r4.data = r4dat assert (lal.swig_lal_test_copyinout_REAL4VectorSequence(r4)) assert ((r4.data == 3 * r4dat).all()) r4.data = r4dat retn, r4 = lal.swig_lal_test_copyinout_REAL4VectorSequence(r4) assert (retn) assert ((r4.data == 3 * r4dat).all()) r4 = r4dat retn, r4 = lal.swig_lal_test_copyinout_REAL4VectorSequence(r4) assert (retn) assert ((r4 == 3 * r4dat).all()) del r4 del r4out del r4dat lal.CheckMemoryLeaks() r8 = lal.CreateREAL8VectorSequence(r8dat.shape[0], r8dat.shape[1]) r8.data = r8dat r8out = lal.CreateREAL8VectorSequence(r8dat.shape[0], r8dat.shape[1]) r8out.data = numpy.zeros(numpy.shape(r8dat), dtype=r8dat.dtype) assert (lal.swig_lal_test_viewin_REAL8VectorSequence(r8out, r8)) assert ((r8out.data == r8.data).all()) r8out.data = numpy.zeros(numpy.shape(r8dat), dtype=r8dat.dtype) assert (lal.swig_lal_test_viewin_REAL8VectorSequence(r8out, r8dat)) assert ((r8out.data == r8dat).all()) r8out.data = numpy.zeros(numpy.shape(r8dat), dtype=r8dat.dtype) assert (lal.swig_lal_test_viewinout_REAL8VectorSequence(r8out, r8)) assert ((2 * r8out.data == r8.data).all()) r8out.data = numpy.zeros(numpy.shape(r8dat), dtype=r8dat.dtype) assert (lal.swig_lal_test_viewinout_REAL8VectorSequence(r8out, r8dat)) assert ((2 * r8out.data == r8dat).all()) r8.data = r8dat
def plotspectrogram(sequencelist, outfile, epoch=0, deltaT=1, f0=0, deltaF=1,\
t0=0, ydata=None, **kwargs):
"""
Plots a list of REAL?VectorSequences on a time-frequency-amplitude colour
map. The epochand deltaT arguments define the spacing in the x direction
for the given VectorSequence, and similarly f0 and deltaF in the
y-direction. If a list of VectorSequences is given, any of these arguments
can be in list form, one for each sequence.
ydata can be given as to explicitly define the frequencies at which the
sequences are sampled.
"""
# construct list of series
if not hasattr(sequencelist, "__contains__"):
sequencelist = [sequencelist]
numseq = len(sequencelist)
# format variables
if isinstance(epoch, numbers.Number) or isinstance(epoch, lal.LIGOTimeGPS):
epoch = [epoch] * numseq
epoch = map(float, epoch)
if not len(epoch) == numseq:
raise ValueError("Wrong number of epoch arguments given.")
if isinstance(deltaT, numbers.Number):
deltaT = [deltaT] * numseq
deltaT = map(float, deltaT)
if not len(deltaT) == numseq:
raise ValueError("Wrong number of deltaT arguments given.")
if not ydata is None:
if isinstance(f0, numbers.Number):
f0 = [f0] * numseq
f0 = map(float, f0)
if not len(f0) == numseq:
raise ValueError("Wrong number of f0 arguments given.")
if isinstance(deltaF, numbers.Number):
deltaF = [deltaF] * numseq
deltaF = map(float, deltaF)
if not len(deltaF) == numseq:
raise ValueError("Wrong number of deltaF arguments given.")
# get limits
xlim = kwargs.pop("xlim", None)
ylim = kwargs.pop("ylim", None)
colorlim = kwargs.pop("colorlim", None)
if xlim:
start, end = xlim
else:
start = min(epoch)
end = max(e + l.length * dt\
for e,l,dt in zip(epoch, sequencelist, deltaT))
if not ydata is None and not ylim:
ylim = [ydata.min(), ydata.max()]
# get axis scales
logx = kwargs.pop("logx", False)
logy = kwargs.pop("logy", False)
logcolor = kwargs.pop("logcolor", False)
# get legend loc
loc = kwargs.pop("loc", 0)
alpha = kwargs.pop("alpha", 0.8)
# get colorbar options
hidden_colorbar = kwargs.pop("hidden_colorbar", False)
# get savefig option
bbox_inches = kwargs.pop("bbox_inches", None)
#
# get labels
#
xlabel = kwargs.pop("xlabel", None)
if xlabel:
unit = 1
if not xlabel:
unit, timestr = plotutils.time_axis_unit(end - start)
if not t0:
t0 = start
t0 = lal.LIGOTimeGPS(t0)
if int(t0.gpsNanoSeconds) == 0:
xlabel = datetime.datetime(*lal.GPSToUTC(int(t0))[:6])\
.strftime("%B %d %Y, %H:%M:%S %ZUTC")
xlabel = "Time (%s) since %s (%s)" % (timestr, xlabel, int(t0))
else:
xlabel = datetime.datetime(*lal.GPSToUTC(t0.gpsSeconds)[:6])\
.strftime("%B %d %Y, %H:%M:%S %ZUTC")
xlabel = "Time (%s) since %s (%s)"\
% (timestr,
xlabel.replace(" UTC",".%.3s UTC" % t0.gpsNanoSeconds),\
t0)
t0 = float(t0)
ylabel = kwargs.pop("ylabel", "Frequency (Hz)")
colorlabel = kwargs.pop("colorlabel", "Amplitude")
title = kwargs.pop("title", "")
subtitle = kwargs.pop("subtitle", "")
#
# restrict data to the correct limits for plotting
#
interpolate = logy and ydata is None
for i, sequence in enumerate(sequencelist):
if interpolate:
# interpolate the data onto a log-scale
sequence, ydata = loginterpolate(sequence, f0[i], deltaF[i])
if logy and ylim:
plotted = (ydata > ylim[0]) & (ydata <= ylim[1])
newVectorLength = int(plotted.sum())
newsequence = lal.CreateREAL8VectorSequence(sequence.length,\
newVectorLength)
for j in range(sequence.length):
newsequence.data[j, :] = sequence.data[j, :][plotted]
del sequence
sequencelist[i] = newsequence
if len(sequencelist) and logy and ylim:
ydata = ydata[plotted]
#
# format bins
#
xbins = []
for i in range(numseq):
xmin = epoch[i]
xmax = epoch[i] + sequencelist[i].length * deltaT[i]
xbins.append(rate.LinearBins(float(xmin-t0)/unit, float(xmax-t0)/unit,\
2))
ybins = []
for i in range(numseq):
if ydata is not None:
ydata = numpy.asarray(ydata)
ymin = ydata.min()
ymax = ydata.max()
else:
ymin = f0[i]
ymax = f0[i] + sequencelist[i].vectorLength * deltaF[i]
if logy:
if ymin == 0:
ymin = deltaF[i]
ybins.append(rate.LogarithmicBins(ymin, ymax, 2))
else:
ybins.append(rate.LinearBins(ymin, ymax, 2))
#
# plot
#
kwargs.setdefault("interpolation", "kaiser")
plot = plotutils.ImagePlot(xlabel=xlabel, ylabel=ylabel, title=title,\
subtitle=subtitle, colorlabel=colorlabel)
for sequence, x, y in zip(sequencelist, xbins, ybins):
data = numpy.ma.masked_where(numpy.isnan(sequence.data), sequence.data,\
copy=False)
plot.add_content(data.T, x, y, **kwargs)
# finalize
plot.finalize(colorbar=True, logcolor=logcolor, minorticks=True,\
clim=colorlim)
if hidden_colorbar:
plotutils.add_colorbar(plot.ax, visible=False)
# set logscale
if logx:
plot.ax.xaxis.set_scale("log")
if logy:
plot.ax.yaxis.set_scale("log")
plot.ax._update_transScale()
# format axes
if xlim:
xlim = (numpy.asarray(xlim).astype(float) - t0) / unit
plot.ax.set_xlim(xlim)
if ylim:
plot.ax.set_ylim(ylim)
# set grid and ticks
plot.ax.grid(True, which="both")
plotutils.set_time_ticks(plot.ax)
plotutils.set_minor_ticks(plot.ax)
# save and close
plot.savefig(outfile, bbox_inches=bbox_inches,\
bbox_extra_artists=plot.ax.texts)
plot.close()