def plot_grace(station_name, filename, out_dir):
grace_ts = gps_io_functions.read_grace(filename)
plt.figure()
plt.plot_date(grace_ts.dtarray, grace_ts.u, '-b')
plt.plot_date(grace_ts.dtarray, grace_ts.v, '-g')
plt.plot_date(grace_ts.dtarray, grace_ts.w, '-r')
plt.legend(['east', 'north', 'vertical'])
plt.grid(True)
plt.xlabel('Time')
plt.ylabel('Displacement (mm)')
plt.savefig(out_dir + station_name + "_gracets.eps")
return
def remove_seasonals_by_GRACE(Data, grace_dir):
# Here we use pre-computed GRACE load model time series to correct the GPS time series.
# We recognize that the horizontals will be bad, and that the resolution of GRACE is coarse.
# For these reasons, this is not an important part of the analysis.
# Read and interpolate GRACE loading model
# Subtract the GRACE model
# Remove a trend from the GPS data
# Return the object.
filename = grace_dir + "scaled_" + Data.name + "_PREM_model_ts.txt";
if not os.path.isfile(filename):
print("Error! GRACE not found for %s" % Data.name);
print("Returning placeholder object.");
wimpyObj = get_wimpy_object(Data)
return wimpyObj, wimpyObj;
# If the station has been pre-computed with GRACE:
Data = gps_ts_functions.remove_nans(Data);
[grace_model] = gps_io_functions.read_grace(filename);
my_paired_ts = grace_ts_functions.pair_GPSGRACE(Data, grace_model);
decyear = gps_ts_functions.get_float_times(my_paired_ts.dtarray);
# Subtract the GRACE object
dE_filt, dN_filt, dU_filt = [], [], [];
for i in range(len(my_paired_ts.dtarray)):
dE_filt.append(my_paired_ts.east[i] - my_paired_ts.u[i]);
dN_filt.append(my_paired_ts.north[i] - my_paired_ts.v[i]);
dU_filt.append(my_paired_ts.vert[i] - my_paired_ts.w[i]);
# A Simple detrending
dE_detrended = np.zeros(np.shape(decyear));
dN_detrended = np.zeros(np.shape(decyear));
dU_detrended = np.zeros(np.shape(decyear));
east_coef = np.polyfit(decyear, dE_filt, 1)[0];
for i in range(len(dE_filt)):
dE_detrended[i] = dE_filt[i] - east_coef * decyear[i] - (dE_filt[0] - east_coef * decyear[0]);
north_coef = np.polyfit(decyear, dN_filt, 1)[0];
for i in range(len(dN_filt)):
dN_detrended[i] = (dN_filt[i] - north_coef * decyear[i]) - (dN_filt[0] - north_coef * decyear[0]);
vert_coef = np.polyfit(decyear, dU_filt, 1)[0];
for i in range(len(dU_filt)):
dU_detrended[i] = (dU_filt[i] - vert_coef * decyear[i]) - (dU_filt[0] - vert_coef * decyear[0]);
detrended = gps_io_functions.Timeseries(name=Data.name, coords=Data.coords, dtarray=my_paired_ts.dtarray,
dN=dN_detrended, dE=dE_detrended, dU=dU_detrended, Sn=my_paired_ts.N_err,
Se=my_paired_ts.E_err, Su=my_paired_ts.V_err, EQtimes=Data.EQtimes);
trended = gps_io_functions.Timeseries(name=Data.name, coords=Data.coords, dtarray=my_paired_ts.dtarray, dN=dN_filt,
dE=dE_filt, dU=dU_filt, Sn=my_paired_ts.N_err, Se=my_paired_ts.E_err,
Su=my_paired_ts.V_err, EQtimes=Data.EQtimes);
return detrended, trended; # 0 = successful completion
def get_grace(data_config_file, station):
Params = gps_io_functions.read_config_file(data_config_file)
filename = Params.grace_dir + "scaled_" + station + "_PREM_model_ts.txt"
[myData] = gps_io_functions.read_grace(filename)
Offset = offsets.get_empty_offsets()
return [myData, Offset, Offset]
def grace_inputs(file_list):
dataobj_list = []
for item in file_list:
grace_ts = gps_io_functions.read_grace(item)
dataobj_list.append(grace_ts)
return [dataobj_list]