Merge branch 'develop' of ariadne.geophysik.ruhr-uni-bochum.de:/data/git/pylot into develop
This commit is contained in:
commit
579461f7b4
@ -4,7 +4,7 @@
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import warnings
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import numpy as np
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from obspy import UTCDateTime
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from pylot.core.util.utils import find_nearest
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from pylot.core.util.utils import find_nearest, clims
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from pylot.core.util.version import get_git_version as _getVersionString
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__version__ = _getVersionString()
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@ -329,28 +329,14 @@ class ProbabilityDensityFunction(object):
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:param r2:
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:param max_npts:
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:return:
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'''
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# >>> manu = ProbabilityDensityFunction.from_pick(0.01, 0.3, 0.5, 0.54)
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# >>> auto = ProbabilityDensityFunction.from_pick(0.01, 0.3, 0.34, 0.54)
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# >>> manu.commonlimits(0.01, auto)
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# (
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l1, r1 = self.limits()
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l2, r2 = other.limits()
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if l1 < l2:
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x0 = l1
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else:
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x0 = l2
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x0, r = clims(self.limits(), other.limits())
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# calculate index for rounding
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ri = self.precision(incr)
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if r1 < r2:
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npts = int(round(r2 - x0, ri) // incr)
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else:
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npts = int(round(r1 - x0, ri) // incr)
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npts = int(round(r - x0, ri) // incr)
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if npts > max_npts:
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raise ValueError('Maximum number of points exceeded:\n'
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@ -31,6 +31,43 @@ def worker(func, input, cores='max', async=False):
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pool.close()
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return result
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def clims(lim1, lim2):
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"""
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takes two pairs of limits and returns one pair of common limts
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:param lim1:
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:param lim2:
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:return:
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>>> clims([0, 4], [1, 3])
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[0, 4]
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>>> clims([1, 4], [0, 3])
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[0, 4]
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>>> clims([1, 3], [0, 4])
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[0, 4]
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>>> clims([0, 3], [1, 4])
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[0, 4]
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>>> clims([0, 3], [0, 4])
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[0, 4]
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>>> clims([1, 4], [0, 4])
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[0, 4]
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>>> clims([0, 4], [0, 4])
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[0, 4]
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>>> clims([0, 4], [1, 4])
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[0, 4]
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>>> clims([0, 4], [0, 3])
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[0, 4]
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"""
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lim = [None, None]
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if lim1[0] < lim2[0]:
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lim[0] = lim1[0]
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else:
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lim[0] = lim2[0]
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if lim1[1] > lim2[1]:
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lim[1] = lim1[1]
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else:
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lim[1] = lim2[1]
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return lim
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def demeanTrace(trace, window):
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"""
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@ -6,6 +6,7 @@ Created on Wed Mar 19 11:27:35 2014
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"""
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import warnings
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import copy
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import datetime
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import numpy as np
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@ -32,7 +33,7 @@ from pylot.core.pick.compare import Comparison
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from pylot.core.util.defaults import OUTPUTFORMATS, FILTERDEFAULTS, LOCTOOLS, \
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COMPPOSITION_MAP
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from pylot.core.util.utils import prepTimeAxis, getGlobalTimes, scaleWFData, \
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demeanTrace, isSorted, findComboBoxIndex
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demeanTrace, isSorted, findComboBoxIndex, clims
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def getDataType(parent):
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@ -46,6 +47,19 @@ def getDataType(parent):
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return type
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def plot_pdf(_axes, x, y, annotation, bbox_props, xlabel=None, ylabel=None,
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title=None):
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_axes.plot(x, y)
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if title:
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_axes.set_title(title)
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if xlabel:
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_axes.set_xlabel(xlabel)
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if ylabel:
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_axes.set_ylabel(ylabel)
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_anno = _axes.annotate(annotation, xy=(.05, .5), xycoords='axes fraction')
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_anno.set_bbox(bbox_props)
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return _axes
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def createAction(parent, text, slot=None, shortcut=None, icon=None,
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tip=None, checkable=False):
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@ -214,55 +228,63 @@ class ComparisonDialog(QDialog):
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_ax1 = self.canvas.figure.add_subplot(_gs[2, 0])
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_ax2 = self.canvas.figure.add_subplot(_gs[2, 1])
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_axes.cla()
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#_axes.cla()
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station = self.plotprops['station']
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phase = self.plotprops['phase']
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pdf = self.data.comparison[station][phase]
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x, y, std, exp = pdf.axis, pdf.data, pdf.standard_deviation(), \
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pdf.expectation()
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_axes.plot(x, y)
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_axes.set_title(phase)
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_axes.set_ylabel('propability density [-]')
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_axes.set_xlabel('time difference [s]')
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annotation = "{phase} difference on {station}\n" \
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"expectation: {exp}\n" \
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"std: {std}".format(station=station, phase=phase,
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std=std, exp=exp)
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_anno = _axes.annotate(annotation, xy=(.05, .5), xycoords='axes '
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'fraction')
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"expectation: {exp}\n" \
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"std: {std}".format(station=station, phase=phase,
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std=std, exp=exp)
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bbox_props = dict(boxstyle='round', facecolor='lightgrey', alpha=.7)
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_anno.set_bbox(bbox_props)
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pdf_a = self.data.get('auto')[station][phase]
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pdf_m = self.data.get('manu')[station][phase]
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plot_pdf(_axes, x, y, annotation, bbox_props, 'time difference [s]',
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'propability density [-]', phase)
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xauto, yauto, stdauto, expauto = pdf_a.axis, pdf_a.data, \
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pdf_a.standard_deviation(), \
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pdf_a.expectation()
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xmanu, ymanu, stdmanu, expmanu = pdf_m.axis, pdf_m.data, \
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pdf_m.standard_deviation(), \
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pdf_m.expectation()
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pdf_a = copy.deepcopy(self.data.get('auto')[station][phase])
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pdf_m = copy.deepcopy(self.data.get('manu')[station][phase])
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_ax1.plot(xmanu, ymanu)
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_ax1.set_xlim(pdf_m.limits())
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xauto, yauto, stdauto, expauto, alim = pdf_a.axis, pdf_a.data, \
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pdf_a.standard_deviation(), \
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pdf_a.expectation(), \
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pdf_a.limits()
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xmanu, ymanu, stdmanu, expmanu, mlim = pdf_m.axis, pdf_m.data, \
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pdf_m.standard_deviation(), \
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pdf_m.expectation(), \
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pdf_m.limits()
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# find common limits
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lims = clims(alim, mlim)
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# relative x axis
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x0 = lims[0]
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xmanu -= x0
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xauto -= x0
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lims = [lim - x0 for lim in lims]
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x0 = UTCDateTime(x0)
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# set annotation text
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mannotation = "probability density for manual pick\n" \
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"expectation: {exp}\n" \
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"std: {std}".format(std=stdmanu, exp=expmanu)
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_anno = _ax1.annotate(mannotation, xy=(.05, .5), xycoords='axes '
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'fraction')
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bbox_props = dict(boxstyle='round', facecolor='lightgrey', alpha=.7)
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_anno.set_bbox(bbox_props)
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"std: {std}".format(std=stdmanu,
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exp=expmanu-x0.timestamp)
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_ax2.plot(xauto, yauto)
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_ax2.set_xlim(pdf_a.limits())
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_ax2.set_ylabel('time [s]')
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aannotation = "probability density for automatic pick\n" \
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"expectation: {exp}\n" \
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"std: {std}".format(std=stdauto, exp=expauto)
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_anno = _ax2.annotate(aannotation, xy=(.05, .5), xycoords='axes '
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'fraction')
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bbox_props = dict(boxstyle='round', facecolor='lightgrey', alpha=.7)
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_anno.set_bbox(bbox_props)
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"std: {std}".format(std=stdauto,
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exp=expauto-x0.timestamp)
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_ax1 = plot_pdf(_ax1, xmanu, ymanu, mannotation,
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bbox_props=bbox_props, xlabel='seconds since '
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'{0}'.format(x0),
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ylabel='probability density [-]')
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_ax1.set_xlim(lims)
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_ax2 = plot_pdf(_ax2, xauto, yauto, aannotation,
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bbox_props=bbox_props, xlabel='seconds since '
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'{0}'.format(x0))
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_ax2.set_xlim(lims)
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_gs.update(wspace=0.5, hspace=0.5)
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