# http://pyrocko.org - GPLv3
#
# The Pyrocko Developers, 21st Century
# ---|P------/S----------~Lg----------
'''
Access to the USGS Global Crustal Database.
Simple queries and statistical analysis
'''
from __future__ import absolute_import
import numpy as num
import copy
import logging
from os import path
from pyrocko.guts import Object, String, Float, Int
from pyrocko.guts_array import Array
from pyrocko.cake import LayeredModel, Material
from pyrocko.plot.cake_plot import my_model_plot, xscaled, yscaled
from .crustdb_abbr import ageKey, provinceKey, referenceKey, pubYear # noqa
logger = logging.getLogger('pyrocko.dataset.crustdb')
THICKNESS_HALFSPACE = 2
db_url = 'https://mirror.pyrocko.org/gsc20130501.txt'
km = 1e3
vel_labels = {
'vp': '$V_P$',
'p': '$V_P$',
'vs': '$V_S$',
's': '$V_S$',
}
class DatabaseError(Exception):
pass
class ProfileEmpty(Exception):
pass
def _getCanvas(axes):
import matplotlib.pyplot as plt
if axes is None:
fig = plt.figure()
return fig, fig.gca()
return axes.figure, axes
def xoffset_scale(offset, scale, ax):
from matplotlib.ticker import ScalarFormatter, AutoLocator
class FormatVelocities(ScalarFormatter):
@staticmethod
def __call__(value, pos):
return u'%.1f' % ((value-offset) * scale)
class OffsetLocator(AutoLocator):
def tick_values(self, vmin, vmax):
return [v + offset for v in
AutoLocator.tick_values(self, vmin, vmax)]
ax.get_xaxis().set_major_formatter(FormatVelocities())
ax.get_xaxis().set_major_locator(OffsetLocator())
[docs]class VelocityProfile(Object):
uid = Int.T(
optional=True,
help='Unique ID of measurement')
lat = Float.T(
help='Latitude [deg]')
lon = Float.T(
help='Longitude [deg]')
elevation = Float.T(
default=num.nan,
help='Elevation [m]')
vp = Array.T(
shape=(None, 1),
help='P Wave velocities [m/s]')
vs = Array.T(
shape=(None, 1),
help='S Wave velocities [m/s]')
d = Array.T(
shape=(None, 1),
help='Interface depth, top [m]')
h = Array.T(
shape=(None, 1),
help='Interface thickness [m]')
heatflow = Float.T(
optional=True,
help='Heatflow [W/m^2]')
geographical_location = String.T(
optional=True,
help='Geographic Location')
geological_province = String.T(
optional=True,
help='Geological Province')
geological_age = String.T(
optional=True,
help='Geological Age')
measurement_method = Int.T(
optional=True,
help='Measurement method')
publication_reference = String.T(
optional=True,
help='Publication Reference')
publication_year__ = Int.T(
help='Publication Date')
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
self.h = num.abs(self.d - num.roll(self.d, -1))
self.h[-1] = 0
self.nlayers = self.h.size
self.geographical_location = '%s (%s)' % (
provinceKey(self.geographical_location),
self.geographical_location)
self.vs[self.vs == 0] = num.nan
self.vp[self.vp == 0] = num.nan
self._step_vp = num.repeat(self.vp, 2)
self._step_vs = num.repeat(self.vs, 2)
self._step_d = num.roll(num.repeat(self.d, 2), -1)
self._step_d[-1] = self._step_d[-2] + THICKNESS_HALFSPACE
@property
def publication_year__(self):
return pubYear(self.publication_reference)
[docs] def interpolateProfile(self, depths, phase='p', stepped=True):
'''
Get a continuous velocity function at arbitrary depth.
:param depth: Depths to interpolate
:type depth: :class:`numpy.ndarray`
:param phase: P or S wave velocity, **p** or **s**
:type phase: str, optional
:param stepped: Use a stepped velocity function or gradient
:type stepped: bool
:returns: velocities at requested depths
:rtype: :class:`numpy.ndarray`
'''
if phase not in ['s', 'p']:
raise AttributeError('Phase has to be either \'p\' or \'s\'.')
if phase == 'p':
vel = self._step_vp if stepped else self.vp
elif phase == 's':
vel = self._step_vs if stepped else self.vs
d = self._step_d if stepped else self.d
if vel.size == 0:
raise ProfileEmpty('Phase %s does not contain velocities' % phase)
try:
res = num.interp(depths, d, vel,
left=num.nan, right=num.nan)
except ValueError:
raise ValueError('Could not interpolate velocity profile.')
return res
[docs] def plot(self, axes=None):
'''
Plot the velocity profile, see :class:`pyrocko.cake`.
:param axes: Axes to plot into.
:type axes: :class:`matplotlib.Axes`
'''
import matplotlib.pyplot as plt
fig, ax = _getCanvas(axes)
my_model_plot(self.getLayeredModel(), axes=axes)
ax.set_title('Global Crustal Database\n'
'Velocity Structure at {p.lat:.4f}N, '
' {p.lat:.4f}E (uid {p.uid})'.format(p=self))
if axes is None:
plt.show()
[docs] def getLayeredModel(self):
'''
Get a layered model, see :class:`pyrocko.cake.LayeredModel`.
'''
def iterLines():
for il, m in enumerate(self.iterLayers()):
yield self.d[il], m, ''
return LayeredModel.from_scanlines(iterLines())
[docs] def iterLayers(self):
'''
Iterator yielding a :class:`pyrocko.cake.Material` for each layer.
'''
for il in range(self.nlayers):
yield Material(vp=self.vp[il],
vs=self.vs[il])
@property
def geog_loc_long(self):
return provinceKey(self.geog_loc)
@property
def geol_age_long(self):
return ageKey(self.geol_age)
@property
def has_s(self):
return num.any(self.vp)
@property
def has_p(self):
return num.any(self.vs)
[docs] def get_weeded(self):
'''
Get weeded representation of layers used in the profile.
See :func:`pyrocko.cake.get_weeded` for details.
'''
weeded = num.zeros((self.nlayers, 4))
weeded[:, 0] = self.d
weeded[:, 1] = self.vp
weeded[:, 2] = self.vs
def _csv(self):
output = ''
for d in range(len(self.h)):
output += (
'{p.uid}, {p.lat}, {p.lon},'
' {vp}, {vs}, {h}, {d}, {p.publication_reference}\n'
).format(
p=self,
vp=self.vp[d], vs=self.vs[d], h=self.h[d], d=self.d[d])
return output
[docs]class CrustDB(object):
'''
CrustDB is a container for :class:`VelocityProfile` and provides
functions for spatial selection, querying, processing and visualising
data from the Global Crustal Database.
'''
def __init__(self, database_file=None, parent=None):
self.profiles = []
self._velocity_matrix_cache = {}
self.data_matrix = None
self.name = None
self.database_file = database_file
if parent is not None:
pass
elif database_file is not None:
self._read(database_file)
else:
self._read(self._getRepositoryDatabase())
def __len__(self):
return len(self.profiles)
def __setitem__(self, key, value):
if not isinstance(value, VelocityProfile):
raise TypeError('Element is not a VelocityProfile')
self.profiles[key] = value
def __delitem__(self, key):
self.profiles.remove(key)
def __getitem__(self, key):
return self.profiles[key]
def __str__(self):
rstr = "Container contains %d velocity profiles:\n\n" % self.nprofiles
return rstr
@property
def nprofiles(self):
return len(self.profiles)
def append(self, value):
if not isinstance(value, VelocityProfile):
raise TypeError('Element is not a VelocityProfile')
self.profiles.append(value)
def copy(self):
return copy.deepcopy(self)
def lats(self):
return num.array(
[p.lat for p in self.profiles])
def lons(self):
return num.array(
[p.lon for p in self.profiles])
def _dataMatrix(self):
if self.data_matrix is not None:
return self.data_matrix
self.data_matrix = num.core.records.fromarrays(
num.vstack([
num.concatenate([p.vp for p in self.profiles]),
num.concatenate([p.vs for p in self.profiles]),
num.concatenate([p.h for p in self.profiles]),
num.concatenate([p.d for p in self.profiles])
]),
names='vp, vs, h, d')
return self.data_matrix
[docs] def velocityMatrix(self, depth_range=(0, 60000.), ddepth=100., phase='p'):
'''
Create a regular sampled velocity matrix.
:param depth_range: Depth range, ``(dmin, dmax)``,
defaults to ``(0, 6000.)``
:type depth_range: tuple
:param ddepth: Stepping in [m], defaults to ``100.``
:type ddepth: float
:param phase: Phase to calculate ``p`` or ``s``,
defaults to ``p``
:type phase: str
:returns: Sample depths, veloctiy matrix
:rtype: tuple, (sample_depth, :class:`numpy.ndarray`)
'''
dmin, dmax = depth_range
uid = '.'.join(map(repr, (dmin, dmax, ddepth, phase)))
sdepth = num.linspace(dmin, dmax, (dmax - dmin) / ddepth)
ndepth = sdepth.size
if uid not in self._velocity_matrix_cache:
vel_mat = num.empty((self.nprofiles, ndepth))
for ip, profile in enumerate(self.profiles):
vel_mat[ip, :] = profile.interpolateProfile(sdepth,
phase=phase)
self._velocity_matrix_cache[uid] = num.ma.masked_invalid(vel_mat)
return sdepth, self._velocity_matrix_cache[uid]
[docs] def rmsRank(self, ref_profile, depth_range=(0, 3500.), ddepth=100.,
phase='p'):
'''
Correlates ``ref_profile`` to each profile in the database.
:param ref_profile: Reference profile
:type ref_profile: :class:`VelocityProfile`
:param depth_range: Depth range in [m], ``(dmin, dmax)``,
defaults to ``(0, 35000.)``
:type depth_range: tuple, optional
:param ddepth: Stepping in [m], defaults to ``100.``
:type ddepth: float
:param phase: Phase to calculate ``p`` or ``s``, defaults to ``p``
:type phase: str
:returns: RMS factor length of N_profiles
:rtype: :class:`numpy.ndarray`
'''
if not isinstance(ref_profile, VelocityProfile):
raise ValueError('ref_profile is not a VelocityProfile')
sdepth, vel_matrix = self.velocityMatrix(depth_range, ddepth,
phase=phase)
ref_vel = ref_profile.interpolateProfile(sdepth, phase=phase)
rms = num.empty(self.nprofiles)
for p in range(self.nprofiles):
profile = vel_matrix[p, :]
rms[p] = num.sqrt(profile**2 - ref_vel**2).sum() / ref_vel.size
return rms
[docs] def histogram2d(self, depth_range=(0., 60000.), vel_range=None,
ddepth=100., dvbin=100., ddbin=2000., phase='p'):
'''
Create a 2D Histogram of all the velocity profiles.
Check :func:`numpy.histogram2d` for more information.
:param depth_range: Depth range in [m], ``(dmin, dmax)``,
defaults to ``(0., 60000.)``
:type depth_range: tuple
:param vel_range: Depth range, ``(vmin, vmax)``,
defaults to ``(5500., 8500.)``
:type vel_range: tuple
:param ddepth: Stepping in [km], defaults to ``100.``
:type ddepth: float
:param dvbin: Bin size in velocity dimension [m/s], defaults to 100.
:type dvbin: float
:param dvbin: Bin size in depth dimension [m], defaults to 2000.
:type dvbin: float
:param phase: Phase to calculate ``p`` or ``s``, defaults to ``p``
:type phase: str
:returns: :func:`numpy.histogram2d`
:rtype: tuple
'''
sdepth, v_mat = self.velocityMatrix(depth_range, ddepth, phase=phase)
d_vec = num.tile(sdepth, self.nprofiles)
# Velocity and depth bins
if vel_range is None:
vel_range = ((v_mat.min() // 1e2) * 1e2,
(v_mat.max() // 1e2) * 1e2)
nvbins = int((vel_range[1] - vel_range[0]) / dvbin)
ndbins = int((depth_range[1] - depth_range[0]) / ddbin)
return num.histogram2d(v_mat.flatten(), d_vec,
range=(vel_range, depth_range),
bins=[nvbins, ndbins],
normed=False)
[docs] def meanVelocity(self, depth_range=(0., 60000.), ddepth=100., phase='p'):
'''
Get mean and standard deviation of velocity profile.
:param depth_range: Depth range in [m], ``(dmin, dmax)``,
defaults to ``(0., 60000.)``
:type depth_range: tuple
:param ddepth: Stepping in [m], defaults to ``100.``
:type ddepth: float
:param phase: Phase to calculate ``p`` or ``s``, defaults to ``p``
:type phase: str
:returns: depth vector, mean velocities, standard deviations
:rtype: tuple of :class:`numpy.ndarray`
'''
sdepth, v_mat = self.velocityMatrix(depth_range, ddepth, phase=phase)
v_mean = num.ma.mean(v_mat, axis=0)
v_std = num.ma.std(v_mat, axis=0)
return sdepth, v_mean.flatten(), v_std.flatten()
[docs] def modeVelocity(self, depth_range=(0., 60000.), ddepth=100., phase='p'):
'''
Get mode velocity profile and standard deviation.
:param depth_range: Depth range in [m], ``(dmin, dmax)``,
defaults to ``(0., 60000.)``
:type depth_range: tuple
:param ddepth: Stepping in [m], defaults to ``100.``
:type ddepth: float
:param phase: Phase to calculate ``p`` or ``s``, defaults to ``p``
:type phase: str
:returns: depth vector, mode velocity, number of counts at each depth
:rtype: tuple of :class:`numpy.ndarray`
'''
import scipy.stats
sdepth, v_mat = self.velocityMatrix(depth_range, ddepth)
v_mode, v_counts = scipy.stats.mstats.mode(v_mat, axis=0)
return sdepth, v_mode.flatten(), v_counts.flatten()
[docs] def plotHistogram(self, vel_range=None, bins=36, phase='vp',
axes=None):
'''
Plot 1D histogram of seismic velocities in the container.
:param vel_range: Velocity range, defaults to (5.5, 8.5)
:type vel_range: tuple, optional
:param bins: bins, defaults to 30 (see :func:`numpy.histogram`)
:type bins: int, optional
:param phase: Property to plot out of ``['vp', 'vs']``,
defaults to 'vp'
:type phase: str, optional
:param figure: Figure to plot in, defaults to None
:type figure: :class:`matplotlib.Figure`, optional
'''
import matplotlib.pyplot as plt
fig, ax = _getCanvas(axes)
if phase not in ['vp', 'vs']:
raise AttributeError('phase has to be either vp or vs')
data = self._dataMatrix()[phase]
ax.hist(data, weights=self.data_matrix['h'],
range=vel_range, bins=bins,
color='g', alpha=.5)
ax.text(.95, .95, '%d Profiles' % self.nprofiles,
transform=ax.transAxes, fontsize=10,
va='top', ha='right', alpha=.7)
ax.set_title('Distribution of %s' % vel_labels[phase])
ax.set_xlabel('%s [km/s]' % vel_labels[phase])
ax.set_ylabel('Cumulative occurrence [N]')
xscaled(1./km, ax)
ax.yaxis.grid(alpha=.4)
if self.name is not None:
ax.set_title('%s for %s' % (ax.get_title(), self.name))
if axes is None:
plt.show()
[docs] def plot(self, depth_range=(0, 60000.), ddepth=100., ddbin=2000.,
vel_range=None, dvbin=100.,
percent=False,
plot_mode=True, plot_median=True, plot_mean=False,
show_cbar=True,
aspect=.02,
phase='p',
axes=None):
'''
Plot a two 2D Histogram of seismic velocities.
:param depth_range: Depth range, ``(dmin, dmax)``,
defaults to ``(0, 60)``
:type depth_range: tuple
:param vel_range: Velocity range, ``(vmin, vmax)``
:type vel_range: tuple
:param ddepth: Stepping in [m], defaults to ``.1``
:type ddepth: float
:param dvbin: Bin size in velocity dimension [m/s], defaults to .1
:type dvbin: float
:param dvbin: Bin size in depth dimension [m], defaults to 2000.
:type dvbin: float
:param phase: Phase to calculate ``p`` or ``s``, defaults to ``p``
:type phase: str
:param plot_mode: Plot the Mode
:type plot_mode: bool
:param plot_mean: Plot the Mean
:type plot_mean: bool
:param plot_median: Plot the Median
:type plot_median: bool
:param axes: Axes to plot into, defaults to None
:type axes: :class:`matplotlib.Axes`
'''
import matplotlib.pyplot as plt
fig, ax = _getCanvas(axes)
ax = fig.gca()
if vel_range is not None:
vmin, vmax = vel_range
dmin, dmax = depth_range
vfield, vedg, dedg = self.histogram2d(vel_range=vel_range,
depth_range=depth_range,
ddepth=ddepth, dvbin=dvbin,
ddbin=ddbin, phase=phase)
vfield /= (ddbin / ddepth)
if percent:
vfield /= vfield.sum(axis=1)[num.newaxis, :]
grid_ext = [vedg[0], vedg[-1], dedg[-1], dedg[0]]
histogram = ax.imshow(vfield.swapaxes(0, 1),
interpolation='nearest',
extent=grid_ext, aspect=aspect)
if show_cbar:
cticks = num.unique(
num.arange(0, vfield.max(), vfield.max() // 10).round())
cbar = fig.colorbar(histogram, ticks=cticks, format='%1i',
orientation='horizontal')
if percent:
cbar.set_label('Percent')
else:
cbar.set_label('Number of Profiles')
if plot_mode:
sdepth, vel_mode, _ = self.modeVelocity(depth_range=depth_range,
ddepth=ddepth)
ax.plot(vel_mode[sdepth < dmax] + ddepth/2,
sdepth[sdepth < dmax],
alpha=.8, color='w', label='Mode')
if plot_mean:
sdepth, vel_mean, _ = self.meanVelocity(depth_range=depth_range,
ddepth=ddepth)
ax.plot(vel_mean[sdepth < dmax] + ddepth/2,
sdepth[sdepth < dmax],
alpha=.8, color='w', linestyle='--', label='Mean')
if plot_median:
sdepth, vel_median, _ = self.medianVelocity(
depth_range=depth_range,
ddepth=ddepth)
ax.plot(vel_median[sdepth < dmax] + ddepth/2,
sdepth[sdepth < dmax],
alpha=.8, color='w', linestyle=':', label='Median')
ax.grid(True, which="both", color="w", linewidth=.8, alpha=.4)
ax.text(.025, .025, '%d Profiles' % self.nprofiles,
color='w', alpha=.7,
transform=ax.transAxes, fontsize=9, va='bottom', ha='left')
ax.set_title('Crustal Velocity Distribution')
ax.set_xlabel('%s [km/s]' % vel_labels[phase])
ax.set_ylabel('Depth [km]')
yscaled(1./km, ax)
xoffset_scale(dvbin/2, 1./km, ax)
ax.set_xlim(vel_range)
if self.name is not None:
ax.set_title('%s for %s' % (ax.get_title(), self.name))
if plot_mode or plot_mean or plot_median:
leg = ax.legend(loc=1, fancybox=True, prop={'size': 10.})
leg.get_frame().set_alpha(.6)
if axes is None:
plt.show()
[docs] def plotVelocitySurface(self, v_max, d_min=0., d_max=6000., axes=None):
'''
Plot a triangulated a depth surface exceeding velocity.
'''
import matplotlib.pyplot as plt
fig, ax = _getCanvas(axes)
d = self.exceedVelocity(v_max, d_min, d_max)
lons = self.lons()[d > 0]
lats = self.lats()[d > 0]
d = d[d > 0]
ax.tricontourf(lats, lons, d)
if axes is None:
plt.show()
def plotMap(self, outfile, **kwargs):
from pyrocko.plot import gmtpy
lats = self.lats()
lons = self.lons()
s, n, w, e = (lats.min(), lats.max(), lons.min(), lons.max())
def darken(c, f=0.7):
return (c[0]*f, c[1]*f, c[2]*f)
gmt = gmtpy.GMT()
gmt.psbasemap(B='40/20',
J='M0/12',
R='%f/%f/%f/%f' % (w, e, s, n))
gmt.pscoast(R=True, J=True,
D='i', S='216/242/254', A=10000,
W='.2p')
gmt.psxy(R=True, J=True,
in_columns=[lons, lats],
S='c2p', G='black')
gmt.save(outfile)
[docs] def exceedVelocity(self, v_max, d_min=0, d_max=60):
'''
Returns the last depth ``v_max`` has not been exceeded.
:param v_max: maximal velocity
:type vmax: float
:param dz: depth is sampled in dz steps
:type dz: float
:param d_max: maximum depth
:type d_max: int
:param d_min: minimum depth
:type d_min: int
:returns: Lat, Lon, Depth and uid where ``v_max`` is exceeded
:rtype: list(num.array)
'''
self.profile_exceed_velocity = num.empty(len(self.profiles))
self.profile_exceed_velocity[:] = num.nan
for ip, profile in enumerate(self.profiles):
for il in range(len(profile.d)):
if profile.d[il] <= d_min\
or profile.d[il] >= d_max:
continue
if profile.vp[il] < v_max:
continue
else:
self.profile_exceed_velocity[ip] = profile.d[il]
break
return self.profile_exceed_velocity
[docs] def selectRegion(self, west, east, south, north):
'''
Select profiles within a region by geographic corner coordinates.
:param west: west corner
:type west: float
:param east: east corner
:type east: float
:param south: south corner
:type south: float
:param north: north corner
:type north: float
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
for profile in self.profiles:
if profile.lon >= west and profile.lon <= east \
and profile.lat <= north and profile.lat >= south:
r_container.append(profile)
return r_container
[docs] def selectPolygon(self, poly):
'''
Select profiles within a polygon.
The algorithm is called the **Ray Casting Method**
:param poly: Latitude Longitude pairs of the polygon
:type param: list of :class:`numpy.ndarray`
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
for profile in self.profiles:
x = profile.lon
y = profile.lat
inside = False
p1x, p1y = poly[0]
for p2x, p2y in poly:
if y >= min(p1y, p2y):
if y <= max(p1y, p2y):
if x <= max(p1x, p2x):
if p1y != p2y:
xints = (y - p1y) * (p2x - p1x) / \
(p2y - p1y) + p1x
if p1x == p2x or x <= xints:
inside = not inside
p1x, p1y = p2x, p2y
if inside:
r_container.append(profile)
return r_container
[docs] def selectLocation(self, lat, lon, radius=10):
'''
Select profiles at a geographic location within a ``radius``.
:param lat: Latitude in [deg]
:type lat: float
:param lon: Longitude in [deg]
:type lon: float
:param radius: Radius in [deg]
:type radius: float
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
logger.info('Selecting location %f, %f (r=%f)...' % (lat, lon, radius))
for profile in self.profiles:
if num.sqrt((lat - profile.lat)**2 +
(lon - profile.lon)**2) <= radius:
r_container.append(profile)
return r_container
[docs] def selectMinLayers(self, nlayers):
'''
Select profiles with more than ``nlayers``.
:param nlayers: Minimum number of layers
:type nlayers: int
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
logger.info('Selecting minimum %d layers...' % nlayers)
for profile in self.profiles:
if profile.nlayers >= nlayers:
r_container.append(profile)
return r_container
[docs] def selectMaxLayers(self, nlayers):
'''
Select profiles with more than ``nlayers``.
:param nlayers: Maximum number of layers
:type nlayers: int
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
logger.info('Selecting maximum %d layers...' % nlayers)
for profile in self.profiles:
if profile.nlayers <= nlayers:
r_container.append(profile)
return r_container
[docs] def selectMinDepth(self, depth):
'''
Select profiles describing layers deeper than ``depth``.
:param depth: Minumum depth in [m]
:type depth: float
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
logger.info('Selecting minimum depth %f m...' % depth)
for profile in self.profiles:
if profile.d.max() >= depth:
r_container.append(profile)
return r_container
[docs] def selectMaxDepth(self, depth):
'''
Select profiles describing layers shallower than ``depth``.
:param depth: Maximum depth in [m]
:type depth: float
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
logger.info('Selecting maximum depth %f m...' % depth)
for profile in self.profiles:
if profile.d.max() <= depth:
r_container.append(profile)
return r_container
[docs] def selectVp(self):
'''
Select profiles describing P wave velocity.
:returns Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
logger.info('Selecting profiles providing Vp...')
for profile in self.profiles:
if not num.all(num.isnan(profile.vp)):
r_container.append(profile)
return r_container
[docs] def selectVs(self):
'''
Select profiles describing P wave velocity.
:returns: Selected profiles
:rtype: :class:`CrustDB`
'''
r_container = self._emptyCopy()
logger.info('Selecting profiles providing Vs...')
for profile in self.profiles:
if not num.all(num.isnan(profile.vs)):
r_container.append(profile)
return r_container
def _emptyCopy(self):
r_container = CrustDB(parent=self)
r_container.name = self.name
return r_container
[docs] def exportCSV(self, filename=None):
'''
Export as CSV file.
:param filename: Export filename
:type filename: str
'''
with open(filename, 'w') as file:
file.write('# uid, Lat, Lon, vp, vs, H, Depth, Reference\n')
for profile in self.profiles:
file.write(profile._csv())
[docs] def exportYAML(self, filename=None):
'''
Export as YAML file.
:param filename: Export filename
:type filename: str
'''
with open(filename, 'w') as file:
for profile in self.profiles:
file.write(profile.__str__())
@classmethod
def readDatabase(cls, database_file):
db = cls()
CrustDB._read(db, database_file)
return db
@staticmethod
def _getRepositoryDatabase():
from pyrocko import config
name = path.basename(db_url)
data_path = path.join(config.config().crustdb_dir, name)
if not path.exists(data_path):
from pyrocko import util
util.download_file(db_url, data_path, None, None)
return data_path
def _read(self, database_file):
'''
Reads in the the GSN database file and puts it in CrustDB.
File format:
uid lat/lon vp vs hc depth
2 29.76N 2.30 .00 2.00 .00 s 25.70 .10 .00 NAC-CO 5 U
96.31W 3.94 .00 5.30 2.00 s 33.00 MCz 39.00 61C.3 EXC
5.38 .00 12.50 7.30 c
6.92 .00 13.20 19.80 c
8.18 .00 .00 33.00 m
3 34.35N 3.00 .00 3.00 .00 s 35.00 1.60 .00 NAC-BR 4 R
117.83W 6.30 .00 16.50 3.00 38.00 MCz 55.00 63R.1 ORO
7.00 .00 18.50 19.50
7.80 .00 .00 38.00 m
:param database_file: path to database file, type string
'''
def get_empty_record():
meta = {
'uid': num.nan,
'geographical_location': None,
'geological_province': None,
'geological_age': None,
'elevation': num.nan,
'heatflow': num.nan,
'measurement_method': None,
'publication_reference': None
}
# vp, vs, h, d, lat, lon, meta
return (num.zeros(128, dtype=num.float32),
num.zeros(128, dtype=num.float32),
num.zeros(128, dtype=num.float32),
num.zeros(128, dtype=num.float32),
0., 0., meta)
nlayers = []
def add_record(vp, vs, h, d, lat, lon, meta, nlayer):
if nlayer == 0:
return
self.append(VelocityProfile(
vp=vp[:nlayer] * km,
vs=vs[:nlayer] * km,
h=h[:nlayer] * km,
d=d[:nlayer] * km,
lat=lat, lon=lon,
**meta))
nlayers.append(nlayer)
vp, vs, h, d, lat, lon, meta = get_empty_record()
ilayer = 0
with open(database_file, 'r') as database:
for line, dbline in enumerate(database):
if dbline.isspace():
if not len(d) == 0:
add_record(vp, vs, h, d, lat, lon, meta, ilayer)
if not len(vp) == len(h):
raise DatabaseError(
'Inconsistent database, check line %d!\n\tDebug: '
% line, lat, lon, vp, vs, h, d, meta)
vp, vs, h, d, lat, lon, meta = get_empty_record()
ilayer = 0
else:
try:
if ilayer == 0:
lat = float(dbline[8:13])
if dbline[13] == b'S':
lat = -lat
# Additional meta data
meta['uid'] = int(dbline[0:6])
meta['elevation'] = float(dbline[52:57])
meta['heatflow'] = float(dbline[58:64])
if meta['heatflow'] == 0.:
meta['heatflow'] = None
meta['geographical_location'] =\
dbline[66:72].strip()
meta['measurement_method'] = dbline[77]
if ilayer == 1:
lon = float(dbline[7:13])
if dbline[13] == b'W':
lon = -lon
# Additional meta data
meta['geological_age'] = dbline[54:58].strip()
meta['publication_reference'] =\
dbline[66:72].strip()
meta['geological_province'] = dbline[74:78].strip()
try:
vp[ilayer] = dbline[17:21]
vs[ilayer] = dbline[23:27]
h[ilayer] = dbline[28:34]
d[ilayer] = dbline[35:41]
except ValueError:
pass
except ValueError:
logger.warning(
'Could not interpret line %d, skipping\n%s' %
(line, dbline))
while not database.readlines():
pass
vp, vs, h, d, lat, lon, meta = get_empty_record()
ilayer += 1
# Append last profile
add_record(vp, vs, h, d, lat, lon, meta, ilayer)
logger.info('Loaded %d profiles from %s' %
(self.nprofiles, database_file))
