def test_findtc():
x = sea()
x1 = x[0:200, :]
itc, iv = findtc(x1[:, 1], 0, 'dw')
assert_allclose(itc, [28, 31, 39, 56, 64, 69, 78, 82, 83, 89, 94, 101, 108,
119, 131, 140, 148, 159, 173, 184])
assert_allclose(iv, [19, 29, 34, 53, 60, 67, 76, 81, 82, 84, 90, 99, 103,
112, 127, 137, 143, 154, 166, 180, 185])
def test_findtc():
x = sea()
x1 = x[0:200, :]
itc, iv = findtc(x1[:, 1], 0, 'dw')
assert_array_almost_equal(
itc,
np.array(
[28, 31, 39, 56, 64, 69, 78, 82, 83, 89, 94, 101, 108,
119, 131, 140, 148, 159, 173, 184]))
assert_array_almost_equal(
iv,
np.array(
[19, 29, 34, 53, 60, 67, 76, 81, 82, 84, 90, 99, 103,
112, 127, 137, 143, 154, 166, 180, 185]))
def _test_findpot():
import matplotlib.pyplot as plt
import wafo.data
from wafo.misc import findtc
x = wafo.data.sea()
t, data = x[:, :].T
itc, _iv = findtc(data, 0, 'dw')
ytc, ttc = data[itc], t[itc]
ymin = 2 * data.std()
tmin = 10 # sec
I = findpot(data, t, ymin, tmin)
yp, tp = data[I], t[I]
Ie = findpot(yp, tp, ymin, tmin)
ye, te = yp[Ie], tp[Ie]
plt.plot(t, data, ttc, ytc, 'ro', t, zeros(len(t)), ':', te, ye, 'kx', tp,
yp, '+')
plt.show()
def _test_findpot():
import pylab
import wafo.data
from wafo.misc import findtc
x = wafo.data.sea()
t, data = x[:, :].T
itc, _iv = findtc(data, 0, 'dw')
ytc, ttc = data[itc], t[itc]
ymin = 2 * data.std()
tmin = 10 # sec
I = findpot(data, t, ymin, tmin)
yp, tp = data[I], t[I]
Ie = findpot(yp, tp, ymin, tmin)
ye, te = yp[Ie], tp[Ie]
pylab.plot(t, data, ttc,ytc,'ro', t, zeros(len(t)), ':', te, ye, 'kx', tp, yp, '+')
pylab.show() #
pass
def statisticalana(t, series, h=0, mod='cw'):
'''
Return: significant values of crests, troughs, and double peaks,
as well as mean level crossing period
Parameters:
-----------
t: time, 1-d array
series: data, 1-d array
h: level, scalar
mod: string, defines type of wave or crossing returned. Possible options are
'dw' : downcrossing wave
'uw' : upcrossing wave
'cw' : crest wave
'tw' : trough wave
None : All crossings will be returned
'''
from wafo.objects import mat2timeseries
from wafo.misc import findtc
t = np.asarray(t, dtype='float')
series = np.asarray(series, dtype='float')
ts = mat2timeseries(np.stack((t, series), axis=-1))
crestTroughID, crossID = findtc(ts.data, h, mod)
ctp = series[crestTroughID] - h # crest and trough point
crests = ctp[ctp > 0]
troughs = ctp[ctp <= 0]
if series[crestTroughID[0]] < h:
troughs = troughs[1:]
n = min(crests.shape[0], troughs.shape[0])
vpps = crests[:n] - troughs[:n]
# mean level crossing period
tLevelCrossing = t[crossID[::2]]
# significant values (1/3 exceeding probability)
ns = n // 3
return np.mean(-np.sort(-crests)[:ns]) + h, np.mean(np.sort(troughs)[:ns]) + h,\
np.mean(-np.sort(-vpps)[:ns]), np.mean(np.diff(tLevelCrossing))
def waves_proc(df, i_burst, len_avg=600, i_burst_good_exactly=1):
if not 'Pressure' in a.columns:
return
import wafo.objects as wo
import wafo.misc as wm
print('waves_proc')
def df2ts(series):
return wo.mat2timeseries(
pd.concat(
[(series.index.to_series() - series.index.values[0]).dt.total_seconds(),
series
], axis=1).values)
# i_burst, mean_burst_size = i_bursts_starts(df.index, dt_between_blocks = pd.Timedelta(minutes=10))
if len(df) - i_burst[-1] < len_avg:
last_burst = i_burst[-1]
i_burst = i_burst[:-1]
else:
last_burst = len(df)
# use freqencies as calculated for normal burst:
wfreq = df2ts(df['Pressure'][slice(*i_burst[i_burst_good_exactly:(i_burst_good_exactly + 2)])]).tospecdata(
L=600).args
Sp = np.empty((i_burst.size, wfreq.size)) + np.NaN
df['P_detrend'] = df['Pressure'] + np.NaN
calc_spectrum_characteristics = ['Hm0', 'Tm_10', 'Tp', 'Ss', 'Rs']
ar_spectrum_characteristics = np.empty((i_burst.size, len(calc_spectrum_characteristics))) + np.NaN
# Hm0 = 4*sqrt(m0) - Significant wave height
# Tm_10 = 2*pi*m_1/m0 - Energy period
# Tp = 2*pi*int S(w)^4 dw / int w*S(w)^4 dw - Peak Period
# Ss = 2*pi*Hm0/(g*Tm02^2) - Significant wave steepness
# Rs = Quality control parameter
i_st = 0
for i, i_en in enumerate(np.append(i_burst[1:], last_burst)):
sl = slice(i_st + 1, i_en) # we have bad 1st data in each burst. removing it
# ?! df['Pressure'][sl].apply(lambda x: wm.detrendma(x, sl.stop - sl.start)).values returns all [0] objects
try:
ps_detr = df['Pressure'][sl]
ps_detr -= ps_detr.rolling(600, min_periods=1, center=True, win_type='blackman').mean()
ps_detr.interpolate('nearest', inplace=True)
df['P_detrend'][sl] = ps_detr
ts = df2ts(ps_detr)
Sest = ts.tospecdata(L=600)
if np.allclose(wfreq, Sest.args):
Sp[i, :] = Sest.data
ar_spectrum_characteristics[i, :] = \
Sest.characteristic(fact=calc_spectrum_characteristics, T=diff(ts.args[[0, -1]]))[0]
else:
print(f'burst #{i} of different length ({i_en - i_st}) skipped')
# Sest.plot('-')
# # Probability distribution of wave trough period
# dt = Sest.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)
# T, index = ts.wave_periods(vh=0, pdef='d2u')
# Tcrcr, ix = ts.wave_periods(vh=0, pdef='c2c', wdef='tw', rate=8)
# Tc, ixc = ts.wave_periods(vh=0, pdef='u2d', wdef='tw', rate=8)
# bins = wm.good_bins(T, num_bins=25, odd=True)
# wm.plot_histgrm(T, bins=bins, normed=True)
# wave_parameters = ts.wave_parameters()
# if wave_parameters['Ac']==[]:
# wave_parameters =
except Exception as e:
print(f'Error in burst #{i}: {e}')
i_st = i_en
###
# save calculated parameters
export_df_to_csv(
pd.DataFrame(ar_spectrum_characteristics, index=df.index[i_burst], columns=calc_spectrum_characteristics),
cfg['out'],
add_subdir='V,P_txt',
add_suffix='spectrum_characteristics')
export_df_to_csv(
pd.DataFrame(Sp, index=df.index[i_burst], columns=wfreq),
cfg['out'],
add_subdir='V,P_txt',
add_suffix='spectrum')
# envelope ("ogibayushchaya")
ts = df2ts(df['P_detrend'].fillna(method='backfill'))
ct = wm.findtc(ts.data, 0)[0] # , kind='cw','tw' # 0 is mean, 0 becouse it is derended
ind_crest = ct[::2]
ind_trough = ct[1::2]
if False:
plot(ps_detr, 'y')
plot(ps_detr[ct[::2]], '.b')
plot(ps_detr[ct[1::2]], '.r')
ct += sl.start
Tcrcr, ix = ts.wave_periods(vh=0, pdef='c2c', wdef='tw', rate=8)
Tc, ixc = ts.wave_periods(vh=0, pdef='u2d', wdef='tw', rate=8)
# @+others
# @+node:korzh.20180608193514.1: *5* save plot
# plt.setp(plt.gca().spines.values(), linewidth=1)
fig = plt.figure();
plt.gcf().set_size_inches(19, 7)
ax = fig.add_axes([0.05, 0.05, 0.93, 0.93])
ax.plot(df.index, df.P_detrend, '-k', linewidth=0.1)
ax.plot(df.index[ind_crest], df.P_detrend[ind_crest], '-b', linewidth=1)
ax.plot(df.index[ind_trough], df.P_detrend[ind_trough], '-r', linewidth=1)
ax.set_ylim(-2, 2)
ax.grid(True)
# plt.savefig(Path(cfg['out']['db_path']).with_name(cfg['out']['table'] + '_P_detrend (600dpi)_18_1930-2300').with_suffix('.png'), dpi=600)
# @-others
return df, ind_crest, ind_trough
