# if data is not complete, move on. No need to fight here.
ndat = 10*60*20 # minutes*seconds/minute*samples/second
if len(data['Sonic_CupEqHorizSpeed_100m'][0][0][0].flatten()) != 12000:
continue
# make a vector of datetimes for the data
timerange = MET.make_datetime_vector(file)
# make a dataframe for the instrument at probeheight
sonicdat = MET.make_dataframe_for_height(data, timerange, probeheight=probeheight)
temp = sonicdat['Sonic_CupEqHorizSpeed_100m'].dropna()
if len(temp)<1000:
continue
# extract variables needed for classificiation of IEC events
cupspeed, winddir, sigma_data, params = MET.setup_IEC_params(sonicdat, probeheight=100)
# look for extreme wind speed model events
Ve01eventfound, Ve50eventfound = MET.find_EWM_events(cupspeed, params)
Ve01events.extend(Ve01eventfound)
Ve50events.extend(Ve50eventfound)
# look for extreme operating gust events
EOGeventfound = MET.find_EOG_events(cupspeed, params)
EOGevents.extend(EOGeventfound)
# look for extreme turbulence model events
ETMeventfound = MET.find_ETM_events(cupspeed, sigma_data, params)
ETMevents.extend(ETMeventfound)
# look for extreme direction change events
# make a dataframe for the instrument at probeheight
sonicdat = MET.make_dataframe_for_height(
data, timerange, probeheight=probeheight)
sonicdat_lo = MET.make_dataframe_for_height(data,
timerange,
probeheight=38)
sonicdat_hi = MET.make_dataframe_for_height(data,
timerange,
probeheight=122)
temp = sonicdat['WS'].dropna()
if len(temp) < 1000:
continue
# extract variables needed for classificiation of IEC events
params = MET.setup_IEC_params(sonicdat, probeheight=100)
# # look for extreme wind speed model events
# Ve01eventfound, Ve50eventfound = MET.find_EWM_events(sonicdat, params)
# Ve01events = pd.concat([Ve01events,Ve01eventfound])
# Ve50events = pd.concat([Ve50events,Ve50eventfound])
# # look for extreme operating gust events
# EOGeventfound = MET.find_EOG_events(sonicdat, params)
# EOGevents = pd.concat([EOGevents,EOGeventfound])
# # look for extreme turbulence model events
# ETMeventfound = MET.find_ETM_events(sonicdat, params)
# ETMevents = pd.concat([ETMevents,ETMeventfound])
# # look for extreme direction change events
# import vis as vis
import utils as utils
import pickle as pkl
# paths (must mount volume smb://nrel.gov/shared/wind/WindWeb/MetData/135mData/)
towerID = 'M5'
metDataPath = '/Volumes/135mData/{}Twr/20Hz/mat/'.format(towerID)
savepath = '/Users/nhamilto/Documents/Wake_Dynamics/SiteChar/data/IEC_4'
try:
os.makedirs(savepath)
except:
pass
# setup IEC parameters for NWTC
params = MET.setup_IEC_params()
# load some threshold data
alpha_pos = np.load(
'/Users/nhamilto/Documents/Wake_Dynamics/SiteChar/data/pos_alpha_limit.npy'
)
alpha_neg = np.load(
'/Users/nhamilto/Documents/Wake_Dynamics/SiteChar/data/neg_alpha_limit.npy'
)
alpha_reference_velocity = np.load(
'/Users/nhamilto/Documents/Wake_Dynamics/SiteChar/data/alpha_reference_velocity.npy'
)
#### Detect over given date range
# time range
years = [int(a) for a in np.arange(2017, 2019, 1)] #