

In [1]:

    
import numpy as np
from importlib import reload
import sowfa_precursor
sowfa_precursor = reload(sowfa_precursor)
%matplotlib inline

Creat sowfa_precursor object and enter neccisary user inputs



In [2]:

    
per48_15_5 =  sowfa_precursor.Sim(
    dir='/projects/windsim/mlawson/wake_steering/stableABLRuns/48HrPer_0.15m_5m',
    log='log.4.ABLSolver',
    time_dir='29000',
    avg_time=29500,
    avg_width=1000,
    z_level=90.0)
per48_15_5.input['heights'] = np.array([0,1/2,1,3/2,2,3,4,5])*per48_15_5.input['windHeight']









    



Read "zCell" from:
	/projects/windsim/mlawson/wake_steering/stableABLRuns/48HrPer_0.15m_5m/postProcessing/averaging/29000/hLevelsCell
Read SOWFA "setUp" file:
	/projects/windsim/mlawson/wake_steering/stableABLRuns/48HrPer_0.15m_5m/setUp
Set uStarMean = 0.34565311706 from SOWFA ".log" file:
	/projects/windsim/mlawson/wake_steering/stableABLRuns/48HrPer_0.15m_5m/log.4.ABLSolver

Make Plots



In [3]:

    
per48_15_5.theta_w_avg_cell()









    



Time from  29000.3421143 to  29999.833039099998



In [4]:

    
per48_15_5.Umean_avg_nonnormalized()



In [5]:

    
per48_15_5.Tmean_avg_nonnormalized()



In [6]:

    
per48_15_5.variances_avg_cell()