def main(rerun,
visit1,
visit2,
op,
ccdno,
datatype='calexp',
scale=None,
root=None,
invert=False,
cmap='gray',
vmax=None,
annotate=None,
bins=16,
rerun2=None,
showCbar=False,
vsig=None,
showAnnotate=False,
percent=None,
hilite=None):
vsigDefault = 5.0
visit1 = int(visit1)
if visit2:
visit2 = int(visit2)
ccdno = set([int(x) for x in hscUtil.idSplit(ccdno)])
butler1 = hscUtil.getButler(rerun, root=root)
if rerun2:
butler2 = hscUtil.getButler(rerun2, root=root)
else:
butler2 = butler1
if datatype not in calibTypes:
dataIds1 = butler1.queryMetadata(datatype,
"ccd",
format=["visit", "ccd"],
dataId={'visit': visit1})
dataIds1 = [{'visit': x[0], 'ccd': x[1]} for x in dataIds1]
else:
dataIds1 = [{'visit': visit1, 'ccd': x} for x in range(104)]
dataRef1 = None
dataRef2 = None
if visit2:
dataIds2 = butler2.queryMetadata(datatype,
"ccd",
format=["visit", "ccd"],
dataId={'visit': visit2})
dataIds2 = [{'visit': x[0], 'ccd': x[1]} for x in dataIds2]
# flip the color map
if invert:
cmap = re.sub("_r$", "", cmap) if re.match('_r$', cmap) else '_r'
# sleezy, but if vmax isn't set and we're linear, just use ccd in the middle to norm
if vmax is None and scale != 'histeq':
vmax = 'c049'
# handle the gray scale normalization
vmin = 0.0
mdRef1 = None
mdRef2 = None
if vmax:
if scale == 'histeq':
raise ValueError("Cannot specify vmax with histeq scaling.")
# if it identifies a CCD to use
if re.match("^c", vmax):
vmaxCcd = int(re.sub("c", "", vmax))
try:
dataRef1 = hscButler.getDataRef(butler1, {
'visit': visit1,
'ccd': vmaxCcd
})
imgRef1 = dataRef1.get(
datatype).getMaskedImage().getImage().getArray()
if datatype not in calibTypes:
mdRef1 = dataRef1.get(datatype + '_md', immediate=True)
if visit2:
dataRef2 = hscButler.getDataRef(butler2, {
'visit': visit2,
'ccd': vmaxCcd
})
imgRef2 = dataRef2.get(
datatype).getMaskedImage().getImage().getArray()
mdRef2 = dataRef2.get(datatype + '_md', immediate=True)
img_op = hscUtil.rebin(compute(imgRef1, imgRef2, op), bins)
med = numpy.median(img_op)
std = numpy.std(img_op)
if not vsig:
vsig = vsigDefault
delta = vsig * std
if percent:
delta = percent * med
vmin = med - abs(delta)
vmax = med + abs(delta)
else:
if showAnnotate:
exp1 = dataRef1.get(datatype)
aval = float(exp1.getMetadata().get(annotate))
med = aval
delta = vsig
if not vsig:
delta = 0.5 * aval
if percent:
delta = percent * aval
vmin = med - abs(delta)
vmax = med + abs(delta)
else:
img_op = imgRef1
med = numpy.median(img_op)
sig = numpy.sqrt(med)
delta = vsigDefault * sig
if vsig:
delta = vsig * sig
if percent:
delta = percent * med
vmin = med - abs(delta)
vmax = med + abs(delta)
if not vsig and not percent:
vmin = 0.5 * med
vmax = med + 5.0 * sig
except Exception, e:
raise RuntimeError("Could not get stats on vmax CCD" +
str(vmax) + " Exiting." + str(e))
elif re.search(":", vmax):
vmaxCcd = None
vmin, vmax = [float(x) for x in vmax.split(":")]
med = 0.5 * (vmax + vmin)
else:
vmaxCcd = None
vmax = float(vmax)
med = 0.5 * (vmax + vmin)
def main(rerun, visit1, visit2, op, ccdno,
datatype='calexp', scale=None, root=None, invert=False, cmap='gray',
vmax=None, annotate=None, bins=16, rerun2=None, showCbar=False, vsig=None,
showAnnotate=False, percent=None, hilite=None):
vsigDefault = 5.0
visit1 = int(visit1)
if visit2:
visit2 = int(visit2)
ccdno = set([int(x) for x in hscUtil.idSplit(ccdno)])
butler1 = hscUtil.getButler(rerun, root=root)
if rerun2:
butler2 = hscUtil.getButler(rerun2, root=root)
else:
butler2 = butler1
if datatype not in calibTypes:
dataIds1 = butler1.queryMetadata(datatype, "ccd", format=["visit", "ccd"], dataId={'visit':visit1})
dataIds1 = [{'visit':x[0], 'ccd':x[1]} for x in dataIds1]
else:
dataIds1 = [{'visit': visit1, 'ccd':x} for x in range(104)]
dataRef1 = None
dataRef2 = None
if visit2:
dataIds2 = butler2.queryMetadata(datatype, "ccd", format=["visit", "ccd"], dataId={'visit':visit2})
dataIds2 = [{'visit':x[0], 'ccd':x[1]} for x in dataIds2]
# flip the color map
if invert:
cmap = re.sub("_r$", "", cmap) if re.match('_r$', cmap) else '_r'
# sleezy, but if vmax isn't set and we're linear, just use ccd in the middle to norm
if vmax is None and scale != 'histeq':
vmax = 'c049'
# handle the gray scale normalization
vmin = 0.0
mdRef1 = None
mdRef2 = None
if vmax:
if scale == 'histeq':
raise ValueError("Cannot specify vmax with histeq scaling.")
# if it identifies a CCD to use
if re.match("^c", vmax):
vmaxCcd = int(re.sub("c", "", vmax))
try:
dataRef1 = hscButler.getDataRef(butler1, {'visit':visit1, 'ccd':vmaxCcd})
imgRef1 = dataRef1.get(datatype).getMaskedImage().getImage().getArray()
if datatype not in calibTypes:
mdRef1 = dataRef1.get(datatype+'_md', immediate=True)
if visit2:
dataRef2 = hscButler.getDataRef(butler2, {'visit':visit2, 'ccd':vmaxCcd})
imgRef2 = dataRef2.get(datatype).getMaskedImage().getImage().getArray()
mdRef2 = dataRef2.get(datatype+'_md', immediate=True)
img_op = hscUtil.rebin(compute(imgRef1, imgRef2, op), bins)
med = numpy.median(img_op)
std = numpy.std(img_op)
if not vsig:
vsig = vsigDefault
delta = vsig*std
if percent:
delta = percent*med
vmin = med - abs(delta)
vmax = med + abs(delta)
else:
if showAnnotate:
exp1 = dataRef1.get(datatype)
aval = float(exp1.getMetadata().get(annotate))
med = aval
delta = vsig
if not vsig:
delta = 0.5*aval
if percent:
delta = percent*aval
vmin = med - abs(delta)
vmax = med + abs(delta)
else:
img_op = imgRef1
med = numpy.median(img_op)
sig = numpy.sqrt(med)
delta = vsigDefault*sig
if vsig:
delta = vsig*sig
if percent:
delta = percent*med
vmin = med - abs(delta)
vmax = med + abs(delta)
if not vsig and not percent:
vmin = 0.5*med
vmax = med + 5.0*sig
except Exception, e:
raise RuntimeError("Could not get stats on vmax CCD" + str(vmax)+ " Exiting." + str(e))
elif re.search(":", vmax):
vmaxCcd = None
vmin, vmax = [float(x) for x in vmax.split(":")]
med = 0.5*(vmax + vmin)
else:
vmaxCcd = None
vmax = float(vmax)
med = 0.5*(vmax + vmin)
imshow_kwargs = {}
if scale == 'linear':
imshow_kwargs = {'vmin': vmin, 'vmax': vmax}
if showAnnotate:
ny, nx = 4, 2
if exp1.getDetector().getOrientation().getNQuarter() % 2:
ny, nx = 2, 4
imtmp = numpy.ones((ny, nx)) * float(aval)
else:
if visit2:
img_op = compute(img1, img2, op)
else:
img_op = img1
# scale as requested
img_op = scaleLookup[scale](img_op)
imtmp = hscUtil.rebin(img_op, bins)[::-1]
im_ax = ax.imshow(imtmp, cmap=cmap, **imshow_kwargs)
if showCbar and im_ax and scale != "histeq":
ylo, yhi = vmin, vmax
rect = (0.91, 0.2, 0.02, 0.6)
cax = fig.add_axes(rect)
cax.set_ylim([ylo, yhi])
cax.get_xaxis().set_ticks([])
cax.get_yaxis().get_major_formatter().set_useOffset(False)
cbar = fig.colorbar(im_ax, cax=cax)
caxp = cax.twinx()
caxp.set_ylim([ylo / med, yhi / med])
imshow_kwargs = {}
if scale == 'linear':
imshow_kwargs = {'vmin': vmin, 'vmax': vmax}
if showAnnotate:
ny, nx = 4, 2
if exp1.getDetector().getOrientation().getNQuarter() % 2:
ny, nx = 2, 4
imtmp = numpy.ones((ny, nx))*float(aval)
else:
if visit2:
img_op = compute(img1, img2, op)
else:
img_op = img1
# scale as requested
img_op = scaleLookup[scale](img_op)
imtmp = hscUtil.rebin(img_op, bins)[::-1]
im_ax = ax.imshow(imtmp, cmap=cmap, **imshow_kwargs)
if showCbar and im_ax and scale != "histeq":
ylo, yhi = vmin, vmax
rect = (0.91, 0.2, 0.02, 0.6)
cax = fig.add_axes(rect)
cax.set_ylim([ylo, yhi])
cax.get_xaxis().set_ticks([])
cax.get_yaxis().get_major_formatter().set_useOffset(False)
cbar = fig.colorbar(im_ax, cax=cax)
