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(dataobj_list, offsetobj_list, eqobj_list, distances, EQtime):
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
detrended_objects = []
for i in range(len(sorted_objects)):
newobj = gps_seasonal_removals.make_detrended_ts(
sorted_objects[i], 0, 'lssq')
detrended_objects.append(newobj)
# Objects with no earthquakes or seasonals
stage1_objects = []
stage2_objects = []
east_slope_obj = []
for i in range(len(dataobj_list)):
# Remove the steps earthquakes
newobj = offsets.remove_antenna_offsets(sorted_objects[i],
sorted_offsets[i])
newobj = offsets.remove_earthquakes(newobj, sorted_eqs[i])
# The detrended TS without earthquakes
stage1obj = gps_seasonal_removals.make_detrended_ts(newobj, 0, 'lssq')
stage1_objects.append(stage1obj)
# The detrended TS without earthquakes or seasonals
stage2obj = gps_seasonal_removals.make_detrended_ts(newobj, 1, 'lssq')
stage2_objects.append(stage2obj)
# Get the pre-event and post-event velocities (earthquakes removed)
[east_slope_before, north_slope_before, vert_slope_before, _, _,
_] = gps_ts_functions.get_slope(stage2obj, endtime=EQtime)
[east_slope_after, north_slope_after, vert_slope_after, _, _,
_] = gps_ts_functions.get_slope(stage2obj, starttime=EQtime)
east_slope_after = np.round(east_slope_after, decimals=1)
east_slope_before = np.round(east_slope_before, decimals=1)
east_slope_obj.append([east_slope_before, east_slope_after])
return [
detrended_objects, stage1_objects, stage2_objects, sorted_distances,
east_slope_obj
]
def compute(dataobj_list, offsetobj_list, eqobj_list, fit_table, grace_dir,start_time_infl, end_time_infl,start_time_velo, end_time_velo, seasonal_type, N, Wn): # Initialize output objects noeq_objects = []; east_filt=[]; north_filt=[]; vert_filt=[]; east_inf_time=[]; north_inf_time=[]; vert_inf_time=[]; east_change=[]; north_change=[]; vert_change=[]; for i in range(len(dataobj_list)): # Remove the earthquakes and offsets newobj=offsets.remove_antenna_offsets(dataobj_list[i], offsetobj_list[i]); newobj=offsets.remove_earthquakes(newobj,eqobj_list[i]); newobj=gps_ts_functions.remove_outliers(newobj,15); # 15mm horizontal outliers newobj=gps_seasonal_removals.make_detrended_ts(newobj, 1, seasonal_type, fit_table, grace_dir); # can remove seasonals a few ways noeq_objects.append(newobj); print(dataobj_list[i].name); # Get the inflection points in the timeseries float_dtarray = gps_ts_functions.get_float_times(newobj.dtarray); [east_filtered, e_inflection_time, echange]=inflection_with_butterworth(newobj.dtarray, float_dtarray, newobj.dE, N, Wn, start_time_infl, end_time_infl,start_time_velo, end_time_velo); [north_filtered, n_inflection_time, nchange]=inflection_with_butterworth(newobj.dtarray, float_dtarray, newobj.dN, N, Wn, start_time_infl, end_time_infl,start_time_velo, end_time_velo); [vert_filtered, v_inflection_time, vchange]=inflection_with_butterworth(newobj.dtarray, float_dtarray, newobj.dU, N, Wn, start_time_infl, end_time_infl,start_time_velo, end_time_velo); east_filt.append(east_filtered); north_filt.append(north_filtered); vert_filt.append(vert_filtered); east_inf_time.append(e_inflection_time); north_inf_time.append(n_inflection_time); vert_inf_time.append(v_inflection_time); east_change.append(echange); north_change.append(nchange); vert_change.append(vchange); return [noeq_objects, east_filt, north_filt, vert_filt, east_inf_time, north_inf_time, vert_inf_time, east_change, north_change, vert_change];
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(myData, offset_obj, eq_obj, MyParams): newData=myData; if MyParams.offsets_remove==1: # First step: remove offsets and earthquakes newData=offsets.remove_antenna_offsets(newData, offset_obj); if MyParams.outliers_remove==1: # Second step: remove outliers newData=gps_ts_functions.remove_outliers(newData, MyParams.outliers_def); if MyParams.earthquakes_remove==1: newData=offsets.remove_earthquakes(newData, eq_obj); # A few different types of seasonal removal. notrend=gps_seasonal_removals.make_detrended_ts(newData, 0, 'lssq'); lssq_fit=gps_seasonal_removals.make_detrended_ts(newData, 1, 'lssq'); notch_filt=gps_seasonal_removals.make_detrended_ts(newData, 1, 'notch'); grace_filt=gps_seasonal_removals.make_detrended_ts(newData, 1, 'grace'); stl_filt=gps_seasonal_removals.make_detrended_ts(newData, 1, 'stl'); # stl_filt=gps_seasonal_removals.make_detrended_ts(newData, 1, 'lssq'); return [notrend, lssq_fit, notch_filt, grace_filt, stl_filt];
def compute(myData, offset_obj, eq_obj, outliers_def, outdir):
for i in range(len(myData)):
newData = myData[i]
newData = offsets.remove_antenna_offsets(newData, offset_obj[i])
newData = gps_ts_functions.remove_outliers(newData, outliers_def)
newData = offsets.remove_earthquakes(newData, eq_obj[i])
stl_filt = gps_seasonal_removals.make_detrended_ts(newData, 1, 'stl')
outputs(stl_filt, outdir)
return
def get_detrended_gps_station(station_name):
datasource = 'pbo'
[newData, offset_obj,
eq_obj] = gps_input_pipeline.get_station_data(station_name, datasource)
newData = offsets.remove_antenna_offsets(newData, offset_obj)
newData = gps_ts_functions.remove_outliers(newData, 5)
# mm for outliers
newData = offsets.remove_earthquakes(newData, eq_obj)
trend_out = gps_seasonal_removals.make_detrended_ts(newData, 1, 'notch')
return trend_out
def compute(myData, offset_obj, eq_obj, MyParams):
newData = myData
if MyParams.offsets_remove == 1: # First step: remove offsets and earthquakes
newData = offsets.remove_antenna_offsets(newData, offset_obj)
if MyParams.outliers_remove == 1: # Second step: remove outliers
newData = gps_ts_functions.remove_outliers(newData,
MyParams.outliers_def)
if MyParams.earthquakes_remove == 1:
newData = offsets.remove_earthquakes(newData, eq_obj)
trend_out = gps_seasonal_removals.make_detrended_ts(
newData, MyParams.seasonals_remove, MyParams.seasonals_type,
MyParams.fit_table, MyParams.grace_dir)
return [newData, trend_out]
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, dt1_start, dt1_end,
dt2_start, dt2_end, fit_type):
# No earthquakes objects
noeq_objects = []
east_slope_obj = []
north_slope_obj = []
vert_slope_obj = []
period_after_start_date = 7
# wait a week.
# 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_antenna_offsets(dataobj_list[i],
offsetobj_list[i])
newobj = offsets.remove_earthquakes(newobj, eqobj_list[i])
if fit_type == 'none':
newobj = gps_seasonal_removals.make_detrended_ts(
newobj, 0, fit_type)
# remove seasonals
else:
newobj = gps_seasonal_removals.make_detrended_ts(
newobj, 1, fit_type)
# remove seasonals
# What is this? It looks like awful code.
if newobj.dN[0] == 1.0000:
print("Passing because we haven't computed GRACE yet...")
noeq_objects.append(newobj)
east_slope_obj.append([np.nan, np.nan])
north_slope_obj.append([np.nan, np.nan])
vert_slope_obj.append([np.nan, np.nan])
continue
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=period_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=period_after_start_date),
endtime=dt2_end)
# When do we ignore stations? When their detrended time series have a large variance.
critical_value = 5
# mm/yr
if abs(esig0) > critical_value or abs(nsig0) > critical_value or abs(
esig1) > critical_value or abs(nsig1) > critical_value:
print("Kicking station out...")
print(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]
else:
east_slope_after = np.round(east_slope_after, decimals=1)
east_slope_before = np.round(east_slope_before, decimals=1)
north_slope_after = np.round(north_slope_after, decimals=1)
north_slope_before = np.round(north_slope_before, decimals=1)
vert_slope_after = np.round(vert_slope_after, decimals=1)
vert_slope_before = np.round(vert_slope_before, decimals=1)
east_slope_obj.append([east_slope_before, east_slope_after])
north_slope_obj.append([north_slope_before, north_slope_after])
vert_slope_obj.append([vert_slope_before, vert_slope_after])
# Adjusting verticals by a reference station.
vert_slope_obj = adjust_by_reference_stations(names, coords,
vert_slope_obj)
return [noeq_objects, east_slope_obj, north_slope_obj, vert_slope_obj]
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