prolint.core.contact_provider¶

Contact provider module.

This module provides classes for computing and managing contact data between molecular groups.

Attributes¶

logger

Classes¶

`ComputedContacts`	Container for computed contact data with analysis methods.
`ContactsProvider`	Orchestrates contact computation between atom groups.

Module Contents¶

prolint.core.contact_provider.logger[source]¶

class prolint.core.contact_provider.ComputedContacts(contact_strategy_instance: prolint.contacts.base.BaseContactStore, provider: ContactsProvider)[source]¶

Container for computed contact data with analysis methods.

This class wraps contact computation results and provides methods for analyzing contacts, computing metrics, and performing set operations.

Parameters:

contact_strategy_instance (BaseContactStore) – Contact storage instance containing computed contacts.
provider (ContactsProvider) – The provider that created this instance.

Examples

>>> contacts = universe.compute_contacts(cutoff=7.0)
>>> result = contacts.analyze("timeseries", database_type="CHOL")

Compute metrics:

>>> occupancy = contacts.compute_metric("occupancy", target_resname="CHOL")

Set operations:

>>> common = contacts1 + contacts2  # Intersection
>>> unique = contacts1 - contacts2  # Difference

See also

Universe.compute_contacts: Method that creates ComputedContacts
AnalysisRegistry: Registry of available analysis types

provider[source]¶

compute_metric(metric: str, target_resname=None)[source]¶

Compute a metric for contacts.

Parameters:

metric (str) – Metric to compute. Options: “occupancy”, “mean”, “max”, “sum”.
target_resname (str, optional) – Filter by residue name (e.g., “CHOL”, “POPC”).

Returns:

Metric values organized by residue ID.

Return type:

dict

Examples

>>> occupancy = contacts.compute_metric("occupancy", target_resname="CHOL")
>>> mean_duration = contacts.compute_metric("mean")

apply_function(func: Callable, target_resname=None)[source]¶

Apply a custom function to contact data.

Parameters:

func (callable) – Function to apply to contact durations.
target_resname (str, optional) – Filter by residue name.

Returns:

Function results organized by residue ID.

Return type:

dict

property contacts[source]¶

Raw contact data.

Returns:: Contact data organized by residue and database molecule.
Return type:: dict

property contact_frames[source]¶

Frame indices where contacts occur.

Returns:: Nested dict mapping residue_id -> database_id -> list of frame indices.
Return type:: dict

property norm_factor: float[source]¶

Normalization factor for duration calculations.

Returns:: Factor used to normalize contact durations.
Return type:: float

intersection(other: ComputedContacts) → ComputedContacts[source]¶

Find contacts common to both ComputedContacts objects.

Parameters:: other (ComputedContacts) – Another computed contacts object.
Returns:: New object containing only contacts present in both.
Return type:: ComputedContacts

Examples

>>> common = contacts1.intersection(contacts2)
>>> # Or using operator
>>> common = contacts1 + contacts2

difference(other: ComputedContacts) → ComputedContacts[source]¶

Find contacts in self but not in other.

Parameters:: other (ComputedContacts) – Another computed contacts object.
Returns:: New object containing contacts unique to self.
Return type:: ComputedContacts

Examples

>>> unique = contacts1.difference(contacts2)
>>> # Or using operator
>>> unique = contacts1 - contacts2

analyze(analysis_type: str, **kwargs) → prolint.analysis.base.AnalysisResult[source]¶

Run an analysis on computed contacts.

Parameters:

analysis_type (str) – Type of analysis to run. Options: - “timeseries”: Contact counts over time - “database_contacts”: Per-molecule contact timeline - “kinetics”: Binding kinetics and residence times - “density_map”: 2D spatial density - “radial_density”: Radial density profile - “shared_contacts”: Residue contact correlations - “distances”: Distance distributions - “atom_distances”: Atom-level distances - “metrics”: Per-residue metrics
**kwargs (dict) – Analysis-specific parameters.

Returns:

Result object containing analysis data.

Return type:

AnalysisResult

Examples

>>> result = contacts.analyze("timeseries", database_type="CHOL")
>>> result = contacts.analyze("kinetics", query_residue=42, mode="accumulated")
>>> result = contacts.analyze("metrics", metric="occupancy")