def echoloc(indata, histlen, startoffset=5.0):
thehist, peakheight, peakloc, peakwidth, centerofmass = makehistogram(
indata, histlen, refine=True
)
thestore = np.zeros((2, len(thehist[0])), dtype="float64")
thestore[0, :] = (thehist[1][1:] + thehist[1][0:-1]) / 2.0
thestore[1, :] = thehist[0][-histlen:]
timestep = thestore[0, 1] - thestore[0, 0]
startpt = np.argmax(thestore[1, :]) + int(startoffset // timestep)
print("primary peak:", peakheight, peakloc, peakwidth)
print("startpt, startloc, timestep:", startpt, thestore[1, startpt], timestep)
while (thestore[1, startpt] > thestore[1, startpt + 1]) and (startpt < len(thehist[0]) - 2):
startpt += 1
echopeakindex = np.argmax(thestore[1, startpt:-2]) + startpt
echopeakloc = thestore[0, echopeakindex + 1]
echopeakheight = thestore[1, echopeakindex + 1]
numbins = 1
while (echopeakindex + numbins < histlen - 1) and (
thestore[1, echopeakindex + numbins] > echopeakheight / 2.0
):
numbins += 1
echopeakwidth = (thestore[0, echopeakindex + numbins] - thestore[0, echopeakindex]) * 2.0
echopeakheight, echopeakloc, echopeakwidth = tide_fit.gaussfit(
echopeakheight, echopeakloc, echopeakwidth, thestore[0, :], thestore[1, :]
)
return echopeakloc - peakloc, (echopeakheight * echopeakwidth) / (peakheight * peakwidth)
def autocorrcheck(corrscale, thexcorr, delta=0.1, acampthresh=0.1, aclagthresh=10.0, displayplots=False, prewindow=True,
detrendorder=1, debug=False):
"""
Parameters
----------
corrscale
thexcorr
delta
acampthresh
aclagthresh
displayplots
prewindow
detrendorder
debug
Returns
-------
"""
lookahead = 2
peaks = tide_fit.peakdetect(thexcorr, x_axis=corrscale, delta=delta, lookahead=lookahead)
maxpeaks = np.asarray(peaks[0], dtype='float64')
minpeaks = np.asarray(peaks[1], dtype='float64')
if len(peaks[0]) > 0:
if debug:
print(peaks)
zeropkindex = np.argmin(abs(maxpeaks[:, 0]))
for i in range(zeropkindex + 1, maxpeaks.shape[0]):
if maxpeaks[i, 0] > aclagthresh:
return None, None
if maxpeaks[i, 1] > acampthresh:
sidelobetime = maxpeaks[i, 0]
sidelobeindex = tide_util.valtoindex(corrscale, sidelobetime)
sidelobeamp = thexcorr[sidelobeindex]
numbins = 1
while (sidelobeindex + numbins < np.shape(corrscale)[0] - 1) and (
thexcorr[sidelobeindex + numbins] > sidelobeamp / 2.0):
numbins += 1
sidelobewidth = (corrscale[sidelobeindex + numbins] - corrscale[sidelobeindex]) * 2.0
fitstart = sidelobeindex - numbins
fitend = sidelobeindex + numbins
sidelobeamp, sidelobetime, sidelobewidth = tide_fit.gaussfit(sidelobeamp, sidelobetime, sidelobewidth,
corrscale[fitstart:fitend + 1],
thexcorr[fitstart:fitend + 1])
if displayplots:
pl.plot(corrscale[fitstart:fitend + 1], thexcorr[fitstart:fitend + 1], 'k',
corrscale[fitstart:fitend + 1],
tide_fit.gauss_eval(corrscale[fitstart:fitend + 1], [sidelobeamp, sidelobetime, sidelobewidth]),
'r')
pl.show()
return sidelobetime, sidelobeamp
return None, None
def gethistprops(indata, histlen, refine=False, therange=None, pickleft=False, peakthresh=0.33):
"""
Parameters
----------
indata
histlen
refine
therange
pickleftpeak
Returns
-------
"""
thestore = np.zeros((2, histlen), dtype="float64")
if therange is None:
thehist = np.histogram(indata, histlen)
else:
thehist = np.histogram(indata, histlen, therange)
thestore[0, :] = thehist[1][-histlen:]
thestore[1, :] = thehist[0][-histlen:]
# get starting values for the peak, ignoring first and last point of histogram
if pickleft:
overallmax = np.max(thestore[1, 1:-2])
peakindex = 1
i = 1
started = False
finished = False
while i < len(thestore[1, :] - 2) and not finished:
if thestore[1, i] > peakthresh * overallmax:
started = True
if thestore[1, i] > thestore[1, peakindex]:
peakindex = i
if started and (thestore[1, i] < 0.75 * thestore[1, peakindex]):
finished = True
i += 1
else:
peakindex = np.argmax(thestore[1, 1:-2])
peaklag = thestore[0, peakindex + 1]
peakheight = thestore[1, peakindex + 1]
numbins = 1
while (peakindex + numbins < histlen - 1) and (
thestore[1, peakindex + numbins] > peakheight / 2.0
):
numbins += 1
peakwidth = (thestore[0, peakindex + numbins] - thestore[0, peakindex]) * 2.0
if refine:
peakheight, peaklag, peakwidth = tide_fit.gaussfit(
peakheight, peaklag, peakwidth, thestore[0, :], thestore[1, :]
)
return peaklag, peakheight, peakwidth
def gethistprops(indata, histlen, refine=False, therange=None, pickleft=False):
"""
Parameters
----------
indata
histlen
refine
therange
pickleftpeak
Returns
-------
"""
thestore = np.zeros((2, histlen), dtype='float64')
if therange is None:
thehist = np.histogram(indata, histlen)
else:
thehist = np.histogram(indata, histlen, therange)
thestore[0, :] = thehist[1][-histlen:]
thestore[1, :] = thehist[0][-histlen:]
# get starting values for the peak, ignoring first and last point of histogram
if pickleft:
overallmax = np.max(thestore[1, 1:-2])
peakindex = 1
i = 1
started = False
finished = False
while i < len(thestore[1, :] - 2) and not finished:
if thestore[1, i] > 0.5 * overallmax:
started = True
i += 1
if thestore[1, i] > thestore[1, peakindex]:
peakindex = i
if started and (thestore[1, i] < 0.75 * thestore[1, peakindex]):
finished = True
else:
peakindex = np.argmax(thestore[1, 1:-2])
peaklag = thestore[0, peakindex + 1]
peakheight = thestore[1, peakindex + 1]
numbins = 1
while (peakindex + numbins < histlen - 1) and (thestore[1, peakindex + numbins] > peakheight / 2.0):
numbins += 1
peakwidth = (thestore[0, peakindex + numbins] - thestore[0, peakindex]) * 2.0
if refine:
peakheight, peaklag, peakwidth = tide_fit.gaussfit(peakheight, peaklag, peakwidth, thestore[0, :], thestore[1, :])
return peaklag, peakheight, peakwidth
def makeandsavehistogram(indata, histlen, endtrim, outname,
binsize=None,
displaytitle='histogram',
displayplots=False,
refine=False,
therange=None):
"""
Parameters
----------
indata
histlen
endtrim
outname
displaytitle
displayplots
refine
therange
Returns
-------
"""
thehist = makehistogram(indata, histlen, binsize=binsize, therange=therange)
thestore = np.zeros((2, len(thehist[0])), dtype='float64')
thestore[0, :] = (thehist[1][1:] + thehist[1][0:-1]) / 2.0
thestore[1, :] = thehist[0][-histlen:]
# get starting values for the peak, ignoring first and last point of histogram
peakindex = np.argmax(thestore[1, 1:-2])
peaklag = thestore[0, peakindex + 1]
peakheight = thestore[1, peakindex + 1]
numbins = 1
while (peakindex + numbins < histlen - 1) and (thestore[1, peakindex + numbins] > peakheight / 2.0):
numbins += 1
peakwidth = (thestore[0, peakindex + numbins] - thestore[0, peakindex]) * 2.0
if refine:
peakheight, peaklag, peakwidth = tide_fit.gaussfit(peakheight, peaklag, peakwidth, thestore[0, :], thestore[1, :])
centerofmass = np.sum(thestore[0, :] * thestore[1, :]) / np.sum(thestore[1, :])
tide_io.writenpvecs(np.array([centerofmass]), outname + '_centerofmass.txt')
tide_io.writenpvecs(np.array([peaklag]), outname + '_peak.txt')
tide_io.writenpvecs(thestore, outname + '.txt')
if displayplots:
fig = pl.figure()
ax = fig.add_subplot(111)
ax.set_title(displaytitle)
pl.plot(thestore[0, :(-1 - endtrim)], thestore[1, :(-1 - endtrim)])
def makehistogram(indata, histlen, binsize=None, therange=None, refine=False):
"""
Parameters
----------
indata
histlen
binsize
therange
Returns
-------
"""
if therange is None:
therange = [indata.min(), indata.max()]
if histlen is None and binsize is None:
thebins = 10
elif binsize is not None:
thebins = np.linspace(
therange[0], therange[1], (therange[1] - therange[0]) / binsize + 1, endpoint=True,
)
else:
thebins = histlen
thehist = np.histogram(indata, thebins, therange)
thestore = np.zeros((2, len(thehist[0])), dtype="float64")
thestore[0, :] = (thehist[1][1:] + thehist[1][0:-1]) / 2.0
thestore[1, :] = thehist[0][-histlen:]
# get starting values for the peak, ignoring first and last point of histogram
peakindex = np.argmax(thestore[1, 1:-2])
peakloc = thestore[0, peakindex + 1]
peakheight = thestore[1, peakindex + 1]
numbins = 1
while (peakindex + numbins < histlen - 1) and (
thestore[1, peakindex + numbins] > peakheight / 2.0
):
numbins += 1
peakwidth = (thestore[0, peakindex + numbins] - thestore[0, peakindex]) * 2.0
if refine:
peakheight, peakloc, peakwidth = tide_fit.gaussfit(
peakheight, peakloc, peakwidth, thestore[0, :], thestore[1, :]
)
centerofmass = np.sum(thestore[0, :] * thestore[1, :]) / np.sum(thestore[1, :])
return thehist, peakheight, peakloc, peakwidth, centerofmass
def autocorrcheck(corrscale,
thexcorr,
delta=0.1,
acampthresh=0.1,
aclagthresh=10.0,
displayplots=False,
prewindow=True,
detrendorder=1,
debug=False):
"""
Parameters
----------
corrscale
thexcorr
delta
acampthresh
aclagthresh
displayplots
prewindow
detrendorder
debug
Returns
-------
"""
lookahead = 2
peaks = tide_fit.peakdetect(thexcorr,
x_axis=corrscale,
delta=delta,
lookahead=lookahead)
maxpeaks = np.asarray(peaks[0], dtype='float64')
minpeaks = np.asarray(peaks[1], dtype='float64')
if len(peaks[0]) > 0:
if debug:
print(peaks)
zeropkindex = np.argmin(abs(maxpeaks[:, 0]))
for i in range(zeropkindex + 1, maxpeaks.shape[0]):
if maxpeaks[i, 0] > aclagthresh:
return None, None
if maxpeaks[i, 1] > acampthresh:
sidelobetime = maxpeaks[i, 0]
sidelobeindex = tide_util.valtoindex(corrscale, sidelobetime)
sidelobeamp = thexcorr[sidelobeindex]
numbins = 1
while (sidelobeindex + numbins < np.shape(corrscale)[0] -
1) and (thexcorr[sidelobeindex + numbins] >
sidelobeamp / 2.0):
numbins += 1
sidelobewidth = (corrscale[sidelobeindex + numbins] -
corrscale[sidelobeindex]) * 2.0
fitstart = sidelobeindex - numbins
fitend = sidelobeindex + numbins
sidelobeamp, sidelobetime, sidelobewidth = tide_fit.gaussfit(
sidelobeamp, sidelobetime, sidelobewidth,
corrscale[fitstart:fitend + 1],
thexcorr[fitstart:fitend + 1])
if displayplots:
pl.plot(
corrscale[fitstart:fitend + 1],
thexcorr[fitstart:fitend + 1], 'k',
corrscale[fitstart:fitend + 1],
tide_fit.gauss_eval(
corrscale[fitstart:fitend + 1],
[sidelobeamp, sidelobetime, sidelobewidth]), 'r')
pl.show()
return sidelobetime, sidelobeamp
return None, None
def check_autocorrelation(
corrscale,
thexcorr,
delta=0.1,
acampthresh=0.1,
aclagthresh=10.0,
displayplots=False,
detrendorder=1,
):
"""Check for autocorrelation in an array.
Parameters
----------
corrscale
thexcorr
delta
acampthresh
aclagthresh
displayplots
windowfunc
detrendorder
Returns
-------
sidelobetime
sidelobeamp
"""
lookahead = 2
peaks = tide_fit.peakdetect(thexcorr, x_axis=corrscale, delta=delta, lookahead=lookahead)
maxpeaks = np.asarray(peaks[0], dtype="float64")
if len(peaks[0]) > 0:
LGR.debug(peaks)
zeropkindex = np.argmin(abs(maxpeaks[:, 0]))
for i in range(zeropkindex + 1, maxpeaks.shape[0]):
if maxpeaks[i, 0] > aclagthresh:
return None, None
if maxpeaks[i, 1] > acampthresh:
sidelobetime = maxpeaks[i, 0]
sidelobeindex = tide_util.valtoindex(corrscale, sidelobetime)
sidelobeamp = thexcorr[sidelobeindex]
numbins = 1
while (sidelobeindex + numbins < np.shape(corrscale)[0] - 1) and (
thexcorr[sidelobeindex + numbins] > sidelobeamp / 2.0
):
numbins += 1
sidelobewidth = (
corrscale[sidelobeindex + numbins] - corrscale[sidelobeindex]
) * 2.0
fitstart = sidelobeindex - numbins
fitend = sidelobeindex + numbins
sidelobeamp, sidelobetime, sidelobewidth = tide_fit.gaussfit(
sidelobeamp,
sidelobetime,
sidelobewidth,
corrscale[fitstart : fitend + 1],
thexcorr[fitstart : fitend + 1],
)
if displayplots:
plt.plot(
corrscale[fitstart : fitend + 1],
thexcorr[fitstart : fitend + 1],
"k",
corrscale[fitstart : fitend + 1],
tide_fit.gauss_eval(
corrscale[fitstart : fitend + 1],
[sidelobeamp, sidelobetime, sidelobewidth],
),
"r",
)
plt.show()
return sidelobetime, sidelobeamp
return None, None