def realtime_image_view(request, image_id=0, size=0, type='jpeg'):
filename = RealtimeImages.objects.filter(id=image_id)
if filename:
object = RealtimeObjects.objects.get(id=filename[0].object_id)
filename = filename[0].filename
else:
return HttpResponse("Image unavailable")
filename = posixpath.join(settings.BASE, filename)
filename = fix_remote_path(filename, object.channel_id)
if type == 'jpeg':
image, xsize, ysize = fitsimage.FitsReadData(filename)
img = fitsimage.FitsImageFromData(image,
xsize,
ysize,
contrast="percentile",
contrast_opts={'max_percent': 99.5},
scale="linear")
if size:
img.thumbnail((size, size), resample=fitsimage.Image.ANTIALIAS)
# now what?
response = HttpResponse(content_type="image/jpeg")
img.save(response, "JPEG", quality=95)
elif type == 'fits':
response = HttpResponse(FileWrapper(file(filename)),
content_type='application/octet-stream')
response[
'Content-Disposition'] = 'attachment; filename=' + os.path.split(
filename)[-1]
response['Content-Length'] = os.path.getsize(filename)
return response
def survey_image_download(request,
id=0,
ra=0,
dec=0,
sr=0.1,
size=200,
plot_objects=True):
image = Images.objects.get(id=id)
filename = image.filename
filename = fix_remote_path(filename, image.channel_id)
header = pyfits.getheader(filename, -1)
wcs = pywcs.WCS(header)
x, y = wcs.all_world2pix(float(ra), float(dec), 0)
pixscale = np.hypot(wcs.wcs.cd[0, 0], wcs.wcs.cd[0, 1])
cutsize = max(10, float(sr) / pixscale)
process = subprocess.Popen([
"./crop", filename, "-",
"x0=%d" % np.round(x),
"y0=%d" % np.round(y),
"width=%d" % np.round(2 * cutsize),
"height=%d" % np.round(2 * cutsize)
],
stdout=subprocess.PIPE)
content = process.communicate()[0]
response = HttpResponse(content, content_type='application/octet-stream')
response[
'Content-Disposition'] = 'attachment; filename=crop_' + os.path.split(
filename)[-1]
response['Content-Length'] = len(content)
return response
def raw_dir(request, dir='', channel=0):
base = fix_remote_path(settings.BASE_RAW, channel_id=channel)
fits = get_index(posixpath.join(base, dir))
has_index = True
if not fits:
fits = [
f for f in os.listdir(posixpath.join(base, dir))
if os.path.isfile(posixpath.join(base, dir, f))
and re.match('^.*\.fits$', f)
]
fits = [{'path': path, 'filter': None} for path in sorted(fits)]
has_index = False
stat = os.statvfs(base)
s_free = stat.f_bavail * stat.f_frsize
s_total = stat.f_blocks * stat.f_frsize
context = {
'raw_dir': fits,
'dir': dir,
'base': posixpath.join(base, dir),
'free': s_free,
'total': s_total,
'has_index': has_index,
'channel': channel if channel else 0
}
return TemplateResponse(request, 'raw_list.html', context=context)
def realtime_object_animation(request, object_id=0):
object = RealtimeObjects.objects.get(id=object_id)
images = RealtimeImages.objects.filter(object=object_id)[:1]
if request.method == 'GET':
channel_id = int(request.GET.get('channel_id', 0))
else:
channel_id = 0
if images:
dirname = posixpath.split(images[0].filename)[0]
dirname = posixpath.join(settings.BASE, dirname)
dirname = fix_remote_path(dirname, object.channel_id)
if channel_id:
filename = glob.glob(
posixpath.join(dirname, "channels", "%d_*" % channel_id,
"animation.gif"))
if filename:
filename = filename[0]
print filename
else:
filename = posixpath.join(dirname, "animation.gif")
# with open(filename) as f:
# string = f.read()
response = HttpResponse(FileWrapper(file(filename)),
content_type='image/gif')
response['Content-Length'] = os.path.getsize(filename)
else:
response = HttpResponse("Animation unavailable")
return response
def raw_slice(request, filename='', channel=0):
base = fix_remote_path(settings.BASE_RAW, channel_id=channel)
fits = pyfits.open(posixpath.join(base, filename))
d = fits[0].data
h, w = fits[0].shape
y0 = int(h / 2)
x0 = int(w / 2)
fig = Figure(facecolor='white', figsize=(800 / 72, 600 / 72))
ax = fig.add_subplot(211)
ax.plot(d[y0], drawstyle='steps')
ax.set_xlabel("X")
ax.set_ylabel("Value")
ax.set_title("Horizontal and Vertical Slices at the Center")
ax.set_ylim(min(0, np.min(d[y0])), np.max(d[y0]))
ax.set_xlim(0, w)
ax = fig.add_subplot(212)
ax.plot(d[:, x0], drawstyle='steps')
ax.set_xlabel("Y")
ax.set_ylabel("Value")
#ax.set_title("Vertical Slice")
ax.set_ylim(min(0, np.min(d[:, x0])), np.max(d[:, x0]))
ax.set_xlim(0, h)
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/png')
canvas.print_png(response)
return response
def image_histogram(request, id, base=settings.BASE):
image_obj = Images.objects.get(id=id)
filename = Images.objects.get(id=id).filename
filename = posixpath.join(base, filename)
filename = fix_remote_path(filename, image_obj.channel_id)
fits = pyfits.open(filename)
d = fits[-1].data.flatten()
fig = Figure(facecolor='white', figsize=(800 / 72, 600 / 72))
ax = fig.add_subplot(111)
if d.max() - d.min() > 10:
ax.hist(d, bins=d.max() - d.min(), histtype='step', log=True)
else:
ax.hist(d, bins=1000, histtype='step', log=True)
ax.set_xlabel("Value")
ax.set_ylabel("")
ax.set_title("Histogram: %s" % (filename))
# ax.set_xlim(0, width)
# ax.set_ylim(height, 0)
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/png')
canvas.print_png(response)
return response
def image_details(request, id=0):
image = Images.objects.get(id=id)
context = {'image': image}
if image.type != 'flat' and image.type != 'dark':
context['flat'] = find_image(image.time, 'superflat', image.channel_id)
context['flat_id'] = find_image_id(image.time, 'superflat',
image.channel_id)
context['dark'] = find_image(image.time, 'dark', image.channel_id)
context['dark_id'] = find_image_id(image.time, 'dark',
image.channel_id)
try:
# Try to read original FITS keywords with comments
filename = posixpath.join(settings.BASE, image.filename)
filename = fix_remote_path(filename, image.channel_id)
hdr = pyfits.getheader(filename, -1)
ignored_keywords = ['COMMENT', 'SIMPLE', 'BZERO', 'BSCALE', 'EXTEND']
keywords = [{
'key': k,
'value': repr(hdr[k]),
'comment': hdr.comments[k]
} for k in hdr.keys() if k not in ignored_keywords]
context['keywords'] = keywords
except:
pass
return TemplateResponse(request, 'image.html', context=context)
def raw_view(request,
filename='',
channel=0,
size=0,
processed=False,
type='jpeg'):
base = fix_remote_path(settings.BASE_RAW, channel_id=channel)
fullname = posixpath.join(base, filename)
image = pyfits.getdata(fullname, -1)
header = pyfits.getheader(fullname, -1)
time = postprocess.get_time_from_filename(posixpath.split(filename)[-1])
if processed:
darkname = find_image(time, 'dark', channel)
darkname = fix_remote_path(darkname, channel)
if posixpath.exists(darkname):
dark = pyfits.getdata(darkname, -1)
image -= dark
if type == 'jpeg':
img = fitsimage.FitsImageFromData(image,
image.shape[1],
image.shape[0],
contrast="percentile",
contrast_opts={'max_percent': 99.9},
scale="linear")
if size:
img.thumbnail((size, size)) #, resample=fitsimage.Image.ANTIALIAS)
# now what?
response = HttpResponse(content_type="image/jpeg")
img.save(response, "JPEG", quality=95)
elif type == 'fits':
response = HttpResponse(FileWrapper(
file(posixpath.join(base, filename))),
content_type='application/octet-stream')
response[
'Content-Disposition'] = 'attachment; filename=' + os.path.split(
filename)[-1]
response['Content-Length'] = os.path.getsize(
posixpath.join(base, filename))
return response
def raw_list(request, channel=0):
context = {}
if settings.CHANNEL_ID > 0 or channel > 0:
base = fix_remote_path(settings.BASE_RAW, channel_id=channel)
dirs = [
d for d in os.listdir(base)
if os.path.isdir(posixpath.join(base, d))
and re.match('^\d{8}_\d{6}$', d)
]
raws = []
prev_time = None
for path in sorted(dirs, reverse=True):
index = get_index(posixpath.join(base, path))
length = len(index)
is_new = False
if index:
first = index[0]
last = index[-1]
# Is the time delay between last file and current one large enough?
if prev_time and prev_time - get_time_from_filename(
last['path']) > datetime.timedelta(seconds=1):
is_new = True
prev_time = get_time_from_filename(first['path'])
else:
first = None
raws.append({
'path': path,
'first': first,
'length': length,
'is_new': is_new
})
stat = os.statvfs(base)
s_free = stat.f_bavail * stat.f_frsize
s_total = stat.f_blocks * stat.f_frsize
context['raw_list'] = raws
context['base'] = base
context['free'] = s_free
context['total'] = s_total
context['channel_list'] = range(1, settings.NCHANNELS + 1)
context['channel'] = int(channel) if channel else 0
return TemplateResponse(request, 'raw_list.html', context=context)
def image_download(request, id, size=800, base=settings.BASE):
image_obj = Images.objects.get(id=id)
filename = Images.objects.get(id=id).filename
filename = posixpath.join(base, filename)
filename = fix_remote_path(filename, image_obj.channel_id)
response = HttpResponse(FileWrapper(file(filename)),
content_type='application/octet-stream')
response['Content-Disposition'] = 'attachment; filename=' + os.path.split(
filename)[-1]
response['Content-Length'] = os.path.getsize(filename)
return response
def scheduler_target_rgb_image(request, id=0):
base = posixpath.join("/tmp/fweb/targets", str(id))
try:
os.makedirs(base)
except:
pass
rgbname = posixpath.join(base, "rgb.jpg")
if not posixpath.isfile(rgbname):
# No cached file
try:
target = SchedulerTargets.objects.get(id=id)
images = Images.objects.raw(
"select *,get_filter_name(filter) as filter_string from images where keywords->'TARGET UUID'='%s' and q3c_radial_query(ra0, dec0, %s, %s, 2.0) order by channel_id"
% (target.uuid, target.ra, target.dec))
files_b, files_v, files_r = [], [], []
for image in images:
filename = posixpath.join(settings.BASE, image.filename)
filename = fix_remote_path(filename, image.channel_id)
if image.filter_string == 'B':
files_b.append(filename)
if image.filter_string == 'V':
files_v.append(filename)
if image.filter_string == 'R':
files_r.append(filename)
if len(files_b) and len(files_v) and len(files_r):
print files_b[0], files_v[0], files_r[0]
coadd_rgb(name_blue=files_b[0],
name_green=files_v[0],
name_red=files_r[0],
out=rgbname)
except:
pass
if not posixpath.isfile(rgbname):
return HttpResponse("Can't create RGB image for target %s" % str(id))
response = HttpResponse(FileWrapper(file(rgbname)),
content_type='image/jpeg')
response['Content-Length'] = posixpath.getsize(rgbname)
return response
def image_slice(request, id, base=settings.BASE):
image_obj = Images.objects.get(id=id)
filename = Images.objects.get(id=id).filename
filename = posixpath.join(base, filename)
filename = fix_remote_path(filename, image_obj.channel_id)
fits = pyfits.open(filename)
fits = pyfits.open(posixpath.join(base, filename))
d = fits[-1].data
h, w = fits[-1].shape
y0 = int(h / 2)
x0 = int(w / 2)
fig = Figure(facecolor='white', figsize=(800 / 72, 600 / 72))
ax = fig.add_subplot(211)
ax.plot(d[y0], drawstyle='steps')
ax.set_xlabel("X")
ax.set_ylabel("Value")
ax.set_title("Horizontal and Vertical Slices at the Center")
if not request.GET.get('nozero', 0):
ax.set_ylim(min(0, np.min(d[y0])), np.max(d[y0]))
ax.set_xlim(0, w)
ax = fig.add_subplot(212)
ax.plot(d[:, x0], drawstyle='steps')
ax.set_xlabel("Y")
ax.set_ylabel("Value")
#ax.set_title("Vertical Slice")
if not request.GET.get('nozero', 0):
ax.set_ylim(min(0, np.min(d[:, x0])), np.max(d[:, x0]))
ax.set_xlim(0, h)
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/png')
canvas.print_png(response)
return response
def raw_histogram(request, filename='', channel=0):
base = fix_remote_path(settings.BASE_RAW, channel_id=channel)
fits = pyfits.open(posixpath.join(base, filename))
d = fits[0].data.flatten()
fig = Figure(facecolor='white', figsize=(800 / 72, 600 / 72))
ax = fig.add_subplot(111)
ax.hist(d, bins=d.max() - d.min(), histtype='step', log=True)
ax.set_xlabel("Value")
ax.set_ylabel("")
ax.set_title("Histogram: %s" % (filename))
# ax.set_xlim(0, width)
# ax.set_ylim(height, 0)
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/png')
canvas.print_png(response)
return response
def raw_info(request, filename='', channel=0):
base = fix_remote_path(settings.BASE_RAW, channel_id=channel)
fits = pyfits.open(posixpath.join(base, filename))
time = postprocess.get_time_from_filename(posixpath.split(filename)[-1])
hdr = fits[0].header
ignored_keywords = ['COMMENT', 'SIMPLE', 'BZERO', 'BSCALE', 'EXTEND']
keywords = [{
'key': k,
'value': repr(hdr[k]),
'comment': hdr.comments[k]
} for k in hdr.keys() if k not in ignored_keywords]
fits.close()
context = {
'filename': filename,
'keywords': keywords,
'channel': channel if channel else 0,
'time': time
}
avg = Images.objects.filter(type='avg', channel_id=channel,
time__lt=time).order_by('-time').first()
survey = Images.objects.filter(type='survey',
channel_id=channel,
time__lt=time).order_by('-time').first()
dark = Images.objects.filter(type='dark',
channel_id=channel,
time__lt=time).order_by('-time').first()
if dark:
context['dark'] = dark
if avg:
context['avg'] = avg
if survey and (time - survey.time).total_seconds() < 20 * 60:
context['survey'] = survey
return TemplateResponse(request, 'raw_info.html', context=context)
def survey_image(request,
id=0,
ra=0,
dec=0,
sr=0.1,
size=300,
plot_objects=True):
image = Images.objects.get(id=id)
filename = image.filename
filename = fix_remote_path(filename, image.channel_id)
imdata = pyfits.getdata(filename, -1)
header = pyfits.getheader(filename, -1)
wcs = pywcs.WCS(header)
x, y = wcs.all_world2pix(float(ra), float(dec), 0)
pixscale = np.hypot(wcs.wcs.cd[0, 0], wcs.wcs.cd[0, 1])
cutsize = max(10, float(sr) / pixscale)
#imdata2,x0,y0 = crop_image(imdata, x, y, cutsize)
if plot_objects:
objects = survey.get_objects_from_file(filename)
else:
objects = None
fig = Figure(facecolor='white',
dpi=72,
figsize=(size * 1.0 / 72, size * 1.0 / 72))
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
# if x > -cutsize and x <= imdata.shape[1] + cutsize and y > -cutsize and y <= imdata.shape[0] + cutsize:
# limits = np.percentile(imdata2, [0.5, 99.0])
# ax.imshow(imdata2, cmap='hot', origin='lower', interpolation='nearest', vmin=limits[0], vmax=limits[1])
# if x < cutsize:
# ax.set_xlim(x - cutsize, x + cutsize - 1)
# if x >= imdata.shape[1] - cutsize:
# ax.set_xlim(0, 2*cutsize - 1)
# if y < cutsize:
# ax.set_ylim(y - cutsize, y + cutsize - 1)
# if y >= imdata.shape[0] - cutsize:
# ax.set_ylim(0, 2*cutsize - 1)
y1, x1 = np.mgrid[0:imdata.shape[0], 0:imdata.shape[1]]
idx = (x1 >= x - cutsize) & (x1 <= x + cutsize) & (y1 >= y - cutsize) & (
y1 <= y + cutsize)
idx = idx & (x1 > 0) & (y1 > 0) & (x1 < imdata.shape[1] - 1) & (
y1 < imdata.shape[0] - 1)
if len(imdata[idx]):
limits = np.percentile(imdata[idx], [0.5, 99.0])
ax.imshow(imdata,
cmap='hot',
origin='lower',
interpolation='nearest',
vmin=limits[0],
vmax=limits[1])
ax.set_xlim(x - cutsize, x + cutsize)
ax.set_ylim(y - cutsize, y + cutsize)
if objects:
ax.autoscale(False)
ax.scatter(objects['x'], objects['y'], c='blue')
if request.method == 'GET':
if request.GET.has_key('ra0') and request.GET.has_key('dec0'):
ra0 = float(request.GET.get('ra0', 0))
dec0 = float(request.GET.get('dec0', 0))
sr0 = float(request.GET.get('sr0', 0.01))
x0, y0 = wcs.all_world2pix(float(ra0), float(dec0), 0)
psr0 = float(sr0) / pixscale
ax.add_patch(
Ellipse(xy=[x0, y0],
width=2 * psr0,
height=2 * psr0,
edgecolor='green',
fc='None',
lw=2))
response = HttpResponse(content_type="image/png")
canvas = FigureCanvas(fig)
canvas.print_png(response, bbox_inches='tight')
return response
def image_processed(request,
id,
size=800,
base=settings.BASE,
raw=False,
format='jpeg',
pmin=0.5,
pmax=97):
if request.method == 'GET':
pmin = float(request.GET.get('pmin', pmin))
pmax = float(request.GET.get('pmax', pmax))
image_obj = Images.objects.get(id=id)
filename = image_obj.filename
filename = posixpath.join(base, filename)
filename = fix_remote_path(filename, image_obj.channel_id)
data = pyfits.getdata(filename, -1).astype(np.float64)
header = pyfits.getheader(filename, -1)
np.seterr(divide='ignore')
if not raw and image_obj.type not in [
'coadd', 'dark', 'mdark', 'flat', 'flat_11', 'bgflat', 'skyflat',
'skyflat_11', 'superflat'
]:
darkname = find_image(image_obj.time, 'dark', image_obj.channel_id)
darkname = fix_remote_path(darkname, image_obj.channel_id)
is_flat = False
if request.method == 'GET' and request.GET.has_key('linearize'):
calib = Calibrator(shutter=int(image_obj.keywords['SHUTTER']),
channel_id=image_obj.channel_id)
data, header = calib.calibrate(data, header)
darkname = None
is_flat = True
if darkname and posixpath.exists(darkname):
dark = pyfits.getdata(darkname, -1)
# if image_obj.type == 'avg' and np.median(data) < np.median(dark):
# # AVG image affected by off-by-one error (summed over 99 frames instead of 100, but divided by 100)
# data *= 100.0/99.0
data -= dark
is_flat = True
if is_flat:
flatname = find_image(image_obj.time, 'superflat',
image_obj.channel_id)
flatname = fix_remote_path(flatname, image_obj.channel_id)
if flatname and posixpath.exists(flatname):
flat = pyfits.getdata(flatname, -1)
flat[np.isnan(flat)] = np.median(flat[~np.isnan(flat)])
data *= np.mean(flat) / flat
if raw and request.method == 'GET' and request.GET.has_key('linearize'):
calib = Calibrator(shutter=int(image_obj.keywords['SHUTTER']),
channel_id=image_obj.channel_id)
data, header = calib.calibrate(data, header)
if format == 'jpeg':
img = fitsimage.FitsImageFromData(data,
data.shape[1],
data.shape[0],
contrast="percentile",
contrast_opts={
'min_percent': pmin,
'max_percent': pmax
},
scale="linear")
if size:
img.thumbnail((size, size), resample=fitsimage.Image.ANTIALIAS)
# now what?
response = HttpResponse(content_type="image/jpeg")
img.save(response, "JPEG", quality=95)
return response
elif format == 'fits':
newname = posixpath.splitext(
posixpath.split(filename)[-1])[0] + '.processed.fits'
obj = StringIO.StringIO()
pyfits.writeto(obj, data, header=header, clobber=True)
response = HttpResponse(obj.getvalue(),
content_type='application/octet-stream')
response['Content-Disposition'] = 'attachment; filename=' + newname
return response
def realtime_object_details(request, object_id=0):
if request.method == 'POST':
assert_permission(request, 'fweb.modify_data')
message = ""
action = request.POST.get('action')
r = None
if action in [
'process', 'process_1s', 'process_5s', 'process_10s', 'preview'
]:
object = RealtimeObjects.objects.get(id=object_id)
if action == 'process_1s':
deltat = 1
elif action == 'process_5s':
deltat = 5
elif action == 'process_10s':
deltat = 10
else:
deltat = 0
if action == 'preview':
preview = True
else:
preview = False
r = favor2_celery.processObjects.apply_async(kwargs={
'id': object_id,
'deltat': deltat,
'reprocess': True,
'preview': preview
},
queue="channel%d" %
object.channel_id)
elif action == 'delete' or action == 'delete_and_next':
object = RealtimeObjects.objects.get(id=object_id)
night = object.night
state = object.state.name
realtime_object_delete(id=object_id)
next_state = request.POST.get('next_state')
if action == 'delete' or not next_state:
return redirect('/realtime/night/%s/%s' % (night, state))
else:
return redirect('realtime_object', object_id=int(next_state))
elif action == 'comment-delete':
for c in RealtimeComments.objects.filter(object_id=object_id):
c.delete()
elif action == 'comment':
comment = request.POST.get('comment').strip()
c, _ = RealtimeComments.objects.get_or_create(object_id=object_id)
if comment:
c.comment = comment
c.save()
else:
c.delete()
elif action == 'change_state':
new_state = request.POST.get('new_state').strip()
if new_state:
realtime_object_change_state(object_id, new_state)
elif action == 'change_state_particle':
realtime_object_change_state(object_id, 'particle')
elif action == 'change_state_misc':
realtime_object_change_state(object_id, 'misc')
if r:
# Redirect to the task page
return redirect('tasks_task', id=r.id)
else:
# Redirect to the same view, but with no POST args. We can't display messages with it!
return redirect('realtime_object', object_id=object_id)
object = RealtimeObjects.objects.annotate(
nrecords=Count('realtimerecords')).get(id=object_id)
context = {'object': object}
next = RealtimeObjects.objects.filter(night=object.night).filter(
id__lt=object.id).order_by('-time_start').order_by('-id').first()
next_state = RealtimeObjects.objects.filter(
night=object.night, state=object.state,
id__lt=object.id).order_by('-time_start').order_by('-id').first()
if next:
context['next'] = next.id
if next_state:
context['next_state'] = next_state.id
prev = RealtimeObjects.objects.filter(night=object.night).filter(
id__gt=object.id).order_by('time_start').order_by('id').first()
prev_state = RealtimeObjects.objects.filter(
night=object.night, state=object.state,
id__gt=object.id).order_by('time_start').order_by('id').first()
if prev:
context['prev'] = prev.id
if prev_state:
context['prev_state'] = prev_state.id
images = RealtimeImages.objects.filter(
object_id=object_id).order_by('time')
if not images:
records = RealtimeRecords.objects.filter(
object_id=object_id).order_by('time')
images = [{'record': r, 'time': r.time} for r in records]
context['processed'] = False
else:
context['processed'] = True
comments = RealtimeComments.objects.filter(object_id=object_id)
if comments:
context['comment'] = comments[0]
if object.params.has_key('related'):
related = object.params['related'].split()
context['related'] = related
avg = Images.objects.filter(type='avg').filter(
channel_id=object.channel_id).filter(
time__gt=object.time_start).order_by('time').first()
if avg:
context['avg_id'] = avg.id
#context['avg_id'] = find_image_id(object.time_start, 'avg', object.channel_id)
if object.channel_id:
# Try to get direct links to RAW files
raw_base = fix_remote_path(settings.BASE_RAW,
channel_id=object.channel_id)
raw_dirs = [
d for d in os.listdir(raw_base)
if os.path.isdir(posixpath.join(raw_base, d))
and re.match('^\d{8}_\d{6}$', d)
]
raw_dirs.sort()
i0 = max(
0,
bisect.bisect_right(
raw_dirs,
get_dirname(object.time_start - datetime.timedelta(seconds=1)))
- 1)
i1 = bisect.bisect_right(
raw_dirs,
get_dirname(object.time_end + datetime.timedelta(seconds=1))) - 1
raw_dirs = raw_dirs[i0:i1 + 1]
for image in images:
raw = None
if raw_dirs:
time = image['time'] if type(image) == dict else image.time
filename = get_filename(time)
for dir in raw_dirs:
idx = get_index(posixpath.join(raw_base, dir))
for i in idx:
if i['path'] == filename:
raw = posixpath.join(dir, i['path'])
break
if raw:
break
if type(image) == dict:
image['raw'] = raw
else:
image.raw = raw
context['images'] = images
return TemplateResponse(request, 'realtime_object.html', context=context)
def survey_dbg_image(request, id=0, size=900, cat='pickles'):
if request.method == 'GET':
if request.GET.has_key('cat'):
cat = request.GET.get('cat')
image_obj = Images.objects.get(id=id)
filename = fix_remote_path(image_obj.filename, image_obj.channel_id)
darkname = find_image(image_obj.time, 'dark', image_obj.channel_id)
darkname = fix_remote_path(darkname, image_obj.channel_id)
flatname = find_image(image_obj.time, 'flat', image_obj.channel_id)
flatname = fix_remote_path(flatname, image_obj.channel_id)
data = pyfits.getdata(filename, -1)
header = pyfits.getheader(filename, -1)
# Prepare
if posixpath.exists(darkname):
dark = pyfits.getdata(darkname, -1)
data -= dark
if posixpath.exists(flatname):
flat = pyfits.getdata(flatname, -1)
data *= np.mean(flat) / flat
# Calibrate
obj = survey.get_objects_from_file(filename, simple=False, filter=True)
wcs = pywcs.WCS(header)
ra0, dec0, sr0 = survey.get_frame_center(header=header, wcs=wcs)
print filename, '-', ra0, dec0, sr0
favor2 = Favor2()
if cat == 'apass':
cat = favor2.get_stars(ra0,
dec0,
sr0,
catalog='apass',
limit=600000,
extra=[
"g<14 and g>8.5",
"b<20 and v<20 and r<20 and i<20 and g<20",
"var = 0", "gerr>0 and rerr>0 and ierr>0"
])
else:
cat = favor2.get_stars(ra0,
dec0,
sr0,
catalog='pickles',
limit=200000,
extra=["var = 0", "rank=1"])
print "%d objects, %d stars" % (len(obj['ra']), len(cat['ra']))
survey.fix_distortion(obj, cat, header=header)
Y, YY, corr, oidx = survey.calibrate_objects(obj,
cat,
sr=10. / 3600,
retval='all')
# Plot
dpi = 72
figsize = (size / dpi, size * 4 * 0.6 / dpi)
fig = Figure(facecolor='white', figsize=figsize, tight_layout=True)
# 1
ax = fig.add_subplot(411)
limits = np.percentile(data, [0.5, 99.5])
i = ax.imshow(data,
origin='lower',
cmap='hot',
interpolation='nearest',
vmin=limits[0],
vmax=limits[1])
fig.colorbar(i)
ax.set_title(
"%d / %s / ch=%d at %s" %
(image_obj.id, image_obj.type, image_obj.channel_id, image_obj.time))
# 2
ax = fig.add_subplot(412)
gmag0, bins, binloc = griddata(obj['x'][oidx],
obj['y'][oidx],
Y,
binsize=100)
limits = np.percentile(gmag0[np.isfinite(gmag0)], [0.5, 95.5])
i = ax.imshow(gmag0,
origin='lower',
extent=[0, header['NAXIS1'], 0, header['NAXIS2']],
interpolation='nearest',
vmin=limits[0],
vmax=limits[1])
fig.colorbar(i)
ax.autoscale(False)
ax.plot(obj['x'][oidx], obj['y'][oidx], 'b.', alpha=0.3)
ax.set_title("Actual zero point")
# 3
ax = fig.add_subplot(413)
gmag0, bins, binloc = griddata(obj['x'][oidx],
obj['y'][oidx],
YY,
binsize=100)
#limits = np.percentile(gmag0[np.isfinite(gmag0)], [0.5, 95.5])
i = ax.imshow(gmag0,
origin='lower',
extent=[0, header['NAXIS1'], 0, header['NAXIS2']],
interpolation='nearest',
vmin=limits[0],
vmax=limits[1])
fig.colorbar(i)
ax.autoscale(False)
ax.plot(obj['x'][oidx], obj['y'][oidx], 'b.', alpha=0.3)
ax.set_title("Fitted zero point")
# 4
ax = fig.add_subplot(414)
gmag0, bins, binloc = griddata(obj['x'][oidx],
obj['y'][oidx],
Y - YY,
binsize=100)
limits = np.percentile(gmag0[np.isfinite(gmag0)], [0.5, 95.5])
i = ax.imshow(gmag0,
origin='lower',
extent=[0, header['NAXIS1'], 0, header['NAXIS2']],
interpolation='nearest',
vmin=limits[0],
vmax=limits[1])
fig.colorbar(i)
ax.autoscale(False)
ax.plot(obj['x'][oidx], obj['y'][oidx], 'b.', alpha=0.3)
ax.set_title("Zero point residuals. std=%g" % np.std(Y - YY))
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/png')
canvas.print_png(response)
return response
