Source code for pycape.function_ref

"""A structured set of metadata representing a deployed Cape function.

A :class:`FunctionRef` is intended to capture any/all metadata related to a Cape
function. The metadata is generally user-supplied, provided to them with the output of
the Cape CLI's ``deploy`` command.



    fid = "asdf231lkg1324afdg"
    fchecksum = str(b"2l1h21jhgb2k1jh3".hex())
    fref = FunctionRef(fid, fchecksum)

    cape = Cape()
from __future__ import annotations

import json
import os
import pathlib
from typing import Optional
from typing import Union

[docs]class FunctionRef: """A reference to a Cape function. Args: id: Required string denoting the function ID of the deployed Cape function. Typically given with the output of the Cape CLI's ``deploy`` command. token: Required string containing a Cape function token generated by the Cape CLI during ``cape token``. checksum: Optional string denoting the checksum of the deployed Cape function. If supplied as part of a ``FunctionRef``, the :class:`~pycape.cape.Cape` client will verify that enclave responses includes a matching checksum whenever the ``FunctionRef`` is included in Cape requests. """ def __init__( self, id: str, token: str, checksum: Optional[str] = None, ): id_ = id if not isinstance(id_, str): raise TypeError(f"Function id must be a string, found {type(id_)}.") if not isinstance(token, str): raise TypeError(f"Function token must be a string, found {type(token)}.") if checksum is not None and not isinstance(checksum, str): raise TypeError( f"Function checksum must be a string, found {type(checksum)}." ) self._id = id_ self._checksum = checksum self._token = token @property def id(self): return self._id @property def checksum(self): return self._checksum @property def token(self): return self._token
[docs] @classmethod def from_json(cls, function_json: Union[str, os.PathLike]) -> FunctionRef: """Construct a :class:`~.function_ref.FunctionRef` from a JSON string or file. Args: function_json: a JSON string or filepath containing function ID, token, and optional checksum, e.g. as generated by the Cape CLI ``token`` command. Returns: A :class:`~.function_ref.FunctionRef` representing the deployed Cape function. Raises: ValueError: if the json token file doesn't exist or, the token file doesn't contain a `function_id` or a `function_token`. """ if isinstance(function_json, pathlib.Path): function_config = _try_load_json_file(function_json) if function_config is None: raise ValueError(f"JSON file not found @ {str(function_json)}") elif isinstance(function_json, str): # try to treat function_json as filepath str json_path = pathlib.Path(function_json) function_config = _try_load_json_file(json_path) # if file not found, treat function_json as json str function_config = function_config or json.loads(function_json) else: raise TypeError( "The function_json argument expects a json string or " f"a path to a json file, found: {type(function_json)}." ) function_id = function_config.get("function_id") if function_id is None: raise ValueError("Couldn't find a `function_id` in the token file provided") function_token = function_config.get("function_token") if function_token is None: raise ValueError( "Couldn't find a `function_token` in the token file provided" ) function_checksum = function_config.get("function_checksum") return cls(function_id, function_token, function_checksum)
[docs] def to_json(self, path: Optional[Union[str, os.PathLike]] = None) -> Optional[str]: """Write this :class:`~.function_ref.FunctionRef` to a JSON string or file. Args: path: Optional file path to write the resulting JSON to. Returns: If ``path`` is None, a string with this :class:`~.function_ref.FunctionRef` as a JSON struct. """ fn_ref_dict = { "function_id": self._id, "function_token": self._token, "function_checksum": self._checksum, } if path is None: return json.dumps(fn_ref_dict) with open(path, "w") as f: json.dump(fn_ref_dict, f)
def _try_load_json_file(json_file: pathlib.Path): if json_file.exists(): with open(json_file, "r") as f: json_output = json.load(f) return json_output