blueetl.repository

Repository.

Classes

BaseExtractor(repo)	BaseExtractor class.
CompartmentReportExtractor(repo)	CompartmentReportExtractor class.
FilteredRepository(parent, simulations_filter)	FilteredRepository class.
NeuronClassesExtractor(repo)	NeuronClassesExtractor class.
NeuronsExtractor(repo)	NeuronsExtractor class.
Repository(simulations_config, ...[, ...])	Repository class.
SimulationsExtractor(repo)	SimulationsExtractor class.
SomaReportExtractor(repo)	SomaReportExtractor class.
SpikesExtractor(repo)	SpikesExtractor class.
WindowsExtractor(repo)	WindowsExtractor class.

class blueetl.repository.BaseExtractor(repo: Repository)

Bases: ABC, Generic[ExtractorT]

BaseExtractor class.

Initialize the object.

extract(name: str) → ExtractorT

Return an object extracted from the cache or as new.

Parameters:

name – name of the dataframe.

abstract extract_cached(df: DataFrame, name: str) → ExtractorT

Instantiate an object from a cached DataFrame.

abstract extract_new() → ExtractorT

Instantiate an object from the configuration.

class blueetl.repository.CompartmentReportExtractor(repo: Repository)

Bases: BaseExtractor[CompartmentReport]

CompartmentReportExtractor class.

Initialize the object.

extract_cached(df: DataFrame, name: str) → CompartmentReport

Instantiate an object from a cached DataFrame.

extract_new() → CompartmentReport

Instantiate an object from the configuration.

class blueetl.repository.FilteredRepository(parent: Repository, simulations_filter: dict[str, Any])

Bases: Repository

FilteredRepository class.

Initialize the object using the given dict of DataFrames.

Filtered dataframes are never written to disk.

needs_filter(name: str) → bool

Return True if the repository needs to be filtered during the extraction.

class blueetl.repository.NeuronClassesExtractor(repo: Repository)

Bases: BaseExtractor[NeuronClasses]

NeuronClassesExtractor class.

Initialize the object.

extract_cached(df: DataFrame, name: str) → NeuronClasses

Instantiate an object from a cached DataFrame.

extract_new() → NeuronClasses

Instantiate an object from the configuration.

class blueetl.repository.NeuronsExtractor(repo: Repository)

Bases: BaseExtractor[Neurons]

NeuronsExtractor class.

Initialize the object.

extract_cached(df: DataFrame, name: str) → Neurons

Instantiate an object from a cached DataFrame.

extract_new() → Neurons

Instantiate an object from the configuration.

class blueetl.repository.Repository(simulations_config: SimulationCampaign, extraction_config: ExtractionConfig, cache_manager: CacheManager, simulations_filter: dict[str, Any] | None = None, resolver: Resolver | None = None)

Bases: object

Repository class.

Initialize the repository.

Parameters:

• simulations_config – simulation campaign configuration.

• extraction_config – extraction configuration.

• cache_manager – cache manager responsible to load and dump dataframes.

• simulations_filter – optional simulations filter.

• resolver – resolver instance.

apply_filter(simulations_filter: dict[str, Any]) → Repository

Apply the given filter and return a new object.

property cache_manager: CacheManager

Access to the cache manager.

check_extractions() → None

Check that all the dataframes have been extracted.

extract() → None

Extract all the dataframes.

property extraction_config: ExtractionConfig

Access to the extraction configuration.

is_cached() → bool

Return True if all the dataframes have been cached (loaded in memory or not).

is_extracted() → bool

Return True if all the dataframes have been extracted and loaded in memory.

missing_simulations() → DataFrame

Return a DataFrame with the simulations ignored because of missing spikes.

Returns:

pd.DataFrame with the simulation conditions and simulation_path as columns,

and one record for each ignored and missing simulation.

property names: list[str]

Return the list of names of the extracted objects.

needs_filter(name: str) → bool

Return True if the repository needs to be filtered during the extraction.

property neuron_classes: NeuronClasses

Return the NeuronClasses extraction.

property neurons: Neurons

Return the Neurons extraction.

property report: ReportExtractor

Return the Report extraction.

property resolver: Resolver | None

Return the resolver.

show() → None

Print some information about the instance, mainly for debug and inspection.

property simulation_ids: list[int]

Return the list of simulation ids, possibly filtered.

property simulations: Simulations

Return the Simulations extraction.

property simulations_config: SimulationCampaign

Access to the simulation campaign configuration.

property simulations_filter: dict[str, Any] | None

Access to the simulations filter.

property spikes: ReportExtractor

Return the Spikes extraction.

property windows: Windows

Return the Windows extraction.

class blueetl.repository.SimulationsExtractor(repo: Repository)

Bases: BaseExtractor[Simulations]

SimulationsExtractor class.

Initialize the object.

extract_cached(df: DataFrame, name: str) → Simulations

Instantiate an object from a cached DataFrame.

extract_new() → Simulations

Instantiate an object from the configuration.

class blueetl.repository.SomaReportExtractor(repo: Repository)

Bases: BaseExtractor[SomaReport]

SomaReportExtractor class.

Initialize the object.

extract_cached(df: DataFrame, name: str) → SomaReport

Instantiate an object from a cached DataFrame.

extract_new() → SomaReport

Instantiate an object from the configuration.

class blueetl.repository.SpikesExtractor(repo: Repository)

Bases: BaseExtractor[Spikes]

SpikesExtractor class.

Initialize the object.

extract_cached(df: DataFrame, name: str) → Spikes

Instantiate an object from a cached DataFrame.

extract_new() → Spikes

Instantiate an object from the configuration.

class blueetl.repository.WindowsExtractor(repo: Repository)

Bases: BaseExtractor[Windows]

WindowsExtractor class.

Initialize the object.

extract_cached(df: DataFrame, name: str) → Windows

Instantiate an object from a cached DataFrame.

extract_new() → Windows

Instantiate an object from the configuration.