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* New filter for quality cuts * PEP8 * Fix test output
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# Copyright 2024 AstroLab Software | ||
# Author: Julien Peloton | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
from pyspark.sql.functions import pandas_udf, PandasUDFType | ||
from pyspark.sql.types import BooleanType | ||
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from fink_filters.tester import spark_unit_tests | ||
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import pandas as pd | ||
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def ztf_quality_cuts_( | ||
rb, | ||
nbad | ||
) -> pd.Series: | ||
"""Return alerts considered as scientifically valid for ZTF | ||
Parameters | ||
---------- | ||
rb: Pandas series | ||
Column containing the Real Bogus score | ||
nbad: Pandas series | ||
Column containing number of bad pixels | ||
Returns | ||
---------- | ||
out: pandas.Series of bool | ||
Return a Pandas DataFrame with the appropriate flag: | ||
false for bad alert, and true for good alert. | ||
Examples | ||
---------- | ||
>>> pdf = pd.read_parquet('datatest/regular') | ||
>>> classification = ztf_quality_cuts_( | ||
... pdf['candidate'].apply(lambda x: x['rb']), | ||
... pdf['candidate'].apply(lambda x: x['nbad'])) | ||
>>> print(len(pdf[classification]['objectId'].values)) | ||
320 | ||
""" | ||
high_rb = rb.astype(float) >= 0.55 | ||
no_nbad = nbad.astype(int) == 0 | ||
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return high_rb & no_nbad | ||
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@pandas_udf(BooleanType(), PandasUDFType.SCALAR) | ||
def ztf_quality_cuts( | ||
rb, | ||
nbad, | ||
) -> pd.Series: | ||
"""Pandas UDF for ztf_quality_cuts_ | ||
Parameters | ||
---------- | ||
rb: Pandas series | ||
Column containing the Real Bogus score | ||
nbad: Pandas series | ||
Column containing the number of bad pixels | ||
Returns | ||
---------- | ||
out: pandas.Series of bool | ||
Return a Pandas DataFrame with the appropriate flag: | ||
false for bad alert, and true for good alert. | ||
Examples | ||
---------- | ||
>>> from fink_utils.spark.utils import apply_user_defined_filter | ||
>>> from fink_utils.spark.utils import concat_col | ||
>>> df = spark.read.format('parquet').load('datatest/regular') | ||
>>> f = 'fink_filters.filter_quality_cuts.filter.ztf_quality_cuts' | ||
>>> df = apply_user_defined_filter(df, f) | ||
>>> print(df.count()) | ||
320 | ||
""" | ||
series = ztf_quality_cuts_( | ||
rb, | ||
nbad, | ||
) | ||
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return series | ||
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if __name__ == "__main__": | ||
"""Execute the test suite""" | ||
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# Run the test suite | ||
globs = globals() | ||
spark_unit_tests(globs) |