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fca6cf70a6d7fbd1d10578387b0b0d8562dfb058f5bf919899c3f93bf7d2346a | def build_transaction(self, tokens_to_wrap: List[ITokenBundleToken], uri_for_wrapped_token: str, recipient: str, tx_params: Optional[TxParams]=None) -> dict:
'Construct calldata to be used as input to the method.'
(tokens_to_wrap, uri_for_wrapped_token, recipient) = self.validate_and_normalize_inputs(tokens_to_wrap, uri_for_wrapped_token, recipient)
tx_params = super().normalize_tx_params(tx_params)
return self._underlying_method(tokens_to_wrap, uri_for_wrapped_token, recipient).buildTransaction(tx_params.as_dict()) | Construct calldata to be used as input to the method. | thirdweb/abi/multiwrap.py | build_transaction | nftlabs/nftlabs-sdk-python | 30 | python | def build_transaction(self, tokens_to_wrap: List[ITokenBundleToken], uri_for_wrapped_token: str, recipient: str, tx_params: Optional[TxParams]=None) -> dict:
(tokens_to_wrap, uri_for_wrapped_token, recipient) = self.validate_and_normalize_inputs(tokens_to_wrap, uri_for_wrapped_token, recipient)
tx_params = super().normalize_tx_params(tx_params)
return self._underlying_method(tokens_to_wrap, uri_for_wrapped_token, recipient).buildTransaction(tx_params.as_dict()) | def build_transaction(self, tokens_to_wrap: List[ITokenBundleToken], uri_for_wrapped_token: str, recipient: str, tx_params: Optional[TxParams]=None) -> dict:
(tokens_to_wrap, uri_for_wrapped_token, recipient) = self.validate_and_normalize_inputs(tokens_to_wrap, uri_for_wrapped_token, recipient)
tx_params = super().normalize_tx_params(tx_params)
return self._underlying_method(tokens_to_wrap, uri_for_wrapped_token, recipient).buildTransaction(tx_params.as_dict())<|docstring|>Construct calldata to be used as input to the method.<|endoftext|> |
2f892c323993d84917535c0b82aac3233f494eb0c70ec4874141cbf1a69f7317 | def estimate_gas(self, tokens_to_wrap: List[ITokenBundleToken], uri_for_wrapped_token: str, recipient: str, tx_params: Optional[TxParams]=None) -> int:
'Estimate gas consumption of method call.'
(tokens_to_wrap, uri_for_wrapped_token, recipient) = self.validate_and_normalize_inputs(tokens_to_wrap, uri_for_wrapped_token, recipient)
tx_params = super().normalize_tx_params(tx_params)
return self._underlying_method(tokens_to_wrap, uri_for_wrapped_token, recipient).estimateGas(tx_params.as_dict()) | Estimate gas consumption of method call. | thirdweb/abi/multiwrap.py | estimate_gas | nftlabs/nftlabs-sdk-python | 30 | python | def estimate_gas(self, tokens_to_wrap: List[ITokenBundleToken], uri_for_wrapped_token: str, recipient: str, tx_params: Optional[TxParams]=None) -> int:
(tokens_to_wrap, uri_for_wrapped_token, recipient) = self.validate_and_normalize_inputs(tokens_to_wrap, uri_for_wrapped_token, recipient)
tx_params = super().normalize_tx_params(tx_params)
return self._underlying_method(tokens_to_wrap, uri_for_wrapped_token, recipient).estimateGas(tx_params.as_dict()) | def estimate_gas(self, tokens_to_wrap: List[ITokenBundleToken], uri_for_wrapped_token: str, recipient: str, tx_params: Optional[TxParams]=None) -> int:
(tokens_to_wrap, uri_for_wrapped_token, recipient) = self.validate_and_normalize_inputs(tokens_to_wrap, uri_for_wrapped_token, recipient)
tx_params = super().normalize_tx_params(tx_params)
return self._underlying_method(tokens_to_wrap, uri_for_wrapped_token, recipient).estimateGas(tx_params.as_dict())<|docstring|>Estimate gas consumption of method call.<|endoftext|> |
5be96e16dcd15ae31233b2fce25711abf14a9605d5a89fe1356a65a73a7a80a6 | def __init__(self, web3_or_provider: Union[(Web3, BaseProvider)], contract_address: str, validator: MultiwrapValidator=None):
'Get an instance of wrapper for smart contract.\n\n :param web3_or_provider: Either an instance of `web3.Web3`:code: or\n `web3.providers.base.BaseProvider`:code:\n :param contract_address: where the contract has been deployed\n :param validator: for validation of method inputs.\n '
self.contract_address = contract_address
if (not validator):
validator = MultiwrapValidator(web3_or_provider, contract_address)
web3 = None
if isinstance(web3_or_provider, BaseProvider):
web3 = Web3(web3_or_provider)
elif isinstance(web3_or_provider, Web3):
web3 = web3_or_provider
else:
raise TypeError(("Expected parameter 'web3_or_provider' to be an instance of either" + ' Web3 or BaseProvider'))
try:
MIDDLEWARE
except NameError:
pass
else:
try:
for middleware in MIDDLEWARE:
web3.middleware_onion.inject(middleware['function'], layer=middleware['layer'])
except ValueError as value_error:
if (value_error.args == ("You can't add the same un-named instance twice",)):
pass
self._web3_eth = web3.eth
functions = self._web3_eth.contract(address=to_checksum_address(contract_address), abi=Multiwrap.abi()).functions
self.default_admin_role = DefaultAdminRoleMethod(web3_or_provider, contract_address, functions.DEFAULT_ADMIN_ROLE)
self.approve = ApproveMethod(web3_or_provider, contract_address, functions.approve, validator)
self.balance_of = BalanceOfMethod(web3_or_provider, contract_address, functions.balanceOf, validator)
self.contract_type = ContractTypeMethod(web3_or_provider, contract_address, functions.contractType)
self.contract_uri = ContractUriMethod(web3_or_provider, contract_address, functions.contractURI)
self.contract_version = ContractVersionMethod(web3_or_provider, contract_address, functions.contractVersion)
self.get_approved = GetApprovedMethod(web3_or_provider, contract_address, functions.getApproved, validator)
self.get_default_royalty_info = GetDefaultRoyaltyInfoMethod(web3_or_provider, contract_address, functions.getDefaultRoyaltyInfo)
self.get_role_admin = GetRoleAdminMethod(web3_or_provider, contract_address, functions.getRoleAdmin, validator)
self.get_role_member = GetRoleMemberMethod(web3_or_provider, contract_address, functions.getRoleMember, validator)
self.get_role_member_count = GetRoleMemberCountMethod(web3_or_provider, contract_address, functions.getRoleMemberCount, validator)
self.get_royalty_info_for_token = GetRoyaltyInfoForTokenMethod(web3_or_provider, contract_address, functions.getRoyaltyInfoForToken, validator)
self.get_token_count_of_bundle = GetTokenCountOfBundleMethod(web3_or_provider, contract_address, functions.getTokenCountOfBundle, validator)
self.get_token_of_bundle = GetTokenOfBundleMethod(web3_or_provider, contract_address, functions.getTokenOfBundle, validator)
self.get_uri_of_bundle = GetUriOfBundleMethod(web3_or_provider, contract_address, functions.getUriOfBundle, validator)
self.get_wrapped_contents = GetWrappedContentsMethod(web3_or_provider, contract_address, functions.getWrappedContents, validator)
self.grant_role = GrantRoleMethod(web3_or_provider, contract_address, functions.grantRole, validator)
self.has_role = HasRoleMethod(web3_or_provider, contract_address, functions.hasRole, validator)
self.has_role_with_switch = HasRoleWithSwitchMethod(web3_or_provider, contract_address, functions.hasRoleWithSwitch, validator)
self.initialize = InitializeMethod(web3_or_provider, contract_address, functions.initialize, validator)
self.is_approved_for_all = IsApprovedForAllMethod(web3_or_provider, contract_address, functions.isApprovedForAll, validator)
self.is_trusted_forwarder = IsTrustedForwarderMethod(web3_or_provider, contract_address, functions.isTrustedForwarder, validator)
self.multicall = MulticallMethod(web3_or_provider, contract_address, functions.multicall, validator)
self.name = NameMethod(web3_or_provider, contract_address, functions.name)
self.next_token_id_to_mint = NextTokenIdToMintMethod(web3_or_provider, contract_address, functions.nextTokenIdToMint)
self.on_erc1155_batch_received = OnErc1155BatchReceivedMethod(web3_or_provider, contract_address, functions.onERC1155BatchReceived, validator)
self.on_erc1155_received = OnErc1155ReceivedMethod(web3_or_provider, contract_address, functions.onERC1155Received, validator)
self.on_erc721_received = OnErc721ReceivedMethod(web3_or_provider, contract_address, functions.onERC721Received, validator)
self.owner = OwnerMethod(web3_or_provider, contract_address, functions.owner)
self.owner_of = OwnerOfMethod(web3_or_provider, contract_address, functions.ownerOf, validator)
self.renounce_role = RenounceRoleMethod(web3_or_provider, contract_address, functions.renounceRole, validator)
self.revoke_role = RevokeRoleMethod(web3_or_provider, contract_address, functions.revokeRole, validator)
self.royalty_info = RoyaltyInfoMethod(web3_or_provider, contract_address, functions.royaltyInfo, validator)
self.safe_transfer_from1 = SafeTransferFrom1Method(web3_or_provider, contract_address, functions.safeTransferFrom, validator)
self.safe_transfer_from2 = SafeTransferFrom2Method(web3_or_provider, contract_address, functions.safeTransferFrom, validator)
self.set_approval_for_all = SetApprovalForAllMethod(web3_or_provider, contract_address, functions.setApprovalForAll, validator)
self.set_contract_uri = SetContractUriMethod(web3_or_provider, contract_address, functions.setContractURI, validator)
self.set_default_royalty_info = SetDefaultRoyaltyInfoMethod(web3_or_provider, contract_address, functions.setDefaultRoyaltyInfo, validator)
self.set_owner = SetOwnerMethod(web3_or_provider, contract_address, functions.setOwner, validator)
self.set_royalty_info_for_token = SetRoyaltyInfoForTokenMethod(web3_or_provider, contract_address, functions.setRoyaltyInfoForToken, validator)
self.supports_interface = SupportsInterfaceMethod(web3_or_provider, contract_address, functions.supportsInterface, validator)
self.symbol = SymbolMethod(web3_or_provider, contract_address, functions.symbol)
self.token_uri = TokenUriMethod(web3_or_provider, contract_address, functions.tokenURI, validator)
self.transfer_from = TransferFromMethod(web3_or_provider, contract_address, functions.transferFrom, validator)
self.unwrap = UnwrapMethod(web3_or_provider, contract_address, functions.unwrap, validator)
self.wrap = WrapMethod(web3_or_provider, contract_address, functions.wrap, validator) | Get an instance of wrapper for smart contract.
:param web3_or_provider: Either an instance of `web3.Web3`:code: or
`web3.providers.base.BaseProvider`:code:
:param contract_address: where the contract has been deployed
:param validator: for validation of method inputs. | thirdweb/abi/multiwrap.py | __init__ | nftlabs/nftlabs-sdk-python | 30 | python | def __init__(self, web3_or_provider: Union[(Web3, BaseProvider)], contract_address: str, validator: MultiwrapValidator=None):
'Get an instance of wrapper for smart contract.\n\n :param web3_or_provider: Either an instance of `web3.Web3`:code: or\n `web3.providers.base.BaseProvider`:code:\n :param contract_address: where the contract has been deployed\n :param validator: for validation of method inputs.\n '
self.contract_address = contract_address
if (not validator):
validator = MultiwrapValidator(web3_or_provider, contract_address)
web3 = None
if isinstance(web3_or_provider, BaseProvider):
web3 = Web3(web3_or_provider)
elif isinstance(web3_or_provider, Web3):
web3 = web3_or_provider
else:
raise TypeError(("Expected parameter 'web3_or_provider' to be an instance of either" + ' Web3 or BaseProvider'))
try:
MIDDLEWARE
except NameError:
pass
else:
try:
for middleware in MIDDLEWARE:
web3.middleware_onion.inject(middleware['function'], layer=middleware['layer'])
except ValueError as value_error:
if (value_error.args == ("You can't add the same un-named instance twice",)):
pass
self._web3_eth = web3.eth
functions = self._web3_eth.contract(address=to_checksum_address(contract_address), abi=Multiwrap.abi()).functions
self.default_admin_role = DefaultAdminRoleMethod(web3_or_provider, contract_address, functions.DEFAULT_ADMIN_ROLE)
self.approve = ApproveMethod(web3_or_provider, contract_address, functions.approve, validator)
self.balance_of = BalanceOfMethod(web3_or_provider, contract_address, functions.balanceOf, validator)
self.contract_type = ContractTypeMethod(web3_or_provider, contract_address, functions.contractType)
self.contract_uri = ContractUriMethod(web3_or_provider, contract_address, functions.contractURI)
self.contract_version = ContractVersionMethod(web3_or_provider, contract_address, functions.contractVersion)
self.get_approved = GetApprovedMethod(web3_or_provider, contract_address, functions.getApproved, validator)
self.get_default_royalty_info = GetDefaultRoyaltyInfoMethod(web3_or_provider, contract_address, functions.getDefaultRoyaltyInfo)
self.get_role_admin = GetRoleAdminMethod(web3_or_provider, contract_address, functions.getRoleAdmin, validator)
self.get_role_member = GetRoleMemberMethod(web3_or_provider, contract_address, functions.getRoleMember, validator)
self.get_role_member_count = GetRoleMemberCountMethod(web3_or_provider, contract_address, functions.getRoleMemberCount, validator)
self.get_royalty_info_for_token = GetRoyaltyInfoForTokenMethod(web3_or_provider, contract_address, functions.getRoyaltyInfoForToken, validator)
self.get_token_count_of_bundle = GetTokenCountOfBundleMethod(web3_or_provider, contract_address, functions.getTokenCountOfBundle, validator)
self.get_token_of_bundle = GetTokenOfBundleMethod(web3_or_provider, contract_address, functions.getTokenOfBundle, validator)
self.get_uri_of_bundle = GetUriOfBundleMethod(web3_or_provider, contract_address, functions.getUriOfBundle, validator)
self.get_wrapped_contents = GetWrappedContentsMethod(web3_or_provider, contract_address, functions.getWrappedContents, validator)
self.grant_role = GrantRoleMethod(web3_or_provider, contract_address, functions.grantRole, validator)
self.has_role = HasRoleMethod(web3_or_provider, contract_address, functions.hasRole, validator)
self.has_role_with_switch = HasRoleWithSwitchMethod(web3_or_provider, contract_address, functions.hasRoleWithSwitch, validator)
self.initialize = InitializeMethod(web3_or_provider, contract_address, functions.initialize, validator)
self.is_approved_for_all = IsApprovedForAllMethod(web3_or_provider, contract_address, functions.isApprovedForAll, validator)
self.is_trusted_forwarder = IsTrustedForwarderMethod(web3_or_provider, contract_address, functions.isTrustedForwarder, validator)
self.multicall = MulticallMethod(web3_or_provider, contract_address, functions.multicall, validator)
self.name = NameMethod(web3_or_provider, contract_address, functions.name)
self.next_token_id_to_mint = NextTokenIdToMintMethod(web3_or_provider, contract_address, functions.nextTokenIdToMint)
self.on_erc1155_batch_received = OnErc1155BatchReceivedMethod(web3_or_provider, contract_address, functions.onERC1155BatchReceived, validator)
self.on_erc1155_received = OnErc1155ReceivedMethod(web3_or_provider, contract_address, functions.onERC1155Received, validator)
self.on_erc721_received = OnErc721ReceivedMethod(web3_or_provider, contract_address, functions.onERC721Received, validator)
self.owner = OwnerMethod(web3_or_provider, contract_address, functions.owner)
self.owner_of = OwnerOfMethod(web3_or_provider, contract_address, functions.ownerOf, validator)
self.renounce_role = RenounceRoleMethod(web3_or_provider, contract_address, functions.renounceRole, validator)
self.revoke_role = RevokeRoleMethod(web3_or_provider, contract_address, functions.revokeRole, validator)
self.royalty_info = RoyaltyInfoMethod(web3_or_provider, contract_address, functions.royaltyInfo, validator)
self.safe_transfer_from1 = SafeTransferFrom1Method(web3_or_provider, contract_address, functions.safeTransferFrom, validator)
self.safe_transfer_from2 = SafeTransferFrom2Method(web3_or_provider, contract_address, functions.safeTransferFrom, validator)
self.set_approval_for_all = SetApprovalForAllMethod(web3_or_provider, contract_address, functions.setApprovalForAll, validator)
self.set_contract_uri = SetContractUriMethod(web3_or_provider, contract_address, functions.setContractURI, validator)
self.set_default_royalty_info = SetDefaultRoyaltyInfoMethod(web3_or_provider, contract_address, functions.setDefaultRoyaltyInfo, validator)
self.set_owner = SetOwnerMethod(web3_or_provider, contract_address, functions.setOwner, validator)
self.set_royalty_info_for_token = SetRoyaltyInfoForTokenMethod(web3_or_provider, contract_address, functions.setRoyaltyInfoForToken, validator)
self.supports_interface = SupportsInterfaceMethod(web3_or_provider, contract_address, functions.supportsInterface, validator)
self.symbol = SymbolMethod(web3_or_provider, contract_address, functions.symbol)
self.token_uri = TokenUriMethod(web3_or_provider, contract_address, functions.tokenURI, validator)
self.transfer_from = TransferFromMethod(web3_or_provider, contract_address, functions.transferFrom, validator)
self.unwrap = UnwrapMethod(web3_or_provider, contract_address, functions.unwrap, validator)
self.wrap = WrapMethod(web3_or_provider, contract_address, functions.wrap, validator) | def __init__(self, web3_or_provider: Union[(Web3, BaseProvider)], contract_address: str, validator: MultiwrapValidator=None):
'Get an instance of wrapper for smart contract.\n\n :param web3_or_provider: Either an instance of `web3.Web3`:code: or\n `web3.providers.base.BaseProvider`:code:\n :param contract_address: where the contract has been deployed\n :param validator: for validation of method inputs.\n '
self.contract_address = contract_address
if (not validator):
validator = MultiwrapValidator(web3_or_provider, contract_address)
web3 = None
if isinstance(web3_or_provider, BaseProvider):
web3 = Web3(web3_or_provider)
elif isinstance(web3_or_provider, Web3):
web3 = web3_or_provider
else:
raise TypeError(("Expected parameter 'web3_or_provider' to be an instance of either" + ' Web3 or BaseProvider'))
try:
MIDDLEWARE
except NameError:
pass
else:
try:
for middleware in MIDDLEWARE:
web3.middleware_onion.inject(middleware['function'], layer=middleware['layer'])
except ValueError as value_error:
if (value_error.args == ("You can't add the same un-named instance twice",)):
pass
self._web3_eth = web3.eth
functions = self._web3_eth.contract(address=to_checksum_address(contract_address), abi=Multiwrap.abi()).functions
self.default_admin_role = DefaultAdminRoleMethod(web3_or_provider, contract_address, functions.DEFAULT_ADMIN_ROLE)
self.approve = ApproveMethod(web3_or_provider, contract_address, functions.approve, validator)
self.balance_of = BalanceOfMethod(web3_or_provider, contract_address, functions.balanceOf, validator)
self.contract_type = ContractTypeMethod(web3_or_provider, contract_address, functions.contractType)
self.contract_uri = ContractUriMethod(web3_or_provider, contract_address, functions.contractURI)
self.contract_version = ContractVersionMethod(web3_or_provider, contract_address, functions.contractVersion)
self.get_approved = GetApprovedMethod(web3_or_provider, contract_address, functions.getApproved, validator)
self.get_default_royalty_info = GetDefaultRoyaltyInfoMethod(web3_or_provider, contract_address, functions.getDefaultRoyaltyInfo)
self.get_role_admin = GetRoleAdminMethod(web3_or_provider, contract_address, functions.getRoleAdmin, validator)
self.get_role_member = GetRoleMemberMethod(web3_or_provider, contract_address, functions.getRoleMember, validator)
self.get_role_member_count = GetRoleMemberCountMethod(web3_or_provider, contract_address, functions.getRoleMemberCount, validator)
self.get_royalty_info_for_token = GetRoyaltyInfoForTokenMethod(web3_or_provider, contract_address, functions.getRoyaltyInfoForToken, validator)
self.get_token_count_of_bundle = GetTokenCountOfBundleMethod(web3_or_provider, contract_address, functions.getTokenCountOfBundle, validator)
self.get_token_of_bundle = GetTokenOfBundleMethod(web3_or_provider, contract_address, functions.getTokenOfBundle, validator)
self.get_uri_of_bundle = GetUriOfBundleMethod(web3_or_provider, contract_address, functions.getUriOfBundle, validator)
self.get_wrapped_contents = GetWrappedContentsMethod(web3_or_provider, contract_address, functions.getWrappedContents, validator)
self.grant_role = GrantRoleMethod(web3_or_provider, contract_address, functions.grantRole, validator)
self.has_role = HasRoleMethod(web3_or_provider, contract_address, functions.hasRole, validator)
self.has_role_with_switch = HasRoleWithSwitchMethod(web3_or_provider, contract_address, functions.hasRoleWithSwitch, validator)
self.initialize = InitializeMethod(web3_or_provider, contract_address, functions.initialize, validator)
self.is_approved_for_all = IsApprovedForAllMethod(web3_or_provider, contract_address, functions.isApprovedForAll, validator)
self.is_trusted_forwarder = IsTrustedForwarderMethod(web3_or_provider, contract_address, functions.isTrustedForwarder, validator)
self.multicall = MulticallMethod(web3_or_provider, contract_address, functions.multicall, validator)
self.name = NameMethod(web3_or_provider, contract_address, functions.name)
self.next_token_id_to_mint = NextTokenIdToMintMethod(web3_or_provider, contract_address, functions.nextTokenIdToMint)
self.on_erc1155_batch_received = OnErc1155BatchReceivedMethod(web3_or_provider, contract_address, functions.onERC1155BatchReceived, validator)
self.on_erc1155_received = OnErc1155ReceivedMethod(web3_or_provider, contract_address, functions.onERC1155Received, validator)
self.on_erc721_received = OnErc721ReceivedMethod(web3_or_provider, contract_address, functions.onERC721Received, validator)
self.owner = OwnerMethod(web3_or_provider, contract_address, functions.owner)
self.owner_of = OwnerOfMethod(web3_or_provider, contract_address, functions.ownerOf, validator)
self.renounce_role = RenounceRoleMethod(web3_or_provider, contract_address, functions.renounceRole, validator)
self.revoke_role = RevokeRoleMethod(web3_or_provider, contract_address, functions.revokeRole, validator)
self.royalty_info = RoyaltyInfoMethod(web3_or_provider, contract_address, functions.royaltyInfo, validator)
self.safe_transfer_from1 = SafeTransferFrom1Method(web3_or_provider, contract_address, functions.safeTransferFrom, validator)
self.safe_transfer_from2 = SafeTransferFrom2Method(web3_or_provider, contract_address, functions.safeTransferFrom, validator)
self.set_approval_for_all = SetApprovalForAllMethod(web3_or_provider, contract_address, functions.setApprovalForAll, validator)
self.set_contract_uri = SetContractUriMethod(web3_or_provider, contract_address, functions.setContractURI, validator)
self.set_default_royalty_info = SetDefaultRoyaltyInfoMethod(web3_or_provider, contract_address, functions.setDefaultRoyaltyInfo, validator)
self.set_owner = SetOwnerMethod(web3_or_provider, contract_address, functions.setOwner, validator)
self.set_royalty_info_for_token = SetRoyaltyInfoForTokenMethod(web3_or_provider, contract_address, functions.setRoyaltyInfoForToken, validator)
self.supports_interface = SupportsInterfaceMethod(web3_or_provider, contract_address, functions.supportsInterface, validator)
self.symbol = SymbolMethod(web3_or_provider, contract_address, functions.symbol)
self.token_uri = TokenUriMethod(web3_or_provider, contract_address, functions.tokenURI, validator)
self.transfer_from = TransferFromMethod(web3_or_provider, contract_address, functions.transferFrom, validator)
self.unwrap = UnwrapMethod(web3_or_provider, contract_address, functions.unwrap, validator)
self.wrap = WrapMethod(web3_or_provider, contract_address, functions.wrap, validator)<|docstring|>Get an instance of wrapper for smart contract.
:param web3_or_provider: Either an instance of `web3.Web3`:code: or
`web3.providers.base.BaseProvider`:code:
:param contract_address: where the contract has been deployed
:param validator: for validation of method inputs.<|endoftext|> |
dd22877dc55dfaaf775c3dfa7353c9d5f7af975284c2ac94d902859f548312f3 | def get_approval_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for Approval event.\n\n :param tx_hash: hash of transaction emitting Approval event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.Approval().processReceipt(tx_receipt) | Get log entry for Approval event.
:param tx_hash: hash of transaction emitting Approval event | thirdweb/abi/multiwrap.py | get_approval_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_approval_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for Approval event.\n\n :param tx_hash: hash of transaction emitting Approval event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.Approval().processReceipt(tx_receipt) | def get_approval_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for Approval event.\n\n :param tx_hash: hash of transaction emitting Approval event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.Approval().processReceipt(tx_receipt)<|docstring|>Get log entry for Approval event.
:param tx_hash: hash of transaction emitting Approval event<|endoftext|> |
2285ce7f1c963f78b0d0f1bf5229cd33ab8caf8f5757d87ed84b783397c2ea2f | def get_approval_for_all_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for ApprovalForAll event.\n\n :param tx_hash: hash of transaction emitting ApprovalForAll event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.ApprovalForAll().processReceipt(tx_receipt) | Get log entry for ApprovalForAll event.
:param tx_hash: hash of transaction emitting ApprovalForAll event | thirdweb/abi/multiwrap.py | get_approval_for_all_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_approval_for_all_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for ApprovalForAll event.\n\n :param tx_hash: hash of transaction emitting ApprovalForAll event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.ApprovalForAll().processReceipt(tx_receipt) | def get_approval_for_all_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for ApprovalForAll event.\n\n :param tx_hash: hash of transaction emitting ApprovalForAll event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.ApprovalForAll().processReceipt(tx_receipt)<|docstring|>Get log entry for ApprovalForAll event.
:param tx_hash: hash of transaction emitting ApprovalForAll event<|endoftext|> |
3b349ae729928ec69ad68f610610383d2c1a14d3dea286ef87c56027f6f487d6 | def get_contract_uri_updated_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for ContractURIUpdated event.\n\n :param tx_hash: hash of transaction emitting ContractURIUpdated event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.ContractURIUpdated().processReceipt(tx_receipt) | Get log entry for ContractURIUpdated event.
:param tx_hash: hash of transaction emitting ContractURIUpdated event | thirdweb/abi/multiwrap.py | get_contract_uri_updated_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_contract_uri_updated_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for ContractURIUpdated event.\n\n :param tx_hash: hash of transaction emitting ContractURIUpdated event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.ContractURIUpdated().processReceipt(tx_receipt) | def get_contract_uri_updated_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for ContractURIUpdated event.\n\n :param tx_hash: hash of transaction emitting ContractURIUpdated event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.ContractURIUpdated().processReceipt(tx_receipt)<|docstring|>Get log entry for ContractURIUpdated event.
:param tx_hash: hash of transaction emitting ContractURIUpdated event<|endoftext|> |
28fe9ab2275ca867c791324fd8c4a9c33d490285382a37aa3881c3e5d191d775 | def get_default_royalty_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for DefaultRoyalty event.\n\n :param tx_hash: hash of transaction emitting DefaultRoyalty event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.DefaultRoyalty().processReceipt(tx_receipt) | Get log entry for DefaultRoyalty event.
:param tx_hash: hash of transaction emitting DefaultRoyalty event | thirdweb/abi/multiwrap.py | get_default_royalty_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_default_royalty_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for DefaultRoyalty event.\n\n :param tx_hash: hash of transaction emitting DefaultRoyalty event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.DefaultRoyalty().processReceipt(tx_receipt) | def get_default_royalty_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for DefaultRoyalty event.\n\n :param tx_hash: hash of transaction emitting DefaultRoyalty event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.DefaultRoyalty().processReceipt(tx_receipt)<|docstring|>Get log entry for DefaultRoyalty event.
:param tx_hash: hash of transaction emitting DefaultRoyalty event<|endoftext|> |
08626ca22ee62846505e77fce7b27dd72386e9d3fe3d63dceff10690dba50d62 | def get_owner_updated_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for OwnerUpdated event.\n\n :param tx_hash: hash of transaction emitting OwnerUpdated event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.OwnerUpdated().processReceipt(tx_receipt) | Get log entry for OwnerUpdated event.
:param tx_hash: hash of transaction emitting OwnerUpdated event | thirdweb/abi/multiwrap.py | get_owner_updated_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_owner_updated_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for OwnerUpdated event.\n\n :param tx_hash: hash of transaction emitting OwnerUpdated event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.OwnerUpdated().processReceipt(tx_receipt) | def get_owner_updated_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for OwnerUpdated event.\n\n :param tx_hash: hash of transaction emitting OwnerUpdated event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.OwnerUpdated().processReceipt(tx_receipt)<|docstring|>Get log entry for OwnerUpdated event.
:param tx_hash: hash of transaction emitting OwnerUpdated event<|endoftext|> |
01614399129aa7d8f47ded10947dfea8ccf3f139c0b413f23e7cf89b6f426776 | def get_role_admin_changed_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleAdminChanged event.\n\n :param tx_hash: hash of transaction emitting RoleAdminChanged event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleAdminChanged().processReceipt(tx_receipt) | Get log entry for RoleAdminChanged event.
:param tx_hash: hash of transaction emitting RoleAdminChanged event | thirdweb/abi/multiwrap.py | get_role_admin_changed_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_role_admin_changed_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleAdminChanged event.\n\n :param tx_hash: hash of transaction emitting RoleAdminChanged event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleAdminChanged().processReceipt(tx_receipt) | def get_role_admin_changed_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleAdminChanged event.\n\n :param tx_hash: hash of transaction emitting RoleAdminChanged event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleAdminChanged().processReceipt(tx_receipt)<|docstring|>Get log entry for RoleAdminChanged event.
:param tx_hash: hash of transaction emitting RoleAdminChanged event<|endoftext|> |
85369f4dec7eaad742669df32906f11a5129264117d415628406695c036f784f | def get_role_granted_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleGranted event.\n\n :param tx_hash: hash of transaction emitting RoleGranted event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleGranted().processReceipt(tx_receipt) | Get log entry for RoleGranted event.
:param tx_hash: hash of transaction emitting RoleGranted event | thirdweb/abi/multiwrap.py | get_role_granted_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_role_granted_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleGranted event.\n\n :param tx_hash: hash of transaction emitting RoleGranted event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleGranted().processReceipt(tx_receipt) | def get_role_granted_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleGranted event.\n\n :param tx_hash: hash of transaction emitting RoleGranted event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleGranted().processReceipt(tx_receipt)<|docstring|>Get log entry for RoleGranted event.
:param tx_hash: hash of transaction emitting RoleGranted event<|endoftext|> |
76d2edfc6ea2a6f2b29216f1efc17372c7500f744902d0945012f199304da3e2 | def get_role_revoked_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleRevoked event.\n\n :param tx_hash: hash of transaction emitting RoleRevoked event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleRevoked().processReceipt(tx_receipt) | Get log entry for RoleRevoked event.
:param tx_hash: hash of transaction emitting RoleRevoked event | thirdweb/abi/multiwrap.py | get_role_revoked_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_role_revoked_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleRevoked event.\n\n :param tx_hash: hash of transaction emitting RoleRevoked event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleRevoked().processReceipt(tx_receipt) | def get_role_revoked_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoleRevoked event.\n\n :param tx_hash: hash of transaction emitting RoleRevoked event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoleRevoked().processReceipt(tx_receipt)<|docstring|>Get log entry for RoleRevoked event.
:param tx_hash: hash of transaction emitting RoleRevoked event<|endoftext|> |
159f615bb7b0a25bde072644d8cce6aadb856ca14bb52d07652209625edc3923 | def get_royalty_for_token_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoyaltyForToken event.\n\n :param tx_hash: hash of transaction emitting RoyaltyForToken event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoyaltyForToken().processReceipt(tx_receipt) | Get log entry for RoyaltyForToken event.
:param tx_hash: hash of transaction emitting RoyaltyForToken event | thirdweb/abi/multiwrap.py | get_royalty_for_token_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_royalty_for_token_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoyaltyForToken event.\n\n :param tx_hash: hash of transaction emitting RoyaltyForToken event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoyaltyForToken().processReceipt(tx_receipt) | def get_royalty_for_token_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for RoyaltyForToken event.\n\n :param tx_hash: hash of transaction emitting RoyaltyForToken event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.RoyaltyForToken().processReceipt(tx_receipt)<|docstring|>Get log entry for RoyaltyForToken event.
:param tx_hash: hash of transaction emitting RoyaltyForToken event<|endoftext|> |
f4cbc28886d1cd9a767e92f1df3a3dd24c49e1006b129aeb696575ccc3476d96 | def get_tokens_unwrapped_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for TokensUnwrapped event.\n\n :param tx_hash: hash of transaction emitting TokensUnwrapped event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.TokensUnwrapped().processReceipt(tx_receipt) | Get log entry for TokensUnwrapped event.
:param tx_hash: hash of transaction emitting TokensUnwrapped event | thirdweb/abi/multiwrap.py | get_tokens_unwrapped_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_tokens_unwrapped_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for TokensUnwrapped event.\n\n :param tx_hash: hash of transaction emitting TokensUnwrapped event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.TokensUnwrapped().processReceipt(tx_receipt) | def get_tokens_unwrapped_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for TokensUnwrapped event.\n\n :param tx_hash: hash of transaction emitting TokensUnwrapped event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.TokensUnwrapped().processReceipt(tx_receipt)<|docstring|>Get log entry for TokensUnwrapped event.
:param tx_hash: hash of transaction emitting TokensUnwrapped event<|endoftext|> |
49e8253d03eaafcd41fc042ae610feba4f2af8e148f70b3fcfa9e59b09ede2b7 | def get_tokens_wrapped_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for TokensWrapped event.\n\n :param tx_hash: hash of transaction emitting TokensWrapped event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.TokensWrapped().processReceipt(tx_receipt) | Get log entry for TokensWrapped event.
:param tx_hash: hash of transaction emitting TokensWrapped event | thirdweb/abi/multiwrap.py | get_tokens_wrapped_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_tokens_wrapped_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for TokensWrapped event.\n\n :param tx_hash: hash of transaction emitting TokensWrapped event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.TokensWrapped().processReceipt(tx_receipt) | def get_tokens_wrapped_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for TokensWrapped event.\n\n :param tx_hash: hash of transaction emitting TokensWrapped event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.TokensWrapped().processReceipt(tx_receipt)<|docstring|>Get log entry for TokensWrapped event.
:param tx_hash: hash of transaction emitting TokensWrapped event<|endoftext|> |
1fc7b178ba0babc6a6972b20d47faead51951f656099c7afd2400738ed51a047 | def get_transfer_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for Transfer event.\n\n :param tx_hash: hash of transaction emitting Transfer event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.Transfer().processReceipt(tx_receipt) | Get log entry for Transfer event.
:param tx_hash: hash of transaction emitting Transfer event | thirdweb/abi/multiwrap.py | get_transfer_event | nftlabs/nftlabs-sdk-python | 30 | python | def get_transfer_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for Transfer event.\n\n :param tx_hash: hash of transaction emitting Transfer event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.Transfer().processReceipt(tx_receipt) | def get_transfer_event(self, tx_hash: Union[(HexBytes, bytes)]) -> Tuple[AttributeDict]:
'Get log entry for Transfer event.\n\n :param tx_hash: hash of transaction emitting Transfer event\n '
tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash)
return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=Multiwrap.abi()).events.Transfer().processReceipt(tx_receipt)<|docstring|>Get log entry for Transfer event.
:param tx_hash: hash of transaction emitting Transfer event<|endoftext|> |
e89a76374e80c559f622e462224c015f22f2158a4295469dc3bc2ca3a42c34d7 | @staticmethod
def abi():
'Return the ABI to the underlying contract.'
return json.loads('[{"inputs":[{"internalType":"address","name":"_nativeTokenWrapper","type":"address"}],"stateMutability":"nonpayable","type":"constructor"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"approved","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"Approval","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"operator","type":"address"},{"indexed":false,"internalType":"bool","name":"approved","type":"bool"}],"name":"ApprovalForAll","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"internalType":"string","name":"prevURI","type":"string"},{"indexed":false,"internalType":"string","name":"newURI","type":"string"}],"name":"ContractURIUpdated","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"internalType":"address","name":"newRoyaltyRecipient","type":"address"},{"indexed":false,"internalType":"uint256","name":"newRoyaltyBps","type":"uint256"}],"name":"DefaultRoyalty","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"internalType":"address","name":"prevOwner","type":"address"},{"indexed":false,"internalType":"address","name":"newOwner","type":"address"}],"name":"OwnerUpdated","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"bytes32","name":"previousAdminRole","type":"bytes32"},{"indexed":true,"internalType":"bytes32","name":"newAdminRole","type":"bytes32"}],"name":"RoleAdminChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"address","name":"account","type":"address"},{"indexed":true,"internalType":"address","name":"sender","type":"address"}],"name":"RoleGranted","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"address","name":"account","type":"address"},{"indexed":true,"internalType":"address","name":"sender","type":"address"}],"name":"RoleRevoked","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"},{"indexed":false,"internalType":"address","name":"royaltyRecipient","type":"address"},{"indexed":false,"internalType":"uint256","name":"royaltyBps","type":"uint256"}],"name":"RoyaltyForToken","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"unwrapper","type":"address"},{"indexed":true,"internalType":"address","name":"recipientOfWrappedContents","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenIdOfWrappedToken","type":"uint256"}],"name":"TokensUnwrapped","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"wrapper","type":"address"},{"indexed":true,"internalType":"address","name":"recipientOfWrappedToken","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenIdOfWrappedToken","type":"uint256"},{"components":[{"internalType":"address","name":"assetContract","type":"address"},{"internalType":"enum ITokenBundle.TokenType","name":"tokenType","type":"uint8"},{"internalType":"uint256","name":"tokenId","type":"uint256"},{"internalType":"uint256","name":"totalAmount","type":"uint256"}],"indexed":false,"internalType":"struct ITokenBundle.Token[]","name":"wrappedContents","type":"tuple[]"}],"name":"TokensWrapped","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"from","type":"address"},{"indexed":true,"internalType":"address","name":"to","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"Transfer","type":"event"},{"inputs":[],"name":"DEFAULT_ADMIN_ROLE","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"approve","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"owner","type":"address"}],"name":"balanceOf","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"contractType","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"pure","type":"function"},{"inputs":[],"name":"contractURI","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"contractVersion","outputs":[{"internalType":"uint8","name":"","type":"uint8"}],"stateMutability":"pure","type":"function"},{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"getApproved","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"getDefaultRoyaltyInfo","outputs":[{"internalType":"address","name":"","type":"address"},{"internalType":"uint16","name":"","type":"uint16"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"}],"name":"getRoleAdmin","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"uint256","name":"index","type":"uint256"}],"name":"getRoleMember","outputs":[{"internalType":"address","name":"member","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"}],"name":"getRoleMemberCount","outputs":[{"internalType":"uint256","name":"count","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_tokenId","type":"uint256"}],"name":"getRoyaltyInfoForToken","outputs":[{"internalType":"address","name":"","type":"address"},{"internalType":"uint16","name":"","type":"uint16"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_bundleId","type":"uint256"}],"name":"getTokenCountOfBundle","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_bundleId","type":"uint256"},{"internalType":"uint256","name":"index","type":"uint256"}],"name":"getTokenOfBundle","outputs":[{"components":[{"internalType":"address","name":"assetContract","type":"address"},{"internalType":"enum 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def abi():
return 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def abi():
return json.loads('[{"inputs":[{"internalType":"address","name":"_nativeTokenWrapper","type":"address"}],"stateMutability":"nonpayable","type":"constructor"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"approved","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"Approval","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"operator","type":"address"},{"indexed":false,"internalType":"bool","name":"approved","type":"bool"}],"name":"ApprovalForAll","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"internalType":"string","name":"prevURI","type":"string"},{"indexed":false,"internalType":"string","name":"newURI","type":"string"}],"name":"ContractURIUpdated","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"internalType":"address","name":"newRoyaltyRecipient","type":"address"},{"indexed":false,"internalType":"uint256","name":"newRoyaltyBps","type":"uint256"}],"name":"DefaultRoyalty","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"internalType":"address","name":"prevOwner","type":"address"},{"indexed":false,"internalType":"address","name":"newOwner","type":"address"}],"name":"OwnerUpdated","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"bytes32","name":"previousAdminRole","type":"bytes32"},{"indexed":true,"internalType":"bytes32","name":"newAdminRole","type":"bytes32"}],"name":"RoleAdminChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"address","name":"account","type":"address"},{"indexed":true,"internalType":"address","name":"sender","type":"address"}],"name":"RoleGranted","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"address","name":"account","type":"address"},{"indexed":true,"internalType":"address","name":"sender","type":"address"}],"name":"RoleRevoked","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"},{"indexed":false,"internalType":"address","name":"royaltyRecipient","type":"address"},{"indexed":false,"internalType":"uint256","name":"royaltyBps","type":"uint256"}],"name":"RoyaltyForToken","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"unwrapper","type":"address"},{"indexed":true,"internalType":"address","name":"recipientOfWrappedContents","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenIdOfWrappedToken","type":"uint256"}],"name":"TokensUnwrapped","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"wrapper","type":"address"},{"indexed":true,"internalType":"address","name":"recipientOfWrappedToken","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenIdOfWrappedToken","type":"uint256"},{"components":[{"internalType":"address","name":"assetContract","type":"address"},{"internalType":"enum ITokenBundle.TokenType","name":"tokenType","type":"uint8"},{"internalType":"uint256","name":"tokenId","type":"uint256"},{"internalType":"uint256","name":"totalAmount","type":"uint256"}],"indexed":false,"internalType":"struct ITokenBundle.Token[]","name":"wrappedContents","type":"tuple[]"}],"name":"TokensWrapped","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"from","type":"address"},{"indexed":true,"internalType":"address","name":"to","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"Transfer","type":"event"},{"inputs":[],"name":"DEFAULT_ADMIN_ROLE","outputs":[{"internalType":"bytes32","name":,"type":"bytes32"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"approve","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"owner","type":"address"}],"name":"balanceOf","outputs":[{"internalType":"uint256","name":,"type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"contractType","outputs":[{"internalType":"bytes32","name":,"type":"bytes32"}],"stateMutability":"pure","type":"function"},{"inputs":[],"name":"contractURI","outputs":[{"internalType":"string","name":,"type":"string"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"contractVersion","outputs":[{"internalType":"uint8","name":,"type":"uint8"}],"stateMutability":"pure","type":"function"},{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"getApproved","outputs":[{"internalType":"address","name":,"type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"getDefaultRoyaltyInfo","outputs":[{"internalType":"address","name":,"type":"address"},{"internalType":"uint16","name":,"type":"uint16"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"}],"name":"getRoleAdmin","outputs":[{"internalType":"bytes32","name":,"type":"bytes32"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"uint256","name":"index","type":"uint256"}],"name":"getRoleMember","outputs":[{"internalType":"address","name":"member","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"}],"name":"getRoleMemberCount","outputs":[{"internalType":"uint256","name":"count","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_tokenId","type":"uint256"}],"name":"getRoyaltyInfoForToken","outputs":[{"internalType":"address","name":,"type":"address"},{"internalType":"uint16","name":,"type":"uint16"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_bundleId","type":"uint256"}],"name":"getTokenCountOfBundle","outputs":[{"internalType":"uint256","name":,"type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_bundleId","type":"uint256"},{"internalType":"uint256","name":"index","type":"uint256"}],"name":"getTokenOfBundle","outputs":[{"components":[{"internalType":"address","name":"assetContract","type":"address"},{"internalType":"enum ITokenBundle.TokenType","name":"tokenType","type":"uint8"},{"internalType":"uint256","name":"tokenId","type":"uint256"},{"internalType":"uint256","name":"totalAmount","type":"uint256"}],"internalType":"struct ITokenBundle.Token","name":,"type":"tuple"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_bundleId","type":"uint256"}],"name":"getUriOfBundle","outputs":[{"internalType":"string","name":,"type":"string"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"_tokenId","type":"uint256"}],"name":"getWrappedContents","outputs":[{"components":[{"internalType":"address","name":"assetContract","type":"address"},{"internalType":"enum ITokenBundle.TokenType","name":"tokenType","type":"uint8"},{"internalType":"uint256","name":"tokenId","type":"uint256"},{"internalType":"uint256","name":"totalAmount","type":"uint256"}],"internalType":"struct ITokenBundle.Token[]","name":"contents","type":"tuple[]"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"grantRole","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"hasRole","outputs":[{"internalType":"bool","name":,"type":"bool"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"hasRoleWithSwitch","outputs":[{"internalType":"bool","name":,"type":"bool"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"_defaultAdmin","type":"address"},{"internalType":"string","name":"_name","type":"string"},{"internalType":"string","name":"_symbol","type":"string"},{"internalType":"string","name":"_contractURI","type":"string"},{"internalType":"address[]","name":"_trustedForwarders","type":"address[]"},{"internalType":"address","name":"_royaltyRecipient","type":"address"},{"internalType":"uint256","name":"_royaltyBps","type":"uint256"}],"name":"initialize","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"owner","type":"address"},{"internalType":"address","name":"operator","type":"address"}],"name":"isApprovedForAll","outputs":[{"internalType":"bool","name":,"type":"bool"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"forwarder","type":"address"}],"name":"isTrustedForwarder","outputs":[{"internalType":"bool","name":,"type":"bool"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes[]","name":"data","type":"bytes[]"}],"name":"multicall","outputs":[{"internalType":"bytes[]","name":"results","type":"bytes[]"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"name","outputs":[{"internalType":"string","name":,"type":"string"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"nextTokenIdToMint","outputs":[{"internalType":"uint256","name":,"type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"index_0","type":"address"},{"internalType":"address","name":"index_1","type":"address"},{"internalType":"uint256[]","name":"index_2","type":"uint256[]"},{"internalType":"uint256[]","name":"index_3","type":"uint256[]"},{"internalType":"bytes","name":"index_4","type":"bytes"}],"name":"onERC1155BatchReceived","outputs":[{"internalType":"bytes4","name":,"type":"bytes4"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"index_0","type":"address"},{"internalType":"address","name":"index_1","type":"address"},{"internalType":"uint256","name":"index_2","type":"uint256"},{"internalType":"uint256","name":"index_3","type":"uint256"},{"internalType":"bytes","name":"index_4","type":"bytes"}],"name":"onERC1155Received","outputs":[{"internalType":"bytes4","name":,"type":"bytes4"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"index_0","type":"address"},{"internalType":"address","name":"index_1","type":"address"},{"internalType":"uint256","name":"index_2","type":"uint256"},{"internalType":"bytes","name":"index_3","type":"bytes"}],"name":"onERC721Received","outputs":[{"internalType":"bytes4","name":,"type":"bytes4"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"owner","outputs":[{"internalType":"address","name":,"type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"ownerOf","outputs":[{"internalType":"address","name":,"type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"renounceRole","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"revokeRole","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"},{"internalType":"uint256","name":"salePrice","type":"uint256"}],"name":"royaltyInfo","outputs":[{"internalType":"address","name":"receiver","type":"address"},{"internalType":"uint256","name":"royaltyAmount","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"from","type":"address"},{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"safeTransferFrom","outputs":[],"stateMutability":"non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ITokenBundle.TokenType","name":"tokenType","type":"uint8"},{"internalType":"uint256","name":"tokenId","type":"uint256"},{"internalType":"uint256","name":"totalAmount","type":"uint256"}],"internalType":"struct ITokenBundle.Token[]","name":"_tokensToWrap","type":"tuple[]"},{"internalType":"string","name":"_uriForWrappedToken","type":"string"},{"internalType":"address","name":"_recipient","type":"address"}],"name":"wrap","outputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"stateMutability":"payable","type":"function"}]')<|docstring|>Return the ABI to the underlying contract.<|endoftext|> |
b4336b2f4ff09a59fdb8a71ccd0ec60fbbfa9d48fcaa737fa0421d6700066b46 | def setUp(self):
'Set up the tests.'
webcompat.app.config['TESTING'] = True
self.app = webcompat.app.test_client() | Set up the tests. | tests/unit/test_urls.py | setUp | softvision-oana-arbuzov/webcompat.com | 2 | python | def setUp(self):
webcompat.app.config['TESTING'] = True
self.app = webcompat.app.test_client() | def setUp(self):
webcompat.app.config['TESTING'] = True
self.app = webcompat.app.test_client()<|docstring|>Set up the tests.<|endoftext|> |
ff56221943b1285762627cbf28c4dbc317906c6ba43c726f3d605cdcfd3bdde0 | def tearDown(self):
'Tear down the tests.'
pass | Tear down the tests. | tests/unit/test_urls.py | tearDown | softvision-oana-arbuzov/webcompat.com | 2 | python | def tearDown(self):
pass | def tearDown(self):
pass<|docstring|>Tear down the tests.<|endoftext|> |
027011c3faaacbe7c461bd1b4ad98fa64714dad3513684cb3fa63d90f8326820 | def test_home(self):
'Test that the home page exists.'
rv = self.app.get('/', environ_base=headers)
self.assertEqual(rv.status_code, 200) | Test that the home page exists. | tests/unit/test_urls.py | test_home | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_home(self):
rv = self.app.get('/', environ_base=headers)
self.assertEqual(rv.status_code, 200) | def test_home(self):
rv = self.app.get('/', environ_base=headers)
self.assertEqual(rv.status_code, 200)<|docstring|>Test that the home page exists.<|endoftext|> |
f934a2d976af52ae2e6baa2ac6f7624e111861b8244eacf4e58a687b4ac2a9d3 | def test_new_issue(self):
'Test that /issues/new exists.'
rv = self.app.get('/issues/new', environ_base=headers)
self.assertEqual(rv.status_code, 200) | Test that /issues/new exists. | tests/unit/test_urls.py | test_new_issue | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_new_issue(self):
rv = self.app.get('/issues/new', environ_base=headers)
self.assertEqual(rv.status_code, 200) | def test_new_issue(self):
rv = self.app.get('/issues/new', environ_base=headers)
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /issues/new exists.<|endoftext|> |
20160344198928338f17075030d30ef61cdf2aa1fd84984366a7721f2b86fa36 | def test_about(self):
'Test that /about exists.'
rv = self.app.get('/about')
self.assertEqual(rv.status_code, 200) | Test that /about exists. | tests/unit/test_urls.py | test_about | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_about(self):
rv = self.app.get('/about')
self.assertEqual(rv.status_code, 200) | def test_about(self):
rv = self.app.get('/about')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /about exists.<|endoftext|> |
8e63b22f49f9ad55b1e238f708f73418f0b57ea3a65b42fd01263aac879375a7 | def test_privacy(self):
'Test that /privacy exists.'
rv = self.app.get('/privacy')
self.assertEqual(rv.status_code, 200) | Test that /privacy exists. | tests/unit/test_urls.py | test_privacy | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_privacy(self):
rv = self.app.get('/privacy')
self.assertEqual(rv.status_code, 200) | def test_privacy(self):
rv = self.app.get('/privacy')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /privacy exists.<|endoftext|> |
81ae576278e8c537f1e6d6a7c55823ffd90607bc41b8d8e1facad211a5cdb9ae | def test_contributors(self):
'Test that /contributors exists.'
rv = self.app.get('/contributors')
self.assertEqual(rv.status_code, 200) | Test that /contributors exists. | tests/unit/test_urls.py | test_contributors | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors(self):
rv = self.app.get('/contributors')
self.assertEqual(rv.status_code, 200) | def test_contributors(self):
rv = self.app.get('/contributors')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors exists.<|endoftext|> |
3de4e5cefee81069134d38ab3d55fac948197733d5688abcccfdd0ba104ed62d | def test_contributors_report_bug(self):
'Test that /contributors/report-bug exists.'
rv = self.app.get('/contributors/report-bug')
self.assertEqual(rv.status_code, 200) | Test that /contributors/report-bug exists. | tests/unit/test_urls.py | test_contributors_report_bug | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors_report_bug(self):
rv = self.app.get('/contributors/report-bug')
self.assertEqual(rv.status_code, 200) | def test_contributors_report_bug(self):
rv = self.app.get('/contributors/report-bug')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors/report-bug exists.<|endoftext|> |
e2b3ac1470e160260ecbdee5d64e5ca309c63640a03bb74f1e2572f1d2bac5cb | def test_contributors_diagnose_bug(self):
'Test that /contributors/diagnose-bug exists.'
rv = self.app.get('/contributors/diagnose-bug')
self.assertEqual(rv.status_code, 200) | Test that /contributors/diagnose-bug exists. | tests/unit/test_urls.py | test_contributors_diagnose_bug | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors_diagnose_bug(self):
rv = self.app.get('/contributors/diagnose-bug')
self.assertEqual(rv.status_code, 200) | def test_contributors_diagnose_bug(self):
rv = self.app.get('/contributors/diagnose-bug')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors/diagnose-bug exists.<|endoftext|> |
7384c441257453045cb2da5361efb95181fd305435f0f422e45b4b33e126d7d1 | def test_contributors_reproduce_bug(self):
'Test that /contributors/reproduce-bug exists.'
rv = self.app.get('/contributors/reproduce-bug')
self.assertEqual(rv.status_code, 200) | Test that /contributors/reproduce-bug exists. | tests/unit/test_urls.py | test_contributors_reproduce_bug | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors_reproduce_bug(self):
rv = self.app.get('/contributors/reproduce-bug')
self.assertEqual(rv.status_code, 200) | def test_contributors_reproduce_bug(self):
rv = self.app.get('/contributors/reproduce-bug')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors/reproduce-bug exists.<|endoftext|> |
5ecb025e9ab1fb3e5ce4cffd9c847a74d6a074900a9892121f3cc5bc4152cb9c | def test_contributors_site_outreach(self):
'Test that /contributors/site-outreach exists.'
rv = self.app.get('/contributors/site-outreach')
self.assertEqual(rv.status_code, 200) | Test that /contributors/site-outreach exists. | tests/unit/test_urls.py | test_contributors_site_outreach | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors_site_outreach(self):
rv = self.app.get('/contributors/site-outreach')
self.assertEqual(rv.status_code, 200) | def test_contributors_site_outreach(self):
rv = self.app.get('/contributors/site-outreach')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors/site-outreach exists.<|endoftext|> |
42a21e2f54fbcf6eadd5734bb40760f64756d72b8ed831414b99cd56fdb4c4d3 | def test_contributors_build_tools(self):
'Test that /contributors/build-tools exists.'
rv = self.app.get('/contributors/build-tools')
self.assertEqual(rv.status_code, 200) | Test that /contributors/build-tools exists. | tests/unit/test_urls.py | test_contributors_build_tools | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors_build_tools(self):
rv = self.app.get('/contributors/build-tools')
self.assertEqual(rv.status_code, 200) | def test_contributors_build_tools(self):
rv = self.app.get('/contributors/build-tools')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors/build-tools exists.<|endoftext|> |
6a3e7b7cbc62fa85593536c4ba0a13f7940d5414550a01210ecc6ce0334d816a | def test_contributors_web_research(self):
'Test that /contributors/web-platform-research exists.'
rv = self.app.get('/contributors/web-platform-research')
self.assertEqual(rv.status_code, 200) | Test that /contributors/web-platform-research exists. | tests/unit/test_urls.py | test_contributors_web_research | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors_web_research(self):
rv = self.app.get('/contributors/web-platform-research')
self.assertEqual(rv.status_code, 200) | def test_contributors_web_research(self):
rv = self.app.get('/contributors/web-platform-research')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors/web-platform-research exists.<|endoftext|> |
065818a6cf17be6beee1933f6312d6948726dab52decd6896428bc98f64d8f29 | def test_contributors_events(self):
'Test that /contributors/organize-webcompat-events exists.'
rv = self.app.get('/contributors/organize-webcompat-events')
self.assertEqual(rv.status_code, 200) | Test that /contributors/organize-webcompat-events exists. | tests/unit/test_urls.py | test_contributors_events | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contributors_events(self):
rv = self.app.get('/contributors/organize-webcompat-events')
self.assertEqual(rv.status_code, 200) | def test_contributors_events(self):
rv = self.app.get('/contributors/organize-webcompat-events')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contributors/organize-webcompat-events exists.<|endoftext|> |
f7096d2910cae83a8e03dc79e58d109af2689c566c6eaac72fc54a332b2de476 | def test_contact(self):
'Test that /contact exists.'
rv = self.app.get('/contact')
self.assertEqual(rv.status_code, 200) | Test that /contact exists. | tests/unit/test_urls.py | test_contact | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_contact(self):
rv = self.app.get('/contact')
self.assertEqual(rv.status_code, 200) | def test_contact(self):
rv = self.app.get('/contact')
self.assertEqual(rv.status_code, 200)<|docstring|>Test that /contact exists.<|endoftext|> |
d9729652a83b8c03452f7e9c01956f9dcead7eb5bb9a3fc77f9376c1f7310d71 | def test_activity_page_401_if_not_logged_in(self):
'Test that asks user to log in before displaying activity.'
rv = self.app.get('/me')
self.assertEqual(rv.status_code, 401) | Test that asks user to log in before displaying activity. | tests/unit/test_urls.py | test_activity_page_401_if_not_logged_in | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_activity_page_401_if_not_logged_in(self):
rv = self.app.get('/me')
self.assertEqual(rv.status_code, 401) | def test_activity_page_401_if_not_logged_in(self):
rv = self.app.get('/me')
self.assertEqual(rv.status_code, 401)<|docstring|>Test that asks user to log in before displaying activity.<|endoftext|> |
239e178b433672cec4f378260cd27e2f9063d40d88bcfdf96a694221717d0b45 | def test_issue_int(self):
'Test if issues are really integer.\n\n * an issue only displays if <number> is an integer\n * /issues/<number> exists, and does not redirect.\n '
rv = self.app.get('/issues/3')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 404)
rv = self.app.get('/issues/three')
self.assertEqual(rv.status_code, 404)
self.assertNotEqual(rv.status_code, 200) | Test if issues are really integer.
* an issue only displays if <number> is an integer
* /issues/<number> exists, and does not redirect. | tests/unit/test_urls.py | test_issue_int | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_issue_int(self):
'Test if issues are really integer.\n\n * an issue only displays if <number> is an integer\n * /issues/<number> exists, and does not redirect.\n '
rv = self.app.get('/issues/3')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 404)
rv = self.app.get('/issues/three')
self.assertEqual(rv.status_code, 404)
self.assertNotEqual(rv.status_code, 200) | def test_issue_int(self):
'Test if issues are really integer.\n\n * an issue only displays if <number> is an integer\n * /issues/<number> exists, and does not redirect.\n '
rv = self.app.get('/issues/3')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 404)
rv = self.app.get('/issues/three')
self.assertEqual(rv.status_code, 404)
self.assertNotEqual(rv.status_code, 200)<|docstring|>Test if issues are really integer.
* an issue only displays if <number> is an integer
* /issues/<number> exists, and does not redirect.<|endoftext|> |
467f8453de91d5a4932bacce6ecbdf12c595d25f2fc2604f8b2eb70e924cc245 | def test_issue_redirect(self):
'Test that the /issues/<number> exists, and does not redirect.'
rv = self.app.get('/issues/3')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 307) | Test that the /issues/<number> exists, and does not redirect. | tests/unit/test_urls.py | test_issue_redirect | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_issue_redirect(self):
rv = self.app.get('/issues/3')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 307) | def test_issue_redirect(self):
rv = self.app.get('/issues/3')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 307)<|docstring|>Test that the /issues/<number> exists, and does not redirect.<|endoftext|> |
59c3c592edf9fc1e9d45b5db24ac90ff5595749756899de8c0cf7865064da33b | def test_issues_list_page(self):
'Test that the /issues route gets 200 and does not redirect.'
rv = self.app.get('/issues')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 307) | Test that the /issues route gets 200 and does not redirect. | tests/unit/test_urls.py | test_issues_list_page | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_issues_list_page(self):
rv = self.app.get('/issues')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 307) | def test_issues_list_page(self):
rv = self.app.get('/issues')
self.assertEqual(rv.status_code, 200)
self.assertNotEqual(rv.status_code, 307)<|docstring|>Test that the /issues route gets 200 and does not redirect.<|endoftext|> |
a9f341fbecdb2fbf7b0b96986290777b1265e283ba6f214b3cdf6ead0e13e19c | def test_csp_report_uri(self):
'Test POST to /csp-report w/ correct content-type returns 204.'
headers = {'Content-Type': 'application/csp-report'}
rv = self.app.post('/csp-report', headers=headers)
self.assertEqual(rv.status_code, 204) | Test POST to /csp-report w/ correct content-type returns 204. | tests/unit/test_urls.py | test_csp_report_uri | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_csp_report_uri(self):
headers = {'Content-Type': 'application/csp-report'}
rv = self.app.post('/csp-report', headers=headers)
self.assertEqual(rv.status_code, 204) | def test_csp_report_uri(self):
headers = {'Content-Type': 'application/csp-report'}
rv = self.app.post('/csp-report', headers=headers)
self.assertEqual(rv.status_code, 204)<|docstring|>Test POST to /csp-report w/ correct content-type returns 204.<|endoftext|> |
90877a27c451a9ef0a45de4b4a3ad7e5f531209931842b3e8f64560be6ed322f | def test_csp_report_uri_bad_content_type(self):
'Test POST w/ wrong content-type to /csp-report returns 400.'
headers = {'Content-Type': 'application/json'}
rv = self.app.post('/csp-report', headers=headers)
self.assertNotEqual(rv.status_code, 204)
self.assertEqual(rv.status_code, 400) | Test POST w/ wrong content-type to /csp-report returns 400. | tests/unit/test_urls.py | test_csp_report_uri_bad_content_type | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_csp_report_uri_bad_content_type(self):
headers = {'Content-Type': 'application/json'}
rv = self.app.post('/csp-report', headers=headers)
self.assertNotEqual(rv.status_code, 204)
self.assertEqual(rv.status_code, 400) | def test_csp_report_uri_bad_content_type(self):
headers = {'Content-Type': 'application/json'}
rv = self.app.post('/csp-report', headers=headers)
self.assertNotEqual(rv.status_code, 204)
self.assertEqual(rv.status_code, 400)<|docstring|>Test POST w/ wrong content-type to /csp-report returns 400.<|endoftext|> |
c4dd8b133567beb6df2241edcaecc2f833aad24f434835b95cc30b0cb3516a8e | def test_tools_cssfixme(self):
'Test that the /tools/cssfixme route gets 200.'
rv = self.app.get('/tools/cssfixme')
self.assertEqual(rv.status_code, 410) | Test that the /tools/cssfixme route gets 200. | tests/unit/test_urls.py | test_tools_cssfixme | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_tools_cssfixme(self):
rv = self.app.get('/tools/cssfixme')
self.assertEqual(rv.status_code, 410) | def test_tools_cssfixme(self):
rv = self.app.get('/tools/cssfixme')
self.assertEqual(rv.status_code, 410)<|docstring|>Test that the /tools/cssfixme route gets 200.<|endoftext|> |
0a0ff17ff4744324eff21296f787e8f447d0da31f32fb7b3472a439cab6e4113 | def test_rate_limit(self):
'Rate Limit URI sends 410 Gone.'
rv = self.app.get('/rate_limit')
self.assertEqual(rv.status_code, 410) | Rate Limit URI sends 410 Gone. | tests/unit/test_urls.py | test_rate_limit | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_rate_limit(self):
rv = self.app.get('/rate_limit')
self.assertEqual(rv.status_code, 410) | def test_rate_limit(self):
rv = self.app.get('/rate_limit')
self.assertEqual(rv.status_code, 410)<|docstring|>Rate Limit URI sends 410 Gone.<|endoftext|> |
ba673270073dbdfd390e8da9a048c09bb4487b8c6c61772e0c5819d38eef3d13 | def test_missing_parameters_for_new_issue(self):
'Sends 400 to POST on /issues/new with missing parameters.'
rv = self.app.post('/issues/new', data=dict(url='foo'))
self.assertEqual(rv.status_code, 400) | Sends 400 to POST on /issues/new with missing parameters. | tests/unit/test_urls.py | test_missing_parameters_for_new_issue | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_missing_parameters_for_new_issue(self):
rv = self.app.post('/issues/new', data=dict(url='foo'))
self.assertEqual(rv.status_code, 400) | def test_missing_parameters_for_new_issue(self):
rv = self.app.post('/issues/new', data=dict(url='foo'))
self.assertEqual(rv.status_code, 400)<|docstring|>Sends 400 to POST on /issues/new with missing parameters.<|endoftext|> |
97302fb35180406d3bfb0dc793c4ce3acba60a24877dd3c94002acb6c89651d7 | def test_new_issue_should_not_crash(self):
'/issues/new POST exit with 400 if missing parameters.'
data = {'problem_category': u'mobile_site_bug', 'description': u'foo', 'submit_type': u'github-proxy-report', 'url': u'http://example.com', 'os': u'Foobar', 'browser': u'BarFoo'}
rv = self.app.post('/issues/new', data=data)
self.assertEqual(rv.status_code, 400) | /issues/new POST exit with 400 if missing parameters. | tests/unit/test_urls.py | test_new_issue_should_not_crash | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_new_issue_should_not_crash(self):
data = {'problem_category': u'mobile_site_bug', 'description': u'foo', 'submit_type': u'github-proxy-report', 'url': u'http://example.com', 'os': u'Foobar', 'browser': u'BarFoo'}
rv = self.app.post('/issues/new', data=data)
self.assertEqual(rv.status_code, 400) | def test_new_issue_should_not_crash(self):
data = {'problem_category': u'mobile_site_bug', 'description': u'foo', 'submit_type': u'github-proxy-report', 'url': u'http://example.com', 'os': u'Foobar', 'browser': u'BarFoo'}
rv = self.app.post('/issues/new', data=data)
self.assertEqual(rv.status_code, 400)<|docstring|>/issues/new POST exit with 400 if missing parameters.<|endoftext|> |
3355bc29e93e8c091130d14cc8c19855f73b540e38031ed8dde9bd4e331266dd | def test_dashboard_triage(self):
'Request to /dashboard/triage should be 200.'
rv = self.app.get('/dashboard/triage')
self.assertEqual(rv.status_code, 200)
self.assertTrue(('<h1><a href="/">Webcompat.com</a> // Triage Dashboard</h1>' in rv.data))
self.assertTrue(('text/html' in rv.content_type)) | Request to /dashboard/triage should be 200. | tests/unit/test_urls.py | test_dashboard_triage | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_dashboard_triage(self):
rv = self.app.get('/dashboard/triage')
self.assertEqual(rv.status_code, 200)
self.assertTrue(('<h1><a href="/">Webcompat.com</a> // Triage Dashboard</h1>' in rv.data))
self.assertTrue(('text/html' in rv.content_type)) | def test_dashboard_triage(self):
rv = self.app.get('/dashboard/triage')
self.assertEqual(rv.status_code, 200)
self.assertTrue(('<h1><a href="/">Webcompat.com</a> // Triage Dashboard</h1>' in rv.data))
self.assertTrue(('text/html' in rv.content_type))<|docstring|>Request to /dashboard/triage should be 200.<|endoftext|> |
0be02369ae9350ac50e8d7348be311c37d749479cbcbdb93c56b6044536d7ea5 | def test_dashboard_route(self):
'Request to /dashboard should be 404.\n\n For now, the dashboard route has no purpose.\n '
rv = self.app.get('/dashboard/')
content_test = ('Lost in Punk Cat Space (404)' in rv.data)
self.assertEqual(rv.status_code, 404)
self.assertTrue(('text/html' in rv.content_type))
self.assertTrue(content_test)
rv = self.app.get('/dashboard')
content_test = ('Lost in Punk Cat Space (404)' in rv.data)
self.assertEqual(rv.status_code, 404)
self.assertTrue(('text/html' in rv.content_type))
self.assertTrue(content_test) | Request to /dashboard should be 404.
For now, the dashboard route has no purpose. | tests/unit/test_urls.py | test_dashboard_route | softvision-oana-arbuzov/webcompat.com | 2 | python | def test_dashboard_route(self):
'Request to /dashboard should be 404.\n\n For now, the dashboard route has no purpose.\n '
rv = self.app.get('/dashboard/')
content_test = ('Lost in Punk Cat Space (404)' in rv.data)
self.assertEqual(rv.status_code, 404)
self.assertTrue(('text/html' in rv.content_type))
self.assertTrue(content_test)
rv = self.app.get('/dashboard')
content_test = ('Lost in Punk Cat Space (404)' in rv.data)
self.assertEqual(rv.status_code, 404)
self.assertTrue(('text/html' in rv.content_type))
self.assertTrue(content_test) | def test_dashboard_route(self):
'Request to /dashboard should be 404.\n\n For now, the dashboard route has no purpose.\n '
rv = self.app.get('/dashboard/')
content_test = ('Lost in Punk Cat Space (404)' in rv.data)
self.assertEqual(rv.status_code, 404)
self.assertTrue(('text/html' in rv.content_type))
self.assertTrue(content_test)
rv = self.app.get('/dashboard')
content_test = ('Lost in Punk Cat Space (404)' in rv.data)
self.assertEqual(rv.status_code, 404)
self.assertTrue(('text/html' in rv.content_type))
self.assertTrue(content_test)<|docstring|>Request to /dashboard should be 404.
For now, the dashboard route has no purpose.<|endoftext|> |
b17b99cbf52823fb268a2890345c280adf303b7d079a2d1e2ab3865480f11ac0 | def load_deps(path):
'Load dependencies from requirements file'
with open(path) as fp:
return [line.strip() for line in fp if (not is_ignored(line))] | Load dependencies from requirements file | setup.py | load_deps | yonittanenbaum/mlrun | 0 | python | def load_deps(path):
with open(path) as fp:
return [line.strip() for line in fp if (not is_ignored(line))] | def load_deps(path):
with open(path) as fp:
return [line.strip() for line in fp if (not is_ignored(line))]<|docstring|>Load dependencies from requirements file<|endoftext|> |
d6868b14ba9352a49c57690d10365ff9c0c6266a6f97a5fd51c7a8efe65ddb8d | def reward_function(params):
'\n Stay in the track and incentive higher speeds\n '
MAX_REWARD = float(10000)
if (params['progress'] == 100):
return MAX_REWARD
all_wheels_on_track = params['all_wheels_on_track']
distance_from_center = params['distance_from_center']
track_width = params['track_width']
reward = 0.001
if (all_wheels_on_track and (((0.5 * track_width) - distance_from_center) >= 0.05)):
reward = 1.0
reward *= (params['speed'] ** 2)
return min(float(reward), MAX_REWARD) | Stay in the track and incentive higher speeds | deepracer/rewards/time-trial/speed_incentivized_simple_track_follow.py | reward_function | spowers42/rl-racing | 0 | python | def reward_function(params):
'\n \n '
MAX_REWARD = float(10000)
if (params['progress'] == 100):
return MAX_REWARD
all_wheels_on_track = params['all_wheels_on_track']
distance_from_center = params['distance_from_center']
track_width = params['track_width']
reward = 0.001
if (all_wheels_on_track and (((0.5 * track_width) - distance_from_center) >= 0.05)):
reward = 1.0
reward *= (params['speed'] ** 2)
return min(float(reward), MAX_REWARD) | def reward_function(params):
'\n \n '
MAX_REWARD = float(10000)
if (params['progress'] == 100):
return MAX_REWARD
all_wheels_on_track = params['all_wheels_on_track']
distance_from_center = params['distance_from_center']
track_width = params['track_width']
reward = 0.001
if (all_wheels_on_track and (((0.5 * track_width) - distance_from_center) >= 0.05)):
reward = 1.0
reward *= (params['speed'] ** 2)
return min(float(reward), MAX_REWARD)<|docstring|>Stay in the track and incentive higher speeds<|endoftext|> |
68841d9e24e51befa61546debcaf2408ae44ef69acb7d1a9ac813e34b8b69f5a | def my_inference_detector(model, data):
'Inference image(s) with the detector.\n\n Args:\n model (nn.Module): The loaded detector.\n imgs (str/ndarray or list[str/ndarray]): Either image files or loaded\n images.\n\n Returns:\n If imgs is a str, a generator will be returned, otherwise return the\n detection results directly.\n '
cfg = model.cfg
device = next(model.parameters()).device
test_pipeline = cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
data = test_pipeline(data)
data = collate([data], samples_per_gpu=1)
if next(model.parameters()).is_cuda:
data = scatter(data, [device])[0]
else:
for m in model.modules():
if isinstance(m, (RoIPool, RoIAlign)):
if (not m.aligned):
m.use_torchvision = True
warnings.warn('We set use_torchvision=True in CPU mode.')
data['img_metas'] = data['img_metas'][0].data
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
return result[0] | Inference image(s) with the detector.
Args:
model (nn.Module): The loaded detector.
imgs (str/ndarray or list[str/ndarray]): Either image files or loaded
images.
Returns:
If imgs is a str, a generator will be returned, otherwise return the
detection results directly. | UFPMP-Det-Tools/eval_script/ufpmp_det_eval.py | my_inference_detector | PuAnysh/UFPMP-Det | 9 | python | def my_inference_detector(model, data):
'Inference image(s) with the detector.\n\n Args:\n model (nn.Module): The loaded detector.\n imgs (str/ndarray or list[str/ndarray]): Either image files or loaded\n images.\n\n Returns:\n If imgs is a str, a generator will be returned, otherwise return the\n detection results directly.\n '
cfg = model.cfg
device = next(model.parameters()).device
test_pipeline = cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
data = test_pipeline(data)
data = collate([data], samples_per_gpu=1)
if next(model.parameters()).is_cuda:
data = scatter(data, [device])[0]
else:
for m in model.modules():
if isinstance(m, (RoIPool, RoIAlign)):
if (not m.aligned):
m.use_torchvision = True
warnings.warn('We set use_torchvision=True in CPU mode.')
data['img_metas'] = data['img_metas'][0].data
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
return result[0] | def my_inference_detector(model, data):
'Inference image(s) with the detector.\n\n Args:\n model (nn.Module): The loaded detector.\n imgs (str/ndarray or list[str/ndarray]): Either image files or loaded\n images.\n\n Returns:\n If imgs is a str, a generator will be returned, otherwise return the\n detection results directly.\n '
cfg = model.cfg
device = next(model.parameters()).device
test_pipeline = cfg.data.test.pipeline[1:]
test_pipeline = Compose(test_pipeline)
data = test_pipeline(data)
data = collate([data], samples_per_gpu=1)
if next(model.parameters()).is_cuda:
data = scatter(data, [device])[0]
else:
for m in model.modules():
if isinstance(m, (RoIPool, RoIAlign)):
if (not m.aligned):
m.use_torchvision = True
warnings.warn('We set use_torchvision=True in CPU mode.')
data['img_metas'] = data['img_metas'][0].data
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
return result[0]<|docstring|>Inference image(s) with the detector.
Args:
model (nn.Module): The loaded detector.
imgs (str/ndarray or list[str/ndarray]): Either image files or loaded
images.
Returns:
If imgs is a str, a generator will be returned, otherwise return the
detection results directly.<|endoftext|> |
207f67dd87ec988b3549a0a139539ecf3ec5edb2114b4bfc7449a2b6d37baada | def py_cpu_nms(dets, thresh):
'Pure Python NMS baseline.'
dets = np.array(dets)
x1 = dets[(:, 0)]
y1 = dets[(:, 1)]
x2 = dets[(:, 2)]
y2 = dets[(:, 3)]
scores = dets[(:, 4)]
areas = (((x2 - x1) + 1) * ((y2 - y1) + 1))
order = scores.argsort()[::(- 1)]
keep = []
while (order.size > 0):
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, ((xx2 - xx1) + 1))
h = np.maximum(0.0, ((yy2 - yy1) + 1))
inter = (w * h)
ovr = (inter / ((areas[i] + areas[order[1:]]) - inter))
inds = np.where((ovr <= thresh))[0]
order = order[(inds + 1)]
return keep | Pure Python NMS baseline. | UFPMP-Det-Tools/eval_script/ufpmp_det_eval.py | py_cpu_nms | PuAnysh/UFPMP-Det | 9 | python | def py_cpu_nms(dets, thresh):
dets = np.array(dets)
x1 = dets[(:, 0)]
y1 = dets[(:, 1)]
x2 = dets[(:, 2)]
y2 = dets[(:, 3)]
scores = dets[(:, 4)]
areas = (((x2 - x1) + 1) * ((y2 - y1) + 1))
order = scores.argsort()[::(- 1)]
keep = []
while (order.size > 0):
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, ((xx2 - xx1) + 1))
h = np.maximum(0.0, ((yy2 - yy1) + 1))
inter = (w * h)
ovr = (inter / ((areas[i] + areas[order[1:]]) - inter))
inds = np.where((ovr <= thresh))[0]
order = order[(inds + 1)]
return keep | def py_cpu_nms(dets, thresh):
dets = np.array(dets)
x1 = dets[(:, 0)]
y1 = dets[(:, 1)]
x2 = dets[(:, 2)]
y2 = dets[(:, 3)]
scores = dets[(:, 4)]
areas = (((x2 - x1) + 1) * ((y2 - y1) + 1))
order = scores.argsort()[::(- 1)]
keep = []
while (order.size > 0):
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, ((xx2 - xx1) + 1))
h = np.maximum(0.0, ((yy2 - yy1) + 1))
inter = (w * h)
ovr = (inter / ((areas[i] + areas[order[1:]]) - inter))
inds = np.where((ovr <= thresh))[0]
order = order[(inds + 1)]
return keep<|docstring|>Pure Python NMS baseline.<|endoftext|> |
b5fdb3435d45051dca51a28210bb8e7086da30e9e01a28fa41b6866c8cb90cb9 | def __call__(self, results, bbox=None, img_data=None):
'Call function to load images into results.\n\n Args:\n results (dict): A result dict contains the file name\n of the image to be read.\n\n Returns:\n dict: ``results`` will be returned containing loaded image.\n '
if isinstance(results['img'], str):
results['filename'] = results['img']
results['ori_filename'] = results['img']
else:
results['filename'] = None
results['ori_filename'] = None
if (img_data is None):
img = mmcv.imread(results['img'])
else:
img = img_data
if bbox:
(x1, x2, y1, y2, _) = bbox
img = img[(x1:x2, y1:y2, :)]
results['img'] = img
results['img_fields'] = ['img']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
return results | Call function to load images into results.
Args:
results (dict): A result dict contains the file name
of the image to be read.
Returns:
dict: ``results`` will be returned containing loaded image. | UFPMP-Det-Tools/eval_script/ufpmp_det_eval.py | __call__ | PuAnysh/UFPMP-Det | 9 | python | def __call__(self, results, bbox=None, img_data=None):
'Call function to load images into results.\n\n Args:\n results (dict): A result dict contains the file name\n of the image to be read.\n\n Returns:\n dict: ``results`` will be returned containing loaded image.\n '
if isinstance(results['img'], str):
results['filename'] = results['img']
results['ori_filename'] = results['img']
else:
results['filename'] = None
results['ori_filename'] = None
if (img_data is None):
img = mmcv.imread(results['img'])
else:
img = img_data
if bbox:
(x1, x2, y1, y2, _) = bbox
img = img[(x1:x2, y1:y2, :)]
results['img'] = img
results['img_fields'] = ['img']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
return results | def __call__(self, results, bbox=None, img_data=None):
'Call function to load images into results.\n\n Args:\n results (dict): A result dict contains the file name\n of the image to be read.\n\n Returns:\n dict: ``results`` will be returned containing loaded image.\n '
if isinstance(results['img'], str):
results['filename'] = results['img']
results['ori_filename'] = results['img']
else:
results['filename'] = None
results['ori_filename'] = None
if (img_data is None):
img = mmcv.imread(results['img'])
else:
img = img_data
if bbox:
(x1, x2, y1, y2, _) = bbox
img = img[(x1:x2, y1:y2, :)]
results['img'] = img
results['img_fields'] = ['img']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
return results<|docstring|>Call function to load images into results.
Args:
results (dict): A result dict contains the file name
of the image to be read.
Returns:
dict: ``results`` will be returned containing loaded image.<|endoftext|> |
fea8e7de41bc3058a86d5ab89ebed39d4df65d648bd945e8fd7195800911ddbb | def __init__(self):
'\n Constructor that will appropriately initialize a supervised learning object\n @ In, None\n @ Out, None\n '
super().__init__()
import sklearn
import sklearn.multiclass
self.model = sklearn.multiclass.OutputCodeClassifier | Constructor that will appropriately initialize a supervised learning object
@ In, None
@ Out, None | framework/SupervisedLearning/ScikitLearn/MultiClass/OutputCodeClassifier.py | __init__ | greenwoodms06/raven | 1 | python | def __init__(self):
'\n Constructor that will appropriately initialize a supervised learning object\n @ In, None\n @ Out, None\n '
super().__init__()
import sklearn
import sklearn.multiclass
self.model = sklearn.multiclass.OutputCodeClassifier | def __init__(self):
'\n Constructor that will appropriately initialize a supervised learning object\n @ In, None\n @ Out, None\n '
super().__init__()
import sklearn
import sklearn.multiclass
self.model = sklearn.multiclass.OutputCodeClassifier<|docstring|>Constructor that will appropriately initialize a supervised learning object
@ In, None
@ Out, None<|endoftext|> |
655c31687e9737678c96d3c37d5f38871acc36ff63a6f2c9f2242a24ecc50cb1 | @classmethod
def getInputSpecification(cls):
'\n Method to get a reference to a class that specifies the input data for\n class cls.\n @ In, cls, the class for which we are retrieving the specification\n @ Out, inputSpecification, InputData.ParameterInput, class to use for\n specifying input of cls.\n '
specs = super().getInputSpecification()
specs.description = 'The \\xmlNode{OutputCodeClassifier} (\\textit{(Error-Correcting) Output-Code multiclass strategy})\n Output-code based strategies consist in representing each class with a binary code (an array of\n 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At\n prediction time, the classifiers are used to project new points in the class space and the class\n closest to the points is chosen. The main advantage of these strategies is that the number of\n classifiers used can be controlled by the user, either for compressing the model\n (0 < code\\_size < 1) or for making the model more robust to errors (code\\_size > 1). See the\n documentation for more details.\n \\zNormalizationNotPerformed{OutputCodeClassifier}\n '
estimatorInput = InputData.assemblyInputFactory('estimator', contentType=InputTypes.StringType, descr='name of a ROM that can be used as an estimator', default='no-default')
specs.addSub(estimatorInput)
specs.addSub(InputData.parameterInputFactory('code_size', contentType=InputTypes.FloatType, descr='Percentage of the number of classes to be used to create\n the code book. A number between 0 and 1 will require fewer classifiers\n than one-vs-the-rest. A number greater than 1 will require more classifiers\n than one-vs-the-rest.', default=1.5))
specs.addSub(InputData.parameterInputFactory('random_state', contentType=InputTypes.IntegerType, descr='The generator used to initialize the codebook. Pass an int\n for reproducible output across multiple function calls. ', default=None))
specs.addSub(InputData.parameterInputFactory('n_jobs', contentType=InputTypes.IntegerType, descr='TThe number of jobs to use for the computation: the n\\_classes one-vs-rest\n problems are computed in parallel. None means 1 unless in a joblib.parallel\\_backend\n context. -1 means using all processors. See Glossary for more details.', default=None))
return specs | Method to get a reference to a class that specifies the input data for
class cls.
@ In, cls, the class for which we are retrieving the specification
@ Out, inputSpecification, InputData.ParameterInput, class to use for
specifying input of cls. | framework/SupervisedLearning/ScikitLearn/MultiClass/OutputCodeClassifier.py | getInputSpecification | greenwoodms06/raven | 1 | python | @classmethod
def getInputSpecification(cls):
'\n Method to get a reference to a class that specifies the input data for\n class cls.\n @ In, cls, the class for which we are retrieving the specification\n @ Out, inputSpecification, InputData.ParameterInput, class to use for\n specifying input of cls.\n '
specs = super().getInputSpecification()
specs.description = 'The \\xmlNode{OutputCodeClassifier} (\\textit{(Error-Correcting) Output-Code multiclass strategy})\n Output-code based strategies consist in representing each class with a binary code (an array of\n 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At\n prediction time, the classifiers are used to project new points in the class space and the class\n closest to the points is chosen. The main advantage of these strategies is that the number of\n classifiers used can be controlled by the user, either for compressing the model\n (0 < code\\_size < 1) or for making the model more robust to errors (code\\_size > 1). See the\n documentation for more details.\n \\zNormalizationNotPerformed{OutputCodeClassifier}\n '
estimatorInput = InputData.assemblyInputFactory('estimator', contentType=InputTypes.StringType, descr='name of a ROM that can be used as an estimator', default='no-default')
specs.addSub(estimatorInput)
specs.addSub(InputData.parameterInputFactory('code_size', contentType=InputTypes.FloatType, descr='Percentage of the number of classes to be used to create\n the code book. A number between 0 and 1 will require fewer classifiers\n than one-vs-the-rest. A number greater than 1 will require more classifiers\n than one-vs-the-rest.', default=1.5))
specs.addSub(InputData.parameterInputFactory('random_state', contentType=InputTypes.IntegerType, descr='The generator used to initialize the codebook. Pass an int\n for reproducible output across multiple function calls. ', default=None))
specs.addSub(InputData.parameterInputFactory('n_jobs', contentType=InputTypes.IntegerType, descr='TThe number of jobs to use for the computation: the n\\_classes one-vs-rest\n problems are computed in parallel. None means 1 unless in a joblib.parallel\\_backend\n context. -1 means using all processors. See Glossary for more details.', default=None))
return specs | @classmethod
def getInputSpecification(cls):
'\n Method to get a reference to a class that specifies the input data for\n class cls.\n @ In, cls, the class for which we are retrieving the specification\n @ Out, inputSpecification, InputData.ParameterInput, class to use for\n specifying input of cls.\n '
specs = super().getInputSpecification()
specs.description = 'The \\xmlNode{OutputCodeClassifier} (\\textit{(Error-Correcting) Output-Code multiclass strategy})\n Output-code based strategies consist in representing each class with a binary code (an array of\n 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At\n prediction time, the classifiers are used to project new points in the class space and the class\n closest to the points is chosen. The main advantage of these strategies is that the number of\n classifiers used can be controlled by the user, either for compressing the model\n (0 < code\\_size < 1) or for making the model more robust to errors (code\\_size > 1). See the\n documentation for more details.\n \\zNormalizationNotPerformed{OutputCodeClassifier}\n '
estimatorInput = InputData.assemblyInputFactory('estimator', contentType=InputTypes.StringType, descr='name of a ROM that can be used as an estimator', default='no-default')
specs.addSub(estimatorInput)
specs.addSub(InputData.parameterInputFactory('code_size', contentType=InputTypes.FloatType, descr='Percentage of the number of classes to be used to create\n the code book. A number between 0 and 1 will require fewer classifiers\n than one-vs-the-rest. A number greater than 1 will require more classifiers\n than one-vs-the-rest.', default=1.5))
specs.addSub(InputData.parameterInputFactory('random_state', contentType=InputTypes.IntegerType, descr='The generator used to initialize the codebook. Pass an int\n for reproducible output across multiple function calls. ', default=None))
specs.addSub(InputData.parameterInputFactory('n_jobs', contentType=InputTypes.IntegerType, descr='TThe number of jobs to use for the computation: the n\\_classes one-vs-rest\n problems are computed in parallel. None means 1 unless in a joblib.parallel\\_backend\n context. -1 means using all processors. See Glossary for more details.', default=None))
return specs<|docstring|>Method to get a reference to a class that specifies the input data for
class cls.
@ In, cls, the class for which we are retrieving the specification
@ Out, inputSpecification, InputData.ParameterInput, class to use for
specifying input of cls.<|endoftext|> |
d5c36203d4cb584faf29b263b27cd91068103eeac8de0405a517e24cd8559e5d | def _handleInput(self, paramInput):
'\n Function to handle the common parts of the distribution parameter input.\n @ In, paramInput, ParameterInput, the already parsed input.\n @ Out, None\n '
super()._handleInput(paramInput)
(settings, notFound) = paramInput.findNodesAndExtractValues(['code_size', 'random_state', 'n_jobs'])
assert (not notFound)
self.settings = settings | Function to handle the common parts of the distribution parameter input.
@ In, paramInput, ParameterInput, the already parsed input.
@ Out, None | framework/SupervisedLearning/ScikitLearn/MultiClass/OutputCodeClassifier.py | _handleInput | greenwoodms06/raven | 1 | python | def _handleInput(self, paramInput):
'\n Function to handle the common parts of the distribution parameter input.\n @ In, paramInput, ParameterInput, the already parsed input.\n @ Out, None\n '
super()._handleInput(paramInput)
(settings, notFound) = paramInput.findNodesAndExtractValues(['code_size', 'random_state', 'n_jobs'])
assert (not notFound)
self.settings = settings | def _handleInput(self, paramInput):
'\n Function to handle the common parts of the distribution parameter input.\n @ In, paramInput, ParameterInput, the already parsed input.\n @ Out, None\n '
super()._handleInput(paramInput)
(settings, notFound) = paramInput.findNodesAndExtractValues(['code_size', 'random_state', 'n_jobs'])
assert (not notFound)
self.settings = settings<|docstring|>Function to handle the common parts of the distribution parameter input.
@ In, paramInput, ParameterInput, the already parsed input.
@ Out, None<|endoftext|> |
786787ce3e5a22bfa39bd78c88d0936f411b5cb0a296604a49272f6e39a50905 | def setEstimator(self, estimatorList):
'\n Initialization method\n @ In, estimatorList, list of ROM instances/estimators used by ROM\n @ Out, None\n '
if (len(estimatorList) != 1):
self.raiseAWarning('ROM', self.name, 'can only accept one estimator, but multiple estimators are provided!', 'Only the first one will be used, i.e.,', estimator.name)
estimator = estimatorList[0]
if estimator._interfaceROM.multioutputWrapper:
sklEstimator = estimator._interfaceROM.model.get_params()['estimator']
else:
sklEstimator = estimator._interfaceROM.model
if (not callable(getattr(sklEstimator, 'fit', None))):
self.raiseAnError(IOError, 'estimator:', estimator.name, 'can not be used! Please change to a different estimator')
else:
self.raiseADebug('A valid estimator', estimator.name, 'is provided!')
settings = {'estimator': sklEstimator}
self.settings.update(settings)
self.initializeModel(self.settings) | Initialization method
@ In, estimatorList, list of ROM instances/estimators used by ROM
@ Out, None | framework/SupervisedLearning/ScikitLearn/MultiClass/OutputCodeClassifier.py | setEstimator | greenwoodms06/raven | 1 | python | def setEstimator(self, estimatorList):
'\n Initialization method\n @ In, estimatorList, list of ROM instances/estimators used by ROM\n @ Out, None\n '
if (len(estimatorList) != 1):
self.raiseAWarning('ROM', self.name, 'can only accept one estimator, but multiple estimators are provided!', 'Only the first one will be used, i.e.,', estimator.name)
estimator = estimatorList[0]
if estimator._interfaceROM.multioutputWrapper:
sklEstimator = estimator._interfaceROM.model.get_params()['estimator']
else:
sklEstimator = estimator._interfaceROM.model
if (not callable(getattr(sklEstimator, 'fit', None))):
self.raiseAnError(IOError, 'estimator:', estimator.name, 'can not be used! Please change to a different estimator')
else:
self.raiseADebug('A valid estimator', estimator.name, 'is provided!')
settings = {'estimator': sklEstimator}
self.settings.update(settings)
self.initializeModel(self.settings) | def setEstimator(self, estimatorList):
'\n Initialization method\n @ In, estimatorList, list of ROM instances/estimators used by ROM\n @ Out, None\n '
if (len(estimatorList) != 1):
self.raiseAWarning('ROM', self.name, 'can only accept one estimator, but multiple estimators are provided!', 'Only the first one will be used, i.e.,', estimator.name)
estimator = estimatorList[0]
if estimator._interfaceROM.multioutputWrapper:
sklEstimator = estimator._interfaceROM.model.get_params()['estimator']
else:
sklEstimator = estimator._interfaceROM.model
if (not callable(getattr(sklEstimator, 'fit', None))):
self.raiseAnError(IOError, 'estimator:', estimator.name, 'can not be used! Please change to a different estimator')
else:
self.raiseADebug('A valid estimator', estimator.name, 'is provided!')
settings = {'estimator': sklEstimator}
self.settings.update(settings)
self.initializeModel(self.settings)<|docstring|>Initialization method
@ In, estimatorList, list of ROM instances/estimators used by ROM
@ Out, None<|endoftext|> |
b11541dfc3544767bd47c523854676ffb0b87e4783f4e3be87c95cd1db8de212 | def add_user(new_user):
'\n Add new user to userlist\n '
User.add_user(new_user) | Add new user to userlist | start.py | add_user | IsaiahKe/flask_Vault | 0 | python | def add_user(new_user):
'\n \n '
User.add_user(new_user) | def add_user(new_user):
'\n \n '
User.add_user(new_user)<|docstring|>Add new user to userlist<|endoftext|> |
ad1146ca3ce7e2c35643f22d723edd87f977e445f22c45e21423601d5ac24dbf | def add_credential(credential):
'\n Add credential to credential list\n '
Credential.add_credential(credential) | Add credential to credential list | start.py | add_credential | IsaiahKe/flask_Vault | 0 | python | def add_credential(credential):
'\n \n '
Credential.add_credential(credential) | def add_credential(credential):
'\n \n '
Credential.add_credential(credential)<|docstring|>Add credential to credential list<|endoftext|> |
2ea4c34a0c292cdc4b42fc68cb4016b5ddb67033d0cd187ca59bbfbbd6e460fb | def delete_all():
'\n empty all list\n '
Credential.delete_all() | empty all list | start.py | delete_all | IsaiahKe/flask_Vault | 0 | python | def delete_all():
'\n \n '
Credential.delete_all() | def delete_all():
'\n \n '
Credential.delete_all()<|docstring|>empty all list<|endoftext|> |
72ff4f9840386d5398e4188c7fd7d31a85f35700963cbad76fff4ef1a91e43d9 | def delete_by_account_name(name):
'\n Delete by account name\n '
Credential.delete_by_account(name) | Delete by account name | start.py | delete_by_account_name | IsaiahKe/flask_Vault | 0 | python | def delete_by_account_name(name):
'\n \n '
Credential.delete_by_account(name) | def delete_by_account_name(name):
'\n \n '
Credential.delete_by_account(name)<|docstring|>Delete by account name<|endoftext|> |
eecf4e5ce1a4e37d170a5a9abe1a06ba3a31f3888e427b22cac0d57f696b3350 | def __init__(self, USER_API_KEY):
"\n Parameters\n ----------\n USER_API_KEY: str\n User's API key generated from the Materials Project database.\n See https://materialsproject.org/open\n "
self.user_api_key = USER_API_KEY
self.fp = {}
self.implemented_features = ['stoichiometry', 'electronegativity', 'mass', 'volume', 'density', 'bulk modulus', 'shear modulus', 'poisson ratio', 'anisotropy', 'spacegroup', 'ionic character']
self.selected_features = None
self.label = None | Parameters
----------
USER_API_KEY: str
User's API key generated from the Materials Project database.
See https://materialsproject.org/open | gibbsml/ellingham/fingerprint.py | __init__ | atomisticnet/gibbsml | 5 | python | def __init__(self, USER_API_KEY):
"\n Parameters\n ----------\n USER_API_KEY: str\n User's API key generated from the Materials Project database.\n See https://materialsproject.org/open\n "
self.user_api_key = USER_API_KEY
self.fp = {}
self.implemented_features = ['stoichiometry', 'electronegativity', 'mass', 'volume', 'density', 'bulk modulus', 'shear modulus', 'poisson ratio', 'anisotropy', 'spacegroup', 'ionic character']
self.selected_features = None
self.label = None | def __init__(self, USER_API_KEY):
"\n Parameters\n ----------\n USER_API_KEY: str\n User's API key generated from the Materials Project database.\n See https://materialsproject.org/open\n "
self.user_api_key = USER_API_KEY
self.fp = {}
self.implemented_features = ['stoichiometry', 'electronegativity', 'mass', 'volume', 'density', 'bulk modulus', 'shear modulus', 'poisson ratio', 'anisotropy', 'spacegroup', 'ionic character']
self.selected_features = None
self.label = None<|docstring|>Parameters
----------
USER_API_KEY: str
User's API key generated from the Materials Project database.
See https://materialsproject.org/open<|endoftext|> |
6f5253652425662f4f5bde9afa1e3bab2faa1e174d3b95070f9699562d87b0de | def extract_mp_features(self, id_mo, id_m1='', id_m2='', id_oxygen='mp-12957', selected_features='all', label=None, mo_energy_correction=True):
"\n Generates a feature set for an oxidation for a given metal\n oxide (AxByOz) from the elements (A and B).\n\n Parameters\n ----------\n id_mo: str\n Materials Project mp-id for the metal oxide or chemical formula\n of the metal oxide, e.g. 'Al2SiO5' or 'mp-4753'.\n id_m1: str\n (optional) Materials Project mp-id for the metal A, e.g. 'mp-134'.\n id_m2: str\n (optional) Materials Project mp-id for the metal B, e.g. 'mp-149'.\n id_oxygen: str\n Materials project mp-id for oxygen in the gas phase.\n selected_features: list or str\n (option 1): list\n List of selected features to be considered to\n generate the fingerprint. Implemented are: 'stoichiometry',\n 'electronegativity', 'mass', 'volume', 'density',\n 'bulk modulus', 'shear modulus', 'poisson ratio',\n 'anisotropy', 'spacegroup' and 'ionic character'.\n (option 2): str\n 'all': Include all implemented features (see option 1).\n 'ellingham': Recommended features for building models for\n predicting Ellingham diagrams. Includes only\n the following features:\n 'stoichiometry', 'electronegativity',\n 'density', 'bulk modulus', 'ionic character'.\n\n 'label': str\n Defines the label tag for the fingerprint. The user can chose a\n name for the fingerprint for the data entry, e.g. 'Al2SiO5-PBEU'.\n 'mo_energy_correction': bool\n If True the algorithm only selects Material Project entries which\n in which energy corrections are available. See:\n https://materialsproject.org/docs/calculations#Total_Energy_Adjustments\n\n "
if (selected_features == 'all'):
self.selected_features = self.implemented_features
if (selected_features == 'ellingham'):
self.selected_features = ['stoichiometry', 'electronegativity', 'density', 'bulk modulus', 'ionic character']
else:
self.selected_features = selected_features
print(('Getting information for ' + id_mo))
if ('mp' not in id_mo):
id_mo = self._find_id(id_mo)
with MPRester(self.user_api_key) as m:
data_mo = m.get_data(id_mo)[0]
with MPRester(self.user_api_key) as m:
try:
e_adjus = m.get_entries(id_mo)[0].__dict__['energy_adjustments']
e_mo_corr = 0
for e_ad in e_adjus:
e_mo_corr += e_ad.value
except:
e_adjus = m.get_entries(id_mo)[0].__dict__['correction']
e_mo_corr = e_adjus
if (mo_energy_correction is False):
e_mo_corr = 0.0
data_o2 = m.get_data(id_oxygen)[0]
e_o2 = ((2 * data_o2['energy']) / data_o2['unit_cell_formula']['O'])
n_elements = data_mo['nelements']
binary_oxide = False
if (n_elements == 3):
binary_oxide = True
msg = 'Only unary and binary oxides are implemented.'
assert (n_elements <= 3), NotImplementedError(msg)
elements = data_mo['elements']
element_no_ox = np.array(elements)[(~ np.isin(elements, 'O'))]
if (binary_oxide is True):
(element_m1, element_m2) = (element_no_ox[0], element_no_ox[1])
else:
(element_m1, element_m2) = (element_no_ox[0], element_no_ox[0])
if ('mp' not in id_m1):
id_m1 = self._find_id(element_m1)
if ('mp' not in id_m2):
id_m2 = self._find_id(element_m2)
data_m1 = m.get_data(id_m1)[0]
data_m2 = m.get_data(id_m2)[0]
formula_mo = data_mo['pretty_formula']
formula_m1 = data_m1['pretty_formula']
formula_m2 = data_m2['pretty_formula']
self.label = label
if (self.label is None):
self.label = ((formula_mo + '_') + id_mo)
self.fp.update({self.label: {}})
self.fp[self.label]['target_features'] = {}
self.fp[self.label]['features'] = {}
atoms = []
for i in ['m1', 'm2', 'mo']:
f_atoms = open('tmp_Atoms.cif', 'w')
f_atoms.write(eval(('data_' + i))['cif'])
f_atoms.close()
atoms.append(read('tmp_Atoms.cif'))
os.remove('tmp_Atoms.cif')
(atoms_m1, atoms_m2, atoms_mo) = atoms
(n_m1, fu_m1) = (2 * (len(atoms_m1),))
(n_m2, fu_m2) = (2 * (len(atoms_m2),))
fu_mo = self._get_atoms_per_unit_formula(atoms_mo)
n_m1_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol=element_m1)
n_m1_in_mo /= fu_mo
n_m2_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol=element_m2)
n_m2_in_mo /= fu_mo
n_ox_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol='O')
n_ox_in_mo /= fu_mo
self.fp[self.label]['raw data'] = {}
self.fp[self.label]['raw data']['data mo'] = data_mo
self.fp[self.label]['raw data']['data m1'] = data_m1
self.fp[self.label]['raw data']['data m2'] = data_m2
(e_m1, e_m2) = (data_m1['energy'], data_m2['energy'])
e_mo = data_mo['energy']
(x, y, z) = (n_m1_in_mo, n_m2_in_mo, n_ox_in_mo)
a = ((2 / z) * x)
b = ((2 / z) * y)
c = (2 / z)
if (not binary_oxide):
a /= 2
b /= 2
dH0 = ((c * (e_mo + e_mo_corr)) / fu_mo)
dH0 -= ((a * e_m1) / fu_m1)
dH0 -= ((b * e_m2) / fu_m2)
dH0 -= e_o2
dH0 *= 96.485
self.add_feature(description='formation energy (kJ/mol)', value=dH0)
balanced_reaction = None
if (not binary_oxide):
balanced_reaction = (((str((2 * a)) + ' ') + formula_m1) + ' + O2')
balanced_reaction += ' --> '
balanced_reaction += ((str(c) + ' ') + formula_mo)
if binary_oxide:
balanced_reaction = (((str(a) + ' ') + formula_m1) + ' + ')
balanced_reaction += (((str(b) + ' ') + formula_m2) + ' + O2')
balanced_reaction += ' --> '
balanced_reaction += ((str(c) + ' ') + formula_mo)
self.fp[self.label]['balanced reaction'] = balanced_reaction
if ('stoichiometry' in self.selected_features):
ratio_o_m1 = (n_m1_in_mo / n_ox_in_mo)
ratio_o_m2 = (n_m2_in_mo / n_ox_in_mo)
av_ox_state = ((n_ox_in_mo * 2) / (n_m1_in_mo + n_m2_in_mo))
(element_m1, element_m2) = (atoms_m1[0].symbol, atoms_m2[0].symbol)
z_m1 = element(element_m1).atomic_number
z_m2 = element(element_m2).atomic_number
self.add_feature(description='ratio metal oxygen (mean)', value=np.mean([ratio_o_m1, ratio_o_m2]))
self.add_feature(description='ratio metal oxygen (var)', value=np.var([ratio_o_m1, ratio_o_m2]))
self.add_feature(description='average oxidation state', value=av_ox_state)
self.add_feature(description='atomic number (mean)', value=np.mean([z_m1, z_m2]))
self.add_feature(description='atomic number (var)', value=np.var([z_m1, z_m2]))
if ('electronegativity' in self.selected_features):
elecneg_m1 = element(element_m1).en_pauling
elecneg_m2 = element(element_m2).en_pauling
self.add_feature(description='pauling electronegativity (mean)', value=np.mean([elecneg_m1, elecneg_m2]))
self.add_feature(description='pauling electronegativity (var)', value=np.var([elecneg_m1, elecneg_m2]))
if ('ionic character' in self.selected_features):
elnegdif_m1 = (element('O').en_pauling - element(element_m1).en_pauling)
elnegdif_m2 = (element('O').en_pauling - element(element_m2).en_pauling)
pio_m1 = (100 * (1 - np.exp((- (((1 / 2) * elnegdif_m1) ** 2)))))
pio_m2 = (100 * (1 - np.exp((- (((1 / 2) * elnegdif_m2) ** 2)))))
self.add_feature(description='% ionic character (mean)', value=np.mean([pio_m1, pio_m2]))
self.add_feature(description='% ionic character (var)', value=np.var([pio_m1, pio_m2]))
if ('volume' in self.selected_features):
V_m1 = atoms_m1.get_volume()
V_per_fu_m1 = (V_m1 / fu_m1)
V_m2 = atoms_m2.get_volume()
V_per_fu_m2 = (V_m2 / fu_m2)
V_mo = atoms_mo.get_volume()
V_per_fu_mo = (V_mo / fu_mo)
self.add_feature(description='volume per formula unit (mean)', value=np.mean([V_per_fu_m1, V_per_fu_m2]))
self.add_feature(description='volume per formula unit (var)', value=np.var([V_per_fu_m1, V_per_fu_m2]))
self.add_feature(description='volume MO per formula unit', value=V_per_fu_mo)
diff_V_per_fu_m1_mo = (V_per_fu_mo - V_per_fu_m1)
diff_V_per_fu_m2_mo = (V_per_fu_mo - V_per_fu_m2)
self.add_feature(description='difference volume (MO-M) (mean)', value=np.mean([diff_V_per_fu_m1_mo, diff_V_per_fu_m2_mo]))
self.add_feature(description='difference volume (MO-M) (var)', value=np.var([diff_V_per_fu_m1_mo, diff_V_per_fu_m2_mo]))
if ('mass' in self.selected_features):
mass_m1 = np.average(atoms_m1.get_masses())
mass_m2 = np.average(atoms_m2.get_masses())
mass_mo = np.average(atoms_mo.get_masses())
mass_per_fu_m1 = (mass_m1 / fu_m1)
mass_per_fu_m2 = (mass_m2 / fu_m2)
mass_per_fu_mo = (mass_mo / fu_mo)
self.add_feature(description='mass per formula unit (mean)', value=np.mean([mass_per_fu_m1, mass_per_fu_m2]))
self.add_feature(description='mass per formula unit (var)', value=np.var([mass_per_fu_m1, mass_per_fu_m2]))
self.add_feature(description='mass MO per formula unit', value=mass_per_fu_mo)
diff_mass_per_fu_m1_mo = (mass_per_fu_mo - mass_per_fu_m1)
diff_mass_per_fu_m2_mo = (mass_per_fu_mo - mass_per_fu_m2)
self.add_feature(description='difference mass (MO-M) (mean)', value=np.mean([diff_mass_per_fu_m1_mo, diff_mass_per_fu_m2_mo]))
self.add_feature(description='difference mass (MO-M) (var)', value=np.var([diff_mass_per_fu_m1_mo, diff_mass_per_fu_m2_mo]))
if ('density' in self.selected_features):
dens_m1 = data_m1['density']
dens_m2 = data_m2['density']
dens_mo = data_mo['density']
self.add_feature(description='density (mean)', value=np.mean([dens_m1, dens_m2]))
self.add_feature(description='density (var)', value=np.var([dens_m1, dens_m2]))
self.add_feature(description='density MO', value=dens_mo)
diff_dens_m1_mo = (dens_mo - dens_m1)
diff_dens_m2_mo = (dens_mo - dens_m2)
self.add_feature(description='difference density (MO-M) (mean)', value=np.mean([diff_dens_m1_mo, diff_dens_m2_mo]))
self.add_feature(description='difference density (MO-M) (var)', value=np.var([diff_dens_m1_mo, diff_dens_m2_mo]))
if ('bulk modulus' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(Kv_m1, Kv_m2) = Kv_mo = (elas_m1['K_Voigt'], elas_m2['K_Voigt'])
if elas_mo:
Kv_mo = elas_mo['K_Voigt']
else:
with MPRester(self.user_api_key) as m:
Kv_mo = m.get_data(id_mo, prop='elastic_moduli', data_type='pred')[0]['elastic_moduli']['K']
self.add_feature(description='bulk modulus (mean)', value=np.mean([Kv_m1, Kv_m2]))
self.add_feature(description='bulk modulus (var)', value=np.var([Kv_m1, Kv_m2]))
self.add_feature(description='bulk modulus MO', value=Kv_mo)
diff_Kv_m1_mo = (Kv_mo - Kv_m1)
diff_Kv_m2_mo = (Kv_mo - Kv_m2)
self.add_feature(description='difference bulk modulus (MO-M) (mean)', value=np.mean([diff_Kv_m1_mo, diff_Kv_m2_mo]))
self.add_feature(description='difference bulk modulus (MO-M) (var)', value=np.var([diff_Kv_m1_mo, diff_Kv_m2_mo]))
if ('shear modulus' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(Gv_m1, Gv_m2) = (elas_m1['G_Voigt'], elas_m2['G_Voigt'])
if elas_mo:
Gv_mo = elas_mo['G_Voigt']
else:
with MPRester(self.user_api_key) as m:
Gv_mo = m.get_data(id_mo, prop='elastic_moduli', data_type='pred')[0]['elastic_moduli']['G']
self.add_feature(description='shear modulus (mean)', value=np.mean([Gv_m1, Gv_m2]))
self.add_feature(description='shear modulus (var)', value=np.var([Gv_m1, Gv_m2]))
self.add_feature(description='shear modulus MO', value=Gv_mo)
diff_Gv_m1_mo = (Gv_mo - Gv_m1)
diff_Gv_m2_mo = (Gv_mo - Gv_m2)
self.add_feature(description='difference shear modulus (MO-M) (mean)', value=np.mean([diff_Gv_m1_mo, diff_Gv_m2_mo]))
self.add_feature(description='difference shear modulus (MO-M) (var)', value=np.var([diff_Gv_m1_mo, diff_Gv_m2_mo]))
if ('poisson ratio' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(pois_m1, pois_m2, pois_mo) = (elas_m1['poisson_ratio'], elas_m2['poisson_ratio'], elas_mo['poisson_ratio'])
self.add_feature(description='poisson ratio (mean)', value=np.mean([pois_m1, pois_m2]))
self.add_feature(description='poisson ratio (var)', value=np.var([pois_m1, pois_m2]))
self.add_feature(description='poisson ratio MO', value=pois_mo)
diff_pois_m1_mo = (pois_mo - pois_m1)
diff_pois_m2_mo = (pois_mo - pois_m2)
self.add_feature(description='difference poisson ratio (MO-M) (mean)', value=np.mean([diff_pois_m1_mo, diff_pois_m2_mo]))
self.add_feature(description='difference poisson ratio (MO-M) (var)', value=np.var([diff_pois_m1_mo, diff_pois_m2_mo]))
if ('anisotropy' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(u_ani_m1, u_ani_m2, u_ani_mo) = (elas_m1['universal_anisotropy'], elas_m2['universal_anisotropy'], elas_mo['universal_anisotropy'])
(el_ani_m1, el_ani_m2, el_ani_mo) = (elas_m1['elastic_anisotropy'], elas_m2['elastic_anisotropy'], elas_mo['elastic_anisotropy'])
self.add_feature(description='universal anisotropy (mean)', value=np.mean([u_ani_m1, u_ani_m2]))
self.add_feature(description='universal anisotropy (var)', value=np.var([u_ani_m1, u_ani_m2]))
self.add_feature(description='universal anisotropy MO', value=u_ani_mo)
diff_u_ani_m1_mo = (u_ani_mo - u_ani_m1)
diff_u_ani_m2_mo = (u_ani_mo - u_ani_m2)
self.add_feature(description='difference universal anisotropy (MO-M) (mean)', value=np.mean([diff_u_ani_m1_mo, diff_u_ani_m2_mo]))
self.add_feature(description='difference universal anisotropy (MO-M) (var)', value=np.var([diff_u_ani_m1_mo, diff_u_ani_m2_mo]))
self.add_feature(description='elastic anisotropy (mean)', value=np.mean([el_ani_m1, el_ani_m2]))
self.add_feature(description='elastic anisotropy (var)', value=np.var([el_ani_m1, el_ani_m2]))
self.add_feature(description='elastic anisotropy MO', value=el_ani_mo)
diff_el_ani_m1_mo = (el_ani_mo - el_ani_m1)
diff_el_ani_m2_mo = (el_ani_mo - el_ani_m2)
self.add_feature(description='difference elastic anisotropy (MO-M) (mean)', value=np.mean([diff_el_ani_m1_mo, diff_el_ani_m2_mo]))
self.add_feature(description='difference elastic anisotropy (MO-M) (var)', value=np.var([diff_el_ani_m1_mo, diff_el_ani_m2_mo]))
if ('spacegroup' in self.selected_features):
spacegroup_m1 = data_m1['spacegroup']['number']
spacegroup_m2 = data_m2['spacegroup']['number']
spacegroup_mo = data_mo['spacegroup']['number']
self.add_feature(description='Spacegroup M1', value=spacegroup_m1)
self.add_feature(description='Spacegroup M2', value=spacegroup_m2)
self.add_feature(description='Spacegroup MO', value=spacegroup_mo)
print((('Fingerprint for ' + self.label) + ' completed.')) | Generates a feature set for an oxidation for a given metal
oxide (AxByOz) from the elements (A and B).
Parameters
----------
id_mo: str
Materials Project mp-id for the metal oxide or chemical formula
of the metal oxide, e.g. 'Al2SiO5' or 'mp-4753'.
id_m1: str
(optional) Materials Project mp-id for the metal A, e.g. 'mp-134'.
id_m2: str
(optional) Materials Project mp-id for the metal B, e.g. 'mp-149'.
id_oxygen: str
Materials project mp-id for oxygen in the gas phase.
selected_features: list or str
(option 1): list
List of selected features to be considered to
generate the fingerprint. Implemented are: 'stoichiometry',
'electronegativity', 'mass', 'volume', 'density',
'bulk modulus', 'shear modulus', 'poisson ratio',
'anisotropy', 'spacegroup' and 'ionic character'.
(option 2): str
'all': Include all implemented features (see option 1).
'ellingham': Recommended features for building models for
predicting Ellingham diagrams. Includes only
the following features:
'stoichiometry', 'electronegativity',
'density', 'bulk modulus', 'ionic character'.
'label': str
Defines the label tag for the fingerprint. The user can chose a
name for the fingerprint for the data entry, e.g. 'Al2SiO5-PBEU'.
'mo_energy_correction': bool
If True the algorithm only selects Material Project entries which
in which energy corrections are available. See:
https://materialsproject.org/docs/calculations#Total_Energy_Adjustments | gibbsml/ellingham/fingerprint.py | extract_mp_features | atomisticnet/gibbsml | 5 | python | def extract_mp_features(self, id_mo, id_m1=, id_m2=, id_oxygen='mp-12957', selected_features='all', label=None, mo_energy_correction=True):
"\n Generates a feature set for an oxidation for a given metal\n oxide (AxByOz) from the elements (A and B).\n\n Parameters\n ----------\n id_mo: str\n Materials Project mp-id for the metal oxide or chemical formula\n of the metal oxide, e.g. 'Al2SiO5' or 'mp-4753'.\n id_m1: str\n (optional) Materials Project mp-id for the metal A, e.g. 'mp-134'.\n id_m2: str\n (optional) Materials Project mp-id for the metal B, e.g. 'mp-149'.\n id_oxygen: str\n Materials project mp-id for oxygen in the gas phase.\n selected_features: list or str\n (option 1): list\n List of selected features to be considered to\n generate the fingerprint. Implemented are: 'stoichiometry',\n 'electronegativity', 'mass', 'volume', 'density',\n 'bulk modulus', 'shear modulus', 'poisson ratio',\n 'anisotropy', 'spacegroup' and 'ionic character'.\n (option 2): str\n 'all': Include all implemented features (see option 1).\n 'ellingham': Recommended features for building models for\n predicting Ellingham diagrams. Includes only\n the following features:\n 'stoichiometry', 'electronegativity',\n 'density', 'bulk modulus', 'ionic character'.\n\n 'label': str\n Defines the label tag for the fingerprint. The user can chose a\n name for the fingerprint for the data entry, e.g. 'Al2SiO5-PBEU'.\n 'mo_energy_correction': bool\n If True the algorithm only selects Material Project entries which\n in which energy corrections are available. See:\n https://materialsproject.org/docs/calculations#Total_Energy_Adjustments\n\n "
if (selected_features == 'all'):
self.selected_features = self.implemented_features
if (selected_features == 'ellingham'):
self.selected_features = ['stoichiometry', 'electronegativity', 'density', 'bulk modulus', 'ionic character']
else:
self.selected_features = selected_features
print(('Getting information for ' + id_mo))
if ('mp' not in id_mo):
id_mo = self._find_id(id_mo)
with MPRester(self.user_api_key) as m:
data_mo = m.get_data(id_mo)[0]
with MPRester(self.user_api_key) as m:
try:
e_adjus = m.get_entries(id_mo)[0].__dict__['energy_adjustments']
e_mo_corr = 0
for e_ad in e_adjus:
e_mo_corr += e_ad.value
except:
e_adjus = m.get_entries(id_mo)[0].__dict__['correction']
e_mo_corr = e_adjus
if (mo_energy_correction is False):
e_mo_corr = 0.0
data_o2 = m.get_data(id_oxygen)[0]
e_o2 = ((2 * data_o2['energy']) / data_o2['unit_cell_formula']['O'])
n_elements = data_mo['nelements']
binary_oxide = False
if (n_elements == 3):
binary_oxide = True
msg = 'Only unary and binary oxides are implemented.'
assert (n_elements <= 3), NotImplementedError(msg)
elements = data_mo['elements']
element_no_ox = np.array(elements)[(~ np.isin(elements, 'O'))]
if (binary_oxide is True):
(element_m1, element_m2) = (element_no_ox[0], element_no_ox[1])
else:
(element_m1, element_m2) = (element_no_ox[0], element_no_ox[0])
if ('mp' not in id_m1):
id_m1 = self._find_id(element_m1)
if ('mp' not in id_m2):
id_m2 = self._find_id(element_m2)
data_m1 = m.get_data(id_m1)[0]
data_m2 = m.get_data(id_m2)[0]
formula_mo = data_mo['pretty_formula']
formula_m1 = data_m1['pretty_formula']
formula_m2 = data_m2['pretty_formula']
self.label = label
if (self.label is None):
self.label = ((formula_mo + '_') + id_mo)
self.fp.update({self.label: {}})
self.fp[self.label]['target_features'] = {}
self.fp[self.label]['features'] = {}
atoms = []
for i in ['m1', 'm2', 'mo']:
f_atoms = open('tmp_Atoms.cif', 'w')
f_atoms.write(eval(('data_' + i))['cif'])
f_atoms.close()
atoms.append(read('tmp_Atoms.cif'))
os.remove('tmp_Atoms.cif')
(atoms_m1, atoms_m2, atoms_mo) = atoms
(n_m1, fu_m1) = (2 * (len(atoms_m1),))
(n_m2, fu_m2) = (2 * (len(atoms_m2),))
fu_mo = self._get_atoms_per_unit_formula(atoms_mo)
n_m1_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol=element_m1)
n_m1_in_mo /= fu_mo
n_m2_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol=element_m2)
n_m2_in_mo /= fu_mo
n_ox_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol='O')
n_ox_in_mo /= fu_mo
self.fp[self.label]['raw data'] = {}
self.fp[self.label]['raw data']['data mo'] = data_mo
self.fp[self.label]['raw data']['data m1'] = data_m1
self.fp[self.label]['raw data']['data m2'] = data_m2
(e_m1, e_m2) = (data_m1['energy'], data_m2['energy'])
e_mo = data_mo['energy']
(x, y, z) = (n_m1_in_mo, n_m2_in_mo, n_ox_in_mo)
a = ((2 / z) * x)
b = ((2 / z) * y)
c = (2 / z)
if (not binary_oxide):
a /= 2
b /= 2
dH0 = ((c * (e_mo + e_mo_corr)) / fu_mo)
dH0 -= ((a * e_m1) / fu_m1)
dH0 -= ((b * e_m2) / fu_m2)
dH0 -= e_o2
dH0 *= 96.485
self.add_feature(description='formation energy (kJ/mol)', value=dH0)
balanced_reaction = None
if (not binary_oxide):
balanced_reaction = (((str((2 * a)) + ' ') + formula_m1) + ' + O2')
balanced_reaction += ' --> '
balanced_reaction += ((str(c) + ' ') + formula_mo)
if binary_oxide:
balanced_reaction = (((str(a) + ' ') + formula_m1) + ' + ')
balanced_reaction += (((str(b) + ' ') + formula_m2) + ' + O2')
balanced_reaction += ' --> '
balanced_reaction += ((str(c) + ' ') + formula_mo)
self.fp[self.label]['balanced reaction'] = balanced_reaction
if ('stoichiometry' in self.selected_features):
ratio_o_m1 = (n_m1_in_mo / n_ox_in_mo)
ratio_o_m2 = (n_m2_in_mo / n_ox_in_mo)
av_ox_state = ((n_ox_in_mo * 2) / (n_m1_in_mo + n_m2_in_mo))
(element_m1, element_m2) = (atoms_m1[0].symbol, atoms_m2[0].symbol)
z_m1 = element(element_m1).atomic_number
z_m2 = element(element_m2).atomic_number
self.add_feature(description='ratio metal oxygen (mean)', value=np.mean([ratio_o_m1, ratio_o_m2]))
self.add_feature(description='ratio metal oxygen (var)', value=np.var([ratio_o_m1, ratio_o_m2]))
self.add_feature(description='average oxidation state', value=av_ox_state)
self.add_feature(description='atomic number (mean)', value=np.mean([z_m1, z_m2]))
self.add_feature(description='atomic number (var)', value=np.var([z_m1, z_m2]))
if ('electronegativity' in self.selected_features):
elecneg_m1 = element(element_m1).en_pauling
elecneg_m2 = element(element_m2).en_pauling
self.add_feature(description='pauling electronegativity (mean)', value=np.mean([elecneg_m1, elecneg_m2]))
self.add_feature(description='pauling electronegativity (var)', value=np.var([elecneg_m1, elecneg_m2]))
if ('ionic character' in self.selected_features):
elnegdif_m1 = (element('O').en_pauling - element(element_m1).en_pauling)
elnegdif_m2 = (element('O').en_pauling - element(element_m2).en_pauling)
pio_m1 = (100 * (1 - np.exp((- (((1 / 2) * elnegdif_m1) ** 2)))))
pio_m2 = (100 * (1 - np.exp((- (((1 / 2) * elnegdif_m2) ** 2)))))
self.add_feature(description='% ionic character (mean)', value=np.mean([pio_m1, pio_m2]))
self.add_feature(description='% ionic character (var)', value=np.var([pio_m1, pio_m2]))
if ('volume' in self.selected_features):
V_m1 = atoms_m1.get_volume()
V_per_fu_m1 = (V_m1 / fu_m1)
V_m2 = atoms_m2.get_volume()
V_per_fu_m2 = (V_m2 / fu_m2)
V_mo = atoms_mo.get_volume()
V_per_fu_mo = (V_mo / fu_mo)
self.add_feature(description='volume per formula unit (mean)', value=np.mean([V_per_fu_m1, V_per_fu_m2]))
self.add_feature(description='volume per formula unit (var)', value=np.var([V_per_fu_m1, V_per_fu_m2]))
self.add_feature(description='volume MO per formula unit', value=V_per_fu_mo)
diff_V_per_fu_m1_mo = (V_per_fu_mo - V_per_fu_m1)
diff_V_per_fu_m2_mo = (V_per_fu_mo - V_per_fu_m2)
self.add_feature(description='difference volume (MO-M) (mean)', value=np.mean([diff_V_per_fu_m1_mo, diff_V_per_fu_m2_mo]))
self.add_feature(description='difference volume (MO-M) (var)', value=np.var([diff_V_per_fu_m1_mo, diff_V_per_fu_m2_mo]))
if ('mass' in self.selected_features):
mass_m1 = np.average(atoms_m1.get_masses())
mass_m2 = np.average(atoms_m2.get_masses())
mass_mo = np.average(atoms_mo.get_masses())
mass_per_fu_m1 = (mass_m1 / fu_m1)
mass_per_fu_m2 = (mass_m2 / fu_m2)
mass_per_fu_mo = (mass_mo / fu_mo)
self.add_feature(description='mass per formula unit (mean)', value=np.mean([mass_per_fu_m1, mass_per_fu_m2]))
self.add_feature(description='mass per formula unit (var)', value=np.var([mass_per_fu_m1, mass_per_fu_m2]))
self.add_feature(description='mass MO per formula unit', value=mass_per_fu_mo)
diff_mass_per_fu_m1_mo = (mass_per_fu_mo - mass_per_fu_m1)
diff_mass_per_fu_m2_mo = (mass_per_fu_mo - mass_per_fu_m2)
self.add_feature(description='difference mass (MO-M) (mean)', value=np.mean([diff_mass_per_fu_m1_mo, diff_mass_per_fu_m2_mo]))
self.add_feature(description='difference mass (MO-M) (var)', value=np.var([diff_mass_per_fu_m1_mo, diff_mass_per_fu_m2_mo]))
if ('density' in self.selected_features):
dens_m1 = data_m1['density']
dens_m2 = data_m2['density']
dens_mo = data_mo['density']
self.add_feature(description='density (mean)', value=np.mean([dens_m1, dens_m2]))
self.add_feature(description='density (var)', value=np.var([dens_m1, dens_m2]))
self.add_feature(description='density MO', value=dens_mo)
diff_dens_m1_mo = (dens_mo - dens_m1)
diff_dens_m2_mo = (dens_mo - dens_m2)
self.add_feature(description='difference density (MO-M) (mean)', value=np.mean([diff_dens_m1_mo, diff_dens_m2_mo]))
self.add_feature(description='difference density (MO-M) (var)', value=np.var([diff_dens_m1_mo, diff_dens_m2_mo]))
if ('bulk modulus' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(Kv_m1, Kv_m2) = Kv_mo = (elas_m1['K_Voigt'], elas_m2['K_Voigt'])
if elas_mo:
Kv_mo = elas_mo['K_Voigt']
else:
with MPRester(self.user_api_key) as m:
Kv_mo = m.get_data(id_mo, prop='elastic_moduli', data_type='pred')[0]['elastic_moduli']['K']
self.add_feature(description='bulk modulus (mean)', value=np.mean([Kv_m1, Kv_m2]))
self.add_feature(description='bulk modulus (var)', value=np.var([Kv_m1, Kv_m2]))
self.add_feature(description='bulk modulus MO', value=Kv_mo)
diff_Kv_m1_mo = (Kv_mo - Kv_m1)
diff_Kv_m2_mo = (Kv_mo - Kv_m2)
self.add_feature(description='difference bulk modulus (MO-M) (mean)', value=np.mean([diff_Kv_m1_mo, diff_Kv_m2_mo]))
self.add_feature(description='difference bulk modulus (MO-M) (var)', value=np.var([diff_Kv_m1_mo, diff_Kv_m2_mo]))
if ('shear modulus' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(Gv_m1, Gv_m2) = (elas_m1['G_Voigt'], elas_m2['G_Voigt'])
if elas_mo:
Gv_mo = elas_mo['G_Voigt']
else:
with MPRester(self.user_api_key) as m:
Gv_mo = m.get_data(id_mo, prop='elastic_moduli', data_type='pred')[0]['elastic_moduli']['G']
self.add_feature(description='shear modulus (mean)', value=np.mean([Gv_m1, Gv_m2]))
self.add_feature(description='shear modulus (var)', value=np.var([Gv_m1, Gv_m2]))
self.add_feature(description='shear modulus MO', value=Gv_mo)
diff_Gv_m1_mo = (Gv_mo - Gv_m1)
diff_Gv_m2_mo = (Gv_mo - Gv_m2)
self.add_feature(description='difference shear modulus (MO-M) (mean)', value=np.mean([diff_Gv_m1_mo, diff_Gv_m2_mo]))
self.add_feature(description='difference shear modulus (MO-M) (var)', value=np.var([diff_Gv_m1_mo, diff_Gv_m2_mo]))
if ('poisson ratio' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(pois_m1, pois_m2, pois_mo) = (elas_m1['poisson_ratio'], elas_m2['poisson_ratio'], elas_mo['poisson_ratio'])
self.add_feature(description='poisson ratio (mean)', value=np.mean([pois_m1, pois_m2]))
self.add_feature(description='poisson ratio (var)', value=np.var([pois_m1, pois_m2]))
self.add_feature(description='poisson ratio MO', value=pois_mo)
diff_pois_m1_mo = (pois_mo - pois_m1)
diff_pois_m2_mo = (pois_mo - pois_m2)
self.add_feature(description='difference poisson ratio (MO-M) (mean)', value=np.mean([diff_pois_m1_mo, diff_pois_m2_mo]))
self.add_feature(description='difference poisson ratio (MO-M) (var)', value=np.var([diff_pois_m1_mo, diff_pois_m2_mo]))
if ('anisotropy' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(u_ani_m1, u_ani_m2, u_ani_mo) = (elas_m1['universal_anisotropy'], elas_m2['universal_anisotropy'], elas_mo['universal_anisotropy'])
(el_ani_m1, el_ani_m2, el_ani_mo) = (elas_m1['elastic_anisotropy'], elas_m2['elastic_anisotropy'], elas_mo['elastic_anisotropy'])
self.add_feature(description='universal anisotropy (mean)', value=np.mean([u_ani_m1, u_ani_m2]))
self.add_feature(description='universal anisotropy (var)', value=np.var([u_ani_m1, u_ani_m2]))
self.add_feature(description='universal anisotropy MO', value=u_ani_mo)
diff_u_ani_m1_mo = (u_ani_mo - u_ani_m1)
diff_u_ani_m2_mo = (u_ani_mo - u_ani_m2)
self.add_feature(description='difference universal anisotropy (MO-M) (mean)', value=np.mean([diff_u_ani_m1_mo, diff_u_ani_m2_mo]))
self.add_feature(description='difference universal anisotropy (MO-M) (var)', value=np.var([diff_u_ani_m1_mo, diff_u_ani_m2_mo]))
self.add_feature(description='elastic anisotropy (mean)', value=np.mean([el_ani_m1, el_ani_m2]))
self.add_feature(description='elastic anisotropy (var)', value=np.var([el_ani_m1, el_ani_m2]))
self.add_feature(description='elastic anisotropy MO', value=el_ani_mo)
diff_el_ani_m1_mo = (el_ani_mo - el_ani_m1)
diff_el_ani_m2_mo = (el_ani_mo - el_ani_m2)
self.add_feature(description='difference elastic anisotropy (MO-M) (mean)', value=np.mean([diff_el_ani_m1_mo, diff_el_ani_m2_mo]))
self.add_feature(description='difference elastic anisotropy (MO-M) (var)', value=np.var([diff_el_ani_m1_mo, diff_el_ani_m2_mo]))
if ('spacegroup' in self.selected_features):
spacegroup_m1 = data_m1['spacegroup']['number']
spacegroup_m2 = data_m2['spacegroup']['number']
spacegroup_mo = data_mo['spacegroup']['number']
self.add_feature(description='Spacegroup M1', value=spacegroup_m1)
self.add_feature(description='Spacegroup M2', value=spacegroup_m2)
self.add_feature(description='Spacegroup MO', value=spacegroup_mo)
print((('Fingerprint for ' + self.label) + ' completed.')) | def extract_mp_features(self, id_mo, id_m1=, id_m2=, id_oxygen='mp-12957', selected_features='all', label=None, mo_energy_correction=True):
"\n Generates a feature set for an oxidation for a given metal\n oxide (AxByOz) from the elements (A and B).\n\n Parameters\n ----------\n id_mo: str\n Materials Project mp-id for the metal oxide or chemical formula\n of the metal oxide, e.g. 'Al2SiO5' or 'mp-4753'.\n id_m1: str\n (optional) Materials Project mp-id for the metal A, e.g. 'mp-134'.\n id_m2: str\n (optional) Materials Project mp-id for the metal B, e.g. 'mp-149'.\n id_oxygen: str\n Materials project mp-id for oxygen in the gas phase.\n selected_features: list or str\n (option 1): list\n List of selected features to be considered to\n generate the fingerprint. Implemented are: 'stoichiometry',\n 'electronegativity', 'mass', 'volume', 'density',\n 'bulk modulus', 'shear modulus', 'poisson ratio',\n 'anisotropy', 'spacegroup' and 'ionic character'.\n (option 2): str\n 'all': Include all implemented features (see option 1).\n 'ellingham': Recommended features for building models for\n predicting Ellingham diagrams. Includes only\n the following features:\n 'stoichiometry', 'electronegativity',\n 'density', 'bulk modulus', 'ionic character'.\n\n 'label': str\n Defines the label tag for the fingerprint. The user can chose a\n name for the fingerprint for the data entry, e.g. 'Al2SiO5-PBEU'.\n 'mo_energy_correction': bool\n If True the algorithm only selects Material Project entries which\n in which energy corrections are available. See:\n https://materialsproject.org/docs/calculations#Total_Energy_Adjustments\n\n "
if (selected_features == 'all'):
self.selected_features = self.implemented_features
if (selected_features == 'ellingham'):
self.selected_features = ['stoichiometry', 'electronegativity', 'density', 'bulk modulus', 'ionic character']
else:
self.selected_features = selected_features
print(('Getting information for ' + id_mo))
if ('mp' not in id_mo):
id_mo = self._find_id(id_mo)
with MPRester(self.user_api_key) as m:
data_mo = m.get_data(id_mo)[0]
with MPRester(self.user_api_key) as m:
try:
e_adjus = m.get_entries(id_mo)[0].__dict__['energy_adjustments']
e_mo_corr = 0
for e_ad in e_adjus:
e_mo_corr += e_ad.value
except:
e_adjus = m.get_entries(id_mo)[0].__dict__['correction']
e_mo_corr = e_adjus
if (mo_energy_correction is False):
e_mo_corr = 0.0
data_o2 = m.get_data(id_oxygen)[0]
e_o2 = ((2 * data_o2['energy']) / data_o2['unit_cell_formula']['O'])
n_elements = data_mo['nelements']
binary_oxide = False
if (n_elements == 3):
binary_oxide = True
msg = 'Only unary and binary oxides are implemented.'
assert (n_elements <= 3), NotImplementedError(msg)
elements = data_mo['elements']
element_no_ox = np.array(elements)[(~ np.isin(elements, 'O'))]
if (binary_oxide is True):
(element_m1, element_m2) = (element_no_ox[0], element_no_ox[1])
else:
(element_m1, element_m2) = (element_no_ox[0], element_no_ox[0])
if ('mp' not in id_m1):
id_m1 = self._find_id(element_m1)
if ('mp' not in id_m2):
id_m2 = self._find_id(element_m2)
data_m1 = m.get_data(id_m1)[0]
data_m2 = m.get_data(id_m2)[0]
formula_mo = data_mo['pretty_formula']
formula_m1 = data_m1['pretty_formula']
formula_m2 = data_m2['pretty_formula']
self.label = label
if (self.label is None):
self.label = ((formula_mo + '_') + id_mo)
self.fp.update({self.label: {}})
self.fp[self.label]['target_features'] = {}
self.fp[self.label]['features'] = {}
atoms = []
for i in ['m1', 'm2', 'mo']:
f_atoms = open('tmp_Atoms.cif', 'w')
f_atoms.write(eval(('data_' + i))['cif'])
f_atoms.close()
atoms.append(read('tmp_Atoms.cif'))
os.remove('tmp_Atoms.cif')
(atoms_m1, atoms_m2, atoms_mo) = atoms
(n_m1, fu_m1) = (2 * (len(atoms_m1),))
(n_m2, fu_m2) = (2 * (len(atoms_m2),))
fu_mo = self._get_atoms_per_unit_formula(atoms_mo)
n_m1_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol=element_m1)
n_m1_in_mo /= fu_mo
n_m2_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol=element_m2)
n_m2_in_mo /= fu_mo
n_ox_in_mo = self._get_number_of_atoms_element(atoms_mo, symbol='O')
n_ox_in_mo /= fu_mo
self.fp[self.label]['raw data'] = {}
self.fp[self.label]['raw data']['data mo'] = data_mo
self.fp[self.label]['raw data']['data m1'] = data_m1
self.fp[self.label]['raw data']['data m2'] = data_m2
(e_m1, e_m2) = (data_m1['energy'], data_m2['energy'])
e_mo = data_mo['energy']
(x, y, z) = (n_m1_in_mo, n_m2_in_mo, n_ox_in_mo)
a = ((2 / z) * x)
b = ((2 / z) * y)
c = (2 / z)
if (not binary_oxide):
a /= 2
b /= 2
dH0 = ((c * (e_mo + e_mo_corr)) / fu_mo)
dH0 -= ((a * e_m1) / fu_m1)
dH0 -= ((b * e_m2) / fu_m2)
dH0 -= e_o2
dH0 *= 96.485
self.add_feature(description='formation energy (kJ/mol)', value=dH0)
balanced_reaction = None
if (not binary_oxide):
balanced_reaction = (((str((2 * a)) + ' ') + formula_m1) + ' + O2')
balanced_reaction += ' --> '
balanced_reaction += ((str(c) + ' ') + formula_mo)
if binary_oxide:
balanced_reaction = (((str(a) + ' ') + formula_m1) + ' + ')
balanced_reaction += (((str(b) + ' ') + formula_m2) + ' + O2')
balanced_reaction += ' --> '
balanced_reaction += ((str(c) + ' ') + formula_mo)
self.fp[self.label]['balanced reaction'] = balanced_reaction
if ('stoichiometry' in self.selected_features):
ratio_o_m1 = (n_m1_in_mo / n_ox_in_mo)
ratio_o_m2 = (n_m2_in_mo / n_ox_in_mo)
av_ox_state = ((n_ox_in_mo * 2) / (n_m1_in_mo + n_m2_in_mo))
(element_m1, element_m2) = (atoms_m1[0].symbol, atoms_m2[0].symbol)
z_m1 = element(element_m1).atomic_number
z_m2 = element(element_m2).atomic_number
self.add_feature(description='ratio metal oxygen (mean)', value=np.mean([ratio_o_m1, ratio_o_m2]))
self.add_feature(description='ratio metal oxygen (var)', value=np.var([ratio_o_m1, ratio_o_m2]))
self.add_feature(description='average oxidation state', value=av_ox_state)
self.add_feature(description='atomic number (mean)', value=np.mean([z_m1, z_m2]))
self.add_feature(description='atomic number (var)', value=np.var([z_m1, z_m2]))
if ('electronegativity' in self.selected_features):
elecneg_m1 = element(element_m1).en_pauling
elecneg_m2 = element(element_m2).en_pauling
self.add_feature(description='pauling electronegativity (mean)', value=np.mean([elecneg_m1, elecneg_m2]))
self.add_feature(description='pauling electronegativity (var)', value=np.var([elecneg_m1, elecneg_m2]))
if ('ionic character' in self.selected_features):
elnegdif_m1 = (element('O').en_pauling - element(element_m1).en_pauling)
elnegdif_m2 = (element('O').en_pauling - element(element_m2).en_pauling)
pio_m1 = (100 * (1 - np.exp((- (((1 / 2) * elnegdif_m1) ** 2)))))
pio_m2 = (100 * (1 - np.exp((- (((1 / 2) * elnegdif_m2) ** 2)))))
self.add_feature(description='% ionic character (mean)', value=np.mean([pio_m1, pio_m2]))
self.add_feature(description='% ionic character (var)', value=np.var([pio_m1, pio_m2]))
if ('volume' in self.selected_features):
V_m1 = atoms_m1.get_volume()
V_per_fu_m1 = (V_m1 / fu_m1)
V_m2 = atoms_m2.get_volume()
V_per_fu_m2 = (V_m2 / fu_m2)
V_mo = atoms_mo.get_volume()
V_per_fu_mo = (V_mo / fu_mo)
self.add_feature(description='volume per formula unit (mean)', value=np.mean([V_per_fu_m1, V_per_fu_m2]))
self.add_feature(description='volume per formula unit (var)', value=np.var([V_per_fu_m1, V_per_fu_m2]))
self.add_feature(description='volume MO per formula unit', value=V_per_fu_mo)
diff_V_per_fu_m1_mo = (V_per_fu_mo - V_per_fu_m1)
diff_V_per_fu_m2_mo = (V_per_fu_mo - V_per_fu_m2)
self.add_feature(description='difference volume (MO-M) (mean)', value=np.mean([diff_V_per_fu_m1_mo, diff_V_per_fu_m2_mo]))
self.add_feature(description='difference volume (MO-M) (var)', value=np.var([diff_V_per_fu_m1_mo, diff_V_per_fu_m2_mo]))
if ('mass' in self.selected_features):
mass_m1 = np.average(atoms_m1.get_masses())
mass_m2 = np.average(atoms_m2.get_masses())
mass_mo = np.average(atoms_mo.get_masses())
mass_per_fu_m1 = (mass_m1 / fu_m1)
mass_per_fu_m2 = (mass_m2 / fu_m2)
mass_per_fu_mo = (mass_mo / fu_mo)
self.add_feature(description='mass per formula unit (mean)', value=np.mean([mass_per_fu_m1, mass_per_fu_m2]))
self.add_feature(description='mass per formula unit (var)', value=np.var([mass_per_fu_m1, mass_per_fu_m2]))
self.add_feature(description='mass MO per formula unit', value=mass_per_fu_mo)
diff_mass_per_fu_m1_mo = (mass_per_fu_mo - mass_per_fu_m1)
diff_mass_per_fu_m2_mo = (mass_per_fu_mo - mass_per_fu_m2)
self.add_feature(description='difference mass (MO-M) (mean)', value=np.mean([diff_mass_per_fu_m1_mo, diff_mass_per_fu_m2_mo]))
self.add_feature(description='difference mass (MO-M) (var)', value=np.var([diff_mass_per_fu_m1_mo, diff_mass_per_fu_m2_mo]))
if ('density' in self.selected_features):
dens_m1 = data_m1['density']
dens_m2 = data_m2['density']
dens_mo = data_mo['density']
self.add_feature(description='density (mean)', value=np.mean([dens_m1, dens_m2]))
self.add_feature(description='density (var)', value=np.var([dens_m1, dens_m2]))
self.add_feature(description='density MO', value=dens_mo)
diff_dens_m1_mo = (dens_mo - dens_m1)
diff_dens_m2_mo = (dens_mo - dens_m2)
self.add_feature(description='difference density (MO-M) (mean)', value=np.mean([diff_dens_m1_mo, diff_dens_m2_mo]))
self.add_feature(description='difference density (MO-M) (var)', value=np.var([diff_dens_m1_mo, diff_dens_m2_mo]))
if ('bulk modulus' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(Kv_m1, Kv_m2) = Kv_mo = (elas_m1['K_Voigt'], elas_m2['K_Voigt'])
if elas_mo:
Kv_mo = elas_mo['K_Voigt']
else:
with MPRester(self.user_api_key) as m:
Kv_mo = m.get_data(id_mo, prop='elastic_moduli', data_type='pred')[0]['elastic_moduli']['K']
self.add_feature(description='bulk modulus (mean)', value=np.mean([Kv_m1, Kv_m2]))
self.add_feature(description='bulk modulus (var)', value=np.var([Kv_m1, Kv_m2]))
self.add_feature(description='bulk modulus MO', value=Kv_mo)
diff_Kv_m1_mo = (Kv_mo - Kv_m1)
diff_Kv_m2_mo = (Kv_mo - Kv_m2)
self.add_feature(description='difference bulk modulus (MO-M) (mean)', value=np.mean([diff_Kv_m1_mo, diff_Kv_m2_mo]))
self.add_feature(description='difference bulk modulus (MO-M) (var)', value=np.var([diff_Kv_m1_mo, diff_Kv_m2_mo]))
if ('shear modulus' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(Gv_m1, Gv_m2) = (elas_m1['G_Voigt'], elas_m2['G_Voigt'])
if elas_mo:
Gv_mo = elas_mo['G_Voigt']
else:
with MPRester(self.user_api_key) as m:
Gv_mo = m.get_data(id_mo, prop='elastic_moduli', data_type='pred')[0]['elastic_moduli']['G']
self.add_feature(description='shear modulus (mean)', value=np.mean([Gv_m1, Gv_m2]))
self.add_feature(description='shear modulus (var)', value=np.var([Gv_m1, Gv_m2]))
self.add_feature(description='shear modulus MO', value=Gv_mo)
diff_Gv_m1_mo = (Gv_mo - Gv_m1)
diff_Gv_m2_mo = (Gv_mo - Gv_m2)
self.add_feature(description='difference shear modulus (MO-M) (mean)', value=np.mean([diff_Gv_m1_mo, diff_Gv_m2_mo]))
self.add_feature(description='difference shear modulus (MO-M) (var)', value=np.var([diff_Gv_m1_mo, diff_Gv_m2_mo]))
if ('poisson ratio' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(pois_m1, pois_m2, pois_mo) = (elas_m1['poisson_ratio'], elas_m2['poisson_ratio'], elas_mo['poisson_ratio'])
self.add_feature(description='poisson ratio (mean)', value=np.mean([pois_m1, pois_m2]))
self.add_feature(description='poisson ratio (var)', value=np.var([pois_m1, pois_m2]))
self.add_feature(description='poisson ratio MO', value=pois_mo)
diff_pois_m1_mo = (pois_mo - pois_m1)
diff_pois_m2_mo = (pois_mo - pois_m2)
self.add_feature(description='difference poisson ratio (MO-M) (mean)', value=np.mean([diff_pois_m1_mo, diff_pois_m2_mo]))
self.add_feature(description='difference poisson ratio (MO-M) (var)', value=np.var([diff_pois_m1_mo, diff_pois_m2_mo]))
if ('anisotropy' in self.selected_features):
elas_m1 = data_m1['elasticity']
elas_m2 = data_m2['elasticity']
elas_mo = data_mo['elasticity']
(u_ani_m1, u_ani_m2, u_ani_mo) = (elas_m1['universal_anisotropy'], elas_m2['universal_anisotropy'], elas_mo['universal_anisotropy'])
(el_ani_m1, el_ani_m2, el_ani_mo) = (elas_m1['elastic_anisotropy'], elas_m2['elastic_anisotropy'], elas_mo['elastic_anisotropy'])
self.add_feature(description='universal anisotropy (mean)', value=np.mean([u_ani_m1, u_ani_m2]))
self.add_feature(description='universal anisotropy (var)', value=np.var([u_ani_m1, u_ani_m2]))
self.add_feature(description='universal anisotropy MO', value=u_ani_mo)
diff_u_ani_m1_mo = (u_ani_mo - u_ani_m1)
diff_u_ani_m2_mo = (u_ani_mo - u_ani_m2)
self.add_feature(description='difference universal anisotropy (MO-M) (mean)', value=np.mean([diff_u_ani_m1_mo, diff_u_ani_m2_mo]))
self.add_feature(description='difference universal anisotropy (MO-M) (var)', value=np.var([diff_u_ani_m1_mo, diff_u_ani_m2_mo]))
self.add_feature(description='elastic anisotropy (mean)', value=np.mean([el_ani_m1, el_ani_m2]))
self.add_feature(description='elastic anisotropy (var)', value=np.var([el_ani_m1, el_ani_m2]))
self.add_feature(description='elastic anisotropy MO', value=el_ani_mo)
diff_el_ani_m1_mo = (el_ani_mo - el_ani_m1)
diff_el_ani_m2_mo = (el_ani_mo - el_ani_m2)
self.add_feature(description='difference elastic anisotropy (MO-M) (mean)', value=np.mean([diff_el_ani_m1_mo, diff_el_ani_m2_mo]))
self.add_feature(description='difference elastic anisotropy (MO-M) (var)', value=np.var([diff_el_ani_m1_mo, diff_el_ani_m2_mo]))
if ('spacegroup' in self.selected_features):
spacegroup_m1 = data_m1['spacegroup']['number']
spacegroup_m2 = data_m2['spacegroup']['number']
spacegroup_mo = data_mo['spacegroup']['number']
self.add_feature(description='Spacegroup M1', value=spacegroup_m1)
self.add_feature(description='Spacegroup M2', value=spacegroup_m2)
self.add_feature(description='Spacegroup MO', value=spacegroup_mo)
print((('Fingerprint for ' + self.label) + ' completed.'))<|docstring|>Generates a feature set for an oxidation for a given metal
oxide (AxByOz) from the elements (A and B).
Parameters
----------
id_mo: str
Materials Project mp-id for the metal oxide or chemical formula
of the metal oxide, e.g. 'Al2SiO5' or 'mp-4753'.
id_m1: str
(optional) Materials Project mp-id for the metal A, e.g. 'mp-134'.
id_m2: str
(optional) Materials Project mp-id for the metal B, e.g. 'mp-149'.
id_oxygen: str
Materials project mp-id for oxygen in the gas phase.
selected_features: list or str
(option 1): list
List of selected features to be considered to
generate the fingerprint. Implemented are: 'stoichiometry',
'electronegativity', 'mass', 'volume', 'density',
'bulk modulus', 'shear modulus', 'poisson ratio',
'anisotropy', 'spacegroup' and 'ionic character'.
(option 2): str
'all': Include all implemented features (see option 1).
'ellingham': Recommended features for building models for
predicting Ellingham diagrams. Includes only
the following features:
'stoichiometry', 'electronegativity',
'density', 'bulk modulus', 'ionic character'.
'label': str
Defines the label tag for the fingerprint. The user can chose a
name for the fingerprint for the data entry, e.g. 'Al2SiO5-PBEU'.
'mo_energy_correction': bool
If True the algorithm only selects Material Project entries which
in which energy corrections are available. See:
https://materialsproject.org/docs/calculations#Total_Energy_Adjustments<|endoftext|> |
0c43707edb9ed6db0e2d54842aa1bc55053e9369b602a335f87721bf53abf3e8 | def _find_id(self, compound):
" Find Materials Project ID for a given compound.\n\n Parameters\n ----------\n compound: str\n Compound formula. Examples: ``'Li2O'``,\n ``'AlLiO2'``, ``'Mg2SiO4'``.\n user_api: str\n Materials Project users API.\n\n Returns\n -------\n id_compound: str\n Materials Project compound ID.\n\n "
with MPRester(self.user_api_key) as m:
info_MOs = m.get_entries(compound, inc_structure='final', property_data=['elasticity', 'e_above_hull', 'Correction'], sort_by_e_above_hull=True)
for i in range(len(info_MOs)):
id_compound = info_MOs[i].__dict__['entry_id']
elasticity = m.get_data(id_compound)[0]['elasticity']
if elasticity:
break
if (not elasticity):
id_compound = info_MOs[0].__dict__['entry_id']
return id_compound | Find Materials Project ID for a given compound.
Parameters
----------
compound: str
Compound formula. Examples: ``'Li2O'``,
``'AlLiO2'``, ``'Mg2SiO4'``.
user_api: str
Materials Project users API.
Returns
-------
id_compound: str
Materials Project compound ID. | gibbsml/ellingham/fingerprint.py | _find_id | atomisticnet/gibbsml | 5 | python | def _find_id(self, compound):
" Find Materials Project ID for a given compound.\n\n Parameters\n ----------\n compound: str\n Compound formula. Examples: ``'Li2O'``,\n ``'AlLiO2'``, ``'Mg2SiO4'``.\n user_api: str\n Materials Project users API.\n\n Returns\n -------\n id_compound: str\n Materials Project compound ID.\n\n "
with MPRester(self.user_api_key) as m:
info_MOs = m.get_entries(compound, inc_structure='final', property_data=['elasticity', 'e_above_hull', 'Correction'], sort_by_e_above_hull=True)
for i in range(len(info_MOs)):
id_compound = info_MOs[i].__dict__['entry_id']
elasticity = m.get_data(id_compound)[0]['elasticity']
if elasticity:
break
if (not elasticity):
id_compound = info_MOs[0].__dict__['entry_id']
return id_compound | def _find_id(self, compound):
" Find Materials Project ID for a given compound.\n\n Parameters\n ----------\n compound: str\n Compound formula. Examples: ``'Li2O'``,\n ``'AlLiO2'``, ``'Mg2SiO4'``.\n user_api: str\n Materials Project users API.\n\n Returns\n -------\n id_compound: str\n Materials Project compound ID.\n\n "
with MPRester(self.user_api_key) as m:
info_MOs = m.get_entries(compound, inc_structure='final', property_data=['elasticity', 'e_above_hull', 'Correction'], sort_by_e_above_hull=True)
for i in range(len(info_MOs)):
id_compound = info_MOs[i].__dict__['entry_id']
elasticity = m.get_data(id_compound)[0]['elasticity']
if elasticity:
break
if (not elasticity):
id_compound = info_MOs[0].__dict__['entry_id']
return id_compound<|docstring|>Find Materials Project ID for a given compound.
Parameters
----------
compound: str
Compound formula. Examples: ``'Li2O'``,
``'AlLiO2'``, ``'Mg2SiO4'``.
user_api: str
Materials Project users API.
Returns
-------
id_compound: str
Materials Project compound ID.<|endoftext|> |
a4aac19791ee77889bb9cf2923e2f607c6613f558c2ccbb1878e15b9669d7eaa | def add_feature(self, description, value):
"\n Parameters\n ----------\n description: str\n Description of the property to append (e.g. 'Formation energy').\n value: float\n Numerical value for a given property.\n\n Returns\n -------\n Adds a feature to the fingerprint (stored in self.fp).\n "
self.fp[self.label]['features'].update({description: value}) | Parameters
----------
description: str
Description of the property to append (e.g. 'Formation energy').
value: float
Numerical value for a given property.
Returns
-------
Adds a feature to the fingerprint (stored in self.fp). | gibbsml/ellingham/fingerprint.py | add_feature | atomisticnet/gibbsml | 5 | python | def add_feature(self, description, value):
"\n Parameters\n ----------\n description: str\n Description of the property to append (e.g. 'Formation energy').\n value: float\n Numerical value for a given property.\n\n Returns\n -------\n Adds a feature to the fingerprint (stored in self.fp).\n "
self.fp[self.label]['features'].update({description: value}) | def add_feature(self, description, value):
"\n Parameters\n ----------\n description: str\n Description of the property to append (e.g. 'Formation energy').\n value: float\n Numerical value for a given property.\n\n Returns\n -------\n Adds a feature to the fingerprint (stored in self.fp).\n "
self.fp[self.label]['features'].update({description: value})<|docstring|>Parameters
----------
description: str
Description of the property to append (e.g. 'Formation energy').
value: float
Numerical value for a given property.
Returns
-------
Adds a feature to the fingerprint (stored in self.fp).<|endoftext|> |
d0094bc0237b9f980ffee3ffdb6ba8ac96a98bf8d905f0f9bf910b6ac5535d7e | def add_target_feature(self, description, value):
"\n Parameters\n ----------\n description: str\n Description of the property to be appended (e.g. 'Reduction\n temperature').\n value: float\n Numerical value for a given property.\n\n Returns\n -------\n Adds a target feature to the fingerprint (stored in self.fp).\n Note: In this case the properties and values will be only used for\n training the model (commonly know as train_y).\n "
self.fp[self.label]['target_features'].update({description: value}) | Parameters
----------
description: str
Description of the property to be appended (e.g. 'Reduction
temperature').
value: float
Numerical value for a given property.
Returns
-------
Adds a target feature to the fingerprint (stored in self.fp).
Note: In this case the properties and values will be only used for
training the model (commonly know as train_y). | gibbsml/ellingham/fingerprint.py | add_target_feature | atomisticnet/gibbsml | 5 | python | def add_target_feature(self, description, value):
"\n Parameters\n ----------\n description: str\n Description of the property to be appended (e.g. 'Reduction\n temperature').\n value: float\n Numerical value for a given property.\n\n Returns\n -------\n Adds a target feature to the fingerprint (stored in self.fp).\n Note: In this case the properties and values will be only used for\n training the model (commonly know as train_y).\n "
self.fp[self.label]['target_features'].update({description: value}) | def add_target_feature(self, description, value):
"\n Parameters\n ----------\n description: str\n Description of the property to be appended (e.g. 'Reduction\n temperature').\n value: float\n Numerical value for a given property.\n\n Returns\n -------\n Adds a target feature to the fingerprint (stored in self.fp).\n Note: In this case the properties and values will be only used for\n training the model (commonly know as train_y).\n "
self.fp[self.label]['target_features'].update({description: value})<|docstring|>Parameters
----------
description: str
Description of the property to be appended (e.g. 'Reduction
temperature').
value: float
Numerical value for a given property.
Returns
-------
Adds a target feature to the fingerprint (stored in self.fp).
Note: In this case the properties and values will be only used for
training the model (commonly know as train_y).<|endoftext|> |
b997254d5a347f2ccb3084da598d166d62109b7889b34dacac5986cd0b899524 | def get_labels(self):
'\n Returns the list of species (labelled), e.g. CaO-mp-2605.\n '
return list(self.fp.keys()) | Returns the list of species (labelled), e.g. CaO-mp-2605. | gibbsml/ellingham/fingerprint.py | get_labels | atomisticnet/gibbsml | 5 | python | def get_labels(self):
'\n \n '
return list(self.fp.keys()) | def get_labels(self):
'\n \n '
return list(self.fp.keys())<|docstring|>Returns the list of species (labelled), e.g. CaO-mp-2605.<|endoftext|> |
02d34ec3eb459c98930034bfb42e40d21aab6d56ac974a9fcf6dfe7efa88ba65 | def get_features_names(self):
'\n Returns a list containing the names of the features, e.g. formation\n energy (kJ/mol).\n '
species = list(self.fp.keys())
features_names = list(self.fp[species[0]]['features'].keys())
return features_names | Returns a list containing the names of the features, e.g. formation
energy (kJ/mol). | gibbsml/ellingham/fingerprint.py | get_features_names | atomisticnet/gibbsml | 5 | python | def get_features_names(self):
'\n Returns a list containing the names of the features, e.g. formation\n energy (kJ/mol).\n '
species = list(self.fp.keys())
features_names = list(self.fp[species[0]]['features'].keys())
return features_names | def get_features_names(self):
'\n Returns a list containing the names of the features, e.g. formation\n energy (kJ/mol).\n '
species = list(self.fp.keys())
features_names = list(self.fp[species[0]]['features'].keys())
return features_names<|docstring|>Returns a list containing the names of the features, e.g. formation
energy (kJ/mol).<|endoftext|> |
0b8d7829f90c22d800cae598dddab71ea44eeac41a39d0e4b5723305accb7ba3 | def get_target_features_names(self):
'\n Returns the list of target features. These features are\n user-defined and must be included with the add_target_features\n function.\n '
species = list(self.fp.keys())
features_names = list(self.fp[species[0]]['target_features'].keys())
return features_names | Returns the list of target features. These features are
user-defined and must be included with the add_target_features
function. | gibbsml/ellingham/fingerprint.py | get_target_features_names | atomisticnet/gibbsml | 5 | python | def get_target_features_names(self):
'\n Returns the list of target features. These features are\n user-defined and must be included with the add_target_features\n function.\n '
species = list(self.fp.keys())
features_names = list(self.fp[species[0]]['target_features'].keys())
return features_names | def get_target_features_names(self):
'\n Returns the list of target features. These features are\n user-defined and must be included with the add_target_features\n function.\n '
species = list(self.fp.keys())
features_names = list(self.fp[species[0]]['target_features'].keys())
return features_names<|docstring|>Returns the list of target features. These features are
user-defined and must be included with the add_target_features
function.<|endoftext|> |
063353fdbf2a2630abffc31bdf9a3e3f45d80df6db0bc9a14deb3c1c20925c8e | def dump_set(self, filename='fingerprint.json'):
'\n Parameters\n ----------\n filename: str\n Name of the file to save the generated Fingerprint class.\n\n Returns\n -------\n Saves the whole Fingerprint class into a json file.\n '
self.label = 0.0
fp_dict = self.__dict__
del fp_dict['user_api_key']
with open(filename, 'w') as fp:
json.dump(fp_dict, fp) | Parameters
----------
filename: str
Name of the file to save the generated Fingerprint class.
Returns
-------
Saves the whole Fingerprint class into a json file. | gibbsml/ellingham/fingerprint.py | dump_set | atomisticnet/gibbsml | 5 | python | def dump_set(self, filename='fingerprint.json'):
'\n Parameters\n ----------\n filename: str\n Name of the file to save the generated Fingerprint class.\n\n Returns\n -------\n Saves the whole Fingerprint class into a json file.\n '
self.label = 0.0
fp_dict = self.__dict__
del fp_dict['user_api_key']
with open(filename, 'w') as fp:
json.dump(fp_dict, fp) | def dump_set(self, filename='fingerprint.json'):
'\n Parameters\n ----------\n filename: str\n Name of the file to save the generated Fingerprint class.\n\n Returns\n -------\n Saves the whole Fingerprint class into a json file.\n '
self.label = 0.0
fp_dict = self.__dict__
del fp_dict['user_api_key']
with open(filename, 'w') as fp:
json.dump(fp_dict, fp)<|docstring|>Parameters
----------
filename: str
Name of the file to save the generated Fingerprint class.
Returns
-------
Saves the whole Fingerprint class into a json file.<|endoftext|> |
4569602b19d261b5d7cd97766c7cb47149ca3dc6952e02177d3d4a202345f2ae | def load_set(self, filename):
'\n Parameters\n ----------\n filename: str\n Name of the file to load (json format).\n\n Returns\n -------\n Load a json file containing a previously saved Fingerprint (see\n dump_set function).\n '
with open(filename, 'r') as fp:
self.__dict__ = json.load(fp) | Parameters
----------
filename: str
Name of the file to load (json format).
Returns
-------
Load a json file containing a previously saved Fingerprint (see
dump_set function). | gibbsml/ellingham/fingerprint.py | load_set | atomisticnet/gibbsml | 5 | python | def load_set(self, filename):
'\n Parameters\n ----------\n filename: str\n Name of the file to load (json format).\n\n Returns\n -------\n Load a json file containing a previously saved Fingerprint (see\n dump_set function).\n '
with open(filename, 'r') as fp:
self.__dict__ = json.load(fp) | def load_set(self, filename):
'\n Parameters\n ----------\n filename: str\n Name of the file to load (json format).\n\n Returns\n -------\n Load a json file containing a previously saved Fingerprint (see\n dump_set function).\n '
with open(filename, 'r') as fp:
self.__dict__ = json.load(fp)<|docstring|>Parameters
----------
filename: str
Name of the file to load (json format).
Returns
-------
Load a json file containing a previously saved Fingerprint (see
dump_set function).<|endoftext|> |
48ce9cdb82e2936e9c4ac660159436f8cab213a00375afce248dcbeba95b67b4 | def initialize(self):
' An overridden initializer method.\n\n This method adds the window to the static set of Windows.\n\n '
super(Window, self).initialize()
Window.windows.add(self) | An overridden initializer method.
This method adds the window to the static set of Windows. | enaml/widgets/window.py | initialize | AndiEcker/enaml | 1,080 | python | def initialize(self):
' An overridden initializer method.\n\n This method adds the window to the static set of Windows.\n\n '
super(Window, self).initialize()
Window.windows.add(self) | def initialize(self):
' An overridden initializer method.\n\n This method adds the window to the static set of Windows.\n\n '
super(Window, self).initialize()
Window.windows.add(self)<|docstring|>An overridden initializer method.
This method adds the window to the static set of Windows.<|endoftext|> |
9003acd451fae75ceb6043314be43e2d382c06c5f26196a07d644631dc38854c | def destroy(self):
' An overridden destructor method.\n\n This method removes the window from the static set of Windows.\n\n '
super(Window, self).destroy()
Window.windows.discard(self) | An overridden destructor method.
This method removes the window from the static set of Windows. | enaml/widgets/window.py | destroy | AndiEcker/enaml | 1,080 | python | def destroy(self):
' An overridden destructor method.\n\n This method removes the window from the static set of Windows.\n\n '
super(Window, self).destroy()
Window.windows.discard(self) | def destroy(self):
' An overridden destructor method.\n\n This method removes the window from the static set of Windows.\n\n '
super(Window, self).destroy()
Window.windows.discard(self)<|docstring|>An overridden destructor method.
This method removes the window from the static set of Windows.<|endoftext|> |
aa21c0ab7c94839b6e2f8a141487cb3939ddf9f5d2a75bde61c319fd21b1e987 | def central_widget(self):
' Get the central widget defined on the window.\n\n The last `Container` child of the window is the central widget.\n\n '
for child in reversed(self.children):
if isinstance(child, Container):
return child | Get the central widget defined on the window.
The last `Container` child of the window is the central widget. | enaml/widgets/window.py | central_widget | AndiEcker/enaml | 1,080 | python | def central_widget(self):
' Get the central widget defined on the window.\n\n The last `Container` child of the window is the central widget.\n\n '
for child in reversed(self.children):
if isinstance(child, Container):
return child | def central_widget(self):
' Get the central widget defined on the window.\n\n The last `Container` child of the window is the central widget.\n\n '
for child in reversed(self.children):
if isinstance(child, Container):
return child<|docstring|>Get the central widget defined on the window.
The last `Container` child of the window is the central widget.<|endoftext|> |
c4976f7da412e8585c9d08ad489680cb94047c2383649f0c1546e0cf09757ef2 | def position(self):
' Get the position of the window frame.\n\n Returns\n -------\n result : Pos\n The current position of the window frame.\n\n '
if self.proxy_is_active:
return self.proxy.position()
return Pos((- 1), (- 1)) | Get the position of the window frame.
Returns
-------
result : Pos
The current position of the window frame. | enaml/widgets/window.py | position | AndiEcker/enaml | 1,080 | python | def position(self):
' Get the position of the window frame.\n\n Returns\n -------\n result : Pos\n The current position of the window frame.\n\n '
if self.proxy_is_active:
return self.proxy.position()
return Pos((- 1), (- 1)) | def position(self):
' Get the position of the window frame.\n\n Returns\n -------\n result : Pos\n The current position of the window frame.\n\n '
if self.proxy_is_active:
return self.proxy.position()
return Pos((- 1), (- 1))<|docstring|>Get the position of the window frame.
Returns
-------
result : Pos
The current position of the window frame.<|endoftext|> |
1cb03f615a5b04c7133f7b9425c27b91b2381a16e4d93ea61b8e61622d74714f | def set_position(self, pos):
' Set the position of the window frame.\n\n Parameters\n ----------\n pos : Pos\n The desired position of the window the window frame.\n\n '
if self.proxy_is_active:
self.proxy.set_position(pos) | Set the position of the window frame.
Parameters
----------
pos : Pos
The desired position of the window the window frame. | enaml/widgets/window.py | set_position | AndiEcker/enaml | 1,080 | python | def set_position(self, pos):
' Set the position of the window frame.\n\n Parameters\n ----------\n pos : Pos\n The desired position of the window the window frame.\n\n '
if self.proxy_is_active:
self.proxy.set_position(pos) | def set_position(self, pos):
' Set the position of the window frame.\n\n Parameters\n ----------\n pos : Pos\n The desired position of the window the window frame.\n\n '
if self.proxy_is_active:
self.proxy.set_position(pos)<|docstring|>Set the position of the window frame.
Parameters
----------
pos : Pos
The desired position of the window the window frame.<|endoftext|> |
d79339f1b08a9bad66c88eace19cbb7640b68c405d074d619968012aa298c83f | def size(self):
' Get the size of the window client area.\n\n Returns\n -------\n result : Size\n The current size of the window client area.\n\n '
if self.proxy_is_active:
return self.proxy.size()
return Size((- 1), (- 1)) | Get the size of the window client area.
Returns
-------
result : Size
The current size of the window client area. | enaml/widgets/window.py | size | AndiEcker/enaml | 1,080 | python | def size(self):
' Get the size of the window client area.\n\n Returns\n -------\n result : Size\n The current size of the window client area.\n\n '
if self.proxy_is_active:
return self.proxy.size()
return Size((- 1), (- 1)) | def size(self):
' Get the size of the window client area.\n\n Returns\n -------\n result : Size\n The current size of the window client area.\n\n '
if self.proxy_is_active:
return self.proxy.size()
return Size((- 1), (- 1))<|docstring|>Get the size of the window client area.
Returns
-------
result : Size
The current size of the window client area.<|endoftext|> |
76f0b4b9c77d5fd6eaa0a197ab03f79972086eace7dbc46df2d5b8ea2f8752e9 | def set_size(self, size):
' Set the size of the window client area.\n\n Parameters\n ----------\n size : Size\n The desired size of the window client area.\n\n '
if self.proxy_is_active:
self.proxy.set_size(size) | Set the size of the window client area.
Parameters
----------
size : Size
The desired size of the window client area. | enaml/widgets/window.py | set_size | AndiEcker/enaml | 1,080 | python | def set_size(self, size):
' Set the size of the window client area.\n\n Parameters\n ----------\n size : Size\n The desired size of the window client area.\n\n '
if self.proxy_is_active:
self.proxy.set_size(size) | def set_size(self, size):
' Set the size of the window client area.\n\n Parameters\n ----------\n size : Size\n The desired size of the window client area.\n\n '
if self.proxy_is_active:
self.proxy.set_size(size)<|docstring|>Set the size of the window client area.
Parameters
----------
size : Size
The desired size of the window client area.<|endoftext|> |
cf096b4dcbf84bcb60fabf9ee9aad1d233291dd7574dbf858b8c06c8e25a86aa | def geometry(self):
' Get the geometry of the window client area.\n\n Returns\n -------\n result : Rect\n The current geometry of the window client area.\n\n '
if self.proxy_is_active:
return self.proxy.geometry()
return Rect((- 1), (- 1), (- 1), (- 1)) | Get the geometry of the window client area.
Returns
-------
result : Rect
The current geometry of the window client area. | enaml/widgets/window.py | geometry | AndiEcker/enaml | 1,080 | python | def geometry(self):
' Get the geometry of the window client area.\n\n Returns\n -------\n result : Rect\n The current geometry of the window client area.\n\n '
if self.proxy_is_active:
return self.proxy.geometry()
return Rect((- 1), (- 1), (- 1), (- 1)) | def geometry(self):
' Get the geometry of the window client area.\n\n Returns\n -------\n result : Rect\n The current geometry of the window client area.\n\n '
if self.proxy_is_active:
return self.proxy.geometry()
return Rect((- 1), (- 1), (- 1), (- 1))<|docstring|>Get the geometry of the window client area.
Returns
-------
result : Rect
The current geometry of the window client area.<|endoftext|> |
ed1180bf23c9ea3eca315a050de183aaed0a04e4733faf969ea8722ea7d94367 | def set_geometry(self, rect):
' Set the geometry of the window client area.\n\n Parameters\n ----------\n rect : Rect\n The desired geometry of the window client area.\n\n '
if self.proxy_is_active:
self.proxy.set_geometry(rect) | Set the geometry of the window client area.
Parameters
----------
rect : Rect
The desired geometry of the window client area. | enaml/widgets/window.py | set_geometry | AndiEcker/enaml | 1,080 | python | def set_geometry(self, rect):
' Set the geometry of the window client area.\n\n Parameters\n ----------\n rect : Rect\n The desired geometry of the window client area.\n\n '
if self.proxy_is_active:
self.proxy.set_geometry(rect) | def set_geometry(self, rect):
' Set the geometry of the window client area.\n\n Parameters\n ----------\n rect : Rect\n The desired geometry of the window client area.\n\n '
if self.proxy_is_active:
self.proxy.set_geometry(rect)<|docstring|>Set the geometry of the window client area.
Parameters
----------
rect : Rect
The desired geometry of the window client area.<|endoftext|> |
97b1985c8a5a7f3e9c549d5f636096ee92965d25a1d120b083b5764262d8e996 | def frame_geometry(self):
' Get the geometry of the window frame.\n\n Returns\n -------\n result : Rect\n The current geometry of the window frame.\n\n '
if self.proxy_is_active:
return self.proxy.frame_geometry()
return Rect((- 1), (- 1), (- 1), (- 1)) | Get the geometry of the window frame.
Returns
-------
result : Rect
The current geometry of the window frame. | enaml/widgets/window.py | frame_geometry | AndiEcker/enaml | 1,080 | python | def frame_geometry(self):
' Get the geometry of the window frame.\n\n Returns\n -------\n result : Rect\n The current geometry of the window frame.\n\n '
if self.proxy_is_active:
return self.proxy.frame_geometry()
return Rect((- 1), (- 1), (- 1), (- 1)) | def frame_geometry(self):
' Get the geometry of the window frame.\n\n Returns\n -------\n result : Rect\n The current geometry of the window frame.\n\n '
if self.proxy_is_active:
return self.proxy.frame_geometry()
return Rect((- 1), (- 1), (- 1), (- 1))<|docstring|>Get the geometry of the window frame.
Returns
-------
result : Rect
The current geometry of the window frame.<|endoftext|> |
636d55ec74b6ba1eefeb1dcc5191725c79a566a6e3eefc8f2d16ba8af9bc00e3 | def maximize(self):
' Maximize the window.\n\n '
if self.proxy_is_active:
self.proxy.maximize() | Maximize the window. | enaml/widgets/window.py | maximize | AndiEcker/enaml | 1,080 | python | def maximize(self):
' \n\n '
if self.proxy_is_active:
self.proxy.maximize() | def maximize(self):
' \n\n '
if self.proxy_is_active:
self.proxy.maximize()<|docstring|>Maximize the window.<|endoftext|> |
032ee8aa2c7c3f5f415d148da6fd2e15f28bce8ea43f31af8ea5967ab7ab5b79 | def is_maximized(self):
' Get whether the window is maximized.\n\n '
if self.proxy_is_active:
return self.proxy.is_maximized()
return False | Get whether the window is maximized. | enaml/widgets/window.py | is_maximized | AndiEcker/enaml | 1,080 | python | def is_maximized(self):
' \n\n '
if self.proxy_is_active:
return self.proxy.is_maximized()
return False | def is_maximized(self):
' \n\n '
if self.proxy_is_active:
return self.proxy.is_maximized()
return False<|docstring|>Get whether the window is maximized.<|endoftext|> |
c18e251ac188c1e879642b18b911afbdbdcb98bb8551ac53c160fa0af044b522 | def minimize(self):
' Minimize the window.\n\n '
if self.proxy_is_active:
self.proxy.minimize() | Minimize the window. | enaml/widgets/window.py | minimize | AndiEcker/enaml | 1,080 | python | def minimize(self):
' \n\n '
if self.proxy_is_active:
self.proxy.minimize() | def minimize(self):
' \n\n '
if self.proxy_is_active:
self.proxy.minimize()<|docstring|>Minimize the window.<|endoftext|> |
3d3b2fa392510dc8bdf164c3cb529a1de9d7dbe97376dc784ede10ee1aca4947 | def is_minimized(self):
' Get whether the window is minimized.\n\n '
if self.proxy_is_active:
return self.proxy.is_minimized()
return False | Get whether the window is minimized. | enaml/widgets/window.py | is_minimized | AndiEcker/enaml | 1,080 | python | def is_minimized(self):
' \n\n '
if self.proxy_is_active:
return self.proxy.is_minimized()
return False | def is_minimized(self):
' \n\n '
if self.proxy_is_active:
return self.proxy.is_minimized()
return False<|docstring|>Get whether the window is minimized.<|endoftext|> |
3b77c392f328813397ce36ac7b8c88e017e4318d0dce6cbf46f3a1e489bc9e75 | def restore(self):
' Restore the window from a maximized or minimized state.\n\n '
if self.proxy_is_active:
self.proxy.restore() | Restore the window from a maximized or minimized state. | enaml/widgets/window.py | restore | AndiEcker/enaml | 1,080 | python | def restore(self):
' \n\n '
if self.proxy_is_active:
self.proxy.restore() | def restore(self):
' \n\n '
if self.proxy_is_active:
self.proxy.restore()<|docstring|>Restore the window from a maximized or minimized state.<|endoftext|> |
77d3c4239a1803a23d6334bc9125f947d75e6d3c1641f0c3d8d2f152c9d9f29e | def send_to_front(self):
' Send the window to the top of the Z-order.\n\n This will only affect the Z-order of the window relative to the\n Z-order of other windows in the same application.\n\n '
if self.proxy_is_active:
self.proxy.send_to_front() | Send the window to the top of the Z-order.
This will only affect the Z-order of the window relative to the
Z-order of other windows in the same application. | enaml/widgets/window.py | send_to_front | AndiEcker/enaml | 1,080 | python | def send_to_front(self):
' Send the window to the top of the Z-order.\n\n This will only affect the Z-order of the window relative to the\n Z-order of other windows in the same application.\n\n '
if self.proxy_is_active:
self.proxy.send_to_front() | def send_to_front(self):
' Send the window to the top of the Z-order.\n\n This will only affect the Z-order of the window relative to the\n Z-order of other windows in the same application.\n\n '
if self.proxy_is_active:
self.proxy.send_to_front()<|docstring|>Send the window to the top of the Z-order.
This will only affect the Z-order of the window relative to the
Z-order of other windows in the same application.<|endoftext|> |
e4d3f16e6e4a7fd131063a9d8169e2d887feda309e5395d2664b9bdbda9e1c62 | def send_to_back(self):
' Send the window to the bottom of the Z-order.\n\n This will only affect the Z-order of the window relative to the\n Z-order of other windows in the same application.\n\n '
if self.proxy_is_active:
self.proxy.send_to_back() | Send the window to the bottom of the Z-order.
This will only affect the Z-order of the window relative to the
Z-order of other windows in the same application. | enaml/widgets/window.py | send_to_back | AndiEcker/enaml | 1,080 | python | def send_to_back(self):
' Send the window to the bottom of the Z-order.\n\n This will only affect the Z-order of the window relative to the\n Z-order of other windows in the same application.\n\n '
if self.proxy_is_active:
self.proxy.send_to_back() | def send_to_back(self):
' Send the window to the bottom of the Z-order.\n\n This will only affect the Z-order of the window relative to the\n Z-order of other windows in the same application.\n\n '
if self.proxy_is_active:
self.proxy.send_to_back()<|docstring|>Send the window to the bottom of the Z-order.
This will only affect the Z-order of the window relative to the
Z-order of other windows in the same application.<|endoftext|> |
484cae1decb96f2be24d74c1770c9bd789e0af83f2a01e20e2dcc552598cc432 | def activate_window(self):
' Set this window to be the active application window.\n\n This performs the same operation as clicking the mouse on the\n title bar of the window, except that it will not effect the Z\n order of the window.\n\n On Windows, this will cause the taskbar icon to flash if the\n window does not belong to the active application.\n\n '
if self.proxy_is_active:
self.proxy.activate_window() | Set this window to be the active application window.
This performs the same operation as clicking the mouse on the
title bar of the window, except that it will not effect the Z
order of the window.
On Windows, this will cause the taskbar icon to flash if the
window does not belong to the active application. | enaml/widgets/window.py | activate_window | AndiEcker/enaml | 1,080 | python | def activate_window(self):
' Set this window to be the active application window.\n\n This performs the same operation as clicking the mouse on the\n title bar of the window, except that it will not effect the Z\n order of the window.\n\n On Windows, this will cause the taskbar icon to flash if the\n window does not belong to the active application.\n\n '
if self.proxy_is_active:
self.proxy.activate_window() | def activate_window(self):
' Set this window to be the active application window.\n\n This performs the same operation as clicking the mouse on the\n title bar of the window, except that it will not effect the Z\n order of the window.\n\n On Windows, this will cause the taskbar icon to flash if the\n window does not belong to the active application.\n\n '
if self.proxy_is_active:
self.proxy.activate_window()<|docstring|>Set this window to be the active application window.
This performs the same operation as clicking the mouse on the
title bar of the window, except that it will not effect the Z
order of the window.
On Windows, this will cause the taskbar icon to flash if the
window does not belong to the active application.<|endoftext|> |
7385616c81817e37e0c33050cef8ec98f3f9f9fdb0f40e0dab1eb33e122e38ce | def center_on_screen(self):
' Center the window on the screen.\n\n '
if self.proxy_is_active:
self.proxy.center_on_screen() | Center the window on the screen. | enaml/widgets/window.py | center_on_screen | AndiEcker/enaml | 1,080 | python | def center_on_screen(self):
' \n\n '
if self.proxy_is_active:
self.proxy.center_on_screen() | def center_on_screen(self):
' \n\n '
if self.proxy_is_active:
self.proxy.center_on_screen()<|docstring|>Center the window on the screen.<|endoftext|> |
0ed84c58394630eb03952684fd5101dc618bf034c1b5f1476d996244db36f444 | def center_on_widget(self, other):
' Center this window on another widget.\n\n Parameters\n ----------\n other : Widget\n The widget onto which to center this window.\n\n '
assert isinstance(other, Widget)
if (self.proxy_is_active and other.proxy_is_active):
self.proxy.center_on_widget(other) | Center this window on another widget.
Parameters
----------
other : Widget
The widget onto which to center this window. | enaml/widgets/window.py | center_on_widget | AndiEcker/enaml | 1,080 | python | def center_on_widget(self, other):
' Center this window on another widget.\n\n Parameters\n ----------\n other : Widget\n The widget onto which to center this window.\n\n '
assert isinstance(other, Widget)
if (self.proxy_is_active and other.proxy_is_active):
self.proxy.center_on_widget(other) | def center_on_widget(self, other):
' Center this window on another widget.\n\n Parameters\n ----------\n other : Widget\n The widget onto which to center this window.\n\n '
assert isinstance(other, Widget)
if (self.proxy_is_active and other.proxy_is_active):
self.proxy.center_on_widget(other)<|docstring|>Center this window on another widget.
Parameters
----------
other : Widget
The widget onto which to center this window.<|endoftext|> |
347b908773a2f89bfa3971880f0880ed0abea71409ac9d927e11db3fd49989da | def close(self):
" Close the window.\n\n This will cause the window to be hidden, the 'closed' event\n to be fired, and the window subsequently destroyed.\n\n "
if self.proxy_is_active:
self.proxy.close() | Close the window.
This will cause the window to be hidden, the 'closed' event
to be fired, and the window subsequently destroyed. | enaml/widgets/window.py | close | AndiEcker/enaml | 1,080 | python | def close(self):
" Close the window.\n\n This will cause the window to be hidden, the 'closed' event\n to be fired, and the window subsequently destroyed.\n\n "
if self.proxy_is_active:
self.proxy.close() | def close(self):
" Close the window.\n\n This will cause the window to be hidden, the 'closed' event\n to be fired, and the window subsequently destroyed.\n\n "
if self.proxy_is_active:
self.proxy.close()<|docstring|>Close the window.
This will cause the window to be hidden, the 'closed' event
to be fired, and the window subsequently destroyed.<|endoftext|> |
25a6ec70c308a245b4a3905324896c965099955a1a9b91bc6f88d9911ee9e591 | def show(self):
' Show the window to the screen.\n\n This is a reimplemented parent class method which will init\n and build the window hierarchy if needed.\n\n '
if (not self.is_initialized):
self.initialize()
if (not self.proxy_is_active):
self.activate_proxy()
super(Window, self).show() | Show the window to the screen.
This is a reimplemented parent class method which will init
and build the window hierarchy if needed. | enaml/widgets/window.py | show | AndiEcker/enaml | 1,080 | python | def show(self):
' Show the window to the screen.\n\n This is a reimplemented parent class method which will init\n and build the window hierarchy if needed.\n\n '
if (not self.is_initialized):
self.initialize()
if (not self.proxy_is_active):
self.activate_proxy()
super(Window, self).show() | def show(self):
' Show the window to the screen.\n\n This is a reimplemented parent class method which will init\n and build the window hierarchy if needed.\n\n '
if (not self.is_initialized):
self.initialize()
if (not self.proxy_is_active):
self.activate_proxy()
super(Window, self).show()<|docstring|>Show the window to the screen.
This is a reimplemented parent class method which will init
and build the window hierarchy if needed.<|endoftext|> |
51d81e2216290eb79b877c6f5f89225c5d43a7a207c0d13381a9aa79f1194cb9 | @observe('title', 'modality', 'icon')
def _update_proxy(self, change):
' Update the ProxyWindow when the Window data changes.\n\n '
super(Window, self)._update_proxy(change) | Update the ProxyWindow when the Window data changes. | enaml/widgets/window.py | _update_proxy | AndiEcker/enaml | 1,080 | python | @observe('title', 'modality', 'icon')
def _update_proxy(self, change):
' \n\n '
super(Window, self)._update_proxy(change) | @observe('title', 'modality', 'icon')
def _update_proxy(self, change):
' \n\n '
super(Window, self)._update_proxy(change)<|docstring|>Update the ProxyWindow when the Window data changes.<|endoftext|> |
4b679ce5f0be647fd4337a0de1d7121e17cb49e5db92266ae52e643d38c5b5b1 | @contextmanager
def cached():
'Context manager that enables the caching system within parametrizations\n registered with :func:`register_parametrization`.\n\n The value of the parametrized objects is computed and cached the first time\n they are required when this context manager is active. The cached values are\n discarded when leaving the context manager.\n\n This is useful when using a parametrized parameter more than once in the forward pass.\n An example of this is when parametrizing the recurrent kernel of an RNN or when\n sharing weights.\n\n The simplest way to activate the cache is by wrapping the forward pass of the neural network\n\n .. code-block:: python\n\n import torch.nn.utils.parametrize as P\n ...\n with P.cached():\n output = model(inputs)\n\n in training and evaluation. One may also wrap the parts of the modules that use\n several times the parametrized tensors. For example, the loop of an RNN with a\n parametrized recurrent kernel:\n\n .. code-block:: python\n\n with P.cached():\n for x in xs:\n out_rnn = self.rnn_cell(x, out_rnn)\n '
global _cache
global _cache_enabled
_cache_enabled += 1
try:
(yield)
finally:
_cache_enabled -= 1
if (not _cache_enabled):
_cache = {} | Context manager that enables the caching system within parametrizations
registered with :func:`register_parametrization`.
The value of the parametrized objects is computed and cached the first time
they are required when this context manager is active. The cached values are
discarded when leaving the context manager.
This is useful when using a parametrized parameter more than once in the forward pass.
An example of this is when parametrizing the recurrent kernel of an RNN or when
sharing weights.
The simplest way to activate the cache is by wrapping the forward pass of the neural network
.. code-block:: python
import torch.nn.utils.parametrize as P
...
with P.cached():
output = model(inputs)
in training and evaluation. One may also wrap the parts of the modules that use
several times the parametrized tensors. For example, the loop of an RNN with a
parametrized recurrent kernel:
.. code-block:: python
with P.cached():
for x in xs:
out_rnn = self.rnn_cell(x, out_rnn) | torch/nn/utils/parametrize.py | cached | joker-eph/pytorch | 60,067 | python | @contextmanager
def cached():
'Context manager that enables the caching system within parametrizations\n registered with :func:`register_parametrization`.\n\n The value of the parametrized objects is computed and cached the first time\n they are required when this context manager is active. The cached values are\n discarded when leaving the context manager.\n\n This is useful when using a parametrized parameter more than once in the forward pass.\n An example of this is when parametrizing the recurrent kernel of an RNN or when\n sharing weights.\n\n The simplest way to activate the cache is by wrapping the forward pass of the neural network\n\n .. code-block:: python\n\n import torch.nn.utils.parametrize as P\n ...\n with P.cached():\n output = model(inputs)\n\n in training and evaluation. One may also wrap the parts of the modules that use\n several times the parametrized tensors. For example, the loop of an RNN with a\n parametrized recurrent kernel:\n\n .. code-block:: python\n\n with P.cached():\n for x in xs:\n out_rnn = self.rnn_cell(x, out_rnn)\n '
global _cache
global _cache_enabled
_cache_enabled += 1
try:
(yield)
finally:
_cache_enabled -= 1
if (not _cache_enabled):
_cache = {} | @contextmanager
def cached():
'Context manager that enables the caching system within parametrizations\n registered with :func:`register_parametrization`.\n\n The value of the parametrized objects is computed and cached the first time\n they are required when this context manager is active. The cached values are\n discarded when leaving the context manager.\n\n This is useful when using a parametrized parameter more than once in the forward pass.\n An example of this is when parametrizing the recurrent kernel of an RNN or when\n sharing weights.\n\n The simplest way to activate the cache is by wrapping the forward pass of the neural network\n\n .. code-block:: python\n\n import torch.nn.utils.parametrize as P\n ...\n with P.cached():\n output = model(inputs)\n\n in training and evaluation. One may also wrap the parts of the modules that use\n several times the parametrized tensors. For example, the loop of an RNN with a\n parametrized recurrent kernel:\n\n .. code-block:: python\n\n with P.cached():\n for x in xs:\n out_rnn = self.rnn_cell(x, out_rnn)\n '
global _cache
global _cache_enabled
_cache_enabled += 1
try:
(yield)
finally:
_cache_enabled -= 1
if (not _cache_enabled):
_cache = {}<|docstring|>Context manager that enables the caching system within parametrizations
registered with :func:`register_parametrization`.
The value of the parametrized objects is computed and cached the first time
they are required when this context manager is active. The cached values are
discarded when leaving the context manager.
This is useful when using a parametrized parameter more than once in the forward pass.
An example of this is when parametrizing the recurrent kernel of an RNN or when
sharing weights.
The simplest way to activate the cache is by wrapping the forward pass of the neural network
.. code-block:: python
import torch.nn.utils.parametrize as P
...
with P.cached():
output = model(inputs)
in training and evaluation. One may also wrap the parts of the modules that use
several times the parametrized tensors. For example, the loop of an RNN with a
parametrized recurrent kernel:
.. code-block:: python
with P.cached():
for x in xs:
out_rnn = self.rnn_cell(x, out_rnn)<|endoftext|> |
6890dd4538583e754317fb95bf50b5db50ed6c674605fac5c52c82b3cb6c506a | def _inject_new_class(module: Module) -> None:
'Sets up a module to be parametrized.\n\n This works by substituting the class of the module by a class\n that extends it to be able to inject a property\n\n Args:\n module (nn.Module): module into which to inject the property\n '
cls = module.__class__
def getstate(self):
raise RuntimeError('Serialization of parametrized modules is only supported through state_dict(). See:\nhttps://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-a-general-checkpoint-for-inference-and-or-resuming-training')
param_cls = type(f'Parametrized{cls.__name__}', (cls,), {'__getstate__': getstate})
module.__class__ = param_cls | Sets up a module to be parametrized.
This works by substituting the class of the module by a class
that extends it to be able to inject a property
Args:
module (nn.Module): module into which to inject the property | torch/nn/utils/parametrize.py | _inject_new_class | joker-eph/pytorch | 60,067 | python | def _inject_new_class(module: Module) -> None:
'Sets up a module to be parametrized.\n\n This works by substituting the class of the module by a class\n that extends it to be able to inject a property\n\n Args:\n module (nn.Module): module into which to inject the property\n '
cls = module.__class__
def getstate(self):
raise RuntimeError('Serialization of parametrized modules is only supported through state_dict(). See:\nhttps://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-a-general-checkpoint-for-inference-and-or-resuming-training')
param_cls = type(f'Parametrized{cls.__name__}', (cls,), {'__getstate__': getstate})
module.__class__ = param_cls | def _inject_new_class(module: Module) -> None:
'Sets up a module to be parametrized.\n\n This works by substituting the class of the module by a class\n that extends it to be able to inject a property\n\n Args:\n module (nn.Module): module into which to inject the property\n '
cls = module.__class__
def getstate(self):
raise RuntimeError('Serialization of parametrized modules is only supported through state_dict(). See:\nhttps://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-a-general-checkpoint-for-inference-and-or-resuming-training')
param_cls = type(f'Parametrized{cls.__name__}', (cls,), {'__getstate__': getstate})
module.__class__ = param_cls<|docstring|>Sets up a module to be parametrized.
This works by substituting the class of the module by a class
that extends it to be able to inject a property
Args:
module (nn.Module): module into which to inject the property<|endoftext|> |
7a39a58d90beb36ee330a43b99c71246ff3c43ceb69956a1adacbb3c1d3dce79 | def _inject_property(module: Module, tensor_name: str) -> None:
'Injects a property into module[tensor_name].\n\n It assumes that the class in the module has already been modified from its\n original one using _inject_new_class and that the tensor under :attr:`tensor_name`\n has already been moved out\n\n Args:\n module (nn.Module): module into which to inject the property\n tensor_name (str): name of the name of the property to create\n '
assert (not hasattr(module, tensor_name))
@torch.jit.unused
def get_cached_parametrization(parametrization) -> Tensor:
global _cache
key = (id(module), tensor_name)
tensor = _cache.get(key)
if (tensor is None):
tensor = parametrization()
_cache[key] = tensor
return tensor
def get_parametrized(self) -> Tensor:
parametrization = self.parametrizations[tensor_name]
if _cache_enabled:
if torch.jit.is_scripting():
raise RuntimeError('Caching is not implemented for scripting. Either disable caching or avoid scripting.')
elif (torch._C._get_tracing_state() is not None):
raise RuntimeError('Cannot trace a model while caching parametrizations.')
else:
return get_cached_parametrization(parametrization)
else:
return parametrization()
def set_original(self, value: Tensor) -> None:
self.parametrizations[tensor_name].right_inverse(value)
setattr(module.__class__, tensor_name, property(get_parametrized, set_original)) | Injects a property into module[tensor_name].
It assumes that the class in the module has already been modified from its
original one using _inject_new_class and that the tensor under :attr:`tensor_name`
has already been moved out
Args:
module (nn.Module): module into which to inject the property
tensor_name (str): name of the name of the property to create | torch/nn/utils/parametrize.py | _inject_property | joker-eph/pytorch | 60,067 | python | def _inject_property(module: Module, tensor_name: str) -> None:
'Injects a property into module[tensor_name].\n\n It assumes that the class in the module has already been modified from its\n original one using _inject_new_class and that the tensor under :attr:`tensor_name`\n has already been moved out\n\n Args:\n module (nn.Module): module into which to inject the property\n tensor_name (str): name of the name of the property to create\n '
assert (not hasattr(module, tensor_name))
@torch.jit.unused
def get_cached_parametrization(parametrization) -> Tensor:
global _cache
key = (id(module), tensor_name)
tensor = _cache.get(key)
if (tensor is None):
tensor = parametrization()
_cache[key] = tensor
return tensor
def get_parametrized(self) -> Tensor:
parametrization = self.parametrizations[tensor_name]
if _cache_enabled:
if torch.jit.is_scripting():
raise RuntimeError('Caching is not implemented for scripting. Either disable caching or avoid scripting.')
elif (torch._C._get_tracing_state() is not None):
raise RuntimeError('Cannot trace a model while caching parametrizations.')
else:
return get_cached_parametrization(parametrization)
else:
return parametrization()
def set_original(self, value: Tensor) -> None:
self.parametrizations[tensor_name].right_inverse(value)
setattr(module.__class__, tensor_name, property(get_parametrized, set_original)) | def _inject_property(module: Module, tensor_name: str) -> None:
'Injects a property into module[tensor_name].\n\n It assumes that the class in the module has already been modified from its\n original one using _inject_new_class and that the tensor under :attr:`tensor_name`\n has already been moved out\n\n Args:\n module (nn.Module): module into which to inject the property\n tensor_name (str): name of the name of the property to create\n '
assert (not hasattr(module, tensor_name))
@torch.jit.unused
def get_cached_parametrization(parametrization) -> Tensor:
global _cache
key = (id(module), tensor_name)
tensor = _cache.get(key)
if (tensor is None):
tensor = parametrization()
_cache[key] = tensor
return tensor
def get_parametrized(self) -> Tensor:
parametrization = self.parametrizations[tensor_name]
if _cache_enabled:
if torch.jit.is_scripting():
raise RuntimeError('Caching is not implemented for scripting. Either disable caching or avoid scripting.')
elif (torch._C._get_tracing_state() is not None):
raise RuntimeError('Cannot trace a model while caching parametrizations.')
else:
return get_cached_parametrization(parametrization)
else:
return parametrization()
def set_original(self, value: Tensor) -> None:
self.parametrizations[tensor_name].right_inverse(value)
setattr(module.__class__, tensor_name, property(get_parametrized, set_original))<|docstring|>Injects a property into module[tensor_name].
It assumes that the class in the module has already been modified from its
original one using _inject_new_class and that the tensor under :attr:`tensor_name`
has already been moved out
Args:
module (nn.Module): module into which to inject the property
tensor_name (str): name of the name of the property to create<|endoftext|> |
ddc7a99efa8c176d6a6e7edd6dd002c5f0a15c5a3909856a486810136e2f3097 | def register_parametrization(module: Module, tensor_name: str, parametrization: Module, *, unsafe: bool=False) -> Module:
'Adds a parametrization to a tensor in a module.\n\n Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``,\n the module will return the parametrized version ``parametrization(module.weight)``.\n If the original tensor requires a gradient, the backward pass will differentiate\n through :attr:`parametrization`, and the optimizer will update the tensor accordingly.\n\n The first time that a module registers a parametrization, this function will add an attribute\n ``parametrizations`` to the module of type :class:`~ParametrizationList`.\n\n The list of parametrizations on the tensor ``weight`` will be accessible under\n ``module.parametrizations.weight``.\n\n The original tensor will be accessible under\n ``module.parametrizations.weight.original``.\n\n Parametrizations may be concatenated by registering several parametrizations\n on the same attribute.\n\n The training mode of a registered parametrization is updated on registration\n to match the training mode of the host module\n\n Parametrized parameters and buffers have an inbuilt caching system that can be activated\n using the context manager :func:`cached`.\n\n A :attr:`parametrization` may optionally implement a method with signature\n\n .. code-block:: python\n\n def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]]\n\n This method is called on the unparametrized tensor when the first parametrization\n is registered to compute the initial value of the original tensor.\n If this method is not implemented, the original tensor will be just the unparametrized tensor.\n\n If all the parametrizations registered on a tensor implement `right_inverse` it is possible\n to initialize a parametrized tensor by assigning to it, as shown in the example below.\n\n It is possible for the first parametrization to depend on several inputs.\n This may be implemented returning a tuple of tensors from ``right_inverse``\n (see the example implementation of a ``RankOne`` parametrization below).\n\n In this case, the unconstrained tensors are also located under ``module.parametrizations.weight``\n with names ``original0``, ``original1``,...\n\n .. note::\n\n If unsafe=False (default) both the forward and right_inverse methods will be called\n once to perform a number of consistency checks.\n If unsafe=True, then right_inverse will be called if the tensor is not parametrized,\n and nothing will be called otherwise.\n\n .. note::\n\n In most situations, ``right_inverse`` will be a function such that\n ``forward(right_inverse(X)) == X`` (see\n `right inverse <https://en.wikipedia.org/wiki/Inverse_function#Right_inverses>`_).\n Sometimes, when the parametrization is not surjective, it may be reasonable\n to relax this.\n\n .. warning::\n\n If a parametrization depends on several inputs, :func:`~register_parametrization`\n will register a number of new parameters. If such parametrization is registered\n after the optimizer is created, these new parameters will need to be added manually\n to the optimizer. See :meth:`torch.Optimizer.add_param_group`.\n\n Args:\n module (nn.Module): module on which to register the parametrization\n tensor_name (str): name of the parameter or buffer on which to register\n the parametrization\n parametrization (nn.Module): the parametrization to register\n Keyword args:\n unsafe (bool): a boolean flag that denotes whether the parametrization\n may change the dtype and shape of the tensor. Default: `False`\n Warning: the parametrization is not checked for consistency upon registration.\n Enable this flag at your own risk.\n\n Raises:\n ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name`\n\n Examples:\n >>> import torch\n >>> import torch.nn as nn\n >>> import torch.nn.utils.parametrize as P\n >>>\n >>> class Symmetric(nn.Module):\n >>> def forward(self, X):\n >>> return X.triu() + X.triu(1).T # Return a symmetric matrix\n >>>\n >>> def right_inverse(self, A):\n >>> return A.triu()\n >>>\n >>> m = nn.Linear(5, 5)\n >>> P.register_parametrization(m, "weight", Symmetric())\n >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric\n True\n >>> A = torch.rand(5, 5)\n >>> A = A + A.T # A is now symmetric\n >>> m.weight = A # Initialize the weight to be the symmetric matrix A\n >>> print(torch.allclose(m.weight, A))\n True\n\n >>> class RankOne(nn.Module):\n >>> def forward(self, x, y):\n >>> # Form a rank 1 matrix multiplying two vectors\n >>> return x.unsqueeze(-1) @ y.unsqueeze(-2)\n >>>\n >>> def right_inverse(self, Z):\n >>> # Project Z onto the rank 1 matrices\n >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False)\n >>> # Return rescaled singular vectors\n >>> s0_sqrt = S[0].sqrt().unsqueeze(-1)\n >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt\n >>>\n >>> linear_rank_one = P.register_parametrization(nn.Linear(4, 4), "weight", RankOne())\n >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item())\n 1\n\n '
parametrization.train(module.training)
if is_parametrized(module, tensor_name):
if (not unsafe):
Y = getattr(module, tensor_name)
X = parametrization(Y)
if (not isinstance(X, Tensor)):
raise ValueError(f'A parametrization must return a tensor. Got {type(X).__name__}.')
if (X.dtype != Y.dtype):
raise ValueError(f'''Registering a parametrization may not change the dtype of the tensor, unless the `unsafe` flag is enabled.
module.{tensor_name}.dtype: {Y.dtype}
parametrization(module.{tensor_name}).dtype: {X.dtype}''')
if (X.shape != Y.shape):
raise ValueError(f'''Registering a parametrization may not change the shape of the tensor, unless the `unsafe` flag is enabled.
module.{tensor_name}.shape: {Y.shape}
parametrization(module.{tensor_name}).shape: {X.shape}''')
if hasattr(parametrization, 'right_inverse'):
try:
Z = parametrization.right_inverse(X)
except NotImplementedError:
pass
else:
if (not isinstance(Z, Tensor)):
raise ValueError(f'parametrization.right_inverse must return a tensor. Got: {type(Z).__name__}')
if (Z.dtype != Y.dtype):
raise ValueError(f'''The tensor returned by parametrization.right_inverse must have the same dtype as module.{tensor_name}, unless the `unsafe` flag is enabled.
module.{tensor_name}.dtype: {Y.dtype}
returned dtype: {Z.dtype}''')
if (Z.shape != Y.shape):
raise ValueError(f'''The tensor returned by parametrization.right_inverse must have the same shape as module.{tensor_name}, unless the `unsafe` flag is enabled.
module.{tensor_name}.shape: {Y.shape}
returned shape: {Z.shape}''')
assert isinstance(module.parametrizations, ModuleDict)
module.parametrizations[tensor_name].append(parametrization)
module.parametrizations[tensor_name].unsafe |= unsafe
elif ((tensor_name in module._buffers) or (tensor_name in module._parameters)):
original = getattr(module, tensor_name)
parametrizations = ParametrizationList([parametrization], original, unsafe=unsafe)
delattr(module, tensor_name)
if (not is_parametrized(module)):
_inject_new_class(module)
module.parametrizations = ModuleDict()
_inject_property(module, tensor_name)
assert isinstance(module.parametrizations, ModuleDict)
module.parametrizations[tensor_name] = parametrizations
else:
raise ValueError(f"Module '{module}' does not have a parameter, a buffer, or a parametrized element with name '{tensor_name}'")
return module | Adds a parametrization to a tensor in a module.
Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``,
the module will return the parametrized version ``parametrization(module.weight)``.
If the original tensor requires a gradient, the backward pass will differentiate
through :attr:`parametrization`, and the optimizer will update the tensor accordingly.
The first time that a module registers a parametrization, this function will add an attribute
``parametrizations`` to the module of type :class:`~ParametrizationList`.
The list of parametrizations on the tensor ``weight`` will be accessible under
``module.parametrizations.weight``.
The original tensor will be accessible under
``module.parametrizations.weight.original``.
Parametrizations may be concatenated by registering several parametrizations
on the same attribute.
The training mode of a registered parametrization is updated on registration
to match the training mode of the host module
Parametrized parameters and buffers have an inbuilt caching system that can be activated
using the context manager :func:`cached`.
A :attr:`parametrization` may optionally implement a method with signature
.. code-block:: python
def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]]
This method is called on the unparametrized tensor when the first parametrization
is registered to compute the initial value of the original tensor.
If this method is not implemented, the original tensor will be just the unparametrized tensor.
If all the parametrizations registered on a tensor implement `right_inverse` it is possible
to initialize a parametrized tensor by assigning to it, as shown in the example below.
It is possible for the first parametrization to depend on several inputs.
This may be implemented returning a tuple of tensors from ``right_inverse``
(see the example implementation of a ``RankOne`` parametrization below).
In this case, the unconstrained tensors are also located under ``module.parametrizations.weight``
with names ``original0``, ``original1``,...
.. note::
If unsafe=False (default) both the forward and right_inverse methods will be called
once to perform a number of consistency checks.
If unsafe=True, then right_inverse will be called if the tensor is not parametrized,
and nothing will be called otherwise.
.. note::
In most situations, ``right_inverse`` will be a function such that
``forward(right_inverse(X)) == X`` (see
`right inverse <https://en.wikipedia.org/wiki/Inverse_function#Right_inverses>`_).
Sometimes, when the parametrization is not surjective, it may be reasonable
to relax this.
.. warning::
If a parametrization depends on several inputs, :func:`~register_parametrization`
will register a number of new parameters. If such parametrization is registered
after the optimizer is created, these new parameters will need to be added manually
to the optimizer. See :meth:`torch.Optimizer.add_param_group`.
Args:
module (nn.Module): module on which to register the parametrization
tensor_name (str): name of the parameter or buffer on which to register
the parametrization
parametrization (nn.Module): the parametrization to register
Keyword args:
unsafe (bool): a boolean flag that denotes whether the parametrization
may change the dtype and shape of the tensor. Default: `False`
Warning: the parametrization is not checked for consistency upon registration.
Enable this flag at your own risk.
Raises:
ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name`
Examples:
>>> import torch
>>> import torch.nn as nn
>>> import torch.nn.utils.parametrize as P
>>>
>>> class Symmetric(nn.Module):
>>> def forward(self, X):
>>> return X.triu() + X.triu(1).T # Return a symmetric matrix
>>>
>>> def right_inverse(self, A):
>>> return A.triu()
>>>
>>> m = nn.Linear(5, 5)
>>> P.register_parametrization(m, "weight", Symmetric())
>>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric
True
>>> A = torch.rand(5, 5)
>>> A = A + A.T # A is now symmetric
>>> m.weight = A # Initialize the weight to be the symmetric matrix A
>>> print(torch.allclose(m.weight, A))
True
>>> class RankOne(nn.Module):
>>> def forward(self, x, y):
>>> # Form a rank 1 matrix multiplying two vectors
>>> return x.unsqueeze(-1) @ y.unsqueeze(-2)
>>>
>>> def right_inverse(self, Z):
>>> # Project Z onto the rank 1 matrices
>>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False)
>>> # Return rescaled singular vectors
>>> s0_sqrt = S[0].sqrt().unsqueeze(-1)
>>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt
>>>
>>> linear_rank_one = P.register_parametrization(nn.Linear(4, 4), "weight", RankOne())
>>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item())
1 | torch/nn/utils/parametrize.py | register_parametrization | joker-eph/pytorch | 60,067 | python | def register_parametrization(module: Module, tensor_name: str, parametrization: Module, *, unsafe: bool=False) -> Module:
'Adds a parametrization to a tensor in a module.\n\n Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``,\n the module will return the parametrized version ``parametrization(module.weight)``.\n If the original tensor requires a gradient, the backward pass will differentiate\n through :attr:`parametrization`, and the optimizer will update the tensor accordingly.\n\n The first time that a module registers a parametrization, this function will add an attribute\n ``parametrizations`` to the module of type :class:`~ParametrizationList`.\n\n The list of parametrizations on the tensor ``weight`` will be accessible under\n ``module.parametrizations.weight``.\n\n The original tensor will be accessible under\n ``module.parametrizations.weight.original``.\n\n Parametrizations may be concatenated by registering several parametrizations\n on the same attribute.\n\n The training mode of a registered parametrization is updated on registration\n to match the training mode of the host module\n\n Parametrized parameters and buffers have an inbuilt caching system that can be activated\n using the context manager :func:`cached`.\n\n A :attr:`parametrization` may optionally implement a method with signature\n\n .. code-block:: python\n\n def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]]\n\n This method is called on the unparametrized tensor when the first parametrization\n is registered to compute the initial value of the original tensor.\n If this method is not implemented, the original tensor will be just the unparametrized tensor.\n\n If all the parametrizations registered on a tensor implement `right_inverse` it is possible\n to initialize a parametrized tensor by assigning to it, as shown in the example below.\n\n It is possible for the first parametrization to depend on several inputs.\n This may be implemented returning a tuple of tensors from ``right_inverse``\n (see the example implementation of a ``RankOne`` parametrization below).\n\n In this case, the unconstrained tensors are also located under ``module.parametrizations.weight``\n with names ``original0``, ``original1``,...\n\n .. note::\n\n If unsafe=False (default) both the forward and right_inverse methods will be called\n once to perform a number of consistency checks.\n If unsafe=True, then right_inverse will be called if the tensor is not parametrized,\n and nothing will be called otherwise.\n\n .. note::\n\n In most situations, ``right_inverse`` will be a function such that\n ``forward(right_inverse(X)) == X`` (see\n `right inverse <https://en.wikipedia.org/wiki/Inverse_function#Right_inverses>`_).\n Sometimes, when the parametrization is not surjective, it may be reasonable\n to relax this.\n\n .. warning::\n\n If a parametrization depends on several inputs, :func:`~register_parametrization`\n will register a number of new parameters. If such parametrization is registered\n after the optimizer is created, these new parameters will need to be added manually\n to the optimizer. See :meth:`torch.Optimizer.add_param_group`.\n\n Args:\n module (nn.Module): module on which to register the parametrization\n tensor_name (str): name of the parameter or buffer on which to register\n the parametrization\n parametrization (nn.Module): the parametrization to register\n Keyword args:\n unsafe (bool): a boolean flag that denotes whether the parametrization\n may change the dtype and shape of the tensor. Default: `False`\n Warning: the parametrization is not checked for consistency upon registration.\n Enable this flag at your own risk.\n\n Raises:\n ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name`\n\n Examples:\n >>> import torch\n >>> import torch.nn as nn\n >>> import torch.nn.utils.parametrize as P\n >>>\n >>> class Symmetric(nn.Module):\n >>> def forward(self, X):\n >>> return X.triu() + X.triu(1).T # Return a symmetric matrix\n >>>\n >>> def right_inverse(self, A):\n >>> return A.triu()\n >>>\n >>> m = nn.Linear(5, 5)\n >>> P.register_parametrization(m, "weight", Symmetric())\n >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric\n True\n >>> A = torch.rand(5, 5)\n >>> A = A + A.T # A is now symmetric\n >>> m.weight = A # Initialize the weight to be the symmetric matrix A\n >>> print(torch.allclose(m.weight, A))\n True\n\n >>> class RankOne(nn.Module):\n >>> def forward(self, x, y):\n >>> # Form a rank 1 matrix multiplying two vectors\n >>> return x.unsqueeze(-1) @ y.unsqueeze(-2)\n >>>\n >>> def right_inverse(self, Z):\n >>> # Project Z onto the rank 1 matrices\n >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False)\n >>> # Return rescaled singular vectors\n >>> s0_sqrt = S[0].sqrt().unsqueeze(-1)\n >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt\n >>>\n >>> linear_rank_one = P.register_parametrization(nn.Linear(4, 4), "weight", RankOne())\n >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item())\n 1\n\n '
parametrization.train(module.training)
if is_parametrized(module, tensor_name):
if (not unsafe):
Y = getattr(module, tensor_name)
X = parametrization(Y)
if (not isinstance(X, Tensor)):
raise ValueError(f'A parametrization must return a tensor. Got {type(X).__name__}.')
if (X.dtype != Y.dtype):
raise ValueError(f'Registering a parametrization may not change the dtype of the tensor, unless the `unsafe` flag is enabled.
module.{tensor_name}.dtype: {Y.dtype}
parametrization(module.{tensor_name}).dtype: {X.dtype}')
if (X.shape != Y.shape):
raise ValueError(f'Registering a parametrization may not change the shape of the tensor, unless the `unsafe` flag is enabled.
module.{tensor_name}.shape: {Y.shape}
parametrization(module.{tensor_name}).shape: {X.shape}')
if hasattr(parametrization, 'right_inverse'):
try:
Z = parametrization.right_inverse(X)
except NotImplementedError:
pass
else:
if (not isinstance(Z, Tensor)):
raise ValueError(f'parametrization.right_inverse must return a tensor. Got: {type(Z).__name__}')
if (Z.dtype != Y.dtype):
raise ValueError(f'The tensor returned by parametrization.right_inverse must have the same dtype as module.{tensor_name}, unless the `unsafe` flag is enabled.
module.{tensor_name}.dtype: {Y.dtype}
returned dtype: {Z.dtype}')
if (Z.shape != Y.shape):
raise ValueError(f'The tensor returned by parametrization.right_inverse must have the same shape as module.{tensor_name}, unless the `unsafe` flag is enabled.
module.{tensor_name}.shape: {Y.shape}
returned shape: {Z.shape}')
assert isinstance(module.parametrizations, ModuleDict)
module.parametrizations[tensor_name].append(parametrization)
module.parametrizations[tensor_name].unsafe |= unsafe
elif ((tensor_name in module._buffers) or (tensor_name in module._parameters)):
original = getattr(module, tensor_name)
parametrizations = ParametrizationList([parametrization], original, unsafe=unsafe)
delattr(module, tensor_name)
if (not is_parametrized(module)):
_inject_new_class(module)
module.parametrizations = ModuleDict()
_inject_property(module, tensor_name)
assert isinstance(module.parametrizations, ModuleDict)
module.parametrizations[tensor_name] = parametrizations
else:
raise ValueError(f"Module '{module}' does not have a parameter, a buffer, or a parametrized element with name '{tensor_name}'")
return module | def register_parametrization(module: Module, tensor_name: str, parametrization: Module, *, unsafe: bool=False) -> Module:
'Adds a parametrization to a tensor in a module.\n\n Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``,\n the module will return the parametrized version ``parametrization(module.weight)``.\n If the original tensor requires a gradient, the backward pass will differentiate\n through :attr:`parametrization`, and the optimizer will update the tensor accordingly.\n\n The first time that a module registers a parametrization, this function will add an attribute\n ``parametrizations`` to the module of type :class:`~ParametrizationList`.\n\n The list of parametrizations on the tensor ``weight`` will be accessible under\n ``module.parametrizations.weight``.\n\n The original tensor will be accessible under\n ``module.parametrizations.weight.original``.\n\n Parametrizations may be concatenated by registering several parametrizations\n on the same attribute.\n\n The training mode of a registered parametrization is updated on registration\n to match the training mode of the host module\n\n Parametrized parameters and buffers have an inbuilt caching system that can be activated\n using the context manager :func:`cached`.\n\n A :attr:`parametrization` may optionally implement a method with signature\n\n .. code-block:: python\n\n def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]]\n\n This method is called on the unparametrized tensor when the first parametrization\n is registered to compute the initial value of the original tensor.\n If this method is not implemented, the original tensor will be just the unparametrized tensor.\n\n If all the parametrizations registered on a tensor implement `right_inverse` it is possible\n to initialize a parametrized tensor by assigning to it, as shown in the example below.\n\n It is possible for the first parametrization to depend on several inputs.\n This may be implemented returning a tuple of tensors from ``right_inverse``\n (see the example implementation of a ``RankOne`` parametrization below).\n\n In this case, the unconstrained tensors are also located under ``module.parametrizations.weight``\n with names ``original0``, ``original1``,...\n\n .. note::\n\n If unsafe=False (default) both the forward and right_inverse methods will be called\n once to perform a number of consistency checks.\n If unsafe=True, then right_inverse will be called if the tensor is not parametrized,\n and nothing will be called otherwise.\n\n .. note::\n\n In most situations, ``right_inverse`` will be a function such that\n ``forward(right_inverse(X)) == X`` (see\n `right inverse <https://en.wikipedia.org/wiki/Inverse_function#Right_inverses>`_).\n Sometimes, when the parametrization is not surjective, it may be reasonable\n to relax this.\n\n .. warning::\n\n If a parametrization depends on several inputs, :func:`~register_parametrization`\n will register a number of new parameters. If such parametrization is registered\n after the optimizer is created, these new parameters will need to be added manually\n to the optimizer. See :meth:`torch.Optimizer.add_param_group`.\n\n Args:\n module (nn.Module): module on which to register the parametrization\n tensor_name (str): name of the parameter or buffer on which to register\n the parametrization\n parametrization (nn.Module): the parametrization to register\n Keyword args:\n unsafe (bool): a boolean flag that denotes whether the parametrization\n may change the dtype and shape of the tensor. Default: `False`\n Warning: the parametrization is not checked for consistency upon registration.\n Enable this flag at your own risk.\n\n Raises:\n ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name`\n\n Examples:\n >>> import torch\n >>> import torch.nn as nn\n >>> import torch.nn.utils.parametrize as P\n >>>\n >>> class Symmetric(nn.Module):\n >>> def forward(self, X):\n >>> return X.triu() + X.triu(1).T # Return a symmetric matrix\n >>>\n >>> def right_inverse(self, A):\n >>> return A.triu()\n >>>\n >>> m = nn.Linear(5, 5)\n >>> P.register_parametrization(m, "weight", Symmetric())\n >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric\n True\n >>> A = torch.rand(5, 5)\n >>> A = A + A.T # A is now symmetric\n >>> m.weight = A # Initialize the weight to be the symmetric matrix A\n >>> print(torch.allclose(m.weight, A))\n True\n\n >>> class RankOne(nn.Module):\n >>> def forward(self, x, y):\n >>> # Form a rank 1 matrix multiplying two vectors\n >>> return x.unsqueeze(-1) @ y.unsqueeze(-2)\n >>>\n >>> def right_inverse(self, Z):\n >>> # Project Z onto the rank 1 matrices\n >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False)\n >>> # Return rescaled singular vectors\n >>> s0_sqrt = S[0].sqrt().unsqueeze(-1)\n >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt\n >>>\n >>> linear_rank_one = P.register_parametrization(nn.Linear(4, 4), "weight", RankOne())\n >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item())\n 1\n\n '
parametrization.train(module.training)
if is_parametrized(module, tensor_name):
if (not unsafe):
Y = getattr(module, tensor_name)
X = parametrization(Y)
if (not isinstance(X, Tensor)):
raise ValueError(f'A parametrization must return a tensor. Got {type(X).__name__}.')
if (X.dtype != Y.dtype):
raise ValueError(f'Registering a parametrization may not change the dtype of the tensor, unless the `unsafe` flag is enabled.
module.{tensor_name}.dtype: {Y.dtype}
parametrization(module.{tensor_name}).dtype: {X.dtype}')
if (X.shape != Y.shape):
raise ValueError(f'Registering a parametrization may not change the shape of the tensor, unless the `unsafe` flag is enabled.
module.{tensor_name}.shape: {Y.shape}
parametrization(module.{tensor_name}).shape: {X.shape}')
if hasattr(parametrization, 'right_inverse'):
try:
Z = parametrization.right_inverse(X)
except NotImplementedError:
pass
else:
if (not isinstance(Z, Tensor)):
raise ValueError(f'parametrization.right_inverse must return a tensor. Got: {type(Z).__name__}')
if (Z.dtype != Y.dtype):
raise ValueError(f'The tensor returned by parametrization.right_inverse must have the same dtype as module.{tensor_name}, unless the `unsafe` flag is enabled.
module.{tensor_name}.dtype: {Y.dtype}
returned dtype: {Z.dtype}')
if (Z.shape != Y.shape):
raise ValueError(f'The tensor returned by parametrization.right_inverse must have the same shape as module.{tensor_name}, unless the `unsafe` flag is enabled.
module.{tensor_name}.shape: {Y.shape}
returned shape: {Z.shape}')
assert isinstance(module.parametrizations, ModuleDict)
module.parametrizations[tensor_name].append(parametrization)
module.parametrizations[tensor_name].unsafe |= unsafe
elif ((tensor_name in module._buffers) or (tensor_name in module._parameters)):
original = getattr(module, tensor_name)
parametrizations = ParametrizationList([parametrization], original, unsafe=unsafe)
delattr(module, tensor_name)
if (not is_parametrized(module)):
_inject_new_class(module)
module.parametrizations = ModuleDict()
_inject_property(module, tensor_name)
assert isinstance(module.parametrizations, ModuleDict)
module.parametrizations[tensor_name] = parametrizations
else:
raise ValueError(f"Module '{module}' does not have a parameter, a buffer, or a parametrized element with name '{tensor_name}'")
return module<|docstring|>Adds a parametrization to a tensor in a module.
Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``,
the module will return the parametrized version ``parametrization(module.weight)``.
If the original tensor requires a gradient, the backward pass will differentiate
through :attr:`parametrization`, and the optimizer will update the tensor accordingly.
The first time that a module registers a parametrization, this function will add an attribute
``parametrizations`` to the module of type :class:`~ParametrizationList`.
The list of parametrizations on the tensor ``weight`` will be accessible under
``module.parametrizations.weight``.
The original tensor will be accessible under
``module.parametrizations.weight.original``.
Parametrizations may be concatenated by registering several parametrizations
on the same attribute.
The training mode of a registered parametrization is updated on registration
to match the training mode of the host module
Parametrized parameters and buffers have an inbuilt caching system that can be activated
using the context manager :func:`cached`.
A :attr:`parametrization` may optionally implement a method with signature
.. code-block:: python
def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]]
This method is called on the unparametrized tensor when the first parametrization
is registered to compute the initial value of the original tensor.
If this method is not implemented, the original tensor will be just the unparametrized tensor.
If all the parametrizations registered on a tensor implement `right_inverse` it is possible
to initialize a parametrized tensor by assigning to it, as shown in the example below.
It is possible for the first parametrization to depend on several inputs.
This may be implemented returning a tuple of tensors from ``right_inverse``
(see the example implementation of a ``RankOne`` parametrization below).
In this case, the unconstrained tensors are also located under ``module.parametrizations.weight``
with names ``original0``, ``original1``,...
.. note::
If unsafe=False (default) both the forward and right_inverse methods will be called
once to perform a number of consistency checks.
If unsafe=True, then right_inverse will be called if the tensor is not parametrized,
and nothing will be called otherwise.
.. note::
In most situations, ``right_inverse`` will be a function such that
``forward(right_inverse(X)) == X`` (see
`right inverse <https://en.wikipedia.org/wiki/Inverse_function#Right_inverses>`_).
Sometimes, when the parametrization is not surjective, it may be reasonable
to relax this.
.. warning::
If a parametrization depends on several inputs, :func:`~register_parametrization`
will register a number of new parameters. If such parametrization is registered
after the optimizer is created, these new parameters will need to be added manually
to the optimizer. See :meth:`torch.Optimizer.add_param_group`.
Args:
module (nn.Module): module on which to register the parametrization
tensor_name (str): name of the parameter or buffer on which to register
the parametrization
parametrization (nn.Module): the parametrization to register
Keyword args:
unsafe (bool): a boolean flag that denotes whether the parametrization
may change the dtype and shape of the tensor. Default: `False`
Warning: the parametrization is not checked for consistency upon registration.
Enable this flag at your own risk.
Raises:
ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name`
Examples:
>>> import torch
>>> import torch.nn as nn
>>> import torch.nn.utils.parametrize as P
>>>
>>> class Symmetric(nn.Module):
>>> def forward(self, X):
>>> return X.triu() + X.triu(1).T # Return a symmetric matrix
>>>
>>> def right_inverse(self, A):
>>> return A.triu()
>>>
>>> m = nn.Linear(5, 5)
>>> P.register_parametrization(m, "weight", Symmetric())
>>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric
True
>>> A = torch.rand(5, 5)
>>> A = A + A.T # A is now symmetric
>>> m.weight = A # Initialize the weight to be the symmetric matrix A
>>> print(torch.allclose(m.weight, A))
True
>>> class RankOne(nn.Module):
>>> def forward(self, x, y):
>>> # Form a rank 1 matrix multiplying two vectors
>>> return x.unsqueeze(-1) @ y.unsqueeze(-2)
>>>
>>> def right_inverse(self, Z):
>>> # Project Z onto the rank 1 matrices
>>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False)
>>> # Return rescaled singular vectors
>>> s0_sqrt = S[0].sqrt().unsqueeze(-1)
>>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt
>>>
>>> linear_rank_one = P.register_parametrization(nn.Linear(4, 4), "weight", RankOne())
>>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item())
1<|endoftext|> |
fdff9622be9221f938618f7dacb4da17e2852b1e5de82d45d404999a1732ae04 | def is_parametrized(module: Module, tensor_name: Optional[str]=None) -> bool:
'Returns ``True`` if module has an active parametrization.\n\n If the argument :attr:`tensor_name` is specified, returns ``True`` if\n ``module[tensor_name]`` is parametrized.\n\n Args:\n module (nn.Module): module to query\n name (str, optional): attribute in the module to query\n Default: ``None``\n '
parametrizations = getattr(module, 'parametrizations', None)
if ((parametrizations is None) or (not isinstance(parametrizations, ModuleDict))):
return False
if (tensor_name is None):
return (len(parametrizations) > 0)
else:
return (tensor_name in parametrizations) | Returns ``True`` if module has an active parametrization.
If the argument :attr:`tensor_name` is specified, returns ``True`` if
``module[tensor_name]`` is parametrized.
Args:
module (nn.Module): module to query
name (str, optional): attribute in the module to query
Default: ``None`` | torch/nn/utils/parametrize.py | is_parametrized | joker-eph/pytorch | 60,067 | python | def is_parametrized(module: Module, tensor_name: Optional[str]=None) -> bool:
'Returns ``True`` if module has an active parametrization.\n\n If the argument :attr:`tensor_name` is specified, returns ``True`` if\n ``module[tensor_name]`` is parametrized.\n\n Args:\n module (nn.Module): module to query\n name (str, optional): attribute in the module to query\n Default: ``None``\n '
parametrizations = getattr(module, 'parametrizations', None)
if ((parametrizations is None) or (not isinstance(parametrizations, ModuleDict))):
return False
if (tensor_name is None):
return (len(parametrizations) > 0)
else:
return (tensor_name in parametrizations) | def is_parametrized(module: Module, tensor_name: Optional[str]=None) -> bool:
'Returns ``True`` if module has an active parametrization.\n\n If the argument :attr:`tensor_name` is specified, returns ``True`` if\n ``module[tensor_name]`` is parametrized.\n\n Args:\n module (nn.Module): module to query\n name (str, optional): attribute in the module to query\n Default: ``None``\n '
parametrizations = getattr(module, 'parametrizations', None)
if ((parametrizations is None) or (not isinstance(parametrizations, ModuleDict))):
return False
if (tensor_name is None):
return (len(parametrizations) > 0)
else:
return (tensor_name in parametrizations)<|docstring|>Returns ``True`` if module has an active parametrization.
If the argument :attr:`tensor_name` is specified, returns ``True`` if
``module[tensor_name]`` is parametrized.
Args:
module (nn.Module): module to query
name (str, optional): attribute in the module to query
Default: ``None``<|endoftext|> |
f3db34c7fb586e0a221851f3482b1ac1681999571a7be33f4965650c421d59eb | def remove_parametrizations(module: Module, tensor_name: str, leave_parametrized: bool=True) -> Module:
'Removes the parametrizations on a tensor in a module.\n\n - If ``leave_parametrized=True``, ``module[tensor_name]`` will be set to\n its current output. In this case, the parametrization shall not change the ``dtype``\n of the tensor.\n - If ``leave_parametrized=False``, ``module[tensor_name]`` will be set to\n the unparametrised tensor in ``module.parametrizations[tensor_name].original``.\n This is only possible when the parametrization depends on just one tensor.\n\n Args:\n module (nn.Module): module from which remove the parametrization\n tensor_name (str): name of the parametrization to be removed\n leave_parametrized (bool, optional): leave the attribute :attr:`tensor_name` parametrized.\n Default: ``True``\n\n Returns:\n Module: module\n\n Raises:\n ValueError: if ``module[tensor_name]`` is not parametrized\n ValueError: if ``leave_parametrized=False`` and the parametrization depends on several tensors\n '
if (not is_parametrized(module, tensor_name)):
raise ValueError(f'Module {module} does not have a parametrization on {tensor_name}')
assert isinstance(module.parametrizations, ModuleDict)
parametrizations = module.parametrizations[tensor_name]
if parametrizations.is_tensor:
original = parametrizations.original
if leave_parametrized:
with torch.no_grad():
t = getattr(module, tensor_name)
with torch.no_grad():
original.set_(t)
elif leave_parametrized:
t = getattr(module, tensor_name)
original = (Parameter(t) if t.requires_grad else t)
else:
raise ValueError('Cannot leave unparametrized (`leave_parametrized=False`) a tensor that is parametrized in terms of a sequence of tensors.')
delattr(module.__class__, tensor_name)
del module.parametrizations[tensor_name]
_register_parameter_or_buffer(module, tensor_name, original)
if (not is_parametrized(module)):
delattr(module, 'parametrizations')
orig_cls = module.__class__.__bases__[0]
module.__class__ = orig_cls
return module | Removes the parametrizations on a tensor in a module.
- If ``leave_parametrized=True``, ``module[tensor_name]`` will be set to
its current output. In this case, the parametrization shall not change the ``dtype``
of the tensor.
- If ``leave_parametrized=False``, ``module[tensor_name]`` will be set to
the unparametrised tensor in ``module.parametrizations[tensor_name].original``.
This is only possible when the parametrization depends on just one tensor.
Args:
module (nn.Module): module from which remove the parametrization
tensor_name (str): name of the parametrization to be removed
leave_parametrized (bool, optional): leave the attribute :attr:`tensor_name` parametrized.
Default: ``True``
Returns:
Module: module
Raises:
ValueError: if ``module[tensor_name]`` is not parametrized
ValueError: if ``leave_parametrized=False`` and the parametrization depends on several tensors | torch/nn/utils/parametrize.py | remove_parametrizations | joker-eph/pytorch | 60,067 | python | def remove_parametrizations(module: Module, tensor_name: str, leave_parametrized: bool=True) -> Module:
'Removes the parametrizations on a tensor in a module.\n\n - If ``leave_parametrized=True``, ``module[tensor_name]`` will be set to\n its current output. In this case, the parametrization shall not change the ``dtype``\n of the tensor.\n - If ``leave_parametrized=False``, ``module[tensor_name]`` will be set to\n the unparametrised tensor in ``module.parametrizations[tensor_name].original``.\n This is only possible when the parametrization depends on just one tensor.\n\n Args:\n module (nn.Module): module from which remove the parametrization\n tensor_name (str): name of the parametrization to be removed\n leave_parametrized (bool, optional): leave the attribute :attr:`tensor_name` parametrized.\n Default: ``True``\n\n Returns:\n Module: module\n\n Raises:\n ValueError: if ``module[tensor_name]`` is not parametrized\n ValueError: if ``leave_parametrized=False`` and the parametrization depends on several tensors\n '
if (not is_parametrized(module, tensor_name)):
raise ValueError(f'Module {module} does not have a parametrization on {tensor_name}')
assert isinstance(module.parametrizations, ModuleDict)
parametrizations = module.parametrizations[tensor_name]
if parametrizations.is_tensor:
original = parametrizations.original
if leave_parametrized:
with torch.no_grad():
t = getattr(module, tensor_name)
with torch.no_grad():
original.set_(t)
elif leave_parametrized:
t = getattr(module, tensor_name)
original = (Parameter(t) if t.requires_grad else t)
else:
raise ValueError('Cannot leave unparametrized (`leave_parametrized=False`) a tensor that is parametrized in terms of a sequence of tensors.')
delattr(module.__class__, tensor_name)
del module.parametrizations[tensor_name]
_register_parameter_or_buffer(module, tensor_name, original)
if (not is_parametrized(module)):
delattr(module, 'parametrizations')
orig_cls = module.__class__.__bases__[0]
module.__class__ = orig_cls
return module | def remove_parametrizations(module: Module, tensor_name: str, leave_parametrized: bool=True) -> Module:
'Removes the parametrizations on a tensor in a module.\n\n - If ``leave_parametrized=True``, ``module[tensor_name]`` will be set to\n its current output. In this case, the parametrization shall not change the ``dtype``\n of the tensor.\n - If ``leave_parametrized=False``, ``module[tensor_name]`` will be set to\n the unparametrised tensor in ``module.parametrizations[tensor_name].original``.\n This is only possible when the parametrization depends on just one tensor.\n\n Args:\n module (nn.Module): module from which remove the parametrization\n tensor_name (str): name of the parametrization to be removed\n leave_parametrized (bool, optional): leave the attribute :attr:`tensor_name` parametrized.\n Default: ``True``\n\n Returns:\n Module: module\n\n Raises:\n ValueError: if ``module[tensor_name]`` is not parametrized\n ValueError: if ``leave_parametrized=False`` and the parametrization depends on several tensors\n '
if (not is_parametrized(module, tensor_name)):
raise ValueError(f'Module {module} does not have a parametrization on {tensor_name}')
assert isinstance(module.parametrizations, ModuleDict)
parametrizations = module.parametrizations[tensor_name]
if parametrizations.is_tensor:
original = parametrizations.original
if leave_parametrized:
with torch.no_grad():
t = getattr(module, tensor_name)
with torch.no_grad():
original.set_(t)
elif leave_parametrized:
t = getattr(module, tensor_name)
original = (Parameter(t) if t.requires_grad else t)
else:
raise ValueError('Cannot leave unparametrized (`leave_parametrized=False`) a tensor that is parametrized in terms of a sequence of tensors.')
delattr(module.__class__, tensor_name)
del module.parametrizations[tensor_name]
_register_parameter_or_buffer(module, tensor_name, original)
if (not is_parametrized(module)):
delattr(module, 'parametrizations')
orig_cls = module.__class__.__bases__[0]
module.__class__ = orig_cls
return module<|docstring|>Removes the parametrizations on a tensor in a module.
- If ``leave_parametrized=True``, ``module[tensor_name]`` will be set to
its current output. In this case, the parametrization shall not change the ``dtype``
of the tensor.
- If ``leave_parametrized=False``, ``module[tensor_name]`` will be set to
the unparametrised tensor in ``module.parametrizations[tensor_name].original``.
This is only possible when the parametrization depends on just one tensor.
Args:
module (nn.Module): module from which remove the parametrization
tensor_name (str): name of the parametrization to be removed
leave_parametrized (bool, optional): leave the attribute :attr:`tensor_name` parametrized.
Default: ``True``
Returns:
Module: module
Raises:
ValueError: if ``module[tensor_name]`` is not parametrized
ValueError: if ``leave_parametrized=False`` and the parametrization depends on several tensors<|endoftext|> |
e515300f7b1b9bddba890a1fb03018a733bc99bcc44669318b0156b6aada77ea | def right_inverse(self, value: Tensor) -> None:
'Calls the methods ``right_inverse`` (see :func:`register_parametrization`)\n of the parametrizations in the inverse order they were registered in.\n Then, it stores the result in ``self.original`` if ``right_inverse`` outputs one tensor\n or in ``self.original0``, ``self.original1``, ... if it outputs several.\n\n Args:\n value (Tensor): Value to which initialize the module\n '
with torch.no_grad():
for module in reversed(self):
if hasattr(module, 'right_inverse'):
value = module.right_inverse(value)
else:
raise RuntimeError(f'parametrization {type(module).__name__} does not implement right_inverse.')
if self.is_tensor:
if (not isinstance(value, Tensor)):
raise ValueError(f'`right_inverse` should return a tensor. Got {type(value).__name__}')
if (value.dtype != self.original.dtype):
raise ValueError(f'The tensor returned by `right_inverse` has dtype {value.dtype} while `original` has dtype {self.original.dtype}')
self.original.set_(value)
else:
if (not isinstance(value, collections.abc.Sequence)):
raise ValueError(f"'right_inverse' must return a sequence of tensors. Got {type(value).__name__}.")
if (len(value) != self.ntensors):
raise ValueError(f"'right_inverse' must return a sequence of tensors of length {self.ntensors}. Got a sequence of lenght {len(value)}.")
for (i, tensor) in enumerate(value):
original_i = getattr(self, f'original{i}')
if (not isinstance(tensor, Tensor)):
raise ValueError(f'`right_inverse` must return a sequence of tensors. Got element {i} of type {type(tensor).__name__}')
if (original_i.dtype != tensor.dtype):
raise ValueError(f'Tensor {i} returned by `right_inverse` has dtype {tensor.dtype} while `original{i}` has dtype {original_i.dtype}')
original_i.set_(tensor) | Calls the methods ``right_inverse`` (see :func:`register_parametrization`)
of the parametrizations in the inverse order they were registered in.
Then, it stores the result in ``self.original`` if ``right_inverse`` outputs one tensor
or in ``self.original0``, ``self.original1``, ... if it outputs several.
Args:
value (Tensor): Value to which initialize the module | torch/nn/utils/parametrize.py | right_inverse | joker-eph/pytorch | 60,067 | python | def right_inverse(self, value: Tensor) -> None:
'Calls the methods ``right_inverse`` (see :func:`register_parametrization`)\n of the parametrizations in the inverse order they were registered in.\n Then, it stores the result in ``self.original`` if ``right_inverse`` outputs one tensor\n or in ``self.original0``, ``self.original1``, ... if it outputs several.\n\n Args:\n value (Tensor): Value to which initialize the module\n '
with torch.no_grad():
for module in reversed(self):
if hasattr(module, 'right_inverse'):
value = module.right_inverse(value)
else:
raise RuntimeError(f'parametrization {type(module).__name__} does not implement right_inverse.')
if self.is_tensor:
if (not isinstance(value, Tensor)):
raise ValueError(f'`right_inverse` should return a tensor. Got {type(value).__name__}')
if (value.dtype != self.original.dtype):
raise ValueError(f'The tensor returned by `right_inverse` has dtype {value.dtype} while `original` has dtype {self.original.dtype}')
self.original.set_(value)
else:
if (not isinstance(value, collections.abc.Sequence)):
raise ValueError(f"'right_inverse' must return a sequence of tensors. Got {type(value).__name__}.")
if (len(value) != self.ntensors):
raise ValueError(f"'right_inverse' must return a sequence of tensors of length {self.ntensors}. Got a sequence of lenght {len(value)}.")
for (i, tensor) in enumerate(value):
original_i = getattr(self, f'original{i}')
if (not isinstance(tensor, Tensor)):
raise ValueError(f'`right_inverse` must return a sequence of tensors. Got element {i} of type {type(tensor).__name__}')
if (original_i.dtype != tensor.dtype):
raise ValueError(f'Tensor {i} returned by `right_inverse` has dtype {tensor.dtype} while `original{i}` has dtype {original_i.dtype}')
original_i.set_(tensor) | def right_inverse(self, value: Tensor) -> None:
'Calls the methods ``right_inverse`` (see :func:`register_parametrization`)\n of the parametrizations in the inverse order they were registered in.\n Then, it stores the result in ``self.original`` if ``right_inverse`` outputs one tensor\n or in ``self.original0``, ``self.original1``, ... if it outputs several.\n\n Args:\n value (Tensor): Value to which initialize the module\n '
with torch.no_grad():
for module in reversed(self):
if hasattr(module, 'right_inverse'):
value = module.right_inverse(value)
else:
raise RuntimeError(f'parametrization {type(module).__name__} does not implement right_inverse.')
if self.is_tensor:
if (not isinstance(value, Tensor)):
raise ValueError(f'`right_inverse` should return a tensor. Got {type(value).__name__}')
if (value.dtype != self.original.dtype):
raise ValueError(f'The tensor returned by `right_inverse` has dtype {value.dtype} while `original` has dtype {self.original.dtype}')
self.original.set_(value)
else:
if (not isinstance(value, collections.abc.Sequence)):
raise ValueError(f"'right_inverse' must return a sequence of tensors. Got {type(value).__name__}.")
if (len(value) != self.ntensors):
raise ValueError(f"'right_inverse' must return a sequence of tensors of length {self.ntensors}. Got a sequence of lenght {len(value)}.")
for (i, tensor) in enumerate(value):
original_i = getattr(self, f'original{i}')
if (not isinstance(tensor, Tensor)):
raise ValueError(f'`right_inverse` must return a sequence of tensors. Got element {i} of type {type(tensor).__name__}')
if (original_i.dtype != tensor.dtype):
raise ValueError(f'Tensor {i} returned by `right_inverse` has dtype {tensor.dtype} while `original{i}` has dtype {original_i.dtype}')
original_i.set_(tensor)<|docstring|>Calls the methods ``right_inverse`` (see :func:`register_parametrization`)
of the parametrizations in the inverse order they were registered in.
Then, it stores the result in ``self.original`` if ``right_inverse`` outputs one tensor
or in ``self.original0``, ``self.original1``, ... if it outputs several.
Args:
value (Tensor): Value to which initialize the module<|endoftext|> |
00eb85dc9797ea90cb99e73ee1fdd2764d9236bcfbdf9d2d30e553d55e40bcfc | def hasPathSum(self, root, sum):
'\n :type root: TreeNode\n :type sum: int\n :rtype: bool\n '
if (not root):
return False
elif ((not root.left) and (not root.right)):
return (root.val == sum)
return (self.hasPathSum(root.left, (sum - root.val)) or self.hasPathSum(root.right, (sum - root.val))) | :type root: TreeNode
:type sum: int
:rtype: bool | 100-199/112-path-sum.py | hasPathSum | bcongdon/leetcode | 4 | python | def hasPathSum(self, root, sum):
'\n :type root: TreeNode\n :type sum: int\n :rtype: bool\n '
if (not root):
return False
elif ((not root.left) and (not root.right)):
return (root.val == sum)
return (self.hasPathSum(root.left, (sum - root.val)) or self.hasPathSum(root.right, (sum - root.val))) | def hasPathSum(self, root, sum):
'\n :type root: TreeNode\n :type sum: int\n :rtype: bool\n '
if (not root):
return False
elif ((not root.left) and (not root.right)):
return (root.val == sum)
return (self.hasPathSum(root.left, (sum - root.val)) or self.hasPathSum(root.right, (sum - root.val)))<|docstring|>:type root: TreeNode
:type sum: int
:rtype: bool<|endoftext|> |
4481db6634b935e149ff85b9c99161ee2ee2dd4bcc225d38d045c25bcd21afc0 | def mock_add_paths(self, path):
'Adds a path and all of its parents to the set of existing paths.'
self._mock_path_exists.add(path) | Adds a path and all of its parents to the set of existing paths. | recipe_modules/path/api.py | mock_add_paths | Acidburn0zzz/luci | 1 | python | def mock_add_paths(self, path):
self._mock_path_exists.add(path) | def mock_add_paths(self, path):
self._mock_path_exists.add(path)<|docstring|>Adds a path and all of its parents to the set of existing paths.<|endoftext|> |
78411f81cb3c1e220f3af2471d59daf45ff1ccca09cbaa1baf361f97fa4c14b5 | def mock_copy_paths(self, source, dest):
'Duplicates a path and all of its children to another path.'
self._mock_path_exists.copy(source, dest) | Duplicates a path and all of its children to another path. | recipe_modules/path/api.py | mock_copy_paths | Acidburn0zzz/luci | 1 | python | def mock_copy_paths(self, source, dest):
self._mock_path_exists.copy(source, dest) | def mock_copy_paths(self, source, dest):
self._mock_path_exists.copy(source, dest)<|docstring|>Duplicates a path and all of its children to another path.<|endoftext|> |
36240cefcbafaa2c63c7963aa62c0dd062dcad4c696e9c002d7a3c49cac39200 | def mock_remove_paths(self, path, filt):
'Removes a path and all of its children from the set of existing paths.'
self._mock_path_exists.remove(path, filt) | Removes a path and all of its children from the set of existing paths. | recipe_modules/path/api.py | mock_remove_paths | Acidburn0zzz/luci | 1 | python | def mock_remove_paths(self, path, filt):
self._mock_path_exists.remove(path, filt) | def mock_remove_paths(self, path, filt):
self._mock_path_exists.remove(path, filt)<|docstring|>Removes a path and all of its children from the set of existing paths.<|endoftext|> |
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