def Coherence_obs_5var():
"Prepare the data for plotting"
pan_obs_gapfill = load('%s/pan_obs_gapfill_good_stations' % workspace)
rn = load('%s/Rn_0.2_0.5_good_stations' % (workspace))
ist = 0
for ibasin in xrange(0, 10):
cohere_obs_basin = []
data = scipy.io.loadmat('%s/%s_AP.mat' % (datadir, ibasin + 1))
for istation in good_stations[ibasin]:
# the PowerSpectrum method take the matrix as different segment, so shoule be a 1d array
input = [
Gapfill(data[v][0, istation][0:tstep].flatten()).flatten()
for v in variables
]
input.insert(1, rn[ist, :].flatten())
panobs = pan_obs_gapfill[ist, :].flatten()
# Compute the coherence
cohere_obs_basin.append(
vstack([
FFT.Coherence(v, panobs, sampling_frequency, 'linear')[1]
for v in input
]).reshape(1, 5, nf))
ist = ist + 1
# store basin average
cohere_obs_basin = vstack(cohere_obs_basin)
cohere_obs_basin.dump('%s/coherence_obs_5var_good_station_%s' %
(workspace, basinlongs[ibasin]))
# cohere_obs_basin.dump('%s/coherence_obs_5var_test2048_%s' %(workspace, basinlongs[ibasin]))
return
def Coherence_Frequency():
data = scipy.io.loadmat('%s/1_AP.mat' % (datadir))
input = data[variables[0]][0, 0][0:tstep].flatten()
pan = data['pan'][0, 0][0:tstep].flatten()
freq = FFT.Coherence(input, pan, sampling_frequency, 'linear')[0]
return freq