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.example_dbimage_datasets(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])
        nd_posns = dbmon.get_nulldetections(imgs[0].id, deRuiter_r=1)
        self.assertEqual(len(nd_posns), 0)
        imgs[0].associate_extracted_sources(deRuiter_r=0.1)

        imgs[1].insert_extracted_sources([upper_steady_src, overlap_steady_src,
                                          overlap_transient])
        nd_posns = dbmon.get_nulldetections(imgs[1].id, deRuiter_r=1)
        self.assertEqual(len(nd_posns), 0)
        imgs[1].associate_extracted_sources(deRuiter_r=0.1)

        runcats = columns_from_table('runningcatalog',
                                where={'dataset': self.dataset.id})
        self.assertEqual(len(runcats), 4) #sanity check.

        monlist = columns_from_table('monitoringlist',
                                where={'dataset': self.dataset.id})
        self.assertEqual(len(monlist), 1)

        transients_qry = """\
        SELECT *
          FROM transient tr
              ,runningcatalog rc
        WHERE rc.dataset = %s
          AND tr.runcat = rc.id
        """
        self.database.cursor.execute(transients_qry, (self.dataset.id,))
        transients = get_db_rows_as_dicts(self.database.cursor)
        self.assertEqual(len(transients), 1)

    def test_infinite(self):
        # Check that database insertion doesn't choke on infinite errors.

        dataset = DataSet(data={'description': 'example dataset'},
                           database=self.database)
        image = Image(dataset=dataset, data=db_subs.example_dbimage_data_dict())

        # Inserting a standard example extractedsource should be fine
        extracted_source = db_subs.example_extractedsource_tuple()
        image.insert_extracted_sources([extracted_source])
        inserted = columns_from_table('extractedsource',
                                      where= {'image' : image.id})
        self.assertEqual(len(inserted), 1)

        # But if the source has infinite errors we drop it and log a warning
        extracted_source = db_subs.example_extractedsource_tuple(error_radius=float('inf'),
                                                                 peak_err=float('inf'),
                                                                 flux_err=float('inf'))

                # We will add a handler to the root logger which catches all log
        # output in a buffer.
        iostream = BytesIO()
        hdlr = logging.StreamHandler(iostream)
        logging.getLogger().addHandler(hdlr)

        image.insert_extracted_sources([extracted_source])

        logging.getLogger().removeHandler(hdlr)
        # We want to be sure that the error has been appropriately logged.
        self.assertIn("Dropped source fit with infinite flux errors",
                      iostream.getvalue())

        inserted = columns_from_table('extractedsource',
                                      where= {'image' : image.id})
        self.assertEqual(len(inserted), 1)

    def test_infinite(self):
        # Check that database insertion doesn't choke on infinite errors
        dataset = DataSet(data={'description': 'example dataset'},
                           database=self.database)
        image = Image(dataset=dataset, data=db_subs.example_dbimage_datasets(1)[0])

        # Inserting an example extractedsource should be fine
        extracted_source = db_subs.example_extractedsource_tuple()
        image.insert_extracted_sources([extracted_source])

        # But it should also be fine if the source has infinite errors
        extracted_source = db_subs.example_extractedsource_tuple(error_radius=float('inf'))
        image.insert_extracted_sources([extracted_source])

    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_steady_source(self):
        """
        Sanity check: Ensure we get no newsource table entries for a steady
        source.
        """
        im_params = self.im_params
        steady_src = db_subs.MockSource(
             template_extractedsource=db_subs.example_extractedsource_tuple(
                 ra=im_params[0]['centre_ra'],
                 dec=im_params[0]['centre_decl'],
             ),
             lightcurve=defaultdict(lambda : self.reliably_detectable_flux)
        )

        inserted_sources = []
        for img_pars in im_params:
            image, _,forced_fits = insert_image_and_simulated_sources(
                self.dataset,img_pars,[steady_src],
                self.new_source_sigma_margin)

            #should not have any nulldetections
            self.assertEqual(len(forced_fits), 0)

            transients = get_sources_filtered_by_final_variability(
                dataset_id=self.dataset.id, **self.search_params)
            newsources = get_newsources_for_dataset(self.dataset.id)

            #or newsources, high variability sources
            self.assertEqual(len(transients), 0)
            self.assertEqual(len(newsources), 0)

    def test_probably_not_a_transient(self):
        """
        No source at 250MHz, but we detect a source at 50MHz.
        Not necessarily a transient.
        Should trivially ignore 250MHz data when looking at a new 50MHz source.
        """
        img_params = self.img_params

        img0 = img_params[0]

        # This time around, we just manually exclude the steady src from
        # the first image detections.
        steady_low_freq_src = MockSource(
            example_extractedsource_tuple(ra=img_params[0]['centre_ra'],
                                          dec=img_params[0]['centre_decl']
                                          ),
            lightcurve=defaultdict(lambda :self.always_detectable_flux)
        )

        # Insert first image, no sources.
        tkp.db.Image(data=img_params[0],dataset=self.dataset)
        # Now set up second image.
        img1 = tkp.db.Image(data=img_params[1],dataset=self.dataset)
        xtr = steady_low_freq_src.simulate_extraction(img1,
                                                    extraction_type='blind')
        insert_extracted_sources(img1._id, [xtr], 'blind')
        associate_extracted_sources(img1._id, deRuiter_r, self.new_source_sigma_margin)
        transients = get_newsources_for_dataset(self.dataset.id)

        # Should have no marked transients
        self.assertEqual(len(transients), 0)

    def test_null_detection_business_as_usual(self):
        """
        If we do not blindly extract a steady source due to increased RMS,
        then we expect a null-detection forced-fit to be triggered.

        However, if the source properties are steady, this should not
        result in the source being identified as transient.
        """

        img0 = self.img_params[0]
        steady_src_flux = self.barely_detectable_flux
        steady_src = MockSource(
            example_extractedsource_tuple(ra=img0['centre_ra'],
                                          dec=img0['centre_decl']
                                          ),
            lightcurve=defaultdict(lambda :steady_src_flux)
        )


        image, blind_xtr,forced_fits = insert_image_and_simulated_sources(
                self.dataset,self.img_params[0],[steady_src],
                self.new_source_sigma_margin)
        self.assertEqual(len(blind_xtr),1)
        self.assertEqual(len(forced_fits),0)

        image, blind_xtr,forced_fits = insert_image_and_simulated_sources(
                self.dataset,self.img_params[1],[steady_src],
                self.new_source_sigma_margin)
        self.assertEqual(len(blind_xtr),0)
        self.assertEqual(len(forced_fits),1)
        get_sources_filtered_by_final_variability(dataset_id=self.dataset.id,
                                    **self.search_params)

        transients=get_newsources_for_dataset(self.dataset.id)
        self.assertEqual(len(transients),0)

    def test_single_epoch_weak_transient(self):
        """
        A weak (barely detected in blind extraction) transient appears at
        field centre in one image, then disappears entirely.

        Because it is a weak extraction, it will not be immediately marked
        as transient, but it will get flagged up after forced-fitting due to
        the variability search.
        """
        im_params = self.im_params

        transient_src = db_subs.MockSource(
             template_extractedsource=db_subs.example_extractedsource_tuple(
                 ra=im_params[0]['centre_ra'],
                 dec=im_params[0]['centre_decl'],
             ),
             lightcurve={im_params[2]['taustart_ts'] :
                             self.barely_detectable_flux}
        )

        inserted_sources = []

        for img_pars in im_params[:3]:
            image, _,forced_fits = insert_image_and_simulated_sources(
                self.dataset,img_pars,[transient_src],
                self.new_source_sigma_margin)
            self.assertEqual(forced_fits, [])
            newsources = get_newsources_for_dataset(self.dataset.id)
            self.assertEqual(len(newsources), 0)
            transients = get_sources_filtered_by_final_variability(
                dataset_id=self.dataset.id, **self.search_params)
            #No variability yet
            self.assertEqual(len(transients), 0)

        #Now, the final, empty image:
        image, blind_extractions, forced_fits = insert_image_and_simulated_sources(
                self.dataset,im_params[3],[transient_src],
                self.new_source_sigma_margin)
        self.assertEqual(len(blind_extractions),0)
        self.assertEqual(len(forced_fits), 1)

        #No changes to newsource table
        newsources = get_newsources_for_dataset(self.dataset.id)
        self.assertEqual(len(newsources), 0)

        #But it now has high variability
        transients = get_sources_filtered_by_final_variability(
            dataset_id=self.dataset.id, **self.search_params)
        self.assertEqual(len(transients), 1)
        transient_properties = transients[0]

        # Check that the bands for the images are the same as the transient's band
        freq_bands = self.dataset.frequency_bands()
        self.assertEqual(len(freq_bands), 1)
        self.assertEqual(freq_bands[0], transient_properties['band'])

        #Sanity check that the runcat is correctly matched
        runcats = self.dataset.runcat_entries()
        self.assertEqual(len(runcats), 1)
        self.assertEqual(runcats[0]['runcat'], transient_properties['runcat_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.example_dbimage_datasets(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)
        self.assertAlmostEqual(avg_f_int[0], 2.3)

    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 TestMeridianLowerEdgeCase(self):
        """What happens if a source is right on the meridian?"""

        dataset = DataSet(data={'description':"Assoc 1-to-1:" +
                                self._testMethodName})
        n_images = 3
        im_params = db_subs.example_dbimage_datasets(n_images, centre_ra=0.5,
                                                      centre_decl=10)
        src_list = []
        src0 = db_subs.example_extractedsource_tuple(ra=0.0002, dec=10.5,
                                             ra_fit_err=0.01, dec_fit_err=0.01)
        src_list.append(src0)
        src1 = src0._replace(ra=0.0003)
        src_list.append(src1)
        src2 = src0._replace(ra=0.0004)
        src_list.append(src2)

        for idx, im in enumerate(im_params):
            im['centre_ra'] = 359.9
            image = tkp.db.Image(dataset=dataset, data=im)
            image.insert_extracted_sources([src_list[idx]])
            associate_extracted_sources(image.id, deRuiter_r=3.717)
        runcat = columns_from_table('runningcatalog', ['datapoints', 'wm_ra'],
                                   where={'dataset':dataset.id})
#        print "***\nRESULTS:", runcat, "\n*****"
        self.assertEqual(len(runcat), 1)
        self.assertEqual(runcat[0]['datapoints'], 3)
        avg_ra = (src0.ra + src1.ra +src2.ra)/3
        self.assertAlmostEqual(runcat[0]['wm_ra'], avg_ra)

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 test_marginal_transient(self):
        """
        ( flux1 > (rms_min0*(det0 + margin) )
        but ( flux1 < (rms_max0*(det0 + margin) )
        --> Possible transient

        If it was in a region of rms_min, we would (almost certainly) have seen
        it in the first image. So new source --> Possible transient.
        But if it was in a region of rms_max, then perhaps we would have missed
        it. In which case, new source --> Just seeing deeper.

        Note that if we are tiling overlapping images, then the first time
        a field is processed with image-centre at the edge of the old field,
        we may get a bunch of unhelpful 'possible transients'.

        Furthermore, this will pick up fluctuating sources near the
        image-margins even with a fixed field of view.
        But without a more complex store of image-rms-per-position, we cannot
        do better.
        Hopefully we can use a 'distance from centre' feature to separate out
        the good and bad candidates in this case.
        """
        img_params = self.img_params

        #Must pick flux value carefully to fire correct logic branch:
        marginal_transient_flux = self.reliably_detected_at_image_centre_flux

        marginal_transient = MockSource(
            example_extractedsource_tuple(ra=img_params[0]['centre_ra'],
                                          dec=img_params[0]['centre_decl']),
            lightcurve={img_params[1]['taustart_ts'] : marginal_transient_flux}
        )


        #First, check that we've set up the test correctly:
        rms_min0 = img_params[0]['rms_min']
        rms_max0 = img_params[0]['rms_max']
        det0 = img_params[0]['detection_thresh']
        self.assertTrue(marginal_transient_flux <
            rms_max0*(det0 + self.new_source_sigma_margin ) )
        self.assertTrue(marginal_transient_flux >
            rms_min0*(det0 + self.new_source_sigma_margin ) )

        for pars in self.img_params:
            img = tkp.db.Image(data=pars,dataset=self.dataset)
            xtr = marginal_transient.simulate_extraction(img,
                                                       extraction_type='blind')
            if xtr is not None:
                img.insert_extracted_sources([xtr],'blind')
            img.associate_extracted_sources(deRuiter_r, self.new_source_sigma_margin)


        newsources = get_newsources_for_dataset(self.dataset.id)

        #Should have one 'possible' transient
        self.assertEqual(len(newsources),1)
        self.assertTrue(
            newsources[0]['low_thresh_sigma'] > self.new_source_sigma_margin)
        self.assertTrue(
            newsources[0]['high_thresh_sigma'] < self.new_source_sigma_margin)

    def test_only_first_epoch_source(self):
        """test_only_first_epoch_source

        - Pretend to extract a source only from the first image.
        - Run source association for each image, as we would in TraP.
        - Check the image source listing works
        - Check runcat and assocxtrsource are correct.

        """


        first_epoch = True
        extracted_source_ids=[]
        for im in self.im_params:
            self.db_imgs.append( Image( data=im, dataset=self.dataset) )
            last_img =self.db_imgs[-1]

            if first_epoch:
                last_img.insert_extracted_sources(
                    [db_subs.example_extractedsource_tuple()],'blind')

            last_img.associate_extracted_sources(deRuiter_r,
                                                 new_source_sigma_margin)

            #First, check the runcat has been updated correctly:
            running_cat = columns_from_table(table="runningcatalog",
                                           keywords=['datapoints'],
                                           where={"dataset":self.dataset.id})
            self.assertEqual(len(running_cat), 1)
            self.assertEqual(running_cat[0]['datapoints'], 1)

            last_img.update()
            last_img.update_sources()
            img_xtrsrc_ids = [src.id for src in last_img.sources]
#            print "ImageID:", last_img.id
#            print "Imgs sources:", img_xtrsrc_ids
            if first_epoch:
                self.assertEqual(len(img_xtrsrc_ids),1)
                extracted_source_ids.extend(img_xtrsrc_ids)
                assocxtrsrcs_rows = columns_from_table(table="assocxtrsource",
                                           keywords=['runcat', 'xtrsrc' ],
                                           where={"xtrsrc":img_xtrsrc_ids[0]})
                self.assertEqual(len(assocxtrsrcs_rows),1)
                self.assertEqual(assocxtrsrcs_rows[0]['xtrsrc'], img_xtrsrc_ids[0])
            else:
                self.assertEqual(len(img_xtrsrc_ids),0)

            first_epoch=False


        #Assocxtrsources still ok after multiple images?
        self.assertEqual(len(extracted_source_ids),1)
        assocxtrsrcs_rows = columns_from_table(table="assocxtrsource",
                                           keywords=['runcat', 'xtrsrc' ],
                                           where={"xtrsrc":extracted_source_ids[0]})
        self.assertEqual(len(assocxtrsrcs_rows),1)

        self.assertEqual(assocxtrsrcs_rows[0]['xtrsrc'], extracted_source_ids[0],
                         "Runcat xtrsrc entry must match the only extracted source")

    def test_single_fixed_source(self):
        """test_single_fixed_source

        - Pretend to extract the same source in each of a series of images.
        - Perform source association
        - Check the image source listing works
        - Check runcat, assocxtrsource.
        """

        fixed_src_runcat_id = None
        for img_idx, im in enumerate(self.im_params):
            self.db_imgs.append( Image(data=im, dataset=self.dataset))
            last_img = self.db_imgs[-1]
            insert_extracted_sources(last_img._id,
                [db_subs.example_extractedsource_tuple()],'blind')
            associate_extracted_sources(last_img._id, deRuiter_r,
                                        new_source_sigma_margin)

            running_cat = columns_from_table(table="runningcatalog",
                                           keywords=['id', 'datapoints'],
                                           where={"dataset":self.dataset.id})
            self.assertEqual(len(running_cat), 1)
            self.assertEqual(running_cat[0]['datapoints'], img_idx+1)

            # Check runcat ID does not change for a steady single source
            if img_idx == 0:
                fixed_src_runcat_id = running_cat[0]['id']
                self.assertIsNotNone(fixed_src_runcat_id, "No runcat id assigned to source")
            self.assertEqual(running_cat[0]['id'], fixed_src_runcat_id,
                             "Multiple runcat ids for same fixed source")


            runcat_flux = columns_from_table(table="runningcatalog_flux",
                               keywords=['f_datapoints'],
                               where={"runcat":fixed_src_runcat_id})
            self.assertEqual(len(runcat_flux),1)
            self.assertEqual(img_idx+1, runcat_flux[0]['f_datapoints'])

            last_img.update()
            last_img.update_sources()
            img_xtrsrc_ids = [src.id for src in last_img.sources]
            self.assertEqual(len(img_xtrsrc_ids), 1)

            #Get the association row for most recent extraction:
            assocxtrsrcs_rows = columns_from_table(table="assocxtrsource",
                                       keywords=['runcat', 'xtrsrc' ],
                                       where={"xtrsrc":img_xtrsrc_ids[0]})
#            print "ImageID:", last_img.id
#            print "Imgs sources:", img_xtrsrc_ids
#            print "Assoc entries:", assocxtrsrcs_rows
#            print "First extracted source id:", ds_source_ids[0]
#            if len(assocxtrsrcs_rows):
#                print "Associated source:", assocxtrsrcs_rows[0]['xtrsrc']
            self.assertEqual(len(assocxtrsrcs_rows),1,
                             msg="No entries in assocxtrsrcs for image number "+str(img_idx))
            self.assertEqual(assocxtrsrcs_rows[0]['runcat'], fixed_src_runcat_id,
                             "Mismatched runcat id in assocxtrsrc table")

    def TestDeRuiterCalculation(self):
        """Check all the unit conversions are correct"""
        dataset = DataSet(data={'description':"Assoc 1-to-1:" + self._testMethodName})
        n_images = 2
        im_params = db_subs.example_dbimage_datasets(n_images, centre_ra=10,
                                                     centre_decl=0)


        #Note ra / ra_fit_err are in degrees.
        # ra_sys_err is in arcseconds, but we set it = 0 so doesn't matter.
        #ra_fit_err cannot be zero or we get div by zero errors.
        #Also, there is a hard limit on association radii:
        #currently this defaults to 0.03 degrees== 108 arcseconds
        src0 = db_subs.example_extractedsource_tuple(ra=10.00, dec=0.0,
                                             ra_fit_err=0.1, dec_fit_err=1.00,
                                             ra_sys_err=0.0, dec_sys_err=0.0)
        src1 = db_subs.example_extractedsource_tuple(ra=10.02, dec=0.0,
                                             ra_fit_err=0.1, dec_fit_err=1.00,
                                             ra_sys_err=0.0, dec_sys_err=0.0)
        src_list = [src0, src1]
        #NB dec_fit_err nonzero, but since delta_dec==0 this simplifies to:
        expected_DR_radius = math.sqrt((src1.ra - src0.ra) ** 2 /
                               (src0.ra_fit_err ** 2 + src1.ra_fit_err ** 2))
#        print "Expected DR", expected_DR_radius

        for idx in [0, 1]:
            image = tkp.db.Image(dataset=dataset,
                                data=im_params[idx])
            image.insert_extracted_sources([src_list[idx]])
            #Peform very loose association since we just want to store DR value.
            associate_extracted_sources(image.id, deRuiter_r=100)
        runcat = columns_from_table('runningcatalog', ['id'],
                                   where={'dataset':dataset.id})
#        print "***\nRESULTS:", runcat, "\n*****"
        self.assertEqual(len(runcat), 1)
        assoc = columns_from_table('assocxtrsource', ['r'],
                                   where={'runcat':runcat[0]['id']})
#        print "Got assocs:", assoc
        self.assertEqual(len(assoc), 2)
        self.assertAlmostEqual(assoc[1]['r'], expected_DR_radius)

    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_previous_image_id(self):
        img_params = self.img_params

        mock_sources=[]
        mock_sources.append( MockSource(
            example_extractedsource_tuple(ra=img_params[0]['centre_ra'],
                                          dec=img_params[0]['centre_decl']),
            lightcurve={img_params[-1]['taustart_ts']:
                            self.always_detectable_flux}
        ))

        mock_sources.append( MockSource(
            example_extractedsource_tuple(ra=img_params[0]['centre_ra']+1,
                                          dec=img_params[0]['centre_decl']),
            lightcurve={img_params[-1]['taustart_ts']:
                            self.always_detectable_flux}
        ))


        image_ids={}

        for img_idx in xrange(self.n_images):
            image, _,_ = insert_image_and_simulated_sources(
                self.dataset,self.img_params[img_idx],mock_sources,
                self.new_source_sigma_margin)
            image_ids[img_idx]=image.id



        newsources = get_newsources_for_dataset(self.dataset.id)

        self.assertEqual(len(newsources),len(mock_sources))
        newsource_properties = newsources[0]
        print "Image IDs:", image_ids
        self.assertEqual(newsource_properties['previous_limits_image'],
                         image_ids[4])

    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)