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Merge branch 'develop' of ariadne.geophysik.ruhr-uni-bochum.de:/data/git/pylot into develop

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This commit is contained in:
Marcel Paffrath 2016-06-16 13:57:50 +02:00
commit 579461f7b4
3 changed files with 97 additions and 52 deletions
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20
pylot/core/util/pdf.py
View File

@ -4,7 +4,7 @@
import warnings
import numpy as np
from obspy import UTCDateTime
from pylot.core.util.utils import find_nearest
from pylot.core.util.utils import find_nearest, clims
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
@ -329,28 +329,14 @@ class ProbabilityDensityFunction(object):
:param r2:
:param max_npts:
:return:
'''
# >>> manu = ProbabilityDensityFunction.from_pick(0.01, 0.3, 0.5, 0.54)
# >>> auto = ProbabilityDensityFunction.from_pick(0.01, 0.3, 0.34, 0.54)
# >>> manu.commonlimits(0.01, auto)
# (
l1, r1 = self.limits()
l2, r2 = other.limits()
if l1 < l2:
x0 = l1
else:
x0 = l2
x0, r = clims(self.limits(), other.limits())
# calculate index for rounding
ri = self.precision(incr)
if r1 < r2:
npts = int(round(r2 - x0, ri) // incr)
else:
npts = int(round(r1 - x0, ri) // incr)
npts = int(round(r - x0, ri) // incr)
if npts > max_npts:
raise ValueError('Maximum number of points exceeded:\n'

37
pylot/core/util/utils.py
View File

@ -31,6 +31,43 @@ def worker(func, input, cores='max', async=False):
pool.close()
return result
def clims(lim1, lim2):
"""
takes two pairs of limits and returns one pair of common limts
:param lim1:
:param lim2:
:return:
>>> clims([0, 4], [1, 3])
[0, 4]
>>> clims([1, 4], [0, 3])
[0, 4]
>>> clims([1, 3], [0, 4])
[0, 4]
>>> clims([0, 3], [1, 4])
[0, 4]
>>> clims([0, 3], [0, 4])
[0, 4]
>>> clims([1, 4], [0, 4])
[0, 4]
>>> clims([0, 4], [0, 4])
[0, 4]
>>> clims([0, 4], [1, 4])
[0, 4]
>>> clims([0, 4], [0, 3])
[0, 4]
"""
lim = [None, None]
if lim1[0] < lim2[0]:
lim[0] = lim1[0]
else:
lim[0] = lim2[0]
if lim1[1] > lim2[1]:
lim[1] = lim1[1]
else:
lim[1] = lim2[1]
return lim
def demeanTrace(trace, window):
"""

82
pylot/core/util/widgets.py
View File

@ -6,6 +6,7 @@ Created on Wed Mar 19 11:27:35 2014
"""
import warnings
import copy
import datetime
import numpy as np
@ -32,7 +33,7 @@ from pylot.core.pick.compare import Comparison
from pylot.core.util.defaults import OUTPUTFORMATS, FILTERDEFAULTS, LOCTOOLS, \
COMPPOSITION_MAP
from pylot.core.util.utils import prepTimeAxis, getGlobalTimes, scaleWFData, \
demeanTrace, isSorted, findComboBoxIndex
demeanTrace, isSorted, findComboBoxIndex, clims
def getDataType(parent):
@ -46,6 +47,19 @@ def getDataType(parent):
return type
def plot_pdf(_axes, x, y, annotation, bbox_props, xlabel=None, ylabel=None,
title=None):
_axes.plot(x, y)
if title:
_axes.set_title(title)
if xlabel:
_axes.set_xlabel(xlabel)
if ylabel:
_axes.set_ylabel(ylabel)
_anno = _axes.annotate(annotation, xy=(.05, .5), xycoords='axes fraction')
_anno.set_bbox(bbox_props)
return _axes
def createAction(parent, text, slot=None, shortcut=None, icon=None,
tip=None, checkable=False):
@ -214,55 +228,63 @@ class ComparisonDialog(QDialog):
_ax1 = self.canvas.figure.add_subplot(_gs[2, 0])
_ax2 = self.canvas.figure.add_subplot(_gs[2, 1])
_axes.cla()
#_axes.cla()
station = self.plotprops['station']
phase = self.plotprops['phase']
pdf = self.data.comparison[station][phase]
x, y, std, exp = pdf.axis, pdf.data, pdf.standard_deviation(), \
pdf.expectation()
_axes.plot(x, y)
_axes.set_title(phase)
_axes.set_ylabel('propability density [-]')
_axes.set_xlabel('time difference [s]')
annotation = "{phase} difference on {station}\n" \
"expectation: {exp}\n" \
"std: {std}".format(station=station, phase=phase,
std=std, exp=exp)
_anno = _axes.annotate(annotation, xy=(.05, .5), xycoords='axes '
'fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey', alpha=.7)
_anno.set_bbox(bbox_props)
pdf_a = self.data.get('auto')[station][phase]
pdf_m = self.data.get('manu')[station][phase]
plot_pdf(_axes, x, y, annotation, bbox_props, 'time difference [s]',
'propability density [-]', phase)
xauto, yauto, stdauto, expauto = pdf_a.axis, pdf_a.data, \
pdf_a = copy.deepcopy(self.data.get('auto')[station][phase])
pdf_m = copy.deepcopy(self.data.get('manu')[station][phase])
xauto, yauto, stdauto, expauto, alim = pdf_a.axis, pdf_a.data, \
pdf_a.standard_deviation(), \
pdf_a.expectation()
xmanu, ymanu, stdmanu, expmanu = pdf_m.axis, pdf_m.data, \
pdf_a.expectation(), \
pdf_a.limits()
xmanu, ymanu, stdmanu, expmanu, mlim = pdf_m.axis, pdf_m.data, \
pdf_m.standard_deviation(), \
pdf_m.expectation()
pdf_m.expectation(), \
pdf_m.limits()
# find common limits
lims = clims(alim, mlim)
# relative x axis
x0 = lims[0]
xmanu -= x0
xauto -= x0
lims = [lim - x0 for lim in lims]
x0 = UTCDateTime(x0)
_ax1.plot(xmanu, ymanu)
_ax1.set_xlim(pdf_m.limits())
# set annotation text
mannotation = "probability density for manual pick\n" \
"expectation: {exp}\n" \
"std: {std}".format(std=stdmanu, exp=expmanu)
_anno = _ax1.annotate(mannotation, xy=(.05, .5), xycoords='axes '
'fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey', alpha=.7)
_anno.set_bbox(bbox_props)
"std: {std}".format(std=stdmanu,
exp=expmanu-x0.timestamp)
_ax2.plot(xauto, yauto)
_ax2.set_xlim(pdf_a.limits())
_ax2.set_ylabel('time [s]')
aannotation = "probability density for automatic pick\n" \
"expectation: {exp}\n" \
"std: {std}".format(std=stdauto, exp=expauto)
_anno = _ax2.annotate(aannotation, xy=(.05, .5), xycoords='axes '
'fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey', alpha=.7)
_anno.set_bbox(bbox_props)
"std: {std}".format(std=stdauto,
exp=expauto-x0.timestamp)
_ax1 = plot_pdf(_ax1, xmanu, ymanu, mannotation,
bbox_props=bbox_props, xlabel='seconds since '
'{0}'.format(x0),
ylabel='probability density [-]')
_ax1.set_xlim(lims)
_ax2 = plot_pdf(_ax2, xauto, yauto, aannotation,
bbox_props=bbox_props, xlabel='seconds since '
'{0}'.format(x0))
_ax2.set_xlim(lims)
_gs.update(wspace=0.5, hspace=0.5)