def camera_types_trend():
period = 'monthly'
_map = {
'monthly': (_("Integration hours by camera type"), '%Y-%m'),
}
flot_data = []
flot_options = {
'xaxis': {'mode': 'time'},
'lines': {'show': 'true'},
'legend': {
'position': 'nw',
'backgroundColor': '#000000',
'backgroundOpacity': 0.75,
},
'grid': {
'hoverable': True,
},
}
for g in Camera.CAMERA_TYPES:
if g[0] > 1:
# Limit to CCD and DSLR
continue
all = DeepSky_Acquisition.objects \
.filter(Q(image__imaging_cameras__type = g[0]) & Q(date__gte = '2011-01-01')) \
.exclude(date = None) \
.order_by('date')
g_dict = {
'label': unicode(g[1]),
'stage_data': {},
'data': [],
}
for i in all:
integration = 0
if i.duration and i.number:
integration += (i.duration * i.number) / 3600.0
key = i.date.strftime(_map[period][1])
if key in g_dict['stage_data']:
g_dict['stage_data'][key] += integration
else:
g_dict['stage_data'][key] = integration
if all:
for date in daterange(all[0].date, datetime.today().date()):
grouped_date = date.strftime(_map[period][1])
t = time.mktime(datetime.strptime(grouped_date, _map[period][1]).timetuple()) * 1000
if grouped_date in g_dict['stage_data'].keys():
g_dict['data'].append([t, g_dict['stage_data'][grouped_date]])
else:
g_dict['data'].append([t, 0])
g_dict['data'] = unique_items(g_dict['data'])
del g_dict['stage_data']
flot_data.append(g_dict)
return (flot_data, flot_options)
def subject_images_monthly(subject_id):
flot_label = _("Uploaded images, monthly")
flot_data = []
flot_options = {
'bars': {
'show': 'true',
'fill': 'true',
'barWidth': 0.9,
'align': 'center',
},
'legend': {
'show': False,
},
'grid': {'hoverable': 'true'},
}
all = Image.objects.filter(subjects__id = subject_id).order_by('uploaded')
data = {}
for i in all:
key = i.uploaded.date().strftime('%m')
if key in data:
data[key] += 1
else:
data[key] = 1
if all:
for date in daterange(all[0].uploaded.date(), datetime.today().date()):
grouped_date = date.strftime('%m')
if grouped_date in data.keys():
flot_data.append([grouped_date, data[grouped_date]])
else:
flot_data.append([grouped_date, 0])
flot_data = unique_items(flot_data)
return (flot_label, flot_data, flot_options)
def integration_hours(user, period='monthly', since=0):
_map = {
'yearly' : (_("Integration hours, yearly") , '%Y'),
'monthly': (_("Integration hours, monthly"), '%Y-%m'),
'daily' : (_("Integration hours, daily") , '%Y-%m-%d'),
}
flot_label = _map[period][0]
flot_data = []
flot_options = {
'xaxis': {'mode': 'time'},
'lines': {'show': 'true', 'fill': 'true'},
'points': {'show': 'true'},
'legend': {
'position': 'nw',
'backgroundColor': '#000000',
'backgroundOpacity': 0.75},
'grid': {'hoverable': 'true'},
}
if period == 'monthly':
flot_options['xaxis']['timeformat'] = '%b'
astrobin = User.objects.get(username = '******')
all = DeepSky_Acquisition.objects.all().exclude(date = None).order_by('date')
if since > 0:
all = all.filter(date__gte = datetime.today().date() - timedelta(days = since))
if user != astrobin:
all = all.filter(image__user = user)
data = {}
for i in all:
integration = 0
if i.duration and i.number:
integration += (i.duration * i.number) / 3600.0
key = i.date.strftime(_map[period][1])
if key in data:
if integration > 0:
data[key] += integration
else:
data[key] = integration
if all:
for date in daterange(all[0].date, datetime.today().date()):
grouped_date = date.strftime(_map[period][1])
t = time.mktime(datetime.strptime(grouped_date, _map[period][1]).timetuple()) * 1000
if grouped_date in data.keys():
flot_data.append([t, data[grouped_date]])
else:
flot_data.append([t, 0])
flot_data = unique_items(flot_data)
return (flot_label, flot_data, flot_options)
def subject_type_trend():
period = 'monthly'
_map = {
'monthly': (_("Number of images by subject type"), '%Y-%m'),
}
flot_data = []
flot_options = {
'xaxis': {'mode': 'time'},
'lines': {'show': 'true'},
'legend': {
'position': 'nw',
'backgroundColor': '#000000',
'backgroundOpacity': 0.75,
},
'grid': {
'hoverable': True,
},
}
for g in Image.SUBJECT_TYPE_CHOICES:
all = Image.objects \
.filter(Q(subject_type = g[0])) \
.order_by('uploaded')
g_dict = {
'label': unicode(g[1]),
'stage_data': {},
'data': [],
}
for i in all:
key = i.uploaded.strftime(_map[period][1])
if key in g_dict['stage_data']:
g_dict['stage_data'][key] += 1
else:
g_dict['stage_data'][key] = 1
if all:
for date in daterange(all[0].uploaded.date(), datetime.today().date()):
grouped_date = date.strftime(_map[period][1])
t = time.mktime(datetime.strptime(grouped_date, _map[period][1]).timetuple()) * 1000
if grouped_date in g_dict['stage_data'].keys():
g_dict['data'].append([t, g_dict['stage_data'][grouped_date]])
else:
g_dict['data'].append([t, 0])
g_dict['data'] = unique_items(g_dict['data'])
del g_dict['stage_data']
flot_data.append(g_dict)
return (flot_data, flot_options)
def hard_merge(modeladmin, request, queryset):
from utils import unique_items
masters = unique_items([x.master for x in queryset])
if len(masters) > 1:
return
master = masters[0]
for merge in queryset:
master.hard_merge(merge.slave)
# Finally, clear up the temporary model
queryset.delete()
return HttpResponseRedirect('/admin/astrobin/gear/')
def hard_merge(modeladmin, request, queryset):
from utils import unique_items
masters = unique_items([x.master for x in queryset])
if len(masters) > 1:
return
master = masters[0]
for merge in queryset:
master.hard_merge(merge.slave)
# Finally, clear up the temporary model
queryset.delete()
return HttpResponseRedirect('/admin/astrobin/gear/')
def uploaded_images(user, period='monthly'):
_map = {
'yearly' : (_("Uploaded images, yearly") , '%Y'),
'monthly': (_("Uploaded images, monthly"), '%Y-%m'),
'daily' : (_("Uploaded images, daily") , '%Y-%m-%d'),
}
flot_label = _map[period][0]
flot_data = []
flot_options = {
'xaxis': {'mode': 'time'},
'lines': {'show': 'true', 'fill': 'true'},
'points': {'show': 'true'},
'legend': {
'position': 'nw',
'backgroundColor': '#000000',
'backgroundOpacity': 0.75},
'grid': {'hoverable': 'true'},
}
if period == 'monthly':
flot_options['xaxis']['timeformat'] = '%b'
astrobin = User.objects.get(username = '******')
all = Image.objects.all().order_by('uploaded')
if user != astrobin:
all = all.filter(user = user)
data = {}
for i in all:
key = i.uploaded.date().strftime(_map[period][1])
if key in data:
data[key] += 1
else:
data[key] = 1
if all:
for date in daterange(all[0].uploaded.date(), datetime.today().date()):
grouped_date = date.strftime(_map[period][1])
t = time.mktime(datetime.strptime(grouped_date, _map[period][1]).timetuple()) * 1000
if grouped_date in data.keys():
flot_data.append([t, data[grouped_date]])
else:
flot_data.append([t, 0])
flot_data = unique_items(flot_data)
return (flot_label, flot_data, flot_options)
def image_views(image_id, period='monthly'):
_map = {
'yearly' : (_("Views, yearly") , '%Y'),
'monthly': (_("Views, monthly"), '%Y-%m'),
'daily' : (_("Views, daily") , '%Y-%m-%d'),
}
flot_label = _map[period][0]
flot_data = []
flot_options = {
'xaxis': {'mode': 'time'},
'lines': {'show': 'true', 'fill': 'true'},
'points': {'show': 'true'},
'legend': {
'position': 'nw',
'backgroundColor': '#000000',
'backgroundOpacity': 0.75},
'grid': {'hoverable': 'true'},
}
if period == 'monthly':
flot_options['xaxis']['timeformat'] = '%b'
all = Hit.objects.filter(hitcount__object_pk = image_id).order_by('created')
data = {}
for i in all:
key = i.created.date().strftime(_map[period][1])
if key in data:
data[key] += 1
else:
data[key] = 1
if all:
for date in daterange(all[0].created.date(), datetime.today().date()):
grouped_date = date.strftime(_map[period][1])
t = time.mktime(datetime.strptime(grouped_date, _map[period][1]).timetuple()) * 1000
if grouped_date in data.keys():
flot_data.append([t, data[grouped_date]])
else:
flot_data.append([t, 0])
flot_data = unique_items(flot_data)
return (flot_label, flot_data, flot_options)
def subject_total_images(subject_id):
period = 'monthly'
_map = {
'monthly': (_("Total images"), '%Y-%m'),
}
flot_label = _map[period][0]
flot_data = []
flot_options = {
'xaxis': {'mode': 'time', 'timeformat': '%b'},
'lines': {'show': 'true', 'fill': 'true'},
'points': {'show': 'true'},
'legend': {
'position': 'nw',
'backgroundColor': '#000000',
'backgroundOpacity': 0.75},
'grid': {'hoverable': 'true'},
}
all = Image.objects.filter(subjects__id = subject_id).order_by('uploaded')
data = {}
total = 0
for i in all:
key = i.uploaded.date().strftime(_map[period][1])
if key in data:
total += 1
data[key] = total
else:
total += 1
data[key] = total
if all:
for date in daterange(all[0].uploaded.date(), datetime.today().date()):
grouped_date = date.strftime(_map[period][1])
t = time.mktime(datetime.strptime(grouped_date, _map[period][1]).timetuple()) * 1000
if grouped_date in data.keys():
flot_data.append([t, data[grouped_date]])
else:
flot_data.append([t, 0])
flot_data = unique_items(flot_data)
return (flot_label, flot_data, flot_options)
def subject_integration_monthly(subject_id):
flot_label = _("Integration hours, monthly")
flot_data = []
flot_options = {
'bars': {
'show': 'true',
'fill': 'true',
'barWidth': 0.9,
'align': 'center',
},
'legend': {
'show': False,
},
'grid': {'hoverable': 'true'},
}
all = DeepSky_Acquisition.objects \
.filter(image__subjects__id = subject_id) \
.exclude(Q(number = None) | Q(duration = None) | Q(date = None)) \
.order_by('date')
data = {}
for i in all:
key = i.date.strftime('%m')
if key in data:
data[key] += (i.number * i.duration / 3600.00)
else:
data[key] = 0
if all:
for date in daterange(all[0].date, datetime.today().date()):
grouped_date = date.strftime('%m')
if grouped_date in data.keys():
flot_data.append([grouped_date, data[grouped_date]])
else:
flot_data.append([grouped_date, 0])
flot_data = unique_items(flot_data)
return (flot_label, flot_data, flot_options)
def integration_hours_by_gear(user, period='monthly'):
_map = {
'yearly' : (_("Integration hours by gear, yearly") , '%Y'),
'monthly': (_("Integration hours by gear, monthly"), '%Y-%m'),
'daily' : (_("Integration hours by gear, daily") , '%Y-%m-%d'),
}
flot_data = []
flot_options = {
'xaxis': {'mode': 'time'},
'lines': {'show': 'true'},
'points': {'show': 'true'},
'legend': {
'noColumns': 3,
},
'grid': {'hoverable': 'true'},
}
if period == 'monthly':
flot_options['xaxis']['timeformat'] = '%b'
profile = user.userprofile
all_telescopes = profile.telescopes.all()
all_cameras = profile.cameras.all()
thickness = all_telescopes.count() + all_cameras.count()
for t in (all_telescopes, all_cameras):
all = DeepSky_Acquisition.objects.filter(image__user = user).exclude(date = None).order_by('date')
for g in t:
if hasattr(t, 'model') and t.model.__name__ == 'Telescope':
all = all.filter(image__imaging_telescopes = g)
if hasattr(t, 'model') and t.model.__name__ == 'Camera':
all = all.filter(image__imaging_cameras = g)
g_dict = {
'label': _map[period][0] + ": " + unicodedata.normalize('NFKD', unicode(g)).encode('ascii', 'ignore'),
'stage_data': {},
'data': [],
'lines': {'lineWidth': thickness},
}
for i in all:
integration = 0
if i.duration and i.number:
integration += (i.duration * i.number) / 3600.0
key = i.date.strftime(_map[period][1])
if key in g_dict['stage_data']:
g_dict['stage_data'][key] += integration
else:
g_dict['stage_data'][key] = integration
if all:
for date in daterange(all[0].date, datetime.today().date()):
grouped_date = date.strftime(_map[period][1])
t = time.mktime(datetime.strptime(grouped_date, _map[period][1]).timetuple()) * 1000
if grouped_date in g_dict['stage_data'].keys():
g_dict['data'].append([t, g_dict['stage_data'][grouped_date]])
else:
g_dict['data'].append([t, 0])
g_dict['data'] = unique_items(g_dict['data'])
del g_dict['stage_data']
flot_data.append(g_dict)
thickness -= 1
return (flot_data, flot_options)