Python Library Guide

Python Library Guide

FleXgeo2 can be used as a Python library when you want to run analyses inside notebooks, inspect data frames in memory, or integrate geometry descriptors into another workflow.

High-level API

The main entry point is FlexGeo2App.

from flexgeo2 import AnalysisConfig, FlexGeo2App

result = FlexGeo2App().run(
    AnalysisConfig(pdb_file="path/to/ensemble.pdb")
)

The top-level configuration objects are:

• AnalysisConfig: full analysis settings
• OutputConfig: output directory and file-writing policy
• ReferenceConfig: reference-state comparison settings
• ClusteringConfig: per-residue and residue-range clustering settings

AnalysisConfig

from flexgeo2 import AnalysisConfig, ClusteringConfig, OutputConfig, ReferenceConfig

config = AnalysisConfig(
    pdb_file="path/to/ensemble.pdb",
    chains=["A"],
    n_jobs=4,
    max_models_in_plot=12,
    hide_model_traces=False,
    dmax_outlier_fraction=0.01,
    reference=ReferenceConfig(model_id="1"),
    clustering=ClusteringConfig(
        cluster_residues=True,
        cluster_residue_ranges=["45-54"],
        min_cluster_size=8,
        min_samples=4,
    ),
    output=OutputConfig(output_dir="path/to/results", verbose=True),
)

Running without writing files

Use OutputConfig(write_files=False) when you only want the in-memory result data frames.

from flexgeo2 import AnalysisConfig, FlexGeo2App, OutputConfig

result = FlexGeo2App().run(
    AnalysisConfig(
        pdb_file="path/to/ensemble.pdb",
        output=OutputConfig(write_files=False),
    )
)

Result objects

FlexGeo2App().run(config) returns an AnalysisResult with:

• pdb_file: resolved input path
• raw_df: normalized Melodia descriptor table
• residue_summary_df: per-residue curvature, torsion, and dmax summary
• model_summary_df: per-chain model summary
• overall_model_summary_df: model summary across selected chains
• distance_result: optional DistanceResult
• residue_clustering: optional ResidueClusteringResult
• residue_range_clustering: optional ResidueRangeClusteringResult
• outputs: OutputArtifacts with paths to written files

DistanceResult contains long_df, summary_df, and reference_label.

ResidueClusteringResult and ResidueRangeClusteringResult each contain assignments_df and summary_df.

CLI to Python examples

Basic run:

flexgeo2 path/to/ensemble.pdb

AnalysisConfig(pdb_file="path/to/ensemble.pdb")

Chain filtering:

flexgeo2 path/to/ensemble.pdb --chain A --n-jobs 4

AnalysisConfig(
    pdb_file="path/to/ensemble.pdb",
    chains=["A"],
    n_jobs=4,
)

Reference model comparison:

flexgeo2 path/to/ensemble.pdb --reference-model 1

AnalysisConfig(
    pdb_file="path/to/ensemble.pdb",
    reference=ReferenceConfig(model_id="1"),
)

External reference PDB:

flexgeo2 path/to/ensemble.pdb \
  --reference-pdb path/to/reference.pdb \
  --reference-pdb-model 1

AnalysisConfig(
    pdb_file="path/to/ensemble.pdb",
    reference=ReferenceConfig(
        pdb_file="path/to/reference.pdb",
        pdb_model_id="1",
    ),
)

Residue clustering:

flexgeo2 path/to/ensemble.pdb \
  --cluster-residues \
  --cluster-min-size 8 \
  --cluster-min-samples 4

AnalysisConfig(
    pdb_file="path/to/ensemble.pdb",
    clustering=ClusteringConfig(
        cluster_residues=True,
        min_cluster_size=8,
        min_samples=4,
    ),
)

Residue-range clustering:

flexgeo2 path/to/ensemble.pdb \
  --chain A \
  --cluster-residue-range 45-54 \
  --cluster-residue-range 90-99

AnalysisConfig(
    pdb_file="path/to/ensemble.pdb",
    chains=["A"],
    clustering=ClusteringConfig(
        cluster_residue_ranges=["45-54", "90-99"],
    ),
)

For a notebook dedicated to these mappings, see notebooks/05_cli_to_python_mapping.ipynb.

Service-level API

Advanced users can call services directly.

from flexgeo2.geometry import GeometryService

geometry = GeometryService()
raw_df = geometry.load_structure("path/to/ensemble.pdb", n_jobs=4)
raw_df = geometry.filter_chains(raw_df, ["A"])
raw_df = geometry.normalize(raw_df)
summary_df = geometry.summarize(raw_df, dmax_outlier_fraction=0.01)

from flexgeo2.clustering import ClusteringService

clusters = ClusteringService()
assignments_df, cluster_summary_df = clusters.cluster_residues(
    raw_df,
    min_cluster_size=5,
    min_samples=None,
)

from flexgeo2.config import OutputConfig
from flexgeo2.outputs import OutputWriter

writer = OutputWriter(OutputConfig(output_dir="path/to/results", verbose=False))
artifacts = writer.write(
    result,
    max_models_in_plot=12,
    hide_model_traces=False,
)