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Merge pull request #197 from brain-bican/autogenerated-1.9.3
Generate "other formats" for all models now that modify_jsonldcontext.py is removed
2 parents 057b173 + ebd3d45 commit f97221d

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-1342
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json-schema-autogen/anatomical_structure.json

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},
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"x_resolution": {
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"description": "The resolution (length / pixel) in along the x axis (numerical value part).",
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"pattern": "^[+]?\\d*\\.?\\d+$",
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"type": [
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"number",
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"null"
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},
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"y_resolution": {
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"description": "The resolution (length / pixel) in along the y axis (numerical value part).",
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"pattern": "^[+]?\\d*\\.?\\d+$",
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"type": [
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"number",
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"null"
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},
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"z_resolution": {
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"description": "The resolution (length / pixel) in along the z axis (numerical value part).",
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"pattern": "^[+]?\\d*\\.?\\d+$",
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"type": [
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"number",
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"null"
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"properties": {
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"color": {
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"description": "A string representing to hex triplet code of a color",
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"pattern": "#[0-9a-fA-F]{6}",
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"type": [
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"string",
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"null"
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"type": "object",
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"version": null
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}
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json-schema-autogen/assertion_evidence.json

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"type": "object",
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"version": null
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}
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json-schema-autogen/bke_taxonomy.json

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},
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"x_resolution": {
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"description": "The resolution (length / pixel) in along the x axis (numerical value part).",
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"pattern": "{PositiveFloat}",
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"type": [
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"number",
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"null"
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},
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"y_resolution": {
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"description": "The resolution (length / pixel) in along the y axis (numerical value part).",
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"pattern": "{PositiveFloat}",
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"type": [
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"number",
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"null"
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},
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"z_resolution": {
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"description": "The resolution (length / pixel) in along the z axis (numerical value part).",
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"pattern": "{PositiveFloat}",
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"type": [
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"number",
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"null"
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"properties": {
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"color": {
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"description": "A string representing to hex triplet code of a color",
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"pattern": "{ColorHexTriplet}",
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"type": [
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"string",
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"null"
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"type": "object",
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"version": null
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}
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json-schema-autogen/genome_annotation.json

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"type": "object",
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"version": null
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}
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json-schema-autogen/library_generation.json

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"type": "object",
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"version": null
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}
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jsonld-context-autogen/anatomical_structure.context.jsonld

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"@type": "@id",
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"@id": "measures"
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},
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"category": "@type",
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"category": {
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"@type": "xsd:anyURI",
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"@id": "biolink:category"
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},
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"checksum_algorithm": {
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"@context": {
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"text": "skos:notation",
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"@id": "VersionedNamedThing"
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}
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}
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}
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}
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jsonld-context-autogen/library_generation.context.jsonld

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"@type": "xsd:integer",
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"@id": "f10e928d-5a2b-4943-af18-d8fe5d05528d"
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},
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"category": "@type",
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"category": {
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"@type": "xsd:anyURI",
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"@id": "biolink:category"
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},
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"amplified_cDNA_PCR_cycles": {
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"@type": "xsd:integer",
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"@id": "3827634c-3f8f-4760-b358-86ce4b030238"
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"@id": "VersionedNamedThing"
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}
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}
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}
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}
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models_py-autogen/anatomical_structure.py

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models_py-autogen/assertion_evidence.py

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from typing import (
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Any,
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ClassVar,
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Dict,
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List,
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Literal,
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Optional,
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Union
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class LinkMLMeta(RootModel):
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root: Dict[str, Any] = {}
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root: dict[str, Any] = {}
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model_config = ConfigDict(frozen=True)
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def __getattr__(self, key:str):
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class EvidenceStrength(str, Enum):
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# P value less than 0.01
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HighlySignificant = "HighlySignificant"
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# P value less than 0.05
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"""
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P value less than 0.01
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"""
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Significant = "Significant"
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# P value less than 0.10
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"""
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P value less than 0.05
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"""
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MarginallySignificant = "MarginallySignificant"
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# P value greater than or equal to 0.10
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"""
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P value less than 0.10
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"""
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NotSignificant = "NotSignificant"
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# Specifies that the significance level is unknown as the information is not present in the study.
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"""
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P value greater than or equal to 0.10
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"""
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Unknown = "Unknown"
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"""
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Specifies that the significance level is unknown as the information is not present in the study.
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"""
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class EvidenceDirection(str, Enum):

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