm.power_setpoints, - axial_induction_functions=self.floris.farm.turbine_axial_induction_functions, - tilt_interps=self.floris.farm.turbine_tilt_interps, - correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt, - turbine_type_map=self.floris.farm.turbine_type_map, - turbine_power_thrust_tables=self.floris.farm.turbine_power_thrust_tables, - average_method=self.floris.grid.average_method, - cubature_weights=self.floris.grid.cubature_weights, - multidim_condition=self.floris.flow_field.multidim_conditions, + velocities=self.core.flow_field.u, + air_density=self.core.flow_field.air_density, + yaw_angles=self.core.farm.yaw_angles, + tilt_angles=self.core.farm.tilt_angles, + power_setpoints=self.core.farm.power_setpoints, + axial_induction_functions=self.core.farm.turbine_axial_induction_functions, + tilt_interps=self.core.farm.turbine_tilt_interps, + correct_cp_ct_for_tilt=self.core.farm.correct_cp_ct_for_tilt, + turbine_type_map=self.core.farm.turbine_type_map, + turbine_power_thrust_tables=self.core.farm.turbine_power_thrust_tables, + average_method=self.core.grid.average_method, + cubature_weights=self.core.grid.cubature_weights, + multidim_condition=self.core.flow_field.multidim_conditions, ) return turbine_ais @property def turbine_average_velocities(self) -> NDArrayFloat: return average_velocity( - velocities=self.floris.flow_field.u, - method=self.floris.grid.average_method, - cubature_weights=self.floris.grid.cubature_weights, + velocities=self.core.flow_field.u, + method=self.core.grid.average_method, + cubature_weights=self.core.grid.cubature_weights, ) def get_turbine_TIs(self) -> NDArrayFloat: - return self.floris.flow_field.turbulence_intensity_field + return self.core.flow_field.turbulence_intensity_field def get_farm_power( self, @@ -902,29 +902,29 @@ def get_farm_power( # the model yet # TODO: Turbines need a switch for using turbulence correction # TODO: Uncomment out the following two lines once the above are resolved - # for turbine in self.floris.farm.turbines: + # for turbine in self.core.farm.turbines: # turbine.use_turbulence_correction = use_turbulence_correction # Confirm calculate wake has been run - if self.floris.state is not State.USED: + if self.core.state is not State.USED: raise RuntimeError( - "Can't run function `FlorisInterface.get_turbine_powers` without " - "first running `FlorisInterface.calculate_wake`." + "Can't run function `FlorisModel.get_turbine_powers` without " + "first running `FlorisModel.calculate_wake`." ) if turbine_weights is None: # Default to equal weighing of all turbines when turbine_weights is None turbine_weights = np.ones( ( - self.floris.flow_field.n_findex, - self.floris.farm.n_turbines, + self.core.flow_field.n_findex, + self.core.farm.n_turbines, ) ) elif len(np.shape(turbine_weights)) == 1: # Deal with situation when 1D array is provided turbine_weights = np.tile( turbine_weights, - (self.floris.flow_field.n_findex, 1), + (self.core.flow_field.n_findex, 1), ) # Calculate all turbine powers and apply weights @@ -986,7 +986,7 @@ def get_farm_AEP( """ # Verify dimensions of the variable "freq" - if np.shape(freq)[0] != self.floris.flow_field.n_findex: + if np.shape(freq)[0] != self.core.flow_field.n_findex: raise UserWarning( "'freq' should be a one-dimensional array with dimensions (n_findex). " f"Given shape is {np.shape(freq)}" @@ -1000,12 +1000,10 @@ def get_farm_AEP( # Copy the full wind speed array from the floris object and initialize # the the farm_power variable as an empty array. - wind_speeds = np.array(self.floris.flow_field.wind_speeds, copy=True) - wind_directions = np.array(self.floris.flow_field.wind_directions, copy=True) - turbulence_intensities = np.array( - self.floris.flow_field.turbulence_intensities, copy=True - ) - farm_power = np.zeros(self.floris.flow_field.n_findex) + wind_speeds = np.array(self.core.flow_field.wind_speeds, copy=True) + wind_directions = np.array(self.core.flow_field.wind_directions, copy=True) + turbulence_intensities = np.array(self.core.flow_field.turbulence_intensities, copy=True) + farm_power = np.zeros(self.core.flow_field.n_findex) # Determine which wind speeds we must evaluate conditions_to_evaluate = wind_speeds >= cut_in_wind_speed @@ -1092,7 +1090,7 @@ def get_farm_AEP_with_wind_data( """ # Verify the wind_data object matches FLORIS' initialization - if wind_data.n_findex != self.floris.flow_field.n_findex: + if wind_data.n_findex != self.core.flow_field.n_findex: raise ValueError("WindData object and floris do not have same findex") # Get freq directly from wind_data @@ -1124,7 +1122,7 @@ def sample_flow_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloa if not len(x) == len(y) == len(z): raise ValueError("x, y, and z must be the same size") - return self.floris.solve_for_points(x, y, z) + return self.core.solve_for_points(x, y, z) def sample_velocity_deficit_profiles( self, @@ -1175,7 +1173,7 @@ def sample_velocity_deficit_profiles( raise ValueError("`direction` must be either `cross-stream` or `vertical`.") if ref_rotor_diameter is None: - unique_rotor_diameters = np.unique(self.floris.farm.rotor_diameters) + unique_rotor_diameters = np.unique(self.core.farm.rotor_diameters) if len(unique_rotor_diameters) == 1: ref_rotor_diameter = unique_rotor_diameters[0] else: @@ -1192,9 +1190,9 @@ def sample_velocity_deficit_profiles( if profile_range is None: profile_range = ref_rotor_diameter * np.array([-2, 2]) - wind_directions_copy = np.array(self.floris.flow_field.wind_directions, copy=True) - wind_speeds_copy = np.array(self.floris.flow_field.wind_speeds, copy=True) - wind_shear_copy = self.floris.flow_field.wind_shear + wind_directions_copy = np.array(self.core.flow_field.wind_directions, copy=True) + wind_speeds_copy = np.array(self.core.flow_field.wind_speeds, copy=True) + wind_shear_copy = self.core.flow_field.wind_shear if wind_direction is None: if len(wind_directions_copy) == 1: @@ -1222,7 +1220,7 @@ def sample_velocity_deficit_profiles( ) if reference_height is None: - reference_height = self.floris.flow_field.reference_wind_height + reference_height = self.core.flow_field.reference_wind_height self.set( wind_directions=[wind_direction], @@ -1230,7 +1228,7 @@ def sample_velocity_deficit_profiles( wind_shear=0.0, ) - velocity_deficit_profiles = self.floris.solve_for_velocity_deficit_profiles( + velocity_deficit_profiles = self.core.solve_for_velocity_deficit_profiles( direction, downstream_dists, profile_range, @@ -1258,7 +1256,7 @@ def layout_x(self): Returns: np.array: Wind turbine x-coordinate. """ - return self.floris.farm.layout_x + return self.core.farm.layout_x @property def layout_y(self): @@ -1268,7 +1266,7 @@ def layout_y(self): Returns: np.array: Wind turbine y-coordinate. """ - return self.floris.farm.layout_y + return self.core.farm.layout_y def get_turbine_layout(self, z=False): """ @@ -1282,7 +1280,7 @@ def get_turbine_layout(self, z=False): np.array: lists of x, y, and (optionally) z coordinates of each turbine """ - xcoords, ycoords, zcoords = self.floris.farm.coordinates.T + xcoords, ycoords, zcoords = self.core.farm.coordinates.T if z: return xcoords, ycoords, zcoords else: diff --git a/floris/floris_model_utils.py b/floris/floris_model_utils.py new file mode 100644 index 000000000..4860a0a42 --- /dev/null +++ b/floris/floris_model_utils.py @@ -0,0 +1,133 @@ + +from __future__ import annotations + +import os +from math import ceil +from typing import ( + Any, + Dict, + List, + Optional, + Tuple, +) + +import numpy as np +import yaml +from attrs import define, field + +from floris import FlorisModel +from floris.type_dec import floris_array_converter, NDArrayFloat + + +def nested_get(dic: Dict[str, Any], + keys: List[str]) -> Any: + """Get a value from a nested dictionary using a list of keys. + Based on: stackoverflow.com/questions/14692690/access-nested-dictionary-items-via-a-list-of-keys + + Args: + dic (Dict[str, Any]): The dictionary to get the value from. + keys (List[str]): A list of keys to traverse the dictionary. + + Returns: + Any: The value at the end of the key traversal. + """ + for key in keys: + dic = dic[key] + return dic + + + +def nested_set(dic: Dict[str, Any], + keys: List[str], + value: Any, + idx: Optional[int] = None) -> None: + """Set a value in a nested dictionary using a list of keys. + Based on: stackoverflow.com/questions/14692690/access-nested-dictionary-items-via-a-list-of-keys + + Args: + dic (Dict[str, Any]): The dictionary to set the value in. + keys (List[str]): A list of keys to traverse the dictionary. + value (Any): The value to set. + idx (Optional[int], optional): If the value is an list, the index to change. + Defaults to None. + """ + dic_in = dic.copy() + + for key in keys[:-1]: + dic = dic.setdefault(key, {}) + if idx is None: + # Parameter is a scaler, set directly + dic[keys[-1]] = value + else: + # Parameter is a list, need to first get the list, change the values at idx + + # # Get the underlying list + par_list = nested_get(dic_in, keys) + par_list[idx] = value + dic[keys[-1]] = par_list + +def print_nested_dict(dictionary: Dict[str, Any], indent: int = 0) -> None: + """Print a nested dictionary with indentation. + + Args: + dictionary (Dict[str, Any]): The dictionary to print. + indent (int, optional): The number of spaces to indent. Defaults to 0. + """ + for key, value in dictionary.items(): + print(" " * indent + str(key)) + if isinstance(value, dict): + print_nested_dict(value, indent + 4) + else: + print(" " * (indent + 4) + str(value)) + +def print_fmodel_dict(fmodel: FlorisModel) -> None: + """Print the FlorisModel dictionary. + + Args: + fmodel (FlorisModel): The FlorisModel to print. + """ + print_nested_dict(fmodel.core.as_dict()) + +def set_fmodel_param( + fmodel_in: FlorisModel, + param: List[str], + value: Any, + param_idx: Optional[int] = None +): + """Set a parameter in a FlorisModel object. + + Args: + fi_in (FlorisModel): The FlorisModel object to modify. + param (List[str]): A list of keys to traverse the FlorisModel dictionary. + value (Any): The value to set. + idx (Optional[int], optional): The index to set the value at. Defaults to None. + + Returns: + FlorisModel: The modified FlorisModel object. + """ + fm_dict_mod = fmodel_in.core.as_dict() + nested_set(fm_dict_mod, param, value, param_idx) + return FlorisModel(fm_dict_mod) + +def get_fmodel_param( + fmodel_in: FlorisModel, + param: List[str], + param_idx: Optional[int] = None +) -> Any: + """Get a parameter from a FlorisModel object. + + Args: + fmodel_in (FlorisModel): The FlorisModel object to get the parameter from. + param (List[str]): A list of keys to traverse the FlorisModel dictionary. + param_idx (Optional[int], optional): The index to get the value at. Defaults to None. + If None, the entire parameter is returned. + + Returns: + Any: The value of the parameter. + """ + fm_dict = fmodel_in.core.as_dict() + + if param_idx is None: + return nested_get(fm_dict, param) + else: + return nested_get(fm_dict, param)[param_idx] diff --git a/floris/tools/flow_visualization.py b/floris/flow_visualization.py similarity index 93% rename from floris/tools/flow_visualization.py rename to floris/flow_visualization.py index 003c770c5..3afaf1a38 100644 --- a/floris/tools/flow_visualization.py +++ b/floris/flow_visualization.py @@ -15,10 +15,10 @@ from matplotlib import rcParams from scipy.spatial import ConvexHull -from floris.simulation import Floris -from floris.simulation.turbine.operation_models import POWER_SETPOINT_DEFAULT -from floris.tools.cut_plane import CutPlane -from floris.tools.floris_interface import FlorisInterface +from floris import FlorisModel +from floris.core import Core +from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT +from floris.cut_plane import CutPlane from floris.type_dec import ( floris_array_converter, NDArrayFloat, @@ -195,7 +195,7 @@ def visualize_cut_plane( def visualize_heterogeneous_cut_plane( cut_plane, - fi, + fmodel, ax=None, vel_component='u', min_speed=None, @@ -215,7 +215,7 @@ def visualize_heterogeneous_cut_plane( Args: cut_plane (:py:class:`~.tools.cut_plane.CutPlane`): 2D plane through wind plant. - fi (:py:class:`~.tools.floris_interface.FlorisInterface`): FlorisInterface object. + fmodel (:py:class:`~.floris_model.FlorisModel`): FlorisModel object. ax (:py:class:`matplotlib.pyplot.axes`): Figure axes. Defaults to None. vel_component (str, optional): The velocity component that the cut plane is @@ -297,8 +297,8 @@ def visualize_heterogeneous_cut_plane( points = np.array( list( zip( - fi.floris.flow_field.heterogenous_inflow_config['x'], - fi.floris.flow_field.heterogenous_inflow_config['y'], + fmodel.core.flow_field.heterogenous_inflow_config['x'], + fmodel.core.flow_field.heterogenous_inflow_config['y'], ) ) ) @@ -423,7 +423,7 @@ def plot_rotor_values( figure objects are returned for custom editing. Example: - from floris.tools.visualization import plot_rotor_values + from floris.visualization import plot_rotor_values plot_rotor_values(floris.flow_field.u, findex=0, n_rows=1, ncols=4) plot_rotor_values(floris.flow_field.v, findex=0, n_rows=1, ncols=4) plot_rotor_values(floris.flow_field.w, findex=0, n_rows=1, ncols=4, show=True) @@ -472,7 +472,7 @@ def plot_rotor_values( plt.show() def calculate_horizontal_plane_with_turbines( - fi_in, + fmodel_in, x_resolution=200, y_resolution=200, x_bounds=None, @@ -494,12 +494,12 @@ def calculate_horizontal_plane_with_turbines( and the flow field is reset to its initial state for every new location. Then, the local velocities are put into a DataFrame and then into a CutPlane. This method is much slower than - `FlorisInterface.calculate_horizontal_plane`, but it is helpful + `FlorisModel.calculate_horizontal_plane`, but it is helpful for models where the visualization capability is not yet available. Args: - fi_in (:py:class:`floris.tools.floris_interface.FlorisInterface`): - Preinitialized FlorisInterface object. + fmodel_in (:py:class:`floris.floris_model.FlorisModel`): + Preinitialized FlorisModel object. x_resolution (float, optional): Output array resolution. Defaults to 200 points. y_resolution (float, optional): Output array resolution. Defaults to 200 points. x_bounds (tuple, optional): Limits of output array (in m). Defaults to None. @@ -515,34 +515,34 @@ def calculate_horizontal_plane_with_turbines( :py:class:`~.tools.cut_plane.CutPlane`: containing values of x, y, u, v, w """ - # Make a local copy of fi to avoid editing passed in fi - fi = copy.deepcopy(fi_in) + # Make a local copy of fmodel to avoid editing passed in fmodel + fmodel = copy.deepcopy(fmodel_in) - # If wd/ws not provided, use what is set in fi + # If wd/ws not provided, use what is set in fmodel if wd is None: - wd = fi.floris.flow_field.wind_directions + wd = fmodel.core.flow_field.wind_directions if ws is None: - ws = fi.floris.flow_field.wind_speeds + ws = fmodel.core.flow_field.wind_speeds if ti is None: - ti = fi.floris.flow_field.turbulence_intensities - fi.check_wind_condition_for_viz(wd=wd, ws=ws, ti=ti) + ti = fmodel.core.flow_field.turbulence_intensities + fmodel.check_wind_condition_for_viz(wd=wd, ws=ws, ti=ti) # Set the ws and wd - fi.set( + fmodel.set( wind_directions=wd, wind_speeds=ws, yaw_angles=yaw_angles, power_setpoints=power_setpoints, disable_turbines=disable_turbines ) - yaw_angles = fi.floris.farm.yaw_angles - power_setpoints = fi.floris.farm.power_setpoints + yaw_angles = fmodel.core.farm.yaw_angles + power_setpoints = fmodel.core.farm.power_setpoints # Grab the turbine layout - layout_x = copy.deepcopy(fi.layout_x) - layout_y = copy.deepcopy(fi.layout_y) - turbine_types = copy.deepcopy(fi.floris.farm.turbine_type) - D = fi.floris.farm.rotor_diameters_sorted[0, 0] + layout_x = copy.deepcopy(fmodel.layout_x) + layout_y = copy.deepcopy(fmodel.layout_y) + turbine_types = copy.deepcopy(fmodel.core.farm.turbine_type) + D = fmodel.core.farm.rotor_diameters_sorted[0, 0] # Declare a new layout array with an extra turbine layout_x_test = np.append(layout_x,[0]) @@ -554,10 +554,10 @@ def calculate_horizontal_plane_with_turbines( turbine_types_test = [turbine_types[0] for i in range(len(layout_x))] + ['nrel_5MW'] else: turbine_types_test = np.append(turbine_types, 'nrel_5MW').tolist() - yaw_angles = np.append(yaw_angles, np.zeros([fi.floris.flow_field.n_findex, 1]), axis=1) + yaw_angles = np.append(yaw_angles, np.zeros([fmodel.core.flow_field.n_findex, 1]), axis=1) power_setpoints = np.append( power_setpoints, - POWER_SETPOINT_DEFAULT * np.ones([fi.floris.flow_field.n_findex, 1]), + POWER_SETPOINT_DEFAULT * np.ones([fmodel.core.flow_field.n_findex, 1]), axis=1 ) @@ -591,7 +591,7 @@ def calculate_horizontal_plane_with_turbines( # Place the test turbine at this location and calculate wake layout_x_test[-1] = x layout_y_test[-1] = y - fi.set( + fmodel.set( layout_x=layout_x_test, layout_y=layout_y_test, yaw_angles=yaw_angles, @@ -599,11 +599,11 @@ def calculate_horizontal_plane_with_turbines( disable_turbines=disable_turbines, turbine_type=turbine_types_test ) - fi.run() + fmodel.run() # Get the velocity of that test turbines central point - center_point = int(np.floor(fi.floris.flow_field.u[0,-1].shape[0] / 2.0)) - u_results[idx] = fi.floris.flow_field.u[0,-1,center_point,center_point] + center_point = int(np.floor(fmodel.core.flow_field.u[0,-1].shape[0] / 2.0)) + u_results[idx] = fmodel.core.flow_field.u[0,-1,center_point,center_point] # Increment index idx = idx + 1 diff --git a/floris/tools/layout_visualization.py b/floris/layout_visualization.py similarity index 87% rename from floris/tools/layout_visualization.py rename to floris/layout_visualization.py index 756fb35c9..c064059c6 100644 --- a/floris/tools/layout_visualization.py +++ b/floris/layout_visualization.py @@ -13,21 +13,21 @@ import pandas as pd from scipy.spatial.distance import pdist, squareform -from floris.tools import FlorisInterface +from floris import FlorisModel from floris.utilities import rotate_coordinates_rel_west, wind_delta def plot_turbine_points( - fi: FlorisInterface, + fmodel: FlorisModel, ax: plt.Axes = None, turbine_indices: List[int] = None, plotting_dict: Dict[str, Any] = {}, ) -> plt.Axes: """ - Plots turbine layout from a FlorisInterface object. + Plots turbine layout from a FlorisModel object. Args: - fi (FlorisInterface): The FlorisInterface object containing layout data. + fmodel (FlorisModel): The FlorisModel object containing layout data. ax (plt.Axes, optional): An existing axes object to plot on. If None, a new figure and axes will be created. Defaults to None. turbine_indices (List[int], optional): A list of turbine indices to plot. @@ -53,11 +53,11 @@ def plot_turbine_points( # If turbine_indices is not none, make sure all elements correspond to real indices if turbine_indices is not None: try: - fi.layout_x[turbine_indices] + fmodel.layout_x[turbine_indices] except IndexError: raise IndexError("turbine_indices does not correspond to turbine indices in fi") else: - turbine_indices = list(range(len(fi.layout_x))) + turbine_indices = list(range(len(fmodel.layout_x))) # Generate plotting dictionary default_plotting_dict = { @@ -70,8 +70,8 @@ def plot_turbine_points( # Plot ax.plot( - fi.layout_x[turbine_indices], - fi.layout_y[turbine_indices], + fmodel.layout_x[turbine_indices], + fmodel.layout_y[turbine_indices], linestyle="None", **plotting_dict, ) @@ -83,7 +83,7 @@ def plot_turbine_points( def plot_turbine_labels( - fi: FlorisInterface, + fmodel: FlorisModel, ax: plt.Axes = None, turbine_names: List[str] = None, turbine_indices: List[int] = None, @@ -96,7 +96,7 @@ def plot_turbine_labels( Adds turbine labels to a turbine layout plot. Args: - fi (FlorisInterface): The FlorisInterface object containing layout data. + fmodel (FlorisModel): The FlorisModel object containing layout data. ax (plt.Axes, optional): An existing axes object to plot on. If None, a new figure and axes will be created. Defaults to None. turbine_names (List[str], optional): Custom turbine labels. If None, @@ -132,26 +132,28 @@ def plot_turbine_labels( # If turbine names not none, confirm has correct number of turbines if turbine_names is not None: - if len(turbine_names) != len(fi.layout_x): - raise ValueError("Length of turbine_names not equal to number turbines in fi object") + if len(turbine_names) != len(fmodel.layout_x): + raise ValueError( + "Length of turbine_names not equal to number turbines in fmodel object" + ) else: # Assign simple default numbering - turbine_names = [f"{i:03d}" for i in range(len(fi.layout_x))] + turbine_names = [f"{i:03d}" for i in range(len(fmodel.layout_x))] # If label_offset is None, use default value of r/8 if label_offset is None: - rotor_diameters = fi.floris.farm.rotor_diameters.flatten() + rotor_diameters = fmodel.core.farm.rotor_diameters.flatten() r = rotor_diameters[0] / 2.0 label_offset = r / 8.0 # If turbine_indices is not none, make sure all elements correspond to real indices if turbine_indices is not None: try: - fi.layout_x[turbine_indices] + fmodel.layout_x[turbine_indices] except IndexError: raise IndexError("turbine_indices does not correspond to turbine indices in fi") else: - turbine_indices = list(range(len(fi.layout_x))) + turbine_indices = list(range(len(fmodel.layout_x))) # Generate plotting dictionary default_plotting_dict = { @@ -167,15 +169,15 @@ def plot_turbine_labels( for ti in turbine_indices: if not show_bbox: ax.text( - fi.layout_x[ti] + label_offset, - fi.layout_y[ti] + label_offset, + fmodel.layout_x[ti] + label_offset, + fmodel.layout_y[ti] + label_offset, turbine_names[ti], **plotting_dict, ) else: ax.text( - fi.layout_x[ti] + label_offset, - fi.layout_y[ti] + label_offset, + fmodel.layout_x[ti] + label_offset, + fmodel.layout_y[ti] + label_offset, turbine_names[ti], bbox=bbox_dict, **plotting_dict, @@ -188,7 +190,7 @@ def plot_turbine_labels( def plot_turbine_rotors( - fi: FlorisInterface, + fmodel: FlorisModel, ax: plt.Axes = None, color: str = "k", wd: float = None, @@ -198,15 +200,15 @@ def plot_turbine_rotors( Plots wind turbine rotors on an existing axes, visually representing their yaw angles. Args: - fi (FlorisInterface): The FlorisInterface object containing layout and turbine data. + fmodel (FlorisModel): The FlorisModel object containing layout and turbine data. ax (plt.Axes, optional): An existing axes object to plot on. If None, a new figure and axes will be created. Defaults to None. color (str, optional): Color of the turbine rotor lines. Defaults to 'k' (black). wd (float, optional): Wind direction (in degrees) relative to global reference. - If None, the first wind direction in `fi.floris.flow_field.wind_directions` is used. + If None, the first wind direction in `fmodel.core.flow_field.wind_directions` is used. Defaults to None. yaw_angles (np.ndarray, optional): Array of turbine yaw angles (in degrees). If None, - the values from `fi.floris.farm.yaw_angles` are used. Defaults to None. + the values from `fmodel.core.farm.yaw_angles` are used. Defaults to None. Returns: plt.Axes: The axes object used for the plot. @@ -214,9 +216,9 @@ def plot_turbine_rotors( if not ax: _, ax = plt.subplots() if yaw_angles is None: - yaw_angles = fi.floris.farm.yaw_angles + yaw_angles = fmodel.core.farm.yaw_angles if wd is None: - wd = fi.floris.flow_field.wind_directions[0] + wd = fmodel.core.flow_field.wind_directions[0] # Rotate yaw angles to inertial frame for plotting turbines relative to wind direction yaw_angles = yaw_angles - wind_delta(np.array(wd)) @@ -229,8 +231,8 @@ def plot_turbine_rotors( if yaw_angles.ndim == 2: yaw_angles = yaw_angles[0, :] - rotor_diameters = fi.floris.farm.rotor_diameters.flatten() - for x, y, yaw, d in zip(fi.layout_x, fi.layout_y, yaw_angles, rotor_diameters): + rotor_diameters = fmodel.core.farm.rotor_diameters.flatten() + for x, y, yaw, d in zip(fmodel.layout_x, fmodel.layout_y, yaw_angles, rotor_diameters): R = d / 2.0 x_0 = x + np.sin(np.deg2rad(yaw)) * R x_1 = x - np.sin(np.deg2rad(yaw)) * R @@ -359,7 +361,7 @@ def put_label(i: int) -> None: def plot_waking_directions( - fi: FlorisInterface, + fmodel: FlorisModel, ax: plt.Axes = None, turbine_indices: List[int] = None, wake_plotting_dict: Dict[str, Any] = {}, @@ -373,7 +375,7 @@ def plot_waking_directions( Plots lines representing potential waking directions between wind turbines in a layout. Args: - fi (FlorisInterface): Instantiated FlorisInterface object containing layout data. + fmodel (FlorisModel): Instantiated FlorisModel object containing layout data. ax (plt.Axes, optional): An existing axes object to plot on. If None, a new figure and axes will be created. Defaults to None. turbine_indices (List[int], optional): Indices of turbines to include in the plot. @@ -408,14 +410,14 @@ def plot_waking_directions( # If turbine_indices is not none, make sure all elements correspond to real indices if turbine_indices is not None: try: - fi.layout_x[turbine_indices] + fmodel.layout_x[turbine_indices] except IndexError: raise IndexError("turbine_indices does not correspond to turbine indices in fi") else: - turbine_indices = list(range(len(fi.layout_x))) + turbine_indices = list(range(len(fmodel.layout_x))) - layout_x = fi.layout_x[turbine_indices] - layout_y = fi.layout_y[turbine_indices] + layout_x = fmodel.layout_x[turbine_indices] + layout_y = fmodel.layout_y[turbine_indices] N_turbs = len(layout_x) # Combine default plotting options @@ -426,13 +428,13 @@ def plot_waking_directions( } wake_plotting_dict = {**def_wake_plotting_dict, **wake_plotting_dict} - # N_turbs = len(fi.floris.farm.turbine_definitions) + # N_turbs = len(fmodel.core.farm.turbine_definitions) if D is None: - D = fi.floris.farm.turbine_definitions[0]["rotor_diameter"] + D = fmodel.core.farm.turbine_definitions[0]["rotor_diameter"] # TODO: build out capability to use multiple diameters, if of interest. # D = np.array([turb['rotor_diameter'] for turb in - # fi.floris.farm.turbine_definitions]) + # fmodel.core.farm.turbine_definitions]) # else: # D = D*np.ones(N_turbs) @@ -468,7 +470,11 @@ def plot_waking_directions( # and i in layout_plotting_dict["turbine_indices"] # and j in layout_plotting_dict["turbine_indices"] ): - (h,) = ax.plot(fi.layout_x[[i, j]], fi.layout_y[[i, j]], **wake_plotting_dict) + (h,) = ax.plot( + fmodel.layout_x[[i, j]], + fmodel.layout_y[[i, j]], + **wake_plotting_dict + ) # Only label in one direction if ~label_exists[i, j]: @@ -495,20 +501,20 @@ def plot_waking_directions( return ax -def plot_farm_terrain(fi: FlorisInterface, ax: plt.Axes = None) -> None: +def plot_farm_terrain(fmodel: FlorisModel, ax: plt.Axes = None) -> None: """ Creates a filled contour plot visualizing terrain-corrected wind turbine hub heights. Args: - fi (FlorisInterface): The FlorisInterface object containing layout data. + fmodel (FlorisModel): The FlorisModel object containing layout data. ax (plt.Axes, optional): An existing axes object to plot on. If None, a new figure and axes will be created. Defaults to None. """ if not ax: _, ax = plt.subplots() - hub_heights = fi.floris.farm.hub_heights.flatten() - cntr = ax.tricontourf(fi.layout_x, fi.layout_y, hub_heights, levels=14, cmap="RdBu_r") + hub_heights = fmodel.core.farm.hub_heights.flatten() + cntr = ax.tricontourf(fmodel.layout_x, fmodel.layout_y, hub_heights, levels=14, cmap="RdBu_r") ax.get_figure().colorbar( cntr, diff --git a/floris/tools/optimization/__init__.py b/floris/optimization/__init__.py similarity index 100% rename from floris/tools/optimization/__init__.py rename to floris/optimization/__init__.py diff --git a/floris/tools/optimization/layout_optimization/__init__.py b/floris/optimization/layout_optimization/__init__.py similarity index 100% rename from floris/tools/optimization/layout_optimization/__init__.py rename to floris/optimization/layout_optimization/__init__.py diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_base.py b/floris/optimization/layout_optimization/layout_optimization_base.py similarity index 86% rename from floris/tools/optimization/layout_optimization/layout_optimization_base.py rename to floris/optimization/layout_optimization/layout_optimization_base.py index ba5a86751..c8e192d1a 100644 --- a/floris/tools/optimization/layout_optimization/layout_optimization_base.py +++ b/floris/optimization/layout_optimization/layout_optimization_base.py @@ -3,13 +3,13 @@ import numpy as np from shapely.geometry import LineString, Polygon -from floris.tools import TimeSeries -from floris.tools.optimization.yaw_optimization.yaw_optimizer_geometric import ( +from floris import TimeSeries +from floris.optimization.yaw_optimization.yaw_optimizer_geometric import ( YawOptimizationGeometric, ) -from floris.tools.wind_data import WindDataBase +from floris.wind_data import WindDataBase -from ....logging_manager import LoggingManager +from ...logging_manager import LoggingManager class LayoutOptimization(LoggingManager): @@ -18,7 +18,7 @@ class LayoutOptimization(LoggingManager): but should be subclassed by a specific optimization method. Args: - fi (FlorisInterface): A FlorisInterface object. + fmodel (FlorisModel): A FlorisModel object. boundaries (iterable(float, float)): Pairs of x- and y-coordinates that represent the boundary's vertices (m). wind_data (TimeSeries | WindRose): A TimeSeries or WindRose object @@ -29,8 +29,8 @@ class LayoutOptimization(LoggingManager): enable_geometric_yaw (bool, optional): If True, enables geometric yaw optimization. Defaults to False. """ - def __init__(self, fi, boundaries, wind_data, min_dist=None, enable_geometric_yaw=False): - self.fi = fi.copy() + def __init__(self, fmodel, boundaries, wind_data, min_dist=None, enable_geometric_yaw=False): + self.fmodel = fmodel.copy() self.boundaries = boundaries self.enable_geometric_yaw = enable_geometric_yaw @@ -59,12 +59,12 @@ def __init__(self, fi, boundaries, wind_data, min_dist=None, enable_geometric_ya # Establish geometric yaw class if self.enable_geometric_yaw: self.yaw_opt = YawOptimizationGeometric( - fi, + fmodel, minimum_yaw_angle=-30.0, maximum_yaw_angle=30.0, ) - self.initial_AEP = fi.get_farm_AEP_with_wind_data(self.wind_data) + self.initial_AEP = fmodel.get_farm_AEP_with_wind_data(self.wind_data) def __str__(self): return "layout" @@ -79,7 +79,7 @@ def _get_geoyaw_angles(self): # NOTE: requires that child class saves x and y locations # as self.x and self.y and updates them during optimization. if self.enable_geometric_yaw: - self.yaw_opt.fi_subset.set(layout_x=self.x, layout_y=self.y) + self.yaw_opt.fmodel_subset.set(layout_x=self.x, layout_y=self.y) df_opt = self.yaw_opt.optimize() self.yaw_angles = np.vstack(df_opt['yaw_angles_opt'])[:, :] else: @@ -137,9 +137,9 @@ def nturbs(self): Returns: nturbs (int): The number of turbines in the FLORIS object. """ - self._nturbs = self.fi.floris.farm.n_turbines + self._nturbs = self.fmodel.core.farm.n_turbines return self._nturbs @property def rotor_diameter(self): - return self.fi.floris.farm.rotor_diameters_sorted[0][0] + return self.fmodel.core.farm.rotor_diameters_sorted[0][0] diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_boundary_grid.py b/floris/optimization/layout_optimization/layout_optimization_boundary_grid.py similarity index 99% rename from floris/tools/optimization/layout_optimization/layout_optimization_boundary_grid.py rename to floris/optimization/layout_optimization/layout_optimization_boundary_grid.py index a17b3e220..c43310017 100644 --- a/floris/tools/optimization/layout_optimization/layout_optimization_boundary_grid.py +++ b/floris/optimization/layout_optimization/layout_optimization_boundary_grid.py @@ -14,7 +14,7 @@ class LayoutOptimizationBoundaryGrid(LayoutOptimization): def __init__( self, - fi, + fmodel, boundaries, start, x_spacing, @@ -27,7 +27,7 @@ def __init__( n_boundary_turbines=None, boundary_spacing=None, ): - self.fi = fi + self.fmodel = fmodel self.boundary_x = np.array([val[0] for val in boundaries]) self.boundary_y = np.array([val[1] for val in boundaries]) @@ -612,13 +612,13 @@ def reinitialize_xy(self): self.boundary_spacing, ) - self.fi.set(layout=(layout_x, layout_y)) + self.fmodel.set(layout=(layout_x, layout_y)) def plot_layout(self): plt.figure(figsize=(9, 6)) fontsize = 16 - plt.plot(self.fi.layout_x, self.fi.layout_y, "ob") + plt.plot(self.fmodel.layout_x, self.fmodel.layout_y, "ob") # plt.plot(locsx, locsy, "or") plt.xlabel("x (m)", fontsize=fontsize) diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse.py b/floris/optimization/layout_optimization/layout_optimization_pyoptsparse.py similarity index 93% rename from floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse.py rename to floris/optimization/layout_optimization/layout_optimization_pyoptsparse.py index f0b519254..9d26bc616 100644 --- a/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse.py +++ b/floris/optimization/layout_optimization/layout_optimization_pyoptsparse.py @@ -10,7 +10,7 @@ class LayoutOptimizationPyOptSparse(LayoutOptimization): def __init__( self, - fi, + fmodel, boundaries, wind_data, min_dist=None, @@ -21,11 +21,11 @@ def __init__( hotStart=None, enable_geometric_yaw=False, ): - super().__init__(fi, boundaries, wind_data=wind_data, min_dist=min_dist, + super().__init__(fmodel, boundaries, wind_data=wind_data, min_dist=min_dist, enable_geometric_yaw=enable_geometric_yaw) - self.x0 = self._norm(self.fi.layout_x, self.xmin, self.xmax) - self.y0 = self._norm(self.fi.layout_y, self.ymin, self.ymax) + self.x0 = self._norm(self.fmodel.layout_x, self.xmin, self.xmax) + self.y0 = self._norm(self.fmodel.layout_y, self.ymin, self.ymax) self.storeHistory = storeHistory self.timeLimit = timeLimit @@ -94,13 +94,13 @@ def _obj_func(self, varDict): # Compute turbine yaw angles using PJ's geometric code (if enabled) yaw_angles = self._get_geoyaw_angles() # Update turbine map with turbine locations and yaw angles - self.fi.set(layout_x=self.x, layout_y=self.y, yaw_angles=yaw_angles) + self.fmodel.set(layout_x=self.x, layout_y=self.y, yaw_angles=yaw_angles) # Compute the objective function funcs = {} funcs["obj"] = ( - -1 * self.fi.get_farm_AEP_with_wind_data(self.wind_data) + -1 * self.fmodel.get_farm_AEP_with_wind_data(self.wind_data) / self.initial_AEP ) diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py b/floris/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py similarity index 97% rename from floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py rename to floris/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py index 7b0ccbe03..aa8d9f54e 100644 --- a/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py +++ b/floris/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py @@ -10,7 +10,7 @@ class LayoutOptimizationPyOptSparse(LayoutOptimization): def __init__( self, - fi, + fmodel, boundaries, wind_data, min_dist=None, @@ -20,7 +20,7 @@ def __init__( storeHistory='hist.hist', hotStart=None ): - super().__init__(fi, boundaries, wind_data=wind_data, min_dist=min_dist) + super().__init__(fmodel, boundaries, wind_data=wind_data, min_dist=min_dist) self._reinitialize(solver=solver, optOptions=optOptions) self.storeHistory = storeHistory @@ -88,8 +88,8 @@ def _obj_func(self, varDict): self.parse_opt_vars(varDict) # Update turbine map with turbince locations - # self.fi.reinitialize(layout=[self.x, self.y]) - # self.fi.calculate_wake() + # self.fmodel.reinitialize(layout=[self.x, self.y]) + # self.fmodel.calculate_wake() # Compute the objective function funcs = {} diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_scipy.py b/floris/optimization/layout_optimization/layout_optimization_scipy.py similarity index 94% rename from floris/tools/optimization/layout_optimization/layout_optimization_scipy.py rename to floris/optimization/layout_optimization/layout_optimization_scipy.py index a2a8bef6f..23c866071 100644 --- a/floris/tools/optimization/layout_optimization/layout_optimization_scipy.py +++ b/floris/optimization/layout_optimization/layout_optimization_scipy.py @@ -11,7 +11,7 @@ class LayoutOptimizationScipy(LayoutOptimization): def __init__( self, - fi, + fmodel, boundaries, wind_data, bnds=None, @@ -24,7 +24,7 @@ def __init__( _summary_ Args: - fi (_type_): _description_ + fmodel (FlorisModel): A FlorisModel object. boundaries (iterable(float, float)): Pairs of x- and y-coordinates that represent the boundary's vertices (m). wind_data (TimeSeries | WindRose): A TimeSeries or WindRose object @@ -39,7 +39,7 @@ def __init__( optOptions (dict, optional): Dicitonary for setting the optimization options. Defaults to None. """ - super().__init__(fi, boundaries, min_dist=min_dist, wind_data=wind_data, + super().__init__(fmodel, boundaries, min_dist=min_dist, wind_data=wind_data, enable_geometric_yaw=enable_geometric_yaw) self.boundaries_norm = [ @@ -51,10 +51,10 @@ def __init__( ] self.x0 = [ self._norm(x, self.xmin, self.xmax) - for x in self.fi.layout_x + for x in self.fmodel.layout_x ] + [ self._norm(y, self.ymin, self.ymax) - for y in self.fi.layout_y + for y in self.fmodel.layout_y ] if bnds is not None: self.bnds = bnds @@ -97,9 +97,9 @@ def _obj_func(self, locs): self._change_coordinates(locs_unnorm) # Compute turbine yaw angles using PJ's geometric code (if enabled) yaw_angles = self._get_geoyaw_angles() - self.fi.set(yaw_angles=yaw_angles) + self.fmodel.set(yaw_angles=yaw_angles) - return (-1 * self.fi.get_farm_AEP_with_wind_data(self.wind_data) / + return (-1 * self.fmodel.get_farm_AEP_with_wind_data(self.wind_data) / self.initial_AEP) @@ -113,7 +113,7 @@ def _change_coordinates(self, locs): self.y = layout_y # Update the turbine map in floris - self.fi.set(layout_x=layout_x, layout_y=layout_y) + self.fmodel.set(layout_x=layout_x, layout_y=layout_y) def _generate_constraints(self): tmp1 = { diff --git a/floris/tools/optimization/other/__init__.py b/floris/optimization/other/__init__.py similarity index 100% rename from floris/tools/optimization/other/__init__.py rename to floris/optimization/other/__init__.py diff --git a/floris/tools/optimization/other/boundary_grid.py b/floris/optimization/other/boundary_grid.py similarity index 97% rename from floris/tools/optimization/other/boundary_grid.py rename to floris/optimization/other/boundary_grid.py index 38b9816e5..9d160b8a6 100644 --- a/floris/tools/optimization/other/boundary_grid.py +++ b/floris/optimization/other/boundary_grid.py @@ -243,13 +243,12 @@ class BoundaryGrid: def __init__(self, fi): """ Initializes a BoundaryGrid object by assigning a - FlorisInterface object. + FlorisModel object. Args: - fi (:py:class:`~.tools.floris_interface.FlorisInterface`): - Interface used to interact with the Floris object. + fmodel (FlorisModel): A FlorisModel object. """ - self.fi = fi + self.fmodel = fi self.n_boundary_turbs = 0 self.start = 0.0 @@ -332,7 +331,7 @@ def reinitialize_xy(self): eps=self.eps, ) - self.fi.reinitialize_flow_field(layout_array=(layout_x, layout_y)) + self.fmodel.reinitialize_flow_field(layout_array=(layout_x, layout_y)) if __name__ == "__main__": diff --git a/floris/tools/optimization/yaw_optimization/__init__.py b/floris/optimization/yaw_optimization/__init__.py similarity index 100% rename from floris/tools/optimization/yaw_optimization/__init__.py rename to floris/optimization/yaw_optimization/__init__.py diff --git a/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py b/floris/optimization/yaw_optimization/yaw_optimization_base.py similarity index 93% rename from floris/tools/optimization/yaw_optimization/yaw_optimization_base.py rename to floris/optimization/yaw_optimization/yaw_optimization_base.py index 5964c2ae1..5608f58f4 100644 --- a/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py +++ b/floris/optimization/yaw_optimization/yaw_optimization_base.py @@ -12,14 +12,14 @@ class YawOptimization(LoggingManager): """ - YawOptimization is a subclass of :py:class:`floris.tools.optimization.scipy. + YawOptimization is a subclass of :py:class:`floris.optimization.scipy. Optimization` that is used to optimize the yaw angles of all turbines in a Floris Farm for a single set of inflow conditions using the SciPy optimize package. """ def __init__( self, - fi, + fmodel, minimum_yaw_angle=0.0, maximum_yaw_angle=25.0, yaw_angles_baseline=None, @@ -31,12 +31,11 @@ def __init__( verify_convergence=False, ): """ - Instantiate YawOptimization object with a FlorisInterface object + Instantiate YawOptimization object with a FlorisModel object and assign parameter values. Args: - fi (:py:class:`~.tools.floris_interface.FlorisInterface`): - Interface used to interact with the Floris object. + fmodel (:py:class:`~.floris_model.FlorisModel`): A FlorisModel object. minimum_yaw_angle (float or ndarray): Minimum constraint on yaw angle (deg). If a single value specified, assumes this value for all turbines. If a 1D array is specified, assumes these @@ -100,11 +99,11 @@ def __init__( """ # Save turbine object to self - self.fi = fi.copy() - self.nturbs = len(self.fi.layout_x) + self.fmodel = fmodel.copy() + self.nturbs = len(self.fmodel.layout_x) # # Check floris options - # if self.fi.floris.flow_field.n_wind_speeds > 1: + # if self.fmodel.core.flow_field.n_wind_speeds > 1: # raise NotImplementedError( # "Optimizer currently does not support more than one wind" + # " speed. Please assign FLORIS a single wind speed." @@ -116,7 +115,7 @@ def __init__( yaw_angles_baseline = self._unpack_variable(yaw_angles_baseline) self.yaw_angles_baseline = yaw_angles_baseline else: - b = self.fi.floris.farm.yaw_angles + b = self.fmodel.core.farm.yaw_angles self.yaw_angles_baseline = self._unpack_variable(b) if np.any(np.abs(b) > 0.0): print( @@ -206,7 +205,7 @@ def _unpack_variable(self, variable, subset=False): # If one-dimensional array, copy over to all atmos. conditions variable = np.tile( variable, - (self.fi.floris.flow_field.n_findex, 1) + (self.fmodel.core.flow_field.n_findex, 1) ) @@ -225,8 +224,8 @@ def _reduce_control_problem(self): self.turbs_to_opt = (self.maximum_yaw_angle - self.minimum_yaw_angle >= 0.001) # Initialize subset variables as full set - self.fi_subset = self.fi.copy() - n_findex_subset = copy.deepcopy(self.fi.floris.flow_field.n_findex) + self.fmodel_subset = self.fmodel.copy() + n_findex_subset = copy.deepcopy(self.fmodel.core.flow_field.n_findex) minimum_yaw_angle_subset = copy.deepcopy(self.minimum_yaw_angle) maximum_yaw_angle_subset = copy.deepcopy(self.maximum_yaw_angle) x0_subset = copy.deepcopy(self.x0) @@ -237,9 +236,9 @@ def _reduce_control_problem(self): # Define which turbines to optimize for if self.exclude_downstream_turbines: - for iw, wd in enumerate(self.fi.floris.flow_field.wind_directions): + for iw, wd in enumerate(self.fmodel.core.flow_field.wind_directions): # Remove turbines from turbs_to_opt that are downstream - downstream_turbines = derive_downstream_turbines(self.fi, wd) + downstream_turbines = derive_downstream_turbines(self.fmodel, wd) downstream_turbines = np.array(downstream_turbines, dtype=int) self.turbs_to_opt[iw, downstream_turbines] = False turbs_to_opt_subset = copy.deepcopy(self.turbs_to_opt) # Update @@ -326,19 +325,19 @@ def _calculate_farm_power( farm_power (float): Weighted wind farm power. """ # Unpack all variables, whichever are defined. - fi_subset = copy.deepcopy(self.fi_subset) + fmodel_subset = copy.deepcopy(self.fmodel_subset) if wd_array is None: - wd_array = fi_subset.floris.flow_field.wind_directions + wd_array = fmodel_subset.core.flow_field.wind_directions if ws_array is None: - ws_array = fi_subset.floris.flow_field.wind_speeds + ws_array = fmodel_subset.core.flow_field.wind_speeds if ti_array is None: - ti_array = fi_subset.floris.flow_field.turbulence_intensities + ti_array = fmodel_subset.core.flow_field.turbulence_intensities if yaw_angles is None: yaw_angles = self._yaw_angles_baseline_subset if turbine_weights is None: turbine_weights = self._turbine_weights_subset if heterogeneous_speed_multipliers is not None: - fi_subset.floris.flow_field.\ + fmodel_subset.core.flow_field.\ heterogenous_inflow_config['speed_multipliers'] = heterogeneous_speed_multipliers # Ensure format [incompatible with _subset notation] @@ -349,14 +348,14 @@ def _calculate_farm_power( # Calculate solutions turbine_power = np.zeros_like(self._minimum_yaw_angle_subset[:, :]) - fi_subset.set( + fmodel_subset.set( wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=ti_array, yaw_angles=yaw_angles, ) - fi_subset.run() - turbine_power = fi_subset.get_turbine_powers() + fmodel_subset.run() + turbine_power = fmodel_subset.get_turbine_powers() # Multiply with turbine weighing terms turbine_power_weighted = np.multiply(turbine_weights, turbine_power) @@ -401,9 +400,9 @@ def _finalize(self, farm_power_opt_subset=None, yaw_angles_opt_subset=None): df_list.append( pd.DataFrame( { - "wind_direction": self.fi.floris.flow_field.wind_directions, - "wind_speed": self.fi.floris.flow_field.wind_speeds, - "turbulence_intensity": self.fi.floris.flow_field.turbulence_intensities, + "wind_direction": self.fmodel.core.flow_field.wind_directions, + "wind_speed": self.fmodel.core.flow_field.wind_speeds, + "turbulence_intensity": self.fmodel.core.flow_field.turbulence_intensities, "yaw_angles_opt": list(self.yaw_angles_opt[:, :]), "farm_power_opt": None if self.farm_power_opt is None @@ -493,11 +492,11 @@ def _verify_solutions_for_convergence( # we copy the atmospheric conditions n_turbs times and for each # copy of atmospheric conditions, we reset that turbine's yaw angle # to its baseline value for all conditions. - n_turbs = len(self.fi.layout_x) + n_turbs = len(self.fmodel.layout_x) sp = (n_turbs, 1) # Tile shape for matrix expansion - wd_array_nominal = self.fi_subset.floris.flow_field.wind_directions - ws_array_nominal = self.fi_subset.floris.flow_field.wind_speeds - ti_array_nominal = self.fi_subset.floris.flow_field.turbulence_intensities + wd_array_nominal = self.fmodel_subset.core.flow_field.wind_directions + ws_array_nominal = self.fmodel_subset.core.flow_field.wind_speeds + ti_array_nominal = self.fmodel_subset.core.flow_field.turbulence_intensities n_wind_directions = len(wd_array_nominal) yaw_angles_verify = np.tile(yaw_angles_opt_subset, sp) yaw_angles_bl_verify = np.tile(yaw_angles_baseline_subset, sp) @@ -565,7 +564,7 @@ def _verify_solutions_for_convergence( diff_uplift = dP_old - dP_new ids_max_loss = np.where(np.nanmax(diff_uplift) == diff_uplift) jj = (ids_max_loss[0][0], ids_max_loss[1][0]) - ws_array_nominal = self.fi_subset.floris.flow_field.wind_speeds + ws_array_nominal = self.fmodel_subset.core.flow_field.wind_speeds print( "Nullified the optimal yaw offset for {:d}".format(n) + " conditions and turbines." diff --git a/floris/tools/optimization/yaw_optimization/yaw_optimization_tools.py b/floris/optimization/yaw_optimization/yaw_optimization_tools.py similarity index 94% rename from floris/tools/optimization/yaw_optimization/yaw_optimization_tools.py rename to floris/optimization/yaw_optimization/yaw_optimization_tools.py index 7b13ece91..dedf8f057 100644 --- a/floris/tools/optimization/yaw_optimization/yaw_optimization_tools.py +++ b/floris/optimization/yaw_optimization/yaw_optimization_tools.py @@ -4,7 +4,7 @@ import pandas as pd -def derive_downstream_turbines(fi, wind_direction, wake_slope=0.30, plot_lines=False): +def derive_downstream_turbines(fmodel, wind_direction, wake_slope=0.30, plot_lines=False): """Determine which turbines have no effect on other turbines in the farm, i.e., which turbines have wakes that do not impact the other turbines in the farm. This allows the user to exclude these turbines @@ -23,7 +23,7 @@ def derive_downstream_turbines(fi, wind_direction, wake_slope=0.30, plot_lines=F time compared to FLORIS. Args: - fi ([floris object]): FLORIS object of the farm of interest. + fmodel (FlorisModel): A FlorisModel object. wind_direction (float): The wind direction in the FLORIS frame of reference for which the downstream turbines are to be determined. wake_slope (float, optional): linear slope of the wake (dy/dx) @@ -37,9 +37,9 @@ def derive_downstream_turbines(fi, wind_direction, wake_slope=0.30, plot_lines=F """ # Get farm layout - x = fi.layout_x - y = fi.layout_y - D = np.ones_like(x) * fi.floris.farm.rotor_diameters_sorted[0][0] + x = fmodel.layout_x + y = fmodel.layout_y + D = np.ones_like(x) * fmodel.core.farm.rotor_diameters_sorted[0][0] n_turbs = len(x) # Rotate farm and determine freestream/waked turbines diff --git a/floris/tools/optimization/yaw_optimization/yaw_optimizer_geometric.py b/floris/optimization/yaw_optimization/yaw_optimizer_geometric.py similarity index 96% rename from floris/tools/optimization/yaw_optimization/yaw_optimizer_geometric.py rename to floris/optimization/yaw_optimization/yaw_optimizer_geometric.py index 8607ee596..e78d48c9d 100644 --- a/floris/tools/optimization/yaw_optimization/yaw_optimizer_geometric.py +++ b/floris/optimization/yaw_optimization/yaw_optimizer_geometric.py @@ -9,24 +9,24 @@ class YawOptimizationGeometric(YawOptimization): """ YawOptimizationGeometric is a subclass of - :py:class:`floris.tools.optimization.general_library.YawOptimization` that is + :py:class:`floris.optimization.general_library.YawOptimization` that is used to provide a rough estimate of optimal yaw angles based purely on the wind farm geometry. Main use case is for coupled layout and yaw optimization. """ def __init__( self, - fi, + fmodel, minimum_yaw_angle=0.0, maximum_yaw_angle=25.0, ): """ - Instantiate YawOptimizationGeometric object with a FlorisInterface + Instantiate YawOptimizationGeometric object with a FlorisModel object assign parameter values. """ super().__init__( - fi=fi, + fmodel=fmodel, minimum_yaw_angle=minimum_yaw_angle, maximum_yaw_angle=maximum_yaw_angle, calc_baseline_power=False @@ -42,14 +42,14 @@ def optimize(self): array is equal in length to the number of turbines in the farm. """ # Loop through every WD individually. WS ignored! - wd_array = self.fi_subset.floris.flow_field.wind_directions + wd_array = self.fmodel_subset.core.flow_field.wind_directions for nwdi, wd in enumerate(wd_array): self._yaw_angles_opt_subset[nwdi, :] = geometric_yaw( - self.fi_subset.layout_x, - self.fi_subset.layout_y, + self.fmodel_subset.layout_x, + self.fmodel_subset.layout_y, wd, - self.fi.floris.farm.turbine_definitions[0]["rotor_diameter"], + self.fmodel.core.farm.turbine_definitions[0]["rotor_diameter"], top_left_yaw_upper=self.maximum_yaw_angle[0, 0], bottom_left_yaw_upper=self.maximum_yaw_angle[0, 0], top_left_yaw_lower=self.minimum_yaw_angle[0, 0], diff --git a/floris/tools/optimization/yaw_optimization/yaw_optimizer_scipy.py b/floris/optimization/yaw_optimization/yaw_optimizer_scipy.py similarity index 86% rename from floris/tools/optimization/yaw_optimization/yaw_optimizer_scipy.py rename to floris/optimization/yaw_optimization/yaw_optimizer_scipy.py index 735296b58..b62649117 100644 --- a/floris/tools/optimization/yaw_optimization/yaw_optimizer_scipy.py +++ b/floris/optimization/yaw_optimization/yaw_optimizer_scipy.py @@ -8,14 +8,14 @@ class YawOptimizationScipy(YawOptimization): """ YawOptimizationScipy is a subclass of - :py:class:`floris.tools.optimization.general_library.YawOptimization` that is + :py:class:`floris.optimization.general_library.YawOptimization` that is used to optimize the yaw angles of all turbines in a Floris Farm for a single set of inflow conditions using the SciPy optimize package. """ def __init__( self, - fi, + fmodel, minimum_yaw_angle=0.0, maximum_yaw_angle=25.0, yaw_angles_baseline=None, @@ -27,7 +27,7 @@ def __init__( verify_convergence=False, ): """ - Instantiate YawOptimizationScipy object with a FlorisInterface object + Instantiate YawOptimizationScipy object with a FlorisModel object and assign parameter values. """ if opt_options is None: @@ -41,7 +41,7 @@ def __init__( } super().__init__( - fi=fi, + fmodel=fmodel, minimum_yaw_angle=minimum_yaw_angle, maximum_yaw_angle=maximum_yaw_angle, yaw_angles_baseline=yaw_angles_baseline, @@ -68,12 +68,12 @@ def optimize(self): array is equal in length to the number of turbines in the farm. """ # Loop through every wind condition individually - wd_array = self.fi_subset.floris.flow_field.wind_directions - ws_array = self.fi_subset.floris.flow_field.wind_speeds - ti_array = self.fi_subset.floris.flow_field.turbulence_intensities + wd_array = self.fmodel_subset.core.flow_field.wind_directions + ws_array = self.fmodel_subset.core.flow_field.wind_speeds + ti_array = self.fmodel_subset.core.flow_field.turbulence_intensities for i, (wd, ws, ti) in enumerate(zip(wd_array, ws_array, ti_array)): - self.fi_subset.set( + self.fmodel_subset.set( wind_directions=[wd], wind_speeds=[ws], turbulence_intensities=[ti] @@ -98,10 +98,10 @@ def optimize(self): turbine_weights = np.tile(turbine_weights, (1, 1)) # Handle heterogeneous inflow, if there is one - if (hasattr(self.fi.floris.flow_field, 'heterogenous_inflow_config') and - self.fi.floris.flow_field.heterogenous_inflow_config is not None): + if (hasattr(self.fmodel.core.flow_field, 'heterogenous_inflow_config') and + self.fmodel.core.flow_field.heterogenous_inflow_config is not None): het_sm_orig = np.array( - self.fi.floris.flow_field.heterogenous_inflow_config['speed_multipliers'] + self.fmodel.core.flow_field.heterogenous_inflow_config['speed_multipliers'] ) het_sm = het_sm_orig[i, :].reshape(1, -1) else: diff --git a/floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py b/floris/optimization/yaw_optimization/yaw_optimizer_sr.py similarity index 92% rename from floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py rename to floris/optimization/yaw_optimization/yaw_optimizer_sr.py index 2175a6fe2..c6d76b04e 100644 --- a/floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py +++ b/floris/optimization/yaw_optimization/yaw_optimizer_sr.py @@ -15,7 +15,7 @@ class YawOptimizationSR(YawOptimization, LoggingManager): def __init__( self, - fi, + fmodel, minimum_yaw_angle=0.0, maximum_yaw_angle=25.0, yaw_angles_baseline=None, @@ -26,13 +26,13 @@ def __init__( verify_convergence=False, ): """ - Instantiate YawOptimizationSR object with a FlorisInterface object + Instantiate YawOptimizationSR object with a FlorisModel object and assign parameter values. """ # Initialize base class super().__init__( - fi=fi, + fmodel=fmodel, minimum_yaw_angle=minimum_yaw_angle, maximum_yaw_angle=maximum_yaw_angle, yaw_angles_baseline=yaw_angles_baseline, @@ -62,8 +62,8 @@ def __init__( # if reduce_ngrid: # for ti in range(self.nturbs): # # Force number of grid points to 2 - # self.fi.floris.farm.turbines[ti].ngrid = 2 - # self.fi.floris.farm.turbines[ti].initialize_turbine() + # self.fmodel.core.farm.turbines[ti].ngrid = 2 + # self.fmodel.core.farm.turbines[ti].initialize_turbine() # print("Reducing ngrid. Unsure if this functionality works!") # Save optimization choices to self @@ -73,10 +73,10 @@ def __init__( self._get_turbine_orders() def _get_turbine_orders(self): - layout_x = self.fi.layout_x - layout_y = self.fi.layout_y + layout_x = self.fmodel.layout_x + layout_y = self.fmodel.layout_y turbines_ordered_array = [] - for wd in self.fi_subset.floris.flow_field.wind_directions: + for wd in self.fmodel_subset.core.flow_field.wind_directions: layout_x_rot = ( np.cos((wd - 270.0) * np.pi / 180.0) * layout_x - np.sin((wd - 270.0) * np.pi / 180.0) * layout_y @@ -90,9 +90,9 @@ def _calc_powers_with_memory(self, yaw_angles_subset, use_memory=True): # Define current optimal solutions and floris wind directions locally yaw_angles_opt_subset = self._yaw_angles_opt_subset farm_power_opt_subset = self._farm_power_opt_subset - wd_array_subset = self.fi_subset.floris.flow_field.wind_directions - ws_array_subset = self.fi_subset.floris.flow_field.wind_speeds - ti_array_subset = self.fi_subset.floris.flow_field.turbulence_intensities + wd_array_subset = self.fmodel_subset.core.flow_field.wind_directions + ws_array_subset = self.fmodel_subset.core.flow_field.wind_speeds + ti_array_subset = self.fmodel_subset.core.flow_field.turbulence_intensities turbine_weights_subset = self._turbine_weights_subset # Reformat yaw_angles_subset, if necessary @@ -129,10 +129,10 @@ def _calc_powers_with_memory(self, yaw_angles_subset, use_memory=True): if not np.all(idx): # Now calculate farm powers for conditions we haven't yet evaluated previously start_time = timerpc() - if (hasattr(self.fi.floris.flow_field, 'heterogenous_inflow_config') and - self.fi.floris.flow_field.heterogenous_inflow_config is not None): + if (hasattr(self.fmodel.core.flow_field, 'heterogenous_inflow_config') and + self.fmodel.core.flow_field.heterogenous_inflow_config is not None): het_sm_orig = np.array( - self.fi.floris.flow_field.heterogenous_inflow_config['speed_multipliers'] + self.fmodel.core.flow_field.heterogenous_inflow_config['speed_multipliers'] ) het_sm = np.tile(het_sm_orig, (Ny, 1))[~idx, :] else: @@ -153,7 +153,7 @@ def _calc_powers_with_memory(self, yaw_angles_subset, use_memory=True): farm_powers, ( Ny, - self.fi_subset.floris.flow_field.n_findex + self.fmodel_subset.core.flow_field.n_findex ) ) diff --git a/floris/tools/parallel_computing_interface.py b/floris/parallel_computing_interface.py similarity index 82% rename from floris/tools/parallel_computing_interface.py rename to floris/parallel_computing_interface.py index 7260b0305..00e749adf 100644 --- a/floris/tools/parallel_computing_interface.py +++ b/floris/parallel_computing_interface.py @@ -7,36 +7,36 @@ import pandas as pd from floris.logging_manager import LoggingManager -from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR -from floris.tools.uncertainty_interface import FlorisInterface, UncertaintyInterface +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR +from floris.uncertainty_interface import FlorisModel, UncertaintyInterface def _load_local_floris_object( - fi_dict, + fmodel_dict, unc_pmfs=None, fix_yaw_in_relative_frame=False ): # Load local FLORIS object if unc_pmfs is None: - fi = FlorisInterface(fi_dict) + fmodel = FlorisModel(fmodel_dict) else: - fi = UncertaintyInterface( - fi_dict, + fmodel = UncertaintyInterface( + fmodel_dict, unc_pmfs=unc_pmfs, fix_yaw_in_relative_frame=fix_yaw_in_relative_frame, ) - return fi + return fmodel -def _get_turbine_powers_serial(fi_information, yaw_angles=None): - fi = _load_local_floris_object(*fi_information) - fi.set(yaw_angles=yaw_angles) - fi.run() - return (fi.get_turbine_powers(), fi.floris.flow_field) +def _get_turbine_powers_serial(fmodel_information, yaw_angles=None): + fmodel = _load_local_floris_object(*fmodel_information) + fmodel.set(yaw_angles=yaw_angles) + fmodel.run() + return (fmodel.get_turbine_powers(), fmodel.core.flow_field) def _optimize_yaw_angles_serial( - fi_information, + fmodel_information, minimum_yaw_angle, maximum_yaw_angle, yaw_angles_baseline, @@ -47,9 +47,9 @@ def _optimize_yaw_angles_serial( verify_convergence, print_progress, ): - fi_opt = _load_local_floris_object(*fi_information) + fmodel_opt = _load_local_floris_object(*fmodel_information) yaw_opt = YawOptimizationSR( - fi=fi_opt, + fmodel=fmodel_opt, minimum_yaw_angle=minimum_yaw_angle, maximum_yaw_angle=maximum_yaw_angle, yaw_angles_baseline=yaw_angles_baseline, @@ -68,7 +68,7 @@ def _optimize_yaw_angles_serial( class ParallelComputingInterface(LoggingManager): def __init__( self, - fi, + fmodel, max_workers, n_wind_condition_splits, interface="multiprocessing", # Options are 'multiprocessing', 'mpi4py' or 'concurrent' @@ -77,11 +77,11 @@ def __init__( print_timings=False ): """A wrapper around the nominal floris_interface class that adds - parallel computing to common FlorisInterface properties. + parallel computing to common FlorisModel properties. Args: - fi (FlorisInterface or UncertaintyInterface object): Interactive FLORIS object used to - perform the wake and turbine calculations. Can either be a regular FlorisInterface + fmodel (FlorisModel or UncertaintyInterface object): Interactive FLORIS object used to + perform the wake and turbine calculations. Can either be a regular FlorisModel object or can be an UncertaintyInterface object. max_workers (int): Number of parallel workers, typically equal to the number of cores you have on your system or HPC. @@ -128,15 +128,15 @@ def __init__( ) # Initialize floris object and copy common properties - self.fi = fi.copy() - self.floris = self.fi.floris # Static copy as a placeholder + self.fmodel = fmodel.copy() + self.core = self.fmodel.core # Static copy as a placeholder # Save to self self._n_wind_condition_splits = n_wind_condition_splits # Save initial user input self._max_workers = max_workers # Save initial user input self.n_wind_condition_splits = int( - np.min([n_wind_condition_splits, self.fi.floris.flow_field.n_findex]) + np.min([n_wind_condition_splits, self.fmodel.core.flow_field.n_findex]) ) self.max_workers = int( np.min([max_workers, self.n_wind_condition_splits]) @@ -148,7 +148,7 @@ def __init__( def copy(self): # Make an independent copy self_copy = copy.deepcopy(self) - self_copy.fi = self.fi.copy() + self_copy.fmodel = self.fmodel.copy() return self_copy def set( @@ -166,8 +166,8 @@ def set( turbine_type=None, solver_settings=None, ): - """Pass to the FlorisInterface set function. To allow users - to directly replace a FlorisInterface object with this + """Pass to the FlorisModel set function. To allow users + to directly replace a FlorisModel object with this UncertaintyInterface object, this function is required.""" if layout is not None: @@ -178,8 +178,8 @@ def set( layout_y = layout[1] # Just passes arguments to the floris object - fi = self.fi.copy() - fi.set( + fmodel = self.fmodel.copy() + fmodel.set( wind_speeds=wind_speeds, wind_directions=wind_directions, wind_shear=wind_shear, @@ -195,7 +195,7 @@ def set( # Reinitialize settings self.__init__( - fi=fi, + fmodel=fmodel, max_workers=self._max_workers, n_wind_condition_splits=self._n_wind_condition_splits, interface=self.interface, @@ -207,45 +207,45 @@ def _preprocessing(self, yaw_angles=None): # Format yaw angles if yaw_angles is None: yaw_angles = np.zeros(( - self.fi.floris.flow_field.n_findex, - self.fi.floris.farm.n_turbines + self.fmodel.core.flow_field.n_findex, + self.fmodel.core.farm.n_turbines )) # Prepare settings n_wind_condition_splits = self.n_wind_condition_splits n_wind_condition_splits = np.min( - [n_wind_condition_splits, self.fi.floris.flow_field.n_findex] + [n_wind_condition_splits, self.fmodel.core.flow_field.n_findex] ) # Prepare the input arguments for parallel execution - fi_dict = self.fi.floris.as_dict() + fmodel_dict = self.fmodel.core.as_dict() wind_condition_id_splits = np.array_split( - np.arange(self.fi.floris.flow_field.n_findex), + np.arange(self.fmodel.core.flow_field.n_findex), n_wind_condition_splits, ) multiargs = [] for wc_id_split in wind_condition_id_splits: # for ws_id_split in wind_speed_id_splits: - fi_dict_split = copy.deepcopy(fi_dict) - wind_directions = self.fi.floris.flow_field.wind_directions[wc_id_split] - wind_speeds = self.fi.floris.flow_field.wind_speeds[wc_id_split] - turbulence_intensities = self.fi.floris.flow_field.turbulence_intensities[wc_id_split] + fmodel_dict_split = copy.deepcopy(fmodel_dict) + wind_directions = self.fmodel.core.flow_field.wind_directions[wc_id_split] + wind_speeds = self.fmodel.core.flow_field.wind_speeds[wc_id_split] + turbulence_intensities = self.fmodel.core.flow_field.turbulence_intensities[wc_id_split] yaw_angles_subset = yaw_angles[wc_id_split[0]:wc_id_split[-1]+1, :] - fi_dict_split["flow_field"]["wind_directions"] = wind_directions - fi_dict_split["flow_field"]["wind_speeds"] = wind_speeds - fi_dict_split["flow_field"]["turbulence_intensities"] = turbulence_intensities + fmodel_dict_split["flow_field"]["wind_directions"] = wind_directions + fmodel_dict_split["flow_field"]["wind_speeds"] = wind_speeds + fmodel_dict_split["flow_field"]["turbulence_intensities"] = turbulence_intensities # Prepare lightweight data to pass along - if isinstance(self.fi, FlorisInterface): - fi_information = (fi_dict_split, None, None) + if isinstance(self.fmodel, FlorisModel): + fmodel_information = (fmodel_dict_split, None, None) else: - fi_information = ( - fi_dict_split, - self.fi.fi.het_map, - self.fi.unc_pmfs, - self.fi.fix_yaw_in_relative_frame + fmodel_information = ( + fmodel_dict_split, + self.fmodel.fmodel.het_map, + self.fmodel.unc_pmfs, + self.fmodel.fix_yaw_in_relative_frame ) - multiargs.append((fi_information, yaw_angles_subset)) + multiargs.append((fmodel_information, yaw_angles_subset)) return multiargs @@ -266,14 +266,14 @@ def _postprocessing(self, output): # Optionally, also merge flow field dictionaries from individual floris solutions if self.propagate_flowfield_from_workers: - self.floris = self.fi.floris # Refresh static copy of underlying floris class - # self.floris.flow_field.u_initial = self._merge_subsets("u_initial", flowfield_subsets) - # self.floris.flow_field.v_initial = self._merge_subsets("v_initial", flowfield_subsets) - # self.floris.flow_field.w_initial = self._merge_subsets("w_initial", flowfield_subsets) - self.floris.flow_field.u = self._merge_subsets("u", flowfield_subsets) - self.floris.flow_field.v = self._merge_subsets("v", flowfield_subsets) - self.floris.flow_field.w = self._merge_subsets("w", flowfield_subsets) - self.floris.flow_field.turbulence_intensity_field = self._merge_subsets( + self.core = self.fmodel.core # Refresh static copy of underlying floris class + # self.core.flow_field.u_initial = self._merge_subsets("u_initial", flowfield_subsets) + # self.core.flow_field.v_initial = self._merge_subsets("v_initial", flowfield_subsets) + # self.core.flow_field.w_initial = self._merge_subsets("w_initial", flowfield_subsets) + self.core.flow_field.u = self._merge_subsets("u", flowfield_subsets) + self.core.flow_field.v = self._merge_subsets("v", flowfield_subsets) + self.core.flow_field.w = self._merge_subsets("w", flowfield_subsets) + self.core.flow_field.turbulence_intensity_field = self._merge_subsets( "turbulence_intensity_field", flowfield_subsets ) @@ -329,8 +329,8 @@ def get_farm_power(self, yaw_angles=None, turbine_weights=None): # Default to equal weighing of all turbines when turbine_weights is None turbine_weights = np.ones( ( - self.fi.floris.flow_field.n_findex, - self.fi.floris.farm.n_turbines + self.fmodel.core.flow_field.n_findex, + self.fmodel.core.farm.n_turbines ) ) elif len(np.shape(turbine_weights)) == 1: @@ -338,7 +338,7 @@ def get_farm_power(self, yaw_angles=None, turbine_weights=None): turbine_weights = np.tile( turbine_weights, ( - self.fi.floris.flow_field.n_findex, + self.fmodel.core.flow_field.n_findex, 1 ) ) @@ -407,7 +407,7 @@ def get_farm_AEP( # If no_wake==True, ignore parallelization because it's fast enough if no_wake: - return self.fi.get_farm_AEP( + return self.fmodel.get_farm_AEP( freq=freq, cut_in_wind_speed=cut_in_wind_speed, cut_out_wind_speed=cut_out_wind_speed, @@ -418,8 +418,8 @@ def get_farm_AEP( # Verify dimensions of the variable "freq" if not ( - (np.shape(freq)[0] == self.fi.floris.flow_field.n_wind_directions) - & (np.shape(freq)[1] == self.fi.floris.flow_field.n_wind_speeds) + (np.shape(freq)[0] == self.fmodel.core.flow_field.n_wind_directions) + & (np.shape(freq)[1] == self.fmodel.core.flow_field.n_wind_speeds) & (len(np.shape(freq)) == 2) ): raise UserWarning( @@ -435,13 +435,13 @@ def get_farm_AEP( # Copy the full wind speed array from the floris object and initialize # the the farm_power variable as an empty array. - wind_speeds = np.array(self.fi.floris.flow_field.wind_speeds, copy=True) - wind_directions = np.array(self.fi.floris.flow_field.wind_directions, copy=True) + wind_speeds = np.array(self.fmodel.core.flow_field.wind_speeds, copy=True) + wind_directions = np.array(self.fmodel.core.flow_field.wind_directions, copy=True) turbulence_intensities = np.array( - self.fi.floris.flow_field.turbulence_intensities, + self.fmodel.core.flow_field.turbulence_intensities, copy=True, ) - farm_power = np.zeros((self.fi.floris.flow_field.n_wind_directions, len(wind_speeds))) + farm_power = np.zeros((self.fmodel.core.flow_field.n_wind_directions, len(wind_speeds))) # Determine which wind speeds we must evaluate in floris conditions_to_evaluate = wind_speeds >= cut_in_wind_speed @@ -456,7 +456,7 @@ def get_farm_AEP( yaw_angles_subset = None if yaw_angles is not None: yaw_angles_subset = yaw_angles[:, conditions_to_evaluate] - self.fi.set( + self.fmodel.set( wind_directions=wind_direction_subset, wind_speeds=wind_speeds_subset, turbulence_intensities=turbulence_intensities_subset, @@ -469,7 +469,7 @@ def get_farm_AEP( aep = np.sum(np.multiply(freq, farm_power) * 365 * 24) # Reset the FLORIS object to the full wind speed array - self.fi.set( + self.fmodel.set( wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=turbulence_intensities_subset, @@ -549,12 +549,12 @@ def optimize_yaw_angles( @property def layout_x(self): - return self.fi.layout_x + return self.fmodel.layout_x @property def layout_y(self): - return self.fi.layout_y + return self.fmodel.layout_y # @property # def floris(self): - # return self.fi.floris + # return self.fmodel.core diff --git a/floris/simulation/wake_combination/__init__.py b/floris/simulation/wake_combination/__init__.py deleted file mode 100644 index 9d8c70ea8..000000000 --- a/floris/simulation/wake_combination/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ - -from floris.simulation.wake_combination.fls import FLS -from floris.simulation.wake_combination.max import MAX -from floris.simulation.wake_combination.sosfs import SOSFS diff --git a/floris/simulation/wake_deflection/__init__.py b/floris/simulation/wake_deflection/__init__.py deleted file mode 100644 index 9c5937913..000000000 --- a/floris/simulation/wake_deflection/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ - -from floris.simulation.wake_deflection.empirical_gauss import EmpiricalGaussVelocityDeflection -from floris.simulation.wake_deflection.gauss import GaussVelocityDeflection -from floris.simulation.wake_deflection.jimenez import JimenezVelocityDeflection -from floris.simulation.wake_deflection.none import NoneVelocityDeflection diff --git a/floris/simulation/wake_turbulence/__init__.py b/floris/simulation/wake_turbulence/__init__.py deleted file mode 100644 index 51bee5f74..000000000 --- a/floris/simulation/wake_turbulence/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ - -from floris.simulation.wake_turbulence.crespo_hernandez import CrespoHernandez -from floris.simulation.wake_turbulence.none import NoneWakeTurbulence -from floris.simulation.wake_turbulence.wake_induced_mixing import WakeInducedMixing diff --git a/floris/simulation/wake_velocity/__init__.py b/floris/simulation/wake_velocity/__init__.py deleted file mode 100644 index f0d3b4c99..000000000 --- a/floris/simulation/wake_velocity/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ - -from floris.simulation.wake_velocity.cumulative_gauss_curl import CumulativeGaussCurlVelocityDeficit -from floris.simulation.wake_velocity.empirical_gauss import EmpiricalGaussVelocityDeficit -from floris.simulation.wake_velocity.gauss import GaussVelocityDeficit -from floris.simulation.wake_velocity.jensen import JensenVelocityDeficit -from floris.simulation.wake_velocity.none import NoneVelocityDeficit -from floris.simulation.wake_velocity.turbopark import TurbOParkVelocityDeficit diff --git a/floris/tools/__init__.py b/floris/tools/__init__.py deleted file mode 100644 index 94160d697..000000000 --- a/floris/tools/__init__.py +++ /dev/null @@ -1,48 +0,0 @@ - -""" -The :py:obj:`floris.tools` package contains the modules used to drive -FLORIS simulations and perform studies in various areas of research and -analysis. - -All modules can be imported with - - >>> import floris.tools - -The ``__init__.py`` file enables the import of all modules in this -package so any additional modules should be included there. - -Examples: - >>> import floris.tools - - >>> dir(floris.tools) - ['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', - '__name__', '__package__', '__path__', '__spec__', 'cut_plane', - 'floris_interface', - 'layout_visualization', 'optimization', 'plotting', 'power_rose', - 'visualization'] -""" - -from .floris_interface import FlorisInterface -from .flow_visualization import ( - plot_rotor_values, - visualize_cut_plane, - visualize_quiver, -) -from .parallel_computing_interface import ParallelComputingInterface -from .uncertainty_interface import UncertaintyInterface -from .wind_data import ( - TimeSeries, - WindRose, - WindTIRose, -) - - -# from floris.tools import ( -# cut_plane, -# floris_interface, -# layout_visualization, -# optimization, -# plotting, -# power_rose, -# visualization, -# ) diff --git a/floris/turbine_library/turbine_previewer.py b/floris/turbine_library/turbine_previewer.py index 2324b51e2..9050bc5f5 100644 --- a/floris/turbine_library/turbine_previewer.py +++ b/floris/turbine_library/turbine_previewer.py @@ -8,7 +8,8 @@ import numpy as np from attrs import define, field -from floris.simulation.turbine.turbine import ( +from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT +from floris.core.turbine.turbine import ( power, thrust_coefficient, Turbine, @@ -107,6 +108,7 @@ def power_curve( power_functions={self.turbine.turbine_type: self.turbine.power_function}, yaw_angles=np.zeros(shape), tilt_angles=np.full(shape, v["ref_tilt"]), + power_setpoints=np.full(shape, POWER_SETPOINT_DEFAULT), tilt_interps={self.turbine.turbine_type: self.turbine.tilt_interp}, turbine_type_map=np.full(shape, self.turbine.turbine_type), turbine_power_thrust_tables={self.turbine.turbine_type: v}, @@ -120,6 +122,7 @@ def power_curve( power_functions={self.turbine.turbine_type: self.turbine.power_function}, yaw_angles=np.zeros(shape), tilt_angles=np.full(shape, self.turbine.power_thrust_table["ref_tilt"]), + power_setpoints=np.full(shape, POWER_SETPOINT_DEFAULT), tilt_interps={self.turbine.turbine_type: self.turbine.tilt_interp}, turbine_type_map=np.full(shape, self.turbine.turbine_type), turbine_power_thrust_tables={ @@ -148,8 +151,10 @@ def thrust_coefficient_curve( ct_curve = { k: thrust_coefficient( velocities=wind_speeds.reshape(shape), + air_density=np.full(shape, v["ref_air_density"]), yaw_angles=np.zeros(shape), tilt_angles=np.full(shape, v["ref_tilt"]), + power_setpoints=np.full(shape, POWER_SETPOINT_DEFAULT), thrust_coefficient_functions={ self.turbine.turbine_type: self.turbine.thrust_coefficient_function }, @@ -163,8 +168,10 @@ def thrust_coefficient_curve( else: ct_curve = thrust_coefficient( velocities=wind_speeds.reshape(shape), + air_density=np.full(shape, self.turbine.power_thrust_table["ref_air_density"]), yaw_angles=np.zeros(shape), tilt_angles=np.full(shape, self.turbine.power_thrust_table["ref_tilt"]), + power_setpoints=np.full(shape, POWER_SETPOINT_DEFAULT), thrust_coefficient_functions={ self.turbine.turbine_type: self.turbine.thrust_coefficient_function }, diff --git a/floris/tools/uncertainty_interface.py b/floris/uncertainty_interface.py similarity index 92% rename from floris/tools/uncertainty_interface.py rename to floris/uncertainty_interface.py index f2be5c02c..9a2acab4d 100644 --- a/floris/tools/uncertainty_interface.py +++ b/floris/uncertainty_interface.py @@ -4,21 +4,22 @@ import numpy as np +from floris import FlorisModel from floris.logging_manager import LoggingManager -from floris.tools import FlorisInterface -from floris.tools.wind_data import WindDataBase from floris.type_dec import ( floris_array_converter, NDArrayBool, NDArrayFloat, ) +from floris.utilities import wrap_360 +from floris.wind_data import WindDataBase class UncertaintyInterface(LoggingManager): """ An interface for handling uncertainty in wind farm simulations. - This class contains a FlorisInterface object and adds functionality to handle + This class contains a FlorisModel object and adds functionality to handle uncertainty in wind direction. Args: @@ -28,7 +29,7 @@ class UncertaintyInterface(LoggingManager): - **farm**: See `floris.simulation.farm.Farm` for more details. - **turbine**: See `floris.simulation.turbine.Turbine` for more details. - **wake**: See `floris.simulation.wake.WakeManager` for more details. - - **logging**: See `floris.simulation.floris.Floris` for more details. + - **logging**: See `floris.core.Core` for more details. wd_resolution (float, optional): The resolution of wind direction, in degrees. Defaults to 1.0. ws_resolution (float, optional): The resolution of wind speed, in m/s. Defaults to 1.0. @@ -64,7 +65,7 @@ def __init__( - **farm**: See `floris.simulation.farm.Farm` for more details. - **turbine**: See `floris.simulation.turbine.Turbine` for more details. - **wake**: See `floris.simulation.wake.WakeManager` for more details. - - **logging**: See `floris.simulation.floris.Floris` for more details. + - **logging**: See `floris.simulation.core.Core` for more details. wd_resolution (float, optional): The resolution of wind direction for generating gaussian blends, in degrees. Defaults to 1.0. ws_resolution (float, optional): The resolution of wind speed, in m/s. Defaults to 1.0. @@ -98,14 +99,14 @@ def __init__( # Get the weights self.weights = self._get_weights(self.wd_std, self.wd_sample_points) - # Instantiate the un-expanded FlorisInterface - self.fi_unexpanded = FlorisInterface(configuration) + # Instantiate the un-expanded FlorisModel + self.fmodel_unexpanded = FlorisModel(configuration) # Call set at this point with no arguments so ready to run self.set() - # Instantiate the expanded FlorisInterface - # self.floris_interface = FlorisInterface(configuration) + # Instantiate the expanded FlorisModel + # self.core_interface = FlorisModel(configuration) def set( @@ -121,7 +122,7 @@ def set( **kwargs: The wind farm conditions to set. """ # Call the nominal set function - self.fi_unexpanded.set( + self.fmodel_unexpanded.set( **kwargs ) @@ -138,13 +139,13 @@ def _set_uncertain( # Grab the unexpanded values of all arrays # These original dimensions are what is returned - self.wind_directions_unexpanded = self.fi_unexpanded.floris.flow_field.wind_directions - self.wind_speeds_unexpanded = self.fi_unexpanded.floris.flow_field.wind_speeds + self.wind_directions_unexpanded = self.fmodel_unexpanded.core.flow_field.wind_directions + self.wind_speeds_unexpanded = self.fmodel_unexpanded.core.flow_field.wind_speeds self.turbulence_intensities_unexpanded = ( - self.fi_unexpanded.floris.flow_field.turbulence_intensities + self.fmodel_unexpanded.core.flow_field.turbulence_intensities ) - self.yaw_angles_unexpanded = self.fi_unexpanded.floris.farm.yaw_angles - self.power_setpoints_unexpanded = self.fi_unexpanded.floris.farm.power_setpoints + self.yaw_angles_unexpanded = self.fmodel_unexpanded.core.farm.yaw_angles + self.power_setpoints_unexpanded = self.fmodel_unexpanded.core.farm.power_setpoints self.n_unexpanded = len(self.wind_directions_unexpanded) # Combine into the complete unexpanded_inputs @@ -186,44 +187,44 @@ def _set_uncertain( print(f"Expanded num rows: {self.n_expanded}") print(f"Unique num rows: {self.n_unique}") - # Initiate the expanded FlorisInterface - self.fi_expanded = self.fi_unexpanded.copy() + # Initiate the expanded FlorisModel + self.fmodel_expanded = self.fmodel_unexpanded.copy() # Now set the underlying wd/ws/ti/yaw/setpoint to check only the unique conditions - self.fi_expanded.set( + self.fmodel_expanded.set( wind_directions=self.unique_inputs[:, 0], wind_speeds=self.unique_inputs[:, 1], turbulence_intensities=self.unique_inputs[:, 2], - yaw_angles=self.unique_inputs[:, 3 : 3 + self.fi_unexpanded.floris.farm.n_turbines], - power_setpoints=self.unique_inputs[:, 3 + self.fi_unexpanded.floris.farm.n_turbines:] + yaw_angles=self.unique_inputs[:, 3 : 3 + self.fmodel_unexpanded.core.farm.n_turbines], + power_setpoints=self.unique_inputs[:, 3 + self.fmodel_unexpanded.core.farm.n_turbines:] ) def run(self): """ - Run the simulation in the underlying FlorisInterface object. + Run the simulation in the underlying FlorisModel object. """ - self.fi_expanded.run() + self.fmodel_expanded.run() def run_no_wake(self): """ - Run the simulation in the underlying FlorisInterface object without wakes. + Run the simulation in the underlying FlorisModel object without wakes. """ - self.fi_expanded.run_no_wake() + self.fmodel_expanded.run_no_wake() def reset_operation(self): """ - Reset the operation of the underlying FlorisInterface object. + Reset the operation of the underlying FlorisModel object. """ - self.fi_unexpanded.set( + self.fmodel_unexpanded.set( wind_directions=self.wind_directions_unexpanded, wind_speeds=self.wind_speeds_unexpanded, turbulence_intensities=self.turbulence_intensities_unexpanded, ) - self.fi_unexpanded.reset_operation() + self.fmodel_unexpanded.reset_operation() - # Calling set_uncertain again to reset the expanded FlorisInterface + # Calling set_uncertain again to reset the expanded FlorisModel self._set_uncertain() def get_turbine_powers(self): @@ -238,7 +239,7 @@ def get_turbine_powers(self): """ # First call the underlying function - unique_turbine_powers = self.fi_expanded.get_turbine_powers() + unique_turbine_powers = self.fmodel_expanded.get_turbine_powers() # Expand back to the expanded value expanded_turbine_powers = unique_turbine_powers[self.map_to_expanded_inputs] @@ -249,7 +250,7 @@ def get_turbine_powers(self): # Reshape expanded_turbine_powers into blocks blocks = np.reshape( expanded_turbine_powers, - (self.n_unexpanded, self.n_sample_points, self.fi_unexpanded.floris.farm.n_turbines), + (self.n_unexpanded, self.n_sample_points, self.fmodel_unexpanded.core.farm.n_turbines), order="F", ) @@ -292,7 +293,7 @@ def get_farm_power( turbine_weights = np.ones( ( self.n_unexpanded, - self.fi_unexpanded.floris.farm.n_turbines, + self.fmodel_unexpanded.core.farm.n_turbines, ) ) elif len(np.shape(turbine_weights)) == 1: @@ -636,7 +637,7 @@ def layout_x(self): Returns: np.array: Wind turbine x-coordinate. """ - return self.floris_interface.floris.farm.layout_x + return self.core_interface.core.farm.layout_x @property def layout_y(self): @@ -646,4 +647,4 @@ def layout_y(self): Returns: np.array: Wind turbine y-coordinate. """ - return self.floris_interface.floris.farm.layout_y + return self.core_interface.core.farm.layout_y diff --git a/floris/tools/wind_data.py b/floris/wind_data.py similarity index 99% rename from floris/tools/wind_data.py rename to floris/wind_data.py index 8f2dd78df..ab202e670 100644 --- a/floris/tools/wind_data.py +++ b/floris/wind_data.py @@ -28,7 +28,7 @@ def unpack(self): def unpack_for_reinitialize(self): """ - Return only the variables need for FlorisInterface.reinitialize + Return only the variables need for FlorisModel.reinitialize """ ( wind_directions_unpack, diff --git a/profiling/profiling.py b/profiling/profiling.py index 272f75730..a4fcc769d 100644 --- a/profiling/profiling.py +++ b/profiling/profiling.py @@ -9,12 +9,12 @@ from conftest import SampleInputs -from floris.simulation import Floris +from floris.core import Core def run_floris(): - floris = Floris.from_file("examples/example_input.yaml") - return floris + core = Core.from_file("examples/example_input.yaml") + return core if __name__=="__main__": # if len(sys.argv) > 1: @@ -30,24 +30,25 @@ def run_floris(): sample_inputs = SampleInputs() - sample_inputs.floris["wake"]["model_strings"]["velocity_model"] = "gauss" - sample_inputs.floris["wake"]["model_strings"]["deflection_model"] = "gauss" - sample_inputs.floris["wake"]["enable_secondary_steering"] = True - sample_inputs.floris["wake"]["enable_yaw_added_recovery"] = True - sample_inputs.floris["wake"]["enable_transverse_velocities"] = True + sample_inputs.core["wake"]["model_strings"]["velocity_model"] = "gauss" + sample_inputs.core["wake"]["model_strings"]["deflection_model"] = "gauss" + sample_inputs.core["wake"]["enable_secondary_steering"] = True + sample_inputs.core["wake"]["enable_yaw_added_recovery"] = True + sample_inputs.core["wake"]["enable_transverse_velocities"] = True N_TURBINES = 100 N_FINDEX = 72 * 25 # Size of a characteristic wind rose - TURBINE_DIAMETER = sample_inputs.floris["farm"]["turbine_type"][0]["rotor_diameter"] - sample_inputs.floris["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * i for i in range(N_TURBINES)] - sample_inputs.floris["farm"]["layout_y"] = [0.0 for i in range(N_TURBINES)] + TURBINE_DIAMETER = sample_inputs.core["farm"]["turbine_type"][0]["rotor_diameter"] + sample_inputs.core["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * i for i in range(N_TURBINES)] + sample_inputs.core["farm"]["layout_y"] = [0.0 for i in range(N_TURBINES)] - sample_inputs.floris["flow_field"]["wind_directions"] = N_FINDEX * [270.0] - sample_inputs.floris["flow_field"]["wind_speeds"] = N_FINDEX * [8.0] + sample_inputs.core["flow_field"]["wind_directions"] = N_FINDEX * [270.0] + sample_inputs.core["flow_field"]["wind_speeds"] = N_FINDEX * [8.0] + sample_inputs.core["flow_field"]["turbulence_intensities"] = N_FINDEX * [0.06] N = 1 for i in range(N): - floris = Floris.from_dict(copy.deepcopy(sample_inputs.floris)) - floris.initialize_domain() - floris.steady_state_atmospheric_condition() + core = Core.from_dict(copy.deepcopy(sample_inputs.core)) + core.initialize_domain() + core.steady_state_atmospheric_condition() diff --git a/profiling/quality_metrics.py b/profiling/quality_metrics.py index 27d7c5aca..142480550 100644 --- a/profiling/quality_metrics.py +++ b/profiling/quality_metrics.py @@ -6,7 +6,7 @@ import numpy as np from linux_perf import perf -from floris.simulation import Floris +from floris.core import Core wd_grid, ws_grid = np.meshgrid( @@ -33,9 +33,9 @@ def run_floris(input_dict): try: start = time.perf_counter() - floris = Floris.from_dict(copy.deepcopy(input_dict.floris)) - floris.initialize_domain() - floris.steady_state_atmospheric_condition() + core = Core.from_dict(copy.deepcopy(input_dict.core)) + core.initialize_domain() + core.steady_state_atmospheric_condition() end = time.perf_counter() return end - start except KeyError: @@ -57,43 +57,43 @@ def time_profile(input_dict): def test_time_jensen_jimenez(sample_inputs_fixture): - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = "jensen" - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = "jimenez" + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = "jensen" + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = "jimenez" return time_profile(sample_inputs_fixture) def test_time_gauss(sample_inputs_fixture): - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = "gauss" - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = "gauss" + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = "gauss" + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = "gauss" return time_profile(sample_inputs_fixture) def test_time_gch(sample_inputs_fixture): - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = "gauss" - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = "gauss" - sample_inputs_fixture.floris["wake"]["enable_transverse_velocities"] = True - sample_inputs_fixture.floris["wake"]["enable_secondary_steering"] = True - sample_inputs_fixture.floris["wake"]["enable_yaw_added_recovery"] = True + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = "gauss" + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = "gauss" + sample_inputs_fixture.core["wake"]["enable_transverse_velocities"] = True + sample_inputs_fixture.core["wake"]["enable_secondary_steering"] = True + sample_inputs_fixture.core["wake"]["enable_yaw_added_recovery"] = True return time_profile(sample_inputs_fixture) def test_time_cumulative(sample_inputs_fixture): - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = "cc" - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = "gauss" + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = "cc" + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = "gauss" return time_profile(sample_inputs_fixture) def memory_profile(input_dict): # Run once to initialize Python and memory - floris = Floris.from_dict(copy.deepcopy(input_dict.floris)) - floris.initialize_domain() - floris.steady_state_atmospheric_condition() + core = Core.from_dict(copy.deepcopy(input_dict.core)) + core.initialize_domain() + core.steady_state_atmospheric_condition() with perf(): for i in range(N_ITERATIONS): - floris = Floris.from_dict(copy.deepcopy(input_dict.floris)) - floris.initialize_domain() - floris.steady_state_atmospheric_condition() + core = Core.from_dict(copy.deepcopy(input_dict.core)) + core.initialize_domain() + core.steady_state_atmospheric_condition() print( "Size of one data array: " @@ -102,8 +102,8 @@ def memory_profile(input_dict): def test_mem_jensen_jimenez(sample_inputs_fixture): - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = "jensen" - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = "jimenez" + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = "jensen" + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = "jimenez" memory_profile(sample_inputs_fixture) @@ -113,11 +113,11 @@ def test_mem_jensen_jimenez(sample_inputs_fixture): from conftest import SampleInputs sample_inputs = SampleInputs() - sample_inputs.floris["farm"]["layout_x"] = X_COORDS - sample_inputs.floris["farm"]["layout_y"] = Y_COORDS - sample_inputs.floris["flow_field"]["wind_directions"] = WIND_DIRECTIONS - sample_inputs.floris["flow_field"]["wind_speeds"] = WIND_SPEEDS - sample_inputs.floris["flow_field"]["turbulence_intensities"] = TURBULENCE_INTENSITIES + sample_inputs.core["farm"]["layout_x"] = X_COORDS + sample_inputs.core["farm"]["layout_y"] = Y_COORDS + sample_inputs.core["flow_field"]["wind_directions"] = WIND_DIRECTIONS + sample_inputs.core["flow_field"]["wind_speeds"] = WIND_SPEEDS + sample_inputs.core["flow_field"]["turbulence_intensities"] = TURBULENCE_INTENSITIES print() print("### Memory profiling") diff --git a/profiling/serial_vectorize.py b/profiling/serial_vectorize.py index 7c6c33207..fb66a1652 100644 --- a/profiling/serial_vectorize.py +++ b/profiling/serial_vectorize.py @@ -11,7 +11,7 @@ def time_vec(input_dict): start = time.time() - floris = Floris(input_dict=input_dict.floris) + floris = Floris(input_dict=input_dict.core) end = time.time() init_time = end - start @@ -29,11 +29,11 @@ def time_serial(input_dict, wd, ws): for i, (d, s) in enumerate(zip(wd, ws)): - input_dict.floris["flow_field"]["wind_directions"] = [d] - input_dict.floris["flow_field"]["wind_speeds"] = [s] + input_dict.core["flow_field"]["wind_directions"] = [d] + input_dict.core["flow_field"]["wind_speeds"] = [s] start = time.time() - floris = Floris(input_dict=input_dict.floris) + floris = Floris(input_dict=input_dict.core) end = time.time() init_times[i] = end - start @@ -48,9 +48,9 @@ def time_serial(input_dict, wd, ws): plt.figure() sample_inputs = SampleInputs() - sample_inputs.floris["flow_field"]["wind_directions"] = [270.0] - sample_inputs.floris["flow_field"]["wind_speeds"] = [8.0] - TURBINE_DIAMETER = sample_inputs.floris["turbine"]["rotor_diameter"] + sample_inputs.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs.core["flow_field"]["wind_speeds"] = [8.0] + TURBINE_DIAMETER = sample_inputs.core["turbine"]["rotor_diameter"] N = 5 simulation_size = np.arange(N) @@ -61,8 +61,8 @@ def time_serial(input_dict, wd, ws): vectorize_scaling_inputs = copy.deepcopy(sample_inputs) factor = (i+1) * 50 - vectorize_scaling_inputs.floris["flow_field"]["wind_directions"] = [270.0] - vectorize_scaling_inputs.floris["flow_field"]["wind_speeds"] = factor * [8.0] + vectorize_scaling_inputs.core["flow_field"]["wind_directions"] = [270.0] + vectorize_scaling_inputs.core["flow_field"]["wind_speeds"] = factor * [8.0] vectorize_init[i], vectorize_calc[i] = time_vec(copy.deepcopy(vectorize_scaling_inputs)) print("vectorize", i, vectorize_calc[i]) @@ -90,16 +90,16 @@ def time_serial(input_dict, wd, ws): # More than 1 turbine n_turbines = 10 - sample_inputs.floris["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * j for j in range(n_turbines)] - sample_inputs.floris["farm"]["layout_y"] = n_turbines * [0.0] + sample_inputs.core["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * j for j in range(n_turbines)] + sample_inputs.core["farm"]["layout_y"] = n_turbines * [0.0] vectorize_init, vectorize_calc = np.zeros(N), np.zeros(N) for i in range(N): vectorize_scaling_inputs = copy.deepcopy(sample_inputs) factor = (i+1) * 50 - vectorize_scaling_inputs.floris["flow_field"]["wind_speeds"] = factor * [8.0] - vectorize_scaling_inputs.floris["flow_field"]["wind_directions"] = [270.0] + vectorize_scaling_inputs.core["flow_field"]["wind_speeds"] = factor * [8.0] + vectorize_scaling_inputs.core["flow_field"]["wind_directions"] = [270.0] vectorize_init[i], vectorize_calc[i] = time_vec(copy.deepcopy(vectorize_scaling_inputs)) print("vectorize", i, vectorize_calc[i]) diff --git a/profiling/timing.py b/profiling/timing.py index 3083403da..b03cd23db 100644 --- a/profiling/timing.py +++ b/profiling/timing.py @@ -11,19 +11,19 @@ def time_profile(input_dict): - floris = Floris.from_dict(input_dict.floris) + floris = Floris.from_dict(input_dict.core) start = time.perf_counter() floris.steady_state_atmospheric_condition() end = time.perf_counter() return end - start def internal_probe(input_dict): - floris = Floris(input_dict=input_dict.floris) + floris = Floris(input_dict=input_dict.core) internal_quantity = floris.steady_state_atmospheric_condition() return internal_quantity def memory_profile(input_dict): - floris = Floris(input_dict=input_dict.floris) + floris = Floris(input_dict=input_dict.core) mem_usage = memory_profiler.memory_usage( (floris.steady_state_atmospheric_condition, (), {}), max_usage=True @@ -32,10 +32,10 @@ def memory_profile(input_dict): if __name__=="__main__": sample_inputs = SampleInputs() - TURBINE_DIAMETER = sample_inputs.floris["turbine"]["rotor_diameter"] + TURBINE_DIAMETER = sample_inputs.core["turbine"]["rotor_diameter"] # Use Gauss models - sample_inputs.floris["wake"]["model_strings"] = { + sample_inputs.core["wake"]["model_strings"] = { "velocity_model": "gauss", "deflection_model": "gauss", "combination_model": None, @@ -51,8 +51,8 @@ def memory_profile(input_dict): # wind_direction_scaling_inputs = copy.deepcopy(sample_inputs) # for i in range(N): # factor = (i+1) * 50 - # wind_direction_scaling_inputs.floris["flow_field"]["wind_directions"] = factor * [270.0] - # wind_direction_scaling_inputs.floris["flow_field"]["wind_speeds"] = [8.0] + # wind_direction_scaling_inputs.core["flow_field"]["wind_directions"] = factor * [270.0] + # wind_direction_scaling_inputs.core["flow_field"]["wind_speeds"] = [8.0] # wd_calc_time[i] = time_profile(copy.deepcopy(wind_direction_scaling_inputs)) # wd_size[i] = factor @@ -64,8 +64,8 @@ def memory_profile(input_dict): # wind_speed_scaling_inputs = copy.deepcopy(sample_inputs) # for i in range(N): # factor = (i+1) * 50 - # wind_speed_scaling_inputs.floris["flow_field"]["wind_directions"] = [270.0] - # wind_speed_scaling_inputs.floris["flow_field"]["wind_speeds"] = factor * [8.0] + # wind_speed_scaling_inputs.core["flow_field"]["wind_directions"] = [270.0] + # wind_speed_scaling_inputs.core["flow_field"]["wind_speeds"] = factor * [8.0] # ws_calc_time[i] = time_profile(copy.deepcopy(wind_speed_scaling_inputs)) # ws_size[i] = factor @@ -77,11 +77,11 @@ def memory_profile(input_dict): # turbine_scaling_inputs = copy.deepcopy(sample_inputs) # for i in range(N): # factor = (i+1) * 3 - # turbine_scaling_inputs.floris["farm"]["layout_x"] = [ + # turbine_scaling_inputs.core["farm"]["layout_x"] = [ # 5 * TURBINE_DIAMETER * j # for j in range(factor) # ] - # turbine_scaling_inputs.floris["farm"]["layout_y"] = factor * [0.0] + # turbine_scaling_inputs.core["farm"]["layout_y"] = factor * [0.0] # turb_calc_time[i] = time_profile(copy.deepcopy(turbine_scaling_inputs)) # turb_size[i] = factor @@ -92,14 +92,14 @@ def memory_profile(input_dict): # scaling_inputs = copy.deepcopy(sample_inputs) # for i in range(5): # factor = (i+1) * 2 - # scaling_inputs.floris["farm"]["layout_x"] = [ + # scaling_inputs.core["farm"]["layout_x"] = [ # 5 * TURBINE_DIAMETER * j # for j in range(factor) # ] - # scaling_inputs.floris["farm"]["layout_y"] = factor * [0.0] + # scaling_inputs.core["farm"]["layout_y"] = factor * [0.0] # factor = (i+1) * 20 - # scaling_inputs.floris["flow_field"]["wind_directions"] = factor * [270.0] - # scaling_inputs.floris["flow_field"]["wind_speeds"] = factor * [8.0] + # scaling_inputs.core["flow_field"]["wind_directions"] = factor * [270.0] + # scaling_inputs.core["flow_field"]["wind_speeds"] = factor * [8.0] # internal_quantity[i] = time_profile(scaling_inputs) # print("n turbine", i, internal_quantity[i]) @@ -118,7 +118,7 @@ def memory_profile(input_dict): n_wind_directions = 1 n_wind_speeds = 1 n_turbines = 3 - sample_inputs.floris["wake"]["model_strings"] = { + sample_inputs.core["wake"]["model_strings"] = { # "velocity_model": "jensen", # "deflection_model": "jimenez", "velocity_model": "cc", @@ -126,18 +126,18 @@ def memory_profile(input_dict): "combination_model": None, "turbulence_model": None, } - sample_inputs.floris["solver"] = { + sample_inputs.core["solver"] = { "type": "turbine_grid", "turbine_grid_points": 5 } - # sample_inputs.floris["wake"]["enable_transverse_velocities"] = False - # sample_inputs.floris["wake"]["enable_secondary_steering"] = False - # sample_inputs.floris["wake"]["enable_yaw_added_recovery"] = False - sample_inputs.floris["flow_field"]["wind_directions"] = n_wind_directions * [270.0] - sample_inputs.floris["flow_field"]["wind_speeds"] = n_wind_speeds * [8.0] - sample_inputs.floris["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * j for j in range(n_turbines)] - sample_inputs.floris["farm"]["layout_y"] = n_turbines * [0.0] + # sample_inputs.core["wake"]["enable_transverse_velocities"] = False + # sample_inputs.core["wake"]["enable_secondary_steering"] = False + # sample_inputs.core["wake"]["enable_yaw_added_recovery"] = False + sample_inputs.core["flow_field"]["wind_directions"] = n_wind_directions * [270.0] + sample_inputs.core["flow_field"]["wind_speeds"] = n_wind_speeds * [8.0] + sample_inputs.core["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * j for j in range(n_turbines)] + sample_inputs.core["farm"]["layout_y"] = n_turbines * [0.0] N = 1 times = np.zeros(N) @@ -158,8 +158,8 @@ def memory_profile(input_dict): # wind_direction_scaling_inputs = copy.deepcopy(sample_inputs) # for i in range(N): # factor = (i+1) * 50 - # wind_direction_scaling_inputs.floris["farm"]["wind_directions"] = factor * [270.0] - # wind_direction_scaling_inputs.floris["farm"]["wind_speeds"] = [8.0] + # wind_direction_scaling_inputs.core["farm"]["wind_directions"] = factor * [270.0] + # wind_direction_scaling_inputs.core["farm"]["wind_speeds"] = [8.0] # wd_space[i] = memory_profile(wind_direction_scaling_inputs) # print("wind direction", i, wd_space[i]) @@ -169,8 +169,8 @@ def memory_profile(input_dict): # wind_speed_scaling_inputs = copy.deepcopy(sample_inputs) # for i in range(N): # factor = (i+1) * 50 - # wind_speed_scaling_inputs.floris["farm"]["wind_directions"] = [270.0] - # wind_speed_scaling_inputs.floris["farm"]["wind_speeds"] = factor * [8.0] + # wind_speed_scaling_inputs.core["farm"]["wind_directions"] = [270.0] + # wind_speed_scaling_inputs.core["farm"]["wind_speeds"] = factor * [8.0] # ws_space[i] = memory_profile(wind_speed_scaling_inputs) # print("wind speed", i, ws_space[i]) @@ -180,11 +180,11 @@ def memory_profile(input_dict): # turbine_scaling_inputs = copy.deepcopy(sample_inputs) # for i in range(N): # factor = (i+1) * 50 - # turbine_scaling_inputs.floris["farm"]["layout_x"] = [ + # turbine_scaling_inputs.core["farm"]["layout_x"] = [ # 5 * TURBINE_DIAMETER * j # for j in range(factor) # ] - # turbine_scaling_inputs.floris["farm"]["layout_y"] = factor * [0.0] + # turbine_scaling_inputs.core["farm"]["layout_y"] = factor * [0.0] # turb_space[i] = memory_profile(turbine_scaling_inputs) # print("n turbine", turb_space[i]) diff --git a/pyproject.toml b/pyproject.toml index 5610ba9f3..330c5a2d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -116,18 +116,18 @@ dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" [tool.ruff.per-file-ignores] # F841 unused-variable: ignore since this file uses numexpr and many variables look unused -"floris/simulation/wake_deflection/jimenez.py" = ["F841"] -"floris/simulation/wake_turbulence/crespo_hernandez.py" = ["F841"] -"floris/simulation/wake_deflection/gauss.py" = ["F841"] -"floris/simulation/wake_velocity/jensen.py" = ["F841"] -"floris/simulation/wake_velocity/gauss.py" = ["F841"] -"floris/simulation/wake_velocity/empirical_gauss.py" = ["F841"] +"floris/core/wake_deflection/jimenez.py" = ["F841"] +"floris/core/wake_turbulence/crespo_hernandez.py" = ["F841"] +"floris/core/wake_deflection/gauss.py" = ["F841"] +"floris/core/wake_velocity/jensen.py" = ["F841"] +"floris/core/wake_velocity/gauss.py" = ["F841"] +"floris/core/wake_velocity/empirical_gauss.py" = ["F841"] # Ignore `F401` (import violations) in all `__init__.py` files, and in `path/to/file.py`. "__init__.py" = ["F401"] # I001 unsorted-imports: ignore because the import order is meaningful to navigate # import dependencies -"floris/simulation/__init__.py" = ["I001"] +"floris/core/__init__.py" = ["I001"] [tool.ruff.isort] combine-as-imports = true diff --git a/setup.py b/setup.py index a50eb738e..54da3219c 100644 --- a/setup.py +++ b/setup.py @@ -67,7 +67,7 @@ url=URL, packages=find_packages(exclude=["tests", "*.tests", "*.tests.*", "tests.*"]), package_data={ - 'floris': ['turbine_library/*.yaml', 'simulation/wake_velocity/turbopark_lookup_table.mat'] + 'floris': ['turbine_library/*.yaml', 'core/wake_velocity/turbopark_lookup_table.mat'] }, install_requires=REQUIRED, extras_require=EXTRAS, diff --git a/tests/base_unit_test.py b/tests/base_unit_test.py index 89a608041..fadae3523 100644 --- a/tests/base_unit_test.py +++ b/tests/base_unit_test.py @@ -3,7 +3,7 @@ from attr import define, field from attrs.exceptions import FrozenAttributeError -from floris.simulation import BaseClass, BaseModel +from floris.core import BaseClass, BaseModel @define diff --git a/tests/conftest.py b/tests/conftest.py index a8dd8fabb..70e1d2ca9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,8 +6,8 @@ import numpy as np import pytest -from floris.simulation import ( - Floris, +from floris.core import ( + Core, FlowField, FlowFieldGrid, PointsGrid, @@ -191,7 +191,7 @@ def points_grid_fixture(sample_inputs_fixture) -> PointsGrid: @pytest.fixture def floris_fixture(): sample_inputs = SampleInputs() - return Floris(sample_inputs.floris) + return Core(sample_inputs.core) @pytest.fixture def sample_inputs_fixture(): @@ -510,7 +510,7 @@ def __init__(self): "enable_transverse_velocities": False, } - self.floris = { + self.core = { "farm": self.farm, "flow_field": self.flow_field, "wake": self.wake, diff --git a/tests/floris_unit_test.py b/tests/core_unit_test.py similarity index 58% rename from tests/floris_unit_test.py rename to tests/core_unit_test.py index ef7d140e5..5e9108354 100644 --- a/tests/floris_unit_test.py +++ b/tests/core_unit_test.py @@ -3,9 +3,9 @@ import yaml -from floris.simulation import ( +from floris.core import ( + Core, Farm, - Floris, FlowField, TurbineGrid, WakeModelManager, @@ -18,29 +18,29 @@ def test_read_yaml(): - fi = Floris.from_file(YAML_INPUT) - assert isinstance(fi, Floris) + fmodel = Core.from_file(YAML_INPUT) + assert isinstance(fmodel, Core) def test_read_dict(): - fi = Floris.from_dict(DICT_INPUT) - assert isinstance(fi, Floris) + fmodel = Core.from_dict(DICT_INPUT) + assert isinstance(fmodel, Core) def test_init(): - fi = Floris.from_dict(DICT_INPUT) - assert isinstance(fi.farm, Farm) - assert isinstance(fi.wake, WakeModelManager) - assert isinstance(fi.flow_field, FlowField) + fmodel = Core.from_dict(DICT_INPUT) + assert isinstance(fmodel.farm, Farm) + assert isinstance(fmodel.wake, WakeModelManager) + assert isinstance(fmodel.flow_field, FlowField) def test_asdict(turbine_grid_fixture: TurbineGrid): - floris = Floris.from_dict(DICT_INPUT) + floris = Core.from_dict(DICT_INPUT) floris.flow_field.initialize_velocity_field(turbine_grid_fixture) dict1 = floris.as_dict() - new_floris = Floris.from_dict(dict1) + new_floris = Core.from_dict(dict1) new_floris.flow_field.initialize_velocity_field(turbine_grid_fixture) dict2 = new_floris.as_dict() diff --git a/tests/farm_unit_test.py b/tests/farm_unit_test.py index 767ba3c0b..38d2b91a7 100644 --- a/tests/farm_unit_test.py +++ b/tests/farm_unit_test.py @@ -5,7 +5,7 @@ import numpy as np import pytest -from floris.simulation import Farm +from floris.core import Farm from floris.utilities import load_yaml from tests.conftest import ( N_FINDEX, diff --git a/tests/floris_interface_integration_test.py b/tests/floris_model_integration_test.py similarity index 55% rename from tests/floris_interface_integration_test.py rename to tests/floris_model_integration_test.py index 0696bea3c..397cbef9d 100644 --- a/tests/floris_interface_integration_test.py +++ b/tests/floris_model_integration_test.py @@ -4,8 +4,8 @@ import pytest import yaml -from floris.simulation.turbine.operation_models import POWER_SETPOINT_DEFAULT -from floris.tools.floris_interface import FlorisInterface +from floris import FlorisModel +from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT TEST_DATA = Path(__file__).resolve().parent / "data" @@ -13,33 +13,33 @@ def test_read_yaml(): - fi = FlorisInterface(configuration=YAML_INPUT) - assert isinstance(fi, FlorisInterface) + fmodel = FlorisModel(configuration=YAML_INPUT) + assert isinstance(fmodel, FlorisModel) def test_assign_setpoints(): - fi = FlorisInterface(configuration=YAML_INPUT) - fi.set(layout_x=[0, 0], layout_y=[0, 1000]) + fmodel = FlorisModel(configuration=YAML_INPUT) + fmodel.set(layout_x=[0, 0], layout_y=[0, 1000]) # Test setting yaw angles via a list, integers, numpy array - fi.set(yaw_angles=[[20.0, 30.0]]) - fi.set(yaw_angles=[[20, 30]]) - fi.set(yaw_angles=np.array([[20.0, 30.0]])) + fmodel.set(yaw_angles=[[20.0, 30.0]]) + fmodel.set(yaw_angles=[[20, 30]]) + fmodel.set(yaw_angles=np.array([[20.0, 30.0]])) # Test setting power setpoints in various ways - fi.set(power_setpoints=[[1e6, 2e6]]) - fi.set(power_setpoints=np.array([[1e6, 2e6]])) + fmodel.set(power_setpoints=[[1e6, 2e6]]) + fmodel.set(power_setpoints=np.array([[1e6, 2e6]])) # Disable turbines - fi.set(disable_turbines=[[True, False]]) - fi.set(disable_turbines=np.array([[True, False]])) + fmodel.set(disable_turbines=[[True, False]]) + fmodel.set(disable_turbines=np.array([[True, False]])) # Combination - fi.set(yaw_angles=[[0, 30]], power_setpoints=np.array([[1e6, None]])) + fmodel.set(yaw_angles=[[0, 30]], power_setpoints=np.array([[1e6, None]])) # power_setpoints and disable_turbines (disable_turbines overrides power_setpoints) - fi.set(power_setpoints=[[1e6, 2e6]], disable_turbines=[[True, False]]) - assert np.allclose(fi.floris.farm.power_setpoints, np.array([[0.001, 2e6]])) + fmodel.set(power_setpoints=[[1e6, 2e6]], disable_turbines=[[True, False]]) + assert np.allclose(fmodel.core.farm.power_setpoints, np.array([[0.001, 2e6]])) def test_set_run(): """ @@ -51,129 +51,131 @@ def test_set_run(): # In FLORIS v3.2, running calculate_wake twice incorrectly set the yaw angles when the # first time has non-zero yaw settings but the second run had all-zero yaw settings. # The test below asserts that the yaw angles are correctly set in subsequent calls to run. - fi = FlorisInterface(configuration=YAML_INPUT) - yaw_angles = 20 * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(yaw_angles=yaw_angles) - fi.run() - assert fi.floris.farm.yaw_angles == yaw_angles + fmodel = FlorisModel(configuration=YAML_INPUT) + yaw_angles = 20 * np.ones((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(yaw_angles=yaw_angles) + fmodel.run() + assert fmodel.core.farm.yaw_angles == yaw_angles - yaw_angles = np.zeros((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(yaw_angles=yaw_angles) - fi.run() - assert fi.floris.farm.yaw_angles == yaw_angles + yaw_angles = np.zeros((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(yaw_angles=yaw_angles) + fmodel.run() + assert fmodel.core.farm.yaw_angles == yaw_angles # Verify making changes to the layout, wind speed, wind direction and # turbulence intensity both before and after running the calculation - fi.reset_operation() - fi.set( + fmodel.reset_operation() + fmodel.set( layout_x=[0, 0], layout_y=[0, 1000], wind_speeds=[8, 8], wind_directions=[270, 270], turbulence_intensities=[0.06, 0.06] ) - assert np.array_equal(fi.floris.farm.layout_x, np.array([0, 0])) - assert np.array_equal(fi.floris.farm.layout_y, np.array([0, 1000])) - assert np.array_equal(fi.floris.flow_field.wind_speeds, np.array([8, 8])) - assert np.array_equal(fi.floris.flow_field.wind_directions, np.array([270, 270])) + assert np.array_equal(fmodel.core.farm.layout_x, np.array([0, 0])) + assert np.array_equal(fmodel.core.farm.layout_y, np.array([0, 1000])) + assert np.array_equal(fmodel.core.flow_field.wind_speeds, np.array([8, 8])) + assert np.array_equal(fmodel.core.flow_field.wind_directions, np.array([270, 270])) # Double check that nothing has changed after running the calculation - fi.run() - assert np.array_equal(fi.floris.farm.layout_x, np.array([0, 0])) - assert np.array_equal(fi.floris.farm.layout_y, np.array([0, 1000])) - assert np.array_equal(fi.floris.flow_field.wind_speeds, np.array([8, 8])) - assert np.array_equal(fi.floris.flow_field.wind_directions, np.array([270, 270])) + fmodel.run() + assert np.array_equal(fmodel.core.farm.layout_x, np.array([0, 0])) + assert np.array_equal(fmodel.core.farm.layout_y, np.array([0, 1000])) + assert np.array_equal(fmodel.core.flow_field.wind_speeds, np.array([8, 8])) + assert np.array_equal(fmodel.core.flow_field.wind_directions, np.array([270, 270])) # Verify that changing wind shear doesn't change the other settings above - fi.set(wind_shear=0.1) - assert fi.floris.flow_field.wind_shear == 0.1 - assert np.array_equal(fi.floris.farm.layout_x, np.array([0, 0])) - assert np.array_equal(fi.floris.farm.layout_y, np.array([0, 1000])) - assert np.array_equal(fi.floris.flow_field.wind_speeds, np.array([8, 8])) - assert np.array_equal(fi.floris.flow_field.wind_directions, np.array([270, 270])) + fmodel.set(wind_shear=0.1) + assert fmodel.core.flow_field.wind_shear == 0.1 + assert np.array_equal(fmodel.core.farm.layout_x, np.array([0, 0])) + assert np.array_equal(fmodel.core.farm.layout_y, np.array([0, 1000])) + assert np.array_equal(fmodel.core.flow_field.wind_speeds, np.array([8, 8])) + assert np.array_equal(fmodel.core.flow_field.wind_directions, np.array([270, 270])) # Verify that operation set-points are retained after changing other settings - yaw_angles = 20 * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(yaw_angles=yaw_angles) - assert np.array_equal(fi.floris.farm.yaw_angles, yaw_angles) - fi.set() - assert np.array_equal(fi.floris.farm.yaw_angles, yaw_angles) - fi.set(wind_speeds=[10, 10]) - assert np.array_equal(fi.floris.farm.yaw_angles, yaw_angles) - power_setpoints = 1e6 * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(power_setpoints=power_setpoints) - assert np.array_equal(fi.floris.farm.yaw_angles, yaw_angles) - assert np.array_equal(fi.floris.farm.power_setpoints, power_setpoints) + yaw_angles = 20 * np.ones((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(yaw_angles=yaw_angles) + assert np.array_equal(fmodel.core.farm.yaw_angles, yaw_angles) + fmodel.set() + assert np.array_equal(fmodel.core.farm.yaw_angles, yaw_angles) + fmodel.set(wind_speeds=[10, 10]) + assert np.array_equal(fmodel.core.farm.yaw_angles, yaw_angles) + power_setpoints = 1e6 * np.ones((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(power_setpoints=power_setpoints) + assert np.array_equal(fmodel.core.farm.yaw_angles, yaw_angles) + assert np.array_equal(fmodel.core.farm.power_setpoints, power_setpoints) # Test that setting power setpoints through the .set() function actually sets the # power setpoints in the floris object - fi.reset_operation() - power_setpoints = 1e6 * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(power_setpoints=power_setpoints) - fi.run() - assert np.array_equal(fi.floris.farm.power_setpoints, power_setpoints) + fmodel.reset_operation() + power_setpoints = 1e6 * np.ones((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(power_setpoints=power_setpoints) + fmodel.run() + assert np.array_equal(fmodel.core.farm.power_setpoints, power_setpoints) # Similar to above, any "None" set-points should be set to the default value power_setpoints = np.array([[1e6, None]]) - fi.set(layout_x=[0, 0], layout_y=[0, 1000], power_setpoints=power_setpoints) - fi.run() + fmodel.set(layout_x=[0, 0], layout_y=[0, 1000], power_setpoints=power_setpoints) + fmodel.run() assert np.array_equal( - fi.floris.farm.power_setpoints, + fmodel.core.farm.power_setpoints, np.array([[power_setpoints[0, 0], POWER_SETPOINT_DEFAULT]]) ) def test_reset_operation(): # Calling the reset function should reset the power setpoints to the default values - fi = FlorisInterface(configuration=YAML_INPUT) - yaw_angles = 20 * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - power_setpoints = 1e6 * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(power_setpoints=power_setpoints, yaw_angles=yaw_angles) - fi.run() - fi.reset_operation() - assert fi.floris.farm.yaw_angles == np.zeros( - (fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines) + fmodel = FlorisModel(configuration=YAML_INPUT) + yaw_angles = 20 * np.ones((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + power_setpoints = 1e6 * np.ones((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(power_setpoints=power_setpoints, yaw_angles=yaw_angles) + fmodel.run() + fmodel.reset_operation() + assert fmodel.core.farm.yaw_angles == np.zeros( + (fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines) ) - assert fi.floris.farm.power_setpoints == ( - POWER_SETPOINT_DEFAULT * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) + assert fmodel.core.farm.power_setpoints == ( + POWER_SETPOINT_DEFAULT * np.ones((fmodel.core.flow_field.n_findex, + fmodel.core.farm.n_turbines)) ) # Double check that running the calculate also doesn't change the operating set points - fi.run() - assert fi.floris.farm.yaw_angles == np.zeros( - (fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines) + fmodel.run() + assert fmodel.core.farm.yaw_angles == np.zeros( + (fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines) ) - assert fi.floris.farm.power_setpoints == ( - POWER_SETPOINT_DEFAULT * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) + assert fmodel.core.farm.power_setpoints == ( + POWER_SETPOINT_DEFAULT * np.ones((fmodel.core.flow_field.n_findex, + fmodel.core.farm.n_turbines)) ) def test_run_no_wake(): # In FLORIS v3.2, running calculate_no_wake twice incorrectly set the yaw angles when the first # time has non-zero yaw settings but the second run had all-zero yaw settings. The test below # asserts that the yaw angles are correctly set in subsequent calls to run_no_wake. - fi = FlorisInterface(configuration=YAML_INPUT) - yaw_angles = 20 * np.ones((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(yaw_angles=yaw_angles) - fi.run_no_wake() - assert fi.floris.farm.yaw_angles == yaw_angles + fmodel = FlorisModel(configuration=YAML_INPUT) + yaw_angles = 20 * np.ones((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(yaw_angles=yaw_angles) + fmodel.run_no_wake() + assert fmodel.core.farm.yaw_angles == yaw_angles - yaw_angles = np.zeros((fi.floris.flow_field.n_findex, fi.floris.farm.n_turbines)) - fi.set(yaw_angles=yaw_angles) - fi.run_no_wake() - assert fi.floris.farm.yaw_angles == yaw_angles + yaw_angles = np.zeros((fmodel.core.flow_field.n_findex, fmodel.core.farm.n_turbines)) + fmodel.set(yaw_angles=yaw_angles) + fmodel.run_no_wake() + assert fmodel.core.farm.yaw_angles == yaw_angles # With no wake and three turbines in a line, the power for all turbines with zero yaw # should be the same - fi.reset_operation() - fi.set(layout_x=[0, 200, 4000], layout_y=[0, 0, 0]) - fi.run_no_wake() - power_no_wake = fi.get_turbine_powers() + fmodel.reset_operation() + fmodel.set(layout_x=[0, 200, 4000], layout_y=[0, 0, 0]) + fmodel.run_no_wake() + power_no_wake = fmodel.get_turbine_powers() assert len(np.unique(power_no_wake)) == 1 def test_get_turbine_powers(): # Get turbine powers should return n_findex x n_turbine powers # Apply the same wind speed and direction multiple times and confirm all equal - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) wind_speeds = np.array([8.0, 8.0, 8.0]) wind_directions = np.array([270.0, 270.0, 270.0]) @@ -184,7 +186,7 @@ def test_get_turbine_powers(): layout_y = np.array([0, 1000]) n_turbines = len(layout_x) - fi.set( + fmodel.set( wind_speeds=wind_speeds, wind_directions=wind_directions, turbulence_intensities=turbulence_intensities, @@ -192,16 +194,16 @@ def test_get_turbine_powers(): layout_y=layout_y, ) - fi.run() + fmodel.run() - turbine_powers = fi.get_turbine_powers() + turbine_powers = fmodel.get_turbine_powers() assert turbine_powers.shape[0] == n_findex assert turbine_powers.shape[1] == n_turbines assert turbine_powers[0, 0] == turbine_powers[1, 0] def test_get_farm_power(): - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) wind_speeds = np.array([8.0, 8.0, 8.0]) wind_directions = np.array([270.0, 270.0, 270.0]) @@ -212,7 +214,7 @@ def test_get_farm_power(): layout_y = np.array([0, 1000]) # n_turbines = len(layout_x) - fi.set( + fmodel.set( wind_speeds=wind_speeds, wind_directions=wind_directions, turbulence_intensities=turbulence_intensities, @@ -220,10 +222,10 @@ def test_get_farm_power(): layout_y=layout_y, ) - fi.run() + fmodel.run() - turbine_powers = fi.get_turbine_powers() - farm_powers = fi.get_farm_power() + turbine_powers = fmodel.get_turbine_powers() + farm_powers = fmodel.get_farm_power() assert farm_powers.shape[0] == n_findex @@ -234,7 +236,7 @@ def test_get_farm_power(): # Test using weights to disable the second turbine turbine_weights = np.array([1.0, 0.0]) - farm_powers = fi.get_farm_power(turbine_weights=turbine_weights) + farm_powers = fmodel.get_farm_power(turbine_weights=turbine_weights) # Assert farm power is now equal to the 0th turbine since 1st is # disabled @@ -245,28 +247,28 @@ def test_get_farm_power(): # findex values turbine_weights = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 0.0]]) - farm_powers = fi.get_farm_power(turbine_weights=turbine_weights) + farm_powers = fmodel.get_farm_power(turbine_weights=turbine_weights) turbine_powers[-1, 1] = 0 farm_power_from_turbine = turbine_powers.sum(axis=1) np.testing.assert_almost_equal(farm_power_from_turbine, farm_powers) def test_disable_turbines(): - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) # Set to mixed turbine model with open( str( - fi.floris.as_dict()["farm"]["turbine_library_path"] - / (fi.floris.as_dict()["farm"]["turbine_type"][0] + ".yaml") + fmodel.core.as_dict()["farm"]["turbine_library_path"] + / (fmodel.core.as_dict()["farm"]["turbine_type"][0] + ".yaml") ) ) as t: turbine_type = yaml.safe_load(t) turbine_type["power_thrust_model"] = "mixed" - fi.set(turbine_type=[turbine_type]) + fmodel.set(turbine_type=[turbine_type]) # Init to n-findex = 2, n_turbines = 3 - fi.set( + fmodel.set( wind_speeds=np.array([8.,8.,]), wind_directions=np.array([270.,270.]), turbulence_intensities=np.array([0.06,0.06]), @@ -276,71 +278,71 @@ def test_disable_turbines(): # Confirm that using a disable value with wrong n_findex raises error with pytest.raises(ValueError): - fi.set(disable_turbines=np.zeros((10, 3), dtype=bool)) - fi.run() + fmodel.set(disable_turbines=np.zeros((10, 3), dtype=bool)) + fmodel.run() # Confirm that using a disable value with wrong n_turbines raises error with pytest.raises(ValueError): - fi.set(disable_turbines=np.zeros((2, 10), dtype=bool)) - fi.run() + fmodel.set(disable_turbines=np.zeros((2, 10), dtype=bool)) + fmodel.run() # Confirm that if all turbines are disabled, power is near 0 for all turbines - fi.set(disable_turbines=np.ones((2, 3), dtype=bool)) - fi.run() - turbines_powers = fi.get_turbine_powers() + fmodel.set(disable_turbines=np.ones((2, 3), dtype=bool)) + fmodel.run() + turbines_powers = fmodel.get_turbine_powers() np.testing.assert_allclose(turbines_powers, 0, atol=0.1) # Confirm the same for run_no_wake - fi.run_no_wake() - turbines_powers = fi.get_turbine_powers() + fmodel.run_no_wake() + turbines_powers = fmodel.get_turbine_powers() np.testing.assert_allclose(turbines_powers, 0, atol=0.1) # Confirm that if all disabled values set to false, equivalent to running normally - fi.reset_operation() - fi.run() - turbines_powers_normal = fi.get_turbine_powers() - fi.set(disable_turbines=np.zeros((2, 3), dtype=bool)) - fi.run() - turbines_powers_false_disable = fi.get_turbine_powers() + fmodel.reset_operation() + fmodel.run() + turbines_powers_normal = fmodel.get_turbine_powers() + fmodel.set(disable_turbines=np.zeros((2, 3), dtype=bool)) + fmodel.run() + turbines_powers_false_disable = fmodel.get_turbine_powers() np.testing.assert_allclose(turbines_powers_normal,turbines_powers_false_disable,atol=0.1) # Confirm the same for run_no_wake - fi.run_no_wake() - turbines_powers_normal = fi.get_turbine_powers() - fi.set(disable_turbines=np.zeros((2, 3), dtype=bool)) - fi.run_no_wake() - turbines_powers_false_disable = fi.get_turbine_powers() + fmodel.run_no_wake() + turbines_powers_normal = fmodel.get_turbine_powers() + fmodel.set(disable_turbines=np.zeros((2, 3), dtype=bool)) + fmodel.run_no_wake() + turbines_powers_false_disable = fmodel.get_turbine_powers() np.testing.assert_allclose(turbines_powers_normal,turbines_powers_false_disable,atol=0.1) # Confirm the shutting off the middle turbine is like removing from the layout # In terms of impact on third turbine disable_turbines = np.zeros((2, 3), dtype=bool) disable_turbines[:,1] = [True, True] - fi.set(disable_turbines=disable_turbines) - fi.run() - power_with_middle_disabled = fi.get_turbine_powers() + fmodel.set(disable_turbines=disable_turbines) + fmodel.run() + power_with_middle_disabled = fmodel.get_turbine_powers() # Two turbine case to compare against above - fi_remove_middle = fi.copy() - fi_remove_middle.set(layout_x=[0,2000], layout_y=[0, 0]) - fi_remove_middle.run() - power_with_middle_removed = fi_remove_middle.get_turbine_powers() + fmodel_remove_middle = fmodel.copy() + fmodel_remove_middle.set(layout_x=[0,2000], layout_y=[0, 0]) + fmodel_remove_middle.run() + power_with_middle_removed = fmodel_remove_middle.get_turbine_powers() np.testing.assert_almost_equal(power_with_middle_disabled[0,2], power_with_middle_removed[0,1]) np.testing.assert_almost_equal(power_with_middle_disabled[1,2], power_with_middle_removed[1,1]) # Check that yaw angles are correctly set when turbines are disabled - fi.set( + fmodel.set( layout_x=[0, 1000, 2000], layout_y=[0, 0, 0], disable_turbines=disable_turbines, yaw_angles=np.ones((2, 3)) ) - fi.run() - assert (fi.floris.farm.yaw_angles == np.array([[1.0, 0.0, 1.0], [1.0, 0.0, 1.0]])).all() + fmodel.run() + assert (fmodel.core.farm.yaw_angles == np.array([[1.0, 0.0, 1.0], [1.0, 0.0, 1.0]])).all() def test_get_farm_aep(): - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) wind_speeds = np.array([8.0, 8.0, 8.0]) wind_directions = np.array([270.0, 270.0, 270.0]) @@ -351,7 +353,7 @@ def test_get_farm_aep(): layout_y = np.array([0, 1000]) # n_turbines = len(layout_x) - fi.set( + fmodel.set( wind_speeds=wind_speeds, wind_directions=wind_directions, turbulence_intensities=turbulence_intensities, @@ -359,15 +361,15 @@ def test_get_farm_aep(): layout_y=layout_y, ) - fi.run() + fmodel.run() - farm_powers = fi.get_farm_power() + farm_powers = fmodel.get_farm_power() # Start with uniform frequency freq = np.ones(n_findex) freq = freq / np.sum(freq) - farm_aep = fi.get_farm_AEP(freq=freq) + farm_aep = fmodel.get_farm_AEP(freq=freq) aep = np.sum(np.multiply(freq, farm_powers) * 365 * 24) @@ -375,7 +377,7 @@ def test_get_farm_aep(): np.testing.assert_allclose(farm_aep, aep) def test_get_farm_aep_with_conditions(): - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) wind_speeds = np.array([5.0, 8.0, 8.0, 8.0, 20.0]) wind_directions = np.array([270.0, 270.0, 270.0, 270.0, 270.0]) @@ -386,7 +388,7 @@ def test_get_farm_aep_with_conditions(): layout_y = np.array([0, 1000]) # n_turbines = len(layout_x) - fi.set( + fmodel.set( wind_speeds=wind_speeds, wind_directions=wind_directions, turbulence_intensities=turbulence_intensities, @@ -394,9 +396,9 @@ def test_get_farm_aep_with_conditions(): layout_y=layout_y, ) - fi.run() + fmodel.run() - farm_powers = fi.get_farm_power() + farm_powers = fmodel.get_farm_power() # Start with uniform frequency freq = np.ones(n_findex) @@ -404,7 +406,7 @@ def test_get_farm_aep_with_conditions(): # Get farm AEP with conditions on minimun and max wind speed # which exclude the first and last findex - farm_aep = fi.get_farm_AEP(freq=freq, cut_in_wind_speed=6.0, cut_out_wind_speed=15.0) + farm_aep = fmodel.get_farm_AEP(freq=freq, cut_in_wind_speed=6.0, cut_out_wind_speed=15.0) # In this case the aep should be computed assuming 0 power # for the 0th and last findex @@ -416,63 +418,62 @@ def test_get_farm_aep_with_conditions(): np.testing.assert_allclose(farm_aep, aep) #Confirm n_findex reset after the operation - assert n_findex == fi.floris.flow_field.n_findex + assert n_findex == fmodel.core.flow_field.n_findex def test_set_ti(): - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) # Set wind directions, wind speeds and turbulence intensities with n_findex = 3 - fi.set( + fmodel.set( wind_speeds=[8.0, 8.0, 8.0], wind_directions=[240.0, 250.0, 260.0], turbulence_intensities=[0.1, 0.1, 0.1], ) # Confirm can change turbulence intensities if not changing the length of the array - fi.set(turbulence_intensities=[0.12, 0.12, 0.12]) + fmodel.set(turbulence_intensities=[0.12, 0.12, 0.12]) # Confirm that changes to wind speeds and directions without changing turbulence intensities # raises an error with pytest.raises(ValueError): - fi.set( + fmodel.set( wind_speeds=[8.0, 8.0, 8.0, 8.0], wind_directions=[240.0, 250.0, 260.0, 270.0], ) - # Changing the length of TI alone is not allowed with pytest.raises(ValueError): - fi.set(turbulence_intensities=[0.12]) + fmodel.set(turbulence_intensities=[0.12]) # Test that applying a float however raises an error with pytest.raises(TypeError): - fi.set(turbulence_intensities=0.12) + fmodel.set(turbulence_intensities=0.12) def test_calculate_planes(): - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) # The calculate_plane functions should run directly with the inputs as given - fi.calculate_horizontal_plane(90.0) - fi.calculate_y_plane(0.0) - fi.calculate_cross_plane(500.0) + fmodel.calculate_horizontal_plane(90.0) + fmodel.calculate_y_plane(0.0) + fmodel.calculate_cross_plane(500.0) # They should also support setting new wind conditions, but they all have to set at once wind_speeds = [8.0, 8.0, 8.0] wind_directions = [270.0, 270.0, 270.0] turbulence_intensities = [0.1, 0.1, 0.1] - fi.calculate_horizontal_plane( + fmodel.calculate_horizontal_plane( 90.0, ws=[wind_speeds[0]], wd=[wind_directions[0]], ti=[turbulence_intensities[0]] ) - fi.calculate_y_plane( + fmodel.calculate_y_plane( 0.0, ws=[wind_speeds[0]], wd=[wind_directions[0]], ti=[turbulence_intensities[0]] ) - fi.calculate_cross_plane( + fmodel.calculate_cross_plane( 500.0, ws=[wind_speeds[0]], wd=[wind_directions[0]], @@ -481,14 +482,14 @@ def test_calculate_planes(): # If Floris is configured with multiple wind conditions prior to this, then all of the # components must be changed together. - fi.set( + fmodel.set( wind_speeds=wind_speeds, wind_directions=wind_directions, turbulence_intensities=turbulence_intensities ) with pytest.raises(ValueError): - fi.calculate_horizontal_plane(90.0, ws=[wind_speeds[0]], wd=[wind_directions[0]]) + fmodel.calculate_horizontal_plane(90.0, ws=[wind_speeds[0]], wd=[wind_directions[0]]) with pytest.raises(ValueError): - fi.calculate_y_plane(0.0, ws=[wind_speeds[0]], wd=[wind_directions[0]]) + fmodel.calculate_y_plane(0.0, ws=[wind_speeds[0]], wd=[wind_directions[0]]) with pytest.raises(ValueError): - fi.calculate_cross_plane(500.0, ws=[wind_speeds[0]], wd=[wind_directions[0]]) + fmodel.calculate_cross_plane(500.0, ws=[wind_speeds[0]], wd=[wind_directions[0]]) diff --git a/tests/floris_model_utils_test.py b/tests/floris_model_utils_test.py new file mode 100644 index 000000000..47b21a798 --- /dev/null +++ b/tests/floris_model_utils_test.py @@ -0,0 +1,57 @@ + +from pathlib import Path + +from floris import FlorisModel +from floris.floris_model_utils import ( + get_fmodel_param, + nested_get, + nested_set, + set_fmodel_param, +) + + +TEST_DATA = Path(__file__).resolve().parent / "data" +YAML_INPUT = TEST_DATA / "input_full.yaml" + +def test_nested_get(): + example_dict = { + 'a': { + 'b': { + 'c': 10 + } + } + } + + assert nested_get(example_dict, ['a', 'b', 'c']) == 10 + +def test_nested_set(): + example_dict = { + 'a': { + 'b': { + 'c': 10 + } + } + } + + nested_set(example_dict, ['a', 'b', 'c'], 20) + assert nested_get(example_dict, ['a', 'b', 'c']) == 20 + + +def test_get_and_set_fmodel_param(): + + + fmodel = FlorisModel(configuration=YAML_INPUT) + + # Get the wind speed + wind_speeds = get_fmodel_param(fmodel, ['flow_field', 'wind_speeds']) + assert wind_speeds[0] == 8.0 + + # Set the wind speed + fmodel = set_fmodel_param(fmodel, ['flow_field', 'wind_speeds'], 10.0, param_idx=0) + wind_speed = get_fmodel_param(fmodel, ['flow_field', 'wind_speeds'], param_idx=0 ) + assert wind_speed == 10.0 + + # Repeat with wake parameter + fmodel = set_fmodel_param(fmodel, ['wake', 'wake_velocity_parameters', 'gauss', 'alpha'], 0.1) + alpha = get_fmodel_param(fmodel, ['wake', 'wake_velocity_parameters', 'gauss', 'alpha']) + assert alpha == 0.1 diff --git a/tests/flow_field_unit_test.py b/tests/flow_field_unit_test.py index 3c5001506..260c1f8df 100644 --- a/tests/flow_field_unit_test.py +++ b/tests/flow_field_unit_test.py @@ -2,7 +2,7 @@ import numpy as np import pytest -from floris.simulation import FlowField, TurbineGrid +from floris.core import FlowField, TurbineGrid from tests.conftest import N_FINDEX, N_TURBINES diff --git a/tests/layout_optimization_integration_test.py b/tests/layout_optimization_integration_test.py index 7e61311a4..dafd5e0d6 100644 --- a/tests/layout_optimization_integration_test.py +++ b/tests/layout_optimization_integration_test.py @@ -3,18 +3,18 @@ import numpy as np import pytest -from floris.tools import ( +from floris import ( + FlorisModel, TimeSeries, WindRose, ) -from floris.tools.floris_interface import FlorisInterface -from floris.tools.optimization.layout_optimization.layout_optimization_base import ( +from floris.optimization.layout_optimization.layout_optimization_base import ( LayoutOptimization, ) -from floris.tools.optimization.layout_optimization.layout_optimization_scipy import ( +from floris.optimization.layout_optimization.layout_optimization_scipy import ( LayoutOptimizationScipy, ) -from floris.tools.wind_data import WindDataBase +from floris.wind_data import WindDataBase TEST_DATA = Path(__file__).resolve().parent / "data" @@ -23,7 +23,7 @@ def test_base_class(): # Get a test fi - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) # Set up a sample boundary boundaries = [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)] @@ -33,23 +33,23 @@ def test_base_class(): freq = np.ones((5, 5)) freq = freq / freq.sum() with pytest.raises(ValueError): - LayoutOptimization(fi, boundaries, freq, 5) + LayoutOptimization(fmodel, boundaries, freq, 5) # Passing as a keyword freq to wind_data should also fail with pytest.raises(ValueError): - LayoutOptimization(fi=fi, boundaries=boundaries, wind_data=freq, min_dist=5,) + LayoutOptimization(fmodel=fmodel, boundaries=boundaries, wind_data=freq, min_dist=5,) time_series = TimeSeries( - wind_directions=fi.floris.flow_field.wind_directions, - wind_speeds=fi.floris.flow_field.wind_speeds, - turbulence_intensities=fi.floris.flow_field.turbulence_intensities, + wind_directions=fmodel.core.flow_field.wind_directions, + wind_speeds=fmodel.core.flow_field.wind_speeds, + turbulence_intensities=fmodel.core.flow_field.turbulence_intensities, ) wind_rose = time_series.to_wind_rose() # Passing wind_data objects in the 3rd position should not fail - LayoutOptimization(fi, boundaries, time_series, 5) - LayoutOptimization(fi, boundaries, wind_rose, 5) + LayoutOptimization(fmodel, boundaries, time_series, 5) + LayoutOptimization(fmodel, boundaries, wind_rose, 5) # Passing wind_data objects by keyword should not fail - LayoutOptimization(fi=fi, boundaries=boundaries, wind_data=time_series, min_dist=5) - LayoutOptimization(fi=fi, boundaries=boundaries, wind_data=wind_rose, min_dist=5) + LayoutOptimization(fmodel=fmodel, boundaries=boundaries, wind_data=time_series, min_dist=5) + LayoutOptimization(fmodel=fmodel, boundaries=boundaries, wind_data=wind_rose, min_dist=5) diff --git a/tests/layout_visualization_test.py b/tests/layout_visualization_test.py index f23340c56..055b15b1b 100644 --- a/tests/layout_visualization_test.py +++ b/tests/layout_visualization_test.py @@ -4,8 +4,8 @@ import matplotlib.pyplot as plt import numpy as np -import floris.tools.layout_visualization as layoutviz -from floris.tools.floris_interface import FlorisInterface +import floris.layout_visualization as layoutviz +from floris import FlorisModel TEST_DATA = Path(__file__).resolve().parent / "data" @@ -24,24 +24,24 @@ def test_get_wake_direction(): def test_plotting_functions(): - fi = FlorisInterface(configuration=YAML_INPUT) + fmodel = FlorisModel(configuration=YAML_INPUT) - ax = layoutviz.plot_turbine_points(fi=fi) + ax = layoutviz.plot_turbine_points(fmodel=fmodel) assert isinstance(ax, plt.Axes) - ax = layoutviz.plot_turbine_labels(fi=fi) + ax = layoutviz.plot_turbine_labels(fmodel=fmodel) assert isinstance(ax, plt.Axes) - ax = layoutviz.plot_turbine_rotors(fi=fi) + ax = layoutviz.plot_turbine_rotors(fmodel=fmodel) assert isinstance(ax, plt.Axes) - ax = layoutviz.plot_waking_directions(fi=fi) + ax = layoutviz.plot_waking_directions(fmodel=fmodel) assert isinstance(ax, plt.Axes) # Add additional turbines to test plot farm terrain - fi.set( + fmodel.set( layout_x=[0, 1000, 0, 1000, 3000], layout_y=[0, 0, 2000, 2000, 3000], ) - ax = layoutviz.plot_farm_terrain(fi=fi) + ax = layoutviz.plot_farm_terrain(fmodel=fmodel) assert isinstance(ax, plt.Axes) diff --git a/tests/parallel_computing_interface_integration_test.py b/tests/parallel_computing_interface_integration_test.py index 6b31297d5..099f65583 100644 --- a/tests/parallel_computing_interface_integration_test.py +++ b/tests/parallel_computing_interface_integration_test.py @@ -3,7 +3,7 @@ import numpy as np -from floris.tools import FlorisInterface, ParallelComputingInterface +from floris import FlorisModel, ParallelComputingInterface from tests.conftest import ( assert_results_arrays, ) @@ -22,24 +22,24 @@ def test_parallel_turbine_powers(sample_inputs_fixture): the serial floris interface. The expected result is that the turbine powers should be exactly the same. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - fi_serial = FlorisInterface(sample_inputs_fixture.floris) - fi_parallel_input = copy.deepcopy(fi_serial) - fi_serial.run() + fmodel_serial = FlorisModel(sample_inputs_fixture.core) + fmodel_parallel_input = copy.deepcopy(fmodel_serial) + fmodel_serial.run() - serial_turbine_powers = fi_serial.get_turbine_powers() + serial_turbine_powers = fmodel_serial.get_turbine_powers() - fi_parallel = ParallelComputingInterface( - fi=fi_parallel_input, + fmodel_parallel = ParallelComputingInterface( + fmodel=fmodel_parallel_input, max_workers=2, n_wind_condition_splits=2, interface="concurrent", print_timings=False, ) - parallel_turbine_powers = fi_parallel.get_turbine_powers() + parallel_turbine_powers = fmodel_parallel.get_turbine_powers() if DEBUG: print(serial_turbine_powers) diff --git a/tests/reg_tests/cumulative_curl_regression_test.py b/tests/reg_tests/cumulative_curl_regression_test.py index 8eba6eac7..8d47d0ebd 100644 --- a/tests/reg_tests/cumulative_curl_regression_test.py +++ b/tests/reg_tests/cumulative_curl_regression_test.py @@ -1,10 +1,10 @@ import numpy as np -from floris.simulation import ( +from floris.core import ( average_velocity, axial_induction, - Floris, + Core, power, rotor_effective_velocity, thrust_coefficient, @@ -189,10 +189,10 @@ def test_regression_tandem(sample_inputs_fixture): """ Tandem turbines """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -302,25 +302,25 @@ def test_regression_rotation(sample_inputs_fixture): """ TURBINE_DIAMETER = 126.0 - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["farm"]["layout_x"] = [ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["farm"]["layout_x"] = [ 0.0, 0.0, 5 * TURBINE_DIAMETER, 5 * TURBINE_DIAMETER, ] - sample_inputs_fixture.floris["farm"]["layout_y"] = [ + sample_inputs_fixture.core["farm"]["layout_y"] = [ 0.0, 5 * TURBINE_DIAMETER, 0.0, 5 * TURBINE_DIAMETER ] - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0, 360.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0, 8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1, 0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0, 360.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0, 8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1, 0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -346,10 +346,10 @@ def test_regression_yaw(sample_inputs_fixture): """ Tandem turbines with the upstream turbine yawed """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -431,14 +431,14 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): correction enabled """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["enable_transverse_velocities"] = True - sample_inputs_fixture.floris["wake"]["enable_secondary_steering"] = False - sample_inputs_fixture.floris["wake"]["enable_yaw_added_recovery"] = True + sample_inputs_fixture.core["wake"]["enable_transverse_velocities"] = True + sample_inputs_fixture.core["wake"]["enable_secondary_steering"] = False + sample_inputs_fixture.core["wake"]["enable_yaw_added_recovery"] = True - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -519,14 +519,14 @@ def test_regression_secondary_steering(sample_inputs_fixture): Tandem turbines with the upstream turbine yawed and secondary steering enabled """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["enable_transverse_velocities"] = True - sample_inputs_fixture.floris["wake"]["enable_secondary_steering"] = True - sample_inputs_fixture.floris["wake"]["enable_yaw_added_recovery"] = False + sample_inputs_fixture.core["wake"]["enable_transverse_velocities"] = True + sample_inputs_fixture.core["wake"]["enable_secondary_steering"] = True + sample_inputs_fixture.core["wake"]["enable_yaw_added_recovery"] = False - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -623,8 +623,8 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): turbine to be affected by its own wake. This test requires that at least in this particular configuration the masking correctly filters grid points. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL X, Y = np.meshgrid( 6.0 * 126.0 * np.arange(0, 5, 1), 6.0 * 126.0 * np.arange(0, 5, 1) @@ -632,10 +632,10 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): X = X.flatten() Y = Y.flatten() - sample_inputs_fixture.floris["farm"]["layout_x"] = X - sample_inputs_fixture.floris["farm"]["layout_y"] = Y + sample_inputs_fixture.core["farm"]["layout_x"] = X + sample_inputs_fixture.core["farm"]["layout_y"] = Y - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -678,20 +678,20 @@ def test_full_flow_solver(sample_inputs_fixture): (n_findex, n_turbines, n grid points in x, n grid points in y, 3 grid points in z). """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["solver"] = { + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["solver"] = { "type": "flow_field_planar_grid", "normal_vector": "z", - "planar_coordinate": sample_inputs_fixture.floris["farm"]["turbine_type"][0]["hub_height"], + "planar_coordinate": sample_inputs_fixture.core["farm"]["turbine_type"][0]["hub_height"], "flow_field_grid_points": [5, 5], "flow_field_bounds": [None, None], } - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.solve_for_viz() velocities = floris.flow_field.u_sorted diff --git a/tests/reg_tests/empirical_gauss_regression_test.py b/tests/reg_tests/empirical_gauss_regression_test.py index fce5e96be..224eb66de 100644 --- a/tests/reg_tests/empirical_gauss_regression_test.py +++ b/tests/reg_tests/empirical_gauss_regression_test.py @@ -1,10 +1,10 @@ import numpy as np -from floris.simulation import ( +from floris.core import ( average_velocity, axial_induction, - Floris, + Core, power, rotor_effective_velocity, thrust_coefficient, @@ -162,11 +162,11 @@ def test_regression_tandem(sample_inputs_fixture): """ Tandem turbines """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -276,26 +276,26 @@ def test_regression_rotation(sample_inputs_fixture): """ TURBINE_DIAMETER = 126.0 - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL - sample_inputs_fixture.floris["farm"]["layout_x"] = [ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL + sample_inputs_fixture.core["farm"]["layout_x"] = [ 0.0, 0.0, 5 * TURBINE_DIAMETER, 5 * TURBINE_DIAMETER, ] - sample_inputs_fixture.floris["farm"]["layout_y"] = [ + sample_inputs_fixture.core["farm"]["layout_y"] = [ 0.0, 5 * TURBINE_DIAMETER, 0.0, 5 * TURBINE_DIAMETER ] - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0, 360.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0, 8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1, 0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0, 360.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0, 8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1, 0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -321,11 +321,11 @@ def test_regression_yaw(sample_inputs_fixture): """ Tandem turbines with the upstream turbine yawed """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -406,15 +406,15 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): correction enabled """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL # Turn on yaw added recovery - sample_inputs_fixture.floris["wake"]["enable_yaw_added_recovery"] = True + sample_inputs_fixture.core["wake"]["enable_yaw_added_recovery"] = True # First pass, leave at default value of 0; should then do nothing - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -483,10 +483,10 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): assert_results_arrays(test_results[0:4], yawed_baseline) # Second pass, use nonzero gain - sample_inputs_fixture.floris["wake"]["wake_deflection_parameters"]\ + sample_inputs_fixture.core["wake"]["wake_deflection_parameters"]\ ["empirical_gauss"]["yaw_added_mixing_gain"] = 0.1 - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -583,9 +583,9 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): turbine to be affected by its own wake. This test requires that at least in this particular configuration the masking correctly filters grid points. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL X, Y = np.meshgrid( 6.0 * 126.0 * np.arange(0, 5, 1), 6.0 * 126.0 * np.arange(0, 5, 1) @@ -593,10 +593,10 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): X = X.flatten() Y = Y.flatten() - sample_inputs_fixture.floris["farm"]["layout_x"] = X - sample_inputs_fixture.floris["farm"]["layout_y"] = Y + sample_inputs_fixture.core["farm"]["layout_x"] = X + sample_inputs_fixture.core["farm"]["layout_y"] = Y - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -647,21 +647,21 @@ def test_full_flow_solver(sample_inputs_fixture): (n_findex, n_turbines, n grid points in x, n grid points in y, 3 grid points in z). """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL - sample_inputs_fixture.floris["solver"] = { + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL + sample_inputs_fixture.core["solver"] = { "type": "flow_field_planar_grid", "normal_vector": "z", - "planar_coordinate": sample_inputs_fixture.floris["farm"]["turbine_type"][0]["hub_height"], + "planar_coordinate": sample_inputs_fixture.core["farm"]["turbine_type"][0]["hub_height"], "flow_field_grid_points": [5, 5], "flow_field_bounds": [None, None], } - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.solve_for_viz() velocities = floris.flow_field.u_sorted diff --git a/tests/reg_tests/gauss_regression_test.py b/tests/reg_tests/gauss_regression_test.py index 561323f72..bc876006b 100644 --- a/tests/reg_tests/gauss_regression_test.py +++ b/tests/reg_tests/gauss_regression_test.py @@ -1,10 +1,10 @@ import numpy as np -from floris.simulation import ( +from floris.core import ( average_velocity, axial_induction, - Floris, + Core, power, rotor_effective_velocity, thrust_coefficient, @@ -281,10 +281,10 @@ def test_regression_tandem(sample_inputs_fixture): """ Tandem turbines """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -394,26 +394,26 @@ def test_regression_rotation(sample_inputs_fixture): """ TURBINE_DIAMETER = 126.0 - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["farm"]["layout_x"] = [ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["farm"]["layout_x"] = [ 0.0, 0.0, 5 * TURBINE_DIAMETER, 5 * TURBINE_DIAMETER, ] - sample_inputs_fixture.floris["farm"]["layout_y"] = [ + sample_inputs_fixture.core["farm"]["layout_y"] = [ 0.0, 5 * TURBINE_DIAMETER, 0.0, 5 * TURBINE_DIAMETER ] - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0, 360.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0, 8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1, 0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0, 360.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0, 8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1, 0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -439,10 +439,10 @@ def test_regression_yaw(sample_inputs_fixture): """ Tandem turbines with the upstream turbine yawed """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -523,12 +523,12 @@ def test_regression_gch(sample_inputs_fixture): Tandem turbines with the upstream turbine yawed, yaw added recovery correction enabled, and secondary steering enabled """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL ### With GCH off (via conftest), GCH should be same as Gauss - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -605,11 +605,11 @@ def test_regression_gch(sample_inputs_fixture): ### With GCH on, the results should change - sample_inputs_fixture.floris["wake"]["enable_transverse_velocities"] = True - sample_inputs_fixture.floris["wake"]["enable_secondary_steering"] = True - sample_inputs_fixture.floris["wake"]["enable_yaw_added_recovery"] = True + sample_inputs_fixture.core["wake"]["enable_transverse_velocities"] = True + sample_inputs_fixture.core["wake"]["enable_secondary_steering"] = True + sample_inputs_fixture.core["wake"]["enable_yaw_added_recovery"] = True - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -691,14 +691,14 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): correction enabled """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["enable_transverse_velocities"] = True - sample_inputs_fixture.floris["wake"]["enable_secondary_steering"] = False - sample_inputs_fixture.floris["wake"]["enable_yaw_added_recovery"] = True + sample_inputs_fixture.core["wake"]["enable_transverse_velocities"] = True + sample_inputs_fixture.core["wake"]["enable_secondary_steering"] = False + sample_inputs_fixture.core["wake"]["enable_yaw_added_recovery"] = True - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -779,14 +779,14 @@ def test_regression_secondary_steering(sample_inputs_fixture): Tandem turbines with the upstream turbine yawed and secondary steering enabled """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["enable_transverse_velocities"] = True - sample_inputs_fixture.floris["wake"]["enable_secondary_steering"] = True - sample_inputs_fixture.floris["wake"]["enable_yaw_added_recovery"] = False + sample_inputs_fixture.core["wake"]["enable_transverse_velocities"] = True + sample_inputs_fixture.core["wake"]["enable_secondary_steering"] = True + sample_inputs_fixture.core["wake"]["enable_yaw_added_recovery"] = False - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -883,8 +883,8 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): turbine to be affected by its own wake. This test requires that at least in this particular configuration the masking correctly filters grid points. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL X, Y = np.meshgrid( 6.0 * 126.0 * np.arange(0, 5, 1), 6.0 * 126.0 * np.arange(0, 5, 1) @@ -892,10 +892,10 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): X = X.flatten() Y = Y.flatten() - sample_inputs_fixture.floris["farm"]["layout_x"] = X - sample_inputs_fixture.floris["farm"]["layout_y"] = Y + sample_inputs_fixture.core["farm"]["layout_x"] = X + sample_inputs_fixture.core["farm"]["layout_y"] = Y - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -937,20 +937,20 @@ def test_full_flow_solver(sample_inputs_fixture): (n_findex, n_turbines, n grid points in x, n grid points in y, 3 grid points in z). """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["solver"] = { + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["solver"] = { "type": "flow_field_planar_grid", "normal_vector": "z", - "planar_coordinate": sample_inputs_fixture.floris["farm"]["turbine_type"][0]["hub_height"], + "planar_coordinate": sample_inputs_fixture.core["farm"]["turbine_type"][0]["hub_height"], "flow_field_grid_points": [5, 5], "flow_field_bounds": [None, None], } - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.solve_for_viz() velocities = floris.flow_field.u_sorted diff --git a/tests/reg_tests/jensen_jimenez_regression_test.py b/tests/reg_tests/jensen_jimenez_regression_test.py index ecb915fbc..775687077 100644 --- a/tests/reg_tests/jensen_jimenez_regression_test.py +++ b/tests/reg_tests/jensen_jimenez_regression_test.py @@ -1,10 +1,10 @@ import numpy as np -from floris.simulation import ( +from floris.core import ( average_velocity, axial_induction, - Floris, + Core, power, rotor_effective_velocity, thrust_coefficient, @@ -131,10 +131,10 @@ def test_regression_tandem(sample_inputs_fixture): """ Tandem turbines """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -244,25 +244,25 @@ def test_regression_rotation(sample_inputs_fixture): """ TURBINE_DIAMETER = 126.0 - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["farm"]["layout_x"] = [ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["farm"]["layout_x"] = [ 0.0, 0.0, 5 * TURBINE_DIAMETER, 5 * TURBINE_DIAMETER, ] - sample_inputs_fixture.floris["farm"]["layout_y"] = [ + sample_inputs_fixture.core["farm"]["layout_y"] = [ 0.0, 5 * TURBINE_DIAMETER, 0.0, 5 * TURBINE_DIAMETER ] - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0, 360.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0, 8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1, 0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0, 360.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0, 8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1, 0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -288,10 +288,10 @@ def test_regression_yaw(sample_inputs_fixture): """ Tandem turbines with the upstream turbine yawed """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -388,8 +388,8 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): turbine to be affected by its own wake. This test requires that at least in this particular configuration the masking correctly filters grid points. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL X, Y = np.meshgrid( 6.0 * 126.0 * np.arange(0, 5, 1), 6.0 * 126.0 * np.arange(0, 5, 1) @@ -397,10 +397,10 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): X = X.flatten() Y = Y.flatten() - sample_inputs_fixture.floris["farm"]["layout_x"] = X - sample_inputs_fixture.floris["farm"]["layout_y"] = Y + sample_inputs_fixture.core["farm"]["layout_x"] = X + sample_inputs_fixture.core["farm"]["layout_y"] = Y - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -454,20 +454,20 @@ def test_full_flow_solver(sample_inputs_fixture): (n_findex, n_turbines, n grid points in x, n grid points in y, 3 grid points in z). """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["solver"] = { + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["solver"] = { "type": "flow_field_planar_grid", "normal_vector": "z", - "planar_coordinate": sample_inputs_fixture.floris["farm"]["turbine_type"][0]["hub_height"], + "planar_coordinate": sample_inputs_fixture.core["farm"]["turbine_type"][0]["hub_height"], "flow_field_grid_points": [5, 5], "flow_field_bounds": [None, None], } - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.solve_for_viz() velocities = floris.flow_field.u_sorted diff --git a/tests/reg_tests/none_regression_test.py b/tests/reg_tests/none_regression_test.py index 5b98fa1a4..aff811938 100644 --- a/tests/reg_tests/none_regression_test.py +++ b/tests/reg_tests/none_regression_test.py @@ -2,10 +2,10 @@ import numpy as np import pytest -from floris.simulation import ( +from floris.core import ( average_velocity, axial_induction, - Floris, + Core, power, rotor_effective_velocity, thrust_coefficient, @@ -132,10 +132,10 @@ def test_regression_tandem(sample_inputs_fixture): """ Tandem turbines """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -245,25 +245,25 @@ def test_regression_rotation(sample_inputs_fixture): """ TURBINE_DIAMETER = 126.0 - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["farm"]["layout_x"] = [ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["farm"]["layout_x"] = [ 0.0, 0.0, 5 * TURBINE_DIAMETER, 5 * TURBINE_DIAMETER, ] - sample_inputs_fixture.floris["farm"]["layout_y"] = [ + sample_inputs_fixture.core["farm"]["layout_y"] = [ 0.0, 5 * TURBINE_DIAMETER, 0.0, 5 * TURBINE_DIAMETER ] - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0, 360.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0, 8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1, 0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0, 360.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0, 8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1, 0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -289,10 +289,10 @@ def test_regression_yaw(sample_inputs_fixture): """ Tandem turbines with the upstream turbine yawed """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -324,8 +324,8 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): turbine to be affected by its own wake. This test requires that at least in this particular configuration the masking correctly filters grid points. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL X, Y = np.meshgrid( 6.0 * 126.0 * np.arange(0, 5, 1), 6.0 * 126.0 * np.arange(0, 5, 1) @@ -333,10 +333,10 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): X = X.flatten() Y = Y.flatten() - sample_inputs_fixture.floris["farm"]["layout_x"] = X - sample_inputs_fixture.floris["farm"]["layout_y"] = Y + sample_inputs_fixture.core["farm"]["layout_x"] = X + sample_inputs_fixture.core["farm"]["layout_y"] = Y - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -379,20 +379,20 @@ def test_full_flow_solver(sample_inputs_fixture): (n_findex, n_turbines, n grid points in x, n grid points in y, 3 grid points in z). """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["solver"] = { + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["solver"] = { "type": "flow_field_planar_grid", "normal_vector": "z", - "planar_coordinate": sample_inputs_fixture.floris["farm"]["turbine_type"][0]["hub_height"], + "planar_coordinate": sample_inputs_fixture.core["farm"]["turbine_type"][0]["hub_height"], "flow_field_grid_points": [5, 5], "flow_field_bounds": [None, None], } - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.solve_for_viz() velocities = floris.flow_field.u_sorted diff --git a/tests/reg_tests/scipy_layout_opt_regression.py b/tests/reg_tests/scipy_layout_opt_regression.py index 570cb964c..049b1b841 100644 --- a/tests/reg_tests/scipy_layout_opt_regression.py +++ b/tests/reg_tests/scipy_layout_opt_regression.py @@ -2,8 +2,8 @@ import numpy as np import pandas as pd -from floris.tools import FlorisInterface -from floris.tools.optimization.layout_optimization.layout_optimization_scipy import ( +from floris import FlorisModel +from floris.optimization.layout_optimization.layout_optimization_scipy import ( LayoutOptimizationScipy, ) from tests.conftest import ( @@ -29,8 +29,8 @@ def test_scipy_layout_opt(sample_inputs_fixture): compares the optimization results from the SciPy layout optimizaiton for a simple farm with a simple wind rose to stored baseline results. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL opt_options = { "maxiter": 5, @@ -42,18 +42,18 @@ def test_scipy_layout_opt(sample_inputs_fixture): boundaries = [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)] - fi = FlorisInterface(sample_inputs_fixture.floris) + fmodel = FlorisModel(sample_inputs_fixture.core) wd_array = np.arange(0, 360.0, 5.0) ws_array = 8.0 * np.ones_like(wd_array) D = 126.0 # Rotor diameter for the NREL 5 MW - fi.reinitialize( + fmodel.reinitialize( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, wind_speeds=ws_array, ) - layout_opt = LayoutOptimizationScipy(fi, boundaries, optOptions=opt_options) + layout_opt = LayoutOptimizationScipy(fmodel, boundaries, optOptions=opt_options) sol = layout_opt.optimize() locations_opt = np.array([sol[0], sol[1]]) diff --git a/tests/reg_tests/turbopark_regression_test.py b/tests/reg_tests/turbopark_regression_test.py index 16be779e4..d4ee6febe 100644 --- a/tests/reg_tests/turbopark_regression_test.py +++ b/tests/reg_tests/turbopark_regression_test.py @@ -1,10 +1,10 @@ import numpy as np -from floris.simulation import ( +from floris.core import ( average_velocity, axial_induction, - Floris, + Core, power, rotor_effective_velocity, thrust_coefficient, @@ -90,11 +90,11 @@ def test_regression_tandem(sample_inputs_fixture): """ Tandem turbines """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -204,26 +204,26 @@ def test_regression_rotation(sample_inputs_fixture): """ TURBINE_DIAMETER = 126.0 - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL - sample_inputs_fixture.floris["farm"]["layout_x"] = [ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL + sample_inputs_fixture.core["farm"]["layout_x"] = [ 0.0, 0.0, 5 * TURBINE_DIAMETER, 5 * TURBINE_DIAMETER, ] - sample_inputs_fixture.floris["farm"]["layout_y"] = [ + sample_inputs_fixture.core["farm"]["layout_y"] = [ 0.0, 5 * TURBINE_DIAMETER, 0.0, 5 * TURBINE_DIAMETER ] - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0, 360.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0, 8.0] - sample_inputs_fixture.floris["flow_field"]["turbulence_intensities"] = [0.1, 0.1] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0, 360.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0, 8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1, 0.1] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -249,10 +249,10 @@ def test_regression_yaw(sample_inputs_fixture): """ Tandem turbines with the upstream turbine yawed """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) yaw_angles[:,0] = 5.0 @@ -343,9 +343,9 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): turbine to be affected by its own wake. This test requires that at least in this particular configuration the masking correctly filters grid points. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL X, Y = np.meshgrid( 6.0 * 126.0 * np.arange(0, 5, 1), 6.0 * 126.0 * np.arange(0, 5, 1) @@ -353,10 +353,10 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): X = X.flatten() Y = Y.flatten() - sample_inputs_fixture.floris["farm"]["layout_x"] = X - sample_inputs_fixture.floris["farm"]["layout_y"] = Y + sample_inputs_fixture.core["farm"]["layout_x"] = X + sample_inputs_fixture.core["farm"]["layout_y"] = Y - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.initialize_domain() floris.steady_state_atmospheric_condition() @@ -399,19 +399,19 @@ def test_full_flow_solver(sample_inputs_fixture): (n_findex, n_turbines, n grid points in x, n grid points in y, 3 grid points in z). """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - sample_inputs_fixture.floris["solver"] = { + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["solver"] = { "type": "flow_field_planar_grid", "normal_vector": "z", - "planar_coordinate": sample_inputs_fixture.floris["farm"]["turbine_type"][0]["hub_height"], + "planar_coordinate": sample_inputs_fixture.core["farm"]["turbine_type"][0]["hub_height"], "flow_field_grid_points": [5, 5], "flow_field_bounds": [None, None], } - sample_inputs_fixture.floris["flow_field"]["wind_directions"] = [270.0] - sample_inputs_fixture.floris["flow_field"]["wind_speeds"] = [8.0] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0] - floris = Floris.from_dict(sample_inputs_fixture.floris) + floris = Core.from_dict(sample_inputs_fixture.core) floris.solve_for_viz() velocities = floris.flow_field.u_sorted diff --git a/tests/reg_tests/yaw_optimization_regression_test.py b/tests/reg_tests/yaw_optimization_regression_test.py index ea353eadc..203856646 100644 --- a/tests/reg_tests/yaw_optimization_regression_test.py +++ b/tests/reg_tests/yaw_optimization_regression_test.py @@ -2,12 +2,12 @@ import numpy as np import pandas as pd -from floris.tools import FlorisInterface -from floris.tools.optimization.yaw_optimization.yaw_optimizer_geometric import ( +from floris import FlorisModel +from floris.optimization.yaw_optimization.yaw_optimizer_geometric import ( YawOptimizationGeometric, ) -from floris.tools.optimization.yaw_optimization.yaw_optimizer_scipy import YawOptimizationScipy -from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR +from floris.optimization.yaw_optimization.yaw_optimizer_scipy import YawOptimizationScipy +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR DEBUG = False @@ -77,16 +77,16 @@ def test_serial_refine(sample_inputs_fixture): optimization scheme. This test compares the optimization results from the SR method for a simple farm with a simple wind rose to stored baseline results. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - fi = FlorisInterface(sample_inputs_fixture.floris) + fmodel = FlorisModel(sample_inputs_fixture.core) wd_array = np.arange(0.0, 360.0, 90.0) ws_array = 8.0 * np.ones_like(wd_array) ti_array = 0.1 * np.ones_like(wd_array) D = 126.0 # Rotor diameter for the NREL 5 MW - fi.set( + fmodel.set( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, @@ -94,7 +94,7 @@ def test_serial_refine(sample_inputs_fixture): turbulence_intensities=ti_array, ) - yaw_opt = YawOptimizationSR(fi) + yaw_opt = YawOptimizationSR(fmodel) df_opt = yaw_opt.optimize() if DEBUG: @@ -110,31 +110,31 @@ def test_geometric_yaw(sample_inputs_fixture): optimal yaw relationships. This test compares the optimization results from the Geometric Yaw optimization for a simple farm with a simple wind rose to stored baseline results. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - fi = FlorisInterface(sample_inputs_fixture.floris) + fmodel = FlorisModel(sample_inputs_fixture.core) wd_array = np.arange(0.0, 360.0, 90.0) ws_array = 8.0 * np.ones_like(wd_array) ti_array = 0.1 * np.ones_like(wd_array) D = 126.0 # Rotor diameter for the NREL 5 MW - fi.set( + fmodel.set( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=ti_array, ) - fi.run() - baseline_farm_power = fi.get_farm_power().squeeze() + fmodel.run() + baseline_farm_power = fmodel.get_farm_power().squeeze() - yaw_opt = YawOptimizationGeometric(fi) + yaw_opt = YawOptimizationGeometric(fmodel) df_opt = yaw_opt.optimize() yaw_angles_opt_geo = np.vstack(yaw_opt.yaw_angles_opt) - fi.set(yaw_angles=yaw_angles_opt_geo) - fi.run() - geo_farm_power = fi.get_farm_power().squeeze() + fmodel.set(yaw_angles=yaw_angles_opt_geo) + fmodel.run() + geo_farm_power = fmodel.get_farm_power().squeeze() df_opt['farm_power_baseline'] = baseline_farm_power df_opt['farm_power_opt'] = geo_farm_power @@ -152,8 +152,8 @@ def test_scipy_yaw_opt(sample_inputs_fixture): compares the optimization results from the SciPy yaw optimization for a simple farm with a simple wind rose to stored baseline results. """ - sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL opt_options = { "maxiter": 5, @@ -163,12 +163,12 @@ def test_scipy_yaw_opt(sample_inputs_fixture): "eps": 0.5, } - fi = FlorisInterface(sample_inputs_fixture.floris) + fmodel = FlorisModel(sample_inputs_fixture.core) wd_array = np.arange(0.0, 360.0, 90.0) ws_array = 8.0 * np.ones_like(wd_array) ti_array = 0.1 * np.ones_like(wd_array) D = 126.0 # Rotor diameter for the NREL 5 MW - fi.set( + fmodel.set( layout_x=[0.0, 5 * D, 10 * D], layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, @@ -176,7 +176,7 @@ def test_scipy_yaw_opt(sample_inputs_fixture): turbulence_intensities=ti_array, ) - yaw_opt = YawOptimizationScipy(fi, opt_options=opt_options) + yaw_opt = YawOptimizationScipy(fmodel, opt_options=opt_options) df_opt = yaw_opt.optimize() if DEBUG: diff --git a/tests/rotor_velocity_unit_test.py b/tests/rotor_velocity_unit_test.py index 30b19f346..468b7a887 100644 --- a/tests/rotor_velocity_unit_test.py +++ b/tests/rotor_velocity_unit_test.py @@ -1,7 +1,7 @@ import numpy as np -from floris.simulation import Turbine -from floris.simulation.rotor_velocity import ( +from floris.core import Turbine +from floris.core.rotor_velocity import ( average_velocity, compute_tilt_angles_for_floating_turbines, compute_tilt_angles_for_floating_turbines_map, diff --git a/tests/turbine_grid_unit_test.py b/tests/turbine_grid_unit_test.py index c65a90a29..3d9b01961 100644 --- a/tests/turbine_grid_unit_test.py +++ b/tests/turbine_grid_unit_test.py @@ -1,7 +1,7 @@ import numpy as np -from floris.simulation import TurbineGrid +from floris.core import TurbineGrid from tests.conftest import ( N_FINDEX, N_TURBINES, diff --git a/tests/turbine_multi_dim_unit_test.py b/tests/turbine_multi_dim_unit_test.py index 39f1b1f1a..55b582e41 100644 --- a/tests/turbine_multi_dim_unit_test.py +++ b/tests/turbine_multi_dim_unit_test.py @@ -5,11 +5,11 @@ import pandas as pd import pytest -from floris.simulation import ( +from floris.core import ( Turbine, ) -from floris.simulation.turbine.operation_models import POWER_SETPOINT_DEFAULT -from floris.simulation.turbine.turbine import ( +from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT +from floris.core.turbine.turbine import ( axial_induction, power, thrust_coefficient, diff --git a/tests/turbine_operation_models_integration_test.py b/tests/turbine_operation_models_integration_test.py index 446695855..4732bd555 100644 --- a/tests/turbine_operation_models_integration_test.py +++ b/tests/turbine_operation_models_integration_test.py @@ -1,7 +1,7 @@ import numpy as np import pytest -from floris.simulation.turbine.operation_models import ( +from floris.core.turbine.operation_models import ( CosineLossTurbine, MixedOperationTurbine, POWER_SETPOINT_DEFAULT, diff --git a/tests/turbine_unit_test.py b/tests/turbine_unit_test.py index e366aeb11..2ef7a7d97 100644 --- a/tests/turbine_unit_test.py +++ b/tests/turbine_unit_test.py @@ -7,14 +7,14 @@ import pytest import yaml -from floris.simulation import ( +from floris.core import ( average_velocity, axial_induction, power, thrust_coefficient, Turbine, ) -from floris.simulation.turbine.operation_models import POWER_SETPOINT_DEFAULT +from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT from tests.conftest import SampleInputs, WIND_SPEEDS diff --git a/tests/uncertainty_interface_integration_test.py b/tests/uncertainty_interface_integration_test.py index 74bf956b0..8e0d4fd8f 100644 --- a/tests/uncertainty_interface_integration_test.py +++ b/tests/uncertainty_interface_integration_test.py @@ -4,9 +4,9 @@ import pytest import yaml -from floris.simulation.turbine.operation_models import POWER_SETPOINT_DEFAULT -from floris.tools.floris_interface import FlorisInterface -from floris.tools.uncertainty_interface import UncertaintyInterface +from floris import FlorisModel +from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT +from floris.uncertainty_interface import UncertaintyInterface TEST_DATA = Path(__file__).resolve().parent / "data" @@ -14,12 +14,12 @@ def test_read_yaml(): - fi = UncertaintyInterface(configuration=YAML_INPUT) - assert isinstance(fi, UncertaintyInterface) + umodel = UncertaintyInterface(configuration=YAML_INPUT) + assert isinstance(umodel, UncertaintyInterface) def test_rounded_inputs(): - fi = UncertaintyInterface(configuration=YAML_INPUT) + umodel = UncertaintyInterface(configuration=YAML_INPUT) # Using defaults # Example input array @@ -29,13 +29,13 @@ def test_rounded_inputs(): expected_output = np.array([[45.0, 8.0, 0.25, 91.0, 700.0], [60.0, 8.0, 0.3, 95.0, 800.0]]) # Call the function - rounded_inputs = fi._get_rounded_inputs(input_array) + rounded_inputs = umodel._get_rounded_inputs(input_array) np.testing.assert_almost_equal(rounded_inputs, expected_output) def test_expand_wind_directions(): - fi = UncertaintyInterface(configuration=YAML_INPUT) + umodel = UncertaintyInterface(configuration=YAML_INPUT) input_array = np.array( [[1, 20, 30], [40, 50, 60], [70, 80, 90], [100, 110, 120], [359, 140, 150]] @@ -44,16 +44,16 @@ def test_expand_wind_directions(): # Test even length with pytest.raises(ValueError): wd_sample_points = [-15, -10, -5, 5, 10, 15] # Even lenght - fi._expand_wind_directions(input_array, wd_sample_points) + umodel._expand_wind_directions(input_array, wd_sample_points) # Test middle element not 0 with pytest.raises(ValueError): wd_sample_points = [-15, -10, -5, 1, 5, 10, 15] # Odd length, not 0 at the middle - fi._expand_wind_directions(input_array, wd_sample_points) + umodel._expand_wind_directions(input_array, wd_sample_points) # Test correction operations wd_sample_points = [-15, -10, -5, 0, 5, 10, 15] # Odd length, 0 at the middle - output_array = fi._expand_wind_directions(input_array, wd_sample_points) + output_array = umodel._expand_wind_directions(input_array, wd_sample_points) # Check if output shape is correct assert output_array.shape[0] == 35 @@ -68,7 +68,7 @@ def test_expand_wind_directions(): def test_get_unique_inputs(): - fi = UncertaintyInterface(configuration=YAML_INPUT) + umodel = UncertaintyInterface(configuration=YAML_INPUT) input_array = np.array( [ @@ -82,7 +82,7 @@ def test_get_unique_inputs(): expected_unique_inputs = np.array([[0, 1], [0, 2], [1, 1]]) - unique_inputs, map_to_expanded_inputs = fi._get_unique_inputs(input_array) + unique_inputs, map_to_expanded_inputs = umodel._get_unique_inputs(input_array) # test expected result assert np.array_equal(unique_inputs, expected_unique_inputs) @@ -92,8 +92,8 @@ def test_get_unique_inputs(): def test_get_weights(): - fi = UncertaintyInterface(configuration=YAML_INPUT) - weights = fi._get_weights(3.0, [-6, -3, 0, 3, 6]) + umodel = UncertaintyInterface(configuration=YAML_INPUT) + weights = umodel._get_weights(3.0, [-6, -3, 0, 3, 6]) np.testing.assert_allclose( weights, np.array([0.05448868, 0.24420134, 0.40261995, 0.24420134, 0.05448868]) ) @@ -102,10 +102,10 @@ def test_get_weights(): def test_uncertainty_interface(): # Recompute uncertain result using certain result with 1 deg - fi_nom = FlorisInterface(configuration=YAML_INPUT) - fi_unc = UncertaintyInterface(configuration=YAML_INPUT, wd_sample_points=[-3, 0, 3], wd_std=3) + fmodel = FlorisModel(configuration=YAML_INPUT) + umodel = UncertaintyInterface(configuration=YAML_INPUT, wd_sample_points=[-3, 0, 3], wd_std=3) - fi_nom.set( + fmodel.set( layout_x=[0, 300], layout_y=[0, 0], wind_speeds=[8.0, 8.0, 8.0], @@ -113,7 +113,7 @@ def test_uncertainty_interface(): turbulence_intensities=[0.06, 0.06, 0.06], ) - fi_unc.set( + umodel.set( layout_x=[0, 300], layout_y=[0, 0], wind_speeds=[8.0], @@ -121,22 +121,22 @@ def test_uncertainty_interface(): turbulence_intensities=[0.06], ) - fi_nom.run() - fi_unc.run() + fmodel.run() + umodel.run() - nom_powers = fi_nom.get_turbine_powers()[:, 1].flatten() - unc_powers = fi_unc.get_turbine_powers()[:, 1].flatten() + nom_powers = fmodel.get_turbine_powers()[:, 1].flatten() + unc_powers = umodel.get_turbine_powers()[:, 1].flatten() - weights = fi_unc.weights + weights = umodel.weights np.testing.assert_allclose(np.sum(nom_powers * weights), unc_powers) def test_uncertainty_interface_setpoints(): - fi_nom = FlorisInterface(configuration=YAML_INPUT) - fi_unc = UncertaintyInterface(configuration=YAML_INPUT, wd_sample_points=[-3, 0, 3], wd_std=3) + fmodel = FlorisModel(configuration=YAML_INPUT) + umodel = UncertaintyInterface(configuration=YAML_INPUT, wd_sample_points=[-3, 0, 3], wd_std=3) - fi_nom.set( + fmodel.set( layout_x=[0, 300], layout_y=[0, 0], wind_speeds=[8.0, 8.0, 8.0], @@ -144,41 +144,41 @@ def test_uncertainty_interface_setpoints(): turbulence_intensities=[0.06, 0.06, 0.06], ) - fi_unc.set( + umodel.set( layout_x=[0, 300], layout_y=[0, 0], wind_speeds=[8.0], wind_directions=[270.0], turbulence_intensities=[0.06], ) - weights = fi_unc.weights + weights = umodel.weights # Check setpoints dimensions are respected and reset_operation works - # Note that fi_nom.set() does NOT raise ValueError---an AttributeError is raised only at - # fi_nom.run()---whereas fi_unc.set raises ValueError immediately. - # fi_nom.set(yaw_angles=np.array([[0.0, 0.0]])) + # Note that fmodel.set() does NOT raise ValueError---an AttributeError is raised only at + # fmodel.run()---whereas umodel.set raises ValueError immediately. + # fmodel.set(yaw_angles=np.array([[0.0, 0.0]])) # with pytest.raises(AttributeError): - # fi_nom.run() + # fmodel.run() # with pytest.raises(ValueError): - # fi_unc.set(yaw_angles=np.array([[0.0, 0.0]])) + # umodel.set(yaw_angles=np.array([[0.0, 0.0]])) - fi_nom.set(yaw_angles=np.array([[20.0, 0.0], [20.0, 0.0], [20.0, 0.0]])) - fi_nom.run() - nom_powers = fi_nom.get_turbine_powers()[:, 1].flatten() + fmodel.set(yaw_angles=np.array([[20.0, 0.0], [20.0, 0.0], [20.0, 0.0]])) + fmodel.run() + nom_powers = fmodel.get_turbine_powers()[:, 1].flatten() - fi_unc.set(yaw_angles=np.array([[20.0, 0.0]])) - fi_unc.run() - unc_powers = fi_unc.get_turbine_powers()[:, 1].flatten() + umodel.set(yaw_angles=np.array([[20.0, 0.0]])) + umodel.run() + unc_powers = umodel.get_turbine_powers()[:, 1].flatten() np.testing.assert_allclose(np.sum(nom_powers * weights), unc_powers) # Drop yaw setpoints and rerun - fi_nom.reset_operation() - fi_nom.run() - nom_powers = fi_nom.get_turbine_powers()[:, 1].flatten() + fmodel.reset_operation() + fmodel.run() + nom_powers = fmodel.get_turbine_powers()[:, 1].flatten() - fi_unc.reset_operation() - fi_unc.run() - unc_powers = fi_unc.get_turbine_powers()[:, 1].flatten() + umodel.reset_operation() + umodel.run() + unc_powers = umodel.get_turbine_powers()[:, 1].flatten() np.testing.assert_allclose(np.sum(nom_powers * weights), unc_powers) diff --git a/tests/wake_unit_tests.py b/tests/wake_unit_tests.py index 09e66787c..90f66057e 100644 --- a/tests/wake_unit_tests.py +++ b/tests/wake_unit_tests.py @@ -1,5 +1,5 @@ -from floris.simulation import WakeModelManager +from floris.core import WakeModelManager from tests.conftest import SampleInputs diff --git a/tests/wind_data_integration_test.py b/tests/wind_data_integration_test.py index 66782733a..ecc8281b3 100644 --- a/tests/wind_data_integration_test.py +++ b/tests/wind_data_integration_test.py @@ -1,12 +1,12 @@ import numpy as np import pytest -from floris.tools import ( +from floris import ( TimeSeries, WindRose, WindTIRose, ) -from floris.tools.wind_data import WindDataBase +from floris.wind_data import WindDataBase class ChildClassTest(WindDataBase):