names = [line.split(' ')[0] for line in f]
gnss_data = num.loadtxt(fname, skiprows=2, usecols=(1, 2, 3, 4, 5, 6, 7, 8))
for ista, sta_data in enumerate(gnss_data):
name = names[ista]
lon, lat, de, dn, du, sde, sdn, sdu = map(float, sta_data)
station = gnss.GNSSStation(code=name, lat=lat, lon=lon, elevation=0.)
if station.distance_to(RIDGECREST_EQ) > DIST_MAX:
continue
station.east = gnss.GNSSComponent(shift=de, sigma=sde)
station.north = gnss.GNSSComponent(shift=dn, sigma=sdn)
station.up = gnss.GNSSComponent(shift=du, sigma=sdu)
campaign.add_station(station)
print('Selected %d stations, saving to ridgecrest_coseis_gnss.yml' %
(campaign.nstations))
campaign.dump(filename='2019-ridgecrest_gnss_unr.yml')
print('Creating map...')
m = Map(lat=RIDGECREST_EQ.lat,
lon=RIDGECREST_EQ.lon,
radius=DIST_MAX * 1.5,
width=20.,
height=14.)
m.add_gnss_campaign(campaign)
m.save('2019-ridgecrest_GNSS-UNR.pdf')
from pyrocko import moment_tensor as pmt
gmtpy.check_have_gmt()
# Download example data
get_example_data('stations_deadsea.pf')
get_example_data('deadsea_events_1976-2017.txt')
# Generate the basic map
m = Map(lat=31.5,
lon=35.5,
radius=150000.,
width=30.,
height=30.,
show_grid=False,
show_topo=True,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=True)
# Draw some larger cities covered by the map area
m.draw_cities()
# Generate with latitute, longitude and labels of the stations
stations = model.load_stations('stations_deadsea.pf')
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
labels = ['.'.join(s.nsl()) for s in stations]
def call(self):
if not self.viewer_connected:
self.get_viewer().about_to_close.connect(
self.file_serving_worker.stop)
self.viewer_connected = True
try:
from OpenGL import GL # noqa
except ImportError:
logger.warn('Could not find package OpenGL, '
'if the map does not work try installing OpenGL\n'
'e.g. sudo pip install PyOpenGL')
self.cleanup()
try:
viewer = self.get_viewer()
cli_mode = False
except NoViewerSet:
viewer = None
cli_mode = True
if not cli_mode:
if self.only_active:
_, active_stations = \
self.get_active_event_and_stations()
else:
active_stations = viewer.stations.values()
elif cli_mode:
active_stations = self.stations
station_list = []
if active_stations:
for stat in active_stations:
is_blacklisted = util.match_nslc(viewer.blacklist, stat.nsl())
if (viewer and not is_blacklisted) or cli_mode:
xml_station_marker = XMLStationMarker(
nsl='.'.join(stat.nsl()),
longitude=float(stat.lon),
latitude=float(stat.lat),
active='yes')
station_list.append(xml_station_marker)
active_station_list = StationMarkerList(stations=station_list)
if self.only_active:
markers = [viewer.get_active_event_marker()]
else:
if cli_mode:
markers = self.markers
else:
markers = self.get_selected_markers()
if len(markers) == 0:
tmin, tmax = self.get_selected_time_range(fallback=True)
markers = [
m for m in viewer.get_markers()
if isinstance(m, gui_util.EventMarker)
and m.tmin >= tmin and m.tmax <= tmax
]
ev_marker_list = []
for m in markers:
if not isinstance(m, gui_util.EventMarker):
continue
xmleventmarker = convert_event_marker(m)
if xmleventmarker is None:
continue
ev_marker_list.append(xmleventmarker)
event_list = EventMarkerList(events=ev_marker_list)
event_station_list = MarkerLists(
station_marker_list=active_station_list,
event_marker_list=event_list)
event_station_list.validate()
if self.map_kind != 'GMT':
tempdir = self.marker_tempdir
if self.map_kind == 'Google Maps':
map_fn = 'map_googlemaps.html'
elif self.map_kind == 'OpenStreetMap':
map_fn = 'map_osm.html'
url = 'http://localhost:' + str(self.port) + '/%s' % map_fn
files = ['loadxmldoc.js', 'map_util.js', 'plates.kml', map_fn]
snuffling_dir = op.dirname(op.abspath(__file__))
for entry in files:
shutil.copy(os.path.join(snuffling_dir, entry),
os.path.join(tempdir, entry))
logger.debug('copied data to %s' % tempdir)
markers_fn = os.path.join(self.marker_tempdir, 'markers.xml')
self.data_proxy.content_to_serve.emit(self.port)
dump_xml(event_station_list, filename=markers_fn)
if self.open_external:
qg.QDesktopServices.openUrl(qc.QUrl(url))
else:
global g_counter
g_counter += 1
self.web_frame(url,
name='Map %i (%s)' % (g_counter, self.map_kind))
else:
lats_all = []
lons_all = []
slats = []
slons = []
slabels = []
for s in active_stations:
slats.append(s.lat)
slons.append(s.lon)
slabels.append('.'.join(s.nsl()))
elats = []
elons = []
elats = []
elons = []
psmeca_input = []
markers = self.get_selected_markers()
for m in markers:
if isinstance(m, gui_util.EventMarker):
e = m.get_event()
elats.append(e.lat)
elons.append(e.lon)
if e.moment_tensor is not None:
mt = e.moment_tensor.m6()
psmeca_input.append((e.lon, e.lat, e.depth / 1000.,
mt[0], mt[1], mt[2], mt[3], mt[4],
mt[5], 1., e.lon, e.lat, e.name))
else:
if e.magnitude is None:
moment = -1.
else:
moment = moment_tensor.magnitude_to_moment(
e.magnitude)
psmeca_input.append(
(e.lon, e.lat, e.depth / 1000., moment / 3.,
moment / 3., moment / 3., 0., 0., 0., 1.,
e.lon, e.lat, e.name))
lats_all.extend(elats)
lons_all.extend(elons)
lats_all.extend(slats)
lons_all.extend(slons)
lats_all = num.array(lats_all)
lons_all = num.array(lons_all)
if len(lats_all) == 0:
return
center_lat, center_lon = ortho.geographic_midpoint(
lats_all, lons_all)
ntotal = len(lats_all)
clats = num.ones(ntotal) * center_lat
clons = num.ones(ntotal) * center_lon
dists = ortho.distance_accurate50m_numpy(clats, clons, lats_all,
lons_all)
maxd = num.max(dists) or 0.
m = Map(lat=center_lat,
lon=center_lon,
radius=max(10000., maxd) * 1.1,
width=35,
height=25,
show_grid=True,
show_topo=True,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=False)
m.gmt.psxy(in_columns=(slons, slats), S='t15p', G='black', *m.jxyr)
for i in range(len(active_stations)):
m.add_label(slats[i], slons[i], slabels[i])
m.gmt.psmeca(in_rows=psmeca_input,
S='m1.0',
G='red',
C='5p,0/0/0',
*m.jxyr)
tmpdir = self.tempdir()
self.outfn = os.path.join(tmpdir, '%i.png' % self.figcount)
m.save(self.outfn)
f = self.pixmap_frame(self.outfn) # noqa
def plot_map(stations, center, events=None, savename=None):
from pyrocko.plot.automap import Map
from pyrocko.example import get_example_data
from pyrocko import model, gmtpy
from pyrocko import moment_tensor as pmt
gmtpy.check_have_gmt()
# Generate the basic map
m = Map(lat=center[0],
lon=center[1],
radius=150000.,
width=30.,
height=30.,
show_grid=False,
show_topo=True,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=False)
# Draw some larger cities covered by the map area
m.draw_cities()
# Generate with latitute, longitude and labels of the stations
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
labels = ['.'.join(s.nsl()) for s in stations]
# Stations as black triangles.
m.gmt.psxy(in_columns=(lons, lats), S='t20p', G='black', *m.jxyr)
# Station labels
for i in range(len(stations)):
m.add_label(lats[i], lons[i], labels[i])
beachball_symbol = 'd'
factor_symbl_size = 5.0
if events is not None:
for ev in events:
mag = ev.magnitude
if ev.moment_tensor is None:
ev_symb = 'c' + str(mag * factor_symbl_size) + 'p'
m.gmt.psxy(in_rows=[[ev.lon, ev.lat]],
S=ev_symb,
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
beachball_size = mag * factor_symbl_size
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
if m.gmt.is_gmt5():
kwargs = dict(M=True,
S='%s%g' % (beachball_symbol[0],
(beachball_size) / gmtpy.cm))
else:
kwargs = dict(S='%s%g' % (beachball_symbol[0],
(beachball_size) * 2 / gmtpy.cm))
m.gmt.psmeca(in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
W='1p,%s' % gmtpy.color('chocolate3'),
*m.jxyr,
**kwargs)
if savename is None:
if events is None:
m.save('pics/stations_ridgecrest.png')
else:
m.save('pics/mechanisms_scedc_ridgecrest.png')
else:
m.save(savename)
gmtpy.check_have_gmt()
# Download example data
get_example_data('stations_deadsea.pf')
get_example_data('deadsea_events_1976-2017.txt')
# Generate the basic map
m = Map(
lat=31.5,
lon=35.5,
radius=150000.,
width=30., height=30.,
show_grid=False,
show_topo=True,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=True)
# Draw some larger cities covered by the map area
m.draw_cities()
# Generate with latitute, longitude and labels of the stations
stations = model.load_stations('stations_deadsea.pf')
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
labels = ['.'.join(s.nsl()) for s in stations]
def plot_corr_angles(ns,
st_lats,
st_lons,
orientfile,
dir_orient,
pl_options,
pl_topo,
mapsize,
ls=False):
'''
Plot correction angles of all stations on a map. nans are igored.
Values for plotting are read from file which was automatically prepared in
the orient section.
:param ls: label position [lon (symbol), lat (symbol + label), lon (label)]
'''
logs = logging.getLogger('plot_corr_angles')
gmtconf = dict(MAP_TICK_PEN_PRIMARY='1.25p',
MAP_TICK_PEN_SECONDARY='1.25p',
MAP_TICK_LENGTH_PRIMARY='0.2c',
MAP_TICK_LENGTH_SECONDARY='0.6c',
FONT_ANNOT_PRIMARY='20p,1,black',
FONT_LABEL='20p,1,black',
PS_CHAR_ENCODING='ISOLatin1+',
MAP_FRAME_TYPE='fancy',
FORMAT_GEO_MAP='D',
PS_PAGE_ORIENTATION='portrait',
MAP_GRID_PEN_PRIMARY='thinnest,0/50/0',
MAP_ANNOT_OBLIQUE='6')
angles_fromfile = load(filename=os.path.join(dir_orient, orientfile))
angle_no_nan = []
lat_no_nan = []
lon_no_nan = []
angle_no_nan_u = []
lat_no_nan_u = []
lon_no_nan_u = []
stats_no_nan = []
stats_no_nan_u = []
for i_ns, ns_now in enumerate(ns):
for l in ['00', '', '01', '10', '60']:
try:
ns_now_ = '%s %s %s' % (ns_now[0], ns_now[1], l)
a = angles_fromfile.CorrectAngl_perStat_median[ns_now_]
nev = angles_fromfile.n_events[ns_now_]
if a > -181.0 and a < 180.0: # not nan
if nev >= 5:
angle_no_nan.append(0.0 - a)
stats_no_nan.append(ns_now[1])
lat_no_nan.append(st_lats[i_ns])
lon_no_nan.append(st_lons[i_ns])
else:
angle_no_nan_u.append(0.0 - a)
stats_no_nan_u.append(ns_now[1])
lat_no_nan_u.append(st_lats[i_ns])
lon_no_nan_u.append(st_lons[i_ns])
except KeyError:
continue
# Generate the basic map
m = Map(
lat=pl_options[0],
lon=pl_options[1],
radius=pl_options[2],
width=mapsize[0],
height=mapsize[1],
show_grid=False,
show_topo=pl_topo,
#color_dry=(143, 188, 143), #(238, 236, 230),
topo_cpt_wet='white_sea_land', #'light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=True,
show_plates=False,
gmt_config=gmtconf)
# Draw some larger cities covered by the map area
# m.draw_cities()
# Draw max. amplitudes at station locations as colored circles
cptfile = 'tempfile2.cpt'
abs_angs = list(num.abs(angle_no_nan))
m.gmt.makecpt(C='jet', T='%g/%g' % (0.1, 180.),
out_filename=cptfile) #, suppress_defaults=True)
# m.gmt.makecpt(
# C='/home/gesap/Documents/CETperceptual_GMT/CET-D8.cpt',
# T='%g/%g' % (0.1, 180.),
# out_filename=cptfile)#, suppress_defaults=True)
# same length for every vector:
length = [1.5 for a in range(len(lat_no_nan))]
length_u = [0.7 for a in range(len(lat_no_nan_u))]
# angle_zero = [1.5 for a in range(len(lat_no_nan))]
# angle_zero_u = [1.5 for a in range(len(lat_no_nan_u))]
# plot obtained rotation vectors:
#m.gmt.psxy(in_columns=(lon_no_nan, lat_no_nan, angle_zero, length),
# S='V0.5c+jc', W='0.07c,black',
# *m.jxyr)
#m.gmt.psxy(in_columns=(lon_no_nan_u, lat_no_nan_u, angle_zero_u, length_u),
# S='V0.5c+jc', W='0.07c,black',
# *m.jxyr)
m.gmt.psxy(in_columns=(lon_no_nan_u, lat_no_nan_u, angle_no_nan_u,
length_u),
S='V0.4c+jc+eA',
W='0.05c,black',
*m.jxyr)
m.gmt.psxy(in_columns=(lon_no_nan, lat_no_nan, abs_angs, angle_no_nan,
length),
C=cptfile,
S='V0.7c+jc+eA',
W='0.1c+cl',
*m.jxyr)
# add handmade label
if ls:
m.gmt.psxy(in_columns=([ls[0]], [ls[1]], [ls[3]], [0.9]),
S='V0.5c+eA',
W='0.07c,red',
*m.jxyr)
labels = ['Sensor orientation']
lat_lab = [ls[1]]
lon_lab = [ls[2]]
for i in range(len(labels)):
m.add_label(lat_lab[i], lon_lab[i], labels[i])
# add a colorbar
B_opt_psscale = 'xaf'
m.gmt.psscale(
B=B_opt_psscale + '+l abs. misorientation [deg]',
D='x9c/6c+w12c/0.5c+jTC+h', # 'x9c/17c+w12c/0.5c+jTC+h'
C=cptfile)
# add station labels
has_label = []
for i in range(len(stats_no_nan)):
if stats_no_nan[i] not in has_label:
m.add_label(lat_no_nan[i], lon_no_nan[i], stats_no_nan[i])
has_label.append(stats_no_nan[i])
has_label = []
for i in range(len(stats_no_nan_u)):
if stats_no_nan_u[i] not in has_label:
m.add_label(lat_no_nan_u[i], lon_no_nan_u[i], stats_no_nan_u[i])
has_label.append(stats_no_nan_u[i])
m.save(os.path.join(dir_orient, 'map_orient.png'))
logs.info('Saved map with corr. angles for sensor orientations')
def plot_median_gain_map_from_file(ns, st_lats, st_lons, pl_options, pl_topo,
gains_file, directory, comp, mapsize):
"""
Plot map with mean relative gain factors
"""
gmtconf = dict(MAP_TICK_PEN_PRIMARY='1.25p',
MAP_TICK_PEN_SECONDARY='1.25p',
MAP_TICK_LENGTH_PRIMARY='0.2c',
MAP_TICK_LENGTH_SECONDARY='0.6c',
FONT_ANNOT_PRIMARY='20p,1,black',
FONT_LABEL='20p,1,black',
PS_CHAR_ENCODING='ISOLatin1+',
MAP_FRAME_TYPE='fancy',
FORMAT_GEO_MAP='D',
PS_PAGE_ORIENTATION='portrait',
MAP_GRID_PEN_PRIMARY='thinnest,0/50/0',
MAP_ANNOT_OBLIQUE='6')
gains_fromfile = load(filename=os.path.join(directory, gains_file))
try:
ns_rel = gains_fromfile.ref_stats
# print(ns_rel)
except Exception:
ns_rel = None
gains_no_nan = []
lat_no_nan = []
lon_no_nan = []
# stats_no_nan = []
# print(gains_fromfile.trace_gains_median)
for i_ns, ns_now in enumerate(ns):
for l in ['00', '', '01', '10', '60']:
try:
nslc = '%s.%s.%s.%s' % (ns_now[0], ns_now[1], l, comp)
g = gains_fromfile.trace_gains_median[nslc]
if g > 0.0 or g < 0.0: # g < 5 and g > 0.2: # g < 10.0 and g > 0.1: #g > 0.0 or g < 0.0:
gains_no_nan.append(g)
# stats_no_nan.append(ns_now[1])
lat_no_nan.append(st_lats[i_ns])
lon_no_nan.append(st_lons[i_ns])
except KeyError:
continue
miny = min(gains_no_nan)
maxy = max(gains_no_nan)
# print(gains_no_nan)
gains_fromfile = None
gc.collect()
gains_no_nan = list(num.log10(gains_no_nan))
# print(gains_no_nan)
m = Map(
lat=pl_options[0],
lon=pl_options[1],
radius=pl_options[2],
width=mapsize[0],
height=mapsize[1],
show_grid=False,
show_topo=pl_topo,
# topo_cpt_dry='/home/gesap/Documents/CETperceptual_GMT/CET-L2.cpt',#'/usr/local/share/cpt/gray.cpt',
color_dry=(143, 188, 143), # grey
illuminate=True,
illuminate_factor_ocean=0.15,
# illuminate_factor_land = 0.2,
show_rivers=True,
show_plates=False,
gmt_config=gmtconf)
# Draw some larger cities covered by the map area
# m.draw_cities()
# Draw max. amplitudes at station locations as colored circles
cptfile = 'tempfile.cpt'
miny = min(gains_no_nan)
maxy = max(gains_no_nan)
# plus/minus 10 % for nicer scale
miny = miny # -0.1*miny
maxy = maxy # +0.1*maxy
m.gmt.makecpt(
C=pl_options[
3], #'split',#'polar', # '/home/gesap/Documents/CETperceptual_GMT/CET-D4.cpt',#'split',#'polar',
T='%g/%g' %
(miny, maxy), # (miny, maxy), # (-1,1),#(-0.7, 0.7), (-20, 20)
Q=True,
out_filename=cptfile,
suppress_defaults=True)
# if ns_rel:
# text = 'Gains relative to station %s, colorbar values 10^x.'\
# % (ns_rel)
# else:
# text = 'colorbar values 10^x'
# m.gmt.psbasemap(B='+t"%s"' % text, *m.jxyr)
m.gmt.psxy(in_columns=(lon_no_nan, lat_no_nan, gains_no_nan),
C=cptfile,
G='blue',
S='t24p',
*m.jxyr)
# add a colorbar
B_opt_psscale = 'xaf'
m.gmt.psscale(B=B_opt_psscale + '+l log10(Md(A@-i,j@-/A@-ref,j@-))',
D='x9c/6c+w12c/0.5c+jTC+h',
C=cptfile)
# add station labels
# for i in range(len(stats_no_nan)):
# m.add_label(lat_no_nan[i], lon_no_nan[i], stats_no_nan[i])
fn = os.path.join(directory, '%s%s_map_log.png' % (gains_file[0:12], comp))
m.save(fn)
logging.info('saved file %s' % fn)