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Source code for galaxy.visualization.data_providers.registry

from typing import Dict, Optional, Type, Union

from typing_extensions import Literal

from galaxy.datatypes.data import Data, Newick, Nexus
from galaxy.datatypes.interval import (
    Bed,
    ChromatinInteractions,
    ENCODEPeak,
    Gff,
    Gtf,
    Interval
)
from galaxy.datatypes.tabular import Tabular, Vcf
from galaxy.datatypes.xml import Phyloxml
from galaxy.model import NoConverterException
from galaxy.visualization.data_providers import genome
from galaxy.visualization.data_providers.basic import BaseDataProvider, ColumnDataProvider
from galaxy.visualization.data_providers.phyloviz import PhylovizDataProvider


# a dict keyed on datatype with a 'default' string key.
PROVIDER_BY_DATATYPE_CLASS_DICT = Dict[Union[Literal["default"], Type[Data]], Type[BaseDataProvider]]
DATA_PROVIDER_BY_TYPE_NAME_DICT = Dict[str, Union[Type[BaseDataProvider], PROVIDER_BY_DATATYPE_CLASS_DICT]]


[docs]class DataProviderRegistry: """ Registry for data providers that enables listing and lookup. """
[docs] def __init__(self): # Mapping from dataset type name to a class that can fetch data from a file of that # type. First key is converted dataset type; if result is another dict, second key # is original dataset type. self.dataset_type_name_to_data_provider: DATA_PROVIDER_BY_TYPE_NAME_DICT = { "tabix": { Vcf: genome.VcfTabixDataProvider, Bed: genome.BedTabixDataProvider, Gtf: genome.GtfTabixDataProvider, ENCODEPeak: genome.ENCODEPeakTabixDataProvider, Interval: genome.IntervalTabixDataProvider, ChromatinInteractions: genome.ChromatinInteractionsTabixDataProvider, "default": genome.TabixDataProvider }, "interval_index": genome.IntervalIndexDataProvider, "bai": genome.BamDataProvider, "bam": genome.SamDataProvider, "bigwig": genome.BigWigDataProvider, "bigbed": genome.BigBedDataProvider, "column_with_stats": ColumnDataProvider }
[docs] def get_data_provider(self, trans, name=None, source='data', raw=False, original_dataset=None): """ Returns data provider matching parameter values. For standalone data sources, source parameter is ignored. """ data_provider: Optional[BaseDataProvider] data_provider_class: Type[BaseDataProvider] # any datatype class that is a subclass of another needs to be # checked before the parent in this conditional. if raw: # Working with raw data. if isinstance(original_dataset.datatype, Gff): data_provider_class = genome.RawGFFDataProvider elif isinstance(original_dataset.datatype, Bed): data_provider_class = genome.RawBedDataProvider elif isinstance(original_dataset.datatype, Vcf): data_provider_class = genome.RawVcfDataProvider elif isinstance(original_dataset.datatype, Tabular): data_provider_class = ColumnDataProvider elif isinstance(original_dataset.datatype, (Nexus, Newick, Phyloxml)): data_provider_class = PhylovizDataProvider data_provider = data_provider_class(original_dataset=original_dataset) else: # Working with converted or standalone dataset. if name: # Provider requested by name; get from mappings. value = self.dataset_type_name_to_data_provider[name] if isinstance(value, dict): # value is a PROVIDER_BY_DATATYPE_CLASS_DICT # Get converter by dataset extension; if there is no data provider, # get the default. default_type = value.get("default") assert default_type data_provider_class = value.get(original_dataset.datatype.__class__, default_type) else: data_provider_class = value # If name is the same as original dataset's type, dataset is standalone. # Otherwise, a converted dataset is being used. if name == original_dataset.ext: data_provider = data_provider_class(original_dataset=original_dataset) else: converted_dataset = original_dataset.get_converted_dataset(trans, name) deps = original_dataset.get_converted_dataset_deps(trans, name) data_provider = data_provider_class(original_dataset=original_dataset, converted_dataset=converted_dataset, dependencies=deps) elif original_dataset: # No name, so look up a provider name from datatype's information. # Dataset must have data sources to get data. if not original_dataset.datatype.data_sources: return None # Get data provider mapping and data provider. data_provider_mapping = original_dataset.datatype.data_sources if 'data_standalone' in data_provider_mapping: data_provider = self.get_data_provider(trans, name=data_provider_mapping['data_standalone'], original_dataset=original_dataset) else: source_list = data_provider_mapping[source] if isinstance(source_list, str): source_list = [source_list] # Find a valid data provider in the source list. for source in source_list: try: data_provider = self.get_data_provider(trans, name=source, original_dataset=original_dataset) break except NoConverterException: pass return data_provider