def spectralfilterprops(thefilter, debug=False):
lowerstop, lowerpass, upperpass, upperstop = thefilter['filter'].getfreqlimits()
lowerstopindex = valtoindex(thefilter['frequencies'], lowerstop)
lowerpassindex = valtoindex(thefilter['frequencies'], lowerpass)
upperpassindex = valtoindex(thefilter['frequencies'], upperpass)
upperstopindex = np.min([valtoindex(thefilter['frequencies'], upperstop), len(thefilter['frequencies']) - 1])
if debug:
print('target freqs:', lowerstop, lowerpass, upperpass, upperstop)
print('actual freqs:', thefilter['frequencies'][lowerstopindex],
thefilter['frequencies'][lowerpassindex],
thefilter['frequencies'][upperpassindex],
thefilter['frequencies'][upperstopindex])
response = {}
passbandmean = np.mean(thefilter['transferfunc'][lowerpassindex:upperpassindex])
passbandmax = np.max(thefilter['transferfunc'][lowerpassindex:upperpassindex])
passbandmin = np.min(thefilter['transferfunc'][lowerpassindex:upperpassindex])
response['passbandripple'] = (passbandmax - passbandmin)/passbandmean
if lowerstopindex > 2:
response['lowerstopmean'] = np.mean(thefilter['transferfunc'][0:lowerstopindex])/passbandmean
response['lowerstopmax'] = np.max(np.abs(thefilter['transferfunc'][0:lowerstopindex]))/passbandmean
else:
response['lowerstopmean'] = 0.0
response['lowerstopmax'] = 0.0
if len(thefilter['transferfunc']) - upperstopindex > 2:
response['upperstopmean'] = np.mean(thefilter['transferfunc'][upperstopindex:-1])/passbandmean
response['upperstopmax'] = np.max(np.abs(thefilter['transferfunc'][upperstopindex:-1]))/passbandmean
else:
response['upperstopmean'] = 0.0
response['upperstopmax'] = 0.0
return response
def track(self, x, fs):
self.freqs, self.times, thespectrogram = sp.signal.spectrogram(
np.concatenate(
[
np.zeros(int(self.nperseg // 2)), x,
np.zeros(int(self.nperseg // 2))
],
axis=0,
),
fs=fs,
detrend="constant",
scaling="spectrum",
nfft=None,
window=np.hamming(self.nfft),
noverlap=(self.nperseg - 1),
)
lowerliminpts = tide_util.valtoindex(self.freqs, self.lowerlim)
upperliminpts = tide_util.valtoindex(self.freqs, self.upperlim)
if self.debug:
print(self.times.shape, self.freqs.shape, thespectrogram.shape)
print(self.times)
# intitialize the peak fitter
thefitter = SimilarityFunctionFitter(
corrtimeaxis=self.freqs,
lagmin=self.lowerlim,
lagmax=self.upperlim,
absmaxsigma=10.0,
absminsigma=0.1,
debug=self.debug,
peakfittype="fastquad",
zerooutbadfit=False,
useguess=False,
)
peakfreqs = np.zeros((thespectrogram.shape[1] - 1), dtype=float)
for i in range(0, thespectrogram.shape[1] - 1):
(
maxindex,
peakfreqs[i],
maxval,
maxsigma,
maskval,
failreason,
peakstart,
peakend,
) = thefitter.fit(thespectrogram[:, i])
if not (lowerliminpts <= maxindex <= upperliminpts):
peakfreqs[i] = -1.0
return self.times[:-1], peakfreqs
def spectralfilterprops(thefilter, debug=False):
lowerstop, lowerpass, upperpass, upperstop = thefilter["filter"].getfreqs()
lowerstopindex = valtoindex(thefilter["frequencies"], lowerstop)
lowerpassindex = valtoindex(thefilter["frequencies"], lowerpass)
upperpassindex = valtoindex(thefilter["frequencies"], upperpass)
upperstopindex = np.min([
valtoindex(thefilter["frequencies"], upperstop),
len(thefilter["frequencies"]) - 1
])
if debug:
print("target freqs:", lowerstop, lowerpass, upperpass, upperstop)
print(
"actual freqs:",
thefilter["frequencies"][lowerstopindex],
thefilter["frequencies"][lowerpassindex],
thefilter["frequencies"][upperpassindex],
thefilter["frequencies"][upperstopindex],
)
response = {}
passbandmean = np.mean(
thefilter["transferfunc"][lowerpassindex:upperpassindex])
passbandmax = np.max(
thefilter["transferfunc"][lowerpassindex:upperpassindex])
passbandmin = np.min(
thefilter["transferfunc"][lowerpassindex:upperpassindex])
response["passbandripple"] = (passbandmax - passbandmin) / passbandmean
if lowerstopindex > 2:
response["lowerstopmean"] = (
np.mean(thefilter["transferfunc"][0:lowerstopindex]) /
passbandmean)
response["lowerstopmax"] = (
np.max(np.abs(thefilter["transferfunc"][0:lowerstopindex])) /
passbandmean)
else:
response["lowerstopmean"] = 0.0
response["lowerstopmax"] = 0.0
if len(thefilter["transferfunc"]) - upperstopindex > 2:
response["upperstopmean"] = (
np.mean(thefilter["transferfunc"][upperstopindex:-1]) /
passbandmean)
response["upperstopmax"] = (
np.max(np.abs(thefilter["transferfunc"][upperstopindex:-1])) /
passbandmean)
else:
response["upperstopmean"] = 0.0
response["upperstopmax"] = 0.0
return response
def setlimits(self, freqmin, freqmax):
self.freqmin = freqmin
self.freqmax = freqmax
if self.freqaxisvalid:
self.freqmininpts = np.max(
[0, tide_util.valtoindex(self.freqaxis, self.freqmin)])
self.freqmaxinpts = np.min([
tide_util.valtoindex(self.freqaxis, self.freqmax),
len(self.freqaxis) - 1,
])
if self.debug:
print("setlimits:")
print("\tfreqmin,freqmax:", self.freqmin, self.freqmax)
print("\tfreqmininpts,freqmaxinpts:", self.freqmininpts,
self.freqmaxinpts)
def getpeaks(xvals, yvals, xrange=None, bipolar=False, display=False):
peaks, dummy = find_peaks(yvals, height=0)
if bipolar:
negpeaks, dummy = find_peaks(-yvals, height=0)
peaks = np.concatenate((peaks, negpeaks))
procpeaks = []
if xrange is None:
lagmin = xvals[0]
lagmax = xvals[-1]
else:
lagmin = xrange[0]
lagmax = xrange[1]
originloc = tide_util.valtoindex(xvals, 0.0, discrete=False)
for thepeak in peaks:
if lagmin <= xvals[thepeak] <= lagmax:
if bipolar:
procpeaks.append([
xvals[thepeak],
yvals[thepeak],
tide_util.valtoindex(xvals, xvals[thepeak], discrete=False)
- originloc,
])
else:
if yvals[thepeak] > 0.0:
procpeaks.append([
xvals[thepeak],
yvals[thepeak],
tide_util.valtoindex(
xvals, xvals[thepeak], discrete=False) - originloc,
])
if display:
plotx = []
ploty = []
offset = []
for thepeak in procpeaks:
plotx.append(thepeak[0])
ploty.append(thepeak[1])
offset.append(thepeak[2])
plt.plot(xvals, yvals)
plt.plot(plotx, ploty, "x")
plt.plot(xvals, np.zeros_like(yvals), "--", color="gray")
plt.show()
return procpeaks
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 test_valtoindex(debug=False):
tr = 1.0
testtr = 0.7
xaxislen = 100
shiftdist = 30
xaxis = np.arange(0.0, tr * xaxislen, tr)
minx = np.min(xaxis)
maxx = np.max(xaxis)
testvec = np.arange(-1.0, 1.1 * maxx, testtr)
for i in range(len(testvec)):
testval = testvec[i]
indclosest = valtoindex(xaxis, testval)
print(testval, xaxis[indclosest])
def test_valtoindex(debug=False):
tr = 1.0
testtr = 0.7
xaxislen = 100
shiftdist = 30
xaxis = np.arange(0.0, tr * xaxislen, tr)
minx = np.min(xaxis)
maxx = np.max(xaxis)
testvec = np.arange(-1.0, 1.1 * maxx, testtr)
for i in range(len(testvec)):
testval = testvec[i]
indclosest = valtoindex(xaxis, testval)
print(testval, xaxis[indclosest])
def setreftc(self, reftc):
self.reftc = reftc + 0.0
self.prepreftc = self.preptc(self.reftc)
# get frequency axis, etc
self.freqaxis, self.thecoherence = sp.signal.coherence(self.prepreftc,
self.prepreftc,
fs=self.Fs)
# window=self.windowfunc)'''
self.similarityfunclen = len(self.thecoherence)
self.similarityfuncorigin = 0
self.freqaxisvalid = True
self.datavalid = False
if self.freqmin is None or self.freqmax is None:
self.setlimits(self.freqaxis[0], self.freqaxis[-1])
self.freqmininpts = tide_util.valtoindex(self.freqaxis,
self.freqmin,
discretization="floor",
debug=self.debug)
self.freqmaxinpts = tide_util.valtoindex(self.freqaxis,
self.freqmax,
discretization="ceiling",
debug=self.debug)
def fit(self, incorrfunc):
# check to make sure xcorr_x and xcorr_y match
if self.corrtimeaxis is None:
print("Correlation time axis is not defined - exiting")
sys.exit()
if len(self.corrtimeaxis) != len(incorrfunc):
print(
"Correlation time axis and values do not match in length (",
len(self.corrtimeaxis),
"!=",
len(incorrfunc),
"- exiting",
)
sys.exit()
# set initial parameters
# absmaxsigma is in seconds
# maxsigma is in Hz
# maxlag is in seconds
warnings.filterwarnings("ignore", "Number*")
failreason = self.FML_NOERROR
maskval = np.uint16(1) # start out assuming the fit will succeed
binwidth = self.corrtimeaxis[1] - self.corrtimeaxis[0]
# set the search range
lowerlim = 0
upperlim = len(self.corrtimeaxis) - 1
if self.debug:
print(
"initial search indices are",
lowerlim,
"to",
upperlim,
"(",
self.corrtimeaxis[lowerlim],
self.corrtimeaxis[upperlim],
")",
)
# make an initial guess at the fit parameters for the gaussian
# start with finding the maximum value and its location
flipfac = 1.0
corrfunc = incorrfunc + 0.0
if self.useguess:
maxindex = tide_util.valtoindex(self.corrtimeaxis, self.maxguess)
if corrfunc[maxindex] < 0.0:
flipfac = -1.0
else:
maxindex, flipfac = self._maxindex_noedge(corrfunc)
corrfunc *= flipfac
maxlag_init = (1.0 * self.corrtimeaxis[maxindex]).astype("float64")
maxval_init = corrfunc[maxindex].astype("float64")
if self.debug:
print(
"maxindex, maxlag_init, maxval_init:",
maxindex,
maxlag_init,
maxval_init,
)
# set the baseline and baselinedev levels
if (self.functype == "correlation") or (self.functype == "hybrid"):
baseline = 0.0
baselinedev = 0.0
else:
# for mutual information, there is a nonzero baseline, so we want the difference from that.
baseline = np.median(corrfunc)
baselinedev = mad(corrfunc)
if self.debug:
print("baseline, baselinedev:", baseline, baselinedev)
# then calculate the width of the peak
if self.peakfittype == "fastquad" or self.peakfittype == "COM":
peakstart = np.max([1, maxindex - 2])
peakend = np.min([len(self.corrtimeaxis) - 2, maxindex + 2])
else:
thegrad = np.gradient(corrfunc).astype(
"float64") # the gradient of the correlation function
if (self.functype == "correlation") or (self.functype == "hybrid"):
if self.peakfittype == "quad":
peakpoints = np.where(
corrfunc > maxval_init - 0.05, 1, 0
) # mask for places where correlation exceeds searchfrac*maxval_init
else:
peakpoints = np.where(
corrfunc > self.searchfrac * maxval_init, 1, 0
) # mask for places where correlation exceeds searchfrac*maxval_init
else:
# for mutual information, there is a flattish, nonzero baseline, so we want the difference from that.
peakpoints = np.where(
corrfunc >
(baseline + self.searchfrac * (maxval_init - baseline)),
1,
0,
)
peakpoints[0] = 0
peakpoints[-1] = 0
peakstart = np.max([1, maxindex - 1])
peakend = np.min([len(self.corrtimeaxis) - 2, maxindex + 1])
if self.debug:
print("initial peakstart, peakend:", peakstart, peakend)
if self.functype == "mutualinfo":
while peakpoints[peakend + 1] == 1:
peakend += 1
while peakpoints[peakstart - 1] == 1:
peakstart -= 1
else:
while thegrad[peakend + 1] <= 0.0 and peakpoints[peakend +
1] == 1:
peakend += 1
while thegrad[peakstart - 1] >= 0.0 and peakpoints[peakstart -
1] == 1:
peakstart -= 1
if self.debug:
print("final peakstart, peakend:", peakstart, peakend)
# deal with flat peak top
while (peakend < (len(self.corrtimeaxis) - 3)
and corrfunc[peakend] == corrfunc[peakend - 1]):
peakend += 1
while peakstart > 2 and corrfunc[peakstart] == corrfunc[peakstart +
1]:
peakstart -= 1
if self.debug:
print("peakstart, peakend after flattop correction:",
peakstart, peakend)
print("\n")
for i in range(peakstart, peakend + 1):
print(self.corrtimeaxis[i], corrfunc[i])
print("\n")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("Peak sent to fitting routine")
plt.plot(
self.corrtimeaxis[peakstart:peakend + 1],
corrfunc[peakstart:peakend + 1],
"r",
)
plt.show()
# This is calculated from first principles, but it's always big by a factor or ~1.4.
# Which makes me think I dropped a factor if sqrt(2). So fix that with a final division
maxsigma_init = np.float64(
((peakend - peakstart + 1) * binwidth /
(2.0 * np.sqrt(-np.log(self.searchfrac)))) / np.sqrt(2.0))
if self.debug:
print("maxsigma_init:", maxsigma_init)
# now check the values for errors
if self.hardlimit:
rangeextension = 0.0
else:
rangeextension = (self.lagmax - self.lagmin) * 0.75
if not ((self.lagmin - rangeextension - binwidth) <= maxlag_init <=
(self.lagmax + rangeextension + binwidth)):
if maxlag_init <= (self.lagmin - rangeextension - binwidth):
failreason |= self.FML_INITLAGLOW
maxlag_init = self.lagmin - rangeextension - binwidth
else:
failreason |= self.FML_INITLAGHIGH
maxlag_init = self.lagmax + rangeextension + binwidth
if self.debug:
print("bad initial")
if maxsigma_init > self.absmaxsigma:
failreason |= self.FML_INITWIDTHHIGH
maxsigma_init = self.absmaxsigma
if self.debug:
print("bad initial width - too high")
if peakend - peakstart < 2:
failreason |= self.FML_INITWIDTHLOW
maxsigma_init = np.float64(
((2 + 1) * binwidth /
(2.0 * np.sqrt(-np.log(self.searchfrac)))) / np.sqrt(2.0))
if self.debug:
print("bad initial width - too low")
if (self.functype == "correlation") or (self.functype == "hybrid"):
if not (self.lthreshval <= maxval_init <=
self.uthreshval) and self.enforcethresh:
failreason |= self.FML_INITAMPLOW
if self.debug:
print(
"bad initial amp:",
maxval_init,
"is less than",
self.lthreshval,
)
if maxval_init < 0.0:
failreason |= self.FML_INITAMPLOW
maxval_init = 0.0
if self.debug:
print("bad initial amp:", maxval_init,
"is less than 0.0")
if maxval_init > 1.0:
failreason |= self.FML_INITAMPHIGH
maxval_init = 1.0
if self.debug:
print("bad initial amp:", maxval_init,
"is greater than 1.0")
else:
# somewhat different rules for mutual information peaks
if ((maxval_init - baseline) < self.lthreshval *
baselinedev) or (maxval_init < baseline):
failreason |= self.FML_INITAMPLOW
maxval_init = 0.0
if self.debug:
print("bad initial amp:", maxval_init,
"is less than 0.0")
if (failreason != self.FML_NOERROR) and self.zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(maxlag_init)
maxsigma = np.float64(maxsigma_init)
# refine if necessary
if self.peakfittype != "None":
if self.peakfittype == "COM":
X = self.corrtimeaxis[peakstart:peakend + 1] - baseline
data = corrfunc[peakstart:peakend + 1]
maxval = maxval_init
maxlag = np.sum(X * data) / np.sum(data)
maxsigma = 10.0
elif self.peakfittype == "gauss":
X = self.corrtimeaxis[peakstart:peakend + 1] - baseline
data = corrfunc[peakstart:peakend + 1]
# do a least squares fit over the top of the peak
# p0 = np.array([maxval_init, np.fmod(maxlag_init, lagmod), maxsigma_init], dtype='float64')
p0 = np.array([maxval_init, maxlag_init, maxsigma_init],
dtype="float64")
if self.debug:
print("fit input array:", p0)
try:
plsq, dummy = sp.optimize.leastsq(tide_fit.gaussresiduals,
p0,
args=(data, X),
maxfev=5000)
maxval = plsq[0] + baseline
maxlag = np.fmod((1.0 * plsq[1]), self.lagmod)
maxsigma = plsq[2]
except:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
if self.debug:
print("fit output array:", [maxval, maxlag, maxsigma])
elif self.peakfittype == "fastgauss":
X = self.corrtimeaxis[peakstart:peakend + 1] - baseline
data = corrfunc[peakstart:peakend + 1]
# do a non-iterative fit over the top of the peak
# 6/12/2015 This is just broken. Gives quantized maxima
maxlag = np.float64(1.0 * np.sum(X * data) / np.sum(data))
maxsigma = np.float64(
np.sqrt(
np.abs(np.sum((X - maxlag)**2 * data) / np.sum(data))))
maxval = np.float64(data.max()) + baseline
elif self.peakfittype == "fastquad":
maxlag, maxval, maxsigma, ismax, badfit = tide_fit.refinepeak_quad(
self.corrtimeaxis, corrfunc, maxindex)
elif self.peakfittype == "quad":
X = self.corrtimeaxis[peakstart:peakend + 1]
data = corrfunc[peakstart:peakend + 1]
try:
thecoffs = np.polyfit(X, data, 2)
a = thecoffs[0]
b = thecoffs[1]
c = thecoffs[2]
maxlag = -b / (2.0 * a)
maxval = a * maxlag * maxlag + b * maxlag + c
maxsigma = 1.0 / np.fabs(a)
if self.debug:
print("poly coffs:", a, b, c)
print("maxlag, maxval, maxsigma:", maxlag, maxval,
maxsigma)
except np.lib.polynomial.RankWarning:
maxlag = 0.0
maxval = 0.0
maxsigma = 0.0
if self.debug:
print("\n")
for i in range(len(X)):
print(X[i], data[i])
print("\n")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("Peak and fit")
plt.plot(X, data, "r")
plt.plot(X, c + b * X + a * X * X, "b")
plt.show()
else:
print("illegal peak refinement type")
# check for errors in fit
fitfail = False
if self.bipolar:
lowestcorrcoeff = -1.0
else:
lowestcorrcoeff = 0.0
if (self.functype == "correlation") or (self.functype == "hybrid"):
if maxval < lowestcorrcoeff:
failreason |= self.FML_FITAMPLOW
maxval = lowestcorrcoeff
if self.debug:
print("bad fit amp: maxval is lower than lower limit")
fitfail = True
if np.abs(maxval) > 1.0:
if not self.allowhighfitamps:
failreason |= self.FML_FITAMPHIGH
if self.debug:
print(
"bad fit amp: magnitude of",
maxval,
"is greater than 1.0",
)
fitfail = True
maxval = 1.0 * np.sign(maxval)
else:
# different rules for mutual information peaks
if ((maxval - baseline) <
self.lthreshval * baselinedev) or (maxval < baseline):
failreason |= self.FML_FITAMPLOW
if self.debug:
if (maxval - baseline) < self.lthreshval * baselinedev:
print(
"FITAMPLOW: maxval - baseline:",
maxval - baseline,
" < lthreshval * baselinedev:",
self.lthreshval * baselinedev,
)
if maxval < baseline:
print("FITAMPLOW: maxval < baseline:", maxval,
baseline)
maxval_init = 0.0
if self.debug:
print("bad fit amp: maxval is lower than lower limit")
if (self.lagmin > maxlag) or (maxlag > self.lagmax):
if self.debug:
print("bad lag after refinement")
if self.lagmin > maxlag:
failreason |= self.FML_FITLAGLOW
maxlag = self.lagmin
else:
failreason |= self.FML_FITLAGHIGH
maxlag = self.lagmax
fitfail = True
if maxsigma > self.absmaxsigma:
failreason |= self.FML_FITWIDTHHIGH
if self.debug:
print("bad width after refinement:", maxsigma, ">",
self.absmaxsigma)
maxsigma = self.absmaxsigma
fitfail = True
if maxsigma < self.absminsigma:
failreason |= self.FML_FITWIDTHLOW
if self.debug:
print("bad width after refinement:", maxsigma, "<",
self.absminsigma)
maxsigma = self.absminsigma
fitfail = True
if fitfail:
if self.debug:
print("fit fail")
if self.zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.uint16(0)
# print(maxlag_init, maxlag, maxval_init, maxval, maxsigma_init, maxsigma, maskval, failreason, fitfail)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(np.fmod(maxlag_init, self.lagmod))
maxsigma = np.float64(maxsigma_init)
if failreason != self.FML_NOERROR:
maskval = np.uint16(0)
if self.debug or self.displayplots:
print(
"init to final: maxval",
maxval_init,
maxval,
", maxlag:",
maxlag_init,
maxlag,
", width:",
maxsigma_init,
maxsigma,
)
if self.displayplots and (self.peakfittype != "None") and (maskval !=
0.0):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("Data and fit")
hiresx = np.arange(X[0], X[-1], (X[1] - X[0]) / 10.0)
plt.plot(
X,
data,
"ro",
hiresx,
tide_fit.gauss_eval(hiresx,
np.array([maxval, maxlag, maxsigma])),
"b-",
)
plt.show()
return (
maxindex,
maxlag,
flipfac * maxval,
maxsigma,
maskval,
failreason,
peakstart,
peakend,
)
def findmaxlag_gauss(thexcorr_x, thexcorr_y, lagmin, lagmax, widthlimit,
edgebufferfrac=0.0, threshval=0.0, uthreshval=30.0,
debug=False, tweaklims=True, zerooutbadfit=True, refine=False, maxguess=0.0, useguess=False,
searchfrac=0.5, fastgauss=False, lagmod=1000.0, enforcethresh=True, displayplots=False):
"""
Parameters
----------
thexcorr_x
thexcorr_y
lagmin
lagmax
widthlimit
edgebufferfrac
threshval
uthreshval
debug
tweaklims
zerooutbadfit
refine
maxguess
useguess
searchfrac
fastgauss
lagmod
enforcethresh
displayplots
Returns
-------
"""
# set initial parameters
# widthlimit is in seconds
# maxsigma is in Hz
# maxlag is in seconds
warnings.filterwarnings("ignore", "Number*")
failreason = np.uint16(0)
maxlag = np.float64(0.0)
maxval = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.uint16(1)
numlagbins = len(thexcorr_y)
binwidth = thexcorr_x[1] - thexcorr_x[0]
searchbins = int(widthlimit // binwidth)
lowerlim = int(numlagbins * edgebufferfrac)
upperlim = numlagbins - lowerlim - 1
if tweaklims:
lowerlim = 0
upperlim = numlagbins - 1
while (thexcorr_y[lowerlim + 1] < thexcorr_y[lowerlim]) and (lowerlim + 1) < upperlim:
lowerlim += 1
while (thexcorr_y[upperlim - 1] < thexcorr_y[upperlim]) and (upperlim - 1) > lowerlim:
upperlim -= 1
FML_BADAMPLOW = np.uint16(0x01)
FML_BADAMPHIGH = np.uint16(0x02)
FML_BADSEARCHWINDOW = np.uint16(0x04)
FML_BADWIDTH = np.uint16(0x08)
FML_BADLAG = np.uint16(0x10)
FML_HITEDGE = np.uint16(0x20)
FML_FITFAIL = np.uint16(0x40)
FML_INITFAIL = np.uint16(0x80)
# make an initial guess at the fit parameters for the gaussian
# start with finding the maximum value
if useguess:
maxindex = tide_util.valtoindex(thexcorr_x, maxguess)
nlowerlim = int(maxindex - widthlimit / 2.0)
nupperlim = int(maxindex + widthlimit / 2.0)
if nlowerlim < lowerlim:
nlowerlim = lowerlim
nupperlim = lowerlim + int(widthlimit)
if nupperlim > upperlim:
nupperlim = upperlim
nlowerlim = upperlim - int(widthlimit)
maxval_init = thexcorr_y[maxindex].astype('float64')
else:
maxindex = (np.argmax(thexcorr_y[lowerlim:upperlim]) + lowerlim).astype('int32')
maxval_init = thexcorr_y[maxindex].astype('float64')
# now get a location for that value
maxlag_init = (1.0 * thexcorr_x[maxindex]).astype('float64')
# and calculate the width of the peak
upperlimit = len(thexcorr_y) - 1
lowerlimit = 0
i = 0
j = 0
while (maxindex + i <= upperlimit) and (thexcorr_y[maxindex + i] > searchfrac * maxval_init) and (i < searchbins):
i += 1
if (maxindex + i > upperlimit) or (i > searchbins):
i -= 1
while (maxindex - j >= lowerlimit) and (thexcorr_y[maxindex - j] > searchfrac * maxval_init) and (j < searchbins):
j += 1
if (maxindex -j < lowerlimit) or (j > searchbins):
j -= 1
# This is calculated from first principles, but it's always big by a factor or ~1.4.
# Which makes me think I dropped a factor if sqrt(2). So fix that with a final division
maxsigma_init = np.float64(((i + j + 1) * binwidth / (2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
# now check the values for errors and refine if necessary
fitend = min(maxindex + i + 1, upperlimit)
fitstart = max(1, maxindex - j)
if not ((lagmin + binwidth) <= maxlag_init <= (lagmax - binwidth)):
failreason += FML_HITEDGE
if lagmin + binwidth <= maxlag_init:
maxlag_init = lagmin + binwidth
else:
maxlag_init = lagmax - binwidth
if i + j + 1 < 3:
failreason += FML_BADSEARCHWINDOW
maxsigma_init = np.float64((3.0 * binwidth / (2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
if maxsigma_init > widthlimit:
failreason += FML_BADWIDTH
maxsigma_init = widthlimit
if (maxval_init < threshval) and enforcethresh:
failreason += FML_BADAMPLOW
if (maxval_init < 0.0):
failreason += FML_BADAMPLOW
maxval_init = 0.0
if (maxval_init > 1.0):
failreason |= FML_BADAMPHIGH
maxval_init = 1.0
if failreason > 0:
maskval = np.uint16(0)
if failreason > 0 and zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
else:
if refine:
data = thexcorr_y[fitstart:fitend]
X = thexcorr_x[fitstart:fitend]
if fastgauss:
# do a non-iterative fit over the top of the peak
# 6/12/2015 This is just broken. Gives quantized maxima
maxlag = np.float64(1.0 * sum(X * data) / sum(data))
# maxsigma = np.sqrt(abs(np.square(sum((X - maxlag)) * data) / sum(data)))
maxsigma = np.float64(np.sqrt(np.fabs(np.sum((X - maxlag) ** 2 * data) / np.sum(data))))
maxval = np.float64(data.max())
else:
# do a least squares fit over the top of the peak
p0 = np.array([maxval_init, maxlag_init, maxsigma_init], dtype='float64')
if fitend - fitstart >= 3:
plsq, dummy = sp.optimize.leastsq(gaussresiduals, p0,
args=(data, X), maxfev=5000)
maxval = plsq[0]
maxlag = np.fmod((1.0 * plsq[1]), lagmod)
maxsigma = plsq[2]
# if maxval > 1.0, fit failed catastrophically, zero out or reset to initial value
# corrected logic for 1.1.6
if (np.fabs(maxval)) > 1.0 or (lagmin > maxlag) or (maxlag > lagmax):
if zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.int16(0)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(maxlag_init)
maxsigma = np.float64(maxsigma_init)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(np.fmod(maxlag_init, lagmod))
maxsigma = np.float64(maxsigma_init)
if maxval == 0.0:
failreason += FML_FITFAIL
if not (lagmin <= maxlag <= lagmax):
failreason += FML_BADLAG
if failreason > 0:
maskval = np.uint16(0)
if failreason > 0 and zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
if debug or displayplots:
print("init to final: maxval", maxval_init, maxval, ", maxlag:", maxlag_init, maxlag, ", width:", maxsigma_init,
maxsigma)
if displayplots and refine and (maskval != 0.0):
fig = pl.figure()
ax = fig.add_subplot(111)
ax.set_title('Data and fit')
hiresx = np.arange(X[0], X[-1], (X[1] - X[0]) / 10.0)
pl.plot(X, data, 'ro', hiresx, gauss_eval(hiresx, np.array([maxval, maxlag, maxsigma])), 'b-')
pl.show()
return maxindex, maxlag, maxval, maxsigma, maskval, failreason, fitstart, fitend
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
def congrid(xaxis, loc, val, width, kernel="kaiser", cyclic=True, debug=False):
"""
Perform a convolution gridding operation with a Kaiser-Bessel or Gaussian kernel of width 'width'. Grid
parameters are cached for performance.
Parameters
----------
xaxis: array-like
The target axis for resampling
loc: float
The location, in x-axis units, of the sample to be gridded
val: float
The value to be gridded
width: float
The width of the gridding kernel in target bins
kernel: {'old', 'gauss', 'kaiser'}, optional
The type of convolution gridding kernel. Default is 'kaiser'.
cyclic: bool, optional
When True, gridding wraps around the endpoints of xaxis. Default is True.
debug: bool, optional
When True, output additional information about the gridding process
Returns
-------
vals: array-like
The input value, convolved with the gridding kernel, projected on to x axis points
weights: array-like
The values of convolution kernel, projected on to x axis points (used for normalization)
indices: array-like
The indices along the x axis where the vals and weights fall.
Notes
-----
See IEEE TRANSACTIONS ON MEDICAL IMAGING. VOL. IO.NO. 3, SEPTEMBER 1991
"""
global congridyvals
if (congridyvals["kernel"] != kernel) or (congridyvals["width"] != width):
if congridyvals["kernel"] != kernel:
if debug:
print(congridyvals["kernel"], "!=", kernel)
if congridyvals["width"] != width:
if debug:
print(congridyvals["width"], "!=", width)
if debug:
print("(re)initializing congridyvals")
congridyvals = {}
congridyvals["kernel"] = kernel
congridyvals["width"] = width * 1.0
optsigma = np.array([0.4241, 0.4927, 0.4839, 0.5063, 0.5516, 0.5695, 0.5682, 0.5974])
optbeta = np.array([1.9980, 2.3934, 3.3800, 4.2054, 4.9107, 5.7567, 6.6291, 7.4302])
xstep = xaxis[1] - xaxis[0]
if (loc < xaxis[0] - xstep / 2.0 or loc > xaxis[-1] + xstep / 2.0) and not cyclic:
print("loc", loc, "not in range", xaxis[0], xaxis[-1])
# choose the smoothing kernel based on the width
if kernel != "old":
if not (1.5 <= width <= 5.0) or (np.fmod(width, 0.5) > 0.0):
print("congrid: width is", width)
print("congrid: width must be a half-integral value between 1.5 and 5.0 inclusive")
sys.exit()
else:
kernelindex = int((width - 1.5) // 0.5)
# find the closest grid point to the target location, calculate relative offsets from this point
center = tide_util.valtoindex(xaxis, loc)
offset = np.fmod(np.round((loc - xaxis[center]) / xstep, 3), 1.0) # will vary from -0.5 to 0.5
if cyclic:
if center == len(xaxis) - 1 and offset > 0.5:
center = 0
offset -= 1.0
if center == 0 and offset < -0.5:
center = len(xaxis) - 1
offset += 1.0
if not (-0.5 <= offset <= 0.5):
print("(loc, xstep, center, offset):", loc, xstep, center, offset)
print("xaxis:", xaxis)
sys.exit()
offsetkey = str(offset)
if kernel == "old":
if debug:
print("gridding with old kernel")
widthinpts = int(np.round(width * 4.6 / xstep))
widthinpts -= widthinpts % 2 - 1
try:
yvals = congridyvals[offsetkey]
except KeyError:
if debug:
print("new key:", offsetkey)
xvals = (
np.linspace(
-xstep * (widthinpts // 2),
xstep * (widthinpts // 2),
num=widthinpts,
endpoint=True,
)
+ offset
)
congridyvals[offsetkey] = tide_fit.gauss_eval(xvals, np.array([1.0, 0.0, width]))
yvals = congridyvals[offsetkey]
startpt = int(center - widthinpts // 2)
indices = range(startpt, startpt + widthinpts)
indices = np.remainder(indices, len(xaxis))
if debug:
print("center, offset, indices, yvals", center, offset, indices, yvals)
return val * yvals, yvals, indices
else:
offsetinpts = center + offset
startpt = int(np.ceil(offsetinpts - width / 2.0))
endpt = int(np.floor(offsetinpts + width / 2.0))
indices = np.remainder(range(startpt, endpt + 1), len(xaxis))
try:
yvals = congridyvals[offsetkey]
except KeyError:
if debug:
print("new key:", offsetkey)
xvals = indices - center + offset
if kernel == "gauss":
sigma = optsigma[kernelindex]
congridyvals[offsetkey] = tide_fit.gauss_eval(xvals, np.array([1.0, 0.0, sigma]))
elif kernel == "kaiser":
beta = optbeta[kernelindex]
congridyvals[offsetkey] = tide_fit.kaiserbessel_eval(
xvals, np.array([beta, width / 2.0])
)
else:
print("illegal kernel value in congrid - exiting")
sys.exit()
yvals = congridyvals[offsetkey]
if debug:
print("xvals, yvals", xvals, yvals)
if debug:
print("center, offset, indices, yvals", center, offset, indices, yvals)
return val * yvals, yvals, indices
def showtc(args):
# set the sample rate
if args.samplerate == "auto":
samplerate = 1.0
args.samplerate = samplerate
else:
samplerate = args.samplerate
# set the appropriate display mode
if args.displaymode == "time":
dospectrum = False
specmode = "power"
elif args.displaymode == "power":
dospectrum = True
specmode = "power"
elif args.displaymode == "phase":
dospectrum = True
specmode = "phase"
else:
print("illegal display mode")
sys.exit()
# determine how to composite multiple plots
if args.plotformat == "overlaid":
separate = False
linky = True
elif args.plotformat == "separate":
separate = True
linky = False
elif args.plotformat == "separatelinked":
separate = True
linky = True
else:
print("illegal formatting mode")
sys.exit()
# set various cosmetic aspects of the plots
if args.colors is not None:
colornames = args.colors.split(",")
else:
colornames = []
if args.legends is not None:
legends = args.legends.split(",")
legendset = True
else:
legends = []
legendset = False
dolegend = args.dolegend
if args.linewidths is not None:
thelinewidth = []
for thestring in args.linewidths.split(","):
thelinewidth.append(float(thestring))
else:
thelinewidth = [1.0]
numlinewidths = len(thelinewidth)
if 0 <= args.legendloc <= 10:
legendloc = args.legendloc
else:
print("illegal legend location:", args.legendloc)
sys.exit()
savespec = False
detrendorder = 1
demean = False
useHamming = True
# check range
if args.theendtime is None:
args.theendtime = 100000000.0
if args.thestarttime is not None:
if args.thestarttime >= args.theendtime:
print("endtime must be greater then starttime;")
sys.exit()
# handle required args first
xvecs = []
yvecs = []
linelabels = []
samplerates = []
numvecs = 0
minlen = 100000000
shortcolnames = True
# read in all the data
for i in range(0, len(args.textfilenames)):
thisfilename, thiscolspec = tide_io.parsefilespec(args.textfilenames[i])
# check file type
(
thissamplerate,
thisstartoffset,
colnames,
invecs,
dummy,
dummy,
) = tide_io.readvectorsfromtextfile(args.textfilenames[i], debug=args.debug)
if args.debug:
print("On return from readvectorsfromtextfile:")
print(f"\targs.samplerate: {args.samplerate}")
print(f"\tthissamplerate: {thissamplerate}")
print(f"\targs.thestarttime: {args.thestarttime}")
print(f"\tthisstartoffset: {thisstartoffset}")
if args.debug:
print("input data dimensions:", invecs.shape)
if thissamplerate is None:
thissamplerate = samplerate
if thisstartoffset is None:
# print("thisstartoffset is None")
if args.thestarttime is None:
print("args.thestarttime is None")
args.thestarttime = 0.0
else:
print(f"args.thestarttime is {args.thestarttime}")
thisstartoffset = args.thestarttime
else:
# print(f"thisstartoffset is {thisstartoffset}")
if args.thestarttime is None:
print("args.thestarttime is None")
args.thestarttime = thisstartoffset
else:
print(f"args.thestarttime is {args.thestarttime}")
thisstartoffset = args.thestarttime
if args.debug:
print("After preprocessing time variables:")
print(f"\targs.samplerate: {args.samplerate}")
print(f"\tthissamplerate: {thissamplerate}")
print(f"\targs.thestarttime: {args.thestarttime}")
print(f"\tthisstartoffset: {thisstartoffset}")
if args.debug:
print(f"file {args.textfilenames[i]} colnames: {colnames}")
if args.dotranspose:
invecs = np.transpose(invecs)
if args.debug:
print(" ", invecs.shape[0], " columns")
for j in range(0, invecs.shape[0]):
if args.debug:
print("appending vector number ", j)
if dospectrum:
if invecs.shape[1] % 2 == 1:
invec = invecs[j, :-1]
else:
invec = invecs[j, :]
if detrendorder > 0:
invec = tide_fit.detrend(invec, order=detrendorder, demean=True)
elif demean:
invec = invec - np.mean(invec)
if useHamming:
freqaxis, spectrum = tide_filt.spectrum(
tide_filt.hamming(len(invec)) * invec, Fs=thissamplerate, mode=specmode,
)
else:
freqaxis, spectrum = tide_filt.spectrum(
invec, Fs=thissamplerate, mode=specmode
)
if savespec:
tide_io.writenpvecs(
np.transpose(np.stack([freqaxis, spectrum], axis=1)), "thespectrum.txt",
)
xvecs.append(freqaxis)
yvecs.append(spectrum)
else:
yvecs.append(invecs[j] * 1.0)
xvecs.append(
thisstartoffset + np.arange(0.0, len(yvecs[-1]), 1.0) / thissamplerate
)
if len(yvecs[-1]) < minlen:
minlen = len(yvecs[-1])
if not legendset:
if invecs.shape[0] > 1:
if colnames is None:
if shortcolnames:
linelabels.append("column" + str(j).zfill(2))
else:
linelabels.append(thisfilename + "_column" + str(j).zfill(2))
else:
if shortcolnames:
linelabels.append(colnames[j])
else:
linelabels.append(thisfilename + "_" + colnames[j])
else:
linelabels.append(thisfilename)
else:
linelabels.append(legends[i % len(legends)])
"""if invecs.shape[0] > 1:
linelabels.append(legends[i % len(legends)] + '_column' + str(j).zfill(2))
else:
linelabels.append(legends[i % len(legends)])"""
samplerates.append(thissamplerate + 0.0)
if args.debug:
print(
"timecourse:",
j,
", len:",
len(xvecs[-1]),
", timerange:",
xvecs[-1][0],
xvecs[-1][-1],
)
numvecs += 1
thestartpoint = tide_util.valtoindex(xvecs[0], args.thestarttime)
theendpoint = tide_util.valtoindex(xvecs[0], args.theendtime)
args.thestarttime = xvecs[0][thestartpoint]
args.theendtime = xvecs[0][theendpoint]
if args.debug:
print("full range (pts):", thestartpoint, theendpoint)
print("full range (time):", args.thestarttime, args.theendtime)
overallxmax = -1e38
overallxmin = 1e38
for thevec in xvecs:
overallxmax = np.max([np.max(thevec), overallxmax])
overallxmin = np.min([np.min(thevec), overallxmin])
xrange = (np.max([overallxmin, args.thestarttime]), np.min([overallxmax, args.theendtime]))
ymins = []
ymaxs = []
for thevec in yvecs:
ymins.append(np.min(np.asarray(thevec[thestartpoint:theendpoint], dtype="float")))
ymaxs.append(np.max(np.asarray(thevec[thestartpoint:theendpoint], dtype="float")))
overallymax = -1e38
overallymin = 1e38
for thevec in yvecs:
overallymax = np.max([np.max(thevec), overallymax])
overallymin = np.min([np.min(thevec), overallymin])
yrange = (overallymin, overallymax)
if args.debug:
print("xrange:", xrange)
print("yrange:", yrange)
if args.voffset < 0.0:
args.voffset = yrange[1] - yrange[0]
if args.debug:
print("voffset:", args.voffset)
if not separate:
for i in range(0, numvecs):
yvecs[i] += (numvecs - i - 1) * args.voffset
overallymax = -1e38
overallymin = 1e38
for thevec in yvecs:
overallymax = np.max([np.max(thevec), overallymax])
overallymin = np.min([np.min(thevec), overallymin])
yrange = (overallymin, overallymax)
if args.dowaterfall:
xstep = (xrange[1] - xrange[0]) / numvecs
ystep = yrange[1] - yrange[0]
for i in range(numvecs):
xvecs[i] = xvecs[i] + i * xstep
yvecs[i] = 10.0 * yvecs[i] / ystep + i * ystep
# now plot it out
if separate:
thexaxfontsize = 6 * args.fontscalefac
theyaxfontsize = 6 * args.fontscalefac
thexlabelfontsize = 6 * args.fontscalefac
theylabelfontsize = 6 * args.fontscalefac
thelegendfontsize = 5 * args.fontscalefac
thetitlefontsize = 6 * args.fontscalefac
thesuptitlefontsize = 10 * args.fontscalefac
else:
thexaxfontsize = 10 * args.fontscalefac
theyaxfontsize = 10 * args.fontscalefac
thexlabelfontsize = 10 * args.fontscalefac
theylabelfontsize = 10 * args.fontscalefac
thelegendfontsize = 8 * args.fontscalefac
thetitlefontsize = 10 * args.fontscalefac
thesuptitlefontsize = 10 * args.fontscalefac
if len(colornames) > 0:
colorlist = [colornames[i % len(colornames)] for i in range(numvecs)]
else:
colorlist = [cm.nipy_spectral(float(i) / numvecs) for i in range(numvecs)]
fig = figure()
if separate:
if args.thetitle is not None:
fig.suptitle(args.thetitle, fontsize=thesuptitlefontsize)
if linky:
axlist = fig.subplots(numvecs, sharex=True, sharey=True)[:]
else:
axlist = fig.subplots(numvecs, sharex=True, sharey=False)[:]
else:
ax = fig.add_subplot(1, 1, 1)
if args.thetitle is not None:
ax.set_title(args.thetitle, fontsize=thetitlefontsize)
for i in range(0, numvecs):
if separate:
ax = axlist[i]
ax.plot(
xvecs[i],
yvecs[i],
color=colorlist[i],
label=linelabels[i],
linewidth=thelinewidth[i % numlinewidths],
)
if dolegend:
ax.legend(fontsize=thelegendfontsize, loc=legendloc)
ax.set_xlim(xrange)
if linky:
# print(yrange)
ax.set_ylim(yrange)
else:
themax = np.max(yvecs[i])
themin = np.min(yvecs[i])
thediff = themax - themin
# print(themin, themax, thediff)
ax.set_ylim(top=(themax + thediff / 20.0), bottom=(themin - thediff / 20.0))
if args.showxax:
ax.tick_params(axis="x", labelsize=thexlabelfontsize, which="both")
if args.showyax:
ax.tick_params(axis="y", labelsize=theylabelfontsize, which="both")
if separate:
fig.subplots_adjust(hspace=0)
setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
if dospectrum:
if args.xlabel is None:
args.xlabel = "Frequency (Hz)"
if specmode == "power":
if args.ylabel is None:
args.ylabel = "Signal power"
else:
if args.ylabel is None:
args.ylabel = "Signal phase"
else:
if args.xlabel is None:
args.xlabel = "Time (s)"
if args.showxax:
ax.set_xlabel(args.xlabel, fontsize=thexlabelfontsize, fontweight="bold")
else:
ax.xaxis.set_visible(False)
if args.showyax:
ax.set_ylabel(args.ylabel, fontsize=theylabelfontsize, fontweight="bold")
else:
ax.yaxis.set_visible(False)
# fig.tight_layout()
if args.outputfile is None:
show()
else:
savefig(args.outputfile, bbox_inches="tight", dpi=args.saveres)
def findmaxlag_gauss_rev(thexcorr_x, thexcorr_y, lagmin, lagmax, widthlimit,
absmaxsigma=1000.0,
hardlimit=True,
bipolar=False,
edgebufferfrac=0.0,
threshval=0.0,
uthreshval=1.0,
debug=False,
tweaklims=True,
zerooutbadfit=True,
refine=False,
maxguess=0.0,
useguess=False,
searchfrac=0.5,
fastgauss=False,
lagmod=1000.0,
enforcethresh=True,
displayplots=False):
"""
Parameters
----------
thexcorr_x: 1D float array
The time axis of the correlation function
thexcorr_y: 1D float array
The values of the correlation function
lagmin: float
The minimum allowed lag time in seconds
lagmax: float
The maximum allowed lag time in seconds
widthlimit: float
The maximum allowed peak halfwidth in seconds
absmaxsigma
hardlimit
bipolar: boolean
If true find the correlation peak with the maximum absolute value, regardless of sign
edgebufferfrac
threshval
uthreshval
debug
tweaklims
zerooutbadfit
refine
maxguess
useguess
searchfrac
fastgauss
lagmod
enforcethresh
displayplots
Returns
-------
"""
# set initial parameters
# widthlimit is in seconds
# maxsigma is in Hz
# maxlag is in seconds
warnings.filterwarnings("ignore", "Number*")
maxlag = np.float64(0.0)
maxval = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.uint16(1) # start out assuming the fit will succeed
numlagbins = len(thexcorr_y)
binwidth = thexcorr_x[1] - thexcorr_x[0]
# define error values
failreason = np.uint16(0)
FML_BADAMPLOW = np.uint16(0x01)
FML_BADAMPHIGH = np.uint16(0x02)
FML_BADSEARCHWINDOW = np.uint16(0x04)
FML_BADWIDTH = np.uint16(0x08)
FML_BADLAG = np.uint16(0x10)
FML_HITEDGE = np.uint16(0x20)
FML_FITFAIL = np.uint16(0x40)
FML_INITFAIL = np.uint16(0x80)
# set the search range
lowerlim = 0
upperlim = len(thexcorr_x) - 1
if debug:
print('initial search indices are', lowerlim, 'to', upperlim,
'(', thexcorr_x[lowerlim], thexcorr_x[upperlim], ')')
# make an initial guess at the fit parameters for the gaussian
# start with finding the maximum value and its location
flipfac = 1.0
if useguess:
maxindex = tide_util.valtoindex(thexcorr_x, maxguess)
else:
maxindex, flipfac = maxindex_noedge(thexcorr_x, thexcorr_y, bipolar=bipolar)
thexcorr_y *= flipfac
maxlag_init = (1.0 * thexcorr_x[maxindex]).astype('float64')
maxval_init = thexcorr_y[maxindex].astype('float64')
if debug:
print('maxindex, maxlag_init, maxval_init:', maxindex, maxlag_init, maxval_init)
# then calculate the width of the peak
thegrad = np.gradient(thexcorr_y).astype('float64') # the gradient of the correlation function
peakpoints = np.where(thexcorr_y > searchfrac * maxval_init, 1,
0) # mask for places where correlaion exceeds serchfrac*maxval_init
peakpoints[0] = 0
peakpoints[-1] = 0
peakstart = maxindex + 0
peakend = maxindex + 0
while peakend < (len(thexcorr_x) - 2) and thegrad[peakend + 1] < 0.0 and peakpoints[peakend + 1] == 1:
peakend += 1
while peakstart > 1 and thegrad[peakstart - 1] > 0.0 and peakpoints[peakstart - 1] == 1:
peakstart -= 1
# This is calculated from first principles, but it's always big by a factor or ~1.4.
# Which makes me think I dropped a factor if sqrt(2). So fix that with a final division
maxsigma_init = np.float64(
((peakend - peakstart + 1) * binwidth / (2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
if debug:
print('maxsigma_init:', maxsigma_init)
# now check the values for errors
if hardlimit:
rangeextension = 0.0
else:
rangeextension = (lagmax - lagmin) * 0.75
if not ((lagmin - rangeextension - binwidth) <= maxlag_init <= (lagmax + rangeextension + binwidth)):
failreason |= (FML_INITFAIL | FML_BADLAG)
if (lagmin - rangeextension - binwidth) <= maxlag_init:
maxlag_init = lagmin - rangeextension - binwidth
else:
maxlag_init = lagmax + rangeextension + binwidth
if debug:
print('bad initial')
if maxsigma_init > absmaxsigma:
failreason |= (FML_INITFAIL | FML_BADWIDTH)
maxsigma_init = absmaxsigma
if debug:
print('bad initial width - too high')
if peakend - peakstart < 2:
failreason |= (FML_INITFAIL | FML_BADSEARCHWINDOW)
maxsigma_init = np.float64(
((2 + 1) * binwidth / (2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
if debug:
print('bad initial width - too low')
if not (threshval <= maxval_init <= uthreshval) and enforcethresh:
failreason |= (FML_INITFAIL | FML_BADAMPLOW)
if debug:
print('bad initial amp:', maxval_init, 'is less than', threshval)
if (maxval_init < 0.0):
failreason |= (FML_INITFAIL | FML_BADAMPLOW)
maxval_init = 0.0
if debug:
print('bad initial amp:', maxval_init, 'is less than 0.0')
if (maxval_init > 1.0):
failreason |= (FML_INITFAIL | FML_BADAMPHIGH)
maxval_init = 1.0
if debug:
print('bad initial amp:', maxval_init, 'is greater than 1.0')
if failreason > 0 and zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(maxlag_init)
maxsigma = np.float64(maxsigma_init)
# refine if necessary
if refine:
data = thexcorr_y[peakstart:peakend]
X = thexcorr_x[peakstart:peakend]
if fastgauss:
# do a non-iterative fit over the top of the peak
# 6/12/2015 This is just broken. Gives quantized maxima
maxlag = np.float64(1.0 * sum(X * data) / sum(data))
maxsigma = np.float64(np.sqrt(np.abs(np.sum((X - maxlag) ** 2 * data) / np.sum(data))))
maxval = np.float64(data.max())
else:
# do a least squares fit over the top of the peak
# p0 = np.array([maxval_init, np.fmod(maxlag_init, lagmod), maxsigma_init], dtype='float64')
p0 = np.array([maxval_init, maxlag_init, maxsigma_init], dtype='float64')
if debug:
print('fit input array:', p0)
try:
plsq, dummy = sp.optimize.leastsq(gaussresiduals, p0, args=(data, X), maxfev=5000)
maxval = plsq[0]
maxlag = np.fmod((1.0 * plsq[1]), lagmod)
maxsigma = plsq[2]
except:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
if debug:
print('fit output array:', [maxval, maxlag, maxsigma])
# check for errors in fit
fitfail = False
failreason = np.uint16(0)
if not (0.0 <= np.fabs(maxval) <= 1.0):
failreason |= (FML_FITFAIL + FML_BADAMPLOW)
if debug:
print('bad amp after refinement')
fitfail = True
if (lagmin > maxlag) or (maxlag > lagmax):
failreason |= (FML_FITFAIL + FML_BADLAG)
if debug:
print('bad lag after refinement')
if lagmin > maxlag:
maxlag = lagmin
else:
maxlag = lagmax
fitfail = True
if maxsigma > absmaxsigma:
failreason |= (FML_FITFAIL + FML_BADWIDTH)
if debug:
print('bad width after refinement')
maxsigma = absmaxsigma
fitfail = True
if not (0.0 < maxsigma):
failreason |= (FML_FITFAIL + FML_BADSEARCHWINDOW)
if debug:
print('bad width after refinement')
maxsigma = 0.0
fitfail = True
if fitfail:
if debug:
print('fit fail')
if zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.int16(0)
# print(maxlag_init, maxlag, maxval_init, maxval, maxsigma_init, maxsigma, maskval, failreason, fitfail)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(np.fmod(maxlag_init, lagmod))
maxsigma = np.float64(maxsigma_init)
if failreason > 0:
maskval = np.uint16(0)
if debug or displayplots:
print("init to final: maxval", maxval_init, maxval, ", maxlag:", maxlag_init, maxlag, ", width:", maxsigma_init,
maxsigma)
if displayplots and refine and (maskval != 0.0):
fig = pl.figure()
ax = fig.add_subplot(111)
ax.set_title('Data and fit')
hiresx = np.arange(X[0], X[-1], (X[1] - X[0]) / 10.0)
pl.plot(X, data, 'ro', hiresx, gauss_eval(hiresx, np.array([maxval, maxlag, maxsigma])), 'b-')
pl.show()
return maxindex, maxlag, flipfac * maxval, maxsigma, maskval, failreason, peakstart, peakend
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 findmaxlag_gauss(
thexcorr_x,
thexcorr_y,
lagmin,
lagmax,
widthlimit,
edgebufferfrac=0.0,
threshval=0.0,
uthreshval=30.0,
debug=False,
tweaklims=True,
zerooutbadfit=True,
refine=False,
maxguess=0.0,
useguess=False,
searchfrac=0.5,
fastgauss=False,
lagmod=1000.0,
enforcethresh=True,
absmaxsigma=1000.0,
absminsigma=0.1,
displayplots=False,
):
"""
Parameters
----------
thexcorr_x
thexcorr_y
lagmin
lagmax
widthlimit
edgebufferfrac
threshval
uthreshval
debug
tweaklims
zerooutbadfit
refine
maxguess
useguess
searchfrac
fastgauss
lagmod
enforcethresh
displayplots
Returns
-------
"""
# set initial parameters
# widthlimit is in seconds
# maxsigma is in Hz
# maxlag is in seconds
warnings.filterwarnings("ignore", "Number*")
failreason = np.uint16(0)
maxlag = np.float64(0.0)
maxval = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.uint16(1)
numlagbins = len(thexcorr_y)
binwidth = thexcorr_x[1] - thexcorr_x[0]
searchbins = int(widthlimit // binwidth)
lowerlim = int(numlagbins * edgebufferfrac)
upperlim = numlagbins - lowerlim - 1
if tweaklims:
lowerlim = 0
upperlim = numlagbins - 1
while (thexcorr_y[lowerlim + 1] <
thexcorr_y[lowerlim]) and (lowerlim + 1) < upperlim:
lowerlim += 1
while (thexcorr_y[upperlim - 1] <
thexcorr_y[upperlim]) and (upperlim - 1) > lowerlim:
upperlim -= 1
FML_BADAMPLOW = np.uint16(0x01)
FML_BADAMPHIGH = np.uint16(0x02)
FML_BADSEARCHWINDOW = np.uint16(0x04)
FML_BADWIDTH = np.uint16(0x08)
FML_BADLAG = np.uint16(0x10)
FML_HITEDGE = np.uint16(0x20)
FML_FITFAIL = np.uint16(0x40)
FML_INITFAIL = np.uint16(0x80)
# make an initial guess at the fit parameters for the gaussian
# start with finding the maximum value
if useguess:
maxindex = tide_util.valtoindex(thexcorr_x, maxguess)
nlowerlim = int(maxindex - widthlimit / 2.0)
nupperlim = int(maxindex + widthlimit / 2.0)
if nlowerlim < lowerlim:
nlowerlim = lowerlim
nupperlim = lowerlim + int(widthlimit)
if nupperlim > upperlim:
nupperlim = upperlim
nlowerlim = upperlim - int(widthlimit)
maxval_init = thexcorr_y[maxindex].astype("float64")
else:
maxindex = (np.argmax(thexcorr_y[lowerlim:upperlim]) +
lowerlim).astype("int32")
maxval_init = thexcorr_y[maxindex].astype("float64")
# now get a location for that value
maxlag_init = (1.0 * thexcorr_x[maxindex]).astype("float64")
# and calculate the width of the peak
upperlimit = len(thexcorr_y) - 1
lowerlimit = 0
i = 0
j = 0
while ((maxindex + i <= upperlimit)
and (thexcorr_y[maxindex + i] > searchfrac * maxval_init)
and (i < searchbins)):
i += 1
if (maxindex + i > upperlimit) or (i > searchbins):
i -= 1
while ((maxindex - j >= lowerlimit)
and (thexcorr_y[maxindex - j] > searchfrac * maxval_init)
and (j < searchbins)):
j += 1
if (maxindex - j < lowerlimit) or (j > searchbins):
j -= 1
# This is calculated from first principles, but it's always big by a factor or ~1.4.
# Which makes me think I dropped a factor if sqrt(2). So fix that with a final division
maxsigma_init = np.float64(
((i + j + 1) * binwidth /
(2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
# now check the values for errors and refine if necessary
fitend = min(maxindex + i + 1, upperlimit)
fitstart = max(1, maxindex - j)
if not (lagmin <= maxlag_init <= lagmax):
failreason += FML_HITEDGE
if maxlag_init <= lagmin:
maxlag_init = lagmin
else:
maxlag_init = lagmax
if i + j + 1 < 3:
failreason += FML_BADSEARCHWINDOW
maxsigma_init = np.float64(
(3.0 * binwidth /
(2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
if maxsigma_init > widthlimit:
failreason += FML_BADWIDTH
maxsigma_init = widthlimit
if (maxval_init < threshval) and enforcethresh:
failreason += FML_BADAMPLOW
if maxval_init < 0.0:
failreason += FML_BADAMPLOW
maxval_init = 0.0
if maxval_init > 1.0:
failreason |= FML_BADAMPHIGH
maxval_init = 1.0
if failreason > 0:
maskval = np.uint16(0)
if failreason > 0 and zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
else:
if refine:
data = thexcorr_y[fitstart:fitend]
X = thexcorr_x[fitstart:fitend]
if fastgauss:
# do a non-iterative fit over the top of the peak
# 6/12/2015 This is just broken. Gives quantized maxima
maxlag = np.float64(1.0 * sum(X * data) / sum(data))
# maxsigma = np.sqrt(abs(np.square(sum((X - maxlag)) * data) / sum(data)))
maxsigma = np.float64(
np.sqrt(
np.fabs(np.sum(
(X - maxlag)**2 * data) / np.sum(data))))
maxval = np.float64(data.max())
else:
# do a least squares fit over the top of the peak
p0 = np.array([maxval_init, maxlag_init, maxsigma_init],
dtype="float64")
if fitend - fitstart >= 3:
plsq, dummy = sp.optimize.leastsq(gaussresiduals,
p0,
args=(data, X),
maxfev=5000)
maxval = plsq[0]
maxlag = np.fmod((1.0 * plsq[1]), lagmod)
maxsigma = plsq[2]
# if maxval > 1.0, fit failed catastrophically, zero out or reset to initial value
# corrected logic for 1.1.6
if (np.fabs(maxval)) > 1.0 or (lagmin > maxlag) or (maxlag >
lagmax):
if zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.int16(0)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(maxlag_init)
maxsigma = np.float64(maxsigma_init)
if not absminsigma <= maxsigma <= absmaxsigma:
if zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.int16(0)
else:
if maxsigma > absmaxsigma:
maxsigma = absmaxsigma
else:
maxsigma = absminsigma
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(np.fmod(maxlag_init, lagmod))
maxsigma = np.float64(maxsigma_init)
if maxval == 0.0:
failreason += FML_FITFAIL
if not (lagmin <= maxlag <= lagmax):
failreason += FML_BADLAG
if failreason > 0:
maskval = np.uint16(0)
if failreason > 0 and zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
if debug or displayplots:
print(
"init to final: maxval",
maxval_init,
maxval,
", maxlag:",
maxlag_init,
maxlag,
", width:",
maxsigma_init,
maxsigma,
)
if displayplots and refine and (maskval != 0.0):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("Data and fit")
hiresx = np.arange(X[0], X[-1], (X[1] - X[0]) / 10.0)
plt.plot(
X,
data,
"ro",
hiresx,
gauss_eval(hiresx, np.array([maxval, maxlag, maxsigma])),
"b-",
)
plt.show()
return maxindex, maxlag, maxval, maxsigma, maskval, failreason, fitstart, fitend
def congrid(xaxis, loc, val, width, kernel='kaiser', cyclic=True, debug=False):
"""
Perform a convolution gridding operation with a Kaiser-Bessel or Gaussian kernel of width 'width'
Parameters
----------
xaxis: array-like
The target axis for resampling
loc: float
The location, in x-axis units, of the sample to be gridded
val: float
The value to be gridded
width: float
The width of the gridding kernel in target bins
kernel: {'old', 'gauss', 'kaiser'}, optional
The type of convolution gridding kernel. Default is 'kaiser'.
debug: bool, optional
When True, output additional information about the gridding process
Returns
-------
vals: array-like
The input value, convolved with the gridding kernel, projected on to x axis points
weights: array-like
The values of convolution kernel, projected on to x axis points (used for normalization)
indices: array-like
The indices along the x axis where the vals and weights fall.
Notes
-----
See IEEE TRANSACTIONS ON MEDICAL IMAGING. VOL. IO.NO. 3, SEPTEMBER 1991
"""
global congridyvals
if (congridyvals['kernel'] != kernel) or (congridyvals['width'] != width):
if congridyvals['kernel'] != kernel:
print(congridyvals['kernel'], '!=', kernel)
if congridyvals['width'] != width:
print(congridyvals['width'],'!=', width)
print('(re)initializing congridyvals')
congridyvals = {}
congridyvals['kernel'] = kernel
congridyvals['width'] = width * 1.0
optsigma = np.array([0.4241, 0.4927, 0.4839, 0.5063, 0.5516, 0.5695, 0.5682, 0.5974])
optbeta = np.array([1.9980, 2.3934, 3.3800, 4.2054, 4.9107, 5.7567, 6.6291, 7.4302])
xstep = xaxis[1] - xaxis[0]
if (loc < xaxis[0] - xstep / 2.0 or loc > xaxis[-1] + xstep / 2.0) and not cyclic:
print('loc', loc, 'not in range', xaxis[0], xaxis[-1])
# choose the smoothing kernel based on the width
if kernel != 'old':
if not (1.5 <= width <= 5.0) or (np.fmod(width, 0.5) > 0.0):
print('congrid: width is', width)
print('congrid: width must be a half-integral value between 1.5 and 5.0 inclusive')
sys.exit()
else:
kernelindex = int((width - 1.5) // 0.5)
# find the closest grid point to the target location, calculate relative offsets from this point
center = tide_util.valtoindex(xaxis, loc)
offset = np.fmod(np.round((loc - xaxis[center]) / xstep, 3), 1.0) # will vary from -0.5 to 0.5
if cyclic:
if center == len(xaxis) - 1 and offset > 0.5:
center = 0
offset -= 1.0
if center == 0 and offset < -0.5:
center = len(xaxis) - 1
offset += 1.0
if not (-0.5 <= offset <= 0.5):
print('(loc, xstep, center, offset):', loc, xstep, center, offset)
print('xaxis:', xaxis)
sys.exit()
offsetkey = str(offset)
if kernel == 'old':
if debug:
print('gridding with old kernel')
widthinpts = int(np.round(width * 4.6 / xstep))
widthinpts -= widthinpts % 2 - 1
try:
yvals = congridyvals[offsetkey]
except KeyError:
if debug:
print('new key:', offsetkey)
xvals = np.linspace(-xstep * (widthinpts // 2), xstep * (widthinpts // 2), num=widthinpts,
endpoint=True) + offset
congridyvals[offsetkey] = tide_fit.gauss_eval(xvals, np.array([1.0, 0.0, width]))
yvals = congridyvals[offsetkey]
startpt = int(center - widthinpts // 2)
indices = range(startpt, startpt + widthinpts)
indices = np.remainder(indices, len(xaxis))
if debug:
print('center, offset, indices, yvals', center, offset, indices, yvals)
return val * yvals, yvals, indices
else:
offsetinpts = center + offset
startpt = int(np.ceil(offsetinpts - width / 2.0))
endpt = int(np.floor(offsetinpts + width / 2.0))
indices = np.remainder(range(startpt, endpt + 1), len(xaxis))
try:
yvals = congridyvals[offsetkey]
except KeyError:
if debug:
print('new key:', offsetkey)
xvals = indices - center + offset
if kernel == 'gauss':
sigma = optsigma[kernelindex]
congridyvals[offsetkey] = tide_fit.gauss_eval(xvals, np.array([1.0, 0.0, sigma]))
elif kernel == 'kaiser':
beta = optbeta[kernelindex]
congridyvals[offsetkey] = tide_fit.kaiserbessel_eval(xvals, np.array([beta, width / 2.0]))
else:
print('illegal kernel value in congrid - exiting')
sys.exit()
yvals = congridyvals[offsetkey]
if debug:
print('xvals, yvals', xvals, yvals)
if debug:
print('center, offset, indices, yvals', center, offset, indices, yvals)
return val * yvals, yvals, indices
def findmaxlag_gauss_rev(
thexcorr_x,
thexcorr_y,
lagmin,
lagmax,
widthlimit,
absmaxsigma=1000.0,
hardlimit=True,
bipolar=False,
edgebufferfrac=0.0,
threshval=0.0,
uthreshval=1.0,
debug=False,
tweaklims=True,
zerooutbadfit=True,
refine=False,
maxguess=0.0,
useguess=False,
searchfrac=0.5,
fastgauss=False,
lagmod=1000.0,
enforcethresh=True,
displayplots=False,
):
"""
Parameters
----------
thexcorr_x: 1D float array
The time axis of the correlation function
thexcorr_y: 1D float array
The values of the correlation function
lagmin: float
The minimum allowed lag time in seconds
lagmax: float
The maximum allowed lag time in seconds
widthlimit: float
The maximum allowed peak halfwidth in seconds
absmaxsigma
hardlimit
bipolar: boolean
If true find the correlation peak with the maximum absolute value, regardless of sign
edgebufferfrac
threshval
uthreshval
debug
tweaklims
zerooutbadfit
refine
maxguess
useguess
searchfrac
fastgauss
lagmod
enforcethresh
displayplots
Returns
-------
"""
# set initial parameters
# widthlimit is in seconds
# maxsigma is in Hz
# maxlag is in seconds
warnings.filterwarnings("ignore", "Number*")
maxlag = np.float64(0.0)
maxval = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.uint16(1) # start out assuming the fit will succeed
numlagbins = len(thexcorr_y)
binwidth = thexcorr_x[1] - thexcorr_x[0]
# define error values
failreason = np.uint16(0)
FML_BADAMPLOW = np.uint16(0x01)
FML_BADAMPHIGH = np.uint16(0x02)
FML_BADSEARCHWINDOW = np.uint16(0x04)
FML_BADWIDTH = np.uint16(0x08)
FML_BADLAG = np.uint16(0x10)
FML_HITEDGE = np.uint16(0x20)
FML_FITFAIL = np.uint16(0x40)
FML_INITFAIL = np.uint16(0x80)
# set the search range
lowerlim = 0
upperlim = len(thexcorr_x) - 1
if debug:
print(
"initial search indices are",
lowerlim,
"to",
upperlim,
"(",
thexcorr_x[lowerlim],
thexcorr_x[upperlim],
")",
)
# make an initial guess at the fit parameters for the gaussian
# start with finding the maximum value and its location
flipfac = 1.0
if useguess:
maxindex = tide_util.valtoindex(thexcorr_x, maxguess)
else:
maxindex, flipfac = maxindex_noedge(thexcorr_x,
thexcorr_y,
bipolar=bipolar)
thexcorr_y *= flipfac
maxlag_init = (1.0 * thexcorr_x[maxindex]).astype("float64")
maxval_init = thexcorr_y[maxindex].astype("float64")
if debug:
print("maxindex, maxlag_init, maxval_init:", maxindex, maxlag_init,
maxval_init)
# then calculate the width of the peak
thegrad = np.gradient(thexcorr_y).astype(
"float64") # the gradient of the correlation function
peakpoints = np.where(
thexcorr_y > searchfrac * maxval_init, 1,
0) # mask for places where correlaion exceeds serchfrac*maxval_init
peakpoints[0] = 0
peakpoints[-1] = 0
peakstart = maxindex + 0
peakend = maxindex + 0
while (peakend < (len(thexcorr_x) - 2) and thegrad[peakend + 1] < 0.0
and peakpoints[peakend + 1] == 1):
peakend += 1
while peakstart > 1 and thegrad[peakstart - 1] > 0.0 and peakpoints[
peakstart - 1] == 1:
peakstart -= 1
# This is calculated from first principles, but it's always big by a factor or ~1.4.
# Which makes me think I dropped a factor if sqrt(2). So fix that with a final division
maxsigma_init = np.float64(
((peakend - peakstart + 1) * binwidth /
(2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
if debug:
print("maxsigma_init:", maxsigma_init)
# now check the values for errors
if hardlimit:
rangeextension = 0.0
else:
rangeextension = (lagmax - lagmin) * 0.75
if not ((lagmin - rangeextension - binwidth) <= maxlag_init <=
(lagmax + rangeextension + binwidth)):
failreason |= FML_INITFAIL | FML_BADLAG
if maxlag_init <= (lagmin - rangeextension - binwidth):
maxlag_init = lagmin - rangeextension - binwidth
else:
maxlag_init = lagmax + rangeextension + binwidth
if debug:
print("bad initial")
if maxsigma_init > absmaxsigma:
failreason |= FML_INITFAIL | FML_BADWIDTH
maxsigma_init = absmaxsigma
if debug:
print("bad initial width - too high")
if peakend - peakstart < 2:
failreason |= FML_INITFAIL | FML_BADSEARCHWINDOW
maxsigma_init = np.float64(
((2 + 1) * binwidth /
(2.0 * np.sqrt(-np.log(searchfrac)))) / np.sqrt(2.0))
if debug:
print("bad initial width - too low")
if not (threshval <= maxval_init <= uthreshval) and enforcethresh:
failreason |= FML_INITFAIL | FML_BADAMPLOW
if debug:
print("bad initial amp:", maxval_init, "is less than", threshval)
if maxval_init < 0.0:
failreason |= FML_INITFAIL | FML_BADAMPLOW
maxval_init = 0.0
if debug:
print("bad initial amp:", maxval_init, "is less than 0.0")
if maxval_init > 1.0:
failreason |= FML_INITFAIL | FML_BADAMPHIGH
maxval_init = 1.0
if debug:
print("bad initial amp:", maxval_init, "is greater than 1.0")
if failreason > 0 and zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(maxlag_init)
maxsigma = np.float64(maxsigma_init)
# refine if necessary
if refine:
data = thexcorr_y[peakstart:peakend]
X = thexcorr_x[peakstart:peakend]
if fastgauss:
# do a non-iterative fit over the top of the peak
# 6/12/2015 This is just broken. Gives quantized maxima
maxlag = np.float64(1.0 * sum(X * data) / sum(data))
maxsigma = np.float64(
np.sqrt(np.abs(np.sum((X - maxlag)**2 * data) / np.sum(data))))
maxval = np.float64(data.max())
else:
# do a least squares fit over the top of the peak
# p0 = np.array([maxval_init, np.fmod(maxlag_init, lagmod), maxsigma_init], dtype='float64')
p0 = np.array([maxval_init, maxlag_init, maxsigma_init],
dtype="float64")
if debug:
print("fit input array:", p0)
try:
plsq, dummy = sp.optimize.leastsq(gaussresiduals,
p0,
args=(data, X),
maxfev=5000)
maxval = plsq[0]
maxlag = np.fmod((1.0 * plsq[1]), lagmod)
maxsigma = plsq[2]
except:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
if debug:
print("fit output array:", [maxval, maxlag, maxsigma])
# check for errors in fit
fitfail = False
failreason = np.uint16(0)
if not (0.0 <= np.fabs(maxval) <= 1.0):
failreason |= FML_FITFAIL + FML_BADAMPLOW
if debug:
print("bad amp after refinement")
fitfail = True
if (lagmin > maxlag) or (maxlag > lagmax):
failreason |= FML_FITFAIL + FML_BADLAG
if debug:
print("bad lag after refinement")
if lagmin > maxlag:
maxlag = lagmin
else:
maxlag = lagmax
fitfail = True
if maxsigma > absmaxsigma:
failreason |= FML_FITFAIL + FML_BADWIDTH
if debug:
print("bad width after refinement")
maxsigma = absmaxsigma
fitfail = True
if not (0.0 < maxsigma):
failreason |= FML_FITFAIL + FML_BADSEARCHWINDOW
if debug:
print("bad width after refinement")
maxsigma = 0.0
fitfail = True
if fitfail:
if debug:
print("fit fail")
if zerooutbadfit:
maxval = np.float64(0.0)
maxlag = np.float64(0.0)
maxsigma = np.float64(0.0)
maskval = np.int16(0)
# print(maxlag_init, maxlag, maxval_init, maxval, maxsigma_init, maxsigma, maskval, failreason, fitfail)
else:
maxval = np.float64(maxval_init)
maxlag = np.float64(np.fmod(maxlag_init, lagmod))
maxsigma = np.float64(maxsigma_init)
if failreason > 0:
maskval = np.uint16(0)
if debug or displayplots:
print(
"init to final: maxval",
maxval_init,
maxval,
", maxlag:",
maxlag_init,
maxlag,
", width:",
maxsigma_init,
maxsigma,
)
if displayplots and refine and (maskval != 0.0):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("Data and fit")
hiresx = np.arange(X[0], X[-1], (X[1] - X[0]) / 10.0)
plt.plot(
X,
data,
"ro",
hiresx,
gauss_eval(hiresx, np.array([maxval, maxlag, maxsigma])),
"b-",
)
plt.show()
return (
maxindex,
maxlag,
flipfac * maxval,
maxsigma,
maskval,
failreason,
peakstart,
peakend,
)