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

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Marcel Paffrath 2016-07-18 11:12:43 +02:00
commit fb8d888845
2 changed files with 53 additions and 57 deletions
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40
pylot/core/pick/compare.py
View File

@ -307,7 +307,7 @@ class PDFDictionary(object):
pdfs = self.pdf_data[station]
for l, phase in enumerate(pdfs.keys()):
try:
axarr[n, l].plot(pdfs[phase].axis, pdfs[phase].data)
axarr[n, l].plot(pdfs[phase].axis, pdfs[phase].data())
if n is 0:
axarr[n, l].set_title(phase)
if l is 0:
@ -322,7 +322,7 @@ class PDFDictionary(object):
except IndexError as e:
print('trying aligned plotting\n{0}'.format(e))
hide_labels = False
axarr[l].plot(pdfs[phase].axis, pdfs[phase].data)
axarr[l].plot(pdfs[phase].axis, pdfs[phase].data())
axarr[l].set_title(phase)
if l is 0:
axann = axarr[l].annotate(station, xy=(.05, .5),
@ -350,13 +350,15 @@ class PDFstatistics(object):
self.stations = {}
self.p_std = {}
self.s_std = {}
self.makeDirlist()
self.getData()
self.getStatistics()
self.arraylen = 0
self.Theta015 = []
self.Theta25 = []
self.Theta485 = []
self.theta015 = []
self.theta1 = []
self.theta2 = []
self.dirlist = list()
self.evtdict = {}
#self.makeDirlist()
#self.getData()
#self.getStatistics()
#self.showData()
@ -367,8 +369,10 @@ class PDFstatistics(object):
self.evtdict[rd] = glob.glob1((os.path.join(self.directory, rd)), '*.xml')
def getData(self):
arraylen = 0
for dir in self.dirlist:
for evt in self.evtdict[dir]:
print evt
self.stations[evt] = []
self.p_std[evt] = []
self.s_std[evt] = []
@ -377,16 +381,24 @@ class PDFstatistics(object):
# print station, pdfs
try:
p_std = pdfs['P'].standard_deviation()
self.theta015.append(pdfs['P'].qtile_dist_quot(0.015))
self.theta1.append(pdfs['P'].qtile_dist_quot(0.1))
self.theta2.append(pdfs['P'].qtile_dist_quot(0.2))
except KeyError:
p_std = np.nan
try:
s_std = pdfs['S'].standard_deviation()
self.theta015.append(pdfs['S'].qtile_dist_quot(0.015))
self.theta1.append(pdfs['S'].qtile_dist_quot(0.1))
self.theta2.append(pdfs['S'].qtile_dist_quot(0.2))
except KeyError:
s_std = np.nan
self.stations[evt].append(station)
self.p_std[evt].append(p_std)
self.s_std[evt].append(s_std)
self.arraylen += 1
arraylen += 1
self.arraylen = arraylen
self.makeArray()
def makeArray(self):
@ -426,5 +438,11 @@ class PDFstatistics(object):
if __name__ == "__main__":
rootdir = '/home/sebastianp/Data/Reassessment/Insheim/'
rootdir = '/home/sebastianp/Data/Reassessment/Insheim/2012.10/'
Insheim = PDFstatistics(rootdir)
Insheim.dirlist = [rootdir]
Insheim.evtdict[rootdir] = ['e0002.294.12.xml']
Insheim.getData()
print Insheim.theta015
print Insheim.theta1
print Insheim.theta2

68
pylot/core/util/pdf.py
View File

@ -149,8 +149,8 @@ class ProbabilityDensityFunction(object):
x0, incr, npts = self.commonparameter(other)
axis = create_axis(x0, incr, npts)
pdf_self = np.array([self.data(x) for x in axis])
pdf_other = np.array([other.data(x) for x in axis])
pdf_self = np.array(self.data(axis))
pdf_other = np.array(other.data(axis))
pdf = np.convolve(pdf_self, pdf_other, 'full') * incr
@ -174,8 +174,8 @@ class ProbabilityDensityFunction(object):
x0, incr, npts = self.commonparameter(other)
axis = create_axis(x0, incr, npts)
pdf_self = np.array([self.data(x) for x in axis])
pdf_other = np.array([other.data(x) for x in axis])
pdf_self = np.array(self.data(axis))
pdf_other = np.array(other.data(axis))
pdf = np.correlate(pdf_self, pdf_other, 'full') * incr
@ -193,21 +193,22 @@ class ProbabilityDensityFunction(object):
def __nonzero__(self):
prec = self.precision(self.incr)
data = np.array([self.data(t) for t in self.axis])
data = np.array(self.data())
gtzero = np.all(data >= 0)
probone = bool(np.round(self.prob_gt_val(self.axis[0]), prec) == 1.)
return bool(gtzero and probone)
def __str__(self):
return str([self.data(val) for val in create_axis(self.x0, self.incr,
self.npts)])
return str([self.data()])
@staticmethod
def precision(incr):
prec = int(np.ceil(np.abs(np.log10(incr)))) - 2
return prec if prec >= 0 else 0
def data(self, value):
def data(self, value=None):
if value is None:
return self._pdf(self.axis, self.params)
return self._pdf(value, self.params)
@property
@ -325,14 +326,15 @@ class ProbabilityDensityFunction(object):
raise ValueError('value out of bounds: {0}'.format(value))
return self.prob_limits((value, self.axis[-1]))
def prob_limits(self, limits):
lim = np.arange(limits[0], limits[1], self.incr)
data = [self.data(t) for t in lim]
def prob_limits(self, limits, oversampling=1.):
sampling = self.incr / oversampling
lim = np.arange(limits[0], limits[1], sampling)
data = self.data(lim)
min_est, max_est = 0., 0.
for n in range(len(data) - 1):
min_est += min(data[n], data[n + 1])
max_est += max(data[n], data[n + 1])
return (min_est + max_est) / 2. * self.incr
return (min_est + max_est) / 2. * sampling
def prob_val(self, value):
if not (self.axis[0] <= value <= self.axis[-1]):
@ -352,7 +354,7 @@ class ProbabilityDensityFunction(object):
m = (r + l) / 2
diff = prob_value - self.prob_lt_val(m)
while abs(diff) > eps:
while abs(diff) > eps and ((r - l) > self.incr):
if diff > 0:
l = m
else:
@ -367,12 +369,17 @@ class ProbabilityDensityFunction(object):
return qu - ql
def qtile_dist_quot(self,x):
if x < 0 or x > 0.5:
raise ValueError('Value out of range.')
return self.quantile_distance(0.5-x)/self.quantile_distance(x)
def plot(self, label=None):
import matplotlib.pyplot as plt
axis = self.axis
plt.plot(axis, self.data(axis))
plt.plot(self.axis, self.data())
plt.xlabel('x')
plt.ylabel('f(x)')
plt.autoscale(axis='x', tight=True)
@ -442,32 +449,3 @@ class ProbabilityDensityFunction(object):
return x0, incr, npts
def rearrange(self, other):
'''
Method rearrange takes another Probability Density Function and returns
a new axis with mid-point 0 and covering positive and negative range
of axis values, either containing the maximum value of both axis or
the sum of the maxima
:param other:
:return:
'''
x0 = self.x0
incr = self.incr
npts = self.npts
pdf_self = np.zeros(npts)
pdf_other = np.zeros(npts)
x = create_axis(x0, incr, npts)
sstart = find_nearest(x, self.x0)
s_end = sstart + self.data.size
ostart = find_nearest(x, other.x0)
o_end = ostart + other.data.size
pdf_self = self.broadcast(pdf_self, sstart, s_end, self.data)
pdf_other = self.broadcast(pdf_other, ostart, o_end, other.data)
return x0, incr, npts, pdf_self, pdf_other