def readFASTOutAscii(filename):
# Read with panda
f = weio.FASTOutFile(filename)
df = f.toDataFrame()
#df=pd.read_csv(filename, sep='\t', skiprows=[0,1,2,3,4,5,7])
#df.rename(columns=lambda x: x.strip(),inplace=True)
return df
def powerCurvePostProWindStep(outfile,
wndfile=None,
tWindow=20,
ColMap={},
WS=None,
KeepAll=False):
def renameCol(x):
for k, v in ColMap.items():
if x == v:
return k
return x
# --- Reading wind and output file
print('Reading input...')
out = weio.FASTOutFile(outfile).toDataFrame()
wnd = weio.FASTWndFile(wndfile).toDataFrame()
if len(ColMap) > 0:
out.rename(columns=renameCol, inplace=True)
#units=['('s.split('[')[1].split(']')[0] for s in out.columns.values
# --- Looping on wind values, and averaging data over a window
if WS is None:
WS = np.sort(np.unique(wnd['WindSpeed_[m/s]']))
print(WS)
tWS = out['WS_[m/s]'].values
t = out['Time_[s]'].values
tol = 0.01
Iall = np.arange(len(t))
result = None
for i, ws in enumerate(WS):
b = abs(tWS - ws) < tol
I = Iall[b]
iEnd = I[-1]
tEnd = t[iEnd]
tStart = t[iEnd] - tWindow
IWindow = (t > tStart) & (t < tEnd)
print('WS ', ws, ' t ', tStart, '-', tEnd)
MeanValues = pd.DataFrame(out[IWindow].mean()).transpose()
if i == 0:
result = MeanValues.copy()
else:
result = result.append(MeanValues, ignore_index=True)
# --- Sorting by WS
result.sort_values(['WS_[m/s]'], inplace=True)
result.reset_index(drop=True, inplace=True)
# --- Eliminating unnecessary columns
if len(ColMap) > 0 and (not KeepAll):
ColNames = list(ColMap.keys())
else:
ColNames = result.columns.values
result = result[ColNames]
# --- Rounding
result = result.round(4)
#result.plot(x='Wind1VelX',y=I)
#plt.show()
return result
def parametricPostPro(workdir,TimeAvgWindow=10,ColKeep=None,ColSort=None):
OutFiles=glob.glob(os.path.join(workdir,'*.outb'))
result=None
if len(OutFiles)<=0:
raise Exception('No outb files found in {}'.format(workdir))
for i,f in enumerate(OutFiles):
print(f)
df=weio.FASTOutFile(f).toDataFrame()
if ColKeep is not None:
df=df[ColKeep]
## Start time and end time of window
iEndTime=df.index[-1]
endTime=df['Time_[s]'][iEndTime]
iStartTime=(df['Time_[s]']-(endTime-TimeAvgWindow)).abs().idxmin()
startTime=df['Time_[s]'][df.index[iStartTime]]
## Absolute and relative differences at wind extremities
DeltaValuesAbs=(df.iloc[iEndTime]-df.iloc[iStartTime]).abs()
DeltaValuesRel=(df.iloc[iEndTime]-df.iloc[iStartTime]).abs()/df.iloc[iEndTime]
EndValues=df.iloc[iEndTime]
## Stats values during window
IWindow=df['Time_[s]']>startTime
MeanValues = pd.DataFrame(df[IWindow].mean()).transpose()
StdValues = df[IWindow].std()
if i==0:
result = MeanValues.copy()
else:
result=result.append(MeanValues, ignore_index=True)
if ColSort is not None:
# Sorting
result.sort_values([ColSort],inplace=True,ascending=True)
result.reset_index(drop=True,inplace=True)
#result.drop(['Time'],axis=1,inplace=True)
#result.drop(['Wind1VelY'],axis=1,inplace=True)
#result.drop(['Wind1VelZ'],axis=1,inplace=True)
#result.rename(columns=lambda x:'WS' if x=='Wind1VelX' else x,inplace=True)
#result.rename(columns=lambda x:'RPM' if x=='RotSpeed' else x,inplace=True)
#result.rename(columns=lambda x:'Pitch' if x=='BldPitch1' else x,inplace=True)
#result.plot(x='Wind1VelX',y=I)
#print(result)
#plt.show()
return result
n = 1
# save plot and/or data?
plt_grid = True # creates subplot for each observer if True
save_fig = False
save_data = True
#########################################################################################################################################
# produces full path
AAfilename = input_dir + '/' + AAname
OF2filename = input_dir + '/' + OF2name
outputfilename = output_dir + '/' + outputname + "AAOutputFile3"
# read file data
AA_3 = weio.FASTOutFile(AAfilename).toDataFrame()
OF2 = weio.FASTOutFile(OF2filename).toDataFrame()
# determine number of observers
num_obs = (AA_3.shape[1] - 1) / (7 * 34)
# calculate sample time for n revolutions
rpm = OF2[["RotSpeed_[rpm]"]].mean()[0]
time_revs = n * 60 / rpm
tot_time = AA_3["Time_[s]"].max()
if time_revs < tot_time:
sample_time = tot_time - time_revs
else:
print(
"Error: Time for number of revolutions exceeds simulation time. Reduce n."
)
) + os.sep + 'reg_tests' + os.sep + 'r-test' + os.sep + 'glue-codes' + os.sep + 'openfast' + os.sep + 'IEA_LB_RWT-AeroAcoustics'
else:
FAST_directory = os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
) + os.sep + 'openfast' + os.sep + 'reg_tests' + os.sep + 'r-test' + os.sep + 'glue-codes' + os.sep + 'openfast' + os.sep + 'IEA_LB_RWT-AeroAcoustics'
AAfilename = FAST_directory + os.sep + 'IEA_LB_RWT-AeroAcoustics_1.out'
OFfilename = FAST_directory + os.sep + 'IEA_LB_RWT-AeroAcoustics.out'
locfilename = FAST_directory + os.sep + 'AA_ObserverLocations_Map.dat'
output_dir = os.path.dirname(os.path.realpath(__file__))
outputfilename = output_dir + os.sep + "data_output1"
#########################################################################################################################################
## Read in data, manipulate it, and plot it
# reads in file data
AA_1 = weio.FASTOutFile(AAfilename).toDataFrame()
OF = weio.FASTOutFile(OFfilename).toDataFrame()
location = pd.read_csv(locfilename,
delimiter='\s+',
skiprows=[0, 1],
names=['x', 'y', 'z'])
# determine number of observers
num_obs = AA_1.shape[1] - 1
# calculate sample time for n revolutions
rpm = OF[["RotSpeed_[rpm]"]].mean()[0]
yaw = OF[["YawPzn_[deg]"]].mean()[0] / 180. * np.pi
time_revs = n * 60 / rpm
tot_time = AA_1["Time_[s]"].max()
if time_revs < tot_time: