Create a hostless transient detection pipeline for ZTF

broker/cloud_run/ztf/cross_match/Dockerfile

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+# Use the official lightweight Python image.
+# https://hub.docker.com/_/python
+FROM python:3.12-slim
+
+# Allow statements and log messages to immediately appear in the Knative logs
+ENV PYTHONUNBUFFERED True
+
+# Copy local code to the container image.
+ENV APP_HOME /app
+WORKDIR $APP_HOME
+COPY . ./
+
+# Install production dependencies.
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Run the web service on container startup. Here we use the gunicorn
+# webserver, with one worker process and 8 threads.
+# For environments with multiple CPU cores, increase the number of workers
+# to be equal to the cores available.
+# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling.
+CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 0 main:app

broker/cloud_run/ztf/cross_match/cloudbuild.yaml

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+# https://cloud.google.com/build/docs/deploying-builds/deploy-cloud-run
+# containerize the module and deploy it to Cloud Run
+steps:
+# Build the image
+- name: 'gcr.io/cloud-builders/docker'
+  args: ['build', '-t', '${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPOSITORY}/${_MODULE_IMAGE_NAME}', '.']
+# Push the image to Artifact Registry
+- name: 'gcr.io/cloud-builders/docker'
+  args: ['push', '${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPOSITORY}/${_MODULE_IMAGE_NAME}']
+# Deploy image to Cloud Run
+- name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
+  entrypoint: gcloud
+  args: ['run', 'deploy', '${_MODULE_NAME}', '--image', '${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPOSITORY}/${_MODULE_IMAGE_NAME}', '--region', '${_REGION}', '--set-env-vars', '${_ENV_VARS}']
+images:
+- '${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPOSITORY}/${_MODULE_IMAGE_NAME}'
+substitutions:
+    _SURVEY: 'ztf'
+    _TESTID: 'testid'
+    _MODULE_NAME: '${_SURVEY}-euclid-crossmatch-${_TESTID}'
+    _MODULE_IMAGE_NAME: 'gcr.io/${PROJECT_ID}/${_REPOSITORY}/${_MODULE_NAME}'
+    _REPOSITORY: 'cloud-run-services'
+    # cloud functions automatically sets the projectid env var using the name "GCP_PROJECT"
+    # use the same name here for consistency
+    # [TODO] PROJECT_ID is set in setup.sh. this is confusing and we should revisit the decision.
+    # i (Raen) think i didn't make it a substitution because i didn't want to set a default for it.
+    _ENV_VARS: 'GCP_PROJECT=${PROJECT_ID},SURVEY=${_SURVEY},TESTID=${_TESTID}'
+    _REGION: 'us-central1'
+options:
+    dynamic_substitutions: true

broker/cloud_run/ztf/cross_match/deploy.sh

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+#! /bin/bash
+# Deploys or deletes broker Cloud Run service
+# This script will not delete Cloud Run services that are in production
+
+# "False" uses production resources
+# any other string will be appended to the names of all resources
+testid="${1:-test}"
+# "True" tearsdown/deletes resources, else setup
+teardown="${2:-False}"
+# name of the survey this broker instance will ingest
+survey="${3:-ztf}"
+region="${4:-us-central1}"
+# get the environment variable
+PROJECT_ID=$GOOGLE_CLOUD_PROJECT
+
+MODULE_NAME="euclid-crossmatch"  # lower case required by cloud run
+ROUTE_RUN="/"  # url route that will trigger main.run()
+
+define_GCP_resources() {
+    local base_name="$1"
+    local separator="${2:--}"
+    local testid_suffix=""
+
+    if [ "$testid" != "False" ] && [ -n "$testid" ]; then
+        testid_suffix="${separator}${testid}"
+    fi
+    echo "${base_name}${testid_suffix}"
+}
+
+#--- GCP resources used in this script
+artifact_registry_repo=$(define_GCP_resources "${survey}-cloud-run-services")
+bq_dataset=$(define_GCP_resources "${survey}" "_")
+bq_table="euclid_crossmatch"
+cr_module_name=$(define_GCP_resources "${survey}-${MODULE_NAME}")  # lower case required by cloud run
+ps_input_subscrip=$(define_GCP_resources "${survey}-${MODULE_NAME}") # pub/sub subscription used to trigger cloud run module
+ps_output_topic=$(define_GCP_resources "${survey}-${MODULE_NAME}")
+ps_trigger_topic=$(define_GCP_resources "${survey}-hostless-transients")
+runinvoker_svcact="cloud-run-invoker@${PROJECT_ID}.iam.gserviceaccount.com"
+# topics and subscriptions involved in writing data to BigQuery
+ps_bigquery_subscription=$(define_GCP_resources "${survey}-${MODULE_NAME}-bigquery-import")
+ps_deadletter_topic=$(define_GCP_resources "${survey}-deadletter")
+
+if [ "${teardown}" = "True" ]; then
+    # ensure that we do not teardown production resources
+    if [ "${testid}" != "False" ]; then
+        echo
+        echo "Deleting resources for ${MODULE_NAME} module..."
+        gcloud pubsub topics delete "${ps_output_topic}"
+        gcloud pubsub subscriptions delete "${ps_bigquery_subscription}"
+        gcloud pubsub subscriptions delete "${ps_input_subscrip}"
+        gcloud run services delete "${cr_module_name}" --region "${region}"
+    fi
+else
+    echo "Configuring Pub/Sub resources..."
+    gcloud pubsub topics create "${ps_output_topic}"
+    gcloud pubsub subscriptions create "${ps_bigquery_subscription}" \
+        --topic="${ps_output_topic}" \
+        --bigquery-table="${PROJECT_ID}:${bq_dataset}.${bq_table}" \
+        --use-table-schema \
+        --drop-unknown-fields \
+        --dead-letter-topic="${ps_deadletter_topic}" \
+        --max-delivery-attempts=5 \
+        --dead-letter-topic-project="${PROJECT_ID}"
+    # set IAM policies on public Pub/Sub resources
+    if [ "$testid" = "False" ]; then
+        user="allUsers"
+        roleid="projects/${GOOGLE_CLOUD_PROJECT}/roles/userPublic"
+        gcloud pubsub topics add-iam-policy-binding "${ps_output_topic}" --member="${user}" --role="${roleid}"
+    fi
+
+    #--- Deploy Cloud Run service
+    echo
+    echo "Creating container image for ${MODULE_NAME} module and deploying to Cloud Run..."
+    moduledir="."  # assumes deploying what's in our current directory
+    config="${moduledir}/cloudbuild.yaml"
+    url=$(gcloud builds submit --config="${config}" \
+        --substitutions="_SURVEY=${survey},_TESTID=${testid},_MODULE_NAME=${cr_module_name},_REPOSITORY=${artifact_registry_repo}" \
+        "${moduledir}" | sed -n 's/^Step #2: Service URL: \(.*\)$/\1/p')
+    echo
+    echo "Creating trigger subscription for ${MODULE_NAME} Cloud Run service..."
+    gcloud pubsub subscriptions create "${ps_input_subscrip}" \
+        --topic "${ps_trigger_topic}" \
+        --topic-project "${PROJECT_ID}" \
+        --ack-deadline=600 \
+        --push-endpoint="${url}${ROUTE_RUN}" \
+        --push-auth-service-account="${runinvoker_svcact}" \
+        --dead-letter-topic="${ps_deadletter_topic}" \
+        --max-delivery-attempts=5
+fi

broker/cloud_run/ztf/cross_match/main.py

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+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+
+"""
+This module uses the value added alert stream ztf-hostless-transients to crossmatch against objects in the Euclid Quick
+Data Release 1.
+"""
+
+import os
+import io
+from typing import Dict
+from astropy.io import fits
+from astropy.stats import sigma_clip
+import numpy as np
+from google.cloud import logging
+import flask
+import pittgoogle
+
+# [FIXME] Make this helpful or else delete it.
+# Connect the python logger to the google cloud logger.
+# By default, this captures INFO level and above.
+# pittgoogle uses the python logger.
+# We don't currently use the python logger directly in this script, but we could.
+logging.Client().setup_logging()
+
+PROJECT_ID = os.getenv("GCP_PROJECT")
+TESTID = os.getenv("TESTID")
+SURVEY = os.getenv("SURVEY")
+
+# Variables for incoming data
+# A url route is used in setup.sh when the trigger subscription is created.
+# It is possible to define multiple routes in a single module and trigger them using different subscriptions.
+ROUTE_RUN = "/"  # HTTP route that will trigger run(). Must match deploy.sh
+
+# Variables for outgoing data
+HTTP_204 = 204  # HTTP code: Success
+HTTP_400 = 400  # HTTP code: Bad Request
+
+# GCP resources used in this module
+TOPIC = pittgoogle.Topic.from_cloud(
+    "hostless-transients", survey=SURVEY, testid=TESTID, projectid=PROJECT_ID
+)
+
+app = flask.Flask(__name__)
+
+
+@app.route(ROUTE_RUN, methods=["POST"])
+def run():
+    """Produces a value-added alert stream (${survey}-hostless-transients) identifying hostless transient candidates
+    Messages in this stream retain fields from the original alert-lite packet.
+
+    This module is intended to be deployed as a Cloud Run service. It will operate as an HTTP endpoint
+    triggered by Pub/Sub messages. This function will be called once for every message sent to this route.
+    It should accept the incoming HTTP request and return a response.
+
+    Returns
+    -------
+    response : tuple(str, int)
+        Tuple containing the response body (string) and HTTP status code (int). Flask will convert the
+        tuple into a proper HTTP response. Note that the response is a status message for the web server.
+    """
+    # extract the envelope from the request that triggered the endpoint
+    # this contains a single Pub/Sub message with the alert to be processed
+    envelope = flask.request.get_json()
+    try:
+        alert_lite = pittgoogle.Alert.from_cloud_run(envelope, schema_name="default")
+    except pittgoogle.exceptions.BadRequest as exc:
+        return str(exc), HTTP_400
+
+    configs = {
+        "sigma_clipping_kwargs": {"sigma": 3, "maxiters": 10},
+        "hostless_detection_with_clipping": {
+            "crop_radius": 7,
+            "max_number_of_pixels_clipped": 5,
+            "min_number_of_pixels_clipped": 3,
+        },
+    }
+    hostless_dict = {
+        "likely_extragalactic_transient": int(alert_lite.attributes["is_extragalactic_transient"]),
+        "likely_hostless_transient": 0,
+    }
+
+    alert_lite_dict = alert_lite.dict["alert_lite"]
+    if _is_candidate(alert_lite, configs):
+        hostless_dict["likely_hostless_transient"] = 1
+
+    # publish results to Pub/Sub
+    TOPIC.publish(
+        pittgoogle.Alert.from_dict(
+            {"alert_lite": alert_lite_dict},
+            attributes={
+                **alert_lite.attributes,
+                **{
+                    "pg_likely_hostless_transient": int(hostless_dict["likely_hostless_transient"])
+                },
+            },
+            schema_name="default",
+        )
+    )
+    return "", HTTP_204
+
+
+def _is_candidate(alert_lite: pittgoogle.Alert, configs: Dict) -> bool:
+    cutouts = ["Template", "Science"]
+    template_stamp, science_stamp = [alert_lite.dict.get(f"cutout{cutout}") for cutout in cutouts]
+    # apply sigma clipping to the bytes data for each stamp
+    template_stamp_clipped = sigma_clip(
+        _read_stamp_data(template_stamp), **configs["sigma_clipping_kwargs"]
+    )
+    science_stamp_clipped = sigma_clip(
+        _read_stamp_data(science_stamp), **configs["sigma_clipping_kwargs"]
+    )
+
+    return _run_hostless_detection_with_clipped_data(
+        science_stamp_clipped, template_stamp_clipped, configs
+    )
+
+
+def _read_stamp_data(cutout):
+    hdul = fits.open(io.BytesIO(cutout))
+
+    return hdul[0].data
+
+
+def _run_hostless_detection_with_clipped_data(
+    science_stamp: np.ndarray, template_stamp: np.ndarray, configs: Dict
+) -> bool:
+    """Adapted from:
+    https://github.com/COINtoolbox/extragalactic_hostless/blob/main/src/pipeline_utils.py#L271
+
+    Detects potential hostless candidates with sigma clipped stamp images by cropping an image patch from the center of
+    the image. If pixels are rejected in scientific image but not in corresponding template image, such candidates are
+    flagged as potential hostless.
+    """
+
+    science_clipped = sigma_clip(science_stamp, **configs["sigma_clipping_kwargs"])
+    template_clipped = sigma_clip(template_stamp, **configs["sigma_clipping_kwargs"])
+    is_hostless_candidate = _check_hostless_conditions(
+        science_clipped, template_clipped, configs["hostless_detection_with_clipping"]
+    )
+
+    if is_hostless_candidate:
+        return is_hostless_candidate
+    science_stamp = _crop_center_patch(
+        science_stamp, configs["hostless_detection_with_clipping"]["crop_radius"]
+    )
+    template_stamp = _crop_center_patch(
+        template_stamp, configs["hostless_detection_with_clipping"]["crop_radius"]
+    )
+    science_clipped = sigma_clip(science_stamp, **configs["sigma_clipping_kwargs"])
+    template_clipped = sigma_clip(template_stamp, **configs["sigma_clipping_kwargs"])
+    is_hostless_candidate = _check_hostless_conditions(
+        science_clipped, template_clipped, configs["hostless_detection_with_clipping"]
+    )
+
+    return is_hostless_candidate
+
+
+def _crop_center_patch(input_image: np.ndarray, patch_radius: int = 12) -> np.ndarray:
+    """Adapted from:
+    https://github.com/COINtoolbox/extragalactic_hostless/blob/main/src/pipeline_utils.py#L234
+
+    Crops rectangular patch around image center with a given patch scale.
+    """
+    image_shape = input_image.shape[0:2]
+    center_coords = [image_shape[0] / 2, image_shape[1] / 2]
+    center_patch_x = int(center_coords[0] - patch_radius)
+    center_patch_y = int(center_coords[1] - patch_radius)
+
+    return input_image[
+        center_patch_x : center_patch_x + patch_radius * 2,
+        center_patch_y : center_patch_y + patch_radius * 2,
+    ]
+
+
+def _check_hostless_conditions(
+    science_clipped: np.ndarray, template_clipped: np.ndarray, detection_config: Dict
+) -> bool:
+    """Adapted from:
+    https://github.com/COINtoolbox/extragalactic_hostless/blob/main/src/pipeline_utils.py#L253
+    """
+
+    science_only_detection = (
+        np.ma.count_masked(science_clipped) > detection_config["max_number_of_pixels_clipped"]
+        and np.ma.count_masked(template_clipped) < detection_config["min_number_of_pixels_clipped"]
+    )
+    template_only_detection = (
+        np.ma.count_masked(template_clipped) > detection_config["max_number_of_pixels_clipped"]
+        and np.ma.count_masked(science_clipped) < detection_config["min_number_of_pixels_clipped"]
+    )
+
+    if science_only_detection or template_only_detection:
+        return True
+
+    return False

broker/cloud_run/ztf/cross_match/requirements.txt

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+# As explained here
+# https://cloud.google.com/functions/docs/writing/specifying-dependencies-python
+# dependencies for a Cloud Function must be specified in a `requirements.txt`
+# file (or packaged with the function) in the same directory as `main.py`
+
+google-cloud-logging
+pittgoogle-client>=0.3.15
+
+# for Cloud Run
+# https://cloud.google.com/run/docs/quickstarts/build-and-deploy/deploy-python-service
+Flask
+gunicorn
+Werkzeug

broker/cloud_run/ztf/hostless_transients/Dockerfile

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+# Use the official lightweight Python image.
+# https://hub.docker.com/_/python
+FROM python:3.12-slim
+
+# Allow statements and log messages to immediately appear in the Knative logs
+ENV PYTHONUNBUFFERED True
+
+# Copy local code to the container image.
+ENV APP_HOME /app
+WORKDIR $APP_HOME
+COPY . ./
+
+# Install production dependencies.
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Run the web service on container startup. Here we use the gunicorn
+# webserver, with one worker process and 8 threads.
+# For environments with multiple CPU cores, increase the number of workers
+# to be equal to the cores available.
+# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling.
+CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 0 main:app