Add/hdruk/2.1.3

available.json

@@ -1,6 +1,7 @@
 {
       "HDRUK": [
             "2.1.2",
+            "2.1.3",
             "2.1.0",
             "2.0.2"
       ],

hdr_schemata/models/HDRUK/__init__.py

@@ -1 +1,2 @@
 from .v2_1_2 import Hdruk212
+from .v2_1_3 import Hdruk213

hdr_schemata/models/HDRUK/create_json_schema.py

@@ -0,0 +1,8 @@
+from pydantic import ValidationError
+import v2_1_2
+import v2_1_3
+import json
+
+v2_1_2.Hdruk212.save_schema()
+v2_1_3.Hdruk213.save_schema()
+v2_1_3.Hdruk213.save_schema("latest/dev/schema.json")

hdr_schemata/models/HDRUK/latest/dev/schema.json

@@ -1607,14 +1607,6 @@
             "Temporal": {
                   "additionalProperties": false,
                   "properties": {
-                        "accrualPeriodicity": {
-                              "allOf": [
-                                    {
-                                          "$ref": "#/$defs/Periodicity"
-                                    }
-                              ],
-                              "description": "Please indicate the frequency of distribution release. If a dataset is distributed regularly please choose a distribution release periodicity from the constrained list and indicate the next release date. When the release date becomes historical, a new release date will be calculated based on the publishing periodicity. If a dataset has been published and will remain static please indicate that it is static and indicated when it was released. If a dataset is released on an irregular basis or \u201con-demand\u201d please indicate that it is Irregular and leave release date as null. If a dataset can be published in real-time or near-real-time please indicate that it is continuous and leave release date as null. Notes: see https://www.dublincore.org/specifications/dublin-core/collection-description/frequency/"
-                        },
                         "distributionReleaseDate": {
                               "anyOf": [
                                     {
@@ -1679,12 +1671,21 @@
                               ],
                               "description": "Please indicate the typical time-lag between an event and the data for that event appearing in the dataset",
                               "title": "Time Lag"
+                        },
+                        "publishingFrequency": {
+                              "allOf": [
+                                    {
+                                          "$ref": "#/$defs/Periodicity"
+                                    }
+                              ],
+                              "description": "Please indicate the frequency of distribution release. If a dataset is distributed regularly please choose a distribution release periodicity from the constrained list and indicate the next release date. When the release date becomes historical, a new release date will be calculated based on the publishing periodicity. If a dataset has been published and will remain static please indicate that it is static and indicated when it was released. If a dataset is released on an irregular basis or \u201con-demand\u201d please indicate that it is Irregular and leave release date as null. If a dataset can be published in real-time or near-real-time please indicate that it is continuous and leave release date as null. Notes: see https://www.dublincore.org/specifications/dublin-core/collection-description/frequency/",
+                              "title": "Publishing Frequency"
                         }
                   },
                   "required": [
-                        "accrualPeriodicity",
                         "startDate",
-                        "timeLag"
+                        "timeLag",
+                        "publishingFrequency"
                   ],
                   "title": "Temporal",
                   "type": "object"
@@ -2009,6 +2010,6 @@
             "accessibility",
             "observations"
       ],
-      "title": "Hdruk212",
+      "title": "Hdruk213",
       "type": "object"
 }

hdr_schemata/models/HDRUK/v2_1_3/Provenance.py

@@ -0,0 +1,6 @@
+from hdr_schemata.models.HDRUK.base import Provenance as BaseProvenance
+from .Temporal import Temporal
+
+
+class Provenance(BaseProvenance):
+    temporal: Temporal

hdr_schemata/models/HDRUK/v2_1_3/Temporal.py

@@ -0,0 +1,16 @@
+from pydantic import Field
+from hdr_schemata.definitions.HDRUK import Periodicity
+from hdr_schemata.models.HDRUK.base.Temporal import Temporal as BaseTemporal
+from pydantic import BaseModel, Field
+from hdr_schemata.models import remove_fields_from_cls
+
+
+class Temporal(BaseTemporal):
+    publishingFrequency: Periodicity = Field(
+        ...,
+        title="Publishing Frequency",
+        description="Please indicate the frequency of distribution release. If a dataset is distributed regularly please choose a distribution release periodicity from the constrained list and indicate the next release date. When the release date becomes historical, a new release date will be calculated based on the publishing periodicity. If a dataset has been published and will remain static please indicate that it is static and indicated when it was released. If a dataset is released on an irregular basis or “on-demand” please indicate that it is Irregular and leave release date as null. If a dataset can be published in real-time or near-real-time please indicate that it is continuous and leave release date as null. Notes: see https://www.dublincore.org/specifications/dublin-core/collection-description/frequency/",
+    )
+
+
+remove_fields_from_cls(Temporal, ["accrualPeriodicity"])

hdr_schemata/models/HDRUK/v2_1_3/__init__.py

@@ -0,0 +1,18 @@
+from hdr_schemata.models.HDRUK.v2_1_2 import Hdruk212
+import json
+from typing import Optional
+from pydantic import Field
+from .Provenance import Provenance
+
+
+class Hdruk213(Hdruk212):
+    provenance: Optional[Provenance] = Field(
+        None,
+        description="Provenance information allows researchers to understand data within the context of its origins and can be an indicator of quality, authenticity and timeliness.",
+        title="Provenance",
+    )
+
+    @classmethod
+    def save_schema(cls, location="./2.1.3/schema.json"):
+        with open(location, "w") as f:
+            json.dump(cls.model_json_schema(), f, indent=6)