from py_wake.wind_turbines import WindTurbine
from py_wake.wind_turbines.power_ct_functions import PowerCtTabular
from py_wake.site._site import UniformSite
from py_wake.site.xrsite import XRSite
from py_wake.wind_farm_models import PropagateDownwind
from py_wake.deficit_models.gaussian import TurboGaussianDeficit
from py_wake.flow_map import XYGrid

import numpy as np
import xarray as xr

D = 126      #Diametro del wT
h = 90       #Altura del WT
U_ref = 8


initial_position = np.array([[  1  , 1],
                             [  2. , 2],
                             [  4  , 4],
                             [  1  , 4],
                             [  2  , 4],
                             [  3  , 4],
                             [  1  , 5],
                             [  2  , 5],
                             [  5  , 5]])


def py_wake_Initial_Cong(D,name,h,U_ref,initial_position=initial_position):
    
    #Curvas del WT
    power_curve = np.array([
			[ 0.1, 1.000],
            [ 4.5, 267.7],
			[ 5.0, 387.6],
			[ 5.5, 534.0],
			[ 6.0, 707.4],
			[ 6.5, 910.0],
			[ 7.0, 1142.7],
			[ 7.5, 1407.5],
			[ 8.0, 1707.1],
			[ 8.5, 2047.3],
			[ 9.0, 2430.6]]) * [1, 1000]


    ct_curve = np.array([
            [ 0.1, 0.100],
			[ 4.5, 0.928],
			[ 5.0, 0.892],
			[ 5.5, 0.861],
			[ 6.0, 0.835],
			[ 6.5, 0.812],
			[ 7.0, 0.792],
			[ 7.5, 0.776],
			[ 8.0, 0.7702530978349217],
			[ 8.5, 0.762],
			[ 9.0, 0.763]])

    
    return WindTurbine(name=name,
                                diameter=D,
                                hub_height=h,
                                powerCtFunction=PowerCtTabular(power_curve[:, 0],
                                                                power_curve[:, 1], 
                                                                'w',ct_curve[:, 1],
                                                                method='linear'))
    





windTurbines = py_wake_Initial_Cong(D, 'NREL_5MW', h, U_ref)

def run(initial_position=initial_position, U_ref=U_ref):
    initial_position = np.array(initial_position);
    #site = UniformSite(p_wd=[1],
    #                        ws=U_ref,
    #                        initial_position=initial_position*D)
    ds = xr.Dataset(
        data_vars={'P': ('wd', [1])},
        coords={'wd': [0]})
    ds['TI'] = 0.1
    site = XRSite(ds, interp_method='nearest', initial_position=initial_position*D, default_ws=np.atleast_1d(U_ref))

    wt_x, wt_y = site.initial_position.T/D
    wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=TurboGaussianDeficit())
    grid = XYGrid(x=np.arange(0, 10.01, 0.1)*126, y=np.arange(0, 10.01, 0.1)*126)
    xa = wfm(x=wt_x*126, y=wt_y*126, wd=0, yaw=0).flow_map(grid)
    ws = xa.WS_eff
    return ws[:,:,0,0].values[:,:,0].tolist()