From 1dbb4df06656364c33ee7806c6fb1d3e2a056d2a Mon Sep 17 00:00:00 2001
From: Joseph Doetsch <joseph.doetsch@erdw.ethz.ch>
Date: Sun, 20 Jan 2019 17:51:31 +0100
Subject: [PATCH] Bug fixes and change in input file. Magnitudes are now
calculated around predicted p-wave arrival, not pick (in window plus minus
diff in p and s traveltime)
---
config/dug-seis.yaml | 101 +++---
dug_seis/processing/event.py | 444 ++----------------------
dug_seis/processing/event_processing.py | 62 +---
dug_seis/processing/get_waveforms.py | 82 +----
dug_seis/processing/processing.py | 129 +------
5 files changed, 93 insertions(+), 725 deletions(-)
diff --git a/config/dug-seis.yaml b/config/dug-seis.yaml
index 42a9aa3..a97d72b 100644
--- a/config/dug-seis.yaml
+++ b/config/dug-seis.yaml
@@ -3,47 +3,47 @@
General:
project_name : Elegancy_CSD
project_location : Mt_Terri
- operator : ['L. Villiger','J. Doetsch','S. Demir']
+ operator : ['J. Doetsch','S. Demir']
sensor_count : 32
sensor_coords : [ # in local coordinate system; x=East, y=North, z=elevation
0,0,0,
-8.652,84.869,2.870,
-16.187,98.370,2.841,
-23.103,110.886,2.747,
-30.700,125.024,2.546,
-42.158,135.681,3.575,
-71.148,135.037,2.476,
-71.169,125.102,2.192,
-70.764,114.158,2.429,
-70.569,104.196,2.495,
-67.306,93.020,3.403,
-71.162,122.331,16.367,
-70.716,104.681,16.150,
-67.236,91.771,16.019,
-70.069,77.752,16.315,
-57.553,96.126,-11.814,
-50.997,96.164,-19.365,
-54.114,96.061,-8.701,
-37.572,96.071,-19.943,
-57.522,111.956,-12.363,
-50.805,111.951,-19.771,
-49.318,111.967,-11.975,
-37.742,111.972,-19.848,
-68.599,107.806,4.081,
-68.565,105.806,4.115,
-68.530,103.807,4.150,
-68.496,101.807,4.184,
-22.974,110.826,2.791,
-42.048,135.768,3.571,
-71.237,134.959,2.515,
-70.852,114.095,2.480,
-67.279,92.885,3.421,]
+20.838,69.465,13.148,
+19.781,70.950,13.195,
+18.655,72.337,13.163,
+17.638,73.809,13.209,
+16.641,75.305,13.232,
+15.495,76.710,13.245,
+14.503,78.192,13.262,
+13.547,79.520,13.338,
+10.478,77.285,13.287,
+11.507,75.913,13.226,
+12.605,74.466,13.232,
+13.574,72.967,13.249,
+14.598,71.448,13.186,
+15.745,70.015,13.207,
+16.707,68.573,13.196,
+17.676, 67.099, 13.218,
+18.790, 65.669, 13.185,
+7.9388,76.7132,-5.4494,
+9.76492,74.75961,-2.4747,
+11.581,72.81184,0.5099,
+12.03274,72.32524,1.2576,
+15.707958,72.294242,-20.5874,
+15.809368,72.223778,-16.5893,
+15.903385,72.162778,-12.5909,
+15.986165,72.104954,-8.5922,
+21.792, 67.946, 13.152,
+0,0,0,
+0,0,0,
+0,0,0,
+0,0,0,
+0,0,0,]
active_trigger_channel : 1
- origin_ch1903_east : 667400.0 # ch1903 easting origin of local coordinate system
- origin_ch1903_north : 158800.0 # ch1903 northing of local coordinate system
- origin_elev : 1730.0 # elevation of origin of local coordinate system
+ origin_ch1903_east : 579300.0 # ch1903 easting origin of local coordinate system
+ origin_ch1903_north : 247500.0 # ch1903 northing of local coordinate system
+ origin_elev : 500.0 # elevation of origin of local coordinate system
stats:
- network : 'GR'
+ network : 'XM'
Acquisition:
hardware_settings:
@@ -53,18 +53,18 @@ Acquisition:
data_folder_tmp : 'e:/20181218_sw_test/tmp/'
compression : 'gzip-3' # default: 'gzip-3', 'gzip-0' to 'gzip-9' or None
file_length_sec : 60.0 # in seconds
- station_naming : [0,2,4,6,8,10,12,14,1,3,5,7,9,11,13,15,16,18,20,22,24,26,28,30,17,19,21,23,25,27,29,31]
+ station_naming : [1, 9, 2, 10, 3, 11, 4, 12, 5, 13, 6, 14, 7, 15, 8, 16, 17, 25, 18, 26, 19, 27, 20, 28, 21, 29, 22, 30, 23, 31, 24, 32]
Trigger:
- asdf_folder : raw
- channels : [1,16,17,18,19,20,21,22,23]
+ asdf_folder : 'raw'
+ channels : [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]
overlap : 0.05
bandpass_f_min : 1000
bandpass_f_max : 12000
algorithm : 'recstalta' #other option: 'threshold'
- coincidence : 4
+ coincidence : 10
sta_lta:
- threshold_on : 5.0
+ threshold_on : 8.0
threshold_off : 2.0
st_window : 70 #in samples
lt_window : 700 #in samples
@@ -74,34 +74,35 @@ Trigger:
# threshold : 100.0 # in miliVolt
classification:
spread_int : 1e-4
- noise_vis : 'False'
- noise_vis_interval : 10
+ noise_vis : 'True'
+ noise_vis_interval : 60 # in seconds
Processing:
- vp : 5000.0 # velocity in m/s
- vs : 2800.0 # velocity in m/s
+ vp : 2600.0 # velocity in m/s
+ vs : 2000.0 # velocity in m/s
bandpass_f_min : 1000
bandpass_f_max : 12000
Picking:
algorithm : 'sta_lta'
sta_lta:
- threshold_on : 5.0
+ threshold_on : 8.0
threshold_off : 2.0
st_window : 70 #in samples #2.1
lt_window : 700 #in samples #4.2
Locate:
- algorithm : 'hom_aniso' # other options: hom_aniso, nonlinloc
+ algorithm : 'hom_iso' # other options: hom_iso, hom_aniso, nonlinloc
min_picks : 6
damping : 0.01
hom_aniso:
- epsilon : 0.0 # Thomsen epsilon
+ vp_min : 2400.0
+ epsilon : 0.263 # Thomsen epsilon
delta : 0.0 # Thomsen delta
- azi : 290.0 # Azimuth of anisotropy vector in degrees
- inc : 30.0 # Inclination of anisotropy vector in degrees
+ azi : 295.0 # Azimuth of anisotropy vector in degrees
+ inc : 38.0 # Inclination of anisotropy vector in degrees
Magnitude:
algorithm : 'relative'
diff --git a/dug_seis/processing/event.py b/dug_seis/processing/event.py
index 66b6304..f79b4e9 100644
--- a/dug_seis/processing/event.py
+++ b/dug_seis/processing/event.py
@@ -35,9 +35,11 @@ class Event(ObsPyEvent):
self.tcalc = []
self.loc_ind = []
if self.classification != 'noise':
- logging.info('Event ' + str(self.event_id) + ': Processing started.')
+ t_start_plot = self.wf_stream.traces[0].stats["starttime"]
+ logging.info('Event ' + str(self.event_id) + ': Time ' + str(t_start_plot)[11:-1] + ', processing started.')
else:
- logging.info('Noise Visualisation ' + ': Processing started.')
+ t_start_plot = self.wf_stream.traces[0].stats["starttime"]
+ logging.info('Noise Visualisation at ' + str(t_start_plot)[11:-1] + ', processing started.')
def bandpass(self):
# bandpass filter using same parameters as for triggering
@@ -64,7 +66,6 @@ class Event(ObsPyEvent):
method_id='recursive_sta_lta'))
logging.info('Event ' + str(self.event_id) + ': ' + str(len(self.picks)) + ' picks.')
-
def locate(self):
lparam = self.prparam['Locate']
if lparam['algorithm'] == 'hom_aniso':
@@ -80,11 +81,6 @@ class Event(ObsPyEvent):
tobs.append((i.time - self.wf_stream.traces[0].stats["starttime"]) * 1000)
npicks = len(tobs)
sensor_coords = self.param['General']['sensor_coords'][trig_ch, :]
- # for i in range(len(sensor_coords) - 1, -1, -1):
- # if not sensor_coords[i, 0]:
- # trig_ch.remove(trig_ch[i])
- # tobs.remove([i])
- # sensor_coords = self.param['General']['sensor_coords'][trig_ch, :]
vp = self.prparam['vp'] * np.ones([npicks]) / 1000.
if lparam['algorithm'] == 'hom_iso' or lparam['algorithm'] == 'hom_aniso':
@@ -104,8 +100,8 @@ class Event(ObsPyEvent):
theta = np.arccos(np.cos(inc) * np.cos(azi) * np.cos(aparam['inc']) * np.cos(aparam['azi']) +
np.cos(inc) * np.sin(azi) * np.cos(aparam['inc']) * np.sin(aparam['azi']) +
np.sin(inc) * np.sin(aparam['inc']))
- vp[i] = self.prparam['vp'] / 1000. * (1.0 + aparam['delta'] * np.sin(theta)**2 * np.cos(theta)**2 +
- aparam['epsilon'] * np.sin(theta)**4)
+ vp[i] = aparam['vp_min'] / 1000. * (1.0 + aparam['delta'] * np.sin(theta)**2 * np.cos(theta)**2 +
+ aparam['epsilon'] * np.sin(theta)**4)
dist = [np.linalg.norm(loc - sensor_coords[i, :]) for i in range(npicks)]
tcalc = [dist[i] / vp[i] + t0 for i in range(npicks)]
@@ -118,8 +114,7 @@ class Event(ObsPyEvent):
jacobian[:, 3] = np.ones(npicks)
dm = np.matmul(np.matmul(np.linalg.inv(np.matmul(np.transpose(jacobian), jacobian) +
- pow(lparam['damping'], 2) * np.eye(4)), np.transpose(jacobian)),
- res)
+ pow(lparam['damping'], 2) * np.eye(4)), np.transpose(jacobian)), res)
loc = loc + dm[0:3]
t0 = t0 + dm[3]
else:
@@ -169,7 +164,6 @@ class Event(ObsPyEvent):
logging.info('Event ' + str(self.event_id) + ': Location %3.2f %3.2f %3.2f; %i iterations, rms %4.3f ms'
% (loc[0], loc[1], loc[2], nit, rms))
-
def est_magnitude(self, origin_number=np.inf):
emparam = self.prparam['Magnitude']
if origin_number > len(self.origins):
@@ -185,24 +179,18 @@ class Event(ObsPyEvent):
ch_in = [int(i.stats['station']) for i in self.wf_stream.traces]
ind_trig = np.where(np.isin(ch_in, trig_ch)) # Finds correct axis
-
if len(self.origins):
self.ts_approx = []
t0_orig = self.t0[origin_number] - self.wf_stream.traces[0].stats["starttime"] # t0 for use in magnitude estimation
+ righthand = []
for i in range(len(self.dist[origin_number])):
- self.ts_approx.append((self.dist[origin_number][i] / (self.prparam['vs']) + t0_orig) * 1000) # time of s wave arrival/end of the segment (time)
+ self.ts_approx.append((self.dist[origin_number][i] / self.prparam['vs'] + t0_orig) * 1000) # time of s wave arrival/end of the segment (time)
+ righthand.append((self.dist[origin_number][i] / self.prparam['vs'] -
+ self.dist[origin_number][i] / self.prparam['vp']) * 1000)
if self.ts_approx[i] > (self.param['Trigger']['endtime']+self.param['Trigger']['endtime'])*1000:
self.ts_approx[i] = (self.param['Trigger']['endtime']+self.param['Trigger']['endtime'])*1000
- righthand = []
relstartwindow = []
- relpicks=[]
- for i in self.picks:
- if self.param['General']['sensor_coords'][int(i.waveform_id['station_code']) - 1, 0]:
- relpicks.append((i.time - self.wf_stream[0].stats["starttime"])*1000)
- for i in range(len(self.ts_approx)):
- righthand.append(
- self.ts_approx[i] - relpicks[i]) # time between picked p arrival and s wave arrival in ms
for i in range(len(self.ts_approx)):
relstartwindow.append(self.ts_approx[i] - 2 * righthand[i]) # begin of the segment (time)
if relstartwindow[i] < 0:
@@ -228,69 +216,13 @@ class Event(ObsPyEvent):
average_magnitude = np.log10(np.mean(mag_exp))
logging.info('Event ' + str(self.event_id) + ': Relative magnitude %4.2f.' % average_magnitude)
- self.magnitudes.append(Magnitude(mag=average_magnitude,
- resource_id='%s/magnitude/%d' % (
- self.resource_id.id, len(self.magnitudes)+1),
- origin_id=self.origins[origin_number].resource_id,
- type='relative_amplitude'))
- self.preferredMagnitudeID = self.magnitudes[-1].resource_id
- # ######oldmagnitude
- # def est_magnitude(self, origin_number=np.inf):
- # emparam = self.prparam['Magnitude']
- # if origin_number > len(self.origins):
- # origin_number = len(self.origins)
- # elif origin_number < 1:
- # logging.warning('Origin number does not exist.')
- # origin_number = origin_number - 1
- #
- # trig_ch = [int(i.waveform_id['station_code']) for i in self.picks]
- # ch_in = [int(i.stats['station']) for i in self.wf_stream.traces]
- # ind_trig = np.where(np.isin(ch_in, trig_ch)) # Finds correct axis
- #
- # if len(self.origins):
- # self.ts_approx = []
- # t0_orig = self.t0[origin_number] - self.wf_stream.traces[0].stats["starttime"] # t0 for use in magnitude estimation
- # for i in range(len(self.dist[origin_number])):
- # self.ts_approx.append((self.dist[origin_number][i] / (self.prparam['vs']) + t0_orig) * 1000) # time of s wave arrival/end of the segment (time)
- # if self.ts_approx[i] > (self.param['Trigger']['endtime']+self.param['Trigger']['endtime'])*1000:
- # self.ts_approx[i] = (self.param['Trigger']['endtime']+self.param['Trigger']['endtime'])*1000
- #
- # righthand = []
- # relstartwindow = []
- # relpicks = [(i.time - self.wf_stream[0].stats["starttime"])*1000 for i in self.picks]
- # for i in range(len(self.ts_approx)):
- # righthand.append(
- # self.ts_approx[i] - relpicks[i]) # time between picked p arrival and s wave arrival in ms
- # for i in range(len(self.ts_approx)):
- # relstartwindow.append(self.ts_approx[i] - 2 * righthand[i]) # begin of the segment (time)
- # if relstartwindow[i] < 0:
- # relstartwindow[i] = 0
- #
- # sample_start = [int(round(i / 1000 * self.wf_stream.traces[0].stats.sampling_rate)) for i in
- # relstartwindow] # beginning of segment (samples)
- # sample_end = [int(round(i / 1000 * self.wf_stream.traces[0].stats.sampling_rate)) for i in
- # self.ts_approx] # end of segment (samples)
- #
- # maxamp = []
- # for k in range(len(ind_trig[0])):
- # maxamp.append(max(abs(self.wf_stream[ind_trig[0][k]].data[
- # sample_start[k]:sample_end[k]]))) # find maximum amplitude in segment
- #
- # corr_fac = []
- # mag_exp= []
- # for i in range(len(self.dist[origin_number])):
- # corr_fac.append(np.exp(np.pi * (self.dist[origin_number][i] - emparam['r0']) * emparam['f0'] / (emparam['q'] * self.prparam['vp'])))
- # mag_exp.append(maxamp[i] * self.dist[origin_number][i] / emparam['r0'] * corr_fac[i])
- # # calculate relative magnitude for each receiver/event recording
- # average_magnitude = np.log10(np.mean(mag_exp))
- #
- # logging.info('Event ' + str(self.event_id) + ': Relative magnitude %4.2f.' % average_magnitude)
- # self.magnitudes.append(Magnitude(mag=average_magnitude,
- # resource_id='%s/magnitude/%d' % (
- # self.resource_id.id, len(self.magnitudes)+1),
- # origin_id=self.origins[origin_number].resource_id,
- # type='relative_amplitude'))
- # self.preferredMagnitudeID = self.magnitudes[-1].resource_id
+ if(average_magnitude < np.inf):
+ self.magnitudes.append(Magnitude(mag=average_magnitude,
+ resource_id='%s/magnitude/%d' % (
+ self.resource_id.id, len(self.magnitudes)+1),
+ origin_id=self.origins[origin_number].resource_id,
+ type='relative_amplitude'))
+ self.preferredMagnitudeID = self.magnitudes[-1].resource_id
def event_plot(self, save_fig=False):
fparam = self.prparam['Folders']
@@ -302,8 +234,6 @@ class Event(ObsPyEvent):
else:
t_title = 'Event ' + str(self.event_id) + ' at ' + t_start_plotn
-
-
sampRate = self.wf_stream.traces[0].stats.sampling_rate
time_v = np.arange(0, self.wf_stream[0].data.shape[0], 1) * 1 / sampRate * 1000
a_max = []
@@ -335,26 +265,22 @@ class Event(ObsPyEvent):
# finding the correct axes(stations)
ind_trig = np.where(np.isin(ch_in, trig_ch)) # Finds correct axis
trig_time = [(i.time - self.wf_stream.traces[0].stats["starttime"]) * 1000 for i in self.picks]
- loc_ind_trig = ind_trig[0]#why was this? if this line gives error switch between ind_trig[0] and self.loc_ind[-1][:]
-
+ loc_ind_trig = self.loc_ind[-1][:] #why was this? if this line gives error switch between ind_trig[0] and self.loc_ind[-1][:]
# plotting pick time
for m in range(len(trig_time)):
axs[ind_trig[0][m]].plot((trig_time[m], trig_time[m]),
- (axs[ind_trig[0][m]].get_ylim()[0],
- axs[ind_trig[0][m]].get_ylim()[1]), 'r-',
- linewidth=1.0)
+ (axs[ind_trig[0][m]].get_ylim()[0], axs[ind_trig[0][m]].get_ylim()[1]),
+ 'r-', linewidth=1.0)
for m in range(len(loc_ind_trig)):
if len(self.origins):
axs[loc_ind_trig[m]].plot((self.tcalc[len(self.origins)-1][m], self.tcalc[len(self.origins)-1][m]),
- (axs[loc_ind_trig[m]].get_ylim()[0],
- axs[loc_ind_trig[m]].get_ylim()[1]), 'g-',
- linewidth=1.0)
+ (axs[loc_ind_trig[m]].get_ylim()[0], axs[loc_ind_trig[m]].get_ylim()[1]),
+ 'g-', linewidth=1.0)
if len(self.magnitudes):
axs[loc_ind_trig[m]].plot((self.ts_approx[m], self.ts_approx[m]),
- (axs[loc_ind_trig[m]].get_ylim()[0],
- axs[loc_ind_trig[m]].get_ylim()[1]), 'y-',
- linewidth=1.0)
+ (axs[loc_ind_trig[m]].get_ylim()[0], axs[loc_ind_trig[m]].get_ylim()[1]),
+ 'y-', linewidth=1.0)
plt.suptitle(t_title, fontsize=17)
fig.text(0.49, 0.035, 'time [ms]', ha='center', fontsize=14)
@@ -373,324 +299,8 @@ class Event(ObsPyEvent):
if save_fig:
fig.savefig(fparam['noise_vis_folder'] + "/noise vis-" + t_start_plotn + ".png", dpi=100)
- logging.info('Noise Visualisation ' + ': Plotted, Figure saved.')
+ logging.info('Noise Visualisation: Plotted, Figure saved.')
else:
- logging.info('Noise Visualisation ' + ': Plotted, Figure not saved.')
+ logging.info('Noise Visualisation: Plotted, Figure not saved.')
plt.close(fig)
-# """
-# Event class of DUG-Seis
-#
-#
-# # Copyright (c) 2018 by SCCER-SoE and SED at ETHZ
-#
-# Version 0.0, 23.10.2018, Joseph Doetsch (doetschj)
-# 23.10.2018, Semih Demir
-# 23.10.2018, Linus Villiger
-#
-#
-# """
-#
-# from matplotlib import pyplot as plt
-# from obspy.core.event.event import Event as ObsPyEvent
-# from obspy.signal.trigger import recursive_sta_lta, trigger_onset
-# from obspy.core.event import WaveformStreamID, Pick, Origin, Arrival, Magnitude
-# import numpy as np
-# import pyproj
-# import logging
-# import pandas as pd
-#
-#
-# class Event(ObsPyEvent):
-# def __init__(self, param, wf_stream, event_id, classification):
-# super().__init__(resource_id='smi:%s/event/%d' % (param['General']['project_name'], event_id))
-#
-# self.param = param
-# self.prparam = param['Processing']
-# self.wf_stream = wf_stream
-# self.event_id = event_id
-# self.classification = classification
-# self.t0 = []
-# self.dist = []
-# self.tcalc = []
-# self.loc_ind = []
-# if self.classification != 'noise':
-# logging.info('Event ' + str(self.event_id) + ': Processing started.')
-# else:
-# logging.info('Noise Visualisation ' + ': Processing started.')
-#
-# def bandpass(self):
-# # bandpass filter using same parameters as for triggering
-# self.wf_stream.filter("bandpass", freqmin=self.prparam['bandpass_f_min'], freqmax=self.prparam['bandpass_f_max'])
-#
-# def pick(self):
-# piparam = self.prparam['Picking']
-# df = self.wf_stream[0].stats.sampling_rate
-#
-# for j in range(len(self.wf_stream)): # do picking for each trace in 20ms snippet
-# cft = recursive_sta_lta(self.wf_stream[j], piparam['sta_lta']['st_window'],
-# piparam['sta_lta']['lt_window']) # create characteristic function
-# trig = trigger_onset(cft, piparam['sta_lta']['threshold_on'],
-# piparam['sta_lta']['threshold_off']) # do picking
-# if len(trig):
-# t_picks=trig[0][0] / df # if a pick is done on a trace, convert it to seconds
-# t_pick_UTC = self.wf_stream[0].stats[
-# "starttime"] + t_picks # and add the start time of the 20ms snippet
-# station_id = j + 1
-# self.picks.append(Pick(time=t_pick_UTC,
-# resource_id='%s/picks/%d' % (self.resource_id.id, len(self.picks)+1),
-# waveform_id=WaveformStreamID(network_code='GR', station_code='%03i' % station_id,
-# channel_code='001', location_code='00'),
-# method_id='recursive_sta_lta'))
-# logging.info('Event ' + str(self.event_id) + ': ' + str(len(self.picks)) + ' picks.')
-#
-#
-# def locate(self):
-# lparam = self.prparam['Locate']
-# if lparam['algorithm'] == 'hom_aniso':
-# aparam = lparam['hom_aniso']
-# if len(self.picks) < lparam['min_picks']:
-# return
-#
-# trig_ch = []
-# tobs = []
-# for i in self.picks:
-# if self.param['General']['sensor_coords'][int(i.waveform_id['station_code'])-1, 0]:
-# trig_ch.append(int(i.waveform_id['station_code']) - 1)
-# tobs.append((i.time - self.wf_stream.traces[0].stats["starttime"]) * 1000)
-# npicks = len(tobs)
-# sensor_coords = self.param['General']['sensor_coords'][trig_ch, :]
-# # for i in range(len(sensor_coords) - 1, -1, -1):
-# # if not sensor_coords[i, 0]:
-# # trig_ch.remove(trig_ch[i])
-# # tobs.remove([i])
-# # sensor_coords = self.param['General']['sensor_coords'][trig_ch, :]
-# vp = self.prparam['vp'] * np.ones([npicks]) / 1000.
-#
-# if lparam['algorithm'] == 'hom_iso' or lparam['algorithm'] == 'hom_aniso':
-# loc = sensor_coords[tobs.index(min(tobs)), :] + 0.1
-# t0 = min(tobs)
-# nit = 0
-# jacobian = np.zeros([npicks, 4])
-# dm = 1. * np.ones(4)
-#
-# while nit < 100 and np.linalg.norm(dm) > 0.00001:
-# nit = nit + 1
-#
-# if lparam['algorithm'] == 'hom_aniso': # calculate anisotropic velocities
-# for i in range(npicks):
-# azi = np.arctan2(sensor_coords[i, 0] - loc[0], sensor_coords[i, 1] - loc[1])
-# inc = np.arctan2(sensor_coords[i, 2] - loc[2], np.linalg.norm(sensor_coords[i,range(2)]-loc[range(2)]))
-# theta = np.arccos(np.cos(inc) * np.cos(azi) * np.cos(aparam['inc']) * np.cos(aparam['azi']) +
-# np.cos(inc) * np.sin(azi) * np.cos(aparam['inc']) * np.sin(aparam['azi']) +
-# np.sin(inc) * np.sin(aparam['inc']))
-# vp[i] = self.prparam['vp'] / 1000. * (1.0 + aparam['delta'] * np.sin(theta)**2 * np.cos(theta)**2 +
-# aparam['epsilon'] * np.sin(theta)**4)
-#
-# dist = [np.linalg.norm(loc - sensor_coords[i, :]) for i in range(npicks)]
-# tcalc = [dist[i] / vp[i] + t0 for i in range(npicks)]
-#
-# res = [tobs[i] - tcalc[i] for i in range(npicks)]
-# rms = np.linalg.norm(res) / npicks
-# for j in range(3):
-# for i in range(npicks):
-# jacobian[i, j] = -(sensor_coords[i, j] - loc[j]) / (vp[i] * dist[i])
-# jacobian[:, 3] = np.ones(npicks)
-#
-# dm = np.matmul(np.matmul(np.linalg.inv(np.matmul(np.transpose(jacobian), jacobian) +
-# pow(lparam['damping'], 2) * np.eye(4)), np.transpose(jacobian)),
-# res)
-# loc = loc + dm[0:3]
-# t0 = t0 + dm[3]
-# else:
-# logging.warning('Location algorithm not implemented. Location not estimated.')
-#
-# x = loc[0] + self.param['General']['origin_ch1903_east']
-# y = loc[1] + self.param['General']['origin_ch1903_north']
-# z = loc[2] + self.param['General']['origin_elev']
-# t0 = self.wf_stream.traces[0].stats["starttime"] + t0 / 1000.
-#
-# # write CSV file with CH1903 coordinates
-# cols = pd.Index(['Event_id', 'datenum', 'x', 'y', 'z'], name='cols')
-# outdata = [self.event_id] + [t0.matplotlib_date] + [x] + [y] + [z]
-# df = pd.DataFrame(data=[outdata], columns=cols)
-# df.to_csv('events.csv', mode='a', header=False, index=False)
-#
-# # convert coordinates to WGS84
-# proj_ch1903 = pyproj.Proj(init="epsg:21781")
-# proj_wgs84 = pyproj.Proj(init="epsg:4326")
-# lon, lat = pyproj.transform(proj_ch1903, proj_wgs84, x, y)
-# # create origin object and append to event
-# o = Origin(time=t0,
-# longitude=lon, latitude=lat, depth=z,
-# resource_id='%s/origin/%d' % (self.resource_id.id, len(self.origins)+1),
-# depthType='elevation',
-# methodID=lparam['algorithm'])
-# for i in self.picks:
-# if self.param['General']['sensor_coords'][int(i.waveform_id['station_code'])-1, 0]:
-# o.arrivals.append(Arrival(pick_id=i.resource_id,
-# time_residual=res[len(o.arrivals)] / 1000,
-# phase='p',
-# resource_id='%s/origin/%d/arrival/%d' % (
-# self.resource_id.id, len(self.origins)+1, len(o.arrivals))))
-# o.time_errors = rms / 1000
-# self.origins.append(o)
-# self.preferredOriginID = o.resource_id
-# self.extra = {'x': {'value': loc[0],
-# 'namespace': 'http://sccer-soe.ch/local_coordinates/1.0'},
-# 'y': {'value': loc[1],
-# 'namespace': 'http://sccer-soe.ch/local_coordinates/1.0'},
-# 'z': {'value': loc[2],
-# 'namespace': 'http://sccer-soe.ch/local_coordinates/1.0'}}
-# self.t0.append(t0)
-# self.dist.append(dist)
-# self.tcalc.append(tcalc)
-# self.loc_ind.append(trig_ch)
-# logging.info('Event ' + str(self.event_id) + ': Location %3.2f %3.2f %3.2f; %i iterations, rms %4.3f ms'
-# % (loc[0], loc[1], loc[2], nit, rms))
-#
-#
-# def est_magnitude(self, origin_number=np.inf):
-# emparam = self.prparam['Magnitude']
-# if origin_number > len(self.origins):
-# origin_number = len(self.origins)
-# elif origin_number < 1:
-# logging.warning('Origin number does not exist.')
-# origin_number = origin_number - 1
-#
-# trig_ch = [int(i.waveform_id['station_code']) for i in self.picks]
-# ch_in = [int(i.stats['station']) for i in self.wf_stream.traces]
-# ind_trig = np.where(np.isin(ch_in, trig_ch)) # Finds correct axis
-#
-# if len(self.origins):
-# self.ts_approx = []
-# t0_orig = self.t0[origin_number] - self.wf_stream.traces[0].stats["starttime"] # t0 for use in magnitude estimation
-# for i in range(len(self.dist[origin_number])):
-# self.ts_approx.append((self.dist[origin_number][i] / (self.prparam['vs']) + t0_orig) * 1000) # time of s wave arrival/end of the segment (time)
-# if self.ts_approx[i] > (self.param['Trigger']['endtime']+self.param['Trigger']['endtime'])*1000:
-# self.ts_approx[i] = (self.param['Trigger']['endtime']+self.param['Trigger']['endtime'])*1000
-#
-# righthand = []
-# relstartwindow = []
-# relpicks = [(i.time - self.wf_stream[0].stats["starttime"])*1000 for i in self.picks]
-# for i in range(len(self.ts_approx)):
-# righthand.append(
-# self.ts_approx[i] - relpicks[i]) # time between picked p arrival and s wave arrival in ms
-# for i in range(len(self.ts_approx)):
-# relstartwindow.append(self.ts_approx[i] - 2 * righthand[i]) # begin of the segment (time)
-# if relstartwindow[i] < 0:
-# relstartwindow[i] = 0
-#
-# sample_start = [int(round(i / 1000 * self.wf_stream.traces[0].stats.sampling_rate)) for i in
-# relstartwindow] # beginning of segment (samples)
-#
-# sample_end = [int(round(i / 1000 * self.wf_stream.traces[0].stats.sampling_rate)) for i in
-# self.ts_approx] # end of segment (samples)
-#
-# maxamp = []
-# for k in range(len(ind_trig[0])):
-# maxamp.append(max(abs(self.wf_stream[ind_trig[0][k]].data[
-# sample_start[k]:sample_end[k]]))) # find maximum amplitude in segment
-#
-# corr_fac = []
-# mag_exp= []
-# for i in range(len(self.dist[origin_number])):
-# corr_fac.append(np.exp(np.pi * (self.dist[origin_number][i] - emparam['r0']) * emparam['f0'] / (emparam['q'] * self.prparam['vp'])))
-# mag_exp.append(maxamp[i] * self.dist[origin_number][i] / emparam['r0'] * corr_fac[i])
-# # calculate relative magnitude for each receiver/event recording
-# average_magnitude = np.log10(np.mean(mag_exp))
-#
-# logging.info('Event ' + str(self.event_id) + ': Relative magnitude %4.2f.' % average_magnitude)
-# self.magnitudes.append(Magnitude(mag=average_magnitude,
-# resource_id='%s/magnitude/%d' % (
-# self.resource_id.id, len(self.magnitudes)+1),
-# origin_id=self.origins[origin_number].resource_id,
-# type='relative_amplitude'))
-# self.preferredMagnitudeID = self.magnitudes[-1].resource_id
-#
-# def event_plot(self, save_fig=False):
-# fparam = self.prparam['Folders']
-# # for name of each saved png, use start and end time of the 20ms snippet
-# t_start_plot = self.wf_stream.traces[0].stats["starttime"]
-# t_start_plotn = str(t_start_plot)[11:-1]
-# if self.classification=='noise':
-# t_title = 'Noise Visualisation at ' + t_start_plotn
-# else:
-# t_title = 'Event ' + str(self.event_id) + ' at ' + t_start_plotn
-#
-#
-#
-# sampRate = self.wf_stream.traces[0].stats.sampling_rate
-# time_v = np.arange(0, self.wf_stream[0].data.shape[0], 1) * 1 / sampRate * 1000
-# a_max = []
-# for k in range(len(self.wf_stream)):
-# a_max.append(format(np.amax(np.absolute(self.wf_stream[k].data)), '.1f'))
-# fig, axs = plt.subplots(len(self.wf_stream), 1, figsize=(20, 10), facecolor='w', edgecolor='k')
-#
-# plt.interactive(False)
-# fig.subplots_adjust(hspace=0, wspace=0)
-#
-# axs = axs.ravel()
-#
-# for i in range(len(self.wf_stream)):
-# axs[i].plot((time_v), self.wf_stream[i].data)
-# axs[i].set_yticklabels([])
-# axs[i].set_ylabel(self.wf_stream.traces[i].stats['station'] + ' ', rotation=0)
-# axs[i].set_xlim([0, (len(time_v) / self.wf_stream[0].stats.sampling_rate) * 1000])
-#
-# ax2 = axs[i].twinx()
-# ax2.set_yticklabels([])
-# ax2.set_ylabel(' ' + str(a_max[i]), rotation=0, color='g')
-#
-# if i < len(self.wf_stream) - 1:
-# axs[i].set_xticklabels([])
-#
-# if self.classification != 'noise':
-# trig_ch = [int(i.waveform_id['station_code']) for i in self.picks]
-# ch_in = [int(i.stats['station']) for i in self.wf_stream.traces]
-# # finding the correct axes(stations)
-# ind_trig = np.where(np.isin(ch_in, trig_ch)) # Finds correct axis
-# trig_time = [(i.time - self.wf_stream.traces[0].stats["starttime"]) * 1000 for i in self.picks]
-# loc_ind_trig = self.loc_ind[-1][:]
-#
-# # plotting pick time
-# for m in range(len(trig_time)):
-# axs[ind_trig[0][m]].plot((trig_time[m], trig_time[m]),
-# (axs[ind_trig[0][m]].get_ylim()[0],
-# axs[ind_trig[0][m]].get_ylim()[1]), 'r-',
-# linewidth=1.0)
-# for m in range(len(loc_ind_trig)):
-# if len(self.origins):
-# axs[loc_ind_trig[m]].plot((self.tcalc[len(self.origins)-1][m], self.tcalc[len(self.origins)-1][m]),
-# (axs[loc_ind_trig[m]].get_ylim()[0],
-# axs[loc_ind_trig[m]].get_ylim()[1]), 'g-',
-# linewidth=1.0)
-# if len(self.magnitudes):
-# axs[loc_ind_trig[m]].plot((self.ts_approx[m], self.ts_approx[m]),
-# (axs[loc_ind_trig[m]].get_ylim()[0],
-# axs[loc_ind_trig[m]].get_ylim()[1]), 'y-',
-# linewidth=1.0)
-#
-# plt.suptitle(t_title, fontsize=17)
-# fig.text(0.49, 0.035, 'time [ms]', ha='center', fontsize=14)
-# fig.text(0.040, 0.5, 'channel []', va='center', rotation='vertical', fontsize=14)
-# fig.text(.988, 0.5, 'peak amplitude [mV]', va='center', rotation=90, fontsize=14, color='g')
-# # plt.show()
-#
-# if self.classification != 'noise':
-# if save_fig:
-# fig.savefig(fparam['plot_folder_'+self.classification] + "/event-" + str(self.event_id) + '_' + t_start_plotn + ".png",
-# dpi=100)
-# logging.info('Event ' + str(self.event_id) + ': Plotted, Figure saved.')
-# else:
-# logging.info('Event ' + str(self.event_id) + ': Plotted, Figure not saved.')
-# else:
-# if save_fig:
-#
-# fig.savefig(fparam['noise_vis_folder'] + "/noise vis-" + t_start_plotn + ".png", dpi=100)
-# logging.info('Noise Visualisation ' + ': Plotted, Figure saved.')
-#
-# else:
-# logging.info('Noise Visualisation ' + ': Plotted, Figure not saved.')
-# plt.close(fig)
-#
\ No newline at end of file
diff --git a/dug_seis/processing/event_processing.py b/dug_seis/processing/event_processing.py
index ab13d8d..6a72e10 100644
--- a/dug_seis/processing/event_processing.py
+++ b/dug_seis/processing/event_processing.py
@@ -31,64 +31,18 @@ def event_processing(param, load_file, trig_time, event_id, classification):
# event.bandpass()
event.pick()
event.locate()
- # event.est_magnitude()
+ event.est_magnitude()
event.event_plot(save_fig=True)
- # event.prparam['Locate']['algorithm'] = 'hom_iso'
- # event.locate()
- # #event.est_magnitude()
- # #event.est_magnitude(origin_number=1)
- # event.classification = 'active'
- # event.event_plot(save_fig=True)
- # classification = 'passive'
+ #event.prparam['Locate']['algorithm'] = 'hom_aniso'
+ #event.locate()
+ #event.est_magnitude()
+ #event.est_magnitude(origin_number=1)
+ #event.classification = 'active'
+ #event.event_plot(save_fig=True)
+ #classification = 'passive'
if classification == 'passive':
event.write('%s/event%s.xml' % (param['Processing']['Folders']['quakeml_folder'], event_id), 'quakeml', validate=True)
elif classification == 'active':
event.write('%s/hammer%s.xml' % (param['Processing']['Folders']['quakeml_folder'], event_id), 'quakeml',
validate=True)
-#
-# """
-# Processing module of DUG-Seis
-#
-#
-# # Copyright (c) 2018 by SCCER-SoE and SED at ETHZ
-#
-# Version 0.0, 23.10.2018, Joseph Doetsch (doetschj)
-# 23.10.2018, Semih Demir
-# 23.10.2018, Linus Villiger
-#
-#
-# """
-#
-#
-# import logging
-# from dug_seis.processing.event import Event
-# from dug_seis.processing.get_waveforms import get_waveforms
-#
-#
-# def event_processing(param, load_file, trig_time, event_id, classification):
-# logging.debug('Starting processing for event %d' % event_id)
-# wf_stream = get_waveforms(param, load_file, trig_time)
-#
-# event = Event(param, wf_stream, event_id, classification)
-# if classification == 'noise':
-# event.event_plot(save_fig=True)
-# elif classification == 'electronic':
-# event.bandpass()
-# event.event_plot(save_fig=True)
-# else:
-# #event.bandpass()
-# event.pick()
-# #event.locate()
-# #event.est_magnitude()
-# #event.prparam['Locate']['algorithm'] = 'hom_iso'
-# #event.locate()
-# #event.est_magnitude()
-#
-# event.event_plot(save_fig=True)
-# if classification == 'passive':
-# event.write('%s/event%s.xml' % (param['Processing']['Folders']['quakeml_folder'], event_id), 'quakeml', validate=True)
-# elif classification == 'active':
-# event.write('%s/hammer%s.xml' % (param['Processing']['Folders']['quakeml_folder'], event_id), 'quakeml',
-# validate=True)
-#
\ No newline at end of file
diff --git a/dug_seis/processing/get_waveforms.py b/dug_seis/processing/get_waveforms.py
index a56726d..33a7f99 100644
--- a/dug_seis/processing/get_waveforms.py
+++ b/dug_seis/processing/get_waveforms.py
@@ -12,15 +12,15 @@ Version 0.0, 23.10.2018, Joseph Doetsch (doetschj)
from obspy import Stream
import pyasdf
+
def get_waveforms(param, load_file, trig_time):
- aparam=param['Acquisition']
- gparam=param['General']
+ aparam = param['Acquisition']
+ gparam = param['General']
asdf_folder = param['Trigger']['asdf_folder']
- print(gparam['stats']['network'] + '_001')
+ #print(gparam['stats']['network'] + '_001')
start_time = trig_time - param['Trigger']['starttime']
end_time = trig_time + param['Trigger']['endtime']
-
ch_in = range(1, gparam['sensor_count' ] +1)
r = range(len(ch_in))
if len(load_file) == 1: # load 2 snippets if event in overlap otherwise load 1
@@ -33,8 +33,6 @@ def get_waveforms(param, load_file, trig_time):
endtime=end_time,
tag="raw_recording")
-
-
else:
ds1 = pyasdf.ASDFDataSet(asdf_folder + '/' + load_file[0], mode='r')
ds2 = pyasdf.ASDFDataSet(asdf_folder + '/' + load_file[1], mode='r') #GR_001
@@ -66,75 +64,5 @@ def get_waveforms(param, load_file, trig_time):
tag="raw_recording")
return wf_stream
-# """
-# Load waveform data for processing
-#
-#
-# # Copyright (c) 2018 by SCCER-SoE and SED at ETHZ
-#
-# Version 0.0, 23.10.2018, Joseph Doetsch (doetschj)
-# 23.10.2018, Semih Demir
-#
-# """
-#
-# from obspy import Stream
-# import pyasdf
-#
-# def get_waveforms(param, load_file, trig_time):
-# aparam=param['Acquisition']
-# gparam=param['General']
-# asdf_folder = param['Trigger']['asdf_folder']
-#
-# start_time = trig_time - param['Trigger']['starttime']
-# end_time = trig_time + param['Trigger']['endtime']
-#
-#
-# ch_in = range(1, gparam['sensor_count' ] +1)
-# r = range(len(ch_in))
-# if len(load_file) == 1: # load 2 snippets if event in overlap otherwise load 1
-# wf_stream = Stream()
-# ds = pyasdf.ASDFDataSet(asdf_folder + '/' + load_file[0], mode='r')
-# for k in r:
-# wf_stream += ds.get_waveforms(network=aparam['stats']['network'], station=str(int(ch_in[k])).zfill(3), location=aparam['stats']['location'],
-# channel=aparam['stats']['channel'],
-# starttime=start_time,
-# endtime=end_time,
-# tag="raw_recording")
-#
-#
-#
-# else:
-# ds1 = pyasdf.ASDFDataSet(asdf_folder + '/' + load_file[0], mode='r')
-# ds2 = pyasdf.ASDFDataSet(asdf_folder + '/' + load_file[1], mode='r')
-# if end_time > ds2.waveforms.GR_001.raw_recording.traces[0].stats["starttime"]:
-# sta_p1 = Stream()
-# sta_p2 = Stream()
-# for k in r:
-# sta_p1 += ds1.get_waveforms(network=aparam['stats']['network'], station=str(int(ch_in[k])).zfill(3),
-# location=aparam['stats']['location'],
-# channel=aparam['stats']['channel'],
-# starttime=start_time,
-# endtime=ds1.waveforms.aparam['stats']['network'] + '_' +
-# aparam['stats']['channel'].raw_recording.traces[0].stats["endtime"],
-# tag="raw_recording")
-# sta_p2 += ds2.get_waveforms(network=aparam['stats']['network'], station=str(int(ch_in[k])).zfill(3),
-# location=aparam['stats']['location'],
-# channel=aparam['stats']['channel'],
-# starttime=ds2.waveforms.print(
-# aparam['stats']['network'] + '_' + aparam['stats'][
-# 'channel']).raw_recording.traces[0].stats["starttime"],
-# endtime=end_time,
-# tag="raw_recording")
-# wf_stream = sta_p1 + sta_p2
-# wf_stream.merge(method=1, interpolation_samples=0)
-#
-# else:
-# wf_stream = Stream()
-# for k in r:
-# wf_stream += ds1.get_waveforms(network=aparam['stats']['network'], station=str(int(ch_in[k])).zfill(3), location=aparam['stats']['location'],
-# channel=aparam['stats']['channel'],
-# starttime=start_time,
-# endtime=end_time,
-# tag="raw_recording")
-# return wf_stream
+
diff --git a/dug_seis/processing/processing.py b/dug_seis/processing/processing.py
index 7f5cff5..a03f8bb 100644
--- a/dug_seis/processing/processing.py
+++ b/dug_seis/processing/processing.py
@@ -109,133 +109,8 @@ def processing(param):
processed_files.append(current_file)
else:
-
flist = sorted([f for f in os.listdir(asdf_folder) if f.endswith('.h5')])
- new_files = [f for f in flist if not f in processed_files]
- logging.info('adding new files')
+ new_files = [f for f in flist if f not in processed_files]
if not len(new_files):
+ logging.info('Waiting for new files.')
time.sleep(1)
- logging.info('sleeping')
-
-
-"""
-Trigger and processing module of DUG-Seis
-
-
-# Copyright (c) 2018 by SCCER-SoE and SED at ETHZ
-
-Version 0.0, 23.10.2018, Joseph Doetsch (doetschj)
- 23.10.2018, Semih Demir
- 23.10.2018, Linus Villiger
-
-
-"""
-#
-# import logging
-# from obspy import Stream
-# import os
-# import time
-# import pyasdf
-# from dug_seis.processing.dug_trigger import dug_trigger
-# from dug_seis.processing.event_processing import event_processing
-#
-#
-# def processing(param):
-# tparam = param['Trigger']
-# logging.info('Starting trigger module')
-#
-# # create folders for processing
-# if not os.path.exists(param['Processing']['Folders']['quakeml_folder']):
-# os.makedirs(param['Processing']['Folders']['quakeml_folder'])
-# if not os.path.exists(param['Processing']['Folders']['plot_folder_active']):
-# os.makedirs(param['Processing']['Folders']['plot_folder_active'])
-# if not os.path.exists(param['Processing']['Folders']['plot_folder_passive']):
-# os.makedirs(param['Processing']['Folders']['plot_folder_passive'])
-# if not os.path.exists(param['Processing']['Folders']['plot_folder_electronic']):
-# os.makedirs(param['Processing']['Folders']['plot_folder_electronic'])
-# if tparam['noise_vis'] and not os.path.exists(param['Processing']['Folders']['noise_vis_folder']):
-# os.makedirs(param['Processing']['Folders']['noise_vis_folder'])
-#
-# # Initialisation
-# asdf_folder = tparam['asdf_folder'] # 'raw' # Location of .h5 file
-# stations = [i - 1 for i in tparam['channels']]
-# if param['General']['active_trigger_channel'] and param['General']['active_trigger_channel']-1 not in stations:
-# stations.insert(0,param['General']['active_trigger_channel']-1)
-# stations.sort()
-# event_nr = 0
-# event_nr_s = []
-# next_noise_vis = 0
-# # load list of ASDF snippets in folder
-# sta_overlap = Stream()
-# new_files = sorted([f for f in os.listdir(asdf_folder) if f.endswith('.h5')]) # generates a list of the .asdf files in asdf_folder
-#
-# processed_files = []
-# while 1:
-# if len(new_files):
-# # print(new_files)
-# current_file = new_files.pop(0)
-# logging.info('Working on file ' + current_file)
-# sta = Stream()
-#
-# ds = pyasdf.ASDFDataSet(asdf_folder + '/' + current_file, mode='r')
-#
-# wf_list = ds.waveforms.list()
-# for k in stations:
-# sta += ds.waveforms[wf_list[k]].raw_recording
-# sta_copy = sta.copy()
-# sta_total = sta_copy + sta_overlap
-#
-# # Use overlap statement for merging
-# for tr in sta_total:
-# tr.stats.delta = sta_total[0].stats.delta
-# sta_total.merge(method=1, interpolation_samples=0)
-#
-# logging.debug(sta_total)
-# trigger_out, event_nr = dug_trigger(sta_total, tparam, event_nr, event_nr_s) # run trigger function
-#
-# overlap = tparam['starttime'] + tparam['endtime'] #+ tparam['endtime']['sta_lta']['lt_window']/sta_total.stats.sampling_rate
-#
-# t_end = sta.traces[0].stats["endtime"]
-# sta_overlap = sta.trim(t_end - overlap, t_end)
-#
-# #trigger_out = trigger_out[trigger_out['Classification'] == 'passive']
-# logging.debug(trigger_out)
-#
-# event_id = [i for i in trigger_out['Event_id']]
-# classification = [i for i in trigger_out['Classification']]
-#
-#
-# trig_time=[i for i in trigger_out['Time'][trigger_out['Time'] <t_end-overlap]] #do not store times in overlap
-# for l in trig_time: #load 2 snippets if event in overlap
-# if l< sta_copy.traces[0].stats["starttime"]+param['Trigger']['starttime']:
-# load_file = [processed_files[-1], current_file]
-# else:
-# load_file = [current_file]
-#
-# for l in range(0, len(trig_time)):
-# event_processing(param, load_file, trig_time[l], event_id[l], classification[l]) # run processing for each event
-#
-# # noise visualization
-# if tparam['noise_vis']=='True':
-# if next_noise_vis == 0:
-# next_noise_vis = sta_copy.traces[0].stats["starttime"]+param['Trigger']['starttime']#start time of current file+starttime
-# while next_noise_vis < sta_copy.traces[0].stats["endtime"]:#end of current file:
-# if next_noise_vis< sta_copy.traces[0].stats["starttime"] + param['Trigger']['starttime']:
-# load_file = [processed_files[-1], current_file]
-# else:
-# load_file = [current_file]
-# event_processing(param, load_file, next_noise_vis, 0, 'noise') # start processing to visualize noise
-# next_noise_vis += tparam['noise_vis_interval']
-#
-# processed_files.append(current_file)
-#
-# else:
-#
-# flist = sorted([f for f in os.listdir(asdf_folder) if f.endswith('.h5')])
-# new_files = [f for f in flist if not f in processed_files]
-# logging.info('adding new files')
-# if not len(new_files):
-# time.sleep(1)
-# logging.info('sleeping')
-#
-#
\ No newline at end of file
--
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