def synthetic(hsfile, spectrum, sr):
curpath = os.path.dirname(os.path.abspath(__file__))
with open(os.path.join(curpath,hsfile),'r') as f:
raw = f.read().split('\n')
data = [[float(r.split(',')[0]),float(r.split(',')[1])] for r in raw if r]
"""
constants for spectrum and sea water level generation. we define them outside the loop to save some time
"""
max_t = 3600 # maximum duration of time series
nyq_f = sr/2 # nyquist frequency of spectra: highest frequency bin
del_f = 1.0/max_t # interval between frequency bins
f = np.linspace(del_f,nyq_f,max_t) # array containing all frequency bins
t = np.linspace(0,max_t,max_t*sr)
#t = np.reshape(t, (t.size,1))
# lists of relevant hourly data
hmean = []
hmax = []
hhts = []
hht10 = []
index = 0
# jonswap
if spectrum == 0:
for dat in data:
index += 1
# generate spectrum
S = wave_util.jonswap(dat[0], dat[1], 3600, f)
# generate elevation data
E = wave_util.ema(S, 0.5, del_f)
# zerodowncross
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
# wallops
elif spectrum == 1:
for dat in data:
index += 1
# generate spectrum
S = wave_util.wallops(dat[0], dat[1], 10.0, f)
# generate elevation data
E = wave_util.ema(S, 0.5, del_f)
# zerodowncross
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
# custom spectrum -- based on bert-schneider
elif spectrum == 2:
for dat in data:
index += 1
#generate spectrum
S = wave_util.genspec(hs=dat[0], ts=dat[1]-1., f=f, foffset=0, A=0.072, C=0)
E = wave_util.ema(S, 0.5, del_f)
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
data[:] = np.asarray(data)
hhts[:] = np.asarray(hhts)
print np.mean(data), np.mean(hhts)
return (hmean, hmax, hhts, hht10, data)
def synthetic(hsfile, spectrum, sr):
curpath = os.path.dirname(os.path.abspath(__file__))
with open(os.path.join(curpath, hsfile), 'r') as f:
raw = f.read().split('\n')
data = [[float(r.split(',')[0]), float(r.split(',')[1])] for r in raw if r]
"""
constants for spectrum and sea water level generation. we define them outside the loop to save some time
"""
max_t = 3600 # maximum duration of time series
nyq_f = sr / 2 # nyquist frequency of spectra: highest frequency bin
del_f = 1.0 / max_t # interval between frequency bins
f = np.linspace(del_f, nyq_f, max_t) # array containing all frequency bins
t = np.linspace(0, max_t, max_t * sr)
#t = np.reshape(t, (t.size,1))
# lists of relevant hourly data
hmean = []
hmax = []
hhts = []
hht10 = []
index = 0
# jonswap
if spectrum == 0:
for dat in data:
index += 1
# generate spectrum
S = wave_util.jonswap(dat[0], dat[1], 3600, f)
# generate elevation data
E = wave_util.ema(S, 0.5, del_f)
# zerodowncross
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
# wallops
elif spectrum == 1:
for dat in data:
index += 1
# generate spectrum
S = wave_util.wallops(dat[0], dat[1], 10.0, f)
# generate elevation data
E = wave_util.ema(S, 0.5, del_f)
# zerodowncross
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
# custom spectrum -- based on bert-schneider
elif spectrum == 2:
for dat in data:
index += 1
#generate spectrum
S = wave_util.genspec(hs=dat[0],
ts=dat[1] - 1.,
f=f,
foffset=0,
A=0.072,
C=0)
E = wave_util.ema(S, 0.5, del_f)
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
data[:] = np.asarray(data)
hhts[:] = np.asarray(hhts)
print np.mean(data), np.mean(hhts)
return (hmean, hmax, hhts, hht10, data)
def maximizem(sr):
curpath = os.path.dirname(os.path.abspath(__file__))
with open(os.path.join(curpath,'hts_data'),'r') as f:
hts_raw = f.read().split('\n')
dhts = [[float(r.split(',')[0]),float(r.split(',')[1])] for r in hts_raw if r]
with open(os.path.join(curpath,'mean_data'),'r') as f:
mean_raw = f.read().split('\n')
dmean = [[float(r.split(',')[0]),float(r.split(',')[1])] for r in mean_raw if r]
with open(os.path.join(curpath,'max_data'),'r') as f:
max_raw = f.read().split('\n')
dmax = [[float(r.split(',')[0]),float(r.split(',')[1])] for r in max_raw if r]
with open(os.path.join(curpath,'ht10_data'),'r') as f:
ht10_raw = f.read().split('\n')
dht10 = [[float(r.split(',')[0]),float(r.split(',')[1])] for r in ht10_raw if r]
"""
constants for spectrum and sea water level generation. we define them outside the loop to save some time
"""
max_t = 3600 # maximum duration of time series
nyq_f = sr/2 # nyquist frequency of spectra: highest frequency bin
del_f = 1.0/max_t # interval between frequency bins
f = np.linspace(del_f,nyq_f,max_t) # array containing all frequency bins
t = np.linspace(0,max_t,max_t*sr)
#t = np.reshape(t, (t.size,1))
# list of hourly error rates
emean = []
emax = []
ehts = []
eht10 = []
for m in xrange(2,10):
# lists of relevant hourly data
hmean = []
hmax = []
hhts = []
hht10 = []
index = 0
for dat in dhts:
index += 1
# generate spectrum
S = wave_util.wallops(dat[0], dat[1], m, f)
# generate elevation data
E = wave_util.ema(S, 0.5, del_f)
# zerodowncross
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
hhts[:] = np.asarray(hhts)
hmean[:] = np.asarray(hmean)
hmax[:] = np.asarray(hmax)
hht10[:] = np.asarray(hht10)
meandmean = np.mean(np.asarray(dmean))
meandmax = np.mean(np.asarray(dmax))
meandhts = np.mean(np.asarray(dhts))
meandht10 = np.mean(np.asarray(dht10))
emean.append((np.mean(hmean) - meandmean) / meandmean)
emax.append((np.mean(hmax) - meandmax) / meandmax)
ehts.append((np.mean(hhts) - meandhts) / meandhts)
eht10.append((np.mean(hht10) - meandht10) / meandht10)
return emean, emax, ehts, eht10
def maximizem(sr):
curpath = os.path.dirname(os.path.abspath(__file__))
with open(os.path.join(curpath, 'hts_data'), 'r') as f:
hts_raw = f.read().split('\n')
dhts = [[float(r.split(',')[0]),
float(r.split(',')[1])] for r in hts_raw if r]
with open(os.path.join(curpath, 'mean_data'), 'r') as f:
mean_raw = f.read().split('\n')
dmean = [[float(r.split(',')[0]),
float(r.split(',')[1])] for r in mean_raw if r]
with open(os.path.join(curpath, 'max_data'), 'r') as f:
max_raw = f.read().split('\n')
dmax = [[float(r.split(',')[0]),
float(r.split(',')[1])] for r in max_raw if r]
with open(os.path.join(curpath, 'ht10_data'), 'r') as f:
ht10_raw = f.read().split('\n')
dht10 = [[float(r.split(',')[0]),
float(r.split(',')[1])] for r in ht10_raw if r]
"""
constants for spectrum and sea water level generation. we define them outside the loop to save some time
"""
max_t = 3600 # maximum duration of time series
nyq_f = sr / 2 # nyquist frequency of spectra: highest frequency bin
del_f = 1.0 / max_t # interval between frequency bins
f = np.linspace(del_f, nyq_f, max_t) # array containing all frequency bins
t = np.linspace(0, max_t, max_t * sr)
#t = np.reshape(t, (t.size,1))
# list of hourly error rates
emean = []
emax = []
ehts = []
eht10 = []
for m in xrange(2, 10):
# lists of relevant hourly data
hmean = []
hmax = []
hhts = []
hht10 = []
index = 0
for dat in dhts:
index += 1
# generate spectrum
S = wave_util.wallops(dat[0], dat[1], m, f)
# generate elevation data
E = wave_util.ema(S, 0.5, del_f)
# zerodowncross
mean, max, hts, ht10 = wave_util.zerodown(E, sr)
print index, [dat[0] - hts[0], dat[1] - hts[1]]
# add to our hourly list
#print file, mean, max, hts, ht10
hmean.append(mean)
hmax.append(max)
hhts.append(hts)
hht10.append(ht10)
hhts[:] = np.asarray(hhts)
hmean[:] = np.asarray(hmean)
hmax[:] = np.asarray(hmax)
hht10[:] = np.asarray(hht10)
meandmean = np.mean(np.asarray(dmean))
meandmax = np.mean(np.asarray(dmax))
meandhts = np.mean(np.asarray(dhts))
meandht10 = np.mean(np.asarray(dht10))
emean.append((np.mean(hmean) - meandmean) / meandmean)
emax.append((np.mean(hmax) - meandmax) / meandmax)
ehts.append((np.mean(hhts) - meandhts) / meandhts)
eht10.append((np.mean(hht10) - meandht10) / meandht10)
return emean, emax, ehts, eht10
