gosling.GffData

class gosling.GffData(indexUrl=Undefined, type=Undefined, url=Undefined, attributesToFields=Undefined, sampleLength=Undefined, **kwds)

GffData schema wrapper

Mapping(required=[type, url, indexUrl]) Generic Feature Format Version 3 (GFF3) format data. It parses files that follow the [GFF3 specification](https://github.com/The-Sequence-Ontology/Specifications/blob/master/gff3.md).

Attributes:

indexUrlstring

URL link to the tabix index file

typestring

urlstring

URL link to the GFF file

attributesToFieldsList(Mapping(required=[attribute, defaultValue]))

Specifies which attributes to include as a fields. GFF files have an “attributes” column which contains a list of attributes which are each tag-value pairs (tag=value). This option allows for specific attributes to be accessible as a field. For example, if you have an attribute called “gene_name” and you want label features on your track using those values, you can use this option so that you can use “field”: “gene_name” in the schema.

If there is a single value corresponding to the tag, Gosling will parse that value as a string. If there are multiple value`s corresponding to a `tag, Gosling will parse it as a comma-separated list string. If a feature does not have a particular attribute, then the attribute value will be set to the defaultValue.

sampleLengthfloat

The maximum number of samples to be shown on the track. Samples are uniformly randomly selected so that this threshold is not exceeded. __Default:__ 1000

__init__(indexUrl=Undefined, type=Undefined, url=Undefined, attributesToFields=Undefined, sampleLength=Undefined, **kwds)

Methods

__init__([indexUrl, type, url, ...])
copy([deep, ignore])	Return a copy of the object
from_dict(dct[, validate, _wrapper_classes])	Construct class from a dictionary representation
from_json(json_string[, validate])	Instantiate the object from a valid JSON string
resolve_references([schema])	Resolve references in the context of this object's schema or root schema.
to_dict([validate, ignore, context])	Return a dictionary representation of the object
to_json([validate, ignore, context, indent, ...])	Emit the JSON representation for this object as a string.
validate(instance[, schema])	Validate the instance against the class schema in the context of the rootschema.
validate_property(name, value[, schema])	Validate a property against property schema in the context of the rootschema