🎨 Format Python code with psf/black

python/mspasspy/algorithms/ml/arrival.py

@@ -40,7 +40,11 @@ def annotate_arrival_time(
 
     # Check the input arguments
     if not 0 <= threshold <= 1:
-        logging.warning('Threshold should be in the range of [0, 1]. Using default threshold {}}'.format(default_threshold))
+        logging.warning(
+            "Threshold should be in the range of [0, 1]. Using default threshold {}}".format(
+                default_threshold
+            )
+        )
         threshold = default_threshold
 
     # convert timeseries to absolute time
@@ -51,31 +55,38 @@ def annotate_arrival_time(
         # 'stead' model was trained on STEAD for 100 epochs with a learning rate of 0.01.
         # use sbm.PhaseNet.list_pretrained(details=True) to list out other supported models
         # when using this model, please reference the SeisBench publications listed at https://github.com/seisbench/seisbench
-        pretrained_model = "stead" if (model_args == None or "name" not in model_args) else model_args["name"]
+        pretrained_model = (
+            "stead"
+            if (model_args == None or "name" not in model_args)
+            else model_args["name"]
+        )
         model = sbm.PhaseNet.from_pretrained(pretrained_model)
 
     ts_ensemble = TimeSeriesEnsemble()
     ts_ensemble.member.append(timeseries)
     stream = ts_ensemble.toStream()
 
     # apply the window if provided and convert time series to stream
-    start_time_utc = stream[0].stats.starttime.timestamp # UTC timestamp
-    end_time_utc = stream[0].stats.endtime.timestamp # UTC timestamp
+    start_time_utc = stream[0].stats.starttime.timestamp  # UTC timestamp
+    end_time_utc = stream[0].stats.endtime.timestamp  # UTC timestamp
 
     # adjust the time window if it is out of the time range of the time series
     if time_window:
         if time_window.end < start_time_utc or time_window.start > end_time_utc:
             time_window.start = start_time_utc
             time_window.end = end_time_utc
-            logging.warning('Time window is out of the time range of the time series. Adjusting the time window to the time range of the time series.')
+            logging.warning(
+                "Time window is out of the time range of the time series. Adjusting the time window to the time range of the time series."
+            )
         if time_window.end > end_time_utc:
             time_window.end = end_time_utc
         if time_window.start < start_time_utc:
             time_window.start = start_time_utc
-    
+
     windowed_stream = (
-        stream.trim(UTCDateTime(time_window.start), UTCDateTime(time_window.end)) \
-        if time_window else stream
+        stream.trim(UTCDateTime(time_window.start), UTCDateTime(time_window.end))
+        if time_window
+        else stream
     )
 
     # prediction result is the probability for picks over time

python/mspasspy/algorithms/resample.py

@@ -208,8 +208,8 @@ def resample(self, mspass_object):
         # We do this test at the top to avoid having returns testing for
         # a dead datum in each of the if conditional blocks below
         if isinstance(
-                mspass_object,
-                (TimeSeries, Seismogram, TimeSeriesEnsemble, SeismogramEnsemble),
+            mspass_object,
+            (TimeSeries, Seismogram, TimeSeriesEnsemble, SeismogramEnsemble),
         ):
             if mspass_object.dead():
                 return mspass_object
@@ -221,7 +221,7 @@ def resample(self, mspass_object):
 
         if isinstance(mspass_object, TimeSeries):
             data_time_span = (
-                    mspass_object.endtime() - mspass_object.t0 + mspass_object.dt
+                mspass_object.endtime() - mspass_object.t0 + mspass_object.dt
             )
             n_resampled = int(data_time_span * self.samprate)
             rsdata = signal.resample(
@@ -236,7 +236,7 @@ def resample(self, mspass_object):
             mspass_object.data = dv
         elif isinstance(mspass_object, Seismogram):
             data_time_span = (
-                    mspass_object.endtime() - mspass_object.t0 + mspass_object.dt
+                mspass_object.endtime() - mspass_object.t0 + mspass_object.dt
             )
             n_resampled = int(data_time_span * self.samprate)
             rsdata = signal.resample(
@@ -348,8 +348,8 @@ def resample(self, mspass_object):
         # We do this test at the top to avoid having returns testing for
         # a dead datum in each of the if conditional blocks below
         if isinstance(
-                mspass_object,
-                (TimeSeries, Seismogram, TimeSeriesEnsemble, SeismogramEnsemble),
+            mspass_object,
+            (TimeSeries, Seismogram, TimeSeriesEnsemble, SeismogramEnsemble),
         ):
             if mspass_object.dead():
                 return mspass_object
@@ -426,16 +426,16 @@ def resample(self, mspass_object):
 
 @mspass_func_wrapper
 def resample(
-        mspass_object,
-        decimator,
-        resampler,
-        verify_operators=True,
-        object_history=False,
-        alg_name="resample",
-        alg_id=None,
-        dryrun=False,
-        inplace_return=False,
-        function_return_key=None,
+    mspass_object,
+    decimator,
+    resampler,
+    verify_operators=True,
+    object_history=False,
+    alg_name="resample",
+    alg_id=None,
+    dryrun=False,
+    inplace_return=False,
+    function_return_key=None,
 ):
     """
     Resample any valid data object to a common sample rate (sample interval).

python/mspasspy/algorithms/window.py

@@ -889,8 +889,8 @@ def WindowData_autopad(
     return dw
 
 
-# TODO:   this function does not support history mechanism because the 
-# standard decorator is does not support a bound std::vector<TimeSeries> 
+# TODO:   this function does not support history mechanism because the
+# standard decorator is does not support a bound std::vector<TimeSeries>
 # container.  I requires one of the four MsPASS data objects.
 def merge(
     tsvector,
@@ -1040,7 +1040,7 @@ def merge(
       dead.  When set True, gaps will be zeroed and with a record of
       gap positions posted to the Metadata of the output.  See above
       for details.
-    :param zero_gaps:  boolean controlling how gaps are to be handled. 
+    :param zero_gaps:  boolean controlling how gaps are to be handled.
     See above for details of the algorithm.
     :type zero_gaps:  boolean (default False)
     :param object_history: boolean to enable or disable saving object

python/mspasspy/util/converter.py

@@ -147,9 +147,7 @@ def TimeSeries2Trace(ts):
         if abs(sampling_rate - 1.0 / ts.dt) > 1e-6:
             # Record inconsistency in error log (elog)
             message = "sampling_rate inconsistent with 1/dt; updating to 1/dt"
-            ts.elog.log_error("TimeSeries2Trace",
-                             message,
-                             ErrorSeverity.Complaint)
+            ts.elog.log_error("TimeSeries2Trace", message, ErrorSeverity.Complaint)
         # Update sampling_rate to 1/dt
         ts["sampling_rate"] = 1.0 / ts.dt
         dresult.stats["sampling_rate"] = 1.0 / ts.dt

python/tests/algorithms/ml/test_arrival.py

@@ -15,6 +15,7 @@
 
 pn_model = sbm.PhaseNet.from_pretrained("stead")
 
+
 def test_annotate_arrival_time():
     """
     Test the annotate_arrival_time function.
@@ -26,15 +27,15 @@ def test_annotate_arrival_time():
     # picks from mspass
     timeseries = Trace2TimeSeries(stream[0])
     annotate_arrival_time(timeseries, 0)
-    mspass_picks = timeseries["p_wave_picks"] # should be a dictionary
+    mspass_picks = timeseries["p_wave_picks"]  # should be a dictionary
     # assert that for each pick, the value is a float number that is between 0 and 1
     assert all(0 <= value <= 1 for value in mspass_picks.values())
 
     # picks from seisbench
     pn_preds = pn_model.annotate(Stream(stream[0]))
     trace = pn_preds[0]
     assert trace.stats.channel == "PhaseNet_P"
-    seis_picks = trace.times("timestamp") # should be an array
+    seis_picks = trace.times("timestamp")  # should be an array
 
     # Convert both to sets of rounded values
     mspass_set = set(round(v, 6) for v in mspass_picks.keys())
@@ -43,6 +44,7 @@ def test_annotate_arrival_time():
     # Compare the sets
     assert mspass_set == seis_set
 
+
 def test_annotate_arrival_time_threshold():
     """
     Test the annotate_arrival_time function with a threshold.
@@ -64,6 +66,7 @@ def test_annotate_arrival_time_threshold():
     # assert that the number of picks from mspass is less than the number of picks from seisbench
     assert len(mspass_picks) < len(seis_picks)
 
+
 def test_annotate_arrival_time_window():
     """
     Test the annotate_arrival_time function with a time window.
@@ -76,7 +79,9 @@ def test_annotate_arrival_time_window():
     timeseries = Trace2TimeSeries(stream[0])
     window_start = UTCDateTime(2009, 4, 6, 1, 30).timestamp
     window_end = window_start + 1000
-    annotate_arrival_time(timeseries, threshold = 0, time_window=TimeWindow(window_start, window_end))
+    annotate_arrival_time(
+        timeseries, threshold=0, time_window=TimeWindow(window_start, window_end)
+    )
     mspass_picks = timeseries["p_wave_picks"]
 
     assert len(mspass_picks.keys()) > 0
@@ -97,6 +102,7 @@ def test_annotate_arrival_time_window():
     # Compare the sets
     assert mspass_set == seis_set
 
+
 def test_annotate_arrival_time_for_mseed():
     """
     Test the annotate_arrival_time function for a mseed file.
@@ -108,8 +114,10 @@ def test_annotate_arrival_time_for_mseed():
     timeseries = Trace2TimeSeries(trace)
 
     window_start = UTCDateTime(2011, 3, 11, 6, 35).timestamp
-    window_end = window_start + 1200 # 20 minutes
-    annotate_arrival_time(timeseries, 0.1, time_window=TimeWindow(window_start, window_end))
+    window_end = window_start + 1200  # 20 minutes
+    annotate_arrival_time(
+        timeseries, 0.1, time_window=TimeWindow(window_start, window_end)
+    )
     mspass_picks = timeseries["p_wave_picks"]
 
     # assert the picks are not empty
@@ -130,14 +138,15 @@ def test_annotate_arrival_time_for_mseed():
 
     # Convert both to sets of rounded values
     mspass_set = set(round(v, 6) for v in mspass_picks.keys())
-    seis_set = set(round(v, 6) for v in seis_picks) 
+    seis_set = set(round(v, 6) for v in seis_picks)
 
     # every pick in mspass should be in seisbench
     assert mspass_set.issubset(seis_set)
 
     # assert that the number of picks from mspass is less than the number of picks from seisbench
     assert len(mspass_picks) < len(seis_picks)
 
+
 def get_mseed_trace_for_test():
     file_path = os.path.join(os.getcwd(), "python/tests/data/db_mseeds/test_277.mseed")
     st = read(file_path)
@@ -158,5 +167,6 @@ def get_trace_for_test():
         endtime=t + 3600,
     )
 
+
 if __name__ == "__main__":
     test_annotate_arrival_time_window()

python/tests/algorithms/test_window.py

@@ -650,14 +650,15 @@ def test_windowdata_exceptions():
             assert d.data[i, j] == 0.0
     assert d.elog.size() == 1
 
+
 def test_WindowData_autopad():
     """
-    Tests function added September 2024 that uses a soft test 
-    for the data range.   Returns a fixed length signal that 
-    is padded unless the actual time span is too short as 
-    defined by the fractional_mismatch_limit argument.   
+    Tests function added September 2024 that uses a soft test
+    for the data range.   Returns a fixed length signal that
+    is padded unless the actual time span is too short as
+    defined by the fractional_mismatch_limit argument.
     The function is a higher level function using WindowData
-    so tests are limited to the features it adds and 
+    so tests are limited to the features it adds and
     additional exceptions it handles.
     """
     # this duplicates code in test_WindowData
@@ -677,58 +678,61 @@ def test_WindowData_autopad():
     # make copies because WindowData can alter content
     ts0 = TimeSeries(ts)
     se0 = Seismogram(se)
-    
-    # first verify the function works correctly if the 
+
+    # first verify the function works correctly if the
     # stime:etime range is entirely within the data
     ts = TimeSeries(ts0)
-    ts = WindowData_autopad(ts,ts.time(10),ts.time(50))
+    ts = WindowData_autopad(ts, ts.time(10), ts.time(50))
     assert ts.live
     assert ts.npts == 41
-    
+
     se = Seismogram(se0)
-    se = WindowData_autopad(se,se.time(10),se.time(50))
+    se = WindowData_autopad(se, se.time(10), se.time(50))
     assert se.live
     assert se.npts == 41
-    
-    # verify automatic zero padding of first and last sample 
+
+    # verify automatic zero padding of first and last sample
     # when stime and etime are specified as that
     ts = TimeSeries(ts0)
-    # the time method works with negative and resolves her 
+    # the time method works with negative and resolves her
     # to one sample before start and one sample after end time
-    ts = WindowData_autopad(ts,ts.time(-1),ts.time(npts))
+    ts = WindowData_autopad(ts, ts.time(-1), ts.time(npts))
     assert ts.live
     assert ts.npts == npts + 2
     # isclose is not needed here as this is a hard set 0
     assert ts.data[0] == 0.0
-    assert ts.data[npts+1] == 0.0
+    assert ts.data[npts + 1] == 0.0
     # similar for Seismogram
     se = Seismogram(se0)
-    # the time method works with negative and resolves her 
+    # the time method works with negative and resolves her
     # to one sample before start and one sample after end time
-    se = WindowData_autopad(se,se.time(-1),se.time(npts))
+    se = WindowData_autopad(se, se.time(-1), se.time(npts))
     assert se.live
     assert se.npts == npts + 2
     for k in range(3):
-        assert se.data[k,0] == 0.0
-        assert se.data[k,npts+1] == 0.0
-    
-    # test error handling for this function - not made a separate function 
-    # as it isn't that complex 
+        assert se.data[k, 0] == 0.0
+        assert se.data[k, npts + 1] == 0.0
+
+    # test error handling for this function - not made a separate function
+    # as it isn't that complex
     d_foo = TimeSeriesEnsemble(2)
     d_foo.member.append(ts)
     d_foo.set_live()
-    with pytest.raises(TypeError,match="arg0 must be either a TimeSeries or Seismogram object"):
+    with pytest.raises(
+        TypeError, match="arg0 must be either a TimeSeries or Seismogram object"
+    ):
         # need to send TimeSeriesEnsemble to bypass type check of function decorator
-        ts = WindowData_autopad(d_foo,se.time(10),se.time(50))
-    
+        ts = WindowData_autopad(d_foo, se.time(10), se.time(50))
+
     # only test this for TimeSeries - no reason to think it would behave differently for Seismogram
     ts = TimeSeries(ts0)
-    ts = WindowData_autopad(ts,ts.t0,ts.time(3*npts))
+    ts = WindowData_autopad(ts, ts.t0, ts.time(3 * npts))
     assert ts.dead()
     # this is actually 2 because it passes through WindowDataAtomic that also posts a log message
     # Using >= to make the test more robust in the event that changes
     assert ts.elog.size() >= 1
 
+
 def test_TopMute():
     ts = TimeSeries(100)
     seis = Seismogram(100)