Added status, error message fields, contamination map to response
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189
views.py
189
views.py
@ -12,7 +12,7 @@ from astropy.stats import poisson_conf_interval
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from collections import defaultdict
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from django.db.models import Sum
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from django.db.models import Sum, Max
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from django.shortcuts import get_object_or_404
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from rest_framework.views import APIView
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from rest_framework.response import Response
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@ -221,118 +221,132 @@ class UpperLimitView(APIView):
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# exclude contaminated pixels from the background calculations
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annulus_pixels = annulus_pixels.exclude(contaminated=True)
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status_int = 0
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error_message = ""
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if not source_pixels.exists() or not annulus_pixels.exists():
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return Response(
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{
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"detail": "No background and/or source pixel data for the given survey selection."
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},
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status=status.HTTP_404_NOT_FOUND,
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)
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status_int = 1 # status 1 if there are no source or bg pixels
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# COMPUTE COUNTS, BACKGROUND ESTIMATE, EXPOSURE
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# **************************************************************
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try:
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# summing counts across all surveys
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N = sum(obj.counts for obj in source_pixels)
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# summing counts across all surveys
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N = sum(obj.counts for obj in source_pixels)
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Nnpix = len(source_pixels)
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Nnpix = len(source_pixels)
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Bcounts = sum(obj.counts for obj in annulus_pixels)
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Bcounts = sum(obj.counts for obj in annulus_pixels)
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Bnpix = len(annulus_pixels)
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Bnpix = len(annulus_pixels)
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B = Bcounts / Bnpix * Nnpix
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B = Bcounts / Bnpix * Nnpix
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# create a dict of exposures keyed by survey
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t_by_survey = defaultdict(list)
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# create a dict of exposures keyed by survey
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t_by_survey = defaultdict(list)
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for pixel in source_pixels:
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t_by_survey[pixel.survey].append(pixel.exposure)
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for pixel in source_pixels:
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t_by_survey[pixel.survey].append(pixel.exposure)
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# create and populate a list of average exposures per survey
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survey_averages = []
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# create and populate a list of average exposures per survey
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survey_averages = []
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for survey_id, exposures in t_by_survey.items():
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average_exposure = sum(exposures) / len(exposures)
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survey_averages.append(average_exposure)
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for survey_id, exposures in t_by_survey.items():
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average_exposure = sum(exposures) / len(exposures)
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survey_averages.append(average_exposure)
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# sum them up across surveys
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t = sum(survey_averages)
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# sum them up across surveys
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t = sum(survey_averages)
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# CONSTANTS
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# **************************************************************
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# CONSTANTS
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# **************************************************************
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# EEF = .9 # eclosed energy fraction, .5 for hpd, .9 for w90
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# ECF = 4e-11 # energy conversion factor
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# EEF = .9 # eclosed energy fraction, .5 for hpd, .9 for w90
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# ECF = 4e-11 # energy conversion factor
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EEF = 0.80091 # use values from the paper
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ECF = 3.3423184e-11
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EEF = 0.80091 # use values from the paper
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ECF = 3.3423184e-11
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# BAYESIAN IMPLEMENTATION VIA POISSON_CONF_INTERVAL
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# **************************************************************
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# BAYESIAN IMPLEMENTATION VIA POISSON_CONF_INTERVAL
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# **************************************************************
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# double sided interval
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low, high = poisson_conf_interval(
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n=N,
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background=B,
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interval="kraft-burrows-nousek",
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confidence_level=confidence_level,
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)
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# because poisson_conf_interval returns lists
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bayesian_count_ul = high[0]
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bayesian_count_ll = low[0]
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bayesian_count_UL = (
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sp.gammaincinv(
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N + 1, confidence_level * sp.gammaincc(N + 1, B) + sp.gammainc(N + 1, B)
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# double sided interval
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low, high = poisson_conf_interval(
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n=N,
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background=B,
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interval="kraft-burrows-nousek",
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confidence_level=confidence_level,
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)
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- B
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)
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bayesian_rate_ul = bayesian_count_ul / t / EEF # count rate limits
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bayesian_rate_ll = bayesian_count_ll / t / EEF
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bayesian_rate_UL = bayesian_count_UL / t / EEF
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# because poisson_conf_interval returns lists
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bayesian_count_ul = high[0]
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bayesian_count_ll = low[0]
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bayesian_flux_ul = bayesian_rate_ul * ECF # flux limits
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bayesian_flux_ll = bayesian_rate_ll * ECF
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bayesian_flux_UL = bayesian_rate_UL * ECF
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bayesian_count_UL = (
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sp.gammaincinv(
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N + 1,
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confidence_level * sp.gammaincc(N + 1, B) + sp.gammainc(N + 1, B),
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)
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- B
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)
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# CLASSICAL IMPLEMENTATION VIA GAMMAINCCINV
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# ****************************************************************
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bayesian_rate_ul = bayesian_count_ul / t / EEF # count rate limits
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bayesian_rate_ll = bayesian_count_ll / t / EEF
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bayesian_rate_UL = bayesian_count_UL / t / EEF
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# confidence level for the double sided interval
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cl2 = (1 + confidence_level) / 2
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# double sided interval
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classic_count_ul = sp.gammainccinv(N + 1, 1 - cl2) - B
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classic_count_ll = sp.gammainccinv(N, cl2) - B
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# one sided interval
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classic_count_UL = sp.gammainccinv(N + 1, 1 - confidence_level) - B
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bayesian_flux_ul = bayesian_rate_ul * ECF # flux limits
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bayesian_flux_ll = bayesian_rate_ll * ECF
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bayesian_flux_UL = bayesian_rate_UL * ECF
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if not np.isfinite(classic_count_ll) or classic_count_ll < 0:
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classic_count_ll = 0.0
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# CLASSICAL IMPLEMENTATION VIA GAMMAINCCINV
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# ****************************************************************
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classic_rate_ul = classic_count_ul / t / EEF # count rate limits
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classic_rate_ll = classic_count_ll / t / EEF
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classic_rate_UL = classic_count_UL / t / EEF
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# confidence level for the double sided interval
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cl2 = (1 + confidence_level) / 2
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# double sided interval
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classic_count_ul = sp.gammainccinv(N + 1, 1 - cl2) - B
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classic_count_ll = sp.gammainccinv(N, cl2) - B
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# one sided interval
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classic_count_UL = sp.gammainccinv(N + 1, 1 - confidence_level) - B
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classic_flux_ul = classic_rate_ul * ECF # flux limits
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classic_flux_ll = classic_rate_ll * ECF
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classic_flux_UL = classic_rate_UL * ECF
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if not np.isfinite(classic_count_ll) or classic_count_ll < 0:
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classic_count_ll = 0.0
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# FLUX ESTIMATION
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# ****************************************************************
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classic_rate_ul = classic_count_ul / t / EEF # count rate limits
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classic_rate_ll = classic_count_ll / t / EEF
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classic_rate_UL = classic_count_UL / t / EEF
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S = N - B # counts as simply counts within aperture
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# with the background estimate subtracted
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classic_flux_ul = classic_rate_ul * ECF # flux limits
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classic_flux_ll = classic_rate_ll * ECF
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classic_flux_UL = classic_rate_UL * ECF
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CR = S / t / EEF # source count rate
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# FLUX ESTIMATION
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# ****************************************************************
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BR = B / t # background rate within aperture
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S = N - B # counts as simply counts within aperture
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# with the background estimate subtracted
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FL = CR * ECF # conversion to flux
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CR = S / t / EEF # source count rate
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Flux = max(FL, 0) # flux cannot be lower than zero
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BR = B / t # background rate within aperture
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FL = CR * ECF # conversion to flux
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Flux = max(FL, 0) # flux cannot be lower than zero
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# handle exceptions: write error text to the response
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# zero out all math results
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except Exception as e:
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error_message = str(e)
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N = Nnpix = Bcounts = Bnpix = B = t = S = CR = BR = 0.0
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Flux = 0.0
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classic_count_ul = classic_count_ll = classic_count_UL = 0.0
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classic_rate_ul = classic_rate_ll = classic_rate_UL = 0.0
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classic_flux_ul = classic_flux_ll = classic_flux_UL = 0.0
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bayesian_count_ul = bayesian_count_ll = bayesian_count_UL = 0.0
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bayesian_rate_ul = bayesian_rate_ll = bayesian_rate_UL = 0.0
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bayesian_flux_ul = bayesian_flux_ll = bayesian_flux_UL = 0.0
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# NEARBY SOURCES CHECK
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# ****************************************************************
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@ -372,7 +386,7 @@ class UpperLimitView(APIView):
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}
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)
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# SQUARE REGION IMAGE SERVING
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# REGION IMAGE SERVING
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# ****************************************************************
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# default value if not specified in the query
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@ -396,7 +410,10 @@ class UpperLimitView(APIView):
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map_pixels_qs = (
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Pixel.objects.filter(hpid__in=map_pixel_list, survey__in=survey_numbers)
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.values("hpid")
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.annotate(counts=Sum("counts"))
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.annotate(
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counts=Sum("counts"),
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contaminated=Max("contaminated"),
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)
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.order_by("hpid")
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)
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@ -406,6 +423,7 @@ class UpperLimitView(APIView):
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# get lists of healpix indices and count values
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map_healpix_list = [d["hpid"] for d in map_pixels_list]
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map_counts_list = [d["counts"] for d in map_pixels_list]
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map_contaminated_list = [d["contaminated"] for d in map_pixels_list]
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# set map nside
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map_nside = 4096
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@ -417,6 +435,7 @@ class UpperLimitView(APIView):
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map_dict = {
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"healpix": map_healpix_list,
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"counts": map_counts_list,
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"contaminated": map_contaminated_list,
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"nside": map_nside,
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"order": map_order,
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"radius_as": map_radius,
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@ -426,6 +445,10 @@ class UpperLimitView(APIView):
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# ****************************************************************
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result = {
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"Status": status_int,
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# 0 OK
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# 1 either source or bg pixels missing
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"ErrorMessage": error_message,
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# frequentist limits
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"ClassicUpperLimit": classic_count_ul,
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"ClassicLowerLimit": classic_count_ll,
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