marcel/pylot
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[bugfixes] made some fixes to the subtraction method; in some cases ValueErrors are raised which are now handled but raised as Warning

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Sebastian Wehling-Benatelli 2016-03-08 20:35:56 +01:00
parent 9f7f00314a
commit db4dd47daa
1 changed files with 13 additions and 9 deletions
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22
pylot/core/util/pdf.py
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@ -1,6 +1,7 @@
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
import warnings
import numpy as np import numpy as np
from obspy import UTCDateTime from obspy import UTCDateTime
from pylot.core.util.utils import find_nearest from pylot.core.util.utils import find_nearest
@ -9,6 +10,13 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString() __version__ = _getVersionString()
__author__ = 'sebastianw' __author__ = 'sebastianw'
def broadcast(pdf, si, ei, data):
try:
pdf[si:ei] = data
except ValueError as e:
warnings.warn(str(e), Warning)
return broadcast(pdf, si, ei, data[:-1])
return pdf
def create_axis(x0, incr, npts): def create_axis(x0, incr, npts):
ax = np.zeros(npts) ax = np.zeros(npts)
@ -16,11 +24,6 @@ def create_axis(x0, incr, npts):
ax[i] = x0 + incr * i ax[i] = x0 + incr * i
return ax return ax
def find_nearest_index(array, value):
return (np.abs(array-value)).argmin()
def gauss_parameter(te, tm, tl, eta): def gauss_parameter(te, tm, tl, eta):
''' '''
takes three onset times and returns the parameters sig1, sig2, a1 and a2 takes three onset times and returns the parameters sig1, sig2, a1 and a2
@ -265,6 +268,7 @@ class ProbabilityDensityFunction(object):
plt.plot(self.axis, self.data) plt.plot(self.axis, self.data)
plt.xlabel('x') plt.xlabel('x')
plt.ylabel('f(x)') plt.ylabel('f(x)')
plt.autoscale(axis='x', tight=True)
if self: if self:
plt.title('Probability density function') plt.title('Probability density function')
else: else:
@ -346,13 +350,13 @@ class ProbabilityDensityFunction(object):
x = create_axis(x0, incr, npts) x = create_axis(x0, incr, npts)
sstart = find_nearest_index(x, self.x0) sstart = find_nearest(x, self.x0)
s_end = sstart + self.data.size s_end = sstart + self.data.size
ostart = find_nearest_index(x, other.x0) ostart = find_nearest(x, other.x0)
o_end = ostart + other.data.size o_end = ostart + other.data.size
pdf_self[sstart:s_end] = self.data broadcast(pdf_self, sstart, s_end, self.data)
pdf_other[ostart:o_end] = other.data broadcast(pdf_other, ostart, o_end, other.data)
return x0, incr, npts, pdf_self, pdf_other return x0, incr, npts, pdf_self, pdf_other