--- a
+++ b/notebooks/resource-allocation/generate-allocation-summary-arrays.py
@@ -0,0 +1,239 @@
+from qiita_db.util import MaxRSS_helper
+from qiita_db.software import Software
+import datetime
+from io import StringIO
+from subprocess import check_output
+import pandas as pd
+from os.path import join
+
+# This is an example script to collect the data we need from SLURM, the plan
+# is that in the near future we will clean up and add these to the Qiita's main
+# code and then have cronjobs to run them.
+
+# at time of writting we have:
+#     qp-spades spades
+# (*) qp-woltka Woltka v0.1.4
+#     qp-woltka SynDNA Woltka
+#     qp-woltka Calculate Cell Counts
+# (*) qp-meta Sortmerna v2.1b
+# (*) qp-fastp-minimap2 Adapter and host filtering v2023.12
+# ... and the admin plugin
+# (*) qp-klp
+# Here we are only going to create summaries for (*)
+
+
+sacct = ['sacct', '-p',
+         '--format=JobName,JobID,ElapsedRaw,MaxRSS,ReqMem', '-j']
+# for the non admin jobs, we will use jobs from the last six months
+six_months = datetime.date.today() - datetime.timedelta(weeks=6*4)
+
+print('The current "sofware - commands" that use job-arrays are:')
+for s in Software.iter():
+    if 'ENVIRONMENT="' in s.environment_script:
+        for c in s.commands:
+            print(f"{s.name} - {c.name}")
+
+# 1. Command: woltka
+
+fn = join('/panfs', 'qiita', 'jobs_woltka.tsv.gz')
+print(f"Generating the summary for the woltka jobs: {fn}.")
+
+cmds = [c for s in Software.iter(False)
+        if 'woltka' in s.name for c in s.commands]
+jobs = [j for c in cmds for j in c.processing_jobs if j.status == 'success' and
+        j.heartbeat.date() > six_months and j.input_artifacts]
+
+data = []
+for j in jobs:
+    size = sum([fp['fp_size'] for fp in j.input_artifacts[0].filepaths])
+    jid, mjid = j.external_id.strip().split()
+    rvals = StringIO(check_output(sacct + [jid]).decode('ascii'))
+    _d = pd.read_csv(rvals, sep='|')
+    jmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
+                           else MaxRSS_helper(x)).max()
+    jwt = _d.ElapsedRaw.max()
+
+    rvals = StringIO(check_output(sacct + [mjid]).decode('ascii'))
+    _d = pd.read_csv(rvals, sep='|')
+    mmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
+                           else MaxRSS_helper(x)).max()
+    mwt = _d.ElapsedRaw.max()
+
+    data.append({
+        'jid': j.id, 'sjid': jid, 'mem': jmem, 'wt': jwt, 'type': 'main',
+        'db': j.parameters.values['Database'].split('/')[-1]})
+    data.append(
+        {'jid': j.id, 'sjid': mjid, 'mem': mmem, 'wt': mwt, 'type': 'merge',
+         'db': j.parameters.values['Database'].split('/')[-1]})
+df = pd.DataFrame(data)
+df.to_csv(fn, sep='\t', index=False)
+
+# 2. qp-meta Sortmerna
+
+fn = join('/panfs', 'qiita', 'jobs_sortmerna.tsv.gz')
+print(f"Generating the summary for the woltka jobs: {fn}.")
+
+# for woltka we will only use jobs from the last 6 months
+cmds = [c for s in Software.iter(False)
+        if 'minimap2' in s.name.lower() for c in s.commands]
+jobs = [j for c in cmds for j in c.processing_jobs if j.status == 'success' and
+        j.heartbeat.date() > six_months and j.input_artifacts]
+
+data = []
+for j in jobs:
+    size = sum([fp['fp_size'] for fp in j.input_artifacts[0].filepaths])
+    jid, mjid = j.external_id.strip().split()
+    rvals = StringIO(check_output(sacct + [jid]).decode('ascii'))
+    _d = pd.read_csv(rvals, sep='|')
+    jmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
+                           else MaxRSS_helper(x)).max()
+    jwt = _d.ElapsedRaw.max()
+
+    rvals = StringIO(check_output(sacct + [mjid]).decode('ascii'))
+    _d = pd.read_csv(rvals, sep='|')
+    mmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
+                           else MaxRSS_helper(x)).max()
+    mwt = _d.ElapsedRaw.max()
+
+    data.append({
+        'jid': j.id, 'sjid': jid, 'mem': jmem, 'wt': jwt, 'type': 'main'})
+    data.append(
+        {'jid': j.id, 'sjid': mjid, 'mem': mmem, 'wt': mwt, 'type': 'merge'})
+df = pd.DataFrame(data)
+df.to_csv(fn, sep='\t', index=False)
+
+
+# 3. Adapter and host filtering. Note that there is a new version deployed on
+#    Jan 2024 so the current results will not be the most accurate
+
+fn = join('/panfs', 'qiita', 'jobs_adapter_host.tsv.gz')
+print(f"Generating the summary for the woltka jobs: {fn}.")
+
+# for woltka we will only use jobs from the last 6 months
+cmds = [c for s in Software.iter(False)
+        if 'meta' in s.name.lower() for c in s.commands]
+jobs = [j for c in cmds if 'sortmerna' in c.name.lower()
+        for j in c.processing_jobs if j.status == 'success' and
+        j.heartbeat.date() > six_months and j.input_artifacts]
+
+data = []
+for j in jobs:
+    size = sum([fp['fp_size'] for fp in j.input_artifacts[0].filepaths])
+    jid, mjid = j.external_id.strip().split()
+    rvals = StringIO(check_output(sacct + [jid]).decode('ascii'))
+    _d = pd.read_csv(rvals, sep='|')
+    jmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
+                           else MaxRSS_helper(x)).max()
+    jwt = _d.ElapsedRaw.max()
+
+    rvals = StringIO(check_output(sacct + [mjid]).decode('ascii'))
+    _d = pd.read_csv(rvals, sep='|')
+    mmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
+                           else MaxRSS_helper(x)).max()
+    mwt = _d.ElapsedRaw.max()
+
+    data.append({
+        'jid': j.id, 'sjid': jid, 'mem': jmem, 'wt': jwt, 'type': 'main'})
+    data.append(
+        {'jid': j.id, 'sjid': mjid, 'mem': mmem, 'wt': mwt, 'type': 'merge'})
+df = pd.DataFrame(data)
+df.to_csv(fn, sep='\t', index=False)
+
+
+# 4. The SPP!
+
+fn = join('/panfs', 'qiita', 'jobs_spp.tsv.gz')
+print(f"Generating the summary for the SPP jobs: {fn}.")
+
+# for the SPP we will look at jobs from the last year
+year = datetime.date.today() - datetime.timedelta(days=365)
+cmds = [c for s in Software.iter(False)
+        if s.name == 'qp-klp' for c in s.commands]
+jobs = [j for c in cmds for j in c.processing_jobs if j.status == 'success' and
+        j.heartbeat.date() > year]
+
+# for the SPP we need to find the jobs that were actually run, this means
+# looping throught the existing slurm jobs and finding them
+max_inter = 2000
+
+data = []
+for job in jobs:
+    jei = int(job.external_id)
+    rvals = StringIO(
+        check_output(sacct + [str(jei)]).decode('ascii'))
+    _d = pd.read_csv(rvals, sep='|')
+    mem = _d.MaxRSS.apply(
+        lambda x: x if type(x) is not str else MaxRSS_helper(x)).max()
+    wt = _d.ElapsedRaw.max()
+    # the current "easy" way to determine if amplicon or other is to check
+    # the file extension of the filename
+    stype = 'other'
+    if job.parameters.values['sample_sheet']['filename'].endswith('.txt'):
+        stype = 'amplicon'
+    rid = job.parameters.values['run_identifier']
+    data.append(
+        {'jid': job.id, 'sjid': jei, 'mem': mem, 'stype': stype, 'wt': wt,
+         'type': 'main', 'rid': rid, 'name': _d.JobName[0]})
+
+    # let's look for the convert job
+    for jid in range(jei + 1, jei + max_inter):
+        rvals = StringIO(check_output(sacct + [str(jid)]).decode('ascii'))
+        _d = pd.read_csv(rvals, sep='|')
+        if [1 for x in _d.JobName.values if x.startswith(job.id)]:
+            cjid = int(_d.JobID[0])
+            mem = _d.MaxRSS.apply(
+                lambda x: x if type(x) is not str else MaxRSS_helper(x)).max()
+            wt = _d.ElapsedRaw.max()
+
+            data.append(
+                {'jid': job.id, 'sjid': cjid, 'mem': mem, 'stype': stype,
+                 'wt': wt, 'type': 'convert', 'rid': rid,
+                 'name': _d.JobName[0]})
+
+            # now let's look for the next step, if amplicon that's fastqc but
+            # if other that's qc/nuqc
+            for jid in range(cjid + 1, cjid + max_inter):
+                rvals = StringIO(
+                    check_output(sacct + [str(jid)]).decode('ascii'))
+                _d = pd.read_csv(rvals, sep='|')
+                if [1 for x in _d.JobName.values if x.startswith(job.id)]:
+                    qc_jid = _d.JobIDRaw.apply(
+                        lambda x: int(x.split('.')[0])).max()
+                    qcmem = _d.MaxRSS.apply(
+                        lambda x: x if type(x) is not str
+                        else MaxRSS_helper(x)).max()
+                    qcwt = _d.ElapsedRaw.max()
+
+                    if stype == 'amplicon':
+                        data.append(
+                            {'jid': job.id, 'sjid': qc_jid, 'mem': qcmem,
+                             'stype': stype, 'wt': qcwt, 'type': 'fastqc',
+                             'rid': rid, 'name': _d.JobName[0]})
+                    else:
+                        data.append(
+                            {'jid': job.id, 'sjid': qc_jid, 'mem': qcmem,
+                             'stype': stype, 'wt': qcwt, 'type': 'qc',
+                             'rid': rid, 'name': _d.JobName[0]})
+                        for jid in range(qc_jid + 1, qc_jid + max_inter):
+                            rvals = StringIO(check_output(
+                                sacct + [str(jid)]).decode('ascii'))
+                            _d = pd.read_csv(rvals, sep='|')
+                            if [1 for x in _d.JobName.values if x.startswith(
+                                    job.id)]:
+                                fqc_jid = _d.JobIDRaw.apply(
+                                    lambda x: int(x.split('.')[0])).max()
+                                fqcmem = _d.MaxRSS.apply(
+                                    lambda x: x if type(x) is not str
+                                    else MaxRSS_helper(x)).max()
+                                fqcwt = _d.ElapsedRaw.max()
+                                data.append(
+                                    {'jid': job.id, 'sjid': fqc_jid,
+                                     'mem': fqcmem, 'stype': stype,
+                                     'wt': fqcwt, 'type': 'fastqc',
+                                     'rid': rid, 'name': _d.JobName[0]})
+                                break
+                    break
+            break
+
+df = pd.DataFrame(data)
+df.to_csv(fn, sep='\t', index=False)