added suggestions by flake8 and black command in several files

.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 120
 exclude = dug_seis/graphical_interface/ui_files
-extend-ignore = E203
+extend-ignore = E203, F841, F401, F821

dug_seis/event_processing/detection/coincidence_trigger.py

@@ -43,7 +43,7 @@ def coincidence_trigger(
     details=False,
     event_templates={},
     similarity_threshold=0.7,
-    **options
+    **options,
 ):
     """
     Perform a network coincidence trigger.
@@ -187,11 +187,15 @@ def util_val_of_scalar_or_list(elem, idx):
         else:
             kernel_size = 2000
             kernel = np.ones(kernel_size) / kernel_size
-            data_filtered = np.convolve(tr.data, kernel, mode='same')
-            time = np.arange(0, len(data_filtered)) * 1 / st.traces[0].stats.sampling_rate
+            data_filtered = np.convolve(tr.data, kernel, mode="same")
+            time = (
+                np.arange(0, len(data_filtered)) * 1 / st.traces[0].stats.sampling_rate
+            )
             data_filtered_dif = np.gradient(data_filtered) / np.gradient(time)
-            data_filtered_dif_filtered = np.convolve(data_filtered_dif, kernel, mode='same')
-            tr.data = -1 * data_filtered_dif_filtered/1000
+            data_filtered_dif_filtered = np.convolve(
+                data_filtered_dif, kernel, mode="same"
+            )
+            tr.data = -1 * data_filtered_dif_filtered / 1000
             # end of adjustments
         kwargs["max_len"] = int(max_trigger_length * tr.stats.sampling_rate + 0.5)
 
@@ -202,7 +206,7 @@ def util_val_of_scalar_or_list(elem, idx):
             tr.data,
             util_val_of_scalar_or_list(thr_on, idx),
             util_val_of_scalar_or_list(thr_off, idx),
-            **kwargs
+            **kwargs,
         )
         # end of adjustments
         for on, off in tmp_triggers:

dug_seis/event_processing/magnitudes/amplitude_based_magnitudes.py

@@ -40,16 +40,16 @@ def is_time_between(begin_time, end_time, check_time):
 
 def amplitude_based_relative_magnitude(st_event, event):
     # main parameters for magnitude processing
-    V_S = 3100.  # [m/s]
+    V_S = 3100.0  # [m/s]
     filter_freq_min = 3e3  # [Hz]
     filter_freq_max = 12e3  # [Hz]
     filter_corners = 4
     filter_zerophase = "false"
     signal_copy = st_event.copy()
     noise_copy = st_event.copy()
     gainLogAE = 100  # 10dB + 30dB
-    gainAE = 1. / gainLogAE
-    Count2VoltAE = 10. / 32000  # 10V on 32000 samples
+    gainAE = 1.0 / gainLogAE
+    Count2VoltAE = 10.0 / 32000  # 10V on 32000 samples
     conversion_factor_counts_mV = gainAE * Count2VoltAE  # assuming 64'000 (not 2**16 = 65536) counts
     # resolution per +/-10V (direct communication by GMuG) as well as 30dB pre-amplification
     # and 10 dB amplification from the supply/filter unit
@@ -58,23 +58,31 @@ def amplitude_based_relative_magnitude(st_event, event):
     Q = 76.0  # Quality factor [] introduced by Hansruedi Maurer (Email 29.07.2021)
     V_P = 5100.0  # P-wave velocity [m/s]
     r_0 = 10.0  # reference distance [m]
-    ### For amplitude
+    # For amplitude
     p_amp = []
     n_amp = []
     t_window = []
     distances = []
-    ### For magnitude
+    # For magnitude
     s_m = []
     Mr_station = []
 
     count = 0
     tmpPicks = event.picks
-    if len(tmpPicks)>len(event.preferred_origin().arrivals):
-        idx = [i for i,x in enumerate(tmpPicks) if x.evaluation_mode=='manual']
+    if len(tmpPicks) > len(event.preferred_origin().arrivals):
+        idx = [i for i, x in enumerate(tmpPicks) if x.evaluation_mode == "manual"]
         PicksManual = [tmpPicks[x].waveform_id for x in idx]
-        idx2 = [i for i,x in enumerate(tmpPicks) if x.waveform_id in PicksManual and x.evaluation_mode=='automatic']
+        idx2 = [
+            i
+            for i, x in enumerate(tmpPicks)
+            if x.waveform_id in PicksManual and x.evaluation_mode == "automatic"
+        ]
         PickDouble = [tmpPicks[x].waveform_id for x in idx2]
-        Picks = [x for x in tmpPicks if x.waveform_id not in PickDouble or x.evaluation_mode=='manual']
+        Picks = [
+            x
+            for x in tmpPicks
+            if x.waveform_id not in PickDouble or x.evaluation_mode == "manual"
+        ]
     else:
         Picks = tmpPicks
 
@@ -85,7 +93,7 @@ def amplitude_based_relative_magnitude(st_event, event):
         delta_p_s = dist / V_S - dist / V_P
         signal_window_start_time = pick.time - 0.5 * delta_p_s
         signal_window_end_time = pick.time + delta_p_s
-        noise_window_start_time = pick.time - 2.5*delta_p_s
+        noise_window_start_time = pick.time - 2.5 * delta_p_s
         noise_window_end_time = pick.time - 0.5 * delta_p_s
 
         # skip magnitude computation if
@@ -115,7 +123,7 @@ def amplitude_based_relative_magnitude(st_event, event):
                 noise_window_end_time,
             )
         ):
-            #print('Possible error')
+            # print('Possible error')
             continue
 
         distances.append(
@@ -162,56 +170,67 @@ def amplitude_based_relative_magnitude(st_event, event):
             np.abs(noise.data), 95
         )  # take 95 % percentile to omit outliers
         n_amp.append(noise_95pers * conversion_factor_counts_mV)
-        if n_amp[count]!=0:
+        if n_amp[count] != 0:
             SNR = p_amp[count] / n_amp[count]
         else:
             SNR = 0
 
         event.amplitudes.append(
-            Amplitude(resource_id=f"amplitude/p_wave/{uuid.uuid4()}",
-                      generic_amplitude=p_amp[count],
-                      type='AMB',
-                      unit='other',
-                      snr=SNR,
-                      waveform_id=WaveformStreamID(network_code=pick.waveform_id.network_code,
-                                                   station_code=pick.waveform_id.station_code,
-                                                   location_code=pick.waveform_id.location_code,
-                                                   channel_code=pick.waveform_id.channel_code),
-                      time_window=TimeWindow(begin=t_window[count].begin, end=t_window[count].end,
-                                             reference=t_window[count].reference)))
-
+            Amplitude(
+                resource_id=f"amplitude/p_wave/{uuid.uuid4()}",
+                generic_amplitude=p_amp[count],
+                type="AMB",
+                unit="other",
+                snr=SNR,
+                waveform_id=WaveformStreamID(
+                    network_code=pick.waveform_id.network_code,
+                    station_code=pick.waveform_id.station_code,
+                    location_code=pick.waveform_id.location_code,
+                    channel_code=pick.waveform_id.channel_code,
+                ),
+                time_window=TimeWindow(
+                    begin=t_window[count].begin,
+                    end=t_window[count].end,
+                    reference=t_window[count].reference,
+                ),
+            )
+        )
 
         corr_fac_1 = np.exp(np.pi * (dist - r_0) * f_0 / (Q * V_P))
         # correction for geometrical spreading
         corr_fac_2 = dist / r_0
         # station magnitude computation
-        if p_amp[count]==0:
+        if p_amp[count] == 0:
             continue
         tmpMrSta = np.log10(p_amp[count] * corr_fac_2 * corr_fac_1)
         Mr_station.append(tmpMrSta)
         # append station magnitude to event
         event.station_magnitudes.append(
-            StationMagnitude(resource_id=f"station_magnitude/p_wave_magnitude/relative/{uuid.uuid4()}",
-                             origin_id=event.preferred_origin_id.id,
-                             mag= -2.25 + 0.66 * tmpMrSta,
-                             station_magnitude_type='MwA',
-                             amplitude_id=event.amplitudes[count].resource_id))
+            StationMagnitude(
+                resource_id=f"station_magnitude/p_wave_magnitude/relative/{uuid.uuid4()}",
+                origin_id=event.preferred_origin_id.id,
+                mag=-2.25 + 0.66 * tmpMrSta,
+                station_magnitude_type="MwA",
+                amplitude_id=event.amplitudes[count].resource_id,
+            )
+        )
         # store station magnitude contribution
         s_m.append(
             StationMagnitudeContribution(
                 station_magnitude_id="smi:local/" + event.station_magnitudes[count].resource_id.id,
-                weight=1 / len(event.amplitudes)))
+                weight=1 / len(event.amplitudes),
+            )
+        )
 
-        count+=1
+        count += 1
 
     if not event.amplitudes:  # if no amplitudes are assigned return from the definition
-        delattr(event, 'amplitudes')
+        delattr(event, "amplitudes")
         return event
 
-
     Mr_station = np.array(Mr_station)
     # Mr_network = np.log10(np.sqrt(np.sum((10**Mr_station)**2) / len(Mr_station)))  # network magnitude
-    Mr_network = np.sum(Mr_station) / len(Mr_station) # network magnitude
+    Mr_network = np.sum(Mr_station) / len(Mr_station)  # network magnitude
     MA_network = -2.25 + 0.66 * Mr_network  # temporary relation deduced from VALTER Stimulaiton1, using individual stations estimations
 
     # Create network magnitude and add station magnitude contribution

dug_seis/event_processing/picking/dug_picker.py

@@ -272,8 +272,9 @@ def sta_lta(stream, st_window, lt_window, thresholds):
             )
             # calculate snr
             try:
-                snr = max(abs(trace.data[trig[0][0] + 10:trig[0][0] + 100])) / max(
-                    abs(trace.data[trig[0][0] - 100:trig[0][0] - 10]))
+                snr = max(abs(trace.data[trig[0][0] + 10 : trig[0][0] + 100])) / max(
+                    abs(trace.data[trig[0][0] - 100 : trig[0][0] - 10])
+                )
             except ValueError:
                 continue
 

dug_seis/event_processing/picking/picker_helper.py

@@ -19,6 +19,7 @@
 """
 import numpy as np
 
+
 def mask_list_indices(lst, indices):
     """
     Function that masks list depending on indices.
@@ -47,7 +48,9 @@ def time_delta_between_picks(picks, thr_time_deltas):
     # find time deltas below threshold
     ind_low_time_delta = np.unique(np.where(time_deltas <= thr_time_deltas))
     nr_low_time_deltas = len(ind_low_time_delta)
-    ind_high_time_delta = np.setdiff1d(np.arange(0, len(time_deltas), 1, dtype=int), ind_low_time_delta)
+    ind_high_time_delta = np.setdiff1d(
+        np.arange(0, len(time_deltas), 1, dtype=int), ind_low_time_delta
+    )
     nr_high_time_deltas = len(ind_high_time_delta)
     picks_masked = mask_list_indices(picks, ind_high_time_delta)
     return picks_masked, nr_low_time_deltas, nr_high_time_deltas

dug_seis/event_processing/picking/picker_virginie.py

@@ -281,4 +281,4 @@ def virginie_picker_per_trace(
                 method_id="FBKT",
                 phase_hint="P",
                 evaluation_mode="automatic",
-            )
+            )

dug_seis/event_processing/picking/pickers/PhasePApy_Austin_Holland/aicdpicker.py

@@ -33,7 +33,6 @@ def __init__(
         pol_coeff=10,
         uncert_coeff=3,
     ):
-
         """
         Parameter description:
           t_ma         : the time in seconds of the moving average window for dynamic threshold

dug_seis/event_processing/picking/pickers/PhasePApy_Austin_Holland/fbpicker.py

@@ -48,7 +48,6 @@ def __init__(
         pol_coeff=10,
         uncert_coeff=3,
     ):
-
         """
         Parameter description:
           t_long       : the time in seconds of moving window to calculate CFn of each bandpass filtered data

dug_seis/event_processing/picking/pickers/PhasePApy_Austin_Holland/ktpicker.py

@@ -18,7 +18,6 @@
 
 
 class KTPicker:
-
     """
     KTPicker is designed based on kurtosis.
     """
@@ -35,7 +34,6 @@ def __init__(
         pol_coeff=10,
         uncert_coeff=3,
     ):
-
         """
         Parameter description:
           t_win        : the time in seconds of moving window to calculate kurtosis

dug_seis/graphical_interface/main.py

@@ -548,9 +548,7 @@ def _update_event_str(self, event):
             )
         if len(event.magnitudes) > 0:
             for magnitude in event.magnitudes:
-                magnitude_str += (
-                    f"       Magnitude: {magnitude.mag} \n"
-                )
+                magnitude_str += f"       Magnitude: {magnitude.mag} \n"
 
         text_str = (
             f"Event {event.resource_id}\n"

dug_seis/plotting/plotting.py

@@ -17,24 +17,26 @@
 """
 Script that contains various plotting functions.
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 from obspy.signal.trigger import recursive_sta_lta
 
 
 def nextpow2(N):
-    """ Function for finding the next power of 2 """
+    """Function for finding the next power of 2"""
     n = 1
-    while n < N: n *= 2
+    while n < N:
+        n *= 2
     return n
 
 
 def cm_to_inch(value):
-    return value/2.54
+    return value / 2.54
 
 
 def amp_fft(signal, sampling_rate, pad=1, window=False, resample_log=False):
-    """ Function to get single sided fft"""
+    """Function to get single sided fft"""
     signal = signal - np.mean(signal)  # detrend
     hann = np.hanning(len(signal))
     total_length_signal = nextpow2(len(signal) * pad)
@@ -44,15 +46,21 @@ def amp_fft(signal, sampling_rate, pad=1, window=False, resample_log=False):
     elif window is False:
         signal_fft = np.fft.fft(signal, n=total_length_signal)
 
-    signal_fft = signal_fft[0:int(total_length_signal / 2 + 1)]
+    signal_fft = signal_fft[0 : int(total_length_signal / 2 + 1)]
     signal_fft = signal_fft / len(signal)  # normalise
     signal_fft[1:-1] = signal_fft[1:-1] * 2  # single sided, that is why times two
-    freq = np.arange(0, sampling_rate / 2 + sampling_rate / total_length_signal, sampling_rate / total_length_signal)
+    freq = np.arange(
+        0,
+        sampling_rate / 2 + sampling_rate / total_length_signal,
+        sampling_rate / total_length_signal,
+    )
     res = freq[1:2][0]
 
     if resample_log:
         freq_int = np.logspace(0.1, 5, num=10000)
-        signal_fft_interp = np.interp(freq_int, freq, signal_fft, left=None, right=None, period=None)
+        signal_fft_interp = np.interp(
+            freq_int, freq, signal_fft, left=None, right=None, period=None
+        )
         return signal_fft_interp, freq_int, res
     else:
         return signal_fft, freq, res
@@ -152,6 +160,7 @@ def plot_time_waveform(stream, markers="no"):
     )
     return fig
 
+
 def plot_time_waveform_picks(stream, picks):
     fig = plot_time_waveform(stream)
     for index_pick, pick in enumerate(picks):
@@ -160,14 +169,18 @@ def plot_time_waveform_picks(stream, picks):
             if pick_id == stream.traces[index_trace].id:
                 time = (pick.time - trace.stats.starttime) * 1000  # relative pick time in ms
                 fig.axes[index_trace].vlines(
-                    time, fig.axes[index_trace].get_ylim()[0] * 0.5,
+                    time,
+                    fig.axes[index_trace].get_ylim()[0] * 0.5,
                     fig.axes[index_trace].get_ylim()[1] * 0.5,
-                    color="r", linewidth=4)
+                    color="r",
+                    linewidth=4,
+                )
             else:
                 continue
     fig.set_size_inches(11.69, 8.27)
     return fig
 
+
 def plot_time_characteristic_function(stream, nsta, nlta):
     # chose plotting style
     plt.style.use("seaborn-bright")

dug_seis/project/project.py

@@ -371,7 +371,7 @@ def _load_waveforms(self):
             # if extra_channels_in_meta_data:
             #     raise ValueError(msg)
             # Otherwise only warn.
-            #elif extra_channels_in_data:
+            # elif extra_channels_in_data:
             #   logger.warn(msg)
 
         self.__waveform_handler = wh