plt.semilogy(t, np.abs((tspline10 - tresp) / tresp), label='Splined 10/dec')
plt.xlabel('Time (s)')
plt.tight_layout()
plt.show()
###############################################################################
# Runtimes and number of required frequencies for each method:
#
# +-------------------+-----------+------------------+
# |Fourier DLF Method | Time (ms) | # of frequencies |
# +===================+===========+==================+
# |Standard | 1442 | 8100 |
# +-------------------+-----------+------------------+
# |Lagged Convolution | 17 | 105 |
# +-------------------+-----------+------------------+
# |Splined 4/dec | 14 | 37 |
# +-------------------+-----------+------------------+
# |Splined 10/dec | 32 | 91 |
# +-------------------+-----------+------------------+
#
# All methods require 201 wavenumbers (1 offset, filter length is 201).
#
# So the lagged convolution has a relative error of roughly 1e-5, hence 0.001
# %, which is more then enough for real-world applications.
#
# If you want to measure the runtime on your machine set ``tparam['verb'] =
# 2``.
empymod.Report()
# (F) Plots
# ---------
plt.figure(figsize=(5, 4))
ax1 = plt.subplot(111)
plt.title("Resistive cube in a deep marine setting")
plt.plot(times, epm_bg * 1e9, ".4", lw=2, label="empymod")
plt.plot(times, spg_bg * 1e9, "C0--", label="SimPEG Background")
plt.plot(times, spg_tg * 1e9, "C1--", label="SimPEG Target")
plt.ylabel("$E_x$ (nV/m)")
plt.xscale("log")
plt.xlim([0.1, 10])
plt.legend(loc=3)
plt.grid(axis="y", c="0.9")
plt.xlabel("Time (s)")
# Switch off spines
ax1.spines["top"].set_visible(False)
ax1.spines["right"].set_visible(False)
plt.tight_layout()
plt.show()
###############################################################################
empymod.Report([SimPEG, discretize, pymatsolver])
