Fixed bugs in relative_TT_residuals_plotter

.gitignore

@@ -89,3 +89,4 @@ seismic/xcorqc/**/*.pbs
 **/junk.*
 **/LOG_CODE_DUPES_*.txt
 **/LOG_LOCATION_DUPES_*.txt
+/seismic/gps_corrections/p_combined.txt

seismic/gps_corrections/gps_clock_correction_gui.py

@@ -143,7 +143,7 @@ def _update_next_button(self, *_unused):
         nc_file_value = self.nc_file.get()
         nc_file_valid = bool(nc_file_value) and os.path.isfile(nc_file_value)
         if nc_file_valid:
-            self.station_code= self._extract_code_from_filename(nc_file_value)
+            self.station_code = self._extract_code_from_filename(nc_file_value)
         else:
             self.station_code = ''
         self.stn_code_label['text'] = "Station code: " + self.station_code

seismic/gps_corrections/relative_tt_residuals_plotter.py

@@ -33,10 +33,7 @@
 else:
     import pathlib  # pylint: disable=import-error
 
-from seismic.gps_corrections.picks_reader_utils import (read_picks_ensemble,
-                                                        get_network_stations,
-                                                        compute_matching_network_mask,
-                                                        generate_large_events_catalog)
+from seismic.gps_corrections.picks_reader_utils import (read_picks_ensemble, get_network_stations, compute_matching_network_mask, generate_large_events_catalog)
 
 # pylint: disable=invalid-name, fixme, too-many-locals, too-many-statements
 # pylint: disable=attribute-defined-outside-init, logging-format-interpolation, logging-not-lazy
@@ -201,9 +198,11 @@ def pandas_timestamp_to_plottable_datetime(data):
     return data.transform(datetime.datetime.utcfromtimestamp).astype('datetime64[ms]').dt.to_pydatetime()
 
 
-def _plot_target_network_rel_residuals(df, target, ref, batch_options, filter_options, tt_scale=50,
+def _plot_target_network_rel_residuals(df, target, ref, batch_options, filter_options, tt_scale=False,
                                        snr_scale=(0, 60), annotator=None):
 
+    if tt_scale is False:
+        tt_scale = 50
     file_label = batch_options.batch_label
     save_file = batch_options.save_file
 
@@ -299,7 +298,7 @@ def _plot_dataset(ds, net_code, ref_code):
         df_export.to_csv(fname, index=False)
 
 
-def _plot_network_relative_to_ref_station(df_plot, ref, target_stns, batch_options, filter_options, display_options):
+def _plot_network_relative_to_ref_station(df_plot, ref, target_stns, batch_options, filter_options, display_options, tt_scale):
     """
     Compute relative residuals and send to plotting function.
 
@@ -318,11 +317,13 @@ def _plot_network_relative_to_ref_station(df_plot, ref, target_stns, batch_optio
     :type display_options: class DisplayOptions
     """
     register_matplotlib_converters()
+    if df_plot is None:
+        return
     df_plot = df_plot.assign(relTtResidual=(df_plot['ttResidual'] - df_plot['ttResidualRef']))
 
     # Re-order columns
     df_plot = df_plot[['#eventID', 'originTimestamp', 'mag', 'originLon', 'originLat', 'originDepthKm',
-                       'net', 'sta', 'cha', 'pickTimestamp', 'phase', 'stationLon', 'stationLat',
+                       'net', 'sta', 'cha', 'pickTimestamp', 'stationLon', 'stationLat',
                        'distance', 'snr', 'ttResidual', 'ttResidualRef', 'relTtResidual',
                        'qualityMeasureCWT', 'qualityMeasureSlope', 'nSigma']]
 
@@ -335,7 +336,7 @@ def _plot_decorator(opts):
         if opts.deployments is not None:
             _add_temporary_deployment_intervals(opts.deployments)
 
-    _plot_target_network_rel_residuals(df_plot, target_stns, ref, batch_options, filter_options,
+    _plot_target_network_rel_residuals(df_plot, target_stns, ref, batch_options, filter_options, tt_scale,
                                        annotator=lambda: _plot_decorator(display_options))
     deregister_matplotlib_converters()
 
@@ -666,13 +667,16 @@ def _get_known_temporary_deployments():
 @click.option('--interactive', is_flag=True, default=False, show_default=True,
               help='If True, plots will be displayed as popup windows instead of saving to file. '
                    'Use this option to interact with the data.')
+@click.option('--tt-scale', type=float, default=50, show_default=True,
+              help='y-axis limits for TT residual plots')
+
 def main(picks_file, network1, networks2, stations1=None, stations2=None,
          min_distance=DEFAULT_MIN_DISTANCE, max_distance=DEFAULT_MAX_DISTANCE,
          min_event_snr=DEFAULT_MIN_EVENT_SNR, cwt_cutoff=DEFAULT_CWT_CUTOFF,
          slope_cutoff=DEFAULT_SLOPE_CUTOFF, nsigma_cutoff=DEFAULT_NSIGMA_CUTOFF,
          min_event_magnitude=DEFAULT_MIN_EVENT_MAG, strict_filtering=DEFAULT_STRICT_FILTERING,
          show_deployments=False, show_historical=True, include_alternate_catalog=True,
-         export_path=None, interactive=False):  # pragma: no cover
+         export_path=None, interactive=False, tt_scale=False):  # pragma: no cover
     """
     Main function for running relative traveltime residual plotting. The picks ensemble file should
     have column headings in accordance with picks_reader_utils.PICKS_TABLE_SCHEMA.
@@ -704,7 +708,7 @@ def main(picks_file, network1, networks2, stations1=None, stations2=None,
     df_raw_picks = read_picks_ensemble(picks_file)
     # Remove unused columns
     df_raw_picks = df_raw_picks[['#eventID', 'originTimestamp', 'mag', 'originLon', 'originLat', 'originDepthKm',
-                                 'net', 'sta', 'cha', 'pickTimestamp', 'phase', 'stationLon', 'stationLat', 'az',
+                                 'net', 'sta', 'cha', 'pickTimestamp', 'stationLon', 'stationLat', 'az',
                                  'baz', 'distance', 'ttResidual', 'snr', 'qualityMeasureCWT', 'qualityMeasureSlope',
                                  'nSigma']]
     log.debug("Number of raw picks: {}".format(len(df_raw_picks)))
@@ -789,7 +793,7 @@ def main(picks_file, network1, networks2, stations1=None, stations2=None,
             df_final = analyze_target_relative_to_ref(df_picks, single_ref, TARGET_STNS, filter_options)
             # Plot results
             _plot_network_relative_to_ref_station(df_final, single_ref, TARGET_STNS, batch_options,
-                                                  filter_options, display_options)
+                                                    filter_options, display_options, tt_scale)
         # end for
     # end for
 

seismic/gps_corrections/requirementsVDI.txt

@@ -0,0 +1,32 @@
+# cd to hiperseis directory
+# module purge
+# module load openmpi
+# module load mpi4py
+# module load hdf5/1.10.5p
+# module load python3-as-python
+# alias python=python3
+# python -m venv venv
+# source venv/bin/activate
+# pip install -r seismic/gps_corrections/requirementsVDI.txt
+pyasdf
+numpy
+obspy
+pandas
+matplotlib
+ordered-set
+psutil
+click
+ujson
+sklearn
+netcdf4
+tqdm
+shapely
+descartes
+pyproj
+pytz
+pathlib2
+jupyter
+#  export PYTHONPATH=$PYTHONPATH:'pwd'
+#  python3 -m seismic.gps_corrections.gps_clock_correction_gui
+# python -m seismic.gps_corrections.relative_tt_residuals_plotter seismic/gps_corrections/p_combined.txt --network1=1Q --networks2=AU --no-strict-filtering
+#  jupyter notebook

seismic/xcorqc/xcorr_station_clock_analysis.py

@@ -150,7 +150,7 @@ def do_spline_regression(self, group_ids, regression_degree):
             y = self.corrections_clean[mask_group]
             # Constrain the knot frequency to be proportional to the order, so that we don't overfit
             # to high frequency variations in the data just for the sake of lowering the spline residual.
-            t = np.linspace(x[0], x[-1], degree - 1)
+            t = np.linspace(x[0], x[-1], int(degree - 1))
             r = LSQUnivariateSpline(x, y, t[1:-1], k=degree)
             regressions[i] = r
 
@@ -176,7 +176,7 @@ def do_spline_resampling(self, group_ids, regressors, sampling_period_seconds):
             timestamp_min = min(x)
             timestamp_max = max(x)
             num_samples = np.round((timestamp_max - timestamp_min) / sampling_period_seconds)
-            lin_times = np.linspace(timestamp_min, timestamp_max, num_samples + 1)
+            lin_times = np.linspace(timestamp_min, timestamp_max, int(num_samples + 1))
             lin_corrections = regressors[i](lin_times)
             regular_corrections[i] = {'times': lin_times, 'corrections': lin_corrections}