frequency_output = frequency_output[cut_array_higher]
# cut lower frequencies than cut_freq_lower
cut_array_lower = np.invert(np.less(frequency_input, cut_freq_lower))
Sww = Sww[cut_array_lower]
Shh = Shh[cut_array_lower]
frequency_input = frequency_input[cut_array_lower]
frequency_output = frequency_output[cut_array_lower]
# fit the power spectrum with the analytical solution
try:
popt, pcov = shh_analytical_fit(
Sww=Sww,
Shh=Shh,
f=frequency_input,
x=x,
m=m,
n=n,
L=L,
norm=False,
convergence=convergence,
)
except RuntimeError:
print("Optimal parameters not found...")
popt, pcov = [np.nan, np.nan], [[np.nan, np.nan], [np.nan, np.nan]]
print("popt and pcov have been set to np.nan")
except ValueError:
print(
"either ydata or xdata contain NaNs, or if incompatible options are used"
)
popt, pcov = [np.nan, np.nan], [[np.nan, np.nan], [np.nan, np.nan]]
except OptimizeWarning:
o_i="o",
)
frequency, Sww = power_spectrum(
input=recharge_time_series,
output=head_time_series,
time_step_size=time_step_size,
method="scipyffthalf",
o_i="i",
)
# fit the power spectrum with the analytical solution
try:
popt, pcov = shh_analytical_fit(
Sww=Sww,
Shh=Shh,
f=frequency,
x=obs_loc,
m=m,
n=n,
L=aquifer_length,
norm=False,
)
except RuntimeError:
print("Optimal parameters not found...")
popt, pcov = [np.nan, np.nan], [[np.nan, np.nan],[np.nan, np.nan]]
print("popt and pcov have been set to np.nan")
except ValueError:
print("either ydata or xdata contain NaNs, or if incompatible options are used")
popt, pcov = [np.nan, np.nan], [[np.nan, np.nan],[np.nan, np.nan]]
except OptimizeWarning:
print("Covariance of the parameters could not be estimated.")
#popt, pcov = [np.nan, np.nan], [[np.nan, np.nan],[np.nan, np.nan]]
