def compute_detection_sequence_supplement(self):
''' Compute the supplement data representing the detection sequence triangles.
'''
# Load the event detection data from the supplement file.
detection_data = util.get_supplement_data(self.event.public_id,
category = 'detectiondata',
name = 'detectiondata',
directory = self.supplement_dir)
detection_data = detection_data['detection_data']
# Load the event metadata from the supplement file.
meta = self.meta
# Compute the dataframe using the trigger data.
sequence_df = None
for cur_pw_key, cur_process_window in detection_data.items():
trigger_data = cur_process_window['trigger_data']
cur_df = self.compute_detection_data_df(trigger_data)
if sequence_df is None:
sequence_df = cur_df
else:
sequence_df = sequence_df.append(cur_df,
ignore_index = True)
# Limit the data to the event timespan.
pre_window = 6
end_window = 6
win_start = meta['start_time'] - pre_window
win_end = meta['end_time'] + end_window
df_utctime = np.array([obspy.UTCDateTime(x) for x in sequence_df.time])
mask = (df_utctime >= win_start) & (df_utctime <= win_end)
sequence_df = sequence_df.loc[mask, :]
# Get some event properties to add to the properties of the feature collections.
props = {'db_id': meta['db_id'],
'event_start': util.isoformat_tz(meta['start_time']),
'event_end': util.isoformat_tz(meta['end_time']),
'sequence_start': min(sequence_df.time),
'sequence_end': max(sequence_df.time),
'author_uri': self.project.author_uri,
'agency_uri': self.project.agency_uri}
# Write the sequence dataframe to a geojson file.
filepath = util.save_supplement(self.event.public_id,
sequence_df.loc[:, ['geom_simp', 'time', 'pgv',
'pgv_min', 'pgv_max', 'triggered',
'added_to_event']],
output_dir = self.supplement_dir,
category = 'detectionsequence',
name = 'simplices',
props = props)
self.logger.info('Saved detectioin sequence simplices to file %s.',
filepath)
def compute_detection_data_df(self, trigger_data):
''' Compute the detection frames for a common time.
'''
df = None
for cur_data in trigger_data:
cur_coord = [(x.x, x.y) for x in cur_data['simplices_stations']]
cur_simp_poly = shapely.geometry.Polygon(cur_coord)
cur_time = [util.isoformat_tz(obspy.UTCDateTime(x)) for x in cur_data['time']]
cur_added_to_event = np.zeros(len(cur_time), dtype = bool)
tmp = np.where(cur_data['trigger'])[0]
if len(tmp) > 0:
cur_first_trigger = int(tmp[0])
cur_added_to_event[cur_first_trigger:] = True
cur_df = gpd.GeoDataFrame({'geom_simp': [cur_simp_poly] * len(cur_data['time']),
'time': cur_time,
'pgv': map(lambda a: [x if not math.isnan(x) else None for x in a], cur_data['pgv'].tolist()),
'pgv_min': np.nanmin(cur_data['pgv'], axis = 1),
'pgv_max': np.nanmax(cur_data['pgv'], axis = 1),
'triggered': cur_data['trigger'],
'added_to_event': cur_added_to_event},
crs = "epsg:4326",
geometry = 'geom_simp')
if df is None:
df = cur_df
else:
df = df.append(cur_df,
ignore_index = True)
df = df.sort_values('time',
ignore_index = True)
return df
def compute_event_metadata_supplement(self):
''' Compute the supplement data based on the event metadata.
'''
# Load the event metadata from the supplement file.
meta = self.meta
# Compute a PGV geodataframe using the event metadata.
pgv_df = self.compute_pgv_df(meta)
self.add_station_amplification(pgv_df)
# Compute the voronoi cells and add them as a geometry to the dataframe.
# Compute the boundary clipping using shapely because geopandas clipping
# throws exceptions e.g. because of None values.
voronoi.compute_voronoi_geometry(pgv_df,
boundary = self.network_boundary.loc[0, 'geometry'])
# Get some event properties to add to the properties of the feature collections.
props = {'db_id': meta['db_id'],
'event_start': util.isoformat_tz(meta['start_time']),
'event_end': util.isoformat_tz(meta['end_time']),
'author_uri': self.project.author_uri,
'agency_uri': self.project.agency_uri}
filepath = util.save_supplement(self.event.public_id,
pgv_df.loc[:, ['geom_stat', 'nsl',
'pgv', 'sa', 'triggered']],
output_dir = self.supplement_dir,
category = 'eventpgv',
name = 'pgvstation',
props = props)
self.logger.info('Saved station pgv points to file %s.', filepath)
pgv_df = pgv_df.set_geometry('geom_vor')
filepath = util.save_supplement(self.event.public_id,
pgv_df.loc[:, ['geom_vor', 'nsl',
'pgv', 'sa', 'triggered']],
output_dir = self.supplement_dir,
category = 'eventpgv',
name = 'pgvvoronoi',
props = props)
self.logger.info('Saved station pgv voronoi cells to file %s.',
filepath)
def compute_isoseismal_supplement(self):
''' Compute the isoseismal contour lines using kriging.
'''
# Load the event metadata from the supplement file.
meta = self.meta
# Compute a PGV geodataframe using the event metadata.
pgv_df = self.compute_pgv_df(meta)
self.add_station_amplification(pgv_df)
# Use only data with valid pgv data.
pgv_df = pgv_df.loc[pgv_df.pgv.notna(), :]
# Interpolate to a regular grid using ordinary kriging.
krig_z, krig_sigmasq, grid_x, grid_y = util.compute_pgv_krigging(x = pgv_df.x_utm.values,
y = pgv_df.y_utm.values,
z = np.log10(pgv_df.pgv),
nlags = 40,
verbose = False,
enable_plotting = False,
weight = True)
# Compute the contours.
intensity = np.arange(2, 6.1, 0.5)
# Add lower and upper limits to catch all the data below or
# above the desired intensity range.
intensity = np.hstack([[-10], intensity, [20]])
intensity_pgv = util.intensity_to_pgv(intensity = intensity)
# Create and delete a figure to prevent pyplot from plotting the
# contours.
fig = plt.figure()
ax = fig.add_subplot(111)
cs = ax.contourf(grid_x, grid_y, krig_z, np.log10(intensity_pgv[:, 1]),
vmin = -6, vmax = -2)
contours = util.contourset_to_shapely(cs)
fig.clear()
plt.close(fig)
del ax
del fig
del cs
# Create a geodataframe of the contour polygons.
data = {'geometry': [],
'intensity': [],
'pgv': []}
for cur_level, cur_poly in contours.items():
cur_intensity = util.pgv_to_intensity(pgv = [10**cur_level] * len(cur_poly))
data['geometry'].extend(cur_poly)
data['intensity'].extend(cur_intensity[:, 1].tolist())
data['pgv'].extend([10**cur_level] * len(cur_poly))
df = gpd.GeoDataFrame(data = data)
# Convert the polygon coordinates to EPSG:4326.
src_proj = pyproj.Proj(init = 'epsg:' + self.project.inventory.get_utm_epsg()[0][0])
dst_proj = pyproj.Proj(init = 'epsg:4326')
df = util.reproject_polygons(df = df,
src_proj = src_proj,
dst_proj = dst_proj)
# Clip to the network boundary.
# Clipping a polygon may created multiple polygons.
# Therefore create a new dataframe to have only one polygon per,
# entry. Thus avoiding possible problems due to a mixture of
# multipolygons and polygons.
data = {'geometry': [],
'intensity': [],
'pgv': []}
for cur_id, cur_row in df.iterrows():
cur_poly = cur_row.geometry
clipped_poly = cur_poly.intersection(self.network_boundary.loc[0, 'geometry'])
self.logger.info(type(clipped_poly))
if isinstance(clipped_poly, shapely.geometry.multipolygon.MultiPolygon):
data['geometry'].extend([x for x in clipped_poly])
data['intensity'].extend([cur_row.intensity] * len(clipped_poly))
data['pgv'].extend([cur_row.pgv] * len(clipped_poly))
else:
data['geometry'].append(clipped_poly)
data['intensity'].append(cur_row.intensity)
data['pgv'].append(cur_row.pgv)
df = gpd.GeoDataFrame(data = data)
props = {'db_id': meta['db_id'],
'event_start': util.isoformat_tz(meta['start_time']),
'event_end': util.isoformat_tz(meta['end_time']),
'author_uri': self.project.author_uri,
'agency_uri': self.project.agency_uri}
filepath = util.save_supplement(self.event.public_id,
df,
output_dir = self.supplement_dir,
category = 'eventpgv',
name = 'isoseismalfilledcontour',
props = props)
self.logger.info('Saved isoseismal contours to file %s.', filepath)
return df
def compute_pgv_sequence_supplement(self):
''' Compute the supplement data representing the PGV sequence.
'''
# Load the event metadata from the supplement file.
meta = self.meta
# Load the PGV data stream.
pgv_stream = util.get_supplement_data(self.event.public_id,
category = 'detectiondata',
name = 'pgv',
directory = self.supplement_dir)
# Trim the stream.
pgv_stream.trim(starttime = meta['start_time'] - 6,
endtime = meta['end_time'] + 6,
pad = True)
inventory = self.project.inventory
station_nsl = [('MSSNet', x.stats.station, x.stats.location) for x in pgv_stream]
station_nsl = [':'.join(x) for x in station_nsl]
stations = [inventory.get_station(nsl_string = x)[0] for x in station_nsl]
times = pgv_stream[0].times("utcdatetime")
data = np.array([x.data for x in pgv_stream]).transpose()
# Get the stations with no available data.
available_stations = inventory.get_station()
no_data_stations = [x for x in available_stations if x.nsl_string not in station_nsl]
detection_limits = meta['detection_limits']
sequence_df = None
for k in range(len(times)):
cur_time = times[k]
triggered = []
for cur_station in stations:
if cur_station.nsl_string not in detection_limits.keys():
cur_trigger = False
else:
cur_detection_limit = detection_limits[cur_station.nsl_string]
if cur_time >= cur_detection_limit[0] and cur_time <= cur_detection_limit[1]:
cur_trigger = True
else:
cur_trigger = False
triggered.append(cur_trigger)
cur_points = [shapely.geometry.Point(x.x, x.y) for x in stations]
cur_df = gpd.GeoDataFrame({'geom_vor': [shapely.geometry.Polygon([])] * len(stations),
'geom_stat': cur_points,
'time': [util.isoformat_tz(cur_time)] * len(stations),
'nsl': [x.nsl_string for x in stations],
'x': [x.x for x in stations],
'y': [x.y for x in stations],
'x_utm': [x.x_utm for x in stations],
'y_utm': [x.y_utm for x in stations],
'pgv': data[k, :],
'triggered': triggered},
crs = "epsg:4326",
geometry = 'geom_stat')
# Add the station amplification factors.
self.add_station_amplification(cur_df)
# Compute the voronoi cells and add them as a geometry to the dataframe.
# Compute the boundary clipping using shapely because geopandas clipping
# throws exceptions e.g. because of None values.
voronoi.compute_voronoi_geometry(cur_df,
boundary = self.network_boundary.loc[0, 'geometry'])
# Add the no-data stations
cur_nd_points = [shapely.geometry.Point(x.x, x.y) for x in no_data_stations]
cur_nd_df = gpd.GeoDataFrame({'geom_vor': [shapely.geometry.Polygon([])] * len(no_data_stations),
'geom_stat': cur_nd_points,
'time': [util.isoformat_tz(cur_time)] * len(no_data_stations),
'nsl': [x.nsl_string for x in no_data_stations],
'x': [x.x for x in no_data_stations],
'y': [x.y for x in no_data_stations],
'x_utm': [x.x_utm for x in no_data_stations],
'y_utm': [x.y_utm for x in no_data_stations],
'pgv': [None] * len(no_data_stations),
'triggered': [None] * len(no_data_stations)},
crs = "epsg:4326",
geometry = 'geom_stat')
# Add the station amplification factors to the no-data stations.
self.add_station_amplification(cur_nd_df)
# Append the no-data stations.
cur_df = cur_df.append(cur_nd_df)
# Add the dataframe to the sequence.
if sequence_df is None:
sequence_df = cur_df
else:
sequence_df = sequence_df.append(cur_df)
# Get some event properties to add to the properties of the feature collections.
props = {'db_id': meta['db_id'],
'event_start': util.isoformat_tz(meta['start_time']),
'event_end': util.isoformat_tz(meta['end_time']),
'sequence_start': min(sequence_df.time),
'sequence_end': max(sequence_df.time),
'author_uri': self.project.author_uri,
'agency_uri': self.project.agency_uri}
# Write the voronoi dataframe to a geojson file.
sequence_df = sequence_df.set_geometry('geom_vor')
filepath = util.save_supplement(self.event.public_id,
sequence_df.loc[:, ['geom_vor', 'time', 'nsl',
'pgv', 'sa', 'triggered']],
output_dir = self.supplement_dir,
category = 'pgvsequence',
name = 'pgvvoronoi',
props = props)
self.logger.info('Saved pgv voronoi sequence to file %s.', filepath)
sequence_df = sequence_df.set_geometry('geom_stat')
filepath = util.save_supplement(self.event.public_id,
sequence_df.loc[:, ['geom_stat', 'time', 'nsl',
'pgv', 'sa', 'triggered']],
output_dir = self.supplement_dir,
category = 'pgvsequence',
name = 'pgvstation',
props = props)
self.logger.info('Saved pgv station marker sequence to file %s.',
filepath)
def compute_pgv_contour_sequence_supplement(self):
''' Compute the supplement data representing the PGV sequence.
'''
# Load the event metadata from the supplement file.
meta = self.meta
# Load the PGV data stream.
pgv_stream = util.get_supplement_data(self.event_public_id,
category = 'detectiondata',
name = 'pgv',
directory = self.supplement_dir)
# Trim the stream.
pgv_stream.trim(starttime = meta['start_time'] - 6,
endtime = meta['end_time'] + 6,
pad = True)
inventory = self.project.inventory
station_nsl = [('MSSNet', x.stats.station, x.stats.location) for x in pgv_stream]
station_nsl = [':'.join(x) for x in station_nsl]
stations = [inventory.get_station(nsl_string = x)[0] for x in station_nsl]
times = pgv_stream[0].times("utcdatetime")
data = np.array([x.data for x in pgv_stream]).transpose()
detection_limits = meta['detection_limits']
sequence_df = None
last_pgv_df = None
last_krig_z = None
no_change_cnt = 0
for k in range(len(times)):
cur_time = times[k]
self.logger.info("Computing frame {time}.".format(time = str(cur_time)))
triggered = []
for cur_station in stations:
if cur_station.nsl_string not in detection_limits.keys():
cur_trigger = False
else:
cur_detection_limit = detection_limits[cur_station.nsl_string]
if cur_time >= cur_detection_limit[0] and cur_time <= cur_detection_limit[1]:
cur_trigger = True
else:
cur_trigger = False
triggered.append(cur_trigger)
cur_points = [shapely.geometry.Point(x.x, x.y) for x in stations]
cur_df = gpd.GeoDataFrame({'geom_vor': [shapely.geometry.Polygon([])] * len(stations),
'geom_stat': cur_points,
'time': [util.isoformat_tz(cur_time)] * len(stations),
'nsl': [x.nsl_string for x in stations],
'x': [x.x for x in stations],
'y': [x.y for x in stations],
'x_utm': [x.x_utm for x in stations],
'y_utm': [x.y_utm for x in stations],
'pgv': data[k, :],
'triggered': triggered},
crs = "epsg:4326",
geometry = 'geom_stat')
# Add the station amplification factors.
self.add_station_amplification(cur_df)
# Compute the corrected pgv values.
cur_df['pgv_corr'] = cur_df.pgv / cur_df.sa
# Use only the stations with a valid corrected pgv.
cur_df = cur_df[cur_df['pgv_corr'].notna()]
cur_df = cur_df.reset_index()
# Update the pgv values to keep the event maximum pgv.
# Track changes of the event maximum pgv.
if last_pgv_df is not None:
# Use the current PGV values only, if they are higher than
# the last ones.
#
# Update the last_pgv_df with the current df. It is possible, that
# rows are missing or new ones are available.
# Remove the rows, that are not present in the cur_df.
tmp_df = last_pgv_df[last_pgv_df.nsl.isin(cur_df.nsl)]
# Add the rows, that are not present in the last_pgv_df.
mask_df = tmp_df.append(cur_df[~cur_df.nsl.isin(last_pgv_df.nsl)],
ignore_index = True)
# Sort the two dataframes using the nsl.
tmp_df = tmp_df.sort_values(by = 'nsl',
ignore_index = True)
mask_df = mask_df.sort_values(by = 'nsl',
ignore_index = True)
# Check for correct station snl.
if (np.any(tmp_df['nsl'].values != mask_df['nsl'].values)):
raise RuntimeError("The statin SNL codes of the two dataframes to compare are not equal.")
# Reset the values for the stations, that already had a larger pgv value.
mask = cur_df.pgv_corr < mask_df.pgv_corr
cur_df.loc[mask, 'pgv_corr'] = mask_df.loc[mask, 'pgv_corr']
if np.all(mask):
no_change_cnt += 1
else:
no_change_cnt = 0
self.logger.info('no_change_cnt: ' + str(no_change_cnt))
# Exit if the was no change of the max event pgv data for some time.
if no_change_cnt >= 5:
self.logger.info('No change for some time, stop computation of contours.')
break
# Keep the last pgv dataframe.
# Get the rows, that are not available in cur_df and keep them.
if last_pgv_df is not None:
tmp_df = last_pgv_df[~last_pgv_df.nsl.isin(cur_df.nsl)]
last_pgv_df = cur_df.copy()
last_pgv_df = last_pgv_df.append(tmp_df.copy(),
ignore_index = True)
else:
last_pgv_df = cur_df.copy()
# Interpolate to a regular grid using ordinary kriging.
self.logger.info("Interpolate")
krig_z, krig_sigmasq, grid_x, grid_y = util.compute_pgv_krigging(x = cur_df.x_utm.values,
y = cur_df.y_utm.values,
z = np.log10(cur_df.pgv_corr),
nlags = 40,
verbose = False,
enable_plotting = False,
weight = True)
# Update the interpolated pgv values only if they are higher than the last ones.
#if last_krig_z is not None:
# cur_mask = krig_z < last_krig_z
# krig_z[cur_mask] = last_krig_z[cur_mask]
#last_krig_z = krig_z
self.logger.info("Contours")
# Compute the contours.
intensity = np.arange(2, 8.1, 0.1)
# Add lower and upper limits to catch all the data below or
# above the desired intensity range.
intensity = np.hstack([[-10], intensity, [20]])
# Use a low intensity_I_pgv value to make sure, that the lowest countour
# level captures all PGV values.
intensity_pgv = util.intensity_to_pgv(intensity = intensity,
intensity_I_pgv = 1e-9)
# Create and delete a figure to prevent pyplot from plotting the
# contours.
fig = plt.figure()
ax = fig.add_subplot(111)
cs = ax.contourf(grid_x, grid_y, krig_z, np.log10(intensity_pgv[:, 1]))
contours = util.contourset_to_shapely(cs)
fig.clear()
plt.close(fig)
del ax
del fig
del cs
self.logger.info('dataframe')
# Create a geodataframe of the contour polygons.
cont_data = {'time': [],
'geometry': [],
'intensity': [],
'pgv': []}
for cur_level, cur_poly in contours.items():
cur_intensity = util.pgv_to_intensity(pgv = [10**cur_level] * len(cur_poly))
cont_data['time'].extend([util.isoformat_tz(cur_time)] * len(cur_poly))
cont_data['geometry'].extend(cur_poly)
cont_data['intensity'].extend(cur_intensity[:, 1].tolist())
cont_data['pgv'].extend([10**cur_level] * len(cur_poly))
cur_cont_df = gpd.GeoDataFrame(data = cont_data)
# Convert the polygon coordinates to EPSG:4326.
src_proj = pyproj.Proj(init = 'epsg:' + self.project.inventory.get_utm_epsg()[0][0])
dst_proj = pyproj.Proj(init = 'epsg:4326')
cur_cont_df = util.reproject_polygons(df = cur_cont_df,
src_proj = src_proj,
dst_proj = dst_proj)
# Clip to the network boundary.
# Clipping a polygon may created multiple polygons.
# Therefore create a new dataframe to have only one polygon per,
# entry. Thus avoiding possible problems due to a mixture of
# multipolygons and polygons.
self.logger.info('Clipping.')
cont_data = {'time': [],
'geometry': [],
'intensity': [],
'pgv': []}
for cur_id, cur_row in cur_cont_df.iterrows():
cur_poly = cur_row.geometry
clipped_poly = cur_poly.intersection(self.network_boundary.loc[0, 'geometry'])
self.logger.info(type(clipped_poly))
if isinstance(clipped_poly, shapely.geometry.multipolygon.MultiPolygon):
cont_data['time'].extend([cur_row.time] * len(clipped_poly))
cont_data['geometry'].extend([x for x in clipped_poly])
cont_data['intensity'].extend([cur_row.intensity] * len(clipped_poly))
cont_data['pgv'].extend([cur_row.pgv] * len(clipped_poly))
else:
cont_data['time'].append(cur_row.time)
cont_data['geometry'].append(clipped_poly)
cont_data['intensity'].append(cur_row.intensity)
cont_data['pgv'].append(cur_row.pgv)
cur_cont_df = gpd.GeoDataFrame(data = cont_data)
# Remove rows having an empty geometry.
self.logger.info(cur_cont_df['geometry'])
cur_cont_df = cur_cont_df[~cur_cont_df['geometry'].is_empty]
self.logger.info(cur_cont_df['geometry'])
self.logger.info('Appending to sequence.')
# Add the dataframe to the sequence.
if sequence_df is None:
sequence_df = cur_cont_df
else:
sequence_df = sequence_df.append(cur_cont_df)
# Get some event properties to add to the properties of the feature collections.
props = {'db_id': meta['db_id'],
'event_start': util.isoformat_tz(meta['start_time']),
'event_end': util.isoformat_tz(meta['end_time']),
'sequence_start': min(sequence_df.time),
'sequence_end': max(sequence_df.time),
'author_uri': self.project.author_uri,
'agency_uri': self.project.agency_uri,
'station_correction_applied': True}
# Write the voronoi dataframe to a geojson file.
filepath = util.save_supplement(self.event_public_id,
sequence_df,
output_dir = self.supplement_dir,
category = 'pgvsequence',
name = 'pgvcontour',
props = props)
self.logger.info('Saved pgv contour sequence to file %s.', filepath)
