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@ -0,0 +1,18 @@
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[package]
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name = "pyrocko-das"
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version = "0.1.0"
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edition = "2018"
|
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# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
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[lib]
|
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name = "pyrocko_das"
|
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crate-type = ["cdylib"]
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[dependencies]
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numpy = "0.14.1"
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ndarray = "0.15.3"
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|
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[dependencies.pyo3]
|
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version = "0.14.5"
|
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features = ["extension-module"]
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@ -0,0 +1,287 @@
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import numpy as num
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from scipy import interpolate, ndimage, signal
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from collections.abc import Iterable
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||||
from pyrocko.guts import Object, Float, List, Tuple, String, Timestamp, Bool
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from pyrocko import gf
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from pyrocko.model import Location
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||||
import pyrocko.orthodrome as od
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||||
import matplotlib.pyplot as plt
|
||||
|
||||
r2d = 180.0 / num.pi
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||||
km = 1e3
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||||
META = {
|
||||
"measure_length": None,
|
||||
"start_distance": None,
|
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"stop_distance": None,
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"gauge_length": None,
|
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"spatial_resolution": None,
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||||
"geo_lat": None,
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||||
"geo_lon": None,
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"geo_elevation": None,
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"channel": None,
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"unit": None,
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}
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class QuantityType(gf.meta.QuantityType):
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choices = gf.meta.QuantityType.choices + ["strain", "strain_rate"]
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class Fiber(Object):
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lat = Float.T(default=0.0)
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lon = Float.T(default=0.0)
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codes = Tuple.T(
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4,
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String.T(),
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default=("", "", "", "HSF"),
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help="network, station, location and channel codes to be set on "
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"the response trace. If station code is empty it will be filled"
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" by the channel number",
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)
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quantity = QuantityType.T(
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default="strain",
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help="Measurement quantity type. If not given, it is guessed from the "
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"channel code. For some common cases, derivatives of the stored "
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||||
"quantities are supported by using finite difference "
|
||||
"approximations (e.g. displacement to velocity or acceleration). "
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||||
"4th order central FD schemes are used.",
|
||||
)
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||||
elevation = Float.T(default=0.0, help="station surface elevation in [m]")
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||||
store_id = gf.meta.StringID.T(
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optional=True,
|
||||
help="ID of Green's function store to use for the computation. "
|
||||
"If not given, the processor may use a system default.",
|
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)
|
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sample_rate = Float.T(
|
||||
optional=True,
|
||||
help="sample rate to produce. "
|
||||
"If not given the GF store's default sample rate is used. "
|
||||
"GF store specific restrictions may apply.",
|
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)
|
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|
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tmin = Timestamp.T(
|
||||
optional=True,
|
||||
help="time of first sample to request in [s]. "
|
||||
"If not given, it is determined from the Green's functions.",
|
||||
)
|
||||
tmax = Timestamp.T(
|
||||
optional=True,
|
||||
help="time of last sample to request in [s]. "
|
||||
"If not given, it is determined from the Green's functions.",
|
||||
)
|
||||
interpolation = gf.meta.InterpolationMethod.T(
|
||||
default="multilinear",
|
||||
help="Interpolation method between Green's functions. Supported are"
|
||||
" ``nearest_neighbor`` and ``multilinear``",
|
||||
)
|
||||
|
||||
coordinates = List.T(
|
||||
help="coordinates of the cable as ``pyrocko.model.Location`` or a tuple"
|
||||
" of (north_shift, east_shift, [elevation])."
|
||||
)
|
||||
|
||||
channel_spacing = Float.T(default=4.0, help="Channel spacing [m].")
|
||||
smoothing_sigma = Float.T(
|
||||
default=10.0, help="Standard deviation for Gaussian kernel in [m]"
|
||||
)
|
||||
|
||||
spectral_differences = Bool.T(
|
||||
default=False, help="Use spectral derivation for estimation of linear strain."
|
||||
)
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
self._locations = []
|
||||
for coord in self.coordinates:
|
||||
if isinstance(coord, Location):
|
||||
loc = coord
|
||||
elif isinstance(coord, Iterable) and len(coord) in (2, 3):
|
||||
loc = Location(
|
||||
lat=self.lat,
|
||||
lon=self.lon,
|
||||
north_shift=coord[0],
|
||||
east_shift=coord[1],
|
||||
elevation=self.elevation if len(coord) == 2 else coord[2],
|
||||
)
|
||||
else:
|
||||
raise AttributeError(
|
||||
"coordinates have to be a list of pyrocko.model.Location"
|
||||
" or tuples of (north_shift, east_shifts, [elevation]),"
|
||||
" not %s" % type(coord)
|
||||
)
|
||||
|
||||
self._locations.append(loc)
|
||||
assert len(self._locations) > 1, "Fiber needs more than 1 coordinates"
|
||||
|
||||
@classmethod
|
||||
def from_stations(cls, stations, **kwargs):
|
||||
return cls(coordinates=stations, **kwargs)
|
||||
|
||||
@property
|
||||
def distances(self):
|
||||
locations = self._locations
|
||||
nlocs = len(locations)
|
||||
|
||||
dists = [0.0]
|
||||
for il in range(1, nlocs):
|
||||
dists.append(locations[il - 1].distance_3d_to(locations[il]))
|
||||
|
||||
return num.array(dists)
|
||||
|
||||
@property
|
||||
def length(self):
|
||||
return num.sum(self.distances)
|
||||
|
||||
@property
|
||||
def nchannels(self):
|
||||
dists = self.length
|
||||
return int(dists // self.channel_spacing)
|
||||
|
||||
def interpolate_channels(self, start_channel=0):
|
||||
dists = num.cumsum(self.distances)
|
||||
|
||||
interp_lat = interpolate.interp1d(
|
||||
dists, tuple(loc.effective_lat for loc in self._locations)
|
||||
)
|
||||
interp_lon = interpolate.interp1d(
|
||||
dists, tuple(loc.effective_lon for loc in self._locations)
|
||||
)
|
||||
interp_elevation = interpolate.interp1d(
|
||||
dists, tuple(loc.elevation for loc in self._locations)
|
||||
)
|
||||
|
||||
interp_distances = num.arange(0.0, dists.max(), step=self.channel_spacing)
|
||||
|
||||
channels = num.arange(interp_distances.size) + start_channel
|
||||
nchannels = channels.size
|
||||
|
||||
lats = interp_lat(interp_distances)
|
||||
lons = interp_lon(interp_distances)
|
||||
elevations = interp_elevation(interp_distances)
|
||||
|
||||
azis = num.empty(nchannels)
|
||||
azis[:-1] = od.azimuth_numpy(
|
||||
lats[:-1],
|
||||
lons[:-1],
|
||||
lats[1:],
|
||||
lons[1:],
|
||||
)
|
||||
azis[-1] = azis[-2]
|
||||
|
||||
ds = num.full(nchannels, self.channel_spacing)
|
||||
dips = -num.arctan2(num.gradient(elevations), ds) * r2d
|
||||
|
||||
return lats, lons, elevations, azis, dips, channels
|
||||
|
||||
def get_targets(self):
|
||||
lats, lons, elevations, azis, dips, channels = self.interpolate_channels()
|
||||
nchannels = self.nchannels
|
||||
|
||||
quantity = self.quantity
|
||||
if self.quantity in ("strain", "strain_rate"):
|
||||
quantity = "displacement"
|
||||
|
||||
targets = []
|
||||
for icha in range(nchannels):
|
||||
codes = list(self.codes)
|
||||
if not codes[1]:
|
||||
codes[1] = "%05d" % icha
|
||||
|
||||
t = gf.Target(
|
||||
lat=lats[icha],
|
||||
lon=lons[icha],
|
||||
elevation=elevations[icha],
|
||||
azimuth=azis[icha],
|
||||
dip=dips[icha],
|
||||
north_shift=0.0,
|
||||
east_shift=0.0,
|
||||
sample_rate=self.sample_rate,
|
||||
codes=codes,
|
||||
store_id=self.store_id,
|
||||
quantity=quantity,
|
||||
interpolation=self.interpolation,
|
||||
)
|
||||
targets.append(t)
|
||||
return targets
|
||||
|
||||
def get_locations(self):
|
||||
return self._locations
|
||||
|
||||
|
||||
class LocalEngine(gf.LocalEngine):
|
||||
def process_fiber(self, source, fiber, **kwargs):
|
||||
assert isinstance(source, Iterable), "Currently only one source is supported!"
|
||||
|
||||
targets = fiber.get_targets()
|
||||
resp = self.process(source, targets, **kwargs)
|
||||
|
||||
traces = resp.pyrocko_traces()
|
||||
ntraces = len(traces)
|
||||
deltat = traces[0].deltat
|
||||
|
||||
all_times = [(tr.tmin, tr.tmax) for tr in traces]
|
||||
all_tmin = num.min(all_times)
|
||||
all_tmax = num.max(all_times)
|
||||
|
||||
meta = {
|
||||
"measure_length": fiber.length,
|
||||
"start_distance": 0.0,
|
||||
"stop_distance": fiber.length,
|
||||
"gauge_length": fiber.channel_spacing,
|
||||
"spatial_resolution": fiber.channel_spacing,
|
||||
}
|
||||
|
||||
for icha, (tr, target) in enumerate(zip(traces, targets)):
|
||||
tr.extend(tmin=all_tmin, tmax=all_tmax, fillmethod="repeat")
|
||||
tr.meta = meta.copy()
|
||||
tr.meta["channel"] = icha
|
||||
tr.meta["geo_lat"] = target.lat
|
||||
tr.meta["geo_lon"] = target.lon
|
||||
tr.meta["geo_elevation"] = target.elevation
|
||||
|
||||
nsamples = set([tr.ydata.size for tr in traces])
|
||||
assert len(nsamples) == 1
|
||||
nsamples = nsamples.pop()
|
||||
|
||||
# Spatial smoothing
|
||||
trs_data = num.array([tr.ydata for tr in traces])
|
||||
if fiber.smoothing_sigma:
|
||||
trs_data = ndimage.gaussian_filter1d(
|
||||
trs_data, sigma=fiber.smoothing_sigma / fiber.channel_spacing, axis=0
|
||||
)
|
||||
|
||||
if fiber.quantity in ("strain_rate", "strain"):
|
||||
assert (
|
||||
ntraces > 3
|
||||
), "Too few channels in fiber for finite-difference derivation"
|
||||
|
||||
if fiber.spectral_differences:
|
||||
kappa = num.fft.fftfreq(ntraces, fiber.channel_spacing)
|
||||
|
||||
trs_spec = num.fft.fft(trs_data, axis=0)
|
||||
trs_spec *= 2 * num.pi * kappa[:, num.newaxis] * 1j
|
||||
trs_data = num.fft.ifft(trs_spec, axis=0).real
|
||||
else:
|
||||
trs_data = num.gradient(trs_data, fiber.channel_spacing, axis=0)
|
||||
|
||||
if fiber.quantity == "strain_rate":
|
||||
trs_data = num.gradient(trs_data, deltat, axis=1)
|
||||
|
||||
signal.detrend(trs_data, type="linear", axis=1, overwrite_data=True)
|
||||
# if fiber.quantity == 'strain':
|
||||
# trs_data = num.cumsum(
|
||||
# trs_data, axis=1)
|
||||
|
||||
for itr, tr in enumerate(traces):
|
||||
tr.set_ydata(trs_data[itr])
|
||||
|
||||
return traces
|
||||
@ -0,0 +1,149 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os.path as op
|
||||
import time
|
||||
from functools import lru_cache
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as num
|
||||
from matplotlib.widgets import Slider
|
||||
from scipy import signal
|
||||
|
||||
from .rust import rust_func as goldstein_rust
|
||||
|
||||
data = num.load(op.join(op.dirname(__file__), "data", "data-DAS-gfz2020wswf.npy"))
|
||||
|
||||
|
||||
@lru_cache
|
||||
def triangular_taper(size: int, plateau: int):
|
||||
if plateau > size:
|
||||
raise ValueError("Plateau cannot be larger than size.")
|
||||
if size % 2 or plateau % 2:
|
||||
raise ValueError("Size and plateau have to be even.")
|
||||
|
||||
ramp_size = int((size - plateau) / 2)
|
||||
ramp = num.linspace(0.0, 1.0, ramp_size)
|
||||
window = num.ones(size)
|
||||
window[:ramp_size] = ramp
|
||||
window[size - ramp_size :] = ramp[::-1]
|
||||
return window * window[:, num.newaxis]
|
||||
|
||||
|
||||
def data_coherency(data):
|
||||
ntraces = data.shape[0]
|
||||
coherency = 0.0
|
||||
for itr in range(ntraces - 1):
|
||||
coherency += signal.coherence(data[itr], data[itr + 1])[1].mean()
|
||||
|
||||
return coherency / (ntraces - 1)
|
||||
|
||||
|
||||
def goldstein_filter(data, window_size: int = 32, overlap: int = 14, exponent=0.3):
|
||||
if num.log2(window_size) % 1.0 or window_size < 4:
|
||||
raise ValueError("window_size has to be pow(2) and > 4.")
|
||||
if overlap > window_size / 2 - 1:
|
||||
raise ValueError("Overlap is large small. Minimum window_size)/ 2 - 1.")
|
||||
|
||||
window_stride = window_size - overlap
|
||||
window_non_overlap = window_size - 2 * overlap
|
||||
|
||||
npx_x, npx_y = data.shape
|
||||
nwin_x = npx_x // window_stride
|
||||
nwin_y = npx_y // window_stride
|
||||
if nwin_x % 1 or nwin_y % 1:
|
||||
raise ValueError("Padding does not match desired data shape")
|
||||
|
||||
filtered_data = num.zeros_like(data)
|
||||
|
||||
for iwin_x in range(nwin_x):
|
||||
px_x = iwin_x * window_stride
|
||||
slice_x = slice(px_x, px_x + window_size)
|
||||
for iwin_y in range(nwin_y):
|
||||
px_y = iwin_y * window_stride
|
||||
slice_y = slice(px_y, px_y + window_size)
|
||||
|
||||
window_data = data[slice_x, slice_y]
|
||||
window_fft = num.fft.rfft2(window_data)
|
||||
window_fft_abs = num.abs(window_fft) / window_fft.size
|
||||
# Optional
|
||||
# window_fft_abs = signal.medfilt2d(window_fft_abs, kernel_size=3)
|
||||
# window_coherency = data_coherency(window_data)
|
||||
window_fft *= window_fft_abs ** exponent
|
||||
window_filtered = num.fft.irfft2(window_fft)
|
||||
|
||||
taper = triangular_taper(window_size, window_non_overlap)
|
||||
taper = taper[: window_filtered.shape[0], : window_filtered.shape[1]]
|
||||
|
||||
filtered_data[
|
||||
px_x : px_x + window_filtered.shape[0],
|
||||
px_y : px_y + window_filtered.shape[1],
|
||||
] += (
|
||||
window_filtered * taper
|
||||
)
|
||||
|
||||
return filtered_data
|
||||
|
||||
|
||||
def normalize_diff(data, filtered_data):
|
||||
norm = num.abs(data).max()
|
||||
filtered_data_norm = filtered_data / num.abs(filtered_data).max() * (1.0 / norm)
|
||||
filtered_data_norm -= data / norm
|
||||
return filtered_data_norm
|
||||
|
||||
|
||||
def plot_goldstein():
|
||||
exponent = 0.5
|
||||
window_size = 64
|
||||
overlap = 30
|
||||
t = time.time()
|
||||
data_filtered = goldstein_filter(
|
||||
data, window_size=window_size, exponent=exponent, overlap=overlap
|
||||
)
|
||||
print(time.time() - t)
|
||||
|
||||
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex=True, sharey=True)
|
||||
ax1.imshow(data, aspect=5)
|
||||
image = ax2.imshow(data_filtered, aspect=5)
|
||||
image_diff = ax3.imshow(normalize_diff(data, data_filtered), aspect=5)
|
||||
|
||||
ax_slider = plt.axes((0.25, 0.1, 0.65, 0.03))
|
||||
|
||||
ax1.set_title("Data Input")
|
||||
ax2.set_title("Data Filtered")
|
||||
ax3.set_title("Data Residual")
|
||||
|
||||
exp_slider = Slider(
|
||||
ax=ax_slider,
|
||||
label="Exponent",
|
||||
valmin=0,
|
||||
valmax=1.0,
|
||||
valinit=exponent,
|
||||
orientation="horizontal",
|
||||
)
|
||||
|
||||
def update(val):
|
||||
global data
|
||||
|
||||
data_filtered = goldstein_filter(
|
||||
data, window_size=window_size, exponent=val, overlap=overlap
|
||||
)
|
||||
image.set_data(data_filtered)
|
||||
image.set_norm(None)
|
||||
|
||||
image_diff.set_data(normalize_diff(data, data_filtered))
|
||||
image_diff.set_norm(None)
|
||||
fig.canvas.draw_idle()
|
||||
|
||||
exp_slider.on_changed(update)
|
||||
plt.show()
|
||||
|
||||
|
||||
def plot_taper():
|
||||
window = triangular_taper(32, 4)
|
||||
plt.imshow(window)
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(goldstein_rust())
|
||||
plot_goldstein()
|
||||
@ -1 +1,4 @@
|
||||
pytest
|
||||
setuptools-rust
|
||||
flake8
|
||||
black
|
||||
|
||||
@ -1,277 +0,0 @@
|
||||
import numpy as num
|
||||
|
||||
from scipy import interpolate, ndimage
|
||||
from collections.abc import Iterable
|
||||
|
||||
from pyrocko.guts import Object, Float, List, Tuple, String, Timestamp
|
||||
from pyrocko import gf
|
||||
from pyrocko.model import Location
|
||||
import pyrocko.orthodrome as od
|
||||
|
||||
r2d = 180./num.pi
|
||||
km = 1e3
|
||||
|
||||
META = {
|
||||
'measure_length': None,
|
||||
'start_distance': None,
|
||||
'stop_distance': None,
|
||||
'gauge_length': None,
|
||||
'spatial_resolution': None,
|
||||
|
||||
'geo_lat': None,
|
||||
'geo_lon': None,
|
||||
'geo_elevation': None,
|
||||
|
||||
'channel': None,
|
||||
'unit': None
|
||||
}
|
||||
|
||||
|
||||
class QuantityType(gf.meta.QuantityType):
|
||||
choices = gf.meta.QuantityType.choices + ['strain', 'strain_rate']
|
||||
|
||||
|
||||
class Fiber(Object):
|
||||
lat = Float.T(
|
||||
default=0.)
|
||||
lon = Float.T(
|
||||
default=0.)
|
||||
|
||||
codes = Tuple.T(
|
||||
4, String.T(), default=('', '', '', 'HSF'),
|
||||
help='network, station, location and channel codes to be set on '
|
||||
'the response trace. If station code is empty it will be filled'
|
||||
' by the channel number')
|
||||
quantity = QuantityType.T(
|
||||
default='strain',
|
||||
help='Measurement quantity type. If not given, it is guessed from the '
|
||||
'channel code. For some common cases, derivatives of the stored '
|
||||
'quantities are supported by using finite difference '
|
||||
'approximations (e.g. displacement to velocity or acceleration). '
|
||||
'4th order central FD schemes are used.')
|
||||
|
||||
elevation = Float.T(
|
||||
default=0.0,
|
||||
help='station surface elevation in [m]')
|
||||
|
||||
store_id = gf.meta.StringID.T(
|
||||
optional=True,
|
||||
help='ID of Green\'s function store to use for the computation. '
|
||||
'If not given, the processor may use a system default.')
|
||||
|
||||
sample_rate = Float.T(
|
||||
optional=True,
|
||||
help='sample rate to produce. '
|
||||
'If not given the GF store\'s default sample rate is used. '
|
||||
'GF store specific restrictions may apply.')
|
||||
|
||||
tmin = Timestamp.T(
|
||||
optional=True,
|
||||
help='time of first sample to request in [s]. '
|
||||
'If not given, it is determined from the Green\'s functions.')
|
||||
tmax = Timestamp.T(
|
||||
optional=True,
|
||||
help='time of last sample to request in [s]. '
|
||||
'If not given, it is determined from the Green\'s functions.')
|
||||
interpolation = gf.meta.InterpolationMethod.T(
|
||||
default='multilinear',
|
||||
help='Interpolation method between Green\'s functions. Supported are'
|
||||
' ``nearest_neighbor`` and ``multilinear``')
|
||||
|
||||
coordinates = List.T(
|
||||
help='coordinates of the cable as ``pyrocko.model.Location`` or a tuple'
|
||||
' of (north_shift, east_shift, [elevation]).')
|
||||
|
||||
channel_spacing = Float.T(
|
||||
default=4.,
|
||||
help='Channel spacing [m].')
|
||||
smoothing_sigma = Float.T(
|
||||
default=30.,
|
||||
help='Standard deviation for Gaussian kernel in [m]'
|
||||
)
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
self._locations = []
|
||||
for coord in self.coordinates:
|
||||
if isinstance(coord, Location):
|
||||
loc = coord
|
||||
elif isinstance(coord, Iterable) and len(coord) in (2, 3):
|
||||
loc = Location(
|
||||
lat=self.lat,
|
||||
lon=self.lon,
|
||||
north_shift=coord[0],
|
||||
east_shift=coord[1],
|
||||
elevation=self.elevation if len(coord) == 2 else coord[2])
|
||||
else:
|
||||
raise AttributeError(
|
||||
'coordinates have to be a list of pyrocko.model.Location'
|
||||
' or tuples of (north_shift, east_shifts, [elevation]),'
|
||||
' not %s' % type(coord))
|
||||
|
||||
self._locations.append(loc)
|
||||
assert len(self._locations) > 1, 'Fiber needs more than 1 coordinates'
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_stations(cls, stations, **kwargs):
|
||||
return cls(
|
||||
coordinates=stations,
|
||||
**kwargs)
|
||||
|
||||
@property
|
||||
def distances(self):
|
||||
locations = self._locations
|
||||
nlocs = len(locations)
|
||||
|
||||
dists = [0.]
|
||||
for il in range(1, nlocs):
|
||||
dists.append(
|
||||
locations[il-1].distance_3d_to(locations[il]))
|
||||
|
||||
return num.array(dists)
|
||||
|
||||
@property
|
||||
def length(self):
|
||||
return num.sum(self.distances)
|
||||
|
||||
@property
|
||||
def nchannels(self):
|
||||
dists = self.length
|
||||
return int(dists // self.channel_spacing)
|
||||
|
||||
def interpolate_channels(self, start_channel=0):
|
||||
dists = num.cumsum(self.distances)
|
||||
|
||||
interp_lat = interpolate.interp1d(
|
||||
dists,
|
||||
tuple(loc.effective_lat for loc in self._locations))
|
||||
interp_lon = interpolate.interp1d(
|
||||
dists,
|
||||
tuple(loc.effective_lon for loc in self._locations))
|
||||
interp_elevation = interpolate.interp1d(
|
||||
dists,
|
||||
tuple(loc.elevation for loc in self._locations))
|
||||
|
||||
interp_distances = num.arange(
|
||||
0., dists.max(),
|
||||
step=self.channel_spacing)
|
||||
|
||||
channels = num.arange(interp_distances.size) + start_channel
|
||||
nchannels = channels.size
|
||||
|
||||
lats = interp_lat(interp_distances)
|
||||
lons = interp_lon(interp_distances)
|
||||
elevations = interp_elevation(interp_distances)
|
||||
|
||||
azis = num.empty(nchannels)
|
||||
azis[:-1] = od.azimuth_numpy(
|
||||
lats[:-1], lons[:-1],
|
||||
lats[1:], lons[1:],
|
||||
)
|
||||
azis[-1] = azis[-2]
|
||||
|
||||
ds = num.full(nchannels, self.channel_spacing)
|
||||
dips = -num.arctan2(
|
||||
num.gradient(elevations), ds) * r2d
|
||||
|
||||
return lats, lons, elevations, azis, dips, channels
|
||||
|
||||
def get_targets(self):
|
||||
lats, lons, elevations, azis, dips, channels = \
|
||||
self.interpolate_channels()
|
||||
nchannels = self.nchannels
|
||||
|
||||
quantity = self.quantity
|
||||
if self.quantity in ('strain', 'strain_rate'):
|
||||
quantity = 'displacement'
|
||||
|
||||
targets = []
|
||||
for icha in range(nchannels):
|
||||
codes = list(self.codes)
|
||||
if not codes[1]:
|
||||
codes[1] = '%05d' % icha
|
||||
|
||||
t = gf.Target(
|
||||
lat=lats[icha],
|
||||
lon=lons[icha],
|
||||
elevation=elevations[icha],
|
||||
azimuth=azis[icha],
|
||||
dip=dips[icha],
|
||||
north_shift=0.,
|
||||
east_shift=0.,
|
||||
sample_rate=self.sample_rate,
|
||||
|
||||
codes=codes,
|
||||
store_id=self.store_id,
|
||||
quantity=quantity,
|
||||
interpolation=self.interpolation
|
||||
)
|
||||
targets.append(t)
|
||||
return targets
|
||||
|
||||
def get_locations(self):
|
||||
return self._locations
|
||||
|
||||
|
||||
class LocalEngine(gf.LocalEngine):
|
||||
|
||||
def process_fiber(self, source, fiber, **kwargs):
|
||||
assert isinstance(source, Iterable), \
|
||||
'Currently only one source is supported!'
|
||||
|
||||
targets = fiber.get_targets()
|
||||
resp = self.process(source, targets, **kwargs)
|
||||
|
||||
traces = resp.pyrocko_traces()
|
||||
ntraces = len(traces)
|
||||
|
||||
all_times = [(tr.tmin, tr.tmax) for tr in traces]
|
||||
all_tmin = num.min(all_times)
|
||||
all_tmax = num.max(all_times)
|
||||
|
||||
meta = {
|
||||
'measure_length': fiber.length,
|
||||
'start_distance': 0.,
|
||||
'stop_distance': fiber.length,
|
||||
'gauge_length': fiber.channel_spacing,
|
||||
'spatial_resolution': fiber.channel_spacing
|
||||
}
|
||||
|
||||
for icha, (tr, target) in enumerate(zip(traces, targets)):
|
||||
tr.extend(tmin=all_tmin, tmax=all_tmax, fillmethod='repeat')
|
||||
tr.meta = meta.copy()
|
||||
tr.meta['channel'] = icha
|
||||
tr.meta['geo_lat'] = target.lat
|
||||
tr.meta['geo_lon'] = target.lon
|
||||
tr.meta['geo_elevation'] = target.elevation
|
||||
|
||||
nsamples = set([tr.ydata.size for tr in traces])
|
||||
assert len(nsamples) == 1
|
||||
nsamples = nsamples.pop()
|
||||
|
||||
# Spatial smoothing
|
||||
trs_data = ndimage.gaussian_filter1d(
|
||||
num.array([tr.ydata for tr in traces]),
|
||||
sigma=fiber.smoothing_sigma/fiber.channel_spacing, axis=0)
|
||||
|
||||
for itr, tr in enumerate(traces):
|
||||
tr.set_ydata(trs_data[itr])
|
||||
|
||||
if fiber.quantity in ('strain', 'strain_rate'):
|
||||
assert ntraces > 3, \
|
||||
'Too few channels in fiber for finite-difference derivation'
|
||||
|
||||
trs_strain_grad = num.gradient(
|
||||
num.array([tr.ydata for tr in traces]), fiber.channel_spacing,
|
||||
axis=0)
|
||||
|
||||
for itr, tr in enumerate(traces):
|
||||
tr.set_ydata(trs_strain_grad[itr])
|
||||
|
||||
if fiber.quantity == 'strain_rate':
|
||||
for tr in traces:
|
||||
tr.differentiate()
|
||||
|
||||
return traces
|
||||
@ -0,0 +1,22 @@
|
||||
use ndarray::Array;
|
||||
use numpy::{IntoPyArray, PyArray1, PyReadonlyArray2};
|
||||
use pyo3::prelude::*;
|
||||
|
||||
fn triangular_taper(taper_size: usize, plateau: usize) -> Array<f32, _> {
|
||||
assert!(plateau < taper_size, "Plateau cannot be larger than size.");
|
||||
assert!(taper_size as f32 % 2.0 != 0., "Size has to be even");
|
||||
}
|
||||
|
||||
/// A Python module implemented in Rust.
|
||||
#[pymodule]
|
||||
fn rust(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
|
||||
#[pyfn(m)]
|
||||
fn rust_func<'py>(
|
||||
py: Python<'py>,
|
||||
data: PyReadonlyArray2<'py, f64>,
|
||||
) -> PyResult<&'py PyArray1<f64>> {
|
||||
let x = vec![1., 2., 3.].into_pyarray(py);
|
||||
Ok(x)
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
@ -0,0 +1,30 @@
|
||||
import pytest
|
||||
from pyrocko_das import gf
|
||||
|
||||
km = 1e3
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def syn_das_data():
|
||||
def get_data(**kwargs):
|
||||
import pyrocko.gf as pgf
|
||||
|
||||
default_kwargs = dict(
|
||||
lat=0.0,
|
||||
lon=0.0,
|
||||
quantity="velocity",
|
||||
store_id="das_test",
|
||||
coordinates=((1100, 0), (2000, 1000), (2000, 3000)),
|
||||
channel_spacing=4.0,
|
||||
)
|
||||
|
||||
default_kwargs.update(kwargs)
|
||||
fiber = gf.Fiber(**default_kwargs)
|
||||
engine = gf.LocalEngine(use_config=True)
|
||||
|
||||
source = pgf.DCSource(lat=0.0, lon=0.0, depth=2 * km, strike=45.0, dip=30.0)
|
||||
|
||||
traces = engine.process_fiber(source, fiber)
|
||||
return traces
|
||||
|
||||
return get_data
|
||||
@ -0,0 +1,40 @@
|
||||
5J.05119. 63.82725 -22.67712 0.0 0.0
|
||||
5J.04992. 63.83083 -22.67155 0.0 0.0
|
||||
5J.04864. 63.83406 -22.66579 0.0 0.0
|
||||
5J.04736. 63.83600 -22.65668 0.0 0.0
|
||||
5J.04608. 63.83991 -22.65147 0.0 0.0
|
||||
5J.04480. 63.84300 -22.64507 0.0 0.0
|
||||
5J.04352. 63.84610 -22.63717 0.0 0.0
|
||||
5J.04224. 63.84933 -22.63000 0.0 0.0
|
||||
5J.04096. 63.85254 -22.62261 0.0 0.0
|
||||
5J.03969. 63.85398 -22.61294 0.0 0.0
|
||||
5J.03841. 63.85595 -22.60373 0.0 0.0
|
||||
5J.03713. 63.85521 -22.59439 0.0 0.0
|
||||
5J.03585. 63.85409 -22.58429 0.0 0.0
|
||||
5J.03457. 63.85324 -22.57465 0.0 0.0
|
||||
5J.03329. 63.85459 -22.56499 0.0 0.0
|
||||
5J.03201. 63.85883 -22.56068 0.0 0.0
|
||||
5J.03073. 63.86222 -22.55340 0.0 0.0
|
||||
5J.02946. 63.86495 -22.54518 0.0 0.0
|
||||
5J.02818. 63.86642 -22.53514 0.0 0.0
|
||||
5J.02690. 63.86757 -22.52550 0.0 0.0
|
||||
5J.02562. 63.86656 -22.51521 0.0 0.0
|
||||
5J.02434. 63.86616 -22.50483 0.0 0.0
|
||||
5J.02306. 63.86613 -22.49439 0.0 0.0
|
||||
5J.02178. 63.86787 -22.48457 0.0 0.0
|
||||
5J.02050. 63.86989 -22.47506 0.0 0.0
|
||||
5J.01923. 63.87134 -22.46520 0.0 0.0
|
||||
5J.01795. 63.87258 -22.45499 0.0 0.0
|
||||
5J.01667. 63.87480 -22.44567 0.0 0.0
|
||||
5J.01539. 63.87709 -22.43664 0.0 0.0
|
||||
5J.01412. 63.87888 -22.43064 0.0 0.0
|
||||
5J.01284. 63.87482 -22.42599 0.0 0.0
|
||||
5J.01156. 63.87039 -22.42549 0.0 0.0
|
||||
5J.01028. 63.86590 -22.42404 0.0 0.0
|
||||
5J.00901. 63.86151 -22.42662 0.0 0.0
|
||||
5J.00773. 63.85709 -22.42943 0.0 0.0
|
||||
5J.00645. 63.85263 -22.43191 0.0 0.0
|
||||
5J.00517. 63.85036 -22.43858 0.0 0.0
|
||||
5J.00389. 63.84684 -22.44513 0.0 0.0
|
||||
5J.00261. 63.84249 -22.44831 0.0 0.0
|
||||
5J.00133. 63.84025 -22.44546 0.0 0.0
|
||||