data = np.vstack((Shh, Shh_fitted))
labels = ["Shh numerical", "Shh fitted"]
linestyle = ["-", "-"]
marker = ["", "d"]
figtxt = (
"Derived Parameter: S = %1.3e, T = %1.3e [m2/s], tc = %1.3e [d]\nInput Parameter: L = %0.0f, x = %0.0f"
% (Sy, T, tc, L, x))
print("Currently plotting: " + str(name_h) + " ...")
# plot only spectrum of Shh
plot_spectrum(
[Shh],
frequency_output,
heading="Shh - Head Power Spectrum " + name_h,
labels=["Shh obs"],
path=save_path,
linestyle=["-"],
marker=[""],
lims=[(2e-9, 7e-6), (1e-5, 1e7)],
name="Shh_" + name_h,
)
# plot only spectrum of Sww
plot_spectrum(
[Sww],
frequency_input,
heading="Sww - Recharge Power Spectrum " + name_r,
labels=["Sww mHM"],
path=save_path,
linestyle=["-"],
marker=[""],
# frequency_output,
# heading="head spectrum " + name_h,
# labels=["power_spec_out"],
# path=save_path,
# linestyle=["-"],
# marker=[""],
# lims=[(2e-9,7e-6),(1e-5,1e7)],
# name="Shh_" + name_h
# )
# plot only spectrum of Sww
plot_spectrum([Sww],
frequency_input,
heading="recharge spectrum " + name_r,
labels=["power_spec_out"],
path=save_path,
linestyle=["-"],
marker=[""],
lims=[(2e-9, 7e-6), (1e-10, 1e4)],
name="Sww_" + name_r)
# plot Shh and the fitted spectrum
plot_spectrum(
data,
frequency_input,
heading="Shh and fited spectrum",
labels=["power_spec_out", "analytical fit"],
path=save_path,
linestyle=["-", " "],
marker=["", "*"],
figtxt=figtxt,
T_in_geo,
T_in_har,
T_in_ari,
) + "\nDerived Parameter: S = %1.3e, T = %1.3e" % (
popt[0],
popt[1],
)
plot_spectrum(
data,
frequency,
labels=labels,
path=path_to_results,
# lims=lims,
linestyle=linestyle,
marker=marker,
heading="Folder: " + project_folder + "\nLocation: " + str(obs_loc),
name="PSD_"
+ project_folder
+ "_"
+ str(obs_loc).zfill(len(str(aquifer_length))),
figtxt=figtxt,
comment=comment,
)
time_1_folder_end = time.time() - time_1_folder_begin
print(str(time_1_folder_end) + " s elapsed for " + project_folder + "...")
# set path to results incl file name of results
path_to_results_df = path_to_results + "/" + comment + "results.csv"
# if os.path.isfile(path_to_results_df): # override = true, not necesarry
results.to_csv(path_to_results_df)
power_spectrum_1001_1100 = np.reshape(power_spectrum_1001_1100,
(len(power_spectrum_1001_1100), ))
figtxt = "OGS Input Parameter: Ss = %1.3e, D_geo = %1.3e, D_har = %1.3e, D_ari = %1.3e" % (
Ss1,
kf_geomean / Ss1,
kf_harmean / Ss1,
kf_arimean / Ss1,
) + "\nDerived Parameter: D = %1.3e, D_cov = %1.1e" % (
D_1001_1100[0],
D_cov[0],
)
plot_spectrum(np.vstack(
(power_spectrum_1001_1100, power_spectrum_1001_1100_anal)),
frequency_1001_1100,
name="white_noise_0.01",
labels=["Sqq", "Sqq_fitted"],
heading="Recharge: white noise, Stor = 0.01",
marker=["", ""],
linestyle=["-", "-"],
path="/Users/houben/Desktop/baseflow_sa",
figtxt=figtxt)
# calculate the power spectrum for 1101_1200
D_1101_1100, D_cov, frequency_1101_1200, power_spectrum_1101_1200 = discharge_ftf_fit(
recharge_1101_1200, baseflow_sum_1101_1200, 86400, 1000)
power_spectrum_1101_1200_anal = discharge_ftf(frequency_1101_1200, D_1101_1100,
aquifer_length)
power_spectrum_1101_1200_anal = np.reshape(
power_spectrum_1101_1200_anal, (len(power_spectrum_1101_1200_anal), ))
power_spectrum_1101_1200 = np.reshape(power_spectrum_1101_1200,
(len(power_spectrum_1101_1200), ))
figtxt = "OGS Input Parameter: Ss = %1.3e, D_geo = %1.3e, D_har = %1.3e, D_ari = %1.3e" % (