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[add] updates to jackknife from GUI (WIP, experimental)

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Marcel Paffrath 2017-08-21 17:19:08 +02:00
parent ed994e987f
commit a5097e1da6
4 changed files with 72 additions and 37 deletions
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38
QtPyLoT.py
View File

@ -79,7 +79,7 @@ from pylot.core.util.event import Event
from pylot.core.io.location import create_creation_info, create_event
from pylot.core.util.widgets import FilterOptionsDialog, NewEventDlg, \
WaveformWidget, WaveformWidgetPG, PropertiesDlg, HelpForm, createAction, PickDlg, \
getDataType, ComparisonWidget, TuneAutopicker, PylotParaBox, AutoPickDlg
getDataType, ComparisonWidget, TuneAutopicker, PylotParaBox, AutoPickDlg, JackknifeWidget
from pylot.core.util.map_projection import map_projection
from pylot.core.util.structure import DATASTRUCTURE
from pylot.core.util.thread import Thread, Worker
@ -1815,16 +1815,8 @@ class MainWindow(QMainWindow):
self.listWidget.addItem(text)
self.listWidget.scrollToBottom()
def tune_autopicker(self):
'''
Initiates TuneAutopicker widget use to interactively
tune parameters for autopicking algorithm.
'''
# figures and canvas have to be iniated from the main GUI
# thread to prevent handling of QPixmap objects outside of
# the main thread
def init_fig_dict(self):
self.fig_dict = {}
self.canvas_dict = {}
self.fig_keys = [
'mainFig',
'aicFig',
@ -1837,11 +1829,27 @@ class MainWindow(QMainWindow):
'el_S2pick',
'refSpick',
'aicARHfig',
'jackknife',
'wadati'
]
for key in self.fig_keys:
fig = Figure()
self.fig_dict[key] = fig
def init_canvas_dict(self):
self.canvas_dict = {}
for key in self.fig_keys:
self.canvas_dict[key] = FigureCanvas(self.fig_dict[key])
def tune_autopicker(self):
'''
Initiates TuneAutopicker widget use to interactively
tune parameters for autopicking algorithm.
'''
# figures and canvas have to be iniated from the main GUI
# thread to prevent handling of QPixmap objects outside of
# the main thread
self.init_fig_dict()
#if not self.tap:
# init TuneAutopicker object
self.tap = TuneAutopicker(self)
@ -1860,8 +1868,7 @@ class MainWindow(QMainWindow):
'''
Create and fill TuneAutopicker tabs with figure canvas.
'''
for key in self.fig_keys:
self.canvas_dict[key] = FigureCanvas(self.fig_dict[key])
self.init_canvas_dict()
self.tap.fill_tabs(picked=True)
def autoPick(self):
@ -1883,12 +1890,14 @@ class MainWindow(QMainWindow):
# self.addListItem('Loading default values from PyLoT-input file %s'
# % self.infile)
self.init_fig_dict()
args = {'parameter': self._inputs,
'station': 'all',
'fnames': 'None',
'eventid': self.get_current_event_path(),
'iplot': 0,
'fig_dict': None,
'fig_dict': self.fig_dict,
'locflag': 0}
self.mp_thread = QtCore.QThreadPool()
@ -1916,6 +1925,9 @@ class MainWindow(QMainWindow):
event.addAutopicks(result)
self.drawPicks(picktype='auto')
self.draw()
self.init_canvas_dict()
jkw = JackknifeWidget(self, self.canvas_dict['jackknife'])
jkw.show()
def addPicks(self, station, picks, type='manual'):
stat_picks = self.getPicksOnStation(station, type)

7
pylot/core/pick/autopick.py
View File

@ -76,13 +76,14 @@ def autopickevent(data, param, iplot=0, fig_dict=None, ncores=0, metadata=None,
pick.pop('station')
all_onsets[station] = pick
return all_onsets
#return all_onsets
# quality control
# median check and jackknife on P-onset times
jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot)
jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, 1, fig_dict)
return jk_checked_onsets
# check S-P times (Wadati)
return wadaticheck(jk_checked_onsets, wdttolerance, iplot)
return wadaticheck(jk_checked_onsets, wdttolerance, iplot, fig_dict)
def call_autopickstation(input_tuple):

53
pylot/core/pick/utils.py
View File

@ -564,7 +564,7 @@ def select_for_phase(st, phase):
return sel_st
def wadaticheck(pickdic, dttolerance, iplot):
def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
'''
Function to calculate Wadati-diagram from given P and S onsets in order
to detect S pick outliers. If a certain S-P time deviates by dttolerance
@ -794,7 +794,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
return returnflag
def checkPonsets(pickdic, dttolerance, iplot):
def checkPonsets(pickdic, dttolerance, iplot=0, fig_dict=None):
'''
Function to check statistics of P-onset times: Control deviation from
median (maximum adjusted deviation = dttolerance) and apply pseudo-
@ -816,17 +816,19 @@ def checkPonsets(pickdic, dttolerance, iplot):
# search for good quality P picks
Ppicks = []
stations = []
for key in pickdic:
if pickdic[key]['P']['weight'] < 4:
for station in pickdic:
if pickdic[station]['P']['weight'] < 4:
# add P onsets to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
UTCPpick = UTCDateTime(pickdic[station]['P']['mpp'])
Ppicks.append(UTCPpick.timestamp)
stations.append(key)
stations.append(station)
# apply jackknife bootstrapping on variance of P onsets
print("###############################################")
print("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
if not xjack:
return
# get pseudo variances smaller than average variances
# (times safety factor), these picks passed jackknife test
ij = np.where(PHI_pseudo <= 5 * xjack)
@ -870,21 +872,30 @@ def checkPonsets(pickdic, dttolerance, iplot):
checkedonsets = pickdic
if iplot > 0:
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'ro', markersize=14)
if len(badstations) < 1 and len(badjkstations) < 1:
p2, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'go', markersize=14)
if fig_dict:
fig = fig_dict['jackknife']
plt_flag = 0
else:
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14)
p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g',
linewidth=2)
for i in range(0, len(Ppicks)):
plt.text(i, Ppicks[i] + 0.01, '{0}'.format(stations[i]))
fig = plt.figure()
plt_flag = 1
ax = fig.add_subplot(111)
plt.xlabel('Number of P Picks')
plt.ylabel('Onset Time [s] from 1.1.1970')
plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'],
loc='best')
plt.title('Jackknifing and Median Tests on P Onsets')
ax.plot(np.arange(0, len(Ppicks)), Ppicks, 'ro', markersize=14)
if len(badstations) < 1 and len(badjkstations) < 1:
ax.plot(np.arange(0, len(Ppicks)), Ppicks, 'go', markersize=14, label='Skipped P Picks')
else:
ax.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14, label='Good P Picks')
ax.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g',
linewidth=2, label='Median')
for i in range(0, len(Ppicks)):
ax.text(i, Ppicks[i] + 0.01, '{0}'.format(stations[i]))
ax.set_xlabel('Number of P Picks')
ax.set_ylabel('Onset Time [s] from 1.1.1970')
ax.legend()
ax.set_title('Jackknifing and Median Tests on P Onsets')
if plt_flag:
fig.show()
return checkedonsets
@ -913,13 +924,13 @@ def jackknife(X, phi, h):
PHI_sub = None
# determine number of subgroups
g = len(X) / h
if type(g) is not int:
if len(X) % h:
print("jackknife: Cannot divide quantity X in equal sized subgroups!")
print("Choose another size for subgroups!")
return PHI_jack, PHI_pseudo, PHI_sub
else:
g = int(len(X) / h)
# estimator of undisturbed spot check
if phi == 'MEA':
phi_sc = np.mean(X)

11
pylot/core/util/widgets.py
View File

@ -2011,6 +2011,17 @@ class PhasePlotWidget(FigureCanvas):
super(PhasePlotWidget, self).__init__(self.fig)
class JackknifeWidget(QWidget):
'''
'''
def __init__(self, parent, canvas):
QtGui.QWidget.__init__(self, parent, 1)
self.main_layout = QtGui.QVBoxLayout()
self.setLayout(self.main_layout)
self.main_layout.addWidget(canvas)
class TuneAutopicker(QWidget):
update = QtCore.Signal(str)
'''