channel_response_filter-->channel_response

aurora/pipelines/time_series_helpers.py

@@ -292,14 +292,14 @@ def calibrate_stft_obj(stft_obj, run_obj, units="MT", channel_scale_factors=None
     """
     for channel_id in stft_obj.keys():
         mth5_channel = run_obj.get_channel(channel_id)
-        channel_filter = mth5_channel.channel_response_filter
+        channel_filter = mth5_channel.channel_response
         if not channel_filter.filters_list:
             msg = f"Channel {channel_id} with empty filters list detected"
             logger.warning(msg)
             if channel_id == "hy":
                 msg = "Channel hy has no filters, try using filters from hx"
                 logger.warning("Channel HY has no filters, try using filters from HX")
-                channel_filter = run_obj.get_channel("hx").channel_response_filter
+                channel_filter = run_obj.get_channel("hx").channel_response
         calibration_response = channel_filter.complex_response(stft_obj.frequency.data)
         if channel_scale_factors:
             try:

aurora/sandbox/mth5_helpers.py

@@ -40,17 +40,21 @@ def enrich_channel_summary(mth5_object, df, keyword):
             channel = mth5_object.get_channel(
                 row.station, row.run, row.component, row.survey
             )
-            num_filters = len(channel.channel_response_filter.filters_list)
+            num_filters = len(channel.channel_response.filters_list)
             df[keyword].iat[i_row] = num_filters
-    elif keyword=="filter_units_in":
+    elif keyword == "filter_units_in":
         for i_row, row in df.iterrows():
-            channel = mth5_object.get_channel(row.station, row.run, row.component, row.survey)
-            units_in = [x.units_in for x in channel.channel_response_filter.filters_list]
+            channel = mth5_object.get_channel(
+                row.station, row.run, row.component, row.survey
+            )
+            units_in = [x.units_in for x in channel.channel_response.filters_list]
             df[keyword].iat[i_row] = units_in
-    elif keyword=="filter_units_out":
+    elif keyword == "filter_units_out":
         for i_row, row in df.iterrows():
-            channel = mth5_object.get_channel(row.station, row.run, row.component, row.survey)
-            units_out = [x.units_out for x in channel.channel_response_filter.filters_list]
+            channel = mth5_object.get_channel(
+                row.station, row.run, row.component, row.survey
+            )
+            units_out = [x.units_out for x in channel.channel_response.filters_list]
             df[keyword].iat[i_row] = units_out
     return df
 
@@ -75,14 +79,20 @@ def augmented_channel_summary(mth5_object, df=None):  # , **kwargs):
         channel = mth5_object.get_channel(
             row.station, row.run, row.component, row.survey
         )
-        n_filters = len(channel.channel_response_filter.filters_list)
+        n_filters = len(channel.channel_response.filters_list)
         df.n_filters.iat[i_row] = n_filters
     return df
 
 
-def build_request_df(network_id, station_id, channels=None,
-                     start=None, end=None, time_period_dict={},
-                     mth5_version='0.2.0'):
+def build_request_df(
+    network_id,
+    station_id,
+    channels=None,
+    start=None,
+    end=None,
+    time_period_dict={},
+    mth5_version="0.2.0",
+):
     """
 
     Parameters
@@ -113,15 +123,15 @@ def build_request_df(network_id, station_id, channels=None,
         A formatted dataframe that can be passed to mth5.clients.FDSN to request metdata or data.
 
     """
-    from mth5.clients import FDSN
+
     def get_time_period_bounds(ch):
         if ch in time_period_dict.keys():
-            time_interval = time_period_dict[ch]
+            # time_interval = time_period_dict[ch]
             ch_start = time_period_dict[ch].left.isoformat()
             ch_end = time_period_dict[ch].right.isoformat()
         else:
             if start is None:
-                ch_start = '1970-01-01 00:00:00'
+                ch_start = "1970-01-01 00:00:00"
             else:
                 ch_start = start
             if end is None:
@@ -138,7 +148,7 @@ def get_time_period_bounds(ch):
     request_list = []
     for channel in channels:
         ch_start, ch_end = get_time_period_bounds(channel)
-        request_list.append([network_id, station_id, '', channel, ch_start, ch_end])
+        request_list.append([network_id, station_id, "", channel, ch_start, ch_end])
 
     logger.info(f"request_list: {request_list}")
 

aurora/test_utils/parkfield/calibration_helpers.py

@@ -125,11 +125,12 @@ def parkfield_sanity_check(
         channel = run_obj.get_channel(key)
 
         # pole-zero calibration response
-        pz_calibration_response = channel.channel_response_filter.complex_response(
+
+        pz_calibration_response = channel.channel_response.complex_response(
             frequencies, include_decimation=include_decimation
         )
 
-        if channel.channel_response_filter.units_in.lower() in ["t", "tesla"]:
+        if channel.channel_response.units_in.lower() in ["t", "tesla"]:
             logger.warning("WARNING: Expecting nT but got T")
 
         # Frequency response table response

aurora/time_series/windowed_time_series.py

@@ -201,7 +201,7 @@ def delay_correction(self, dataset, run_obj):
         # "NOT TESTED - PSEUDOCODE ONLY"
         for channel_id in dataset.keys():
             mth5_channel = run_obj.get_channel(channel_id)
-            channel_filter = mth5_channel.channel_response_filter
+            channel_filter = mth5_channel.channel_response
             delay_in_seconds = channel_filter.total_delay
             true_time_axis = dataset.time + delay_in_seconds
             interpolator = interp1d(

tests/parkfield/test_calibrate_parkfield.py

@@ -46,7 +46,9 @@ def validate_bulk_spectra_have_correct_units(run_obj, run_ts_obj, show_spectra=F
         num_samples_overlap=0,
         sample_rate=run_ts_obj.sample_rate,  # 40.0 sps
     )
-    windowed_obj = windowing_scheme.apply_sliding_window(run_ts_obj.dataset)
+    windowed_obj = windowing_scheme.apply_sliding_window(
+        run_ts_obj.dataset, dt=1.0 / run_ts_obj.sample_rate
+    )
     tapered_obj = windowing_scheme.apply_taper(windowed_obj)
 
     fft_obj = windowing_scheme.apply_fft(tapered_obj)