def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(
data={'description':"Trans:" +self._testMethodName},
database=self.database)
self.n_images = 2
self.rms_min = 1e-3 #1mJy
self.rms_max = 2e-3 #2mJy
self.new_source_sigma_margin = 3
self.detection_thresh=10
self.first_image_freq = 250e6 # 250 MHz
self.second_image_freq = 50e6 # 50 MHz
dt=self.detection_thresh
margin = self.new_source_sigma_margin
self.always_detectable_flux = 1.01*self.rms_max*(dt+ margin)
self.search_params = dict(eta_min=1,
v_min=0.1,
minpoints=1, )
test_specific_img_params = dict(
freq_eff = self.first_image_freq,
rms_qc = self.rms_min,
rms_min = self.rms_min,
rms_max = self.rms_max,
detection_thresh = self.detection_thresh)
self.img_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
self.img_params[1]['freq_eff']=self.second_image_freq
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(database=self.database,
data={'description':"Trans:"
+ self._testMethodName})
self.n_images = 4
self.new_source_sigma_margin = 3
image_rms = 1e-3
detection_thresh=10
self.search_params = dict(eta_min=1,
v_min=0.1,
# minpoints=1,
)
self.barely_detectable_flux = 1.01*image_rms*(detection_thresh)
self.reliably_detectable_flux = (
1.01*image_rms*(detection_thresh+self.new_source_sigma_margin))
# 1mJy image RMS, 10-sigma detection threshold = 10mJy threshold.
test_specific_img_params = dict(rms_qc = image_rms,
rms_min = image_rms,
rms_max = image_rms,
detection_thresh = detection_thresh)
self.im_params = db_subs.generate_timespaced_dbimages_data(
self.n_images,**test_specific_img_params)
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(
data={'description':"Trans:" +self._testMethodName},
database=self.database)
self.n_images = 2
self.rms_min_initial = 2e-3 #2mJy
self.rms_max_initial = 5e-3 #5mJy
self.new_source_sigma_margin = 3
self.detection_thresh=10
dt=self.detection_thresh
self.barely_detectable_flux = 1.01*dt*self.rms_min_initial
test_specific_img_params = dict(rms_qc = self.rms_min_initial,
rms_min = self.rms_min_initial,
rms_max = self.rms_max_initial,
detection_thresh = self.detection_thresh)
self.img_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
#Increase RMS to 4 mJy / 10mJy in second image.
rms_increase_factor = 2.0
self.img_params[1]['rms_qc']*=rms_increase_factor
self.img_params[1]['rms_min']*=rms_increase_factor
self.img_params[1]['rms_max']*=rms_increase_factor
self.search_params = dict(eta_min=1,
v_min=0.1,
# minpoints=1,
)
def test_basic_same_field_case(self):
""" Here we start with 1 source in image0.
We then add image1 (same field as image0), with a double association
for the source, and check assocskyrgn updates correctly.
"""
n_images = 2
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
idx = 0
src_a = db_subs.example_extractedsource_tuple(
ra=im_params[idx]['centre_ra'],
dec=im_params[idx]['centre_decl'])
src_b = src_a._replace(ra=src_a.ra + 1. / 60.) # 1 arcminute offset
imgs = []
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[idx]))
imgs[idx].insert_extracted_sources([src_a])
imgs[idx].associate_extracted_sources(deRuiter_r, new_source_sigma_margin)
idx = 1
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[idx]))
imgs[idx].insert_extracted_sources([src_a, src_b])
imgs[idx].associate_extracted_sources(deRuiter_r, new_source_sigma_margin)
imgs[idx].update()
runcats = columns_from_table('runningcatalog',
where={'dataset':self.dataset.id})
self.assertEqual(len(runcats), 2) #Just a sanity check.
skyassocs = columns_from_table('assocskyrgn',
where={'skyrgn':imgs[idx]._data['skyrgn']})
self.assertEqual(len(skyassocs), 2)
def main():
database = tkp.db.Database()
dataset = tkp.db.DataSet(data={'description': "Banana test data"},
database=database)
n_images = 4
new_source_sigma_margin = 3
image_rms = 1e-3
detection_thresh = 10
reliably_detectable_flux = 1.01 * image_rms * (detection_thresh +
new_source_sigma_margin)
# 1mJy image RMS, 10-sigma detection threshold = 10mJy threshold.
test_specific_img_params = {'rms_qc': image_rms, 'rms_min': image_rms,
'rms_max': image_rms,
'detection_thresh': detection_thresh}
im_params = db_subs.generate_timespaced_dbimages_data(n_images,
**test_specific_img_params)
src_tuple = db_subs.example_extractedsource_tuple(ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],)
transient_src = db_subs.MockSource(
template_extractedsource=src_tuple,
lightcurve={im_params[2]['taustart_ts']:
reliably_detectable_flux}
)
for img_pars in im_params:
db_subs.insert_image_and_simulated_sources(dataset, img_pars,
[transient_src],
new_source_sigma_margin)
tkp.db.execute("insert into monitor values(1, 1, 1, 1, 1, 'bla')", commit=True)
def setUp(self):
self.session = self.db.Session()
self.fake_images = db_subs.generate_timespaced_dbimages_data(n_images=1)
self.dataset = tkp.db.DataSet(data={'description':
"Reject:" + self._testMethodName})
self.image = tkp.db.Image(data=self.fake_images[0],
dataset=self.dataset)
def setUp(self):
self.database = tkp.db.database.Database()
self.dataset = DataSet(data={'description': "Src. assoc:" +
self._testMethodName},
database=self.database)
self.im_params = db_subs.generate_timespaced_dbimages_data(n_images=8)
self.db_imgs=[]
def test_two_field_overlap_new_transient(self):
"""Now for something more interesting - two overlapping fields, 4 sources:
one steady source only in lower field,
one steady source in both fields,
one steady source only in upper field,
one transient source in both fields but only at 2nd timestep.
"""
n_images = 2
xtr_radius = 1.5
im_params = db_subs.generate_timespaced_dbimages_data(n_images,
xtr_radius=xtr_radius)
im_params[1]['centre_decl'] += xtr_radius * 1
imgs = []
lower_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] - 0.5 * xtr_radius)
upper_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[1]['centre_ra'],
dec=im_params[1]['centre_decl'] + 0.5 * xtr_radius)
overlap_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] + 0.2 * xtr_radius)
overlap_transient = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] + 0.8 * xtr_radius)
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[0]))
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[1]))
imgs[0].insert_extracted_sources([lower_steady_src, overlap_steady_src])
imgs[0].associate_extracted_sources(deRuiter_r=0.1,
new_source_sigma_margin=new_source_sigma_margin)
nd_posns = dbnd.get_nulldetections(imgs[0].id)
self.assertEqual(len(nd_posns), 0)
imgs[1].insert_extracted_sources([upper_steady_src, overlap_steady_src,
overlap_transient])
imgs[1].associate_extracted_sources(deRuiter_r=0.1,
new_source_sigma_margin=new_source_sigma_margin)
nd_posns = dbnd.get_nulldetections(imgs[1].id)
self.assertEqual(len(nd_posns), 0)
runcats = columns_from_table('runningcatalog',
where={'dataset': self.dataset.id})
self.assertEqual(len(runcats), 4) #sanity check.
newsources_qry = """\
SELECT *
FROM newsource tr
,runningcatalog rc
WHERE rc.dataset = %s
AND tr.runcat = rc.id
"""
self.database.cursor.execute(newsources_qry, (self.dataset.id,))
newsources = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(newsources), 1)
def test_rejected_initial_image(self):
"""
An image which is rejected should not be taken into account when
deciding whether a patch of sky has been previously observed, and
hence whether any detections in that area are (potential) transients.
Here, we create a database with two images. The first
(choronologically) is rejected; the second contains a source. That
source should not be marked as a transient.
"""
dataset = tkp.db.DataSet(data={"description": "Trans:" + self._testMethodName}, database=tkp.db.Database())
# We use a dataset with two images
# NB the routine in db_subs automatically increments time between
# images.
n_images = 2
db_imgs = [
tkp.db.Image(data=im_params, dataset=dataset)
for im_params in db_subs.generate_timespaced_dbimages_data(n_images)
]
# The first image is rejected for an arbitrary reason
# (for the sake of argument, we use an unacceptable RMS).
db_quality.reject(
imageid=db_imgs[0].id,
reason=db_quality.reject_reasons["rms"],
comment=self._testMethodName,
session=self.session,
)
# Have to commit here: old DB code makes queries in a separate transaction.
self.session.commit()
# Since we rejected the first image, we only find a source in the
# second.
source = db_subs.example_extractedsource_tuple()
insert_extracted_sources(db_imgs[1]._id, [source])
# Standard source association procedure etc.
associate_extracted_sources(db_imgs[1].id, deRuiter_r=3.7, new_source_sigma_margin=3)
# Our source should _not_ be a transient. That is, there should be no
# entries in the newsource table for this dataset.
cursor = tkp.db.execute(
"""\
SELECT t.id FROM newsource t, runningcatalog rc
WHERE t.runcat = rc.id
AND rc.dataset = %(ds_id)s
""",
{"ds_id": dataset.id},
)
self.assertEqual(cursor.rowcount, 0)
def test_create(self):
image_data_dicts = db_subs.generate_timespaced_dbimages_data(n_images=2)
dataset1 = DataSet(data={'description': 'dataset with images'},
database=self.database)
#self.assertEqual(dataset1.images, set())
image1 = Image(dataset=dataset1, data=image_data_dicts[0])
# Images are automatically added to their dataset
#self.assertEqual(dataset1.images, set([image1]))
self.assertEqual(image1.tau_time, image_data_dicts[0]['tau_time'])
self.assertAlmostEqual(image1.freq_eff, image_data_dicts[0]['freq_eff'])
image2 = Image(dataset=dataset1, data=image_data_dicts[1])
#self.assertEqual(dataset1.images, set([image1, image2]))
dataset2 = DataSet(database=self.database, id=dataset1.id)
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(
data={'description':"Trans:" +self._testMethodName},
database=self.database)
self.n_images = 8
self.rms_min_initial = 2e-3 #2mJy
self.rms_max_initial = 5e-3 #5mJy
self.new_source_sigma_margin = 3
self.detection_thresh=10
dt=self.detection_thresh
margin = self.new_source_sigma_margin
self.always_detectable_flux = 1.01*(dt+ margin)*self.rms_max_initial
self.search_params = dict(eta_min=1,
v_min=0.1,
# minpoints=1,
)
test_specific_img_params = dict(rms_qc = self.rms_min_initial,
rms_min = self.rms_min_initial,
rms_max = self.rms_max_initial,
detection_thresh = self.detection_thresh)
self.img_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
#Raise RMS in images 0,1,2,6
rms_keys = ['rms_qc', 'rms_min', 'rms_max']
rms_increase_factor = 1.2
for img_index in (0,1,2,6):
for k in rms_keys:
self.img_params[img_index][k]*=rms_increase_factor
# Now, images 3,4,5 are equally good. But if we raise the rms_max in
# images, 3,5 (leave rms_min equal) then we should pick 4 as the best.
# (NB Careful ordering - ensures we're not just picking the best by
# default due to it being first or last in the matching set.)
for img_index in (3,5):
self.img_params[img_index]['rms_max']*=rms_increase_factor
#Drop RMS significantly in last image so we get a detection. (index=7)
rms_decrease_factor = 0.5
for k in rms_keys:
self.img_params[-1][k]*=rms_decrease_factor
def test_basic_insertion(self):
"""Here we begin with a single insertion, and check a relevant entry
exists in the skyregion table.
The key logic checked here is that inserting an image with duplicate
skyregion will return the same skyrgn id as the first image of that field,
conversely a new region results in a new skyrgn entry.
"""
self.database = tkp.db.Database()
db_subs.delete_test_database(self.database)
self.dataset = tkp.db.DataSet(database=self.database,
data={'description': "Skyrgn:" + self._testMethodName})
n_images = 3
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
##First image:
image0 = tkp.db.Image(dataset=self.dataset, data=im_params[0])
image0.update()
skyrgns = columns_from_table('skyregion',
where={'dataset':self.dataset.id})
# if self.clean_table:
self.assertEqual(len(skyrgns), 1)
rgn_keys = ['centre_ra', 'centre_decl', 'xtr_radius']
first_skyrgn_id = None
for db_row in skyrgns:
if all([db_row[k] == im_params[0][k] for k in rgn_keys]):
first_skyrgn_id = db_row['id']
self.assertNotEqual(first_skyrgn_id, None)
self.assertEqual(image0._data['skyrgn'], first_skyrgn_id)
##Second, identical image:
image1 = tkp.db.Image(dataset=self.dataset, data=im_params[1])
image1.update()
self.assertEqual(image1._data['skyrgn'], first_skyrgn_id)
##Third, different image:
im_params[2]['centre_ra'] += im_params[2]['xtr_radius'] * 0.5
image2 = tkp.db.Image(dataset=self.dataset, data=im_params[2])
image2.update()
self.assertNotEqual(image2._data['skyrgn'], first_skyrgn_id)
skyrgns = columns_from_table('skyregion',
where={'dataset':self.dataset.id})
for db_row in skyrgns:
if all([db_row[k] == im_params[2][k] for k in rgn_keys]):
second_skyrgn_id = db_row['id']
self.assertNotEqual(second_skyrgn_id, None)
self.assertEqual(image2._data['skyrgn'], second_skyrgn_id)
def test_two_field_overlap_nulling_src(self):
"""Similar to above, but one source disappears:
Two overlapping fields, 4 sources:
one steady source only in lower field,
one steady source in both fields,
one steady source only in upper field,
one transient source in both fields but only at *1st* timestep.
"""
n_images = 2
xtr_radius = 1.5
im_params = db_subs.generate_timespaced_dbimages_data(n_images,
xtr_radius=xtr_radius)
im_params[1]['centre_decl'] += xtr_radius * 1
imgs = []
lower_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] - 0.5 * xtr_radius)
upper_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[1]['centre_ra'],
dec=im_params[1]['centre_decl'] + 0.5 * xtr_radius)
overlap_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] + 0.2 * xtr_radius)
overlap_transient = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] + 0.8 * xtr_radius)
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[0]))
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[1]))
imgs[0].insert_extracted_sources([lower_steady_src, overlap_steady_src,
overlap_transient])
imgs[0].associate_extracted_sources(deRuiter_r=0.1,
new_source_sigma_margin=new_source_sigma_margin)
nd_posns = dbnd.get_nulldetections(imgs[0].id)
self.assertEqual(len(nd_posns), 0)
imgs[1].insert_extracted_sources([upper_steady_src, overlap_steady_src])
imgs[1].associate_extracted_sources(deRuiter_r=0.1,
new_source_sigma_margin=new_source_sigma_margin)
#This time we don't expect to get an immediate transient detection,
#but we *do* expect to get a null-source forced extraction request:
nd_posns = dbnd.get_nulldetections(imgs[1].id)
self.assertEqual(len(nd_posns), 1)
runcats = columns_from_table('runningcatalog',
where={'dataset':self.dataset.id})
self.assertEqual(len(runcats), 4) #sanity check.
def test_new_skyregion_insertion(self):
"""Here we test the association logic executed upon insertion of a
new skyregion.
We expect that any pre-existing entries in the runningcatalog
which lie within the field of view will be marked as
'within this region', through the presence of an entry in table
``assocskyrgn``.
Conversely sources outside the FoV should not be marked as related.
We begin with img0, with a source at centre.
Then we add 2 more (empty) images/fields at varying positions.
"""
n_images = 6
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
src_in_img0 = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],)
##First image:
image0 = tkp.db.Image(dataset=self.dataset, data=im_params[0])
image0.insert_extracted_sources([src_in_img0])
image0.associate_extracted_sources(deRuiter_r, new_source_sigma_margin)
image0.update()
runcats = columns_from_table('runningcatalog',
where={'dataset':self.dataset.id})
self.assertEqual(len(runcats), 1) #Just a sanity check.
##Second, different *But overlapping* image:
idx = 1
im_params[idx]['centre_decl'] += im_params[idx]['xtr_radius'] * 0.9
image1 = tkp.db.Image(dataset=self.dataset, data=im_params[idx])
image1.update()
assocs = columns_from_table('assocskyrgn',
where={'skyrgn':image1._data['skyrgn']})
self.assertEqual(len(assocs), 1)
self.assertEqual(assocs[0]['runcat'], runcats[0]['id'])
##Third, different *and NOT overlapping* image:
idx = 2
im_params[idx]['centre_decl'] += im_params[idx]['xtr_radius'] * 1.1
image2 = tkp.db.Image(dataset=self.dataset, data=im_params[idx])
image2.update()
assocs = columns_from_table('assocskyrgn',
where={'skyrgn':image2._data['skyrgn']})
self.assertEqual(len(assocs), 0)
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(database=self.database,
data={'description':"Median test"
+ self._testMethodName})
self.n_images = 5
self.im_params = db_subs.generate_timespaced_dbimages_data(self.n_images)
for idx, impar in enumerate(self.im_params):
impar['rms_max'] = (idx+1)*1e-4
self.image_ids = []
for img_pars in self.im_params:
image,_,_ = db_subs.insert_image_and_simulated_sources(
self.dataset,img_pars,[],
new_source_sigma_margin=3)
self.image_ids.append(image.id)
def test_one2oneflux(self):
dataset = tkp.db.DataSet(database=self.database, data={'description': 'flux test set: 1-1'})
n_images = 3
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
src_list = []
src = db_subs.example_extractedsource_tuple()
src0 = src._replace(flux=2.0)
src_list.append(src0)
src1 = src._replace(flux=2.5)
src_list.append(src1)
src2 = src._replace(flux=2.4)
src_list.append(src2)
for idx, im in enumerate(im_params):
image = tkp.db.Image(database=self.database, dataset=dataset, data=im)
image.insert_extracted_sources([src_list[idx]])
associate_extracted_sources(image.id, deRuiter_r=3.717)
query = """\
SELECT rf.avg_f_int
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
"""
self.database.cursor.execute(query, {'dataset': dataset.id})
result = zip(*self.database.cursor.fetchall())
avg_f_int = result[0]
self.assertEqual(len(avg_f_int), 1)
py_metrics = db_subs.lightcurve_metrics(src_list)
self.assertAlmostEqual(avg_f_int[0], py_metrics[-1]['avg_f_int'])
runcat_id = columns_from_table('runningcatalog',
where={'dataset':dataset.id})
self.assertEqual(len(runcat_id),1)
runcat_id = runcat_id[0]['id']
# Check evolution of variability indices
db_metrics = db_queries.get_assoc_entries(self.database,
runcat_id)
self.assertEqual(len(db_metrics), n_images)
# Compare the python- and db-calculated values
for i in range(len(db_metrics)):
for key in ('v_int','eta_int'):
self.assertAlmostEqual(db_metrics[i][key], py_metrics[i][key])
def setUp(self):
"""
create a fake transient. Taken from the transient test.
:return:
"""
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(data={'description':
"Augmented Runningcatalog test"},
database=self.database)
self.n_images = 4
self.new_source_sigma_margin = 3
image_rms = 1e-3
detection_thresh = 10
self.search_params = {'eta_min': 1, 'v_min': 0.1}
self.barely_detectable_flux = 1.01 * image_rms * detection_thresh
self.reliably_detectable_flux = 1.01 * image_rms * (detection_thresh +
self.new_source_sigma_margin)
# 1mJy image RMS, 10-sigma detection threshold = 10mJy threshold.
test_specific_img_params = {'rms_qc': image_rms, 'rms_min': image_rms,
'rms_max': image_rms,
'detection_thresh': detection_thresh}
self.im_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
im_params = self.im_params
src_tuple = db_subs.example_extractedsource_tuple(ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],)
transient_src = db_subs.MockSource(
template_extractedsource=src_tuple,
lightcurve={im_params[2]['taustart_ts']:
self.reliably_detectable_flux}
)
for img_pars in im_params:
db_subs.insert_image_and_simulated_sources(self.dataset, img_pars,
[transient_src],
self.new_source_sigma_margin)
def test_two_field_basic_case(self):
"""
Here we create 2 disjoint image fields, with one source at centre of
each, and check that the second source inserted does not get flagged as
newsource.
"""
n_images = 2
xtr_radius = 1.5
im_params = db_subs.generate_timespaced_dbimages_data(n_images,
xtr_radius=xtr_radius)
im_params[1]['centre_decl'] += xtr_radius * 2 + 0.5
imgs = []
for idx in range(len(im_params)):
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[idx]))
for idx in range(len(im_params)):
central_src = db_subs.example_extractedsource_tuple(
ra=im_params[idx]['centre_ra'],
dec=im_params[idx]['centre_decl'])
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[idx]))
imgs[idx].insert_extracted_sources([central_src])
imgs[idx].associate_extracted_sources(deRuiter_r, new_source_sigma_margin)
runcats = columns_from_table('runningcatalog',
where={'dataset':self.dataset.id})
self.assertEqual(len(runcats), 2) #Just a sanity check.
newsources_qry = """\
SELECT *
FROM newsource tr
,runningcatalog rc
WHERE rc.dataset = %s
AND tr.runcat = rc.id
"""
self.database.cursor.execute(newsources_qry, (self.dataset.id,))
newsources = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(newsources), 0)
def test_infinite(self):
# Check that database insertion doesn't choke on infinite beam
# parameters.
test_specific_image_data = {'beam_smaj_pix': float('inf'),
'beam_smin_pix': float('inf'),
'beam_pa_rad': float('inf'),}
image_data_dicts = db_subs.generate_timespaced_dbimages_data(n_images=1,
**test_specific_image_data)
image_data = image_data_dicts[0]
dataset1 = DataSet(data={'description': 'dataset with images'},
database=self.database)
image1 = Image(dataset=dataset1, data=image_data)
bmaj, bmin, bpa = tkp.db.execute("""
SELECT rb_smaj, rb_smin, rb_pa
FROM image
WHERE image.id = %(id)s
""", {"id": image1.id}).fetchone()
self.assertEqual(bmaj, float('inf'))
self.assertEqual(bmin, float('inf'))
self.assertEqual(bpa, float('inf'))
def test_one2one_flux_infinite_error(self):
dataset = tkp.db.DataSet(database=self.database, data={'description': 'flux test set: 1-1'})
n_images = 3
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
src_list = []
src = db_subs.example_extractedsource_tuple()
src0 = src._replace(flux=2.0)
src_list.append(src0)
src1 = src._replace(flux=2.5)
src_list.append(src1)
src2 = src._replace(flux=0.0001, flux_err=float('inf'),
peak=0.0001, peak_err=float('inf'))
src_list.append(src2)
for idx, im in enumerate(im_params):
image = tkp.db.Image(database=self.database, dataset=dataset, data=im)
image.insert_extracted_sources([src_list[idx]])
associate_extracted_sources(image.id, deRuiter_r=3.717)
query = """\
SELECT rf.avg_f_int
,rf.f_datapoints
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
"""
cursor = tkp.db.execute(query, {'dataset': dataset.id})
results = db_subs.get_db_rows_as_dicts(cursor)
self.assertEqual(len(results),1)
self.assertEqual(results[0]['f_datapoints'],2)
self.assertAlmostEqual(results[0]['avg_f_int'],
(src0.flux + src1.flux)/2.0 )
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(
data={'description':"Trans:" +self._testMethodName},
database=self.database)
self.n_images = 2
self.rms_min_initial = 2e-3 #2mJy
self.rms_max_initial = 5e-3 #5mJy
self.new_source_sigma_margin = 3
self.detection_thresh=10
dt=self.detection_thresh
margin = self.new_source_sigma_margin
#These all refer to the first image, they should all be clearly
#detected in the second image:
self.barely_detectable_flux = 1.01*self.rms_min_initial*dt
self.reliably_detected_at_image_centre_flux = (
1.01*self.rms_min_initial*(dt+ margin))
self.always_detectable_flux = 1.01*self.rms_max_initial*(dt+ margin)
test_specific_img_params = dict(rms_qc = self.rms_min_initial,
rms_min = self.rms_min_initial,
rms_max = self.rms_max_initial,
detection_thresh = self.detection_thresh)
self.img_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
#Drop RMS to 1 mJy / 2.5mJy in second image.
rms_decrease_factor = 0.5
self.img_params[1]['rms_qc']*=rms_decrease_factor
self.img_params[1]['rms_min']*=rms_decrease_factor
self.img_params[1]['rms_max']*=rms_decrease_factor
def test_lightcurve(self):
# make 4 images with different date
images = []
image_datasets = db_subs.generate_timespaced_dbimages_data(
n_images=4, taustart_ts=datetime.datetime(2010, 3, 3))
for dset in image_datasets:
image = Image(dataset=self.dataset, data=dset)
images.append(image)
# 3 sources per image, with different coordinates & flux
data_list = []
for i in range(1, 4):
data_list.append({
'ra': 111.11 + i,
'decl': 11.11 + i,
'i_peak': 10. * i,
'i_peak_err': 0.1,
})
# Insert the 3 sources in each image, while further varying the flux
lightcurves_sorted_by_ra = [[], [], []]
for im_idx, image in enumerate(images):
# Create the "source finding results"
# Note that we reuse 'i_peak' as both peak & integrated flux.
img_sources = []
for src_idx, data in enumerate(data_list):
src = db_subs.example_extractedsource_tuple(
ra=data['ra'],
dec=data['decl'],
peak=data['i_peak'] * (1 + im_idx),
flux=data['i_peak'] * (1 + im_idx))
lightcurves_sorted_by_ra[src_idx].append(src)
img_sources.append(src)
image.insert_extracted_sources(img_sources)
image.associate_extracted_sources(deRuiter_r=3.7,
new_source_sigma_margin=3)
# updates the dataset and its set of images
self.dataset.update()
self.dataset.update_images()
# update the images and their sets of sources
for image in self.dataset.images:
image.update()
image.update_sources()
# Now pick last image, select the first source (smallest RA)
# and extract its light curve
sources = self.dataset.images[-1].sources
sources = sorted(sources, key=attrgetter('ra'))
lightcurve = sources[0].lightcurve()
# check if the sources are associated in all images
self.assertEqual(len(images), len(lightcurve))
self.assertEqual(lightcurve[0][0], datetime.datetime(2010, 3, 3, 0, 0))
self.assertEqual(lightcurve[1][0], datetime.datetime(2010, 3, 4, 0, 0))
self.assertEqual(lightcurve[2][0], datetime.datetime(2010, 3, 5, 0, 0))
self.assertEqual(lightcurve[3][0], datetime.datetime(2010, 3, 6, 0, 0))
self.assertAlmostEqual(lightcurve[0][2], 10.)
self.assertAlmostEqual(lightcurve[1][2], 20.)
self.assertAlmostEqual(lightcurve[2][2], 30.)
self.assertAlmostEqual(lightcurve[3][2], 40.)
#Check the summary statistics (avg flux, etc)
query = """\
SELECT rf.avg_f_int
,rf.avg_f_int_sq
,avg_weighted_f_int
,avg_f_int_weight
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
ORDER BY r.wm_ra
"""
self.database.cursor.execute(query, {'dataset': self.dataset.id})
runcat_flux_entries = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(runcat_flux_entries),
len(lightcurves_sorted_by_ra))
for idx, flux_summary in enumerate(runcat_flux_entries):
py_results = db_subs.lightcurve_metrics(
lightcurves_sorted_by_ra[idx])
for key in flux_summary.keys():
self.assertAlmostEqual(flux_summary[key], py_results[-1][key])
#Now check the per-timestep statistics (variability indices)
sorted_runcat_ids = columns_from_table(
'runningcatalog',
where={'dataset': self.dataset.id},
order='wm_ra')
sorted_runcat_ids = [entry['id'] for entry in sorted_runcat_ids]
for idx, rcid in enumerate(sorted_runcat_ids):
db_indices = db_queries.get_assoc_entries(self.database, rcid)
py_indices = db_subs.lightcurve_metrics(
lightcurves_sorted_by_ra[idx])
self.assertEqual(len(db_indices), len(py_indices))
for nstep in range(len(db_indices)):
for key in ('v_int', 'eta_int', 'f_datapoints'):
self.assertAlmostEqual(db_indices[nstep][key],
py_indices[nstep][key],
places=5)
def setUp(self):
data = {'description': "monitoringlist:" + self._testMethodName}
self.dataset = DataSet(data=data)
self.im_params = db_subs.generate_timespaced_dbimages_data(n_images=3)
def setUp(self):
self.dataset = DataSet(
data={'description': "Src. assoc:" + self._testMethodName})
self.im_params = db_subs.generate_timespaced_dbimages_data(n_images=8)
self.db_imgs = []
def test_two_field_overlap_new_transient(self):
"""Now for something more interesting - two overlapping fields, 4 sources:
one steady source only in lower field,
one steady source in both fields,
one steady source only in upper field,
one transient source in both fields but only at 2nd timestep.
"""
n_images = 2
xtr_radius = 1.5
im_params = db_subs.generate_timespaced_dbimages_data(
n_images, xtr_radius=xtr_radius)
im_params[1]['centre_decl'] += xtr_radius * 1
imgs = []
lower_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] - 0.5 * xtr_radius)
upper_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[1]['centre_ra'],
dec=im_params[1]['centre_decl'] + 0.5 * xtr_radius)
overlap_steady_src = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] + 0.2 * xtr_radius)
overlap_transient = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'] + 0.8 * xtr_radius)
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[0]))
imgs.append(tkp.db.Image(dataset=self.dataset, data=im_params[1]))
insert_extracted_sources(imgs[0]._id,
[lower_steady_src, overlap_steady_src])
associate_extracted_sources(
imgs[0]._id,
deRuiter_r=0.1,
new_source_sigma_margin=new_source_sigma_margin)
nd_posns = dbnd.get_nulldetections(imgs[0].id)
self.assertEqual(len(nd_posns), 0)
insert_extracted_sources(
imgs[1]._id,
[upper_steady_src, overlap_steady_src, overlap_transient])
associate_extracted_sources(
imgs[1]._id,
deRuiter_r=0.1,
new_source_sigma_margin=new_source_sigma_margin)
nd_posns = dbnd.get_nulldetections(imgs[1].id)
self.assertEqual(len(nd_posns), 0)
runcats = columns_from_table('runningcatalog',
where={'dataset': self.dataset.id})
self.assertEqual(len(runcats), 4) #sanity check.
newsources_qry = """\
SELECT *
FROM newsource tr
,runningcatalog rc
WHERE rc.dataset = %s
AND tr.runcat = rc.id
"""
self.database.cursor.execute(newsources_qry, (self.dataset.id, ))
newsources = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(newsources), 1)
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(
data={'description':"Trans:" +self._testMethodName},
database=self.database)
self.n_images = 8
self.image_rms = 1e-3 # 1mJy
self.new_source_sigma_margin = 3
self.search_params = dict(eta_min=1,
v_min=0.1,
# minpoints=1,
)
detection_thresh=10
barely_detectable_flux = 1.01*self.image_rms*(detection_thresh)
reliably_detectable_flux = (
1.01*self.image_rms*(detection_thresh+self.new_source_sigma_margin))
test_specific_img_params = dict(rms_qc =self.image_rms,
rms_min = self.image_rms,
rms_max = self.image_rms,
detection_thresh = detection_thresh)
self.img_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
imgs=self.img_params
first_img = imgs[0]
centre_ra = first_img['centre_ra']
centre_decl = first_img['centre_decl']
xtr_radius = first_img['xtr_radius']
#At centre
fixed_source = MockSource(
example_extractedsource_tuple(ra=centre_ra, dec=centre_decl),
lightcurve=defaultdict(lambda: barely_detectable_flux))
#How many transients should we know about after each image?
self.n_transients_after_image = defaultdict(lambda:0)
self.n_newsources_after_image = defaultdict(lambda:0)
#shifted to +ve RA
bright_fast_transient = MockSource(
example_extractedsource_tuple(ra=centre_ra + xtr_radius * 0.5,
dec=centre_decl),
lightcurve={imgs[3]['taustart_ts']: reliably_detectable_flux}
)
#Detect immediately
for img_idx in range(3,self.n_images):
self.n_newsources_after_image[img_idx]+=1
#But only variable after non-detection
for img_idx in range(4,self.n_images):
self.n_transients_after_image[img_idx]+=1
# shifted to -ve RA
weak_fast_transient = MockSource(
example_extractedsource_tuple(ra=centre_ra - xtr_radius * 0.5,
dec=centre_decl),
lightcurve={imgs[3]['taustart_ts']: barely_detectable_flux}
)
# Not flagged as a newsource, could just be a weakly detected
# steady-source at first.
# But, shows high-variance after forced-fit in image[4]
for img_idx in range(4,self.n_images):
self.n_transients_after_image[img_idx]+=1
# shifted to +ve Dec
weak_slow_transient = MockSource(
example_extractedsource_tuple(ra=centre_ra,
dec=centre_decl + xtr_radius * 0.5),
lightcurve={imgs[5]['taustart_ts']: barely_detectable_flux,
imgs[6]['taustart_ts']: barely_detectable_flux*0.95}
)
# Not flagged as a newsource, could just be a weakly detected
# steady-source at first.
# Should not be flagged as transient until forced-fit in image[7]
for img_idx in range(7,self.n_images):
self.n_transients_after_image[img_idx]+=1
self.all_mock_sources = [fixed_source, weak_slow_transient,
bright_fast_transient, weak_fast_transient]
def test_m2m_nullDetection(self):
"""
This tests that two sources (close-by to be associated if they were
detected at different timesteps) which are not seen in the next
image and thus have forced fits, will have separate light curves.
The postions are from the previous test.
"""
data = {'description': "null detection:" + self._testMethodName}
dataset = DataSet(data=data)
# Three timesteps, just 1 band -> 3 images.
taustart_tss = [datetime.datetime(2013, 8, 1),
datetime.datetime(2013, 9, 1),
datetime.datetime(2013, 10, 1)]
freq_effs = [124]
freq_effs = [f * 1e6 for f in freq_effs]
im_params = db_subs.generate_timespaced_dbimages_data(len(freq_effs)
* len(taustart_tss))
timestamps = itertools.repeat(taustart_tss, len(freq_effs))
for im, freq, ts in zip(im_params, itertools.cycle(freq_effs),
itertools.chain.from_iterable(zip(*timestamps))):
im['freq_eff'] = freq
im['taustart_ts'] = ts
images = []
for im in im_params:
image = tkp.db.Image(dataset=dataset, data=im)
images.append(image)
# Arbitrary parameters, except that they fall inside our image
# and close together (see previous test)
src0 = db_subs.example_extractedsource_tuple(ra=122.985, dec=10.5)
src1 = db_subs.example_extractedsource_tuple(ra=123.015, dec=10.5)
# Group images in blocks of 4, corresponding to all frequency bands at
# a given timestep.
for images in zip(*(iter(images),) * len(freq_effs)):
for image in images:
# The sources are only seen at timestep 0
if (image.taustart_ts == taustart_tss[0]):
dbgen.insert_extracted_sources(image.id, [src0,src1],
'blind')
else:
pass
for image in images:
dbass.associate_extracted_sources(image.id, deRuiter_r=5.68,
new_source_sigma_margin=3)
nd_ids_pos = dbnd.get_nulldetections(image.id)
# The null_detections are the positional inputs for the forced
# fits, which on their turn return additional parameters,
# e.g. from src0, src1
if image.taustart_ts == taustart_tss[0]:
# There are no null detections at the first timestep
self.assertEqual(len(nd_ids_pos), 0)
elif image.taustart_ts == taustart_tss[1]:
# src0 & src1 are null detections at the second timestep
self.assertEqual(len(nd_ids_pos), 2)
dbgen.insert_extracted_sources(image.id, [src0,src1],
'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
else:
# All other images have two null detections.
self.assertEqual(len(nd_ids_pos), 2)
dbgen.insert_extracted_sources(image.id, [src0, src1],
'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
# And here we have to associate the null detections with the
# runcat sources...
dbnd.associate_nd(image.id)
query = """\
SELECT id
,datapoints
FROM runningcatalog r
WHERE dataset = %(dataset_id)s
ORDER BY datapoints
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have two runningcatalog sources, with a datapoint for
# every image in which the sources were seen.
self.assertEqual(len(result), 2)
query = """\
SELECT r.id
,rf.band
,rf.f_datapoints
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset_id)s
AND rf.runcat = r.id
ORDER BY r.id
,rf.band
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have two runningcatalog_flux entries,
# one for every source in the band, i.e. 2 x 1.
self.assertEqual(len(result), 2)
# Source 0: inserted into timestep 0.
# Force-fits in images at next timesteps,
# so 1+2 for band 0.
self.assertEqual(result[0][2], 3)
# Source 1: inserted into timestep 0
# Force-fits in images at next timesteps.
# so 1+2 for bands 0
self.assertEqual(result[1][2], 3)
#self.assertEqual(result[2][2], 2)
#self.assertEqual(result[3][2], 2)
# We should also have two lightcurves for both sources,
# where source 1 has 3 datapoints in band0 (t1,t2,t3).
# Source 2 also has 3 datapoints for band0 (t1,t2,t3).
query = """\
SELECT a.runcat
,a.xtrsrc
,a.type
,i.band
,i.taustart_ts
FROM assocxtrsource a
,extractedsource x
,image i
WHERE a.xtrsrc = x.id
AND x.image = i.id
AND i.dataset = %(dataset_id)s
ORDER BY a.runcat
,i.band
,i.taustart_ts
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# 3 + 3 entries for source 0 and 1 resp.
self.assertEqual(len(result), 6)
# The individual light-curve datapoints
# Source1: new at t1, band0
self.assertEqual(result[0][2], 4)
self.assertEqual(result[0][4], taustart_tss[0])
# Source1: Forced fit at t2, same band
self.assertEqual(result[1][2], 7)
self.assertEqual(result[1][3], result[0][3])
self.assertEqual(result[1][4], taustart_tss[1])
# Source1: Forced fit at t3, same band
self.assertEqual(result[2][2], 7)
self.assertEqual(result[2][3], result[1][3])
self.assertEqual(result[2][4], taustart_tss[2])
# Source2: new at t1, band0
self.assertEqual(result[3][2], 4)
self.assertEqual(result[3][3], result[1][3])
self.assertEqual(result[3][4], taustart_tss[0])
# Source2: Forced fit at t2, band0
self.assertEqual(result[4][2], 7)
self.assertEqual(result[4][3], result[3][3])
self.assertEqual(result[4][4], taustart_tss[1])
# Source2: Forced fit at t3, band0
self.assertEqual(result[5][2], 7)
self.assertEqual(result[5][3], result[4][3])
self.assertEqual(result[5][4], taustart_tss[2])
def setUp(self):
data = {'description': "monitoringlist:" + self._testMethodName}
self.dataset = DataSet(data=data)
self.im_params = db_subs.generate_timespaced_dbimages_data(n_images=3)
def test_1to1_nullDetection(self):
"""
This tests that the two sources are associated if they were
detected at different timesteps. The positions are used in
the next test as well.
"""
data = {'description': "null detection:" + self._testMethodName}
dataset = DataSet(data=data)
# Two timesteps, just 1 band -> 2 images.
taustart_tss = [
datetime.datetime(2013, 8, 1),
datetime.datetime(2013, 9, 1)
]
freq_effs = [124]
freq_effs = [f * 1e6 for f in freq_effs]
im_params = db_subs.generate_timespaced_dbimages_data(
len(freq_effs) * len(taustart_tss))
timestamps = itertools.repeat(taustart_tss, len(freq_effs))
for im, freq, ts in zip(
im_params, itertools.cycle(freq_effs),
itertools.chain.from_iterable(zip(*timestamps))):
im['freq_eff'] = freq
im['taustart_ts'] = ts
images = []
for im in im_params:
image = tkp.db.Image(dataset=dataset, data=im)
images.append(image)
# Arbitrary parameters, except that they fall inside our image
# and close together (see next test)
src0 = db_subs.example_extractedsource_tuple(ra=122.985, dec=10.5)
src1 = db_subs.example_extractedsource_tuple(ra=123.015, dec=10.5)
# Group images in blocks of 4, corresponding to all frequency bands at
# a given timestep.
for images in zip(*(iter(images), ) * len(freq_effs)):
for image in images:
# The sources are only seen at timestep 0
if (image.taustart_ts == taustart_tss[0]):
dbgen.insert_extracted_sources(image.id, [src0], 'blind')
elif (image.taustart_ts == taustart_tss[1]):
dbgen.insert_extracted_sources(image.id, [src1], 'blind')
else:
pass
for image in images:
dbass.associate_extracted_sources(image.id,
deRuiter_r=5.68,
new_source_sigma_margin=3)
query = """\
SELECT id
,datapoints
FROM runningcatalog r
WHERE dataset = %(dataset_id)s
ORDER BY datapoints
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have one runningcatalog sources, with two datapoints
# for the images in which the sources were seen.
self.assertEqual(len(result), 1)
self.assertEqual(result[0][1], 2)
query = """\
SELECT r.id
,rf.band
,rf.f_datapoints
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset_id)s
AND rf.runcat = r.id
ORDER BY r.id
,rf.band
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have one runningcatalog_flux entry,
# where the source has two flux datapoints
self.assertEqual(len(result), 1)
self.assertEqual(result[0][2], 2)
def test_nullDetection(self):
data = {'description': "null detection:" + self._testMethodName}
dataset = DataSet(data=data)
# Three timesteps, each with 4 bands -> 12 images.
taustart_tss = [datetime.datetime(2013, 8, 1),
datetime.datetime(2013, 9, 1),
datetime.datetime(2013, 10, 1)]
freq_effs = [124, 149, 156, 185]
freq_effs = [f * 1e6 for f in freq_effs]
im_params = db_subs.generate_timespaced_dbimages_data(len(freq_effs)
* len(taustart_tss))
timestamps = itertools.repeat(taustart_tss, len(freq_effs))
for im, freq, ts in zip(im_params, itertools.cycle(freq_effs),
itertools.chain.from_iterable(zip(*timestamps))):
im['freq_eff'] = freq
im['taustart_ts'] = ts
images = []
for im in im_params:
image = tkp.db.Image(dataset=dataset, data=im)
images.append(image)
# Arbitrary parameters, except that they fall inside our image.
src0 = db_subs.example_extractedsource_tuple(ra=122.5, dec=9.5)
src1 = db_subs.example_extractedsource_tuple(ra=123.5, dec=10.5)
# Group images in blocks of 4, corresponding to all frequency bands at
# a given timestep.
for images in zip(*(iter(images),) * len(freq_effs)):
for image in images:
# The first source is only seen at timestep 0, band 0.
# The second source is only seen at timestep 1, band 3.
if (image.taustart_ts == taustart_tss[0] and
image.freq_eff == freq_effs[0]):
dbgen.insert_extracted_sources(image.id, [src0], 'blind')
elif (image.taustart_ts == taustart_tss[1] and
image.freq_eff == freq_effs[3]):
dbgen.insert_extracted_sources(image.id, [src1], 'blind')
else:
pass
for image in images:
dbass.associate_extracted_sources(image.id, deRuiter_r=5.68,
new_source_sigma_margin=3)
nd_ids_pos = dbnd.get_nulldetections(image.id)
# The null_detections are the positional inputs for the forced
# fits, which on their turn return additional parameters,
# e.g. from src0, src1
if image.taustart_ts == taustart_tss[0]:
# There are no null detections at the first timestep
self.assertEqual(len(nd_ids_pos), 0)
elif image.taustart_ts == taustart_tss[1]:
# src0 is a null detection at the second timestep
self.assertEqual(len(nd_ids_pos), 1)
dbgen.insert_extracted_sources(image.id, [src0], 'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
else:
# All other images have two null detections.
self.assertEqual(len(nd_ids_pos), 2)
dbgen.insert_extracted_sources(image.id, [src0, src1],
'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
# And here we have to associate the null detections with the
# runcat sources...
dbnd.associate_nd(image.id)
query = """\
SELECT id
,datapoints
FROM runningcatalog r
WHERE dataset = %(dataset_id)s
ORDER BY datapoints
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have two runningcatalog sources, with a datapoint for
# every image in which the sources were seen.
self.assertEqual(len(result), 2)
query = """\
SELECT r.id
,rf.band
,rf.f_datapoints
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset_id)s
AND rf.runcat = r.id
ORDER BY r.id
,rf.band
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have eight runningcatalog_flux entries,
# one for every source in every band, i.e. 2 x 4.
# The number of flux datapoints differ per source, though
self.assertEqual(len(result), 8)
# Source 1: inserted into timestep 0, band 0.
# Force-fits in band 0 images at next timesteps,
# so 1+2 for band 0.
self.assertEqual(result[0][2], 3)
# Source 1: inserted into timestep 0, band 0.
# Force-fits in bands 1,2,3 images at next timesteps.
# so 0+2 for bands 1,2,3.
self.assertEqual(result[1][2], 2)
self.assertEqual(result[2][2], 2)
self.assertEqual(result[3][2], 2)
# Source 2: inserted into timestep 1, band 3.
# Force-fits in band 0,1,2 images at next timestep,
# so 1 for band 0,1,2
self.assertEqual(result[4][2], 1)
self.assertEqual(result[5][2], 1)
self.assertEqual(result[6][2], 1)
# Source 2: inserted into timestep 1, band 3.
# Force-fit in band 3 image at next timestep,
# so 1+1 for band 3
self.assertEqual(result[7][2], 2)
# We should also have two lightcurves for both sources,
# where source 1 has 3 datapoints in band0 (t1,t2,t3)
# and 2 datapoints for the other three bands (t2,t3).
# Source 2 has two datapoints for band3 (t2,t3) and
# one for the other three bands (t3).
query = """\
SELECT a.runcat
,a.xtrsrc
,a.type
,i.band
,i.taustart_ts
FROM assocxtrsource a
,extractedsource x
,image i
WHERE a.xtrsrc = x.id
AND x.image = i.id
AND i.dataset = %(dataset_id)s
ORDER BY a.runcat
,i.band
,i.taustart_ts
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# 9 + 5 entries for source 1 and 2 resp.
self.assertEqual(len(result), 14)
# The individual light-curve datapoints
# Source1: new at t1, band0
self.assertEqual(result[0][2], 4)
self.assertEqual(result[0][4], taustart_tss[0])
# Source1: Forced fit at t2, same band
self.assertEqual(result[1][2], 7)
self.assertEqual(result[1][3], result[0][3])
self.assertEqual(result[1][4], taustart_tss[1])
# Source1: Forced fit at t3, same band
self.assertEqual(result[2][2], 7)
self.assertEqual(result[2][3], result[1][3])
self.assertEqual(result[2][4], taustart_tss[2])
# Source1: Forced fit at t2, band1
self.assertEqual(result[3][2], 7)
self.assertTrue(result[3][3] > result[2][3])
self.assertEqual(result[3][4], taustart_tss[1])
# Source1: Forced fit at t3, band1
self.assertEqual(result[4][2], 7)
self.assertEqual(result[4][3], result[3][3])
self.assertEqual(result[4][4], taustart_tss[2])
# Source1: Forced fit at t2, band2
self.assertEqual(result[5][2], 7)
self.assertTrue(result[5][3] > result[4][3])
self.assertEqual(result[5][4], taustart_tss[1])
# Source1: Forced fit at t3, band2
self.assertEqual(result[6][2], 7)
self.assertEqual(result[6][3], result[5][3])
self.assertEqual(result[6][4], taustart_tss[2])
# Source1: Forced fit at t2, band3
self.assertEqual(result[7][2], 7)
self.assertTrue(result[7][3] > result[6][3])
self.assertEqual(result[7][4], taustart_tss[1])
# Source1: Forced fit at t3, band3
self.assertEqual(result[8][2], 7)
self.assertEqual(result[8][3], result[7][3])
self.assertEqual(result[8][4], taustart_tss[2])
# Source2: Forced fit at t3, band0
self.assertEqual(result[9][2], 7)
self.assertEqual(result[9][3], result[0][3])
self.assertEqual(result[9][4], taustart_tss[2])
# Source2: Forced fit at t3, band1
self.assertEqual(result[10][2], 7)
self.assertTrue(result[10][3] > result[9][3])
self.assertEqual(result[10][4], taustart_tss[2])
# Source2: Forced fit at t3, band2
self.assertEqual(result[11][2], 7)
self.assertTrue(result[11][3] > result[10][3])
self.assertEqual(result[11][4], taustart_tss[2])
# Source2: new at t2, band3
self.assertEqual(result[12][2], 4)
self.assertTrue(result[12][3] > result[11][3])
self.assertEqual(result[12][4], taustart_tss[1])
# Source2: Forced fit at t3, band3
self.assertEqual(result[13][2], 7)
self.assertEqual(result[13][3], result[12][3])
self.assertEqual(result[13][4], taustart_tss[2])
def test_new_runcat_insertion(self):
"""Here we test the association logic executed upon insertion of a
new runningcatalog source.
We add an empty image0, then proceed to image1,
which is partially overlapping.
We add one new overlapping source, and one source only in image1's skyrgn.
Then we check that the back-associations to image0 are correct.
"""
n_images = 6
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
#We first create 2 overlapping images,
#one above the other in dec by 1.0*xtr_radius
idx = 0
image0 = tkp.db.Image(dataset=self.dataset, data=im_params[idx])
image0.update()
#Bump up the centre of img1 to higher declination
im_params[1]['centre_decl'] += im_params[1]['xtr_radius']
#We place one source half-way between the field centres (i.e. in both)
src_in_imgs_0_1 = db_subs.example_extractedsource_tuple(
ra=im_params[1]['centre_ra'],
dec=im_params[1]['centre_decl'] - im_params[1]['xtr_radius'] * 0.5)
#And one source only in field 1
src_in_img_1_only = db_subs.example_extractedsource_tuple(
ra=im_params[1]['centre_ra'],
dec=im_params[1]['centre_decl'] + im_params[1]['xtr_radius'] * 0.5)
# First insert new sources in img1 and check association to parent field
# This is always asserted without calculation, for efficiency
image1 = tkp.db.Image(dataset=self.dataset, data=im_params[1])
insert_extracted_sources(image1._id,
[src_in_imgs_0_1, src_in_img_1_only])
associate_extracted_sources(image1._id, deRuiter_r,
new_source_sigma_margin)
image1.update()
runcats = columns_from_table('runningcatalog',
where={'dataset': self.dataset.id})
#We now expect to see both runcat entries in the field of im1
im1_assocs = columns_from_table(
'assocskyrgn', where={'skyrgn': image1._data['skyrgn']})
self.assertEqual(len(im1_assocs), 2)
runcat_ids = [r['id'] for r in runcats]
for assoc in im1_assocs:
self.assertTrue(assoc['runcat'] in runcat_ids)
#The new sources are *also checked against previous regions*
#Only expect one in field of im0 ( the first source).
im0_assocs = columns_from_table(
'assocskyrgn', where={'skyrgn': image0._data['skyrgn']})
runcats_only_in_im0 = columns_from_table('runningcatalog',
where={
'dataset':
self.dataset.id,
'wm_decl': 15
})
self.assertEqual(len(im0_assocs), 1)
self.assertEqual(len(runcats_only_in_im0), 1)
self.assertEqual(im0_assocs[0]['runcat'], runcats_only_in_im0[0]['id'])
def test_one2manyflux(self):
dataset = tkp.db.DataSet(database=self.database,
data={'description': 'flux test set: 1-n'})
n_images = 2
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
central_ra, central_dec = 123.1235, 10.55,
position_offset_deg = 100./3600 #100 arcsec = 0.03 deg approx
# image 1
image = tkp.db.Image(database=self.database, dataset=dataset, data=im_params[0])
imageid1 = image.id
img1_srclist = []
# 1 source
img1_srclist.append(db_subs.example_extractedsource_tuple(central_ra, central_dec,
peak = 1.5, peak_err = 5e-1,
flux = 3.0, flux_err = 5e-1,
))
dbgen.insert_extracted_sources(imageid1, img1_srclist, 'blind')
associate_extracted_sources(imageid1, deRuiter_r=3.717)
# image 2
image = tkp.db.Image(database=self.database, dataset=dataset, data=im_params[1])
imageid2 = image.id
img2_srclist = []
# 2 sources (both close to source 1, catching the 1-to-many case)
img2_srclist.append(db_subs.example_extractedsource_tuple(
central_ra,
central_dec,
peak = 1.6, peak_err = 5e-1,
flux = 3.2, flux_err = 5e-1,
))
img2_srclist.append(db_subs.example_extractedsource_tuple(
central_ra + position_offset_deg,
central_dec,
peak = 1.9, peak_err = 5e-1,
flux = 3.4, flux_err = 5e-1,
))
dbgen.insert_extracted_sources(imageid2, img2_srclist, 'blind')
associate_extracted_sources(imageid2, deRuiter_r=3.717)
# Manually compose the lists of sources we expect to see associated
# into runningcatalog entries:
# NB img2_srclist[1] has larger RA value.
lightcurves_sorted_by_ra =[]
lightcurves_sorted_by_ra.append( [img1_srclist[0], img2_srclist[0]])
lightcurves_sorted_by_ra.append( [img1_srclist[0], img2_srclist[1]])
#Check the summary statistics (avg flux, etc)
query = """\
SELECT rf.avg_f_int
,rf.avg_f_int_sq
,avg_weighted_f_int
,avg_f_int_weight
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
ORDER BY r.wm_ra
"""
self.database.cursor.execute(query, {'dataset': dataset.id})
runcat_flux_entries = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(runcat_flux_entries), 2)
for idx, flux_summary in enumerate(runcat_flux_entries):
py_results = db_subs.lightcurve_metrics(lightcurves_sorted_by_ra[idx])
for key in flux_summary.keys():
self.assertAlmostEqual(flux_summary[key], py_results[-1][key])
#Now check the per-timestep statistics (variability indices)
sorted_runcat_ids = columns_from_table('runningcatalog',
where={'dataset':dataset.id},
order='wm_ra')
sorted_runcat_ids = [entry['id'] for entry in sorted_runcat_ids]
for idx, rcid in enumerate(sorted_runcat_ids):
db_indices = db_queries.get_assoc_entries(self.database,
rcid)
py_indices = db_subs.lightcurve_metrics(lightcurves_sorted_by_ra[idx])
self.assertEqual(len(db_indices), len(py_indices))
for nstep in range(len(db_indices)):
for key in ('v_int', 'eta_int', 'f_datapoints'):
self.assertAlmostEqual(db_indices[nstep][key],
py_indices[nstep][key])
def test_many2manyflux_reduced_to_two_1to1(self):
"""
(See also assoc. test test_many2many_reduced_to_two_1to1 )
In this test-case we cross-associate between a rhombus of sources spread
about a central position, east-west in the first image,
north-south in the second.
The latter, north-south pair are slightly offset towards positive RA
and negative RA respectively.
The result is that the candidate associations are pruned down to
two one-to-one pairings..
"""
dataset = tkp.db.DataSet(database=self.database, data={'description': 'flux test set: n-m, ' + self._testMethodName})
n_images = 2
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
centre_ra, centre_dec = 123., 10.5,
offset_deg = 20 / 3600. #20 arcsec
tiny_offset_deg = 1 / 3600. #1 arcsec
eastern_src = db_subs.example_extractedsource_tuple(
ra=centre_ra + offset_deg,
dec=centre_dec,
peak = 1.5, peak_err = 1e-1,
flux = 3.0, flux_err = 1e-1,)
western_src = db_subs.example_extractedsource_tuple(
ra=centre_ra - offset_deg,
dec=centre_dec,
peak = 1.7, peak_err = 1e-1,
flux = 3.2, flux_err = 1e-1,)
northern_source = db_subs.example_extractedsource_tuple(
ra=centre_ra + tiny_offset_deg,
dec=centre_dec + offset_deg,
peak = 1.8, peak_err = 1e-1,
flux = 3.3, flux_err = 1e-1,
)
southern_source = db_subs.example_extractedsource_tuple(
ra=centre_ra - tiny_offset_deg,
dec=centre_dec - offset_deg,
peak = 1.4, peak_err = 1e-1,
flux = 2.9, flux_err = 1e-1,)
# image 1
image1 = tkp.db.Image(database=self.database, dataset=dataset,
data=im_params[0])
dbgen.insert_extracted_sources(
image1.id, [eastern_src,western_src], 'blind')
associate_extracted_sources(image1.id, deRuiter_r = 3.717)
# image 2
image2 = tkp.db.Image(database=self.database, dataset=dataset,
data=im_params[1])
dbgen.insert_extracted_sources(
image2.id, [northern_source, southern_source], 'blind')
associate_extracted_sources(image2.id, deRuiter_r = 3.717)
# Manually compose the lists of sources we expect to see associated
# into runningcatalog entries:
# NB img1_srclist[1] has larger RA value.
lightcurves_sorted_by_ra =[]
lightcurves_sorted_by_ra.append( [western_src, southern_source])
lightcurves_sorted_by_ra.append( [eastern_src, northern_source])
#Check the summary statistics (avg flux, etc)
query = """\
SELECT rf.avg_f_int
,rf.avg_f_int_sq
,avg_weighted_f_int
,avg_f_int_weight
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
ORDER BY r.wm_ra, r.wm_decl
"""
self.database.cursor.execute(query, {'dataset': dataset.id})
runcat_flux_entries = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(runcat_flux_entries), len(lightcurves_sorted_by_ra))
for idx, flux_summary in enumerate(runcat_flux_entries):
py_results = db_subs.lightcurve_metrics(lightcurves_sorted_by_ra[idx])
for key in flux_summary.keys():
self.assertAlmostEqual(flux_summary[key], py_results[-1][key])
#Now check the per-timestep statistics (variability indices)
sorted_runcat_ids = columns_from_table('runningcatalog',
where={'dataset':dataset.id},
order='wm_ra,wm_decl')
sorted_runcat_ids = [entry['id'] for entry in sorted_runcat_ids]
for idx, rcid in enumerate(sorted_runcat_ids):
db_indices = db_queries.get_assoc_entries(self.database,
rcid)
py_indices = db_subs.lightcurve_metrics(lightcurves_sorted_by_ra[idx])
self.assertEqual(len(db_indices), len(py_indices))
for nstep in range(len(db_indices)):
for key in ('v_int', 'eta_int', 'f_datapoints'):
self.assertAlmostEqual(db_indices[nstep][key],
py_indices[nstep][key])
def test_nullDetection(self):
data = {'description': "null detection:" + self._testMethodName}
dataset = DataSet(data=data)
# Three timesteps, each with 4 bands -> 12 images.
taustart_tss = [
datetime.datetime(2013, 8, 1),
datetime.datetime(2013, 9, 1),
datetime.datetime(2013, 10, 1)
]
freq_effs = [124, 149, 156, 185]
freq_effs = [f * 1e6 for f in freq_effs]
im_params = db_subs.generate_timespaced_dbimages_data(
len(freq_effs) * len(taustart_tss))
timestamps = itertools.repeat(taustart_tss, len(freq_effs))
for im, freq, ts in zip(
im_params, itertools.cycle(freq_effs),
itertools.chain.from_iterable(zip(*timestamps))):
im['freq_eff'] = freq
im['taustart_ts'] = ts
images = []
for im in im_params:
image = tkp.db.Image(dataset=dataset, data=im)
images.append(image)
# Arbitrary parameters, except that they fall inside our image.
src0 = db_subs.example_extractedsource_tuple(ra=122.5, dec=9.5)
src1 = db_subs.example_extractedsource_tuple(ra=123.5, dec=10.5)
# Group images in blocks of 4, corresponding to all frequency bands at
# a given timestep.
for images in zip(*(iter(images), ) * len(freq_effs)):
for image in images:
# The first source is only seen at timestep 0, band 0.
# The second source is only seen at timestep 1, band 3.
if (image.taustart_ts == taustart_tss[0]
and image.freq_eff == freq_effs[0]):
dbgen.insert_extracted_sources(image.id, [src0], 'blind')
elif (image.taustart_ts == taustart_tss[1]
and image.freq_eff == freq_effs[3]):
dbgen.insert_extracted_sources(image.id, [src1], 'blind')
else:
pass
for image in images:
dbass.associate_extracted_sources(image.id,
deRuiter_r=5.68,
new_source_sigma_margin=3)
nd_ids_pos = dbnd.get_nulldetections(image.id)
# The null_detections are the positional inputs for the forced
# fits, which on their turn return additional parameters,
# e.g. from src0, src1
if image.taustart_ts == taustart_tss[0]:
# There are no null detections at the first timestep
self.assertEqual(len(nd_ids_pos), 0)
elif image.taustart_ts == taustart_tss[1]:
# src0 is a null detection at the second timestep
self.assertEqual(len(nd_ids_pos), 1)
dbgen.insert_extracted_sources(
image.id, [src0],
'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
else:
# All other images have two null detections.
self.assertEqual(len(nd_ids_pos), 2)
dbgen.insert_extracted_sources(
image.id, [src0, src1],
'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
# And here we have to associate the null detections with the
# runcat sources...
dbnd.associate_nd(image.id)
query = """\
SELECT id
,datapoints
FROM runningcatalog r
WHERE dataset = %(dataset_id)s
ORDER BY datapoints
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have two runningcatalog sources, with a datapoint for
# every image in which the sources were seen.
self.assertEqual(len(result), 2)
query = """\
SELECT r.id
,rf.band
,rf.f_datapoints
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset_id)s
AND rf.runcat = r.id
ORDER BY r.id
,rf.band
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have eight runningcatalog_flux entries,
# one for every source in every band, i.e. 2 x 4.
# The number of flux datapoints differ per source, though
self.assertEqual(len(result), 8)
# Source 1: inserted into timestep 0, band 0.
# Force-fits in band 0 images at next timesteps,
# so 1+2 for band 0.
self.assertEqual(result[0][2], 3)
# Source 1: inserted into timestep 0, band 0.
# Force-fits in bands 1,2,3 images at next timesteps.
# so 0+2 for bands 1,2,3.
self.assertEqual(result[1][2], 2)
self.assertEqual(result[2][2], 2)
self.assertEqual(result[3][2], 2)
# Source 2: inserted into timestep 1, band 3.
# Force-fits in band 0,1,2 images at next timestep,
# so 1 for band 0,1,2
self.assertEqual(result[4][2], 1)
self.assertEqual(result[5][2], 1)
self.assertEqual(result[6][2], 1)
# Source 2: inserted into timestep 1, band 3.
# Force-fit in band 3 image at next timestep,
# so 1+1 for band 3
self.assertEqual(result[7][2], 2)
# We should also have two lightcurves for both sources,
# where source 1 has 3 datapoints in band0 (t1,t2,t3)
# and 2 datapoints for the other three bands (t2,t3).
# Source 2 has two datapoints for band3 (t2,t3) and
# one for the other three bands (t3).
query = """\
SELECT a.runcat
,a.xtrsrc
,a.type
,i.band
,i.taustart_ts
FROM assocxtrsource a
,extractedsource x
,image i
WHERE a.xtrsrc = x.id
AND x.image = i.id
AND i.dataset = %(dataset_id)s
ORDER BY a.runcat
,i.band
,i.taustart_ts
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# 9 + 5 entries for source 1 and 2 resp.
self.assertEqual(len(result), 14)
# The individual light-curve datapoints
# Source1: new at t1, band0
self.assertEqual(result[0][2], 4)
self.assertEqual(result[0][4], taustart_tss[0])
# Source1: Forced fit at t2, same band
self.assertEqual(result[1][2], 7)
self.assertEqual(result[1][3], result[0][3])
self.assertEqual(result[1][4], taustart_tss[1])
# Source1: Forced fit at t3, same band
self.assertEqual(result[2][2], 7)
self.assertEqual(result[2][3], result[1][3])
self.assertEqual(result[2][4], taustart_tss[2])
# Source1: Forced fit at t2, band1
self.assertEqual(result[3][2], 7)
self.assertTrue(result[3][3] > result[2][3])
self.assertEqual(result[3][4], taustart_tss[1])
# Source1: Forced fit at t3, band1
self.assertEqual(result[4][2], 7)
self.assertEqual(result[4][3], result[3][3])
self.assertEqual(result[4][4], taustart_tss[2])
# Source1: Forced fit at t2, band2
self.assertEqual(result[5][2], 7)
self.assertTrue(result[5][3] > result[4][3])
self.assertEqual(result[5][4], taustart_tss[1])
# Source1: Forced fit at t3, band2
self.assertEqual(result[6][2], 7)
self.assertEqual(result[6][3], result[5][3])
self.assertEqual(result[6][4], taustart_tss[2])
# Source1: Forced fit at t2, band3
self.assertEqual(result[7][2], 7)
self.assertTrue(result[7][3] > result[6][3])
self.assertEqual(result[7][4], taustart_tss[1])
# Source1: Forced fit at t3, band3
self.assertEqual(result[8][2], 7)
self.assertEqual(result[8][3], result[7][3])
self.assertEqual(result[8][4], taustart_tss[2])
# Source2: Forced fit at t3, band0
self.assertEqual(result[9][2], 7)
self.assertEqual(result[9][3], result[0][3])
self.assertEqual(result[9][4], taustart_tss[2])
# Source2: Forced fit at t3, band1
self.assertEqual(result[10][2], 7)
self.assertTrue(result[10][3] > result[9][3])
self.assertEqual(result[10][4], taustart_tss[2])
# Source2: Forced fit at t3, band2
self.assertEqual(result[11][2], 7)
self.assertTrue(result[11][3] > result[10][3])
self.assertEqual(result[11][4], taustart_tss[2])
# Source2: new at t2, band3
self.assertEqual(result[12][2], 4)
self.assertTrue(result[12][3] > result[11][3])
self.assertEqual(result[12][4], taustart_tss[1])
# Source2: Forced fit at t3, band3
self.assertEqual(result[13][2], 7)
self.assertEqual(result[13][3], result[12][3])
self.assertEqual(result[13][4], taustart_tss[2])
def test_1to1_nullDetection(self):
"""
This tests that the two sources are associated if they were
detected at different timesteps. The positions are used in
the next test as well.
"""
data = {'description': "null detection:" + self._testMethodName}
dataset = DataSet(data=data)
# Two timesteps, just 1 band -> 2 images.
taustart_tss = [datetime.datetime(2013, 8, 1),
datetime.datetime(2013, 9, 1)]
freq_effs = [124]
freq_effs = [f * 1e6 for f in freq_effs]
im_params = db_subs.generate_timespaced_dbimages_data(len(freq_effs)
* len(taustart_tss))
timestamps = itertools.repeat(taustart_tss, len(freq_effs))
for im, freq, ts in zip(im_params, itertools.cycle(freq_effs),
itertools.chain.from_iterable(zip(*timestamps))):
im['freq_eff'] = freq
im['taustart_ts'] = ts
images = []
for im in im_params:
image = tkp.db.Image(dataset=dataset, data=im)
images.append(image)
# Arbitrary parameters, except that they fall inside our image
# and close together (see next test)
src0 = db_subs.example_extractedsource_tuple(ra=122.985, dec=10.5)
src1 = db_subs.example_extractedsource_tuple(ra=123.015, dec=10.5)
# Group images in blocks of 4, corresponding to all frequency bands at
# a given timestep.
for images in zip(*(iter(images),) * len(freq_effs)):
for image in images:
# The sources are only seen at timestep 0
if (image.taustart_ts == taustart_tss[0]):
dbgen.insert_extracted_sources(image.id, [src0], 'blind')
elif (image.taustart_ts == taustart_tss[1]):
dbgen.insert_extracted_sources(image.id, [src1], 'blind')
else:
pass
for image in images:
dbass.associate_extracted_sources(image.id, deRuiter_r=5.68,
new_source_sigma_margin=3)
query = """\
SELECT id
,datapoints
FROM runningcatalog r
WHERE dataset = %(dataset_id)s
ORDER BY datapoints
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have one runningcatalog sources, with two datapoints
# for the images in which the sources were seen.
self.assertEqual(len(result), 1)
self.assertEqual(result[0][1], 2)
query = """\
SELECT r.id
,rf.band
,rf.f_datapoints
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset_id)s
AND rf.runcat = r.id
ORDER BY r.id
,rf.band
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have one runningcatalog_flux entry,
# where the source has two flux datapoints
self.assertEqual(len(result), 1)
self.assertEqual(result[0][2], 2)
def test_m2m_nullDetection(self):
"""
This tests that two sources (close-by to be associated if they were
detected at different timesteps) which are not seen in the next
image and thus have forced fits, will have separate light curves.
The postions are from the previous test.
"""
data = {'description': "null detection:" + self._testMethodName}
dataset = DataSet(data=data)
# Three timesteps, just 1 band -> 3 images.
taustart_tss = [
datetime.datetime(2013, 8, 1),
datetime.datetime(2013, 9, 1),
datetime.datetime(2013, 10, 1)
]
freq_effs = [124]
freq_effs = [f * 1e6 for f in freq_effs]
im_params = db_subs.generate_timespaced_dbimages_data(
len(freq_effs) * len(taustart_tss))
timestamps = itertools.repeat(taustart_tss, len(freq_effs))
for im, freq, ts in zip(
im_params, itertools.cycle(freq_effs),
itertools.chain.from_iterable(zip(*timestamps))):
im['freq_eff'] = freq
im['taustart_ts'] = ts
images = []
for im in im_params:
image = tkp.db.Image(dataset=dataset, data=im)
images.append(image)
# Arbitrary parameters, except that they fall inside our image
# and close together (see previous test)
src0 = db_subs.example_extractedsource_tuple(ra=122.985, dec=10.5)
src1 = db_subs.example_extractedsource_tuple(ra=123.015, dec=10.5)
# Group images in blocks of 4, corresponding to all frequency bands at
# a given timestep.
for images in zip(*(iter(images), ) * len(freq_effs)):
for image in images:
# The sources are only seen at timestep 0
if (image.taustart_ts == taustart_tss[0]):
dbgen.insert_extracted_sources(image.id, [src0, src1],
'blind')
else:
pass
for image in images:
dbass.associate_extracted_sources(image.id,
deRuiter_r=5.68,
new_source_sigma_margin=3)
nd_ids_pos = dbnd.get_nulldetections(image.id)
# The null_detections are the positional inputs for the forced
# fits, which on their turn return additional parameters,
# e.g. from src0, src1
if image.taustart_ts == taustart_tss[0]:
# There are no null detections at the first timestep
self.assertEqual(len(nd_ids_pos), 0)
elif image.taustart_ts == taustart_tss[1]:
# src0 & src1 are null detections at the second timestep
self.assertEqual(len(nd_ids_pos), 2)
dbgen.insert_extracted_sources(
image.id, [src0, src1],
'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
else:
# All other images have two null detections.
self.assertEqual(len(nd_ids_pos), 2)
dbgen.insert_extracted_sources(
image.id, [src0, src1],
'ff_nd',
ff_runcat_ids=[ids for ids, ra, decl in nd_ids_pos])
# And here we have to associate the null detections with the
# runcat sources...
dbnd.associate_nd(image.id)
query = """\
SELECT id
,datapoints
FROM runningcatalog r
WHERE dataset = %(dataset_id)s
ORDER BY datapoints
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have two runningcatalog sources, with a datapoint for
# every image in which the sources were seen.
self.assertEqual(len(result), 2)
query = """\
SELECT r.id
,rf.band
,rf.f_datapoints
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset_id)s
AND rf.runcat = r.id
ORDER BY r.id
,rf.band
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# We should have two runningcatalog_flux entries,
# one for every source in the band, i.e. 2 x 1.
self.assertEqual(len(result), 2)
# Source 0: inserted into timestep 0.
# Force-fits in images at next timesteps,
# so 1+2 for band 0.
self.assertEqual(result[0][2], 3)
# Source 1: inserted into timestep 0
# Force-fits in images at next timesteps.
# so 1+2 for bands 0
self.assertEqual(result[1][2], 3)
#self.assertEqual(result[2][2], 2)
#self.assertEqual(result[3][2], 2)
# We should also have two lightcurves for both sources,
# where source 1 has 3 datapoints in band0 (t1,t2,t3).
# Source 2 also has 3 datapoints for band0 (t1,t2,t3).
query = """\
SELECT a.runcat
,a.xtrsrc
,a.type
,i.band
,i.taustart_ts
FROM assocxtrsource a
,extractedsource x
,image i
WHERE a.xtrsrc = x.id
AND x.image = i.id
AND i.dataset = %(dataset_id)s
ORDER BY a.runcat
,i.band
,i.taustart_ts
"""
cursor = tkp.db.execute(query, {'dataset_id': dataset.id})
result = cursor.fetchall()
# 3 + 3 entries for source 0 and 1 resp.
self.assertEqual(len(result), 6)
# The individual light-curve datapoints
# Source1: new at t1, band0
self.assertEqual(result[0][2], 4)
self.assertEqual(result[0][4], taustart_tss[0])
# Source1: Forced fit at t2, same band
self.assertEqual(result[1][2], 7)
self.assertEqual(result[1][3], result[0][3])
self.assertEqual(result[1][4], taustart_tss[1])
# Source1: Forced fit at t3, same band
self.assertEqual(result[2][2], 7)
self.assertEqual(result[2][3], result[1][3])
self.assertEqual(result[2][4], taustart_tss[2])
# Source2: new at t1, band0
self.assertEqual(result[3][2], 4)
self.assertEqual(result[3][3], result[1][3])
self.assertEqual(result[3][4], taustart_tss[0])
# Source2: Forced fit at t2, band0
self.assertEqual(result[4][2], 7)
self.assertEqual(result[4][3], result[3][3])
self.assertEqual(result[4][4], taustart_tss[1])
# Source2: Forced fit at t3, band0
self.assertEqual(result[5][2], 7)
self.assertEqual(result[5][3], result[4][3])
self.assertEqual(result[5][4], taustart_tss[2])
def test_many2manyflux_reduced_to_two_1to1(self):
"""
(See also assoc. test test_many2many_reduced_to_two_1to1 )
In this test-case we cross-associate between a rhombus of sources spread
about a central position, east-west in the first image,
north-south in the second.
The latter, north-south pair are slightly offset towards positive RA
and negative RA respectively.
The result is that the candidate associations are pruned down to
two one-to-one pairings..
"""
dataset = tkp.db.DataSet(database=self.database,
data={
'description':
'flux test set: n-m, ' +
self._testMethodName
})
n_images = 2
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
centre_ra, centre_dec = 123., 10.5,
offset_deg = 20 / 3600. #20 arcsec
tiny_offset_deg = 1 / 3600. #1 arcsec
eastern_src = db_subs.example_extractedsource_tuple(
ra=centre_ra + offset_deg,
dec=centre_dec,
peak=1.5,
peak_err=1e-1,
flux=3.0,
flux_err=1e-1,
)
western_src = db_subs.example_extractedsource_tuple(
ra=centre_ra - offset_deg,
dec=centre_dec,
peak=1.7,
peak_err=1e-1,
flux=3.2,
flux_err=1e-1,
)
northern_source = db_subs.example_extractedsource_tuple(
ra=centre_ra + tiny_offset_deg,
dec=centre_dec + offset_deg,
peak=1.8,
peak_err=1e-1,
flux=3.3,
flux_err=1e-1,
)
southern_source = db_subs.example_extractedsource_tuple(
ra=centre_ra - tiny_offset_deg,
dec=centre_dec - offset_deg,
peak=1.4,
peak_err=1e-1,
flux=2.9,
flux_err=1e-1,
)
# image 1
image1 = tkp.db.Image(database=self.database,
dataset=dataset,
data=im_params[0])
dbgen.insert_extracted_sources(image1.id, [eastern_src, western_src],
'blind')
associate_extracted_sources(image1.id, deRuiter_r=3.717)
# image 2
image2 = tkp.db.Image(database=self.database,
dataset=dataset,
data=im_params[1])
dbgen.insert_extracted_sources(image2.id,
[northern_source, southern_source],
'blind')
associate_extracted_sources(image2.id, deRuiter_r=3.717)
# Manually compose the lists of sources we expect to see associated
# into runningcatalog entries:
# NB img1_srclist[1] has larger RA value.
lightcurves_sorted_by_ra = []
lightcurves_sorted_by_ra.append([western_src, southern_source])
lightcurves_sorted_by_ra.append([eastern_src, northern_source])
#Check the summary statistics (avg flux, etc)
query = """\
SELECT rf.avg_f_int
,rf.avg_f_int_sq
,avg_weighted_f_int
,avg_f_int_weight
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
ORDER BY r.wm_ra, r.wm_decl
"""
self.database.cursor.execute(query, {'dataset': dataset.id})
runcat_flux_entries = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(runcat_flux_entries),
len(lightcurves_sorted_by_ra))
for idx, flux_summary in enumerate(runcat_flux_entries):
py_results = db_subs.lightcurve_metrics(
lightcurves_sorted_by_ra[idx])
for key in flux_summary.keys():
self.assertAlmostEqual(flux_summary[key], py_results[-1][key])
#Now check the per-timestep statistics (variability indices)
sorted_runcat_ids = columns_from_table('runningcatalog',
where={'dataset': dataset.id},
order='wm_ra,wm_decl')
sorted_runcat_ids = [entry['id'] for entry in sorted_runcat_ids]
for idx, rcid in enumerate(sorted_runcat_ids):
db_indices = db_queries.get_assoc_entries(self.database, rcid)
py_indices = db_subs.lightcurve_metrics(
lightcurves_sorted_by_ra[idx])
self.assertEqual(len(db_indices), len(py_indices))
for nstep in range(len(db_indices)):
for key in ('v_int', 'eta_int', 'f_datapoints'):
self.assertAlmostEqual(db_indices[nstep][key],
py_indices[nstep][key])
def test_one2manyflux(self):
dataset = tkp.db.DataSet(database=self.database,
data={'description': 'flux test set: 1-n'})
n_images = 2
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
central_ra, central_dec = 123.1235, 10.55,
position_offset_deg = 100. / 3600 #100 arcsec = 0.03 deg approx
# image 1
image = tkp.db.Image(database=self.database,
dataset=dataset,
data=im_params[0])
imageid1 = image.id
img1_srclist = []
# 1 source
img1_srclist.append(
db_subs.example_extractedsource_tuple(
central_ra,
central_dec,
peak=1.5,
peak_err=5e-1,
flux=3.0,
flux_err=5e-1,
))
dbgen.insert_extracted_sources(imageid1, img1_srclist, 'blind')
associate_extracted_sources(imageid1, deRuiter_r=3.717)
# image 2
image = tkp.db.Image(database=self.database,
dataset=dataset,
data=im_params[1])
imageid2 = image.id
img2_srclist = []
# 2 sources (both close to source 1, catching the 1-to-many case)
img2_srclist.append(
db_subs.example_extractedsource_tuple(
central_ra,
central_dec,
peak=1.6,
peak_err=5e-1,
flux=3.2,
flux_err=5e-1,
))
img2_srclist.append(
db_subs.example_extractedsource_tuple(
central_ra + position_offset_deg,
central_dec,
peak=1.9,
peak_err=5e-1,
flux=3.4,
flux_err=5e-1,
))
dbgen.insert_extracted_sources(imageid2, img2_srclist, 'blind')
associate_extracted_sources(imageid2, deRuiter_r=3.717)
# Manually compose the lists of sources we expect to see associated
# into runningcatalog entries:
# NB img2_srclist[1] has larger RA value.
lightcurves_sorted_by_ra = []
lightcurves_sorted_by_ra.append([img1_srclist[0], img2_srclist[0]])
lightcurves_sorted_by_ra.append([img1_srclist[0], img2_srclist[1]])
#Check the summary statistics (avg flux, etc)
query = """\
SELECT rf.avg_f_int
,rf.avg_f_int_sq
,avg_weighted_f_int
,avg_f_int_weight
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
ORDER BY r.wm_ra
"""
self.database.cursor.execute(query, {'dataset': dataset.id})
runcat_flux_entries = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(runcat_flux_entries), 2)
for idx, flux_summary in enumerate(runcat_flux_entries):
py_results = db_subs.lightcurve_metrics(
lightcurves_sorted_by_ra[idx])
for key in flux_summary.keys():
self.assertAlmostEqual(flux_summary[key], py_results[-1][key])
#Now check the per-timestep statistics (variability indices)
sorted_runcat_ids = columns_from_table('runningcatalog',
where={'dataset': dataset.id},
order='wm_ra')
sorted_runcat_ids = [entry['id'] for entry in sorted_runcat_ids]
for idx, rcid in enumerate(sorted_runcat_ids):
db_indices = db_queries.get_assoc_entries(self.database, rcid)
py_indices = db_subs.lightcurve_metrics(
lightcurves_sorted_by_ra[idx])
self.assertEqual(len(db_indices), len(py_indices))
for nstep in range(len(db_indices)):
for key in ('v_int', 'eta_int', 'f_datapoints'):
self.assertAlmostEqual(db_indices[nstep][key],
py_indices[nstep][key])
def setUp(self):
self.fake_images = db_subs.generate_timespaced_dbimages_data(n_images=1)
self.dataset = tkp.db.DataSet(data={'description':
"Reject:" + self._testMethodName})
self.image = tkp.db.Image(data=self.fake_images[0],
dataset=self.dataset)
def test_new_runcat_insertion(self):
"""Here we test the association logic executed upon insertion of a
new runningcatalog source.
We add an empty image0, then proceed to image1,
which is partially overlapping.
We add one new overlapping source, and one source only in image1's skyrgn.
Then we check that the back-associations to image0 are correct.
"""
n_images = 6
im_params = db_subs.generate_timespaced_dbimages_data(n_images)
#We first create 2 overlapping images,
#one above the other in dec by 1.0*xtr_radius
idx = 0
image0 = tkp.db.Image(dataset=self.dataset, data=im_params[idx])
image0.update()
#Bump up the centre of img1 to higher declination
im_params[1]['centre_decl'] += im_params[1]['xtr_radius']
#We place one source half-way between the field centres (i.e. in both)
src_in_imgs_0_1 = db_subs.example_extractedsource_tuple(
ra=im_params[1]['centre_ra'],
dec=im_params[1]['centre_decl'] -
im_params[1]['xtr_radius'] * 0.5)
#And one source only in field 1
src_in_img_1_only = db_subs.example_extractedsource_tuple(
ra=im_params[1]['centre_ra'],
dec=im_params[1]['centre_decl'] +
im_params[1]['xtr_radius'] * 0.5)
##First insert new sources in img1 and check association to parent field:
## (This is always asserted without calculation, for efficiency)
image1 = tkp.db.Image(dataset=self.dataset, data=im_params[1])
image1.insert_extracted_sources([src_in_imgs_0_1, src_in_img_1_only])
image1.associate_extracted_sources(deRuiter_r, new_source_sigma_margin)
image1.update()
runcats = columns_from_table('runningcatalog',
where={'dataset':self.dataset.id})
#We now expect to see both runcat entries in the field of im1
im1_assocs = columns_from_table('assocskyrgn',
where={'skyrgn':image1._data['skyrgn']})
self.assertEqual(len(im1_assocs), 2)
runcat_ids = [r['id'] for r in runcats]
for assoc in im1_assocs:
self.assertTrue(assoc['runcat'] in runcat_ids)
#The new sources are *also checked against previous regions*
#Only expect one in field of im0 ( the first source).
im0_assocs = columns_from_table('assocskyrgn',
where={'skyrgn':image0._data['skyrgn']})
runcats_only_in_im0 = columns_from_table('runningcatalog',
where={'dataset':self.dataset.id,
'wm_decl':15})
self.assertEqual(len(im0_assocs), 1)
self.assertEqual(len(runcats_only_in_im0), 1)
self.assertEqual(im0_assocs[0]['runcat'], runcats_only_in_im0[0]['id'])
def test_lightcurve(self):
# make 4 images with different date
images = []
image_datasets = db_subs.generate_timespaced_dbimages_data(n_images=4,
taustart_ts= datetime.datetime(2010, 3, 3)
)
for dset in image_datasets:
image = Image(dataset=self.dataset, data=dset)
images.append(image)
# 3 sources per image, with different coordinates & flux
data_list = []
for i in range(1, 4):
data_list.append({
'ra': 111.11 + i,
'decl': 11.11 + i,
'i_peak': 10. * i ,
'i_peak_err': 0.1,
})
# Insert the 3 sources in each image, while further varying the flux
lightcurves_sorted_by_ra = [[],[],[]]
for im_idx, image in enumerate(images):
# Create the "source finding results"
# Note that we reuse 'i_peak' as both peak & integrated flux.
img_sources = []
for src_idx, data in enumerate(data_list):
src = db_subs.example_extractedsource_tuple(
ra = data['ra'],dec=data['decl'],
peak=data['i_peak']* (1 + im_idx),
flux = data['i_peak']* (1 + im_idx)
)
lightcurves_sorted_by_ra[src_idx].append(src)
img_sources.append(src)
insert_extracted_sources(image._id, img_sources)
associate_extracted_sources(image._id, deRuiter_r=3.7,
new_source_sigma_margin=3)
# updates the dataset and its set of images
self.dataset.update()
self.dataset.update_images()
# update the images and their sets of sources
for image in self.dataset.images:
image.update()
image.update_sources()
# Now pick last image, select the first source (smallest RA)
# and extract its light curve
sources = self.dataset.images[-1].sources
sources = sorted(sources, key=attrgetter('ra'))
lightcurve = ligtcurve_func(sources[0]._id)
# check if the sources are associated in all images
self.assertEqual(len(images), len(lightcurve))
self.assertEqual(lightcurve[0][0], datetime.datetime(2010, 3, 3, 0, 0))
self.assertEqual(lightcurve[1][0], datetime.datetime(2010, 3, 4, 0, 0))
self.assertEqual(lightcurve[2][0], datetime.datetime(2010, 3, 5, 0, 0))
self.assertEqual(lightcurve[3][0], datetime.datetime(2010, 3, 6, 0, 0))
self.assertAlmostEqual(lightcurve[0][2], 10.)
self.assertAlmostEqual(lightcurve[1][2], 20.)
self.assertAlmostEqual(lightcurve[2][2], 30.)
self.assertAlmostEqual(lightcurve[3][2], 40.)
#Check the summary statistics (avg flux, etc)
query = """\
SELECT rf.avg_f_int
,rf.avg_f_int_sq
,avg_weighted_f_int
,avg_f_int_weight
FROM runningcatalog r
,runningcatalog_flux rf
WHERE r.dataset = %(dataset)s
AND r.id = rf.runcat
ORDER BY r.wm_ra
"""
self.database.cursor.execute(query, {'dataset': self.dataset.id})
runcat_flux_entries = get_db_rows_as_dicts(self.database.cursor)
self.assertEqual(len(runcat_flux_entries), len(lightcurves_sorted_by_ra))
for idx, flux_summary in enumerate(runcat_flux_entries):
py_results = db_subs.lightcurve_metrics(lightcurves_sorted_by_ra[idx])
for key in flux_summary.keys():
self.assertAlmostEqual(flux_summary[key], py_results[-1][key])
#Now check the per-timestep statistics (variability indices)
sorted_runcat_ids = columns_from_table('runningcatalog',
where={'dataset':self.dataset.id},
order='wm_ra')
sorted_runcat_ids = [entry['id'] for entry in sorted_runcat_ids]
for idx, rcid in enumerate(sorted_runcat_ids):
db_indices = db_queries.get_assoc_entries(self.database,
rcid)
py_indices = db_subs.lightcurve_metrics(lightcurves_sorted_by_ra[idx])
self.assertEqual(len(db_indices), len(py_indices))
for nstep in range(len(db_indices)):
for key in ('v_int', 'eta_int', 'f_datapoints'):
self.assertAlmostEqual(db_indices[nstep][key],
py_indices[nstep][key],
places=5)
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(
data={'description': "Trans:" + self._testMethodName},
database=self.database)
self.n_images = 8
self.image_rms = 1e-3 # 1mJy
self.new_source_sigma_margin = 3
self.search_params = dict(
eta_min=1,
v_min=0.1,
# minpoints=1,
)
detection_thresh = 10
barely_detectable_flux = 1.01 * self.image_rms * (detection_thresh)
reliably_detectable_flux = (
1.01 * self.image_rms *
(detection_thresh + self.new_source_sigma_margin))
test_specific_img_params = dict(rms_qc=self.image_rms,
rms_min=self.image_rms,
rms_max=self.image_rms,
detection_thresh=detection_thresh)
self.img_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
imgs = self.img_params
first_img = imgs[0]
centre_ra = first_img['centre_ra']
centre_decl = first_img['centre_decl']
xtr_radius = first_img['xtr_radius']
#At centre
fixed_source = MockSource(
example_extractedsource_tuple(ra=centre_ra, dec=centre_decl),
lightcurve=defaultdict(lambda: barely_detectable_flux))
#How many transients should we know about after each image?
self.n_transients_after_image = defaultdict(lambda: 0)
self.n_newsources_after_image = defaultdict(lambda: 0)
#shifted to +ve RA
bright_fast_transient = MockSource(
example_extractedsource_tuple(ra=centre_ra + xtr_radius * 0.5,
dec=centre_decl),
lightcurve={imgs[3]['taustart_ts']: reliably_detectable_flux})
#Detect immediately
for img_idx in range(3, self.n_images):
self.n_newsources_after_image[img_idx] += 1
#But only variable after non-detection
for img_idx in range(4, self.n_images):
self.n_transients_after_image[img_idx] += 1
# shifted to -ve RA
weak_fast_transient = MockSource(
example_extractedsource_tuple(ra=centre_ra - xtr_radius * 0.5,
dec=centre_decl),
lightcurve={imgs[3]['taustart_ts']: barely_detectable_flux})
# Not flagged as a newsource, could just be a weakly detected
# steady-source at first.
# But, shows high-variance after forced-fit in image[4]
for img_idx in range(4, self.n_images):
self.n_transients_after_image[img_idx] += 1
# shifted to +ve Dec
weak_slow_transient = MockSource(example_extractedsource_tuple(
ra=centre_ra, dec=centre_decl + xtr_radius * 0.5),
lightcurve={
imgs[5]['taustart_ts']:
barely_detectable_flux,
imgs[6]['taustart_ts']:
barely_detectable_flux * 0.95
})
# Not flagged as a newsource, could just be a weakly detected
# steady-source at first.
# Should not be flagged as transient until forced-fit in image[7]
for img_idx in range(7, self.n_images):
self.n_transients_after_image[img_idx] += 1
self.all_mock_sources = [
fixed_source, weak_slow_transient, bright_fast_transient,
weak_fast_transient
]
