02 - Summarize and filter a batch¶

A batch is the collection-level API. This notebook uses the bundled sample to show summary columns and the fact that filtering returns a new batch. Replace the sample path with a glob such as results/*.log for a research dataset.

In [1]:

from pathlib import Path

from molop import AutoParser, molopconfig

molopconfig.quiet()
sample_candidates = (
    Path("docs/assets/examples/water_mp2.out"),
    Path("../../assets/examples/water_mp2.out"),
    Path("water_mp2.out"),
)
sample_path = next((path for path in sample_candidates if path.is_file()), None)
if sample_path is None:
    raise FileNotFoundError("Place water_mp2.out beside the notebook or use the bundled example.")
batch = AutoParser(sample_path, n_jobs=1)

print("batch size:", len(batch))
print("files:", batch.file_names)

from pathlib import Path from molop import AutoParser, molopconfig molopconfig.quiet() sample_candidates = ( Path("docs/assets/examples/water_mp2.out"), Path("../../assets/examples/water_mp2.out"), Path("water_mp2.out"), ) sample_path = next((path for path in sample_candidates if path.is_file()), None) if sample_path is None: raise FileNotFoundError("Place water_mp2.out beside the notebook or use the bundled example.") batch = AutoParser(sample_path, n_jobs=1) print("batch size:", len(batch)) print("files:", batch.file_names)

batch size: 1
files: ['water_mp2.out']

A full summary adds fields that are available in the source. Flattened columns are convenient for CSV and ordinary DataFrame operations.

In [2]:

import pandas as pd
from IPython.display import display

summary = batch.to_summary_df(
    frame=-1,
    brief=False,
    flatten_columns=True,
)
with pd.option_context("display.max_columns", None, "display.max_rows", None, "display.width", 240):
    display(summary)

import pandas as pd from IPython.display import display summary = batch.to_summary_df( frame=-1, brief=False, flatten_columns=True, ) with pd.option_context("display.max_columns", None, "display.max_rows", None, "display.width", 240): display(summary)

	DiskStorage.FilePath	DiskStorage.FileFormat	General.Charge	General.Multiplicity	General.CanonicalSMILES	General.NumAtoms	General.FrameID	Calc Parameter.Software	Calc Parameter.Version	Calc Parameter.Method	Calc Parameter.BasisSet	Calc Parameter.Functional	Calc Parameter.Keywords	Environment.SolventModel	Environment.Solvent	Status.IsError	Status.IsNormal	Status.IsTS	Status.IsOptimized	Energy.electronic_energy.hartree	Energy.reference_energy.hartree	Energy.mp2_energy.hartree	Energy.total_energy.hartree
0	/home/runner/work/MolOP/MolOP/docs/assets/exam...	.out	0	1	O	3	0	ORCA	6.0.1	MP2	sto-3g		MP2 sto-3g	None	None	False	True	False	False	-74.999375	-74.963574	-74.999375	-74.999375

State filters can be chained. They do not mutate the original batch.

In [3]:

normal = batch.filter_state("normal")
optimized = normal.filter_state("opt")
transition_states = normal.filter_state("ts")

print({
    "parsed": len(batch),
    "normal": len(normal),
    "optimized": len(optimized),
    "transition_states": len(transition_states),
})
print("original batch still contains:", len(batch))

normal = batch.filter_state("normal") optimized = normal.filter_state("opt") transition_states = normal.filter_state("ts") print({ "parsed": len(batch), "normal": len(normal), "optimized": len(optimized), "transition_states": len(transition_states), }) print("original batch still contains:", len(batch))

{'parsed': 1, 'normal': 1, 'optimized': 0, 'transition_states': 0}
original batch still contains: 1

Use the selected batch for export, or write the summary to a CSV file.

In [4]:

summary.to_csv("summary.csv", index=False)
print("wrote summary.csv")

summary.to_csv("summary.csv", index=False) print("wrote summary.csv")

wrote summary.csv