<gh_stars>10-100
''' This example runs a linear turbine simulation
0. Run gen_linear_model() in WEIS/wies/aeroelasticse/LinearFAST.py  (separately)
1. Set up linear turbine model by running mbc transformation
2. Load controller from ROSCO_toolbox and set up linear control model
3. Use wind disturbance to determine operating point for turbine, pitch controller
4. Run linear simulation

'''

import numpy as np
import pandas as pd
import os
import weis.control.LinearModel as lin_mod
import weis.aeroelasticse.LinearFAST as lin_fast
from ROSCO_toolbox import utilities as ROSCO_utilities
from ROSCO_toolbox.ofTools.fast_io import output_processing
from ROSCO_toolbox import controller as ROSCO_controller
from ROSCO_toolbox import turbine as ROSCO_turbine
from pCrunch.Analysis import Loads_Analysis
import yaml
import matplotlib.pyplot as plt

PLOT = False


if __name__ == '__main__':

    weis_dir = os.path.dirname(os.path.dirname(os.path.dirname( __file__)))
    
    # 0. Load linear models from gen_linear_model() in WEIS/wies/aeroelasticse/LinearFAST.py
    lin_fast.gen_linear_model([16])


    # 1. Set up linear turbine model by running mbc transformation
    if True:  
        # Load system from OpenFAST .lin files
        lin_file_dir = os.path.join(weis_dir,'outputs/iea_semi_lin')
        linTurb = lin_mod.LinearTurbineModel(lin_file_dir,reduceStates=False)
    
    else:  
        # Load system from .mat file
        linTurb = lin_mod.LinearTurbineModel('/Users/dzalkind/Tools/matlab-toolbox/Simulations/SaveData/LinearModels/PitTwr.mat', \
            fromMat=True)


    # 1.5 Set up wind disturbance
    if True:   
        # step wind input
        tt = np.arange(0,200,1/80)
        u_h = 16 * np.ones((tt.shape))
        u_h[tt > 100] = 17

    else:
        # load wind disturbance from output file
        fast_io     = ROSCO_utilities.FAST_IO()
        fast_out    = output_processing.load_fast_out('/Users/dzalkind/Tools/matlab-toolbox/Simulations/SaveData/072720_183300.out')
        u_h         = fast_out[0]['RtVAvgxh']
        tt          = fast_out[0]['Time']

    
    # 2. Load controller from ROSCO_toolbox and set up linear control model
    if True:
        # Load controller from yaml file 
        parameter_filename  = os.path.join(weis_dir,'ROSCO_toolbox/Tune_Cases/IEA15MW.yaml')
        inps = yaml.safe_load(open(parameter_filename))
        path_params         = inps['path_params']
        turbine_params      = inps['turbine_params']
        controller_params   = inps['controller_params']

        controller_params['omega_pc'] = 0.2

        # Instantiate turbine, controller, and file processing classes
        turbine         = ROSCO_turbine.Turbine(turbine_params)
        controller      = ROSCO_controller.Controller(controller_params)

        # Load turbine data from OpenFAST and rotor performance text file
        turbine.load_from_fast(path_params['FAST_InputFile'],path_params['FAST_directory'],dev_branch=True,rot_source='txt',txt_filename=path_params['rotor_performance_filename'])

        # Tune controller 
        controller.tune_controller(turbine)

        controller.turbine = turbine

        linCont = lin_mod.LinearControlModel(controller)
        
    else:
        # Load Controller from DISCON.IN
        fp = ROSCO_utilities.FileProcessing()
        f = fp.read_DISCON('/Users/dzalkind/Tools/SaveData/Float_Test/UM_DLC0_100_DISCON.IN')

        linCont = lin_mod.LinearControlModel([],fromDISCON_IN=True,DISCON_file=f)


    # 3. Use wind disturbance to determine operating point for turbine, pitch controller
    # 4. Run linear simulation
    Lin_OutList, Lin_OutData, P_cl = linTurb.solve(tt,u_h,Plot=False,open_loop=False,controller=linCont)
    # linear plot
    if True:
        channels = ['RtVAvgxh','GenSpeed','BldPitch','TwrBsMyt','PtfmPitch']
        ax = [None] * len(channels)
        plt.figure(2)

        for iPlot in range(0,len(channels)):
            ax[iPlot] = plt.subplot(len(channels),1,iPlot+1)
            try:
                ax[iPlot].plot(tt,Lin_OutData[channels[iPlot]])
            except:
                print(channels[iPlot] + ' is not in Linearization OutList')
            ax[iPlot].set_ylabel(channels[iPlot])
            ax[iPlot].grid(True)
            if not iPlot == (len(channels) - 1):
                ax[iPlot].set_xticklabels([])

        if PLOT:
            plt.show()


    # sweep controller.pc_omega and run linearization
    fast_data_lin = []

    if True:
        ww = np.linspace(.05,.45,4)

        for om in ww:
            # Tune controller 
            controller.omega_pc = om
            controller.tune_controller(turbine)

            controller.turbine = turbine

            # update linear controller
            linCont = lin_mod.LinearControlModel(controller)

            # solve 
            Lin_OutList, Lin_OutData, P_cl = linTurb.solve(tt,u_h,Plot=False,open_loop=False,controller=linCont)

            # convert into pCrunch form
            fd_lin  = {}
            fd_lin['TwrBsMyt'] = Lin_OutData['TwrBsMyt']
            fd_lin['meta']    = {}
            fd_lin['meta']['name'] = 'omega: ' + str(om)
            fast_data_lin.append(fd_lin)

            comp_channels = ['RtVAvgxh','GenSpeed','TwrBsMyt','PtfmPitch']
            ax = [None] * len(comp_channels)
            plt.figure(3)

            for iPlot in range(0,len(comp_channels)):
                ax[iPlot] = plt.subplot(len(comp_channels),1,iPlot+1,label=comp_channels[iPlot])

                try:
                    ax[iPlot].plot(tt,Lin_OutData[comp_channels[iPlot]])
                except:
                    print(comp_channels[iPlot] + ' is not in Linearization OutList')
                ax[iPlot].set_ylabel(comp_channels[iPlot])
                ax[iPlot].grid(True)
                if not iPlot == (len(comp_channels) - 1):
                    ax[iPlot].set_xticklabels([])

        if PLOT:
            plt.show()
        

    # Try some post processing using pCrunch
    chan_info = [('TwrBsMyt',4)]

    la = Loads_Analysis()
    fDEL = la.get_DEL(fast_data_lin,chan_info)
    # print('here')





''' This example runs a linear turbine simulation
0. Run gen_linear_model() in WEIS/wies/aeroelasticse/LinearFAST.py  (separately)
1. Set up linear turbine model by running mbc transformation
2. Load controller from ROSCO_toolbox and set up linear control model
3. Use wind disturbance to determine operating point for turbine, pitch controller
4. Run linear simulation

'''

import numpy as np
import pandas as pd
import os
import weis.control.LinearModel as lin_mod
import weis.aeroelasticse.LinearFAST as lin_fast
from ROSCO_toolbox import utilities as ROSCO_utilities
from ROSCO_toolbox.ofTools.fast_io import output_processing
from ROSCO_toolbox import controller as ROSCO_controller
from ROSCO_toolbox import turbine as ROSCO_turbine
from pCrunch.Analysis import Loads_Analysis
import yaml
import matplotlib.pyplot as plt

PLOT = False


if __name__ == '__main__':

    weis_dir = os.path.dirname(os.path.dirname(os.path.dirname( __file__)))
    
    # 0. Load linear models from gen_linear_model() in WEIS/wies/aeroelasticse/LinearFAST.py
    lin_fast.gen_linear_model([16])


    # 1. Set up linear turbine model by running mbc transformation
    if True:  
        # Load system from OpenFAST .lin files
        lin_file_dir = os.path.join(weis_dir,'outputs/iea_semi_lin')
        linTurb = lin_mod.LinearTurbineModel(lin_file_dir,reduceStates=False)
    
    else:  
        # Load system from .mat file
        linTurb = lin_mod.LinearTurbineModel('/Users/dzalkind/Tools/matlab-toolbox/Simulations/SaveData/LinearModels/PitTwr.mat', \
            fromMat=True)


    # 1.5 Set up wind disturbance
    if True:   
        # step wind input
        tt = np.arange(0,200,1/80)
        u_h = 16 * np.ones((tt.shape))
        u_h[tt > 100] = 17

    else:
        # load wind disturbance from output file
        fast_io     = ROSCO_utilities.FAST_IO()
        fast_out    = output_processing.load_fast_out('/Users/dzalkind/Tools/matlab-toolbox/Simulations/SaveData/072720_183300.out')
        u_h         = fast_out[0]['RtVAvgxh']
        tt          = fast_out[0]['Time']

    
    # 2. Load controller from ROSCO_toolbox and set up linear control model
    if True:
        # Load controller from yaml file 
        parameter_filename  = os.path.join(weis_dir,'ROSCO_toolbox/Tune_Cases/IEA15MW.yaml')
        inps = yaml.safe_load(open(parameter_filename))
        path_params         = inps['path_params']
        turbine_params      = inps['turbine_params']
        controller_params   = inps['controller_params']

        controller_params['omega_pc'] = 0.2

        # Instantiate turbine, controller, and file processing classes
        turbine         = ROSCO_turbine.Turbine(turbine_params)
        controller      = ROSCO_controller.Controller(controller_params)

        # Load turbine data from OpenFAST and rotor performance text file
        turbine.load_from_fast(path_params['FAST_InputFile'],path_params['FAST_directory'],dev_branch=True,rot_source='txt',txt_filename=path_params['rotor_performance_filename'])

        # Tune controller 
        controller.tune_controller(turbine)

        controller.turbine = turbine

        linCont = lin_mod.LinearControlModel(controller)
        
    else:
        # Load Controller from DISCON.IN
        fp = ROSCO_utilities.FileProcessing()
        f = fp.read_DISCON('/Users/dzalkind/Tools/SaveData/Float_Test/UM_DLC0_100_DISCON.IN')

        linCont = lin_mod.LinearControlModel([],fromDISCON_IN=True,DISCON_file=f)


    # 3. Use wind disturbance to determine operating point for turbine, pitch controller
    # 4. Run linear simulation
    Lin_OutList, Lin_OutData, P_cl = linTurb.solve(tt,u_h,Plot=False,open_loop=False,controller=linCont)
    # linear plot
    if True:
        channels = ['RtVAvgxh','GenSpeed','BldPitch','TwrBsMyt','PtfmPitch']
        ax = [None] * len(channels)
        plt.figure(2)

        for iPlot in range(0,len(channels)):
            ax[iPlot] = plt.subplot(len(channels),1,iPlot+1)
            try:
                ax[iPlot].plot(tt,Lin_OutData[channels[iPlot]])
            except:
                print(channels[iPlot] + ' is not in Linearization OutList')
            ax[iPlot].set_ylabel(channels[iPlot])
            ax[iPlot].grid(True)
            if not iPlot == (len(channels) - 1):
                ax[iPlot].set_xticklabels([])

        if PLOT:
            plt.show()


    # sweep controller.pc_omega and run linearization
    fast_data_lin = []

    if True:
        ww = np.linspace(.05,.45,4)

        for om in ww:
            # Tune controller 
            controller.omega_pc = om
            controller.tune_controller(turbine)

            controller.turbine = turbine

            # update linear controller
            linCont = lin_mod.LinearControlModel(controller)

            # solve 
            Lin_OutList, Lin_OutData, P_cl = linTurb.solve(tt,u_h,Plot=False,open_loop=False,controller=linCont)

            # convert into pCrunch form
            fd_lin  = {}
            fd_lin['TwrBsMyt'] = Lin_OutData['TwrBsMyt']
            fd_lin['meta']    = {}
            fd_lin['meta']['name'] = 'omega: ' + str(om)
            fast_data_lin.append(fd_lin)

            comp_channels = ['RtVAvgxh','GenSpeed','TwrBsMyt','PtfmPitch']
            ax = [None] * len(comp_channels)
            plt.figure(3)

            for iPlot in range(0,len(comp_channels)):
                ax[iPlot] = plt.subplot(len(comp_channels),1,iPlot+1,label=comp_channels[iPlot])

                try:
                    ax[iPlot].plot(tt,Lin_OutData[comp_channels[iPlot]])
                except:
                    print(comp_channels[iPlot] + ' is not in Linearization OutList')
                ax[iPlot].set_ylabel(comp_channels[iPlot])
                ax[iPlot].grid(True)
                if not iPlot == (len(comp_channels) - 1):
                    ax[iPlot].set_xticklabels([])

        if PLOT:
            plt.show()
        

    # Try some post processing using pCrunch
    chan_info = [('TwrBsMyt',4)]

    la = Loads_Analysis()
    fDEL = la.get_DEL(fast_data_lin,chan_info)
    # print('here')
This example runs a linear turbine simulation 0. Run gen_linear_model() in WEIS/wies/aeroelasticse/LinearFAST.py  (separately) 1. Set up linear turbine model by running mbc transformation 2. Load controller from ROSCO_toolbox and set up linear control model 3. Use wind disturbance to determine operating point for turbine, pitch controller 4. Run linear simulation # 0. Load linear models from gen_linear_model() in WEIS/wies/aeroelasticse/LinearFAST.py # 1. Set up linear turbine model by running mbc transformation # Load system from OpenFAST .lin files # Load system from .mat file # 1.5 Set up wind disturbance # step wind input # load wind disturbance from output file # 2. Load controller from ROSCO_toolbox and set up linear control model # Load controller from yaml file # Instantiate turbine, controller, and file processing classes # Load turbine data from OpenFAST and rotor performance text file # Tune controller # Load Controller from DISCON.IN # 3. Use wind disturbance to determine operating point for turbine, pitch controller # 4. Run linear simulation # linear plot # sweep controller.pc_omega and run linearization # Tune controller # update linear controller # solve # convert into pCrunch form # Try some post processing using pCrunch # print('here')