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Fill alerts with (nonphysical) data to allow performance benchmarking #63

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Fill alerts with (nonphysical) data to allow performance benchmarking #63

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8450bfa
simulate.py: Generalize type inference and avoid defaulting to null
mef51 May 7, 2026
6e73a8b
simulate.py: implement arrayCount prvDiaSources
mef51 Jul 21, 2026
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35 changes: 26 additions & 9 deletions python/lsst/alert/packet/simulate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,8 @@

__all__ = ["simulate_alert"]

global _schemas # schema cache for use during alert simulation
_schemas = {}

def randomNull():
"""Provide a random value of the Avro `null` type.
Expand Down Expand Up @@ -73,12 +75,12 @@ def randomString():
for _ in range(random.randint(0, 10)))


def randomBytes(max_bytes=1000):
def randomBytes(min_bytes=1000, max_bytes=32000):
"""Provide a random value of the Avro `bytes` type.

Up to `max_bytes` bytes are returned.
"""
return numpy.random.bytes(random.randint(0, max_bytes))
return numpy.random.bytes(random.randint(min_bytes, max_bytes))


randomizerFunctionsByType = {
Expand Down Expand Up @@ -132,24 +134,38 @@ def simulate_alert(schema, keepNull=None, arrayCount=None):

if arrayCount is None:
arrayCount = {}
else:
# cache the schemas we need to satisfy the arrayCount argument
if schema["name"].endswith("alert"):
# we are at the top level of the schema and not in a recursive call
_schemas['diaSource'] = schema['fields'][3]

if type(schema['type']) is list:
# potentially nullable
if ('null' in schema['type']) and (schema['name'] in keepNull):
return {schema['name']: None}
else:
# inferring the type like this this is not general but works
# for our application
schema['type'] = schema['type'][0]
if 'null' not in schema['type']:
schema['type'] = schema['type'][0]
elif 'null' in schema['type']: # pick the non-null type by default
idxs = list(range(0, len(schema['type'])))
idxs.pop(schema['type'].index('null'))
schema['type'] = schema['type'][idxs[0]]


if type(schema['type']) is dict:
# either an array, a record, or a nested type
if schema['type']['type'] == 'array':
if schema['name'] in arrayCount:
output_array = []
# infer the schema name (e.g. map prvDiaSources -> diaSource, etc.)
arr_schema = schema['name'].split('prv')[1][0].lower() + schema['name'].split('prv')[1][1:-1]
for i in range(arrayCount[schema['name']]):
output_array.append(simulate_alert(
schema['type']['items'], keepNull=keepNull, arrayCount=arrayCount))
output_array.append(
simulate_alert(
_schemas[arr_schema], keepNull=keepNull, arrayCount=arrayCount
)
)
return {schema['name']: output_array}
else:
return {schema['name']: None}
Expand All @@ -159,8 +175,9 @@ def simulate_alert(schema, keepNull=None, arrayCount=None):
return output
else:
# a nested type
output.update({schema['name']: simulate_alert(
schema['type'], keepNull=keepNull, arrayCount=arrayCount)})
output.update({
schema["name"]: simulate_alert(schema["type"], keepNull=keepNull, arrayCount=arrayCount)
})
return output

if schema['type'] == 'record':
Expand Down