Forced photometry

List of arguments

The list of arguments for accessing forced photometry can be found at https://api.lsst.fink-portal.org . The schema of the returned payload can be found on the schema page and you can also retrieve it programmatically.

"Forced" photometry means a measurement made at a fixed coordinate in an image, regardless of whether an above-threshold region was detected there in that particular image. For all objects, starting from the second detection, forced photometry measurements for previous detections are included.

Data access

Alerts emitted by the same astronomical object share the same diaObjectId identifier. This identifier is a long integer (64-bit integer). The main table for forced photometry measurements is indexed against this identifier, and you can efficiently query all measurement from the same diaObjectId:

Pythoncurlwget

import io
import requests
import pandas as pd

# get forced data for 313761043604045880
r = requests.post(
  "https://api.lsst.fink-portal.org/api/v1/fp",
  json={
    "diaObjectId": "313761043604045880",
    "columns": "r:diaSourceId,r:midpointMjdTai,r:psfFlux,r:psfFluxErr",
    "output-format": "json"
  }
)

# Format output in a DataFrame
if r.status_code == 200:
  pdf = pd.read_json(io.BytesIO(r.content))

# Get forced data for 313761043604045880 and save it in a CSV file
curl -H "Content-Type: application/json" -X POST \
    -d '{"diaObjectId":"313761043604045880", "output-format":"csv"}' \
    https://api.lsst.fink-portal.org/api/v1/fp -o fp_169830579938263176.csv

# you can also specify parameters in the URL, e.g. with wget:
wget "https://api.lsst.fink-portal.org/api/v1/fp?diaObjectId=313761043604045880&output-format=json" -O fp_313761043604045880.json

You can also retrieve the data for several objects at once:

Python

import io
import requests
import pandas as pd

# ID as string
mylist = ["313761043604045880", "313699514821640259"]

# get forced data for many objects
r = requests.post(
  "https://api.lsst.fink-portal.org/api/v1/fp",
  json={
    "diaObjectId": ",".join(mylist),
    "columns": "r:diaSourceId,r:midpointMjdTai,r:psfFlux,r:psfFluxErr",
    "output-format": "json"
  }
)

# Format output in a DataFrame
pdf = pd.read_json(io.BytesIO(r.content))

Do not abuse!

Although the REST API gives you access to hundreds of millions of alerts without account, it is not designed to massively download data. If you have hundreds of objects to query, you probably want to select only a small subset of columns, or you can use the Data Transfer service.

You can also choose different output format:

jsoncsvParquetvotable

import io
import requests
import pandas as pd

# get forced data for 313761043604045880
r = requests.post(
  "https://api.lsst.fink-portal.org/api/v1/fp",
  json={
    "diaObjectId": "313761043604045880",
    "columns": "r:diaSourceId,r:midpointMjdTai,r:psfFlux,r:psfFluxErr",
    "output-format": "json"
  }
)

# Format output in a DataFrame
pdf = pd.read_json(io.BytesIO(r.content))

import io
import requests
import pandas as pd

# get forced data for 313761043604045880
r = requests.post(
  "https://api.lsst.fink-portal.org/api/v1/fp",
  json={
    "diaObjectId": "313761043604045880",
    "columns": "r:diaSourceId,r:midpointMjdTai,r:psfFlux,r:psfFluxErr",
    "output-format": "csv"
  }
)

# Format output in a DataFrame
pd.read_csv(io.BytesIO(r.content))

import io
import requests
import pandas as pd

# get forced data for 313761043604045880
r = requests.post(
  "https://api.lsst.fink-portal.org/api/v1/fp",
  json={
    "diaObjectId": "313761043604045880",
    "columns": "r:diaSourceId,r:midpointMjdTai,r:psfFlux,r:psfFluxErr",
    "output-format": "parquet"
  }
)

# Format output in a DataFrame
pdf = pd.read_parquet(io.BytesIO(r.content))

import io
import requests
from astropy.io import votable

# get forced data for 313761043604045880
r = requests.post(
  "https://api.lsst.fink-portal.org/api/v1/fp",
  json={
    "diaObjectId": "313761043604045880",
    "columns": "r:diaSourceId,r:midpointMjdTai,r:psfFlux,r:psfFluxErr",
    "output-format": "votable"
  }
)

# VO table
vt = votable.parse(io.BytesIO(r.content))