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some minor changes
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CHANGELOG.md

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@@ -5,6 +5,7 @@ Version 2024.07
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Deployed on July 15th, 2024
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* On June 14th, 2024 we modified the SPP to use ["fastp & minimap2 against GRCh38.p14 + Phi X 174 + T2T-CHM13v2.0, then Movi against GRCh38.p14, T2T-CHM13v2.0 + Human Pangenome Reference Consortium release 2023"](https://github.com/cguccione/human_host_filtration) to filter human-reads.
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* Full refactor of the [DB patching system](https://github.com/qiita-spots/qiita/blob/master/CONTRIBUTING.md#patch-91sql) to make sure that a new production deployment has a fully empty database.
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* Fully removed Qiimp from Qiita.
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* Users can now add `ORCID`, `ResearchGate` and/or `GoogleScholar` information to their profile and the creation (registration) timestamp is kept in the database. Thank you @jlab.
@@ -16,7 +17,6 @@ Deployed on July 15th, 2024
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* Added `current_human_filtering` to the prep-information and `human_reads_filter_method` to the artifact to keep track of the method that it was used to human reads filter the raw artifact and know if it's up to date with what is expected via the best practices.
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* Added `reprocess_job_id` to the prep-information so we keep track if a preparation has been reprocessed with another job.
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* Other general fixes, like [#3385](https://github.com/qiita-spots/qiita/pull/3385), [#3397](https://github.com/qiita-spots/qiita/pull/3397), [#3399](https://github.com/qiita-spots/qiita/pull/3399), [#3400](https://github.com/qiita-spots/qiita/pull/3400), [#3409](https://github.com/qiita-spots/qiita/pull/3409), [#3410](https://github.com/qiita-spots/qiita/pull/3410).
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* On June 14th, 2024 we modified the SPP to use XXXX to filter human-reads from the per-sample-FASTQ loaded to Qiita.
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Version 2024.02

qiita_db/util.py

Lines changed: 3 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -2766,11 +2766,9 @@ def update_resource_allocation_table(weeks=1, test=None):
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"""
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df = pd.DataFrame()
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with qdb.sql_connection.TRN:
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sql = sql_command
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qdb.sql_connection.TRN.add(sql, sql_args=[slurm_external_id])
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qdb.sql_connection.TRN.add(sql_command, sql_args=[slurm_external_id])
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res = qdb.sql_connection.TRN.execute_fetchindex()
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columns = ["processing_job_id", 'external_id']
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df = pd.DataFrame(res, columns=columns)
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df = pd.DataFrame(res, columns=["processing_job_id", 'external_id'])
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df['external_id'] = df['external_id'].astype(int)
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data = []
@@ -2784,10 +2782,7 @@ def update_resource_allocation_table(weeks=1, test=None):
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if test is not None:
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slurm_data = test
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else:
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try:
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rvals = StringIO(check_output(sacct)).decode('ascii')
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except TypeError as e:
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raise e
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rvals = check_output(sacct).decode('ascii')
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slurm_data = pd.read_csv(StringIO(rvals), sep='|')
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# In slurm, each JobID is represented by 3 rows in the dataframe:

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