def test_catshtm_redshift(requests_mock):
alerce = Alerce()
requests_mock.register_uri(
"GET",
alerce.config["CATSHTM_API_URL"] +
alerce.config["CATSHTM_ROUTES"]["crossmatch_all"],
json=COSSMATCH_ALL_RESPONSE,
)
r = alerce.catshtm_redshift(ra=357.73373, dec=14.20514, radius=10)
assert r is not None
def test_catshtm_crossmatch_catalog(requests_mock):
alerce = Alerce()
requests_mock.register_uri(
"GET",
alerce.config["CATSHTM_API_URL"] +
alerce.config["CATSHTM_ROUTES"]["crossmatch"],
json=CROSSMATCH_CATALOG_RESPONSE,
)
r = alerce.catshtm_crossmatch(ra=357.73373,
dec=14.20514,
radius=10,
format="pandas",
catalog_name="2MASS")
assert r is not None
def test_catshtm_conesearch_all(requests_mock):
alerce = Alerce()
requests_mock.register_uri(
"GET",
alerce.config["CATSHTM_API_URL"] +
alerce.config["CATSHTM_ROUTES"]["conesearch_all"],
json=CONESEARCH_ALL_RESPONSE,
)
r = alerce.catshtm_conesearch(ra=357.73373,
dec=14.20514,
radius=10,
format="pandas",
catalog_name="all")
assert r is not None
from unittest.mock import Mock, patch
from requests import Session
from requests import Response
from pandas import DataFrame
from astropy.table import Table
import sys
import pytest
sys.path.append("..")
from alerce.core import Alerce
from alerce.exceptions import ObjectNotFoundError, FormatValidationError, ParseError
alerce = Alerce()
@patch.object(Session, "request")
def test_query_objects(mock_request):
mock_request.return_value.status_code = 200
r = alerce.query_objects(classifier="late")
assert r is not None
@patch.object(Session, "request")
def test_query_objects_not_found(mock_request):
mock_request.return_value.status_code = 404
with pytest.raises(ObjectNotFoundError):
alerce.query_objects(classifier="late")
@patch.object(Session, "request")
def test_query_objects_parser_error(mock_request):
def test_exception_stamp(mock_fits):
alerce = Alerce()
with pytest.warns(RuntimeWarning):
r = alerce.plot_stamps(oid="ZTF18abjpdlh", candid="570448435315010001")
assert r is None
def test_get_stamp(mock_fits):
alerce = Alerce()
r = alerce.get_stamps(oid="ZTF18abjpdlh", candid="570448435315010000")
assert isinstance(r, HDUList)
def test_plot_stamp():
alerce = Alerce()
with pytest.warns(RuntimeWarning):
r = alerce.plot_stamps(oid="ZTF18abjpdlh", candid="570448435315010000")
assert r is None
class SNParametricForecast(Resource):
"""
Supernova Parametric Model.
"""
client = Alerce()
extractor = SNParametricModelExtractor(bands=[1, 2])
def check_object(self, oid):
try:
object = self.client.query_object(oid, format="pandas")
object = object.iloc[0]
return object
except ObjectNotFoundError:
return abort(404,
'Not Found',
errors='Object ID not found in ALeRCE database')
def mjd_now(self):
now_datetime = datetime.datetime.utcnow()
astro_time = Time(now_datetime)
return astro_time.mjd
def shift_mjd(self, mjd, days=0):
mjd += days
return mjd
def fit_parameters(self, oid):
detections = self.client.query_detections(oid, format="pandas")
detections["oid"] = oid
detections.set_index("oid", inplace=True)
params = self.extractor.compute_features(detections)
return params
def clean_response(self, values):
new_values = []
for value in values:
if np.isnan(value):
new_values.append(value)
else:
new_values.append(value)
return new_values
def get_parameters(self, oid):
try:
features = self.client.query_features(oid, format="pandas")
if len(features) == 0:
raise Exception("Features not found, fitting model")
params = features[features.name.isin(MODEL_PARAMS)]
return True, params
except Exception:
# Fitting model and getting params
params = self.fit_parameters(oid).iloc[0]
# Renaming index and getting fid
fids = [int(i.rsplit("_", 1)[1]) for i in params.index]
params.index = [i.rsplit("_", 1)[0] for i in params.index]
# Transforming to dataframe and resetting index
params = params.to_frame()
params.reset_index(inplace=True)
# Setting new column names
params.columns = ["name", "value"]
# Adding fid
params["fid"] = fids
return False, params
def infer(self, params, mjd):
flux_forecast = model_inference(mjd, params.SPM_A, params.SPM_t0,
params.SPM_gamma, params.SPM_beta,
params.SPM_tau_rise,
params.SPM_tau_fall)
magnitude_forecast = flux_to_mag(flux_forecast)
return magnitude_forecast
@api.marshal_with(parametric_response)
@api.expect(parametric_parser)
def get(self):
args = parametric_parser.parse_args()
object = self.check_object(args.oid)
min_mjd = object["firstmjd"]
if not args.mjd:
forecast_mjd = np.array([self.mjd_now()])
else:
forecast_mjd = np.array(args.mjd)
shifted_mjd = forecast_mjd - min_mjd
features_on_db, parameters = self.get_parameters(args.oid)
message = "Forecast based on modified Villar et al. 2019. analytic model"
message += " (see [https://arxiv.org/abs/1905.07422] and [https://arxiv.org/abs/2008.03311]). "
if features_on_db:
message += "Using precomputed ALeRCE [http://alerce.science] parameters."
else:
message += "On-demand parameters computed in ALeRCE [http://alerce.science] API."
message += " Warning: This forecast was made with few points."
forecasts = []
for fid in parameters.fid.unique():
fid_params = parameters[parameters.fid == fid]
fid_params.set_index("name", inplace=True)
fid_params = fid_params.value
magpsf = self.infer(fid_params, shifted_mjd)
forecasts.append({
'magpsf': self.clean_response(magpsf),
'mjd': forecast_mjd,
'fid': fid,
})
return {'oid': args.oid, "forecast": forecasts, "comment": message}
def test_error(requests_mock):
alerce = Alerce()
requests_mock.register_uri("GET", "mock://test.com", status_code=500)
with pytest.raises(APIError):
alerce._request_catshtm("GET", "mock://test.com")
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._client = Alerce()