Scripts.DatabankLib.analyze module

DatabankLib.analyze contains top-level methods for properties’ computing.

Module includes APL, TH, MAICoS, NMRPCA, and OP core analysis functions. Code-intensive computations are performed inside corresponding modules. Here, we gather only methods exported to the end-user if one wants to start recomputing any of Databank’s systems via python interface.

Scripts.DatabankLib.analyze.computeNMRPCA(system: System, logger: Logger, *, recompute: bool = False) → int[source]

Compute eq_times.json using NMR PCA analysis.

Parameters:

  • system – System of the Databank

  • logger – Logger object

  • recompute – Delete previous apl.json and recompute it if True. Defaults to False.

Returns:

int success code (RCODE_…)

Scripts.DatabankLib.analyze.computeAPL(system: System, logger: Logger, *, recompute: bool = False) → int[source]

Generate apl.json analysis file for a system.

Parameters:

  • system (dict) – one of systems of the Databank

  • recompute (bool, optional) – Delete previous apl.json and recompute it if True.

  • False. (Defaults to)

Return type:

int success code (RCODE_…)

Scripts.DatabankLib.analyze.computeTH(system: System, logger: Logger, *, recompute: bool = False) → int[source]
Scripts.DatabankLib.analyze.computeOP(system: System, logger: Logger, *, recompute: bool = False) → int[source]

_summary_

Parameters:

  • system (dict) – _description_

  • recompute (bool, optional) – _description_. Defaults to False.

Returns:

int

Return type:

_description_

Scripts.DatabankLib.analyze.computeMAICOS(system: System, logger: Logger, *, ffonly: bool = True, recompute: bool = False) → int[source]