def compute(dataobj_list, offsetobj_list, eqobj_list):
print("Computing common mode from %d stations " % (len(dataobj_list)))
detrended_objects = []
raw_objects = []
cmr_objects = []
# common-mode-removed objects
cmr_deviations = []
# Objects with no earthquakes or seasonals
for i in range(len(dataobj_list)):
newobj = offsets.remove_offsets(dataobj_list[i], offsetobj_list[i])
newobj = offsets.remove_offsets(newobj, eqobj_list[i])
newobj = gps_seasonal_removals.make_detrended_ts(newobj, 0, 'lssq')
detrended_objects.append(newobj)
common_mode_obj = define_common_mode(detrended_objects)
print("Removing common mode from %d objects " % (len(dataobj_list)))
for i in range(len(dataobj_list)):
cmr_object = remove_cm_from_object(detrended_objects[i],
common_mode_obj)
cmr_objects.append(cmr_object)
# Keep a measure of the spread of this data
for i in range(len(cmr_objects)):
udata = scipy.ndimage.median_filter(cmr_objects[i].dU, size=365)
cmr_deviations.append(np.nanmax(udata) - np.nanmin(udata))
return [common_mode_obj, detrended_objects, cmr_objects, cmr_deviations]
def compute(dataobj_list, offsetobj_list, eqobj_list, distances,
data_config_file):
latitudes_list = [i.coords[1] for i in dataobj_list]
sorted_objects = [x for _, x in sorted(zip(latitudes_list, dataobj_list))]
# the raw, sorted data.
sorted_offsets = [
x for _, x in sorted(zip(latitudes_list, offsetobj_list))
]
# the raw, sorted data.
sorted_eqs = [x for _, x in sorted(zip(latitudes_list, eqobj_list))]
# the raw, sorted data.
sorted_distances = [x for _, x in sorted(zip(latitudes_list, distances))]
# the sorted distances.
detrended_objects = []
no_offset_objects = []
no_offsets_no_trends = []
no_offsets_no_trends_no_seasons = []
# Detrended objects (or objects with trends and no offsets; depends on what you want.)
for i in range(len(sorted_objects)):
newobj = gps_seasonal_removals.make_detrended_ts(
sorted_objects[i], 0, 'lssq', data_config_file)
detrended_objects.append(newobj)
# still has offsets, doesn't have trends
newobj = offsets.remove_offsets(sorted_objects[i], sorted_offsets[i])
newobj = offsets.remove_offsets(newobj, sorted_eqs[i])
no_offset_objects.append(newobj)
# still has trends, doesn't have offsets
# Objects with no earthquakes or seasonals
for i in range(len(dataobj_list)):
# Remove the steps earthquakes
newobj = offsets.remove_offsets(sorted_objects[i], sorted_offsets[i])
newobj = offsets.remove_offsets(newobj, sorted_eqs[i])
newobj = gps_ts_functions.remove_outliers(newobj, 20)
# 20mm outlier definition
# The detrended TS without earthquakes
stage1obj = gps_seasonal_removals.make_detrended_ts(
newobj, 0, 'lssq', data_config_file)
no_offsets_no_trends.append(stage1obj)
# The detrended TS without earthquakes or seasonals
stage2obj = gps_seasonal_removals.make_detrended_ts(
stage1obj, 1, 'lssq', data_config_file)
no_offsets_no_trends_no_seasons.append(stage2obj)
return [
detrended_objects, no_offset_objects, no_offsets_no_trends,
no_offsets_no_trends_no_seasons, sorted_distances
]
def compute(myData, offset_obj, eq_obj, MyParams, starttime, endtime):
if starttime is None:
starttime = myData.dtarray[0]
if endtime is None:
endtime = myData.dtarray[-1]
newData = gps_ts_functions.impose_time_limits(myData, starttime, endtime)
if MyParams.offsets_remove == 1: # Remove offsets and antenna changes
newData = offsets.remove_offsets(newData, offset_obj)
if MyParams.outliers_remove == 1: # Remove outliers
newData = gps_ts_functions.remove_outliers(newData,
MyParams.outliers_def)
if MyParams.earthquakes_remove == 1: # Remove earthquakes
newData = offsets.remove_offsets(newData, eq_obj)
trend_out = gps_seasonal_removals.make_detrended_ts(
newData, MyParams.seasonals_remove, MyParams.seasonals_type,
MyParams.data_config_file)
return [newData, trend_out]
def compute(dataobj_list, offsetobj_list, eqobj_list):
east_slope_list = []
north_slope_list = []
vert_slope_list = []
# Objects with no earthquakes or seasonals
for i in range(len(dataobj_list)):
# Remove the steps earthquakes
newobj = offsets.remove_offsets(dataobj_list[i], offsetobj_list[i])
newobj = offsets.remove_offsets(newobj, eqobj_list[i])
[east_slope, north_slope, vert_slope, east_std, north_std,
vert_std] = gps_ts_functions.get_slope(newobj)
east_slope_list.append(east_slope)
north_slope_list.append(north_slope)
vert_slope_list.append(vert_slope)
return [east_slope_list, north_slope_list, vert_slope_list]
def remove_by_model(Data0, data_config_file):
# Right now configured for the Hines data.
starttime1 = dt.datetime.strptime("20100403", "%Y%m%d");
endtime1 = dt.datetime.strptime("20100405", "%Y%m%d");
starttime2 = dt.datetime.strptime("20200328", "%Y%m%d");
endtime2 = dt.datetime.strptime("20200330", "%Y%m%d");
# Input Hines data.
model_data = get_station_hines(Data0.name, data_config_file);
if not model_data: # if None
return Data0;
if len(Data0.dtarray) == 0: # if empty
return Data0;
if model_data.dtarray[-1] < Data0.dtarray[-1]:
print("\nWARNING! Trying to use a short postseismic model to fix a long GNSS time series.");
print("PROBLEMS MAY OCCUR- tread carefully!!\n\n");
# These will be the same size.
Data0, model = gps_ts_functions.pair_gps_model_keeping_gps(Data0, model_data);
# pair_gps_model_keeping_gps leaves data outside of the model timespan
# Data0, model = gps_ts_functions.pair_gps_model(Data0, model_data); # removes data outside of the model timespan.
# Subtract the model from the data.
dtarray, dE_gps, dN_gps, dU_gps = [], [], [], [];
Se_gps, Sn_gps, Su_gps = [], [], [];
for i in range(len(Data0.dtarray)):
dtarray.append(Data0.dtarray[i]);
dE_gps.append(Data0.dE[i] - model.dE[i]);
dN_gps.append(Data0.dN[i] - model.dN[i]);
dU_gps.append(Data0.dU[i] - model.dU[i]);
Se_gps.append(Data0.Se[i]);
Sn_gps.append(Data0.Sn[i]);
Su_gps.append(Data0.Su[i]);
# In this method, we correct for offsets at the beginning and end of the modeled time series.
interval1 = [starttime1, endtime1];
east_offset1 = offsets.fit_single_offset(dtarray, dE_gps, interval1, 20);
north_offset1 = offsets.fit_single_offset(dtarray, dN_gps, interval1, 20);
vert_offset1 = offsets.fit_single_offset(dtarray, dU_gps, interval1, 20);
interval2 = [starttime2, endtime2];
east_offset2 = offsets.fit_single_offset(dtarray, dE_gps, interval2, 20);
north_offset2 = offsets.fit_single_offset(dtarray, dN_gps, interval2, 20);
vert_offset2 = offsets.fit_single_offset(dtarray, dU_gps, interval2, 20);
offsets_obj = offsets.Offsets(e_offsets=[east_offset1, east_offset2],
n_offsets=[north_offset1, north_offset2], u_offsets=[vert_offset1, vert_offset2],
evdts=[starttime1, starttime2]);
corrected_data = gps_io_functions.Timeseries(name=Data0.name, coords=Data0.coords, dtarray=dtarray, dE=dE_gps,
dN=dN_gps, dU=dU_gps, Se=Se_gps, Sn=Sn_gps, Su=Su_gps,
EQtimes=Data0.EQtimes);
corrected_data = offsets.remove_offsets(corrected_data, offsets_obj);
return corrected_data;
#!/usr/bin/env python
"""
Example driver for individual time series reading and writing.
Read, Process a little, and Write Back Out.
"""
import gps_input_pipeline
import offsets
import gps_io_functions
station = "P325"
data_config_file = "/Users/kmaterna/Documents/B_Research/GEOPHYS_DATA/GPS_POS_DATA/config.txt"
outfile = station + "_noearthquake.pos"
[myData, offset_obj,
eq_obj] = gps_input_pipeline.get_station_data(station,
'unr',
data_config_file,
refframe='ITRF')
newobj = offsets.remove_offsets(myData, offset_obj)
# remove antenna changes and instrument changes
newobj = offsets.remove_offsets(newobj, eq_obj)
# remove earthquakes
gps_io_functions.write_pbo_pos_file(newobj,
outfile,
comment="Writing a station's .pos file")
def compute(dataobj_list, offsetobj_list, eqobj_list, deltat1, deltat2,
fit_type, time_after_start_date, critical_variance):
dt1_start = dt.datetime.strptime(deltat1[0], "%Y%m%d")
dt1_end = dt.datetime.strptime(deltat1[1], "%Y%m%d")
dt2_start = dt.datetime.strptime(deltat2[0], "%Y%m%d")
dt2_end = dt.datetime.strptime(deltat2[1], "%Y%m%d")
# No earthquakes objects
noeq_objects = []
east_slope_obj = []
north_slope_obj = []
vert_slope_obj = []
# For the vertical correction.
names = []
coords = []
for i in range(len(dataobj_list)):
names.append(dataobj_list[i].name)
coords.append(dataobj_list[i].coords)
# The main processing loop for slopes.
for i in range(len(dataobj_list)):
# Remove the earthquakes
print(names[i])
newobj = offsets.remove_offsets(dataobj_list[i], offsetobj_list[i])
newobj = offsets.remove_offsets(newobj, eqobj_list[i])
if fit_type == 'none':
newobj = gps_seasonal_removals.make_detrended_ts(
newobj, 0, fit_type)
# remove seasonals
else:
if newobj.name == 'P349':
newobj = gps_seasonal_removals.make_detrended_ts(
newobj, 1, 'shasta')
if newobj.name == 'ORVB':
newobj = gps_seasonal_removals.make_detrended_ts(
newobj, 1, 'oroville')
if fit_type != 'none':
newobj = gps_seasonal_removals.make_detrended_ts(
newobj, 1, fit_type)
# remove seasonals
# NOTE: WRITTEN IN JUNE 2019
# An experiment for removing ETS events
# if newobj.name in ["P349","P060","P330","P331","P332","P343","P338","P341"]:
ets_intervals = remove_ets_events.input_tremor_days()
# newobj=gps_ts_functions.remove_outliers(newobj,3.0); # 3 mm outlier def.
# newobj=remove_ets_events.remove_ETS_times(newobj,ets_intervals, offset_num_days=15); # 30 days on either end of the offsets
newobj = remove_ets_events.remove_characteristic_ETS(
newobj, ets_intervals)
# using only the characteristic offset
noeq_objects.append(newobj)
# Get the pre-event and post-event velocities (earthquakes removed)
[
east_slope_before, north_slope_before, vert_slope_before, esig0,
nsig0, usig0
] = gps_ts_functions.get_slope(
newobj,
starttime=dt1_start + dt.timedelta(days=time_after_start_date),
endtime=dt1_end)
[
east_slope_after, north_slope_after, vert_slope_after, esig1,
nsig1, usig1
] = gps_ts_functions.get_slope(
newobj,
starttime=dt2_start + dt.timedelta(days=time_after_start_date),
endtime=dt2_end)
# Get the uncertainties on the velocity-change estimate
[east_slope_unc1, north_slope_unc1,
vert_slope_unc1] = gps_ts_functions.get_slope_unc(
newobj, dt1_start + dt.timedelta(days=time_after_start_date),
dt1_end)
[east_slope_unc2, north_slope_unc2,
vert_slope_unc2] = gps_ts_functions.get_slope_unc(
newobj, dt2_start + dt.timedelta(days=time_after_start_date),
dt2_end)
east_dv_unc = gps_ts_functions.add_two_unc_quadrature(
east_slope_unc1, east_slope_unc2)
north_dv_unc = gps_ts_functions.add_two_unc_quadrature(
north_slope_unc1, north_slope_unc2)
vert_dv_unc = gps_ts_functions.add_two_unc_quadrature(
vert_slope_unc1, vert_slope_unc2)
# When do we ignore stations? When their detrended time series have a large variance.
if abs(esig0) > critical_variance or abs(
nsig0) > critical_variance or abs(
esig1) > critical_variance or abs(
nsig1) > critical_variance:
print("Kicking station %s out..." % dataobj_list[i].name)
[east_slope_after, north_slope_after,
vert_slope_after] = [np.nan, np.nan, np.nan]
[east_slope_before, north_slope_before,
vert_slope_before] = [np.nan, np.nan, np.nan]
[east_slope_unc1, north_slope_unc1,
vert_slope_unc1] = [np.nan, np.nan, np.nan]
[east_slope_unc2, north_slope_unc2,
vert_slope_unc2] = [np.nan, np.nan, np.nan]
east_slope_obj.append(
[east_slope_before, east_slope_after, east_dv_unc])
north_slope_obj.append(
[north_slope_before, north_slope_after, north_dv_unc])
vert_slope_obj.append(
[vert_slope_before, vert_slope_after, vert_dv_unc])
# Adjusting verticals by a reference station.
vert_slope_obj = vert_adjust_by_reference_stations(names, coords,
vert_slope_obj)
return [noeq_objects, east_slope_obj, north_slope_obj, vert_slope_obj]
def read_station_ts(gps_bbox, gps_reference, remove_coseismic=0):
"""A reading function specific to Brawley right now. """
blacklist = []
network = 'pbo'
station_names, _, _ = stations_within_radius.get_stations_within_box(
gnss_object.gps_data_config_file, coord_box=gps_bbox, network=network)
print(station_names)
[dataobj_list, offsetobj_list, eqobj_list, _] = \
gps_input_pipeline.multi_station_inputs(station_names, blacklist, network, "NA",
gnss_object.gps_data_config_file)
# Importing BRAW
[myData, offset_obj, eq_obj
] = gps_input_pipeline.get_station_data("BRAW",
"unr",
gnss_object.gps_data_config_file,
refframe="NA")
myData = gps_ts_functions.impose_time_limits(
myData, dt.datetime.strptime("20080505", "%Y%m%d"),
dt.datetime.strptime("20200101", "%Y%m%d"))
dataobj_list.append(myData)
offsetobj_list.append(offset_obj)
eqobj_list.append(eq_obj)
# Now we are doing a bit of adjustments, for seasonal corrections and base station.
cleaned_objects = []
for i in range(len(dataobj_list)):
one_object = dataobj_list[i]
newobj = offsets.remove_offsets(one_object, offsetobj_list[i])
# will remove antenna offsets from everything
if newobj.name == 'BRAW':
newobj = gps_seasonal_removals.make_detrended_ts(
newobj,
seasonals_remove=1,
seasonals_type="lssq",
data_config_file=gnss_object.gps_data_config_file,
remove_trend=0)
else:
newobj = gps_seasonal_removals.make_detrended_ts(
newobj,
seasonals_remove=1,
seasonals_type="nldas",
data_config_file=gnss_object.gps_data_config_file,
remove_trend=0)
if remove_coseismic:
print("Removing coseismic offsets")
newobj = offsets.remove_offsets(newobj, eqobj_list[i])
newobj = gps_ts_functions.remove_outliers(newobj, 20)
# 20mm outlier definition
# Here we detrend using pre-2010 velocities,
# assuming tectonic strain accumulation won't contribute to geothermal field deformation.
# Remove the postseismic by the Hines model
endtime = dt.datetime.strptime("2010-04-01", "%Y-%m-%d")
[east_slope, north_slope, vert_slope, _, _,
_] = gps_ts_functions.get_slope(newobj,
endtime=endtime,
missing_fraction=0.2)
east_params = [east_slope, 0, 0, 0, 0]
north_params = [north_slope, 0, 0, 0, 0]
vert_params = [vert_slope, 0, 0, 0, 0]
newobj = gps_postseismic_remove.remove_by_model(
newobj, gnss_object.gps_data_config_file)
# This will actually remove the coseismic offset if within the window.
newobj = gps_ts_functions.detrend_data_by_value(
newobj, east_params, north_params, vert_params)
cleaned_objects.append(newobj)
# Subtracting the reference GPS station.
ref_dataobjlist = []
reference_station = [
x for x in cleaned_objects if x.name == gps_reference
][0]
for one_object in cleaned_objects:
refobj = gps_ts_functions.get_referenced_data(one_object,
reference_station)
ref_dataobjlist.append(refobj)
return ref_dataobjlist