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import logging |
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import genson |
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import jsonschema |
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logger = logging.getLogger(__name__) |
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def _generate_schema_from_example_and_description(input, description): |
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""" |
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With an example input, a schema is automatically generated that conforms |
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to the example in json-schema.org. The description given by the users |
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is then added to the schema. |
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""" |
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s = genson.SchemaBuilder(None) |
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s.add_object(input) |
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input_schema = s.to_schema() |
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if description is not None: |
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if "properties" in input_schema: |
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# Case for input = {'x':1}, input_description='not a dict' |
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if not isinstance(description, dict): |
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msg = f"{input} and {description} do not match" |
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logger.error(msg) |
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raise Exception(msg) |
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for key in description: |
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# Case for input = {'x':1}, |
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# input_description={'x':'x value', 'y':'y value'} |
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if key not in input_schema["properties"]: |
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msg = f"{key} not found in {input}" |
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logger.error(msg) |
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raise Exception(msg) |
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else: |
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input_schema["properties"][key]["description"] = description[key] |
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else: |
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if isinstance(description, dict): |
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raise Exception(f"{input} and {description} do not match") |
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else: |
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input_schema["description"] = description |
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try: |
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# This should not fail unless there are bugs with either genson or |
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# jsonschema. |
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jsonschema.validate(input, input_schema) |
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except Exception as e: |
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logger.error(f"Internal error validating schema: {str(e)}") |
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raise |
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return input_schema |
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def generate_schema(input, output, input_description=None, output_description=None): |
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""" |
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Generate schema from a given sample input and output. |
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A generated schema can be passed to a server together with a function to |
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annotate it with information about input and output parameters, and |
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examples thereof. The schema needs to follow the conventions of JSON Schema |
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(see json-schema.org). |
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Parameters |
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----------- |
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input : any python type | dict |
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output: any python type | dict |
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input_description : str | dict, optional |
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output_description : str | dict, optional |
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References |
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----------- |
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- `Json Schema <http://json-schema.org/documentation.html>` |
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Examples |
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---------- |
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.. sourcecode:: python |
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For just one input parameter, state the example directly. |
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>>> from tabpy.tabpy_tools.schema import generate_schema |
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>>> schema = generate_schema( |
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input=5, |
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output=25, |
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input_description='input value', |
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output_description='the squared value of input') |
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>>> schema |
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{'sample': 5, |
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'input': {'type': 'integer', 'description': 'input value'}, |
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'output': {'type': 'integer', 'description': 'the squared value of input'}} |
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For two or more input parameters, specify them using a dictionary. |
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>>> import graphlab |
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>>> schema = generate_schema( |
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input={'x': 3, 'y': 2}, |
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output=6, |
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input_description={'x': 'value of x', |
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'y': 'value of y'}, |
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output_description='x times y') |
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>>> schema |
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{'sample': {'y': 2, 'x': 3}, |
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'input': {'required': ['x', 'y'], |
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'type': 'object', |
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'properties': {'y': {'type': 'integer', 'description': 'value of y'}, |
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'x': {'type': 'integer', 'description': 'value of x'}}}, |
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'output': {'type': 'integer', 'description': 'x times y'}} |
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""" # noqa: E501 |
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input_schema = _generate_schema_from_example_and_description( |
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input, input_description |
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) |
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output_schema = _generate_schema_from_example_and_description( |
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output, output_description |
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) |
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return {"input": input_schema, "sample": input, "output": output_schema} |
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tableau /
TabPy
