dt = 12.0
nt = 300
np.random.seed(seed=1)
model_param = [10.0, 1.77, -100.0]
pls1 = SimplePowerLawSpectrumWithConstantBackground(model_param,
nt=nt,
dt=dt)
data = TimeSeriesFromPowerSpectrum(pls1).sample
t = dt * np.arange(0, nt)
amplitude = 0.0
data = data + amplitude * (data.max() - data.min()) * np.sin(2 * np.pi * t / 300.0)
# Create a time series object
ts = TimeSeries(t, data)
ts.label = 'emission'
ts.units = 'arb. units'
ts.name = 'simulated data [n=%4.2f]' % (model_param[1])
# Get the normalized power and the positive frequencies
iobs = ts.PowerSpectrum.ppower
this = ([ts.PowerSpectrum.frequencies.positive, iobs],)
# _____________________________________________________________________________
# -----------------------------------------------------------------------------
# Wavelet transform using a white noise background
# -----------------------------------------------------------------------------
var = ts.data
# Range of periods to average
avg1, avg2 = (150.0, 400.0)
# Significance level
print('Loading data from ' + directory)
dc = get_datacube(directory)
ny = dc.shape[0]
nx = dc.shape[1]
nt = dc.shape[2]
# Create a time series object
dt = 12.0
t = dt * np.arange(0, nt)
tsdummy = TimeSeries(t, t)
iobs = np.zeros(tsdummy.PowerSpectrum.Npower.shape)
# Result # 1 - add up all the emission and do the analysis on the full FOV
full_ts = np.zeros((nt))
for i in range(0, nx):
for j in range(0, ny):
d = dc[j, i, :].flatten()
# Fix the data for any non-finite entries
d = tsutils.fix_nonfinite(d)
d = d - np.mean(d)
d = d / np.std(d)
ts = TimeSeries(t, d)
iobs = iobs + ts.PowerSpectrum.Npower
iobs = iobs / (1.0 * nx * ny)
ts.label = 'emission (AIA ' + wave + ')'
ts.units = 'counts'
# Get the normalized power and the positive frequencies
iobs = ts.PowerSpectrum.Npower
this = ([ts.PowerSpectrum.frequencies.positive, iobs],)
tsoriginal = TimeSeries(t, data) plt.figure(10) tsoriginal.peek() meandata = np.mean(data) # relative data = (data - meandata) / meandata #data = data - smooth(data, window_len=84) # Create a time series object ts = TimeSeries(t, data) ts.label = 'emission' ts.units = 'arb. units' ts.name = 'simulated data [n=%4.2f]' % (model_param[1]) # Get the normalized power and the positive frequencies iobs = ts.PowerSpectrum.ppower this = ([ts.PowerSpectrum.frequencies.positive, iobs],) # _____________________________________________________________________________ # ----------------------------------------------------------------------------- # Wavelet transform using a white noise background # ----------------------------------------------------------------------------- var = ts.data # Range of periods to average avg1, avg2 = (150.0, 400.0) # Significance level
