def test_printstartfinish(capsys):
t0 = utils.printstartfinish(0)
assert isinstance(t0, float)
out, _ = capsys.readouterr()
assert out == ""
t0 = utils.printstartfinish(3)
assert isinstance(t0, float)
out, _ = capsys.readouterr()
assert ":: empymod START :: v" in out
utils.printstartfinish(0, t0)
out, _ = capsys.readouterr()
assert out == ""
utils.printstartfinish(3, t0)
out, _ = capsys.readouterr()
assert out[:27] == "\n:: empymod END; runtime = "
utils.printstartfinish(3, t0, 13)
out, _ = capsys.readouterr()
assert out[-19:] == "13 kernel call(s)\n\n"
def dipole(src,
rec,
depth,
res,
freqtime,
aniso=None,
eperm=None,
mperm=None,
verb=2):
r"""Return the electromagnetic field due to a dipole source.
This is a modified version of :func:`empymod.model.dipole`. It returns the
separated contributions of TM--, TM-+, TM+-, TM++, TMdirect, TE--, TE-+,
TE+-, TE++, and TEdirect.
Parameters
----------
src, rec : list of floats or arrays
Source and receiver coordinates (m): [x, y, z].
The x- and y-coordinates can be arrays, z is a single value.
The x- and y-coordinates must have the same dimension.
Sources or receivers placed on a layer interface are considered in the
upper layer.
Sources and receivers must be in the same layer.
depth : list
Absolute layer interfaces z (m); #depth = #res - 1
(excluding +/- infinity).
res : array_like
Horizontal resistivities rho_h (Ohm.m); #res = #depth + 1.
freqtime : float
Frequency f (Hz). (The name `freqtime` is kept for consistency with
:func:`empymod.model.dipole`. Only one frequency at once.
aniso : array_like, optional
Anisotropies lambda = sqrt(rho_v/rho_h) (-); #aniso = #res.
Defaults to ones.
eperm : array_like, optional
Relative electric permittivities epsilon (-);
#eperm = #res. Default is ones.
mperm : array_like, optional
Relative magnetic permeabilities mu (-);
#mperm = #res. Default is ones.
verb : {0, 1, 2, 3, 4}, optional
Level of verbosity, default is 2:
- 0: Print nothing.
- 1: Print warnings.
- 2: Print additional runtime and kernel calls
- 3: Print additional start/stop, condensed parameter information.
- 4: Print additional full parameter information
Returns
-------
TM, TE : list of ndarrays, (nfreq, nrec, nsrc)
Frequency-domain EM field [V/m], separated into
TM = [TM--, TM-+, TM+-, TM++, TMdirect]
and
TE = [TE--, TE-+, TE+-, TE++, TEdirect].
However, source and receiver are normalised. So the source strength is
1 A and its length is 1 m. Therefore the electric field could also be
written as [V/(A.m2)].
The shape of EM is (nfreq, nrec, nsrc). However, single dimensions
are removed.
"""
# === 1. LET'S START ============
t0 = printstartfinish(verb)
# === 2. CHECK INPUT ============
# Check layer parameters
model = check_model(depth, res, aniso, eperm, eperm, mperm, mperm, False,
verb)
depth, res, aniso, epermH, epermV, mpermH, mpermV, _ = model
# Check frequency => get etaH, etaV, zetaH, and zetaV
frequency = check_frequency(freqtime, res, aniso, epermH, epermV, mpermH,
mpermV, verb)
freq, etaH, etaV, zetaH, zetaV = frequency
# Check src and rec
src, nsrc = check_dipole(src, 'src', verb)
rec, nrec = check_dipole(rec, 'rec', verb)
# Get offsets
off, ang = get_off_ang(src, rec, nsrc, nrec, verb)
# Get layer number in which src and rec reside (lsrc/lrec)
lsrc, zsrc = get_layer_nr(src, depth)
lrec, zrec = get_layer_nr(rec, depth)
# Check limitations of this routine compared to the standard `dipole`
if lsrc != lrec: # src and rec in same layer
raise ValueError("src and rec must be in the same layer; "
f"<lsrc>/<lrec> provided: {lsrc}/{lrec}.")
if depth.size < 2: # at least two layers
raise ValueError("model must have more than one layer; "
f"<depth> provided: {_strvar(depth[1:])}.")
if freq.size > 1: # only 1 frequency
raise ValueError("only one frequency permitted; "
f"<freqtime> provided: {_strvar(freqtime)}.")
# === 3. EM-FIELD CALCULATION ============
# This part is a simplification of:
# - model.fem()
# - transform.dlf()
# - kernel.wavenumber()
# DLF filter we use
filt = key_201_2012()
# 3.1. COMPUTE REQUIRED LAMBDAS for given hankel-filter-base
lambd = filt.base / off[:, None]
# 3.2. CALL THE KERNEL
PTM, PTE = greenfct(zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH,
zetaV, lambd)
# 3.3. CARRY OUT THE HANKEL TRANSFORM WITH DLF
ang_fact = angle_factor(ang, 11, False, False)
zm_ang_fact = (ang_fact[:, np.newaxis] - 1) / 2
zp_ang_fact = (ang_fact[:, np.newaxis] + 1) / 2
fact = 4 * np.pi * off
# TE [uu, ud, du, dd, df]
for i, val in enumerate(PTE):
PTE[i] = (ang_fact * np.dot(-val, filt.j1) / off +
np.dot(zm_ang_fact * val * lambd, filt.j0)) / fact
# TM [uu, ud, du, dd, df]
for i, val in enumerate(PTM):
PTM[i] = (ang_fact * np.dot(-val, filt.j1) / off +
np.dot(zp_ang_fact * val * lambd, filt.j0)) / fact
# 3.4. Remove non-physical contributions
# (Note: The T*dd corrections differ slightly from the equations given in
# the accompanying pdf, due to the way the direct field is accounted for
# in the book.)
# General parameters
Gam = np.sqrt((zetaH * etaH)[:, None, :, None]) # Gam for lambd=0
iGam = Gam[:, :, lsrc, 0]
lgam = np.sqrt(zetaH[:, lsrc] * etaH[:, lsrc])
ddepth = np.r_[depth, np.inf]
ds = ddepth[lsrc + 1] - ddepth[lsrc]
def get_rp_rm(z_eta):
r"""Return Rp, Rm."""
# Get Rp/Rm for lambd=0
Rp, Rm = reflections(depth, z_eta, Gam, lrec, lsrc)
# Depending on model Rp/Rm have 3 or 4 dimensions. Last two are
# wavenumbers and layers btw src and rec, which both are 1.
if Rp.ndim == 4:
Rp = np.squeeze(Rp, axis=3)
if Rm.ndim == 4:
Rm = np.squeeze(Rm, axis=3)
Rp = np.squeeze(Rp, axis=2)
Rm = np.squeeze(Rm, axis=2)
# Calculate reverberation M and general factor npfct
Ms = 1 - Rp * Rm * np.exp(-2 * iGam * ds)
npfct = ang_fact * zetaH[:, lsrc] / (fact * off * lgam * Ms)
return Rp, Rm, npfct
# TE modes TE[uu, ud, du, dd]
Rp, Rm, npfct = get_rp_rm(zetaH)
PTE[0] += npfct * Rp * Rm * np.exp(-lgam * (2 * ds - zrec + zsrc))
PTE[1] += npfct * Rp * np.exp(-lgam * (2 * ddepth[lrec + 1] - zrec - zsrc))
PTE[2] += npfct * Rm * np.exp(-lgam * (zrec + zsrc))
PTE[3] += npfct * Rp * Rm * np.exp(-lgam * (2 * ds + zrec - zsrc))
# TM modes TM[uu, ud, du, dd]
Rp, Rm, npfct = get_rp_rm(etaH)
PTM[0] -= npfct * Rp * Rm * np.exp(-lgam * (2 * ds - zrec + zsrc))
PTM[1] += npfct * Rp * np.exp(-lgam * (2 * ddepth[lrec + 1] - zrec - zsrc))
PTM[2] += npfct * Rm * np.exp(-lgam * (zrec + zsrc))
PTM[3] -= npfct * Rp * Rm * np.exp(-lgam * (2 * ds + zrec - zsrc))
# 3.5 Reshape for number of sources
for i, val in enumerate(PTE):
PTE[i] = np.squeeze(val.reshape((-1, nrec, nsrc), order='F'))
for i, val in enumerate(PTM):
PTM[i] = np.squeeze(val.reshape((-1, nrec, nsrc), order='F'))
# === 4. FINISHED ============
printstartfinish(verb, t0)
# return [TMuu, TMud, TMdu, TMdd, TMdf], [TEuu, TEud, TEdu, TEdd, TEdf]
return PTM, PTE
def design(n, spacing, shift, fI, fC=False, r=None, r_def=(1, 1, 2), reim=None,
cvar='amp', error=0.01, name=None, full_output=False, finish=False,
save=True, path='filters', verb=2, plot=1):
r"""Digital linear filter (DLF) design
This routine can be used to design digital linear filters for the Hankel or
Fourier transform, or for any linear transform ([Ghos70]_). For this
included or provided theoretical transform pairs can be used.
Alternatively, one can use the EM modeller empymod to use the responses to
an arbitrary 1D model as numerical transform pair.
This filter designing tool uses the direct matrix inversion method as
described in [Kong07]_ and is based on scripts by [Key12]_. The tool is an
add-on to the electromagnetic modeller empymod [Wert17]_. Fruitful
discussions with Evert Slob and Kerry Key improved the add-on
substantially.
Example notebooks of its usage can be found in the documentation-gallery,
https://empymod.readthedocs.io/en/stable/examples
Parameters
----------
n : int
Filter length.
spacing: float or tuple (start, stop, num)
Spacing between filter points. If tuple, it corresponds to the input
for np.linspace with endpoint=True.
shift: float or tuple (start, stop, num)
Shift of base from zero. If tuple, it corresponds to the input for
np.linspace with endpoint=True.
fI, fC : transform pairs
Theoretical or numerical transform pair(s) for the inversion (I) and
for the check of goodness (fC). fC is optional. If not provided, fI is
used for both fI and fC.
r : array, optional
Right-hand side evaluation points for the check of goodness (fC).
Defaults to r = np.logspace(0, 5, 1000), which are a lot of evaluation
points, and depending on the transform pair way too long r's.
r_def : tuple (add_left, add_right, factor), optional
Definition of the right-hand side evaluation points r of the inversion.
r is derived from the base values, default is (1, 1, 2).
- rmin = log10(1/max(base)) - add_left
- rmax = log10(1/min(base)) + add_right
- r = logspace(rmin, rmax, factor*n)
reim : np.real or np.imag, optional
Which part of complex transform pairs is used for the inversion.
Defaults to np.real.
cvar : string {'amp', 'r'}, optional
If 'amp', the inversion minimizes the amplitude. If 'r', the inversion
maximizes the right-hand side evaluation point r. Defaults is 'amp'.
error : float, optional
Up to which relative error the transformation is considered good in the
evaluation of the goodness. Default is 0.01 (1 %).
name : str, optional
Name of the filter. Defaults to dlf_+str(n).
full_output : bool, optional
If True, returns best filter and output from scipy.optimize.brute; else
only filter. Default is False.
finish : None, True, or callable, optional
If callable, it is passed through to scipy.optimize.brute: minimization
function to find minimize best result from brute-force approach.
Default is None. You can simply provide True in order to use
scipy.optimize.fmin_powell(). Set this to None if you are only
interested in the actually provided spacing/shift-values.
save : bool, optional
If True, best filter is saved to plain text files in ./filters/. Can be
loaded with fdesign.load_filter(name). If full, the inversion output is
stored too. You can add '.gz' to `name`, which will then save the full
inversion output in a compressed file instead of plain text.
path : string, optional
Absolute or relative path where output will be saved if `save=True`.
Default is 'filters'.
verb : {0, 1, 2}, optional
Level of verbosity, default is 2:
- 0: Print nothing.
- 1: Print warnings.
- 2: Print additional time, progress, and result
plot : {0, 1, 2, 3}, optional
Level of plot-verbosity, default is 1:
- 0: Plot nothing.
- 1: Plot brute-force result
- 2: Plot additional theoretical transform pairs, and best inv.
- 3: Plot additional inversion result
(can result in lots of plots depending on spacing and shift)
If you are using a notebook, use %matplotlib notebook to have
all inversion results appear in the same plot.
Returns
-------
filter : empymod.filter.DigitalFilter instance
Best filter for the input parameters.
full : tuple
Output from scipy.optimize.brute with full_output=True. (Returned when
`full_output` is True.)
"""
# === 1. LET'S START ============
t0 = printstartfinish(verb)
# Check plot with matplotlib (soft dependency)
if plot > 0 and not plt:
plot = 0
if verb > 0:
print(plt_msg)
# Ensure fI, fC are lists
def check_f(f):
if hasattr(f, 'name'): # put into list if single tp
f = [f, ]
else: # ensure list (works for lists, tuples, arrays)
f = list(f)
return f
if not fC: # copy fI if fC not provided
fC = dc(fI)
fI = check_f(fI)
if fI[0].name == 'j2':
raise ValueError("j2 (jointly j0 and j1) is only implemented for "
"fC, not for fI!")
fC = check_f(fC)
# Check default input values
if finish and not callable(finish):
finish = fmin_powell
if name is None:
name = 'dlf_'+str(n)
if r is None:
r = np.logspace(0, 5, 1000)
if reim not in [np.real, np.imag]:
reim = np.real
# Get spacing and shift slices, cast r
ispacing = _ls2ar(spacing, 'spacing')
ishift = _ls2ar(shift, 'shift')
r = np.atleast_1d(r)
# Initialize log-dict to keep track in brute-force minimization-function.
log = {'cnt1': -1, # Counter
'cnt2': -1, # %-counter; v Total number of iterations v
'totnr': np.arange(*ispacing).size*np.arange(*ishift).size,
'time': t0, # Timer
'warn-r': 0} # Warning for short r
# === 2. THEORETICAL MODEL rhs ============
# Calculate rhs
for i, f in enumerate(fC):
fC[i].rhs = f.rhs(r)
# Plot
if plot > 1:
_call_qc_transform_pairs(n, ispacing, ishift, fI, fC, r, r_def, reim)
# === 3. RUN BRUTE FORCE OVER THE GRID ============
full = brute(_get_min_val, (ispacing, ishift), full_output=True,
args=(n, fI, fC, r, r_def, error, reim, cvar, verb, plot,
log), finish=finish)
# Add cvar-information to full: 0 for 'amp', 1 for 'r'
if cvar == 'r':
full += (1, )
else:
full += (0, )
# Finish output from brute/fmin; depending if finish or not
if verb > 1:
print("")
if callable(finish):
print("")
# Get best filter (full[0] contains spacing/shift of the best result).
dlf = _calculate_filter(n, full[0][0], full[0][1], fI, r_def, reim, name)
# If verbose, print result
if verb > 1:
print_result(dlf, full)
# === 4. FINISHED ============
printstartfinish(verb, t0)
# If plot, show result
if plot > 0:
print("* QC: Overview of brute-force inversion:")
plot_result(dlf, full, False)
if plot > 1:
print("* QC: Inversion result of best filter (minimum amplitude):")
_get_min_val(full[0], n, fI, fC, r, r_def, error, reim, cvar, 0,
plot+1, log)
# Save if desired
if save:
if full_output:
save_filter(name, dlf, full, path=path)
else:
save_filter(name, dlf, path=path)
# Output, depending on full_output
if full_output:
return dlf, full
else:
return dlf
