def plotSensitivity(sm):
"""Histogram the sensitivity in all pixels"""
data = sm.sensmap.data.ravel()
dummy = pylab.hist(data, 75, [25, 100], fc="red")
pylab.xlabel("Sensitivity in $\mathrm{\mu}$K$_{\mathrm{cmb}}$-arcmin")
pylab.ylabel("Pixels")
pylab.title("Pixel sensitivity distribution derived from hit map")
pylab.text(90, 400000, "Mean: %.3f uK" % data[data < 100].mean(), ha="right")
pylab.text(90, 370000, "Median: %.3f uK" % numpy.median(data[data < 100]), ha="right")
pylab.text(90, 340000, "RMS: %.3f uK" % data[data < 100].std(), ha="right")
utils.saveAndShow("sensitivity_histogram.png")
def plotSky_das(
map, boxRAs=None, boxDecs=None, colorMapName="jet", show=True, colorBar=True, cutLevels=[-200, 200], **kwargs
):
ip = ImagePlot(
map.data, map.wcs, colorMapName=colorMapName, cutLevels=cutLevels, axesFontFamily="sans-serif", **kwargs
)
if boxRAs != None:
print "boxRAs", boxRAs
assert len(boxRAs) == len(boxDecs)
ip.addPlotBoxes(boxRAs, boxDecs, "patches", color="black", width=1.5)
pylab.ylabel("Dec.")
if colorBar:
cb = pylab.colorbar(orientation="horizontal", shrink=0.8, aspect=80, pad=0.375)
if show:
utils.saveAndShow()
return ip
def plotValues(sm):
"""Histogram the data (temperature) values in the filtered map and difference map"""
ax = pylab.subplot(111)
x0, y0 = sm.map.skyToPix(102.70, -55.263)
x1, y1 = sm.map.skyToPix(12.00, -51.0854)
print x0, x1, y0, y1
data = sm.diff.data[y0:y1, x0:x1].ravel()
hist = pylab.hist(data, 200, [-400, 400], histtype="step", ec="red", axes=ax)
pylab.text(100, 55000, "Values in CMB microK:")
pylab.text(350, 50000, "Diff mean = %6.2f" % data.mean(), ha="right", color="red")
pylab.text(350, 45000, "Diff rms = %6.2f" % data.std(), ha="right", color="red")
data = sm.fmap.data[y0:y1, x0:x1].ravel()
hist = pylab.hist(data, 200, [-400, 400], histtype="step", ec="green", axes=ax)
pylab.text(350, 40000, "Data mean = %6.2f" % data.mean(), ha="right", color="green")
pylab.text(350, 35000, "Data rms = %6.2f" % data.std(), ha="right", color="green")
utils.saveAndShow("pixel_histogram.png")
def plotSky(map, boxes=True, colorMapName="jet", show=True, colorBar=True, cutLevels=[-200, 200], **kwargs):
ip = ImagePlot(
map.data, map.wcs, colorMapName=colorMapName, cutLevels=cutLevels, axesFontFamily="sans-serif", **kwargs
)
if boxes:
RAs = []
Decs = []
dra = (102.700630 - 12.007338) / 13.0
for i in range(13):
RAs.append([12.07338 + dra * i, 12.07338 + dra * (i + 1)])
Decs.append([-55.263018, -51.085420])
ip.addPlotBoxes(RAs, Decs, "patches", color="black", width=1.5)
pylab.ylabel("Dec.")
if colorBar:
cb = pylab.colorbar(orientation="horizontal", shrink=0.8, aspect=80, pad=0.375)
if show:
utils.saveAndShow()
return ip
def filter(self, minEll=300, deltaEll=400, maxEll=12000, plotFilter=False, plotMap=False, valueRange=[-250, 250]):
"""Apply a filter that rises as sin^2 in the given ell band.
The 0.50 point is at minEll. The full width is deltaEll.
A separate Gaussian low-pass filter falls to 60% at maxEll.
"""
ell = numpy.arange(0, 20000, 10.0)
lowestEll = minEll - 0.5 * deltaEll
Fell = numpy.sin(numpy.pi / 2 * (ell - lowestEll) / (deltaEll)) ** 2
Fell[ell < minEll - 0.5 * deltaEll] = 0.0
Fell[ell > minEll + 0.5 * deltaEll] = 1.0
Fell *= numpy.exp(-0.5 * (ell / maxEll) ** 2)
if plotFilter:
pylab.clf()
pylab.plot(ell, Fell)
pylab.xlim([10, 20000])
pylab.semilogx()
utils.saveAndShow()
print "The filters are plotted"
self.fmap = filterFromList(self.map, [ell, Fell])
print "The map is filtered now"
if plotMap:
plotSky(self.fmap, boxes=True)
self.ftwoway = [filterFromList(m, [ell, Fell]) for m in self.twoway]
if plotMap:
pylab.clf()
self.ftwoway[0].plot(
valueRange=valueRange,
colBarOrient="horizontal",
colorMapName="copper",
axesLabels="sexagesimal",
RATickSteps="auto",
)
utils.saveAndShow()
pylab.clf()
self.ftwoway[1].plot(
valueRange=valueRange,
colBarOrient="horizontal",
colorMapName="copper",
axesLabels="sexagesimal",
RATickSteps="auto",
)
utils.saveAndShow()
def compute_variance(self, minHits=2000, plot=False):
"""Compute the average variance across the map of temperature times sqrt(N)
for N hits in each pixel. Given this number V, we can convert the hit map
(of N) to a sensitivity map (of sqrt(V/N))."""
# Make 1d vectors of map, weights
if self.diff:
mv = self.diff.data.ravel()
wv = self.wt.data.ravel()
else:
mv = self.fmap.data.ravel()
wv = self.wt.data.ravel()
mv = mv[wv > minHits]
wv = wv[wv > minHits]
tn = numpy.sqrt(wv) * mv
contents, bins, _ignore = pylab.hist(tn, 150, [-2e5, 2e5], fc="gold")
# Estimate a gaussian by the moments
mu = tn.mean()
sig = tn.std()
y0 = contents.max()
binctr = 0.5 * (bins[1:] + bins[:-1])
print "Moments mu, sig: ", mu, sig
if plot:
pylab.plot(binctr, y0 * numpy.exp(-0.5 * ((binctr - mu) / sig) ** 2), "red")
# Estimate a different gaussian by the 16, 84 %ile points
h = numpy.cumsum(contents) * 1.0
h /= h[-1]
f = scipy.interpolate.interp1d(h, binctr)
p16 = f(0.1587) # The -1 sigma point on a Gaussian
p84 = f(0.8413) # The +1 sigma point on a Gaussian
sig = 0.5 * (p84 - p16)
median = f(0.5)
print "Percentile mu, sig: ", median, sig
if plot:
pylab.plot(binctr, y0 * numpy.exp(-0.5 * ((binctr - median) / sig) ** 2), "blue")
print "The raw one-hit variance is %.5g, or sqrt(V)=%.2f" % (sig ** 2, sig)
size_arcmin = numpy.sqrt(self.fmap.pixScaleX * self.fmap.pixScaleY) * 180 / numpy.pi * 60.0
sig *= size_arcmin
self.StdDevHit = sig
print "The calibrated one-hit std dev is sqrt(V)=%.2f uK-arcmin" % sig
if plot:
utils.saveAndShow()
self.sensmap = self.wt.copy()
self.sensmap.data[self.sensmap.data < 100] = 100
self.sensmap.data = self.StdDevHit / numpy.sqrt(self.sensmap.data)
if plot:
pylab.clf()
self.sensmap.plot(
valueRange=[25, 75],
colBarOrient="horizontal",
colorMapName="hot",
axesLabels="sexagesimal",
RATickSteps="auto",
)
utils.saveAndShow()
