build.log

+ DATA=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/spark-mic_cleaned-2510.csv
+ DATA_OUTPUT_DIR=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02
+ FEATURE=smiles
+ LABEL=pmic
+ python -m venv /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist
+ /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/bin/pip install 'schemist>=0.0.4.post1' pandas
Requirement already satisfied: schemist>=0.0.4.post1 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (0.0.4.post1)
Requirement already satisfied: pandas in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (2.2.3)
Requirement already satisfied: carabiner-tools[pd]>=0.0.4 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from schemist>=0.0.4.post1) (0.0.4)
Requirement already satisfied: nemony in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from schemist>=0.0.4.post1) (0.0.2)
Requirement already satisfied: rdkit>=2022.09.5 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from schemist>=0.0.4.post1) (2024.9.6)
Requirement already satisfied: openpyxl==3.1.0 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from schemist>=0.0.4.post1) (3.1.0)
Requirement already satisfied: descriptastorus>=2.7 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from schemist>=0.0.4.post1) (2.8.0)
Requirement already satisfied: selfies in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from schemist>=0.0.4.post1) (2.2.0)
Requirement already satisfied: requests in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from schemist>=0.0.4.post1) (2.32.3)
Requirement already satisfied: et-xmlfile in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from openpyxl==3.1.0->schemist>=0.0.4.post1) (2.0.0)
Requirement already satisfied: pytz>=2020.1 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from pandas) (2025.2)
Requirement already satisfied: tzdata>=2022.7 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from pandas) (2025.2)
Requirement already satisfied: numpy>=1.22.4 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from pandas) (2.2.4)
Requirement already satisfied: python-dateutil>=2.8.2 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from pandas) (2.9.0.post0)
Requirement already satisfied: tqdm in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from carabiner-tools[pd]>=0.0.4->schemist>=0.0.4.post1) (4.67.1)
Requirement already satisfied: scipy in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from descriptastorus>=2.7->schemist>=0.0.4.post1) (1.15.2)
Requirement already satisfied: pandas-flavor in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from descriptastorus>=2.7->schemist>=0.0.4.post1) (0.6.0)
Requirement already satisfied: six>=1.5 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)
Requirement already satisfied: Pillow in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from rdkit>=2022.09.5->schemist>=0.0.4.post1) (11.1.0)
Requirement already satisfied: pyyaml in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from nemony->schemist>=0.0.4.post1) (6.0.2)
Requirement already satisfied: urllib3<3,>=1.21.1 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from requests->schemist>=0.0.4.post1) (2.3.0)
Requirement already satisfied: idna<4,>=2.5 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from requests->schemist>=0.0.4.post1) (3.10)
Requirement already satisfied: certifi>=2017.4.17 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from requests->schemist>=0.0.4.post1) (2025.1.31)
Requirement already satisfied: charset-normalizer<4,>=2 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from requests->schemist>=0.0.4.post1) (3.4.1)
Requirement already satisfied: xarray in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from pandas-flavor->descriptastorus>=2.7->schemist>=0.0.4.post1) (2025.3.0)
Requirement already satisfied: packaging>=23.2 in /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/lib/python3.10/site-packages (from xarray->pandas-flavor->descriptastorus>=2.7->schemist>=0.0.4.post1) (24.2)

[notice] A new release of pip is available: 23.0.1 -> 25.2
[notice] To update, run: python -m pip install --upgrade pip
+ source /nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/bin/activate
++ deactivate nondestructive
++ '[' -n '' ']'
++ '[' -n '' ']'
++ '[' -n /usr/bin/bash -o -n '' ']'
++ hash -r
++ '[' -n '' ']'
++ unset VIRTUAL_ENV
++ unset VIRTUAL_ENV_PROMPT
++ '[' '!' nondestructive = nondestructive ']'
++ VIRTUAL_ENV=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist
++ export VIRTUAL_ENV
++ _OLD_VIRTUAL_PATH=/camp/home/johnsoe/.conda/envs/dev/bin:/camp/apps/eb/software/Anaconda3/2023.09-0/condabin:/usr/share/Modules/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/usr/lpp/mmfs/bin:/camp/home/johnsoe/.local/bin:/camp/home/johnsoe/bin
++ PATH=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/.schemist/bin:/camp/home/johnsoe/.conda/envs/dev/bin:/camp/apps/eb/software/Anaconda3/2023.09-0/condabin:/usr/share/Modules/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/usr/lpp/mmfs/bin:/camp/home/johnsoe/.local/bin:/camp/home/johnsoe/bin
++ export PATH
++ '[' -n '' ']'
++ '[' -z '' ']'
++ _OLD_VIRTUAL_PS1=
++ PS1='(.schemist) '
++ export PS1
++ VIRTUAL_ENV_PROMPT='(.schemist) '
++ export VIRTUAL_ENV_PROMPT
++ '[' -n /usr/bin/bash -o -n '' ']'
++ hash -r
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02
+ wt_data=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv
+ sed '1s/^\xEF\xBB\xBF//' /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/spark-mic_cleaned-2510.csv
+ pandas '; val = "Wild type"; df.query("accumulation_phenotype == @val")'
+ pandas '.drop(columns=[
    "spark_mwt", 
    "compound_cas_no", 
    "approval_date", 
    "publication_lab", 
    "is_buffered", 
    "molecule_chembl_id", 
    "molecule_chembl_source", 
    "pubchem_link",
    "data_source_info",
    "data_source_id",
    "external_compound_id",
    "strain_notes",
    "strain_phenotype",
    "incubation_conditions",
    "spark_extractor_notes",
    "solvent_percent",

])'
+ local 'cmd=; val = "Wild type"; df.query("accumulation_phenotype == @val")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); val = "Wild type"; df.query("accumulation_phenotype == @val").to_csv(sys.stdout, index=False, sep=",")'
+ pandas '.query("not spark_SMILES.isna()")'
+ pandas '.query("not pmic.isna()")'
+ local 'cmd=.drop(columns=[
    "spark_mwt", 
    "compound_cas_no", 
    "approval_date", 
    "publication_lab", 
    "is_buffered", 
    "molecule_chembl_id", 
    "molecule_chembl_source", 
    "pubchem_link",
    "data_source_info",
    "data_source_id",
    "external_compound_id",
    "strain_notes",
    "strain_phenotype",
    "incubation_conditions",
    "spark_extractor_notes",
    "solvent_percent",

])'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).drop(columns=[
    "spark_mwt", 
    "compound_cas_no", 
    "approval_date", 
    "publication_lab", 
    "is_buffered", 
    "molecule_chembl_id", 
    "molecule_chembl_source", 
    "pubchem_link",
    "data_source_info",
    "data_source_id",
    "external_compound_id",
    "strain_notes",
    "strain_phenotype",
    "incubation_conditions",
    "spark_extractor_notes",
    "solvent_percent",

]).to_csv(sys.stdout, index=False, sep=",")'
+ local 'cmd=.query("not spark_SMILES.isna()")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("not spark_SMILES.isna()").to_csv(sys.stdout, index=False, sep=",")'
+ pandas '.query("not species.isna()")'
+ pandas '; val = "#NUM!"; df.query("spark_SMILES != @val")'
+ pandas '; val = "#NUM!"; df.query("pmic != @val")'
+ local 'cmd=; val = "#NUM!"; df.query("spark_SMILES != @val")'
+ local 'cmd=; val = "#NUM!"; df.query("pmic != @val")'
+ local sep1=,
+ pandas '.query("not smiles.isna() and not species.isna()")'
+ local sep1=,
+ local idx=False
+ local idx=False
+ local sep2=,
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); val = "#NUM!"; df.query("spark_SMILES != @val").to_csv(sys.stdout, index=False, sep=",")'
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); val = "#NUM!"; df.query("pmic != @val").to_csv(sys.stdout, index=False, sep=",")'
+ pandas '; import numpy as np; df.assign(
    species=lambda x: np.where(x["species"].isna() & x["strain_name"].str.startswith("PAO1-"), "Pseudomonas aeruginosa", x["species"]),
    full_strain_name=lambda x: x["species"].str.cat(x["strain_name"].fillna(""), sep=" ").str.rstrip(),
    full_strain_name_with_genotype=lambda x: x["full_strain_name"].str.cat(x["strain_genotype"].fillna(""), sep=" ").str.rstrip(),
)'
+ schemist convert -c spark_SMILES -2 id smiles inchikey scaffold mwt clogp tpsa -f CSV -x prefix=SCB-
+ local 'cmd=.query("not pmic.isna()")'
+ schemist split -f CSV --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("not pmic.isna()").to_csv(sys.stdout, index=False, sep=",")'
+ local 'cmd=.query("not smiles.isna() and not species.isna()")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("not smiles.isna() and not species.isna()").to_csv(sys.stdout, index=False, sep=",")'
+ local 'cmd=.query("not species.isna()")'
+ local 'cmd=; import numpy as np; df.assign(
    species=lambda x: np.where(x["species"].isna() & x["strain_name"].str.startswith("PAO1-"), "Pseudomonas aeruginosa", x["species"]),
    full_strain_name=lambda x: x["species"].str.cat(x["strain_name"].fillna(""), sep=" ").str.rstrip(),
    full_strain_name_with_genotype=lambda x: x["full_strain_name"].str.cat(x["strain_genotype"].fillna(""), sep=" ").str.rstrip(),
)'
+ local sep1=,
+ local idx=False
+ local sep1=,
+ local sep2=,
+ local idx=False
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("not species.isna()").to_csv(sys.stdout, index=False, sep=",")'
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); import numpy as np; df.assign(
    species=lambda x: np.where(x["species"].isna() & x["strain_name"].str.startswith("PAO1-"), "Pseudomonas aeruginosa", x["species"]),
    full_strain_name=lambda x: x["species"].str.cat(x["strain_name"].fillna(""), sep=" ").str.rstrip(),
    full_strain_name_with_genotype=lambda x: x["full_strain_name"].str.cat(x["strain_genotype"].fillna(""), sep=" ").str.rstrip(),
).to_csv(sys.stdout, index=False, sep=",")'
🚀 Converting between string representations with the following parameters:
	subcommand: convert
	output: <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>
	format: CSV
	input: <_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>
	representation: SMILES
	column: spark_SMILES
	prefix: None
	to: ['id', 'smiles', 'inchikey', 'scaffold', 'mwt', 'clogp', 'tpsa']
	options: ['prefix=SCB-']
	func: <function _convert at 0x7f8591585fc0>
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv' mode='w' encoding='UTF-8'>
	format: CSV
	input: <_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7f8f6ae12170>

0it [00:00, ?it/s]
1it [00:01,  1.72s/it]
2it [00:03,  1.61s/it]
3it [00:04,  1.33s/it]
4it [00:05,  1.25s/it]
5it [00:06,  1.19s/it]
6it [00:07,  1.19s/it]
7it [00:08,  1.18s/it]
8it [00:09,  1.13s/it]
9it [00:10,  1.11s/it]
10it [00:11,  1.10s/it]
11it [00:13,  1.09s/it]
12it [00:14,  1.08s/it]
13it [00:15,  1.07s/it]
14it [00:16,  1.07s/it]
15it [00:17,  1.08s/it]
16it [00:18,  1.09s/it]
17it [00:19,  1.08s/it]
18it [00:20,  1.08s/it]
19it [00:21,  1.10s/it]
20it [00:22,  1.09s/it]
21it [00:23,  1.10s/it]
22it [00:25,  1.11s/it]
23it [00:26,  1.11s/it]
24it [00:27,  1.11s/it]
25it [00:28,  1.11s/it]
26it [00:29,  1.11s/it]
27it [00:30,  1.12s/it]
28it [00:31,  1.11s/it]
29it [00:32,  1.11s/it]
30it [00:33,  1.11s/it]
31it [00:34,  1.08s/it]
32it [00:36,  1.12s/it]
33it [00:37,  1.15s/it]
34it [00:38,  1.17s/it]
35it [00:39,  1.21s/it]
36it [00:41,  1.27s/it]
37it [00:42,  1.40s/it]
38it [00:44,  1.39s/it]
39it [00:45,  1.36s/it]
40it [00:46,  1.36s/it]
41it [00:48,  1.32s/it]
42it [00:49,  1.29s/it]
43it [00:50,  1.29s/it]
44it [00:51,  1.29s/it]
45it [00:53,  1.31s/it]
46it [00:54,  1.31s/it]
47it [00:55,  1.31s/it]
48it [00:57,  1.34s/it]
49it [00:58,  1.32s/it]
50it [00:59,  1.31s/it]
51it [01:01,  1.33s/it]
52it [01:02,  1.31s/it]
53it [01:03,  1.31s/it]
54it [01:05,  1.31s/it]
55it [01:06,  1.31s/it]
56it [01:07,  1.32s/it]
57it [01:09,  1.33s/it]
58it [01:10,  1.35s/it]
59it [01:11,  1.33s/it]
60it [01:13,  1.32s/it]
61it [01:14,  1.35s/it]
62it [01:15,  1.35s/it]
63it [01:17,  1.35s/it]
64it [01:18,  1.36s/it]
65it [01:20,  1.36s/it]
66it [01:21,  1.50s/it]
67it [01:23,  1.60s/it]
68it [01:25,  1.75s/it]
69it [01:27,  1.77s/it]
70it [01:29,  1.73s/it]
71it [01:31,  1.85s/it]
72it [01:33,  2.07s/it]
73it [01:35,  1.95s/it]
74it [01:37,  1.91s/it]
75it [01:38,  1.79s/it]
76it [01:40,  1.66s/it]
77it [01:41,  1.53s/it]
78it [01:42,  1.25s/it]
78it [01:42,  1.31s/it]
Error counts:
	id: 0
	smiles: 0
	inchikey: 0
	scaffold: 3
	mwt: 0
	clogp: 0
	tpsa: 0
⏰ Completed process in 0:01:43.397173

0it [00:00, ?it/s]
1it [00:00,  1.13it/s]
2it [00:01,  1.23it/s]
3it [00:02,  1.48it/s]
4it [00:02,  1.57it/s]
5it [00:03,  1.67it/s]
6it [00:03,  1.65it/s]
7it [00:04,  1.68it/s]
8it [00:04,  1.77it/s]
9it [00:05,  1.84it/s]
10it [00:06,  1.84it/s]
11it [00:06,  1.86it/s]
12it [00:07,  1.90it/s]
13it [00:07,  1.92it/s]
14it [00:08,  1.94it/s]
15it [00:08,  1.90it/s]
16it [00:09,  1.85it/s]
17it [00:09,  1.87it/s]
18it [00:10,  1.89it/s]
19it [00:10,  1.87it/s]
20it [00:11,  1.89it/s]
21it [00:11,  1.87it/s]
22it [00:12,  1.87it/s]
23it [00:12,  1.87it/s]
24it [00:13,  1.87it/s]
25it [00:13,  1.87it/s]
26it [00:14,  1.89it/s]
27it [00:15,  1.86it/s]
28it [00:15,  1.87it/s]
29it [00:16,  1.90it/s]
30it [00:16,  1.90it/s]
31it [00:17,  1.97it/s]
32it [00:17,  1.87it/s]
33it [00:18,  1.82it/s]
34it [00:18,  1.76it/s]
35it [00:19,  1.70it/s]
36it [00:20,  1.60it/s]
37it [00:21,  1.41it/s]
38it [00:21,  1.42it/s]
39it [00:22,  1.43it/s]
40it [00:23,  1.43it/s]
41it [00:23,  1.48it/s]
42it [00:24,  1.51it/s]
43it [00:25,  1.53it/s]
44it [00:25,  1.60it/s]
45it [00:26,  1.62it/s]
46it [00:26,  1.64it/s]
47it [00:27,  1.67it/s]
48it [00:28,  1.64it/s]
49it [00:28,  1.69it/s]
50it [00:29,  1.70it/s]
51it [00:29,  1.68it/s]
52it [00:30,  1.72it/s]
53it [00:30,  1.73it/s]
54it [00:31,  1.73it/s]
55it [00:32,  1.72it/s]
56it [00:32,  1.70it/s]
57it [00:33,  1.72it/s]
58it [00:33,  1.67it/s]
59it [00:34,  1.71it/s]
60it [00:35,  1.71it/s]
61it [00:35,  1.66it/s]
62it [00:36,  1.67it/s]
63it [00:36,  1.65it/s]
64it [00:37,  1.63it/s]
65it [00:38,  1.58it/s]
66it [00:39,  1.36it/s]
67it [00:40,  1.23it/s]
68it [00:41,  1.08it/s]
69it [00:42,  1.08it/s]
70it [00:43,  1.09it/s]
71it [00:44,  1.01it/s]
72it [00:45,  1.12s/it]
73it [00:46,  1.04s/it]
74it [00:47,  1.01s/it]
75it [00:48,  1.08it/s]
76it [00:48,  1.19it/s]
77it [00:49,  1.31it/s]
78it [00:49,  1.60it/s]
78it [00:49,  1.57it/s]

0it [00:00, ?it/s]
1it [00:00,  2.84it/s]
2it [00:00,  2.94it/s]
3it [00:01,  2.90it/s]
4it [00:01,  2.86it/s]
5it [00:01,  2.91it/s]
6it [00:02,  2.93it/s]
7it [00:02,  2.95it/s]
8it [00:02,  2.94it/s]
9it [00:03,  2.96it/s]
10it [00:03,  2.96it/s]
11it [00:03,  2.94it/s]
12it [00:04,  3.04it/s]
13it [00:04,  3.02it/s]
14it [00:04,  3.02it/s]
15it [00:05,  2.98it/s]
16it [00:05,  2.97it/s]
17it [00:05,  2.98it/s]
18it [00:06,  2.95it/s]
19it [00:06,  2.91it/s]
20it [00:06,  2.96it/s]
21it [00:07,  2.90it/s]
22it [00:07,  2.89it/s]
23it [00:07,  2.89it/s]
24it [00:08,  2.87it/s]
25it [00:08,  2.88it/s]
26it [00:08,  2.93it/s]
27it [00:09,  2.93it/s]
28it [00:09,  2.97it/s]
29it [00:09,  2.93it/s]
30it [00:10,  2.92it/s]
31it [00:10,  2.95it/s]
32it [00:10,  2.87it/s]
33it [00:11,  2.88it/s]
34it [00:11,  2.81it/s]
35it [00:12,  2.75it/s]
36it [00:12,  2.71it/s]
37it [00:12,  2.76it/s]
38it [00:13,  2.81it/s]
39it [00:13,  2.94it/s]
40it [00:13,  3.03it/s]
41it [00:13,  3.21it/s]
42it [00:14,  3.29it/s]
43it [00:14,  3.32it/s]
44it [00:14,  3.49it/s]
45it [00:15,  3.62it/s]
46it [00:15,  3.70it/s]
47it [00:15,  3.78it/s]
48it [00:15,  3.75it/s]
49it [00:16,  3.84it/s]
50it [00:16,  3.95it/s]
51it [00:16,  3.90it/s]
52it [00:16,  3.99it/s]
53it [00:17,  4.11it/s]
54it [00:17,  3.99it/s]
55it [00:17,  4.02it/s]
56it [00:17,  3.98it/s]
57it [00:18,  4.06it/s]
58it [00:18,  4.02it/s]
59it [00:18,  3.99it/s]
60it [00:18,  3.92it/s]
61it [00:19,  3.89it/s]
62it [00:19,  3.90it/s]
63it [00:19,  3.92it/s]
64it [00:19,  3.65it/s]
65it [00:20,  3.43it/s]
66it [00:20,  3.22it/s]
67it [00:20,  3.04it/s]
68it [00:21,  3.09it/s]
69it [00:21,  3.00it/s]
70it [00:22,  2.88it/s]
71it [00:22,  2.82it/s]
72it [00:22,  2.86it/s]
73it [00:23,  2.80it/s]
74it [00:23,  2.78it/s]
75it [00:23,  2.99it/s]
76it [00:24,  3.07it/s]
77it [00:24,  3.13it/s]
78it [00:24,  3.78it/s]
78it [00:24,  3.19it/s]
Split counts:
	train: 54186
	test: 11612
	validation: 11610
⏰ Completed process in 0:03:00.171263
+ for split in "train" "test" "validation"
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all
+ logger 'Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv train...'
+ local 'message=Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv train...'
++ date
+ local '_date=Wed 22 Oct 16:36:45 BST 2025'
+ local 'prefix=Wed 22 Oct 16:36:45 BST 2025'
+ echo 'Wed 22 Oct 16:36:45 BST 2025 :: Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv train...'
Wed 22 Oct 16:36:45 BST 2025 :: Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv train...
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all
+ logger 'Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv test...'
+ local 'message=Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv test...'
++ date
+ local '_date=Wed 22 Oct 16:36:52 BST 2025'
+ local 'prefix=Wed 22 Oct 16:36:52 BST 2025'
+ echo 'Wed 22 Oct 16:36:52 BST 2025 :: Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv test...'
Wed 22 Oct 16:36:52 BST 2025 :: Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv test...
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all
+ logger 'Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv validation...'
+ local 'message=Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv validation...'
++ date
+ local '_date=Wed 22 Oct 16:36:53 BST 2025'
+ local 'prefix=Wed 22 Oct 16:36:53 BST 2025'
+ echo 'Wed 22 Oct 16:36:53 BST 2025 :: Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv validation...'
Wed 22 Oct 16:36:53 BST 2025 :: Processing /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv validation...
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/all/scaffold-split-validation.csv
+ unique_values species ,
+ tail -n+2
+ local col=species
+ local sep=,
+ pandas '[["species"]].drop_duplicates().sort_values("species")' ,
+ local 'cmd=[["species"]].drop_duplicates().sort_values("species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False)[["species"]].drop_duplicates().sort_values("species").to_csv(sys.stdout, index=False, sep=",")'
+ readarray -t unique_organisms
+ printf 'species_name\tn_rows\n'
+ echo 'Acinetobacter baumannii' 'Bacillus subtilis' 'Brucella abortus' 'Burkholderia thailandensis' 'Caulobacter crescentus' 'Enterobacter cloacae' 'Enterococcus faecalis' 'Escherichia coli' 'Francisella novicida' 'Francisella tularensis' 'Haemophilus influenzae' 'Klebsiella pneumoniae' 'Moraxella catarrhalis' 'Mycobacterium vaccae' 'Proteus hauseri' 'Proteus mirabilis' 'Pseudomonas aeruginosa' 'Pseudomonas fluorescens' 'Pseudomonas syringae' 'Salmonella enterica serovar Typhimurium' 'Staphylococcus aureus' 'Stenotrophomonas maltophilia' 'Streptococcus pneumoniae' 'Vibrio cholerae' 'Yersinia enterocolitica' 'Yersinia pestis' 'Yersinia pseudotuberculosis'
Acinetobacter baumannii Bacillus subtilis Brucella abortus Burkholderia thailandensis Caulobacter crescentus Enterobacter cloacae Enterococcus faecalis Escherichia coli Francisella novicida Francisella tularensis Haemophilus influenzae Klebsiella pneumoniae Moraxella catarrhalis Mycobacterium vaccae Proteus hauseri Proteus mirabilis Pseudomonas aeruginosa Pseudomonas fluorescens Pseudomonas syringae Salmonella enterica serovar Typhimurium Staphylococcus aureus Stenotrophomonas maltophilia Streptococcus pneumoniae Vibrio cholerae Yersinia enterocolitica Yersinia pestis Yersinia pseudotuberculosis
+ for species in "${unique_organisms[@]}"
+ species_safe=Acinetobacter-baumannii
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii
+ logger 'Processing Acinetobacter baumannii...'
+ local 'message=Processing Acinetobacter baumannii...'
++ date
+ local '_date=Wed 22 Oct 16:36:55 BST 2025'
+ local 'prefix=Wed 22 Oct 16:36:55 BST 2025'
+ echo 'Wed 22 Oct 16:36:55 BST 2025 :: Processing Acinetobacter baumannii...'
Wed 22 Oct 16:36:55 BST 2025 :: Processing Acinetobacter baumannii...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii
+ pandas '; species = "Acinetobacter baumannii"; df.query("species == @species")' ,
+ local 'cmd=; species = "Acinetobacter baumannii"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Acinetobacter baumannii"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/full.csv
++ wc -l
+ data_size=2146
+ logger 'Data for Acinetobacter baumannii has 2146 rows'
+ local 'message=Data for Acinetobacter baumannii has 2146 rows'
++ date
+ local '_date=Wed 22 Oct 16:36:56 BST 2025'
+ local 'prefix=Wed 22 Oct 16:36:56 BST 2025'
+ echo 'Wed 22 Oct 16:36:56 BST 2025 :: Data for Acinetobacter baumannii has 2146 rows'
Wed 22 Oct 16:36:56 BST 2025 :: Data for Acinetobacter baumannii has 2146 rows
+ '[' 2146 -gt 1000 ']'
+ printf 'Acinetobacter baumannii\t2146\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7f9965d1e170>

0it [00:00, ?it/s]
1it [00:00,  1.48it/s]
2it [00:01,  1.24it/s]
3it [00:01,  2.06it/s]
3it [00:01,  1.79it/s]

0it [00:00, ?it/s]
3it [00:00, 39.68it/s]
Split counts:
	train: 1503
	test: 322
	validation: 321
⏰ Completed process in 0:00:01.778784
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Acinetobacter-baumannii/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Bacillus-subtilis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Bacillus-subtilis
+ logger 'Processing Bacillus subtilis...'
+ local 'message=Processing Bacillus subtilis...'
++ date
+ local '_date=Wed 22 Oct 16:37:10 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:10 BST 2025'
+ echo 'Wed 22 Oct 16:37:10 BST 2025 :: Processing Bacillus subtilis...'
Wed 22 Oct 16:37:10 BST 2025 :: Processing Bacillus subtilis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Bacillus-subtilis
+ pandas '; species = "Bacillus subtilis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Bacillus subtilis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Bacillus subtilis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Bacillus-subtilis/full.csv
++ wc -l
+ data_size=13
+ logger 'Data for Bacillus subtilis has 13 rows'
+ local 'message=Data for Bacillus subtilis has 13 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:10 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:10 BST 2025'
+ echo 'Wed 22 Oct 16:37:10 BST 2025 :: Data for Bacillus subtilis has 13 rows'
Wed 22 Oct 16:37:10 BST 2025 :: Data for Bacillus subtilis has 13 rows
+ '[' 13 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Bacillus-subtilis
+ for species in "${unique_organisms[@]}"
+ species_safe=Brucella-abortus
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus
+ logger 'Processing Brucella abortus...'
+ local 'message=Processing Brucella abortus...'
++ date
+ local '_date=Wed 22 Oct 16:37:10 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:10 BST 2025'
+ echo 'Wed 22 Oct 16:37:10 BST 2025 :: Processing Brucella abortus...'
Wed 22 Oct 16:37:10 BST 2025 :: Processing Brucella abortus...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus
+ pandas '; species = "Brucella abortus"; df.query("species == @species")' ,
+ local 'cmd=; species = "Brucella abortus"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Brucella abortus"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/full.csv
++ wc -l
+ data_size=9947
+ logger 'Data for Brucella abortus has 9947 rows'
+ local 'message=Data for Brucella abortus has 9947 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:12 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:12 BST 2025'
+ echo 'Wed 22 Oct 16:37:12 BST 2025 :: Data for Brucella abortus has 9947 rows'
Wed 22 Oct 16:37:12 BST 2025 :: Data for Brucella abortus has 9947 rows
+ '[' 9947 -gt 1000 ']'
+ printf 'Brucella abortus\t9947\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7fcc81f0a170>

0it [00:00, ?it/s]
1it [00:00,  1.80it/s]
2it [00:01,  1.76it/s]
3it [00:01,  1.81it/s]
4it [00:02,  1.84it/s]
5it [00:02,  1.82it/s]
6it [00:03,  1.83it/s]
7it [00:03,  1.84it/s]
8it [00:04,  1.84it/s]
9it [00:04,  1.83it/s]
10it [00:05,  1.88it/s]
10it [00:05,  1.84it/s]

0it [00:00, ?it/s]
2it [00:00, 16.50it/s]
4it [00:00, 17.04it/s]
6it [00:00, 16.91it/s]
8it [00:00, 16.71it/s]
10it [00:00, 16.92it/s]
10it [00:00, 16.87it/s]
Split counts:
	train: 6963
	test: 1493
	validation: 1491
⏰ Completed process in 0:00:06.073482
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Brucella-abortus/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Burkholderia-thailandensis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Burkholderia-thailandensis
+ logger 'Processing Burkholderia thailandensis...'
+ local 'message=Processing Burkholderia thailandensis...'
++ date
+ local '_date=Wed 22 Oct 16:37:22 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:22 BST 2025'
+ echo 'Wed 22 Oct 16:37:22 BST 2025 :: Processing Burkholderia thailandensis...'
Wed 22 Oct 16:37:22 BST 2025 :: Processing Burkholderia thailandensis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Burkholderia-thailandensis
+ pandas '; species = "Burkholderia thailandensis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Burkholderia thailandensis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Burkholderia thailandensis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Burkholderia-thailandensis/full.csv
++ wc -l
+ data_size=725
+ logger 'Data for Burkholderia thailandensis has 725 rows'
+ local 'message=Data for Burkholderia thailandensis has 725 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:23 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:23 BST 2025'
+ echo 'Wed 22 Oct 16:37:23 BST 2025 :: Data for Burkholderia thailandensis has 725 rows'
Wed 22 Oct 16:37:23 BST 2025 :: Data for Burkholderia thailandensis has 725 rows
+ '[' 725 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Burkholderia-thailandensis
+ for species in "${unique_organisms[@]}"
+ species_safe=Caulobacter-crescentus
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Caulobacter-crescentus
+ logger 'Processing Caulobacter crescentus...'
+ local 'message=Processing Caulobacter crescentus...'
++ date
+ local '_date=Wed 22 Oct 16:37:23 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:23 BST 2025'
+ echo 'Wed 22 Oct 16:37:23 BST 2025 :: Processing Caulobacter crescentus...'
Wed 22 Oct 16:37:23 BST 2025 :: Processing Caulobacter crescentus...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Caulobacter-crescentus
+ pandas '; species = "Caulobacter crescentus"; df.query("species == @species")' ,
+ local 'cmd=; species = "Caulobacter crescentus"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Caulobacter crescentus"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Caulobacter-crescentus/full.csv
++ wc -l
+ data_size=37
+ logger 'Data for Caulobacter crescentus has 37 rows'
+ local 'message=Data for Caulobacter crescentus has 37 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:24 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:24 BST 2025'
+ echo 'Wed 22 Oct 16:37:24 BST 2025 :: Data for Caulobacter crescentus has 37 rows'
Wed 22 Oct 16:37:24 BST 2025 :: Data for Caulobacter crescentus has 37 rows
+ '[' 37 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Caulobacter-crescentus
+ for species in "${unique_organisms[@]}"
+ species_safe=Enterobacter-cloacae
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterobacter-cloacae
+ logger 'Processing Enterobacter cloacae...'
+ local 'message=Processing Enterobacter cloacae...'
++ date
+ local '_date=Wed 22 Oct 16:37:24 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:24 BST 2025'
+ echo 'Wed 22 Oct 16:37:24 BST 2025 :: Processing Enterobacter cloacae...'
Wed 22 Oct 16:37:24 BST 2025 :: Processing Enterobacter cloacae...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterobacter-cloacae
+ pandas '; species = "Enterobacter cloacae"; df.query("species == @species")' ,
+ local 'cmd=; species = "Enterobacter cloacae"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Enterobacter cloacae"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterobacter-cloacae/full.csv
++ wc -l
+ data_size=101
+ logger 'Data for Enterobacter cloacae has 101 rows'
+ local 'message=Data for Enterobacter cloacae has 101 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:24 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:24 BST 2025'
+ echo 'Wed 22 Oct 16:37:24 BST 2025 :: Data for Enterobacter cloacae has 101 rows'
Wed 22 Oct 16:37:24 BST 2025 :: Data for Enterobacter cloacae has 101 rows
+ '[' 101 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterobacter-cloacae
+ for species in "${unique_organisms[@]}"
+ species_safe=Enterococcus-faecalis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterococcus-faecalis
+ logger 'Processing Enterococcus faecalis...'
+ local 'message=Processing Enterococcus faecalis...'
++ date
+ local '_date=Wed 22 Oct 16:37:24 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:24 BST 2025'
+ echo 'Wed 22 Oct 16:37:24 BST 2025 :: Processing Enterococcus faecalis...'
Wed 22 Oct 16:37:24 BST 2025 :: Processing Enterococcus faecalis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterococcus-faecalis
+ pandas '; species = "Enterococcus faecalis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Enterococcus faecalis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Enterococcus faecalis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterococcus-faecalis/full.csv
++ wc -l
+ data_size=961
+ logger 'Data for Enterococcus faecalis has 961 rows'
+ local 'message=Data for Enterococcus faecalis has 961 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:25 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:25 BST 2025'
+ echo 'Wed 22 Oct 16:37:25 BST 2025 :: Data for Enterococcus faecalis has 961 rows'
Wed 22 Oct 16:37:25 BST 2025 :: Data for Enterococcus faecalis has 961 rows
+ '[' 961 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Enterococcus-faecalis
+ for species in "${unique_organisms[@]}"
+ species_safe=Escherichia-coli
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli
+ logger 'Processing Escherichia coli...'
+ local 'message=Processing Escherichia coli...'
++ date
+ local '_date=Wed 22 Oct 16:37:25 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:25 BST 2025'
+ echo 'Wed 22 Oct 16:37:25 BST 2025 :: Processing Escherichia coli...'
Wed 22 Oct 16:37:25 BST 2025 :: Processing Escherichia coli...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli
+ pandas '; species = "Escherichia coli"; df.query("species == @species")' ,
+ local 'cmd=; species = "Escherichia coli"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Escherichia coli"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/full.csv
++ wc -l
+ data_size=19660
+ logger 'Data for Escherichia coli has 19660 rows'
+ local 'message=Data for Escherichia coli has 19660 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:27 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:27 BST 2025'
+ echo 'Wed 22 Oct 16:37:27 BST 2025 :: Data for Escherichia coli has 19660 rows'
Wed 22 Oct 16:37:27 BST 2025 :: Data for Escherichia coli has 19660 rows
+ '[' 19660 -gt 1000 ']'
+ printf 'Escherichia coli\t19660\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7fa128a06170>

0it [00:00, ?it/s]
1it [00:00,  1.31it/s]
2it [00:01,  1.41it/s]
3it [00:02,  1.33it/s]
4it [00:02,  1.39it/s]
5it [00:03,  1.46it/s]
6it [00:04,  1.50it/s]
7it [00:04,  1.55it/s]
8it [00:05,  1.57it/s]
9it [00:06,  1.57it/s]
10it [00:06,  1.57it/s]
11it [00:07,  1.42it/s]
12it [00:08,  1.21it/s]
13it [00:09,  1.12it/s]
14it [00:10,  1.08it/s]
15it [00:11,  1.08it/s]
16it [00:12,  1.03it/s]
17it [00:13,  1.03s/it]
18it [00:14,  1.00it/s]
19it [00:15,  1.06it/s]
20it [00:15,  1.27it/s]
20it [00:15,  1.25it/s]

0it [00:00, ?it/s]
1it [00:00,  9.96it/s]
2it [00:00,  9.61it/s]
4it [00:00, 10.40it/s]
6it [00:00, 11.13it/s]
8it [00:00, 11.78it/s]
10it [00:00, 11.86it/s]
12it [00:01, 11.19it/s]
14it [00:01, 10.85it/s]
16it [00:01, 10.48it/s]
18it [00:01, 10.34it/s]
20it [00:01, 11.03it/s]
20it [00:01, 10.93it/s]
Split counts:
	train: 13762
	test: 2949
	validation: 2949
⏰ Completed process in 0:00:17.925069
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Escherichia-coli/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Francisella-novicida
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-novicida
+ logger 'Processing Francisella novicida...'
+ local 'message=Processing Francisella novicida...'
++ date
+ local '_date=Wed 22 Oct 16:37:49 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:49 BST 2025'
+ echo 'Wed 22 Oct 16:37:49 BST 2025 :: Processing Francisella novicida...'
Wed 22 Oct 16:37:49 BST 2025 :: Processing Francisella novicida...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-novicida
+ pandas '; species = "Francisella novicida"; df.query("species == @species")' ,
+ local 'cmd=; species = "Francisella novicida"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Francisella novicida"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-novicida/full.csv
++ wc -l
+ data_size=4
+ logger 'Data for Francisella novicida has 4 rows'
+ local 'message=Data for Francisella novicida has 4 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:50 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:50 BST 2025'
+ echo 'Wed 22 Oct 16:37:50 BST 2025 :: Data for Francisella novicida has 4 rows'
Wed 22 Oct 16:37:50 BST 2025 :: Data for Francisella novicida has 4 rows
+ '[' 4 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-novicida
+ for species in "${unique_organisms[@]}"
+ species_safe=Francisella-tularensis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis
+ logger 'Processing Francisella tularensis...'
+ local 'message=Processing Francisella tularensis...'
++ date
+ local '_date=Wed 22 Oct 16:37:50 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:50 BST 2025'
+ echo 'Wed 22 Oct 16:37:50 BST 2025 :: Processing Francisella tularensis...'
Wed 22 Oct 16:37:50 BST 2025 :: Processing Francisella tularensis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis
+ pandas '; species = "Francisella tularensis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Francisella tularensis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Francisella tularensis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/full.csv
++ wc -l
+ data_size=9672
+ logger 'Data for Francisella tularensis has 9672 rows'
+ local 'message=Data for Francisella tularensis has 9672 rows'
++ date
+ local '_date=Wed 22 Oct 16:37:51 BST 2025'
+ local 'prefix=Wed 22 Oct 16:37:51 BST 2025'
+ echo 'Wed 22 Oct 16:37:51 BST 2025 :: Data for Francisella tularensis has 9672 rows'
Wed 22 Oct 16:37:51 BST 2025 :: Data for Francisella tularensis has 9672 rows
+ '[' 9672 -gt 1000 ']'
+ printf 'Francisella tularensis\t9672\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7f59d26d6170>

0it [00:00, ?it/s]
1it [00:00,  1.81it/s]
2it [00:01,  1.76it/s]
3it [00:01,  1.80it/s]
4it [00:02,  1.84it/s]
5it [00:02,  1.82it/s]
6it [00:03,  1.82it/s]
7it [00:03,  1.81it/s]
8it [00:04,  1.82it/s]
9it [00:04,  1.81it/s]
10it [00:05,  2.05it/s]
10it [00:05,  1.88it/s]

0it [00:00, ?it/s]
2it [00:00, 16.45it/s]
4it [00:00, 16.96it/s]
6it [00:00, 16.74it/s]
8it [00:00, 16.59it/s]
10it [00:00, 17.62it/s]
10it [00:00, 17.19it/s]
Split counts:
	train: 6771
	test: 1451
	validation: 1450
⏰ Completed process in 0:00:05.940047
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Francisella-tularensis/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Haemophilus-influenzae
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Haemophilus-influenzae
+ logger 'Processing Haemophilus influenzae...'
+ local 'message=Processing Haemophilus influenzae...'
++ date
+ local '_date=Wed 22 Oct 16:38:01 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:01 BST 2025'
+ echo 'Wed 22 Oct 16:38:01 BST 2025 :: Processing Haemophilus influenzae...'
Wed 22 Oct 16:38:01 BST 2025 :: Processing Haemophilus influenzae...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Haemophilus-influenzae
+ pandas '; species = "Haemophilus influenzae"; df.query("species == @species")' ,
+ local 'cmd=; species = "Haemophilus influenzae"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Haemophilus influenzae"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Haemophilus-influenzae/full.csv
++ wc -l
+ data_size=27
+ logger 'Data for Haemophilus influenzae has 27 rows'
+ local 'message=Data for Haemophilus influenzae has 27 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:02 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:02 BST 2025'
+ echo 'Wed 22 Oct 16:38:02 BST 2025 :: Data for Haemophilus influenzae has 27 rows'
Wed 22 Oct 16:38:02 BST 2025 :: Data for Haemophilus influenzae has 27 rows
+ '[' 27 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Haemophilus-influenzae
+ for species in "${unique_organisms[@]}"
+ species_safe=Klebsiella-pneumoniae
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae
+ logger 'Processing Klebsiella pneumoniae...'
+ local 'message=Processing Klebsiella pneumoniae...'
++ date
+ local '_date=Wed 22 Oct 16:38:02 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:02 BST 2025'
+ echo 'Wed 22 Oct 16:38:02 BST 2025 :: Processing Klebsiella pneumoniae...'
Wed 22 Oct 16:38:02 BST 2025 :: Processing Klebsiella pneumoniae...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae
+ pandas '; species = "Klebsiella pneumoniae"; df.query("species == @species")' ,
+ local 'cmd=; species = "Klebsiella pneumoniae"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Klebsiella pneumoniae"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/full.csv
++ wc -l
+ data_size=3713
+ logger 'Data for Klebsiella pneumoniae has 3713 rows'
+ local 'message=Data for Klebsiella pneumoniae has 3713 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:03 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:03 BST 2025'
+ echo 'Wed 22 Oct 16:38:03 BST 2025 :: Data for Klebsiella pneumoniae has 3713 rows'
Wed 22 Oct 16:38:03 BST 2025 :: Data for Klebsiella pneumoniae has 3713 rows
+ '[' 3713 -gt 1000 ']'
+ printf 'Klebsiella pneumoniae\t3713\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7fe9299f6170>

0it [00:00, ?it/s]
1it [00:00,  1.47it/s]
2it [00:01,  1.51it/s]
3it [00:01,  1.62it/s]
4it [00:02,  1.66it/s]
4it [00:02,  1.62it/s]

0it [00:00, ?it/s]
3it [00:00, 26.12it/s]
4it [00:00, 27.66it/s]
Split counts:
	train: 2600
	test: 557
	validation: 556
⏰ Completed process in 0:00:02.641324
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Klebsiella-pneumoniae/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Moraxella-catarrhalis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Moraxella-catarrhalis
+ logger 'Processing Moraxella catarrhalis...'
+ local 'message=Processing Moraxella catarrhalis...'
++ date
+ local '_date=Wed 22 Oct 16:38:08 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:08 BST 2025'
+ echo 'Wed 22 Oct 16:38:08 BST 2025 :: Processing Moraxella catarrhalis...'
Wed 22 Oct 16:38:08 BST 2025 :: Processing Moraxella catarrhalis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Moraxella-catarrhalis
+ pandas '; species = "Moraxella catarrhalis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Moraxella catarrhalis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Moraxella catarrhalis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Moraxella-catarrhalis/full.csv
++ wc -l
+ data_size=2
+ logger 'Data for Moraxella catarrhalis has 2 rows'
+ local 'message=Data for Moraxella catarrhalis has 2 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:09 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:09 BST 2025'
+ echo 'Wed 22 Oct 16:38:09 BST 2025 :: Data for Moraxella catarrhalis has 2 rows'
Wed 22 Oct 16:38:09 BST 2025 :: Data for Moraxella catarrhalis has 2 rows
+ '[' 2 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Moraxella-catarrhalis
+ for species in "${unique_organisms[@]}"
+ species_safe=Mycobacterium-vaccae
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Mycobacterium-vaccae
+ logger 'Processing Mycobacterium vaccae...'
+ local 'message=Processing Mycobacterium vaccae...'
++ date
+ local '_date=Wed 22 Oct 16:38:09 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:09 BST 2025'
+ echo 'Wed 22 Oct 16:38:09 BST 2025 :: Processing Mycobacterium vaccae...'
Wed 22 Oct 16:38:09 BST 2025 :: Processing Mycobacterium vaccae...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Mycobacterium-vaccae
+ pandas '; species = "Mycobacterium vaccae"; df.query("species == @species")' ,
+ local 'cmd=; species = "Mycobacterium vaccae"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Mycobacterium vaccae"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Mycobacterium-vaccae/full.csv
++ wc -l
+ data_size=7
+ logger 'Data for Mycobacterium vaccae has 7 rows'
+ local 'message=Data for Mycobacterium vaccae has 7 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:10 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:10 BST 2025'
+ echo 'Wed 22 Oct 16:38:10 BST 2025 :: Data for Mycobacterium vaccae has 7 rows'
Wed 22 Oct 16:38:10 BST 2025 :: Data for Mycobacterium vaccae has 7 rows
+ '[' 7 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Mycobacterium-vaccae
+ for species in "${unique_organisms[@]}"
+ species_safe=Proteus-hauseri
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-hauseri
+ logger 'Processing Proteus hauseri...'
+ local 'message=Processing Proteus hauseri...'
++ date
+ local '_date=Wed 22 Oct 16:38:10 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:10 BST 2025'
+ echo 'Wed 22 Oct 16:38:10 BST 2025 :: Processing Proteus hauseri...'
Wed 22 Oct 16:38:10 BST 2025 :: Processing Proteus hauseri...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-hauseri
+ pandas '; species = "Proteus hauseri"; df.query("species == @species")' ,
+ local 'cmd=; species = "Proteus hauseri"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Proteus hauseri"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-hauseri/full.csv
++ wc -l
+ data_size=4
+ logger 'Data for Proteus hauseri has 4 rows'
+ local 'message=Data for Proteus hauseri has 4 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:11 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:11 BST 2025'
+ echo 'Wed 22 Oct 16:38:11 BST 2025 :: Data for Proteus hauseri has 4 rows'
Wed 22 Oct 16:38:11 BST 2025 :: Data for Proteus hauseri has 4 rows
+ '[' 4 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-hauseri
+ for species in "${unique_organisms[@]}"
+ species_safe=Proteus-mirabilis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-mirabilis
+ logger 'Processing Proteus mirabilis...'
+ local 'message=Processing Proteus mirabilis...'
++ date
+ local '_date=Wed 22 Oct 16:38:11 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:11 BST 2025'
+ echo 'Wed 22 Oct 16:38:11 BST 2025 :: Processing Proteus mirabilis...'
Wed 22 Oct 16:38:11 BST 2025 :: Processing Proteus mirabilis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-mirabilis
+ pandas '; species = "Proteus mirabilis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Proteus mirabilis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Proteus mirabilis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-mirabilis/full.csv
++ wc -l
+ data_size=20
+ logger 'Data for Proteus mirabilis has 20 rows'
+ local 'message=Data for Proteus mirabilis has 20 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:11 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:11 BST 2025'
+ echo 'Wed 22 Oct 16:38:11 BST 2025 :: Data for Proteus mirabilis has 20 rows'
Wed 22 Oct 16:38:11 BST 2025 :: Data for Proteus mirabilis has 20 rows
+ '[' 20 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Proteus-mirabilis
+ for species in "${unique_organisms[@]}"
+ species_safe=Pseudomonas-aeruginosa
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa
+ logger 'Processing Pseudomonas aeruginosa...'
+ local 'message=Processing Pseudomonas aeruginosa...'
++ date
+ local '_date=Wed 22 Oct 16:38:11 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:11 BST 2025'
+ echo 'Wed 22 Oct 16:38:11 BST 2025 :: Processing Pseudomonas aeruginosa...'
Wed 22 Oct 16:38:11 BST 2025 :: Processing Pseudomonas aeruginosa...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa
+ pandas '; species = "Pseudomonas aeruginosa"; df.query("species == @species")' ,
+ local 'cmd=; species = "Pseudomonas aeruginosa"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Pseudomonas aeruginosa"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/full.csv
++ wc -l
+ data_size=15454
+ logger 'Data for Pseudomonas aeruginosa has 15454 rows'
+ local 'message=Data for Pseudomonas aeruginosa has 15454 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:13 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:13 BST 2025'
+ echo 'Wed 22 Oct 16:38:13 BST 2025 :: Data for Pseudomonas aeruginosa has 15454 rows'
Wed 22 Oct 16:38:13 BST 2025 :: Data for Pseudomonas aeruginosa has 15454 rows
+ '[' 15454 -gt 1000 ']'
+ printf 'Pseudomonas aeruginosa\t15454\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7efd112ba170>

0it [00:00, ?it/s]
1it [00:00,  1.26it/s]
2it [00:01,  1.24it/s]
3it [00:02,  1.34it/s]
4it [00:02,  1.47it/s]
5it [00:03,  1.54it/s]
6it [00:04,  1.58it/s]
7it [00:04,  1.62it/s]
8it [00:05,  1.67it/s]
9it [00:05,  1.69it/s]
10it [00:06,  1.69it/s]
11it [00:06,  1.68it/s]
12it [00:07,  1.70it/s]
13it [00:08,  1.69it/s]
14it [00:08,  1.61it/s]
15it [00:09,  1.52it/s]
16it [00:09,  1.83it/s]
16it [00:09,  1.62it/s]

0it [00:00, ?it/s]
2it [00:00, 11.34it/s]
4it [00:00, 12.32it/s]
6it [00:00, 13.05it/s]
8it [00:00, 13.75it/s]
10it [00:00, 14.02it/s]
12it [00:00, 14.16it/s]
14it [00:01, 13.88it/s]
16it [00:01, 14.67it/s]
16it [00:01, 13.90it/s]
Split counts:
	train: 10818
	test: 2319
	validation: 2317
⏰ Completed process in 0:00:11.092012
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-aeruginosa/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Pseudomonas-fluorescens
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-fluorescens
+ logger 'Processing Pseudomonas fluorescens...'
+ local 'message=Processing Pseudomonas fluorescens...'
++ date
+ local '_date=Wed 22 Oct 16:38:27 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:27 BST 2025'
+ echo 'Wed 22 Oct 16:38:27 BST 2025 :: Processing Pseudomonas fluorescens...'
Wed 22 Oct 16:38:27 BST 2025 :: Processing Pseudomonas fluorescens...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-fluorescens
+ pandas '; species = "Pseudomonas fluorescens"; df.query("species == @species")' ,
+ local 'cmd=; species = "Pseudomonas fluorescens"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Pseudomonas fluorescens"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-fluorescens/full.csv
++ wc -l
+ data_size=219
+ logger 'Data for Pseudomonas fluorescens has 219 rows'
+ local 'message=Data for Pseudomonas fluorescens has 219 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:28 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:28 BST 2025'
+ echo 'Wed 22 Oct 16:38:28 BST 2025 :: Data for Pseudomonas fluorescens has 219 rows'
Wed 22 Oct 16:38:28 BST 2025 :: Data for Pseudomonas fluorescens has 219 rows
+ '[' 219 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-fluorescens
+ for species in "${unique_organisms[@]}"
+ species_safe=Pseudomonas-syringae
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-syringae
+ logger 'Processing Pseudomonas syringae...'
+ local 'message=Processing Pseudomonas syringae...'
++ date
+ local '_date=Wed 22 Oct 16:38:28 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:28 BST 2025'
+ echo 'Wed 22 Oct 16:38:28 BST 2025 :: Processing Pseudomonas syringae...'
Wed 22 Oct 16:38:28 BST 2025 :: Processing Pseudomonas syringae...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-syringae
+ pandas '; species = "Pseudomonas syringae"; df.query("species == @species")' ,
+ local 'cmd=; species = "Pseudomonas syringae"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Pseudomonas syringae"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-syringae/full.csv
++ wc -l
+ data_size=16
+ logger 'Data for Pseudomonas syringae has 16 rows'
+ local 'message=Data for Pseudomonas syringae has 16 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:29 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:29 BST 2025'
+ echo 'Wed 22 Oct 16:38:29 BST 2025 :: Data for Pseudomonas syringae has 16 rows'
Wed 22 Oct 16:38:29 BST 2025 :: Data for Pseudomonas syringae has 16 rows
+ '[' 16 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Pseudomonas-syringae
+ for species in "${unique_organisms[@]}"
+ species_safe=Salmonella-enterica-serovar-Typhimurium
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Salmonella-enterica-serovar-Typhimurium
+ logger 'Processing Salmonella enterica serovar Typhimurium...'
+ local 'message=Processing Salmonella enterica serovar Typhimurium...'
++ date
+ local '_date=Wed 22 Oct 16:38:29 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:29 BST 2025'
+ echo 'Wed 22 Oct 16:38:29 BST 2025 :: Processing Salmonella enterica serovar Typhimurium...'
Wed 22 Oct 16:38:29 BST 2025 :: Processing Salmonella enterica serovar Typhimurium...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Salmonella-enterica-serovar-Typhimurium
+ pandas '; species = "Salmonella enterica serovar Typhimurium"; df.query("species == @species")' ,
+ local 'cmd=; species = "Salmonella enterica serovar Typhimurium"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Salmonella enterica serovar Typhimurium"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Salmonella-enterica-serovar-Typhimurium/full.csv
++ wc -l
+ data_size=66
+ logger 'Data for Salmonella enterica serovar Typhimurium has 66 rows'
+ local 'message=Data for Salmonella enterica serovar Typhimurium has 66 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:29 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:29 BST 2025'
+ echo 'Wed 22 Oct 16:38:29 BST 2025 :: Data for Salmonella enterica serovar Typhimurium has 66 rows'
Wed 22 Oct 16:38:29 BST 2025 :: Data for Salmonella enterica serovar Typhimurium has 66 rows
+ '[' 66 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Salmonella-enterica-serovar-Typhimurium
+ for species in "${unique_organisms[@]}"
+ species_safe=Staphylococcus-aureus
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus
+ logger 'Processing Staphylococcus aureus...'
+ local 'message=Processing Staphylococcus aureus...'
++ date
+ local '_date=Wed 22 Oct 16:38:29 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:29 BST 2025'
+ echo 'Wed 22 Oct 16:38:29 BST 2025 :: Processing Staphylococcus aureus...'
Wed 22 Oct 16:38:29 BST 2025 :: Processing Staphylococcus aureus...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus
+ pandas '; species = "Staphylococcus aureus"; df.query("species == @species")' ,
+ local 'cmd=; species = "Staphylococcus aureus"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Staphylococcus aureus"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/full.csv
++ wc -l
+ data_size=1914
+ logger 'Data for Staphylococcus aureus has 1914 rows'
+ local 'message=Data for Staphylococcus aureus has 1914 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:30 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:30 BST 2025'
+ echo 'Wed 22 Oct 16:38:30 BST 2025 :: Data for Staphylococcus aureus has 1914 rows'
Wed 22 Oct 16:38:30 BST 2025 :: Data for Staphylococcus aureus has 1914 rows
+ '[' 1914 -gt 1000 ']'
+ printf 'Staphylococcus aureus\t1914\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7f2045836170>

0it [00:00, ?it/s]
1it [00:00,  1.55it/s]
2it [00:01,  1.60it/s]
2it [00:01,  1.59it/s]

0it [00:00, ?it/s]
2it [00:00, 30.23it/s]
Split counts:
	train: 1340
	test: 288
	validation: 286
⏰ Completed process in 0:00:01.336805
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Staphylococcus-aureus/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Stenotrophomonas-maltophilia
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Stenotrophomonas-maltophilia
+ logger 'Processing Stenotrophomonas maltophilia...'
+ local 'message=Processing Stenotrophomonas maltophilia...'
++ date
+ local '_date=Wed 22 Oct 16:38:34 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:34 BST 2025'
+ echo 'Wed 22 Oct 16:38:34 BST 2025 :: Processing Stenotrophomonas maltophilia...'
Wed 22 Oct 16:38:34 BST 2025 :: Processing Stenotrophomonas maltophilia...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Stenotrophomonas-maltophilia
+ pandas '; species = "Stenotrophomonas maltophilia"; df.query("species == @species")' ,
+ local 'cmd=; species = "Stenotrophomonas maltophilia"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Stenotrophomonas maltophilia"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Stenotrophomonas-maltophilia/full.csv
++ wc -l
+ data_size=13
+ logger 'Data for Stenotrophomonas maltophilia has 13 rows'
+ local 'message=Data for Stenotrophomonas maltophilia has 13 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:35 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:35 BST 2025'
+ echo 'Wed 22 Oct 16:38:35 BST 2025 :: Data for Stenotrophomonas maltophilia has 13 rows'
Wed 22 Oct 16:38:35 BST 2025 :: Data for Stenotrophomonas maltophilia has 13 rows
+ '[' 13 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Stenotrophomonas-maltophilia
+ for species in "${unique_organisms[@]}"
+ species_safe=Streptococcus-pneumoniae
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae
+ logger 'Processing Streptococcus pneumoniae...'
+ local 'message=Processing Streptococcus pneumoniae...'
++ date
+ local '_date=Wed 22 Oct 16:38:35 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:35 BST 2025'
+ echo 'Wed 22 Oct 16:38:35 BST 2025 :: Processing Streptococcus pneumoniae...'
Wed 22 Oct 16:38:35 BST 2025 :: Processing Streptococcus pneumoniae...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae
+ pandas '; species = "Streptococcus pneumoniae"; df.query("species == @species")' ,
+ local 'cmd=; species = "Streptococcus pneumoniae"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Streptococcus pneumoniae"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/full.csv
++ wc -l
+ data_size=1254
+ logger 'Data for Streptococcus pneumoniae has 1254 rows'
+ local 'message=Data for Streptococcus pneumoniae has 1254 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:36 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:36 BST 2025'
+ echo 'Wed 22 Oct 16:38:36 BST 2025 :: Data for Streptococcus pneumoniae has 1254 rows'
Wed 22 Oct 16:38:36 BST 2025 :: Data for Streptococcus pneumoniae has 1254 rows
+ '[' 1254 -gt 1000 ']'
+ printf 'Streptococcus pneumoniae\t1254\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7fc9c0082170>

0it [00:00, ?it/s]
1it [00:00,  1.43it/s]
2it [00:00,  2.52it/s]
2it [00:00,  2.26it/s]

0it [00:00, ?it/s]
2it [00:00, 44.92it/s]
Split counts:
	train: 878
	test: 189
	validation: 187
⏰ Completed process in 0:00:00.939748
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Streptococcus-pneumoniae/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Vibrio-cholerae
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Vibrio-cholerae
+ logger 'Processing Vibrio cholerae...'
+ local 'message=Processing Vibrio cholerae...'
++ date
+ local '_date=Wed 22 Oct 16:38:39 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:39 BST 2025'
+ echo 'Wed 22 Oct 16:38:39 BST 2025 :: Processing Vibrio cholerae...'
Wed 22 Oct 16:38:39 BST 2025 :: Processing Vibrio cholerae...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Vibrio-cholerae
+ pandas '; species = "Vibrio cholerae"; df.query("species == @species")' ,
+ local 'cmd=; species = "Vibrio cholerae"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Vibrio cholerae"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Vibrio-cholerae/full.csv
++ wc -l
+ data_size=9
+ logger 'Data for Vibrio cholerae has 9 rows'
+ local 'message=Data for Vibrio cholerae has 9 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:40 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:40 BST 2025'
+ echo 'Wed 22 Oct 16:38:40 BST 2025 :: Data for Vibrio cholerae has 9 rows'
Wed 22 Oct 16:38:40 BST 2025 :: Data for Vibrio cholerae has 9 rows
+ '[' 9 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Vibrio-cholerae
+ for species in "${unique_organisms[@]}"
+ species_safe=Yersinia-enterocolitica
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica
+ logger 'Processing Yersinia enterocolitica...'
+ local 'message=Processing Yersinia enterocolitica...'
++ date
+ local '_date=Wed 22 Oct 16:38:40 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:40 BST 2025'
+ echo 'Wed 22 Oct 16:38:40 BST 2025 :: Processing Yersinia enterocolitica...'
Wed 22 Oct 16:38:40 BST 2025 :: Processing Yersinia enterocolitica...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica
+ pandas '; species = "Yersinia enterocolitica"; df.query("species == @species")' ,
+ local 'cmd=; species = "Yersinia enterocolitica"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Yersinia enterocolitica"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/full.csv
++ wc -l
+ data_size=1405
+ logger 'Data for Yersinia enterocolitica has 1405 rows'
+ local 'message=Data for Yersinia enterocolitica has 1405 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:41 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:41 BST 2025'
+ echo 'Wed 22 Oct 16:38:41 BST 2025 :: Data for Yersinia enterocolitica has 1405 rows'
Wed 22 Oct 16:38:41 BST 2025 :: Data for Yersinia enterocolitica has 1405 rows
+ '[' 1405 -gt 1000 ']'
+ printf 'Yersinia enterocolitica\t1405\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7fc3280f6170>

0it [00:00, ?it/s]
1it [00:00,  1.60it/s]
2it [00:00,  2.44it/s]
2it [00:00,  2.26it/s]

0it [00:00, ?it/s]
2it [00:00, 43.41it/s]
Split counts:
	train: 984
	test: 211
	validation: 210
⏰ Completed process in 0:00:00.939103
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-enterocolitica/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Yersinia-pestis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis
+ logger 'Processing Yersinia pestis...'
+ local 'message=Processing Yersinia pestis...'
++ date
+ local '_date=Wed 22 Oct 16:38:44 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:44 BST 2025'
+ echo 'Wed 22 Oct 16:38:44 BST 2025 :: Processing Yersinia pestis...'
Wed 22 Oct 16:38:44 BST 2025 :: Processing Yersinia pestis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis
+ pandas '; species = "Yersinia pestis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Yersinia pestis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Yersinia pestis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/full.csv
++ wc -l
+ data_size=10003
+ logger 'Data for Yersinia pestis has 10003 rows'
+ local 'message=Data for Yersinia pestis has 10003 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:45 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:45 BST 2025'
+ echo 'Wed 22 Oct 16:38:45 BST 2025 :: Data for Yersinia pestis has 10003 rows'
Wed 22 Oct 16:38:45 BST 2025 :: Data for Yersinia pestis has 10003 rows
+ '[' 10003 -gt 1000 ']'
+ printf 'Yersinia pestis\t10003\n'
+ schemist split /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/full.csv --type scaffold --train 0.7 --test 0.15 --seed 0 --output /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/scaffold-split.csv
🚀 Splitting table with the following parameters:
	subcommand: split
	output: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/scaffold-split.csv' mode='w' encoding='UTF-8'>
	format: None
	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/full.csv' mode='r' encoding='UTF-8'>
	representation: SMILES
	column: smiles
	prefix: None
	type: scaffold
	train: 0.7
	test: 0.15
	seed: 0
	func: <function _split at 0x7fbc3f82a170>

0it [00:00, ?it/s]
1it [00:00,  1.81it/s]
2it [00:01,  1.74it/s]
3it [00:01,  1.80it/s]
4it [00:02,  1.84it/s]
5it [00:02,  1.82it/s]
6it [00:03,  1.83it/s]
7it [00:03,  1.82it/s]
8it [00:04,  1.82it/s]
9it [00:04,  1.82it/s]
10it [00:05,  1.84it/s]
11it [00:05,  2.00it/s]

0it [00:00, ?it/s]
2it [00:00, 16.03it/s]
4it [00:00, 16.55it/s]
6it [00:00, 16.41it/s]
8it [00:00, 16.23it/s]
10it [00:00, 16.28it/s]
11it [00:00, 17.85it/s]
Split counts:
	train: 7003
	test: 1501
	validation: 1499
⏰ Completed process in 0:00:06.156438
+ for split in "train" "test" "validation"
+ pandas '.query("is_train")'
+ local 'cmd=.query("is_train")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_train").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/scaffold-split-train.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_test")'
+ local 'cmd=.query("is_test")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_test").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/scaffold-split-test.csv
+ for split in "train" "test" "validation"
+ pandas '.query("is_validation")'
+ local 'cmd=.query("is_validation")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False).query("is_validation").to_csv(sys.stdout, index=False, sep=",")'
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/scaffold-split-validation.csv
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/scaffold-split.csv
+ rm /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pestis/full.csv
+ for species in "${unique_organisms[@]}"
+ species_safe=Yersinia-pseudotuberculosis
+ output_data_dir=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pseudotuberculosis
+ logger 'Processing Yersinia pseudotuberculosis...'
+ local 'message=Processing Yersinia pseudotuberculosis...'
++ date
+ local '_date=Wed 22 Oct 16:38:54 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:54 BST 2025'
+ echo 'Wed 22 Oct 16:38:54 BST 2025 :: Processing Yersinia pseudotuberculosis...'
Wed 22 Oct 16:38:54 BST 2025 :: Processing Yersinia pseudotuberculosis...
+ mkdir -p /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pseudotuberculosis
+ pandas '; species = "Yersinia pseudotuberculosis"; df.query("species == @species")' ,
+ local 'cmd=; species = "Yersinia pseudotuberculosis"; df.query("species == @species")'
+ local sep1=,
+ local idx=False
+ local sep2=,
+ python -c 'import sys; import pandas as pd; df = pd.read_csv(sys.stdin, sep=",", low_memory=False); species = "Yersinia pseudotuberculosis"; df.query("species == @species").to_csv(sys.stdout, index=False, sep=",")'
++ tail -n+2 /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pseudotuberculosis/full.csv
++ wc -l
+ data_size=16
+ logger 'Data for Yersinia pseudotuberculosis has 16 rows'
+ local 'message=Data for Yersinia pseudotuberculosis has 16 rows'
++ date
+ local '_date=Wed 22 Oct 16:38:55 BST 2025'
+ local 'prefix=Wed 22 Oct 16:38:55 BST 2025'
+ echo 'Wed 22 Oct 16:38:55 BST 2025 :: Data for Yersinia pseudotuberculosis has 16 rows'
Wed 22 Oct 16:38:55 BST 2025 :: Data for Yersinia pseudotuberculosis has 16 rows
+ '[' 16 -gt 1000 ']'
+ rm -r /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/Yersinia-pseudotuberculosis
+ gzip --best -f /nemo/lab/johnsone/home/users/johnsoe/data/datasets/spark/species-wt-v02/spark-accumulation-wt.csv
+ set +x
Wed 22 Oct 16:38:56 BST 2025 :: Done!