def phaserate(plotar, ms2mappings):
if plotar.plotType != 'phatime':
raise RuntimeError("phaserate() cannot run on plot type {0}".format(
plotar.plotType))
spm = ms2mappings.spectralMap
# iterate over all plots and all data sets within the plots
for k in plotar.keys():
for d in plotar[k].keys():
# get a reference to the data set
dsref = plotar[k][d]
# get the full data set label - we have access to all the data set's properties (FQ, SB, POL etc)
n = plots.join_label(k, d)
# fit a line through the unwrapped phase
unw = numpy.unwrap(numpy.deg2rad(dsref.yval))
coeffs = numpy.polyfit(dsref.xval, unw, 1)
# evaluate the fitted polynomial at the x-loci
extray = numpy.polyval(coeffs, dsref.xval)
# here we could compute the reliability of the fit
diff = unw - extray
ss_tot = numpy.sum(numpy.square(unw - unw.mean()))
ss_res = numpy.sum(numpy.square(diff))
r_sq = 1.0 - ss_res / ss_tot
# compare std deviation and variance in the residuals after fit
std_r = numpy.std(diff)
var_r = numpy.var(diff)
f = spm.frequencyOfFREQ_SB(n.FQ, n.SB)
rate = coeffs[0]
if var_r < std_r:
print("{0}: {1:.8f} ps/s @ {2:5.4f}MHz [R2={3:.3f}]".format(
n, rate / (2.0 * numpy.pi * f * 1.0e-12), f / 1.0e6, r_sq))
# before plotting wrap back to -pi,pi and transform to degrees
dsref.extra = [
drawline_fn(n, dsref.xval, numpy.rad2deg(do_wrap(extray)))
]
def plotar2unidict(plotar):
rv = plots.Dict()
rv.plotType = plotar.plotType
# loop over all plots and datasets-within-plot
for k in plotar.keys():
for d in plotar[k].keys():
# get the full data set label - we have access to all the data set's properties (FQ, SB, POL etc)
n = plots.join_label(k, d)
# and make a copy of the dataset
rv[n] = COPY(plotar[k][d])
return rv
def phasedbg(plotar, ms2mappings):
global cache
if plotar.plotType != 'phatime':
raise RuntimeError("phasedbg() cannot run on plot type {0}".format(
plotar.plotType))
store = len(cache) == 0
# iterate over all plots and all data sets within the plots
for k in plotar.keys():
for d in plotar[k].keys():
# get a reference to the data set
dsref = plotar[k][d]
# get the full data set label - we have access to all the data set's properties (FQ, SB, POL etc)
n = plots.join_label(k, d)
# fit a line through the unwrapped phase
unw = numpy.unwrap(numpy.deg2rad(dsref.yval))
#coeffs = numpy.polyfit(dsref.xval, unw, 1)
coeffs = numpy.polyfit(xrange(len(dsref.yval)), unw, 1)
# evaluate the fitted polynomial at the x-loci
extray = numpy.polyval(coeffs, dsref.xval)
# here we could compute the reliability of the fit
diff = unw - extray
# compare std deviation and variance in the residuals after fit
std_r = numpy.std(diff)
var_r = numpy.var(diff)
coeffs = numpy.rad2deg(coeffs)
if var_r < std_r:
# decide what to do
if store:
cache[n] = coeffs
else:
# check if current key exists in cache; if so
# do differencing
otherCoeffs = cache.get(n, None)
if otherCoeffs is None:
print("{0}: not found in cache".format(n))
else:
delta = otherCoeffs - coeffs
print(
"{0.BL} {0.SB} {0.P}: dRate={1:5.4f} dOff={2:4.1f}"
.format(
n, delta[0], delta[1] +
360.0 if delta[1] < 0.0 else delta[1]))
# before plotting wrap back to -pi,pi and transform to degrees
#dsref.extra = [ drawline_fn(n, dsref.xval, numpy.rad2deg(do_wrap(extray))) ]
if not store:
cache = {}