From 12788617d846258a6f58d4c178d62f4776a8fca8 Mon Sep 17 00:00:00 2001 From: Anais Moller Date: Tue, 24 Feb 2026 20:32:34 +1100 Subject: [PATCH 1/9] README ztf --- ztf/README.md | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 ztf/README.md diff --git a/ztf/README.md b/ztf/README.md new file mode 100644 index 0000000..6f940bd --- /dev/null +++ b/ztf/README.md @@ -0,0 +1,50 @@ +# Fink broker tutorials + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/astrolabsoftware/fink-notebook-template/blob/main) + +This repository contains materials (notebooks & presentation) to explore the [Fink broker](https://fink-broker.org) alert data. As of November 2024, Fink has processed more than 180 millions alerts from the ZTF public alert stream. Among these, you will find extragalatic sources (supernovae, AGN, ...), galactic sources (many classes of transients incl. variables stars from our galaxy or gravitational microlensing events, ...) and moving objects from our Solar System (asteroids, comets, and made-man objects like space-debris!). Some sources are already confirmed, many are candidates! + +## Materials + +The repository contains a number of notebooks focusing on the use of the Fink REST API. We shortly present different science cases: + +- Extragalactic science: AGN & supernovae ([see notebook](extragalactic/extragalactic.ipynb)) +- Galactic science: variable stars & microlensing ([see notebook](galactic/galactic.ipynb)) +- Solar system science: asteroids, comets & space debris ([see notebook](sso/sso.ipynb)) +- Solar system science: phase curves ([see notebook](sso/fink_sso_imcce.ipynb)) +- Multi-messenger astronomy: searching for kilonovae ([see notebook](MMA/MMA.ipynb)) +- Multi-messenger astronomy: correlating with gravitational waves sky maps ([see notebook](MMA/gravitational_waves.ipynb)) +- Broker interfaces: presentation on the livestream service, the Science Portal and its API, and the Fink TOM module ([see the presentation](interfaces/README.md)) + +These sciences are not exhaustive and we welcome new collaborations to expand them! In addition, there are notebooks focusing on other specific aspects: + +- How to tune the output rate of a Fink filter? Example for the Early SN Ia candidate filter ([see notebook](extragalactic/tuning_snia_output_rate.ipynb)) + +You can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally. Most notebooks will work with any Python verson higher than 3.5, except the one on [microlensing](galactic/galactic.ipynb) which requires Python < 3.12 due to dependencies. We advise to work on a virtual environment: + +``` +python -m venv myenv +source myenv/bin/activate + +pip install -r requirements.txt +# ... wait a bit + +jupyter-notebook +# play with notebooks +``` + +We also provide a Singularity script to work in a contained environment (thanks @bregeon): + +- Build with `singularity build --fakeroot fink.sif Singularity` +- Run with `singularity run fink.sif` +- Open the link in your browser (from the host) + + +## How to contribute + +How to contribute: + +- Clone (or fork) this repo, and open a new branch. +- Create a new folder with a meaningful name (e.g. `supernovae`, `grb`, ...) +- Read and copy an existing notebook to get an idea of the structure of a tutorial. +- Once your notebook is finished, open a Pull Request such that we review the tutorial and merge it! From 34a44e9530cbc347d7883296755f746a8466690d Mon Sep 17 00:00:00 2001 From: Anais Moller Date: Tue, 24 Feb 2026 20:34:52 +1100 Subject: [PATCH 2/9] mv to ztf folder --- {MMA => ztf/MMA}/MMA.ipynb | 0 {MMA => ztf/MMA}/gravitational_waves.ipynb | 0 {events => ztf/events}/2022/README.md | 0 {events => ztf/events}/2022/bayestar.fits.gz | Bin {events => ztf/events}/2022/images/rest_api.png | Bin {events => ztf/events}/2022/mycatalog.csv | 0 {events => ztf/events}/2022/rest_api_how_to.ipynb | 0 {events => ztf/events}/2022/supernova.ipynb | 0 {events => ztf/events}/2022/xmatch.ipynb | 0 {events => ztf/events}/2022/zooniverse_template.py | 0 .../extragalactic}/extragalactic.ipynb | 0 .../extragalactic}/tuning_snia_output_rate.ipynb | 0 {gaia => ztf/gaia}/Fink_Gaia_alerts.ipynb | 0 {gaia => ztf/gaia}/gaia_xmatch.csv | 0 {galactic => ztf/galactic}/galactic.ipynb | 0 {galactic => ztf/galactic}/legacy/Gaia.dat | 0 {galactic => ztf/galactic}/legacy/README.md | 0 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b/ztf/sso/phase_curve_models.ipynb similarity index 100% rename from sso/phase_curve_models.ipynb rename to ztf/sso/phase_curve_models.ipynb diff --git a/sso/riskList.csv b/ztf/sso/riskList.csv similarity index 100% rename from sso/riskList.csv rename to ztf/sso/riskList.csv diff --git a/sso/sso_search.png b/ztf/sso/sso_search.png similarity index 100% rename from sso/sso_search.png rename to ztf/sso/sso_search.png diff --git a/statistics/fink_stats.ipynb b/ztf/statistics/fink_stats.ipynb similarity index 100% rename from statistics/fink_stats.ipynb rename to ztf/statistics/fink_stats.ipynb From e0147340b4b97914a9c33689c246c877e51b2571 Mon Sep 17 00:00:00 2001 From: Anais Moller Date: Tue, 24 Feb 2026 20:41:04 +1100 Subject: [PATCH 3/9] lsst --- lsst/README.md | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 lsst/README.md diff --git a/lsst/README.md b/lsst/README.md new file mode 100644 index 0000000..9a16b1f --- /dev/null +++ b/lsst/README.md @@ -0,0 +1,38 @@ +# Fink broker tutorials + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/astrolabsoftware/fink-notebook-template/blob/main) + +This repository contains materials (notebooks & presentation) to explore the [Fink broker](https://fink-broker.org) alert data from Vera C. Rubin Legacy Survey of Space and Time (LSST). + +## Materials + +The repository contains a number of notebooks focusing on the use of the Fink LSST REST API. + +You can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally. Most notebooks will work with any Python verson higher than 3.5. We advise to work on a virtual environment: + +``` +python -m venv myenv +source myenv/bin/activate + +pip install -r requirements.txt +# ... wait a bit + +jupyter-notebook +# play with notebooks +``` + +We also provide a Singularity script to work in a contained environment (thanks @bregeon): + +- Build with `singularity build --fakeroot fink.sif Singularity` +- Run with `singularity run fink.sif` +- Open the link in your browser (from the host) + + +## How to contribute + +How to contribute: + +- Clone (or fork) this repo, and open a new branch. +- Create a new folder with a meaningful name (e.g. `supernovae`, `grb`, ...) +- Read and copy an existing notebook to get an idea of the structure of a tutorial. +- Once your notebook is finished, open a Pull Request such that we review the tutorial and merge it! From 1909eb373a2ad8221327e4cbd03f784822396c07 Mon Sep 17 00:00:00 2001 From: Anais Moller Date: Tue, 24 Feb 2026 20:43:18 +1100 Subject: [PATCH 4/9] README --- README.md | 17 ++++------------- 1 file changed, 4 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 6f940bd..90f3ebf 100644 --- a/README.md +++ b/README.md @@ -6,21 +6,12 @@ This repository contains materials (notebooks & presentation) to explore the [Fi ## Materials -The repository contains a number of notebooks focusing on the use of the Fink REST API. We shortly present different science cases: +The repository contains a number of notebooks focusing on the use of the Fink REST API for two different surveys: LSST and ZTF. +- LSST: The Vera C. Rubin Legacy Survey of Space and Time. Fink is one of the Fink Community Brokers processing LSST data from 2026. +- ZTF: Zwicky Transient Facility. Fink has been processing data from ZTF since November 2019. -- Extragalactic science: AGN & supernovae ([see notebook](extragalactic/extragalactic.ipynb)) -- Galactic science: variable stars & microlensing ([see notebook](galactic/galactic.ipynb)) -- Solar system science: asteroids, comets & space debris ([see notebook](sso/sso.ipynb)) -- Solar system science: phase curves ([see notebook](sso/fink_sso_imcce.ipynb)) -- Multi-messenger astronomy: searching for kilonovae ([see notebook](MMA/MMA.ipynb)) -- Multi-messenger astronomy: correlating with gravitational waves sky maps ([see notebook](MMA/gravitational_waves.ipynb)) -- Broker interfaces: presentation on the livestream service, the Science Portal and its API, and the Fink TOM module ([see the presentation](interfaces/README.md)) -These sciences are not exhaustive and we welcome new collaborations to expand them! In addition, there are notebooks focusing on other specific aspects: - -- How to tune the output rate of a Fink filter? Example for the Early SN Ia candidate filter ([see notebook](extragalactic/tuning_snia_output_rate.ipynb)) - -You can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally. Most notebooks will work with any Python verson higher than 3.5, except the one on [microlensing](galactic/galactic.ipynb) which requires Python < 3.12 due to dependencies. We advise to work on a virtual environment: +You can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally. Most notebooks will work with any Python verson higher than 3.5, except the one on [ZTF microlensing](ztf/galactic/galactic.ipynb) which requires Python < 3.12 due to dependencies. We advise to work on a virtual environment: ``` python -m venv myenv From 3abc0f4faae822ce79640f549c1bf43e78367ca6 Mon Sep 17 00:00:00 2001 From: Anais Moller Date: Tue, 24 Feb 2026 20:44:25 +1100 Subject: [PATCH 5/9] deprecated events --- ztf/{events => deprecated_events}/2022/README.md | 0 .../2022/bayestar.fits.gz | Bin .../2022/images/rest_api.png | Bin .../2022/mycatalog.csv | 0 .../2022/rest_api_how_to.ipynb | 0 .../2022/supernova.ipynb | 0 ztf/{events => deprecated_events}/2022/xmatch.ipynb | 0 .../2022/zooniverse_template.py | 0 8 files changed, 0 insertions(+), 0 deletions(-) rename ztf/{events => deprecated_events}/2022/README.md (100%) rename ztf/{events => deprecated_events}/2022/bayestar.fits.gz (100%) rename ztf/{events => deprecated_events}/2022/images/rest_api.png (100%) rename ztf/{events => deprecated_events}/2022/mycatalog.csv (100%) rename ztf/{events => deprecated_events}/2022/rest_api_how_to.ipynb (100%) rename ztf/{events => deprecated_events}/2022/supernova.ipynb (100%) rename ztf/{events => deprecated_events}/2022/xmatch.ipynb (100%) rename ztf/{events => deprecated_events}/2022/zooniverse_template.py (100%) diff --git a/ztf/events/2022/README.md b/ztf/deprecated_events/2022/README.md similarity index 100% rename from ztf/events/2022/README.md rename to ztf/deprecated_events/2022/README.md diff --git a/ztf/events/2022/bayestar.fits.gz b/ztf/deprecated_events/2022/bayestar.fits.gz similarity index 100% rename from ztf/events/2022/bayestar.fits.gz rename to ztf/deprecated_events/2022/bayestar.fits.gz diff --git a/ztf/events/2022/images/rest_api.png b/ztf/deprecated_events/2022/images/rest_api.png similarity index 100% rename from ztf/events/2022/images/rest_api.png rename to ztf/deprecated_events/2022/images/rest_api.png diff --git a/ztf/events/2022/mycatalog.csv b/ztf/deprecated_events/2022/mycatalog.csv similarity index 100% rename from ztf/events/2022/mycatalog.csv rename to ztf/deprecated_events/2022/mycatalog.csv diff --git a/ztf/events/2022/rest_api_how_to.ipynb b/ztf/deprecated_events/2022/rest_api_how_to.ipynb similarity index 100% rename from ztf/events/2022/rest_api_how_to.ipynb rename to ztf/deprecated_events/2022/rest_api_how_to.ipynb diff --git a/ztf/events/2022/supernova.ipynb b/ztf/deprecated_events/2022/supernova.ipynb similarity index 100% rename from ztf/events/2022/supernova.ipynb rename to ztf/deprecated_events/2022/supernova.ipynb diff --git a/ztf/events/2022/xmatch.ipynb b/ztf/deprecated_events/2022/xmatch.ipynb similarity index 100% rename from ztf/events/2022/xmatch.ipynb rename to ztf/deprecated_events/2022/xmatch.ipynb diff --git a/ztf/events/2022/zooniverse_template.py b/ztf/deprecated_events/2022/zooniverse_template.py similarity index 100% rename from ztf/events/2022/zooniverse_template.py rename to ztf/deprecated_events/2022/zooniverse_template.py From 82f458bbb66dca41d3e7abc55d42b984186739b0 Mon Sep 17 00:00:00 2001 From: emilleishida Date: Tue, 24 Feb 2026 18:23:13 +0100 Subject: [PATCH 6/9] add conesearch notebool --- lsst/cone_search/Fink_lsst_conesearch.ipynb | 557 ++++++++++++++++++++ lsst/cone_search/cone_search_example.csv | 26 + 2 files changed, 583 insertions(+) create mode 100644 lsst/cone_search/Fink_lsst_conesearch.ipynb create mode 100644 lsst/cone_search/cone_search_example.csv diff --git a/lsst/cone_search/Fink_lsst_conesearch.ipynb b/lsst/cone_search/Fink_lsst_conesearch.ipynb new file mode 100644 index 0000000..f570f8f --- /dev/null +++ b/lsst/cone_search/Fink_lsst_conesearch.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "7f6cee5c-1bf8-4562-a661-a4b259c82838", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Fink service demonstration: Cone Search" + ] + }, + { + "cell_type": "markdown", + "id": "c6c81b13-f8ad-4f6c-a84b-7cad87f3d1e7", + "metadata": {}, + "source": [ + "### Goal\n", + "\n", + "The goal of this notebook is to demonstrate how to use Fink to match a local list of sources to LSST alerts.\n", + "\n", + "For Rubin, Fink API is updated in real time, even during observation periods in Chile. \n", + "Thus, it is possible to use the examples shown for quick decision making and inspection. " + ] + }, + { + "cell_type": "markdown", + "id": "646fbd81-1db3-496f-9c84-f97f81d7cb6f", + "metadata": {}, + "source": [ + "### Environment set-up\n", + "\n", + "To run this notebook, you need to import the following libraries (already installed in colab):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5ba9641-bc16-436b-9bd4-e860d97a1459", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import io\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set_context('talk')\n", + "\n", + "APIURL = 'https://wert038nx.fink-portal.org/'" + ] + }, + { + "cell_type": "markdown", + "id": "2edc7b4e-ba0c-4da9-87ce-7aa15883c152", + "metadata": {}, + "source": [ + "### Case 1: cone search around one position and error" + ] + }, + { + "cell_type": "markdown", + "id": "7bb9072d-ac3e-4687-b463-64579bfa86e0", + "metadata": {}, + "source": [ + "First set is to identify all the `objects` within your circle of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "232753db-9994-46c4-af79-956279f5add2", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the diaObjectId for the alert(s) within a circle on the sky\n", + "r0 = requests.post(\n", + " '{}/api/v1/conesearch'.format(APIURL),\n", + " \n", + " json={\n", + " \"ra\": \"61.9648\",\n", + " \"dec\": \"-48.713\",\n", + " \"radius\": \"10\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c52bf34c-2fef-46f2-8451-6bbf724f93b9", + "metadata": {}, + "source": [ + "The query above will return the last alert per objectId found within your search area.\n", + "\n", + "The second step is to extract the `objectId` themselves and subsequently, the photometric data." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "af7b9a56-578d-44c3-99d4-0165e4a91101", + "metadata": {}, + "outputs": [], + "source": [ + "# extract the list of objectIds\n", + "mylist = [val[\"r:diaObjectId\"] for val in r0.json()]" + ] + }, + { + "cell_type": "markdown", + "id": "c3f92f7a-28af-4f53-ba9f-cf98da1ce637", + "metadata": {}, + "source": [ + "You can directly inspect these objects in the portal" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "56fcf162-d083-48c3-8954-33d18f1ddaf4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://lsst.fink-portal.org/313761043604045880\n", + "https://lsst.fink-portal.org/170028498820792325\n" + ] + } + ], + "source": [ + "for name in mylist:\n", + " print('https://lsst.fink-portal.org/{}'.format(name))" + ] + }, + { + "cell_type": "markdown", + "id": "88d40bd5-ffbe-413e-bb3d-b1ca71b5364f", + "metadata": {}, + "source": [ + "In order to get full light curves for these two objects, you should query the `sources` endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "365320d0-1193-44e4-bcbc-d7e639656a29", + "metadata": {}, + "outputs": [], + "source": [ + "# get full lightcurves for all these alerts\n", + "r1 = requests.post(\n", + " '{}/api/v1/sources'.format(APIURL),\n", + "\n", + " json={\n", + " \"diaObjectId\": \", \".join(map(str, mylist)),\n", + " \n", + " # choose which columns you want to transfer: https://lsst.fink-portal.org/schemas\n", + " \"columns\": \"r:diaObjectId,r:midpointMjdTai,r:psfFlux,r:psfFluxErr,r:band\",\n", + " \"output-format\": \"json\"\n", + " }\n", + ")\n", + "\n", + "# Format output in a DataFrame\n", + "pdf = pd.read_json(io.BytesIO(r1.content))" + ] + }, + { + "cell_type": "markdown", + "id": "521837a5-80ca-47ff-8cb4-82a52e3257e8", + "metadata": {}, + "source": [ + "You can now plot the resulting light curves for any of the given objectIds" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "f79f4db7-6306-4318-9024-98571b6129ff", + "metadata": {}, + "outputs": [], + "source": [ + "# get only data for the first object in the list\n", + "mask = pdf['r:diaObjectId'] == mylist[0]\n", + "obj_lc = pdf[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "e53f4783-c782-4d31-bcdc-56292b8af984", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# all lsst bands\n", + "all_bands = ['u', 'g', 'r', 'i', 'z', 'y']\n", + "symbols = ['o', '<', '>', 's', '*', 'p']\n", + "\n", + "# get unique filters\n", + "lc_bands = np.unique(obj_lc['r:band'])\n", + "\n", + "plt.figure(figsize=(16,10))\n", + "\n", + "for i in range(len(all_bands)):\n", + " if all_bands[i] in lc_bands:\n", + " flag = obj_lc['r:band'] == all_bands[i] # identify one filter at a time\n", + " \n", + " mjd = obj_lc['r:midpointMjdTai'][flag] # get observation days (MJD)\n", + " flux = obj_lc['r:psfFlux'][flag] # get difference flux\n", + " fluxerr = obj_lc['r:psfFluxErr'][flag] # get difference flux error\n", + "\n", + " plt.errorbar(mjd, flux, yerr=fluxerr, fmt=symbols[i], label=all_bands[i])\n", + "\n", + "plt.xlabel('MJD')\n", + "plt.ylabel('difference flux (milijansky)')\n", + "plt.legend(loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "45985478-08cb-4970-b9f7-3b7ed29afed4", + "metadata": {}, + "source": [ + "### Case 2: cone search for a list of positions" + ] + }, + { + "cell_type": "markdown", + "id": "a8404a7e-0458-4e61-8138-ac910a82bf86", + "metadata": {}, + "source": [ + "In order to cross-match with an entire catalog, you can loop over your sources" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "ab00345e-3328-423d-a316-787ca0703f15", + "metadata": {}, + "outputs": [], + "source": [ + "# read your catalog\n", + "my_cat = pd.read_csv('cone_search_example.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "6ae204a2-7c31-4102-be6a-c0db03b8c095", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "string indices must be integers, not 'str'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[75]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 1\u001b[39m store_objs = []\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m my_cat:\n\u001b[32m 5\u001b[39m r = requests.post(\n\u001b[32m 6\u001b[39m \u001b[33m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m/api/v1/conesearch\u001b[39m\u001b[33m'\u001b[39m.format(APIURL),\n\u001b[32m 7\u001b[39m \n\u001b[32m 8\u001b[39m json={\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mra\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mstr\u001b[39m(\u001b[43mitem\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mra\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m),\n\u001b[32m 10\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mdec\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mstr\u001b[39m(item[\u001b[33m'\u001b[39m\u001b[33mdec\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 11\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mradius\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m1.5\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 12\u001b[39m }\n\u001b[32m 13\u001b[39m )\n\u001b[32m 15\u001b[39m pdf = pd.read_json(io.BytesIO(r.content))\n\u001b[32m 16\u001b[39m store_objs.append(pdf)\n", + "\u001b[31mTypeError\u001b[39m: string indices must be integers, not 'str'" + ] + } + ], + "source": [ + "store_objs = []\n", + "\n", + "for item in my_cat:\n", + " \n", + " r = requests.post(\n", + " '{}/api/v1/conesearch'.format(APIURL),\n", + " \n", + " json={\n", + " \"ra\": str(item['ra']),\n", + " \"dec\": str(item['dec']),\n", + " \"radius\": \"1.5\"\n", + " }\n", + " )\n", + "\n", + " pdf = pd.read_json(io.BytesIO(r.content))\n", + " store_objs.append(pdf)\n", + "\n", + "data_all = pd.concat(store_objs, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "ec1e24a0-c133-4450-bed6-c4267da2103c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idradec
0SN 2025hlf185.9885278.763203
1SN 2025mbp151.5925821.158333
2SN 2025hlf185.9885238.763211
3SN 2022and150.0277203.756888
4TDE 2018hyz151.7119921.692788
5SN 2025mbp151.5925801.158335
6SN 2025hlf185.9885228.763210
7SN 2025mbp151.5925791.158329
8TDE 2018hyz151.7119911.692776
9SN 2025mbp151.5925791.158323
10SN 2022and150.0272793.757105
11SN 2025mbp151.5925831.158336
12SN 2025mbp151.5925821.158331
13SN 2025mbp151.5925851.158326
14SN 2025hlf185.9885248.763210
15SN 2025hlf185.9885238.763206
16SN 2025hlf185.9885268.763201
17SN 2022and150.0273663.757268
18SN 2025hlf185.9885268.763208
19SN 2022and150.0271823.757157
20SN 2025mbp151.5925891.158323
21TDE 2018hyz151.7119051.692987
22SN 2025mbp151.5925801.158335
23SN 2025mbp151.5925821.158332
24SN 2025hlf185.9885238.763208
\n", + "
" + ], + "text/plain": [ + " id ra dec\n", + "0 SN 2025hlf 185.988527 8.763203\n", + "1 SN 2025mbp 151.592582 1.158333\n", + "2 SN 2025hlf 185.988523 8.763211\n", + "3 SN 2022and 150.027720 3.756888\n", + "4 TDE 2018hyz 151.711992 1.692788\n", + "5 SN 2025mbp 151.592580 1.158335\n", + "6 SN 2025hlf 185.988522 8.763210\n", + "7 SN 2025mbp 151.592579 1.158329\n", + "8 TDE 2018hyz 151.711991 1.692776\n", + "9 SN 2025mbp 151.592579 1.158323\n", + "10 SN 2022and 150.027279 3.757105\n", + "11 SN 2025mbp 151.592583 1.158336\n", + "12 SN 2025mbp 151.592582 1.158331\n", + "13 SN 2025mbp 151.592585 1.158326\n", + "14 SN 2025hlf 185.988524 8.763210\n", + "15 SN 2025hlf 185.988523 8.763206\n", + "16 SN 2025hlf 185.988526 8.763201\n", + "17 SN 2022and 150.027366 3.757268\n", + "18 SN 2025hlf 185.988526 8.763208\n", + "19 SN 2022and 150.027182 3.757157\n", + "20 SN 2025mbp 151.592589 1.158323\n", + "21 TDE 2018hyz 151.711905 1.692987\n", + "22 SN 2025mbp 151.592580 1.158335\n", + "23 SN 2025mbp 151.592582 1.158332\n", + "24 SN 2025hlf 185.988523 8.763208" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "['']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8aa8ce8f-ad0c-4183-a80c-29eb9cd6cfc4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lsst/cone_search/cone_search_example.csv b/lsst/cone_search/cone_search_example.csv new file mode 100644 index 0000000..22ae7c6 --- /dev/null +++ b/lsst/cone_search/cone_search_example.csv @@ -0,0 +1,26 @@ +id,ra,dec +SN 2025hlf,185.9885269576657,8.763203076474348 +SN 2025mbp,151.59258155968166,1.1583334010976534 +SN 2025hlf,185.98852305065634,8.763210957025105 +SN 2022and,150.02772046270303,3.7568883682336343 +TDE 2018hyz,151.71199219490367,1.6927875596017365 +SN 2025mbp,151.59257968790465,1.1583349887200571 +SN 2025hlf,185.9885222759368,8.763210159176577 +SN 2025mbp,151.5925787772887,1.158328777812482 +TDE 2018hyz,151.71199074831256,1.6927760569545407 +SN 2025mbp,151.59257891450594,1.1583230163050986 +SN 2022and,150.02727924147476,3.757105282227618 +SN 2025mbp,151.59258269417936,1.158336006078109 +SN 2025mbp,151.59258210001013,1.1583314546095491 +SN 2025mbp,151.59258461644018,1.158326331969271 +SN 2025hlf,185.9885238018117,8.763209705258399 +SN 2025hlf,185.98852345314916,8.763206377352834 +SN 2025hlf,185.9885257866466,8.763201263017224 +SN 2022and,150.02736558904886,3.757268112550088 +SN 2025hlf,185.9885261861473,8.76320846794194 +SN 2022and,150.02718191139454,3.7571565483459395 +SN 2025mbp,151.592588557289,1.1583225689171535 +TDE 2018hyz,151.7119053572508,1.6929866612208528 +SN 2025mbp,151.59257951482942,1.1583349512890457 +SN 2025mbp,151.5925823998322,1.1583323434104336 +SN 2025hlf,185.98852255870577,8.76320814912579 From f43b43f98aa1e5d6fca7ad64893cf9b344f4ef78 Mon Sep 17 00:00:00 2001 From: emilleishida Date: Tue, 24 Feb 2026 18:43:27 +0100 Subject: [PATCH 7/9] fix api path --- lsst/cone_search/Fink_lsst_conesearch.ipynb | 349 +++++++------------- 1 file changed, 113 insertions(+), 236 deletions(-) diff --git a/lsst/cone_search/Fink_lsst_conesearch.ipynb b/lsst/cone_search/Fink_lsst_conesearch.ipynb index f570f8f..ef0f5ab 100644 --- a/lsst/cone_search/Fink_lsst_conesearch.ipynb +++ b/lsst/cone_search/Fink_lsst_conesearch.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "e5ba9641-bc16-436b-9bd4-e860d97a1459", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ "import seaborn as sns\n", "sns.set_context('talk')\n", "\n", - "APIURL = 'https://wert038nx.fink-portal.org/'" + "APIURL = 'https://api.lsst.fink-portal.org'" ] }, { @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "id": "232753db-9994-46c4-af79-956279f5add2", "metadata": {}, "outputs": [], @@ -84,7 +84,7 @@ " json={\n", " \"ra\": \"61.9648\",\n", " \"dec\": \"-48.713\",\n", - " \"radius\": \"10\"\n", + " \"radius\": \"10\" # radius should be in arcsec\n", " }\n", ")" ] @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 3, "id": "af7b9a56-578d-44c3-99d4-0165e4a91101", "metadata": {}, "outputs": [], @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 5, "id": "56fcf162-d083-48c3-8954-33d18f1ddaf4", "metadata": {}, "outputs": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 6, "id": "365320d0-1193-44e4-bcbc-d7e639656a29", "metadata": {}, "outputs": [], @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 7, "id": "f79f4db7-6306-4318-9024-98571b6129ff", "metadata": {}, "outputs": [], @@ -192,13 +192,13 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 8, "id": "e53f4783-c782-4d31-bcdc-56292b8af984", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 9, "id": "ab00345e-3328-423d-a316-787ca0703f15", "metadata": {}, "outputs": [], @@ -262,34 +262,45 @@ }, { "cell_type": "code", - "execution_count": 75, - "id": "6ae204a2-7c31-4102-be6a-c0db03b8c095", + "execution_count": 15, + "id": "68a9c34b-523d-4941-8bf8-411794c637e6", "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "string indices must be integers, not 'str'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[75]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 1\u001b[39m store_objs = []\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m my_cat:\n\u001b[32m 5\u001b[39m r = requests.post(\n\u001b[32m 6\u001b[39m \u001b[33m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m/api/v1/conesearch\u001b[39m\u001b[33m'\u001b[39m.format(APIURL),\n\u001b[32m 7\u001b[39m \n\u001b[32m 8\u001b[39m json={\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mra\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mstr\u001b[39m(\u001b[43mitem\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mra\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m),\n\u001b[32m 10\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mdec\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mstr\u001b[39m(item[\u001b[33m'\u001b[39m\u001b[33mdec\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 11\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mradius\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m1.5\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 12\u001b[39m }\n\u001b[32m 13\u001b[39m )\n\u001b[32m 15\u001b[39m pdf = pd.read_json(io.BytesIO(r.content))\n\u001b[32m 16\u001b[39m store_objs.append(pdf)\n", - "\u001b[31mTypeError\u001b[39m: string indices must be integers, not 'str'" - ] + "data": { + "text/plain": [ + "(25, 3)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" } ], + "source": [ + "# check the size of your catalog\n", + "my_cat.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ae204a2-7c31-4102-be6a-c0db03b8c095", + "metadata": {}, + "outputs": [], "source": [ "store_objs = []\n", "\n", - "for item in my_cat:\n", + "# launch a sequence of queries\n", + "for i in range(my_cat.shape[0]):\n", " \n", " r = requests.post(\n", " '{}/api/v1/conesearch'.format(APIURL),\n", " \n", " json={\n", - " \"ra\": str(item['ra']),\n", - " \"dec\": str(item['dec']),\n", - " \"radius\": \"1.5\"\n", + " \"ra\": str(my_cat['ra'][i]),\n", + " \"dec\": str(my_cat['dec'][i]),\n", + " \"radius\": \"1.5\" # radius should be in arcsec\n", " }\n", " )\n", "\n", @@ -301,233 +312,99 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "ec1e24a0-c133-4450-bed6-c4267da2103c", + "execution_count": 24, + "id": "9f90bf87-4b4b-470b-bbb6-36f273c2cd97", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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idradec
0SN 2025hlf185.9885278.763203
1SN 2025mbp151.5925821.158333
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3SN 2022and150.0277203.756888
4TDE 2018hyz151.7119921.692788
5SN 2025mbp151.5925801.158335
6SN 2025hlf185.9885228.763210
7SN 2025mbp151.5925791.158329
8TDE 2018hyz151.7119911.692776
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19SN 2022and150.0271823.757157
20SN 2025mbp151.5925891.158323
21TDE 2018hyz151.7119051.692987
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" - ], "text/plain": [ - " id ra dec\n", - "0 SN 2025hlf 185.988527 8.763203\n", - "1 SN 2025mbp 151.592582 1.158333\n", - "2 SN 2025hlf 185.988523 8.763211\n", - "3 SN 2022and 150.027720 3.756888\n", - "4 TDE 2018hyz 151.711992 1.692788\n", - "5 SN 2025mbp 151.592580 1.158335\n", - "6 SN 2025hlf 185.988522 8.763210\n", - "7 SN 2025mbp 151.592579 1.158329\n", - "8 TDE 2018hyz 151.711991 1.692776\n", - "9 SN 2025mbp 151.592579 1.158323\n", - "10 SN 2022and 150.027279 3.757105\n", - "11 SN 2025mbp 151.592583 1.158336\n", - "12 SN 2025mbp 151.592582 1.158331\n", - "13 SN 2025mbp 151.592585 1.158326\n", - "14 SN 2025hlf 185.988524 8.763210\n", - "15 SN 2025hlf 185.988523 8.763206\n", - "16 SN 2025hlf 185.988526 8.763201\n", - "17 SN 2022and 150.027366 3.757268\n", - "18 SN 2025hlf 185.988526 8.763208\n", - "19 SN 2022and 150.027182 3.757157\n", - "20 SN 2025mbp 151.592589 1.158323\n", - "21 TDE 2018hyz 151.711905 1.692987\n", - "22 SN 2025mbp 151.592580 1.158335\n", - "23 SN 2025mbp 151.592582 1.158332\n", - "24 SN 2025hlf 185.988523 8.763208" + "(41, 186)" ] }, - "execution_count": 77, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "['']" + "#check how many objects were found\n", + "data_all.shape" + ] + }, + { + "cell_type": "markdown", + "id": "b7c64d2d-1c42-4a41-bb7e-01e2eddce30d", + "metadata": {}, + "source": [ + "Note that each one of the searches identifies the last alert of each object, but the same alert can be associated to more than one source in your catalog.\n", + "\n", + "In this example, we found 41 alerts, but the number of objects is different." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "9187d8c3-e103-4743-8b6a-a641f5453ab8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(11,)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obj_ids = np.unique(data_all['r:diaObjectId'])\n", + "obj_ids.shape" + ] + }, + { + "cell_type": "markdown", + "id": "0a4aa8f4-1d90-4072-807c-c8e1506f3b8b", + "metadata": {}, + "source": [ + "You can now extract the `objectId` for every source which got cross-matched and check them at the Fink portal" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ec1e24a0-c133-4450-bed6-c4267da2103c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://lsst.fink-portal.org/170028510695916335\n", + "https://lsst.fink-portal.org/170028510831181832\n", + "https://lsst.fink-portal.org/170028521943466017\n", + "https://lsst.fink-portal.org/170028527544959074\n", + "https://lsst.fink-portal.org/170032908006326285\n", + "https://lsst.fink-portal.org/313862203201028173\n", + "https://lsst.fink-portal.org/313871014369951753\n", + "https://lsst.fink-portal.org/313875416991924268\n", + "https://lsst.fink-portal.org/313998569677259171\n", + "https://lsst.fink-portal.org/314007379158499424\n", + "https://lsst.fink-portal.org/314007379963805782\n" + ] + } + ], + "source": [ + "for name in obj_ids:\n", + " print('https://lsst.fink-portal.org/{}'.format(name))" ] }, { "cell_type": "code", "execution_count": null, - "id": "8aa8ce8f-ad0c-4183-a80c-29eb9cd6cfc4", + "id": "c6bc3eeb-9530-46b0-aa55-8ae7f6545369", "metadata": {}, "outputs": [], "source": [] From 238f809f54d2de8be785fe888f34315ec612b015 Mon Sep 17 00:00:00 2001 From: emilleishida Date: Tue, 24 Feb 2026 18:49:59 +0100 Subject: [PATCH 8/9] add warning on catalog size --- lsst/cone_search/Fink_lsst_conesearch.ipynb | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/lsst/cone_search/Fink_lsst_conesearch.ipynb b/lsst/cone_search/Fink_lsst_conesearch.ipynb index ef0f5ab..7334a15 100644 --- a/lsst/cone_search/Fink_lsst_conesearch.ipynb +++ b/lsst/cone_search/Fink_lsst_conesearch.ipynb @@ -401,10 +401,20 @@ " print('https://lsst.fink-portal.org/{}'.format(name))" ] }, + { + "cell_type": "markdown", + "id": "c6bc3eeb-9530-46b0-aa55-8ae7f6545369", + "metadata": {}, + "source": [ + "> **⚠️ Warning**\n", + "> This service should not be used for catalogs containing more than a few thousand entries\n", + "> The xmatch service for larger samples will be made available shortly" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "c6bc3eeb-9530-46b0-aa55-8ae7f6545369", + "id": "4f194fa5-c43c-4828-8876-dfb870b1eeba", "metadata": {}, "outputs": [], "source": [] From 116df531b7b6f9c8adf42f84785a829046bbd9ab Mon Sep 17 00:00:00 2001 From: emilleishida Date: Tue, 24 Feb 2026 19:00:14 +0100 Subject: [PATCH 9/9] fix path to logo --- lsst/cone_search/Fink_lsst_conesearch.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lsst/cone_search/Fink_lsst_conesearch.ipynb b/lsst/cone_search/Fink_lsst_conesearch.ipynb index 7334a15..2bb61f5 100644 --- a/lsst/cone_search/Fink_lsst_conesearch.ipynb +++ b/lsst/cone_search/Fink_lsst_conesearch.ipynb @@ -6,7 +6,7 @@ "id": "7f6cee5c-1bf8-4562-a661-a4b259c82838", "metadata": {}, "source": [ - "\n", + "\n", "\n", "# Fink service demonstration: Cone Search" ]