diff --git a/examples_artificial_data/04_floris_tuning/develop_ws_est.ipynb b/examples_artificial_data/04_floris_tuning/develop_ws_est.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Develop ws estimator\n",
+ "\n",
+ "This is likely a temporary example while working out the wins speed estimator method"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from floris import FlorisModel, TimeSeries\n",
+ "\n",
+ "from flasc.utilities.floris_tools import estimate_ws_with_floris"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load FlorisModel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "file_path = Path.cwd()\n",
+ "fm_path = file_path / \"../floris_input_artificial/gch.yaml\"\n",
+ "fm = FlorisModel(fm_path)\n",
+ "\n",
+ "# Set to 1 turbine layout\n",
+ "fm.set(layout_x=[0], layout_y=[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set to\n",
+ "N = 25\n",
+ "wind_speeds = np.linspace(0.01, 20.0, N)\n",
+ "time_series = TimeSeries(\n",
+ " wind_speeds=wind_speeds, wind_directions=270.0, turbulence_intensities=0.06\n",
+ ")\n",
+ "\n",
+ "fm.set(wind_data=time_series)\n",
+ "\n",
+ "fm.run()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " time | \n",
+ " pow_000 | \n",
+ " ws_000 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 0 | \n",
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+ " 2 | \n",
+ " 2 | \n",
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+ " 3 | \n",
+ " 3 | \n",
+ " 0.000000 | \n",
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+ " 4 | \n",
+ " 4 | \n",
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+ " 5 | \n",
+ " 5 | \n",
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+ " \n",
+ " 6 | \n",
+ " 6 | \n",
+ " 401.871370 | \n",
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+ " \n",
+ " 7 | \n",
+ " 7 | \n",
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+ " \n",
+ " 8 | \n",
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+ " \n",
+ " 9 | \n",
+ " 9 | \n",
+ " 1450.108488 | \n",
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+ " \n",
+ " 10 | \n",
+ " 10 | \n",
+ " 2004.154084 | \n",
+ " 9.339167 | \n",
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\n",
+ " \n",
+ " 11 | \n",
+ " 11 | \n",
+ " 2650.508644 | \n",
+ " 10.172083 | \n",
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+ " \n",
+ " 12 | \n",
+ " 12 | \n",
+ " 3422.430855 | \n",
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+ " \n",
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+ " \n",
+ " 14 | \n",
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+ " \n",
+ " 15 | \n",
+ " 15 | \n",
+ " 5000.000000 | \n",
+ " 13.503750 | \n",
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+ " \n",
+ " 16 | \n",
+ " 16 | \n",
+ " 5000.000000 | \n",
+ " 14.336667 | \n",
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+ " \n",
+ " 17 | \n",
+ " 17 | \n",
+ " 5000.000000 | \n",
+ " 15.169583 | \n",
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+ " \n",
+ " 18 | \n",
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+ " 5000.000000 | \n",
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+ " 19 | \n",
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+ " 20 | \n",
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+ " 5000.000000 | \n",
+ " 17.668333 | \n",
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+ " \n",
+ " 21 | \n",
+ " 21 | \n",
+ " 5000.000000 | \n",
+ " 18.501250 | \n",
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+ " 22 | \n",
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+ " 5000.000000 | \n",
+ " 19.334167 | \n",
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+ " \n",
+ " 23 | \n",
+ " 23 | \n",
+ " 5000.000000 | \n",
+ " 20.167083 | \n",
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+ " \n",
+ " 24 | \n",
+ " 24 | \n",
+ " 5000.000000 | \n",
+ " 21.000000 | \n",
+ "
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+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " time pow_000 ws_000\n",
+ "0 0 0.000000 1.010000\n",
+ "1 1 0.000000 1.842917\n",
+ "2 2 0.000000 2.675833\n",
+ "3 3 0.000000 3.508750\n",
+ "4 4 85.871284 4.341667\n",
+ "5 5 214.061051 5.174583\n",
+ "6 6 401.871370 6.007500\n",
+ "7 7 677.927512 6.840417\n",
+ "8 8 1030.443191 7.673333\n",
+ "9 9 1450.108488 8.506250\n",
+ "10 10 2004.154084 9.339167\n",
+ "11 11 2650.508644 10.172083\n",
+ "12 12 3422.430855 11.005000\n",
+ "13 13 4328.813099 11.837917\n",
+ "14 14 5000.000000 12.670833\n",
+ "15 15 5000.000000 13.503750\n",
+ "16 16 5000.000000 14.336667\n",
+ "17 17 5000.000000 15.169583\n",
+ "18 18 5000.000000 16.002500\n",
+ "19 19 5000.000000 16.835417\n",
+ "20 20 5000.000000 17.668333\n",
+ "21 21 5000.000000 18.501250\n",
+ "22 22 5000.000000 19.334167\n",
+ "23 23 5000.000000 20.167083\n",
+ "24 24 5000.000000 21.000000"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Make a df_scada from the data\n",
+ "df_scada = pd.DataFrame(\n",
+ " {\n",
+ " \"time\": np.arange(0, N),\n",
+ " \"pow_000\": fm.get_turbine_powers().squeeze() / 1000.0,\n",
+ " \"ws_000\": wind_speeds + 1.0, # Add 1m/s bias\n",
+ " }\n",
+ ")\n",
+ "df_scada"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ " \n",
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+ " ws_000 | \n",
+ " ws_est_000 | \n",
+ " ws_est_gain_000 | \n",
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+ " 5 | \n",
+ " 5 | \n",
+ " 214.061051 | \n",
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+ " 4.182593 | \n",
+ " 0.978554 | \n",
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+ " \n",
+ " 6 | \n",
+ " 6 | \n",
+ " 401.871370 | \n",
+ " 6.007500 | \n",
+ " 4.991029 | \n",
+ " 1.000000 | \n",
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\n",
+ " \n",
+ " 7 | \n",
+ " 7 | \n",
+ " 677.927512 | \n",
+ " 6.840417 | \n",
+ " 5.821206 | \n",
+ " 1.000000 | \n",
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\n",
+ " \n",
+ " 8 | \n",
+ " 8 | \n",
+ " 1030.443191 | \n",
+ " 7.673333 | \n",
+ " 6.651383 | \n",
+ " 1.000000 | \n",
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\n",
+ " \n",
+ " 9 | \n",
+ " 9 | \n",
+ " 1450.108488 | \n",
+ " 8.506250 | \n",
+ " 7.481560 | \n",
+ " 1.000000 | \n",
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+ " \n",
+ " 10 | \n",
+ " 10 | \n",
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+ " \n",
+ " 11 | \n",
+ " 11 | \n",
+ " 2650.508644 | \n",
+ " 10.172083 | \n",
+ " 9.141914 | \n",
+ " 1.000000 | \n",
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\n",
+ " \n",
+ " 12 | \n",
+ " 12 | \n",
+ " 3422.430855 | \n",
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+ " 10.090822 | \n",
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\n",
+ " \n",
+ " 13 | \n",
+ " 13 | \n",
+ " 4328.813099 | \n",
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+ " 11.837917 | \n",
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\n",
+ " \n",
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+ " 14 | \n",
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+ " \n",
+ " 16 | \n",
+ " 16 | \n",
+ " 5000.000000 | \n",
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+ " 14.336667 | \n",
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+ " 5000.000000 | \n",
+ " 16.002500 | \n",
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+ " 19 | \n",
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+ " 5000.000000 | \n",
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+ " 0.000000 | \n",
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+ " \n",
+ " 20 | \n",
+ " 20 | \n",
+ " 5000.000000 | \n",
+ " 17.668333 | \n",
+ " 17.668333 | \n",
+ " 0.000000 | \n",
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+ " \n",
+ " 21 | \n",
+ " 21 | \n",
+ " 5000.000000 | \n",
+ " 18.501250 | \n",
+ " 18.501250 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " 22 | \n",
+ " 22 | \n",
+ " 5000.000000 | \n",
+ " 19.334167 | \n",
+ " 19.334167 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " 23 | \n",
+ " 23 | \n",
+ " 5000.000000 | \n",
+ " 20.167083 | \n",
+ " 20.167083 | \n",
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+ " \n",
+ " 24 | \n",
+ " 24 | \n",
+ " 5000.000000 | \n",
+ " 21.000000 | \n",
+ " 21.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " time pow_000 ws_000 ws_est_000 ws_est_gain_000\n",
+ "0 0 0.000000 1.010000 1.370988 0.190999\n",
+ "1 1 0.000000 1.842917 2.211321 0.348510\n",
+ "2 2 0.000000 2.675833 2.789266 0.506021\n",
+ "3 3 0.000000 3.508750 3.104825 0.663532\n",
+ "4 4 85.871284 4.341667 3.511599 0.821043\n",
+ "5 5 214.061051 5.174583 4.182593 0.978554\n",
+ "6 6 401.871370 6.007500 4.991029 1.000000\n",
+ "7 7 677.927512 6.840417 5.821206 1.000000\n",
+ "8 8 1030.443191 7.673333 6.651383 1.000000\n",
+ "9 9 1450.108488 8.506250 7.481560 1.000000\n",
+ "10 10 2004.154084 9.339167 8.311737 1.000000\n",
+ "11 11 2650.508644 10.172083 9.141914 1.000000\n",
+ "12 12 3422.430855 11.005000 10.090822 0.885052\n",
+ "13 13 4328.813099 11.837917 11.837917 0.000000\n",
+ "14 14 5000.000000 12.670833 12.670833 0.000000\n",
+ "15 15 5000.000000 13.503750 13.503750 0.000000\n",
+ "16 16 5000.000000 14.336667 14.336667 0.000000\n",
+ "17 17 5000.000000 15.169583 15.169583 0.000000\n",
+ "18 18 5000.000000 16.002500 16.002500 0.000000\n",
+ "19 19 5000.000000 16.835417 16.835417 0.000000\n",
+ "20 20 5000.000000 17.668333 17.668333 0.000000\n",
+ "21 21 5000.000000 18.501250 18.501250 0.000000\n",
+ "22 22 5000.000000 19.334167 19.334167 0.000000\n",
+ "23 23 5000.000000 20.167083 20.167083 0.000000\n",
+ "24 24 5000.000000 21.000000 21.000000 0.000000"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_scada = estimate_ws_with_floris(df_scada, fm)\n",
+ "df_scada"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Calculate power with wind speed and estimated wind speed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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