diff --git a/docs/notebooks/exercises/CombineModels.ipynb b/docs/notebooks/exercises/CombineModels.ipynb
index 6a398bd42e9ea426e441f44802df36bd6ff5d701..3b72d223e802ed33fcff33f81718d5d317f429c2 100644
--- a/docs/notebooks/exercises/CombineModels.ipynb
+++ b/docs/notebooks/exercises/CombineModels.ipynb
@@ -1,235 +1,259 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Experiment: Combine Models\n",
- "\n",
- "In this notebook, you can combine the difference models of py_wake and see the effects in terms of AEP and a flow map"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Initialization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Install PyWake if needed\n",
- "try:\n",
- " import py_wake\n",
- "except ModuleNotFoundError:\n",
- " !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake.git"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import all available models\n",
- "from py_wake.deficit_models import *\n",
- "from py_wake.wind_farm_models import *\n",
- "from py_wake.rotor_avg_models import *\n",
- "from py_wake.superposition_models import *\n",
- "from py_wake.deflection_models import *\n",
- "from py_wake.turbulence_models import *\n",
- "from py_wake.ground_models import *"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setup site, wind turbines\n",
- "from py_wake.examples.data.iea37._iea37 import IEA37Site, IEA37_WindTurbines\n",
- "site = IEA37Site(16)\n",
- "windTurbines = IEA37_WindTurbines()\n",
- "x,y = site.initial_position.T"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>.widget-label { min-width: 20ex !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# prepare for the model combination tool\n",
- "from py_wake.utils.model_utils import get_models, get_signature\n",
- "from ipywidgets import interact\n",
- "from IPython.display import HTML, display, Javascript\n",
- "import time\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# Fix ipywidget label width\n",
- "display(HTML('''<style>.widget-label { min-width: 20ex !important; }</style>'''))\n",
- "\n",
- "\n",
- "def print_signature(windFarmModel, **kwargs):\n",
- " s = \"\"\"# windFarmModel autogenerated by dropdown boxes\n",
- "t = time.time()\n",
- "wfm = %s\n",
- "sim_res = wfm(x,y)\n",
- "plt.figure(figsize=(12,8))\n",
- "sim_res.flow_map(wd=270).plot_wake_map()\n",
- "print (wfm)\n",
- "print (\"Computation time (AEP + flowmap):\", time.time()-t)\n",
- "plt.title('AEP: %%.2fGWh'%%(sim_res.aep().sum()))\n",
- "\"\"\"% get_signature(windFarmModel, kwargs, 1)\n",
- " \n",
- " # Write windFarmModel code to cell starting \"# windFarmModel autogenerated by dropdown boxes\"\n",
- " display(Javascript(\"\"\"\n",
- "for (var cell of IPython.notebook.get_cells()) {\n",
- " if (cell.get_text().startsWith(\"# windFarmModel autogenerated by dropdown boxes\")){\n",
- " cell.set_text(`%s`);\n",
- " cell.execute();\n",
- " }\n",
- "}\"\"\"%s))\n",
- "\n",
- "# setup list of models\n",
- "models = {n:[(getattr(m,'__name__',m), m) for m in get_models(cls)] \n",
- " for n,cls in [('windFarmModel', WindFarmModel),\n",
- " ('wake_deficitModel', WakeDeficitModel),\n",
- " ('rotorAvgModel', RotorAvgModel),\n",
- " ('superpositionModel', SuperpositionModel),\n",
- " ('blockage_deficitModel', BlockageDeficitModel),\n",
- " ('deflectionModel',DeflectionModel),\n",
- " ('turbulenceModel', TurbulenceModel),\n",
- " ('groundModel', GroundModel)\n",
- " ]}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Combine and execute model\n",
- "\n",
- "Combine your model via the dropdown boxes below\n",
- "Choosing a different model updates and executes the the code cell below which runs the wind farm model, prints the AEP and plots a flow map"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "171330f143a1445b9c63829ba26c8455",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Dropdown(description='windFarmModel', options=(('PropagateDownwind', <class 'py_wake.win\u2026"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "_ = interact(print_signature, **models)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "ename": "AssertionError",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-7-5850474ce68c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# windFarmModel autogenerated by dropdown boxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m wfm = PropagateDownwind(\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0msite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mwindTurbines\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mc:\\mmpe\\programming\\python\\topfarm\\pywake\\py_wake\\wind_farm_models\\engineering_models.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, site, windTurbines, wake_deficitModel, rotorAvgModel, superpositionModel, deflectionModel, turbulenceModel, groundModel)\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[0mModel\u001b[0m \u001b[0mdescribing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mamount\u001b[0m \u001b[0mof\u001b[0m \u001b[0madded\u001b[0m \u001b[0mturbulence\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mwake\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 392\u001b[0m \"\"\"\n\u001b[1;32m--> 393\u001b[1;33m EngineeringWindFarmModel.__init__(self, site, windTurbines, wake_deficitModel, rotorAvgModel, superpositionModel,\n\u001b[0m\u001b[0;32m 394\u001b[0m \u001b[0mblockage_deficitModel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeflectionModel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdeflectionModel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 395\u001b[0m turbulenceModel=turbulenceModel, groundModel=groundModel)\n",
- "\u001b[1;32mc:\\mmpe\\programming\\python\\topfarm\\pywake\\py_wake\\wind_farm_models\\engineering_models.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, site, windTurbines, wake_deficitModel, rotorAvgModel, superpositionModel, blockage_deficitModel, deflectionModel, turbulenceModel, groundModel)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwake_deficitModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeficitModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrotorAvgModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRotorAvgModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 53\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msuperpositionModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSuperpositionModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 54\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mblockage_deficitModel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblockage_deficitModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeficitModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mdeflectionModel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeflectionModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeflectionModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mAssertionError\u001b[0m: "
- ]
- }
- ],
- "source": [
- "# windFarmModel autogenerated by dropdown boxes\n",
- "t = time.time()\n",
- "wfm = PropagateDownwind(\n",
- " site,\n",
- " windTurbines,\n",
- " wake_deficitModel=NOJDeficit(\n",
- " k=0.1,\n",
- " use_effective_ws=False),\n",
- " rotorAvgModel=RotorCenter(),\n",
- " superpositionModel=LinearSum(),\n",
- " deflectionModel=None,\n",
- " turbulenceModel=None,\n",
- " groundModel=NoGround())\n",
- "sim_res = wfm(x,y)\n",
- "plt.figure(figsize=(12,8))\n",
- "sim_res.flow_map(wd=270).plot_wake_map()\n",
- "print (wfm)\n",
- "print (\"Computation time (AEP + flowmap):\", time.time()-t)\n",
- "plt.title('AEP: %.2fGWh'%(sim_res.aep().sum()))\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- },
- "toc": {
- "base_numbering": 1,
- "nav_menu": {},
- "number_sections": true,
- "sideBar": true,
- "skip_h1_title": false,
- "title_cell": "Table of Contents",
- "title_sidebar": "Contents",
- "toc_cell": false,
- "toc_position": {},
- "toc_section_display": true,
- "toc_window_display": true
- }
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Experiment: Combine Models\n",
+ "\n",
+ "In this notebook, you can combine the difference models of py_wake and see the effects in terms of AEP and a flow map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initialization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install PyWake if needed\n",
+ "try:\n",
+ " import py_wake\n",
+ "except ModuleNotFoundError:\n",
+ " !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import all available models\n",
+ "from py_wake.deficit_models import *\n",
+ "from py_wake.wind_farm_models import *\n",
+ "from py_wake.rotor_avg_models import *\n",
+ "from py_wake.superposition_models import *\n",
+ "from py_wake.deflection_models import *\n",
+ "from py_wake.turbulence_models import *\n",
+ "from py_wake.ground_models import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup site, wind turbines\n",
+ "from py_wake.examples.data.iea37._iea37 import IEA37Site, IEA37_WindTurbines\n",
+ "site = IEA37Site(16)\n",
+ "windTurbines = IEA37_WindTurbines()\n",
+ "x,y = site.initial_position.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>.widget-label { min-width: 20ex !important; }</style>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# prepare for the model combination tool\n",
+ "from py_wake.utils.model_utils import get_models, get_signature\n",
+ "from ipywidgets import interact\n",
+ "from IPython.display import HTML, display, Javascript\n",
+ "import time\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Fix ipywidget label width\n",
+ "display(HTML('''<style>.widget-label { min-width: 20ex !important; }</style>'''))\n",
+ "\n",
+ "\n",
+ "def print_signature(windFarmModel, **kwargs):\n",
+ " s = \"\"\"# windFarmModel autogenerated by dropdown boxes\n",
+ "t = time.time()\n",
+ "wfm = %s\n",
+ "sim_res = wfm(x,y)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "sim_res.flow_map(wd=270).plot_wake_map()\n",
+ "print (wfm)\n",
+ "print (\"Computation time (AEP + flowmap):\", time.time()-t)\n",
+ "plt.title('AEP: %%.2fGWh'%%(sim_res.aep().sum()))\n",
+ "\"\"\"% get_signature(windFarmModel, kwargs, 1)\n",
+ " \n",
+ " # Write windFarmModel code to cell starting \"# windFarmModel autogenerated by dropdown boxes\"\n",
+ " display(Javascript(\"\"\"\n",
+ "for (var cell of IPython.notebook.get_cells()) {\n",
+ " if (cell.get_text().startsWith(\"# windFarmModel autogenerated by dropdown boxes\")){\n",
+ " cell.set_text(`%s`);\n",
+ " cell.execute();\n",
+ " }\n",
+ "}\"\"\"%s))\n",
+ "\n",
+ "# setup list of models\n",
+ "models = {n:[(getattr(m,'__name__',m), m) for m in get_models(cls)] \n",
+ " for n,cls in [('windFarmModel', WindFarmModel),\n",
+ " ('wake_deficitModel', WakeDeficitModel),\n",
+ " ('rotorAvgModel', RotorAvgModel),\n",
+ " ('superpositionModel', SuperpositionModel),\n",
+ " ('blockage_deficitModel', BlockageDeficitModel),\n",
+ " ('deflectionModel',DeflectionModel),\n",
+ " ('turbulenceModel', TurbulenceModel),\n",
+ " ('groundModel', GroundModel)\n",
+ " ]}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Combine and execute model\n",
+ "\n",
+ "Combine your model via the dropdown boxes below\n",
+ "Choosing a different model updates and executes the the code cell below which runs the wind farm model, prints the AEP and plots a flow map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c288db0e4d214e6e9f533ab492afc97a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(Dropdown(description='windFarmModel', options=(('PropagateDownwind', <class 'py_wake.win…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "_ = interact(print_signature, **models)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PropagateDownwind(EngineeringWindFarmModel, NOJDeficit-wake, RotorCenter-rotor-average, LinearSum-superposition)\n",
+ "Computation time (AEP + flowmap): 2.721977949142456\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'AEP: 355.83GWh')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x576 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# windFarmModel autogenerated by dropdown boxes\n",
+ "t = time.time()\n",
+ "wfm = PropagateDownwind(\n",
+ " site,\n",
+ " windTurbines,\n",
+ " wake_deficitModel=NOJDeficit(\n",
+ " k=0.1,\n",
+ " use_effective_ws=False),\n",
+ " rotorAvgModel=RotorCenter(),\n",
+ " superpositionModel=LinearSum(),\n",
+ " deflectionModel=None,\n",
+ " turbulenceModel=None,\n",
+ " groundModel=NoGround())\n",
+ "sim_res = wfm(x,y)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "sim_res.flow_map(wd=270).plot_wake_map()\n",
+ "print (wfm)\n",
+ "print (\"Computation time (AEP + flowmap):\", time.time()-t)\n",
+ "plt.title('AEP: %.2fGWh'%(sim_res.aep().sum()))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/notebooks/exercises/WakeDeflection.ipynb b/docs/notebooks/exercises/WakeDeflection.ipynb
index 268631552453ef2d2918020ca7221fde7b312ce2..5204195df63dbed49b6724d94d8c40e329a4a95d 100644
--- a/docs/notebooks/exercises/WakeDeflection.ipynb
+++ b/docs/notebooks/exercises/WakeDeflection.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Exercise: Combine Models\n",
+ "# Exercise: Wake deflection\n",
"\n",
"In this exercise you can investigate the wake-deflection effects of yaw-misalignment"
]
@@ -56,7 +56,7 @@
" ax1 = plt.figure(figsize=(20,4)).gca()\n",
" ax2 = plt.figure(figsize=(10,3)).gca()\n",
" \n",
- " sim_res = wfm(x, y, yaw_ilk=np.reshape([WT0,WT1,0],(3,1,1)), wd=270, ws=10)\n",
+ " sim_res = wfm(x, y, yaw=np.reshape([WT0,WT1,0],(3,1,1)), wd=270, ws=10)\n",
" sim_res.flow_map(HorizontalGrid(x = np.linspace(0,1400,200), y=np.linspace(-200,200,50))).plot_wake_map(ax=ax1)\n",
" ax1.set_xlim([-200,1400])\n",
" aep.append(sim_res.aep().values[:,0,0])\n",
@@ -80,7 +80,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e1be6c3aa87b45ef95efe446bd8914cf",
+ "model_id": "1a5be0d4df8746d19bd085b9e7501831",
"version_major": 2,
"version_minor": 0
},