paulo037/starcoder · Datasets at Hugging Face

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integration-testing/test/test_single_network_one.py	MicrohexHQ/CasperLabs	0	151763	import os import logging import pytest import json from pathlib import Path from pytest import fixture, raises from test.cl_node.contract_address import contract_address from test.cl_node.common import testing_root_path, HELLO_NAME_CONTRACT from test.cl_node.casperlabs_accounts import Account, GENESIS_ACCOUNT from test.cl_node.common import extract_block_hash_from_propose_output from test.cl_node.docker_node import DockerNode from test.cl_node.errors import NonZeroExitCodeError from test.cl_node.wait import wait_for_genesis_block from casperlabs_client import ABI from test.cl_node.cli import CLI, DockerCLI, CLIErrorExit """ Test account state retrieval with query-state. Example output of the Scala client: account { public_key: "3030303030303030303030303030303030303030303030303030303030303030" purse_id { uref: "0000000000000000000000000000000000000000000000000000000000000000" access_rights: READ_ADD_WRITE } associated_keys { public_key: "3030303030303030303030303030303030303030303030303030303030303030" weight: 1 } action_thresholds { deployment_threshold: 1 key_management_threshold: 1 } account_activity { key_management_last_used: 0 deployment_last_used: 0 inactivity_period_limit: 100 } } """ def deploy_and_propose_from_genesis(node, contract): return node.deploy_and_propose( session_contract=contract, payment_contract=contract, from_address=GENESIS_ACCOUNT.public_key_hex, public_key=GENESIS_ACCOUNT.public_key_path, private_key=GENESIS_ACCOUNT.private_key_path, ) def account_state(node, block_hash, account=GENESIS_ACCOUNT): return node.d_client.query_state( block_hash=block_hash, key_type="address", key=account.public_key_hex, path="" ) def test_account_state(one_node_network): node = one_node_network.docker_nodes[0] block_hash = deploy_and_propose_from_genesis(node, "test_counterdefine.wasm") deploys = node.client.show_deploys(block_hash) assert not deploys[0].is_error acct_state = account_state(node, block_hash) known_urefs = acct_state.account[0].known_urefs names = [uref.name for uref in known_urefs] assert "counter" in names block_hash = deploy_and_propose_from_genesis(node, "test_countercall.wasm") acct_state = account_state(node, block_hash) known_urefs = acct_state.account[0].known_urefs names = [uref.name for uref in known_urefs] assert "counter" in names def test_transfer_with_overdraft(one_node_network): acct1 = Account(1) acct2 = Account(2) node: DockerNode = one_node_network.docker_nodes[0] # Transfer 1000000 from genesis... to acct1... # For compatibility with EE with no execution cost # payment_contract="transfer_to_account.wasm" block_hash = node.transfer_to_account( to_account_id=1, amount=1000000, from_account_id="genesis", payment_contract="transfer_to_account.wasm", ) deploys = node.client.show_deploys(block_hash) assert not deploys[0].is_error, f"error_message: {deploys[0].error_message}" # Response not used, but assures account exist _ = account_state(node, block_hash, acct1) # Should error as account doesn't exist. with raises(Exception): _ = account_state(block_hash, acct2.public_key_hex) # No API currently exists for getting balance to check transfer. # Transfer 750000 from acct1... to acct2... block_hash = node.transfer_to_account( to_account_id=2, amount=750000, from_account_id=1, payment_contract="transfer_to_account.wasm", ) deploys = node.client.show_deploys(block_hash) assert not deploys[0].is_error, f"error_message: {deploys[0].error_message}" # Response not used, but assures account exist _ = account_state(node, block_hash, acct2) # Transfer 750000000000 from acct1... to acct2... # Should fail with acct1 overdrawn. Requires assert in contract to generate is_error. with raises(Exception): _ = node.transfer_to_account( to_account_id=2, amount=750000000000, from_account_id=1, payment_contract="transfer_to_account.wasm", ) def test_transfer_to_accounts(one_node_network): node: DockerNode = one_node_network.docker_nodes[0] # Perform multiple transfers with end result of Acct1 = 100, Acct2 = 100, Acct3 = 800 node.transfer_to_accounts([(1, 1000), (2, 900, 1), (3, 800, 2)]) with raises(Exception): # Acct 1 has not enough funds so it should fail node.transfer_to_account( to_account_id=4, amount=100000000000, from_account_id=1, payment_contract="transfer_to_account.wasm", ) node.transfer_to_account( to_account_id=4, amount=100, from_account_id=2, payment_contract="transfer_to_account.wasm", ) # TODO: Improve checks once balance is easy to read. def balance(node, account_address, block_hash): try: return node.client.get_balance(account_address, block_hash) except Exception: return 0 def test_scala_client_balance(one_node_network): node: DockerNode = one_node_network.docker_nodes[0] accounts = [Account(i) for i in range(1, 4)] block_hash = list(node.p_client.show_blocks(1))[0].summary.block_hash.hex() initial = [ balance(node, account.public_key_hex, block_hash) for account in accounts ] # Perform multiple transfers with end result of Acct1 = 200, Acct2 = 100, Acct3 = 700 hashes = node.transfer_to_accounts([(1, 1000), (2, 800, 1), (3, 700, 2)]) for acct_num, value in ((0, 200), (1, 100), (2, 700)): addr = accounts[acct_num].public_key_hex bal = node.d_client.get_balance(account_address=addr, block_hash=hashes[-1]) assert bal == initial[acct_num] + value ffi_test_contracts = [ ("getcallerdefine.wasm", "getcallercall.wasm"), ("listknownurefsdefine.wasm", "listknownurefscall.wasm"), ] def deploy_and_propose_expect_no_errors(node, contract): client = node.d_client block_hash = node.deploy_and_propose( session_contract=contract, payment_contract=contract, from_address=node.genesis_account.public_key_hex, public_key=node.genesis_account.public_key_path, private_key=node.genesis_account.private_key_path, ) r = client.show_deploys(block_hash)[0] assert r.is_error is False, f"error_message: {r.error_message}" @pytest.mark.parametrize("define_contract, call_contract", ffi_test_contracts) def test_get_caller(one_node_network, define_contract, call_contract): node = one_node_network.docker_nodes[0] deploy_and_propose_expect_no_errors(node, define_contract) deploy_and_propose_expect_no_errors(node, call_contract) @pytest.mark.parametrize("wasm", [HELLO_NAME_CONTRACT, "old_wasm/test_helloname.wasm"]) def test_multiple_propose(one_node_network, wasm): """ Feature file: propose.feature Scenario: Single node deploy and multiple propose generates an Exception. OP-182: First propose should be success, and subsequent propose calls should throw an error/exception. """ node = one_node_network.docker_nodes[0] deploy_and_propose_from_genesis(node, wasm) number_of_blocks = node.client.get_blocks_count(100) try: _ = node.client.propose() assert False, "Second propose must not succeed, should throw" except NonZeroExitCodeError as e: assert e.exit_code == 1, "Second propose should fail" wait_for_genesis_block(node) # Number of blocks after second propose should not change assert node.client.get_blocks_count(100) == number_of_blocks # Examples of query-state executed with the Scala client that result in errors: # CasperLabs/docker $ ./client.sh node-0 propose # Response: Success! Block 9d38836598... created and added. # CasperLabs/docker $ ./client.sh node-0 query-state --block-hash '"9d"' --key '"a91208047c"' --path file.xxx --type hash # NOT_FOUND: Cannot find block matching hash "9d" # CasperLabs/docker$ ./client.sh node-0 query-state --block-hash 9d --key '"a91208047c"' --path file.xxx --type hash # INVALID_ARGUMENT: Key of type hash must have exactly 32 bytes, 5 =/= 32 provided. # CasperLabs/docker$ ./client.sh node-0 query-state --block-hash 9d --key 3030303030303030303030303030303030303030303030303030303030303030 --path file.xxx --type hash # INVALID_ARGUMENT: Value not found: " Hash([48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48])" @pytest.fixture() # scope="module") def node(one_node_network): return one_node_network.docker_nodes[0] @pytest.fixture() # (scope="module") def client(node): return node.d_client @pytest.fixture() # (scope="module") def block_hash(node): return node.deploy_and_propose( session_contract="test_helloname.wasm", payment_contract="test_helloname.wasm" ) block_hash_queries = [ ( { "block_hash": "9d000000", "key": "a91208047c", "path": "file.xxx", "key_type": "hash", }, "NOT_FOUND: Cannot find block matching", ), ( {"key": "a91208047c", "path": "file.xxx", "key_type": "hash"}, "INVALID_ARGUMENT: Key of type hash must have exactly 32 bytes", ), ({"path": "file.xxx", "key_type": "hash"}, "INVALID_ARGUMENT: Value not found"), ] @pytest.mark.parametrize("query, expected", block_hash_queries) def test_query_state_error(node, client, block_hash, query, expected): if "block_hash" not in query: query["block_hash"] = block_hash if "key" not in query: query["key"] = GENESIS_ACCOUNT.public_key_hex with pytest.raises(NonZeroExitCodeError) as excinfo: _ = client.query_state(*query) assert expected in excinfo.value.output def test_revert_subcall(client, node): # This contract calls another contract that calls revert(2) block_hash = deploy_and_propose_from_genesis( node, "test_subcall_revert_define.wasm" ) r = client.show_deploys(block_hash)[0] assert not r.is_error assert r.error_message == "" deploy_hash = r.deploy.deploy_hash r = client.show_deploy(deploy_hash) assert r.deploy.deploy_hash == deploy_hash block_hash = deploy_and_propose_from_genesis(node, "test_subcall_revert_call.wasm") r = client.show_deploys(block_hash)[0] assert r.is_error assert r.error_message == "Exit code: 2" def test_revert_direct(client, node): # This contract calls revert(1) directly block_hash = deploy_and_propose_from_genesis(node, "test_direct_revert_call.wasm") r = client.show_deploys(block_hash)[0] assert r.is_error assert r.error_message == "Exit code: 1" def test_deploy_with_valid_signature(one_node_network): """ Feature file: deploy.feature Scenario: Deploy with valid signature """ node0 = one_node_network.docker_nodes[0] block_hash = deploy_and_propose_from_genesis(node0, "test_helloname.wasm") deploys = node0.client.show_deploys(block_hash) assert deploys[0].is_error is False def test_deploy_with_invalid_signature(one_node_network): """ Feature file: deploy.feature Scenario: Deploy with invalid signature """ node0 = one_node_network.docker_nodes[0] with pytest.raises(NonZeroExitCodeError): node0.client.deploy( from_address=GENESIS_ACCOUNT.public_key_hex, session_contract="test_helloname.wasm", payment_contract="test_helloname.wasm", private_key="validator-0-private-invalid.pem", public_key="validator-0-public-invalid.pem", ) """ Feature file: ~/CasperLabs/integration-testing/features/deploy.feature """ def deploy_and_propose(node, contract): node.client.deploy( session_contract=contract, payment_contract=contract, from_address=GENESIS_ACCOUNT.public_key_hex, public_key=GENESIS_ACCOUNT.public_key_path, private_key=GENESIS_ACCOUNT.private_key_path, ) return extract_block_hash_from_propose_output(node.client.propose()) def deploy(node, contract): message = node.client.deploy( from_address=GENESIS_ACCOUNT.public_key_hex, public_key=GENESIS_ACCOUNT.public_key_path, private_key=GENESIS_ACCOUNT.private_key_path, session_contract=contract, payment_contract=contract, ) assert "Success!" in message return message.split()[2] def propose(node): return extract_block_hash_from_propose_output(node.client.propose()) def deploy_hashes(node, block_hash): return set(d.deploy.deploy_hash for d in node.client.show_deploys(block_hash)) # Python Client (library) # fmt: off def resource(fn): cur_path = Path(os.path.realpath(__file__)).parent while cur_path.name != "integration-testing": cur_path = cur_path.parent return cur_path / "resources" / fn def test_args_parser(): account = ( b"\x00\x01\x00\x02\x00\x03\x00\x04\x00\x05\x00\x06\x00\x07\x00\x08" b"\x00\x00\x00\x01\x00\x02\x00\x03\x00\x04\x00\x05\x00\x06\x00\x07" ) amount = 123456 args = [{"name": "amount", "value": {"long_value": amount}}, {"name": "account", "value": {"bytes_value": account.hex()}}, {"name": "purse_id", "value": {"optional_value": {}}}, {"name": "number", "value": {"big_int": {"value": "2", "bit_width": 512}}}] json_str = json.dumps(args) assert ABI.args_from_json(json_str) == ABI.args( [ABI.long_value(amount), ABI.account(account), ABI.optional_value(None), ABI.big_int(2)] ) @fixture() def genesis_public_signing_key(): with GENESIS_ACCOUNT.public_key_binary_file() as f: yield f def test_deploy_with_args(one_node_network, genesis_public_signing_key): """ Deploys test contracts that do: revert(get_arg(0)); // for u32 and u512 and revert(sum(address_bytes[u8; 32]) + u32); for multiple argument test. Tests args get correctly encoded and decoded in the contract. Test expects the test contracts test_args_u32.wasm and test_args_u512.wasm to deserialize correctly their arguments and then call revert with value of the argument (converted to a Rust native int, as expected by revert). If the test contracts don't fail or if their exit code is different than expected, the test will fail. """ node = one_node_network.docker_nodes[0] client = node.p_client.client for wasm, encode in [ (resource("test_args_u32.wasm"), ABI.u32), (resource("test_args_u512.wasm"), ABI.u512), ]: for number in [1, 12, 256, 1024]: response, deploy_hash = client.deploy( payment=wasm, session=wasm, public_key=resource("accounts/account-public-genesis.pem"), private_key=resource("accounts/account-private-genesis.pem"), session_args=ABI.args([encode(number)]), ) logging.info( f"DEPLOY RESPONSE: {response} deploy_hash: {deploy_hash.hex()}" ) response = client.propose() # Need to convert to hex string from bytes block_hash = response.block_hash.hex() for deploy_info in client.showDeploys(block_hash): assert deploy_info.is_error is True assert deploy_info.error_message == f"Exit code: {number}" wasm = resource("test_args_multi.wasm") account_hex = "0101010102020202030303030404040405050505060606060707070708080808" number = 1000 total_sum = sum([1, 2, 3, 4, 5, 6, 7, 8]) 4 + number response, deploy_hash = client.deploy( payment=wasm, session=wasm, public_key=resource("accounts/account-public-genesis.pem"), private_key=resource("accounts/account-private-genesis.pem"), session_args=ABI.args( [ABI.account(bytes.fromhex(account_hex)), ABI.u32(number)] ), ) logging.info(f"DEPLOY RESPONSE: {response} deploy_hash: {deploy_hash.hex()}") response = client.propose() block_hash = response.block_hash.hex() for deploy_info in client.showDeploys(block_hash): assert deploy_info.is_error is True assert deploy_info.error_message == f"Exit code: {total_sum}" for blockInfo in client.showBlocks(10): assert blockInfo.status.stats.block_size_bytes > 0 # Python CLI # @pytest.fixture() # scope="module") def cli(one_node_network): return CLI(one_node_network.docker_nodes[0], "casperlabs_client") @pytest.fixture() # scope="module") def scala_cli(one_node_network): return DockerCLI(one_node_network.docker_nodes[0]) def test_cli_no_parameters(cli): with raises(CLIErrorExit) as ex_info: cli() assert "You must provide a command" in str(ex_info.value) def test_cli_help(cli): out = cli("--help") # deploy,propose,show-block,show-blocks,show-deploy,show-deploys,vdag,query-state assert "Casper" in out def test_cli_show_blocks_and_show_block(cli): blocks = cli("show-blocks", "--depth", "1") assert len(blocks) > 0 for block in blocks: block_hash = block.summary.block_hash assert len(block_hash) == 32 * 2 # hex b = cli("show-block", block_hash) assert block_hash == b.summary.block_hash def test_cli_show_block_not_found(cli): block_hash = "00" * 32 with raises(CLIErrorExit) as ex_info: cli("show-block", block_hash) # StatusCode.NOT_FOUND: Cannot find block matching hash # 0000000000000000000000000000000000000000000000000000000000000000 assert "NOT_FOUND" in str(ex_info.value) assert "Cannot find block matching hash" in str(ex_info.value) @pytest.mark.skip def test_cli_deploy_propose_show_deploys_show_deploy_query_state_and_balance(cli): resources_path = testing_root_path() / "resources" account = GENESIS_ACCOUNT deploy_hash = cli( "deploy", f"--from {account.public_key_hex}", f"--payment {str(resources_path / 'test_helloname.wasm')}", f"--session {str(resources_path / 'test_helloname.wasm')}", f"--private-key {str(account.private_key_path)}", f"--public-key {str(account.public_key_path)}", ) block_hash = cli("propose") deploys = cli("show-deploys", block_hash) deploy_hashes = [d.deploy.deploy_hash for d in deploys] assert deploy_hash in deploy_hashes deploy_info = cli("show-deploy", deploy_hash) assert deploy_info.deploy.deploy_hash == deploy_hash result = cli("query-state", "--block-hash", block_hash, "--type", "address", "--key", account.public_key_hex, "--path", "",) assert "hello_name" in [u.name for u in result.account.known_urefs] balance = int( cli("balance", "--address", account.public_key_hex, "--block-hash", block_hash) ) # TODO Need constant for where this 1000000000 is from. assert balance == 1000000000 # genesis # CLI ABI def int_value(x): return x def big_int_value(x): return {'value': str(x), 'bit_width': 512} abi_unsigned_test_data = [ ("int_value", 'test_args_u32.wasm', int_value), ("big_int", 'test_args_u512.wasm', big_int_value), ] @pytest.mark.parametrize("unsigned_type, test_contract, value", abi_unsigned_test_data) def test_cli_abi_unsigned_scala(node, unsigned_type, test_contract, value): check_cli_abi_unsigned(DockerCLI(node), lambda s: f"'{s}'", '/data', unsigned_type, value, test_contract, lambda a: (a.private_key_docker_path, a.public_key_docker_path)) @pytest.mark.parametrize("unsigned_type, test_contract, value", abi_unsigned_test_data) def test_cli_abi_unsigned_python(node, unsigned_type, test_contract, value): check_cli_abi_unsigned(CLI(node), lambda s: s, 'resources', unsigned_type, value, test_contract, lambda a: (a.private_key_path, a.public_key_path)) def check_cli_abi_unsigned(cli, quote, resource_directory, unsigned_type, value, test_contract, keys): account = GENESIS_ACCOUNT private_key, public_key = keys(account) for number in [2, 256, 1024]: test_contract_path = '/'.join((resource_directory, test_contract)) session_args = json.dumps([{"name": "number", "value": {unsigned_type: value(number)}}]) args = ('deploy', '--from', account.public_key_hex, '--session', test_contract_path, '--session-args', quote(session_args), '--payment', test_contract_path, '--private-key', private_key, '--public-key', public_key) logging.info(f"EXECUTING {' '.join(cli.expand_args(args))}") deploy_hash = cli(args) cli('propose') deploy_info = cli("show-deploy", deploy_hash) assert deploy_info.processing_results[0].is_error is True assert deploy_info.processing_results[0].error_message == f"Exit code: {number}" def test_cli_abi_multiple(cli): account = GENESIS_ACCOUNT test_contract = resource("test_args_multi.wasm") account_hex = "0101010102020202030303030404040405050505060606060707070708080808" number = 1000 total_sum = sum([1, 2, 3, 4, 5, 6, 7, 8]) 4 + number session_args = json.dumps([{'name': 'account', 'value': {'account': account_hex}}, {'name': 'number', 'value': {'int_value': number}}]) deploy_hash = cli('deploy', '--from', account.public_key_hex, '--session', resource(test_contract), '--session-args', session_args, '--payment', resource(test_contract), '--private-key', account.private_key_path, '--public-key', account.public_key_path,) cli('propose') deploy_info = cli("show-deploy", deploy_hash) assert deploy_info.processing_results[0].is_error is True assert deploy_info.processing_results[0].error_message == f"Exit code: {total_sum}" def test_cli_scala_help(scala_cli): output = scala_cli('--help') assert 'Subcommand: make-deploy' in output def test_cli_scala_extended_deploy(scala_cli): cli = scala_cli account = GENESIS_ACCOUNT # TODO: when paralelizing tests, make sure test don't collide # when trying to access the same file, perhaps map containers /tmp # to a unique hosts's directory. test_contract = "/data/test_helloname.wasm" cli('make-deploy', '-o', '/tmp/unsigned.deploy', '--from', account.public_key_hex, '--session', test_contract, '--payment', test_contract) cli('sign-deploy', '-i', '/tmp/unsigned.deploy', '-o', '/tmp/signed.deploy', '--private-key', account.private_key_docker_path, '--public-key', account.public_key_docker_path) deploy_hash = cli('send-deploy', '-i', '/tmp/signed.deploy') cli('propose') deploy_info = cli("show-deploy", deploy_hash) assert not deploy_info.processing_results[0].is_error try: os.remove('/tmp/unsigned.deploy') os.remove('/tmp/signed.deploy') except Exception as e: logging.warning(f"Could not delete temporary files: {str(e)}") def test_cli_scala_direct_call_by_hash_and_name(scala_cli): check_cli_direct_call_by_hash_and_name(scala_cli, scala_cli) def test_cli_python_direct_call_by_hash_and_name(cli, scala_cli): check_cli_direct_call_by_hash_and_name(cli, scala_cli) def check_cli_direct_call_by_hash_and_name(cli, scala_cli): # TODO: For now using scala_cli for assertions because for some # strange reason Python CLI doesn't show is_error and error_message # in the output of show-deploys. This has to be fixed asap. account = cli.node.test_account cli.set_default_deploy_args('--from', account.public_key_hex, '--private-key', cli.private_key_path(account), '--public-key', cli.public_key_path(account)) # First, deploy a contract that stores a function # and saves pointer to it under UREF "revert_test". # The stored function calls revert(2). test_contract = cli.resource("test_subcall_revert_define.wasm") first_deploy_hash = cli('deploy', '--session', test_contract, '--payment', test_contract) block_hash = cli("propose") logging.info(f"""EXECUTING {' '.join(scala_cli.expand_args(["show-deploys", block_hash]))}""") deploys = scala_cli("show-deploys", block_hash) assert len(list(deploys)) == 1 for deploy_info in deploys: assert deploy_info.deploy.deploy_hash == first_deploy_hash assert not deploy_info.is_error # Call by name deploy_hash = cli("deploy", '--session-name', "revert_test", '--payment-name', "revert_test") block_hash = cli("propose") deploys = scala_cli("show-deploys", block_hash) for deploy_info in deploys: assert deploy_info.deploy.deploy_hash == deploy_hash assert deploy_info.error_message == 'Exit code: 2' # Expected: contract called revert(2) # Call by function address revert_test_addr = contract_address(first_deploy_hash, 0).hex() # assume fn_store_id starts from 0 deploy_hash = cli("deploy", '--session-hash', revert_test_addr, '--payment-hash', revert_test_addr) block_hash = cli("propose") deploys = scala_cli("show-deploys", block_hash) for deploy_info in deploys: assert deploy_info.deploy.deploy_hash == deploy_hash assert deploy_info.error_message == 'Exit code: 2'	[ 1, 1053, 2897, 13, 5215, 12183, 13, 5215, 11451, 1688, 13, 5215, 4390, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 11451, 1688, 1053, 5713, 15546, 29892, 1153, 4637, 13, 3166, 1243, 29889, 695, 29918, 3177, 29889, 1285, 1461, 29918, 7328, 1053, 8078, 29918, 7328, 13, 3166, 1243, 29889, 695, 29918, 3177, 29889, 9435, 1053, 6724, 29918, 4632, 29918, 2084, 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cleaning/partition_domains_fixed_block_size.py	AGLDWG/ld-link-harvester	0	95143	import pandas as pd IN_FILE = 'aus-domain-urls.txt' START_IDX = 1186 BLOCK_SIZE = 100 OUT_FILE_PREFIX = 'partition_data/aus-domain-urls' data = pd.read_csv(IN_FILE) data_length = len(data) for i in range(int(data_length - START_IDX / BLOCK_SIZE)): if i == 0: lower_bound = START_IDX else: lower_bound = upper_bound upper_bound = lower_bound + BLOCK_SIZE if upper_bound >= data_length: upper_bound = data_length - 1 out_file = '{}_{}_{}_{}.txt'.format(OUT_FILE_PREFIX, lower_bound, upper_bound, upper_bound - lower_bound) (data.iloc[lower_bound:upper_bound, :]).to_csv(out_file, header=False, index=None, sep=" ") break out_file = '{}_{}_{}_{}.txt'.format(OUT_FILE_PREFIX, lower_bound, upper_bound, upper_bound - lower_bound) (data.iloc[ lower_bound:upper_bound, : ]).to_csv(out_file, header=False, index=None, sep=" ")	[ 1, 1053, 11701, 408, 10518, 13, 13, 1177, 29918, 7724, 353, 525, 1485, 29899, 7247, 29899, 26045, 29889, 3945, 29915, 13, 25826, 29918, 1367, 29990, 353, 29871, 29896, 29896, 29947, 29953, 13, 29933, 21339, 29918, 14226, 353, 29871, 29896, 29900, 29900, 13, 12015, 29918, 7724, 29918, 15094, 25634, 353, 525, 16707, 29918, 1272, 29914, 1485, 29899, 7247, 29899, 26045, 29915, 13, 13, 1272, 353, 10518, 29889, 949, 29918, 7638, 29898, 1177, 29918, 7724, 29897, 13, 1272, 29918, 2848, 353, 7431, 29898, 1272, 29897, 13, 1454, 474, 297, 3464, 29898, 524, 29898, 1272, 29918, 2848, 448, 6850, 8322, 29918, 1367, 29990, 847, 350, 21339, 29918, 14226, 22164, 13, 1678, 565, 474, 1275, 29871, 29900, 29901, 13, 4706, 5224, 29918, 9917, 353, 6850, 8322, 29918, 1367, 29990, 13, 1678, 1683, 29901, 13, 4706, 5224, 29918, 9917, 353, 7568, 29918, 9917, 13, 1678, 7568, 29918, 9917, 353, 5224, 29918, 9917, 718, 350, 21339, 29918, 14226, 13, 1678, 565, 7568, 29918, 9917, 6736, 848, 29918, 2848, 29901, 13, 4706, 7568, 29918, 9917, 353, 848, 29918, 2848, 448, 29871, 29896, 13, 4706, 714, 29918, 1445, 353, 22372, 3227, 3227, 3227, 1836, 3945, 4286, 4830, 29898, 12015, 29918, 7724, 29918, 15094, 25634, 29892, 5224, 29918, 9917, 29892, 7568, 29918, 9917, 29892, 7568, 29918, 9917, 448, 5224, 29918, 9917, 29897, 13, 4706, 313, 1272, 29889, 309, 542, 29961, 13609, 29918, 9917, 29901, 21064, 29918, 9917, 29892, 584, 14664, 517, 29918, 7638, 29898, 449, 29918, 1445, 29892, 4839, 29922, 8824, 29892, 2380, 29922, 8516, 29892, 16345, 543, 16521, 13, 4706, 2867, 13, 1678, 714, 29918, 1445, 353, 22372, 3227, 3227, 3227, 1836, 3945, 4286, 4830, 29898, 12015, 29918, 7724, 29918, 15094, 25634, 29892, 5224, 29918, 9917, 29892, 7568, 29918, 9917, 29892, 7568, 29918, 9917, 448, 5224, 29918, 9917, 29897, 13, 1678, 313, 1272, 29889, 309, 542, 29961, 5224, 29918, 9917, 29901, 21064, 29918, 9917, 29892, 584, 4514, 467, 517, 29918, 7638, 29898, 449, 29918, 1445, 29892, 4839, 29922, 8824, 29892, 2380, 29922, 8516, 29892, 16345, 543, 16521, 13, 13, 2 ]
simple_chatbot/responses.py	Codingplace42/django-simple-chatbot	9	120528	<reponame>Codingplace42/django-simple-chatbot from abc import ABC, abstractmethod from random import choice class BaseResponse(ABC): @abstractmethod def get_response(self): pass class GenericRandomResponse(BaseResponse): choices = () def get_response(self): return choice(self.choices)	[ 1, 529, 276, 1112, 420, 29958, 29907, 3689, 6689, 29946, 29906, 29914, 14095, 29899, 12857, 29899, 13496, 7451, 13, 3166, 25638, 1053, 16417, 29892, 9846, 5696, 13, 3166, 4036, 1053, 7348, 13, 13, 13, 1990, 7399, 5103, 29898, 19658, 1125, 13, 1678, 732, 16595, 5696, 13, 1678, 822, 679, 29918, 5327, 29898, 1311, 1125, 13, 4706, 1209, 13, 13, 13, 1990, 3251, 293, 17875, 5103, 29898, 5160, 5103, 1125, 13, 1678, 19995, 353, 3861, 13, 13, 1678, 822, 679, 29918, 5327, 29898, 1311, 1125, 13, 4706, 736, 7348, 29898, 1311, 29889, 1859, 1575, 29897, 13, 2 ]
saleor/core/transactions.py	fairhopeweb/saleor	15,337	1534	from contextlib import contextmanager from django.db import DatabaseError from ..core.tracing import traced_atomic_transaction @contextmanager def transaction_with_commit_on_errors(): """Perform transaction and raise an error in any occurred.""" error = None with traced_atomic_transaction(): try: yield except DatabaseError: raise except Exception as e: error = e if error: raise error	[ 1, 515, 3030, 1982, 1053, 3030, 12847, 13, 13, 3166, 9557, 29889, 2585, 1053, 5470, 2392, 13, 13, 3166, 6317, 3221, 29889, 29873, 945, 292, 1053, 16703, 287, 29918, 21641, 29918, 20736, 13, 13, 13, 29992, 4703, 12847, 13, 1753, 10804, 29918, 2541, 29918, 15060, 29918, 265, 29918, 12523, 7295, 13, 1678, 9995, 5894, 689, 10804, 322, 12020, 385, 1059, 297, 738, 10761, 1213, 15945, 13, 1678, 1059, 353, 6213, 13, 1678, 411, 16703, 287, 29918, 21641, 29918, 20736, 7295, 13, 4706, 1018, 29901, 13, 9651, 7709, 13, 4706, 5174, 5470, 2392, 29901, 13, 9651, 12020, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 1059, 353, 321, 13, 1678, 565, 1059, 29901, 13, 4706, 12020, 1059, 13, 2 ]
test/python_unit/collision.py	proyan/hpp-fcl	0	188747	<filename>test/python_unit/collision.py import unittest from test_case import TestCase import hppfcl hppfcl.switchToNumpyArray() import numpy as np def tetahedron(): pts = hppfcl.StdVec_Vec3f() pts.append(np.array((0, 0, 0))) pts.append(np.array((0, 1, 0))) pts.append(np.array((1, 0, 0))) pts.append(np.array((0, 0, 1))) tri = hppfcl.StdVec_Triangle() tri.append(hppfcl.Triangle(0, 1, 2)) tri.append(hppfcl.Triangle(0, 1, 3)) tri.append(hppfcl.Triangle(0, 2, 3)) tri.append(hppfcl.Triangle(1, 2, 3)) return hppfcl.Convex(pts, tri) class TestMainAPI(TestCase): def test_convex_halfspace(self): convex = tetahedron() halfspace = hppfcl.Halfspace(np.array((0, 0, 1)), 0) req = hppfcl.CollisionRequest() res = hppfcl.CollisionResult() M1 = hppfcl.Transform3f() M2 = hppfcl.Transform3f(np.eye(3), np.array([0, 0, -0.1])) res.clear() hppfcl.collide(convex, M1, halfspace, M2, req, res) self.assertFalse(hppfcl.collide(convex, M1, halfspace, M2, req, res)) M1 = hppfcl.Transform3f() M2 = hppfcl.Transform3f(np.eye(3), np.array([0, 0, 0.1])) res.clear() self.assertTrue(hppfcl.collide(convex, M1, halfspace, M2, req, res)) M1 = hppfcl.Transform3f() M2 = hppfcl.Transform3f(np.eye(3), np.array([0, 0, 2])) res.clear() self.assertTrue(hppfcl.collide(convex, M1, halfspace, M2, req, res)) if __name__ == "__main__": unittest.main()	[ 1, 529, 9507, 29958, 1688, 29914, 4691, 29918, 5441, 29914, 22017, 2459, 29889, 2272, 13, 5215, 443, 27958, 13, 3166, 1243, 29918, 4878, 1053, 4321, 8259, 13, 5215, 298, 407, 29888, 695, 13, 13, 29623, 29888, 695, 29889, 15123, 1762, 8009, 2272, 2588, 580, 13, 5215, 12655, 408, 7442, 13, 13, 13, 1753, 260, 300, 801, 287, 1617, 7295, 13, 1678, 282, 1372, 353, 298, 407, 29888, 695, 29889, 855, 29881, 25987, 29918, 25987, 29941, 29888, 580, 13, 1678, 282, 1372, 29889, 4397, 29898, 9302, 29889, 2378, 3552, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 4961, 13, 1678, 282, 1372, 29889, 4397, 29898, 9302, 29889, 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turdshovel/_stubs/System/Collections/Generic.py	daddycocoaman/turdshovel	39	117303	# encoding: utf-8 # module System.Collections.Generic calls itself Generic # from mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Microsoft.Bcl.AsyncInterfaces, Version=1.0.0.0, Culture=neutral, PublicKeyToken=<KEY> # by generator 1.145 # no doc # no imports # no functions # classes class Comparer(object, IComparer, IComparer[T]): # no doc def Compare(self, x, y): """ Compare(self: Comparer[T], x: T, y: T) -> int """ pass @staticmethod def Create(comparison): """ Create(comparison: Comparison[T]) -> Comparer[T] """ pass def __cmp__(self, args): #cannot find CLR method """ x.__cmp__(y) <==> cmp(x,y) """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: object) -> str """ pass class Dictionary(object, IDictionary[TKey, TValue], ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable, IDictionary, ICollection, IReadOnlyDictionary[TKey, TValue], IReadOnlyCollection[KeyValuePair[TKey, TValue]], ISerializable, IDeserializationCallback): """ Dictionary[TKey, TValue]() Dictionary[TKey, TValue](capacity: int) Dictionary[TKey, TValue](comparer: IEqualityComparer[TKey]) Dictionary[TKey, TValue](capacity: int, comparer: IEqualityComparer[TKey]) Dictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue]) Dictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue], comparer: IEqualityComparer[TKey]) """ def Add(self, key, value): """ Add(self: Dictionary[TKey, TValue], key: TKey, value: TValue) """ pass def Clear(self): """ Clear(self: Dictionary[TKey, TValue]) """ pass def ContainsKey(self, key): """ ContainsKey(self: Dictionary[TKey, TValue], key: TKey) -> bool """ pass def ContainsValue(self, value): """ ContainsValue(self: Dictionary[TKey, TValue], value: TValue) -> bool """ pass def GetEnumerator(self): """ GetEnumerator(self: Dictionary[TKey, TValue]) -> Enumerator """ pass def GetObjectData(self, info, context): """ GetObjectData(self: Dictionary[TKey, TValue], info: SerializationInfo, context: StreamingContext) """ pass def OnDeserialization(self, sender): """ OnDeserialization(self: Dictionary[TKey, TValue], sender: object) """ pass def Remove(self, key): """ Remove(self: Dictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: Dictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: IDictionary[TKey, TValue], key: TKey) -> bool __contains__(self: IDictionary, key: object) -> bool """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, __args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, comparer: IEqualityComparer[TKey]) __new__(cls: type, capacity: int, comparer: IEqualityComparer[TKey]) __new__(cls: type, dictionary: IDictionary[TKey, TValue]) __new__(cls: type, dictionary: IDictionary[TKey, TValue], comparer: IEqualityComparer[TKey]) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: Dictionary[TKey, TValue]) -> str __repr__(self: Dictionary[K, V]) -> str """ pass def __setitem__(self, args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: Dictionary[TKey, TValue]) -> IEqualityComparer[TKey] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: Dictionary[TKey, TValue]) -> int """ Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: Dictionary[TKey, TValue]) -> KeyCollection """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: Dictionary[TKey, TValue]) -> ValueCollection """ Enumerator = None KeyCollection = None ValueCollection = None class EqualityComparer(object, IEqualityComparer, IEqualityComparer[T]): # no doc def Equals(self, __args): """ Equals(self: EqualityComparer[T], x: T, y: T) -> bool """ pass def GetHashCode(self, obj=None): """ GetHashCode(self: EqualityComparer[T], obj: T) -> int """ pass def __eq__(self, args): #cannot find CLR method """ x.__eq__(y) <==> x==y """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: object) -> str """ pass class IAsyncEnumerable: # no doc def GetAsyncEnumerator(self, cancellationToken): """ GetAsyncEnumerator(self: IAsyncEnumerable[T], cancellationToken: CancellationToken) -> IAsyncEnumerator[T] Returns an enumerator that iterates asynchronously through the collection. cancellationToken: A System.Threading.CancellationToken that may be used to cancel the asynchronous iteration. Returns: An enumerator that can be used to iterate asynchronously through the collection. """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IAsyncEnumerator(IAsyncDisposable): # no doc def MoveNextAsync(self): """ MoveNextAsync(self: IAsyncEnumerator[T]) -> ValueTask[bool] Advances the enumerator asynchronously to the next element of the collection. Returns: A System.Threading.Tasks.ValueTask that will complete with a result of true if the enumerator was successfully advanced to the next element, or false if the enumerator has passed the end of the collection. """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass Current = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Gets the element in the collection at the current position of the enumerator. Get: Current(self: IAsyncEnumerator[T]) -> T """ class ICollection(IEnumerable[T], IEnumerable): # no doc def Add(self, item): """ Add(self: ICollection[T], item: T) """ pass def Clear(self): """ Clear(self: ICollection[T]) """ pass def Contains(self, item): """ Contains(self: ICollection[T], item: T) -> bool """ pass def CopyTo(self, array, arrayIndex): """ CopyTo(self: ICollection[T], array: Array[T], arrayIndex: int) """ pass def Remove(self, item): """ Remove(self: ICollection[T], item: T) -> bool """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: ICollection[T]) -> int """ IsReadOnly = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: IsReadOnly(self: ICollection[T]) -> bool """ class IComparer: # no doc def Compare(self, x, y): """ Compare(self: IComparer[T], x: T, y: T) -> int """ pass def __cmp__(self, args): #cannot find CLR method """ x.__cmp__(y) <==> cmp(x,y) """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IDictionary(ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable): # no doc def Add(self, key, value): """ Add(self: IDictionary[TKey, TValue], key: TKey, value: TValue) """ pass def ContainsKey(self, key): """ ContainsKey(self: IDictionary[TKey, TValue], key: TKey) -> bool """ pass def Remove(self, key): """ Remove(self: IDictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: IDictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: ICollection[KeyValuePair[TKey, TValue]], item: KeyValuePair[TKey, TValue]) -> bool """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass def __setitem__(self, args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: IDictionary[TKey, TValue]) -> ICollection[TKey] """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: IDictionary[TKey, TValue]) -> ICollection[TValue] """ class IEnumerable(IEnumerable): # no doc def GetEnumerator(self): """ GetEnumerator(self: IEnumerable[T]) -> IEnumerator[T] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IEnumerator(IDisposable, IEnumerator): # no doc def next(self, args): #cannot find CLR method """ next(self: object) -> object """ pass def __enter__(self, args): #cannot find CLR method """ __enter__(self: IDisposable) -> object """ pass def __exit__(self, args): #cannot find CLR method """ __exit__(self: IDisposable, exc_type: object, exc_value: object, exc_back: object) """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__[T](self: IEnumerator[T]) -> object """ pass Current = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Current(self: IEnumerator[T]) -> T """ class IEqualityComparer: # no doc def Equals(self, x, y): """ Equals(self: IEqualityComparer[T], x: T, y: T) -> bool """ pass def GetHashCode(self, obj): """ GetHashCode(self: IEqualityComparer[T], obj: T) -> int """ pass def __eq__(self, args): #cannot find CLR method """ x.__eq__(y) <==> x==y """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IList(ICollection[T], IEnumerable[T], IEnumerable): # no doc def IndexOf(self, item): """ IndexOf(self: IList[T], item: T) -> int """ pass def Insert(self, index, item): """ Insert(self: IList[T], index: int, item: T) """ pass def RemoveAt(self, index): """ RemoveAt(self: IList[T], index: int) """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass def __setitem__(self, args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass class IReadOnlyCollection(IEnumerable[T], IEnumerable): # no doc def __contains__(self, args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: IReadOnlyCollection[T]) -> int """ class IReadOnlyDictionary(IReadOnlyCollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable): # no doc def ContainsKey(self, key): """ ContainsKey(self: IReadOnlyDictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: IReadOnlyDictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __contains__(self, args): #cannot find CLR method """ __contains__[KeyValuePair`2](enumerable: IEnumerable[KeyValuePair[TKey, TValue]], value: KeyValuePair[TKey, TValue]) -> bool """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: IReadOnlyDictionary[TKey, TValue]) -> IEnumerable[TKey] """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: IReadOnlyDictionary[TKey, TValue]) -> IEnumerable[TValue] """ class IReadOnlyList(IReadOnlyCollection[T], IEnumerable[T], IEnumerable): # no doc def __contains__(self, args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass class ISet(ICollection[T], IEnumerable[T], IEnumerable): # no doc def Add(self, item): """ Add(self: ISet[T], item: T) -> bool """ pass def ExceptWith(self, other): """ ExceptWith(self: ISet[T], other: IEnumerable[T]) """ pass def IntersectWith(self, other): """ IntersectWith(self: ISet[T], other: IEnumerable[T]) """ pass def IsProperSubsetOf(self, other): """ IsProperSubsetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def IsProperSupersetOf(self, other): """ IsProperSupersetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def IsSubsetOf(self, other): """ IsSubsetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def IsSupersetOf(self, other): """ IsSupersetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def Overlaps(self, other): """ Overlaps(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def SetEquals(self, other): """ SetEquals(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def SymmetricExceptWith(self, other): """ SymmetricExceptWith(self: ISet[T], other: IEnumerable[T]) """ pass def UnionWith(self, other): """ UnionWith(self: ISet[T], other: IEnumerable[T]) """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass class KeyNotFoundException(SystemException, ISerializable, _Exception): """ KeyNotFoundException() KeyNotFoundException(message: str) KeyNotFoundException(message: str, innerException: Exception) """ def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod # known case of __new__ def __new__(self, message=None, innerException=None): """ __new__(cls: type) __new__(cls: type, message: str) __new__(cls: type, message: str, innerException: Exception) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __str__(self, args): #cannot find CLR method pass SerializeObjectState = None class KeyValuePair(object): """ KeyValuePair[TKey, TValue](key: TKey, value: TValue) """ def ToString(self): """ ToString(self: KeyValuePair[TKey, TValue]) -> str """ pass @staticmethod # known case of __new__ def __new__(self, key, value): """ __new__[KeyValuePair`2]() -> KeyValuePair[TKey, TValue] __new__(cls: type, key: TKey, value: TValue) """ pass Key = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Key(self: KeyValuePair[TKey, TValue]) -> TKey """ Value = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Value(self: KeyValuePair[TKey, TValue]) -> TValue """ class LinkedList(object, ICollection[T], IEnumerable[T], IEnumerable, ICollection, IReadOnlyCollection[T], ISerializable, IDeserializationCallback): """ LinkedList[T]() LinkedList[T](collection: IEnumerable[T]) """ def AddAfter(self, node, __args): """ AddAfter(self: LinkedList[T], node: LinkedListNode[T], newNode: LinkedListNode[T])AddAfter(self: LinkedList[T], node: LinkedListNode[T], value: T) -> LinkedListNode[T] """ pass def AddBefore(self, node, __args): """ AddBefore(self: LinkedList[T], node: LinkedListNode[T], value: T) -> LinkedListNode[T] AddBefore(self: LinkedList[T], node: LinkedListNode[T], newNode: LinkedListNode[T]) """ pass def AddFirst(self, __args): """ AddFirst(self: LinkedList[T], value: T) -> LinkedListNode[T] AddFirst(self: LinkedList[T], node: LinkedListNode[T]) """ pass def AddLast(self, __args): """ AddLast(self: LinkedList[T], value: T) -> LinkedListNode[T] AddLast(self: LinkedList[T], node: LinkedListNode[T]) """ pass def Clear(self): """ Clear(self: LinkedList[T]) """ pass def Contains(self, value): """ Contains(self: LinkedList[T], value: T) -> bool """ pass def CopyTo(self, array, index): """ CopyTo(self: LinkedList[T], array: Array[T], index: int) """ pass def Find(self, value): """ Find(self: LinkedList[T], value: T) -> LinkedListNode[T] """ pass def FindLast(self, value): """ FindLast(self: LinkedList[T], value: T) -> LinkedListNode[T] """ pass def GetEnumerator(self): """ GetEnumerator(self: LinkedList[T]) -> Enumerator """ pass def GetObjectData(self, info, context): """ GetObjectData(self: LinkedList[T], info: SerializationInfo, context: StreamingContext) """ pass def OnDeserialization(self, sender): """ OnDeserialization(self: LinkedList[T], sender: object) """ pass def Remove(self, __args): """ Remove(self: LinkedList[T], value: T) -> bool Remove(self: LinkedList[T], node: LinkedListNode[T]) """ pass def RemoveFirst(self): """ RemoveFirst(self: LinkedList[T]) """ pass def RemoveLast(self): """ RemoveLast(self: LinkedList[T]) """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, collection=None): """ __new__(cls: type) __new__(cls: type, collection: IEnumerable[T]) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: LinkedList[T]) -> int """ First = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: First(self: LinkedList[T]) -> LinkedListNode[T] """ Last = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Last(self: LinkedList[T]) -> LinkedListNode[T] """ Enumerator = None class LinkedListNode(object): """ LinkedListNode[T](value: T) """ @staticmethod # known case of __new__ def __new__(self, value): """ __new__(cls: type, value: T) """ pass List = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: List(self: LinkedListNode[T]) -> LinkedList[T] """ Next = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Next(self: LinkedListNode[T]) -> LinkedListNode[T] """ Previous = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Previous(self: LinkedListNode[T]) -> LinkedListNode[T] """ Value = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Value(self: LinkedListNode[T]) -> T Set: Value(self: LinkedListNode[T]) = value """ class List(object, IList[T], ICollection[T], IEnumerable[T], IEnumerable, IList, ICollection, IReadOnlyList[T], IReadOnlyCollection[T]): """ List[T]() List[T](capacity: int) List[T](collection: IEnumerable[T]) """ def Add(self, item): """ Add(self: List[T], item: T) """ pass def AddRange(self, collection): """ AddRange(self: List[T], collection: IEnumerable[T]) """ pass def AsReadOnly(self): """ AsReadOnly(self: List[T]) -> ReadOnlyCollection[T] """ pass def BinarySearch(self, __args): """ BinarySearch(self: List[T], index: int, count: int, item: T, comparer: IComparer[T]) -> int BinarySearch(self: List[T], item: T) -> int BinarySearch(self: List[T], item: T, comparer: IComparer[T]) -> int """ pass def Clear(self): """ Clear(self: List[T]) """ pass def Contains(self, item): """ Contains(self: List[T], item: T) -> bool """ pass def ConvertAll(self, converter): """ ConvertAll[TOutput](self: List[T], converter: Converter[T, TOutput]) -> List[TOutput] """ pass def CopyTo(self, __args): """ CopyTo(self: List[T], index: int, array: Array[T], arrayIndex: int, count: int)CopyTo(self: List[T], array: Array[T])CopyTo(self: List[T], array: Array[T], arrayIndex: int) """ pass def Exists(self, match): """ Exists(self: List[T], match: Predicate[T]) -> bool """ pass def Find(self, match): """ Find(self: List[T], match: Predicate[T]) -> T """ pass def FindAll(self, match): """ FindAll(self: List[T], match: Predicate[T]) -> List[T] """ pass def FindIndex(self, __args): """ FindIndex(self: List[T], match: Predicate[T]) -> int FindIndex(self: List[T], startIndex: int, match: Predicate[T]) -> int FindIndex(self: List[T], startIndex: int, count: int, match: Predicate[T]) -> int """ pass def FindLast(self, match): """ FindLast(self: List[T], match: Predicate[T]) -> T """ pass def FindLastIndex(self, __args): """ FindLastIndex(self: List[T], match: Predicate[T]) -> int FindLastIndex(self: List[T], startIndex: int, match: Predicate[T]) -> int FindLastIndex(self: List[T], startIndex: int, count: int, match: Predicate[T]) -> int """ pass def ForEach(self, action): """ ForEach(self: List[T], action: Action[T]) """ pass def GetEnumerator(self): """ GetEnumerator(self: List[T]) -> Enumerator """ pass def GetRange(self, index, count): """ GetRange(self: List[T], index: int, count: int) -> List[T] """ pass def IndexOf(self, item, index=None, count=None): """ IndexOf(self: List[T], item: T) -> int IndexOf(self: List[T], item: T, index: int) -> int IndexOf(self: List[T], item: T, index: int, count: int) -> int """ pass def Insert(self, index, item): """ Insert(self: List[T], index: int, item: T) """ pass def InsertRange(self, index, collection): """ InsertRange(self: List[T], index: int, collection: IEnumerable[T]) """ pass def LastIndexOf(self, item, index=None, count=None): """ LastIndexOf(self: List[T], item: T) -> int LastIndexOf(self: List[T], item: T, index: int) -> int LastIndexOf(self: List[T], item: T, index: int, count: int) -> int """ pass def Remove(self, item): """ Remove(self: List[T], item: T) -> bool """ pass def RemoveAll(self, match): """ RemoveAll(self: List[T], match: Predicate[T]) -> int """ pass def RemoveAt(self, index): """ RemoveAt(self: List[T], index: int) """ pass def RemoveRange(self, index, count): """ RemoveRange(self: List[T], index: int, count: int) """ pass def Reverse(self, index=None, count=None): """ Reverse(self: List[T], index: int, count: int)Reverse(self: List[T]) """ pass def Sort(self, __args): """ Sort(self: List[T])Sort(self: List[T], comparer: IComparer[T])Sort(self: List[T], index: int, count: int, comparer: IComparer[T])Sort(self: List[T], comparison: Comparison[T]) """ pass def ToArray(self): """ ToArray(self: List[T]) -> Array[T] """ pass def TrimExcess(self): """ TrimExcess(self: List[T]) """ pass def TrueForAll(self, match): """ TrueForAll(self: List[T], match: Predicate[T]) -> bool """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool __contains__(self: IList, value: object) -> bool """ pass def __delitem__(self, args): #cannot find CLR method """ x.__delitem__(y) <==> del x[y]x.__delitem__(y) <==> del x[y]x.__delitem__(y) <==> del x[y] """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __getslice__(self, args): #cannot find CLR method """ __getslice__(self: List[T], x: int, y: int) -> List[T] __getslice__(self: List[T], x: int, y: int) -> List[T] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, __args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, collection: IEnumerable[T]) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: List[T]) -> str __repr__(self: List[T]) -> str """ pass def __setitem__(self, args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Capacity = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Capacity(self: List[T]) -> int Set: Capacity(self: List[T]) = value """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: List[T]) -> int """ Enumerator = None class Queue(object, IEnumerable[T], IEnumerable, ICollection, IReadOnlyCollection[T]): """ Queue[T]() Queue[T](capacity: int) Queue[T](collection: IEnumerable[T]) """ def Clear(self): """ Clear(self: Queue[T]) """ pass def Contains(self, item): """ Contains(self: Queue[T], item: T) -> bool """ pass def CopyTo(self, array, arrayIndex): """ CopyTo(self: Queue[T], array: Array[T], arrayIndex: int) """ pass def Dequeue(self): """ Dequeue(self: Queue[T]) -> T """ pass def Enqueue(self, item): """ Enqueue(self: Queue[T], item: T) """ pass def GetEnumerator(self): """ GetEnumerator(self: Queue[T]) -> Enumerator """ pass def Peek(self): """ Peek(self: Queue[T]) -> T """ pass def ToArray(self): """ ToArray(self: Queue[T]) -> Array[T] """ pass def TrimExcess(self): """ TrimExcess(self: Queue[T]) """ pass def __contains__(self, args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, __args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, collection: IEnumerable[T]) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: Queue[T]) -> int """ Enumerator = None class SortedDictionary(object, IDictionary[TKey, TValue], ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable, IDictionary, ICollection, IReadOnlyDictionary[TKey, TValue], IReadOnlyCollection[KeyValuePair[TKey, TValue]]): """ SortedDictionary[TKey, TValue]() SortedDictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue]) SortedDictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) SortedDictionary[TKey, TValue](comparer: IComparer[TKey]) """ def Add(self, key, value): """ Add(self: SortedDictionary[TKey, TValue], key: TKey, value: TValue) """ pass def Clear(self): """ Clear(self: SortedDictionary[TKey, TValue]) """ pass def ContainsKey(self, key): """ ContainsKey(self: SortedDictionary[TKey, TValue], key: TKey) -> bool """ pass def ContainsValue(self, value): """ ContainsValue(self: SortedDictionary[TKey, TValue], value: TValue) -> bool """ pass def CopyTo(self, array, index): """ CopyTo(self: SortedDictionary[TKey, TValue], array: Array[KeyValuePair[TKey, TValue]], index: int) """ pass def GetEnumerator(self): """ GetEnumerator(self: SortedDictionary[TKey, TValue]) -> Enumerator """ pass def Remove(self, key): """ Remove(self: SortedDictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: SortedDictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: IDictionary[TKey, TValue], key: TKey) -> bool __contains__(self: IDictionary, key: object) -> bool """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, __args): """ __new__(cls: type) __new__(cls: type, dictionary: IDictionary[TKey, TValue]) __new__(cls: type, dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) __new__(cls: type, comparer: IComparer[TKey]) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: object) -> str """ pass def __setitem__(self, args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: SortedDictionary[TKey, TValue]) -> IComparer[TKey] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: SortedDictionary[TKey, TValue]) -> int """ Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: SortedDictionary[TKey, TValue]) -> KeyCollection """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: SortedDictionary[TKey, TValue]) -> ValueCollection """ Enumerator = None KeyCollection = None ValueCollection = None class SortedList(object, IDictionary[TKey, TValue], ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable, IDictionary, ICollection, IReadOnlyDictionary[TKey, TValue], IReadOnlyCollection[KeyValuePair[TKey, TValue]]): """ SortedList[TKey, TValue]() SortedList[TKey, TValue](capacity: int) SortedList[TKey, TValue](comparer: IComparer[TKey]) SortedList[TKey, TValue](capacity: int, comparer: IComparer[TKey]) SortedList[TKey, TValue](dictionary: IDictionary[TKey, TValue]) SortedList[TKey, TValue](dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) """ def Add(self, key, value): """ Add(self: SortedList[TKey, TValue], key: TKey, value: TValue) """ pass def Clear(self): """ Clear(self: SortedList[TKey, TValue]) """ pass def ContainsKey(self, key): """ ContainsKey(self: SortedList[TKey, TValue], key: TKey) -> bool """ pass def ContainsValue(self, value): """ ContainsValue(self: SortedList[TKey, TValue], value: TValue) -> bool """ pass def GetEnumerator(self): """ GetEnumerator(self: SortedList[TKey, TValue]) -> IEnumerator[KeyValuePair[TKey, TValue]] """ pass def IndexOfKey(self, key): """ IndexOfKey(self: SortedList[TKey, TValue], key: TKey) -> int """ pass def IndexOfValue(self, value): """ IndexOfValue(self: SortedList[TKey, TValue], value: TValue) -> int """ pass def Remove(self, key): """ Remove(self: SortedList[TKey, TValue], key: TKey) -> bool """ pass def RemoveAt(self, index): """ RemoveAt(self: SortedList[TKey, TValue], index: int) """ pass def TrimExcess(self): """ TrimExcess(self: SortedList[TKey, TValue]) """ pass def TryGetValue(self, key, value): """ TryGetValue(self: SortedList[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: IDictionary[TKey, TValue], key: TKey) -> bool __contains__(self: IDictionary, key: object) -> bool """ pass def __getitem__(self, args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, __args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, comparer: IComparer[TKey]) __new__(cls: type, capacity: int, comparer: IComparer[TKey]) __new__(cls: type, dictionary: IDictionary[TKey, TValue]) __new__(cls: type, dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: object) -> str """ pass def __setitem__(self, args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Capacity = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Capacity(self: SortedList[TKey, TValue]) -> int Set: Capacity(self: SortedList[TKey, TValue]) = value """ Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: SortedList[TKey, TValue]) -> IComparer[TKey] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: SortedList[TKey, TValue]) -> int """ Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: SortedList[TKey, TValue]) -> IList[TKey] """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: SortedList[TKey, TValue]) -> IList[TValue] """ class SortedSet(object, ISet[T], ICollection[T], IEnumerable[T], IEnumerable, ICollection, ISerializable, IDeserializationCallback, IReadOnlyCollection[T]): """ SortedSet[T]() SortedSet[T](comparer: IComparer[T]) SortedSet[T](collection: IEnumerable[T]) SortedSet[T](collection: IEnumerable[T], comparer: IComparer[T]) """ def Add(self, item): """ Add(self: SortedSet[T], item: T) -> bool """ pass def Clear(self): """ Clear(self: SortedSet[T]) """ pass def Contains(self, item): """ Contains(self: SortedSet[T], item: T) -> bool """ pass def CopyTo(self, array, index=None, count=None): """ CopyTo(self: SortedSet[T], array: Array[T])CopyTo(self: SortedSet[T], array: Array[T], index: int)CopyTo(self: SortedSet[T], array: Array[T], index: int, count: int) """ pass @staticmethod def CreateSetComparer(memberEqualityComparer=None): """ CreateSetComparer() -> IEqualityComparer[SortedSet[T]] CreateSetComparer(memberEqualityComparer: IEqualityComparer[T]) -> IEqualityComparer[SortedSet[T]] """ pass def ExceptWith(self, other): """ ExceptWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def GetEnumerator(self): """ GetEnumerator(self: SortedSet[T]) -> Enumerator """ pass def GetObjectData(self, args): #cannot find CLR method """ GetObjectData(self: SortedSet[T], info: SerializationInfo, context: StreamingContext) """ pass def GetViewBetween(self, lowerValue, upperValue): """ GetViewBetween(self: SortedSet[T], lowerValue: T, upperValue: T) -> SortedSet[T] """ pass def IntersectWith(self, other): """ IntersectWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def IsProperSubsetOf(self, other): """ IsProperSubsetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def IsProperSupersetOf(self, other): """ IsProperSupersetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def IsSubsetOf(self, other): """ IsSubsetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def IsSupersetOf(self, other): """ IsSupersetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def OnDeserialization(self, args): #cannot find CLR method """ OnDeserialization(self: SortedSet[T], sender: object) """ pass def Overlaps(self, other): """ Overlaps(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def Remove(self, item): """ Remove(self: SortedSet[T], item: T) -> bool """ pass def RemoveWhere(self, match): """ RemoveWhere(self: SortedSet[T], match: Predicate[T]) -> int """ pass def Reverse(self): """ Reverse(self: SortedSet[T]) -> IEnumerable[T] """ pass def SetEquals(self, other): """ SetEquals(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def SymmetricExceptWith(self, other): """ SymmetricExceptWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def TryGetValue(self, equalValue, actualValue): """ TryGetValue(self: SortedSet[T], equalValue: T) -> (bool, T) """ pass def UnionWith(self, other): """ UnionWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def __add__(self, args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, __args): """ __new__(cls: type) __new__(cls: type, comparer: IComparer[T]) __new__(cls: type, collection: IEnumerable[T]) __new__(cls: type, collection: IEnumerable[T], comparer: IComparer[T]) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: SortedSet[T]) -> IComparer[T] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: SortedSet[T]) -> int """ Max = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Max(self: SortedSet[T]) -> T """ Min = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Min(self: SortedSet[T]) -> T """ Enumerator = None class Stack(object, IEnumerable[T], IEnumerable, ICollection, IReadOnlyCollection[T]): """ Stack[T]() Stack[T](capacity: int) Stack[T](collection: IEnumerable[T]) """ def Clear(self): """ Clear(self: Stack[T]) """ pass def Contains(self, item): """ Contains(self: Stack[T], item: T) -> bool """ pass def CopyTo(self, array, arrayIndex): """ CopyTo(self: Stack[T], array: Array[T], arrayIndex: int) """ pass def GetEnumerator(self): """ GetEnumerator(self: Stack[T]) -> Enumerator """ pass def Peek(self): """ Peek(self: Stack[T]) -> T """ pass def Pop(self): """ Pop(self: Stack[T]) -> T """ pass def Push(self, item): """ Push(self: Stack[T], item: T) """ pass def ToArray(self): """ ToArray(self: Stack[T]) -> Array[T] """ pass def TrimExcess(self): """ TrimExcess(self: Stack[T]) """ pass def __contains__(self, args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, __args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, collection: IEnumerable[T]) """ pass def __reduce_ex__(self, args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: Stack[T]) -> int """ Enumerator = None	[ 1, 396, 8025, 29901, 23616, 29899, 29947, 30004, 13, 29937, 3883, 2184, 29889, 19466, 29889, 15809, 5717, 3528, 3251, 293, 30004, 13, 29937, 515, 286, 1557, 272, 1982, 29892, 10079, 29922, 29946, 29889, 29900, 29889, 29900, 29889, 29900, 29892, 14062, 29922, 17821, 1705, 29892, 5236, 2558, 6066, 29922, 29890, 29955, 29955, 29874, 29945, 29883, 29945, 29953, 29896, 29929, 29941, 29946, 29872, 29900, 29947, 29929, 29892, 2184, 29892, 10079, 29922, 29946, 29889, 29900, 29889, 29900, 29889, 29900, 29892, 14062, 29922, 17821, 1705, 29892, 5236, 2558, 6066, 29922, 29890, 29955, 29955, 29874, 29945, 29883, 29945, 29953, 29896, 29929, 29941, 29946, 29872, 29900, 29947, 29929, 29892, 7783, 29889, 29933, 695, 29889, 8123, 4074, 8726, 29892, 10079, 29922, 29896, 29889, 29900, 29889, 29900, 29889, 29900, 29892, 14062, 29922, 17821, 1705, 29892, 5236, 2558, 6066, 29922, 29966, 10818, 3238, 13, 29937, 491, 15299, 29871, 29896, 29889, 29896, 29946, 29945, 30004, 13, 29937, 694, 1574, 30004, 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LinkUp/core/apis/algorithm_api.py	Justin-sd/LinkUp	0	67204	from ..models import * from .availability_calendar_api import * from .calendar_api import * import json from datetime import datetime, timedelta def get_best(event_id): """ :param event_id: the id of the event we want to get best times of :return: A list of sorted pairs: [ (time, [users]), (time, [users]).... ] where time is the starting time and users is list of users who can make it. """ event_set = Event.objects.filter(event_id=event_id) event = event_set[0] # make the queryset of users into a list of users users = list(event.members.all()) # make all these in minutes duration = int(event.duration) st = event.potential_start_date # round up the potential starting minutes if st.minute > 30: new_st = st.replace(minute=0) new_st = new_st + timedelta(hours=1) elif st.minute > 0: new_st = st.replace(minute=30) elif st.minute == 0 or st.minute == 30: new_st = st start = convert_to_minutes(new_st, new_st) et = event.potential_end_date # round down potential ending minutes if et.minute > 30: new_et = et.replace(minute=30) elif et.minute > 0: new_et = et.replace(minute=0) elif et.minute == 0 or et.minute == 30: new_et = et end = convert_to_minutes(new_et, new_st) min_hour = event.no_earlier_than.hour min_minute = event.no_earlier_than.minute max_hour = event.no_later_than.hour max_minute = event.no_later_than.minute # Dictionary: starting times as keys and values is list of people who can make it, # keys incremented by duration optimal_times = {} # from start to end time, add keys of 30 minute increments with querysets of every user attending for i in range(start,end+1, 30): if i + duration > end: break # only add times later than min time and earlier than max time time = convert_to_datetime(new_st, i) if min_hour < time.hour < max_hour: optimal_times[i] = users.copy() elif time.hour == min_hour: if time.minute >= min_minute: optimal_times[i] = users.copy() elif time.hour == max_hour: if time.minute <= max_minute: optimal_times[i] = users.copy() # have a list of all users times for u in users: # user_sched = free_busy_month(u) # schedule = json.dumps(user_sched, default=json_datetime_handler) # Schedule.objects.create(user=u, availability=schedule) # get user's schedules in datetime format for times in get_users_saved_schedule(u): start_time = list(times.values())[0] # round DOWN the starting minutes if start_time.minute > 30: starting = start_time.replace(minute=30) elif start_time.minute > 0: starting = start_time.replace(minute=0) elif start_time.minute == 0 or start_time.minute == 30: starting = start_time the_start = convert_to_minutes(starting, new_st) end_time = list(times.values())[1] # round UP the ending minutes if et.minute > 30: ending = et.replace(minute=0) ending = ending + timedelta(hours=1) elif et.minute > 0: ending = et.replace(minute=30) elif et.minute == 0 or et.minute == 30: ending = end_time the_end = convert_to_minutes(ending, new_st) # try to find the keys in 30 minute increments and remove the user # from the corresponding list for i in range(the_start, the_end+1, 30): if i in optimal_times: dict_value = optimal_times.get(i) if u in dict_value: dict_value.remove(u) new_dict = {i: dict_value} optimal_times.update(new_dict) # go through the optimal times and find which list contains # most users then append to new list curr_max = 0 if len(optimal_times) > 0: curr_max = len(list(optimal_times.values())[0]) append_list = [] for times in optimal_times: if len(optimal_times[times]) >= curr_max: # append a list of pairs, first = datetime of start second = list of attending # with the ending of the list having more people available append_list.append((convert_to_datetime(new_st, times), optimal_times.get(times))) curr_max = len(optimal_times[times]) # return the reversed list return append_list[::-1] # convert a datetime to minutes elapsed def convert_to_minutes(time, starting): elapsed = time - starting minutes = int(elapsed.total_seconds()/60) return minutes # convert minutes to a datetime by getting starting datetime and timedelta by minutes def convert_to_datetime(starting, mins): time = starting + timedelta(minutes=mins) return time	[ 1, 515, 6317, 9794, 1053, 334, 13, 3166, 869, 485, 737, 3097, 29918, 23392, 29918, 2754, 1053, 334, 13, 3166, 869, 23392, 29918, 2754, 1053, 334, 13, 5215, 4390, 13, 3166, 12865, 1053, 12865, 29892, 5335, 287, 2554, 13, 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src/starter/models.py	rehive/django-starter	2	149545	from logging import getLogger import datetime from uuid import uuid4 from django.contrib.postgres.fields import JSONField from django.db import models from django.utils.timezone import utc logger = getLogger('django') class Message(models.Model): first_name = models.CharField(max_length=300, null=True, blank=True) last_name = models.CharField(max_length=300, null=True, blank=True) message = models.CharField(max_length=300, null=False, blank=False) created = models.DateTimeField() updated = models.DateTimeField() def __str__(self): return str(self.id) def save(self, args, kwargs): if not self.id: # On create self.created = datetime.datetime.now(tz=utc) self.updated = datetime.datetime.now(tz=utc) return super(Message, self).save(args, **kwargs)	[ 1, 515, 12183, 1053, 679, 16363, 13, 13, 5215, 12865, 13, 3166, 318, 5416, 1053, 318, 5416, 29946, 13, 13, 3166, 9557, 29889, 21570, 29889, 2490, 7201, 29889, 9621, 1053, 4663, 3073, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 3166, 9557, 29889, 13239, 29889, 2230, 8028, 1053, 3477, 29883, 13, 13, 21707, 353, 679, 16363, 877, 14095, 1495, 13, 13, 1990, 7777, 29898, 9794, 29889, 3195, 1125, 13, 1678, 937, 29918, 978, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29941, 29900, 29900, 29892, 1870, 29922, 5574, 29892, 9654, 29922, 5574, 29897, 13, 1678, 1833, 29918, 978, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29941, 29900, 29900, 29892, 1870, 29922, 5574, 29892, 9654, 29922, 5574, 29897, 13, 1678, 2643, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29941, 29900, 29900, 29892, 1870, 29922, 8824, 29892, 9654, 29922, 8824, 29897, 13, 1678, 2825, 353, 4733, 29889, 11384, 3073, 580, 13, 1678, 4784, 353, 4733, 29889, 11384, 3073, 580, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 851, 29898, 1311, 29889, 333, 29897, 13, 13, 13, 1678, 822, 4078, 29898, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 565, 451, 1583, 29889, 333, 29901, 29871, 396, 1551, 1653, 13, 9651, 1583, 29889, 11600, 353, 12865, 29889, 12673, 29889, 3707, 29898, 17559, 29922, 329, 29883, 29897, 13, 13, 4706, 1583, 29889, 21402, 353, 12865, 29889, 12673, 29889, 3707, 29898, 17559, 29922, 329, 29883, 29897, 13, 4706, 736, 2428, 29898, 3728, 29892, 1583, 467, 7620, 10456, 5085, 29892, 3579, 19290, 29897, 13, 13, 13, 2 ]
app/home/views.py	1IllI1/BBS_MIGRATE	0	31423	# coding:utf8 # 调用蓝图 from . import home from flask import render_template, redirect, url_for, flash, session, request,current_app from app.home.forms import RegistForm, LoginForm, UserdetailForm, PwdForm, CommentForm, PostForm from app.models import User, UserLoginLog, Comment, Post,Col from werkzeug.security import generate_password_hash from werkzeug.utils import secure_filename import uuid from app import db from app.home.email import send_mail from functools import wraps import time import os # 定义用户登录判断装饰器 def user_login_req(func): @wraps(func) def decorated_function(args, kwargs): # session不存在时请求登录 if "user" not in session: return redirect(url_for("home.user_login", next=request.url)) return func(args, **kwargs) return decorated_function # html测试路由 @home.route('/usefortest/') def ust(): return render_template('home/USERFORTEST.html') # 首页路由 @home.route('/') def index(): # posts = Post.query.all() current_user_id = 0 current_user_name ="" if "user" in session: current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id page_index = request.args.get('page', 1, type=int) query = Post.query.join(User).filter(User.id == Post.user_id).order_by(Post.addtime.desc()) pagination = query.paginate(page_index, per_page=10, error_out=False) posts = pagination.items return render_template('home/index.html', posts=posts, pagination=pagination,current_user_name=current_user_name,current_user_id=current_user_id) #首页删除个人发布的内容 @home.route("/index/del/") @user_login_req def index_del(): #获取当前登录用户id current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id index_id = request.args.get("id", '0') post = Post.query.get_or_404(int(index_id)) if post.user_id != current_user_id: flash("删除不合法") return redirect(url_for("home.index")) db.session.delete(post) db.session.commit() flash("删除成功") return redirect(url_for("home.index")) #设置帖子关注 @home.route("/index/col/") @user_login_req def index_col(): #获取当前登录用户id current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id index_id = request.args.get("id", '0') col_check = Col.query.filter_by(id=index_id).count() if col_check == 0: col=Col( post_id=index_id, user_id=current_user_id ) db.session.add(col) db.session.commit() flash("收藏成功","ok") flash("收藏已存在","err") return redirect(url_for("home.index")) #设置评论关注 @home.route("/play/col/") @user_login_req def play_col(): #获取当前登录用户id current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id index_id = request.args.get("id", '0') col_check = Col.query.filter_by(id=index_id).count() if col_check == 0: col=Col( comment_id=index_id, user_id=current_user_id ) db.session.add(col) db.session.commit() flash("收藏成功","ok") flash("收藏已存在","err") return redirect(url_for("home.index")) # # from io import BytesIO # from . import verify_code # @home.route('/code') # def get_code(): # image, code = verify_code.get_verify_code() # # 图片以二进制形式写入 # buf = BytesIO() # image.save(buf, 'jpeg') # buf_str = buf.getvalue() # # 把buf_str作为response返回前端，并设置首部字段 # response = verify_code.make_response(buf_str) # response.headers['Content-Type'] = 'image/gif' # # 将验证码字符串储存在session中 # session['image'] = code # return response @home.route('/activate/<token>') def activate(token): #验证token 提取id if User.check_active_token(token): flash("账户已经激活") return redirect(url_for("home.user_login")) else: flash("激活失败") return redirect(url_for("home.index")) # 登录路由 @home.route("/login/", methods=["POST", "GET"]) def user_login(): form = LoginForm() if form.validate_on_submit(): data = form.data user = User.query.filter_by(name=data["name"]).first() print("登录按钮被点击") # if session.get('image').lower() != form.verify_code.data.lower(): # flash('验证码错误') # return render_template('home/user_login.html', form=form) print("用户激活状态"+str(user.activate)) if user.activate: if not user.check_pwd(data["pwd"]): flash("用户名或密码错误！") return redirect(url_for("home.user_login")) session["user"] = data["name"] #session["user_id"] = user.id userloginlog = UserLoginLog( user_id=user.id, ip=request.remote_addr, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) db.session.add(userloginlog) db.session.commit() return redirect(request.args.get('next') or url_for("home.index")) else: flash("用户尚未激活,请激活以后再登录") return render_template('home/user_login.html', form=form) # 登出路由 @home.route("/logout/") @user_login_req def logout(): session.pop("user") return redirect(url_for("home.user_login")) # 会员注册 @home.route("/register/", methods=['GET', "POST"]) def register(): form = RegistForm() if form.validate_on_submit(): data = form.data user = User( name=data["name"], email=data["email"], phone=data["phone"], pwd=generate_password_hash(data["pwd"]), uuid=uuid.uuid4().hex, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) print(user) check = User.query.filter_by(name=data["name"]).count() if check == 0: db.session.add(user) db.session.commit() print("用户数据提交到数据库") token = user.generate_active_token() # 发送用户账户激活的邮件 send_mail(user.email, '激活您的账户', 'email/activate', username=user.name, token=token) # 弹出消息提示用户 flash("注册成功，请点击邮件中的链接完成激活",'ok') return redirect(url_for("home.user_login")) flash("用户名已存在","err") return render_template('home/register.html', form=form) # 修改文件名称 def change_filename(filename): fileinfo = os.path.splitext(filename) # 对名字进行前后缀分离 #注意此处datetime.now() filename = time.strftime("%Y%m%d%H%M%S") + "_" + fileinfo[-1] # 生成新文件名 return filename # 用户中心 @home.route("/user/", methods=["GET", "POST"]) @user_login_req def user(): form = UserdetailForm() user = User.query.filter_by(name=(session["user"])).first() if user.face is not None: form.face.validators = [] if request.method == "GET": form.name.data = user.name form.email.data = user.email form.phone.data = user.phone form.info.data = user.info if form.validate_on_submit(): print('button pressed') data = form.data # if data["name"] != user.name and name_count == 1: # flash("用户名已被占用") # return redirect(url_for("home.user")) if request.method == 'POST': if request.files['imageup']: file = request.files['imageup'] print("获取文件成功") filename = secure_filename(str(hash(file.filename)))+str(user.id)+".jpg" print("secure成功"+filename) del_face = user.face file.save(os.path.join(current_app.config['UP_DIR']+os.sep+"users",filename)) print("上传成功" + filename) #os.remove(os.path.join(app.config['UP_DIR'] + os.sep+"users", del_face)) print("删除文件"+del_face+"成功") user.face = filename user.name=data["name"] user.email=data["email"] user.phone=data["phone"] user.info=data["info"] db.session.add(user) db.session.commit() flash("修改成功！") return redirect(url_for("home.user")) flash("失败") return render_template('home/user.html', form=form, user=user) @home.route("/pwd/", methods=["GET", "POST"]) @user_login_req def pwd(): form = PwdForm() if form.validate_on_submit(): data = form.data user = User.query.filter_by(name=session["user"]).first() user.pwd = <PASSWORD>_password_hash(data["new_pwd"]) db.session.add(user) db.session.commit() flash("修改密码成功，请重新登录！", "ok") return redirect(url_for("home.logout")) return render_template('home/pwd.html', form=form) # 会员中心评论列表评论功能在paly路由中 @home.route("/comments/") @user_login_req def comments(): user_name = session["user"] user = User.query.filter_by(name=user_name).first() page = request.args.get('page', 1, type=int) # query = Comment.query.order_by(Comment.addtime.desc()) query = Comment.query.filter(Comment.user_id == user.id).order_by(Comment.addtime.desc()) pagination = query.paginate(page, per_page=10, error_out=False) comments = pagination.items return render_template('home/comments.html', user=user,user_name=user_name, comments=comments,pagination=pagination) @home.route("/comments/del/") @user_login_req def comment_del(): comment_id = request.args.get("id", '') comment = Comment.query.get_or_404(int(comment_id)) db.session.delete(comment) db.session.commit() flash("评论删除成功") return redirect(url_for("home.comments")) @home.route("/postrecords/") @user_login_req def postrecords(): user_name = session["user"] user = User.query.filter_by(name=user_name).first() user_id = user.id user = User.query.filter_by(id=user_id).first() page = request.args.get('page', 1, type=int) # query = Comment.query.order_by(Comment.addtime.desc()) query = Post.query.filter(Post.user_id == user_id).order_by(Post.addtime.desc()) pagination = query.paginate(page, per_page=5, error_out=False) posts = pagination.items return render_template('home/post_records.html', user=user,user_name=user_name, posts=posts, pagination=pagination) @home.route("/postrecords/del/") @user_login_req def post_del(): post_id = request.args.get("id", '') post = Post.query.get_or_404(int(post_id)) comment = Comment.query.filter_by(post_id=post_id).all() db.session.delete(post) db.session.commit() db.session.delete(comment) db.session.commit() flash("主题帖删除成功") return redirect(url_for("home.postrecords")) @home.route("/loginlog/", methods=["POST", "GET"]) @user_login_req def loginlog(): current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() user_login_log = UserLoginLog.query.filter_by( user_id=user.id ).order_by( UserLoginLog.addtime.desc() # 此处限制了查寻到的登录日志为前15条 ).limit(15).all() return render_template("home/loginlog.html", user_login_log=user_login_log) @home.route("/col/del/") @user_login_req def col_del(): current_user_name = session["user"] user= User.query.filter_by(name=current_user_name).first() current_user_id = user.id col_id = request.args.get("id", '') col = Col.query.get_or_404(int(col_id)) if col.user_id != current_user_id: flash("收藏删除不合法") return redirect(url_for("home.col")) db.session.delete(col) db.session.commit() flash("收藏删除成功") return redirect(url_for("home.col")) ##会员中心收藏列表 @home.route("/col/") @user_login_req def col(): current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() user_id = user.id # 获取当前分页页面编号（编号，默认值，类型） page = request.args.get('page', 1, type=int) # 从数据库中查找对应用户的收藏 #query =Col.query.filter_by(user_id =user_id).order_by(Col.addtime.desc()) query = Col.query.join(Post).join(User).filter(Col.user_id==user_id,Col.post_id == Col.post_id).order_by(Col.addtime.desc()) # 对当前贴的评论进行分页（分页号，每页展示的数量，error） pagination = query.paginate(page, per_page=5, error_out=False) # 获得分页后当前页显示的评论 cols = pagination.items # 渲染主题帖展示页面 print(query) return render_template('home/col.html',cols=cols,pagination=pagination) @home.route("/index/") def reindex(): # z此处index重复 return redirect(url_for("home.index")) @home.route('/animation/') def animation(): data = {'sgd.jpg', 'sutstudent.jpg', 'sutsight01.jpg', 'sutsight02.jpg', 'hxxy.jpg'} return render_template('home/animation.html', data=data) @home.route('/search/') def search(): current_user_id = 0 current_user_name = "" if "user" in session: current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id # 获取查询的内容 # search=request.args.get("search",'',type=str) search = request.args.get("search", "搜索结果为空") # print("搜索的的内容"+search) # 获取当前分页页面编号（编号，默认值，类型） page = request.args.get('page', 1, type=int) # 从数据库中查找对应当前主题贴的评论 query = Post.query.filter(Post.title.ilike('%' + search + '%')).order_by(Post.addtime.desc()) # 对当前主题帖的评论数量进行统计 post_count = Post.query.filter(Post.title.ilike('%' + search + '%')).count() # 对当前贴的评论进行分页（分页号，每页展示的数量，error） pagination = query.paginate(page, per_page=5, error_out=False) # 获得分页后当前页显示的评论 comments = pagination.items # 渲染主题帖展示页面 return render_template("home/search.html", search=search, count=post_count, current_user_name=current_user_name,pagination=pagination, results=comments,current_user_id=current_user_id) # 主题帖详情页 @home.route('/play/', methods=["GET", "POST"]) def play(): # 从请求参数拿到请求的post_id post_id = request.args.get("post_id", "") # 评论表单 form = CommentForm() # 清除表单内容 form.data['content'] = "" # 利用post_id找到要显示的主题贴 post = Post.query.filter(Post.id == post_id).first() # 利用post_id在User表中查找作者姓名 author = User.query.filter(User.id == post.user_id).first() # 从session中取得当前登陆中的用户名 current_user_id = 0 current_user_name = '游客' if "user" in session: current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id # 若用户登录则显示评论发布表单 if "user" in session and form.validate_on_submit(): comment = Comment( content=form.data["content"], post_id=int(post_id), user_id=current_user_id, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) db.session.add(comment) db.session.commit() flash("评论提交成功！") # 获取当前分页页面编号（编号，默认值，类型） page = request.args.get('page', 1, type=int) # 从数据库中查找对应当前主题贴的评论 query = Comment.query.join(User).filter(Comment.post_id == post_id).order_by(Comment.addtime.desc()) # 对当前主题帖的评论数量进行统计 comment_count = Comment.query.filter(Comment.post_id == post_id).count() # 对当前贴的评论进行分页（分页号，每页展示的数量，error） pagination = query.paginate(page, per_page=5, error_out=False) # 获得分页后当前页显示的评论 comments = pagination.items # 渲染主题帖展示页面 return render_template("home/play.html", post=post, form=form, comments=comments, pagination=pagination, author=author,current_user_name=current_user_name, count=comment_count,current_user_id=current_user_id) @home.route('/post/', methods=["GET", "POST"]) @user_login_req def post(): form = PostForm() current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id if form.validate_on_submit(): data = form.data post = Post( title=data["title"], content=data["content"], user_id=current_user_id, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) db.session.add(post) db.session.commit() flash("发布主题帖成功") return 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networks/larflow/models/larflow_uresnet.py	LArbys/ublarcvserver	2	221	<filename>networks/larflow/models/larflow_uresnet.py import torch.nn as nn import torch as torch import math import torch.utils.model_zoo as model_zoo ########################################################### # # U-ResNet # U-net witih ResNet modules # # Semantic segmentation network used by MicroBooNE # to label track/shower pixels # # resnet implementation from pytorch.torchvision module # U-net from (cite) # # meant to be copy of caffe version # ########################################################### def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu1 = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.stride = stride self.bypass = None if inplanes!=planes or stride>1: self.bypass = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False) self.relu = nn.ReLU(inplace=True) def forward(self, x): out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) if self.bypass is not None: outbp = self.bypass(x) out += outbp else: out += x out = self.relu(out) return out class Bottleneck(nn.Module): def __init__(self, inplanes, planes, stride=1 ): super(Bottleneck, self).__init__() # residual path self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.stride = stride # if stride >1, then we need to subsamble the input if stride>1: self.shortcut = nn.Conv2d(inplanes,planes,kernel_size=1,stride=stride,bias=False) else: self.shortcut = None def forward(self, x): if self.shortcut is None: bypass = x else: bypass = self.shortcut(x) residual = self.conv1(x) residual = self.bn1(residual) residual = self.relu(residual) residual = self.conv2(residual) residual = self.bn2(residual) residual = self.relu(residual) residual = self.conv3(residual) residual = self.bn3(residual) out = bypass+residual out = self.relu(out) return out class PreactivationBlock(nn.Module): def __init__(self, inplanes, planes, stride=1 ): super(Preactivation, self).__init__() # residual path self.bn1 = nn.BatchNorm2d(inplanes) self.relu1 = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.relu2 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) # if stride >1, then we need to subsamble the input if stride>1: self.shortcut = nn.Conv2d(inplanes,planes,kernel_size=1,stride=stride,bias=False) else: self.shortcut = None def forward(self, x): if self.shortcut is None: bypass = x else: bypass = self.shortcut(x) class DoubleResNet(nn.Module): def __init__(self,Block,inplanes,planes,stride=1): super(DoubleResNet,self).__init__() self.res1 = Block(inplanes,planes,stride) self.res2 = Block( planes,planes, 1) def forward(self, x): out = self.res1(x) out = self.res2(out) return out class ConvTransposeLayer(nn.Module): def __init__(self,deconv_inplanes,skip_inplanes,deconv_outplanes,res_outplanes): super(ConvTransposeLayer,self).__init__() self.deconv = nn.ConvTranspose2d( deconv_inplanes, deconv_outplanes, kernel_size=4, stride=2, padding=1, bias=False ) self.res = DoubleResNet(BasicBlock,deconv_outplanes+skip_inplanes,res_outplanes,stride=1) def forward(self,x,skip_x): out = self.deconv(x,output_size=skip_x.size()) # concat skip connections out = torch.cat( [out,skip_x], 1 ) out = self.res(out) return out class LArFlowUResNet(nn.Module): def __init__(self, num_classes=3, input_channels=3, inplanes=16, showsizes=False, use_visi=True): self.inplanes =inplanes super(LArFlowUResNet, self).__init__() self._showsizes = showsizes # print size at each layer self.use_visi = use_visi # Encoder # stem # one big stem self.conv1 = nn.Conv2d(input_channels, self.inplanes, kernel_size=7, stride=1, padding=3, bias=True) # initial conv layer self.bn1 = nn.BatchNorm2d(self.inplanes) self.relu1 = nn.ReLU(inplace=True) self.pool1 = nn.MaxPool2d( 3, stride=2, padding=1 ) self.enc_layer1 = self._make_encoding_layer( self.inplanes1, self.inplanes2, stride=1) # 16->32 self.enc_layer2 = self._make_encoding_layer( self.inplanes2, self.inplanes4, stride=2) # 32->64 self.enc_layer3 = self._make_encoding_layer( self.inplanes4, self.inplanes8, stride=2) # 64->128 self.enc_layer4 = self._make_encoding_layer( self.inplanes8, self.inplanes16, stride=2) # 128->256 self.enc_layer5 = self._make_encoding_layer( self.inplanes16, self.inplanes32, stride=2) # 256->512 # decoding flow #self.num_final_flow_features = self.inplanes self.num_final_flow_features = self.inplanes self.flow_dec_layer5 = self._make_decoding_layer( self.inplanes322, self.inplanes16, self.inplanes16, self.inplanes16 ) # 512->256 self.flow_dec_layer4 = self._make_decoding_layer( self.inplanes16, self.inplanes8, self.inplanes8, self.inplanes8 ) # 256->128 self.flow_dec_layer3 = self._make_decoding_layer( self.inplanes8, self.inplanes4, self.inplanes4, self.inplanes4 ) # 128->64 self.flow_dec_layer2 = self._make_decoding_layer( self.inplanes4, self.inplanes2, self.inplanes2, self.inplanes2 ) # 64->32 #self.flow_dec_layer1 = self._make_decoding_layer( self.inplanes2, self.inplanes, self.inplanes ) # 32->16 self.flow_dec_layer1 = self._make_decoding_layer( self.inplanes2, self.inplanes, self.inplanes, self.num_final_flow_features ) # 32->200 # decoding matchability if self.use_visi: self.visi_dec_layer5 = self._make_decoding_layer( self.inplanes322, self.inplanes16, self.inplanes16, self.inplanes16 ) # 512->256 self.visi_dec_layer4 = self._make_decoding_layer( self.inplanes16, self.inplanes8, self.inplanes8, self.inplanes8 ) # 256->128 self.visi_dec_layer3 = self._make_decoding_layer( self.inplanes8, self.inplanes4, self.inplanes4, self.inplanes4 ) # 128->64 self.visi_dec_layer2 = self._make_decoding_layer( self.inplanes4, self.inplanes2, self.inplanes2, self.inplanes2 ) # 64->32 self.visi_dec_layer1 = self._make_decoding_layer( self.inplanes2, self.inplanes, self.inplanes, self.inplanes ) # 32->16 # 1x1 conv for flow self.flow_conv = nn.Conv2d( self.num_final_flow_features, 1, kernel_size=1, stride=1, padding=0, bias=True ) # 1x1 conv for mathability if self.use_visi: self.visi_conv = nn.Conv2d( self.inplanes, 2, kernel_size=1, stride=1, padding=0, bias=True ) # 2 classes, 0=not vis, 1=vis self.visi_softmax = nn.LogSoftmax(dim=1) # initialization for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m,nn.ConvTranspose2d): n = m.kernel_size[0] m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_encoding_layer(self, inplanes, planes, stride=2): return DoubleResNet(BasicBlock,inplanes,planes,stride=stride) def _make_decoding_layer(self, inplanes, skipplanes, deconvplanes, resnetplanes ): return ConvTransposeLayer( inplanes, skipplanes, deconvplanes, resnetplanes ) def encode(self,x): # stem x = self.conv1(x) x = self.bn1(x) x0 = self.relu1(x) x = self.pool1(x0) x1 = self.enc_layer1(x) x2 = self.enc_layer2(x1) x3 = self.enc_layer3(x2) x4 = self.enc_layer4(x3) x5 = self.enc_layer5(x4) if self._showsizes: print "after encoding: " print " x1: ",x1.size() print " x2: ",x2.size() print " x3: ",x3.size() print " x4: ",x4.size() print " x5: ",x5.size() return x5,x0,x1,x2,x3,x4 def flow(self,merged_encode,x0,x1,x2,x3,x4): """ decoding to flow prediction """ x = self.flow_dec_layer5(merged_encode,x4) if self._showsizes: print "after decoding:" print " dec5: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer4(x,x3) if self._showsizes: print " dec4: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer3(x,x2) if self._showsizes: print " dec3: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer2(x,x1) if self._showsizes: print " dec2: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer1(x,x0) if self._showsizes: print " dec1: ",x.size()," iscuda=",x.is_cuda return x def visibility(self,merged_encode,x0,x1,x2,x3,x4): """ decoding to flow prediction """ x = self.visi_dec_layer5(merged_encode,x4) if self._showsizes: print "after decoding:" print " dec5: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer4(x,x3) if self._showsizes: print " dec4: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer3(x,x2) if self._showsizes: print " dec3: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer2(x,x1) if self._showsizes: print " dec2: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer1(x,x0) if self._showsizes: print " dec1: ",x.size()," iscuda=",x.is_cuda return x def forward(self, src, target): if self._showsizes: print "input: ",x.size()," is_cuda=",x.is_cuda src_encode, s0, s1, s2, s3, s4 = self.encode(src) target_encode, t0, t1, t2, t3, t4 = self.encode(target) merged_encode = torch.cat( [target_encode,src_encode], 1 ) flowout = self.flow( merged_encode, s0, s1, s2, s3, s4 ) if self.use_visi: visiout = self.visibility( merged_encode, t0, t1, t2, t3, t4 ) flow_predict = self.flow_conv( flowout ) if self.use_visi: visi_predict = self.visi_conv( visiout ) visi_predict = self.visi_softmax(visi_predict) else: visi_predict = None if self._showsizes: print " softmax: ",x.size() return flow_predict,visi_predict	[ 1, 529, 9507, 29958, 11618, 29879, 29914, 4675, 1731, 29914, 9794, 29914, 4675, 1731, 29918, 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relaax/algorithms/ddpg/parameter_server.py	deeplearninc/relaax	71	6738	from __future__ import absolute_import from relaax.server.parameter_server import parameter_server_base from relaax.server.common import session from . import ddpg_model class ParameterServer(parameter_server_base.ParameterServerBase): def init_session(self): self.session = session.Session(ddpg_model.SharedParameters()) self.session.op_initialize() self.session.op_init_target_weights() def n_step(self): return self.session.op_n_step() def score(self): return self.session.op_score() def get_session(self): return self.session	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 13, 3166, 10208, 1165, 29889, 2974, 29889, 15501, 29918, 2974, 1053, 3443, 29918, 2974, 29918, 3188, 13, 3166, 10208, 1165, 29889, 2974, 29889, 9435, 1053, 4867, 13, 13, 3166, 869, 1053, 24488, 4061, 29918, 4299, 13, 13, 13, 1990, 24953, 6004, 29898, 15501, 29918, 2974, 29918, 3188, 29889, 9329, 6004, 5160, 1125, 13, 1678, 822, 2069, 29918, 7924, 29898, 1311, 1125, 13, 4706, 1583, 29889, 7924, 353, 4867, 29889, 7317, 29898, 1289, 4061, 29918, 4299, 29889, 21741, 11507, 3101, 13, 4706, 1583, 29889, 7924, 29889, 459, 29918, 24926, 580, 13, 4706, 1583, 29889, 7924, 29889, 459, 29918, 2344, 29918, 5182, 29918, 705, 5861, 580, 13, 13, 1678, 822, 302, 29918, 10568, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 7924, 29889, 459, 29918, 29876, 29918, 10568, 580, 13, 13, 1678, 822, 8158, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 7924, 29889, 459, 29918, 13628, 580, 13, 13, 1678, 822, 679, 29918, 7924, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 7924, 13, 2 ]
PyBank/main.py	fannie-chang/Python-Challenge	0	123615	<reponame>fannie-chang/Python-Challenge<filename>PyBank/main.py # Import os module to read CSV file import os import csv # Path to collect data from the PyBank folder budget_csv = os.path.join('..' ,'PyBank','Resources','budget_data.csv') # Open the file with open(budget_csv, newline='') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') # Skip the first row of the CSV file csv_header = next (csvreader, None) #Lists to budget data date=[] profit_losses=[] trend_change = [] total_net_amount = 0 total_trend_change = 0 # Loop through the data for row in csvreader: date.append(row[0]) profit_losses.append(row[1]) # Calculate total months total_months = list(date) num_months = len(total_months) # Calculate total net amount total_net_amount = total_net_amount + int(row[1]) # Loop through data for profit_losses list for i in range(len(profit_losses)-1): # Calculate average trend changes (current value - previous value) trend_change.append(int(profit_losses[i+1]) - int(profit_losses[i])) total_trend_change = total_trend_change + (int(profit_losses[i+1]) - int(profit_losses[i])) trend_average = round(total_trend_change / (len(total_months)-1),2) # Calculate greatest increase and decrease greatest_increase = max (trend_change) greatest_decrease = min (trend_change) # Find date for greatest increase and decrease max_date_index = trend_change.index(greatest_increase) max_date = date[max_date_index +1] min_date_index = trend_change.index(greatest_decrease) min_date = date[min_date_index +1] # Print result print(f"Financial Analysis ") print(f"-------------------------------- ") print(f"Total Months: {num_months}") print(f"Total:${total_net_amount} ") print(f"Average Change: ${trend_average}") print(f"Greatest Increase in Profits: {max_date} ${greatest_increase}") print(f"Greatest Decrease in Profits: {min_date} ${greatest_decrease}") #Print result to text file output_path = os.path.join('..' ,'PyBank','Analysis','financial_analysis.txt') with open (output_path, 'w', newline="") as textfile: print(f"Financial Analysis ", file=textfile) print(f"--------------------------------", file=textfile ) print(f"Total Months: {num_months}", file=textfile) print(f"Total:${total_net_amount} ", file=textfile) print(f"Average Change: ${trend_average}", file=textfile) print(f"Greatest Increase in Profits: {max_date} ${greatest_increase}", file=textfile) print(f"Greatest Decrease in Profits: {min_date} ${greatest_decrease}", file=textfile)	[ 1, 529, 276, 1112, 420, 29958, 29888, 812, 347, 29899, 305, 574, 29914, 11980, 29899, 1451, 11768, 29966, 9507, 29958, 19737, 29933, 804, 29914, 3396, 29889, 2272, 13, 13, 29937, 16032, 2897, 3883, 304, 1303, 16874, 934, 13, 13, 5215, 2897, 13, 5215, 11799, 13, 13, 13, 29937, 10802, 304, 6314, 848, 515, 278, 10772, 29933, 804, 4138, 13, 13, 15841, 657, 29918, 7638, 353, 2897, 29889, 2084, 29889, 7122, 877, 636, 29915, 1919, 29915, 19737, 29933, 804, 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aiidalab_widgets_base/export.py	danielhollas/aiidalab-widgets-base	2	68805	<filename>aiidalab_widgets_base/export.py """Widgets to manage AiiDA export.""" import os from IPython.display import display from ipywidgets import Button class ExportButtonWidget(Button): """Export Node button.""" def __init__(self, process, kwargs): self.process = process if "description" not in kwargs: kwargs["description"] = "Export workflow ({})".format(self.process.id) if "layout" not in kwargs: kwargs["layout"] = {} kwargs["layout"]["width"] = "initial" super(ExportButtonWidget, self).__init__(kwargs) self.on_click(self.export_aiida_subgraph) def export_aiida_subgraph(self, change=None): # pylint: disable=unused-argument """Perform export when the button is pressed""" import base64 import subprocess from tempfile import mkdtemp from IPython.display import Javascript fname = os.path.join(mkdtemp(), "export.aiida") subprocess.call( ["verdi", "export", "create", fname, "-N", str(self.process.id)] ) with open(fname, "rb") as fobj: b64 = base64.b64encode(fobj.read()) payload = b64.decode() javas = Javascript( """ var link = document.createElement('a'); link.href = "data:;base64,{payload}" link.download = "{filename}" document.body.appendChild(link); link.click(); document.body.removeChild(link); """.format( payload=payload, filename="export_{}.aiida".format(self.process.id) ) ) display(javas)	[ 1, 529, 9507, 29958, 1794, 23670, 370, 29918, 8030, 29879, 29918, 3188, 29914, 15843, 29889, 2272, 13, 15945, 29908, 8801, 29879, 304, 10933, 319, 2236, 7698, 5609, 1213, 15945, 13, 5215, 2897, 13, 13, 3166, 5641, 1656, 29889, 4990, 1053, 2479, 13, 3166, 474, 2272, 8030, 29879, 1053, 11025, 13, 13, 13, 1990, 1222, 637, 3125, 8801, 29898, 3125, 1125, 13, 1678, 9995, 26382, 9071, 2826, 1213, 15945, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1889, 29892, 3579, 19290, 1125, 13, 4706, 1583, 29889, 5014, 353, 1889, 13, 4706, 565, 376, 8216, 29908, 451, 297, 9049, 5085, 29901, 13, 9651, 9049, 5085, 3366, 8216, 3108, 353, 376, 26382, 27321, 21313, 1800, 1642, 4830, 29898, 1311, 29889, 5014, 29889, 333, 29897, 13, 4706, 565, 376, 2680, 29908, 451, 297, 9049, 5085, 29901, 13, 9651, 9049, 5085, 3366, 2680, 3108, 353, 6571, 13, 4706, 9049, 5085, 3366, 2680, 3108, 3366, 2103, 3108, 353, 376, 11228, 29908, 13, 4706, 2428, 29898, 26382, 3125, 8801, 29892, 1583, 467, 1649, 2344, 12035, 1068, 19290, 29897, 13, 4706, 1583, 29889, 265, 29918, 3808, 29898, 1311, 29889, 15843, 29918, 1794, 1458, 29918, 1491, 4262, 29897, 13, 13, 1678, 822, 5609, 29918, 1794, 1458, 29918, 1491, 4262, 29898, 1311, 29892, 1735, 29922, 8516, 1125, 29871, 396, 282, 2904, 524, 29901, 11262, 29922, 348, 3880, 29899, 23516, 13, 4706, 9995, 5894, 689, 5609, 746, 278, 2826, 338, 15385, 15945, 29908, 13, 4706, 1053, 2967, 29953, 29946, 13, 4706, 1053, 1014, 5014, 13, 4706, 515, 5694, 1445, 1053, 14690, 29881, 7382, 13, 13, 4706, 515, 5641, 1656, 29889, 4990, 1053, 12728, 13, 13, 4706, 285, 978, 353, 2897, 29889, 2084, 29889, 7122, 29898, 11256, 29881, 7382, 3285, 376, 15843, 29889, 1794, 1458, 1159, 13, 4706, 1014, 5014, 29889, 4804, 29898, 13, 9651, 6796, 369, 6051, 613, 376, 15843, 613, 376, 3258, 613, 285, 978, 29892, 11663, 29940, 613, 851, 29898, 1311, 29889, 5014, 29889, 333, 4638, 13, 4706, 1723, 13, 4706, 411, 1722, 29898, 29888, 978, 29892, 376, 6050, 1159, 408, 285, 5415, 29901, 13, 9651, 289, 29953, 29946, 353, 2967, 29953, 29946, 29889, 29890, 29953, 29946, 12508, 29898, 29888, 5415, 29889, 949, 3101, 13, 9651, 20092, 353, 289, 29953, 29946, 29889, 13808, 580, 13, 4706, 432, 2449, 353, 12728, 29898, 13, 9651, 9995, 13, 9651, 722, 1544, 353, 1842, 29889, 22662, 877, 29874, 2157, 13, 9651, 1544, 29889, 12653, 353, 376, 1272, 29901, 29936, 3188, 29953, 29946, 29892, 29912, 23813, 5038, 13, 9651, 1544, 29889, 10382, 353, 29850, 9507, 5038, 13, 9651, 1842, 29889, 2587, 29889, 23850, 29898, 2324, 416, 13, 9651, 1544, 29889, 3808, 890, 13, 9651, 1842, 29889, 2587, 29889, 5992, 5938, 29898, 2324, 416, 13, 9651, 5124, 1642, 4830, 29898, 13, 18884, 20092, 29922, 23813, 29892, 10422, 543, 15843, 648, 1836, 1794, 1458, 1642, 4830, 29898, 1311, 29889, 5014, 29889, 333, 29897, 13, 9651, 1723, 13, 4706, 1723, 13, 4706, 2479, 29898, 29926, 2449, 29897, 13, 2 ]
flopy/utils/mfgrdfile.py	pjhaest/flopy	0	162412	import numpy as np import collections from ..utils.utils_def import FlopyBinaryData from ..utils.reference import SpatialReference class MfGrdFile(FlopyBinaryData): def __init__(self, filename, precision='double', verbose=False): """ Class constructor. """ super(MfGrdFile, self).__init__() self.set_float(precision=precision) self.verbose = verbose self._initial_len = 50 self._recorddict = collections.OrderedDict() self._datadict = collections.OrderedDict() self._recordkeys = [] self.file = open(filename, 'rb') """ # read header information GRID DISV VERSION 1 NTXT 13 LENTXT 100 """ # grid type line = self.read_text(self._initial_len).strip() t = line.split() self._grid = t[1] # version line = self.read_text(self._initial_len).strip() t = line.split() self._version = t[1] # version line = self.read_text(self._initial_len).strip() t = line.split() self._ntxt = int(t[1]) # length of text line = self.read_text(self._initial_len).strip() t = line.split() self._lentxt = int(t[1]) # read text strings for idx in range(self._ntxt): line = self.read_text(self._lentxt).strip() t = line.split() key = t[0] dt = t[1] if dt == 'INTEGER': dtype = np.int32 elif dt == 'SINGLE': dtype = np.float32 elif dt == 'DOUBLE': dtype = np.float64 else: dtype = None nd = int(t[3]) if nd > 0: shp = [int(v) for v in t[4:]] shp = tuple(shp[::-1]) else: shp = (0,) self._recorddict[key] = (dtype, nd, shp) self._recordkeys.append(key) if self.verbose: print('read {} records from {}'.format(self._ntxt, filename)) for key in self._recordkeys: dt, nd, shp = self._recorddict[key] # read array data if nd > 0: count = 1 for v in shp: count *= v v = self.read_record(count=count, dtype=dt) # read variable data else: if dt == np.int32: v = self.read_integer() elif dt == np.float32: v = self.read_real() elif dt == np.float64: v = self.read_real() self._datadict[key] = v # set the spatial reference self.sr = self._set_spatialreference() def _set_spatialreference(self): try: if self._grid == 'DISV': sr = None elif self._grid == 'DIS': delr, delc = self._datadict['DELR'], self._datadict['DELC'] xorigin, yorigin, rot = self._datadict['XORIGIN'], \ self._datadict['YORIGIN'], \ self._datadict['ANGROT'] sr = SpatialReference(delr=delr, delc=delc, xll=xorigin, yll=yorigin, rotation=rot) except: sr = None print('could not set spatial reference for {}'.format(self.file.name)) return sr def get_spatialreference(self): return self.sr def get_centroids(self): x, y = None, None try: if self._grid == 'DISV': x = self._datadict['CELLX'] y = self._datadict['CELLY'] elif self._grid == 'DIS': nlay = self._datadict['NLAY'] x = np.tile(self.sr.xcentergrid.flatten(), nlay) y = np.tile(self.sr.ycentergrid.flatten(), nlay) except: print('could not return centroids' + ' for {}'.format(self.file.name)) return np.column_stack((x, y)) def get_verts(self): if self._grid == 'DISV': try: iverts = [] iavert = self._datadict['IAVERT'] javert = self._datadict['JAVERT'] shpvert = self._recorddict['VERTICES'][2] for ivert in range(self._datadict['NCPL']): i0 = iavert[ivert] - 1 i1 = iavert[ivert+1] - 1 iverts.append((javert[i0:i1]-1).tolist()) if self.verbose: print('returning vertices for {}'.format(self.file.name)) return iverts, self._datadict['VERTICES'].reshape(shpvert) except: print('could not return vertices for {}'.format(self.file.name)) elif self._grid == 'DIS': try: nlay, nrow, ncol = self._datadict['NLAY'], \ self._datadict['NROW'], \ self._datadict['NCOL'] iv = 0 verts = [] iverts = [] for k in range(nlay): for i in range(nrow): for j in range(ncol): ivlist = [] v = self.sr.get_vertices(i, j) for (x, y) in v: verts.append((x, y)) ivlist.append(iv) iv += 1 iverts.append(ivlist) verts = np.array(verts) return iverts, verts except: print('could not return vertices for {}'.format(self.file.name))	[ 1, 1053, 12655, 408, 7442, 13, 5215, 16250, 13, 13, 3166, 6317, 13239, 29889, 13239, 29918, 1753, 1053, 26043, 2272, 25196, 1469, 13, 3166, 6317, 13239, 29889, 5679, 1053, 1706, 15238, 7422, 13, 13, 1990, 341, 29888, 3338, 29881, 2283, 29898, 29943, 417, 2272, 25196, 1469, 1125, 13, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 10422, 29892, 16716, 2433, 8896, 742, 26952, 29922, 8824, 1125, 13, 4706, 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dataworkspace/dataworkspace/tests/eventlog/test_views.py	uktrade/jupyterhub-data-auth-admin	1	130622	<filename>dataworkspace/dataworkspace/tests/eventlog/test_views.py from django.urls import reverse from dataworkspace.tests import factories from dataworkspace.tests.common import BaseAdminTestCase class TestEventLogAdmin(BaseAdminTestCase): def test_event_log_csv_download(self): event1 = factories.EventLogFactory.create( user=self.user, related_object=factories.ReferenceDatasetFactory.create() ) event2 = factories.EventLogFactory.create( related_object=factories.DatabaseFactory.create() ) response = self._authenticated_post( reverse("admin:eventlog_eventlog_changelist"), {"action": "export_events", "_selected_action": [event1.id, event2.id]}, ) self.assertContains(response, '"timestamp","user","event_type","related_object","extra"') self.assertEqual(len(response.content.splitlines()), 3)	[ 1, 529, 9507, 29958, 1272, 1287, 3493, 29914, 1272, 1287, 3493, 29914, 21150, 29914, 3696, 1188, 29914, 1688, 29918, 7406, 29889, 2272, 13, 3166, 9557, 29889, 26045, 1053, 11837, 13, 13, 3166, 848, 1287, 3493, 29889, 21150, 1053, 2114, 3842, 13, 3166, 848, 1287, 3493, 29889, 21150, 29889, 9435, 1053, 7399, 12754, 3057, 8259, 13, 13, 13, 1990, 4321, 2624, 3403, 12754, 29898, 5160, 12754, 3057, 8259, 1125, 13, 1678, 822, 1243, 29918, 3696, 29918, 1188, 29918, 7638, 29918, 10382, 29898, 1311, 1125, 13, 4706, 1741, 29896, 353, 2114, 3842, 29889, 2624, 3403, 5126, 29889, 3258, 29898, 13, 9651, 1404, 29922, 1311, 29889, 1792, 29892, 4475, 29918, 3318, 29922, 17028, 3842, 29889, 7422, 16390, 24541, 5126, 29889, 3258, 580, 13, 4706, 1723, 13, 4706, 1741, 29906, 353, 2114, 3842, 29889, 2624, 3403, 5126, 29889, 3258, 29898, 13, 9651, 4475, 29918, 3318, 29922, 17028, 3842, 29889, 9112, 5126, 29889, 3258, 580, 13, 4706, 1723, 13, 4706, 2933, 353, 1583, 3032, 27218, 630, 29918, 2490, 29898, 13, 9651, 11837, 703, 6406, 29901, 3696, 1188, 29918, 3696, 1188, 29918, 305, 9477, 391, 4968, 13, 9651, 8853, 2467, 1115, 376, 15843, 29918, 13604, 613, 11119, 8391, 29918, 2467, 1115, 518, 3696, 29896, 29889, 333, 29892, 1741, 29906, 29889, 333, 29962, 1118, 13, 4706, 1723, 13, 4706, 1583, 29889, 9294, 21409, 29898, 5327, 29892, 18793, 16394, 3284, 1792, 3284, 3696, 29918, 1853, 3284, 12817, 29918, 3318, 3284, 17833, 29908, 1495, 13, 4706, 1583, 29889, 9294, 9843, 29898, 2435, 29898, 5327, 29889, 3051, 29889, 5451, 9012, 25739, 29871, 29941, 29897, 13, 2 ]
onirim/card/_base.py	cwahbong/onirim-py	0	148550	""" Inner module for enumerations and base types. """ import logging from onirim import util LOGGER = logging.getLogger(__name__) class Color(util.AutoNumberEnum): """ Enumerated colors of cards. Attributes: red blue green yellow """ red = () blue = () green = () yellow = () class Card: """ A card in onirim. All cards in onirim inherits from this class. Methods called by some event has a single parameter `core` (:py:class:`onirim.core.Core`) for accessing the game information. """ _color = None _kind = None def _color_name(self): return self._color.name if self._color else "nocolor" def _kind_name(self): return self._kind.name if self._kind else "nokind" def _class_name(self): return self.__class__.__name__[1:].lower() @property def color(self): """ Color of a card. Returns: The color of card if it has color, None otherwise. Currently only nightmare cards have no color. """ return self._color @property def kind(self): """ Kind of a card if it is a location. Returns: The kind of card if it is a location, None otherwise. """ return self._kind def _do_drawn(self, core): raise NotImplementedError def drawn(self, core): """ Called while this card is drawn. """ LOGGER.debug("A card is drawn, %r.", self) self._do_drawn(core) def _do_play(self, core): raise NotImplementedError def play(self, core): """ Called while this card is played. """ LOGGER.debug( "A card is played, color=%s, kind=%s.", self._color_name(), self._kind_name()) self._do_play(core) def _do_discard(self, core): raise NotImplementedError def discard(self, core): """ Called while this card is discarded. """ LOGGER.debug( "A card is discarded, color=%s, kind=%s.", self._color_name(), self._kind_name()) self._do_discard(core) def __eq__(self, other): return self._kind == other.kind and self._color == other.color def __hash__(self): key_int = self.kind.value if self.kind else 0 color_int = self.color.value if self.color else 0 return key_int * 10 + color_int def __str__(self): return "{1} {0} card".format(self._class_name(), self._color_name()) def __repr__(self): return "Card{{color: {}, kind: {}}}".format(self._color, self._kind) class ColorCard(Card): """A card with color.""" def __init__(self, color): # pylint is not smart enough to recognize enum class so we disable the # function arguments check here. self._color = Color(color) #pylint: disable=too-many-function-args	[ 1, 9995, 13, 27748, 3883, 363, 22447, 800, 322, 2967, 4072, 29889, 13, 15945, 29908, 13, 13, 5215, 12183, 13, 13, 3166, 373, 381, 326, 1053, 3667, 13, 13, 13, 14480, 17070, 353, 12183, 29889, 657, 16363, 22168, 978, 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DAG/pipeline/service.py	zj-myidea/MYIDEA	0	78966	<gh_stars>0 from .model import Vertex, Graph, Edge,db, Pipeline, Track from .config import getlogger from functools import wraps from collections import defaultdict logger = getlogger(__name__,'./recode.log') def transactional(fn): @wraps(fn) def wrapper(args, kwargs): ret = fn(args, **kwargs) try: db.session.commit() except Exception as e: db.session.rollback() logger.error(e) return ret return wrapper @transactional def create_graph(name,desc=None): graph = Graph() graph.name = name graph.desc = desc db.session.add(graph) return graph @transactional def add_vertex(graph:Graph, name:str, input=None, script=None,): vertex = Vertex() vertex.g_id = graph.id vertex.name = name vertex.input = input vertex.script = script db.session.add(vertex) return vertex @transactional def add_edge(graph:Graph, tail:Vertex, head:Vertex): edge = Edge() edge.g_id = graph.id edge.head = head.id edge.tail = tail.id db.session.add(edge) return edge def delete_vertex(id): query = db.session.query(Vertex).filter(Vertex.id==id) v = query.first() if v: db.session.query(Edge).filter(Edge.tail == v.id \| Edge.head == v.id).delete() query.delete() return v def check_graph(graph:Graph): query = db.session.query(Vertex).filter(Vertex.g_id==graph.id) vertexs = {vertex.id for vertex in query} query = db.session.query(Edge).filter(Edge.g_id==graph.id) edges = defaultdict(list) ids = set() for edge in query: edges[edge.tail].append((edge.tail, edge.head)) ids.add(edge.head) if len(edges) == 0: print('edge') return False zds = vertexs - ids if len(zds): # {a:[(a,b),(a,c)],b:[(b,d)]} for zd in zds: if zd in edges: edges.pop(zd) while edges: print(edges) vertexs = ids ids = set() for lis in edges.values(): for edge in lis: ids.add(edge[1]) zds = vertexs - ids if not len(zds): print('zds') break else: for zd in zds: edges.pop(zd) if len(edges) == 0: try: graph = db.session.query(Graph).filter(Graph.id ==graph.id).first() if graph: graph.checked = 1 db.session.add(graph) db.session.commit() print('++++++++++++++++++++') return True except Exception as e: db.session.rollback() raise e print('~~~~~~~~~~~~') return False	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 869, 4299, 1053, 1798, 4776, 29892, 12367, 29892, 21086, 29892, 2585, 29892, 349, 23828, 29892, 17026, 13, 3166, 869, 2917, 1053, 679, 21707, 13, 3166, 2090, 312, 8789, 1053, 11463, 567, 13, 3166, 16250, 1053, 2322, 8977, 13, 13, 13, 13, 21707, 353, 679, 21707, 22168, 978, 1649, 29892, 4286, 29914, 276, 401, 29889, 1188, 1495, 13, 13, 1753, 10804, 284, 29898, 9144, 1125, 13, 13, 1678, 732, 29893, 336, 567, 29898, 9144, 29897, 13, 1678, 822, 14476, 10456, 5085, 29892, 3579, 19290, 1125, 13, 13, 4706, 3240, 353, 7876, 10456, 5085, 29892, 3579, 19290, 29897, 13, 4706, 1018, 29901, 13, 9651, 4833, 29889, 7924, 29889, 15060, 580, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 4833, 29889, 7924, 29889, 1245, 1627, 580, 13, 9651, 17927, 29889, 2704, 29898, 29872, 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bin/snaptools_wrapper.py	mr-c/sc-atac-seq-pipeline	0	130559	<filename>bin/snaptools_wrapper.py #!/usr/bin/env python3 from argparse import ArgumentParser from os import fspath from pathlib import Path from subprocess import run from typing import Callable, Dict, List, Tuple, Union import snaptools_defaults from utils import find_base_index_path, identity # 3-tuples: # [0] command-line argument to snaptools # [1] default path # [2] function used to adjust the path (both default path and user-provided) # (if no adjustment required, use 'identity') CommandData = Tuple[ str, Path, Callable[ [Path], Path, ], ] SNAPTOOLS_COMMAND_DEFAULTS: Dict[str, List[CommandData]] = { 'align-paired-end': [ ('--input-reference', snaptools_defaults.DEFAULT_ALIGNMENT_INDEX, find_base_index_path), ], 'snap-pre': [ ('--genome-size', snaptools_defaults.DEFAULT_SIZE_INDEX, identity), ], } def run_with_defaults(snaptools_command: str, other_args: List[Union[Path, str]]): args = other_args.copy() # For each command, check known options/arguments with default values. # If the option is present, transform with the provided function (e.g. # finding the "base" path of a genome index). If the option is not present, # append with the default value for option, default, func in SNAPTOOLS_COMMAND_DEFAULTS[snaptools_command]: if option in args: existing_option_index = args.index(option) value_index = existing_option_index + 1 args[value_index] = func(args[value_index]) else: args.append(option) args.append(func(default)) command = ['snaptools', snaptools_command] command.extend(fspath(arg) for arg in args) print('Running:', ' '.join(command)) run(command, check=True) if __name__ == '__main__': p = ArgumentParser() p.add_argument('snaptools_command') p.add_argument( 'other_arg', nargs='*', help='Arguments to the chosen snaptools command', ) args = p.parse_args() run_with_defaults(args.snaptools_command, args.other_arg)	[ 1, 529, 9507, 29958, 2109, 29914, 29879, 1056, 415, 8789, 29918, 17699, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 3166, 1852, 5510, 1053, 23125, 11726, 13, 3166, 2897, 1053, 285, 1028, 493, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 1014, 5014, 1053, 1065, 13, 3166, 19229, 1053, 8251, 519, 29892, 360, 919, 29892, 2391, 29892, 12603, 552, 29892, 7761, 13, 13, 5215, 269, 1056, 415, 8789, 29918, 4381, 29879, 13, 3166, 3667, 29879, 1053, 1284, 29918, 3188, 29918, 2248, 29918, 2084, 29892, 10110, 13, 13, 29937, 29871, 29941, 29899, 9161, 2701, 29901, 13, 29937, 29871, 518, 29900, 29962, 1899, 29899, 1220, 2980, 304, 269, 1056, 415, 8789, 13, 29937, 29871, 518, 29896, 29962, 2322, 2224, 13, 29937, 29871, 518, 29906, 29962, 740, 1304, 304, 10365, 278, 2224, 313, 20313, 2322, 2224, 322, 1404, 29899, 16123, 2618, 29897, 13, 29937, 418, 313, 361, 694, 10365, 358, 3734, 29892, 671, 525, 22350, 1495, 13, 13, 6255, 1469, 353, 12603, 552, 29961, 13, 1678, 851, 29892, 13, 1678, 10802, 29892, 13, 1678, 8251, 519, 29961, 13, 4706, 518, 2605, 1402, 13, 4706, 10802, 29892, 13, 1678, 21251, 13, 29962, 13, 13, 19296, 3301, 4986, 5607, 29903, 29918, 19795, 1529, 2797, 29918, 23397, 29903, 29901, 360, 919, 29961, 710, 29892, 2391, 29961, 6255, 1469, 5262, 353, 426, 13, 1678, 525, 2520, 29899, 3274, 2859, 29899, 355, 2396, 518, 13, 4706, 6702, 489, 2080, 29899, 5679, 742, 269, 1056, 415, 8789, 29918, 4381, 29879, 29889, 23397, 29918, 1964, 17298, 13780, 29918, 27992, 29892, 1284, 29918, 3188, 29918, 2248, 29918, 2084, 511, 13, 1678, 21251, 13, 1678, 525, 29879, 8971, 29899, 1457, 2396, 518, 13, 4706, 6702, 489, 1885, 608, 29899, 2311, 742, 269, 1056, 415, 8789, 29918, 4381, 29879, 29889, 23397, 29918, 14226, 29918, 27992, 29892, 10110, 511, 13, 1678, 21251, 13, 29913, 13, 13, 1753, 1065, 29918, 2541, 29918, 4381, 29879, 29898, 29879, 1056, 415, 8789, 29918, 6519, 29901, 851, 29892, 916, 29918, 5085, 29901, 2391, 29961, 19986, 29961, 2605, 29892, 851, 5262, 1125, 13, 1678, 6389, 353, 916, 29918, 5085, 29889, 8552, 580, 13, 13, 1678, 396, 1152, 1269, 1899, 29892, 1423, 2998, 3987, 29914, 25699, 411, 2322, 1819, 29889, 13, 1678, 396, 960, 278, 2984, 338, 2198, 29892, 4327, 411, 278, 4944, 740, 313, 29872, 29889, 29887, 29889, 13, 1678, 396, 9138, 278, 376, 3188, 29908, 2224, 310, 263, 2531, 608, 2380, 467, 960, 278, 2984, 338, 451, 2198, 29892, 13, 1678, 396, 9773, 411, 278, 2322, 995, 13, 1678, 363, 2984, 29892, 2322, 29892, 3653, 297, 21989, 3301, 4986, 5607, 29903, 29918, 19795, 1529, 2797, 29918, 23397, 29903, 29961, 29879, 1056, 415, 8789, 29918, 6519, 5387, 13, 4706, 565, 2984, 297, 6389, 29901, 13, 9651, 5923, 29918, 3385, 29918, 2248, 353, 6389, 29889, 2248, 29898, 3385, 29897, 13, 9651, 995, 29918, 2248, 353, 5923, 29918, 3385, 29918, 2248, 718, 29871, 29896, 13, 9651, 6389, 29961, 1767, 29918, 2248, 29962, 353, 3653, 29898, 5085, 29961, 1767, 29918, 2248, 2314, 13, 4706, 1683, 29901, 13, 9651, 6389, 29889, 4397, 29898, 3385, 29897, 13, 9651, 6389, 29889, 4397, 29898, 9891, 29898, 4381, 876, 13, 13, 1678, 1899, 353, 6024, 29879, 1056, 415, 8789, 742, 269, 1056, 415, 8789, 29918, 6519, 29962, 13, 1678, 1899, 29889, 21843, 29898, 29888, 1028, 493, 29898, 1191, 29897, 363, 1852, 297, 6389, 29897, 13, 13, 1678, 1596, 877, 27795, 29901, 742, 525, 15300, 7122, 29898, 6519, 876, 13, 1678, 1065, 29898, 6519, 29892, 1423, 29922, 5574, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 282, 353, 23125, 11726, 580, 13, 1678, 282, 29889, 1202, 29918, 23516, 877, 29879, 1056, 415, 8789, 29918, 6519, 1495, 13, 1678, 282, 29889, 1202, 29918, 23516, 29898, 13, 4706, 525, 1228, 29918, 1191, 742, 13, 4706, 302, 5085, 2433, 29930, 742, 13, 4706, 1371, 2433, 26915, 304, 278, 10434, 269, 1056, 415, 8789, 1899, 742, 13, 1678, 1723, 13, 1678, 6389, 353, 282, 29889, 5510, 29918, 5085, 580, 13, 13, 1678, 1065, 29918, 2541, 29918, 4381, 29879, 29898, 5085, 29889, 29879, 1056, 415, 8789, 29918, 6519, 29892, 6389, 29889, 1228, 29918, 1191, 29897, 13, 2 ]
api/app.py	sai-krishna-msk/KickAssist	0	8588	<gh_stars>0 from ml_model.model import KickModel import numpy as np import pandas as pd import eli5 import joblib import flask from flask import Flask, render_template, request, jsonify app = Flask(__name__) model_oh = joblib.load('ml_model/estimators/model_oh.sav') model_hel = joblib.load('ml_model/estimators/model_hel.sav') encoder_oh = joblib.load('ml_model/estimators/encoder_oh.sav') encoder_hel = joblib.load('ml_model/estimators/encoder_hel.sav') encoder_label = joblib.load('ml_model/estimators/encoder_label.sav') def get_predict(launch_date , deadline_date , goal , subcategory , category , currency , country , description, rewards): pred_dict={ "launched_at":launch_date, "deadline":deadline_date, "goal":int(goal), "sub_category":subcategory, "category":category, "currency":currency, "location_country":country, "blurb":description, "rewards":[] } try: for reward in rewards.split(","): pred_dict["rewards"].append(int(reward)) except Exception as e: raise Exception(f"Error sanatizing rewards with {e} error") return pred_dict @app.route('/predict/<launch_date>/<deadline_date>/<goal>/<subcategory>/<category>/<currency>/<country>/<description>/<rewards>') def GetURL(launch_date , deadline_date , goal , subcategory , category , currency , country , description, rewards): pred_dict = get_predict(launch_date , deadline_date , goal , subcategory , category , currency , country , description, rewards) obj = KickModel(model_oh , model_hel , encoder_oh , encoder_hel , encoder_label) obj.load_data(pred_dict) obj.pred() oh_pred = float(obj.pred_oh[0][1]) hel_pred = float(obj.pred_hel[0][1]) response = { "prediction_oh":oh_pred, "prediction_hel":hel_pred, "prediction_oh_df":obj.pred_oh_intr.to_dict(), "prediction_hel_intr":obj.pred_hel_intr.to_dict() } return response if __name__=="__main__": app.run(debug =True)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 286, 29880, 29918, 4299, 29889, 4299, 1053, 476, 860, 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wbia/algo/verif/clf_helpers.py	WildMeOrg/wildbook-ia	20	71552	# -- coding: utf-8 -- """ This module is a work in progress, as such concepts are subject to change. MAIN IDEA: `MultiTaskSamples` serves as a structure to contain and manipulate a set of samples with potentially many different types of labels and features. """ import logging import utool as ut import ubelt as ub import numpy as np from wbia import dtool as dt import pandas as pd import sklearn import sklearn.metrics import sklearn.ensemble import sklearn.impute import sklearn.pipeline import sklearn.neural_network from wbia.algo.verif import sklearn_utils print, rrr, profile = ut.inject2(__name__) logger = logging.getLogger('wbia') class XValConfig(dt.Config): _param_info_list = [ # ut.ParamInfo('type', 'StratifiedKFold'), ut.ParamInfo('type', 'StratifiedGroupKFold'), ut.ParamInfo('n_splits', 3), ut.ParamInfo( 'shuffle', True, hideif=lambda cfg: cfg['type'] == 'StratifiedGroupKFold' ), ut.ParamInfo( 'random_state', 3953056901, hideif=lambda cfg: cfg['type'] == 'StratifiedGroupKFold', ), ] @ut.reloadable_class class ClfProblem(ut.NiceRepr): def __init__(pblm): pblm.deploy_task_clfs = None pblm.eval_task_clfs = None pblm.xval_kw = XValConfig() pblm.eval_task_clfs = None pblm.task_combo_res = None pblm.verbose = True def set_pandas_options(pblm): # pd.options.display.max_rows = 10 pd.options.display.max_rows = 20 pd.options.display.max_columns = 40 pd.options.display.width = 160 pd.options.display.float_format = lambda x: '%.4f' % (x,) def set_pandas_options_low(pblm): # pd.options.display.max_rows = 10 pd.options.display.max_rows = 5 pd.options.display.max_columns = 40 pd.options.display.width = 160 pd.options.display.float_format = lambda x: '%.4f' % (x,) def set_pandas_options_normal(pblm): # pd.options.display.max_rows = 10 pd.options.display.max_rows = 20 pd.options.display.max_columns = 40 pd.options.display.width = 160 pd.options.display.float_format = lambda x: '%.4f' % (x,) def learn_evaluation_classifiers(pblm, task_keys=None, clf_keys=None, data_keys=None): """ Evaluates by learning classifiers using cross validation. Do not use this to learn production classifiers. python -m wbia.algo.verif.vsone evaluate_classifiers --db PZ_PB_RF_TRAIN --show Example: CommandLine: python -m clf_helpers learn_evaluation_classifiers Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> pblm = IrisProblem() >>> pblm.setup() >>> pblm.verbose = True >>> pblm.eval_clf_keys = ['Logit', 'RF'] >>> pblm.eval_task_keys = ['iris'] >>> pblm.eval_data_keys = ['learn(all)'] >>> result = pblm.learn_evaluation_classifiers() >>> res = pblm.task_combo_res['iris']['Logit']['learn(all)'] >>> res.print_report() >>> res = pblm.task_combo_res['iris']['RF']['learn(all)'] >>> res.print_report() >>> print(result) """ pblm.eval_task_clfs = ut.AutoVivification() pblm.task_combo_res = ut.AutoVivification() if task_keys is None: task_keys = pblm.eval_task_keys if data_keys is None: data_keys = pblm.eval_data_keys if clf_keys is None: clf_keys = pblm.eval_clf_keys if task_keys is None: task_keys = [pblm.primary_task_key] if data_keys is None: data_keys = [pblm.default_data_key] if clf_keys is None: clf_keys = [pblm.default_clf_key] if pblm.verbose: ut.cprint('[pblm] learn_evaluation_classifiers', color='blue') ut.cprint('[pblm] task_keys = {}'.format(task_keys)) ut.cprint('[pblm] data_keys = {}'.format(data_keys)) ut.cprint('[pblm] clf_keys = {}'.format(clf_keys)) Prog = ut.ProgPartial(freq=1, adjust=False, prehack='%s') task_prog = Prog(task_keys, label='Task') for task_key in task_prog: dataset_prog = Prog(data_keys, label='Data') for data_key in dataset_prog: clf_prog = Prog(clf_keys, label='CLF') for clf_key in clf_prog: pblm._ensure_evaluation_clf(task_key, data_key, clf_key) def _ensure_evaluation_clf(pblm, task_key, data_key, clf_key, use_cache=True): """ Learns and caches an evaluation (cross-validated) classifier and tests and caches the results. data_key = 'learn(sum,glob)' clf_key = 'RF' """ # TODO: add in params used to construct features into the cfgstr if hasattr(pblm.samples, 'sample_hashid'): ibs = pblm.infr.ibs sample_hashid = pblm.samples.sample_hashid() feat_dims = pblm.samples.X_dict[data_key].columns.values.tolist() # cfg_prefix = sample_hashid + pblm.qreq_.get_cfgstr() + feat_cfgstr est_kw1, est_kw2 = pblm._estimator_params(clf_key) param_id = ut.get_dict_hashid(est_kw1) xval_id = pblm.xval_kw.get_cfgstr() cfgstr = '_'.join( [ sample_hashid, param_id, xval_id, task_key, data_key, clf_key, ut.hashid_arr(feat_dims, 'feats'), ] ) fname = 'eval_clfres_' + ibs.dbname else: fname = 'foo' feat_dims = None cfgstr = 'bar' use_cache = False # TODO: ABI class should not be caching cacher_kw = dict(appname='vsone_rf_train', enabled=use_cache, verbose=1) cacher_clf = ub.Cacher(fname, cfgstr=cfgstr, meta=[feat_dims], *cacher_kw) data = cacher_clf.tryload() if not data: data = pblm._train_evaluation_clf(task_key, data_key, clf_key) cacher_clf.save(data) clf_list, res_list = data labels = pblm.samples.subtasks[task_key] combo_res = ClfResult.combine_results(res_list, labels) pblm.eval_task_clfs[task_key][clf_key][data_key] = clf_list pblm.task_combo_res[task_key][clf_key][data_key] = combo_res def _train_evaluation_clf(pblm, task_key, data_key, clf_key, feat_dims=None): """ Learns a cross-validated classifier on the dataset Ignore: >>> from wbia.algo.verif.vsone import # NOQA >>> pblm = OneVsOneProblem() >>> pblm.load_features() >>> pblm.load_samples() >>> data_key = 'learn(all)' >>> task_key = 'photobomb_state' >>> clf_key = 'RF-OVR' >>> task_key = 'match_state' >>> data_key = pblm.default_data_key >>> clf_key = pblm.default_clf_key """ X_df = pblm.samples.X_dict[data_key] labels = pblm.samples.subtasks[task_key] assert np.all(labels.encoded_df.index == X_df.index) clf_partial = pblm._get_estimator(clf_key) xval_kw = pblm.xval_kw.asdict() clf_list = [] res_list = [] skf_list = pblm.samples.stratified_kfold_indices(xval_kw) skf_prog = ut.ProgIter(skf_list, label='skf-train-eval') for train_idx, test_idx in skf_prog: X_df_train = X_df.iloc[train_idx] assert X_df_train.index.tolist() == ut.take(pblm.samples.index, train_idx) # train_uv = X_df.iloc[train_idx].index # X_train = X_df.loc[train_uv] # y_train = labels.encoded_df.loc[train_uv] if feat_dims is not None: X_df_train = X_df_train[feat_dims] X_train = X_df_train.values y_train = labels.encoded_df.iloc[train_idx].values.ravel() clf = clf_partial() clf.fit(X_train, y_train) # Note: There is a corner case where one fold doesn't get any # labels of a certain class. Because y_train is an encoded integer, # the clf.classes_ attribute will cause predictions to agree with # other classifiers trained on the same labels. # Evaluate results res = ClfResult.make_single( clf, X_df, test_idx, labels, data_key, feat_dims=feat_dims ) clf_list.append(clf) res_list.append(res) return clf_list, res_list def _external_classifier_result( pblm, clf, task_key, data_key, feat_dims=None, test_idx=None ): """ Given an external classifier (ensure its trained on disjoint data) evaluate all data on it. Args: test_idx (list): subset of this classifier to test on (defaults to all if None) """ X_df = pblm.samples.X_dict[data_key] if test_idx is None: test_idx = np.arange(len(X_df)) labels = pblm.samples.subtasks[task_key] res = ClfResult.make_single( clf, X_df, test_idx, labels, data_key, feat_dims=feat_dims ) return res def learn_deploy_classifiers(pblm, task_keys=None, clf_key=None, data_key=None): """ Learns on data without any train/validation split """ if pblm.verbose > 0: ut.cprint('[pblm] learn_deploy_classifiers', color='blue') if clf_key is None: clf_key = pblm.default_clf_key if data_key is None: data_key = pblm.default_data_key if task_keys is None: task_keys = list(pblm.samples.supported_tasks()) if pblm.deploy_task_clfs is None: pblm.deploy_task_clfs = ut.AutoVivification() Prog = ut.ProgPartial(freq=1, adjust=False, prehack='%s') task_prog = Prog(task_keys, label='Task') task_clfs = {} for task_key in task_prog: clf = pblm._train_deploy_clf(task_key, data_key, clf_key) task_clfs[task_key] = clf pblm.deploy_task_clfs[task_key][clf_key][data_key] = clf return task_clfs def _estimator_params(pblm, clf_key): est_type = clf_key.split('-')[0] if est_type in {'RF', 'RandomForest'}: est_kw1 = { # 'max_depth': 4, 'bootstrap': True, 'class_weight': None, 'criterion': 'entropy', 'max_features': 'sqrt', # 'max_features': None, 'min_samples_leaf': 5, 'min_samples_split': 2, # 'n_estimators': 64, 'n_estimators': 256, } # Hack to only use missing values if we have the right sklearn if 'missing_values' in ut.get_func_kwargs( sklearn.ensemble.RandomForestClassifier.__init__ ): est_kw1['missing_values'] = np.nan est_kw2 = { 'random_state': 3915904814, 'verbose': 0, 'n_jobs': -1, } elif est_type in {'SVC', 'SVM'}: est_kw1 = dict(kernel='linear') est_kw2 = {} elif est_type in {'Logit', 'LogisticRegression'}: est_kw1 = {} est_kw2 = {} elif est_type in {'MLP'}: est_kw1 = dict( activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(10, 10), learning_rate='constant', learning_rate_init=0.001, max_iter=200, momentum=0.9, nesterovs_momentum=True, power_t=0.5, random_state=3915904814, shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1, warm_start=False, ) est_kw2 = dict(verbose=False) else: raise KeyError('Unknown Estimator') return est_kw1, est_kw2 def _get_estimator(pblm, clf_key): """ Returns sklearn classifier """ tup = clf_key.split('-') wrap_type = None if len(tup) == 1 else tup[1] est_type = tup[0] multiclass_wrapper = { None: ut.identity, 'OVR': sklearn.multiclass.OneVsRestClassifier, 'OVO': sklearn.multiclass.OneVsOneClassifier, }[wrap_type] est_class = { 'RF': sklearn.ensemble.RandomForestClassifier, 'SVC': sklearn.svm.SVC, 'Logit': sklearn.linear_model.LogisticRegression, 'MLP': sklearn.neural_network.MLPClassifier, }[est_type] est_kw1, est_kw2 = pblm._estimator_params(est_type) est_params = ut.merge_dicts(est_kw1, est_kw2) # steps = [] # steps.append((est_type, est_class(est_params))) # if wrap_type is not None: # steps.append((wrap_type, multiclass_wrapper)) if est_type == 'MLP': def clf_partial(): pipe = sklearn.pipeline.Pipeline( [ ('inputer', sklearn.impute.SimpleImputer(strategy='mean')), # ('scale', sklearn.preprocessing.StandardScaler), ('est', est_class(est_params)), ] ) return multiclass_wrapper(pipe) elif est_type == 'Logit': def clf_partial(): pipe = sklearn.pipeline.Pipeline( [ ('inputer', sklearn.impute.SimpleImputer(strategy='mean')), ('est', est_class(est_params)), ] ) return multiclass_wrapper(pipe) else: def clf_partial(): return multiclass_wrapper(est_class(*est_params)) return clf_partial def _train_deploy_clf(pblm, task_key, data_key, clf_key): X_df = pblm.samples.X_dict[data_key] labels = pblm.samples.subtasks[task_key] assert np.all(labels.encoded_df.index == X_df.index) clf_partial = pblm._get_estimator(clf_key) logger.info( 'Training deployment {} classifier on {} for {}'.format( clf_key, data_key, task_key ) ) clf = clf_partial() index = X_df.index X = X_df.loc[index].values y = labels.encoded_df.loc[index].values.ravel() clf.fit(X, y) return clf def _optimize_rf_hyperparams(pblm, data_key=None, task_key=None): """ helper script I've only run interactively Example: >>> # DISABLE_DOCTEST >>> from wbia.algo.verif.vsone import # NOQA >>> pblm = OneVsOneProblem.from_empty('PZ_PB_RF_TRAIN') #>>> pblm = OneVsOneProblem.from_empty('GZ_Master1') >>> pblm.load_samples() >>> pblm.load_features() >>> pblm.build_feature_subsets() >>> data_key=None >>> task_key=None """ from sklearn.model_selection import RandomizedSearchCV # NOQA from sklearn.model_selection import GridSearchCV # NOQA from sklearn.ensemble import RandomForestClassifier from wbia.algo.verif import sklearn_utils if data_key is None: data_key = pblm.default_data_key if task_key is None: task_key = pblm.primary_task_key # Load data X = pblm.samples.X_dict[data_key].values y = pblm.samples.subtasks[task_key].y_enc groups = pblm.samples.group_ids # Define estimator and parameter search space grid = { 'bootstrap': [True, False], 'class_weight': [None, 'balanced'], 'criterion': ['entropy', 'gini'], # 'max_features': ['sqrt', 'log2'], 'max_features': ['sqrt'], 'min_samples_leaf': list(range(2, 11)), 'min_samples_split': list(range(2, 11)), 'n_estimators': [8, 64, 128, 256, 512, 1024], } est = RandomForestClassifier(missing_values=np.nan) if False: # debug params = ut.util_dict.all_dict_combinations(grid)[0] est.set_params(verbose=10, n_jobs=1, *params) est.fit(X=X, y=y) cv = sklearn_utils.StratifiedGroupKFold(n_splits=3) if True: n_iter = 25 SearchCV = ut.partial(RandomizedSearchCV, n_iter=n_iter) else: n_iter = ut.prod(map(len, grid.values())) SearchCV = GridSearchCV search = SearchCV(est, grid, cv=cv, verbose=10) n_cpus = ut.num_cpus() thresh = n_cpus 1.5 n_jobs_est = 1 n_jobs_ser = min(n_cpus, n_iter) if n_iter < thresh: n_jobs_est = int(max(1, thresh / n_iter)) est.set_params(n_jobs=n_jobs_est) search.set_params(n_jobs=n_jobs_ser) search.fit(X=X, y=y, groups=groups) res = search.cv_results_.copy() alias = ut.odict( [ ('rank_test_score', 'rank'), ('mean_test_score', 'μ-test'), ('std_test_score', 'σ-test'), ('mean_train_score', 'μ-train'), ('std_train_score', 'σ-train'), ('mean_fit_time', 'fit_time'), ('params', 'params'), ] ) res = ut.dict_subset(res, alias.keys()) cvresult_df = pd.DataFrame(res).rename(columns=alias) cvresult_df = cvresult_df.sort_values('rank').reset_index(drop=True) params = pd.DataFrame.from_dict(cvresult_df['params'].values.tolist()) logger.info('Varied params:') logger.info(ut.repr4(ut.map_vals(set, params.to_dict('list')))) logger.info('Ranked Params') logger.info(params) logger.info('Ranked scores on development set:') logger.info(cvresult_df) logger.info('Best parameters set found on hyperparam set:') logger.info('best_params_ = %s' % (ut.repr4(search.best_params_),)) logger.info('Fastest params') cvresult_df.loc[cvresult_df['fit_time'].idxmin()]['params'] def _dev_calib(pblm): """ interactive script only """ from sklearn.ensemble import RandomForestClassifier from sklearn.calibration import CalibratedClassifierCV from sklearn.calibration import calibration_curve from sklearn.metrics import log_loss, brier_score_loss # Load data data_key = pblm.default_data_key task_key = pblm.primary_task_key X = pblm.samples.X_dict[data_key].values y = pblm.samples.subtasks[task_key].y_enc groups = pblm.samples.group_ids # Split into test/train/valid cv = sklearn_utils.StratifiedGroupKFold(n_splits=2) test_idx, train_idx = next(cv.split(X, y, groups)) # valid_idx = train_idx[0::2] # train_idx = train_idx[1::2] # train_valid_idx = np.hstack([train_idx, valid_idx]) # Train Uncalibrated RF est_kw = pblm._estimator_params('RF')[0] uncal_clf = RandomForestClassifier(est_kw) uncal_clf.fit(X[train_idx], y[train_idx]) uncal_probs = uncal_clf.predict_proba(X[test_idx]).T[1] uncal_score = log_loss(y[test_idx] == 1, uncal_probs) uncal_brier = brier_score_loss(y[test_idx] == 1, uncal_probs) # Train Calibrated RF method = 'isotonic' if len(test_idx) > 2000 else 'sigmoid' precal_clf = RandomForestClassifier(est_kw) # cv = sklearn_utils.StratifiedGroupKFold(n_splits=3) cal_clf = CalibratedClassifierCV(precal_clf, cv=2, method=method) cal_clf.fit(X[train_idx], y[train_idx]) cal_probs = cal_clf.predict_proba(X[test_idx]).T[1] cal_score = log_loss(y[test_idx] == 1, cal_probs) cal_brier = brier_score_loss(y[test_idx] == 1, cal_probs) logger.info('cal_brier = %r' % (cal_brier,)) logger.info('uncal_brier = %r' % (uncal_brier,)) logger.info('uncal_score = %r' % (uncal_score,)) logger.info('cal_score = %r' % (cal_score,)) import wbia.plottool as pt ut.qtensure() pt.figure() ax = pt.gca() y_test = y[test_idx] == 1 fraction_of_positives, mean_predicted_value = calibration_curve( y_test, uncal_probs, n_bins=10 ) ax.plot([0, 1], [0, 1], 'k:', label='Perfectly calibrated') ax.plot( mean_predicted_value, fraction_of_positives, 's-', label='%s (%1.3f)' % ('uncal-RF', uncal_brier), ) fraction_of_positives, mean_predicted_value = calibration_curve( y_test, cal_probs, n_bins=10 ) ax.plot( mean_predicted_value, fraction_of_positives, 's-', label='%s (%1.3f)' % ('cal-RF', cal_brier), ) pt.legend() @ut.reloadable_class class ClfResult(ut.NiceRepr): r""" Handles evaluation statistics for a multiclass classifier trained on a specific dataset with specific labels. """ # Attributes that identify the task and data the classifier is evaluated on _key_attrs = ['task_key', 'data_key', 'class_names'] # Attributes about results and labels of individual samples _datafame_attrs = ['probs_df', 'probhats_df', 'target_bin_df', 'target_enc_df'] def __init__(res): pass def __nice__(res): return '{}, {}, {}'.format(res.task_key, res.data_key, len(res.index)) @property def index(res): return res.probs_df.index @classmethod def make_single(ClfResult, clf, X_df, test_idx, labels, data_key, feat_dims=None): """ Make a result for a single cross validiation subset """ X_df_test = X_df.iloc[test_idx] if feat_dims is not None: X_df_test = X_df_test[feat_dims] index = X_df_test.index # clf_probs = clf.predict_proba(X_df_test) # index = pd.Series(test_idx, name='test_idx') # Ensure shape corresponds with all classes def align_cols(arr, arr_cols, target_cols): import utool as ut alignx = ut.list_alignment(arr_cols, target_cols, missing=True) aligned_arrT = ut.none_take(arr.T, alignx) aligned_arrT = ut.replace_nones(aligned_arrT, np.zeros(len(arr))) aligned_arr = np.vstack(aligned_arrT).T return aligned_arr res = ClfResult() res.task_key = labels.task_name res.data_key = data_key res.class_names = ut.lmap(str, labels.class_names) res.feat_dims = feat_dims res.probs_df = sklearn_utils.predict_proba_df(clf, X_df_test, res.class_names) res.target_bin_df = labels.indicator_df.iloc[test_idx] res.target_enc_df = labels.encoded_df.iloc[test_idx] if hasattr(clf, 'estimators_') and labels.n_classes > 2: # The n-th estimator in the OVR classifier predicts the prob of the # n-th class (as label 1). probs_hat = np.hstack( [est.predict_proba(X_df_test)[:, 1:2] for est in clf.estimators_] ) res.probhats_df = pd.DataFrame( align_cols(probs_hat, clf.classes_, labels.classes_), index=index, columns=res.class_names, ) # In the OVR-case, ideally things will sum to 1, but when they # don't normalization happens. An Z-value of more than 1 means # overconfidence, and under 0 means underconfidence. res.confidence_ratio = res.probhats_df.sum(axis=1) else: res.probhats_df = None return res def compress(res, flags): res2 = ClfResult() res2.task_key = res.task_key res2.data_key = res.data_key res2.class_names = res.class_names res2.probs_df = res.probs_df[flags] res2.target_bin_df = res.target_bin_df[flags] res2.target_enc_df = res.target_enc_df[flags] if res.probhats_df is None: res2.probhats_df = None else: res2.probhats_df = res.probhats_df[flags] # res2.confidence_ratio = res.confidence_ratio[flags] return res2 @classmethod def combine_results(ClfResult, res_list, labels=None): """ Combine results from cross validation runs into a single result representing the performance of the entire dataset """ # Ensure that res_lists are not overlapping for r1, r2 in ut.combinations(res_list, 2): assert ( len(r1.index.intersection(r2.index)) == 0 ), 'ClfResult dataframes must be disjoint' # sanity check for r in res_list: assert np.all(r.index == r.probs_df.index) assert np.all(r.index == r.target_bin_df.index) assert np.all(r.index == r.target_enc_df.index) # Combine them with pandas res = ClfResult() res0 = res_list[0] # Transfer single attributes (which should all be the same) for attr in ClfResult._key_attrs: val = getattr(res0, attr) setattr(res, attr, val) assert all( [getattr(r, attr) == val for r in res_list] ), 'ClfResult with different key attributes are incompatible' # Combine dataframe properties (which should all have disjoint indices) for attr in ClfResult._datafame_attrs: if getattr(res0, attr) is not None: combo_attr = pd.concat([getattr(r, attr) for r in res_list]) setattr(res, attr, combo_attr) else: setattr(res, attr, None) for attr in ClfResult._datafame_attrs: val = getattr(res, attr) if val is not None: assert np.all(res.index == val.index), 'index got weird' return res def hardness_analysis(res, samples, infr=None, method='argmax'): """ samples = pblm.samples # TODO MWE with sklearn data # ClfResult.make_single(ClfResult, clf, X_df, test_idx, labels, # data_key, feat_dims=None): import sklearn.datasets iris = sklearn.datasets.load_iris() # TODO: make this setup simpler pblm = ClfProblem() task_key, clf_key, data_key = 'iris', 'RF', 'learn(all)' X_df = pd.DataFrame(iris.data, columns=iris.feature_names) samples = MultiTaskSamples(X_df.index) samples.apply_indicators({'iris': {name: iris.target == idx for idx, name in enumerate(iris.target_names)}}) samples.X_dict = {'learn(all)': X_df} pblm.samples = samples pblm.xval_kw['type'] = 'StratifiedKFold' clf_list, res_list = pblm._train_evaluation_clf( task_key, data_key, clf_key) labels = pblm.samples.subtasks[task_key] res = ClfResult.combine_results(res_list, labels) res.get_thresholds('mcc', 'maximize') predict_method = 'argmax' """ meta = {} easiness = ut.ziptake(res.probs_df.values, res.target_enc_df.values) # pred = sklearn_utils.predict_from_probs(res.probs_df, predict_method) if method == 'max-mcc': method = res.get_thresholds('mcc', 'maximize') pred = sklearn_utils.predict_from_probs(res.probs_df, method, force=True) meta['easiness'] = np.array(easiness).ravel() meta['hardness'] = 1 - meta['easiness'] meta['aid1'] = res.probs_df.index.get_level_values(0) meta['aid2'] = res.probs_df.index.get_level_values(1) # meta['aid1'] = samples.aid_pairs.T[0].take(res.probs_df.index.values) # meta['aid2'] = samples.aid_pairs.T[1].take(res.probs_df.index.values) # meta['pred'] = res.probs_df.values.argmax(axis=1) meta['pred'] = pred.values meta['real'] = res.target_enc_df.values.ravel() meta['failed'] = meta['pred'] != meta['real'] meta = pd.DataFrame(meta) meta = meta.set_index(['aid1', 'aid2'], drop=False) if infr is not None: ibs = infr.ibs edges = list(meta.index.tolist()) conf_dict = infr.get_edge_attrs( 'confidence', edges, on_missing='filter', default=ibs.const.CONFIDENCE.CODE.UNKNOWN, ) conf_df = pd.DataFrame.from_dict(conf_dict, orient='index') conf_df = conf_df[0].map(ibs.const.CONFIDENCE.CODE_TO_INT) meta = meta.assign(real_conf=conf_df) meta['real_conf'] = np.nan_to_num(meta['real_conf']).astype(np.int) meta = meta.sort_values('hardness', ascending=False) res.meta = meta return res.meta def missing_classes(res): # Find classes that were never predicted unique_predictions = np.unique(res.probs_df.values.argmax(axis=1)) n_classes = len(res.class_names) missing_classes = ut.index_complement(unique_predictions, n_classes) return missing_classes def augment_if_needed(res): """ Adds in dummy values for missing classes """ missing_classes = res.missing_classes() n_classes = len(res.class_names) y_test_enc_aug = res.target_enc_df.values y_test_bin_aug = res.target_bin_df.values clf_probs_aug = res.probs_df.values sample_weight = np.ones(len(y_test_enc_aug)) n_missing = len(missing_classes) if res.probhats_df is not None: clf_probhats_aug = res.probhats_df.values else: clf_probhats_aug = None # Check if augmentation is necessary if n_missing > 0: missing_bin = np.zeros((n_missing, n_classes)) missing_bin[(np.arange(n_missing), missing_classes)] = 1.0 missing_enc = np.array(missing_classes)[:, None] y_test_enc_aug = np.vstack([y_test_enc_aug, missing_enc]) y_test_bin_aug = np.vstack([y_test_bin_aug, missing_bin]) clf_probs_aug = np.vstack([clf_probs_aug, missing_bin]) # make sample weights where dummies have no weight sample_weight = np.hstack([sample_weight, np.full(n_missing, 0)]) if res.probhats_df is not None: clf_probhats_aug = np.vstack([clf_probhats_aug, missing_bin]) res.clf_probs = clf_probs_aug res.clf_probhats = clf_probhats_aug res.y_test_enc = y_test_enc_aug res.y_test_bin = y_test_bin_aug res.sample_weight = sample_weight def extended_clf_report(res, verbose=True): res.augment_if_needed() pred_enc = res.clf_probs.argmax(axis=1) y_pred = pred_enc y_true = res.y_test_enc sample_weight = res.sample_weight target_names = res.class_names report = sklearn_utils.classification_report2( y_true, y_pred, target_names=target_names, sample_weight=sample_weight, verbose=verbose, ) return report def print_report(res): res.augment_if_needed() pred_enc = res.clf_probs.argmax(axis=1) res.extended_clf_report() report = sklearn.metrics.classification_report( y_true=res.y_test_enc, y_pred=pred_enc, target_names=res.class_names, sample_weight=res.sample_weight, ) logger.info('Precision/Recall Report:') logger.info(report) def get_thresholds(res, metric='mcc', value='maximize'): """ get_metric = 'thresholds' at_metric = metric = 'mcc' at_value = value = 'maximize' a = [] b = [] for x in np.linspace(0, 1, 1000): a += [cfms.get_metric_at_metric('thresholds', 'fpr', x, subindex=True)] b += [cfms.get_thresh_at_metric('fpr', x)] a = np.array(a) b = np.array(b) d = (a - b) logger.info((d.min(), d.max())) """ threshes = {} for class_name in res.class_names: cfms = res.confusions(class_name) thresh = cfms.get_metric_at_metric('thresh', metric, value) threshes[class_name] = thresh return threshes @profile def get_pos_threshes( res, metric='fpr', value=1e-4, maximize=False, warmup=200, priors=None, min_thresh=0.5, ): """ Finds a threshold that achieves the desired `value` for the desired metric, while maximizing or minimizing the threshold. For positive classification you want to minimize the threshold. Priors can be passed in to augment probabilities depending on support. By default a class prior is 1 for threshold minimization and 0 for maximization. """ pos_threshes = {} if priors is None: priors = {name: float(not maximize) for name in res.class_names} for class_name in res.class_names: cfms = res.confusions(class_name) learned_thresh = cfms.get_metric_at_metric('thresh', metric, value) # learned_thresh = cfms.get_thresh_at_metric( # metric, value, maximize=maximize) prior_thresh = priors[class_name] n_support = cfms.n_pos if warmup is not None: """ python -m wbia.plottool.draw_func2 plot_func --show --range=0,1 \ --func="lambda x: np.maximum(0, (x - .6) / (1 - .6))" """ # If n_support < warmup: then interpolate to learned thresh nmax = warmup if isinstance(warmup, int) else warmup[class_name] # alpha varies from 0 to 1 alpha = min(nmax, n_support) / nmax # transform alpha through nonlinear function (similar to ReLU) p = 0.6 # transition point alpha = max(0, (alpha - p) / (1 - p)) thresh = prior_thresh * (1 - alpha) + learned_thresh * (alpha) else: thresh = learned_thresh pos_threshes[class_name] = max(min_thresh, thresh) return pos_threshes def report_thresholds(res, warmup=200): # import vtool as vt ut.cprint('Threshold Report', 'yellow') y_test_bin = res.target_bin_df.values # y_test_enc = y_test_bin.argmax(axis=1) # clf_probs = res.probs_df.values # The maximum allowed false positive rate # We expect that we will make 1 error every 1,000 decisions # thresh_df['foo'] = [1, 2, 3] # thresh_df['foo'][res.class_names[k]] = 1 # for k in [2, 0, 1]: choice_mv = ut.odict( [ ('@fpr=.01', ('fpr', 0.01)), ('@fpr=.001', ('fpr', 0.001)), ('@fpr=.0001', ('fpr', 1e-4)), ('@fpr=.0000', ('fpr', 0)), ('@max(mcc)', ('mcc', 'max')), # (class_name + '@max(acc)', ('acc', 'max')), # (class_name + '@max(mk)', ('mk', 'max')), # (class_name + '@max(bm)', ('bm', 'max')), ] ) for k in range(y_test_bin.shape[1]): thresh_dict = ut.odict() class_name = res.class_names[k] cfms = res.confusions(class_name) # probs, labels = clf_probs.T[k], y_test_bin.T[k] # cfms = vt.ConfusionMetrics().fit(probs, labels) for k, mv in choice_mv.items(): metric, value = mv idx = cfms.get_index_at_metric(metric, value) key = class_name + k thresh_dict[key] = ut.odict() for metric in ['thresh', 'fpr', 'tpr', 'tpa', 'bm', 'mk', 'mcc']: thresh_dict[key][metric] = cfms.get_metric_at_index(metric, idx) thresh_df = pd.DataFrame.from_dict(thresh_dict, orient='index') thresh_df = thresh_df.loc[list(thresh_dict.keys())] if cfms.n_pos > 0 and cfms.n_neg > 0: logger.info('Raw 1vR {} Thresholds'.format(class_name)) logger.info(ut.indent(thresh_df.to_string(float_format='{:.4f}'.format))) # chosen_type = class_name + '@fpr=0' # pos_threshes[class_name] = thresh_df.loc[chosen_type]['thresh'] for choice_k, choice_mv in iter(choice_mv.items()): metric, value = choice_mv pos_threshes = res.get_pos_threshes(metric, value, warmup=warmup) logger.info('Choosing threshold based on %s' % (choice_k,)) res.report_auto_thresholds(pos_threshes) def report_auto_thresholds(res, threshes, verbose=True): report_lines = [] print_ = report_lines.append print_( 'Chosen thresholds = %s' % (ut.repr2(threshes, nl=1, precision=4, align=True),) ) res.augment_if_needed() target_names = res.class_names sample_weight = res.sample_weight y_true = res.y_test_enc.ravel() y_pred, can_autodecide = sklearn_utils.predict_from_probs( res.clf_probs, threshes, res.class_names, force=False, multi=False, return_flags=True, ) can_autodecide[res.sample_weight == 0] = False auto_pred = y_pred[can_autodecide].astype(np.int) auto_true = y_true[can_autodecide].ravel() auto_probs = res.clf_probs[can_autodecide] total_cases = int(sample_weight.sum()) print_('Will autodecide for %r/%r cases' % (can_autodecide.sum(), (total_cases))) def frac_str(a, b): return '{:}/{:} = {:.2f}%'.format(int(a), int(b), a / b) y_test_bin = res.target_bin_df.values supported_class_idxs = [k for k, y in enumerate(y_test_bin.T) if y.sum() > 0] print_(' * Auto-Decide Per-Class Summary') for k in supported_class_idxs: # Look at fail/succs in threshold name = res.class_names[k] # number of times this class appears overall n_total_k = (y_test_bin.T[k]).sum() # get the cases where this class was predicted auto_true_k = auto_true == k auto_pred_k = auto_pred == k # number of cases auto predicted n_pred_k = auto_pred_k.sum() # number of times auto was right n_tp = (auto_true_k & auto_pred_k).sum() # number of times auto was wrong n_fp = (~auto_true_k & auto_pred_k).sum() fail_str = frac_str(n_fp, n_pred_k) pass_str = frac_str(n_tp, n_total_k) fmtstr = '\n'.join( [ '{name}:', ' {n_total_k} samples existed, and did {n_pred_k} auto predictions', ' got {pass_str} right', ' made {fail_str} errors', ] ) print_(ut.indent(fmtstr.format(*locals()))) report = sklearn_utils.classification_report2( y_true, y_pred, target_names=target_names, sample_weight=can_autodecide.astype(np.float), verbose=False, ) print_(' Auto-Decide Confusion') print_(ut.indent(str(report['confusion']))) print_(' * Auto-Decide Metrics') print_(ut.indent(str(report['metrics']))) if 'mcc' in report: print_(ut.indent(str(report['mcc']))) try: auto_truth_bin = res.y_test_bin[can_autodecide] for k in supported_class_idxs: auto_truth_k = auto_truth_bin.T[k] auto_probs_k = auto_probs.T[k] if auto_probs_k.sum(): auc = sklearn.metrics.roc_auc_score(auto_truth_k, auto_probs_k) print_( ' * Auto AUC(Macro): {:.4f} for class={}'.format( auc, res.class_names[k] ) ) except ValueError: pass report = '\n'.join(report_lines) if verbose: logger.info(report) return report def confusions(res, class_name): import vtool as vt y_test_bin = res.target_bin_df.values clf_probs = res.probs_df.values k = res.class_names.index(class_name) probs, labels = clf_probs.T[k], y_test_bin.T[k] confusions = vt.ConfusionMetrics().fit(probs, labels) return confusions def ishow_roc(res): import vtool as vt import wbia.plottool as pt ut.qtensure() y_test_bin = res.target_bin_df.values # The maximum allowed false positive rate # We expect that we will make 1 error every 1,000 decisions # thresh_df['foo'] = [1, 2, 3] # thresh_df['foo'][res.class_names[k]] = 1 # for k in [2, 0, 1]: for k in range(y_test_bin.shape[1]): if y_test_bin.shape[1] == 2 and k == 0: # only show one in the binary case continue class_name = res.class_names[k] confusions = res.confusions(class_name) ROCInteraction = vt.interact_roc_factory( confusions, show_operating_point=True ) fnum = pt.ensure_fnum(k) # ROCInteraction.static_plot(fnum, None, name=class_name) inter = ROCInteraction(fnum=fnum, pnum=None, name=class_name) inter.start() # if False: # X = probs # y = labels # encoder = vt.ScoreNormalizer() # encoder.fit(probs, labels) # learn_thresh = encoder.learn_threshold2() # encoder.inverse_normalize(learn_thresh) # encoder.visualize(fnum=k) pass def show_roc(res, class_name, kwargs): import vtool as vt labels = res.target_bin_df[class_name].values probs = res.probs_df[class_name].values confusions = vt.ConfusionMetrics().fit(probs, labels) confusions.draw_roc_curve(kwargs) def roc_scores_ovr_hat(res): res.augment_if_needed() for k in range(len(res.class_names)): class_k_truth = res.y_test_bin.T[k] class_k_probs = res.probhats_df.values.T[k] auc = sklearn.metrics.roc_auc_score(class_k_truth, class_k_probs) yield auc def roc_scores_ovr(res): res.augment_if_needed() for k in range(res.y_test_bin.shape[1]): class_k_truth = res.y_test_bin.T[k] class_k_probs = res.clf_probs.T[k] auc = sklearn.metrics.roc_auc_score(class_k_truth, class_k_probs) yield auc def confusions_ovr(res): # one_vs_rest confusions import vtool as vt res.augment_if_needed() for k in range(res.y_test_bin.shape[1]): class_k_truth = res.y_test_bin.T[k] class_k_probs = res.clf_probs.T[k] cfms = vt.ConfusionMetrics().fit(class_k_probs, class_k_truth) # auc = sklearn.metrics.roc_auc_score(class_k_truth, class_k_probs) yield res.class_names[k], cfms def roc_score(res): res.augment_if_needed() auc_learn = sklearn.metrics.roc_auc_score(res.y_test_bin, res.clf_probs) return auc_learn @ut.reloadable_class class MultiTaskSamples(ut.NiceRepr): """ Handles samples (i.e. feature-label pairs) with a combination of non-mutually exclusive subclassification labels CommandLine: python -m wbia.algo.verif.clf_helpers MultiTaskSamples Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> samples = MultiTaskSamples([0, 1, 2, 3]) >>> tasks_to_indicators = ut.odict([ >>> ('task1', ut.odict([ >>> ('state1', [0, 0, 0, 1]), >>> ('state2', [0, 0, 1, 0]), >>> ('state3', [1, 1, 0, 0]), >>> ])), >>> ('task2', ut.odict([ >>> ('state4', [0, 0, 0, 1]), >>> ('state5', [1, 1, 1, 0]), >>> ])) >>> ]) >>> samples.apply_indicators(tasks_to_indicators) """ def __init__(samples, index): samples.index = index samples.subtasks = ut.odict() # def set_simple_scores(samples, simple_scores): # if simple_scores is not None: # edges = ut.emap(tuple, samples.aid_pairs.tolist()) # assert (edges == simple_scores.index.tolist()) # samples.simple_scores = simple_scores # def set_feats(samples, X_dict): # if X_dict is not None: # edges = ut.emap(tuple, samples.aid_pairs.tolist()) # for X in X_dict.values(): # assert np.all(edges == X.index.tolist()) # samples.X_dict = X_dict def supported_tasks(samples): for task_key, labels in samples.subtasks.items(): labels = samples.subtasks[task_key] if labels.has_support(): yield task_key def apply_indicators(samples, tasks_to_indicators): """ Adds labels for a specific task Args: tasks_to_indicators (dict): takes the form: { `my_task_name1' { 'class1': [list of bools indicating class membership] ... 'classN': [list of bools indicating class membership] } ... `my_task_nameN': ... } """ n_samples = None samples.n_tasks = len(tasks_to_indicators) for task_name, indicator in tasks_to_indicators.items(): labels = MultiClassLabels.from_indicators( indicator, task_name=task_name, index=samples.index ) samples.subtasks[task_name] = labels if n_samples is None: n_samples = labels.n_samples elif n_samples != labels.n_samples: raise ValueError('numer of samples is different') samples.n_samples = n_samples def apply_encoded_labels(samples, y_enc, class_names, task_name): """ Adds labels for a specific task. Alternative to `apply_indicators` Args: y_enc (list): integer label indicating the class for each sample class_names (list): list of strings indicating the class-domain task_name (str): key for denoting this specific task """ # convert to indicator structure and use that tasks_to_indicators = ut.odict( [ ( task_name, ut.odict( [ (name, np.array(y_enc) == i) for i, name in enumerate(class_names) ] ), ) ] ) samples.apply_indicators(tasks_to_indicators) # @ut.memoize def encoded_2d(samples): encoded_2d = pd.concat([v.encoded_df for k, v in samples.items()], axis=1) return encoded_2d def class_name_basis(samples): """corresponds with indexes returned from encoded1d""" class_name_basis = [ t[::-1] for t in ut.product([v.class_names for k, v in samples.items()][::-1]) ] # class_name_basis = [(b, a) for a, b in ut.product([ # v.class_names for k, v in samples.items()][::-1])] return class_name_basis def class_idx_basis_2d(samples): """2d-index version of class_name_basis""" class_idx_basis_2d = [ (b, a) for a, b in ut.product( [range(v.n_classes) for k, v in samples.items()][::-1] ) ] return class_idx_basis_2d def class_idx_basis_1d(samples): """1d-index version of class_name_basis""" n_states = np.prod([v.n_classes for k, v in samples.items()]) class_idx_basis_1d = np.arange(n_states, dtype=np.int) return class_idx_basis_1d # @ut.memoize def encoded_1d(samples): """Returns a unique label for each combination of samples""" # from sklearn.preprocessing import MultiLabelBinarizer encoded_2d = samples.encoded_2d() class_space = [v.n_classes for k, v in samples.items()] offsets = np.array([1] + np.cumprod(class_space).tolist()[:-1])[None, :] encoded_1d = (offsets encoded_2d).sum(axis=1) # e = MultiLabelBinarizer() # bin_coeff = e.fit_transform(encoded_2d) # bin_basis = (2 ** np.arange(bin_coeff.shape[1]))[None, :] # # encoded_1d = (bin_coeff * bin_basis).sum(axis=1) # encoded_1d = (bin_coeff * bin_basis[::-1]).sum(axis=1) # # vt.unique_rows(sklearn.preprocessing.MultiLabelBinarizer().fit_transform(encoded_2d)) # [v.encoded_df.values for k, v in samples.items()] # encoded_df_1d = pd.concat([v.encoded_df for k, v in samples.items()], axis=1) return encoded_1d def __nice__(samples): return 'nS=%r, nT=%r' % (len(samples), samples.n_tasks) def __getitem__(samples, task_key): return samples.subtasks[task_key] def __len__(samples): return samples.n_samples def print_info(samples): for task_name, labels in samples.items(): labels.print_info() logger.info('hist(all) = %s' % (ut.repr4(samples.make_histogram()))) logger.info('len(all) = %s' % (len(samples))) def make_histogram(samples): """label histogram""" class_name_basis = samples.class_name_basis() class_idx_basis_1d = samples.class_idx_basis_1d() # logger.info('class_idx_basis_1d = %r' % (class_idx_basis_1d,)) # logger.info(samples.encoded_1d()) multi_task_idx_hist = ut.dict_hist( samples.encoded_1d().values, labels=class_idx_basis_1d ) multi_task_hist = ut.map_keys(lambda k: class_name_basis[k], multi_task_idx_hist) return multi_task_hist def items(samples): for task_name, labels in samples.subtasks.items(): yield task_name, labels # def take(samples, idxs): # mask = ut.index_to_boolmask(idxs, len(samples)) # return samples.compress(mask) @property def group_ids(samples): return None def stratified_kfold_indices(samples, xval_kw): """ TODO: check xval label frequency """ from sklearn import model_selection X = np.empty((len(samples), 0)) y = samples.encoded_1d().values groups = samples.group_ids type_ = xval_kw.pop('type', 'StratifiedGroupKFold') if type_ == 'StratifiedGroupKFold': assert groups is not None # FIXME: The StratifiedGroupKFold could be implemented better. splitter = sklearn_utils.StratifiedGroupKFold(xval_kw) skf_list = list(splitter.split(X=X, y=y, groups=groups)) elif type_ == 'StratifiedKFold': splitter = model_selection.StratifiedKFold(xval_kw) skf_list = list(splitter.split(X=X, y=y)) return skf_list def subsplit_indices(samples, subset_idx, xval_kw): """split an existing set""" from sklearn import model_selection X = np.empty((len(subset_idx), 0)) y = samples.encoded_1d().values[subset_idx] groups = samples.group_ids[subset_idx] xval_kw_ = xval_kw.copy() if 'n_splits' not in xval_kw_: xval_kw_['n_splits'] = 3 type_ = xval_kw_.pop('type', 'StratifiedGroupKFold') if type_ == 'StratifiedGroupKFold': assert groups is not None # FIXME: The StratifiedGroupKFold could be implemented better. splitter = sklearn_utils.StratifiedGroupKFold(xval_kw_) rel_skf_list = list(splitter.split(X=X, y=y, groups=groups)) elif type_ == 'StratifiedKFold': splitter = model_selection.StratifiedKFold(xval_kw_) rel_skf_list = list(splitter.split(X=X, y=y)) # map back into original coords skf_list = [ (subset_idx[rel_idx1], subset_idx[rel_idx2]) for rel_idx1, rel_idx2 in rel_skf_list ] for idx1, idx2 in skf_list: assert len(np.intersect1d(subset_idx, idx1)) == len(idx1) assert len(np.intersect1d(subset_idx, idx2)) == len(idx2) # assert return skf_list @ut.reloadable_class class MultiClassLabels(ut.NiceRepr): """ Used by samples to encode a single set of mutually exclusive labels. These can either be binary or multiclass. import pandas as pd pd.options.display.max_rows = 10 # pd.options.display.max_rows = 20 pd.options.display.max_columns = 40 pd.options.display.width = 160 """ def __init__(labels): # Helper Info labels.task_name = None labels.n_samples = None labels.n_classes = None labels.class_names = None labels.classes_ = None # Core data labels.indicator_df = None labels.encoded_df = None labels.default_class = None def has_support(labels): return len(labels.make_histogram()) > 1 def lookup_class_idx(labels, class_name): return ut.dzip(labels.class_names, labels.classes_)[class_name] @classmethod def from_indicators(MultiClassLabels, indicator, index=None, task_name=None): labels = MultiClassLabels() n_samples = len(next(iter(indicator.values()))) # if index is None: # index = pd.Series(np.arange(n_samples), name='index') indicator_df = pd.DataFrame(indicator, index=index) assert np.all( indicator_df.sum(axis=1).values ), 'states in the same task must be mutually exclusive' labels.indicator_df = indicator_df labels.class_names = indicator_df.columns.values labels.encoded_df = pd.DataFrame( indicator_df.values.argmax(axis=1), columns=[task_name], index=index ) labels.task_name = task_name labels.n_samples = n_samples labels.n_classes = len(labels.class_names) if labels.n_classes == 1: labels.n_classes = 2 # 1 column means binary case labels.classes_ = np.arange(labels.n_classes) labels.default_class_name = labels.class_names[1] return labels @property def target_type(labels): return sklearn.utils.multiclass.type_of_target(labels.y_enc) def one_vs_rest_task_names(labels): return [ labels.task_name + '(' + labels.class_names[k] + '-v-rest)' for k in range(labels.n_classes) ] def gen_one_vs_rest_labels(labels): """ Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> indicator = ut.odict([ >>> ('state1', [0, 0, 0, 1]), >>> ('state2', [0, 0, 1, 0]), >>> ('state3', [1, 1, 0, 0]), >>> ]) >>> labels = MultiClassLabels.from_indicators(indicator, task_name='task1') >>> sublabels = list(labels.gen_one_vs_rest_labels()) >>> sublabel = sublabels[0] """ if labels.target_type == 'binary': yield labels return task_names_1vR = labels.one_vs_rest_task_names() for k in range(labels.n_classes): class_name = labels.class_names[k] task_name = task_names_1vR[k] index = labels.indicator_df.index indicator_df = pd.DataFrame() indicator_df['not-' + class_name] = 1 - labels.indicator_df[class_name] indicator_df[class_name] = labels.indicator_df[class_name] indicator_df.index = index # indicator = labels.encoded_df == k # indicator.rename(columns={indicator.columns[0]: class_name}, inplace=True) n_samples = len(indicator_df) sublabel = MultiClassLabels() sublabel.indicator_df = indicator_df sublabel.class_names = indicator_df.columns.values # if len(indicator_df.columns) == 1: # sublabel.encoded_df = pd.DataFrame( # indicator_df.values.T[0], # columns=[task_name] # ) # else: sublabel.encoded_df = pd.DataFrame( indicator_df.values.argmax(axis=1), columns=[task_name], index=index ) sublabel.task_name = task_name sublabel.n_samples = n_samples sublabel.n_classes = len(sublabel.class_names) # if sublabel.n_classes == 1: # sublabel.n_classes = 2 # 1 column means binary case sublabel.classes_ = np.arange(sublabel.n_classes) # sublabel = MultiClassLabels.from_indicators(indicator, # task_name=subname, index=samples.index) yield sublabel @property def y_bin(labels): return labels.indicator_df.values @property def y_enc(labels): return labels.encoded_df.values.ravel() def __nice__(labels): parts = [] if labels.task_name is not None: parts.append(labels.task_name) parts.append('nD=%r' % (labels.n_samples)) parts.append('nC=%r' % (labels.n_classes)) return ' '.join(parts) def __len__(labels): return labels.n_samples def make_histogram(labels): class_idx_hist = ut.dict_hist(labels.y_enc) class_hist = ut.map_keys(lambda idx: labels.class_names[idx], class_idx_hist) return class_hist def print_info(labels): logger.info( 'hist(%s) = %s' % (labels.task_name, ut.repr4(labels.make_histogram())) ) logger.info('len(%s) = %s' % (labels.task_name, len(labels))) class IrisProblem(ClfProblem): """ Simple demo using the abstract clf problem to work on the iris dataset. Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> pblm = IrisProblem() >>> pblm.setup() >>> pblm.samples """ def setup(pblm): import sklearn.datasets iris = sklearn.datasets.load_iris() pblm.primary_task_key = 'iris' pblm.default_data_key = 'learn(all)' pblm.default_clf_key = 'RF' X_df = pd.DataFrame(iris.data, columns=iris.feature_names) samples = MultiTaskSamples(X_df.index) samples.apply_indicators( { 'iris': { name: iris.target == idx for idx, name in enumerate(iris.target_names) } } ) samples.X_dict = {'learn(all)': X_df} pblm.samples = samples pblm.xval_kw['type'] = 'StratifiedKFold'	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 4013, 3883, 338, 263, 664, 297, 6728, 29892, 408, 1316, 22001, 526, 4967, 304, 1735, 29889, 13, 13, 29032, 15004, 29909, 29901, 13, 1678, 421, 15329, 5398, 29903, 9422, 29952, 19700, 408, 263, 3829, 304, 1712, 322, 26749, 263, 731, 310, 13, 1678, 11916, 411, 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src/RNN_utils.py	SergioEG/ScalableAVITM	3	152536	<reponame>SergioEG/ScalableAVITM<filename>src/RNN_utils.py """ Grouping all parsers specifications here @authors: <NAME> (<EMAIL>) AND <NAME> (<EMAIL>) """ import argparse import pickle from pandas import read_csv import numpy as np from sklearn.feature_extraction.text import CountVectorizer import logging logging.getLogger().setLevel(logging.INFO) def parse_args(): desc = 'Variational Inference for Topic Models based on Dense Layers' parser = argparse.ArgumentParser(description=desc) parser.add_argument('--batch_size', type=int, default=200, help='Size of the batches') parser.add_argument('--epochs',type=int,default=10, help='Number of epochs of the simulations') parser.add_argument('--train', type=int,default=1, help='Training model flag') parser.add_argument('--verbose', type=int,default=1, help='Verbose option flag') parser.add_argument('--save_it', type=int,default=1000, help='Save variables every save_it iterations') parser.add_argument('--restore', type=int,default=0, help='To restore session, to keep training or evaluation') parser.add_argument('--save_file', type=str, default='abcnews', help='Save file name (will be extended with other conf. parameters)') parser.add_argument('--corpus', type=str, default='abcnews', help='Corpus. Instructions to read each corpus are given in utils.py') parser.add_argument('--ntopics', type=int, default=10, help='Number of topics') parser.add_argument('--display', type=int, default=1, help='If verbose, print results every display epochs') parser.add_argument('--nlayers', type=int, default=2, help='Number of dense layers for the inference network') parser.add_argument('--hidden_list','--list', nargs='+', help='List of hidden units per layer',default=100) #If only one number, same number of units perlayer! parser.add_argument('--rate', type=float,default=0.005, help='Learning rate') parser.add_argument('--vocabulary_size', type=int,default=10000, help='Vocabulary size') parser.add_argument('--alpha', type=float,default=1.0, help='Dirich hiper.') return parser.parse_args() def get_corpus(corpus_name,nwords): bow = None dictionary = None logging.info(f"Getting corpus and embeds of {corpus_name}") if(corpus_name == 'FECYT'): documents = list(pickle.load(open( "data/df_proyectosFECYT.pkl", "rb" ) )['LEMAS_UC3M']) vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=nwords, stop_words='english') bow = vectorizer.fit_transform(documents) dictionary = vectorizer.get_feature_names() embeds = np.load("data/embeds_FECYT_256_LSTM.npy") if(corpus_name == 'abcnews'): data = read_csv('Data/abcnews-date-text.csv', error_bad_lines=False) documents = data[['headline_text']].values.reshape(-1).tolist() vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=nwords, stop_words='english') bow = vectorizer.fit_transform(documents) dictionary = vectorizer.get_feature_names() embeds = np.load("data/embeds_abcnews_512.npy") logging.info(f"Corpus and embeds obtained") return documents, bow,dictionary, embeds def display_topics(lda_matrix, dictionary, no_top_words): for topic_idx, topic in enumerate(lda_matrix): print ("Topic %d:" % (topic_idx)) print (" ".join([dictionary[i] for i in topic.argsort()[:-no_top_words - 1:-1]])) def 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migrations/versions/5ae213c6353_.py	eugeneandrienko/PyArtistsGallery	0	193247	"""empty message Revision ID: 5ae213c6353 Revises: 3c73f5517a2 Create Date: 2015-07-01 16:08:50.298141 """ # revision identifiers, used by Alembic. revision = '<KEY>' down_revision = '3c73f5517a2' from alembic import op import sqlalchemy as sa def upgrade(): op.drop_index('ix_pictures_path_to_image') op.drop_index('ix_pictures_path_to_thumbnail') op.drop_index('ix_pictures_name') op.drop_index('ix_pictures_description') op.drop_index('ix_pictures_upload_date') op.drop_index('ix_pictures_size') with op.batch_alter_table( 'pictures', reflect_args=[sa.Column( 'description', sa.VARCHAR())] ) as batch_op: batch_op.alter_column('description', existing_type=sa.VARCHAR(), nullable=True) op.create_index( op.f('ix_pictures_path_to_image'), 'pictures', ['path_to_image'], unique=False) op.create_index( op.f('ix_pictures_path_to_thumbnail'), 'pictures', ['path_to_thumbnail'], unique=False) op.create_index(op.f('ix_pictures_name'), 'pictures', ['name'], unique=False) op.create_index(op.f('ix_pictures_description'), 'pictures', ['description'], unique=False) op.create_index( op.f('ix_pictures_upload_date'), 'pictures', ['upload_date'], unique=False) op.create_index(op.f('ix_pictures_size'), 'pictures', ['size'], unique=False) def downgrade(): op.drop_index('ix_pictures_path_to_image') op.drop_index('ix_pictures_path_to_thumbnail') op.drop_index('ix_pictures_name') op.drop_index('ix_pictures_description') op.drop_index('ix_pictures_upload_date') op.drop_index('ix_pictures_size') with op.batch_alter_table( 'pictures', reflect_args=[sa.Column( 'description', sa.VARCHAR())] ) as batch_op: batch_op.alter_column('description', existing_type=sa.VARCHAR(), nullable=False)	[ 1, 9995, 6310, 2643, 13, 13, 1123, 4924, 3553, 29901, 29871, 29945, 3660, 29906, 29896, 29941, 29883, 29953, 29941, 29945, 29941, 13, 1123, 1730, 267, 29901, 29871, 29941, 29883, 29955, 29941, 29888, 29945, 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zink/templatetags/markdownify.py	jw/zink	2	195206	<reponame>jw/zink import logging from django import template import mistune from pygments import highlight from pygments.formatters import html from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.util import ClassNotFound logger = logging.getLogger(__name__) register = template.Library() class HighlightRenderer(mistune.Renderer): def block_code(self, code, lang): if not lang: first_line, _, code = code.partition('\n') name = first_line.replace(":", "") try: lexer = get_lexer_by_name(name, stripall=True) formatter = html.HtmlFormatter() return highlight(code, lexer, formatter) except ClassNotFound as e: return f'<pre><em>{e}</em><code>{mistune.escape(code)}</code></pre>' lexer = get_lexer_by_name(lang, stripall=True) formatter = html.HtmlFormatter() return highlight(code, lexer, formatter) @register.filter def markdown(value): markdown = mistune.Markdown(renderer=HighlightRenderer()) return markdown(value)	[ 1, 529, 276, 1112, 420, 29958, 29926, 29893, 29914, 29920, 682, 13, 5215, 12183, 13, 13, 3166, 9557, 1053, 4472, 13, 5215, 5862, 1540, 13, 3166, 19484, 1860, 1053, 12141, 13, 3166, 19484, 1860, 29889, 4830, 2153, 1053, 3472, 13, 3166, 19484, 1860, 29889, 2506, 414, 1053, 679, 29918, 2506, 261, 29918, 1609, 29918, 978, 29892, 4140, 29918, 2506, 261, 13, 3166, 19484, 1860, 29889, 4422, 1053, 4134, 17413, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 9573, 353, 4472, 29889, 12284, 580, 13, 13, 13, 1990, 5057, 4366, 21323, 29898, 29885, 391, 1540, 29889, 21323, 1125, 13, 1678, 822, 2908, 29918, 401, 29898, 1311, 29892, 775, 29892, 6361, 1125, 13, 4706, 565, 451, 6361, 29901, 13, 9651, 937, 29918, 1220, 29892, 17117, 775, 353, 775, 29889, 16707, 28909, 29876, 1495, 13, 9651, 1024, 353, 937, 29918, 1220, 29889, 6506, 703, 29901, 613, 20569, 13, 9651, 1018, 29901, 13, 18884, 19566, 261, 353, 679, 29918, 2506, 261, 29918, 1609, 29918, 978, 29898, 978, 29892, 17820, 497, 29922, 5574, 29897, 13, 18884, 883, 2620, 353, 3472, 29889, 10922, 18522, 580, 13, 18884, 736, 12141, 29898, 401, 29892, 19566, 261, 29892, 883, 2620, 29897, 13, 9651, 5174, 4134, 17413, 408, 321, 29901, 13, 18884, 736, 285, 29915, 29966, 1457, 5299, 331, 26208, 29872, 16040, 331, 5299, 401, 26208, 29885, 391, 1540, 29889, 21587, 29898, 401, 2915, 829, 401, 2565, 1457, 16299, 13, 4706, 19566, 261, 353, 679, 29918, 2506, 261, 29918, 1609, 29918, 978, 29898, 3893, 29892, 17820, 497, 29922, 5574, 29897, 13, 4706, 883, 2620, 353, 3472, 29889, 10922, 18522, 580, 13, 4706, 736, 12141, 29898, 401, 29892, 19566, 261, 29892, 883, 2620, 29897, 13, 13, 13, 29992, 9573, 29889, 4572, 13, 1753, 2791, 3204, 29898, 1767, 1125, 13, 1678, 2791, 3204, 353, 5862, 1540, 29889, 9802, 3204, 29898, 9482, 261, 29922, 16382, 4366, 21323, 3101, 13, 1678, 736, 2791, 3204, 29898, 1767, 29897, 13, 2 ]
build/lib/tnetwork/DCD/analytics/dynamic_partition.py	Yquetzal/tnetwork	4	87368	import tnetwork as tn import sklearn import sklearn.metrics import scipy import statistics import networkx as nx from tnetwork.DCD.analytics.onmi import onmi import numpy as np __all__ = ["longitudinal_similarity", "consecutive_sn_similarity", "similarity_at_each_step", "entropy_by_node","nb_node_change","quality_at_each_step","SM_N","SM_P","SM_L"] def longitudinal_similarity(dynamicCommunityReference:tn.DynCommunitiesSN, dynamicCommunityObserved:tn.DynCommunitiesSN, score=None,convert_coms_sklearn_format=True): """ Longitudinal similarity The longitudinal similarity between two dynamic clusters is computed by considering each couple (node,time) as an element belong to a cluster, a cluster containing therefore nodes in differnt times It takes into account the fact that the reference might by incomplete by removing from the partition to evaluate all (node,time) not present in the reference. :param dynamicCommunityReference: the dynamic partition used as reference (ground truth) :param dynamicCommunityObserved: the dynamic partition to evaluate (result of an algorithm) :param score: community comparison score, by default the adjsted NMI. (sklearn) :param convert_coms_sklearn_format: if the score expect in input clusters represented as in sklearn, True. if False, score will receive in input lists of sets of nodes :return: score """ if score==None: score=lambda x,y : sklearn.metrics.adjusted_mutual_info_score(x,y,average_method="arithmetic") affilReference=[] affilToEvaluate=[] if convert_coms_sklearn_format: comsToEvaluate = dynamicCommunityObserved.snapshot_affiliations() #for each step for t,affils in dynamicCommunityReference.snapshot_affiliations().items(): #for each node for n,comId in affils.items(): affilReference.append(str(list(comId)[0])) if n in comsToEvaluate[t]: affilToEvaluate.append(str(list(comsToEvaluate[t][n])[0])) else: print("node not in partition to evaluate: ",str(n)," ",str(t)) affilToEvaluate.append("-1") else: affilReference={} affilToEvaluate={} for t,coms in dynamicCommunityReference.snapshot_communities().items(): all_nodes = set() for id,nodes in coms.items(): node_sn = {(n,t) for n in nodes} all_nodes.update(node_sn) affilReference.setdefault(id,set()).update(node_sn) for id,nodes in dynamicCommunityObserved.snapshot_communities(t).items(): node_sn = {(n,t) for n in nodes} affilToEvaluate.setdefault(id,set()).update(node_sn & all_nodes) affilReference = list(affilReference.values()) affilToEvaluate = list(affilToEvaluate.values()) return score(affilReference,affilToEvaluate) def consecutive_sn_similarity(dynamicCommunity:tn.DynCommunitiesSN,score=None): """ Similarity between partitions in consecutive snapshots. Compute the average of a similarity score between all pair of successive partitions :param dynamicCommunity: the dynamic partition to evaluate :param score: the score to use for computing the similarity between each pair of snapshots. default: Overlapping NMI :return: pair (list of scores, list of partition sizes (avg both partitions)) """ if score==None: score=onmi #We use onmi because the number of labels can be different scores=[] sizes=[] #for each step com_snapshots = list(dynamicCommunity.snapshot_communities().values()) #print(com_snapshots) for i in range(len(com_snapshots)-1): partition_before = list(com_snapshots[i].values()) partition_after = list(com_snapshots[i+1].values()) elts_before = sum([len(x) for x in partition_before]) elts_after = sum([len(x) for x in partition_after]) scores.append(score(partition_before,partition_after)) sizes.append((elts_after+elts_before)/2) return scores,sizes def similarity_at_each_step(dynamicCommunityReference:tn.DynCommunitiesSN, dynamicCommunityObserved:tn.DynCommunitiesSN, score=None): """ Compute similarity at each step It takes into account the fact that the reference might by incomplete. (remove from the observations all nodes/time not present in the reference) :param dynamicCommunityReference: the dynamic partition to use as reference :param dynamicCommunityObserved: the dynamic partition to evaluate :param score: score to use, default adjusted NMI :return: pair (list of scores, list of sizes) """ if score==None: score=sklearn.metrics.adjusted_mutual_info_score scores=[] sizes=[] comsToEvaluate = dynamicCommunityObserved.snapshot_affiliations() #for each step for t,affils in dynamicCommunityReference.snapshot_affiliations().items(): affilReference = [] affilToEvaluate = [] #for each node for n,comId in affils.items(): affilReference.append(list(comId)[0]) if n in comsToEvaluate[t]: affilToEvaluate.append(list(comsToEvaluate[t][n])[0]) else: affilToEvaluate.append("-1") scores.append(score(affilReference,affilToEvaluate)) sizes.append(len(affilReference)) return scores,sizes def quality_at_each_step(dynamicCommunities:tn.DynCommunitiesSN,dynamicGraph:tn.DynGraphSN, score=None): """ Compute a community quality at each step :param dynamicCommunities: dynamic communities as SN :param score: score to use, default: Modularity :return: pair(scores, sizes) """ if score==None: score=nx.algorithms.community.modularity scores=[] sizes=[] #for each step for t,affils in dynamicCommunities.snapshot_communities().items(): g = dynamicGraph.snapshots(t) partition = list(affils.values()) try: sc = score(g,partition) scores.append(sc) except: #print("problem to compute with partition: ",partition," nodes",g.nodes()) scores.append(None) sizes.append(len(g.nodes)) return scores,sizes def nb_node_change(dyn_com:tn.DynCommunitiesSN): """ Compute the total number of node changes Measure of smoothness at the level of nodes, adapated to evaluate glitches :param dyn_com: The dynamic community :return: total number of node changes """ coms_by_nodes={} for t,coms in dyn_com.snapshot_communities().items(): #print(t,coms) for com,nodes in coms.items(): #print(n,com) for n in nodes: coms_by_nodes.setdefault(n,[com]) if coms_by_nodes[n][-1]!=com: coms_by_nodes[n].append(com) nb_changes = 0 for n in coms_by_nodes: #print(n,coms_by_nodes[n]) nb_changes+=len(coms_by_nodes[n])-1 return nb_changes # def entropy(dyn_com,sn_duration=1): # """ # Compute the entropy. # # Consider each community label as a data value. The probability of observing this data value is the frequency of a random node to belong to the corresponding community. # # Interpretation: The less communities, the lower the score. The less homogeneous the community sizes, the lower the score. # # This score does not take into account the order of the community changes. # # Be careful, convert SN graph into IG. # # # :param dyn_com: dynamic community to evaluate, can be SN or IG # :param sn_duration: if graph is SN, used to # :return: # """ # dc2 = dyn_com # fractions = [] # if isinstance(dc2,tn.DynCommunitiesSN): # dc2 = dc2.to_DynCommunitiesIG(sn_duration=sn_duration) # for com,nodes in dc2.communities().items(): # this_com_cumulated = 0 # for n,times in nodes.items(): # this_com_cumulated += times.duration() # fractions.append(this_com_cumulated) # sum_durations = sum(fractions) # fractions = [x/sum_durations for x in fractions] # # return scipy.stats.entropy(fractions) def entropy_by_node(dyn_com,sn_duration=1,fast_on_sn=False): """ Compute the entropy by node. For each node, compute the shannon entropy of its labels. (always same label=min entropy, every step a new label=max entropy) return the average value for all nodes :param dyn_com: dynamic community to evaluate, can be SN or IG :param sn_duration: if graph is SN, used to discretize :return: """ dc2 = dyn_com if not fast_on_sn: if isinstance(dc2,tn.DynCommunitiesSN): if sn_duration==1: dc2 = dc2._to_DynCommunitiesIG_fast() else: dc2 = dc2.to_DynCommunitiesIG(sn_duration=sn_duration) all_entropies = [] for n,coms in dc2.affiliations().items(): fractions = [] for com,times in coms.items(): fractions.append(times.duration()) sum_durations = sum(fractions) fractions = [x/sum_durations for x in fractions] ent_this_node = scipy.stats.entropy(fractions) all_entropies.append(ent_this_node) return statistics.mean(all_entropies) def SM_N(dyn_com): """ Smoothness for nodes Inverse of the number of node changes :param dyn_com: dynamic partition :return: SM-N score """ return 1/nb_node_change(dyn_com) def SM_P(dyn_com): """ Smoothness for partitions Averge 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core/homography.py	Kaktusava/Vis-MVSNet	131	153493	import time import os import torch from utils.utils import NanError def depth2class(depth, depth_start, depth_interval, depth_num, inv=False): if not inv: return (depth - depth_start) / (depth_interval + 1e-9) else: depth_end = depth_start + (depth_num-1) * depth_interval inv_interv = (1/(depth_start+1e-9) - 1/(depth_end+1e-9)) / (depth_num-1+1e-9) return (1/(depth+1e-9) - 1/(depth_end+1e-9)) / (inv_interv + 1e-9) def class2depth(class_, depth_start, depth_interval, depth_num, inv=False): if not inv: return depth_start + class_ * depth_interval else: depth_end = depth_start + (depth_num-1) * depth_interval inv_interv = (1/(depth_start+1e-9) - 1/(depth_end+1e-9)) / (depth_num-1+1e-9) return 1/( 1/(depth_end+1e-9) + class_ * inv_interv + 1e-9) def get_homographies(left_cam, right_cam, depth_num, depth_start, depth_interval, inv=False): # n244 n244 1 n111/n1hw n111/n1hw with torch.no_grad(): n, _, sh, sw = depth_start.size() n, _, ih, iw = depth_interval.size() d = depth_num # cameras (K, R, t) R_left = left_cam[:, 0, :3, :3] # n33 R_right = right_cam[:, 0, :3, :3] # n33 t_left = left_cam[:, 0, :3, 3:4] # n31 t_right = right_cam[:, 0, :3, 3:4] # n31 K_left = left_cam[:, 1, :3, :3] # n33 K_right = right_cam[:, 1, :3, :3] # n33 # depth nd1111/ndhw11 if not inv: depth = depth_start + depth_interval * torch.arange(0, depth_num, dtype=left_cam.dtype, device=left_cam.device).view(1,d,1,1) else: depth_end = depth_start + (depth_num-1) * depth_interval inv_interv = (1/(depth_start+1e-9) - 1/(depth_end+1e-9)) / (depth_num-1+1e-9) depth = 1/( 1/(depth_end+1e-9) + inv_interv * torch.arange(0, depth_num, dtype=left_cam.dtype, device=left_cam.device).view(1,d,1,1) ) depth = depth.unsqueeze(-1).unsqueeze(-1) # preparation K_left_inv = K_left.float().inverse().to(left_cam.dtype) R_left_trans = R_left.transpose(-2, -1) R_right_trans = R_right.transpose(-2, -1) fronto_direction = R_left[:, 2:3, :3] # n13 c_left = -R_left_trans @ t_left c_right = -R_right_trans @ t_right # n31 c_relative = c_right - c_left # compute temp_vec = (c_relative @ fronto_direction).view(n,1,1,1,3,3) # n11133 middle_mat0 = torch.eye(3, dtype=left_cam.dtype, device=left_cam.device).view(1,1,1,1,3,3) - temp_vec / (depth + 1e-9) # ndhw33 middle_mat1 = (R_left_trans @ K_left_inv).view(n,1,1,1,3,3) # n11133 middle_mat2 = (middle_mat0 @ middle_mat1) # ndhw33 homographies = K_right.view(n,1,1,1,3,3) @ R_right.view(n,1,1,1,3,3) @ middle_mat2 # ndhw33 if (homographies!=homographies).any(): raise NanError return homographies def get_pixel_grids(height, width): x_coord = (torch.arange(width, dtype=torch.float32).cuda() + 0.5).repeat(height, 1) y_coord = (torch.arange(height, dtype=torch.float32).cuda() + 0.5).repeat(width, 1).t() ones = torch.ones_like(x_coord) indices_grid = torch.stack([x_coord, y_coord, ones], dim=-1).unsqueeze(-1) # hw31 return indices_grid def interpolate(image, coord): # nchw, nhw2 => nchw with torch.no_grad(): warped_coord = coord.clone() warped_coord[..., 0] /= (warped_coord.size()[2]) warped_coord[..., 1] /= (warped_coord.size()[1]) warped_coord = (warped_coord * 2 - 1).clamp(-1.1, 1.1) warped = torch.nn.functional.grid_sample(image, warped_coord, mode='bilinear', padding_mode='zeros', align_corners=False) if (warped != warped).any(): raise NanError return warped def homography_warping(input, H): # nchw, n33/nhw33 -> nchw if len(H.size()) == 3: H = H.view(-1, 1, 1, 3, 3) with torch.no_grad(): pixel_grids = get_pixel_grids(*input.size()[-2:]).unsqueeze(0) # 1hw31 warped_homo_coord = (H @ pixel_grids).squeeze(-1) # nhw3 warped_coord = warped_homo_coord[..., :2] / (warped_homo_coord[..., 2:3] + 1e-9) # nhw2 warped = interpolate(input, warped_coord) return warped # nchw	[ 1, 1053, 931, 13, 5215, 2897, 13, 5215, 4842, 305, 13, 13, 3166, 3667, 29879, 29889, 13239, 1053, 25701, 2392, 13, 13, 13, 1753, 10809, 29906, 1990, 29898, 19488, 29892, 10809, 29918, 2962, 29892, 10809, 29918, 19207, 29892, 10809, 29918, 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calixis/plan/submodels/weighted.py	kyleady/SectorsOfInequity	0	72591	from django.db import models from django.forms.models import model_to_dict class Weighted_Value(models.Model): class Meta: abstract = True def get_weights_as_str(self): weights = [] has_conditional_weights = False for weight in self.weights.all(): if not weight.required_flags: weights.append(str(weight)) else: has_conditional_weights = True weights_as_str = "+".join(weights) if has_conditional_weights: weights_as_str += "*" return weights_as_str def __repr__(self): return json.dumps(model_to_dict( self, fields=[field.name for field in self._meta.fields] )) def __str__(self): return "({weights}) {value_name}".format(weights=self.get_weights_as_str(), value_name=self.value.name) weights = models.ManyToManyField('Roll', related_name='+') order = models.ManyToManyField('Roll', related_name='+') value = None class Weighted_Inspiration(Weighted_Value): value = models.ForeignKey('Inspiration', on_delete=models.CASCADE) class Weighted_Region(Weighted_Value): value = models.ForeignKey('Config_Region', on_delete=models.CASCADE) class Weighted_Type(Weighted_Value): value = models.ForeignKey('Config_Name', on_delete=models.CASCADE) class Weighted_Table(Weighted_Value): value = models.ForeignKey('Inspiration_Table', on_delete=models.CASCADE)	[ 1, 515, 9557, 29889, 2585, 1053, 4733, 30004, 13, 3166, 9557, 29889, 9514, 29889, 9794, 1053, 1904, 29918, 517, 29918, 8977, 30004, 13, 30004, 13, 1990, 1334, 523, 287, 29918, 1917, 29898, 9794, 29889, 3195, 1125, 30004, 13, 1678, 770, 20553, 29901, 30004, 13, 4706, 9846, 353, 5852, 30004, 13, 30004, 13, 1678, 822, 679, 29918, 705, 5861, 29918, 294, 29918, 710, 29898, 1311, 1125, 30004, 13, 4706, 18177, 353, 5159, 30004, 13, 4706, 756, 29918, 1116, 3245, 29918, 705, 5861, 353, 7700, 30004, 13, 4706, 363, 7688, 297, 1583, 29889, 705, 5861, 29889, 497, 7295, 30004, 13, 9651, 565, 451, 7688, 29889, 12403, 29918, 15764, 29901, 30004, 13, 18884, 18177, 29889, 4397, 29898, 710, 29898, 7915, 876, 30004, 13, 9651, 1683, 29901, 30004, 13, 18884, 756, 29918, 1116, 3245, 29918, 705, 5861, 353, 5852, 30004, 13, 30004, 13, 4706, 18177, 29918, 294, 29918, 710, 353, 15691, 1642, 7122, 29898, 705, 5861, 8443, 13, 4706, 565, 756, 29918, 1116, 3245, 29918, 705, 5861, 29901, 30004, 13, 9651, 18177, 29918, 294, 29918, 710, 4619, 26345, 19451, 13, 4706, 736, 18177, 29918, 294, 29918, 710, 30004, 13, 30004, 13, 1678, 822, 4770, 276, 558, 12035, 1311, 1125, 30004, 13, 4706, 736, 4390, 29889, 29881, 17204, 29898, 4299, 29918, 517, 29918, 8977, 29898, 30004, 13, 9651, 1583, 11167, 13, 9651, 4235, 11759, 2671, 29889, 978, 363, 1746, 297, 1583, 3032, 7299, 29889, 9621, 29962, 30004, 13, 308, 876, 30004, 13, 30004, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 30004, 13, 4706, 736, 376, 3319, 705, 5861, 1800, 426, 1767, 29918, 978, 29913, 1642, 4830, 29898, 705, 5861, 29922, 1311, 29889, 657, 29918, 705, 5861, 29918, 294, 29918, 710, 3285, 995, 29918, 978, 29922, 1311, 29889, 1767, 29889, 978, 8443, 13, 30004, 13, 1678, 18177, 353, 4733, 29889, 14804, 1762, 14804, 3073, 877, 29934, 3028, 742, 4475, 29918, 978, 2433, 29974, 1495, 30004, 13, 1678, 1797, 353, 4733, 29889, 14804, 1762, 14804, 3073, 877, 29934, 3028, 742, 4475, 29918, 978, 2433, 29974, 1495, 30004, 13, 1678, 995, 353, 6213, 30004, 13, 30004, 13, 1990, 1334, 523, 287, 29918, 797, 1028, 12232, 29898, 22676, 287, 29918, 1917, 1125, 30004, 13, 1678, 995, 353, 4733, 29889, 27755, 2558, 877, 797, 1028, 12232, 742, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 8443, 13, 30004, 13, 1990, 1334, 523, 287, 29918, 18457, 29898, 22676, 287, 29918, 1917, 1125, 30004, 13, 1678, 995, 353, 4733, 29889, 27755, 2558, 877, 3991, 29918, 18457, 742, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 8443, 13, 30004, 13, 1990, 1334, 523, 287, 29918, 1542, 29898, 22676, 287, 29918, 1917, 1125, 30004, 13, 1678, 995, 353, 4733, 29889, 27755, 2558, 877, 3991, 29918, 1170, 742, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 8443, 13, 30004, 13, 1990, 1334, 523, 287, 29918, 3562, 29898, 22676, 287, 29918, 1917, 1125, 30004, 13, 1678, 995, 353, 4733, 29889, 27755, 2558, 877, 797, 1028, 12232, 29918, 3562, 742, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 8443, 13, 2 ]
tests/test_github.py	xhusar2/repocribro	5	136567	<filename>tests/test_github.py import pytest REPOSITORY = 'MarekSuchanek/pyplayground' NOT_REPOSITORY = 'MarekSuchanek/pyplaygroundzzzz7' def test_login_bad(github_api): assert not github_api.login('random_code') def test_get_user_self(github_api): user = github_api.get('/user') assert user.is_ok assert 'login' in user.data def test_get_webhooks_bad(github_api): res = github_api.webhooks_get(NOT_REPOSITORY) assert not res.is_ok def test_get_webhook_bad(github_api): res = github_api.webhook_get(REPOSITORY, 666) assert not res.is_ok def test_get_webhook_create_check(github_api): res = github_api.webhook_create( REPOSITORY, 'http://localhost:5000/test' ) assert res is not None webhook_id = res['id'] res = github_api.webhooks_get(REPOSITORY) assert len(res.data) >= 1 found = False for webhook in res.data: if webhook['id'] == webhook_id: found = True break assert found res = github_api.webhook_get(REPOSITORY, webhook_id) assert res.is_ok assert res.data['id'] == webhook_id # Delivery on localhost must fail assert not github_api.webhook_tests(REPOSITORY, webhook_id) res = github_api.webhook_delete(REPOSITORY, webhook_id) assert res def test_get_webhook_delete_check(github_api): res = github_api.webhook_create( REPOSITORY, 'http://localhost:5000/test' ) assert res is not None webhook_id = res['id'] res = github_api.webhook_delete(REPOSITORY, webhook_id) assert res res = github_api.webhook_get(REPOSITORY, webhook_id) assert not res.is_ok def compute_signature(payload, secret): import hmac import hashlib h = hmac.new( secret.encode('utf-8'), payload.encode('utf-8'), hashlib.sha1 ) return h.hexdigest() @pytest.mark.parametrize('payload', [ 'aasas', '["a","b","c"]', '{"nope":"nope"}' ]) def test_get_webhook_verify(github_api, payload): sig = compute_signature(payload, github_api.webhooks_secret) assert github_api.webhook_verify_signature( payload.encode('utf-8'), sig ) @pytest.mark.parametrize('payload', [ 'aasas', '["a","b","c"]', '{"nope":"nope"}' ]) def test_get_webhook_unverify(github_api, payload): sig = compute_signature(payload, github_api.webhooks_secret) + 'xxx' assert not github_api.webhook_verify_signature( payload.encode('utf-8'), sig )	[ 1, 529, 9507, 29958, 21150, 29914, 1688, 29918, 3292, 29889, 2272, 13, 5215, 11451, 1688, 13, 13, 1525, 24815, 1806, 18929, 353, 525, 29924, 598, 29895, 29903, 987, 273, 1416, 29914, 2272, 1456, 2057, 29915, 13, 12256, 29918, 1525, 24815, 1806, 18929, 353, 525, 29924, 598, 29895, 29903, 987, 273, 1416, 29914, 2272, 1456, 2057, 5617, 5617, 29955, 29915, 13, 13, 13, 1753, 1243, 29918, 7507, 29918, 12313, 29898, 3292, 29918, 2754, 1125, 13, 1678, 4974, 451, 18546, 29918, 2754, 29889, 7507, 877, 8172, 29918, 401, 1495, 13, 13, 13, 1753, 1243, 29918, 657, 29918, 1792, 29918, 1311, 29898, 3292, 29918, 2754, 1125, 13, 1678, 1404, 353, 18546, 29918, 2754, 29889, 657, 11219, 1792, 1495, 13, 1678, 4974, 1404, 29889, 275, 29918, 554, 13, 1678, 4974, 525, 7507, 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qsBack/app/urls.py	joest67/qsProj	0	58903	from django.conf.urls import patterns, url from qs import views urlpatterns = patterns('', url(r'^$', views.index, name='index'), url(r'^sendqs/$', views.sendqs, name='sendqs'), url(r'^show/$', views.showall, name='showall'), )	[ 1, 515, 9557, 29889, 5527, 29889, 26045, 1053, 15038, 29892, 3142, 13, 13, 3166, 3855, 29879, 1053, 8386, 13, 13, 2271, 11037, 29879, 353, 15038, 877, 742, 13, 1678, 3142, 29898, 29878, 29915, 29985, 29938, 742, 8386, 29889, 2248, 29892, 1024, 2433, 2248, 5477, 13, 1678, 3142, 29898, 29878, 29915, 29985, 6717, 29939, 29879, 13346, 742, 8386, 29889, 6717, 29939, 29879, 29892, 1024, 2433, 6717, 29939, 29879, 5477, 13, 1678, 3142, 29898, 29878, 29915, 29985, 4294, 13346, 742, 8386, 29889, 4294, 497, 29892, 1024, 2433, 4294, 497, 5477, 13, 13, 29897, 29871, 13, 2 ]
6.0_prs_random_forest.py.py	psohn/Forest_Fires_Regression	2	53061	<reponame>psohn/Forest_Fires_Regression ### because how can you not use random forest regression on a forest fire ### regression? this is lazily coded and uncommented. purely for entertainment ### and completion import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import r2_score, mean_squared_error train_X = pd.read_csv('data/features_train.csv', index_col = 0) train_y = pd.read_csv('data/target_train.csv', index_col = 0).astype(int).values.ravel() test_X = pd.read_csv('data/features_test.csv', index_col = 0) test_y = pd.read_csv('data/target_test.csv', index_col = 0).astype(int).values.ravel() model = RandomForestClassifier(random_state = 0, max_depth = 5).fit(X = train_X, y = train_y) pred_train = model.predict(train_X) pred_test = model.predict(test_X) r2_train = r2_score(train_y, pred_train) r2_test = r2_score(test_y, pred_test) mse_train = mean_squared_error(train_y, pred_train, squared = False) mse_test = mean_squared_error(test_y, pred_test, squared = False) print(r2_train, mse_train) print(r2_test, mse_test) ### r2_train = -0.1386 ### mse_train = 0.5807 ### r2_test = -0.1518 ### mse_test = 0.6487	[ 1, 529, 276, 1112, 420, 29958, 567, 6547, 29914, 2831, 342, 29918, 29943, 2658, 29918, 4597, 23881, 13, 2277, 29937, 1363, 920, 508, 366, 451, 671, 4036, 13569, 17855, 373, 263, 13569, 3974, 30004, 13, 2277, 29937, 17855, 29973, 445, 338, 425, 29920, 2354, 274, 6797, 322, 443, 9342, 287, 29889, 24837, 363, 22684, 358, 30004, 13, 2277, 29937, 322, 13285, 30004, 13, 30004, 13, 5215, 11701, 408, 10518, 30004, 13, 3166, 2071, 19668, 29889, 24031, 1053, 16968, 2831, 342, 2385, 3709, 30004, 13, 3166, 2071, 19668, 29889, 2527, 10817, 1053, 364, 29906, 29918, 13628, 29892, 2099, 29918, 26613, 1965, 29918, 2704, 30004, 13, 30004, 13, 14968, 29918, 29990, 353, 10518, 29889, 949, 29918, 7638, 877, 1272, 29914, 22100, 29918, 14968, 29889, 7638, 742, 2380, 29918, 1054, 353, 29871, 29900, 8443, 13, 14968, 29918, 29891, 353, 10518, 29889, 949, 29918, 7638, 877, 1272, 29914, 5182, 29918, 14968, 29889, 7638, 742, 2380, 29918, 1054, 353, 29871, 29900, 467, 579, 668, 29898, 524, 467, 5975, 29889, 336, 955, 26471, 13, 30004, 13, 1688, 29918, 29990, 353, 10518, 29889, 949, 29918, 7638, 877, 1272, 29914, 22100, 29918, 1688, 29889, 7638, 742, 2380, 29918, 1054, 353, 29871, 29900, 8443, 13, 1688, 29918, 29891, 353, 10518, 29889, 949, 29918, 7638, 877, 1272, 29914, 5182, 29918, 1688, 29889, 7638, 742, 2380, 29918, 1054, 353, 29871, 29900, 467, 579, 668, 29898, 524, 467, 5975, 29889, 336, 955, 26471, 13, 30004, 13, 4299, 353, 16968, 2831, 342, 2385, 3709, 29898, 8172, 29918, 3859, 353, 29871, 29900, 29892, 4236, 29918, 19488, 353, 29871, 29945, 467, 9202, 29898, 29990, 353, 7945, 29918, 29990, 29892, 343, 353, 7945, 29918, 29891, 8443, 13, 30004, 13, 11965, 29918, 14968, 353, 1904, 29889, 27711, 29898, 14968, 29918, 29990, 8443, 13, 11965, 29918, 1688, 353, 1904, 29889, 27711, 29898, 1688, 29918, 29990, 8443, 13, 30004, 13, 29878, 29906, 29918, 14968, 353, 364, 29906, 29918, 13628, 29898, 14968, 29918, 29891, 29892, 4450, 29918, 14968, 8443, 13, 29878, 29906, 29918, 1688, 353, 364, 29906, 29918, 13628, 29898, 1688, 29918, 29891, 29892, 4450, 29918, 1688, 8443, 13, 30004, 13, 29885, 344, 29918, 14968, 353, 2099, 29918, 26613, 1965, 29918, 2704, 29898, 14968, 29918, 29891, 29892, 4450, 29918, 14968, 29892, 10674, 1965, 353, 7700, 8443, 13, 29885, 344, 29918, 1688, 353, 2099, 29918, 26613, 1965, 29918, 2704, 29898, 1688, 29918, 29891, 29892, 4450, 29918, 1688, 29892, 10674, 1965, 353, 7700, 8443, 13, 30004, 13, 2158, 29898, 29878, 29906, 29918, 14968, 29892, 286, 344, 29918, 14968, 8443, 13, 2158, 29898, 29878, 29906, 29918, 1688, 29892, 286, 344, 29918, 1688, 8443, 13, 30004, 13, 2277, 29937, 364, 29906, 29918, 14968, 353, 448, 29900, 29889, 29896, 29941, 29947, 29953, 30004, 13, 2277, 29937, 286, 344, 29918, 14968, 353, 29871, 29900, 29889, 29945, 29947, 29900, 29955, 30004, 13, 30004, 13, 2277, 29937, 364, 29906, 29918, 1688, 353, 448, 29900, 29889, 29896, 29945, 29896, 29947, 30004, 13, 2277, 29937, 286, 344, 29918, 1688, 353, 29871, 29900, 29889, 29953, 29946, 29947, 29955, 2 ]
ch02-安装OpenCV/最简单-使用pip安装opencv-python和opencv-contrib-python/test_video.py	makelove/OpenCV-Python-Tutorial	2,875	47463	# -- coding: utf-8 -- # @Time : 2017/8/2 10:46 # @Author : play4fun # @File : test_video.py # @Software: PyCharm """ test_video.py: """ import numpy as np import cv2 from matplotlib import pyplot as plt cap = cv2.VideoCapture('../../data/vtest.avi')#不支持读取视频 # cap = cv2.VideoCapture('output.avi') # cap = cv2.VideoCapture('Minions_banana.mp4') # 帧率 fps = cap.get(cv2.CAP_PROP_FPS) # 25.0 print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps)) # 总共有多少帧 num_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) print('共有', num_frames, '帧') # frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) frame_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) print('高：', frame_height, '宽：', frame_width) FRAME_NOW = cap.get(cv2.CAP_PROP_POS_FRAMES) # 第0帧 print('当前帧数', FRAME_NOW) # 当前帧数 0.0 # 读取指定帧,对视频文件才有效，对摄像头无效？？ # frame_no = 121 # cap.set(1, frame_no) # Where frame_no is the frame you want ret, frame = cap.read() # 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tools/train.py	RocketFlash/SiameseNet	24	1611133	import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(ROOT_DIR) import numpy as np from embedding_net.models import EmbeddingNet, TripletNet, SiameseNet from tensorflow.keras.callbacks import TensorBoard, LearningRateScheduler from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint from embedding_net.datagenerators import ENDataLoader, SimpleDataGenerator, TripletsDataGenerator, SimpleTripletsDataGenerator, SiameseDataGenerator from embedding_net.utils import parse_params, plot_grapths from embedding_net.backbones import pretrain_backbone_softmax from embedding_net.losses_and_accuracies import contrastive_loss, triplet_loss, accuracy import argparse from tensorflow import keras from tensorflow.keras.utils import multi_gpu_model import tensorflow as tf def parse_args(): parser = argparse.ArgumentParser(description='Train a classificator') parser.add_argument('config', help='model config file path') parser.add_argument('--resume_from', help='the checkpoint file to resume from') args = parser.parse_args() return args def create_save_folders(params): work_dir_path = os.path.join(params['work_dir'], params['project_name']) weights_save_path = os.path.join(work_dir_path, 'weights/') weights_pretrained_save_path = os.path.join(work_dir_path, 'pretraining_model/weights/') encodings_save_path = os.path.join(work_dir_path, 'encodings/') plots_save_path = os.path.join(work_dir_path, 'plots/') tensorboard_save_path = os.path.join(work_dir_path, 'tf_log/') tensorboard_pretrained_save_path = os.path.join(work_dir_path, 'pretraining_model/tf_log/') weights_save_file_path = os.path.join(weights_save_path, 'epoch_{epoch:03d}' + '.hdf5') os.makedirs(work_dir_path , exist_ok=True) os.makedirs(weights_save_path, exist_ok=True) os.makedirs(weights_pretrained_save_path, exist_ok=True) os.makedirs(encodings_save_path, exist_ok=True) os.makedirs(plots_save_path, exist_ok=True) os.makedirs(tensorboard_pretrained_save_path, exist_ok=True) return tensorboard_save_path, weights_save_file_path, plots_save_path def main(): print('LOAD PARAMETERS') args = parse_args() cfg_params = parse_params(args.config) params_train = cfg_params['train'] params_model = cfg_params['model'] params_dataloader = cfg_params['dataloader'] params_generator = cfg_params['generator'] tensorboard_save_path, weights_save_file_path, plots_save_path = create_save_folders(cfg_params['general']) work_dir_path = os.path.join(cfg_params['general']['work_dir'], cfg_params['general']['project_name']) weights_save_path = os.path.join(work_dir_path, 'weights/') initial_lr = params_train['learning_rate'] decay_factor = params_train['decay_factor'] step_size = params_train['step_size'] if params_dataloader['validate']: callback_monitor = 'val_loss' else: callback_monitor = 'loss' print('LOADING COMPLETED') callbacks = [ LearningRateScheduler(lambda x: initial_lr * decay_factor np.floor(x/step_size)), ReduceLROnPlateau(monitor=callback_monitor, factor=0.1, patience=4, verbose=1), EarlyStopping(monitor=callback_monitor, patience=10, verbose=1), ModelCheckpoint(filepath=weights_save_file_path, monitor=callback_monitor, save_best_only=True, verbose=1) ] print('CREATE DATALOADER') data_loader = ENDataLoader(params_dataloader) print('DATALOADER CREATED!') if cfg_params['general']['tensorboard_callback']: callbacks.append(TensorBoard(log_dir=tensorboard_save_path)) if cfg_params['general']['wandb_callback']: import wandb from wandb.keras import WandbCallback wandb.init() callbacks.append(WandbCallback(data_type="image", labels=data_loader.class_names)) val_generator = None print('CREATE MODEL AND DATA GENETATORS') if params_model['mode'] == 'siamese': model = SiameseNet(cfg_params, training=True) train_generator = SiameseDataGenerator(class_files_paths=data_loader.train_data, class_names=data_loader.class_names, params_generator) if data_loader.validate: val_generator = SiameseDataGenerator(class_files_paths=data_loader.val_data, class_names=data_loader.class_names, val_gen = True, params_generator) losses = {'output_siamese' : contrastive_loss} metric = {'output_siamese' : accuracy} else: if cfg_params['general']['gpu_ids']: print('Multiple gpu mode') gpu_ids = cfg_params['general']['gpu_ids'] os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids print(f'Using gpu ids: {gpu_ids}') gpu_ids_list = gpu_ids.split(',') n_gpu = len(gpu_ids_list) else: os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = '0' n_gpu = 1 print('Use single gpu mode') model = TripletNet(cfg_params, training=True) if n_gpu>1: strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model.base_model = multi_gpu_model(model.base_model, gpus=n_gpu) # model.base_model = tf.keras.utils.multi_gpu_model(model.base_model, gpus=n_gpu) train_generator = TripletsDataGenerator(embedding_model=model.base_model, class_files_paths=data_loader.train_data, class_names=data_loader.class_names, params_generator) if data_loader.validate: val_generator = SimpleTripletsDataGenerator(data_loader.val_data, data_loader.class_names, params_generator) losses = triplet_loss(params_generator['margin']) metric = ['accuracy'] print('DONE') if args.resume_from is not None: model.load_model(args.resume_from) print('COMPILE MODEL') model.model.compile(loss=losses, optimizer=params_train['optimizer'], metrics=metric) if 'softmax' in cfg_params: params_softmax = cfg_params['softmax'] params_save_paths = cfg_params['general'] pretrain_backbone_softmax(model.backbone_model, data_loader, params_softmax, params_save_paths) history = model.model.fit_generator(train_generator, validation_data=val_generator, epochs=params_train['n_epochs'], callbacks=callbacks, verbose=1, 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osprey/open.py	cjbassi/claw	14	107087	import subprocess import sys config_dir_path = None history_file_path = None log_file_path = None def open(path): if sys.platform == 'darwin': subprocess.Popen(['open', path]) elif sys.platform == 'win32': subprocess.Popen(['start', '', path]) else: subprocess.Popen(['xdg-open', path]) def open_config_dir(): global config_dir_path open(config_dir_path) def open_history_file(): global history_file_path open(history_file_path) def open_log_file(): global log_file_path open(log_file_path)	[ 1, 1053, 1014, 5014, 13, 5215, 10876, 13, 13, 2917, 29918, 3972, 29918, 2084, 353, 6213, 13, 18434, 29918, 1445, 29918, 2084, 353, 6213, 13, 1188, 29918, 1445, 29918, 2084, 353, 6213, 13, 13, 13, 1753, 1722, 29898, 2084, 1125, 13, 1678, 565, 10876, 29889, 12120, 1275, 525, 16702, 5080, 2396, 13, 4706, 1014, 5014, 29889, 29925, 3150, 18959, 3150, 742, 2224, 2314, 13, 1678, 25342, 10876, 29889, 12120, 1275, 525, 5080, 29941, 29906, 2396, 13, 4706, 1014, 5014, 29889, 29925, 3150, 18959, 2962, 742, 15516, 2224, 2314, 13, 1678, 1683, 29901, 13, 4706, 1014, 5014, 29889, 29925, 3150, 18959, 29916, 20726, 29899, 3150, 742, 2224, 2314, 13, 13, 13, 1753, 1722, 29918, 2917, 29918, 3972, 7295, 13, 1678, 5534, 2295, 29918, 3972, 29918, 2084, 13, 1678, 1722, 29898, 2917, 29918, 3972, 29918, 2084, 29897, 13, 13, 13, 1753, 1722, 29918, 18434, 29918, 1445, 7295, 13, 1678, 5534, 4955, 29918, 1445, 29918, 2084, 13, 1678, 1722, 29898, 18434, 29918, 1445, 29918, 2084, 29897, 13, 13, 13, 1753, 1722, 29918, 1188, 29918, 1445, 7295, 13, 1678, 5534, 1480, 29918, 1445, 29918, 2084, 13, 1678, 1722, 29898, 1188, 29918, 1445, 29918, 2084, 29897, 13, 2 ]
django_render/url_patterns_maker/url_patterns_maker.py	jasonmaoverlord/django_render	76	81123	<gh_stars>10-100 #!/usr/bin/env python # -- coding: utf-8 -- # Last modified: <NAME> (<EMAIL>) """urlpatterns_maker This urlpatterns_maker is used to auto load url configurations loosely distributed in the ``views`` package. Usage: 1. got views like this #################################### my_app/views/default.py ##################################################### from django_render.annotations import * @url(ur'^login/auth$', method=M.POST) @post(auth_type=int, auth_id=str, auth_token=str, jpush_registration_id=str) def login_auth(request, auth_type, auth_id, auth_token, jpush_registration_id): pass return True, {'token': login_token, 'user_id': user_id} #################################### my_app/views/profile.py ##################################################### ... #################################### my_app/views/friend.py ###################################################### ... #################################### my_app/views/feed.py ######################################################## ... 2. config urlpatterns as follow #################################### my_app/views/__init__.py #################################################### from django_render.url_patterns_maker import urlpatterns_maker urlpatterns = urlpatterns_maker(default='^', profile='^my/') 3. equivalent to urlpatterns = patterns('', url(ur'^', include('chooper_api.views.default')), url(ur'^my/', include('chooper_api.views.profile')), url(ur'^friend/', include('chooper_api.views.friend')), url(ur'^feed/', include('chooper_api.views.feed')), ) note that unspecified views(friend.py and feed.py here) got their file name(without extension, and surrounded by '^' and '/') as the url regex """ import inspect import os from django.conf.urls import patterns, include, url revision = '0.1' def urlpatterns_maker(**kwargs): path_init = inspect.getouterframes(inspect.currentframe())[1][1] path_views, file_name_init = os.path.split(path_init) path_app, folder_name_views = os.path.split(path_views) app_name = os.path.split(path_app)[1] files = [f.split('.')[0] for f in os.listdir(path_views) if not f.startswith('_') and (f.endswith('.py') or os.path.isdir(os.path.join(path_views, f))) and file_name_init != f] path_list = path_init.split('/') views_index = path_list.index('views') prefix = '.'.join(path_list[views_index-1:-1]) # print(path_init) # print(file_name_init) # print(path_views) # print(path_app) # print(folder_name_views) # print(app_name) # print(files) urlpatterns = [] for file_name in files: if file_name in kwargs: urlpatterns.append( url(r'{0}'.format(kwargs[file_name]), include('{0}.{1}'.format(prefix, file_name))) ) else: urlpatterns.append( url(r'^{0}/'.format(file_name), include('{0}.{1}'.format(prefix, file_name))) ) for urlpattern in urlpatterns: urlpattern.regex # url pattern check, thanks to django return urlpatterns	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 9208, 9120, 29901, 529, 5813, 29958, 313, 29966, 26862, 6227, 12948, 13, 13, 15945, 29908, 2271, 11037, 29879, 29918, 28107, 13, 4013, 3142, 11037, 29879, 29918, 28107, 338, 1304, 304, 4469, 2254, 3142, 22920, 658, 359, 873, 13235, 29871, 297, 278, 4954, 7406, 16159, 3577, 29889, 13, 13, 27573, 29901, 13, 259, 29896, 29889, 2355, 8386, 763, 445, 13, 1678, 835, 13383, 13383, 29937, 590, 29918, 932, 29914, 7406, 29914, 4381, 29889, 2272, 835, 13383, 13383, 13383, 2277, 13, 1678, 515, 9557, 29918, 9482, 29889, 6735, 800, 1053, 334, 13, 1678, 732, 2271, 29898, 332, 29915, 29985, 7507, 29914, 5150, 29938, 742, 1158, 29922, 29924, 29889, 5438, 29897, 13, 1678, 732, 2490, 29898, 5150, 29918, 1853, 29922, 524, 29892, 4817, 29918, 333, 29922, 710, 29892, 4817, 29918, 6979, 29922, 710, 29892, 432, 5910, 29918, 1727, 8306, 29918, 333, 29922, 710, 29897, 13, 1678, 822, 6464, 29918, 5150, 29898, 3827, 29892, 4817, 29918, 1853, 29892, 4817, 29918, 333, 29892, 4817, 29918, 6979, 29892, 432, 5910, 29918, 1727, 8306, 29918, 333, 1125, 13, 4706, 1209, 13, 4706, 736, 5852, 29892, 11117, 6979, 2396, 6464, 29918, 6979, 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13, 276, 4924, 353, 525, 29900, 29889, 29896, 29915, 13, 13, 13, 1753, 3142, 11037, 29879, 29918, 28107, 29898, 1068, 19290, 1125, 13, 1678, 2224, 29918, 2344, 353, 16096, 29889, 657, 5561, 19935, 29898, 1144, 1103, 29889, 3784, 2557, 3101, 29961, 29896, 3816, 29896, 29962, 13, 1678, 2224, 29918, 7406, 29892, 934, 29918, 978, 29918, 2344, 353, 2897, 29889, 2084, 29889, 5451, 29898, 2084, 29918, 2344, 29897, 13, 1678, 2224, 29918, 932, 29892, 4138, 29918, 978, 29918, 7406, 353, 2897, 29889, 2084, 29889, 5451, 29898, 2084, 29918, 7406, 29897, 13, 1678, 623, 29918, 978, 353, 2897, 29889, 2084, 29889, 5451, 29898, 2084, 29918, 932, 9601, 29896, 29962, 13, 1678, 2066, 353, 518, 29888, 29889, 5451, 12839, 29861, 29900, 29962, 363, 285, 297, 2897, 29889, 1761, 3972, 29898, 2084, 29918, 7406, 29897, 565, 451, 285, 29889, 27382, 2541, 877, 29918, 1495, 322, 13, 632, 313, 29888, 29889, 1975, 2541, 12839, 2272, 1495, 470, 2897, 29889, 2084, 29889, 275, 3972, 29898, 359, 29889, 2084, 29889, 7122, 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mlops/data_validation/__init__.py	colingwuyu/mlops	1	58361	<reponame>colingwuyu/mlops from mlops.data_validation.view import view_report	[ 1, 529, 276, 1112, 420, 29958, 1054, 292, 29893, 8631, 29884, 29914, 29885, 417, 567, 13, 3166, 286, 417, 567, 29889, 1272, 29918, 18157, 29889, 1493, 1053, 1776, 29918, 12276, 13, 2 ]
DML/datasets_ids.py	yuichikano/Fashion-Image-Retrieval-System	0	1613209	# Copyright 2019 <NAME> and <NAME> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== ################# LIBRARIES ############################### import warnings warnings.filterwarnings("ignore") import numpy as np, os, sys, pandas as pd, csv, copy import torch, torch.nn as nn, matplotlib.pyplot as plt, random from torch.utils.data import Dataset from PIL import Image from torchvision import transforms from tqdm import tqdm import pretrainedmodels.utils as utils import auxiliaries as aux """============================================================================""" ################ FUNCTION TO RETURN ALL DATALOADERS NECESSARY #################### def give_dataloaders(dataset, opt): """ Args: dataset: string, name of dataset for which the dataloaders should be returned. opt: argparse.Namespace, contains all training-specific parameters. Returns: dataloaders: dict of dataloaders for training, testing and evaluation on training. """ #Dataset selection if opt.dataset=='cub200': datasets = give_CUB200_datasets(opt) elif opt.dataset=='cars196': datasets = give_CARS196_datasets(opt) elif opt.dataset=='online_products': datasets = give_OnlineProducts_datasets(opt) elif opt.dataset=='in-shop': datasets = give_InShop_datasets(opt) elif opt.dataset=='vehicle_id': datasets = give_VehicleID_datasets(opt) else: raise Exception('No Dataset >{}< available!'.format(dataset)) #Move datasets to dataloaders. dataloaders = {} for key,dataset in datasets.items(): is_val = dataset.is_validation dataloaders[key] = torch.utils.data.DataLoader(dataset, batch_size=opt.bs, num_workers=opt.kernels, shuffle=not is_val, pin_memory=True, drop_last=not is_val) return dataloaders """============================================================================""" ################# FUNCTIONS TO RETURN TRAIN/VAL PYTORCH DATASETS FOR CUB200, CARS196, STANFORD ONLINE PRODUCTS, IN-SHOP CLOTHES, PKU VEHICLE-ID #################################### def give_CUB200_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the CUB-200-2011 dataset. For Metric Learning, the dataset classes are sorted by name, and the first half used for training while the last half is used for testing. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ image_sourcepath = opt.source_path+'/images' #Find available data classes. image_classes = sorted([x for x in os.listdir(image_sourcepath) if '._' not in x], key=lambda x: int(x.split('.')[0])) #Make a index-to-labelname conversion dict. conversion = {int(x.split('.')[0]):x.split('.')[-1] for x in image_classes} #Generate a list of tuples (class_label, image_path) image_list = {int(key.split('.')[0]):sorted([image_sourcepath+'/'+key+'/'+x for x in os.listdir(image_sourcepath+'/'+key) if '._' not in x]) for key in image_classes} image_list = [[(key,img_path) for img_path in image_list[key]] for key in image_list.keys()] image_list = [x for y in image_list for x in y] #Image-dict of shape {class_idx:[list of paths to images belong to this class] ...} image_dict = {} for key, img_path in image_list: key = key-1 if not key in image_dict.keys(): image_dict[key] = [] image_dict[key].append(img_path) keys = sorted(list(image_dict.keys())) #Following "Deep Metric Learning via Lifted Structured Feature Embedding", we use the first half of classes for training. train,test = keys[:len(keys)//2], keys[len(keys)//2:] train_image_dict, val_image_dict = {key:image_dict[key] for key in train},{key:image_dict[key] for key in test} train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) val_dataset = BaseTripletDataset(val_image_dict, opt, is_validation=True) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) train_dataset.conversion = conversion val_dataset.conversion = conversion eval_dataset.conversion = conversion return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset} def give_CARS196_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the CARS196 dataset. For Metric Learning, the dataset classes are sorted by name, and the first half used for training while the last half is used for testing. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ image_sourcepath = opt.source_path+'/images' #Find available data classes. image_classes = sorted([x for x in os.listdir(image_sourcepath)]) #Make a index-to-labelname conversion dict. conversion = {i:x for i,x in enumerate(image_classes)} #Generate a list of tuples (class_label, image_path) image_list = {i:sorted([image_sourcepath+'/'+key+'/'+x for x in os.listdir(image_sourcepath+'/'+key)]) for i,key in enumerate(image_classes)} image_list = [[(key,img_path) for img_path in image_list[key]] for key in image_list.keys()] image_list = [x for y in image_list for x in y] #Image-dict of shape {class_idx:[list of paths to images belong to this class] ...} image_dict = {} for key, img_path in image_list: key = key # key = key-1 if not key in image_dict.keys(): image_dict[key] = [] image_dict[key].append(img_path) keys = sorted(list(image_dict.keys())) #Following "Deep Metric Learning via Lifted Structured Feature Embedding", we use the first half of classes for training. train,test = keys[:len(keys)//2], keys[len(keys)//2:] train_image_dict, val_image_dict = {key:image_dict[key] for key in train},{key:image_dict[key] for key in test} train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) val_dataset = BaseTripletDataset(val_image_dict, opt, is_validation=True) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) train_dataset.conversion = conversion val_dataset.conversion = conversion eval_dataset.conversion = conversion return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset} def give_OnlineProducts_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the Online-Products dataset. For Metric Learning, training and test sets are provided by given text-files, Ebay_train.txt & Ebay_test.txt. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ image_sourcepath = opt.source_path+'/images' #Load text-files containing classes and imagepaths. training_files = pd.read_table(opt.source_path+'/Info_Files/Ebay_train.txt', header=0, delimiter=' ') test_files = pd.read_table(opt.source_path+'/Info_Files/Ebay_test.txt', header=0, delimiter=' ') #Generate Conversion dict. conversion = {} for class_id, path in zip(training_files['class_id'],training_files['path']): conversion[class_id] = path.split('/')[0] for class_id, path in zip(test_files['class_id'],test_files['path']): conversion[class_id] = path.split('/')[0] #Generate image_dicts of shape {class_idx:[list of paths to images belong to this class] ...} train_image_dict, val_image_dict = {},{} for key, img_path in zip(training_files['class_id'],training_files['path']): key = key-1 if not key in train_image_dict.keys(): train_image_dict[key] = [] train_image_dict[key].append(image_sourcepath+'/'+img_path) for key, img_path in zip(test_files['class_id'],test_files['path']): key = key-1 if not key in val_image_dict.keys(): val_image_dict[key] = [] val_image_dict[key].append(image_sourcepath+'/'+img_path) ### Uncomment this if super-labels should be used to generate resp.datasets # super_conversion = {} # for super_class_id, path in zip(training_files['super_class_id'],training_files['path']): # conversion[super_class_id] = path.split('/')[0] # for key, img_path in zip(training_files['super_class_id'],training_files['path']): # key = key-1 # if not key in super_train_image_dict.keys(): # super_train_image_dict[key] = [] # super_train_image_dict[key].append(image_sourcepath+'/'+img_path) # super_train_dataset = BaseTripletDataset(super_train_image_dict, opt, is_validation=True) # super_train_dataset.conversion = super_conversion train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) val_dataset = BaseTripletDataset(val_image_dict, opt, is_validation=True) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) train_dataset.conversion = conversion val_dataset.conversion = conversion eval_dataset.conversion = conversion return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset} # return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset, 'super_evaluation':super_train_dataset} def give_InShop_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the In-Shop Clothes dataset. For Metric Learning, training and test sets are provided by one text file, list_eval_partition.txt. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing (by query and gallery separation) and evaluation. """ #Load train-test-partition text file. data_info = np.array(pd.read_table(opt.source_path+'/Eval/list_eval_partition.txt', header=1, delim_whitespace=True))[1:,:] #Separate into training dataset and query/gallery dataset for testing. train, query, gallery = data_info[data_info[:,2]=='train'][:,:2], data_info[data_info[:,2]=='query'][:,:2], data_info[data_info[:,2]=='gallery'][:,:2] #Generate conversions(id verson) # use_train_image_num = 10000 # use_val_image_num = int(use_train_image_num/3) # np.random.seed(0) # train_idx = np.random.choice(len(train), size=use_train_image_num, replace = False) # train = train[train_idx] # query_idx = np.random.choice(len(query), size=use_val_image_num, replace = False) # query = query[query_idx] # gallery_idx = np.random.choice(len(gallery), size=use_val_image_num, replace = False) # gallery = gallery[gallery_idx] #Generate conversions lab_conv = {x:i for i,x in enumerate(np.unique(np.array([int(x.split('_')[-1]) for x in train[:,1]])))} train[:,1] = np.array([lab_conv[int(x.split('_')[-1])] for x in train[:,1]]) lab_conv = {x:i for i,x in enumerate(np.unique(np.array([int(x.split('_')[-1]) for x in np.concatenate([query[:,1], gallery[:,1]])])))} query[:,1] = np.array([lab_conv[int(x.split('_')[-1])] for x in query[:,1]]) gallery[:,1] = np.array([lab_conv[int(x.split('_')[-1])] for x in gallery[:,1]]) #Generate Image-Dicts for training, query and gallery of shape {class_idx:[list of paths to images belong to this class] ...} train_image_dict = {} for img_path, key in train: if not key in train_image_dict.keys(): train_image_dict[key] = [] train_image_dict[key].append(opt.source_path+'/'+img_path) query_image_dict = {} for img_path, key in query: if not key in query_image_dict.keys(): query_image_dict[key] = [] query_image_dict[key].append(opt.source_path+'/'+img_path) gallery_image_dict = {} for img_path, key in gallery: if not key in gallery_image_dict.keys(): gallery_image_dict[key] = [] gallery_image_dict[key].append(opt.source_path+'/'+img_path) ### Uncomment this if super-labels should be used to generate resp.datasets # super_train_image_dict, counter, super_assign = {},0,{} # for img_path, _ in train: # key = '_'.join(img_path.split('/')[1:3]) # if key not in super_assign.keys(): # super_assign[key] = counter # counter += 1 # key = super_assign[key] # # if not key in super_train_image_dict.keys(): # super_train_image_dict[key] = [] # super_train_image_dict[key].append(opt.source_path+'/'+img_path) # super_train_dataset = BaseTripletDataset(super_train_image_dict, opt, is_validation=True) train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) query_dataset = BaseTripletDataset(query_image_dict, opt, is_validation=True) gallery_dataset = BaseTripletDataset(gallery_image_dict, opt, is_validation=True) return {'training':train_dataset, 'testing_query':query_dataset, 'evaluation':eval_dataset, 'testing_gallery':gallery_dataset} # return {'training':train_dataset, 'testing_query':query_dataset, 'evaluation':eval_dataset, 'testing_gallery':gallery_dataset, 'super_evaluation':super_train_dataset} def give_VehicleID_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the PKU Vehicle dataset. For Metric Learning, training and (multiple) test sets are provided by separate text files, train_list and test_list_<n_classes_2_test>.txt. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ #Load respective text-files train = np.array(pd.read_table(opt.source_path+'/train_test_split/train_list.txt', header=None, delim_whitespace=True)) small_test = np.array(pd.read_table(opt.source_path+'/train_test_split/test_list_800.txt', header=None, delim_whitespace=True)) medium_test = np.array(pd.read_table(opt.source_path+'/train_test_split/test_list_1600.txt', header=None, delim_whitespace=True)) big_test = np.array(pd.read_table(opt.source_path+'/train_test_split/test_list_2400.txt', header=None, delim_whitespace=True)) #Generate conversions lab_conv = {x:i for i,x in enumerate(np.unique(train[:,1]))} train[:,1] = np.array([lab_conv[x] for x in train[:,1]]) lab_conv = {x:i for i,x in enumerate(np.unique(np.concatenate([small_test[:,1], medium_test[:,1], big_test[:,1]])))} small_test[:,1] = np.array([lab_conv[x] for x in small_test[:,1]]) medium_test[:,1] = np.array([lab_conv[x] for x in medium_test[:,1]]) big_test[:,1] = np.array([lab_conv[x] for x in big_test[:,1]]) #Generate Image-Dicts for training and different testings of shape {class_idx:[list of paths to images belong to this class] ...} train_image_dict = {} for img_path, key in train: if not key in train_image_dict.keys(): train_image_dict[key] = [] train_image_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) small_test_dict = {} for img_path, key in small_test: if not key in small_test_dict.keys(): small_test_dict[key] = [] small_test_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) medium_test_dict = {} for img_path, key in medium_test: if not key in medium_test_dict.keys(): medium_test_dict[key] = [] medium_test_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) big_test_dict = {} for img_path, key in big_test: if not key in big_test_dict.keys(): big_test_dict[key] = [] big_test_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) val_small_dataset = BaseTripletDataset(small_test_dict, opt, is_validation=True) val_medium_dataset = BaseTripletDataset(medium_test_dict, opt, is_validation=True) val_big_dataset = BaseTripletDataset(big_test_dict, opt, is_validation=True) return {'training':train_dataset, 'testing_set1':val_small_dataset, 'testing_set2':val_medium_dataset, \ 'testing_set3':val_big_dataset, 'evaluation':eval_dataset} ################## BASIC PYTORCH DATASET USED FOR ALL DATASETS ################################## class BaseTripletDataset(Dataset): """ Dataset class to provide (augmented) correctly prepared training samples corresponding to standard DML literature. This includes normalizing to ImageNet-standards, and Random & Resized cropping of shapes 224 for ResNet50 and 227 for GoogLeNet during Training. During validation, only resizing to 256 or center cropping to 224/227 is performed. """ def __init__(self, image_dict, opt, samples_per_class=8, is_validation=False): """ Dataset Init-Function. Args: image_dict: dict, Dictionary of shape {class_idx:[list of paths to images belong to this class] ...} providing all the training paths and classes. opt: argparse.Namespace, contains all training-specific parameters. samples_per_class: Number of samples to draw from one class before moving to the next when filling the batch. is_validation: If is true, dataset properties for validation/testing are used instead of ones for training. Returns: Nothing! """ #Define length of dataset self.n_files = np.sum([len(image_dict[key]) for key in image_dict.keys()]) self.is_validation = is_validation self.pars = opt self.image_dict = image_dict self.avail_classes = sorted(list(self.image_dict.keys())) #Convert image dictionary from classname:content to class_idx:content, because the initial indices are not necessarily from 0 - <n_classes>. self.image_dict = {i:self.image_dict[key] for i,key in enumerate(self.avail_classes)} self.avail_classes = sorted(list(self.image_dict.keys())) #Init. properties that are used when filling up batches. if not self.is_validation: self.samples_per_class = samples_per_class #Select current class to sample images from up to <samples_per_class> self.current_class = np.random.randint(len(self.avail_classes)) self.classes_visited = [self.current_class, self.current_class] self.n_samples_drawn = 0 #Data augmentation/processing methods. normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) transf_list = [] if not self.is_validation: transf_list.extend([transforms.RandomResizedCrop(size=224) if opt.arch=='resnet50' else transforms.RandomResizedCrop(size=227), transforms.RandomHorizontalFlip(0.5)]) else: transf_list.extend([transforms.Resize(256), transforms.CenterCrop(224) if opt.arch=='resnet50' else transforms.CenterCrop(227)]) transf_list.extend([transforms.ToTensor(), normalize]) self.transform = transforms.Compose(transf_list) #Convert Image-Dict to list of (image_path, image_class). Allows for easier direct sampling. self.image_list = [[(x,key) for x in self.image_dict[key]] for key in self.image_dict.keys()] self.image_list = [x for y in self.image_list for x in y] #Flag that denotes if dataset is called for the first time. self.is_init = True def ensure_3dim(self, img): """ Function that ensures that the input img is three-dimensional. Args: img: PIL.Image, image which is to be checked for three-dimensionality (i.e. if some images are black-and-white in an otherwise coloured dataset). Returns: Checked PIL.Image img. """ if len(img.size)==2: img = img.convert('RGB') return img def __getitem__(self, idx): """ Args: idx: Sample idx for training sample Returns: tuple of form (sample_class, torch.Tensor() of input image) """ if self.is_init: self.current_class = self.avail_classes[idx%len(self.avail_classes)] self.is_init = False if not self.is_validation: if self.samples_per_class==1: return self.image_list[idx][-1], self.transform(self.ensure_3dim(Image.open(self.image_list[idx][0]))) if self.n_samples_drawn==self.samples_per_class: #Once enough samples per class have been drawn, we choose another class to draw samples from. #Note that we ensure with self.classes_visited that no class is chosen if it had been chosen #previously or one before that. counter = copy.deepcopy(self.avail_classes) for prev_class in self.classes_visited: if prev_class in counter: counter.remove(prev_class) self.current_class = counter[idx%len(counter)] self.classes_visited = self.classes_visited[1:]+[self.current_class] self.n_samples_drawn = 0 class_sample_idx = idx%len(self.image_dict[self.current_class]) self.n_samples_drawn += 1 out_img = self.transform(self.ensure_3dim(Image.open(self.image_dict[self.current_class][class_sample_idx]))) return self.current_class,out_img else: return self.image_list[idx][-1], self.transform(self.ensure_3dim(Image.open(self.image_list[idx][0]))) def __len__(self): return self.n_files	[ 1, 396, 14187, 1266, 29871, 29906, 29900, 29896, 29929, 529, 5813, 29958, 322, 529, 5813, 29958, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 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tests/component_tests/test_session.py	lsawade/radical.pilot	0	121768	# pylint: disable=protected-access, unused-argument __copyright__ = 'Copyright 2020, The RADICAL-Cybertools Team' __license__ = 'MIT' import glob import os import shutil from unittest import TestCase, mock import radical.pilot as rp TEST_CASES_PATH = '%s/test_cases' % os.path.dirname(__file__) # ------------------------------------------------------------------------------ # class TestSession(TestCase): # -------------------------------------------------------------------------- # @classmethod @mock.patch.object(rp.Session, '_initialize_primary', return_value=None) def setUpClass(cls, args, kwargs): cls._session = rp.Session() # -------------------------------------------------------------------------- # @classmethod def tearDownClass(cls): for d in glob.glob('./rp.session.'): if os.path.isdir(d): try: shutil.rmtree(d) except OSError as e: print('[ERROR] %s - %s' % (e.filename, e.strerror)) # -------------------------------------------------------------------------- # def test_list_resources(self): listed_resources = self._session.list_resources() self.assertIsInstance(listed_resources, list) self.assertIn('local.localhost', listed_resources) # -------------------------------------------------------------------------- # def test_get_resource_config(self): rcfg_label = 'xsede.comet_ssh' # schemas are ["ssh", "gsissh"] rcfg = self._session.get_resource_config(rcfg_label) self.assertEqual(rcfg.job_manager_endpoint, rcfg[rcfg.schemas[0]].job_manager_endpoint) new_schema = 'gsissh' rcfg = self._session.get_resource_config(rcfg_label, schema=new_schema) self.assertEqual(rcfg.job_manager_endpoint, rcfg[new_schema].job_manager_endpoint) # check exceptions with self.assertRaises(RuntimeError): self._session.get_resource_config(resource='wrong_domain.host') with self.assertRaises(RuntimeError): self._session.get_resource_config(resource='local.wrong_host') with self.assertRaises(RuntimeError): self._session.get_resource_config( resource='local.localhost', schema='wrong_schema') # 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js_search/cms_apps.py	compoundpartners/js-search	0	133077	# -- coding: utf-8 -- from __future__ import unicode_literals from django.utils.translation import ugettext_lazy as _ from cms.app_base import CMSApp from cms.apphook_pool import apphook_pool from . import DEFAULT_APP_NAMESPACE class SearchApp(CMSApp): name = _('Search') app_name = DEFAULT_APP_NAMESPACE urls = ['js_search.urls'] def get_urls(self, args, *kwargs): return self.urls apphook_pool.register(SearchApp)	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 13239, 29889, 3286, 18411, 1053, 318, 657, 726, 29918, 433, 1537, 408, 903, 13, 13, 3166, 274, 1516, 29889, 932, 29918, 3188, 1053, 315, 4345, 2052, 13, 3166, 274, 1516, 29889, 932, 20849, 29918, 10109, 1053, 623, 20849, 29918, 10109, 13, 13, 3166, 869, 1053, 22236, 29918, 20576, 29918, 5813, 5550, 11538, 13, 13, 13, 1990, 11856, 2052, 29898, 29907, 4345, 2052, 1125, 13, 1678, 1024, 353, 903, 877, 7974, 1495, 13, 1678, 623, 29918, 978, 353, 22236, 29918, 20576, 29918, 5813, 5550, 11538, 13, 1678, 23942, 353, 6024, 1315, 29918, 4478, 29889, 26045, 2033, 13, 13, 1678, 822, 679, 29918, 26045, 29898, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 736, 1583, 29889, 26045, 13, 13, 932, 20849, 29918, 10109, 29889, 9573, 29898, 7974, 2052, 29897, 13, 2 ]
model/gif2png.py	THCloud9/naruto	20	119220	<reponame>THCloud9/naruto<filename>model/gif2png.py #-- coding: UTF-8 -- import os from PIL import Image def analyseImage(path): ''''' Pre-process pass over the image to determine the mode (full or additive). Necessary as assessing single frames isn't reliable. Need to know the mode before processing all frames. ''' im = Image.open(path) results = { 'size': im.size, 'mode': 'full', } try: while True: if im.tile: tile = im.tile[0] update_region = tile[1] update_region_dimensions = update_region[2:] if update_region_dimensions != im.size: results['mode'] = 'partial' break im.seek(im.tell() + 1) except EOFError: pass return results def processImage(path): ''''' 传入GIF图的完整路径名字,自动在该位置创建png文件夹来保存所有图片 ''' mode = analyseImage(path)['mode'] im = Image.open(path) i = 0 p = im.getpalette() last_frame = im.convert('RGBA') try: while True: print ("saving %s (%s) frame %d, %s %s" % (path, mode, i, im.size, im.tile) ) ''''' If the GIF uses local colour tables, each frame will have its own palette. If not, we need to apply the global palette to the new frame. ''' if not im.getpalette(): im.putpalette(p) new_frame = Image.new('RGBA', im.size) ''''' Is this file a "partial"-mode GIF where frames update a region of a different size to the entire image? If so, we need to construct the new frame by pasting it on top of the preceding frames. ''' if mode == 'partial': new_frame.paste(last_frame) new_frame.paste(im, (0,0), im.convert('RGBA')) fp = "/".join(path.split('/')[:-1]) + '/png' make_path(fp) fp = "/".join(path.split('/')[:-1]) + '/png/' + 'action' new_frame.save('%s-%02d.png' % (fp , i), 'PNG') i += 1 last_frame = new_frame im.seek(im.tell() + 1) except EOFError: pass def make_path(p): if not os.path.exists(p): # 判断文件夹是否存在 os.makedirs(p) # 创建文件夹 def main(): processImage('image/shuidun/shuidun.gif') if __name__ == "__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 4690, 20442, 29929, 29914, 24156, 3066, 29966, 9507, 29958, 4299, 29914, 18660, 29906, 2732, 29889, 2272, 13, 29937, 29899, 29930, 29899, 14137, 29901, 18351, 29899, 29947, 448, 29930, 29899, 1678, 6756, 13, 5215, 2897, 29871, 6756, 13, 3166, 349, 6227, 1053, 7084, 29871, 6756, 13, 29871, 6756, 13, 1753, 16455, 344, 2940, 29898, 2084, 1125, 29871, 6756, 13, 1678, 14550, 4907, 6756, 13, 1678, 4721, 29899, 5014, 1209, 975, 278, 1967, 304, 8161, 278, 4464, 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ambry/dbexceptions.py	kball/ambry	1	1605968	"""Common exception objects Copyright (c) 2013 Clarinova. This file is licensed under the terms of the Revised BSD License, included in this distribution as LICENSE.txt """ import textwrap class BundleError(Exception): def __init__(self, message, args, kwargs): # Call the base class constructor with the parameters it needs #Exception.__init__(self,textwrap.fill(message, 120), args, *kwargs) Exception.__init__(self, message, args, **kwargs) class BadRequest(BundleError): '''The functioncall or request was malformed or incorrect''' class ProcessError(BundleError): '''Error in the configuration files''' class ObjectStateError(BundleError): '''Object not put into appropriate state before uses''' class ConfigurationError(BundleError): '''Error in the configuration files''' class ResultCountError(BundleError): '''Got too many or too few results''' class FilesystemError(BundleError): '''Missing file, etc. ''' class NotFoundError(BundleError): '''Failed to find resource''' class DependencyError(Exception): """Required bundle dependencies not satisfied""" class NoLock(BundleError): '''Error in the configuration files''' class Locked(BundleError): '''Error in the configuration files''' class QueryError(BundleError): """Error while executing a query""" class ConflictError(BundleError): """Conflict with existing resource""" class SyncError(BundleError): """Could not sync a resource""" class FatalError(BundleError): """A Fatal Bundle Error, generated in testing instead of a system exit. """ class DatabaseError(BundleError): """A general database error """ class DatabaseMissingError(DatabaseError): """A general database error """	[ 1, 9995, 18877, 3682, 3618, 13, 13, 11882, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29941, 15183, 262, 4273, 29889, 910, 934, 338, 7794, 21144, 1090, 278, 4958, 310, 278, 13, 1123, 11292, 350, 7230, 19245, 29892, 5134, 297, 445, 4978, 408, 365, 2965, 1430, 1660, 29889, 3945, 13, 15945, 29908, 13, 13, 5215, 1426, 6312, 13, 13, 1990, 24470, 2392, 29898, 2451, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2643, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 13, 4706, 396, 8251, 278, 2967, 770, 5823, 411, 278, 4128, 372, 4225, 13, 4706, 396, 2451, 17255, 2344, 12035, 1311, 29892, 726, 6312, 29889, 5589, 29898, 4906, 29892, 29871, 29896, 29906, 29900, 511, 334, 5085, 29892, 3579, 19290, 29897, 13, 4706, 8960, 17255, 2344, 12035, 1311, 29892, 2643, 29892, 334, 5085, 29892, 3579, 19290, 29897, 13, 13, 1990, 9178, 3089, 29898, 9534, 2392, 1125, 13, 1678, 14550, 1576, 740, 4804, 470, 2009, 471, 4439, 15628, 470, 10240, 12008, 13, 13, 1990, 10554, 2392, 29898, 9534, 2392, 1125, 13, 1678, 14550, 2392, 297, 278, 5285, 2066, 12008, 13, 13, 1990, 4669, 2792, 2392, 29898, 9534, 2392, 1125, 13, 1678, 14550, 2061, 451, 1925, 964, 8210, 2106, 1434, 3913, 12008, 13, 13, 1990, 20999, 2392, 29898, 9534, 2392, 1125, 13, 1678, 14550, 2392, 297, 278, 5285, 2066, 12008, 13, 268, 13, 1990, 7867, 3981, 2392, 29898, 9534, 2392, 1125, 13, 1678, 14550, 29954, 327, 2086, 1784, 470, 2086, 2846, 2582, 12008, 13, 268, 13, 1990, 12745, 973, 2392, 29898, 9534, 2392, 1125, 13, 1678, 14550, 18552, 292, 934, 29892, 2992, 29889, 14550, 13, 13, 1990, 2216, 9692, 2392, 29898, 9534, 2392, 1125, 13, 1678, 14550, 17776, 304, 1284, 6503, 12008, 13, 268, 13, 1990, 10034, 5197, 2392, 29898, 2451, 1125, 13, 1678, 9995, 19347, 11846, 9962, 451, 15787, 15945, 29908, 13, 13, 1990, 1939, 16542, 29898, 9534, 2392, 1125, 13, 1678, 14550, 2392, 297, 278, 5285, 2066, 12008, 13, 13, 1990, 18199, 287, 29898, 9534, 2392, 1125, 13, 1678, 14550, 2392, 297, 278, 5285, 2066, 12008, 13, 13, 1990, 13641, 2392, 29898, 9534, 2392, 1125, 13, 1678, 9995, 2392, 1550, 14012, 263, 2346, 15945, 29908, 13, 13, 1990, 10811, 29176, 2392, 29898, 9534, 2392, 1125, 13, 1678, 9995, 16376, 29176, 411, 5923, 6503, 15945, 29908, 13, 13, 13, 1990, 317, 2720, 2392, 29898, 9534, 2392, 1125, 13, 1678, 9995, 23323, 451, 16523, 263, 6503, 15945, 29908, 13, 13, 1990, 383, 2075, 2392, 29898, 9534, 2392, 1125, 13, 1678, 9995, 29909, 383, 2075, 24470, 4829, 29892, 5759, 297, 6724, 2012, 310, 263, 1788, 6876, 29889, 9995, 13, 13, 1990, 5470, 2392, 29898, 9534, 2392, 1125, 13, 1678, 9995, 29909, 2498, 2566, 1059, 9995, 13, 13, 1990, 5470, 18552, 292, 2392, 29898, 9112, 2392, 1125, 13, 1678, 9995, 29909, 2498, 2566, 1059, 9995, 2 ]
test_file.py	lulivi/gh-actions-tests	0	66498	<reponame>lulivi/gh-actions-tests import pathlib from os import environ import path # type: ignore def sample_function(text: str) -> None: print(f"This is a test function") print(f"The argument value is {text}") def main() -> None: """Multiples errors.""" print("Sample file with bad formatting!!!") environ["SAMPLE_VAR"] = "test" This_isAList: list = ["Sample object", "bad", "Formatted", "Ups" ] sample_function( "12345" ) if __name__ == "__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 29880, 352, 8107, 29914, 12443, 29899, 7387, 29899, 21150, 13, 5215, 2224, 1982, 13, 3166, 2897, 1053, 12471, 13, 13, 5215, 2224, 29871, 396, 1134, 29901, 11455, 13, 13, 13, 1753, 4559, 29918, 2220, 29898, 726, 29901, 851, 29897, 1599, 6213, 29901, 13, 1678, 1596, 29898, 29888, 29908, 4013, 338, 263, 1243, 740, 1159, 13, 1678, 1596, 29898, 29888, 29908, 1576, 2980, 995, 338, 426, 726, 27195, 13, 13, 13, 1753, 1667, 580, 1599, 6213, 29901, 13, 1678, 9995, 15329, 2701, 4436, 1213, 15945, 13, 1678, 1596, 703, 17708, 934, 411, 4319, 15998, 21004, 1159, 13, 1678, 12471, 3366, 8132, 3580, 1307, 29918, 26865, 3108, 353, 376, 1688, 29908, 13, 13, 1678, 910, 29918, 275, 29909, 1293, 29901, 1051, 353, 6796, 17708, 1203, 613, 29871, 13, 462, 3986, 376, 12313, 613, 13, 462, 3986, 376, 2500, 19667, 613, 13, 462, 3986, 376, 29965, 567, 29908, 13, 1678, 4514, 13, 13, 1678, 4559, 29918, 2220, 29898, 13, 4706, 376, 29896, 29906, 29941, 29946, 29945, 29908, 13, 1678, 1723, 13, 13, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1667, 580, 13, 2 ]
build_dataset.py	KrishnaGarg/named-entity-recognition	1	95575	<gh_stars>1-10 import csv import os import sys def load_dataset(path_csv): """Load dataset from csv file""" with open(path_csv, encoding="windows-1252") as f: csv_file = csv.reader(f, delimiter=',') dataset = []; words = []; tags = [] # Each line of csv corresponds to one word for idx, row in enumerate(csv_file): if idx == 0: continue # first row has only labels sentence, word, pos, tag = row # Non-empty sentence marks beginning of new sentence if len(sentence) != 0: if len(words) > 0: assert len(words) == len(tags) dataset.append((words, tags)) words, tags = [], [] # try: word, tag = str(word), str(tag) words.append(word) tags.append(tag) return dataset def save_dataset(dataset, save_dir): """Create splits from dataset""" print("Saving in {}...".format(save_dir)) if not os.path.exists(save_dir): os.makedirs(save_dir) # Export the dataset with open(os.path.join(save_dir, 'sentences.txt'), 'w') as file_sentences: with open(os.path.join(save_dir, 'labels.txt'), 'w') as file_labels: for words, tags in dataset: file_sentences.write("{}\n".format(" ".join(words))) file_labels.write("{}\n".format(" ".join(tags))) print("- done.") if __name__ == "__main__": dataset_path = 'data/ner_dataset.csv' msg = '{} file not found. Make sure you have downloaded the right dataset'.format(dataset_path) assert os.path.isfile(dataset_path), msg # Load the dataset print("Loading dataset...") dataset = load_dataset(dataset_path) print("- done.") # Split the dataset into train, val and test train_dataset = dataset[:int(0.7len(dataset))] val_dataset = dataset[int(0.7len(dataset)) : int(0.85len(dataset))] test_dataset = dataset[int(0.85len(dataset)):] save_dataset(train_dataset, 'data/train') save_dataset(val_dataset, 'data/val') save_dataset(test_dataset, 'data/test')	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 11799, 13, 5215, 2897, 13, 5215, 10876, 13, 13, 1753, 2254, 29918, 24713, 29898, 2084, 29918, 7638, 1125, 13, 12, 15945, 29908, 5896, 8783, 515, 11799, 934, 15945, 29908, 13, 13, 12, 2541, 1722, 29898, 2084, 29918, 7638, 29892, 8025, 543, 10499, 29899, 29896, 29906, 29945, 29906, 1159, 408, 285, 29901, 13, 12, 12, 7638, 29918, 1445, 353, 11799, 29889, 16950, 29898, 29888, 29892, 28552, 29922, 742, 1495, 13, 12, 12, 24713, 353, 13769, 3838, 353, 13769, 8282, 353, 5159, 13, 13, 12, 12, 29937, 7806, 1196, 310, 11799, 16161, 304, 697, 1734, 13, 12, 12, 1454, 22645, 29892, 1948, 297, 26985, 29898, 7638, 29918, 1445, 1125, 13, 12, 12, 12, 361, 22645, 1275, 29871, 29900, 29901, 6773, 12, 29937, 937, 1948, 756, 871, 11073, 13, 12, 12, 12, 18616, 663, 29892, 1734, 29892, 926, 29892, 4055, 353, 1948, 13, 12, 12, 12, 13, 12, 12, 12, 29937, 10050, 29899, 6310, 10541, 17997, 6763, 310, 716, 10541, 13, 12, 12, 12, 361, 7431, 29898, 18616, 663, 29897, 2804, 29871, 29900, 29901, 13, 12, 12, 12, 12, 361, 7431, 29898, 9303, 29897, 1405, 29871, 29900, 29901, 13, 12, 12, 12, 12, 12, 9294, 7431, 29898, 9303, 29897, 1275, 7431, 29898, 11338, 29897, 13, 12, 12, 12, 12, 12, 24713, 29889, 4397, 3552, 9303, 29892, 8282, 876, 13, 12, 12, 12, 12, 12, 9303, 29892, 8282, 353, 19997, 5159, 13, 12, 12, 12, 29937, 1018, 29901, 13, 12, 12, 12, 1742, 29892, 4055, 353, 851, 29898, 1742, 511, 851, 29898, 4039, 29897, 13, 12, 12, 12, 9303, 29889, 4397, 29898, 1742, 29897, 13, 12, 12, 12, 11338, 29889, 4397, 29898, 4039, 29897, 13, 13, 12, 2457, 8783, 13, 13, 1753, 4078, 29918, 24713, 29898, 24713, 29892, 4078, 29918, 3972, 1125, 13, 12, 15945, 29908, 4391, 8536, 1169, 515, 8783, 15945, 29908, 13, 13, 12, 2158, 703, 29903, 5555, 297, 6571, 856, 1642, 4830, 29898, 7620, 29918, 3972, 876, 13, 12, 361, 451, 2897, 29889, 2084, 29889, 9933, 29898, 7620, 29918, 3972, 1125, 13, 12, 12, 359, 29889, 29885, 12535, 12935, 29898, 7620, 29918, 3972, 29897, 13, 13, 12, 29937, 1222, 637, 278, 8783, 13, 12, 2541, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 7620, 29918, 3972, 29892, 525, 18616, 2063, 29889, 3945, 5477, 525, 29893, 1495, 408, 934, 29918, 18616, 2063, 29901, 13, 12, 12, 2541, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 7620, 29918, 3972, 29892, 525, 21134, 29889, 3945, 5477, 525, 29893, 1495, 408, 934, 29918, 21134, 29901, 13, 12, 12, 12, 1454, 3838, 29892, 8282, 297, 8783, 29901, 13, 12, 12, 12, 12, 1445, 29918, 18616, 2063, 29889, 3539, 703, 29912, 1012, 29876, 1642, 4830, 703, 11393, 7122, 29898, 9303, 4961, 13, 12, 12, 12, 12, 1445, 29918, 21134, 29889, 3539, 703, 29912, 1012, 29876, 1642, 4830, 703, 11393, 7122, 29898, 11338, 4961, 13, 12, 2158, 703, 29899, 2309, 23157, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 13, 12, 24713, 29918, 2084, 353, 525, 1272, 29914, 1089, 29918, 24713, 29889, 7638, 29915, 13, 12, 7645, 353, 525, 8875, 934, 451, 1476, 29889, 8561, 1854, 366, 505, 16532, 278, 1492, 8783, 4286, 4830, 29898, 24713, 29918, 2084, 29897, 13, 12, 9294, 2897, 29889, 2084, 29889, 275, 1445, 29898, 24713, 29918, 2084, 511, 10191, 13, 13, 12, 29937, 16012, 278, 8783, 13, 12, 2158, 703, 23456, 8783, 856, 1159, 13, 12, 24713, 353, 2254, 29918, 24713, 29898, 24713, 29918, 2084, 29897, 13, 12, 2158, 703, 29899, 2309, 23157, 13, 13, 12, 29937, 26178, 278, 8783, 964, 7945, 29892, 659, 322, 1243, 13, 12, 14968, 29918, 24713, 353, 8783, 7503, 524, 29898, 29900, 29889, 29955, 29930, 2435, 29898, 24713, 28166, 13, 12, 791, 29918, 24713, 353, 8783, 29961, 524, 29898, 29900, 29889, 29955, 29930, 2435, 29898, 24713, 876, 584, 938, 29898, 29900, 29889, 29947, 29945, 29930, 2435, 29898, 24713, 28166, 13, 12, 1688, 29918, 24713, 353, 8783, 29961, 524, 29898, 29900, 29889, 29947, 29945, 29930, 2435, 29898, 24713, 876, 17531, 13, 13, 12, 7620, 29918, 24713, 29898, 14968, 29918, 24713, 29892, 525, 1272, 29914, 14968, 1495, 13, 12, 7620, 29918, 24713, 29898, 791, 29918, 24713, 29892, 525, 1272, 29914, 791, 1495, 13, 12, 7620, 29918, 24713, 29898, 1688, 29918, 24713, 29892, 525, 1272, 29914, 1688, 1495, 2 ]
sdk/python/pulumi_azure/automation/connection.py	suresh198526/pulumi-azure	0	165971	<gh_stars>0 # coding=utf-8 # * WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. * # * Do not edit by hand unless you're certain you know what you are doing! * import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from .. import _utilities, _tables __all__ = ['Connection'] class Connection(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, automation_account_name: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[str]] = None, values: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, __props__=None, __name__=None, __opts__=None): """ Manages an Automation Connection. ## Import Automation Connection can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:automation/connection:Connection example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Automation/automationAccounts/account1/connections/conn1 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] automation_account_name: The name of the automation account in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] description: A description for this Connection. :param pulumi.Input[str] name: Specifies the name of the Connection. Changing this forces a new resource to be created. :param pulumi.Input[str] resource_group_name: The name of the resource group in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] type: The type of the Connection - can be either builtin type such as `Azure`, `AzureClassicCertificate`, and `AzureServicePrincipal`, or a user defined types. Changing this forces a new resource to be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] values: A mapping of key value pairs passed to the connection. Different `type` needs different parameters in the `values`. Builtin types have required field values as below: """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() if automation_account_name is None: raise TypeError("Missing required property 'automation_account_name'") __props__['automation_account_name'] = automation_account_name __props__['description'] = description __props__['name'] = name if resource_group_name is None: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name if type is None: raise TypeError("Missing required property 'type'") __props__['type'] = type if values is None: raise TypeError("Missing required property 'values'") __props__['values'] = values super(Connection, __self__).__init__( 'azure:automation/connection:Connection', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, automation_account_name: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[str]] = None, values: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None) -> 'Connection': """ Get an existing Connection resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] automation_account_name: The name of the automation account in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] description: A description for this Connection. :param pulumi.Input[str] name: Specifies the name of the Connection. Changing this forces a new resource to be created. :param pulumi.Input[str] resource_group_name: The name of the resource group in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] type: The type of the Connection - can be either builtin type such as `Azure`, `AzureClassicCertificate`, and `AzureServicePrincipal`, or a user defined types. Changing this forces a new resource to be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] values: A mapping of key value pairs passed to the connection. Different `type` needs different parameters in the `values`. Builtin types have required field values as below: """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["automation_account_name"] = automation_account_name __props__["description"] = description __props__["name"] = name __props__["resource_group_name"] = resource_group_name __props__["type"] = type __props__["values"] = values return Connection(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="automationAccountName") def automation_account_name(self) -> pulumi.Output[str]: """ The name of the automation account in which the Connection is created. Changing this forces a new resource to be created. """ return pulumi.get(self, "automation_account_name") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ A description for this Connection. """ return pulumi.get(self, "description") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Specifies the name of the Connection. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Output[str]: """ The name of the resource group in which the Connection is created. Changing this forces a new resource to be created. """ return pulumi.get(self, "resource_group_name") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The type of the Connection - can be either builtin type such as `Azure`, `AzureClassicCertificate`, and `AzureServicePrincipal`, or a user defined types. Changing this forces a new resource to be created. """ return pulumi.get(self, "type") @property @pulumi.getter def values(self) -> pulumi.Output[Mapping[str, str]]: """ A mapping of key value pairs passed to the connection. Different `type` needs different parameters in the `values`. 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start.py	AndersonOyama/TCC	0	121507	import re from spellchecker import SpellChecker def countSpellError(file): spell = SpellChecker(language='pt') lines = [] with open(file, 'r') as f: lines = [word.replace('.', '').replace(',','') for line in f for word in line.split()] f.close() count = 0 misspelled = spell.unknown(lines) for word in misspelled: # spell.correction(word) # print(word) # print(word, spell.correction(word)) count += 1 return misspelled, count	[ 1, 1053, 337, 13, 13, 3166, 24779, 3198, 261, 1053, 1706, 514, 5596, 261, 13, 13, 1753, 2302, 5592, 514, 2392, 29898, 1445, 1125, 13, 13, 1678, 24779, 353, 1706, 514, 5596, 261, 29898, 11675, 2433, 415, 1495, 13, 1678, 3454, 353, 5159, 13, 1678, 411, 1722, 29898, 1445, 29892, 525, 29878, 1495, 408, 285, 29901, 13, 4706, 3454, 353, 518, 1742, 29889, 6506, 12839, 742, 525, 2824, 6506, 29317, 3788, 1495, 363, 1196, 297, 285, 363, 1734, 297, 1196, 29889, 5451, 580, 29962, 13, 1678, 285, 29889, 5358, 580, 13, 1678, 2302, 353, 29871, 29900, 13, 1678, 3052, 13111, 839, 353, 24779, 29889, 26690, 29898, 9012, 29897, 13, 13, 1678, 363, 1734, 297, 3052, 13111, 839, 29901, 13, 4706, 396, 24779, 29889, 2616, 276, 428, 29898, 1742, 29897, 13, 4706, 396, 1596, 29898, 1742, 29897, 13, 4706, 396, 1596, 29898, 1742, 29892, 24779, 29889, 2616, 276, 428, 29898, 1742, 876, 13, 4706, 2302, 4619, 29871, 29896, 13, 268, 13, 268, 13, 1678, 736, 3052, 13111, 839, 29892, 2302, 13, 2 ]
test-projects/django_1_0/urls.py	TheLitvinoff/django-async	13	182659	from django.conf.urls.defaults import * from django.contrib import admin admin.autodiscover() urlpatterns = patterns('', # Enable Slumber (r'^slumber/', include('slumber.urls')), # Uncomment the next line to enable the admin: (r'^admin/(.*)', admin.site.root), )	[ 1, 515, 9557, 29889, 5527, 29889, 26045, 29889, 4381, 29879, 1053, 334, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 13, 13, 6406, 29889, 1300, 397, 10669, 957, 580, 13, 13, 2271, 11037, 29879, 353, 15038, 877, 742, 13, 1678, 396, 1174, 519, 14866, 2807, 13, 1678, 313, 29878, 29915, 29985, 2536, 2807, 29914, 742, 3160, 877, 2536, 2807, 29889, 26045, 1495, 511, 13, 13, 1678, 396, 853, 9342, 278, 2446, 1196, 304, 9025, 278, 4113, 29901, 13, 1678, 313, 29878, 29915, 29985, 6406, 14571, 5575, 29897, 742, 4113, 29889, 2746, 29889, 4632, 511, 13, 29897, 13, 2 ]
dockerimp.py	N4SJAMK/dockerimp	0	195181	#!/usr/bin/env python DOCUMENTATION = ''' --- module: dockerimp short_description: improved docker container management module description: - Manage docker containers with ansible options: name: description: - Set the name of the container required: false default: null aliases: ["id"] state: description: - Set the state of the container required: true default: null choices: [ "present", "running", "stopped", "absent", "restarted", "image_present", "image_latest", "image_absent" ] aliases: [] image: description: - Set the image for the container required: false default: null aliases: [] env: description: - Set the container environment variables required: false default: null aliases: [] volumes: description: - Mount voulumes for the container required: false default: null aliases: [] ports: description: - Map ports for container required: false default: null aliases: [] command: description: - Set the command for container required: false default: null aliases: [] expose: description: - Expose ports required: false default: null aliases: [] links: description: - Link containers required: false default: null aliases: [] client_url: description: - Client base url required: false default: "unix://var/run/docker.sock" aliases: [] insecure_registry: description: - Trust insecure registrys required: false default: false aliases: [] ''' import sys import copy try: import docker.client except ImportError as e: print("Failed to import module {0}".format(e)) sys.exit(1) class ContainerManagerException(Exception): pass class ContainerManager(): def __init__(self, module): self.module = module self.client = docker.Client(base_url = module.params.get('client_url')) self.changed = False self.check_mode = module.check_mode self.changes_made = [] self.params = self.fix_parameters() def fix_parameters(self): params = copy.deepcopy(self.module.params) if params.get('volumes'): try: if type(params['volumes']) is str: volumes = params['volumes'].split(",") elif type(params['volumes']) is list: volumes = params['volumes'] else: raise ContainerManagerException({'Invalid argument': params['volumes']}) mount_points = [x.split(":")[1] for x in volumes] binds = {} for i in volumes: j = i.split(":") len_j = len(j) if len_j != 2 and len_j != 3: raise ContainerManagerException({'Invalid argument': params['volumes']}) ro = False if len_j == 3: if j[2] == "ro": ro = True elif j[2] == "rw": ro = False else: raise ContainerManagerException({'Invalid argument': params['volumes']}) binds[j[0]] = {'bind': j[1], 'ro': ro} params['binds'] = binds params['volumes'] = mount_points except IndexError as e: raise ContainerManagerException({'Invalid argument': params['volumes']}) if params.get('image'): # add 'latest' tag to the image name if no tag is already provided image = params['image'] image_split = image.split("/")[-1].split(":") if len(image_split) == 1: params['image'] = "{0}:latest".format(params['image']) if params.get('ports'): try: if type(params['ports']) is str: port_params = params['ports'].split(",") elif type(params['ports']) is list: port_params = params['ports'] else: raise ContainerManagerException({'Invalid argument': params['ports']}) ports = [] for i in port_params: values = i.split(":") len_values = len(values) if len_values != 2 and len_values != 3: raise ContainerManagerException({'Invalid argument': params['ports']}) port_and_prot = values[-1].split("/") len_port_and_prot = len(port_and_prot) if len_port_and_prot > 2: raise ContainerManagerException({'Invalid argument': params['ports']}) p = (port_and_prot[0], port_and_prot[1]) if len_port_and_prot == 2 else port_and_prot[0] ports.append(p) port_bindings = {} for i in port_params: values = i.split(":") len_values = len(values) if len_values == 2: host_port = values[0] prot_and_port = values[1] bind_ip = None elif len_values == 3: host_port = values[1] prot_and_port = values[2] bind_ip = values[0] else: raise ContainerManagerException({'Invalid argument': params['ports']}) prot_and_port = prot_and_port.split("/") len_prot_and_port = len(prot_and_port) if len_prot_and_port == 2: key = "{0}/{1}".format(prot_and_port[0], prot_and_port[1]) elif len_prot_and_port == 1: key = prot_and_port[0] else: raise ContainerManagerException({'Invalid argument': params['ports']}) if bind_ip: val = (bind_ip, host_port) if host_port else (bind_ip,) else: val = host_port or None port_bindings[key] = val params['ports'] = ports params['port_bindings'] = port_bindings except IndexError as e: raise ContainerManagerException({'Invalid argument': params['ports'], 'error': e}) if params.get('env'): if type(params['env']) is str: envs = params['env'].split(",") elif type(params['env']) is list: envs = params['env'] elif type(params['env']) is dict: envs = ["{0}={1}".format(x, params['env'][x]) for x in params['env']] else: raise ContainerManagerException({'Invalid argument': params['env']}) # Add special ANSIBLE_MANAGED_ENVS variable so we can track which # variables are managed by ansible envs.append("ANSIBLE_MANAGED_ENVS={0}".format(":".join([x.split("=")[0] for x in envs]))) params['environment'] = envs return params def ensure_present(self): required_params = ("name", "image") self.check_required_parameters(required_params) container = self.find_container(self.params['name']) self.__ensure_present(container) def ensure_running(self): required_params = ("name", "image") self.check_required_parameters(required_params) container = self.find_container(self.params['name']) container = self.__ensure_present(container) if not container['State']['Running']: container = self.start_container(container) def ensure_running_latest(self): required_params = ("name", "image") self.check_required_parameters(required_params) container = self.find_container(self.params['name']) image = self.find_image(self.params['image']) if not container: container = self.__ensure_present(container) elif not self.is_running_latest_image(container, image): self.remove_container(container) container = self.__ensure_present() elif not self.ensure_same(container): self.ensure_absent() container = self.__ensure_present() if not container['State']['Running']: self.start_container(container) def ensure_stopped(self): required_params = ("name",) self.check_required_parameters(required_params) container = self.find_container(self.params['name']) if not container: raise ContainerManagerException("Container not found") if container['State']['Running']: self.stop_container(container) def ensure_absent(self): required_params = ("name",) self.check_required_parameters(required_params) container = self.find_container(self.params['name']) self.remove_container(container) def restart(self): required_params = ("name",) self.check_required_parameters(required_params) container = self.find_container(self.params.get('name')) if not container: raise ContainerManagerException("Container not found") if not container['State']['Running']: raise ContainerManagerException("Container not running") self.restart_container(container) def ensure_image_present(self): required_params = ("image",) self.check_required_parameters(required_params) self.__ensure_image_present(self.params['image']) def ensure_image_latest(self): required_params = ("image",) self.check_required_parameters(required_params) self.__ensure_image_latest(self.params['image']) def ensure_image_absent(self): required_params = ("image",) self.check_required_parameters(required_params) name = self.params['image'] if self.find_image(name): self.client.remove_image(name) def __ensure_present(self, container = None): if not container: self.__ensure_image_present(self.params['image']) container = self.create_container() elif not self.ensure_same(container): self.ensure_absent() container = self.__ensure_present() return container def __ensure_image_present(self, name): image = self.find_image(name) if not image: self.pull_image(name) def __ensure_image_latest(self, name): self.pull_image(name) return self.find_image(name) def check_required_parameters(self, required): for i in required: if not self.params.get(i): state = self.params['state'] error_msg = "{0} required for {1} satate".format(i, state) raise ContainerManagerException(error_msg) def find_container(self, name): containers = self.client.containers(all = True) c = [x for x in containers if ((x['Names'] or [""])[0] == "/{0}".format(name)) or (len(name) > 9 and x['Id'].startswith(name))] if len(c) > 1: error_msg = "Found more than one container with name or id" raise ContainerManagerException({'Unexpected error': error_msg}) if c: container = self.get_info(c[0]) return container return None def find_image(self, name): # client.images method does not throw an error if image is not found, it just # returns an empty array. client.inspect_image throws an error if image is not # found. Propably cleaner to do this way than to catch an error. image_name = name.split(":") # image name may contain port, so rejoin everything exept last item which is tag images = self.client.images(name = ":".join(image_name[:-1])) image_len = len(images) if image_len == 0: return None else: for i in images: if name in i['RepoTags']: return self.client.inspect_image(name) else: return None def is_running_latest_image(self, container, image): if not image: return False if image['Id'] == container['Image']: return True else: return False def get_info(self, container): return self.client.inspect_container(container) def get_image_info(self, image): return self.client.inspect_image(image) def pull_image(self, name): insecure_registry = self.params['insecure_registry'] old = self.find_image(name) self.client.pull(name, insecure_registry = insecure_registry) new = self.find_image(name) if not new: error_msg = "Cannot find {0}".format(name) raise ContainerManagerException({'Image not found': error_msg}) elif new['Id'] != (old or {}).get('Id'): self.write_log('PULLED', new) def create_container(self): params = self.params key_filter = ( 'image', 'command', 'hostname', 'user', 'detach', 'stdin_open', 'tty', 'mem_limit', 'ports', 'environment', 'dns', 'volumes', 'volumes_from', 'network_disabled', 'name', 'entrypoint', 'cpu_shares', 'working_dir', 'memswap_limit' ) filtered = { x: params[x] for x in key_filter if x in params } c = self.client.create_container(filtered) container = self.get_info(c) self.write_log('CREATED', container) return container def start_container(self, container): params = self.params key_filter = ( 'binds', 'port_bindings', 'lxc_conf', 'publish_all_ports', 'links', 'privileged', 'dns', 'dns_search', 'volumes_from', 'network_mode', 'restart_policy', 'cap_add', 'cap_drop' ) filtered = { x: params[x] for x in key_filter if x in params } self.client.start(container, filtered) container = self.get_info(container) self.write_log('STARTED', container) return container def stop_container(self, container): self.client.stop(container) container = self.get_info(container) self.write_log('STOPPED', container) return container def remove_container(self, container): if container['State']['Running']: container = self.stop_container(container) self.client.remove_container(container) c = self.find_container(container['Id']) if c: raise ContainerManagerException("Could not remove the container") self.write_log('REMOVED', container) def restart_container(self, container): self.client.restart(container) container = self.get_info(container) self.write_log('RESTARTED', container) def ensure_same(self, container): params = self.params require_restart = False # Ensure running the right image if container['Config']['Image'] != params['image']: require_restart = True # Ensure running latest image if the parameter is provided same = True if params.get('latest_image'): self.client.pull(params['image']) if not self.running_latest_image(container, params['image']): same = False require_restart = True # Ensure environment vars are up to date for i in container['Config']['Env']: if "ANSIBLE_MANAGED_ENVS" in i: ansible_managed_envs = i.split("=")[1].split(":") # Add the magic ANSIBLE_MANAGED_ENVS key value here # so that the two lists are easily comparable with # set() below ansible_managed_envs.append("ANSIBLE_MANAGED_ENVS") has_ansible_managed_envs = True break else: has_ansible_managed_envs = False has_env_params = params.get('environment') != None if has_env_params or has_ansible_managed_envs: if has_env_params and has_ansible_managed_envs: env_params = params['environment'] # Check same variables are set if set(ansible_managed_envs) != set([x.split("=")[0] for x in env_params]): require_restart = True # Check that the values are right else: for env in env_params: if env not in container['Config']['Env']: require_restart = True break else: require_restart = True # Ensure volume mountings are right container_binds = container['HostConfig']['Binds'] bind_params = params.get('binds') if container_binds or bind_params: if container_binds and bind_params: _bind_params = [ ":".join([ x, bind_params[x]['bind'], "ro" if bind_params[x]['ro'] else "rw" ]) for x in bind_params ] if set(_bind_params) != set(container_binds): require_restart = True else: require_restart = True # Ensure command is right if params.get('command'): if params['command'] != container['Command']: require_restart = True return require_restart != True def generate_message(self): if not self.has_changes(): msg = "Up to date. No changes made" else: msg = self.changes_made return msg def write_log(self, action, info): key_filter = ( 'Name', 'Id', 'Image', ) filtered = { x: info[x] for x in key_filter if x in info } self.changes_made.append({action: filtered}) def has_changes(self): if self.changes_made: return True return False def main(): arguments = { 'state': { 'required': True, 'choices': [ "present", "running", "running_latest", "stopped", "absent", "restarted", "image_present", "image_latest", ] }, 'name': { 'default': None, 'aliases': ["id"] }, 'image': { 'default': None }, 'env': { 'default': None }, 'volumes': { 'default': None }, 'ports': { 'default': None }, 'command': { 'default': None }, 'expose': { 'default': None }, 'links': { 'default': None }, 'insecure_registry': { 'default': False, 'choises': BOOLEANS }, } #module = AnsibleModule(argument_spec = arguments, supports_check_mode = True) module = AnsibleModule(argument_spec = arguments) try: manager = ContainerManager(module) state = module.params.get('state') if state == "present": manager.ensure_present() elif state == "running": manager.ensure_running() elif state == "running_latest": manager.ensure_running_latest() elif state == "stopped": manager.ensure_stopped() elif state == "absent": manager.ensure_absent() elif state == "restarted": manager.restart() elif state == "image_present": manager.ensure_image_present() elif state == "image_latest": manager.ensure_image_latest() elif state == "image_absent": manager.ensure_image_absent() module.exit_json(changed = manager.has_changes(), msg = manager.generate_message()) except ContainerManagerException as e: module.fail_json(msg = str(e)) except docker.errors.APIError as e: module.fail_json(msg = str(e)) except docker.errors.DockerException as e: module.fail_json(msg = str(e)) from ansible.module_utils.basic import * main()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 28665, 5005, 3919, 8098, 353, 14550, 13, 5634, 13, 5453, 29901, 10346, 6574, 13, 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test/sql/test_insert.py	smarkets/sqlalchemy	1	1607613	<filename>test/sql/test_insert.py<gh_stars>1-10 #! coding:utf-8 from sqlalchemy import Column, Integer, MetaData, String, Table,\ bindparam, exc, func, insert, select from sqlalchemy.dialects import mysql, postgresql from sqlalchemy.engine import default from sqlalchemy.testing import AssertsCompiledSQL,\ assert_raises_message, fixtures class _InsertTestBase(object): @classmethod def define_tables(cls, metadata): Table('mytable', metadata, Column('myid', Integer), Column('name', String(30)), Column('description', String(30))) Table('myothertable', metadata, Column('otherid', Integer), Column('othername', String(30))) class InsertTest(_InsertTestBase, fixtures.TablesTest, AssertsCompiledSQL): __dialect__ = 'default' def test_generic_insert_bind_params_all_columns(self): table1 = self.tables.mytable self.assert_compile(insert(table1), 'INSERT INTO mytable (myid, name, description) ' 'VALUES (:myid, :name, :description)') def test_insert_with_values_dict(self): table1 = self.tables.mytable checkparams = { 'myid': 3, 'name': 'jack' } self.assert_compile(insert(table1, dict(myid=3, name='jack')), 'INSERT INTO mytable (myid, name) VALUES (:myid, :name)', checkparams=checkparams) def test_insert_with_values_tuple(self): table1 = self.tables.mytable checkparams = { 'myid': 3, 'name': 'jack', 'description': 'mydescription' } self.assert_compile(insert(table1, (3, 'jack', 'mydescription')), 'INSERT INTO mytable (myid, name, description) ' 'VALUES (:myid, :name, :description)', checkparams=checkparams) def test_insert_with_values_func(self): table1 = self.tables.mytable self.assert_compile(insert(table1, values=dict(myid=func.lala())), 'INSERT INTO mytable (myid) VALUES (lala())') def test_insert_with_user_supplied_bind_params(self): table1 = self.tables.mytable values = { table1.c.myid: bindparam('userid'), table1.c.name: bindparam('username') } self.assert_compile(insert(table1, values), 'INSERT INTO mytable (myid, name) VALUES (:userid, :username)') def test_insert_values(self): table1 = self.tables.mytable values1 = {table1.c.myid: bindparam('userid')} values2 = {table1.c.name: bindparam('username')} self.assert_compile(insert(table1, values=values1).values(values2), 'INSERT INTO mytable (myid, name) VALUES (:userid, :username)') def test_prefix_with(self): table1 = self.tables.mytable stmt = table1.insert().\ prefix_with('A', 'B', dialect='mysql').\ prefix_with('C', 'D') self.assert_compile(stmt, 'INSERT C D INTO mytable (myid, name, description) ' 'VALUES (:myid, :name, :description)') self.assert_compile(stmt, 'INSERT A B C D INTO mytable (myid, name, description) ' 'VALUES (%s, %s, %s)', dialect=mysql.dialect()) def test_inline_default(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('foo', Integer, default=func.foobar())) self.assert_compile(table.insert(values={}, inline=True), 'INSERT INTO sometable (foo) VALUES (foobar())') self.assert_compile(table.insert(inline=True), 'INSERT INTO sometable (foo) VALUES (foobar())', params={}) def test_insert_returning_not_in_default(self): table1 = self.tables.mytable stmt = table1.insert().returning(table1.c.myid) assert_raises_message( exc.CompileError, "RETURNING is not supported by this dialect's statement compiler.", stmt.compile, dialect=default.DefaultDialect() ) def test_insert_from_select_select(self): table1 = self.tables.mytable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().\ from_select(("otherid", "othername"), sel) self.assert_compile( ins, "INSERT INTO myothertable (otherid, othername) " "SELECT mytable.myid, mytable.name FROM mytable " "WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) def test_insert_from_select_select_alt_ordering(self): table1 = self.tables.mytable sel = select([table1.c.name, table1.c.myid]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().\ from_select(("othername", "otherid"), sel) self.assert_compile( ins, "INSERT INTO myothertable (othername, otherid) " "SELECT mytable.name, mytable.myid FROM mytable " "WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) def test_insert_from_select_select_no_defaults(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('foo', Integer, default=func.foobar())) table1 = self.tables.mytable sel = select([table1.c.myid]).where(table1.c.name == 'foo') ins = table.insert().\ from_select(["id"], sel) self.assert_compile( ins, "INSERT INTO sometable (id) SELECT mytable.myid " "FROM mytable WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) def test_insert_mix_select_values_exception(self): table1 = self.tables.mytable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().\ from_select(("otherid", "othername"), sel) assert_raises_message( exc.InvalidRequestError, "This construct already inserts from a SELECT", ins.values, othername="5" ) def test_insert_mix_values_select_exception(self): table1 = self.tables.mytable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().values(othername="5") assert_raises_message( exc.InvalidRequestError, "This construct already inserts value expressions", ins.from_select, ("otherid", "othername"), sel ) def test_insert_from_select_table(self): table1 = self.tables.mytable ins = self.tables.myothertable.insert().\ from_select(("otherid", "othername"), table1) # note we aren't checking the number of columns right now self.assert_compile( ins, "INSERT INTO myothertable (otherid, othername) " "SELECT mytable.myid, mytable.name, mytable.description " "FROM mytable", checkparams={} ) def test_insert_from_select_union(self): mytable = self.tables.mytable name = 'name' description = 'desc' sel = select( [name, mytable.c.description], ).union( select([name, description]) ) ins = mytable.insert().\ from_select( [mytable.c.name, mytable.c.description], sel) self.assert_compile( ins, "INSERT INTO mytable (name, description) " "SELECT name, mytable.description FROM mytable " "UNION SELECT name, desc" ) def test_insert_from_select_col_values(self): table1 = self.tables.mytable table2 = self.tables.myothertable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = table2.insert().\ from_select((table2.c.otherid, table2.c.othername), sel) self.assert_compile( ins, "INSERT INTO myothertable (otherid, othername) " "SELECT mytable.myid, mytable.name FROM mytable " "WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) class EmptyTest(_InsertTestBase, fixtures.TablesTest, AssertsCompiledSQL): __dialect__ = 'default' def test_empty_insert_default(self): table1 = self.tables.mytable stmt = table1.insert().values({}) # hide from 2to3 self.assert_compile(stmt, 'INSERT INTO mytable () VALUES ()') def test_supports_empty_insert_true(self): table1 = self.tables.mytable dialect = default.DefaultDialect() dialect.supports_empty_insert = dialect.supports_default_values = True stmt = table1.insert().values({}) # hide from 2to3 self.assert_compile(stmt, 'INSERT INTO mytable DEFAULT VALUES', dialect=dialect) def test_supports_empty_insert_false(self): table1 = self.tables.mytable dialect = default.DefaultDialect() dialect.supports_empty_insert = dialect.supports_default_values = False stmt = table1.insert().values({}) # hide from 2to3 assert_raises_message(exc.CompileError, "The 'default' dialect with current database version " "settings does not support empty inserts.", stmt.compile, dialect=dialect) def _test_insert_with_empty_collection_values(self, collection): table1 = self.tables.mytable ins = table1.insert().values(collection) self.assert_compile(ins, 'INSERT INTO mytable () VALUES ()', checkparams={}) # empty dict populates on next values call self.assert_compile(ins.values(myid=3), 'INSERT INTO mytable (myid) VALUES (:myid)', checkparams={'myid': 3}) def test_insert_with_empty_list_values(self): self._test_insert_with_empty_collection_values([]) def test_insert_with_empty_dict_values(self): self._test_insert_with_empty_collection_values({}) def test_insert_with_empty_tuple_values(self): self._test_insert_with_empty_collection_values(()) class MultirowTest(_InsertTestBase, fixtures.TablesTest, AssertsCompiledSQL): __dialect__ = 'default' def test_not_supported(self): table1 = self.tables.mytable dialect = default.DefaultDialect() stmt = table1.insert().values([{'myid': 1}, {'myid': 2}]) assert_raises_message( exc.CompileError, "The 'default' dialect with current database version settings " "does not support in-place multirow inserts.", stmt.compile, dialect=dialect) def test_named(self): table1 = self.tables.mytable values = [ {'myid': 1, 'name': 'a', 'description': 'b'}, {'myid': 2, 'name': 'c', 'description': 'd'}, {'myid': 3, 'name': 'e', 'description': 'f'} ] checkparams = { 'myid_0': 1, 'myid_1': 2, 'myid_2': 3, 'name_0': 'a', 'name_1': 'c', 'name_2': 'e', 'description_0': 'b', 'description_1': 'd', 'description_2': 'f', } dialect = default.DefaultDialect() dialect.supports_multivalues_insert = True self.assert_compile(table1.insert().values(values), 'INSERT INTO mytable (myid, name, description) VALUES ' '(:myid_0, :name_0, :description_0), ' '(:myid_1, :name_1, :description_1), ' '(:myid_2, :name_2, :description_2)', checkparams=checkparams, dialect=dialect) def test_positional(self): table1 = self.tables.mytable values = [ {'myid': 1, 'name': 'a', 'description': 'b'}, {'myid': 2, 'name': 'c', 'description': 'd'}, {'myid': 3, 'name': 'e', 'description': 'f'} ] checkpositional = (1, 'a', 'b', 2, 'c', 'd', 3, 'e', 'f') dialect = default.DefaultDialect() dialect.supports_multivalues_insert = True dialect.paramstyle = 'format' dialect.positional = True self.assert_compile(table1.insert().values(values), 'INSERT INTO mytable (myid, name, description) VALUES ' '(%s, %s, %s), (%s, %s, %s), (%s, %s, %s)', checkpositional=checkpositional, dialect=dialect) def test_inline_default(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('data', String), Column('foo', Integer, default=func.foobar())) values = [ {'id': 1, 'data': 'data1'}, {'id': 2, 'data': 'data2', 'foo': 'plainfoo'}, {'id': 3, 'data': 'data3'}, ] checkparams = { 'id_0': 1, 'id_1': 2, 'id_2': 3, 'data_0': 'data1', 'data_1': 'data2', 'data_2': 'data3', 'foo_1': 'plainfoo', } self.assert_compile(table.insert().values(values), 'INSERT INTO sometable (id, data, foo) VALUES ' '(%(id_0)s, %(data_0)s, foobar()), ' '(%(id_1)s, %(data_1)s, %(foo_1)s), ' '(%(id_2)s, %(data_2)s, foobar())', checkparams=checkparams, dialect=postgresql.dialect()) def test_server_default(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('data', String), Column('foo', Integer, server_default=func.foobar())) values = [ {'id': 1, 'data': 'data1'}, {'id': 2, 'data': 'data2', 'foo': 'plainfoo'}, {'id': 3, 'data': 'data3'}, ] checkparams = { 'id_0': 1, 'id_1': 2, 'id_2': 3, 'data_0': 'data1', 'data_1': 'data2', 'data_2': 'data3', } self.assert_compile(table.insert().values(values), 'INSERT INTO sometable (id, data) VALUES ' '(%(id_0)s, %(data_0)s), ' '(%(id_1)s, %(data_1)s), ' '(%(id_2)s, %(data_2)s)', checkparams=checkparams, dialect=postgresql.dialect()) def test_server_default_absent_value(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('data', String), Column('foo', Integer, server_default=func.foobar())) values = [ {'id': 1, 'data': 'data1', 'foo': 'plainfoo'}, {'id': 2, 'data': 'data2'}, {'id': 3, 'data': 'data3', 'foo': 'otherfoo'}, ] checkparams = { 'id_0': 1, 'id_1': 2, 'id_2': 3, 'data_0': 'data1', 'data_1': 'data2', 'data_2': 'data3', 'foo_0': 'plainfoo', 'foo_2': 'otherfoo', } # note the effect here is that the first set of params # takes effect for the rest of them, when one is absent self.assert_compile(table.insert().values(values), 'INSERT INTO sometable (id, data, foo) VALUES ' '(%(id_0)s, %(data_0)s, %(foo_0)s), ' '(%(id_1)s, %(data_1)s, %(foo_0)s), ' '(%(id_2)s, %(data_2)s, %(foo_2)s)', checkparams=checkparams, dialect=postgresql.dialect())	[ 1, 529, 9507, 29958, 1688, 29914, 2850, 29914, 1688, 29918, 7851, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 29991, 14137, 29901, 9420, 29899, 29947, 13, 13, 3166, 4576, 284, 305, 6764, 1053, 12481, 29892, 8102, 29892, 20553, 1469, 29892, 1714, 29892, 6137, 2053, 13, 1678, 7868, 3207, 29892, 5566, 29892, 3653, 29892, 4635, 29892, 1831, 13, 3166, 4576, 284, 305, 6764, 29889, 15321, 781, 29879, 1053, 5749, 29892, 27035, 13, 3166, 4576, 284, 305, 6764, 29889, 10599, 1053, 2322, 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shadow/apis/item.py	f1uzz/shadow	1	16634	from functools import lru_cache from typing import Optional import requests from .patches import Patches class Item: """ Manipulation of static item data """ ITEM_URL = f"http://ddragon.leagueoflegends.com/cdn/{Patches.get_current_patch()}/data/en_US/item.json" items = requests.get(ITEM_URL).json() @classmethod @lru_cache() def id_for_name(cls, name: str) -> Optional[str]: """ Finds the id for an item given its name Returns the id, None if not found name - full name of item """ for item_id, item in cls.items["data"].items(): if item["name"].casefold() == name.casefold(): return item_id @classmethod @lru_cache() def name_for_id(cls, item_id: str) -> Optional[str]: """ Finds the name for an item given its id Returns the name, None if not found item_id - id of item """ for found_item_id, item in cls.items["data"].items(): if found_item_id == item_id: return item["name"]	[ 1, 515, 2090, 312, 8789, 1053, 301, 582, 29918, 8173, 13, 3166, 19229, 1053, 28379, 13, 13, 5215, 7274, 13, 13, 3166, 869, 5041, 267, 1053, 349, 905, 267, 13, 13, 13, 1990, 10976, 29901, 13, 1678, 9995, 13, 1678, 2315, 666, 2785, 310, 2294, 2944, 848, 13, 1678, 9995, 13, 13, 1678, 306, 4330, 29924, 29918, 4219, 353, 285, 29908, 1124, 597, 1289, 1431, 265, 29889, 280, 3437, 974, 1397, 1975, 29889, 510, 29914, 13687, 19248, 29925, 905, 267, 29889, 657, 29918, 3784, 29918, 5041, 580, 6822, 1272, 29914, 264, 29918, 3308, 29914, 667, 29889, 3126, 29908, 13, 1678, 4452, 353, 7274, 29889, 657, 29898, 9094, 29924, 29918, 4219, 467, 3126, 580, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 732, 29880, 582, 29918, 8173, 580, 13, 1678, 822, 1178, 29918, 1454, 29918, 978, 29898, 25932, 29892, 1024, 29901, 851, 29897, 1599, 28379, 29961, 710, 5387, 13, 4706, 9995, 13, 4706, 10987, 29879, 278, 1178, 363, 385, 2944, 2183, 967, 1024, 13, 4706, 16969, 278, 1178, 29892, 6213, 565, 451, 1476, 13, 13, 4706, 1024, 448, 2989, 1024, 310, 2944, 13, 4706, 9995, 13, 13, 4706, 363, 2944, 29918, 333, 29892, 2944, 297, 1067, 29879, 29889, 7076, 3366, 1272, 16862, 7076, 7295, 13, 9651, 565, 2944, 3366, 978, 16862, 4878, 8771, 580, 1275, 1024, 29889, 4878, 8771, 7295, 13, 18884, 736, 2944, 29918, 333, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 732, 29880, 582, 29918, 8173, 580, 13, 1678, 822, 1024, 29918, 1454, 29918, 333, 29898, 25932, 29892, 2944, 29918, 333, 29901, 851, 29897, 1599, 28379, 29961, 710, 5387, 13, 4706, 9995, 13, 4706, 10987, 29879, 278, 1024, 363, 385, 2944, 2183, 967, 1178, 13, 4706, 16969, 278, 1024, 29892, 6213, 565, 451, 1476, 13, 13, 4706, 2944, 29918, 333, 448, 1178, 310, 2944, 13, 4706, 9995, 13, 13, 4706, 363, 1476, 29918, 667, 29918, 333, 29892, 2944, 297, 1067, 29879, 29889, 7076, 3366, 1272, 16862, 7076, 7295, 13, 9651, 565, 1476, 29918, 667, 29918, 333, 1275, 2944, 29918, 333, 29901, 13, 18884, 736, 2944, 3366, 978, 3108, 2 ]
data/data_utils.py	hjonnala/deeplab2	0	91394	<filename>data/data_utils.py # coding=utf-8 # Copyright 2022 The Deeplab2 Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Contains common utility functions and classes for building dataset.""" import collections import io import numpy as np from PIL import Image from PIL import ImageOps import tensorflow as tf from deeplab2 import common _PANOPTIC_LABEL_FORMAT = 'raw' def read_image(image_data): """Decodes image from in-memory data. Args: image_data: Bytes data representing encoded image. Returns: Decoded PIL.Image object. """ image = Image.open(io.BytesIO(image_data)) try: image = ImageOps.exif_transpose(image) except TypeError: # capture and ignore this bug: # https://github.com/python-pillow/Pillow/issues/3973 pass return image def get_image_dims(image_data, check_is_rgb=False): """Decodes image and return its height and width. Args: image_data: Bytes data representing encoded image. check_is_rgb: Whether to check encoded image is RGB. Returns: Decoded image size as a tuple of (height, width) Raises: ValueError: If check_is_rgb is set and input image has other format. """ image = read_image(image_data) if check_is_rgb and image.mode != 'RGB': raise ValueError('Expects RGB image data, gets mode: %s' % image.mode) width, height = image.size return height, width def _int64_list_feature(values): """Returns a TF-Feature of int64_list. Args: values: A scalar or an iterable of integer values. Returns: A TF-Feature. """ if not isinstance(values, collections.Iterable): values = [values] return tf.train.Feature(int64_list=tf.train.Int64List(value=values)) def _bytes_list_feature(values): """Returns a TF-Feature of bytes. Args: values: A string. Returns: A TF-Feature. """ if isinstance(values, str): values = values.encode() return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values])) def create_features(image_data, image_format, filename, label_data=None, label_format=None): """Creates image/segmentation features. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg', 'jpg', or 'png'. filename: String, image filename. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. Returns: A dictionary of feature name to tf.train.Feature maaping. """ if image_format not in ('jpeg', 'png', 'jpg'): raise ValueError('Unsupported image format: %s' % image_format) # Check color mode, and convert grey image to rgb image. image = read_image(image_data) if image.mode != 'RGB': image = image.convert('RGB') image_data = io.BytesIO() image.save(image_data, format=image_format) image_data = image_data.getvalue() height, width = get_image_dims(image_data, check_is_rgb=True) feature_dict = { common.KEY_ENCODED_IMAGE: _bytes_list_feature(image_data), common.KEY_IMAGE_FILENAME: _bytes_list_feature(filename), common.KEY_IMAGE_FORMAT: _bytes_list_feature(image_format), common.KEY_IMAGE_HEIGHT: _int64_list_feature(height), common.KEY_IMAGE_WIDTH: _int64_list_feature(width), common.KEY_IMAGE_CHANNELS: _int64_list_feature(3), } if label_data is None: return feature_dict if label_format == 'png': label_height, label_width = get_image_dims(label_data) if (label_height, label_width) != (height, width): raise ValueError('Image (%s) and label (%s) shape mismatch' % ((height, width), (label_height, label_width))) elif label_format == 'raw': # Raw label encodes int32 array. expected_label_size = height * width * np.dtype(np.int32).itemsize if len(label_data) != expected_label_size: raise ValueError('Expects raw label data length %d, gets %d' % (expected_label_size, len(label_data))) else: raise ValueError('Unsupported label format: %s' % label_format) feature_dict.update({ common.KEY_ENCODED_LABEL: _bytes_list_feature(label_data), common.KEY_LABEL_FORMAT: _bytes_list_feature(label_format) }) return feature_dict def create_tfexample(image_data, image_format, filename, label_data=None, label_format=None): """Converts one image/segmentation pair to TF example. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg' or 'png'. filename: String, image filename. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. Returns: TF example proto. """ feature_dict = create_features(image_data, image_format, filename, label_data, label_format) return tf.train.Example(features=tf.train.Features(feature=feature_dict)) def create_video_tfexample(image_data, image_format, filename, sequence_id, image_id, label_data=None, label_format=None, prev_image_data=None, prev_label_data=None): """Converts one video frame/panoptic segmentation pair to TF example. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg' or 'png'. filename: String, image filename. sequence_id: ID of the video sequence as a string. image_id: ID of the image as a string. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. prev_image_data: An optional string or byte stream of encoded previous image data. prev_label_data: An optional string or byte stream of (potentially) encoded previous label data. Returns: TF example proto. """ feature_dict = create_features(image_data, image_format, filename, label_data, label_format) feature_dict.update({ common.KEY_SEQUENCE_ID: _bytes_list_feature(sequence_id), common.KEY_FRAME_ID: _bytes_list_feature(image_id) }) if prev_image_data is not None: feature_dict[common.KEY_ENCODED_PREV_IMAGE] = _bytes_list_feature( prev_image_data) if prev_label_data is not None: feature_dict[common.KEY_ENCODED_PREV_LABEL] = _bytes_list_feature( prev_label_data) return tf.train.Example(features=tf.train.Features(feature=feature_dict)) def create_video_and_depth_tfexample(image_data, image_format, filename, sequence_id, image_id, label_data=None, label_format=None, next_image_data=None, next_label_data=None, depth_data=None, depth_format=None): """Converts an image/segmentation pair and depth of first frame to TF example. The image pair contains the current frame and the next frame with the current frame including depth label. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg' or 'png'. filename: String, image filename. sequence_id: ID of the video sequence as a string. image_id: ID of the image as a string. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. next_image_data: An optional string or byte stream of encoded next image data. next_label_data: An optional string or byte stream of (potentially) encoded next label data. depth_data: An optional string or byte sream of encoded depth data. depth_format: String, depth data format, should be either 'png' or 'raw'. Returns: TF example proto. """ feature_dict = create_features(image_data, image_format, filename, label_data, label_format) feature_dict.update({ common.KEY_SEQUENCE_ID: _bytes_list_feature(sequence_id), common.KEY_FRAME_ID: _bytes_list_feature(image_id) }) if next_image_data is not None: feature_dict[common.KEY_ENCODED_NEXT_IMAGE] = _bytes_list_feature( next_image_data) if next_label_data is not None: feature_dict[common.KEY_ENCODED_NEXT_LABEL] = _bytes_list_feature( next_label_data) if depth_data is not None: feature_dict[common.KEY_ENCODED_DEPTH] = _bytes_list_feature( depth_data) feature_dict[common.KEY_DEPTH_FORMAT] = _bytes_list_feature( depth_format) return tf.train.Example(features=tf.train.Features(feature=feature_dict)) class SegmentationDecoder(object): """Basic parser to decode serialized tf.Example.""" def __init__(self, is_panoptic_dataset=True, is_video_dataset=False, is_depth_dataset=False, use_two_frames=False, use_next_frame=False, decode_groundtruth_label=True): self._is_panoptic_dataset = is_panoptic_dataset self._is_video_dataset = is_video_dataset self._is_depth_dataset = is_depth_dataset self._use_two_frames = use_two_frames self._use_next_frame = use_next_frame self._decode_groundtruth_label = decode_groundtruth_label string_feature = tf.io.FixedLenFeature((), tf.string) int_feature = tf.io.FixedLenFeature((), tf.int64) self._keys_to_features = { common.KEY_ENCODED_IMAGE: string_feature, common.KEY_IMAGE_FILENAME: string_feature, common.KEY_IMAGE_FORMAT: string_feature, common.KEY_IMAGE_HEIGHT: int_feature, common.KEY_IMAGE_WIDTH: int_feature, common.KEY_IMAGE_CHANNELS: int_feature, } if decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_LABEL] = string_feature if self._is_video_dataset: self._keys_to_features[common.KEY_SEQUENCE_ID] = string_feature self._keys_to_features[common.KEY_FRAME_ID] = string_feature # Two-frame specific processing. if self._use_two_frames: self._keys_to_features[common.KEY_ENCODED_PREV_IMAGE] = string_feature if decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_PREV_LABEL] = string_feature # Next-frame specific processing. if self._use_next_frame: self._keys_to_features[common.KEY_ENCODED_NEXT_IMAGE] = string_feature if decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_NEXT_LABEL] = string_feature # Depth specific processing. if self._is_depth_dataset and decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_DEPTH] = string_feature def _decode_image(self, parsed_tensors, key): """Decodes image udner key from parsed tensors.""" image = tf.io.decode_image( parsed_tensors[key], channels=3, dtype=tf.dtypes.uint8, expand_animations=False) image.set_shape([None, None, 3]) return image def _decode_label(self, parsed_tensors, label_key): """Decodes segmentation label under label_key from parsed tensors.""" if self._is_panoptic_dataset: flattened_label = tf.io.decode_raw( parsed_tensors[label_key], out_type=tf.int32) label_shape = tf.stack([ parsed_tensors[common.KEY_IMAGE_HEIGHT], parsed_tensors[common.KEY_IMAGE_WIDTH], 1 ]) label = tf.reshape(flattened_label, label_shape) return label label = tf.io.decode_image(parsed_tensors[label_key], channels=1) label.set_shape([None, None, 1]) return label def __call__(self, serialized_example): parsed_tensors = tf.io.parse_single_example( serialized_example, features=self._keys_to_features) return_dict = { 'image': self._decode_image(parsed_tensors, common.KEY_ENCODED_IMAGE), 'image_name': parsed_tensors[common.KEY_IMAGE_FILENAME], 'height': tf.cast(parsed_tensors[common.KEY_IMAGE_HEIGHT], dtype=tf.int32), 'width': tf.cast(parsed_tensors[common.KEY_IMAGE_WIDTH], dtype=tf.int32), } return_dict['label'] = None if self._decode_groundtruth_label: return_dict['label'] = self._decode_label(parsed_tensors, common.KEY_ENCODED_LABEL) if self._is_video_dataset: return_dict['sequence'] = parsed_tensors[common.KEY_SEQUENCE_ID] if self._use_two_frames: return_dict['prev_image'] = self._decode_image( parsed_tensors, common.KEY_ENCODED_PREV_IMAGE) if self._decode_groundtruth_label: return_dict['prev_label'] = self._decode_label( parsed_tensors, common.KEY_ENCODED_PREV_LABEL) if self._use_next_frame: return_dict['next_image'] = self._decode_image( parsed_tensors, common.KEY_ENCODED_NEXT_IMAGE) if self._decode_groundtruth_label: return_dict['next_label'] = self._decode_label( parsed_tensors, common.KEY_ENCODED_NEXT_LABEL) if self._is_depth_dataset and self._decode_groundtruth_label: return_dict['depth'] = self._decode_label( parsed_tensors, common.KEY_ENCODED_DEPTH) return return_dict	[ 1, 529, 9507, 29958, 1272, 29914, 1272, 29918, 13239, 29889, 2272, 13, 29937, 14137, 29922, 9420, 29899, 29947, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29906, 450, 897, 29872, 572, 370, 29906, 13189, 943, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 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project/graddetmf.py	felixzheng02/pypkpd	9	185599	<gh_stars>1-10 """ ## Function translated automatically using 'matlab.to.r()' ## Author: <NAME>, <NAME> """ import numpy as np from project.cell import cell from project.size import size from project.zeros import zeros from project.mftot import mftot from project.mf_all import mf_all from project.get_fim_size import get_fim_size from project.update_designinlist import update_designinlist def graddetmf(model_switch,aX,groupsize,ni,xt,x,a,bpop,d,sigma,docc,poped_db,lndet=False,gradxt=False): n = get_fim_size(poped_db) m = size(ni)[0] if (gradxt == False): gdmf = np.ones(msize(a)[1]).reshape(m, size(a)[1]) else: gdmf = np.ones(msize(xt)[1]).reshape(m, size(xt)[1]) iParallelN = (poped_db["settings"]["parallel"]["bParallelSG"] == 1) + 1 #1 if no parallel, 2 if parallel if iParallelN == 2: designsin = cell(1,0) it = 1 for p in range(0, iParallelN): if p == 2: #Execute parallel designs designout = designsin raise Exception("Parallel execution not yet implemented in PopED for R") #designout = execute_parallel(designsin,poped_db) designout = designsin if iParallelN == 1: returnArgs = mftot(model_switch,groupsize,ni,xt,x,a,bpop,d,sigma,docc,poped_db) mft = returnArgs[0] poped_db = returnArgs[1] else: if p == 1: designsin = update_designinlist(designsin,groupsize,ni,xt,x,a,-1,0) else: mft = designout["it"]["FIM"] it = it + 1 if iParallelN == 1 or p == 2: if all(size(poped_db["settings"]["prior_fim"]) == size(mft)): mft = mft + poped_db["settings"]["prior_fim"] imft = np.linalg.inv(mft) if np.isinf(imft[1,1]): imft = zeros(size(mft)) for i in range(0, m): if groupsize[i] == 0: gdmf[i, 1:ni[i]] = zeros(1, ni(i)) else: if x.size != 0: x_i = np.transpose(x[i,:]) else: x_i = zeros(0,1) if a.size != 0: a_i = np.transpose(a[i,:]) else: a_i = zeros(0,1) if iParallelN == 1: returnArgs = mf_all(np.transpose(model_switch[i,1:ni[i]]), np.transpose(xt[i,1:ni[i]]),x_i,a_i,bpop,d,sigma,docc,poped_db) mf_tmp = returnArgs[0] poped_db = returnArgs[1] else: if p == 1: designsin = update_designinlist(designsin, 1, ni, xt, x, a, -1, i) else: mf_tmp = designout["it"]["FIM"] it = it + 1 mfb = groupsize[i]mf_tmp a0 = a xt0 = xt if gradxt is False: nCtl = size(poped_db["design"]["a"])[1] else: nCtl = ni[i] for ct1 in range(0, nCtl): if aX[i,ct1] != 0: if gradxt == False: a = a0 a[i,ct1] = a[i,ct1] + poped_db["settings"]["hgd"] else: xt = xt0 xt[i,ct1] = xt[i,ct1] + poped_db["settings"]["hgd"] if iParallelN == 1: returnArgs = mf_all(np.transpose(model_switch[i,1:ni[i]]), np.transpose(xt[i,1:ni[i]]), x_i, np.transpose(a[i,:]), bpop, d, sigma, docc, poped_db) mf_tmp = returnArgs[0] poped_db = returnArgs[1] else: if p == 1: designsin = update_designinlist(designsin, 1, ni, xt, x, a, -1, i) else: mf_tmp = designout["it"]["FIM"] it = it + 1 if iParallelN == 1 or p == 2: mf_plus = groupsize[i]mf_tmp ir = (mf_plus-mfb)/poped_db["settings"]["hgd"] s = 0 #Calc the tr(A^-1 * dA/dX) for some X for ct2 in range(0, n): s = s + np.matmul(imft[ct2,:], ir[:,ct2]) if s == 0: #The model doesn't depend on design variable (a or t), e.g. PD is only dependent on time and not dose, fix the a-gradient to a small value or PD with Placebo dose, fix the xt-gradient to a small value. s = 1e-12 gdmf[i, ct1] = s if lndet == False: ret = gdmf*np.linalg.det(mft) else: ret = gdmf return {"ret": ret, "poped_db": poped_db}	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 19451, 13, 30004, 13, 30004, 13, 2277, 6680, 20512, 6336, 773, 525, 2922, 8205, 29889, 517, 29889, 29878, 580, 29915, 30004, 13, 2277, 13361, 29901, 529, 5813, 10202, 529, 5813, 3238, 13, 15945, 19451, 13, 30004, 13, 30004, 13, 5215, 12655, 408, 7442, 30004, 13, 3166, 2060, 29889, 3729, 1053, 3038, 30004, 13, 3166, 2060, 29889, 2311, 1053, 2159, 30004, 13, 3166, 2060, 29889, 3298, 359, 1053, 24786, 30004, 13, 3166, 2060, 29889, 29885, 615, 327, 1053, 286, 615, 327, 30004, 13, 3166, 2060, 29889, 29885, 29888, 29918, 497, 1053, 286, 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main.py	Meat0Project/ChatBot	4	1682	''' Made by - <NAME> Purpose - Python mini project Date - 18 october 2020 ''' from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer form termcolor import cprint import time chatbot = ChatBot('Bot') trainer = ChatterBotCorpusTrainer(chatbot) trainer.train('chatterbot.corpus.english') cprint("#" * 50, "magenta") cprint((f"A Chatot ").center(50), "yellow") cprint("#" * 50, "magenta") print('You can exit by type exit\n') while True: query = input(">> ") if 'exit' in query: exit() else: print(chatbot.get_response(query))	[ 1, 14550, 13, 29924, 1943, 491, 448, 529, 5813, 29958, 13, 29925, 332, 4220, 448, 5132, 20629, 2060, 13, 2539, 29871, 448, 29871, 29896, 29947, 4725, 4950, 29871, 29906, 29900, 29906, 29900, 13, 12008, 13, 3166, 521, 2620, 7451, 1053, 678, 271, 29933, 327, 13, 3166, 521, 2620, 7451, 29889, 14968, 414, 1053, 678, 2620, 29933, 327, 12521, 13364, 5323, 4983, 13, 689, 1840, 2780, 1053, 274, 2158, 13, 5215, 931, 13, 13, 13496, 7451, 353, 678, 271, 29933, 327, 877, 29933, 327, 1495, 13, 3018, 4983, 353, 678, 2620, 29933, 327, 12521, 13364, 5323, 4983, 29898, 13496, 7451, 29897, 13, 13, 3018, 4983, 29889, 14968, 877, 305, 2620, 7451, 29889, 2616, 13364, 29889, 996, 1674, 1495, 13, 13, 29883, 2158, 14822, 29908, 334, 29871, 29945, 29900, 29892, 376, 11082, 6381, 1159, 13, 29883, 2158, 3552, 29888, 29908, 29909, 678, 271, 327, 376, 467, 5064, 29898, 29945, 29900, 511, 376, 29136, 1159, 13, 29883, 2158, 14822, 29908, 334, 29871, 29945, 29900, 29892, 376, 11082, 6381, 1159, 13, 2158, 877, 3492, 508, 6876, 491, 1134, 6876, 29905, 29876, 1495, 13, 8000, 5852, 29901, 13, 1678, 2346, 353, 1881, 703, 6778, 16521, 13, 1678, 565, 525, 13322, 29915, 297, 2346, 29901, 13, 4706, 6876, 580, 13, 1678, 1683, 29901, 13, 4706, 1596, 29898, 13496, 7451, 29889, 657, 29918, 5327, 29898, 1972, 876, 13, 2 ]
188.py	RafaelHuang87/Leet-Code-Practice	0	138961	<filename>188.py class Solution: def maxProfit(self, k: int, prices: List[int]) -> int: n, res = len(prices), 0 if n < 2: return 0 if k > n // 2: for i in range(1, n): if prices[i] > prices[i - 1]: res += prices[i] - prices[i - 1] return res hold, sold = [float('-inf')] * (k + 1), [0] * (k + 1) for price in prices: for j in range(1, k + 1): hold[j] = max(hold[j], sold[j - 1] - price) sold[j] = max(sold[j], hold[j] + price) return sold[k]	[ 1, 529, 9507, 29958, 29896, 29947, 29947, 29889, 2272, 13, 1990, 24380, 29901, 13, 1678, 822, 4236, 1184, 9202, 29898, 1311, 29892, 413, 29901, 938, 29892, 26094, 29901, 2391, 29961, 524, 2314, 1599, 938, 29901, 13, 4706, 302, 29892, 620, 353, 7431, 29898, 558, 1575, 511, 29871, 29900, 13, 4706, 565, 302, 529, 29871, 29906, 29901, 13, 9651, 736, 29871, 29900, 13, 4706, 565, 413, 1405, 302, 849, 29871, 29906, 29901, 13, 9651, 363, 474, 297, 3464, 29898, 29896, 29892, 302, 1125, 13, 18884, 565, 26094, 29961, 29875, 29962, 1405, 26094, 29961, 29875, 448, 29871, 29896, 5387, 13, 462, 1678, 620, 4619, 26094, 29961, 29875, 29962, 448, 26094, 29961, 29875, 448, 29871, 29896, 29962, 13, 9651, 736, 620, 13, 13, 4706, 4808, 29892, 5239, 353, 518, 7411, 877, 29899, 7192, 1495, 29962, 334, 313, 29895, 718, 29871, 29896, 511, 518, 29900, 29962, 334, 313, 29895, 718, 29871, 29896, 29897, 13, 4706, 363, 8666, 297, 26094, 29901, 13, 9651, 363, 432, 297, 3464, 29898, 29896, 29892, 413, 718, 29871, 29896, 1125, 13, 18884, 4808, 29961, 29926, 29962, 353, 4236, 29898, 8948, 29961, 29926, 1402, 5239, 29961, 29926, 448, 29871, 29896, 29962, 448, 8666, 29897, 13, 18884, 5239, 29961, 29926, 29962, 353, 4236, 29898, 29879, 1025, 29961, 29926, 1402, 4808, 29961, 29926, 29962, 718, 8666, 29897, 13, 4706, 736, 5239, 29961, 29895, 29962, 13, 2 ]
run_create_ec2_keypair.py	HardBoiledSmith/johanna	64	119448	<reponame>HardBoiledSmith/johanna<filename>run_create_ec2_keypair.py #!/usr/bin/env python3 import os import subprocess from multiprocessing import Process env = dict(os.environ) def read_file(file_path): f = open(file_path) lines = list() for ll in f.readlines(): lines.append(ll) f.close() return lines # noinspection PyShadowingNames def run(cmd, file_path_name=None, cwd=None): def _f(): if not file_path_name: _p = subprocess.Popen(cmd, cwd=cwd, env=env) _p.communicate() if _p.returncode != 0: raise Exception() else: with open(file_path_name, 'a') as f: _p = subprocess.Popen(cmd, stdout=f, cwd=cwd, env=env) _p.communicate() if _p.returncode != 0: raise Exception() pp = Process(target=_f) pp.start() pp.join() if pp.exitcode != 0: raise Exception() def run_create_ec2_keypair(key_name): cmd = ['rm', '-f'] cmd += [f'{key_name}.pem'] cmd += [f'{key_name}.pub'] run(cmd) cmd = ['openssl', 'genrsa'] cmd += ['-out', f'{key_name}.pem'] cmd += ['2048'] run(cmd) print('Private key file:', f'{key_name}.pem', '\n') run(['chmod', '400', f'{key_name}.pem']) cmd = ['openssl', 'rsa'] cmd += ['-in', f'{key_name}.pem'] cmd += ['-pubout'] run(cmd, file_path_name=f'{key_name}.pub') print('Public key file:', f'{key_name}.pub', '\n') print('AWS_KEY_PAIR_NAME:', key_name, '\n') pub_key = read_file(f'{key_name}.pub') pub_key = pub_key[1:-1] pp_list = list() for pk in pub_key: pk = pk.strip() pp_list.append(pk) pub_key = ''.join(pp_list) print('AWS_KEY_PAIR_MATERIAL:', pub_key, '\n') run(['chmod', '400', f'{key_name}.pub']) cmd = ['ssh-keygen', '-y'] cmd += ['-f', f'{key_name}.pem'] result, error = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE).communicate() # noinspection PyUnresolvedReferences result = result.decode('utf-8') print('OpenSSH public key:', result.strip(), '\n') cmd = ['openssl', 'pkey'] cmd += ['-in', f'{key_name}.pem'] cmd += ['-pubout'] cmd += ['-outform', 'DER'] proc = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE) cmd = ['openssl', 'md5', '-c'] result, _ = subprocess.Popen(cmd, env=env, stdin=proc.stdout, stdout=subprocess.PIPE).communicate() # noinspection PyUnresolvedReferences result = result.decode('utf-8') print('OpenSSH finger print:', result) return pub_key ################################################################################ # # start # ################################################################################ if __name__ == "__main__": from run_common import parse_args _, args = parse_args() if len(args) != 2: print('usage:', args[0], '<key-name>') raise Exception() run_create_ec2_keypair(args[1])	[ 1, 529, 276, 1112, 420, 29958, 29950, 538, 8431, 2356, 29209, 29914, 29926, 1148, 9713, 29966, 9507, 29958, 3389, 29918, 3258, 29918, 687, 29906, 29918, 446, 1478, 1466, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 5215, 2897, 13, 5215, 1014, 5014, 13, 3166, 6674, 307, 985, 292, 1053, 10554, 13, 13, 6272, 353, 9657, 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exercises/es/test_04_10.py	mariacamilagl/spacy-course	0	167693	def test(): assert len(TRAINING_DATA) == 4, "Los datos de entrenamiento no concuerdan – esperaba 4 ejemplos." assert all( len(entry) == 2 and isinstance(entry[1], dict) for entry in TRAINING_DATA ), "Formato incorrecto de los datos de entrenamiento. Esperaba una lista de tuples dónde el segundo elemento es un dict." assert all( entry[1].get("entities") for entry in TRAINING_DATA ), "Todas las anotaciones en los datos de entrenamiento deberían incluir entidades." assert TRAINING_DATA[0][1]["entities"] == [ (10, 19, "GPE") ], "Vuelve a revisar las entidades en el primer ejemplo." assert TRAINING_DATA[1][1]["entities"] == [ (17, 22, "GPE") ], "Vuelve a revisar las entidades en el segundo ejemplo." assert TRAINING_DATA[2][1]["entities"] == [ (15, 20, "GPE"), (24, 32, "GPE"), ], "Vuelve a revisar las entidades en el tercer ejemplo." assert TRAINING_DATA[3][1]["entities"] == [ (0, 6, "GPE") ], "Vuelve a revisar las entidades en el cuarto ejemplo." __msg__.good( "¡Muy buen trabajo! Una vez el modelo logra buenos resultados detectando entidades GPE " "en los comentarios de los viajeros, podrías añadir un componente basado " "en reglas para determinar si la entidad es un destino turístico en " "este contexto. Por ejemplo, puedes resolver los tipos de entidades en relación con un knowledge base o " "buscarlas en un wiki de viajes." )	[ 1, 822, 1243, 7295, 13, 1678, 4974, 7431, 29898, 29911, 4717, 1177, 4214, 29918, 14573, 29897, 1275, 29871, 29946, 29892, 376, 29286, 18683, 316, 875, 1267, 9993, 694, 3022, 29884, 2018, 273, 785, 17451, 5363, 29871, 29946, 8574, 16305, 359, 1213, 13, 1678, 4974, 599, 29898, 13, 4706, 7431, 29898, 8269, 29897, 1275, 29871, 29906, 322, 338, 8758, 29898, 8269, 29961, 29896, 1402, 9657, 29897, 363, 6251, 297, 323, 4717, 1177, 4214, 29918, 14573, 13, 1678, 10353, 376, 2500, 1219, 10240, 29877, 316, 1232, 18683, 316, 875, 1267, 9993, 29889, 3423, 546, 5363, 1185, 15023, 316, 5291, 2701, 270, 29980, 17720, 560, 14729, 1543, 29877, 831, 443, 9657, 1213, 13, 1678, 4974, 599, 29898, 13, 4706, 6251, 29961, 29896, 1822, 657, 703, 296, 1907, 1159, 363, 6251, 297, 323, 4717, 1177, 4214, 29918, 14573, 13, 1678, 10353, 376, 29911, 397, 294, 1869, 385, 327, 6027, 427, 1232, 18683, 316, 875, 1267, 9993, 316, 495, 8202, 13654, 381, 875, 7305, 1213, 13, 1678, 4974, 323, 4717, 1177, 4214, 29918, 14573, 29961, 29900, 3816, 29896, 29962, 3366, 296, 1907, 3108, 1275, 518, 13, 4706, 313, 29896, 29900, 29892, 29871, 29896, 29929, 29892, 376, 29954, 4162, 1159, 13, 1678, 21251, 376, 29963, 2491, 345, 263, 23484, 279, 1869, 875, 7305, 427, 560, 7130, 20270, 1213, 13, 1678, 4974, 323, 4717, 1177, 4214, 29918, 14573, 29961, 29896, 3816, 29896, 29962, 3366, 296, 1907, 3108, 1275, 518, 13, 4706, 313, 29896, 29955, 29892, 29871, 29906, 29906, 29892, 376, 29954, 4162, 1159, 13, 1678, 21251, 376, 29963, 2491, 345, 263, 23484, 279, 1869, 875, 7305, 427, 560, 14729, 20270, 1213, 13, 1678, 4974, 323, 4717, 1177, 4214, 29918, 14573, 29961, 29906, 3816, 29896, 29962, 3366, 296, 1907, 3108, 1275, 518, 13, 4706, 313, 29896, 29945, 29892, 29871, 29906, 29900, 29892, 376, 29954, 4162, 4968, 13, 4706, 313, 29906, 29946, 29892, 29871, 29941, 29906, 29892, 376, 29954, 4162, 4968, 13, 1678, 21251, 376, 29963, 2491, 345, 263, 23484, 279, 1869, 875, 7305, 427, 560, 1935, 2265, 20270, 1213, 13, 1678, 4974, 323, 4717, 1177, 4214, 29918, 14573, 29961, 29941, 3816, 29896, 29962, 3366, 296, 1907, 3108, 1275, 518, 13, 4706, 313, 29900, 29892, 29871, 29953, 29892, 376, 29954, 4162, 1159, 13, 1678, 21251, 376, 29963, 2491, 345, 263, 23484, 279, 1869, 875, 7305, 427, 560, 2723, 22171, 20270, 1213, 13, 13, 1678, 4770, 7645, 26914, 16773, 29898, 13, 4706, 376, 30180, 29924, 8631, 1321, 264, 21844, 29991, 29871, 12127, 7763, 560, 29472, 1480, 336, 1321, 264, 359, 1121, 2255, 6459, 1743, 875, 7305, 402, 4162, 376, 13, 4706, 376, 264, 1232, 419, 296, 8596, 316, 1232, 3025, 6846, 359, 29892, 2532, 29878, 8577, 4503, 328, 381, 443, 2964, 2016, 2362, 912, 376, 13, 4706, 376, 264, 1072, 3333, 1702, 11806, 279, 1354, 425, 875, 2368, 831, 443, 2731, 1789, 7013, 6141, 1417, 427, 376, 13, 4706, 376, 4196, 3030, 29877, 29889, 7102, 20270, 29892, 2653, 11696, 3770, 369, 1232, 6872, 359, 316, 875, 7305, 427, 10208, 1290, 378, 443, 7134, 2967, 288, 376, 13, 4706, 376, 8262, 4287, 3333, 427, 443, 281, 10058, 316, 3025, 10246, 1213, 13, 1678, 1723, 13, 2 ]
python_fundamentals/lists_basics_exs/football_cards.py	deboradyankova/python_education	0	1605434	<gh_stars>0 team_a_players = 11 team_b_players = 11 cards_list = set(input().split()) is_terminated = False for player in cards_list: if player.startswith('A'): team_a_players -= 1 else: team_b_players -= 1 if team_a_players < 7 or team_b_players < 7: is_terminated = True break print(f'Team A - {team_a_players}; Team B - {team_b_players}') if is_terminated: print('Game was terminated')	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 14318, 29918, 29874, 29918, 1456, 414, 353, 29871, 29896, 29896, 30004, 13, 14318, 29918, 29890, 29918, 1456, 414, 353, 29871, 29896, 29896, 30004, 13, 30004, 13, 28160, 29918, 1761, 353, 731, 29898, 2080, 2141, 5451, 3101, 30004, 13, 30004, 13, 275, 29918, 18821, 630, 353, 7700, 30004, 13, 30004, 13, 1454, 4847, 297, 15889, 29918, 1761, 29901, 30004, 13, 1678, 565, 4847, 29889, 27382, 2541, 877, 29909, 29374, 30004, 13, 4706, 3815, 29918, 29874, 29918, 1456, 414, 22361, 29871, 29896, 30004, 13, 1678, 1683, 29901, 30004, 13, 4706, 3815, 29918, 29890, 29918, 1456, 414, 22361, 29871, 29896, 30004, 13, 30004, 13, 1678, 565, 3815, 29918, 29874, 29918, 1456, 414, 529, 29871, 29955, 470, 3815, 29918, 29890, 29918, 1456, 414, 529, 29871, 29955, 29901, 30004, 13, 4706, 338, 29918, 18821, 630, 353, 5852, 30004, 13, 4706, 2867, 30004, 13, 30004, 13, 2158, 29898, 29888, 29915, 19409, 319, 448, 426, 14318, 29918, 29874, 29918, 1456, 414, 3400, 8583, 350, 448, 426, 14318, 29918, 29890, 29918, 1456, 414, 29913, 1495, 30004, 13, 361, 338, 29918, 18821, 630, 29901, 30004, 13, 1678, 1596, 877, 14199, 471, 29185, 1495, 2 ]
modules/image/image_processing/prnet/utils/rotate_vertices.py	AK391/PaddleHub	1	132611	# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np # import scipy.io as def frontalize(vertices): canonical_vertices = np.load('Data/uv-data/canonical_vertices.npy') vertices_homo = np.hstack((vertices, np.ones([vertices.shape[0], 1]))) #n x 4 P = np.linalg.lstsq(vertices_homo, canonical_vertices)[0].T # Affine matrix. 3 x 4 front_vertices = vertices_homo.dot(P.T) return front_vertices	[ 1, 396, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29906, 29896, 349, 22352, 29925, 22352, 13189, 943, 29889, 2178, 26863, 2538, 9841, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 13, 29937, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 8707, 29928, 22122, 29903, 8079, 13764, 29979, 476, 22255, 29892, 2845, 4653, 470, 2411, 2957, 29889, 13, 29937, 2823, 278, 19245, 363, 278, 2702, 4086, 14765, 1076, 11239, 322, 13, 29937, 27028, 1090, 278, 19245, 29889, 13, 5215, 12655, 408, 7442, 13, 13, 13, 29937, 1053, 4560, 2272, 29889, 601, 408, 13, 1753, 4565, 284, 675, 29898, 1765, 1575, 1125, 13, 1678, 24420, 29918, 1765, 1575, 353, 7442, 29889, 1359, 877, 1469, 29914, 4090, 29899, 1272, 29914, 3068, 265, 936, 29918, 1765, 1575, 29889, 29876, 2272, 1495, 13, 13, 1678, 13791, 29918, 9706, 29877, 353, 7442, 29889, 29882, 1429, 3552, 1765, 1575, 29892, 7442, 29889, 2873, 4197, 1765, 1575, 29889, 12181, 29961, 29900, 1402, 29871, 29896, 29962, 4961, 29871, 396, 29876, 921, 29871, 29946, 13, 1678, 349, 353, 7442, 29889, 29880, 979, 29887, 29889, 20155, 3044, 29898, 1765, 1575, 29918, 9706, 29877, 29892, 24420, 29918, 1765, 1575, 9601, 29900, 1822, 29911, 29871, 396, 13737, 457, 4636, 29889, 29871, 29941, 921, 29871, 29946, 13, 1678, 4565, 29918, 1765, 1575, 353, 13791, 29918, 9706, 29877, 29889, 6333, 29898, 29925, 29889, 29911, 29897, 13, 13, 1678, 736, 4565, 29918, 1765, 1575, 13, 2 ]
src/ethogram.py	dstern/SongExplorer	5	1602902	<reponame>dstern/SongExplorer #!/usr/bin/python3 # apply per-class thresholds to discretize probabilities # ethogram.py <logdir> <model> <thresholds-file> <tf-file> <wav-tic-rate> # e.g. # ethogram.py `pwd`/trained-classifier 1k 50 `pwd`/groundtruth-data/round1/20161207T102314_ch1_p1.tf 5000 import sys import os import re import numpy as np import csv from sys import argv from scipy.io import wavfile from itertools import cycle sys.path.append(os.path.dirname(os.path.realpath(__file__))) from lib import * _,logdir,model,thresholds_file,tf_file,audio_tic_rate = argv print('logdir: '+logdir) print('model: '+model) print('thresholds_file: '+thresholds_file) print('tf_file: '+tf_file) print('audio_tic_rate: '+audio_tic_rate) audio_tic_rate=float(audio_tic_rate) tfpath, tfname = os.path.split(tf_file) tfname_noext = os.path.splitext(tfname)[0] if os.path.isfile(os.path.join(tfpath,tfname_noext+'.wav')): wavname = tfname_noext+'.wav' elif os.path.isfile(os.path.join(tfpath,tfname_noext+'.WAV')): wavname = tfname_noext+'.WAV' else: print('cannot find corresponding WAV file') exit() precision_recall_ratios, thresholds = read_thresholds(logdir, model, thresholds_file) labels = [x[0] for x in thresholds] thresholds = np.array([x[1:] for x in thresholds], dtype=np.float64) sample_rate_probabilities, half_stride_sec, probability_matrix = \ read_probabilities(os.path.join(tfpath, tfname_noext), labels) for ithreshold in range(len(precision_recall_ratios)): features, start_tics, stop_tics = discretize_probabilites(probability_matrix, thresholds[:,[ithreshold]], labels, sample_rate_probabilities, half_stride_sec, audio_tic_rate) filename = os.path.join(tfpath, tfname_noext+'-predicted-'+precision_recall_ratios[ithreshold]+'pr.csv') isort = np.argsort(start_tics) with open(filename,'w') as fid: csvwriter = csv.writer(fid) csvwriter.writerows(zip(cycle([wavname]), \ start_tics[isort], stop_tics[isort], \ cycle(['predicted']), 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tests/test_script.py	sharad28/Tensile_-_compressive_strength	1	1611631	import unittest from main import app import os class TestToPerform(unittest.TestCase): def setUp(self): self.app = app.test_client() def tearDown(self): pass def test_page(self): response = self.app.get('/', follow_redirects=True) print(response) self.assertEqual(response.status_code, 200) if __name__ == '__main__': unittest.main()	[ 1, 1053, 443, 27958, 13, 13, 3166, 1667, 1053, 623, 13, 5215, 2897, 13, 13, 13, 1990, 4321, 1762, 5894, 689, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1583, 29889, 932, 353, 623, 29889, 1688, 29918, 4645, 580, 13, 13, 1678, 822, 734, 279, 6767, 29898, 1311, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 1243, 29918, 3488, 29898, 1311, 1125, 13, 4706, 2933, 353, 1583, 29889, 932, 29889, 657, 11219, 742, 1101, 29918, 17886, 29879, 29922, 5574, 29897, 13, 4706, 1596, 29898, 5327, 29897, 13, 4706, 1583, 29889, 9294, 9843, 29898, 5327, 29889, 4882, 29918, 401, 29892, 29871, 29906, 29900, 29900, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 2 ]
ad2/aula_09/leitura_com_close.py	renzon/fundamentos-de-programacao	6	138559	<gh_stars>1-10 arquivo = open('exemplo.txt', 'r', encoding='utf8') for linha in arquivo: print(linha.strip()) arquivo.close()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 19862, 4243, 353, 1722, 877, 735, 13141, 29889, 3945, 742, 525, 29878, 742, 8025, 2433, 9420, 29947, 1495, 13, 13, 1454, 6276, 2350, 297, 19325, 4243, 29901, 13, 1678, 1596, 29898, 1915, 2350, 29889, 17010, 3101, 13, 13, 19862, 4243, 29889, 5358, 580, 13, 2 ]
bin/analyze_path.py	sjtuzyk/chandra-acis-analysis	3	104108	<reponame>sjtuzyk/chandra-acis-analysis #!/usr/bin/env python3 # # Copyright (c) 2017-2018 <NAME> <<EMAIL>> # MIT license # """ Extract the object name and observation ID from the directory path. The base directory of the object data has the format: <name>_oi<obsid> """ import os import argparse from _context import acispy from acispy.utils import get_name_from_path, get_obsid_from_path def main(): parser = argparse.ArgumentParser( description="Extract object name and ObsID from data directory") parser.add_argument("-b", "--brief", dest="brief", action="store_true", help="Be brief") parser.add_argument("-n", "--name", dest="name", action="store_true", help="Only get object name") parser.add_argument("-i", "--obsid", dest="obsid", action="store_true", help="Only get observation ID") parser.add_argument("path", nargs="?", default=os.getcwd(), help="Path to the data directory " + "(default: current working directory)") args = parser.parse_args() path = os.path.abspath(args.path) b_get_name = False if args.obsid else True b_get_obsid = False if args.name else True if b_get_name: if not args.brief: print("Name:", end=" ") print(get_name_from_path(path)) if b_get_obsid: if not args.brief: print("ObsID:", end=" ") print(get_obsid_from_path(path)) if __name__ == "__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 29879, 29926, 9161, 20973, 29914, 305, 10738, 29899, 562, 275, 29899, 15916, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29955, 29899, 29906, 29900, 29896, 29947, 529, 5813, 29958, 3532, 26862, 6227, 6778, 13, 29937, 341, 1806, 19405, 13, 29937, 13, 13, 15945, 29908, 13, 5647, 1461, 278, 1203, 1024, 322, 15500, 3553, 515, 278, 3884, 2224, 29889, 13, 13, 1576, 2967, 3884, 310, 278, 1203, 848, 756, 278, 3402, 29901, 529, 978, 29958, 29918, 7768, 29966, 26290, 333, 29958, 13, 15945, 29908, 13, 13, 5215, 2897, 13, 5215, 1852, 5510, 13, 13, 3166, 903, 4703, 1053, 1274, 275, 2272, 13, 3166, 1274, 275, 2272, 29889, 13239, 1053, 679, 29918, 978, 29918, 3166, 29918, 2084, 29892, 679, 29918, 26290, 333, 29918, 3166, 29918, 2084, 13, 13, 13, 1753, 1667, 7295, 13, 1678, 13812, 353, 1852, 5510, 29889, 15730, 11726, 29898, 13, 4706, 6139, 543, 5647, 1461, 1203, 1024, 322, 4250, 29879, 1367, 515, 848, 3884, 1159, 13, 1678, 13812, 29889, 1202, 29918, 23516, 703, 29899, 29890, 613, 376, 489, 1182, 2575, 613, 2731, 543, 1182, 2575, 613, 13, 462, 4706, 3158, 543, 8899, 29918, 3009, 613, 1371, 543, 3629, 11473, 1159, 13, 1678, 13812, 29889, 1202, 29918, 23516, 703, 29899, 29876, 613, 376, 489, 978, 613, 2731, 543, 978, 613, 13, 462, 4706, 3158, 543, 8899, 29918, 3009, 613, 1371, 543, 11730, 679, 1203, 1024, 1159, 13, 1678, 13812, 29889, 1202, 29918, 23516, 703, 29899, 29875, 613, 376, 489, 26290, 333, 613, 2731, 543, 26290, 333, 613, 13, 462, 4706, 3158, 543, 8899, 29918, 3009, 613, 1371, 543, 11730, 679, 15500, 3553, 1159, 13, 1678, 13812, 29889, 1202, 29918, 23516, 703, 2084, 613, 302, 5085, 543, 29973, 613, 2322, 29922, 359, 29889, 657, 29883, 9970, 3285, 13, 462, 4706, 1371, 543, 2605, 304, 278, 848, 3884, 376, 718, 13, 462, 4706, 18227, 4381, 29901, 1857, 1985, 3884, 25760, 13, 1678, 6389, 353, 13812, 29889, 5510, 29918, 5085, 580, 13, 1678, 2224, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 5085, 29889, 2084, 29897, 13, 13, 1678, 289, 29918, 657, 29918, 978, 353, 7700, 565, 6389, 29889, 26290, 333, 1683, 5852, 13, 1678, 289, 29918, 657, 29918, 26290, 333, 353, 7700, 565, 6389, 29889, 978, 1683, 5852, 13, 13, 1678, 565, 289, 29918, 657, 29918, 978, 29901, 13, 4706, 565, 451, 6389, 29889, 1182, 2575, 29901, 13, 9651, 1596, 703, 1170, 29901, 613, 1095, 543, 16521, 13, 4706, 1596, 29898, 657, 29918, 978, 29918, 3166, 29918, 2084, 29898, 2084, 876, 13, 1678, 565, 289, 29918, 657, 29918, 26290, 333, 29901, 13, 4706, 565, 451, 6389, 29889, 1182, 2575, 29901, 13, 9651, 1596, 703, 29949, 5824, 1367, 29901, 613, 1095, 543, 16521, 13, 4706, 1596, 29898, 657, 29918, 26290, 333, 29918, 3166, 29918, 2084, 29898, 2084, 876, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1667, 580, 13, 2 ]
Collection_Resources/Email_Integration.py	data4d/Data_Collect	0	199085	<reponame>data4d/Data_Collect # Email Integration for Notifications import sys import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart ## Import Passwords - Importing Passwords from a file stored elsewhere on my computer # sys.path.append('C:/Users/GTayl/Desktop/Finance Modeling') # from passwords import Gmail as pwd def send_mail(fromaddr,toaddr,password,subject,text): fromaddr = fromaddr toaddr = toaddr msg = MIMEMultipart() msg['From'] = fromaddr msg['To'] = toaddr msg['Subject'] = subject body = text msg.attach(MIMEText(body, 'plain')) server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login(fromaddr, password) text = msg.as_string() server.sendmail(fromaddr, toaddr, text) server.quit()	[ 1, 529, 276, 1112, 420, 29958, 1272, 29946, 29881, 29914, 1469, 29918, 28916, 13, 396, 22608, 17100, 362, 363, 2216, 8232, 13, 13, 5215, 10876, 13, 5215, 1560, 9392, 1982, 13, 3166, 4876, 29889, 29885, 603, 29889, 726, 1053, 341, 8890, 1626, 13, 3166, 4876, 29889, 29885, 603, 29889, 18056, 442, 1053, 341, 8890, 6857, 27494, 13, 29871, 13, 2277, 16032, 6978, 9303, 448, 16032, 292, 6978, 9303, 515, 263, 934, 6087, 17551, 373, 590, 6601, 13, 29937, 10876, 29889, 2084, 29889, 4397, 877, 29907, 8419, 5959, 29914, 23799, 388, 29880, 29914, 17600, 29914, 12881, 749, 8125, 292, 1495, 13, 29937, 515, 27630, 1053, 402, 2549, 408, 282, 9970, 13, 13, 1753, 3638, 29918, 2549, 29898, 3166, 10030, 29892, 517, 10030, 29892, 5630, 29892, 16009, 29892, 726, 1125, 13, 268, 13, 1678, 515, 10030, 353, 515, 10030, 13, 1678, 304, 10030, 353, 304, 10030, 13, 1678, 10191, 353, 341, 8890, 6857, 27494, 580, 13, 1678, 10191, 1839, 4591, 2033, 353, 515, 10030, 13, 1678, 10191, 1839, 1762, 2033, 353, 304, 10030, 13, 1678, 10191, 1839, 20622, 2033, 353, 4967, 13, 13, 1678, 3573, 353, 1426, 13, 1678, 10191, 29889, 14930, 29898, 29924, 8890, 1626, 29898, 2587, 29892, 525, 24595, 8785, 13, 13, 1678, 1923, 353, 1560, 9392, 1982, 29889, 17061, 3557, 877, 3844, 9392, 29889, 21980, 29889, 510, 742, 29871, 29945, 29947, 29955, 29897, 13, 1678, 1923, 29889, 2962, 29873, 3137, 580, 13, 1678, 1923, 29889, 7507, 29898, 3166, 10030, 29892, 4800, 29897, 13, 1678, 1426, 353, 10191, 29889, 294, 29918, 1807, 580, 13, 1678, 1923, 29889, 6717, 2549, 29898, 3166, 10030, 29892, 304, 10030, 29892, 1426, 29897, 13, 1678, 1923, 29889, 28358, 580, 2 ]
scripts/git2types/git2_type_rebase.py	git24j/git24j	25	125347	import re from .git2_types import Git2Type from .git2_type_common import ( Git2TypeConstObject, Git2TypeOutObject, PAT1_STR, PAT2_STR, PAT3_STR, ) class Git2TypeConstRebaseOptions(Git2TypeConstObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_options)" + PAT2_STR) class Git2TypeOutRebaseOptions(Git2TypeOutObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_options)" + PAT3_STR) class Git2TypeConstRebase(Git2TypeConstObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase)" + PAT2_STR) class Git2TypeOutRebase(Git2TypeOutObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase)" + PAT3_STR) class Git2TypeConstRebaseOperation(Git2TypeConstObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_operation)" + PAT2_STR) class Git2TypeOutRebaseOperation(Git2TypeOutObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_operation)" + PAT3_STR)	[ 1, 1053, 337, 13, 13, 3166, 869, 5559, 29906, 29918, 8768, 1053, 11786, 29906, 1542, 13, 3166, 869, 5559, 29906, 29918, 1853, 29918, 9435, 1053, 313, 13, 1678, 11786, 29906, 1542, 12075, 2061, 29892, 13, 1678, 11786, 29906, 1542, 3744, 2061, 29892, 13, 1678, 349, 1299, 29896, 29918, 10810, 29892, 13, 1678, 349, 1299, 29906, 29918, 10810, 29892, 13, 1678, 349, 1299, 29941, 29918, 10810, 29892, 13, 29897, 13, 13, 1990, 11786, 29906, 1542, 12075, 29934, 774, 559, 5856, 29898, 28712, 29906, 1542, 12075, 2061, 1125, 13, 1678, 349, 1299, 353, 337, 29889, 12198, 29898, 29925, 1299, 29896, 29918, 10810, 718, 376, 10780, 29925, 29966, 5415, 29918, 978, 29958, 276, 3188, 29918, 6768, 5513, 718, 349, 1299, 29906, 29918, 10810, 29897, 13, 13, 13, 1990, 11786, 29906, 1542, 3744, 29934, 774, 559, 5856, 29898, 28712, 29906, 1542, 3744, 2061, 1125, 13, 1678, 349, 1299, 353, 337, 29889, 12198, 29898, 29925, 1299, 29896, 29918, 10810, 718, 376, 10780, 29925, 29966, 5415, 29918, 978, 29958, 276, 3188, 29918, 6768, 5513, 718, 349, 1299, 29941, 29918, 10810, 29897, 13, 13, 13, 1990, 11786, 29906, 1542, 12075, 29934, 774, 559, 29898, 28712, 29906, 1542, 12075, 2061, 1125, 13, 1678, 349, 1299, 353, 337, 29889, 12198, 29898, 29925, 1299, 29896, 29918, 10810, 718, 376, 10780, 29925, 29966, 5415, 29918, 978, 29958, 276, 3188, 5513, 718, 349, 1299, 29906, 29918, 10810, 29897, 13, 13, 13, 1990, 11786, 29906, 1542, 3744, 29934, 774, 559, 29898, 28712, 29906, 1542, 3744, 2061, 1125, 13, 1678, 349, 1299, 353, 337, 29889, 12198, 29898, 29925, 1299, 29896, 29918, 10810, 718, 376, 10780, 29925, 29966, 5415, 29918, 978, 29958, 276, 3188, 5513, 718, 349, 1299, 29941, 29918, 10810, 29897, 13, 13, 1990, 11786, 29906, 1542, 12075, 29934, 774, 559, 10925, 29898, 28712, 29906, 1542, 12075, 2061, 1125, 13, 1678, 349, 1299, 353, 337, 29889, 12198, 29898, 29925, 1299, 29896, 29918, 10810, 718, 376, 10780, 29925, 29966, 5415, 29918, 978, 29958, 276, 3188, 29918, 16453, 5513, 718, 349, 1299, 29906, 29918, 10810, 29897, 13, 13, 13, 1990, 11786, 29906, 1542, 3744, 29934, 774, 559, 10925, 29898, 28712, 29906, 1542, 3744, 2061, 1125, 13, 1678, 349, 1299, 353, 337, 29889, 12198, 29898, 29925, 1299, 29896, 29918, 10810, 718, 376, 10780, 29925, 29966, 5415, 29918, 978, 29958, 276, 3188, 29918, 16453, 5513, 718, 349, 1299, 29941, 29918, 10810, 29897, 13, 2 ]
app.py	Andrew-Ritchie/Web-Based-Dashboard-for-Nanoindentation-Experiments	1	1602932	import dash import dash_bootstrap_components as dbc from flask_caching import Cache app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP]) app.config.suppress_callback_exceptions = True cache = Cache(app.server, config={ 'CACHE_TYPE': 'filesystem', 'CACHE_DIR': 'cache-directory' }) server = app.server	[ 1, 1053, 12569, 13, 5215, 12569, 29918, 8704, 29918, 14036, 408, 4833, 29883, 13, 3166, 29784, 29918, 29883, 9733, 1053, 28540, 13, 13, 13, 13, 932, 353, 12569, 29889, 29928, 1161, 29898, 23176, 29918, 9783, 354, 1691, 11759, 11140, 29889, 386, 13826, 29889, 8456, 2891, 10810, 3301, 2314, 13, 932, 29889, 2917, 29889, 19303, 1253, 29918, 14035, 29918, 11739, 29879, 353, 5852, 13, 8173, 353, 28540, 29898, 932, 29889, 2974, 29892, 2295, 3790, 13, 1678, 525, 29907, 2477, 9606, 29918, 11116, 2396, 525, 5325, 973, 742, 13, 1678, 525, 29907, 2477, 9606, 29918, 9464, 2396, 525, 8173, 29899, 12322, 29915, 13, 1800, 13, 13, 2974, 353, 623, 29889, 2974, 13, 13, 2 ]
PytoTests/test_pyto.py	snazari/Pyto	701	67576	<gh_stars>100-1000 """ Tests for Pyto before submitting to the App Store. """ import unittest import runpy import sys import os class TestPyto(unittest.TestCase): def test_lib(self): from Lib import (apps, location, mainthread, motion, multipeer, music, notification_center, notifications, pasteboard, photos, pyto_core, pyto_ui, remote_notifications, sharing, sound, speech, userkeys, xcallback) def test_modules(self): runpy.run_module("download_all") def test_pip(self): import pip pip.main(["install", "sympy"]) import sympy pip.main(["uninstall", "sympy"]) def test_command_runner(self): expected_result = "usage: pip.py [-h] [--verbose] [-6]\n sub-command ...\npip.py: error: argument sub-command: invalid choice: '—-help' (choose from 'list', 'install', 'download', 'search', 'versions', 'uninstall', 'update')" out_path = os.path.join(os.path.expanduser("~/tmp"), "out.txt") out = open(out_path, "w+") _out = sys.stdout _err = sys.stderr sys.stdout = out sys.stderr = out sys.argv = ["pyto", "pip", "—-help"] runpy.run_module("command_runner") sys.stdout = _out sys.stderr = _err out.close() out = open(out_path, "r") res = out.read() out.close() res = res.replace(" ", "") res = res.replace("\n", "") res = res.replace("\t", "") expected_result = expected_result.replace(" ", "") expected_result = expected_result.replace("\n", "") expected_result = expected_result.replace("\t", "") self.assertEqual(res, expected_result) if __name__ == '__main__': try: unittest.main() except SystemExit: pass	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 15945, 29908, 13, 24376, 363, 10772, 517, 1434, 11834, 5367, 304, 278, 2401, 14491, 29889, 13, 15945, 29908, 13, 13, 5215, 443, 27958, 13, 5215, 1065, 2272, 13, 5215, 10876, 13, 5215, 2897, 13, 13, 1990, 4321, 19737, 517, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 1678, 822, 1243, 29918, 1982, 29898, 1311, 1125, 13, 4706, 515, 8153, 1053, 313, 13371, 29892, 4423, 29892, 1667, 7097, 29892, 10884, 29892, 13, 4706, 2473, 412, 261, 29892, 4696, 29892, 12519, 29918, 5064, 29892, 25913, 29892, 13, 4706, 11417, 3377, 29892, 20612, 29892, 11451, 517, 29918, 3221, 29892, 11451, 517, 29918, 1481, 29892, 13, 4706, 7592, 29918, 1333, 8232, 29892, 19383, 29892, 6047, 29892, 12032, 29892, 1404, 8149, 29892, 13, 4706, 921, 14035, 29897, 13, 13, 1678, 822, 1243, 29918, 7576, 29898, 1311, 1125, 13, 4706, 1065, 2272, 29889, 3389, 29918, 5453, 703, 10382, 29918, 497, 1159, 13, 308, 13, 1678, 822, 1243, 29918, 13096, 29898, 1311, 1125, 13, 4706, 1053, 8450, 13, 4706, 8450, 29889, 3396, 29898, 3366, 6252, 613, 376, 11967, 2272, 20068, 13, 308, 13, 4706, 1053, 5016, 2272, 13, 308, 13, 4706, 8450, 29889, 3396, 29898, 3366, 348, 6252, 613, 376, 11967, 2272, 20068, 13, 13, 1678, 822, 1243, 29918, 6519, 29918, 27492, 29898, 1311, 1125, 13, 308, 13, 4706, 3806, 29918, 2914, 353, 376, 21125, 29901, 8450, 29889, 2272, 21069, 29882, 29962, 518, 489, 369, 15828, 29962, 21069, 29953, 10725, 29876, 795, 1014, 29899, 6519, 2023, 29905, 9302, 666, 29889, 2272, 29901, 1059, 29901, 2980, 1014, 29899, 6519, 29901, 8340, 7348, 29901, 525, 30003, 29899, 8477, 29915, 313, 21803, 515, 525, 1761, 742, 525, 6252, 742, 525, 10382, 742, 525, 4478, 742, 525, 26100, 742, 525, 348, 6252, 742, 525, 5504, 1495, 29908, 13, 308, 13, 4706, 714, 29918, 2084, 353, 2897, 29889, 2084, 29889, 7122, 29898, 359, 29889, 2084, 29889, 18837, 1792, 703, 20038, 7050, 4968, 376, 449, 29889, 3945, 1159, 13, 308, 13, 4706, 714, 353, 1722, 29898, 449, 29918, 2084, 29892, 376, 29893, 29974, 1159, 13, 4706, 903, 449, 353, 10876, 29889, 25393, 13, 4706, 903, 3127, 353, 10876, 29889, 303, 20405, 13, 4706, 10876, 29889, 25393, 353, 714, 13, 4706, 10876, 29889, 303, 20405, 353, 714, 13, 308, 13, 4706, 10876, 29889, 19218, 353, 6796, 2272, 517, 613, 376, 13096, 613, 376, 30003, 29899, 8477, 3108, 13, 4706, 1065, 2272, 29889, 3389, 29918, 5453, 703, 6519, 29918, 27492, 1159, 13, 308, 13, 4706, 10876, 29889, 25393, 353, 903, 449, 13, 4706, 10876, 29889, 303, 20405, 353, 903, 3127, 13, 308, 13, 4706, 714, 29889, 5358, 580, 13, 4706, 714, 353, 1722, 29898, 449, 29918, 2084, 29892, 376, 29878, 1159, 13, 4706, 620, 353, 714, 29889, 949, 580, 13, 4706, 714, 29889, 5358, 580, 13, 308, 13, 4706, 620, 353, 620, 29889, 6506, 703, 9162, 20569, 13, 4706, 620, 353, 620, 29889, 6506, 14182, 29876, 613, 20569, 13, 4706, 620, 353, 620, 29889, 6506, 14182, 29873, 613, 20569, 13, 4706, 3806, 29918, 2914, 353, 3806, 29918, 2914, 29889, 6506, 703, 9162, 20569, 13, 4706, 3806, 29918, 2914, 353, 3806, 29918, 2914, 29889, 6506, 14182, 29876, 613, 20569, 13, 4706, 3806, 29918, 2914, 353, 3806, 29918, 2914, 29889, 6506, 14182, 29873, 613, 20569, 13, 4706, 1583, 29889, 9294, 9843, 29898, 690, 29892, 3806, 29918, 2914, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 13, 1678, 1018, 29901, 13, 4706, 443, 27958, 29889, 3396, 580, 13, 1678, 5174, 2184, 24365, 29901, 13, 4706, 1209, 13, 2 ]
CodingInterviews/python/48_add.py	YorkFish/git_study	0	62080	#!/usr/bin/env python3 # coding:utf-8 class Solution: def Add(self, num1, num2): """ num1 ^ num2: 不计进位的相加 (num1 & num2) << 1: 进位循环至进位为零 ~(num1 ^ 0xffffffff): 模仿 C，当 num1 为负数时，把它从无符号数转为有符号数 C 的 int 的高位是符号位，Python 没有 """ while num2 != 0: num1, num2 =\ (num1 ^ num2) & 0xffffffff, ((num1 & num2) << 1) & 0xffffffff return num1 if num1 <= 0x7fffffff else ~(num1 ^ 0xffffffff) if __name__ == "__main__": num1 = 66 num2 = 66 s = Solution() ans = s.Add(num1, num2) print(ans)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 14137, 29901, 9420, 29899, 29947, 13, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 3462, 29898, 1311, 29892, 954, 29896, 29892, 954, 29906, 1125, 13, 4706, 9995, 13, 4706, 954, 29896, 6228, 954, 29906, 29901, 29871, 30413, 31466, 31174, 30956, 30210, 30990, 30666, 13, 4706, 313, 1949, 29896, 669, 954, 29906, 29897, 3532, 29871, 29896, 29901, 29871, 31174, 30956, 13, 308, 232, 193, 173, 234, 145, 178, 235, 138, 182, 31174, 30956, 30573, 236, 158, 185, 13, 13, 4706, 3695, 29898, 1949, 29896, 6228, 29871, 29900, 29916, 17156, 17156, 1125, 29871, 31382, 231, 190, 194, 315, 30214, 30948, 954, 29896, 29871, 30573, 235, 183, 162, 30354, 30594, 30214, 233, 141, 141, 232, 177, 134, 31594, 31352, 31277, 30850, 30354, 31415, 30573, 30417, 31277, 30850, 30354, 13, 4706, 315, 29871, 30210, 938, 29871, 30210, 30528, 30956, 30392, 31277, 30850, 30956, 30214, 11980, 29871, 31423, 30417, 13, 4706, 9995, 13, 4706, 1550, 954, 29906, 2804, 29871, 29900, 29901, 13, 9651, 954, 29896, 29892, 954, 29906, 17313, 13, 18884, 313, 1949, 29896, 6228, 954, 29906, 29897, 669, 29871, 29900, 29916, 17156, 17156, 29892, 5135, 1949, 29896, 669, 954, 29906, 29897, 3532, 29871, 29896, 29897, 669, 29871, 29900, 29916, 17156, 17156, 13, 4706, 736, 954, 29896, 565, 954, 29896, 5277, 29871, 29900, 29916, 29955, 17156, 18725, 1683, 3695, 29898, 1949, 29896, 6228, 29871, 29900, 29916, 17156, 17156, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 954, 29896, 353, 29871, 29953, 29953, 13, 1678, 954, 29906, 353, 29871, 29953, 29953, 13, 13, 1678, 269, 353, 24380, 580, 13, 1678, 6063, 353, 269, 29889, 2528, 29898, 1949, 29896, 29892, 954, 29906, 29897, 13, 1678, 1596, 29898, 550, 29897, 13, 2 ]
Server/website/serializers.py	HarshShah1997/Rotary-Website	0	121020	""" Author: harsh """ from rest_framework.serializers import ModelSerializer from website.models import Event, Announcement, BoardMember, Photo class EventSerializer(ModelSerializer): class Meta: model = Event fields = '__all__' class AnnouncementSerializer(ModelSerializer): class Meta: model = Announcement fields = '__all__' class BoardMemberSerializer(ModelSerializer): class Meta: model = BoardMember fields = '__all__' class PhotoSerializer(ModelSerializer): class Meta: model = Photo fields = '__all__'	[ 1, 9995, 13, 13720, 29901, 4023, 845, 13, 15945, 29908, 13, 13, 3166, 1791, 29918, 4468, 29889, 15550, 19427, 1053, 8125, 17679, 13, 13, 3166, 4700, 29889, 9794, 1053, 6864, 29892, 8081, 1309, 13561, 29892, 12590, 13404, 29892, 1963, 3747, 13, 13, 13, 1990, 6864, 17679, 29898, 3195, 17679, 1125, 13, 1678, 770, 20553, 29901, 13, 4706, 1904, 353, 6864, 13, 4706, 4235, 353, 525, 1649, 497, 1649, 29915, 13, 13, 13, 1990, 8081, 1309, 13561, 17679, 29898, 3195, 17679, 1125, 13, 1678, 770, 20553, 29901, 13, 4706, 1904, 353, 8081, 1309, 13561, 13, 4706, 4235, 353, 525, 1649, 497, 1649, 29915, 13, 13, 13, 1990, 12590, 13404, 17679, 29898, 3195, 17679, 1125, 13, 1678, 770, 20553, 29901, 13, 4706, 1904, 353, 12590, 13404, 13, 4706, 4235, 353, 525, 1649, 497, 1649, 29915, 13, 13, 13, 1990, 1963, 3747, 17679, 29898, 3195, 17679, 1125, 13, 1678, 770, 20553, 29901, 13, 4706, 1904, 353, 1963, 3747, 13, 4706, 4235, 353, 525, 1649, 497, 1649, 29915, 13, 2 ]
linkedSPLs/LinkedSPLs-update/mappings/ChEBI-DrugBank-bio2rdf-mapping/scripts/backups/parseDBIdAndUNIIsByInChikey.py	kqingcan/bio2rdf-scripts	76	137787	<gh_stars>10-100 ''' Created 03/20/2014 @authors: <NAME> @summary: parse inchikey and drugbank_id from drugbank.xml then parse inchikey, FDA Preferred Term and UNII from UNIIs records match the results from drugbank and results of parse UNIIs records output terms: FDA Preferred Term, UNII, Drugbank URI output file: PT-UNIIs-Drugbank-03202014.txt ''' from lxml import etree from lxml.etree import XMLParser, parse import os, sys DRUGBANK_XML = "drugbank.xml" UNIIS_RECORDS = "UNIIs 27Jun2014 Records.txt" NS = "{http://www.drugbank.ca}" DRUGBANK_BIO2RDF = "http://bio2rdf.org/drugbank:" DRUGBANK_CA = "http://www.drugbank.ca/drugs/" dict_ickey_dbid = {} ''' data structure of drugbank.xml </drug><drug type="small molecule" created="2005-06-13 07:24:05 -0600" updated="2013-09-16 17:11:29 -0600" version="4.0"> <drugbank-id>DB00641</drugbank-id> <name>Simvastatin</name> <calculated-properties> <property> <kind>InChIKey</kind> <value>InChIKey=RYMZZMVNJRMUDD-HGQWONQESA-N</value> <source>ChemAxon</source> </property> ''' #get mappings of inchikey and drugbank id def parseDbIdAndInChiKey(root): for childDrug in root.iter(tag=NS + "drug"): subId = childDrug.find(NS + "drugbank-id") if subId == None: continue else: drugbankid = subId.text drugbankName = unicode(childDrug.find(NS + "name").text) for subProp in childDrug.iter(NS + "property"): subKind = subProp.find(NS + "kind") if subKind == None: continue elif subKind.text == "InChIKey": subValue = subProp.find(NS + "value") if subValue == None: continue else: #print drugbankid + '\t' + subValue.text[9:] ikey = subValue.text[9:] dict_ickey_dbid [ikey] = (drugbankid,drugbankName) p = XMLParser(huge_tree=True) tree = parse(DRUGBANK_XML,parser=p) root = tree.getroot() parseDbIdAndInChiKey(root) #print dict_ickey_dbid #read mapping file that contains UNII PT INCHIKEY for line in open(UNIIS_RECORDS,'r').readlines(): row = line.split('\t') inchikey = row[4] if len(inchikey) == 0: continue #print "mapping inchikey:" + inchikey if dict_ickey_dbid.has_key(inchikey): drugbankid = dict_ickey_dbid[inchikey][0] drugbankName = dict_ickey_dbid[inchikey][1] output = row[1] +'\t'+ row[0] +'\t'+ drugbankName +'\t'+ DRUGBANK_CA + drugbankid +'\t'+ DRUGBANK_BIO2RDF + drugbankid print output.encode('utf-8').strip()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 12008, 13, 20399, 29871, 29900, 29941, 29914, 29906, 29900, 29914, 29906, 29900, 29896, 29946, 13, 13, 29992, 5150, 943, 29901, 529, 5813, 29958, 13, 13, 29992, 7727, 29901, 6088, 297, 4161, 1989, 322, 15721, 9157, 29918, 333, 515, 15721, 9157, 29889, 3134, 769, 6088, 13, 3986, 297, 4161, 1989, 29892, 383, 7698, 4721, 14373, 11814, 322, 8291, 2687, 515, 8291, 2687, 29879, 6475, 13, 3986, 1993, 278, 2582, 515, 15721, 9157, 322, 2582, 310, 6088, 8291, 2687, 29879, 13, 3986, 6475, 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groclient/constants.py	eric-gro/api-client	18	6915	"""Constants about the Gro ontology that can be imported and re-used anywhere.""" REGION_LEVELS = { 'world': 1, 'continent': 2, 'country': 3, 'province': 4, # Equivalent to state in the United States 'district': 5, # Equivalent to county in the United States 'city': 6, 'market': 7, 'other': 8, 'coordinate': 9 } ENTITY_TYPES_PLURAL = ['metrics', 'items', 'regions', 'frequencies', 'sources', 'units'] DATA_SERIES_UNIQUE_TYPES_ID = [ 'metric_id', 'item_id', 'region_id', 'partner_region_id', 'frequency_id', 'source_id' ] ENTITY_KEY_TO_TYPE = { 'metric_id': 'metrics', 'item_id': 'items', 'region_id': 'regions', 'partner_region_id': 'regions', 'source_id': 'sources', 'frequency_id': 'frequencies', 'unit_id': 'units' } DATA_POINTS_UNIQUE_COLS = DATA_SERIES_UNIQUE_TYPES_ID + [ 'reporting_date', 'start_date', 'end_date' ]	[ 1, 9995, 26570, 1048, 278, 6070, 4625, 3002, 393, 508, 367, 19673, 322, 337, 29899, 3880, 12214, 1213, 15945, 13, 13, 18166, 2725, 29918, 1307, 29963, 6670, 29903, 353, 426, 13, 1678, 525, 11526, 2396, 29871, 29896, 29892, 13, 1678, 525, 1285, 8946, 2396, 29871, 29906, 29892, 13, 1678, 525, 13509, 2396, 29871, 29941, 29892, 13, 1678, 525, 16123, 1239, 2396, 29871, 29946, 29892, 29871, 396, 11243, 27445, 304, 2106, 297, 278, 3303, 3900, 13, 1678, 525, 29881, 6801, 2396, 29871, 29945, 29892, 29871, 396, 11243, 27445, 304, 15178, 297, 278, 3303, 3900, 13, 1678, 525, 12690, 2396, 29871, 29953, 29892, 13, 1678, 525, 28549, 2396, 29871, 29955, 29892, 13, 1678, 525, 1228, 2396, 29871, 29947, 29892, 13, 1678, 525, 29302, 2396, 29871, 29929, 13, 29913, 13, 13, 3919, 11937, 29918, 15631, 29925, 2890, 29918, 7390, 4574, 1964, 353, 6024, 2527, 10817, 742, 525, 7076, 742, 525, 1727, 1080, 742, 525, 10745, 339, 15942, 742, 525, 29879, 2863, 742, 525, 348, 1169, 2033, 13, 13, 14573, 29918, 6304, 29059, 29918, 3904, 29902, 11144, 29918, 15631, 29925, 2890, 29918, 1367, 353, 518, 13, 1678, 525, 16414, 29918, 333, 742, 13, 1678, 525, 667, 29918, 333, 742, 13, 1678, 525, 12803, 29918, 333, 742, 13, 1678, 525, 1595, 1089, 29918, 12803, 29918, 333, 742, 13, 1678, 525, 10745, 23860, 29918, 333, 742, 13, 1678, 525, 4993, 29918, 333, 29915, 13, 29962, 13, 13, 3919, 11937, 29918, 10818, 29918, 4986, 29918, 11116, 353, 426, 13, 1678, 525, 16414, 29918, 333, 2396, 525, 2527, 10817, 742, 13, 1678, 525, 667, 29918, 333, 2396, 525, 7076, 742, 13, 1678, 525, 12803, 29918, 333, 2396, 525, 1727, 1080, 742, 13, 1678, 525, 1595, 1089, 29918, 12803, 29918, 333, 2396, 525, 1727, 1080, 742, 13, 1678, 525, 4993, 29918, 333, 2396, 525, 29879, 2863, 742, 13, 1678, 525, 10745, 23860, 29918, 333, 2396, 525, 10745, 339, 15942, 742, 13, 1678, 525, 5441, 29918, 333, 2396, 525, 348, 1169, 29915, 13, 29913, 13, 13, 14573, 29918, 29925, 6992, 9375, 29918, 3904, 29902, 11144, 29918, 3217, 8547, 353, 360, 8254, 29918, 6304, 29059, 29918, 3904, 29902, 11144, 29918, 15631, 29925, 2890, 29918, 1367, 718, 518, 13, 1678, 525, 12276, 292, 29918, 1256, 742, 13, 1678, 525, 2962, 29918, 1256, 742, 13, 1678, 525, 355, 29918, 1256, 29915, 13, 29962, 13, 2 ]
processing/router.py	uncharted-distil/distil-auto-ml	2	49246	<filename>processing/router.py #!/usr/bin/env python """ distil/router.py """ import logging import sys from copy import deepcopy from typing import Sequence, Tuple from d3m.metadata import problem as _problem logger = logging.getLogger(__name__) SMALL_DATASET_THRESH = 2000 # -- # Detect semantic problem type def get_resource_types(dataset_doc: dict) -> Sequence[str]: return sorted([dr["resType"] for dr in dataset_doc["dataResources"]]) def is_tabular(dataset_doc: dict, problem_desc: dict) -> bool: resource_types = get_resource_types(dataset_doc) task_keywords = problem_desc["problem"]["task_keywords"] if ( len( set(task_keywords) & set( [_problem.TaskKeyword.REGRESSION, _problem.TaskKeyword.CLASSIFICATION] ) ) == 0 ): return False elif ( len( set(task_keywords) & set( [ _problem.TaskKeyword.SEMISUPERVISED, _problem.TaskKeyword.REMOTE_SENSING, ] ) ) != 0 ): return False elif resource_types == ["table"]: return True elif ( set(resource_types) == {"table"} and len(resource_types) > 2 and not is_question_answering(dataset_doc) ): logger.warn( "is_tabular: found more than two tables, which we can't handle. falling back to single table" ) return True else: return False def is_edgelist(dataset_doc: dict, problem_desc: dict) -> bool: resource_types = get_resource_types(dataset_doc) task_keywords = problem_desc["problem"]["task_keywords"] if ( len( set(task_keywords) & set( [ _problem.TaskKeyword.VERTEX_CLASSIFICATION, _problem.TaskKeyword.VERTEX_NOMINATION, _problem.TaskKeyword.LINK_PREDICTION, ] ) ) == 0 ): return False elif "edgeList" in resource_types: return True else: return False def is_multitable(dataset_doc: dict) -> bool: return ["table", "table"] == get_resource_types(dataset_doc) def is_timeseries_classification(dataset_doc: dict, problem: dict) -> bool: timeseries_resource = ["table", "timeseries"] == get_resource_types(dataset_doc) classification_task = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) return timeseries_resource and classification_task def is_timeseries_forecasting(problem: dict) -> bool: return _problem.TaskKeyword.FORECASTING in problem["problem"]["task_keywords"] def is_question_answering(dataset_doc: dict) -> bool: res_paths = sorted([r["resPath"] for r in dataset_doc["dataResources"]]) return res_paths == [ "tables/learningData.csv", "tables/questions.csv", "tables/sentences.csv", "tables/vocabulary.csv", ] def is_audio(dataset_doc: dict) -> bool: return "audio" in get_resource_types(dataset_doc) def is_image(dataset_doc: dict, problem: dict) -> bool: classification = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) regression = _problem.TaskKeyword.REGRESSION in problem["problem"]["task_keywords"] remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( "image" in get_resource_types(dataset_doc) and (classification or regression) and not remote_sensing ) def is_remote_sensing_pretrained(dataset_doc: dict, problem: dict) -> bool: classification = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) regression = _problem.TaskKeyword.REGRESSION in problem["problem"]["task_keywords"] remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( "image" not in get_resource_types(dataset_doc) and (classification or regression) and remote_sensing and _problem.TaskKeyword.SEMISUPERVISED not in problem["problem"]["task_keywords"] ) def is_remote_sensing(dataset_doc: dict, problem: dict) -> bool: classification = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) regression = _problem.TaskKeyword.REGRESSION in problem["problem"]["task_keywords"] remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( "image" in get_resource_types(dataset_doc) and (classification or regression) and remote_sensing and _problem.TaskKeyword.SEMISUPERVISED not in problem["problem"]["task_keywords"] ) def is_object_detection(problem: dict) -> bool: return _problem.TaskKeyword.OBJECT_DETECTION in problem["problem"]["task_keywords"] def is_graph_matching(problem: dict) -> bool: return _problem.TaskKeyword.GRAPH_MATCHING in problem["problem"]["task_keywords"] def is_community_detection(problem: dict) -> bool: return ( _problem.TaskKeyword.COMMUNITY_DETECTION in problem["problem"]["task_keywords"] ) def is_clustering(problem: dict) -> bool: return _problem.TaskKeyword.CLUSTERING in problem["problem"]["task_keywords"] def is_vertex_classification(problem: dict) -> bool: return ( _problem.TaskKeyword.VERTEX_CLASSIFICATION in problem["problem"]["task_keywords"] ) def is_vertex_nomination(problem: dict) -> bool: return _problem.TaskKeyword.VERTEX_NOMINATION in problem["problem"]["task_keywords"] def is_collaborative_filtering(problem: dict) -> bool: return ( _problem.TaskKeyword.COLLABORATIVE_FILTERING in problem["problem"]["task_keywords"] ) def is_link_prediction(problem: dict) -> bool: return _problem.TaskKeyword.LINK_PREDICTION in problem["problem"]["task_keywords"] def is_text(dataset_doc: dict) -> bool: return ["table", "text"] == get_resource_types(dataset_doc) def is_semisupervised_tabular(problem: dict) -> bool: remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( not remote_sensing and _problem.TaskKeyword.SEMISUPERVISED in problem["problem"]["task_keywords"] ) def is_semisupervised_remote_sensing_pretrained(problem: dict) -> bool: remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( remote_sensing and _problem.TaskKeyword.SEMISUPERVISED in problem["problem"]["task_keywords"] ) # -- # Routing def get_routing_info(dataset_doc: dict, problem: dict, metric: str) -> Tuple[str, dict]: # Shouldn't evaluate these in serial -- should do in parallel, then check for # conflicts if is_tabular(dataset_doc, problem): return "table", {} elif is_multitable(dataset_doc): resources = deepcopy(dataset_doc["dataResources"]) learning_resource = [ r for r in resources if "learningData.csv" in r["resPath"] ][0] resources.remove(learning_resource) assert len(resources) == 1 other_resource = resources[0] is_table = other_resource["resType"] == "table" is_collection = other_resource.get("isCollection", False) if is_table and is_collection: # return 'multitable', {} return "table", {"multi": True} else: print( ( "get_routing_info: is_multitable, but other_resource is not a collection\n" " falling back to table (eg. ignoring resources)" ), file=sys.stderr, ) return "table", {} elif is_question_answering(dataset_doc): return "question_answering", {} elif is_text(dataset_doc): return "text", {} elif is_image(dataset_doc, problem): return "image", {} elif is_semisupervised_remote_sensing_pretrained(problem): return "semisupervised_remote_sensing_pretrained", {} elif is_remote_sensing_pretrained(dataset_doc, problem): return "remote_sensing_pretrained", {} elif is_remote_sensing(dataset_doc, problem): return "remote_sensing", {} elif is_object_detection(problem): return "object_detection", {} elif is_clustering(problem): resources = dataset_doc["dataResources"] assert len(resources) == 1 learning_resource = resources[0] # !! Not sure if I'm supposed to be looking at this n_clusters = problem["inputs"][0]["targets"][0]["clusters_number"] all_float = set( [ r["colType"] for r in learning_resource["columns"] if ("suggestedTarget" not in r["role"]) and ("d3mIndex" != r["colName"]) ] ) == {"real"} return "clustering", { "n_clusters": n_clusters, "all_float": all_float, } elif is_timeseries_classification(dataset_doc, problem): return ( "timeseries_classification", { # "metrics": ['euclidean', 'cityblock', 'dtw'], # "diffusion": True, # "forest": True, # "ensemble_size": 3, }, ) elif is_timeseries_forecasting(problem): return "timeseries_forecasting", {} elif is_audio(dataset_doc): return "audio", {} elif is_graph_matching(problem): return "graph_matching", {} elif is_vertex_nomination(problem): return "vertex_nomination", {} elif is_vertex_classification(problem): return "vertex_nomination", {"is_edgelist": is_edgelist(dataset_doc, problem)} elif is_collaborative_filtering(problem): return 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src/resource_resolver/core/ResourceProxy.py	Moyoka22/resource-resolver	0	102860	<reponame>Moyoka22/resource-resolver<gh_stars>0 import logging import shutil from io import StringIO, TextIOBase from pathlib import Path import tempfile from typing import IO, Literal, Union, cast from .errors import ResourceResolverError from .managers import ManagerRegistry class ResourceProxy: GET_AS_FORMATS = ['str', 'buffer', 'file_handle'] def __init__(self, location: Union[str, IO[str], Path], read_only=False, kwargs): self._location = location self._read_only = read_only Manager = ManagerRegistry.get_manager(location) if not Manager: location = repr(location) raise ResourceResolverError.UnsupportedProtocol(location) self._manager = Manager(location, kwargs) def put(self, data: Union[str, IO[str]]) -> None: if self.is_read_only: raise ResourceResolverError.ReadOnly(self._location) if not (isinstance(data, str) or isinstance(data, TextIOBase)): raise ResourceResolverError.UnsupportedWriteType(data) if isinstance(data, str): data = cast(str, data) data = self._produce_stream_from_data(data) data = cast(IO[str], data) self._manager.put(data) def get(self, as_a: str) -> Union[str, IO[str], StringIO]: supported_formats = self.GET_AS_FORMATS if as_a not in supported_formats: raise ResourceResolverError.\ UnsupportedGetAsFormat(as_a, supported_formats) if as_a == 'str': return self._manager.get().read() elif as_a == 'buffer': buffer = StringIO() data = self._manager.get() shutil.copyfileobj(data, buffer) buffer.seek(0, 0) return buffer else: as_a = cast(Literal['file_handle'], as_a) return self._manager.get() @property def is_read_only(self) -> bool: return self._read_only def _produce_stream_from_data(self, data: str) -> IO[str]: try: buffer: IO[str] = StringIO(data) buffer.seek(0, 0) except MemoryError: logging.warning('Failed to allocation memory for string. 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orange3/Orange/tests/test_domain.py	rgschmitz1/BioDepot-workflow-builder	54	63788	# Test methods with long descriptive names can omit docstrings # pylint: disable=missing-docstring import warnings from time import time from numbers import Real from itertools import starmap, chain import unittest import pickle import numpy as np from numpy.testing import assert_array_equal from Orange.data import ( ContinuousVariable, DiscreteVariable, StringVariable, TimeVariable, Variable, Domain, Table, DomainConversion, ) from Orange.data.domain import filter_visible from Orange.preprocess import Continuize, Impute from Orange.tests.base import create_pickling_tests from Orange.util import OrangeDeprecationWarning def create_domain(ss): Variable._clear_all_caches() vars = dict( age=ContinuousVariable(name="AGE"), gender=DiscreteVariable(name="Gender", values=["M", "F"]), incomeA=ContinuousVariable(name="incomeA"), income=ContinuousVariable(name="income"), education=DiscreteVariable(name="education", values=["GS", "HS", "C"]), ssn=StringVariable(name="SSN"), race=DiscreteVariable( name="race", values=["White", "Hypsanic", "African", "Other"] ), arrival=TimeVariable("arrival"), ) def map_vars(s): return [vars[x] for x in s] return Domain([map_vars(s) for s in ss]) PickleDomain = create_pickling_tests( "PickleDomain", ("empty_domain", lambda: create_domain([])), ("with_continuous_variable", lambda: create_domain(["age"])), ("with_discrete_variable", lambda: create_domain(["gender"])), ("with_mixed_variables", lambda: create_domain(["age", "gender"])), ("with_continuous_class", lambda: create_domain(["age", "gender"], ["incomeA"])), ("with_discrete_class", lambda: create_domain(["age", "gender"], ["education"])), ( "with_multiple_classes", lambda: create_domain(["age", "gender"], ["incomeA", "education"]), ), ("with_metas", lambda: create_domain(["age", "gender"], [], ["ssn"])), ( "with_class_and_metas", lambda: create_domain(["age", "gender"], ["incomeA", "education"], ["ssn"]), ), ) age, gender, incomeA, income, education, ssn, race, arrival = create_domain( [], [], ["age", "gender", "incomeA", "income", "education", "ssn", "race", "arrival"], ).metas class TestDomainInit(unittest.TestCase): def test_init_class(self): attributes = (age, gender, income) d = Domain(attributes, race) self.assertEqual(d.variables, attributes + (race,)) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, race) self.assertEqual(d.class_vars, (race,)) self.assertEqual(d.metas, ()) def test_init_class_list(self): attributes = (age, gender, income) d = Domain(attributes, [race]) self.assertEqual(d.variables, attributes + (race,)) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, race) self.assertEqual(d.class_vars, (race,)) self.assertEqual(d.metas, ()) def test_init_no_class(self): attributes = (age, gender, income) d = Domain(attributes) self.assertEqual(d.variables, attributes) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, None) self.assertEqual(d.class_vars, ()) self.assertEqual(d.metas, ()) def test_init_no_class_false(self): attributes = (age, gender, income) d = Domain(attributes, None) self.assertEqual(d.variables, attributes) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, None) self.assertEqual(d.class_vars, ()) self.assertEqual(d.metas, ()) def test_init_multi_class(self): attributes = (age, gender, income) d = Domain(attributes, (education, race)) self.assertEqual(d.variables, attributes + (education, race)) self.assertEqual(d.attributes, attributes) self.assertIsNone(d.class_var) self.assertEqual(d.class_vars, (education, race)) self.assertEqual(d.metas, ()) def test_init_source(self): attributes = (age, gender, income) d = Domain(attributes, (education, race)) d2 = Domain(["Gender", 0, income], source=d) self.assertEqual(d2.variables, (gender, age, income)) def test_init_source_class(self): attributes = (age, gender, income) d = Domain(attributes, (education, race)) d2 = Domain(["Gender", 0], "income", source=d) self.assertEqual(d2.variables, (gender, age, income)) def test_init_metas(self): attributes = (age, gender, income) metas = (ssn, race) d = Domain(attributes, race, metas=metas) self.assertEqual(d.variables, attributes + (race,)) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, race) self.assertEqual(d.class_vars, (race,)) self.assertEqual(d.metas, metas) def test_from_numpy_names(self): for n_cols, name in [ (5, "Feature {}"), (99, "Feature {:02}"), (100, "Feature {:03}"), ]: d = Domain.from_numpy(np.zeros((1, n_cols))) self.assertTrue(d.anonymous) self.assertEqual( [var.name for var in d.attributes], [name.format(i) for i in range(1, n_cols + 1)], ) d = Domain.from_numpy(np.zeros((1, 1))) self.assertTrue(d.anonymous) self.assertEqual(d.attributes[0].name, "Feature") d = Domain.from_numpy(np.zeros((1, 3)), np.zeros((1, 1)), np.zeros((1, 100))) self.assertTrue(d.anonymous) self.assertEqual( [var.name for var in d.attributes], ["Feature {}".format(i) for i in range(1, 4)], ) self.assertEqual(d.class_var.name, "Target") self.assertEqual( [var.name for var in d.metas], ["Meta {:03}".format(i) for i in range(1, 101)], ) def test_from_numpy_dimensions(self): for dimension in [[5], [5, 1]]: d = Domain.from_numpy(np.zeros((1, 1)), np.zeros(dimension)) self.assertTrue(d.anonymous) self.assertEqual(len(d.class_vars), 1) self.assertRaises(ValueError, Domain.from_numpy, np.zeros(2)) self.assertRaises(ValueError, Domain.from_numpy, np.zeros((2, 2, 2))) self.assertRaises( ValueError, Domain.from_numpy, np.zeros((2, 2)), np.zeros((2, 2, 2)) ) def test_from_numpy_values(self): for aran_min, aran_max, vartype in [ (1, 3, ContinuousVariable), (0, 2, DiscreteVariable), (18, 23, ContinuousVariable), ]: n_rows, n_cols, = aran_max - aran_min, 1 d = Domain.from_numpy( np.zeros((1, 1)), np.arange(aran_min, aran_max).reshape(n_rows, n_cols) ) self.assertTrue(d.anonymous) self.assertIsInstance(d.class_var, vartype) if isinstance(vartype, DiscreteVariable): self.assertEqual( d.class_var.values, ["v{}".format(i) for i in range(1, 3)] ) def test_wrong_vartypes(self): attributes = (age, gender, income) for args in ((attributes, ssn), (attributes + (ssn,)), ((ssn,) + attributes)): with self.assertRaises(TypeError): Domain(args) def test_wrong_vartypes_w_source(self): d = Domain((age, gender), metas=(ssn,)) with self.assertRaises(TypeError): Domain(-1, source=d) def test_wrong_types(self): with self.assertRaises(TypeError): Domain((age, [])) with self.assertRaises(TypeError): Domain((age, "income")) with self.assertRaises(TypeError): Domain(([], age)) with self.assertRaises(TypeError): Domain(("income", age)) with self.assertRaises(TypeError): Domain((age,), self) with self.assertRaises(TypeError): Domain((age,), metas=("income",)) def test_get_item(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx, var in [ (age, age), ("AGE", age), (0, age), (income, income), ("income", income), (2, income), (ssn, ssn), ("SSN", ssn), (-1, ssn), (-2, race), ]: self.assertEqual(d[idx], var) def test_index(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx, var in [ (age, 0), ("AGE", 0), (0, 0), (np.int_(0), 0), (income, 2), ("income", 2), (2, 2), (np.int_(2), 2), (ssn, -1), ("SSN", -1), (-1, -1), (np.int_(-1), -1), (-2, -2), (np.int_(-2), -2), ]: self.assertEqual(d.index(idx), var) def test_get_item_slices(self): d = Domain((age, gender, income, race), metas=(ssn, race)) self.assertEqual(d[:2], (age, gender)) self.assertEqual(d[1:3], (gender, income)) self.assertEqual(d[2:], (income, race)) def test_get_item_error(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx in (3, -3, incomeA, "no_such_thing"): with self.assertRaises(KeyError): _ = d[idx] with self.assertRaises(TypeError): _ = d[[2]] def test_index_error(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx in (3, np.int(3), -3, np.int(-3), incomeA, "no_such_thing"): with self.assertRaises(ValueError): d.index(idx) with self.assertRaises(TypeError): d.index([2]) def test_contains(self): d = Domain((age, gender, income), metas=(ssn,)) for var in [ "AGE", age, 0, np.int_(0), "income", income, 2, np.int_(2), "SSN", ssn, -1, np.int_(-1), ]: self.assertIn(var, d) for var in ["no_such_thing", race, 3, np.int_(3), -2, np.int_(-2)]: self.assertNotIn(var, d) with self.assertRaises(TypeError): {} in d with self.assertRaises(TypeError): [] in d def test_iter(self): with warnings.catch_warnings(record=True): warnings.simplefilter("error") d = Domain((age, gender, income), metas=(ssn,)) with self.assertRaises(OrangeDeprecationWarning): list(d) warnings.simplefilter("ignore") self.assertEqual([var for var in d], [age, gender, income]) d = Domain((age,), metas=(ssn,)) self.assertEqual([var for var in d], [age]) d = Domain((), metas=(ssn,)) self.assertEqual([var for var in d], []) def test_str(self): cases = ( (((),), "[]"), (((age,),), "[AGE]"), (((), age), "[ \| AGE]"), (((gender,), age), "[Gender \| AGE]"), (((gender, income), None), "[Gender, income]"), (((gender, income), age), "[Gender, income \| AGE]"), (((gender,), (age, income)), "[Gender \| AGE, income]"), (((gender,), (age, income), (ssn,)), "[Gender \| AGE, income] {SSN}"), ( ((gender,), (age, income), (ssn, race)), "[Gender \| AGE, income] {SSN, race}", ), (((), (), (ssn, race)), "[] {SSN, race}"), ) for args, printout in cases: self.assertEqual(str(Domain(args)), printout) def test_has_discrete(self): self.assertFalse(Domain([]).has_discrete_attributes()) self.assertFalse(Domain([], [age]).has_discrete_attributes()) self.assertFalse(Domain([], race).has_discrete_attributes()) self.assertFalse(Domain([age], None).has_discrete_attributes()) self.assertTrue(Domain([race], None).has_discrete_attributes()) self.assertTrue(Domain([age, race], None).has_discrete_attributes()) self.assertTrue(Domain([race, age], None).has_discrete_attributes()) self.assertFalse(Domain([], [age]).has_discrete_attributes(True)) self.assertTrue(Domain([], [race]).has_discrete_attributes(True)) self.assertFalse(Domain([age], None).has_discrete_attributes(True)) self.assertTrue(Domain([race], None).has_discrete_attributes(True)) self.assertTrue(Domain([age], race).has_discrete_attributes(True)) self.assertTrue(Domain([race], age).has_discrete_attributes(True)) self.assertTrue(Domain([], [race, age]).has_discrete_attributes(True)) d = Domain([], None, [gender]) self.assertTrue(d.has_discrete_attributes(False, True)) d = Domain([], None, [age]) self.assertFalse(d.has_discrete_attributes(False, True)) d = Domain([], [age], [gender]) self.assertTrue(d.has_discrete_attributes(True, True)) d = Domain([], [incomeA], [age]) self.assertFalse(d.has_discrete_attributes(True, True)) def test_has_continuous(self): self.assertFalse(Domain([]).has_continuous_attributes()) self.assertFalse(Domain([], [age]).has_continuous_attributes()) self.assertFalse(Domain([], [race]).has_continuous_attributes()) self.assertTrue(Domain([age], None).has_continuous_attributes()) self.assertFalse(Domain([race], None).has_continuous_attributes()) self.assertTrue(Domain([age, race], None).has_continuous_attributes()) self.assertTrue(Domain([race, age], None).has_continuous_attributes()) self.assertTrue(Domain([], [age]).has_continuous_attributes(True)) self.assertFalse(Domain([], [race]).has_continuous_attributes(True)) self.assertTrue(Domain([age], None).has_continuous_attributes(True)) self.assertFalse(Domain([race], None).has_continuous_attributes(True)) self.assertTrue(Domain([age], race).has_continuous_attributes(True)) self.assertTrue(Domain([race], age).has_continuous_attributes(True)) self.assertTrue(Domain([], [race, age]).has_continuous_attributes(True)) d = Domain([], None, [age]) self.assertTrue(d.has_continuous_attributes(False, True)) d = Domain([], None, [gender]) self.assertFalse(d.has_continuous_attributes(False, True)) d = Domain([], [gender], [age]) self.assertTrue(d.has_continuous_attributes(True, True)) d = Domain([], [race], [gender]) self.assertFalse(d.has_continuous_attributes(True, True)) def test_has_time(self): self.assertFalse(Domain([]).has_time_attributes()) self.assertFalse(Domain([], [age]).has_time_attributes()) self.assertFalse(Domain([], [race]).has_time_attributes()) self.assertFalse(Domain([], [arrival]).has_time_attributes()) self.assertFalse(Domain([], [], [arrival]).has_time_attributes()) self.assertTrue(Domain([arrival], []).has_time_attributes()) self.assertTrue(Domain([], [arrival]).has_time_attributes(include_class=True)) self.assertTrue( Domain([], [], [arrival]).has_time_attributes(include_metas=True) ) self.assertFalse(Domain([arrival], []).has_time_class) self.assertTrue(Domain([], [arrival]).has_time_class) self.assertFalse(Domain([], [], [arrival]).has_time_class) def test_get_conversion(self): compute_value = lambda: 42 new_income = income.copy(compute_value=compute_value) d = Domain((age, gender, income), metas=(ssn, race)) e = Domain((gender, race), None, metas=(age, gender, ssn)) f = Domain((gender,), (race, income), metas=(age, income, ssn)) g = Domain((), metas=(age, gender, ssn)) h = Domain((gender,), (race, new_income), metas=(age, new_income, ssn)) for conver, domain, attr, class_vars, metas in ( (d, e, [1, -2], [], [0, 1, -1]), (d, f, [1], [-2, 2], [0, 2, -1]), (f, g, [], [], [-1, 0, -3]), (g, h, [-2], [None, compute_value], [-1, compute_value, -3]), ): to_domain = domain.get_conversion(conver) self.assertIs(to_domain.source, conver) self.assertEqual(to_domain.attributes, attr) self.assertEqual(to_domain.class_vars, class_vars) self.assertEqual(to_domain.metas, metas) def test_conversion(self): domain = Domain([age, income], [race], [gender, education, ssn]) x, y, metas = domain.convert([42, 13, "White"]) assert_array_equal(x, np.array([42, 13])) assert_array_equal(y, np.array([0])) metas_exp = [gender.Unknown, education.Unknown, ssn.Unknown] def equal(a, b): if ( isinstance(a, Real) and isinstance(b, Real) and np.isnan(a) and np.isnan(b) ): return True else: return a == b self.assertTrue(all(starmap(equal, zip(metas, metas_exp)))) x, y, metas = domain.convert([42, 13, "White", "M", "HS", "1234567"]) assert_array_equal(x, np.array([42, 13])) assert_array_equal(y, np.array([0])) assert_array_equal(metas, np.array([0, 1, "1234567"], dtype=object)) def test_conversion_size(self): domain = Domain([age, gender, income], [race]) self.assertRaises(ValueError, domain.convert, [0] * 3) self.assertRaises(ValueError, domain.convert, [0] * 5) domain = Domain([age, income], [race], [gender, education, ssn]) self.assertRaises(ValueError, domain.convert, [0] * 2) self.assertRaises(ValueError, domain.convert, [0] * 4) self.assertRaises(ValueError, domain.convert, [0] * 7) domain.convert([0] * 3) domain.convert([0] * 6) def test_preprocessor_chaining(self): domain = Domain( [DiscreteVariable("a", values="01"), DiscreteVariable("b", values="01")], DiscreteVariable("y", values="01"), ) table = Table(domain, [[0, 1], [1, np.NaN]], [0, 1]) pre1 = Continuize()(Impute()(table)) pre2 = Table(pre1.domain, table) np.testing.assert_almost_equal(pre1.X, pre2.X) def test_unpickling_recreates_known_domains(self): Variable._clear_all_caches() domain = Domain([]) unpickled_domain = pickle.loads(pickle.dumps(domain)) self.assertTrue(hasattr(unpickled_domain, "_known_domains")) def test_different_domains_with_same_attributes_are_equal(self): domain1 = Domain([]) domain2 = Domain([]) self.assertEqual(domain1, domain2) var1 = ContinuousVariable("var1") domain1.attributes = (var1,) self.assertNotEqual(domain1, domain2) domain2.attributes = (var1,) self.assertEqual(domain1, domain2) domain1.class_vars = (var1,) self.assertNotEqual(domain1, domain2) domain2.class_vars = (var1,) self.assertEqual(domain1, domain2) domain1._metas = (var1,) self.assertNotEqual(domain1, domain2) domain2._metas = (var1,) self.assertEqual(domain1, domain2) def test_domain_conversion_is_fast_enough(self): attrs = [ContinuousVariable("f%i" % i) for i in range(10000)] class_vars = [ContinuousVariable("c%i" % i) for i in range(10)] metas = [ContinuousVariable("m%i" % i) for i in range(10)] source = Domain(attrs, class_vars, metas) start = time() cases = ( ( (attrs[:1000], class_vars, metas), list(range(1000)), list(range(10000, 10010)), list(range(-1, -11, -1)), ), ( (metas, attrs[:1000], class_vars), list(range(-1, -11, -1)), list(range(1000)), list(range(10000, 10010)), ), ( (class_vars, metas, attrs[:1000]), list(range(10000, 10010)), list(range(-1, -11, -1)), list(range(1000)), ), ) for domain_args, attributes, class_vars, metas in cases: c1 = DomainConversion(source, Domain(*domain_args)) self.assertEqual(c1.attributes, attributes) self.assertEqual(c1.class_vars, class_vars) self.assertEqual(c1.metas, metas) self.assertLessEqual(time() - start, 1) def test_copy(self): age.number_of_decimals = 5 attributes = (age, gender, income) domain = Domain(attributes, [race], [ssn]) new_domain = domain.copy() new_domain[age].number_of_decimals = 10 self.assertEqual(domain[age].number_of_decimals, 5) self.assertEqual(new_domain[age].number_of_decimals, 10) def test_domain_conversion_sparsity(self): destination = Domain( attributes=[ ContinuousVariable(name="a"), ContinuousVariable(name="b"), ContinuousVariable(name="c"), ], class_vars=[DiscreteVariable("d", values=["e"])], metas=[StringVariable("f")], ) # all dense source = Domain(attributes=[]) conversion = DomainConversion(source, destination) self.assertFalse(conversion.sparse_X) self.assertFalse(conversion.sparse_Y) self.assertFalse(conversion.sparse_metas) # set destination attributes as sparse for a in destination.attributes: a.sparse = True source = Domain(attributes=[]) conversion = DomainConversion(source, destination) self.assertTrue(conversion.sparse_X) self.assertFalse(conversion.sparse_Y) self.assertFalse(conversion.sparse_metas) # set all destination variable as sparse for a in chain(destination.variables, destination.metas): a.sparse = True source = Domain(attributes=[]) conversion = DomainConversion(source, destination) self.assertTrue(conversion.sparse_X) self.assertTrue(conversion.sparse_Y) self.assertFalse(conversion.sparse_metas) class TestDomainFilter(unittest.TestCase): def setUp(self): self.iris = Table("iris") def test_filter_visible(self): n_feats = len(self.iris.domain.attributes) self.iris.domain.attributes[0].attributes.update({"hidden": True}) filtered = list(filter_visible(self.iris.domain.attributes)) self.assertNotIn(self.iris.domain.attributes[0], filtered) self.assertEqual(len(filtered), n_feats - 1) if __name__ == "__main__": unittest.main()	[ 1, 396, 4321, 3519, 411, 1472, 29037, 573, 2983, 508, 288, 2415, 1574, 19651, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 27259, 29899, 1514, 1807, 13, 5215, 18116, 13, 3166, 931, 1053, 931, 13, 3166, 3694, 1053, 8195, 13, 3166, 4256, 8504, 1053, 5810, 1958, 29892, 9704, 13, 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utils/save_result.py	AnnLIU15/SegCovid	0	146313	<reponame>AnnLIU15/SegCovid import os import cv2 import numpy as np def torch2imgs(output): ''' 函数为将torch数据转为2维图片保存 ''' shapeOFoutput = output.shape if len(shapeOFoutput) == 3 and shapeOFoutput[0] > 1: # 将one-hot结果选取最大值 imgs_array = output.argmax(dim=0).clone().detach().cpu().numpy() elif len(shapeOFoutput) == 3 and shapeOFoutput[0] == 1: # 去掉无用维度 imgs_array = output.clone().detach().cpu().numpy() elif len(shapeOFoutput) == 4: # batchsize~=1，递归保存(反正不考虑效率) imgs_array = np.zeros( shape=(shapeOFoutput[0], shapeOFoutput[2], shapeOFoutput[3])) for idx in range(shapeOFoutput[0]): imgs_array[idx] = torch2imgs(output[idx]) else: assert False, '2D图片的输出维度应该是4.维度不匹配,目前维度'+str(len(shapeOFoutput)) return imgs_array def saveImage(imgs_array, name_of_imgs, save_dir='./output/segResult/', npy_type=False): ''' 使用OPENCV保存图片 ''' # 转为ndarray get_numpy = torch2imgs(imgs_array).astype(np.uint8) shapeofimg = get_numpy.shape if not os.path.exists(save_dir): os.mkdir(save_dir) # 保存 if len(shapeofimg) == 3: for idx in range(shapeofimg[0]): if isinstance(name_of_imgs[idx], tuple): writer_name = name_of_imgs[idx][0] else: writer_name = name_of_imgs[idx] if npy_type: writer_name = writer_name+'.png' cv2.imwrite(save_dir+'/'+writer_name, get_numpy[idx], [ int(cv2.IMWRITE_PNG_COMPRESSION), 0]) else: if isinstance(name_of_imgs, tuple): name_of_imgs = name_of_imgs[0] if npy_type: writer_name = name_of_imgs+'.png' cv2.imwrite(save_dir+'/'+writer_name, get_numpy, [int(cv2.IMWRITE_PNG_COMPRESSION), 0]) if __name__ == '__main__': # import torch # imgs=torch.randint(0,256,(512,512)) # cv2.imwrite('./output/lena.png',imgs.numpy(), [int(cv2.IMWRITE_PNG_COMPRESSION), 0])#第三个参数表示的是压缩级别。默认为3. # cv2.imwrite('./output/1lena.png',imgs.numpy(), [int(cv2.IMWRITE_PNG_COMPRESSION), 9])#第三个参数表示的是压缩级别。默认为3.取最小就好 imgs1 = cv2.imread('./output/lena.png', cv2.IMREAD_GRAYSCALE) imgs = cv2.imread('./output/1lena.png', cv2.IMREAD_GRAYSCALE) print(imgs1 == imgs, 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test/dpt_tests/dpt_float_test.py	kistlin/xknx	1	130004	"""Unit test for KNX 2 and 4 byte float objects.""" import math import struct from unittest.mock import patch import pytest from xknx.dpt import ( DPT2ByteFloat, DPT4ByteFloat, DPTElectricCurrent, DPTElectricPotential, DPTEnthalpy, DPTFrequency, DPTHumidity, DPTLux, DPTPartsPerMillion, DPTPhaseAngleDeg, DPTPower, DPTTemperature, DPTVoltage, ) from xknx.exceptions import ConversionError class TestDPTFloat: """Test class for KNX 2 & 4 byte/octet float object.""" # #################################################################### # DPT2ByteFloat # def test_value_from_documentation(self): """Test parsing and streaming of DPT2ByteFloat -30.00. Example from the internet[tm].""" assert DPT2ByteFloat.to_knx(-30.00) == (0x8A, 0x24) assert DPT2ByteFloat.from_knx((0x8A, 0x24)) == -30.00 def test_value_taken_from_live_thermostat(self): """Test parsing and streaming of DPT2ByteFloat 19.96.""" assert DPT2ByteFloat.to_knx(16.96) == (0x06, 0xA0) assert DPT2ByteFloat.from_knx((0x06, 0xA0)) == 16.96 def test_zero_value(self): """Test parsing and streaming of DPT2ByteFloat zero value.""" assert DPT2ByteFloat.to_knx(0.00) == (0x00, 0x00) assert DPT2ByteFloat.from_knx((0x00, 0x00)) == 0.00 def test_room_temperature(self): """Test parsing and streaming of DPT2ByteFloat 21.00. Room temperature.""" assert DPT2ByteFloat.to_knx(21.00) == (0x0C, 0x1A) assert DPT2ByteFloat.from_knx((0x0C, 0x1A)) == 21.00 def test_high_temperature(self): """Test parsing and streaming of DPT2ByteFloat 500.00, 499.84, 500.16. Testing rounding issues.""" assert DPT2ByteFloat.to_knx(500.00) == (0x2E, 0x1A) assert round(abs(DPT2ByteFloat.from_knx((0x2E, 0x1A)) - 499.84), 7) == 0 assert round(abs(DPT2ByteFloat.from_knx((0x2E, 0x1B)) - 500.16), 7) == 0 assert DPT2ByteFloat.to_knx(499.84) == (0x2E, 0x1A) assert DPT2ByteFloat.to_knx(500.16) == (0x2E, 0x1B) def test_minor_negative_temperature(self): """Test parsing and streaming of DPT2ByteFloat -10.00. Testing negative values.""" assert DPT2ByteFloat.to_knx(-10.00) == (0x84, 0x18) assert DPT2ByteFloat.from_knx((0x84, 0x18)) == -10.00 def test_very_cold_temperature(self): """ Test parsing and streaming of DPT2ByteFloat -1000.00,-999.68, -1000.32. Testing rounding issues of negative values. """ assert DPT2ByteFloat.to_knx(-1000.00) == (0xB1, 0xE6) assert DPT2ByteFloat.from_knx((0xB1, 0xE6)) == -999.68 assert DPT2ByteFloat.from_knx((0xB1, 0xE5)) == -1000.32 assert DPT2ByteFloat.to_knx(-999.68) == (0xB1, 0xE6) assert DPT2ByteFloat.to_knx(-1000.32) == (0xB1, 0xE5) def test_max(self): """Test parsing and streaming of DPT2ByteFloat with maximum value.""" assert DPT2ByteFloat.to_knx(DPT2ByteFloat.value_max) == (0x7F, 0xFF) assert DPT2ByteFloat.from_knx((0x7F, 0xFF)) == DPT2ByteFloat.value_max def test_min(self): """Test parsing and streaming of DPT2ByteFloat with minimum value.""" assert DPT2ByteFloat.to_knx(DPT2ByteFloat.value_min) == (0xF8, 0x00) assert DPT2ByteFloat.from_knx((0xF8, 0x00)) == DPT2ByteFloat.value_min def test_close_to_max(self): """Test parsing and streaming of DPT2ByteFloat with maximum value -1.""" assert DPT2ByteFloat.to_knx(670433.28) == (0x7F, 0xFE) assert DPT2ByteFloat.from_knx((0x7F, 0xFE)) == 670433.28 def test_close_to_min(self): """Test parsing and streaming of DPT2ByteFloat with minimum value +1.""" assert DPT2ByteFloat.to_knx(-670760.96) == (0xF8, 0x01) assert DPT2ByteFloat.from_knx((0xF8, 0x01)) == -670760.96 def test_to_knx_min_exceeded(self): """Test parsing of DPT2ByteFloat with wrong value (underflow).""" with pytest.raises(ConversionError): DPT2ByteFloat.to_knx(DPT2ByteFloat.value_min - 1) def test_to_knx_max_exceeded(self): """Test parsing of DPT2ByteFloat with wrong value (overflow).""" with pytest.raises(ConversionError): DPT2ByteFloat.to_knx(DPT2ByteFloat.value_max + 1) def test_to_knx_wrong_parameter(self): """Test parsing of DPT2ByteFloat with wrong value (string).""" with pytest.raises(ConversionError): DPT2ByteFloat.to_knx("fnord") def test_from_knx_wrong_parameter(self): """Test parsing of DPT2ByteFloat with wrong value (wrong number of bytes).""" with pytest.raises(ConversionError): DPT2ByteFloat.from_knx((0xF8, 0x01, 0x23)) def test_from_knx_wrong_parameter2(self): """Test parsing of DPT2ByteFloat with wrong value (second parameter is a string).""" with pytest.raises(ConversionError): DPT2ByteFloat.from_knx((0xF8, "0x23")) # # DPTTemperature # def test_temperature_settings(self): """Test attributes of DPTTemperature.""" assert DPTTemperature.value_min == -273 assert DPTTemperature.value_max == 670760 assert DPTTemperature.unit == "°C" assert DPTTemperature.resolution == 0.01 def test_temperature_assert_min_exceeded(self): """Testing parsing of DPTTemperature with wrong value.""" with pytest.raises(ConversionError): DPTTemperature.to_knx(-274) def test_temperature_assert_min_exceeded_from_knx(self): """Testing parsing of DPTTemperature with wrong value.""" with pytest.raises(ConversionError): DPTTemperature.from_knx((0xB1, 0xE6)) # -1000 # # DPTLux # def test_lux_settings(self): """Test attributes of DPTLux.""" assert DPTLux.value_min == 0 assert DPTLux.value_max == 670760 assert DPTLux.unit == "lx" assert DPTLux.resolution == 0.01 def test_lux_assert_min_exceeded(self): """Test parsing of DPTLux with wrong value.""" with pytest.raises(ConversionError): DPTLux.to_knx(-1) # # DPTHumidity # def test_humidity_settings(self): """Test attributes of DPTHumidity.""" assert DPTHumidity.value_min == 0 assert DPTHumidity.value_max == 670760 assert DPTHumidity.unit == "%" assert DPTHumidity.resolution == 0.01 def test_humidity_assert_min_exceeded(self): """Test parsing of DPTHumidity with wrong value.""" with pytest.raises(ConversionError): DPTHumidity.to_knx(-1) # # DPTEnthalpy # def test_enthalpy_settings(self): """Test attributes of DPTEnthalpy.""" assert DPTEnthalpy.unit == "H" # # DPTPartsPerMillion # def test_partspermillion_settings(self): """Test attributes of DPTPartsPerMillion.""" assert DPTPartsPerMillion.unit == "ppm" # # DPTVoltage # def test_voltage_settings(self): """Test attributes of DPTVoltage.""" assert DPTVoltage.unit == "mV" # #################################################################### # DPT4ByteFloat # def test_4byte_float_values_from_power_meter(self): """Test parsing DPT4ByteFloat value from power meter.""" assert DPT4ByteFloat.from_knx((0x43, 0xC6, 0x80, 00)) == 397 assert DPT4ByteFloat.to_knx(397) == (0x43, 0xC6, 0x80, 00) assert DPT4ByteFloat.from_knx((0x42, 0x38, 0x00, 00)) == 46 assert DPT4ByteFloat.to_knx(46) == (0x42, 0x38, 0x00, 00) def test_14_033(self): """Test parsing DPTFrequency unit.""" assert DPTFrequency.unit == "Hz" def test_14_055(self): """Test DPTPhaseAngleDeg object.""" assert DPT4ByteFloat.from_knx((0x42, 0xEF, 0x00, 0x00)) == 119.5 assert DPT4ByteFloat.to_knx(119.5) == (0x42, 0xEF, 0x00, 0x00) assert DPTPhaseAngleDeg.unit == "°" def test_14_057(self): """Test DPT4ByteFloat object.""" assert DPT4ByteFloat.from_knx((0x3F, 0x71, 0xEB, 0x86)) == 0.9450001 assert DPT4ByteFloat.to_knx(0.945000052452) == (0x3F, 0x71, 0xEB, 0x86) assert DPT4ByteFloat.unit == "" def test_4byte_float_values_from_voltage_meter(self): """Test parsing DPT4ByteFloat from voltage meter.""" assert DPT4ByteFloat.from_knx((0x43, 0x65, 0xE3, 0xD7)) == 229.89 assert DPT4ByteFloat.to_knx(229.89) == (0x43, 0x65, 0xE3, 0xD7) def test_4byte_float_zero_value(self): """Test parsing and streaming of DPT4ByteFloat zero value.""" assert DPT4ByteFloat.from_knx((0x00, 0x00, 0x00, 0x00)) == 0.00 assert DPT4ByteFloat.to_knx(0.00) == (0x00, 0x00, 0x00, 0x00) def test_4byte_float_special_value(self): """Test parsing and streaming of DPT4ByteFloat special value.""" assert math.isnan(DPT4ByteFloat.from_knx((0x7F, 0xC0, 0x00, 0x00))) assert DPT4ByteFloat.to_knx(float("nan")) == (0x7F, 0xC0, 0x00, 0x00) assert math.isinf(DPT4ByteFloat.from_knx((0x7F, 0x80, 0x00, 0x00))) assert DPT4ByteFloat.to_knx(float("inf")) == (0x7F, 0x80, 0x00, 0x00) assert DPT4ByteFloat.from_knx((0xFF, 0x80, 0x00, 0x00)) == float("-inf") assert DPT4ByteFloat.to_knx(float("-inf")) == (0xFF, 0x80, 0x00, 0x00) assert DPT4ByteFloat.from_knx((0x80, 0x00, 0x00, 0x00)) == float("-0") assert DPT4ByteFloat.to_knx(float("-0")) == (0x80, 0x00, 0x00, 0x00) def test_4byte_float_to_knx_wrong_parameter(self): """Test parsing of DPT4ByteFloat with wrong value (string).""" with pytest.raises(ConversionError): DPT4ByteFloat.to_knx("fnord") def test_4byte_float_from_knx_wrong_parameter(self): """Test parsing of DPT4ByteFloat with wrong value (wrong number of bytes).""" with pytest.raises(ConversionError): DPT4ByteFloat.from_knx((0xF8, 0x01, 0x23)) def test_4byte_float_from_knx_wrong_parameter2(self): """Test parsing of DPT4ByteFloat with wrong value (second parameter is a string).""" with pytest.raises(ConversionError): DPT4ByteFloat.from_knx((0xF8, "0x23", 0x00, 0x00)) def test_4byte_flaot_from_knx_unpack_error(self): """Test DPT4ByteFloat parsing with unpack error.""" with patch("struct.unpack") as unpack_mock: unpack_mock.side_effect = struct.error() with pytest.raises(ConversionError): DPT4ByteFloat.from_knx((0x01, 0x23, 0x02, 0x02)) # # DPTElectricCurrent # def test_electric_current_settings(self): """Test 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liftaway/low_level.py	elj/liftaway	0	167872	#!/usr/bin/env python # -- coding: utf-8 -- """Liftaway Low-Level (GPIO and PCA) module.""" import random import busio import RPi.GPIO as GPIO from adafruit_pca9685 import PCA9685 from board import SCL, SDA from liftaway.constants import control_outputs i2c_bus = busio.I2C(SCL, SDA) pca = PCA9685(i2c_bus) def init(): """Initialize LEDs.""" pca.frequency = 60 # Turn all lights off all_lights_off() # Init Overhead/Ceiling Light green = pca.channels[13] red = pca.channels[14] blue = pca.channels[15] # TODO: make this changable red.duty_cycle = 0xFFFF green.duty_cycle = 0x4000 blue.duty_cycle = 0x0000 def gpio_output(gpio: int, high: bool = True): """Set a GPIO Output High or Low.""" if high: GPIO.output(gpio, GPIO.HIGH) else: GPIO.output(gpio, GPIO.LOW) def cancel_call_led(on: bool = True): """Turn on/off the cancel call LED.""" gpio_output(control_outputs.get("cancel_call"), on) def direction_led(on: bool = True): """Turn on/off the direction lights; pick a rando direction...""" if on: if random.choice((True, False)): # noqa gpio_output(control_outputs.get("direction_up"), on) else: gpio_output(control_outputs.get("direction_dn"), on) else: # on == False -> Off gpio_output(control_outputs.get("direction_up"), on) gpio_output(control_outputs.get("direction_dn"), on) def door_close_led(on: bool = True): """Turn on/off the door close LED.""" gpio_output(control_outputs.get("door_close"), on) def door_open_led(on: bool = True): """Turn on/off the door open LED.""" gpio_output(control_outputs.get("door_open"), on) def floor_button_led(floor, on: bool = True): """Turn on/off floor LED.""" if on: pca.channels[floor].duty_cycle = 0xFFFF else: pca.channels[floor].duty_cycle = 0 def all_lights_off(): """Turn all Lights/LEDS off.""" for i in range(0, 15): pca.channels[i].duty_cycle = 0 for i in control_outputs.values(): gpio_output(i, False)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 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examples/retrieve/owners_retrieve.py	jhurd-tc/threatconnect-python	0	127972	# -- coding: utf-8 -- """ standard """ import ConfigParser from random import randint import sys """ custom """ from threatconnect import ThreatConnect from threatconnect.Config.FilterOperator import FilterSetOperator # configuration file config_file = "tc.conf" # retrieve configuration file config = ConfigParser.RawConfigParser() config.read(config_file) try: api_access_id = config.get('threatconnect', 'api_access_id') api_secret_key = config.get('threatconnect', 'api_secret_key') api_default_org = config.get('threatconnect', 'api_default_org') api_base_url = config.get('threatconnect', 'api_base_url') api_result_limit = int(config.get('threatconnect', 'api_result_limit')) except ConfigParser.NoOptionError: print('Could not retrieve configuration file.') sys.exit(1) tc = ThreatConnect(api_access_id, api_secret_key, api_default_org, api_base_url) tc.set_api_result_limit(api_result_limit) tc.report_enable() """ Get Owners """ enable_example1 = False enable_example2 = False enable_example3 = False # shared method to display results from examples below def show_data(result_obj): """ """ for obj in result_obj: print('\n{0!s:_^80}'.format(obj.name)) print('{0!s:<20}{1!s:<50}'.format('ID', obj.id)) print('{0!s:<20}{1!s:<50}'.format('Type', obj.type)) # # api_uris # if len(obj.request_uris) > 0: print('\n{0!s:-^40}'.format(' Request URIs ')) for request_uri in obj.request_uris: print('{0!s:<20}{1!s:<50}'.format('URI', request_uri)) # # matched filters # if len(obj.matched_filters) > 0: print('\n{0!s:-^40}'.format(' API Matched Filters ')) for api_filter in obj.matched_filters: print('{0!s:<20}{1!s:<50}'.format('Filter', api_filter)) # # print report # print(tc.report.stats) def main(): """ """ # set threat connect log (tcl) level tc.set_tcl_file('log/tc.log', 'debug') tc.set_tcl_console_level('critical') if enable_example1: """ This is a basic example that pull all owners. """ # optionally set the max results the api should return in one request tc.set_api_result_limit(500) # get owner object owners = tc.owners() # retrieve owners try: owners.retrieve() except RuntimeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # show owner data show_data(owners) if enable_example2: """ This example retrieves all owners that a particular indicator appears. """ # get owner object owners = tc.owners() # filter results try: filter1 = owners.add_filter() filter1.add_indicator('10.20.30.40') filter1.add_pf_name('Example Community') filter1.add_pf_type('Community') # Organization, Community, Source except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # retrieve owners try: owners.retrieve() except RuntimeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # show owner data show_data(owners) """ Method: get_owners() -> This method can be used to get a object containing owners filtered by indicator. """ if enable_example3: # get owner object owners = tc.owners() # filter results try: filter1 = owners.add_filter() filter1.add_indicator('10.20.30.40') except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) try: filter2 = owners.add_filter() filter2.add_filter_operator(FilterSetOperator.AND) filter2.add_indicator('<EMAIL>') except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # retrieve owners try: owners.retrieve() except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # show owner data show_data(owners) if __name__ == "__main__": main()	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 15945, 29908, 3918, 9995, 13, 5215, 12782, 11726, 13, 3166, 4036, 1053, 20088, 524, 13, 5215, 10876, 13, 13, 15945, 29908, 2888, 9995, 13, 3166, 28469, 6915, 1053, 498, 276, 271, 17918, 13, 3166, 28469, 6915, 29889, 3991, 29889, 5072, 26486, 1053, 19916, 2697, 26486, 13, 13, 29937, 5285, 934, 13, 2917, 29918, 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anime_downloader/__version__.py	cmjesz/anime-downloader	1	110593	__version__ = '4.0.0.r1'	[ 1, 4770, 3259, 1649, 353, 525, 29946, 29889, 29900, 29889, 29900, 29889, 29878, 29896, 29915, 13, 2 ]
drogher/package/fedex.py	thisisnotmyuserid/drogher	13	14932	<filename>drogher/package/fedex.py import itertools from .base import Package class FedEx(Package): shipper = 'FedEx' class FedExExpress(FedEx): barcode_pattern = r'^\d{34}$' @property def tracking_number(self): return self.barcode[20:22].lstrip('0') + self.barcode[22:] @property def valid_checksum(self): chars, check_digit = self.tracking_number[:-1], self.tracking_number[-1] total = 0 for digit, char in zip(itertools.cycle([1, 3, 7]), reversed(chars)): total += int(char) * digit return total % 11 % 10 == int(check_digit) class FedExGround96(FedEx): barcode_pattern = r'^96\d{20}$' @property def tracking_number(self): return self.barcode[7:] @property def valid_checksum(self): chars, check_digit = self.tracking_number[:-1], self.tracking_number[-1] odd = even = 0 for i, char in enumerate(reversed(chars)): if i & 0x1: odd += int(char) else: even += int(char) check = ((even * 3) + odd) % 10 if check != 0: check = 10 - check return check == int(check_digit)	[ 1, 529, 9507, 29958, 29881, 9102, 2276, 29914, 5113, 29914, 29888, 287, 735, 29889, 2272, 13, 5215, 4256, 8504, 13, 13, 3166, 869, 3188, 1053, 22029, 13, 13, 13, 1990, 17972, 1252, 29898, 14459, 1125, 13, 1678, 528, 29875, 2496, 353, 525, 29943, 287, 1252, 29915, 13, 13, 13, 1990, 17972, 1252, 27404, 29898, 29943, 287, 1252, 1125, 13, 1678, 2594, 401, 29918, 11037, 353, 364, 29915, 3823, 29881, 29912, 29941, 29946, 1042, 29915, 13, 13, 1678, 732, 6799, 13, 1678, 822, 23110, 29918, 4537, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 1646, 401, 29961, 29906, 29900, 29901, 29906, 29906, 1822, 29880, 17010, 877, 29900, 1495, 718, 1583, 29889, 1646, 401, 29961, 29906, 29906, 17531, 13, 13, 1678, 732, 6799, 13, 1678, 822, 2854, 29918, 3198, 2083, 29898, 1311, 1125, 13, 4706, 22524, 29892, 1423, 29918, 26204, 353, 1583, 29889, 11294, 292, 29918, 4537, 7503, 29899, 29896, 1402, 1583, 29889, 11294, 292, 29918, 4537, 14352, 29896, 29962, 13, 4706, 3001, 353, 29871, 29900, 13, 4706, 363, 13615, 29892, 1373, 297, 14319, 29898, 1524, 8504, 29889, 23090, 4197, 29896, 29892, 29871, 29941, 29892, 29871, 29955, 11724, 18764, 287, 29898, 305, 1503, 22164, 13, 9651, 3001, 4619, 938, 29898, 3090, 29897, 334, 13615, 13, 4706, 736, 3001, 1273, 29871, 29896, 29896, 1273, 29871, 29896, 29900, 1275, 938, 29898, 3198, 29918, 26204, 29897, 13, 13, 13, 1990, 17972, 1252, 3338, 618, 29929, 29953, 29898, 29943, 287, 1252, 1125, 13, 1678, 2594, 401, 29918, 11037, 353, 364, 29915, 29985, 29929, 29953, 29905, 29881, 29912, 29906, 29900, 1042, 29915, 13, 13, 1678, 732, 6799, 13, 1678, 822, 23110, 29918, 4537, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 1646, 401, 29961, 29955, 17531, 13, 13, 1678, 732, 6799, 13, 1678, 822, 2854, 29918, 3198, 2083, 29898, 1311, 1125, 13, 4706, 22524, 29892, 1423, 29918, 26204, 353, 1583, 29889, 11294, 292, 29918, 4537, 7503, 29899, 29896, 1402, 1583, 29889, 11294, 292, 29918, 4537, 14352, 29896, 29962, 13, 4706, 7736, 353, 1584, 353, 29871, 29900, 13, 4706, 363, 474, 29892, 1373, 297, 26985, 29898, 276, 874, 287, 29898, 305, 1503, 22164, 13, 9651, 565, 474, 669, 29871, 29900, 29916, 29896, 29901, 13, 18884, 7736, 4619, 938, 29898, 3090, 29897, 13, 9651, 1683, 29901, 13, 18884, 1584, 4619, 938, 29898, 3090, 29897, 13, 4706, 1423, 353, 5135, 11884, 334, 29871, 29941, 29897, 718, 7736, 29897, 1273, 29871, 29896, 29900, 13, 4706, 565, 1423, 2804, 29871, 29900, 29901, 13, 9651, 1423, 353, 29871, 29896, 29900, 448, 1423, 13, 4706, 736, 1423, 1275, 938, 29898, 3198, 29918, 26204, 29897, 13, 2 ]
tests/eth_detectors.py	Srinivas11789/manticore	0	57757	<filename>tests/eth_detectors.py """ File name is purposefully not test_* to run this test separately. """ import inspect import shutil import struct import tempfile import unittest import os from manticore.core.smtlib import operators from eth_general import make_mock_evm_state from manticore.ethereum import ManticoreEVM, DetectInvalid, DetectIntegerOverflow, Detector, NoAliveStates, ABI, \ EthereumError, DetectReentrancySimple, DetectReentrancyAdvanced, DetectUnusedRetVal, DetectSelfdestruct, LoopDepthLimiter, DetectDelegatecall, \ DetectEnvInstruction, DetectExternalCallAndLeak, DetectEnvInstruction import shutil THIS_DIR = os.path.dirname(os.path.abspath(__file__)) # FIXME(mark): Remove these two lines when logging works for ManticoreEVM from manticore.utils.log import init_logging, set_verbosity init_logging() set_verbosity(0) class EthDetectorTest(unittest.TestCase): """ Subclasses must assign this class variable to the class for the detector """ DETECTOR_CLASS = None def setUp(self): self.mevm = ManticoreEVM() self.mevm.verbosity(0) self.worksp = self.mevm.workspace def tearDown(self): self.mevm = None shutil.rmtree(self.worksp) def _test(self, name, should_find): """ Tests DetectInvalid over the consensys benchmark suit """ mevm = self.mevm filename = os.path.join(THIS_DIR, 'binaries', 'detectors', '{}.sol'.format(name)) self.mevm.register_detector(self.DETECTOR_CLASS()) mevm.multi_tx_analysis(filename, contract_name='DetectThis', args=(mevm.make_symbolic_value(),)) expected_findings = set(((c, d) for b, c, d in should_find)) actual_findings = set(((c, d) for a, b, c, d in mevm.global_findings)) self.assertEqual(expected_findings, actual_findings) class EthRetVal(EthDetectorTest): """ Detect when a return value of a low level transaction instruction is ignored """ DETECTOR_CLASS = DetectUnusedRetVal def test_retval_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_retval_not_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(337, 'Returned value at CALL instruction is not used', False)}) def test_retval_crazy(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_retval_lunatic(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) class EthSelfdestruct(EthDetectorTest): DETECTOR_CLASS = DetectSelfdestruct def test_selfdestruct_true_pos(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(307, 'Reachable SELFDESTRUCT', False)}) def test_selfdestruct_true_pos1(self): self.mevm.register_plugin(LoopDepthLimiter(2)) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(307, 'Reachable SELFDESTRUCT', False)}) def test_selfdestruct_true_neg(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_selfdestruct_true_neg1(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) class EthExternalCallAndLeak(EthDetectorTest): DETECTOR_CLASS = DetectExternalCallAndLeak def test_etherleak_true_neg(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_etherleak_true_neg1(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_etherleak_true_neg2(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False)}) def test_etherleak_true_neg3(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False)}) def test_etherleak_true_pos_argument(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable ether leak to sender via argument", False)}) def test_etherleak_true_pos_argument1(self): self.mevm.register_plugin(LoopDepthLimiter(5)) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable ether leak to sender via argument", False)}) def test_etherleak_true_pos_argument2(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable ether leak to user controlled address via argument", False)}) def test_etherleak_true_pos_msgsender(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False), (0x1c5, "Reachable ether leak to sender", False)}) def test_etherleak_true_pos_msgsender1(self): self.mevm.register_plugin(LoopDepthLimiter(5)) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False), (0x1c5, "Reachable ether leak to sender", False)}) class EthIntegerOverflow(unittest.TestCase): def setUp(self): self.io = DetectIntegerOverflow() self.state = make_mock_evm_state() def test_mul_no_overflow(self): """ Regression test added for issue 714, where we were using the ADD ovf check for MUL """ arguments = [1 << 248, self.state.new_symbolic_value(256)] self.state.constrain(operators.ULT(arguments[1], 256)) cond = self.io._unsigned_mul_overflow(self.state, arguments) check = self.state.can_be_true(cond) self.assertFalse(check) def test_mul_overflow0(self): arguments = [1 << 249, self.state.new_symbolic_value(256)] self.state.constrain(operators.ULT(arguments[1], 256)) cond = self.io._unsigned_mul_overflow(self.state, arguments) check = self.state.can_be_true(cond) self.assertTrue(check) def test_mul_overflow1(self): arguments = [1 << 255, self.state.new_symbolic_value(256)] cond = self.io._unsigned_mul_overflow(self.state, *arguments) check = self.state.can_be_true(cond) self.assertTrue(check) class DetectEnvInstruction(EthDetectorTest): DETECTOR_CLASS = DetectEnvInstruction def test_predictable_not_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(174, 'Warning ORIGIN instruction used', False), (157, 'Warning DIFFICULTY instruction used', False), (129, 'Warning TIMESTAMP instruction used', False), (165, 'Warning NUMBER instruction used', False), (132, 'Warning COINBASE instruction used', False), (167, 'Warning BLOCKHASH instruction used', False), (160, 'Warning NUMBER instruction used', False), (199, 'Warning GASPRICE instruction used', False), (202, 'Warning GASLIMIT instruction used', False)}) class EthDelegatecall(EthDetectorTest): """ Test the detection of funny delegatecalls """ DETECTOR_CLASS = DetectDelegatecall def test_delegatecall_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_delegatecall_ok1(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_delegatecall_ok2(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) @unittest.skip("Too slow for this modern times") def test_delegatecall_ok3(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_delegatecall_not_ok(self): self.mevm.register_plugin(LoopDepthLimiter()) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(179, 'Delegatecall to user controlled function', False), (179, 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29898, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 7029, 1246, 304, 10004, 613, 7700, 26972, 13, 13, 1678, 822, 1243, 29918, 1979, 280, 557, 29918, 3009, 29918, 10052, 29941, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 7029, 1246, 304, 10004, 613, 7700, 26972, 13, 13, 1678, 822, 1243, 29918, 1979, 280, 557, 29918, 3009, 29918, 1066, 29918, 23516, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 29871, 1979, 24993, 304, 10004, 3025, 2980, 613, 7700, 26972, 13, 13, 1678, 822, 1243, 29918, 1979, 280, 557, 29918, 3009, 29918, 1066, 29918, 23516, 29896, 29898, 1311, 1125, 13, 4706, 1583, 29889, 1004, 6925, 29889, 9573, 29918, 8582, 29898, 18405, 8498, 386, 29931, 19657, 29898, 29945, 876, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 29871, 1979, 24993, 304, 10004, 3025, 2980, 613, 7700, 26972, 13, 13, 1678, 822, 1243, 29918, 1979, 280, 557, 29918, 3009, 29918, 1066, 29918, 23516, 29906, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 29871, 1979, 24993, 304, 1404, 20704, 3211, 3025, 2980, 613, 7700, 26972, 13, 13, 1678, 822, 1243, 29918, 1979, 280, 557, 29918, 3009, 29918, 1066, 29918, 1516, 3174, 1581, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 7029, 1246, 304, 10004, 613, 7700, 511, 313, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 29871, 1979, 24993, 304, 10004, 613, 7700, 26972, 13, 13, 1678, 822, 1243, 29918, 1979, 280, 557, 29918, 3009, 29918, 1066, 29918, 1516, 3174, 1581, 29896, 29898, 1311, 1125, 13, 4706, 1583, 29889, 1004, 6925, 29889, 9573, 29918, 8582, 29898, 18405, 8498, 386, 29931, 19657, 29898, 29945, 876, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 7029, 1246, 304, 10004, 613, 7700, 511, 313, 29900, 29916, 29896, 29883, 29945, 29892, 376, 1123, 496, 519, 29871, 1979, 24993, 304, 10004, 613, 7700, 26972, 13, 13, 13, 1990, 13772, 7798, 23773, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1583, 29889, 601, 353, 5953, 522, 7798, 23773, 580, 13, 4706, 1583, 29889, 3859, 353, 1207, 29918, 17640, 29918, 5750, 29885, 29918, 3859, 580, 13, 13, 1678, 822, 1243, 29918, 16109, 29918, 1217, 29918, 2262, 29898, 1311, 1125, 13, 4706, 9995, 13, 4706, 2169, 23881, 1243, 2715, 363, 2228, 29871, 29955, 29896, 29946, 29892, 988, 591, 892, 773, 278, 27827, 15397, 29888, 1423, 363, 341, 13309, 13, 4706, 9995, 13, 4706, 6273, 353, 518, 29896, 3532, 29871, 29906, 29946, 29947, 29892, 1583, 29889, 3859, 29889, 1482, 29918, 18098, 293, 29918, 1767, 29898, 29906, 29945, 29953, 4638, 13, 4706, 1583, 29889, 3859, 29889, 3075, 6038, 29898, 3372, 4097, 29889, 8647, 29898, 25699, 29961, 29896, 1402, 29871, 29906, 29945, 29953, 876, 13, 13, 4706, 2148, 353, 1583, 29889, 601, 3032, 15395, 29918, 16109, 29918, 2262, 29898, 1311, 29889, 3859, 29892, 334, 25699, 29897, 13, 4706, 1423, 353, 1583, 29889, 3859, 29889, 3068, 29918, 915, 29918, 3009, 29898, 1116, 29897, 13, 4706, 1583, 29889, 9294, 8824, 29898, 3198, 29897, 13, 13, 1678, 822, 1243, 29918, 16109, 29918, 2262, 29900, 29898, 1311, 1125, 13, 4706, 6273, 353, 518, 29896, 3532, 29871, 29906, 29946, 29929, 29892, 1583, 29889, 3859, 29889, 1482, 29918, 18098, 293, 29918, 1767, 29898, 29906, 29945, 29953, 4638, 13, 4706, 1583, 29889, 3859, 29889, 3075, 6038, 29898, 3372, 4097, 29889, 8647, 29898, 25699, 29961, 29896, 1402, 29871, 29906, 29945, 29953, 876, 13, 13, 4706, 2148, 353, 1583, 29889, 601, 3032, 15395, 29918, 16109, 29918, 2262, 29898, 1311, 29889, 3859, 29892, 334, 25699, 29897, 13, 4706, 1423, 353, 1583, 29889, 3859, 29889, 3068, 29918, 915, 29918, 3009, 29898, 1116, 29897, 13, 4706, 1583, 29889, 9294, 5574, 29898, 3198, 29897, 13, 13, 1678, 822, 1243, 29918, 16109, 29918, 2262, 29896, 29898, 1311, 1125, 13, 4706, 6273, 353, 518, 29896, 3532, 29871, 29906, 29945, 29945, 29892, 1583, 29889, 3859, 29889, 1482, 29918, 18098, 293, 29918, 1767, 29898, 29906, 29945, 29953, 4638, 13, 13, 4706, 2148, 353, 1583, 29889, 601, 3032, 15395, 29918, 16109, 29918, 2262, 29898, 1311, 29889, 3859, 29892, 334, 25699, 29897, 13, 4706, 1423, 353, 1583, 29889, 3859, 29889, 3068, 29918, 915, 29918, 3009, 29898, 1116, 29897, 13, 4706, 1583, 29889, 9294, 5574, 29898, 3198, 29897, 13, 13, 1990, 5953, 522, 21745, 3379, 4080, 29898, 29923, 386, 6362, 3019, 3057, 1125, 13, 1678, 360, 2544, 13845, 1955, 29918, 13875, 1799, 353, 5953, 522, 21745, 3379, 4080, 13, 13, 1678, 822, 1243, 29918, 27711, 519, 29918, 1333, 29918, 554, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29896, 29955, 29946, 29892, 525, 22709, 438, 22789, 1177, 15278, 1304, 742, 7700, 511, 313, 29896, 29945, 29955, 29892, 525, 22709, 22471, 4198, 2965, 8647, 29979, 15278, 1304, 742, 7700, 511, 313, 29896, 29906, 29929, 29892, 525, 22709, 323, 8890, 1254, 19297, 15278, 1304, 742, 7700, 511, 313, 29896, 29953, 29945, 29892, 525, 22709, 28019, 13635, 15278, 1304, 742, 7700, 511, 313, 29896, 29941, 29906, 29892, 525, 22709, 4810, 1177, 25416, 15278, 1304, 742, 7700, 511, 313, 29896, 29953, 29955, 29892, 525, 22709, 350, 21339, 29950, 24943, 15278, 1304, 742, 7700, 511, 313, 29896, 29953, 29900, 29892, 525, 22709, 28019, 13635, 15278, 1304, 742, 7700, 511, 313, 29896, 29929, 29929, 29892, 525, 22709, 402, 3289, 10593, 12107, 15278, 1304, 742, 7700, 511, 313, 29906, 29900, 29906, 29892, 525, 22709, 402, 3289, 5265, 26349, 15278, 1304, 742, 7700, 26972, 13, 13, 13, 13, 1990, 13772, 11494, 4804, 29898, 29923, 386, 6362, 3019, 3057, 1125, 13, 1678, 9995, 4321, 278, 15326, 310, 2090, 1460, 13341, 29883, 4293, 9995, 13, 1678, 360, 2544, 13845, 1955, 29918, 13875, 1799, 353, 5953, 522, 11494, 4804, 13, 13, 1678, 822, 1243, 29918, 21234, 4804, 29918, 554, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 731, 3101, 13, 13, 1678, 822, 1243, 29918, 21234, 4804, 29918, 554, 29896, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 731, 3101, 13, 13, 1678, 822, 1243, 29918, 21234, 4804, 29918, 554, 29906, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 731, 3101, 13, 13, 1678, 732, 348, 27958, 29889, 11014, 703, 1762, 29877, 5232, 363, 445, 5400, 3064, 1159, 13, 1678, 822, 1243, 29918, 21234, 4804, 29918, 554, 29941, 29898, 1311, 1125, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 731, 3101, 13, 13, 1678, 822, 1243, 29918, 21234, 4804, 29918, 1333, 29918, 554, 29898, 1311, 1125, 13, 4706, 1583, 29889, 1004, 6925, 29889, 9573, 29918, 8582, 29898, 18405, 8498, 386, 29931, 19657, 3101, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29896, 29955, 29929, 29892, 525, 11494, 4804, 304, 1404, 20704, 740, 742, 7700, 511, 313, 29896, 29955, 29929, 29892, 525, 11494, 4804, 304, 1404, 20704, 3211, 742, 7700, 26972, 13, 13, 1678, 732, 348, 27958, 29889, 11014, 703, 1762, 29877, 5232, 363, 445, 5400, 3064, 1159, 13, 1678, 822, 1243, 29918, 21234, 4804, 29918, 1333, 29918, 554, 29896, 29898, 1311, 1125, 13, 4706, 1583, 29889, 1004, 6925, 29889, 9573, 29918, 8582, 29898, 18405, 8498, 386, 29931, 19657, 29898, 7888, 29918, 2798, 29918, 386, 12268, 29922, 29945, 29900, 29900, 876, 13, 4706, 1024, 353, 16096, 29889, 3784, 2557, 2141, 29888, 29918, 401, 29889, 1111, 29918, 978, 29961, 29945, 17531, 13, 4706, 1583, 3032, 1688, 29898, 978, 29892, 426, 29898, 29896, 29955, 29929, 29892, 525, 11494, 4804, 304, 1404, 20704, 740, 742, 7700, 26972, 13, 13, 13, 2 ]
dataactcore/migrations/versions/20b5109967bf_.py	brianherman/data-act-broker-backend	1	138424	"""empty message Revision ID: 20b5109967bf Revises: 17ec44522729, <PASSWORD> Create Date: 2017-04-03 11:25:27.381702 """ # revision identifiers, used by Alembic. revision = '<KEY>' down_revision = ('17ec44522729', '<PASSWORD>') branch_labels = None depends_on = None from alembic import op import sqlalchemy as sa def upgrade(engine_name): globals()["upgrade_%s" % engine_name]() def downgrade(engine_name): globals()["downgrade_%s" % engine_name]() def upgrade_data_broker(): pass def downgrade_data_broker(): pass	[ 1, 9995, 6310, 2643, 13, 13, 1123, 4924, 3553, 29901, 29871, 29906, 29900, 29890, 29945, 29896, 29900, 29929, 29929, 29953, 29955, 1635, 13, 1123, 1730, 267, 29901, 29871, 29896, 29955, 687, 29946, 29946, 29945, 29906, 29906, 29955, 29906, 29929, 29892, 529, 25711, 17013, 29958, 13, 4391, 4712, 29901, 29871, 29906, 29900, 29896, 29955, 29899, 29900, 29946, 29899, 29900, 29941, 29871, 29896, 29896, 29901, 29906, 29945, 29901, 29906, 29955, 29889, 29941, 29947, 29896, 29955, 29900, 29906, 13, 13, 15945, 29908, 13, 13, 29937, 26554, 2893, 14903, 29892, 1304, 491, 319, 2409, 29890, 293, 29889, 13, 276, 4924, 353, 12801, 10818, 16299, 13, 3204, 29918, 276, 4924, 353, 6702, 29896, 29955, 687, 29946, 29946, 29945, 29906, 29906, 29955, 29906, 29929, 742, 12801, 25711, 17013, 29958, 1495, 13, 17519, 29918, 21134, 353, 6213, 13, 2716, 1975, 29918, 265, 353, 6213, 13, 13, 3166, 20712, 29890, 293, 1053, 1015, 13, 5215, 4576, 284, 305, 6764, 408, 872, 13, 13, 13, 1753, 14955, 29898, 10599, 29918, 978, 1125, 13, 1678, 13149, 1338, 580, 3366, 786, 8228, 29918, 29995, 29879, 29908, 1273, 6012, 29918, 978, 29962, 580, 13, 13, 13, 1753, 1623, 8228, 29898, 10599, 29918, 978, 1125, 13, 1678, 13149, 1338, 580, 3366, 3204, 8228, 29918, 29995, 29879, 29908, 1273, 6012, 29918, 978, 29962, 580, 13, 13, 13, 13, 13, 13, 1753, 14955, 29918, 1272, 29918, 6729, 3946, 7295, 13, 1678, 1209, 13, 13, 13, 1753, 1623, 8228, 29918, 1272, 29918, 6729, 3946, 7295, 13, 1678, 1209, 13, 13, 2 ]
python/edna/src/edna/emit/StdoutEmit.py	prernaagarwal/cs6235Project	3	75778	from edna.emit import BaseEmit from sys import stdout class StdoutEmit(BaseEmit): """An Emitter that writes to Stdout""" def write(self): """Writes the message to the standard output. Ideally, the message should be a string. This is useful only for debugging.""" # NOT an efficient method, but really, who uses this for a real Job? for buffer_idx in range(self.emit_buffer_index+1): print(self.emit_buffer[buffer_idx], file=stdout)	[ 1, 515, 1226, 1056, 29889, 21976, 1053, 7399, 29923, 2415, 13, 13, 3166, 10876, 1053, 27591, 13, 13, 1990, 624, 29881, 449, 29923, 2415, 29898, 5160, 29923, 2415, 1125, 13, 1678, 9995, 2744, 382, 2415, 357, 393, 15873, 304, 624, 29881, 449, 15945, 29908, 13, 1678, 822, 2436, 29898, 1311, 1125, 13, 4706, 9995, 29956, 768, 267, 278, 2643, 304, 278, 3918, 1962, 29889, 13001, 635, 29892, 278, 2643, 881, 367, 263, 1347, 29889, 910, 338, 5407, 871, 363, 13490, 1213, 15945, 13, 4706, 396, 6058, 385, 8543, 1158, 29892, 541, 2289, 29892, 1058, 3913, 445, 363, 263, 1855, 17163, 29973, 13, 4706, 363, 6835, 29918, 13140, 297, 3464, 29898, 1311, 29889, 21976, 29918, 9040, 29918, 2248, 29974, 29896, 1125, 13, 9651, 1596, 29898, 1311, 29889, 21976, 29918, 9040, 29961, 9040, 29918, 13140, 1402, 934, 29922, 25393, 29897, 13, 13, 308, 2 ]
tests/creditcrawler_test.py	Mivinci/cqupt-piper	3	16606	import requests from bs4 import BeautifulSoup from prettytable import PrettyTable # html = requests.get( # 'http://jwzx.cqu.pt/student/xkxfTj.php', # cookies={'PHPSESSID': 'o2r2fpddrj892dp1ntqddcp2hv'}).text # soup = BeautifulSoup(html, 'html.parser') # for tr in soup.find('table', {'id': 'AxfTjTable'}).findAll('tr')[1:]: # tds = tr.findAll('td') # print(tds[1:5]) table = PrettyTable(['aaa', 'bbb']) print(table)	[ 1, 1053, 7274, 13, 3166, 24512, 29946, 1053, 25685, 29903, 1132, 13, 3166, 5051, 2371, 1053, 4721, 4349, 3562, 13, 13, 29937, 3472, 353, 7274, 29889, 657, 29898, 13, 29937, 268, 525, 1124, 597, 29926, 29893, 29920, 29916, 29889, 29883, 339, 29889, 415, 29914, 18945, 29914, 29916, 29895, 24660, 29911, 29926, 29889, 1961, 742, 13, 29937, 268, 21046, 3790, 29915, 17130, 1660, 1799, 1367, 2396, 525, 29877, 29906, 29878, 29906, 18091, 1289, 29878, 29926, 29947, 29929, 29906, 6099, 29896, 593, 29939, 1289, 6814, 29906, 29882, 29894, 29915, 7690, 726, 13, 13, 29937, 22300, 353, 25685, 29903, 1132, 29898, 1420, 29892, 525, 1420, 29889, 16680, 1495, 13, 13, 29937, 363, 534, 297, 22300, 29889, 2886, 877, 2371, 742, 11117, 333, 2396, 525, 29909, 24660, 29911, 29926, 3562, 29915, 7690, 2886, 3596, 877, 509, 29861, 29896, 29901, 5387, 13, 29937, 268, 260, 6289, 353, 534, 29889, 2886, 3596, 877, 1594, 1495, 13, 29937, 268, 1596, 29898, 1594, 29879, 29961, 29896, 29901, 29945, 2314, 13, 13, 13, 2371, 353, 4721, 4349, 3562, 18959, 7340, 29874, 742, 525, 1327, 29890, 11287, 13, 13, 2158, 29898, 2371, 29897, 2 ]
antlr/SevenBillionHumansParser.py	behrmann/7billionhumans	45	28907	from antlr4 import * from antlr4.error.ErrorListener import ErrorListener from antlr.SBHasmLexer import SBHasmLexer from antlr.SBHasmListener import SBHasmListener from antlr.SBHasmParser import SBHasmParser class MyErrorListener(ErrorListener): def __init__(self): super(MyErrorListener, self).__init__() def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e): raise Exception("SyntaxError in {},{} msg={}".format(line, column, msg)) def reportAmbiguity(self, recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs): raise Exception("reportAmbiguity") def reportAttemptingFullContext(self, recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs): raise Exception("reportAttemptingFullContext") def reportContextSensitivity(self, recognizer, dfa, startIndex, stopIndex, prediction, configs): raise Exception("reportContextSensitivity") class SBHCodeSizeListener(SBHasmListener): def __init__(self): self.cmd_cnt = 0 def enterCmd(self, ctx): self.cmd_cnt += 1 def enterSonst(self, ctx): self.cmd_cnt += 1 class Pickup: def __init__(self, item): self.item = item def __str__(self): return "Pickup" class Mem: def __init__(self, slot): self.slot = slot def __str__(self): return self.slot class Dir: def __init__(self, direction): self.dir = direction class SevenBillionHumansParser: def __init__(self, filepath=None, source=None): if source: self.parse(InputStream(source)) elif filepath: self.parse(FileStream(filepath)) def parse(self, source_stream): lexer = SBHasmLexer(source_stream) stream = CommonTokenStream(lexer) parser = SBHasmParser(stream) # parser._listeners = [ MyErrorListener() ] tree = parser.asm() printer = SBHCodeSizeListener() walker = ParseTreeWalker() walker.walk(printer, tree) self.cmd_size = printer.cmd_cnt if __name__ == '__main__': s = SevenBillionHumansParser("../solutions/55 - Data Flowers/size-10_speed-23.asm")	[ 1, 515, 3677, 29212, 29946, 1053, 334, 13, 3166, 3677, 29212, 29946, 29889, 2704, 29889, 2392, 3962, 1053, 4829, 3962, 13, 13, 3166, 3677, 29212, 29889, 1744, 29950, 11625, 29931, 735, 261, 1053, 317, 29933, 29950, 11625, 29931, 735, 261, 13, 3166, 3677, 29212, 29889, 1744, 29950, 11625, 3962, 1053, 317, 29933, 29950, 11625, 3962, 13, 3166, 3677, 29212, 29889, 1744, 29950, 11625, 11726, 1053, 317, 29933, 29950, 11625, 11726, 13, 13, 13, 1990, 1619, 2392, 3962, 29898, 2392, 3962, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 2428, 29898, 3421, 2392, 3962, 29892, 1583, 467, 1649, 2344, 1649, 580, 13, 13, 1678, 822, 5877, 2392, 29898, 1311, 29892, 5936, 3950, 29892, 1283, 2548, 14730, 29892, 1196, 29892, 1897, 29892, 10191, 29892, 321, 1125, 13, 4706, 12020, 8960, 703, 16676, 2392, 297, 24335, 8875, 10191, 3790, 29913, 1642, 4830, 29898, 1220, 29892, 1897, 29892, 10191, 876, 13, 13, 1678, 822, 3461, 6833, 3752, 29884, 537, 29898, 1311, 29892, 5936, 3950, 29892, 4489, 29874, 29892, 1369, 3220, 29892, 5040, 3220, 29892, 2684, 29892, 3181, 335, 2499, 1372, 29892, 2295, 29879, 1125, 13, 4706, 12020, 8960, 703, 12276, 6833, 3752, 29884, 537, 1159, 13, 13, 1678, 822, 3461, 4165, 3456, 292, 13658, 2677, 29898, 1311, 29892, 5936, 3950, 29892, 4489, 29874, 29892, 1369, 3220, 29892, 5040, 3220, 29892, 9476, 1259, 2499, 1372, 29892, 2295, 29879, 1125, 13, 4706, 12020, 8960, 703, 12276, 4165, 3456, 292, 13658, 2677, 1159, 13, 13, 1678, 822, 3461, 2677, 29903, 575, 24858, 29898, 1311, 29892, 5936, 3950, 29892, 4489, 29874, 29892, 1369, 3220, 29892, 5040, 3220, 29892, 18988, 29892, 2295, 29879, 1125, 13, 4706, 12020, 8960, 703, 12276, 2677, 29903, 575, 24858, 1159, 13, 13, 13, 1990, 317, 29933, 29950, 3399, 3505, 3962, 29898, 1744, 29950, 11625, 3962, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 29889, 9006, 29918, 20047, 353, 29871, 29900, 13, 13, 1678, 822, 3896, 23651, 29898, 1311, 29892, 12893, 1125, 13, 4706, 1583, 29889, 9006, 29918, 20047, 4619, 29871, 29896, 13, 13, 1678, 822, 3896, 29903, 265, 303, 29898, 1311, 29892, 12893, 1125, 13, 4706, 1583, 29889, 9006, 29918, 20047, 4619, 29871, 29896, 13, 13, 13, 1990, 23868, 786, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2944, 1125, 13, 4706, 1583, 29889, 667, 353, 2944, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 376, 29925, 860, 786, 29908, 13, 13, 13, 1990, 8133, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 21497, 1125, 13, 4706, 1583, 29889, 2536, 327, 353, 21497, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 2536, 327, 13, 13, 13, 1990, 19378, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 5305, 1125, 13, 4706, 1583, 29889, 3972, 353, 5305, 13, 13, 13, 1990, 26647, 29933, 453, 291, 29950, 398, 550, 11726, 29901, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 934, 2084, 29922, 8516, 29892, 2752, 29922, 8516, 1125, 13, 4706, 565, 2752, 29901, 13, 9651, 1583, 29889, 5510, 29898, 13828, 29898, 4993, 876, 13, 4706, 25342, 934, 2084, 29901, 13, 9651, 1583, 29889, 5510, 29898, 2283, 3835, 29898, 1445, 2084, 876, 13, 13, 1678, 822, 6088, 29898, 1311, 29892, 2752, 29918, 5461, 1125, 13, 4706, 19566, 261, 353, 317, 29933, 29950, 11625, 29931, 735, 261, 29898, 4993, 29918, 5461, 29897, 13, 4706, 4840, 353, 13103, 6066, 3835, 29898, 2506, 261, 29897, 13, 4706, 13812, 353, 317, 29933, 29950, 11625, 11726, 29898, 5461, 29897, 13, 4706, 396, 13812, 3032, 20631, 414, 353, 518, 1619, 2392, 3962, 580, 4514, 13, 4706, 5447, 353, 13812, 29889, 11625, 580, 13, 4706, 23028, 353, 317, 29933, 29950, 3399, 3505, 3962, 580, 13, 4706, 6686, 261, 353, 20969, 9643, 29956, 2235, 261, 580, 13, 4706, 6686, 261, 29889, 20919, 29898, 558, 1639, 29892, 5447, 29897, 13, 4706, 1583, 29889, 9006, 29918, 2311, 353, 23028, 29889, 9006, 29918, 20047, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 269, 353, 26647, 29933, 453, 291, 29950, 398, 550, 11726, 703, 6995, 2929, 17925, 29914, 29945, 29945, 448, 3630, 22787, 414, 29914, 2311, 29899, 29896, 29900, 29918, 19322, 29899, 29906, 29941, 29889, 11625, 1159, 13, 13, 2 ]
analyzer/codechecker_analyzer/analyzers/clangsa/ctu_autodetection.py	ryankurte/codechecker	1,601	97498	# ------------------------------------------------------------------------- # # Part of the CodeChecker project, under the Apache License v2.0 with # LLVM Exceptions. See LICENSE for license information. # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception # # ------------------------------------------------------------------------- """ Clang Static Analyzer related functions. """ import subprocess from codechecker_common.logger import get_logger from codechecker_analyzer import host_check from codechecker_analyzer.analyzers.clangsa import clang_options, version LOG = get_logger('analyzer.clangsa') CTU_ON_DEMAND_OPTION_NAME = 'ctu-invocation-list' def invoke_binary_checked(binary_path, args=None, environ=None): """ Invoke the binary with the specified args, and return the output if the command finished running with zero exit code. Return False otherwise. Possible usage can be used to check the existence binaries. :param binary_path: The path to the executable to invoke :param args: The arguments of the invocation :type binary_path: str :type args: list :rtype str """ args = args or [] invocation = [binary_path] invocation.extend(args) try: output = subprocess.check_output( invocation, env=environ, encoding="utf-8", errors="ignore") except (subprocess.CalledProcessError, OSError) as e: LOG.debug('Command invocation failed because of non-zero exit code!' 'Details: %s', str(e)) return False return output class CTUAutodetection: """ CTUAutodetection is responsible for providing the availability information of CTU feature, the the relevant mapping tool path and the mapping file name. """ def __init__(self, analyzer_binary, environ): self.__analyzer_binary = analyzer_binary self.environ = environ self.__analyzer_version_info = None if self.__analyzer_binary is None: LOG.debug( 'Trying to detect CTU capability, but analyzer binary is not ' 'set!') return None analyzer_version = invoke_binary_checked( self.__analyzer_binary, ['--version'], self.environ) if analyzer_version is False: LOG.debug('Failed to invoke command to get Clang version!') return None version_parser = version.ClangVersionInfoParser(self.__analyzer_binary) version_info = version_parser.parse(analyzer_version) if not version_info: LOG.debug('Failed to parse Clang version information!') return None self.__analyzer_version_info = version_info @property def analyzer_version_info(self): """ Returns the relevant parameters of the analyzer by parsing the output of the analyzer binary when called with version flag. """ if not self.__analyzer_version_info: return False return self.__analyzer_version_info @property def major_version(self): """ Returns the major version of the analyzer, which is used for CTU analysis. """ return self.analyzer_version_info.major_version @property def installed_dir(self): """ Returns the installed directory of the analyzer, which is used for CTU analysis. """ return self.analyzer_version_info.installed_dir @property def mapping_tool_path(self): """Return the path to the mapping tool.""" tool_path, _ = clang_options.ctu_mapping(self.analyzer_version_info) if tool_path: return tool_path return False @property def display_progress(self): """ Return analyzer args if it is capable to display ctu progress. Returns None if the analyzer can not display ctu progress. The ctu display progress arguments depend on the clang analyzer version. """ if not self.analyzer_version_info: return None ctu_display_progress_args = ['-Xclang', '-analyzer-config', '-Xclang', 'display-ctu-progress=true'] ok = host_check.has_analyzer_config_option( self.__analyzer_binary, "display-ctu-progress", self.environ) if not ok: return None return ctu_display_progress_args @property def mapping_file_name(self): """ Returns the installed directory of the analyzer, which is used for CTU analysis. """ _, mapping_file_name = \ clang_options.ctu_mapping(self.analyzer_version_info) if mapping_file_name: return mapping_file_name return False @property def is_ctu_capable(self): """ Detects if the current clang is CTU compatible. Tries to autodetect the correct one based on clang version. """ tool_path = self.mapping_tool_path if not tool_path: return False return invoke_binary_checked(tool_path, ['-version'], self.environ) \ is not False @property def is_on_demand_ctu_available(self): """ Detects if the current Clang supports on-demand parsing of ASTs for CTU analysis. """ analyzer_options = invoke_binary_checked( self.__analyzer_binary, ['-cc1', '-analyzer-config-help'], self.environ) if analyzer_options is False: return False return CTU_ON_DEMAND_OPTION_NAME in analyzer_options	[ 1, 396, 448, 2683, 2683, 2683, 2683, 1378, 13, 29937, 13, 29937, 29871, 3455, 310, 278, 5920, 5596, 261, 2060, 29892, 1090, 278, 13380, 19245, 325, 29906, 29889, 29900, 411, 13, 29937, 29871, 27624, 9219, 8960, 29879, 29889, 2823, 365, 2965, 1430, 1660, 363, 19405, 2472, 29889, 13, 29937, 29871, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 13380, 29899, 29906, 29889, 29900, 22659, 27624, 9219, 29899, 11739, 13, 29937, 13, 29937, 448, 2683, 2683, 2683, 2683, 1378, 13, 15945, 29908, 13, 29907, 3893, 624, 2454, 11597, 29891, 3298, 4475, 3168, 29889, 13, 15945, 29908, 13, 13, 5215, 1014, 5014, 13, 13, 3166, 775, 3198, 261, 29918, 9435, 29889, 21707, 1053, 679, 29918, 21707, 13, 3166, 775, 3198, 261, 29918, 24209, 3298, 1053, 3495, 29918, 3198, 13, 3166, 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stsc_autograd.py	NathanHowell/STSC	27	162068	from autograd import numpy as npy from functools import reduce from scipy.optimize import minimize from autograd import grad def generate_Givens_rotation(i, j, theta, size): g = npy.eye(size) c = npy.cos(theta) s = npy.sin(theta) g[i, i] = 0 g[j, j] = 0 g[j, i] = 0 g[i, j] = 0 ii_mat = npy.zeros_like(g) ii_mat[i, i] = 1 jj_mat = npy.zeros_like(g) jj_mat[j, j] = 1 ji_mat = npy.zeros_like(g) ji_mat[j, i] = 1 ij_mat = npy.zeros_like(g) ij_mat[i, j] = 1 return g + c * ii_mat + c * jj_mat + s * ji_mat - s * ij_mat def generate_U_list(ij_list, theta_list, size): return [generate_Givens_rotation(ij[0], ij[1], theta, size) for ij, theta in zip(ij_list, theta_list)] def get_rotation_matrix(X, C): ij_list = [(i, j) for i in range(C) for j in range(C) if i < j] def cost(theta_list): U_list = generate_U_list(ij_list, theta_list, C) R = reduce(npy.dot, U_list, npy.eye(C)) Z = X.dot(R) M = npy.max(Z, axis=1, keepdims=True) return npy.sum((Z / M) ** 2) theta_list_init = npy.array([0.0] * int(C * (C - 1) / 2)) opt = minimize(cost, x0=theta_list_init, method='CG', jac=grad(cost), options={'disp': False}) return opt.fun, reduce(npy.dot, generate_U_list(ij_list, opt.x, C), npy.eye(C))	[ 1, 515, 1120, 468, 3665, 1053, 12655, 408, 302, 2272, 13, 3166, 2090, 312, 8789, 1053, 10032, 13, 3166, 4560, 2272, 29889, 20640, 675, 1053, 6260, 675, 13, 3166, 1120, 468, 3665, 1053, 4656, 13, 13, 13, 1753, 5706, 29918, 29954, 440, 575, 29918, 5450, 362, 29898, 29875, 29892, 432, 29892, 278, 941, 29892, 2159, 1125, 13, 1678, 330, 353, 302, 2272, 29889, 1032, 29872, 29898, 2311, 29897, 13, 1678, 274, 353, 302, 2272, 29889, 3944, 29898, 3416, 29897, 13, 1678, 269, 353, 302, 2272, 29889, 5223, 29898, 3416, 29897, 13, 1678, 330, 29961, 29875, 29892, 474, 29962, 353, 29871, 29900, 13, 1678, 330, 29961, 29926, 29892, 432, 29962, 353, 29871, 29900, 13, 1678, 330, 29961, 29926, 29892, 474, 29962, 353, 29871, 29900, 13, 1678, 330, 29961, 29875, 29892, 432, 29962, 353, 29871, 29900, 13, 1678, 13607, 29918, 2922, 353, 302, 2272, 29889, 3298, 359, 29918, 4561, 29898, 29887, 29897, 13, 1678, 13607, 29918, 2922, 29961, 29875, 29892, 474, 29962, 353, 29871, 29896, 13, 1678, 432, 29926, 29918, 2922, 353, 302, 2272, 29889, 3298, 359, 29918, 4561, 29898, 29887, 29897, 13, 1678, 432, 29926, 29918, 2922, 29961, 29926, 29892, 432, 29962, 353, 29871, 29896, 13, 1678, 19841, 29918, 2922, 353, 302, 2272, 29889, 3298, 359, 29918, 4561, 29898, 29887, 29897, 13, 1678, 19841, 29918, 2922, 29961, 29926, 29892, 474, 29962, 353, 29871, 29896, 13, 1678, 474, 29926, 29918, 2922, 353, 302, 2272, 29889, 3298, 359, 29918, 4561, 29898, 29887, 29897, 13, 1678, 474, 29926, 29918, 2922, 29961, 29875, 29892, 432, 29962, 353, 29871, 29896, 13, 1678, 736, 330, 718, 274, 334, 13607, 29918, 2922, 718, 274, 334, 432, 29926, 29918, 2922, 718, 269, 334, 19841, 29918, 2922, 448, 269, 334, 474, 29926, 29918, 2922, 13, 13, 13, 1753, 5706, 29918, 29965, 29918, 1761, 29898, 823, 29918, 1761, 29892, 278, 941, 29918, 1761, 29892, 2159, 1125, 13, 1678, 736, 518, 17158, 29918, 29954, 440, 575, 29918, 5450, 362, 29898, 823, 29961, 29900, 1402, 474, 29926, 29961, 29896, 1402, 278, 941, 29892, 2159, 29897, 13, 9651, 363, 474, 29926, 29892, 278, 941, 297, 14319, 29898, 823, 29918, 1761, 29892, 278, 941, 29918, 1761, 4638, 13, 13, 13, 1753, 679, 29918, 5450, 362, 29918, 5344, 29898, 29990, 29892, 315, 1125, 13, 1678, 474, 29926, 29918, 1761, 353, 17288, 29875, 29892, 432, 29897, 363, 474, 297, 3464, 29898, 29907, 29897, 363, 432, 297, 3464, 29898, 29907, 29897, 565, 474, 529, 432, 29962, 13, 13, 1678, 822, 3438, 29898, 3416, 29918, 1761, 1125, 13, 4706, 501, 29918, 1761, 353, 5706, 29918, 29965, 29918, 1761, 29898, 823, 29918, 1761, 29892, 278, 941, 29918, 1761, 29892, 315, 29897, 13, 4706, 390, 353, 10032, 29898, 29876, 2272, 29889, 6333, 29892, 501, 29918, 1761, 29892, 302, 2272, 29889, 1032, 29872, 29898, 29907, 876, 13, 4706, 796, 353, 1060, 29889, 6333, 29898, 29934, 29897, 13, 4706, 341, 353, 302, 2272, 29889, 3317, 29898, 29999, 29892, 9685, 29922, 29896, 29892, 3013, 6229, 29879, 29922, 5574, 29897, 13, 4706, 736, 302, 2272, 29889, 2083, 3552, 29999, 847, 341, 29897, 3579, 29871, 29906, 29897, 13, 13, 1678, 278, 941, 29918, 1761, 29918, 2344, 353, 302, 2272, 29889, 2378, 4197, 29900, 29889, 29900, 29962, 334, 938, 29898, 29907, 334, 313, 29907, 448, 29871, 29896, 29897, 847, 29871, 29906, 876, 13, 1678, 3523, 353, 6260, 675, 29898, 18253, 29892, 13, 462, 259, 921, 29900, 29922, 3416, 29918, 1761, 29918, 2344, 29892, 13, 462, 259, 1158, 2433, 11135, 742, 13, 462, 259, 432, 562, 29922, 5105, 29898, 18253, 511, 13, 462, 259, 3987, 3790, 29915, 2218, 29886, 2396, 7700, 1800, 13, 1678, 736, 3523, 29889, 7692, 29892, 10032, 29898, 29876, 2272, 29889, 6333, 29892, 5706, 29918, 29965, 29918, 1761, 29898, 823, 29918, 1761, 29892, 3523, 29889, 29916, 29892, 315, 511, 302, 2272, 29889, 1032, 29872, 29898, 29907, 876, 13, 2 ]
grpc_api_client/grpc/api.py	djtaylor/python-grpc-api-client	0	184513	<reponame>djtaylor/python-grpc-api-client import json from importlib import import_module from google.protobuf.json_format import MessageToJson class gRPC_API_Response(object): """ Class object for handling responses from Dex gRPC server. """ def __init__(self, response, output_handler, fields): self.json = json.loads(MessageToJson(response)) self.protobuf = output_handler(*self._get_field_values(response, fields)) def _get_field_values(self, response, fields): """ Return an object of field/key values from a request object post-request. """ response_fields = {} for field in fields: if hasattr(response, field[0]): response_fields[field[0]] = getattr(response, field[0]) return response_fields class gRPC_API_Method_IO_Map(object): """ Map the i/o type (from the descriptor) and the pb2.py file """ def __init__(self, io_type, io_handler): self.type = io_type self.handler = io_handler class gRPC_API_Path(object): """ Class object for storing API path attributes and objects. """ def __init__(self, method, settings): """ Initialize and store the stub object for later use. """ self.name = method['name'] # Client settings self._settings = settings # Input and output objects self.input = gRPC_API_Method_IO_Map(method['input_type'], method['input_handler']) self.output = gRPC_API_Method_IO_Map(method['output_type'], method['output_handler']) # Stub instance self.stub = self._settings.bindings.stub_object def input_fields(self): """ Return an iterator for available input fields. 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data_scout/transformations/math_custom.py	janthiemen/data_scout	0	5718	<filename>data_scout/transformations/math_custom.py from __future__ import division from .transformation import Transformation from pyparsing import (Literal, CaselessLiteral, Word, Combine, Group, Optional, ZeroOrMore, Forward, nums, alphas, oneOf) import math import re import operator __author__ = '<NAME>' __version__ = '$Revision: 0.0 $' __date__ = '$Date: 2009-03-20 $' __source__ = '''http://pyparsing.wikispaces.com/file/view/fourFn.py http://pyparsing.wikispaces.com/message/view/home/15549426 ''' __note__ = ''' All I've done is rewrap Paul McGuire's fourFn.py as a class, so I can use it more easily in other places. ''' class Custom(Transformation): """ Most of this code comes from the fourFn.py pyparsing example """ title = "Custom equation" key = "Math equation" fields = { "equation": {"name": "Equation", "type": "string", "help": "The equation to evaluate. Column values should be entered as {COLUMN NAME}", "required": True, "input": "text", "default": ""}, "output": {"name": "Output column", "type": "string", "input": "text", "required": True, "help": "The name of the (newly created) column that contains the results", "default": ""}, } def __init__(self, arguments: dict, sample_size: int, example: dict = None): """ Initialize the transformation with the given parameters. expop :: '^' multop :: '' \| '/' addop :: '+' \| '-' integer :: ['+' \| '-'] '0'..'9'+ atom :: PI \| E \| real \| fn '(' expr ')' \| '(' expr ')' factor :: atom [ expop factor ] term :: factor [ multop factor ]* expr :: term [ addop term ]* Arguments: arguments {dict} -- The arguments """ super().__init__(arguments, sample_size, example) self.equation = arguments["equation"] self.output = arguments["output"] point = Literal(".") e = CaselessLiteral("E") fnumber = Combine(Word("+-" + nums, nums) + Optional(point + Optional(Word(nums))) + Optional(e + Word("+-" + nums, nums))) ident = Word(alphas, alphas + nums + "_$") plus = Literal("+") minus = Literal("-") mult = Literal("") div = Literal("/") mod = Literal("%") lpar = Literal("(").suppress() rpar = Literal(")").suppress() addop = plus \| minus multop = mult \| div \| mod expop = Literal("^") pi = CaselessLiteral("PI") expr = Forward() atom = ((Optional(oneOf("- +")) + (ident + lpar + expr + rpar \| pi \| e \| fnumber).setParseAction(self.push_first)) \| Optional(oneOf("- +")) + Group(lpar + expr + rpar) ).setParseAction(self.push_u_minus) # by defining exponentiation as "atom [ ^ factor ]..." instead of # "atom [ ^ atom ]...", we get right-to-left exponents, instead of left-to-right # that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor << atom + \ ZeroOrMore((expop + factor).setParseAction(self.push_first)) term = factor + \ ZeroOrMore((multop + factor).setParseAction(self.push_first)) expr << term + \ ZeroOrMore((addop + term).setParseAction(self.push_first)) # addop_term = ( addop + term ).setParseAction( self.push_first ) # general_term = term + ZeroOrMore( addop_term ) \| OneOrMore( addop_term) # expr << general_term self.bnf = expr # map operator symbols to corresponding arithmetic operations epsilon = 1e-12 self.opn = {"+": operator.add, "-": operator.sub, "": operator.mul, "%": operator.mod, "/": operator.truediv, "^": operator.pow} self.expr_stack = None self.fn = {"sin": math.sin, "sinh": math.sinh, "cos": math.cos, "cosh": math.cosh, "tan": math.tan, "tanh": math.tanh, "exp": math.exp, "sqrt": math.sqrt, "radians": math.radians, "degrees": math.degrees, "sign": lambda x: 0 if x == 0 else x / abs(x), "log": math.log10, "ln": math.log, "abs": abs, "trunc": lambda a: int(a), "round": round, "floor": math.floor, "ceil": math.ceil, "sgn": lambda a: abs(a) > epsilon and cmp(a, 0) or 0} def push_first(self, strg, loc, toks): self.expr_stack.append(toks[0]) def push_u_minus(self, strg, loc, toks): if toks and toks[0] == '-': self.expr_stack.append('unary -') def evaluate_stack(self, s): op = s.pop() if op == 'unary -': return -self.evaluate_stack(s) if op in "+-*/^%": op2 = self.evaluate_stack(s) op1 = self.evaluate_stack(s) return self.opn[op](op1, op2) elif op == "PI": return math.pi # 3.1415926535 elif op == "E": return math.e # 2.718281828 elif op in self.fn: return self.fn[op](self.evaluate_stack(s)) elif op[0].isalpha(): return 0 else: return float(op) def eval(self, num_string, parse_all=True): self.expr_stack = [] results = self.bnf.parseString(num_string, parse_all) val = self.evaluate_stack(self.expr_stack[:]) return val def __call__(self, row, index: int): """This class is called on each row. 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web-server/webserver/urls.py	ApLight/groenlandicus	0	25625	<reponame>ApLight/groenlandicus<gh_stars>0 """webserver URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.11/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url from django.contrib import admin from account import views as account_views from quiz import views as quiz_views from entry import views as entry_views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^login/', account_views.login, name="login"), url(r'^feed/', account_views.feed_seolgi, name="feed"), url(r'^quizzes/', quiz_views.get_quizzes, name="get_quizzes"), url(r'^index/', entry_views.get_index, name="get_index"), ] quiz_views.update_quizzes() # When the project starts, execute "update_quizzes".	[ 1, 529, 276, 1112, 420, 29958, 17396, 20769, 29914, 17170, 264, 1049, 12473, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 2676, 2974, 3988, 20999, 13, 13, 1576, 421, 2271, 11037, 29879, 29952, 1051, 12049, 24295, 304, 8386, 29889, 1152, 901, 2472, 3113, 1074, 29901, 13, 1678, 2045, 597, 2640, 29889, 19776, 574, 26555, 622, 29889, 510, 29914, 264, 29914, 29896, 29889, 29896, 29896, 29914, 3332, 1199, 29914, 1124, 29914, 26045, 29914, 13, 1252, 9422, 29901, 13, 6678, 8386, 13, 268, 29896, 29889, 3462, 385, 1053, 29901, 29871, 515, 590, 29918, 932, 1053, 8386, 13, 268, 29906, 29889, 3462, 263, 3988, 304, 3142, 11037, 29879, 29901, 29871, 3142, 29898, 29878, 29915, 29985, 29938, 742, 8386, 29889, 5184, 29892, 1024, 2433, 5184, 1495, 13, 2385, 29899, 6707, 8386, 13, 268, 29896, 29889, 3462, 385, 1053, 29901, 29871, 515, 916, 29918, 932, 29889, 7406, 1053, 8778, 13, 268, 29906, 29889, 3462, 263, 3988, 304, 3142, 11037, 29879, 29901, 29871, 3142, 29898, 29878, 29915, 29985, 29938, 742, 8778, 29889, 294, 29918, 1493, 3285, 1024, 2433, 5184, 1495, 13, 797, 22368, 1790, 3988, 5527, 13, 268, 29896, 29889, 16032, 278, 3160, 580, 740, 29901, 515, 9557, 29889, 5527, 29889, 26045, 1053, 3142, 29892, 3160, 13, 268, 29906, 29889, 3462, 263, 3988, 304, 3142, 11037, 29879, 29901, 29871, 3142, 29898, 29878, 29915, 29985, 7312, 29914, 742, 3160, 877, 7312, 29889, 26045, 8785, 13, 15945, 29908, 13, 3166, 9557, 29889, 5527, 29889, 26045, 1053, 3142, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 13, 3166, 3633, 1053, 8386, 408, 3633, 29918, 7406, 13, 3166, 439, 466, 1053, 8386, 408, 439, 466, 29918, 7406, 13, 3166, 6251, 1053, 8386, 408, 6251, 29918, 7406, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 3142, 29898, 29878, 29915, 29985, 6406, 29914, 742, 4113, 29889, 2746, 29889, 26045, 511, 13, 1678, 3142, 29898, 29878, 29915, 29985, 7507, 29914, 742, 3633, 29918, 7406, 29889, 7507, 29892, 1024, 543, 7507, 4968, 13, 1678, 3142, 29898, 29878, 29915, 29985, 18798, 29914, 742, 3633, 29918, 7406, 29889, 18798, 29918, 344, 324, 3146, 29892, 1024, 543, 18798, 4968, 13, 1678, 3142, 29898, 29878, 29915, 29985, 339, 4981, 267, 29914, 742, 439, 466, 29918, 7406, 29889, 657, 29918, 339, 4981, 267, 29892, 1024, 543, 657, 29918, 339, 4981, 267, 4968, 13, 1678, 3142, 29898, 29878, 29915, 29985, 2248, 29914, 742, 6251, 29918, 7406, 29889, 657, 29918, 2248, 29892, 1024, 543, 657, 29918, 2248, 4968, 13, 29962, 13, 13, 339, 466, 29918, 7406, 29889, 5504, 29918, 339, 4981, 267, 580, 396, 1932, 278, 2060, 8665, 29892, 6222, 376, 5504, 29918, 339, 4981, 267, 1642, 13, 2 ]
day-11/package_demo/package1/pathprint.py	JohnLockwood/100-days-of-python	0	151207	<filename>day-11/package_demo/package1/pathprint.py """Demo module to show python search path""" import sys import pprint def print_path(): """Pretty print the system path""" print("Printing the search path:") pprint.pprint(sys.path)	[ 1, 529, 9507, 29958, 3250, 29899, 29896, 29896, 29914, 5113, 29918, 17482, 29914, 5113, 29896, 29914, 2084, 2158, 29889, 2272, 13, 15945, 29908, 23444, 3883, 304, 1510, 3017, 2740, 2224, 15945, 29908, 13, 5215, 10876, 13, 5215, 282, 2158, 13, 13, 1753, 1596, 29918, 2084, 7295, 13, 1678, 9995, 6572, 4349, 1596, 278, 1788, 2224, 15945, 29908, 13, 1678, 1596, 703, 11816, 292, 278, 2740, 2224, 29901, 1159, 13, 1678, 282, 2158, 29889, 407, 29878, 524, 29898, 9675, 29889, 2084, 29897, 2 ]
invoicer/_units/forms.py	mtik00/invoicer	0	44231	<filename>invoicer/_units/forms.py from flask_wtf import FlaskForm from wtforms import StringField, FloatField from wtforms.validators import DataRequired class UnitForm(FlaskForm): description = StringField(u'Description', validators=[DataRequired()]) unit_price = FloatField(u'Unit Price', validators=[DataRequired()]) units = StringField(u'Units (e.g. hr, day, etc)')	[ 1, 529, 9507, 29958, 262, 1365, 293, 261, 19891, 348, 1169, 29914, 9514, 29889, 2272, 13, 3166, 29784, 29918, 29893, 13264, 1053, 2379, 1278, 2500, 13, 3166, 281, 29873, 9514, 1053, 1714, 3073, 29892, 27842, 3073, 13, 3166, 281, 29873, 9514, 29889, 3084, 4097, 1053, 3630, 19347, 13, 13, 13, 1990, 13223, 2500, 29898, 8754, 1278, 2500, 1125, 13, 1678, 6139, 353, 1714, 3073, 29898, 29884, 29915, 9868, 742, 2854, 4097, 11759, 1469, 19347, 580, 2314, 13, 1678, 5190, 29918, 9175, 353, 27842, 3073, 29898, 29884, 29915, 8325, 20743, 742, 2854, 4097, 11759, 1469, 19347, 580, 2314, 13, 1678, 10340, 353, 1714, 3073, 29898, 29884, 29915, 2525, 1169, 313, 29872, 29889, 29887, 29889, 22157, 29892, 2462, 29892, 2992, 29897, 1495, 13, 2 ]
tests/test_transform_compile_releases_from_records.py	matiasSanabria/kingfisher-process	1	132254	import datetime import os import sqlalchemy as sa from ocdskingfisherprocess.store import Store from ocdskingfisherprocess.transform import TRANSFORM_TYPE_COMPILE_RELEASES from ocdskingfisherprocess.transform.compile_releases import CompileReleasesTransform from tests.base import BaseDataBaseTest class TestTransformCompileReleasesFromRecords(BaseDataBaseTest): def _setup_collections_and_data_run_transform(self, filename, load_a_second_time=False): # Make source collection source_collection_id = self.database.get_or_create_collection_id("test", datetime.datetime.now(), False) source_collection = self.database.get_collection(source_collection_id) # Load some data store = Store(self.config, self.database) store.set_collection(source_collection) json_filename = os.path.join(os.path.dirname( os.path.realpath(__file__)), 'fixtures', filename ) store.store_file_from_local("test.json", "http://example.com", "record_package", "utf-8", json_filename) if load_a_second_time: store.store_file_from_local("test2.json", "http://example.com", "record_package", "utf-8", json_filename) # Make destination collection destination_collection_id = self.database.get_or_create_collection_id( source_collection.source_id, source_collection.data_version, source_collection.sample, transform_from_collection_id=source_collection_id, transform_type=TRANSFORM_TYPE_COMPILE_RELEASES) destination_collection = self.database.get_collection(destination_collection_id) # transform! Nothing should happen because source is not finished transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 0 == result.rowcount # Mark source collection as finished self.database.mark_collection_store_done(source_collection_id) # transform! This should do the work. transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() return source_collection_id, source_collection, destination_collection_id, destination_collection def test_compiled_release(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check it's the compiled release in the record table # Because releases are linked, the only way to get data is to take the compiled release compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table])\ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings == [ 'This already had a compiledRelease in the record! It was passed through this transform unchanged.', ] # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 0 # transform again! This should be fine transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # destination collection should be closed destination_collection = self.database.get_collection(destination_collection_id) assert destination_collection.store_end_at is not None def test_no_compiled_release_linked_records_so_cant_do_anything(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_linked_releases_not_compiled.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 0 == result.rowcount # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 1 assert 'OCID ocds-213czf-000-00001 could not be compiled because at least one release in the releases ' +\ 'array is a linked release or there are no releases with dates, ' +\ 'and the record has neither a compileRelease nor a release with a tag of "compiled".' == \ notes[0].note def test_transform_compiles(self): # This data files has full releases and nothing else, so the transform should compile itself using ocdsmerge source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_releases_not_compiled.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check we've compiled something here. # The only way it could get data here is if it compiled it itself. compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table])\ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings is None # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 0 # transform again! This should be fine transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # destination collection should be closed destination_collection = self.database.get_collection(destination_collection_id) assert destination_collection.store_end_at is not None def test_two_records_same_ocid(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform( 'sample_1_1_record_releases_not_compiled.json', load_a_second_time=True ) # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check it's the compiled release in the record table # Because releases are linked, the only way to get data is to take the compiled release compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table]) \ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings == [ 'There are multiple records for this OCID! The record to pass through was selected arbitrarily.', ] # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 0 # transform again! This should be fine transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # destination collection should be closed destination_collection = self.database.get_collection(destination_collection_id) assert destination_collection.store_end_at is not None def test_no_dates(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_releases_not_compiled_no_dates.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 0 == result.rowcount # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 1 assert 'OCID ocds-213czf-000-00001 could not be compiled ' +\ 'because at least one release in the releases array is a ' +\ 'linked release or there are no releases with dates, ' +\ 'and the record has neither a compileRelease nor a release with a tag of "compiled".' == \ notes[0].note def test_some_dates(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_releases_not_compiled_some_dates.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check it's the compiled release in the record table # Because releases are linked, the only way to get data is to take the compiled release compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table]) \ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings == [ 'This OCID had some releases without a date element. 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grAdapt/sampling/equidistributed/Mitchell.py	mkduong-ai/grAdapt	25	24506	# Python Standard Libraries import warnings # grAdapt # from .base import Equidistributed from .MaximalMinDistance import MaximalMinDistance class Mitchell(MaximalMinDistance): """ [Mitchell et al., 1991], Spectrally optimal sampling for distribution ray tracing """ def __init__(self, m=3): """ Parameters ---------- m : integer number of candidates = m * n """ warnings.warn('Mitchell\' best candidate has a time complexity of O(n^3) ' 'and memory issues when dealing with higher sample numbers. ' 'Use MaximalMinDistance instead which is an improved version ' 'with linear time complexity.', ResourceWarning) super().__init__(n_candidates=m, window_size=0) self.candidates_set = False def sample(self, bounds, n, x_history=None): """Samples low discrepancy/equidistributed sequences according to Mitchell. Method has to handle with new bounds and n. Parameters ---------- bounds : list of tuples or list of grAdapt.space.datatype.base Each tuple in the list defines the bounds for the corresponding variable Example: [(1, 2), (2, 3), (-1, 4)...] n : int number of points to be sampled x_history : array-like (2d) History points. Consider those to prevent sampling in dense regions. Returns ------- array-like (n, len(bounds)) Returns a 2D array. dim is the dimension of a single point Each row corresponds to a single point. Each column corresponds to a dimension. """ if self.candidates_set is False: self.n_candidates = self.n_candidates * n self.candidates_set = True return super().sample(bounds, n, x_history)	[ 1, 396, 5132, 10117, 365, 4626, 4314, 13, 5215, 18116, 13, 13, 29937, 867, 29909, 1388, 415, 13, 29937, 515, 869, 3188, 1053, 11243, 333, 391, 7541, 13, 3166, 869, 7976, 3039, 8140, 27469, 1053, 5918, 3039, 8140, 27469, 13, 13, 13, 1990, 26676, 29898, 7976, 3039, 8140, 27469, 1125, 13, 1678, 9995, 13, 1678, 518, 29924, 2335, 514, 634, 394, 1696, 29871, 29896, 29929, 29929, 29896, 1402, 13, 1678, 27738, 29878, 635, 14413, 23460, 363, 4978, 15570, 16703, 292, 13, 1678, 9995, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 286, 29922, 29941, 1125, 13, 4706, 9995, 13, 4706, 12662, 2699, 13, 4706, 448, 1378, 29899, 13, 4706, 286, 584, 6043, 13, 9651, 1353, 310, 21669, 353, 286, 334, 302, 13, 4706, 9995, 13, 4706, 18116, 29889, 25442, 877, 29924, 2335, 514, 20333, 1900, 14020, 756, 263, 931, 13644, 310, 438, 29898, 29876, 29985, 29941, 29897, 525, 13, 462, 418, 525, 392, 3370, 5626, 746, 16743, 411, 6133, 4559, 3694, 29889, 525, 13, 462, 418, 525, 11403, 5918, 3039, 8140, 27469, 2012, 607, 338, 385, 16710, 1873, 525, 13, 462, 418, 525, 2541, 5608, 931, 13644, 29889, 742, 18981, 22709, 29897, 13, 4706, 2428, 2141, 1649, 2344, 12035, 29876, 29918, 29883, 5380, 1078, 29922, 29885, 29892, 3474, 29918, 2311, 29922, 29900, 29897, 13, 4706, 1583, 29889, 29883, 5380, 1078, 29918, 842, 353, 7700, 13, 13, 1678, 822, 4559, 29898, 1311, 29892, 13451, 29892, 302, 29892, 921, 29918, 18434, 29922, 8516, 1125, 13, 4706, 9995, 29903, 9422, 4482, 766, 1037, 29886, 6906, 29914, 1686, 333, 391, 7541, 15602, 5034, 304, 26676, 29889, 13, 4706, 8108, 756, 304, 4386, 411, 716, 13451, 322, 302, 29889, 13, 13, 4706, 12662, 2699, 13, 4706, 448, 1378, 29899, 13, 4706, 13451, 584, 1051, 310, 5291, 2701, 470, 1051, 310, 867, 29909, 1388, 415, 29889, 3493, 29889, 4130, 23179, 29889, 3188, 13, 9651, 7806, 18761, 297, 278, 1051, 17645, 278, 13451, 363, 278, 6590, 2286, 13, 9651, 8741, 29901, 17288, 29896, 29892, 29871, 29906, 511, 313, 29906, 29892, 29871, 29941, 511, 8521, 29896, 29892, 29871, 29946, 467, 636, 29962, 13, 4706, 302, 584, 938, 13, 9651, 1353, 310, 3291, 304, 367, 4559, 29881, 13, 4706, 921, 29918, 18434, 584, 1409, 29899, 4561, 313, 29906, 29881, 29897, 13, 9651, 5298, 3291, 29889, 10056, 1906, 304, 5557, 23460, 297, 20619, 12786, 29889, 13, 13, 4706, 16969, 13, 4706, 448, 22158, 13, 4706, 1409, 29899, 4561, 313, 29876, 29892, 7431, 29898, 23687, 876, 13, 9651, 16969, 263, 29871, 29906, 29928, 1409, 29889, 3964, 338, 278, 9927, 310, 263, 2323, 1298, 13, 9651, 7806, 1948, 16161, 304, 263, 2323, 1298, 29889, 13, 9651, 7806, 1897, 16161, 304, 263, 9927, 29889, 13, 4706, 9995, 13, 4706, 565, 1583, 29889, 29883, 5380, 1078, 29918, 842, 338, 7700, 29901, 13, 9651, 1583, 29889, 29876, 29918, 29883, 5380, 1078, 353, 1583, 29889, 29876, 29918, 29883, 5380, 1078, 334, 302, 13, 9651, 1583, 29889, 29883, 5380, 1078, 29918, 842, 353, 5852, 13, 4706, 736, 2428, 2141, 11249, 29898, 23687, 29892, 302, 29892, 921, 29918, 18434, 29897, 13, 2 ]
cubedash/_product.py	vconrado/datacube-explorer	0	3561	<reponame>vconrado/datacube-explorer import logging from datetime import timedelta from flask import Blueprint, Response, abort, redirect, url_for from cubedash import _model, _utils, _utils as utils _LOG = logging.getLogger(__name__) bp = Blueprint("product", __name__) @bp.route("/about.csv") def legacy_about_csv(): return redirect(".storage_csv") @bp.route("/audit/storage.csv") def storage_csv(): """Get the product storage table as a CSV""" product_locations = _model.STORE.products_location_samples_all() return utils.as_csv( filename_prefix="product-information", headers=( "name", "count", "locations", "license", "definition", "summary_time", "metadata_type", ), rows=( ( product.name, summary.dataset_count, [ location.common_prefix for location in (product_locations.get(product.name) or []) ], _utils.product_license(product), url_for("product.raw_product_doc", name=product.name, _external=True), summary.last_refresh_time, product.metadata_type.name, ) for product, summary in _model.get_products_with_summaries() ), ) @bp.route("/products.txt") def product_list_text(): # This is useful for bash scripts when we want to loop products :) return Response( "\n".join(t.name for t in _model.STORE.all_dataset_types()), content_type="text/plain", ) @bp.route("/metadata-types.txt") def metadata_type_list_text(): # This is useful for bash scripts when we want to loop them :) return Response( "\n".join(t.name for t in _model.STORE.all_metadata_types()), content_type="text/plain", ) @bp.route("/audit/storage") def storage_page(): product_locations = _model.STORE.products_location_samples_all() return utils.render( "storage.html", product_summary_and_location=[ (product, summary, (product_locations.get(product.name) or [])) for product, summary in _model.get_products_with_summaries() ], ) @bp.route("/product") def product_redirect(): """ If people remove the name from a "/product/<name>" url, take them somewhere useful """ return redirect(url_for(".products_page")) @bp.route("/products") def products_page(): return utils.render( "products.html", ) @bp.route("/metadata-types") def metadata_types_page(): return utils.render( "metadata-types.html", ) @bp.route("/product/<name>.odc-product.yaml") def legacy_raw_product_doc(name): return redirect(url_for(".raw_product_doc", name=name)) @bp.route("/products/<name>.odc-product.yaml") def raw_product_doc(name): product = _model.STORE.index.products.get_by_name(name) if not product: abort(404, f"Unknown product {name!r}") ordered_metadata = utils.prepare_document_formatting( product.definition, "Product", include_source_url=True ) return utils.as_yaml(ordered_metadata) @bp.route("/metadata-type/<name>") def legacy_metadata_type_page(name): return redirect(url_for(".metadata_type_page", name=name)) @bp.route("/metadata-types/<name>") def metadata_type_page(name): metadata_type = _model.STORE.index.metadata_types.get_by_name(name) if not metadata_type: abort(404, f"Unknown metadata type {name!r}") ordered_metadata = utils.prepare_document_formatting(metadata_type.definition) products_using_it = sorted( ( p for p in _model.STORE.index.products.get_all() if p.metadata_type.name == name ), key=lambda p: p.name, ) return utils.render( "metadata-type.html", metadata_type=metadata_type, metadata_doc=ordered_metadata, products_using_it=products_using_it, ) @bp.route("/metadata-type/<name>.odc-type.yaml") def legacy_metadata_type_doc(name): return redirect(url_for(".raw_metadata_type_doc", name=name)) @bp.route("/metadata-types/<name>.odc-type.yaml") def raw_metadata_type_doc(name): metadata_type = _model.STORE.index.metadata_types.get_by_name(name) if not metadata_type: abort(404, f"Unknown metadata type {name!r}") ordered_metadata = utils.prepare_document_formatting( metadata_type.definition, "Metadata Type", include_source_url=True ) return utils.as_yaml(ordered_metadata) @bp.route("/products.odc-product.yaml") def raw_all_products_doc(): resp = utils.as_yaml( ( utils.prepare_document_formatting( product.definition, f"Product {product.name}", include_source_url=url_for( ".raw_product_doc", name=product.name, _external=True ), ) for product in _model.STORE.all_dataset_types() ) ) # Add Explorer ID to the download filename if they have one. utils.suggest_download_filename( resp, prefix="products", suffix=".odc-product.yaml", ) return resp @bp.route("/metadata-types.odc-type.yaml") def raw_all_metadata_types_doc(): resp = utils.as_yaml( ( utils.prepare_document_formatting( type_.definition, f"Metadata Type {type_.name}", include_source_url=url_for( ".raw_metadata_type_doc", name=type_.name, _external=True ), ) for type_ in _model.STORE.all_metadata_types() ), ) # Add Explorer ID to the download filename if they have one. utils.suggest_download_filename( resp, prefix="metadata-types", suffix=".odc-type.yaml", ) return resp def _iso8601_duration(tdelta: timedelta): """ Format a timedelta as an iso8601 duration >>> 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model.py	melorian94/minerl_imitation_learning	18	166230	import torch from torch import nn from torch.nn import functional as F import math class Network(nn.Module): def __init__(self, num_actions, image_channels, vec_size, cnn_module, hidden_size=256, dueling=True, double_channels=False): super().__init__() self.num_actions = num_actions self.dueling = dueling self.cnn = cnn_module(image_channels) self.conv_output_size = self.cnn.output_size self.fc_im = nn.Linear(self.conv_output_size, hidden_size) if not double_channels: vec_channel_size = 128 else: vec_channel_size = 256 self.fc_vec = nn.Linear(vec_size, vec_channel_size) self.fc_h_a = nn.Linear(hidden_size + vec_channel_size, hidden_size) self.fc_a = nn.Linear(hidden_size, num_actions) if self.dueling: self.fc_h_v = nn.Linear(hidden_size + vec_channel_size, hidden_size) self.fc_v = nn.Linear(hidden_size, 1) def forward(self, x, vec): x = self.cnn(x) x = x.view(-1, self.conv_output_size) x = self.fc_im(x) vec = self.fc_vec(vec) x = F.relu(torch.cat((x, vec), 1)) output = self.fc_a(F.relu(self.fc_h_a(x))) if self.dueling: v = self.fc_v(F.relu(self.fc_h_v(x))) output = v + output - output.mean(1, keepdim=True) return output class AtariCNN(nn.Module): def __init__(self, input_channels): super().__init__() self.conv_layers = nn.Sequential(nn.Conv2d(input_channels, 32, 8, stride=4, padding=0), nn.ReLU(), nn.Conv2d(32, 64, 4, stride=2, padding=0), nn.ReLU(), nn.Conv2d(64, 64, 3, stride=1, padding=0), nn.ReLU()) self.output_size = 64 * 4 * 4 def forward(self, x): return self.conv_layers(x) class ImpalaResNetCNN(nn.Module): class _ImpalaResidual(nn.Module): def __init__(self, depth): super().__init__() self.conv1 = nn.Conv2d(depth, depth, 3, padding=1) self.conv2 = nn.Conv2d(depth, depth, 3, padding=1) def forward(self, x): out = F.relu(x) out = self.conv1(out) out = F.relu(out) out = self.conv2(out) return out + x def __init__(self, input_channels): super().__init__() depth_in = input_channels layers = [] for depth_out in [32, 64, 64]: layers.extend([ nn.Conv2d(depth_in, depth_out, 3, padding=1), nn.MaxPool2d(3, stride=2, padding=1), self._ImpalaResidual(depth_out), self._ImpalaResidual(depth_out), ]) depth_in = depth_out self.conv_layers = nn.Sequential(layers, nn.ReLU()) self.output_size = math.ceil(64 / 8) * 2 * depth_in def forward(self, x): return self.conv_layers(x) class FixupResNetCNN(nn.Module): """source: https://github.com/unixpickle/obs-tower2/blob/master/obs_tower2/model.py""" class _FixupResidual(nn.Module): def __init__(self, depth, num_residual): super().__init__() self.conv1 = nn.Conv2d(depth, depth, 3, padding=1, bias=False) self.conv2 = nn.Conv2d(depth, depth, 3, padding=1, bias=False) for p in self.conv1.parameters(): p.data.mul_(1 / math.sqrt(num_residual)) for p in self.conv2.parameters(): p.data.zero_() self.bias1 = nn.Parameter(torch.zeros([depth, 1, 1])) self.bias2 = nn.Parameter(torch.zeros([depth, 1, 1])) self.bias3 = nn.Parameter(torch.zeros([depth, 1, 1])) self.bias4 = nn.Parameter(torch.zeros([depth, 1, 1])) self.scale = nn.Parameter(torch.ones([depth, 1, 1])) def forward(self, x): x = F.relu(x) out = x + self.bias1 out = self.conv1(out) out = out + self.bias2 out = F.relu(out) out = out + self.bias3 out = self.conv2(out) out = out * self.scale out = out + self.bias4 return out + x def __init__(self, input_channels, double_channels=False): super().__init__() depth_in = input_channels layers = [] if not double_channels: channel_sizes = [32, 64, 64] else: channel_sizes = [64, 128, 128] for depth_out in channel_sizes: layers.extend([ nn.Conv2d(depth_in, depth_out, 3, padding=1), nn.MaxPool2d(3, stride=2, padding=1), self._FixupResidual(depth_out, 8), self._FixupResidual(depth_out, 8), ]) depth_in = depth_out layers.extend([ self._FixupResidual(depth_in, 8), self._FixupResidual(depth_in, 8), ]) self.conv_layers = nn.Sequential(layers, nn.ReLU()) self.output_size = math.ceil(64 / 8) * 2 * depth_in def forward(self, x): return self.conv_layers(x)	[ 1, 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pythonAlgorithm/linklist/reverse_linklist.py	Sky-zzt/lintcodePractice	1	154446	<reponame>Sky-zzt/lintcodePractice class Node: def __init__(self, val=0): self.value = val self.next = None class LinkList: c = 10 def __init__(self, args): self.val = (args,) self.head = self.__constructLinklist() #self.printLinklist(self.head) # self.pre = self.revserlinklist() # self.printLinklist(self.pre) def printLinklist(self, head): while head is not None: print(head.val) head = head.next def __constructLinklist(self): dummyNode = Node() head = Node(self.val[0]) dummyNode.next = head for i in range(1, len(self.val)): next = Node(self.val[i]) head.next = next head = next return dummyNode.next def revserlinklist(self): pre = None while self.head is not None: next = self.head.next self.head.next = pre pre = self.head self.head = next return pre def revserFromM2N(self, m, n): #if m<0 or n<0 or n>self.linklength():return None head2=self.head head3=self.head head2.next.next.next=None self.printLinklist(self.head) def linklength(self): length = 0 while self.head is not None: self.head = self.head.next length += 1 return length a = LinkList(1, 2, 3, 4, 5) a.revserFromM2N(2,3)	[ 1, 529, 276, 1112, 420, 29958, 29903, 3459, 29899, 29920, 2065, 29914, 27854, 401, 29925, 1461, 625, 13, 1990, 9071, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 659, 29922, 29900, 1125, 13, 4706, 1583, 29889, 1767, 353, 659, 13, 4706, 1583, 29889, 4622, 353, 6213, 13, 13, 13, 1990, 6645, 1293, 29901, 13, 1678, 274, 353, 29871, 29896, 29900, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 334, 5085, 1125, 13, 4706, 1583, 29889, 791, 353, 3070, 5085, 29892, 29897, 13, 4706, 1583, 29889, 2813, 353, 1583, 17255, 11433, 6595, 1761, 580, 13, 4706, 396, 1311, 29889, 2158, 6595, 1761, 29898, 1311, 29889, 2813, 29897, 13, 4706, 396, 1583, 29889, 1457, 353, 1583, 29889, 13478, 643, 2324, 1761, 580, 13, 4706, 396, 1583, 29889, 2158, 6595, 1761, 29898, 1311, 29889, 1457, 29897, 13, 13, 1678, 822, 1596, 6595, 1761, 29898, 1311, 29892, 2343, 1125, 13, 4706, 1550, 2343, 338, 451, 6213, 29901, 13, 9651, 1596, 29898, 2813, 29889, 791, 29897, 13, 9651, 2343, 353, 2343, 29889, 4622, 13, 13, 1678, 822, 4770, 11433, 6595, 1761, 29898, 1311, 1125, 13, 4706, 20254, 4247, 353, 9071, 580, 13, 4706, 2343, 353, 9071, 29898, 1311, 29889, 791, 29961, 29900, 2314, 13, 4706, 20254, 4247, 29889, 4622, 353, 2343, 13, 4706, 363, 474, 297, 3464, 29898, 29896, 29892, 7431, 29898, 1311, 29889, 791, 22164, 13, 9651, 2446, 353, 9071, 29898, 1311, 29889, 791, 29961, 29875, 2314, 13, 9651, 2343, 29889, 4622, 353, 2446, 13, 9651, 2343, 353, 2446, 13, 4706, 736, 20254, 4247, 29889, 4622, 13, 13, 1678, 822, 6664, 643, 2324, 1761, 29898, 1311, 1125, 13, 4706, 758, 353, 6213, 13, 4706, 1550, 1583, 29889, 2813, 338, 451, 6213, 29901, 13, 9651, 2446, 353, 1583, 29889, 2813, 29889, 4622, 13, 9651, 1583, 29889, 2813, 29889, 4622, 353, 758, 13, 9651, 758, 353, 1583, 29889, 2813, 13, 9651, 1583, 29889, 2813, 353, 2446, 13, 4706, 736, 758, 13, 13, 1678, 822, 6664, 643, 4591, 29924, 29906, 29940, 29898, 1311, 29892, 286, 29892, 302, 1125, 13, 4706, 396, 361, 286, 29966, 29900, 470, 302, 29966, 29900, 470, 302, 29958, 1311, 29889, 2324, 2848, 7295, 2457, 6213, 13, 4706, 2343, 29906, 29922, 1311, 29889, 2813, 13, 4706, 2343, 29941, 29922, 1311, 29889, 2813, 13, 4706, 2343, 29906, 29889, 4622, 29889, 4622, 29889, 4622, 29922, 8516, 13, 4706, 1583, 29889, 2158, 6595, 1761, 29898, 1311, 29889, 2813, 29897, 13, 13, 13, 13, 1678, 822, 1544, 2848, 29898, 1311, 1125, 13, 4706, 3309, 353, 29871, 29900, 13, 4706, 1550, 1583, 29889, 2813, 338, 451, 6213, 29901, 13, 9651, 1583, 29889, 2813, 353, 1583, 29889, 2813, 29889, 4622, 13, 9651, 3309, 4619, 29871, 29896, 13, 4706, 736, 3309, 13, 13, 13, 29874, 353, 6645, 1293, 29898, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29892, 29871, 29945, 29897, 13, 13, 29874, 29889, 13478, 643, 4591, 29924, 29906, 29940, 29898, 29906, 29892, 29941, 29897, 13, 2 ]
one_fm/grd/utils.py	ONE-F-M/One-FM	16	90158	# -- coding: utf-8 -- # encoding: utf-8 from __future__ import unicode_literals import frappe from frappe import _ from frappe.utils import today, add_days, get_url, date_diff, getdate from frappe.model.document import Document from frappe.utils import cstr, cint, get_fullname from frappe.utils import today, add_days, get_url from datetime import date from frappe.model.mapper import get_mapped_doc from dateutil.relativedelta import relativedelta from one_fm.api.notification import create_notification_log def todo_after_insert(doc, method): doctypes = ['Work Permit', 'Medical Insurance'] if doc.reference_type in doctypes and doc.reference_name and doc.owner and 'GRD Operator' in frappe.get_roles(doc.owner): if not frappe.db.get_value(doc.reference_type, doc.reference_name, 'grd_operator'): frappe.db.set_value(doc.reference_type, doc.reference_name, 'grd_operator', doc.owner) def sendmail_reminder_to_book_appointment_for_pifss(): #before 1 week of the new month today = date.today() first_day = today.replace(day=1) + relativedelta(months=1) if date_diff(first_day,today) == 7: operator = frappe.db.get_single_value('GRD Settings', 'default_grd_operator_pifss') supervisor = frappe.db.get_single_value('GRD Settings', 'default_grd_supervisor') if ('@' in operator): email_name = operator.split('@')[0] content = "<h4>Dear "+ email_name +",</h4><p>This month will end soon. Please make sure to book an apointment now for collecting PIFSS documents.</p>" content = content frappe.sendmail(recipients=[operator], sender=supervisor, subject="Book Apointment For PIFSS", content=content) def sendmail_reminder_to_collect_pifss_documents(): #before 1 day of the new month today = date.today() first_day = today.replace(day=1) + relativedelta(months=1) if date_diff(first_day,today) == 1: operator = frappe.db.get_single_value('GRD Settings', 'default_grd_operator') supervisor = frappe.db.get_single_value('GRD Settings', 'default_grd_supervisor') if ('@' in operator): email_name = operator.split('@')[0] content = "<h4>Dear "+ email_name +",</h4><p> This email is reminder for you to collect PIFSS documents.</p>" content = content frappe.sendmail(recipients=[operator], sender=supervisor, subject="Collect PIFSS Documents", content=content) @frappe.whitelist() def mappe_to_work_permit_cancellation(source_name, target_doc=None): pifss_103_record = frappe.get_doc('PIFSS Form 103',source_name) print(pifss_103_record.employee) doc = get_mapped_doc("PIFSS Form 103", source_name, { "PIFSS Form 103": { "doctype": "Work Permit", "field_map": { "attach_end_of_service_from_pifss_website":"end_of_service_screenshot", "date_of_acceptance":"date_of_application", "work_permit_type":"work_permit_type", "employee":"employee" } } }, target_doc) return doc @frappe.whitelist() def mappe_to_work_permit_registration(source_name, target_doc=None): pifss_103_record = frappe.get_doc('PIFSS Form 103',source_name) print(pifss_103_record.employee) doc = get_mapped_doc("PIFSS Form 103", source_name, { "PIFSS Form 103": { "doctype": "Work Permit", "field_map": { "attach_registration_from_pifss_website":"registration_from_pifss_website", "date_of_acceptance":"date_of_application", "work_permit_type":"work_permit_type", "employee":"employee" } } }, target_doc) return doc @frappe.whitelist() def mappe_to_mgrp(source_name, target_doc=None): work_permit_record = frappe.get_doc('Work Permit',source_name) print(work_permit_record.employee) doc = get_mapped_doc("Work Permit", source_name, { "Work Permit": { "doctype": "MGRP", "field_map": { "work_permit_type":"status", "employee":"employee", "first_name":"first_name", "civil_id":"civil_id", "last_name":"last_name", "employee_id":"employee_id", "end_of_service_date":"end_of_service_date", } } }, target_doc) return doc	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 8025, 29901, 23616, 29899, 29947, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 5215, 5227, 4798, 13, 3166, 5227, 4798, 1053, 903, 13, 3166, 5227, 4798, 29889, 13239, 1053, 9826, 29892, 788, 29918, 16700, 29892, 679, 29918, 2271, 29892, 2635, 29918, 12765, 29892, 679, 1256, 13, 3166, 5227, 4798, 29889, 4299, 29889, 3225, 1053, 10854, 13, 3166, 5227, 4798, 29889, 13239, 1053, 274, 710, 29892, 274, 524, 29892, 679, 29918, 8159, 978, 13, 3166, 5227, 4798, 29889, 13239, 1053, 9826, 29892, 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lastpass/blob.py	guilt/lastpass-python	1	130575	<gh_stars>1-10 # coding: utf-8 class Blob(object): def __init__(self, bytes_, key_iteration_count): self.bytes = bytes_ self.key_iteration_count = key_iteration_count def encryption_key(self, username, password): from . import fetcher return fetcher.make_key(username, password, self.key_iteration_count) def __eq__(self, other): return self.bytes == other.bytes and self.key_iteration_count == other.key_iteration_count	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14137, 29901, 23616, 29899, 29947, 13, 1990, 350, 2127, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 6262, 3383, 1820, 29918, 1524, 362, 29918, 2798, 1125, 13, 4706, 1583, 29889, 13193, 353, 6262, 29918, 13, 4706, 1583, 29889, 1989, 29918, 1524, 362, 29918, 2798, 353, 1820, 29918, 1524, 362, 29918, 2798, 13, 13, 1678, 822, 20956, 29918, 1989, 29898, 1311, 29892, 8952, 29892, 4800, 1125, 13, 4706, 515, 869, 1053, 6699, 261, 13, 4706, 736, 6699, 261, 29889, 5675, 29918, 1989, 29898, 6786, 29892, 4800, 29892, 1583, 29889, 1989, 29918, 1524, 362, 29918, 2798, 29897, 13, 13, 1678, 822, 4770, 1837, 12035, 1311, 29892, 916, 1125, 13, 4706, 736, 1583, 29889, 13193, 1275, 916, 29889, 13193, 322, 1583, 29889, 1989, 29918, 1524, 362, 29918, 2798, 1275, 916, 29889, 1989, 29918, 1524, 362, 29918, 2798, 13, 2 ]
tf_trace_publisher/src/tf_trace_publisher/text_publisher.py	ipa-lth/rviz_utils	2	1603343	<filename>tf_trace_publisher/src/tf_trace_publisher/text_publisher.py #!/usr/bin/env python import rospy from visualization_msgs.msg import Marker # Give ourselves the ability to run a dynamic reconfigure server. # from dynamic_reconfigure.server import Server as DynamicReconfigureServer # from ipa325_itasc_commons.cfg import textMarkerPublisher_paramsConfig as ConfigType class TextPublisher(): def __init__(self): # initalize textMarker = Marker() textMarker.header.frame_id = "/textFrame" textMarker.id = rospy.get_param('~id', 1) textMarker.type= Marker.TEXT_VIEW_FACING textMarker.action = Marker.MODIFY # textMarker.ns = tf_child # textMarker.scale.x = rospy.get_param('~scale_X', 0.01) # textMarker.scale.y = rospy.get_param('~scale_Y', 0.01) textMarker.scale.z = rospy.get_param('~scale_Z', 0.1) textMarker.text = 'x_' textMarker.color.r = rospy.get_param('~color_Red', 0.0) textMarker.color.g = rospy.get_param('~color_Green', 1.0) textMarker.color.b = rospy.get_param('~color_Blue', 0.0) textMarker.color.a = rospy.get_param('~color_Alpha', 1.0) rate = rospy.Rate(rospy.get_param('~rate', 5)) pub_markerLine = rospy.Publisher('visualization_marker', Marker) cnt = 0 maxCnt = 10 self.text = 'x_' while not rospy.is_shutdown(): if cnt < maxCnt: textMarker.action = Marker.MODIFY self.text += str(cnt) cnt += 1 else: self.text = 'x_' textMarker.action = Marker.DELETE cnt = 0 textMarker.text = self.text pub_markerLine.publish(textMarker) rate.sleep() if __name__ == '__main__': rospy.init_node('tf_textMarker_Publisher', anonymous=True) try: node = TextPublisher() except rospy.ROSInterruptException: pass	[ 1, 529, 9507, 29958, 13264, 29918, 15003, 29918, 23679, 261, 29914, 4351, 29914, 13264, 29918, 15003, 29918, 23679, 261, 29914, 726, 29918, 23679, 261, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 5215, 696, 1028, 29891, 13, 13, 3166, 7604, 2133, 29918, 1516, 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progue/ai/actions/misc.py	BradyKieffer/progue	0	177051	""" Misc / Weird actions can go here """ import random from action import Action from progue.utils.actor_constants import ATTRIBUTE_IDLE, ATTR_VALUE, MAX_TURNS, CURRENT_TURN, RANGE class ActionIdle(Action): """ Used for lazy monsters """ def __init__(self, actor): Action.__init__(self, actor=actor, update_func=self.idle) self.actor.attributes[ATTRIBUTE_IDLE][ATTR_VALUE] = True rng = self.actor.attributes[ATTRIBUTE_IDLE][RANGE] self.percent_idle = max(min(rng[1], random.random()), rng[0]) self.curr_turn = self.actor.attributes[ATTRIBUTE_IDLE][CURRENT_TURN] self.max_turns = self.actor.attributes[ATTRIBUTE_IDLE][ MAX_TURNS] + random.randrange(-5, 5) def idle(self): if (self.curr_turn > self.max_turns) or (random.random() > self.percent_idle): self.complete = True self.actor.attributes[ATTRIBUTE_IDLE][ATTR_VALUE] = False self.curr_turn += 1	[ 1, 9995, 341, 10669, 847, 1334, 1823, 8820, 508, 748, 1244, 9995, 13, 5215, 4036, 13, 3166, 3158, 1053, 9123, 13, 3166, 410, 13000, 29889, 13239, 29889, 7168, 29918, 3075, 1934, 1053, 15531, 29911, 3960, 29933, 26027, 29918, 1367, 1307, 29892, 15531, 5659, 29918, 19143, 29892, 18134, 29918, 29911, 4574, 3059, 29892, 315, 4574, 29450, 29918, 29911, 24015, 29892, 390, 24336, 13, 13, 13, 1990, 9123, 1204, 280, 29898, 4276, 1125, 13, 1678, 9995, 501, 8485, 363, 17366, 1601, 23080, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 11339, 1125, 13, 4706, 9123, 17255, 2344, 12035, 1311, 29892, 11339, 29922, 7168, 29892, 2767, 29918, 9891, 29922, 1311, 29889, 333, 280, 29897, 13, 13, 4706, 1583, 29889, 7168, 29889, 15697, 29961, 1299, 29911, 3960, 29933, 26027, 29918, 1367, 1307, 3816, 1299, 5659, 29918, 19143, 29962, 353, 5852, 13, 4706, 364, 865, 353, 1583, 29889, 7168, 29889, 15697, 29961, 1299, 29911, 3960, 29933, 26027, 29918, 1367, 1307, 3816, 29934, 24336, 29962, 13, 4706, 1583, 29889, 25376, 29918, 333, 280, 353, 4236, 29898, 1195, 29898, 29878, 865, 29961, 29896, 1402, 4036, 29889, 8172, 25739, 364, 865, 29961, 29900, 2314, 13, 4706, 1583, 29889, 21962, 29918, 685, 353, 1583, 29889, 7168, 29889, 15697, 29961, 1299, 29911, 3960, 29933, 26027, 29918, 1367, 1307, 3816, 22484, 29450, 29918, 29911, 24015, 29962, 13, 4706, 1583, 29889, 3317, 29918, 685, 29879, 353, 1583, 29889, 7168, 29889, 15697, 29961, 1299, 29911, 3960, 29933, 26027, 29918, 1367, 1307, 3816, 13, 9651, 18134, 29918, 29911, 4574, 3059, 29962, 718, 4036, 29889, 9502, 3881, 6278, 29945, 29892, 29871, 29945, 29897, 13, 13, 1678, 822, 28132, 29898, 1311, 1125, 13, 4706, 565, 313, 1311, 29889, 21962, 29918, 685, 1405, 1583, 29889, 3317, 29918, 685, 29879, 29897, 470, 313, 8172, 29889, 8172, 580, 1405, 1583, 29889, 25376, 29918, 333, 280, 1125, 13, 9651, 1583, 29889, 8835, 353, 5852, 13, 9651, 1583, 29889, 7168, 29889, 15697, 29961, 1299, 29911, 3960, 29933, 26027, 29918, 1367, 1307, 3816, 1299, 5659, 29918, 19143, 29962, 353, 7700, 13, 13, 4706, 1583, 29889, 21962, 29918, 685, 4619, 29871, 29896, 13, 2 ]
tsdf/utils.py	changgyhub/semantic-tsdf	9	139659	<reponame>changgyhub/semantic-tsdf ''' TSDF utility functions. ''' import numpy as np # Get corners of 3D camera view frustum of depth image. def get_view_frustum(depth_im, cam_intr, cam_pose): im_h = depth_im.shape[0] im_w = depth_im.shape[1] max_depth = np.max(depth_im) view_frust_pts = np.array([(np.array([0, 0, 0, im_w, im_w]) - cam_intr[0, 2]) * np.array([0, max_depth, max_depth, max_depth, max_depth]) / cam_intr[0, 0], (np.array([0, 0, im_h, 0, im_h]) - cam_intr[1, 2]) * np.array([0, max_depth, max_depth, max_depth, max_depth]) / cam_intr[1, 1], np.array([0, max_depth, max_depth, max_depth, max_depth])]) view_frust_pts = np.dot(cam_pose[:3, :3], view_frust_pts) + np.tile(cam_pose[:3, 3].reshape(3, 1), (1, view_frust_pts.shape[1])) # from camera to world coordinates return view_frust_pts # Save 3D mesh to a polygon .ply file. def meshwrite(filename, verts, faces, norms, colors): # Write header. ply_file = open(filename,'w') ply_file.write("ply\n") ply_file.write("format ascii 1.0\n") ply_file.write("element vertex {:d}\n".format(verts.shape[0])) ply_file.write("property float x\n") ply_file.write("property float y\n") ply_file.write("property float z\n") ply_file.write("property float nx\n") ply_file.write("property float ny\n") ply_file.write("property float nz\n") ply_file.write("property uchar red\n") ply_file.write("property uchar green\n") ply_file.write("property uchar blue\n") ply_file.write("element face {:d}\n".format(faces.shape[0])) ply_file.write("property list uchar int vertex_index\n") ply_file.write("end_header\n") # Write vertex list. for i in range(verts.shape[0]): ply_file.write("{:f} {:f} {:f} {:f} {:f} {:f} {:d} {:d} {:d}\n".format( verts[i, 0], verts[i, 1], verts[i, 2], norms[i, 0], norms[i, 1], norms[i, 2], colors[i, 0], colors[i, 1], colors[i, 2])) # Write face list. for i in range(faces.shape[0]): ply_file.write("3 {:d} {:d} {:d}\n".format(faces[i, 0], faces[i, 1], faces[i, 2])) ply_file.close()	[ 1, 529, 276, 1112, 420, 29958, 305, 574, 1927, 29882, 431, 29914, 12846, 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PresentMonAnalysis.py	CarHol/PresentMonAnalysis	0	148635	<filename>PresentMonAnalysis.py import argparse import numpy as np import time import ntpath import style import ArrayUtils from bokeh.plotting import figure, output_file, show from bokeh.models import Span, Label, Tabs, Panel from bokeh.models.widgets import Div from bokeh.layouts import column, row, widgetbox from PresentMonResult import PresentMonResult, LoadFromCsv # Debug? debug = False # FUNCTIONS def CreateHistogram(data, width, histTitle, xLabel, yLabel, indicateMu=True): """ Generates a Bokeh histogram from a numpy array of data. :param data: Any numpy array of ints or floats :param width: Target rendering width :param histTitle: Chart title :param xLabel: X-axis label :param yLabel: Y-axis label :param indicateMu: Whether or not to draw a vertical line indicating the data mean :return: A Bokeh quad-graph with range from mu +/- (4sigma). """ # Get data from result: # indicateMu = True, indicateSigma=0 # TODO: Add automatic fps conversion in PresentMonResult class # TODO: Add parallellization for large datasets mu = np.mean(data) sigma = np.std(data) # Create plot (hard coded settings for now) p = figure(title=histTitle, x_axis_label=xLabel, y_axis_label=yLabel) p.x_range.end = mu + 4 sigma p.x_range.start = mu - 4 * sigma p.width = width p.toolbar.logo = None hist, edges = np.histogram(data, density=True, bins=min(2000, data.size)) p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color=style.histogramStyle['fill_color'], line_color=style.histogramStyle['line_color']) # Add markers for mean, stdev if indicateMu: muLine = Span(location=mu, dimension='height', line_color='red', line_width=1) p.renderers.extend([muLine]) return p def CreateTabbedHistogram(dataSet, width): """ Creates a panel containing two histograms, one with frametimes and one with framerate :param dataSet: A numpy array of frametimes in ms :param width: Target rendering width :return: A Bokeh panel with one tab for each histogram """ # Create the charts frequencyData = 1000. / np.array(dataSet) timeHistogram = CreateHistogram(dataSet, width, histTitle="Frametimes", xLabel="Frame time (ms)", yLabel="Relative frequency") frequencyHistogram = CreateHistogram(frequencyData, width, histTitle="Framerate", xLabel="Framerate (fps)", yLabel="Relative frequency") # Create panels timeTab = Panel(child=timeHistogram, title="Frametimes") frequencyTab = Panel(child=frequencyHistogram, title="FPS") # Create tabs return Tabs(tabs=[timeTab, frequencyTab]) def CreateLineDiagram(frameData, timeData, width, chartTitle, xLabel, yLabel): """ Creates a line diagram over time from a numpy array :param frameData: A numpy array containing framerate or frametimes :param timeData: A numpy array containing timestamps :param width: Target rendering width :param chartTitle: The title of the chart :param xLabel: X-axis label :param yLabel: Y-axis label :return: A Bokeh line diagram with frameData plotted against timeData """ # Generate figure p = figure(title=chartTitle, x_axis_label=xLabel, y_axis_label=yLabel) p.width = width p.toolbar.logo = None # Add line p.line(timeData, frameData, line_width=0.5) return p def CreateTabbedLineDiagram(dataSet, width): """ Generates a panel with two tabs, one for framerate and one for frametimes :param dataSet: A PresentMonResult object :param width: Target rendering width of the panel :return: A Bokeh panel containing two tabs """ # Create charts frequencyData = 1000. / dataSet.msBetweenPresents timeChart = CreateLineDiagram(dataSet.msBetweenPresents, dataSet.timeStamps, width, "Framtimes", "Runtime (s)", "Frame time (ms)") frequencyChart = CreateLineDiagram(frequencyData, dataSet.timeStamps, width, "Framerate", "Runtime (s)", "Framerate (fps)") # Create panels timeTab = Panel(child=timeChart, title="Frametimes") frequencyTab = Panel(child=frequencyChart, title="Framerate") return Tabs(tabs=[timeTab, frequencyTab]) def GenerateTextStatistics(data, width): """ Generates a Bokeh Div containing statistics in text form. :param data: A numpy array of frametimes in ms. :param width: Target rendering width. :return: A Bokeh Div based on the input data. """ # Get components: basicStatistics = GenerateBasicStatisticsText(data) frameToFrameStatistics = GenerateFrameToFrameStatistics(data) thresholdStatistics = GenerateThresholdStatistics(data, [60, 90, 120, 144]) # Generate graphic text = "<div style=\"padding-left:10px\">" + \ basicStatistics + frameToFrameStatistics + thresholdStatistics + \ "</div>" div = Div(text=text, width=width) return div # Text block generation: def GenerateBasicStatisticsText(data): """ Generates a basic statistics section in HTML format. :param data: A PresentMonResult object. :return: Basic statistics as an HTML string (header + paragraphs). """ nFrames = data.timeStamps.size # Mean, meadian and standard deviation times meanFrametime = np.mean(data.msBetweenPresents) medianFrameTime = np.median(data.msBetweenPresents) stdDevFrameTime = np.std(data.msBetweenPresents) # Corresponding framerates: meanFramerate = 1000. / meanFrametime medianFramerate = 1000. / medianFrameTime stdDevFramerate = 1000. / stdDevFrameTime # Generate and return text return "<h3>Basic statistics</h3>" + \ "<p>Number of processed frames: " + str(nFrames) + \ "<p>Median: " + "{:10.3f}".format(medianFramerate) + " fps (" + "{:10.3f}".format(medianFrameTime) + " ms)" + \ "<p>Mean: " + "{:10.3f}".format(meanFramerate) + " fps (" + "{:10.3f}".format(meanFrametime) + " ms)" + \ "<p>Standard deviation: " + "{:10.3f}".format(stdDevFramerate) + " fps (" + "{:10.3f}".format( stdDevFrameTime) + " ms)" def GenerateFrameToFrameStatistics(data): """ Generates frame-to-frame statistics in HTML format. :param data: A PresentMonResult object. :return: Frame-to-frame statistics as an HTML string (header + paragraphs). """ # Calulate average frame-to-frame difference magnitude diffsMs = ArrayUtils.getArrDiffs(data.msBetweenPresents) maxDiffAmp = np.max(diffsMs) minDiffAmp = np.min(diffsMs) avgDiffAmp = np.mean(diffsMs) # Generate and return text return "<h3>Frame-to-frame statistics</h3>" + \ "<p>Average magnitude of difference: " + "{:10.3f}".format(avgDiffAmp) + " ms</p>" + \ "<p>Maximum magnitude of difference: " + "{:10.3f}".format(maxDiffAmp) + " ms</p>" + \ "<p>Minimum magnitude of difference: " + "{:10.3f}".format(minDiffAmp) + " ms</p>" def GenerateThresholdStatistics(data, thresholdFramerates): """ Generates threshold statistics in HTML data. :param data: A PresentMonResult object. :param thresholdFramerates: A list of framerates to use as thresholds. :return: Threshold statistics as an HTML string (header + paragraphs). """ totalTimeSeconds = data.timeStamps[-1] thresholds = [] for frameRate in thresholdFramerates: thresholds.append((frameRate, [], 0.0)) # Thresholds (time spent above x fps) # (threshold fps, list of frames, fraction of total time spent) for frame in data.msBetweenPresents: for index in range(0, len(thresholds)): # for threshold in thresholds: if (1000. / frame) > thresholds[index][0]: thresholds[index][1].append(frame) thresholds[index] = ( thresholds[index][0], thresholds[index][1], thresholds[index][2] + (frame / 1000) / totalTimeSeconds) # Piece together the values and return text = "<h3>Framerate threshold statistics:</h3>" for threshold in thresholds: text = text + "<p>Fraction of time spent above " + str(threshold[0]) + " fps: " + "{:10.3f}".format( threshold[2] * 100) + "%</p>" return text # SETTINGS plotWidth = 1068 # Parse arguments parser = argparse.ArgumentParser() parser.add_argument("-debug", help="Enable debug mode", action="store_true") parser.add_argument("-file", help="Path to a PresentMon generated CSV-file") startTime = time.time() args = parser.parse_args() fileSource = "TestData/csgo-2.csv" if args.file: fileSource = args.file elif args.debug: debug = True else: parser.error('Either file or debug mode most be selected') # Load data data = LoadFromCsv(fileSource) # Set output file output_file("Analysis.html") # Generate header fileName = ntpath.basename(fileSource) header = Div(text="<div class=headerDiv><h1>Analysis: {}</h1></div>".format(fileName), width=plotWidth, style=style.headerStyle) # Generate plots histogram = CreateTabbedHistogram(data.msBetweenPresents, plotWidth) # Relative frequency histograms frameToFrameHistogram = CreateHistogram(ArrayUtils.getArrDiffs(data.msBetweenPresents), plotWidth, histTitle="Frame-to-frame deviations", xLabel="Frame time difference (ms)", yLabel="Relative frequency") lineDiagram = CreateTabbedLineDiagram(data, plotWidth) # Framerate/frametimes as line diagram textStatistics = 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components/studio/projects/migrations/0019_mlflow.py	aitmlouk/stackn	25	1618034	# Generated by Django 3.1.6 on 2021-03-18 21:32 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('apps', '0049_auto_20210318_1401'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('projects', '0018_auto_20210308_2132'), ] operations = [ migrations.CreateModel( name='MLFlow', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=512)), ('mlflow_url', models.CharField(max_length=512)), ('s3_host', models.CharField(max_length=512)), ('access_key', models.CharField(max_length=512)), ('secret_key', models.CharField(max_length=512)), ('region', models.CharField(blank=True, default='', max_length=512)), ('ba_username', models.CharField(blank=True, default='', max_length=100)), ('ba_password', models.CharField(blank=True, default='', max_length=100)), ('updated_on', models.DateTimeField(auto_now=True)), ('created_on', models.DateTimeField(auto_now_add=True)), ('app', models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='mlflowobj', to='apps.appinstance')), ('owner', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)), ('project', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='mlflow_project', to='projects.project')), ], ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 29889, 29953, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29941, 29899, 29896, 29947, 29871, 29906, 29896, 29901, 29941, 29906, 13, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 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integration-testing/test/test_single_network_one.py

MicrohexHQ/CasperLabs

0

151763

import os import logging import pytest import json from pathlib import Path from pytest import fixture, raises from test.cl_node.contract_address import contract_address from test.cl_node.common import testing_root_path, HELLO_NAME_CONTRACT from test.cl_node.casperlabs_accounts import Account, GENESIS_ACCOUNT from test.cl_node.common import extract_block_hash_from_propose_output from test.cl_node.docker_node import DockerNode from test.cl_node.errors import NonZeroExitCodeError from test.cl_node.wait import wait_for_genesis_block from casperlabs_client import ABI from test.cl_node.cli import CLI, DockerCLI, CLIErrorExit """ Test account state retrieval with query-state. Example output of the Scala client: account { public_key: "3030303030303030303030303030303030303030303030303030303030303030" purse_id { uref: "0000000000000000000000000000000000000000000000000000000000000000" access_rights: READ_ADD_WRITE } associated_keys { public_key: "3030303030303030303030303030303030303030303030303030303030303030" weight: 1 } action_thresholds { deployment_threshold: 1 key_management_threshold: 1 } account_activity { key_management_last_used: 0 deployment_last_used: 0 inactivity_period_limit: 100 } } """ def deploy_and_propose_from_genesis(node, contract): return node.deploy_and_propose( session_contract=contract, payment_contract=contract, from_address=GENESIS_ACCOUNT.public_key_hex, public_key=GENESIS_ACCOUNT.public_key_path, private_key=GENESIS_ACCOUNT.private_key_path, ) def account_state(node, block_hash, account=GENESIS_ACCOUNT): return node.d_client.query_state( block_hash=block_hash, key_type="address", key=account.public_key_hex, path="" ) def test_account_state(one_node_network): node = one_node_network.docker_nodes[0] block_hash = deploy_and_propose_from_genesis(node, "test_counterdefine.wasm") deploys = node.client.show_deploys(block_hash) assert not deploys[0].is_error acct_state = account_state(node, block_hash) known_urefs = acct_state.account[0].known_urefs names = [uref.name for uref in known_urefs] assert "counter" in names block_hash = deploy_and_propose_from_genesis(node, "test_countercall.wasm") acct_state = account_state(node, block_hash) known_urefs = acct_state.account[0].known_urefs names = [uref.name for uref in known_urefs] assert "counter" in names def test_transfer_with_overdraft(one_node_network): acct1 = Account(1) acct2 = Account(2) node: DockerNode = one_node_network.docker_nodes[0] # Transfer 1000000 from genesis... to acct1... # For compatibility with EE with no execution cost # payment_contract="transfer_to_account.wasm" block_hash = node.transfer_to_account( to_account_id=1, amount=1000000, from_account_id="genesis", payment_contract="transfer_to_account.wasm", ) deploys = node.client.show_deploys(block_hash) assert not deploys[0].is_error, f"error_message: {deploys[0].error_message}" # Response not used, but assures account exist _ = account_state(node, block_hash, acct1) # Should error as account doesn't exist. with raises(Exception): _ = account_state(block_hash, acct2.public_key_hex) # No API currently exists for getting balance to check transfer. # Transfer 750000 from acct1... to acct2... block_hash = node.transfer_to_account( to_account_id=2, amount=750000, from_account_id=1, payment_contract="transfer_to_account.wasm", ) deploys = node.client.show_deploys(block_hash) assert not deploys[0].is_error, f"error_message: {deploys[0].error_message}" # Response not used, but assures account exist _ = account_state(node, block_hash, acct2) # Transfer 750000000000 from acct1... to acct2... # Should fail with acct1 overdrawn. Requires assert in contract to generate is_error. with raises(Exception): _ = node.transfer_to_account( to_account_id=2, amount=750000000000, from_account_id=1, payment_contract="transfer_to_account.wasm", ) def test_transfer_to_accounts(one_node_network): node: DockerNode = one_node_network.docker_nodes[0] # Perform multiple transfers with end result of Acct1 = 100, Acct2 = 100, Acct3 = 800 node.transfer_to_accounts([(1, 1000), (2, 900, 1), (3, 800, 2)]) with raises(Exception): # Acct 1 has not enough funds so it should fail node.transfer_to_account( to_account_id=4, amount=100000000000, from_account_id=1, payment_contract="transfer_to_account.wasm", ) node.transfer_to_account( to_account_id=4, amount=100, from_account_id=2, payment_contract="transfer_to_account.wasm", ) # TODO: Improve checks once balance is easy to read. def balance(node, account_address, block_hash): try: return node.client.get_balance(account_address, block_hash) except Exception: return 0 def test_scala_client_balance(one_node_network): node: DockerNode = one_node_network.docker_nodes[0] accounts = [Account(i) for i in range(1, 4)] block_hash = list(node.p_client.show_blocks(1))[0].summary.block_hash.hex() initial = [ balance(node, account.public_key_hex, block_hash) for account in accounts ] # Perform multiple transfers with end result of Acct1 = 200, Acct2 = 100, Acct3 = 700 hashes = node.transfer_to_accounts([(1, 1000), (2, 800, 1), (3, 700, 2)]) for acct_num, value in ((0, 200), (1, 100), (2, 700)): addr = accounts[acct_num].public_key_hex bal = node.d_client.get_balance(account_address=addr, block_hash=hashes[-1]) assert bal == initial[acct_num] + value ffi_test_contracts = [ ("getcallerdefine.wasm", "getcallercall.wasm"), ("listknownurefsdefine.wasm", "listknownurefscall.wasm"), ] def deploy_and_propose_expect_no_errors(node, contract): client = node.d_client block_hash = node.deploy_and_propose( session_contract=contract, payment_contract=contract, from_address=node.genesis_account.public_key_hex, public_key=node.genesis_account.public_key_path, private_key=node.genesis_account.private_key_path, ) r = client.show_deploys(block_hash)[0] assert r.is_error is False, f"error_message: {r.error_message}" @pytest.mark.parametrize("define_contract, call_contract", ffi_test_contracts) def test_get_caller(one_node_network, define_contract, call_contract): node = one_node_network.docker_nodes[0] deploy_and_propose_expect_no_errors(node, define_contract) deploy_and_propose_expect_no_errors(node, call_contract) @pytest.mark.parametrize("wasm", [HELLO_NAME_CONTRACT, "old_wasm/test_helloname.wasm"]) def test_multiple_propose(one_node_network, wasm): """ Feature file: propose.feature Scenario: Single node deploy and multiple propose generates an Exception. OP-182: First propose should be success, and subsequent propose calls should throw an error/exception. """ node = one_node_network.docker_nodes[0] deploy_and_propose_from_genesis(node, wasm) number_of_blocks = node.client.get_blocks_count(100) try: _ = node.client.propose() assert False, "Second propose must not succeed, should throw" except NonZeroExitCodeError as e: assert e.exit_code == 1, "Second propose should fail" wait_for_genesis_block(node) # Number of blocks after second propose should not change assert node.client.get_blocks_count(100) == number_of_blocks # Examples of query-state executed with the Scala client that result in errors: # CasperLabs/docker $ ./client.sh node-0 propose # Response: Success! Block 9d38836598... created and added. # CasperLabs/docker $ ./client.sh node-0 query-state --block-hash '"9d"' --key '"a91208047c"' --path file.xxx --type hash # NOT_FOUND: Cannot find block matching hash "9d" # CasperLabs/docker$ ./client.sh node-0 query-state --block-hash 9d --key '"a91208047c"' --path file.xxx --type hash # INVALID_ARGUMENT: Key of type hash must have exactly 32 bytes, 5 =/= 32 provided. # CasperLabs/docker$ ./client.sh node-0 query-state --block-hash 9d --key 3030303030303030303030303030303030303030303030303030303030303030 --path file.xxx --type hash # INVALID_ARGUMENT: Value not found: " Hash([48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48])" @pytest.fixture() # scope="module") def node(one_node_network): return one_node_network.docker_nodes[0] @pytest.fixture() # (scope="module") def client(node): return node.d_client @pytest.fixture() # (scope="module") def block_hash(node): return node.deploy_and_propose( session_contract="test_helloname.wasm", payment_contract="test_helloname.wasm" ) block_hash_queries = [ ( { "block_hash": "9d000000", "key": "a91208047c", "path": "file.xxx", "key_type": "hash", }, "NOT_FOUND: Cannot find block matching", ), ( {"key": "a91208047c", "path": "file.xxx", "key_type": "hash"}, "INVALID_ARGUMENT: Key of type hash must have exactly 32 bytes", ), ({"path": "file.xxx", "key_type": "hash"}, "INVALID_ARGUMENT: Value not found"), ] @pytest.mark.parametrize("query, expected", block_hash_queries) def test_query_state_error(node, client, block_hash, query, expected): if "block_hash" not in query: query["block_hash"] = block_hash if "key" not in query: query["key"] = GENESIS_ACCOUNT.public_key_hex with pytest.raises(NonZeroExitCodeError) as excinfo: _ = client.query_state(**query) assert expected in excinfo.value.output def test_revert_subcall(client, node): # This contract calls another contract that calls revert(2) block_hash = deploy_and_propose_from_genesis( node, "test_subcall_revert_define.wasm" ) r = client.show_deploys(block_hash)[0] assert not r.is_error assert r.error_message == "" deploy_hash = r.deploy.deploy_hash r = client.show_deploy(deploy_hash) assert r.deploy.deploy_hash == deploy_hash block_hash = deploy_and_propose_from_genesis(node, "test_subcall_revert_call.wasm") r = client.show_deploys(block_hash)[0] assert r.is_error assert r.error_message == "Exit code: 2" def test_revert_direct(client, node): # This contract calls revert(1) directly block_hash = deploy_and_propose_from_genesis(node, "test_direct_revert_call.wasm") r = client.show_deploys(block_hash)[0] assert r.is_error assert r.error_message == "Exit code: 1" def test_deploy_with_valid_signature(one_node_network): """ Feature file: deploy.feature Scenario: Deploy with valid signature """ node0 = one_node_network.docker_nodes[0] block_hash = deploy_and_propose_from_genesis(node0, "test_helloname.wasm") deploys = node0.client.show_deploys(block_hash) assert deploys[0].is_error is False def test_deploy_with_invalid_signature(one_node_network): """ Feature file: deploy.feature Scenario: Deploy with invalid signature """ node0 = one_node_network.docker_nodes[0] with pytest.raises(NonZeroExitCodeError): node0.client.deploy( from_address=GENESIS_ACCOUNT.public_key_hex, session_contract="test_helloname.wasm", payment_contract="test_helloname.wasm", private_key="validator-0-private-invalid.pem", public_key="validator-0-public-invalid.pem", ) """ Feature file: ~/CasperLabs/integration-testing/features/deploy.feature """ def deploy_and_propose(node, contract): node.client.deploy( session_contract=contract, payment_contract=contract, from_address=GENESIS_ACCOUNT.public_key_hex, public_key=GENESIS_ACCOUNT.public_key_path, private_key=GENESIS_ACCOUNT.private_key_path, ) return extract_block_hash_from_propose_output(node.client.propose()) def deploy(node, contract): message = node.client.deploy( from_address=GENESIS_ACCOUNT.public_key_hex, public_key=GENESIS_ACCOUNT.public_key_path, private_key=GENESIS_ACCOUNT.private_key_path, session_contract=contract, payment_contract=contract, ) assert "Success!" in message return message.split()[2] def propose(node): return extract_block_hash_from_propose_output(node.client.propose()) def deploy_hashes(node, block_hash): return set(d.deploy.deploy_hash for d in node.client.show_deploys(block_hash)) # Python Client (library) # fmt: off def resource(fn): cur_path = Path(os.path.realpath(__file__)).parent while cur_path.name != "integration-testing": cur_path = cur_path.parent return cur_path / "resources" / fn def test_args_parser(): account = ( b"\x00\x01\x00\x02\x00\x03\x00\x04\x00\x05\x00\x06\x00\x07\x00\x08" b"\x00\x00\x00\x01\x00\x02\x00\x03\x00\x04\x00\x05\x00\x06\x00\x07" ) amount = 123456 args = [{"name": "amount", "value": {"long_value": amount}}, {"name": "account", "value": {"bytes_value": account.hex()}}, {"name": "purse_id", "value": {"optional_value": {}}}, {"name": "number", "value": {"big_int": {"value": "2", "bit_width": 512}}}] json_str = json.dumps(args) assert ABI.args_from_json(json_str) == ABI.args( [ABI.long_value(amount), ABI.account(account), ABI.optional_value(None), ABI.big_int(2)] ) @fixture() def genesis_public_signing_key(): with GENESIS_ACCOUNT.public_key_binary_file() as f: yield f def test_deploy_with_args(one_node_network, genesis_public_signing_key): """ Deploys test contracts that do: revert(get_arg(0)); // for u32 and u512 and revert(sum(address_bytes[u8; 32]) + u32); for multiple argument test. Tests args get correctly encoded and decoded in the contract. Test expects the test contracts test_args_u32.wasm and test_args_u512.wasm to deserialize correctly their arguments and then call revert with value of the argument (converted to a Rust native int, as expected by revert). If the test contracts don't fail or if their exit code is different than expected, the test will fail. """ node = one_node_network.docker_nodes[0] client = node.p_client.client for wasm, encode in [ (resource("test_args_u32.wasm"), ABI.u32), (resource("test_args_u512.wasm"), ABI.u512), ]: for number in [1, 12, 256, 1024]: response, deploy_hash = client.deploy( payment=wasm, session=wasm, public_key=resource("accounts/account-public-genesis.pem"), private_key=resource("accounts/account-private-genesis.pem"), session_args=ABI.args([encode(number)]), ) logging.info( f"DEPLOY RESPONSE: {response} deploy_hash: {deploy_hash.hex()}" ) response = client.propose() # Need to convert to hex string from bytes block_hash = response.block_hash.hex() for deploy_info in client.showDeploys(block_hash): assert deploy_info.is_error is True assert deploy_info.error_message == f"Exit code: {number}" wasm = resource("test_args_multi.wasm") account_hex = "0101010102020202030303030404040405050505060606060707070708080808" number = 1000 total_sum = sum([1, 2, 3, 4, 5, 6, 7, 8]) * 4 + number response, deploy_hash = client.deploy( payment=wasm, session=wasm, public_key=resource("accounts/account-public-genesis.pem"), private_key=resource("accounts/account-private-genesis.pem"), session_args=ABI.args( [ABI.account(bytes.fromhex(account_hex)), ABI.u32(number)] ), ) logging.info(f"DEPLOY RESPONSE: {response} deploy_hash: {deploy_hash.hex()}") response = client.propose() block_hash = response.block_hash.hex() for deploy_info in client.showDeploys(block_hash): assert deploy_info.is_error is True assert deploy_info.error_message == f"Exit code: {total_sum}" for blockInfo in client.showBlocks(10): assert blockInfo.status.stats.block_size_bytes > 0 # Python CLI # @pytest.fixture() # scope="module") def cli(one_node_network): return CLI(one_node_network.docker_nodes[0], "casperlabs_client") @pytest.fixture() # scope="module") def scala_cli(one_node_network): return DockerCLI(one_node_network.docker_nodes[0]) def test_cli_no_parameters(cli): with raises(CLIErrorExit) as ex_info: cli() assert "You must provide a command" in str(ex_info.value) def test_cli_help(cli): out = cli("--help") # deploy,propose,show-block,show-blocks,show-deploy,show-deploys,vdag,query-state assert "Casper" in out def test_cli_show_blocks_and_show_block(cli): blocks = cli("show-blocks", "--depth", "1") assert len(blocks) > 0 for block in blocks: block_hash = block.summary.block_hash assert len(block_hash) == 32 * 2 # hex b = cli("show-block", block_hash) assert block_hash == b.summary.block_hash def test_cli_show_block_not_found(cli): block_hash = "00" * 32 with raises(CLIErrorExit) as ex_info: cli("show-block", block_hash) # StatusCode.NOT_FOUND: Cannot find block matching hash # 0000000000000000000000000000000000000000000000000000000000000000 assert "NOT_FOUND" in str(ex_info.value) assert "Cannot find block matching hash" in str(ex_info.value) @pytest.mark.skip def test_cli_deploy_propose_show_deploys_show_deploy_query_state_and_balance(cli): resources_path = testing_root_path() / "resources" account = GENESIS_ACCOUNT deploy_hash = cli( "deploy", f"--from {account.public_key_hex}", f"--payment {str(resources_path / 'test_helloname.wasm')}", f"--session {str(resources_path / 'test_helloname.wasm')}", f"--private-key {str(account.private_key_path)}", f"--public-key {str(account.public_key_path)}", ) block_hash = cli("propose") deploys = cli("show-deploys", block_hash) deploy_hashes = [d.deploy.deploy_hash for d in deploys] assert deploy_hash in deploy_hashes deploy_info = cli("show-deploy", deploy_hash) assert deploy_info.deploy.deploy_hash == deploy_hash result = cli("query-state", "--block-hash", block_hash, "--type", "address", "--key", account.public_key_hex, "--path", "",) assert "hello_name" in [u.name for u in result.account.known_urefs] balance = int( cli("balance", "--address", account.public_key_hex, "--block-hash", block_hash) ) # TODO Need constant for where this 1000000000 is from. assert balance == 1000000000 # genesis # CLI ABI def int_value(x): return x def big_int_value(x): return {'value': str(x), 'bit_width': 512} abi_unsigned_test_data = [ ("int_value", 'test_args_u32.wasm', int_value), ("big_int", 'test_args_u512.wasm', big_int_value), ] @pytest.mark.parametrize("unsigned_type, test_contract, value", abi_unsigned_test_data) def test_cli_abi_unsigned_scala(node, unsigned_type, test_contract, value): check_cli_abi_unsigned(DockerCLI(node), lambda s: f"'{s}'", '/data', unsigned_type, value, test_contract, lambda a: (a.private_key_docker_path, a.public_key_docker_path)) @pytest.mark.parametrize("unsigned_type, test_contract, value", abi_unsigned_test_data) def test_cli_abi_unsigned_python(node, unsigned_type, test_contract, value): check_cli_abi_unsigned(CLI(node), lambda s: s, 'resources', unsigned_type, value, test_contract, lambda a: (a.private_key_path, a.public_key_path)) def check_cli_abi_unsigned(cli, quote, resource_directory, unsigned_type, value, test_contract, keys): account = GENESIS_ACCOUNT private_key, public_key = keys(account) for number in [2, 256, 1024]: test_contract_path = '/'.join((resource_directory, test_contract)) session_args = json.dumps([{"name": "number", "value": {unsigned_type: value(number)}}]) args = ('deploy', '--from', account.public_key_hex, '--session', test_contract_path, '--session-args', quote(session_args), '--payment', test_contract_path, '--private-key', private_key, '--public-key', public_key) logging.info(f"EXECUTING {' '.join(cli.expand_args(args))}") deploy_hash = cli(*args) cli('propose') deploy_info = cli("show-deploy", deploy_hash) assert deploy_info.processing_results[0].is_error is True assert deploy_info.processing_results[0].error_message == f"Exit code: {number}" def test_cli_abi_multiple(cli): account = GENESIS_ACCOUNT test_contract = resource("test_args_multi.wasm") account_hex = "0101010102020202030303030404040405050505060606060707070708080808" number = 1000 total_sum = sum([1, 2, 3, 4, 5, 6, 7, 8]) * 4 + number session_args = json.dumps([{'name': 'account', 'value': {'account': account_hex}}, {'name': 'number', 'value': {'int_value': number}}]) deploy_hash = cli('deploy', '--from', account.public_key_hex, '--session', resource(test_contract), '--session-args', session_args, '--payment', resource(test_contract), '--private-key', account.private_key_path, '--public-key', account.public_key_path,) cli('propose') deploy_info = cli("show-deploy", deploy_hash) assert deploy_info.processing_results[0].is_error is True assert deploy_info.processing_results[0].error_message == f"Exit code: {total_sum}" def test_cli_scala_help(scala_cli): output = scala_cli('--help') assert 'Subcommand: make-deploy' in output def test_cli_scala_extended_deploy(scala_cli): cli = scala_cli account = GENESIS_ACCOUNT # TODO: when paralelizing tests, make sure test don't collide # when trying to access the same file, perhaps map containers /tmp # to a unique hosts's directory. test_contract = "/data/test_helloname.wasm" cli('make-deploy', '-o', '/tmp/unsigned.deploy', '--from', account.public_key_hex, '--session', test_contract, '--payment', test_contract) cli('sign-deploy', '-i', '/tmp/unsigned.deploy', '-o', '/tmp/signed.deploy', '--private-key', account.private_key_docker_path, '--public-key', account.public_key_docker_path) deploy_hash = cli('send-deploy', '-i', '/tmp/signed.deploy') cli('propose') deploy_info = cli("show-deploy", deploy_hash) assert not deploy_info.processing_results[0].is_error try: os.remove('/tmp/unsigned.deploy') os.remove('/tmp/signed.deploy') except Exception as e: logging.warning(f"Could not delete temporary files: {str(e)}") def test_cli_scala_direct_call_by_hash_and_name(scala_cli): check_cli_direct_call_by_hash_and_name(scala_cli, scala_cli) def test_cli_python_direct_call_by_hash_and_name(cli, scala_cli): check_cli_direct_call_by_hash_and_name(cli, scala_cli) def check_cli_direct_call_by_hash_and_name(cli, scala_cli): # TODO: For now using scala_cli for assertions because for some # strange reason Python CLI doesn't show is_error and error_message # in the output of show-deploys. This has to be fixed asap. account = cli.node.test_account cli.set_default_deploy_args('--from', account.public_key_hex, '--private-key', cli.private_key_path(account), '--public-key', cli.public_key_path(account)) # First, deploy a contract that stores a function # and saves pointer to it under UREF "revert_test". # The stored function calls revert(2). test_contract = cli.resource("test_subcall_revert_define.wasm") first_deploy_hash = cli('deploy', '--session', test_contract, '--payment', test_contract) block_hash = cli("propose") logging.info(f"""EXECUTING {' '.join(scala_cli.expand_args(["show-deploys", block_hash]))}""") deploys = scala_cli("show-deploys", block_hash) assert len(list(deploys)) == 1 for deploy_info in deploys: assert deploy_info.deploy.deploy_hash == first_deploy_hash assert not deploy_info.is_error # Call by name deploy_hash = cli("deploy", '--session-name', "revert_test", '--payment-name', "revert_test") block_hash = cli("propose") deploys = scala_cli("show-deploys", block_hash) for deploy_info in deploys: assert deploy_info.deploy.deploy_hash == deploy_hash assert deploy_info.error_message == 'Exit code: 2' # Expected: contract called revert(2) # Call by function address revert_test_addr = contract_address(first_deploy_hash, 0).hex() # assume fn_store_id starts from 0 deploy_hash = cli("deploy", '--session-hash', revert_test_addr, '--payment-hash', revert_test_addr) block_hash = cli("propose") deploys = scala_cli("show-deploys", block_hash) for deploy_info in deploys: assert deploy_info.deploy.deploy_hash == deploy_hash assert deploy_info.error_message == 'Exit code: 2'

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cleaning/partition_domains_fixed_block_size.py

AGLDWG/ld-link-harvester

0

95143

import pandas as pd IN_FILE = 'aus-domain-urls.txt' START_IDX = 1186 BLOCK_SIZE = 100 OUT_FILE_PREFIX = 'partition_data/aus-domain-urls' data = pd.read_csv(IN_FILE) data_length = len(data) for i in range(int(data_length - START_IDX / BLOCK_SIZE)): if i == 0: lower_bound = START_IDX else: lower_bound = upper_bound upper_bound = lower_bound + BLOCK_SIZE if upper_bound >= data_length: upper_bound = data_length - 1 out_file = '{}_{}_{}_{}.txt'.format(OUT_FILE_PREFIX, lower_bound, upper_bound, upper_bound - lower_bound) (data.iloc[lower_bound:upper_bound, :]).to_csv(out_file, header=False, index=None, sep=" ") break out_file = '{}_{}_{}_{}.txt'.format(OUT_FILE_PREFIX, lower_bound, upper_bound, upper_bound - lower_bound) (data.iloc[ lower_bound:upper_bound, : ]).to_csv(out_file, header=False, index=None, sep=" ")

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simple_chatbot/responses.py

Codingplace42/django-simple-chatbot

9

120528

<reponame>Codingplace42/django-simple-chatbot from abc import ABC, abstractmethod from random import choice class BaseResponse(ABC): @abstractmethod def get_response(self): pass class GenericRandomResponse(BaseResponse): choices = () def get_response(self): return choice(self.choices)

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saleor/core/transactions.py

fairhopeweb/saleor

15,337

1534

from contextlib import contextmanager from django.db import DatabaseError from ..core.tracing import traced_atomic_transaction @contextmanager def transaction_with_commit_on_errors(): """Perform transaction and raise an error in any occurred.""" error = None with traced_atomic_transaction(): try: yield except DatabaseError: raise except Exception as e: error = e if error: raise error

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test/python_unit/collision.py

proyan/hpp-fcl

0

188747

<filename>test/python_unit/collision.py import unittest from test_case import TestCase import hppfcl hppfcl.switchToNumpyArray() import numpy as np def tetahedron(): pts = hppfcl.StdVec_Vec3f() pts.append(np.array((0, 0, 0))) pts.append(np.array((0, 1, 0))) pts.append(np.array((1, 0, 0))) pts.append(np.array((0, 0, 1))) tri = hppfcl.StdVec_Triangle() tri.append(hppfcl.Triangle(0, 1, 2)) tri.append(hppfcl.Triangle(0, 1, 3)) tri.append(hppfcl.Triangle(0, 2, 3)) tri.append(hppfcl.Triangle(1, 2, 3)) return hppfcl.Convex(pts, tri) class TestMainAPI(TestCase): def test_convex_halfspace(self): convex = tetahedron() halfspace = hppfcl.Halfspace(np.array((0, 0, 1)), 0) req = hppfcl.CollisionRequest() res = hppfcl.CollisionResult() M1 = hppfcl.Transform3f() M2 = hppfcl.Transform3f(np.eye(3), np.array([0, 0, -0.1])) res.clear() hppfcl.collide(convex, M1, halfspace, M2, req, res) self.assertFalse(hppfcl.collide(convex, M1, halfspace, M2, req, res)) M1 = hppfcl.Transform3f() M2 = hppfcl.Transform3f(np.eye(3), np.array([0, 0, 0.1])) res.clear() self.assertTrue(hppfcl.collide(convex, M1, halfspace, M2, req, res)) M1 = hppfcl.Transform3f() M2 = hppfcl.Transform3f(np.eye(3), np.array([0, 0, 2])) res.clear() self.assertTrue(hppfcl.collide(convex, M1, halfspace, M2, req, res)) if __name__ == "__main__": unittest.main()

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turdshovel/_stubs/System/Collections/Generic.py

daddycocoaman/turdshovel

39

117303

# encoding: utf-8 # module System.Collections.Generic calls itself Generic # from mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Microsoft.Bcl.AsyncInterfaces, Version=1.0.0.0, Culture=neutral, PublicKeyToken=<KEY> # by generator 1.145 # no doc # no imports # no functions # classes class Comparer(object, IComparer, IComparer[T]): # no doc def Compare(self, x, y): """ Compare(self: Comparer[T], x: T, y: T) -> int """ pass @staticmethod def Create(comparison): """ Create(comparison: Comparison[T]) -> Comparer[T] """ pass def __cmp__(self, *args): #cannot find CLR method """ x.__cmp__(y) <==> cmp(x,y) """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass class Dictionary(object, IDictionary[TKey, TValue], ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable, IDictionary, ICollection, IReadOnlyDictionary[TKey, TValue], IReadOnlyCollection[KeyValuePair[TKey, TValue]], ISerializable, IDeserializationCallback): """ Dictionary[TKey, TValue]() Dictionary[TKey, TValue](capacity: int) Dictionary[TKey, TValue](comparer: IEqualityComparer[TKey]) Dictionary[TKey, TValue](capacity: int, comparer: IEqualityComparer[TKey]) Dictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue]) Dictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue], comparer: IEqualityComparer[TKey]) """ def Add(self, key, value): """ Add(self: Dictionary[TKey, TValue], key: TKey, value: TValue) """ pass def Clear(self): """ Clear(self: Dictionary[TKey, TValue]) """ pass def ContainsKey(self, key): """ ContainsKey(self: Dictionary[TKey, TValue], key: TKey) -> bool """ pass def ContainsValue(self, value): """ ContainsValue(self: Dictionary[TKey, TValue], value: TValue) -> bool """ pass def GetEnumerator(self): """ GetEnumerator(self: Dictionary[TKey, TValue]) -> Enumerator """ pass def GetObjectData(self, info, context): """ GetObjectData(self: Dictionary[TKey, TValue], info: SerializationInfo, context: StreamingContext) """ pass def OnDeserialization(self, sender): """ OnDeserialization(self: Dictionary[TKey, TValue], sender: object) """ pass def Remove(self, key): """ Remove(self: Dictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: Dictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: IDictionary[TKey, TValue], key: TKey) -> bool __contains__(self: IDictionary, key: object) -> bool """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, *__args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, comparer: IEqualityComparer[TKey]) __new__(cls: type, capacity: int, comparer: IEqualityComparer[TKey]) __new__(cls: type, dictionary: IDictionary[TKey, TValue]) __new__(cls: type, dictionary: IDictionary[TKey, TValue], comparer: IEqualityComparer[TKey]) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: Dictionary[TKey, TValue]) -> str __repr__(self: Dictionary[K, V]) -> str """ pass def __setitem__(self, *args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: Dictionary[TKey, TValue]) -> IEqualityComparer[TKey] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: Dictionary[TKey, TValue]) -> int """ Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: Dictionary[TKey, TValue]) -> KeyCollection """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: Dictionary[TKey, TValue]) -> ValueCollection """ Enumerator = None KeyCollection = None ValueCollection = None class EqualityComparer(object, IEqualityComparer, IEqualityComparer[T]): # no doc def Equals(self, *__args): """ Equals(self: EqualityComparer[T], x: T, y: T) -> bool """ pass def GetHashCode(self, obj=None): """ GetHashCode(self: EqualityComparer[T], obj: T) -> int """ pass def __eq__(self, *args): #cannot find CLR method """ x.__eq__(y) <==> x==y """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass class IAsyncEnumerable: # no doc def GetAsyncEnumerator(self, cancellationToken): """ GetAsyncEnumerator(self: IAsyncEnumerable[T], cancellationToken: CancellationToken) -> IAsyncEnumerator[T] Returns an enumerator that iterates asynchronously through the collection. cancellationToken: A System.Threading.CancellationToken that may be used to cancel the asynchronous iteration. Returns: An enumerator that can be used to iterate asynchronously through the collection. """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IAsyncEnumerator(IAsyncDisposable): # no doc def MoveNextAsync(self): """ MoveNextAsync(self: IAsyncEnumerator[T]) -> ValueTask[bool] Advances the enumerator asynchronously to the next element of the collection. Returns: A System.Threading.Tasks.ValueTask that will complete with a result of true if the enumerator was successfully advanced to the next element, or false if the enumerator has passed the end of the collection. """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass Current = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Gets the element in the collection at the current position of the enumerator. Get: Current(self: IAsyncEnumerator[T]) -> T """ class ICollection(IEnumerable[T], IEnumerable): # no doc def Add(self, item): """ Add(self: ICollection[T], item: T) """ pass def Clear(self): """ Clear(self: ICollection[T]) """ pass def Contains(self, item): """ Contains(self: ICollection[T], item: T) -> bool """ pass def CopyTo(self, array, arrayIndex): """ CopyTo(self: ICollection[T], array: Array[T], arrayIndex: int) """ pass def Remove(self, item): """ Remove(self: ICollection[T], item: T) -> bool """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: ICollection[T]) -> int """ IsReadOnly = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: IsReadOnly(self: ICollection[T]) -> bool """ class IComparer: # no doc def Compare(self, x, y): """ Compare(self: IComparer[T], x: T, y: T) -> int """ pass def __cmp__(self, *args): #cannot find CLR method """ x.__cmp__(y) <==> cmp(x,y) """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IDictionary(ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable): # no doc def Add(self, key, value): """ Add(self: IDictionary[TKey, TValue], key: TKey, value: TValue) """ pass def ContainsKey(self, key): """ ContainsKey(self: IDictionary[TKey, TValue], key: TKey) -> bool """ pass def Remove(self, key): """ Remove(self: IDictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: IDictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: ICollection[KeyValuePair[TKey, TValue]], item: KeyValuePair[TKey, TValue]) -> bool """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass def __setitem__(self, *args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: IDictionary[TKey, TValue]) -> ICollection[TKey] """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: IDictionary[TKey, TValue]) -> ICollection[TValue] """ class IEnumerable(IEnumerable): # no doc def GetEnumerator(self): """ GetEnumerator(self: IEnumerable[T]) -> IEnumerator[T] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IEnumerator(IDisposable, IEnumerator): # no doc def next(self, *args): #cannot find CLR method """ next(self: object) -> object """ pass def __enter__(self, *args): #cannot find CLR method """ __enter__(self: IDisposable) -> object """ pass def __exit__(self, *args): #cannot find CLR method """ __exit__(self: IDisposable, exc_type: object, exc_value: object, exc_back: object) """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__[T](self: IEnumerator[T]) -> object """ pass Current = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Current(self: IEnumerator[T]) -> T """ class IEqualityComparer: # no doc def Equals(self, x, y): """ Equals(self: IEqualityComparer[T], x: T, y: T) -> bool """ pass def GetHashCode(self, obj): """ GetHashCode(self: IEqualityComparer[T], obj: T) -> int """ pass def __eq__(self, *args): #cannot find CLR method """ x.__eq__(y) <==> x==y """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass class IList(ICollection[T], IEnumerable[T], IEnumerable): # no doc def IndexOf(self, item): """ IndexOf(self: IList[T], item: T) -> int """ pass def Insert(self, index, item): """ Insert(self: IList[T], index: int, item: T) """ pass def RemoveAt(self, index): """ RemoveAt(self: IList[T], index: int) """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass def __setitem__(self, *args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass class IReadOnlyCollection(IEnumerable[T], IEnumerable): # no doc def __contains__(self, *args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: IReadOnlyCollection[T]) -> int """ class IReadOnlyDictionary(IReadOnlyCollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable): # no doc def ContainsKey(self, key): """ ContainsKey(self: IReadOnlyDictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: IReadOnlyDictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__[KeyValuePair`2](enumerable: IEnumerable[KeyValuePair[TKey, TValue]], value: KeyValuePair[TKey, TValue]) -> bool """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: IReadOnlyDictionary[TKey, TValue]) -> IEnumerable[TKey] """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: IReadOnlyDictionary[TKey, TValue]) -> IEnumerable[TValue] """ class IReadOnlyList(IReadOnlyCollection[T], IEnumerable[T], IEnumerable): # no doc def __contains__(self, *args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass class ISet(ICollection[T], IEnumerable[T], IEnumerable): # no doc def Add(self, item): """ Add(self: ISet[T], item: T) -> bool """ pass def ExceptWith(self, other): """ ExceptWith(self: ISet[T], other: IEnumerable[T]) """ pass def IntersectWith(self, other): """ IntersectWith(self: ISet[T], other: IEnumerable[T]) """ pass def IsProperSubsetOf(self, other): """ IsProperSubsetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def IsProperSupersetOf(self, other): """ IsProperSupersetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def IsSubsetOf(self, other): """ IsSubsetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def IsSupersetOf(self, other): """ IsSupersetOf(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def Overlaps(self, other): """ Overlaps(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def SetEquals(self, other): """ SetEquals(self: ISet[T], other: IEnumerable[T]) -> bool """ pass def SymmetricExceptWith(self, other): """ SymmetricExceptWith(self: ISet[T], other: IEnumerable[T]) """ pass def UnionWith(self, other): """ UnionWith(self: ISet[T], other: IEnumerable[T]) """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass class KeyNotFoundException(SystemException, ISerializable, _Exception): """ KeyNotFoundException() KeyNotFoundException(message: str) KeyNotFoundException(message: str, innerException: Exception) """ def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod # known case of __new__ def __new__(self, message=None, innerException=None): """ __new__(cls: type) __new__(cls: type, message: str) __new__(cls: type, message: str, innerException: Exception) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __str__(self, *args): #cannot find CLR method pass SerializeObjectState = None class KeyValuePair(object): """ KeyValuePair[TKey, TValue](key: TKey, value: TValue) """ def ToString(self): """ ToString(self: KeyValuePair[TKey, TValue]) -> str """ pass @staticmethod # known case of __new__ def __new__(self, key, value): """ __new__[KeyValuePair`2]() -> KeyValuePair[TKey, TValue] __new__(cls: type, key: TKey, value: TValue) """ pass Key = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Key(self: KeyValuePair[TKey, TValue]) -> TKey """ Value = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Value(self: KeyValuePair[TKey, TValue]) -> TValue """ class LinkedList(object, ICollection[T], IEnumerable[T], IEnumerable, ICollection, IReadOnlyCollection[T], ISerializable, IDeserializationCallback): """ LinkedList[T]() LinkedList[T](collection: IEnumerable[T]) """ def AddAfter(self, node, *__args): """ AddAfter(self: LinkedList[T], node: LinkedListNode[T], newNode: LinkedListNode[T])AddAfter(self: LinkedList[T], node: LinkedListNode[T], value: T) -> LinkedListNode[T] """ pass def AddBefore(self, node, *__args): """ AddBefore(self: LinkedList[T], node: LinkedListNode[T], value: T) -> LinkedListNode[T] AddBefore(self: LinkedList[T], node: LinkedListNode[T], newNode: LinkedListNode[T]) """ pass def AddFirst(self, *__args): """ AddFirst(self: LinkedList[T], value: T) -> LinkedListNode[T] AddFirst(self: LinkedList[T], node: LinkedListNode[T]) """ pass def AddLast(self, *__args): """ AddLast(self: LinkedList[T], value: T) -> LinkedListNode[T] AddLast(self: LinkedList[T], node: LinkedListNode[T]) """ pass def Clear(self): """ Clear(self: LinkedList[T]) """ pass def Contains(self, value): """ Contains(self: LinkedList[T], value: T) -> bool """ pass def CopyTo(self, array, index): """ CopyTo(self: LinkedList[T], array: Array[T], index: int) """ pass def Find(self, value): """ Find(self: LinkedList[T], value: T) -> LinkedListNode[T] """ pass def FindLast(self, value): """ FindLast(self: LinkedList[T], value: T) -> LinkedListNode[T] """ pass def GetEnumerator(self): """ GetEnumerator(self: LinkedList[T]) -> Enumerator """ pass def GetObjectData(self, info, context): """ GetObjectData(self: LinkedList[T], info: SerializationInfo, context: StreamingContext) """ pass def OnDeserialization(self, sender): """ OnDeserialization(self: LinkedList[T], sender: object) """ pass def Remove(self, *__args): """ Remove(self: LinkedList[T], value: T) -> bool Remove(self: LinkedList[T], node: LinkedListNode[T]) """ pass def RemoveFirst(self): """ RemoveFirst(self: LinkedList[T]) """ pass def RemoveLast(self): """ RemoveLast(self: LinkedList[T]) """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, collection=None): """ __new__(cls: type) __new__(cls: type, collection: IEnumerable[T]) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: LinkedList[T]) -> int """ First = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: First(self: LinkedList[T]) -> LinkedListNode[T] """ Last = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Last(self: LinkedList[T]) -> LinkedListNode[T] """ Enumerator = None class LinkedListNode(object): """ LinkedListNode[T](value: T) """ @staticmethod # known case of __new__ def __new__(self, value): """ __new__(cls: type, value: T) """ pass List = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: List(self: LinkedListNode[T]) -> LinkedList[T] """ Next = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Next(self: LinkedListNode[T]) -> LinkedListNode[T] """ Previous = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Previous(self: LinkedListNode[T]) -> LinkedListNode[T] """ Value = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Value(self: LinkedListNode[T]) -> T Set: Value(self: LinkedListNode[T]) = value """ class List(object, IList[T], ICollection[T], IEnumerable[T], IEnumerable, IList, ICollection, IReadOnlyList[T], IReadOnlyCollection[T]): """ List[T]() List[T](capacity: int) List[T](collection: IEnumerable[T]) """ def Add(self, item): """ Add(self: List[T], item: T) """ pass def AddRange(self, collection): """ AddRange(self: List[T], collection: IEnumerable[T]) """ pass def AsReadOnly(self): """ AsReadOnly(self: List[T]) -> ReadOnlyCollection[T] """ pass def BinarySearch(self, *__args): """ BinarySearch(self: List[T], index: int, count: int, item: T, comparer: IComparer[T]) -> int BinarySearch(self: List[T], item: T) -> int BinarySearch(self: List[T], item: T, comparer: IComparer[T]) -> int """ pass def Clear(self): """ Clear(self: List[T]) """ pass def Contains(self, item): """ Contains(self: List[T], item: T) -> bool """ pass def ConvertAll(self, converter): """ ConvertAll[TOutput](self: List[T], converter: Converter[T, TOutput]) -> List[TOutput] """ pass def CopyTo(self, *__args): """ CopyTo(self: List[T], index: int, array: Array[T], arrayIndex: int, count: int)CopyTo(self: List[T], array: Array[T])CopyTo(self: List[T], array: Array[T], arrayIndex: int) """ pass def Exists(self, match): """ Exists(self: List[T], match: Predicate[T]) -> bool """ pass def Find(self, match): """ Find(self: List[T], match: Predicate[T]) -> T """ pass def FindAll(self, match): """ FindAll(self: List[T], match: Predicate[T]) -> List[T] """ pass def FindIndex(self, *__args): """ FindIndex(self: List[T], match: Predicate[T]) -> int FindIndex(self: List[T], startIndex: int, match: Predicate[T]) -> int FindIndex(self: List[T], startIndex: int, count: int, match: Predicate[T]) -> int """ pass def FindLast(self, match): """ FindLast(self: List[T], match: Predicate[T]) -> T """ pass def FindLastIndex(self, *__args): """ FindLastIndex(self: List[T], match: Predicate[T]) -> int FindLastIndex(self: List[T], startIndex: int, match: Predicate[T]) -> int FindLastIndex(self: List[T], startIndex: int, count: int, match: Predicate[T]) -> int """ pass def ForEach(self, action): """ ForEach(self: List[T], action: Action[T]) """ pass def GetEnumerator(self): """ GetEnumerator(self: List[T]) -> Enumerator """ pass def GetRange(self, index, count): """ GetRange(self: List[T], index: int, count: int) -> List[T] """ pass def IndexOf(self, item, index=None, count=None): """ IndexOf(self: List[T], item: T) -> int IndexOf(self: List[T], item: T, index: int) -> int IndexOf(self: List[T], item: T, index: int, count: int) -> int """ pass def Insert(self, index, item): """ Insert(self: List[T], index: int, item: T) """ pass def InsertRange(self, index, collection): """ InsertRange(self: List[T], index: int, collection: IEnumerable[T]) """ pass def LastIndexOf(self, item, index=None, count=None): """ LastIndexOf(self: List[T], item: T) -> int LastIndexOf(self: List[T], item: T, index: int) -> int LastIndexOf(self: List[T], item: T, index: int, count: int) -> int """ pass def Remove(self, item): """ Remove(self: List[T], item: T) -> bool """ pass def RemoveAll(self, match): """ RemoveAll(self: List[T], match: Predicate[T]) -> int """ pass def RemoveAt(self, index): """ RemoveAt(self: List[T], index: int) """ pass def RemoveRange(self, index, count): """ RemoveRange(self: List[T], index: int, count: int) """ pass def Reverse(self, index=None, count=None): """ Reverse(self: List[T], index: int, count: int)Reverse(self: List[T]) """ pass def Sort(self, *__args): """ Sort(self: List[T])Sort(self: List[T], comparer: IComparer[T])Sort(self: List[T], index: int, count: int, comparer: IComparer[T])Sort(self: List[T], comparison: Comparison[T]) """ pass def ToArray(self): """ ToArray(self: List[T]) -> Array[T] """ pass def TrimExcess(self): """ TrimExcess(self: List[T]) """ pass def TrueForAll(self, match): """ TrueForAll(self: List[T], match: Predicate[T]) -> bool """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool __contains__(self: IList, value: object) -> bool """ pass def __delitem__(self, *args): #cannot find CLR method """ x.__delitem__(y) <==> del x[y]x.__delitem__(y) <==> del x[y]x.__delitem__(y) <==> del x[y] """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __getslice__(self, *args): #cannot find CLR method """ __getslice__(self: List[T], x: int, y: int) -> List[T] __getslice__(self: List[T], x: int, y: int) -> List[T] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, *__args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, collection: IEnumerable[T]) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: List[T]) -> str __repr__(self: List[T]) -> str """ pass def __setitem__(self, *args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Capacity = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Capacity(self: List[T]) -> int Set: Capacity(self: List[T]) = value """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: List[T]) -> int """ Enumerator = None class Queue(object, IEnumerable[T], IEnumerable, ICollection, IReadOnlyCollection[T]): """ Queue[T]() Queue[T](capacity: int) Queue[T](collection: IEnumerable[T]) """ def Clear(self): """ Clear(self: Queue[T]) """ pass def Contains(self, item): """ Contains(self: Queue[T], item: T) -> bool """ pass def CopyTo(self, array, arrayIndex): """ CopyTo(self: Queue[T], array: Array[T], arrayIndex: int) """ pass def Dequeue(self): """ Dequeue(self: Queue[T]) -> T """ pass def Enqueue(self, item): """ Enqueue(self: Queue[T], item: T) """ pass def GetEnumerator(self): """ GetEnumerator(self: Queue[T]) -> Enumerator """ pass def Peek(self): """ Peek(self: Queue[T]) -> T """ pass def ToArray(self): """ ToArray(self: Queue[T]) -> Array[T] """ pass def TrimExcess(self): """ TrimExcess(self: Queue[T]) """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, *__args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, collection: IEnumerable[T]) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: Queue[T]) -> int """ Enumerator = None class SortedDictionary(object, IDictionary[TKey, TValue], ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable, IDictionary, ICollection, IReadOnlyDictionary[TKey, TValue], IReadOnlyCollection[KeyValuePair[TKey, TValue]]): """ SortedDictionary[TKey, TValue]() SortedDictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue]) SortedDictionary[TKey, TValue](dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) SortedDictionary[TKey, TValue](comparer: IComparer[TKey]) """ def Add(self, key, value): """ Add(self: SortedDictionary[TKey, TValue], key: TKey, value: TValue) """ pass def Clear(self): """ Clear(self: SortedDictionary[TKey, TValue]) """ pass def ContainsKey(self, key): """ ContainsKey(self: SortedDictionary[TKey, TValue], key: TKey) -> bool """ pass def ContainsValue(self, value): """ ContainsValue(self: SortedDictionary[TKey, TValue], value: TValue) -> bool """ pass def CopyTo(self, array, index): """ CopyTo(self: SortedDictionary[TKey, TValue], array: Array[KeyValuePair[TKey, TValue]], index: int) """ pass def GetEnumerator(self): """ GetEnumerator(self: SortedDictionary[TKey, TValue]) -> Enumerator """ pass def Remove(self, key): """ Remove(self: SortedDictionary[TKey, TValue], key: TKey) -> bool """ pass def TryGetValue(self, key, value): """ TryGetValue(self: SortedDictionary[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: IDictionary[TKey, TValue], key: TKey) -> bool __contains__(self: IDictionary, key: object) -> bool """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, *__args): """ __new__(cls: type) __new__(cls: type, dictionary: IDictionary[TKey, TValue]) __new__(cls: type, dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) __new__(cls: type, comparer: IComparer[TKey]) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass def __setitem__(self, *args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: SortedDictionary[TKey, TValue]) -> IComparer[TKey] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: SortedDictionary[TKey, TValue]) -> int """ Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: SortedDictionary[TKey, TValue]) -> KeyCollection """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: SortedDictionary[TKey, TValue]) -> ValueCollection """ Enumerator = None KeyCollection = None ValueCollection = None class SortedList(object, IDictionary[TKey, TValue], ICollection[KeyValuePair[TKey, TValue]], IEnumerable[KeyValuePair[TKey, TValue]], IEnumerable, IDictionary, ICollection, IReadOnlyDictionary[TKey, TValue], IReadOnlyCollection[KeyValuePair[TKey, TValue]]): """ SortedList[TKey, TValue]() SortedList[TKey, TValue](capacity: int) SortedList[TKey, TValue](comparer: IComparer[TKey]) SortedList[TKey, TValue](capacity: int, comparer: IComparer[TKey]) SortedList[TKey, TValue](dictionary: IDictionary[TKey, TValue]) SortedList[TKey, TValue](dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) """ def Add(self, key, value): """ Add(self: SortedList[TKey, TValue], key: TKey, value: TValue) """ pass def Clear(self): """ Clear(self: SortedList[TKey, TValue]) """ pass def ContainsKey(self, key): """ ContainsKey(self: SortedList[TKey, TValue], key: TKey) -> bool """ pass def ContainsValue(self, value): """ ContainsValue(self: SortedList[TKey, TValue], value: TValue) -> bool """ pass def GetEnumerator(self): """ GetEnumerator(self: SortedList[TKey, TValue]) -> IEnumerator[KeyValuePair[TKey, TValue]] """ pass def IndexOfKey(self, key): """ IndexOfKey(self: SortedList[TKey, TValue], key: TKey) -> int """ pass def IndexOfValue(self, value): """ IndexOfValue(self: SortedList[TKey, TValue], value: TValue) -> int """ pass def Remove(self, key): """ Remove(self: SortedList[TKey, TValue], key: TKey) -> bool """ pass def RemoveAt(self, index): """ RemoveAt(self: SortedList[TKey, TValue], index: int) """ pass def TrimExcess(self): """ TrimExcess(self: SortedList[TKey, TValue]) """ pass def TryGetValue(self, key, value): """ TryGetValue(self: SortedList[TKey, TValue], key: TKey) -> (bool, TValue) """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: IDictionary[TKey, TValue], key: TKey) -> bool __contains__(self: IDictionary, key: object) -> bool """ pass def __getitem__(self, *args): #cannot find CLR method """ x.__getitem__(y) <==> x[y] """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, *__args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, comparer: IComparer[TKey]) __new__(cls: type, capacity: int, comparer: IComparer[TKey]) __new__(cls: type, dictionary: IDictionary[TKey, TValue]) __new__(cls: type, dictionary: IDictionary[TKey, TValue], comparer: IComparer[TKey]) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass def __setitem__(self, *args): #cannot find CLR method """ x.__setitem__(i, y) <==> x[i]= """ pass Capacity = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Capacity(self: SortedList[TKey, TValue]) -> int Set: Capacity(self: SortedList[TKey, TValue]) = value """ Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: SortedList[TKey, TValue]) -> IComparer[TKey] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: SortedList[TKey, TValue]) -> int """ Keys = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Keys(self: SortedList[TKey, TValue]) -> IList[TKey] """ Values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Values(self: SortedList[TKey, TValue]) -> IList[TValue] """ class SortedSet(object, ISet[T], ICollection[T], IEnumerable[T], IEnumerable, ICollection, ISerializable, IDeserializationCallback, IReadOnlyCollection[T]): """ SortedSet[T]() SortedSet[T](comparer: IComparer[T]) SortedSet[T](collection: IEnumerable[T]) SortedSet[T](collection: IEnumerable[T], comparer: IComparer[T]) """ def Add(self, item): """ Add(self: SortedSet[T], item: T) -> bool """ pass def Clear(self): """ Clear(self: SortedSet[T]) """ pass def Contains(self, item): """ Contains(self: SortedSet[T], item: T) -> bool """ pass def CopyTo(self, array, index=None, count=None): """ CopyTo(self: SortedSet[T], array: Array[T])CopyTo(self: SortedSet[T], array: Array[T], index: int)CopyTo(self: SortedSet[T], array: Array[T], index: int, count: int) """ pass @staticmethod def CreateSetComparer(memberEqualityComparer=None): """ CreateSetComparer() -> IEqualityComparer[SortedSet[T]] CreateSetComparer(memberEqualityComparer: IEqualityComparer[T]) -> IEqualityComparer[SortedSet[T]] """ pass def ExceptWith(self, other): """ ExceptWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def GetEnumerator(self): """ GetEnumerator(self: SortedSet[T]) -> Enumerator """ pass def GetObjectData(self, *args): #cannot find CLR method """ GetObjectData(self: SortedSet[T], info: SerializationInfo, context: StreamingContext) """ pass def GetViewBetween(self, lowerValue, upperValue): """ GetViewBetween(self: SortedSet[T], lowerValue: T, upperValue: T) -> SortedSet[T] """ pass def IntersectWith(self, other): """ IntersectWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def IsProperSubsetOf(self, other): """ IsProperSubsetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def IsProperSupersetOf(self, other): """ IsProperSupersetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def IsSubsetOf(self, other): """ IsSubsetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def IsSupersetOf(self, other): """ IsSupersetOf(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def OnDeserialization(self, *args): #cannot find CLR method """ OnDeserialization(self: SortedSet[T], sender: object) """ pass def Overlaps(self, other): """ Overlaps(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def Remove(self, item): """ Remove(self: SortedSet[T], item: T) -> bool """ pass def RemoveWhere(self, match): """ RemoveWhere(self: SortedSet[T], match: Predicate[T]) -> int """ pass def Reverse(self): """ Reverse(self: SortedSet[T]) -> IEnumerable[T] """ pass def SetEquals(self, other): """ SetEquals(self: SortedSet[T], other: IEnumerable[T]) -> bool """ pass def SymmetricExceptWith(self, other): """ SymmetricExceptWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def TryGetValue(self, equalValue, actualValue): """ TryGetValue(self: SortedSet[T], equalValue: T) -> (bool, T) """ pass def UnionWith(self, other): """ UnionWith(self: SortedSet[T], other: IEnumerable[T]) """ pass def __add__(self, *args): #cannot find CLR method """ x.__add__(y) <==> x+y """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__(self: ICollection[T], item: T) -> bool """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, *__args): """ __new__(cls: type) __new__(cls: type, comparer: IComparer[T]) __new__(cls: type, collection: IEnumerable[T]) __new__(cls: type, collection: IEnumerable[T], comparer: IComparer[T]) __new__(cls: type, info: SerializationInfo, context: StreamingContext) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Comparer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Comparer(self: SortedSet[T]) -> IComparer[T] """ Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: SortedSet[T]) -> int """ Max = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Max(self: SortedSet[T]) -> T """ Min = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Min(self: SortedSet[T]) -> T """ Enumerator = None class Stack(object, IEnumerable[T], IEnumerable, ICollection, IReadOnlyCollection[T]): """ Stack[T]() Stack[T](capacity: int) Stack[T](collection: IEnumerable[T]) """ def Clear(self): """ Clear(self: Stack[T]) """ pass def Contains(self, item): """ Contains(self: Stack[T], item: T) -> bool """ pass def CopyTo(self, array, arrayIndex): """ CopyTo(self: Stack[T], array: Array[T], arrayIndex: int) """ pass def GetEnumerator(self): """ GetEnumerator(self: Stack[T]) -> Enumerator """ pass def Peek(self): """ Peek(self: Stack[T]) -> T """ pass def Pop(self): """ Pop(self: Stack[T]) -> T """ pass def Push(self, item): """ Push(self: Stack[T], item: T) """ pass def ToArray(self): """ ToArray(self: Stack[T]) -> Array[T] """ pass def TrimExcess(self): """ TrimExcess(self: Stack[T]) """ pass def __contains__(self, *args): #cannot find CLR method """ __contains__[T](enumerable: IEnumerable[T], value: T) -> bool """ pass def __init__(self, *args): #cannot find CLR method """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __iter__(self, *args): #cannot find CLR method """ __iter__(self: IEnumerable) -> object """ pass def __len__(self, *args): #cannot find CLR method """ x.__len__() <==> len(x) """ pass @staticmethod # known case of __new__ def __new__(self, *__args): """ __new__(cls: type) __new__(cls: type, capacity: int) __new__(cls: type, collection: IEnumerable[T]) """ pass def __reduce_ex__(self, *args): #cannot find CLR method pass def __repr__(self, *args): #cannot find CLR method """ __repr__(self: object) -> str """ pass Count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Count(self: Stack[T]) -> int """ Enumerator = None

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LinkUp/core/apis/algorithm_api.py

Justin-sd/LinkUp

0

67204

from ..models import * from .availability_calendar_api import * from .calendar_api import * import json from datetime import datetime, timedelta def get_best(event_id): """ :param event_id: the id of the event we want to get best times of :return: A list of sorted pairs: [ (time, [users]), (time, [users]).... ] where time is the starting time and users is list of users who can make it. """ event_set = Event.objects.filter(event_id=event_id) event = event_set[0] # make the queryset of users into a list of users users = list(event.members.all()) # make all these in minutes duration = int(event.duration) st = event.potential_start_date # round up the potential starting minutes if st.minute > 30: new_st = st.replace(minute=0) new_st = new_st + timedelta(hours=1) elif st.minute > 0: new_st = st.replace(minute=30) elif st.minute == 0 or st.minute == 30: new_st = st start = convert_to_minutes(new_st, new_st) et = event.potential_end_date # round down potential ending minutes if et.minute > 30: new_et = et.replace(minute=30) elif et.minute > 0: new_et = et.replace(minute=0) elif et.minute == 0 or et.minute == 30: new_et = et end = convert_to_minutes(new_et, new_st) min_hour = event.no_earlier_than.hour min_minute = event.no_earlier_than.minute max_hour = event.no_later_than.hour max_minute = event.no_later_than.minute # Dictionary: starting times as keys and values is list of people who can make it, # keys incremented by duration optimal_times = {} # from start to end time, add keys of 30 minute increments with querysets of every user attending for i in range(start,end+1, 30): if i + duration > end: break # only add times later than min time and earlier than max time time = convert_to_datetime(new_st, i) if min_hour < time.hour < max_hour: optimal_times[i] = users.copy() elif time.hour == min_hour: if time.minute >= min_minute: optimal_times[i] = users.copy() elif time.hour == max_hour: if time.minute <= max_minute: optimal_times[i] = users.copy() # have a list of all users times for u in users: # user_sched = free_busy_month(u) # schedule = json.dumps(user_sched, default=json_datetime_handler) # Schedule.objects.create(user=u, availability=schedule) # get user's schedules in datetime format for times in get_users_saved_schedule(u): start_time = list(times.values())[0] # round DOWN the starting minutes if start_time.minute > 30: starting = start_time.replace(minute=30) elif start_time.minute > 0: starting = start_time.replace(minute=0) elif start_time.minute == 0 or start_time.minute == 30: starting = start_time the_start = convert_to_minutes(starting, new_st) end_time = list(times.values())[1] # round UP the ending minutes if et.minute > 30: ending = et.replace(minute=0) ending = ending + timedelta(hours=1) elif et.minute > 0: ending = et.replace(minute=30) elif et.minute == 0 or et.minute == 30: ending = end_time the_end = convert_to_minutes(ending, new_st) # try to find the keys in 30 minute increments and remove the user # from the corresponding list for i in range(the_start, the_end+1, 30): if i in optimal_times: dict_value = optimal_times.get(i) if u in dict_value: dict_value.remove(u) new_dict = {i: dict_value} optimal_times.update(new_dict) # go through the optimal times and find which list contains # most users then append to new list curr_max = 0 if len(optimal_times) > 0: curr_max = len(list(optimal_times.values())[0]) append_list = [] for times in optimal_times: if len(optimal_times[times]) >= curr_max: # append a list of pairs, first = datetime of start second = list of attending # with the ending of the list having more people available append_list.append((convert_to_datetime(new_st, times), optimal_times.get(times))) curr_max = len(optimal_times[times]) # return the reversed list return append_list[::-1] # convert a datetime to minutes elapsed def convert_to_minutes(time, starting): elapsed = time - starting minutes = int(elapsed.total_seconds()/60) return minutes # convert minutes to a datetime by getting starting datetime and timedelta by minutes def convert_to_datetime(starting, mins): time = starting + timedelta(minutes=mins) return time

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src/starter/models.py

rehive/django-starter

2

149545

from logging import getLogger import datetime from uuid import uuid4 from django.contrib.postgres.fields import JSONField from django.db import models from django.utils.timezone import utc logger = getLogger('django') class Message(models.Model): first_name = models.CharField(max_length=300, null=True, blank=True) last_name = models.CharField(max_length=300, null=True, blank=True) message = models.CharField(max_length=300, null=False, blank=False) created = models.DateTimeField() updated = models.DateTimeField() def __str__(self): return str(self.id) def save(self, *args, **kwargs): if not self.id: # On create self.created = datetime.datetime.now(tz=utc) self.updated = datetime.datetime.now(tz=utc) return super(Message, self).save(*args, **kwargs)

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app/home/views.py

1IllI1/BBS_MIGRATE

0

31423

# coding:utf8 # 调用蓝图 from . import home from flask import render_template, redirect, url_for, flash, session, request,current_app from app.home.forms import RegistForm, LoginForm, UserdetailForm, PwdForm, CommentForm, PostForm from app.models import User, UserLoginLog, Comment, Post,Col from werkzeug.security import generate_password_hash from werkzeug.utils import secure_filename import uuid from app import db from app.home.email import send_mail from functools import wraps import time import os # 定义用户登录判断装饰器 def user_login_req(func): @wraps(func) def decorated_function(*args, **kwargs): # session不存在时请求登录 if "user" not in session: return redirect(url_for("home.user_login", next=request.url)) return func(*args, **kwargs) return decorated_function # html测试路由 @home.route('/usefortest/') def ust(): return render_template('home/USERFORTEST.html') # 首页路由 @home.route('/') def index(): # posts = Post.query.all() current_user_id = 0 current_user_name ="" if "user" in session: current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id page_index = request.args.get('page', 1, type=int) query = Post.query.join(User).filter(User.id == Post.user_id).order_by(Post.addtime.desc()) pagination = query.paginate(page_index, per_page=10, error_out=False) posts = pagination.items return render_template('home/index.html', posts=posts, pagination=pagination,current_user_name=current_user_name,current_user_id=current_user_id) #首页删除个人发布的内容 @home.route("/index/del/") @user_login_req def index_del(): #获取当前登录用户id current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id index_id = request.args.get("id", '0') post = Post.query.get_or_404(int(index_id)) if post.user_id != current_user_id: flash("删除不合法") return redirect(url_for("home.index")) db.session.delete(post) db.session.commit() flash("删除成功") return redirect(url_for("home.index")) #设置帖子关注 @home.route("/index/col/") @user_login_req def index_col(): #获取当前登录用户id current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id index_id = request.args.get("id", '0') col_check = Col.query.filter_by(id=index_id).count() if col_check == 0: col=Col( post_id=index_id, user_id=current_user_id ) db.session.add(col) db.session.commit() flash("收藏成功","ok") flash("收藏已存在","err") return redirect(url_for("home.index")) #设置评论关注 @home.route("/play/col/") @user_login_req def play_col(): #获取当前登录用户id current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id index_id = request.args.get("id", '0') col_check = Col.query.filter_by(id=index_id).count() if col_check == 0: col=Col( comment_id=index_id, user_id=current_user_id ) db.session.add(col) db.session.commit() flash("收藏成功","ok") flash("收藏已存在","err") return redirect(url_for("home.index")) # # from io import BytesIO # from . import verify_code # @home.route('/code') # def get_code(): # image, code = verify_code.get_verify_code() # # 图片以二进制形式写入 # buf = BytesIO() # image.save(buf, 'jpeg') # buf_str = buf.getvalue() # # 把buf_str作为response返回前端，并设置首部字段 # response = verify_code.make_response(buf_str) # response.headers['Content-Type'] = 'image/gif' # # 将验证码字符串储存在session中 # session['image'] = code # return response @home.route('/activate/<token>') def activate(token): #验证token 提取id if User.check_active_token(token): flash("账户已经激活") return redirect(url_for("home.user_login")) else: flash("激活失败") return redirect(url_for("home.index")) # 登录路由 @home.route("/login/", methods=["POST", "GET"]) def user_login(): form = LoginForm() if form.validate_on_submit(): data = form.data user = User.query.filter_by(name=data["name"]).first() print("登录按钮被点击") # if session.get('image').lower() != form.verify_code.data.lower(): # flash('验证码错误') # return render_template('home/user_login.html', form=form) print("用户激活状态"+str(user.activate)) if user.activate: if not user.check_pwd(data["pwd"]): flash("用户名或密码错误！") return redirect(url_for("home.user_login")) session["user"] = data["name"] #session["user_id"] = user.id userloginlog = UserLoginLog( user_id=user.id, ip=request.remote_addr, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) db.session.add(userloginlog) db.session.commit() return redirect(request.args.get('next') or url_for("home.index")) else: flash("用户尚未激活,请激活以后再登录") return render_template('home/user_login.html', form=form) # 登出路由 @home.route("/logout/") @user_login_req def logout(): session.pop("user") return redirect(url_for("home.user_login")) # 会员注册 @home.route("/register/", methods=['GET', "POST"]) def register(): form = RegistForm() if form.validate_on_submit(): data = form.data user = User( name=data["name"], email=data["email"], phone=data["phone"], pwd=generate_password_hash(data["pwd"]), uuid=uuid.uuid4().hex, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) print(user) check = User.query.filter_by(name=data["name"]).count() if check == 0: db.session.add(user) db.session.commit() print("用户数据提交到数据库") token = user.generate_active_token() # 发送用户账户激活的邮件 send_mail(user.email, '激活您的账户', 'email/activate', username=user.name, token=token) # 弹出消息提示用户 flash("注册成功，请点击邮件中的链接完成激活",'ok') return redirect(url_for("home.user_login")) flash("用户名已存在","err") return render_template('home/register.html', form=form) # 修改文件名称 def change_filename(filename): fileinfo = os.path.splitext(filename) # 对名字进行前后缀分离 #注意此处datetime.now() filename = time.strftime("%Y%m%d%H%M%S") + "_" + fileinfo[-1] # 生成新文件名 return filename # 用户中心 @home.route("/user/", methods=["GET", "POST"]) @user_login_req def user(): form = UserdetailForm() user = User.query.filter_by(name=(session["user"])).first() if user.face is not None: form.face.validators = [] if request.method == "GET": form.name.data = user.name form.email.data = user.email form.phone.data = user.phone form.info.data = user.info if form.validate_on_submit(): print('button pressed') data = form.data # if data["name"] != user.name and name_count == 1: # flash("用户名已被占用") # return redirect(url_for("home.user")) if request.method == 'POST': if request.files['imageup']: file = request.files['imageup'] print("获取文件成功") filename = secure_filename(str(hash(file.filename)))+str(user.id)+".jpg" print("secure成功"+filename) del_face = user.face file.save(os.path.join(current_app.config['UP_DIR']+os.sep+"users",filename)) print("上传成功" + filename) #os.remove(os.path.join(app.config['UP_DIR'] + os.sep+"users", del_face)) print("删除文件"+del_face+"成功") user.face = filename user.name=data["name"] user.email=data["email"] user.phone=data["phone"] user.info=data["info"] db.session.add(user) db.session.commit() flash("修改成功！") return redirect(url_for("home.user")) flash("失败") return render_template('home/user.html', form=form, user=user) @home.route("/pwd/", methods=["GET", "POST"]) @user_login_req def pwd(): form = PwdForm() if form.validate_on_submit(): data = form.data user = User.query.filter_by(name=session["user"]).first() user.pwd = <PASSWORD>_password_hash(data["new_pwd"]) db.session.add(user) db.session.commit() flash("修改密码成功，请重新登录！", "ok") return redirect(url_for("home.logout")) return render_template('home/pwd.html', form=form) # 会员中心评论列表评论功能在paly路由中 @home.route("/comments/") @user_login_req def comments(): user_name = session["user"] user = User.query.filter_by(name=user_name).first() page = request.args.get('page', 1, type=int) # query = Comment.query.order_by(Comment.addtime.desc()) query = Comment.query.filter(Comment.user_id == user.id).order_by(Comment.addtime.desc()) pagination = query.paginate(page, per_page=10, error_out=False) comments = pagination.items return render_template('home/comments.html', user=user,user_name=user_name, comments=comments,pagination=pagination) @home.route("/comments/del/") @user_login_req def comment_del(): comment_id = request.args.get("id", '') comment = Comment.query.get_or_404(int(comment_id)) db.session.delete(comment) db.session.commit() flash("评论删除成功") return redirect(url_for("home.comments")) @home.route("/postrecords/") @user_login_req def postrecords(): user_name = session["user"] user = User.query.filter_by(name=user_name).first() user_id = user.id user = User.query.filter_by(id=user_id).first() page = request.args.get('page', 1, type=int) # query = Comment.query.order_by(Comment.addtime.desc()) query = Post.query.filter(Post.user_id == user_id).order_by(Post.addtime.desc()) pagination = query.paginate(page, per_page=5, error_out=False) posts = pagination.items return render_template('home/post_records.html', user=user,user_name=user_name, posts=posts, pagination=pagination) @home.route("/postrecords/del/") @user_login_req def post_del(): post_id = request.args.get("id", '') post = Post.query.get_or_404(int(post_id)) comment = Comment.query.filter_by(post_id=post_id).all() db.session.delete(post) db.session.commit() db.session.delete(comment) db.session.commit() flash("主题帖删除成功") return redirect(url_for("home.postrecords")) @home.route("/loginlog/", methods=["POST", "GET"]) @user_login_req def loginlog(): current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() user_login_log = UserLoginLog.query.filter_by( user_id=user.id ).order_by( UserLoginLog.addtime.desc() # 此处限制了查寻到的登录日志为前15条 ).limit(15).all() return render_template("home/loginlog.html", user_login_log=user_login_log) @home.route("/col/del/") @user_login_req def col_del(): current_user_name = session["user"] user= User.query.filter_by(name=current_user_name).first() current_user_id = user.id col_id = request.args.get("id", '') col = Col.query.get_or_404(int(col_id)) if col.user_id != current_user_id: flash("收藏删除不合法") return redirect(url_for("home.col")) db.session.delete(col) db.session.commit() flash("收藏删除成功") return redirect(url_for("home.col")) ##会员中心收藏列表 @home.route("/col/") @user_login_req def col(): current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() user_id = user.id # 获取当前分页页面编号（编号，默认值，类型） page = request.args.get('page', 1, type=int) # 从数据库中查找对应用户的收藏 #query =Col.query.filter_by(user_id =user_id).order_by(Col.addtime.desc()) query = Col.query.join(Post).join(User).filter(Col.user_id==user_id,Col.post_id == Col.post_id).order_by(Col.addtime.desc()) # 对当前贴的评论进行分页（分页号，每页展示的数量，error） pagination = query.paginate(page, per_page=5, error_out=False) # 获得分页后当前页显示的评论 cols = pagination.items # 渲染主题帖展示页面 print(query) return render_template('home/col.html',cols=cols,pagination=pagination) @home.route("/index/") def reindex(): # z此处index重复 return redirect(url_for("home.index")) @home.route('/animation/') def animation(): data = {'sgd.jpg', 'sutstudent.jpg', 'sutsight01.jpg', 'sutsight02.jpg', 'hxxy.jpg'} return render_template('home/animation.html', data=data) @home.route('/search/') def search(): current_user_id = 0 current_user_name = "" if "user" in session: current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id # 获取查询的内容 # search=request.args.get("search",'',type=str) search = request.args.get("search", "搜索结果为空") # print("搜索的的内容"+search) # 获取当前分页页面编号（编号，默认值，类型） page = request.args.get('page', 1, type=int) # 从数据库中查找对应当前主题贴的评论 query = Post.query.filter(Post.title.ilike('%' + search + '%')).order_by(Post.addtime.desc()) # 对当前主题帖的评论数量进行统计 post_count = Post.query.filter(Post.title.ilike('%' + search + '%')).count() # 对当前贴的评论进行分页（分页号，每页展示的数量，error） pagination = query.paginate(page, per_page=5, error_out=False) # 获得分页后当前页显示的评论 comments = pagination.items # 渲染主题帖展示页面 return render_template("home/search.html", search=search, count=post_count, current_user_name=current_user_name,pagination=pagination, results=comments,current_user_id=current_user_id) # 主题帖详情页 @home.route('/play/', methods=["GET", "POST"]) def play(): # 从请求参数拿到请求的post_id post_id = request.args.get("post_id", "") # 评论表单 form = CommentForm() # 清除表单内容 form.data['content'] = "" # 利用post_id找到要显示的主题贴 post = Post.query.filter(Post.id == post_id).first() # 利用post_id在User表中查找作者姓名 author = User.query.filter(User.id == post.user_id).first() # 从session中取得当前登陆中的用户名 current_user_id = 0 current_user_name = '游客' if "user" in session: current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id # 若用户登录则显示评论发布表单 if "user" in session and form.validate_on_submit(): comment = Comment( content=form.data["content"], post_id=int(post_id), user_id=current_user_id, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) db.session.add(comment) db.session.commit() flash("评论提交成功！") # 获取当前分页页面编号（编号，默认值，类型） page = request.args.get('page', 1, type=int) # 从数据库中查找对应当前主题贴的评论 query = Comment.query.join(User).filter(Comment.post_id == post_id).order_by(Comment.addtime.desc()) # 对当前主题帖的评论数量进行统计 comment_count = Comment.query.filter(Comment.post_id == post_id).count() # 对当前贴的评论进行分页（分页号，每页展示的数量，error） pagination = query.paginate(page, per_page=5, error_out=False) # 获得分页后当前页显示的评论 comments = pagination.items # 渲染主题帖展示页面 return render_template("home/play.html", post=post, form=form, comments=comments, pagination=pagination, author=author,current_user_name=current_user_name, count=comment_count,current_user_id=current_user_id) @home.route('/post/', methods=["GET", "POST"]) @user_login_req def post(): form = PostForm() current_user_name = session["user"] user = User.query.filter_by(name=current_user_name).first() current_user_id = user.id if form.validate_on_submit(): data = form.data post = Post( title=data["title"], content=data["content"], user_id=current_user_id, addtime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ) db.session.add(post) db.session.commit() flash("发布主题帖成功") return render_template("home/post_add.html", form=form,current_user_name=current_user_name) #404 @home.errorhandler(404) def page_not_found(error): return render_template("home/404.html"),404

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networks/larflow/models/larflow_uresnet.py

LArbys/ublarcvserver

2

221

<filename>networks/larflow/models/larflow_uresnet.py import torch.nn as nn import torch as torch import math import torch.utils.model_zoo as model_zoo ########################################################### # # U-ResNet # U-net witih ResNet modules # # Semantic segmentation network used by MicroBooNE # to label track/shower pixels # # resnet implementation from pytorch.torchvision module # U-net from (cite) # # meant to be copy of caffe version # ########################################################### def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu1 = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.stride = stride self.bypass = None if inplanes!=planes or stride>1: self.bypass = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False) self.relu = nn.ReLU(inplace=True) def forward(self, x): out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) if self.bypass is not None: outbp = self.bypass(x) out += outbp else: out += x out = self.relu(out) return out class Bottleneck(nn.Module): def __init__(self, inplanes, planes, stride=1 ): super(Bottleneck, self).__init__() # residual path self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.stride = stride # if stride >1, then we need to subsamble the input if stride>1: self.shortcut = nn.Conv2d(inplanes,planes,kernel_size=1,stride=stride,bias=False) else: self.shortcut = None def forward(self, x): if self.shortcut is None: bypass = x else: bypass = self.shortcut(x) residual = self.conv1(x) residual = self.bn1(residual) residual = self.relu(residual) residual = self.conv2(residual) residual = self.bn2(residual) residual = self.relu(residual) residual = self.conv3(residual) residual = self.bn3(residual) out = bypass+residual out = self.relu(out) return out class PreactivationBlock(nn.Module): def __init__(self, inplanes, planes, stride=1 ): super(Preactivation, self).__init__() # residual path self.bn1 = nn.BatchNorm2d(inplanes) self.relu1 = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.relu2 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) # if stride >1, then we need to subsamble the input if stride>1: self.shortcut = nn.Conv2d(inplanes,planes,kernel_size=1,stride=stride,bias=False) else: self.shortcut = None def forward(self, x): if self.shortcut is None: bypass = x else: bypass = self.shortcut(x) class DoubleResNet(nn.Module): def __init__(self,Block,inplanes,planes,stride=1): super(DoubleResNet,self).__init__() self.res1 = Block(inplanes,planes,stride) self.res2 = Block( planes,planes, 1) def forward(self, x): out = self.res1(x) out = self.res2(out) return out class ConvTransposeLayer(nn.Module): def __init__(self,deconv_inplanes,skip_inplanes,deconv_outplanes,res_outplanes): super(ConvTransposeLayer,self).__init__() self.deconv = nn.ConvTranspose2d( deconv_inplanes, deconv_outplanes, kernel_size=4, stride=2, padding=1, bias=False ) self.res = DoubleResNet(BasicBlock,deconv_outplanes+skip_inplanes,res_outplanes,stride=1) def forward(self,x,skip_x): out = self.deconv(x,output_size=skip_x.size()) # concat skip connections out = torch.cat( [out,skip_x], 1 ) out = self.res(out) return out class LArFlowUResNet(nn.Module): def __init__(self, num_classes=3, input_channels=3, inplanes=16, showsizes=False, use_visi=True): self.inplanes =inplanes super(LArFlowUResNet, self).__init__() self._showsizes = showsizes # print size at each layer self.use_visi = use_visi # Encoder # stem # one big stem self.conv1 = nn.Conv2d(input_channels, self.inplanes, kernel_size=7, stride=1, padding=3, bias=True) # initial conv layer self.bn1 = nn.BatchNorm2d(self.inplanes) self.relu1 = nn.ReLU(inplace=True) self.pool1 = nn.MaxPool2d( 3, stride=2, padding=1 ) self.enc_layer1 = self._make_encoding_layer( self.inplanes*1, self.inplanes*2, stride=1) # 16->32 self.enc_layer2 = self._make_encoding_layer( self.inplanes*2, self.inplanes*4, stride=2) # 32->64 self.enc_layer3 = self._make_encoding_layer( self.inplanes*4, self.inplanes*8, stride=2) # 64->128 self.enc_layer4 = self._make_encoding_layer( self.inplanes*8, self.inplanes*16, stride=2) # 128->256 self.enc_layer5 = self._make_encoding_layer( self.inplanes*16, self.inplanes*32, stride=2) # 256->512 # decoding flow #self.num_final_flow_features = self.inplanes self.num_final_flow_features = self.inplanes self.flow_dec_layer5 = self._make_decoding_layer( self.inplanes*32*2, self.inplanes*16, self.inplanes*16, self.inplanes*16 ) # 512->256 self.flow_dec_layer4 = self._make_decoding_layer( self.inplanes*16, self.inplanes*8, self.inplanes*8, self.inplanes*8 ) # 256->128 self.flow_dec_layer3 = self._make_decoding_layer( self.inplanes*8, self.inplanes*4, self.inplanes*4, self.inplanes*4 ) # 128->64 self.flow_dec_layer2 = self._make_decoding_layer( self.inplanes*4, self.inplanes*2, self.inplanes*2, self.inplanes*2 ) # 64->32 #self.flow_dec_layer1 = self._make_decoding_layer( self.inplanes*2, self.inplanes, self.inplanes ) # 32->16 self.flow_dec_layer1 = self._make_decoding_layer( self.inplanes*2, self.inplanes, self.inplanes, self.num_final_flow_features ) # 32->200 # decoding matchability if self.use_visi: self.visi_dec_layer5 = self._make_decoding_layer( self.inplanes*32*2, self.inplanes*16, self.inplanes*16, self.inplanes*16 ) # 512->256 self.visi_dec_layer4 = self._make_decoding_layer( self.inplanes*16, self.inplanes*8, self.inplanes*8, self.inplanes*8 ) # 256->128 self.visi_dec_layer3 = self._make_decoding_layer( self.inplanes*8, self.inplanes*4, self.inplanes*4, self.inplanes*4 ) # 128->64 self.visi_dec_layer2 = self._make_decoding_layer( self.inplanes*4, self.inplanes*2, self.inplanes*2, self.inplanes*2 ) # 64->32 self.visi_dec_layer1 = self._make_decoding_layer( self.inplanes*2, self.inplanes, self.inplanes, self.inplanes ) # 32->16 # 1x1 conv for flow self.flow_conv = nn.Conv2d( self.num_final_flow_features, 1, kernel_size=1, stride=1, padding=0, bias=True ) # 1x1 conv for mathability if self.use_visi: self.visi_conv = nn.Conv2d( self.inplanes, 2, kernel_size=1, stride=1, padding=0, bias=True ) # 2 classes, 0=not vis, 1=vis self.visi_softmax = nn.LogSoftmax(dim=1) # initialization for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m,nn.ConvTranspose2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_encoding_layer(self, inplanes, planes, stride=2): return DoubleResNet(BasicBlock,inplanes,planes,stride=stride) def _make_decoding_layer(self, inplanes, skipplanes, deconvplanes, resnetplanes ): return ConvTransposeLayer( inplanes, skipplanes, deconvplanes, resnetplanes ) def encode(self,x): # stem x = self.conv1(x) x = self.bn1(x) x0 = self.relu1(x) x = self.pool1(x0) x1 = self.enc_layer1(x) x2 = self.enc_layer2(x1) x3 = self.enc_layer3(x2) x4 = self.enc_layer4(x3) x5 = self.enc_layer5(x4) if self._showsizes: print "after encoding: " print " x1: ",x1.size() print " x2: ",x2.size() print " x3: ",x3.size() print " x4: ",x4.size() print " x5: ",x5.size() return x5,x0,x1,x2,x3,x4 def flow(self,merged_encode,x0,x1,x2,x3,x4): """ decoding to flow prediction """ x = self.flow_dec_layer5(merged_encode,x4) if self._showsizes: print "after decoding:" print " dec5: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer4(x,x3) if self._showsizes: print " dec4: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer3(x,x2) if self._showsizes: print " dec3: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer2(x,x1) if self._showsizes: print " dec2: ",x.size()," iscuda=",x.is_cuda x = self.flow_dec_layer1(x,x0) if self._showsizes: print " dec1: ",x.size()," iscuda=",x.is_cuda return x def visibility(self,merged_encode,x0,x1,x2,x3,x4): """ decoding to flow prediction """ x = self.visi_dec_layer5(merged_encode,x4) if self._showsizes: print "after decoding:" print " dec5: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer4(x,x3) if self._showsizes: print " dec4: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer3(x,x2) if self._showsizes: print " dec3: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer2(x,x1) if self._showsizes: print " dec2: ",x.size()," iscuda=",x.is_cuda x = self.visi_dec_layer1(x,x0) if self._showsizes: print " dec1: ",x.size()," iscuda=",x.is_cuda return x def forward(self, src, target): if self._showsizes: print "input: ",x.size()," is_cuda=",x.is_cuda src_encode, s0, s1, s2, s3, s4 = self.encode(src) target_encode, t0, t1, t2, t3, t4 = self.encode(target) merged_encode = torch.cat( [target_encode,src_encode], 1 ) flowout = self.flow( merged_encode, s0, s1, s2, s3, s4 ) if self.use_visi: visiout = self.visibility( merged_encode, t0, t1, t2, t3, t4 ) flow_predict = self.flow_conv( flowout ) if self.use_visi: visi_predict = self.visi_conv( visiout ) visi_predict = self.visi_softmax(visi_predict) else: visi_predict = None if self._showsizes: print " softmax: ",x.size() return flow_predict,visi_predict

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relaax/algorithms/ddpg/parameter_server.py

deeplearninc/relaax

71

6738

from __future__ import absolute_import from relaax.server.parameter_server import parameter_server_base from relaax.server.common import session from . import ddpg_model class ParameterServer(parameter_server_base.ParameterServerBase): def init_session(self): self.session = session.Session(ddpg_model.SharedParameters()) self.session.op_initialize() self.session.op_init_target_weights() def n_step(self): return self.session.op_n_step() def score(self): return self.session.op_score() def get_session(self): return self.session

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PyBank/main.py

fannie-chang/Python-Challenge

0

123615

<reponame>fannie-chang/Python-Challenge<filename>PyBank/main.py # Import os module to read CSV file import os import csv # Path to collect data from the PyBank folder budget_csv = os.path.join('..' ,'PyBank','Resources','budget_data.csv') # Open the file with open(budget_csv, newline='') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') # Skip the first row of the CSV file csv_header = next (csvreader, None) #Lists to budget data date=[] profit_losses=[] trend_change = [] total_net_amount = 0 total_trend_change = 0 # Loop through the data for row in csvreader: date.append(row[0]) profit_losses.append(row[1]) # Calculate total months total_months = list(date) num_months = len(total_months) # Calculate total net amount total_net_amount = total_net_amount + int(row[1]) # Loop through data for profit_losses list for i in range(len(profit_losses)-1): # Calculate average trend changes (current value - previous value) trend_change.append(int(profit_losses[i+1]) - int(profit_losses[i])) total_trend_change = total_trend_change + (int(profit_losses[i+1]) - int(profit_losses[i])) trend_average = round(total_trend_change / (len(total_months)-1),2) # Calculate greatest increase and decrease greatest_increase = max (trend_change) greatest_decrease = min (trend_change) # Find date for greatest increase and decrease max_date_index = trend_change.index(greatest_increase) max_date = date[max_date_index +1] min_date_index = trend_change.index(greatest_decrease) min_date = date[min_date_index +1] # Print result print(f"Financial Analysis ") print(f"-------------------------------- ") print(f"Total Months: {num_months}") print(f"Total:${total_net_amount} ") print(f"Average Change: ${trend_average}") print(f"Greatest Increase in Profits: {max_date} ${greatest_increase}") print(f"Greatest Decrease in Profits: {min_date} ${greatest_decrease}") #Print result to text file output_path = os.path.join('..' ,'PyBank','Analysis','financial_analysis.txt') with open (output_path, 'w', newline="") as textfile: print(f"Financial Analysis ", file=textfile) print(f"--------------------------------", file=textfile ) print(f"Total Months: {num_months}", file=textfile) print(f"Total:${total_net_amount} ", file=textfile) print(f"Average Change: ${trend_average}", file=textfile) print(f"Greatest Increase in Profits: {max_date} ${greatest_increase}", file=textfile) print(f"Greatest Decrease in Profits: {min_date} ${greatest_decrease}", file=textfile)

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aiidalab_widgets_base/export.py

danielhollas/aiidalab-widgets-base

2

68805

<filename>aiidalab_widgets_base/export.py """Widgets to manage AiiDA export.""" import os from IPython.display import display from ipywidgets import Button class ExportButtonWidget(Button): """Export Node button.""" def __init__(self, process, **kwargs): self.process = process if "description" not in kwargs: kwargs["description"] = "Export workflow ({})".format(self.process.id) if "layout" not in kwargs: kwargs["layout"] = {} kwargs["layout"]["width"] = "initial" super(ExportButtonWidget, self).__init__(**kwargs) self.on_click(self.export_aiida_subgraph) def export_aiida_subgraph(self, change=None): # pylint: disable=unused-argument """Perform export when the button is pressed""" import base64 import subprocess from tempfile import mkdtemp from IPython.display import Javascript fname = os.path.join(mkdtemp(), "export.aiida") subprocess.call( ["verdi", "export", "create", fname, "-N", str(self.process.id)] ) with open(fname, "rb") as fobj: b64 = base64.b64encode(fobj.read()) payload = b64.decode() javas = Javascript( """ var link = document.createElement('a'); link.href = "data:;base64,{payload}" link.download = "{filename}" document.body.appendChild(link); link.click(); document.body.removeChild(link); """.format( payload=payload, filename="export_{}.aiida".format(self.process.id) ) ) display(javas)

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flopy/utils/mfgrdfile.py

pjhaest/flopy

0

162412

import numpy as np import collections from ..utils.utils_def import FlopyBinaryData from ..utils.reference import SpatialReference class MfGrdFile(FlopyBinaryData): def __init__(self, filename, precision='double', verbose=False): """ Class constructor. """ super(MfGrdFile, self).__init__() self.set_float(precision=precision) self.verbose = verbose self._initial_len = 50 self._recorddict = collections.OrderedDict() self._datadict = collections.OrderedDict() self._recordkeys = [] self.file = open(filename, 'rb') """ # read header information GRID DISV VERSION 1 NTXT 13 LENTXT 100 """ # grid type line = self.read_text(self._initial_len).strip() t = line.split() self._grid = t[1] # version line = self.read_text(self._initial_len).strip() t = line.split() self._version = t[1] # version line = self.read_text(self._initial_len).strip() t = line.split() self._ntxt = int(t[1]) # length of text line = self.read_text(self._initial_len).strip() t = line.split() self._lentxt = int(t[1]) # read text strings for idx in range(self._ntxt): line = self.read_text(self._lentxt).strip() t = line.split() key = t[0] dt = t[1] if dt == 'INTEGER': dtype = np.int32 elif dt == 'SINGLE': dtype = np.float32 elif dt == 'DOUBLE': dtype = np.float64 else: dtype = None nd = int(t[3]) if nd > 0: shp = [int(v) for v in t[4:]] shp = tuple(shp[::-1]) else: shp = (0,) self._recorddict[key] = (dtype, nd, shp) self._recordkeys.append(key) if self.verbose: print('read {} records from {}'.format(self._ntxt, filename)) for key in self._recordkeys: dt, nd, shp = self._recorddict[key] # read array data if nd > 0: count = 1 for v in shp: count *= v v = self.read_record(count=count, dtype=dt) # read variable data else: if dt == np.int32: v = self.read_integer() elif dt == np.float32: v = self.read_real() elif dt == np.float64: v = self.read_real() self._datadict[key] = v # set the spatial reference self.sr = self._set_spatialreference() def _set_spatialreference(self): try: if self._grid == 'DISV': sr = None elif self._grid == 'DIS': delr, delc = self._datadict['DELR'], self._datadict['DELC'] xorigin, yorigin, rot = self._datadict['XORIGIN'], \ self._datadict['YORIGIN'], \ self._datadict['ANGROT'] sr = SpatialReference(delr=delr, delc=delc, xll=xorigin, yll=yorigin, rotation=rot) except: sr = None print('could not set spatial reference for {}'.format(self.file.name)) return sr def get_spatialreference(self): return self.sr def get_centroids(self): x, y = None, None try: if self._grid == 'DISV': x = self._datadict['CELLX'] y = self._datadict['CELLY'] elif self._grid == 'DIS': nlay = self._datadict['NLAY'] x = np.tile(self.sr.xcentergrid.flatten(), nlay) y = np.tile(self.sr.ycentergrid.flatten(), nlay) except: print('could not return centroids' + ' for {}'.format(self.file.name)) return np.column_stack((x, y)) def get_verts(self): if self._grid == 'DISV': try: iverts = [] iavert = self._datadict['IAVERT'] javert = self._datadict['JAVERT'] shpvert = self._recorddict['VERTICES'][2] for ivert in range(self._datadict['NCPL']): i0 = iavert[ivert] - 1 i1 = iavert[ivert+1] - 1 iverts.append((javert[i0:i1]-1).tolist()) if self.verbose: print('returning vertices for {}'.format(self.file.name)) return iverts, self._datadict['VERTICES'].reshape(shpvert) except: print('could not return vertices for {}'.format(self.file.name)) elif self._grid == 'DIS': try: nlay, nrow, ncol = self._datadict['NLAY'], \ self._datadict['NROW'], \ self._datadict['NCOL'] iv = 0 verts = [] iverts = [] for k in range(nlay): for i in range(nrow): for j in range(ncol): ivlist = [] v = self.sr.get_vertices(i, j) for (x, y) in v: verts.append((x, y)) ivlist.append(iv) iv += 1 iverts.append(ivlist) verts = np.array(verts) return iverts, verts except: print('could not return vertices for {}'.format(self.file.name))

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dataworkspace/dataworkspace/tests/eventlog/test_views.py

uktrade/jupyterhub-data-auth-admin

1

130622

<filename>dataworkspace/dataworkspace/tests/eventlog/test_views.py from django.urls import reverse from dataworkspace.tests import factories from dataworkspace.tests.common import BaseAdminTestCase class TestEventLogAdmin(BaseAdminTestCase): def test_event_log_csv_download(self): event1 = factories.EventLogFactory.create( user=self.user, related_object=factories.ReferenceDatasetFactory.create() ) event2 = factories.EventLogFactory.create( related_object=factories.DatabaseFactory.create() ) response = self._authenticated_post( reverse("admin:eventlog_eventlog_changelist"), {"action": "export_events", "_selected_action": [event1.id, event2.id]}, ) self.assertContains(response, '"timestamp","user","event_type","related_object","extra"') self.assertEqual(len(response.content.splitlines()), 3)

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onirim/card/_base.py

cwahbong/onirim-py

0

148550

""" Inner module for enumerations and base types. """ import logging from onirim import util LOGGER = logging.getLogger(__name__) class Color(util.AutoNumberEnum): """ Enumerated colors of cards. Attributes: red blue green yellow """ red = () blue = () green = () yellow = () class Card: """ A card in onirim. All cards in onirim inherits from this class. Methods called by some event has a single parameter `core` (:py:class:`onirim.core.Core`) for accessing the game information. """ _color = None _kind = None def _color_name(self): return self._color.name if self._color else "nocolor" def _kind_name(self): return self._kind.name if self._kind else "nokind" def _class_name(self): return self.__class__.__name__[1:].lower() @property def color(self): """ Color of a card. Returns: The color of card if it has color, None otherwise. Currently only nightmare cards have no color. """ return self._color @property def kind(self): """ Kind of a card if it is a location. Returns: The kind of card if it is a location, None otherwise. """ return self._kind def _do_drawn(self, core): raise NotImplementedError def drawn(self, core): """ Called while this card is drawn. """ LOGGER.debug("A card is drawn, %r.", self) self._do_drawn(core) def _do_play(self, core): raise NotImplementedError def play(self, core): """ Called while this card is played. """ LOGGER.debug( "A card is played, color=%s, kind=%s.", self._color_name(), self._kind_name()) self._do_play(core) def _do_discard(self, core): raise NotImplementedError def discard(self, core): """ Called while this card is discarded. """ LOGGER.debug( "A card is discarded, color=%s, kind=%s.", self._color_name(), self._kind_name()) self._do_discard(core) def __eq__(self, other): return self._kind == other.kind and self._color == other.color def __hash__(self): key_int = self.kind.value if self.kind else 0 color_int = self.color.value if self.color else 0 return key_int * 10 + color_int def __str__(self): return "{1} {0} card".format(self._class_name(), self._color_name()) def __repr__(self): return "Card{{color: {}, kind: {}}}".format(self._color, self._kind) class ColorCard(Card): """A card with color.""" def __init__(self, color): # pylint is not smart enough to recognize enum class so we disable the # function arguments check here. self._color = Color(color) #pylint: disable=too-many-function-args

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DAG/pipeline/service.py

zj-myidea/MYIDEA

0

78966

<gh_stars>0 from .model import Vertex, Graph, Edge,db, Pipeline, Track from .config import getlogger from functools import wraps from collections import defaultdict logger = getlogger(__name__,'./recode.log') def transactional(fn): @wraps(fn) def wrapper(*args, **kwargs): ret = fn(*args, **kwargs) try: db.session.commit() except Exception as e: db.session.rollback() logger.error(e) return ret return wrapper @transactional def create_graph(name,desc=None): graph = Graph() graph.name = name graph.desc = desc db.session.add(graph) return graph @transactional def add_vertex(graph:Graph, name:str, input=None, script=None,): vertex = Vertex() vertex.g_id = graph.id vertex.name = name vertex.input = input vertex.script = script db.session.add(vertex) return vertex @transactional def add_edge(graph:Graph, tail:Vertex, head:Vertex): edge = Edge() edge.g_id = graph.id edge.head = head.id edge.tail = tail.id db.session.add(edge) return edge def delete_vertex(id): query = db.session.query(Vertex).filter(Vertex.id==id) v = query.first() if v: db.session.query(Edge).filter(Edge.tail == v.id | Edge.head == v.id).delete() query.delete() return v def check_graph(graph:Graph): query = db.session.query(Vertex).filter(Vertex.g_id==graph.id) vertexs = {vertex.id for vertex in query} query = db.session.query(Edge).filter(Edge.g_id==graph.id) edges = defaultdict(list) ids = set() for edge in query: edges[edge.tail].append((edge.tail, edge.head)) ids.add(edge.head) if len(edges) == 0: print('edge') return False zds = vertexs - ids if len(zds): # {a:[(a,b),(a,c)],b:[(b,d)]} for zd in zds: if zd in edges: edges.pop(zd) while edges: print(edges) vertexs = ids ids = set() for lis in edges.values(): for edge in lis: ids.add(edge[1]) zds = vertexs - ids if not len(zds): print('zds') break else: for zd in zds: edges.pop(zd) if len(edges) == 0: try: graph = db.session.query(Graph).filter(Graph.id ==graph.id).first() if graph: graph.checked = 1 db.session.add(graph) db.session.commit() print('++++++++++++++++++++') return True except Exception as e: db.session.rollback() raise e print('~~~~~~~~~~~~') return False

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bin/snaptools_wrapper.py

mr-c/sc-atac-seq-pipeline

0

130559

<filename>bin/snaptools_wrapper.py #!/usr/bin/env python3 from argparse import ArgumentParser from os import fspath from pathlib import Path from subprocess import run from typing import Callable, Dict, List, Tuple, Union import snaptools_defaults from utils import find_base_index_path, identity # 3-tuples: # [0] command-line argument to snaptools # [1] default path # [2] function used to adjust the path (both default path and user-provided) # (if no adjustment required, use 'identity') CommandData = Tuple[ str, Path, Callable[ [Path], Path, ], ] SNAPTOOLS_COMMAND_DEFAULTS: Dict[str, List[CommandData]] = { 'align-paired-end': [ ('--input-reference', snaptools_defaults.DEFAULT_ALIGNMENT_INDEX, find_base_index_path), ], 'snap-pre': [ ('--genome-size', snaptools_defaults.DEFAULT_SIZE_INDEX, identity), ], } def run_with_defaults(snaptools_command: str, other_args: List[Union[Path, str]]): args = other_args.copy() # For each command, check known options/arguments with default values. # If the option is present, transform with the provided function (e.g. # finding the "base" path of a genome index). If the option is not present, # append with the default value for option, default, func in SNAPTOOLS_COMMAND_DEFAULTS[snaptools_command]: if option in args: existing_option_index = args.index(option) value_index = existing_option_index + 1 args[value_index] = func(args[value_index]) else: args.append(option) args.append(func(default)) command = ['snaptools', snaptools_command] command.extend(fspath(arg) for arg in args) print('Running:', ' '.join(command)) run(command, check=True) if __name__ == '__main__': p = ArgumentParser() p.add_argument('snaptools_command') p.add_argument( 'other_arg', nargs='*', help='Arguments to the chosen snaptools command', ) args = p.parse_args() run_with_defaults(args.snaptools_command, args.other_arg)

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api/app.py

sai-krishna-msk/KickAssist

0

8588

<gh_stars>0 from ml_model.model import KickModel import numpy as np import pandas as pd import eli5 import joblib import flask from flask import Flask, render_template, request, jsonify app = Flask(__name__) model_oh = joblib.load('ml_model/estimators/model_oh.sav') model_hel = joblib.load('ml_model/estimators/model_hel.sav') encoder_oh = joblib.load('ml_model/estimators/encoder_oh.sav') encoder_hel = joblib.load('ml_model/estimators/encoder_hel.sav') encoder_label = joblib.load('ml_model/estimators/encoder_label.sav') def get_predict(launch_date , deadline_date , goal , subcategory , category , currency , country , description, rewards): pred_dict={ "launched_at":launch_date, "deadline":deadline_date, "goal":int(goal), "sub_category":subcategory, "category":category, "currency":currency, "location_country":country, "blurb":description, "rewards":[] } try: for reward in rewards.split(","): pred_dict["rewards"].append(int(reward)) except Exception as e: raise Exception(f"Error sanatizing rewards with {e} error") return pred_dict @app.route('/predict/<launch_date>/<deadline_date>/<goal>/<subcategory>/<category>/<currency>/<country>/<description>/<rewards>') def GetURL(launch_date , deadline_date , goal , subcategory , category , currency , country , description, rewards): pred_dict = get_predict(launch_date , deadline_date , goal , subcategory , category , currency , country , description, rewards) obj = KickModel(model_oh , model_hel , encoder_oh , encoder_hel , encoder_label) obj.load_data(pred_dict) obj.pred() oh_pred = float(obj.pred_oh[0][1]) hel_pred = float(obj.pred_hel[0][1]) response = { "prediction_oh":oh_pred, "prediction_hel":hel_pred, "prediction_oh_df":obj.pred_oh_intr.to_dict(), "prediction_hel_intr":obj.pred_hel_intr.to_dict() } return response if __name__=="__main__": app.run(debug =True)

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wbia/algo/verif/clf_helpers.py

WildMeOrg/wildbook-ia

20

71552

# -*- coding: utf-8 -*- """ This module is a work in progress, as such concepts are subject to change. MAIN IDEA: `MultiTaskSamples` serves as a structure to contain and manipulate a set of samples with potentially many different types of labels and features. """ import logging import utool as ut import ubelt as ub import numpy as np from wbia import dtool as dt import pandas as pd import sklearn import sklearn.metrics import sklearn.ensemble import sklearn.impute import sklearn.pipeline import sklearn.neural_network from wbia.algo.verif import sklearn_utils print, rrr, profile = ut.inject2(__name__) logger = logging.getLogger('wbia') class XValConfig(dt.Config): _param_info_list = [ # ut.ParamInfo('type', 'StratifiedKFold'), ut.ParamInfo('type', 'StratifiedGroupKFold'), ut.ParamInfo('n_splits', 3), ut.ParamInfo( 'shuffle', True, hideif=lambda cfg: cfg['type'] == 'StratifiedGroupKFold' ), ut.ParamInfo( 'random_state', 3953056901, hideif=lambda cfg: cfg['type'] == 'StratifiedGroupKFold', ), ] @ut.reloadable_class class ClfProblem(ut.NiceRepr): def __init__(pblm): pblm.deploy_task_clfs = None pblm.eval_task_clfs = None pblm.xval_kw = XValConfig() pblm.eval_task_clfs = None pblm.task_combo_res = None pblm.verbose = True def set_pandas_options(pblm): # pd.options.display.max_rows = 10 pd.options.display.max_rows = 20 pd.options.display.max_columns = 40 pd.options.display.width = 160 pd.options.display.float_format = lambda x: '%.4f' % (x,) def set_pandas_options_low(pblm): # pd.options.display.max_rows = 10 pd.options.display.max_rows = 5 pd.options.display.max_columns = 40 pd.options.display.width = 160 pd.options.display.float_format = lambda x: '%.4f' % (x,) def set_pandas_options_normal(pblm): # pd.options.display.max_rows = 10 pd.options.display.max_rows = 20 pd.options.display.max_columns = 40 pd.options.display.width = 160 pd.options.display.float_format = lambda x: '%.4f' % (x,) def learn_evaluation_classifiers(pblm, task_keys=None, clf_keys=None, data_keys=None): """ Evaluates by learning classifiers using cross validation. Do not use this to learn production classifiers. python -m wbia.algo.verif.vsone evaluate_classifiers --db PZ_PB_RF_TRAIN --show Example: CommandLine: python -m clf_helpers learn_evaluation_classifiers Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> pblm = IrisProblem() >>> pblm.setup() >>> pblm.verbose = True >>> pblm.eval_clf_keys = ['Logit', 'RF'] >>> pblm.eval_task_keys = ['iris'] >>> pblm.eval_data_keys = ['learn(all)'] >>> result = pblm.learn_evaluation_classifiers() >>> res = pblm.task_combo_res['iris']['Logit']['learn(all)'] >>> res.print_report() >>> res = pblm.task_combo_res['iris']['RF']['learn(all)'] >>> res.print_report() >>> print(result) """ pblm.eval_task_clfs = ut.AutoVivification() pblm.task_combo_res = ut.AutoVivification() if task_keys is None: task_keys = pblm.eval_task_keys if data_keys is None: data_keys = pblm.eval_data_keys if clf_keys is None: clf_keys = pblm.eval_clf_keys if task_keys is None: task_keys = [pblm.primary_task_key] if data_keys is None: data_keys = [pblm.default_data_key] if clf_keys is None: clf_keys = [pblm.default_clf_key] if pblm.verbose: ut.cprint('[pblm] learn_evaluation_classifiers', color='blue') ut.cprint('[pblm] task_keys = {}'.format(task_keys)) ut.cprint('[pblm] data_keys = {}'.format(data_keys)) ut.cprint('[pblm] clf_keys = {}'.format(clf_keys)) Prog = ut.ProgPartial(freq=1, adjust=False, prehack='%s') task_prog = Prog(task_keys, label='Task') for task_key in task_prog: dataset_prog = Prog(data_keys, label='Data') for data_key in dataset_prog: clf_prog = Prog(clf_keys, label='CLF') for clf_key in clf_prog: pblm._ensure_evaluation_clf(task_key, data_key, clf_key) def _ensure_evaluation_clf(pblm, task_key, data_key, clf_key, use_cache=True): """ Learns and caches an evaluation (cross-validated) classifier and tests and caches the results. data_key = 'learn(sum,glob)' clf_key = 'RF' """ # TODO: add in params used to construct features into the cfgstr if hasattr(pblm.samples, 'sample_hashid'): ibs = pblm.infr.ibs sample_hashid = pblm.samples.sample_hashid() feat_dims = pblm.samples.X_dict[data_key].columns.values.tolist() # cfg_prefix = sample_hashid + pblm.qreq_.get_cfgstr() + feat_cfgstr est_kw1, est_kw2 = pblm._estimator_params(clf_key) param_id = ut.get_dict_hashid(est_kw1) xval_id = pblm.xval_kw.get_cfgstr() cfgstr = '_'.join( [ sample_hashid, param_id, xval_id, task_key, data_key, clf_key, ut.hashid_arr(feat_dims, 'feats'), ] ) fname = 'eval_clfres_' + ibs.dbname else: fname = 'foo' feat_dims = None cfgstr = 'bar' use_cache = False # TODO: ABI class should not be caching cacher_kw = dict(appname='vsone_rf_train', enabled=use_cache, verbose=1) cacher_clf = ub.Cacher(fname, cfgstr=cfgstr, meta=[feat_dims], **cacher_kw) data = cacher_clf.tryload() if not data: data = pblm._train_evaluation_clf(task_key, data_key, clf_key) cacher_clf.save(data) clf_list, res_list = data labels = pblm.samples.subtasks[task_key] combo_res = ClfResult.combine_results(res_list, labels) pblm.eval_task_clfs[task_key][clf_key][data_key] = clf_list pblm.task_combo_res[task_key][clf_key][data_key] = combo_res def _train_evaluation_clf(pblm, task_key, data_key, clf_key, feat_dims=None): """ Learns a cross-validated classifier on the dataset Ignore: >>> from wbia.algo.verif.vsone import * # NOQA >>> pblm = OneVsOneProblem() >>> pblm.load_features() >>> pblm.load_samples() >>> data_key = 'learn(all)' >>> task_key = 'photobomb_state' >>> clf_key = 'RF-OVR' >>> task_key = 'match_state' >>> data_key = pblm.default_data_key >>> clf_key = pblm.default_clf_key """ X_df = pblm.samples.X_dict[data_key] labels = pblm.samples.subtasks[task_key] assert np.all(labels.encoded_df.index == X_df.index) clf_partial = pblm._get_estimator(clf_key) xval_kw = pblm.xval_kw.asdict() clf_list = [] res_list = [] skf_list = pblm.samples.stratified_kfold_indices(**xval_kw) skf_prog = ut.ProgIter(skf_list, label='skf-train-eval') for train_idx, test_idx in skf_prog: X_df_train = X_df.iloc[train_idx] assert X_df_train.index.tolist() == ut.take(pblm.samples.index, train_idx) # train_uv = X_df.iloc[train_idx].index # X_train = X_df.loc[train_uv] # y_train = labels.encoded_df.loc[train_uv] if feat_dims is not None: X_df_train = X_df_train[feat_dims] X_train = X_df_train.values y_train = labels.encoded_df.iloc[train_idx].values.ravel() clf = clf_partial() clf.fit(X_train, y_train) # Note: There is a corner case where one fold doesn't get any # labels of a certain class. Because y_train is an encoded integer, # the clf.classes_ attribute will cause predictions to agree with # other classifiers trained on the same labels. # Evaluate results res = ClfResult.make_single( clf, X_df, test_idx, labels, data_key, feat_dims=feat_dims ) clf_list.append(clf) res_list.append(res) return clf_list, res_list def _external_classifier_result( pblm, clf, task_key, data_key, feat_dims=None, test_idx=None ): """ Given an external classifier (ensure its trained on disjoint data) evaluate all data on it. Args: test_idx (list): subset of this classifier to test on (defaults to all if None) """ X_df = pblm.samples.X_dict[data_key] if test_idx is None: test_idx = np.arange(len(X_df)) labels = pblm.samples.subtasks[task_key] res = ClfResult.make_single( clf, X_df, test_idx, labels, data_key, feat_dims=feat_dims ) return res def learn_deploy_classifiers(pblm, task_keys=None, clf_key=None, data_key=None): """ Learns on data without any train/validation split """ if pblm.verbose > 0: ut.cprint('[pblm] learn_deploy_classifiers', color='blue') if clf_key is None: clf_key = pblm.default_clf_key if data_key is None: data_key = pblm.default_data_key if task_keys is None: task_keys = list(pblm.samples.supported_tasks()) if pblm.deploy_task_clfs is None: pblm.deploy_task_clfs = ut.AutoVivification() Prog = ut.ProgPartial(freq=1, adjust=False, prehack='%s') task_prog = Prog(task_keys, label='Task') task_clfs = {} for task_key in task_prog: clf = pblm._train_deploy_clf(task_key, data_key, clf_key) task_clfs[task_key] = clf pblm.deploy_task_clfs[task_key][clf_key][data_key] = clf return task_clfs def _estimator_params(pblm, clf_key): est_type = clf_key.split('-')[0] if est_type in {'RF', 'RandomForest'}: est_kw1 = { # 'max_depth': 4, 'bootstrap': True, 'class_weight': None, 'criterion': 'entropy', 'max_features': 'sqrt', # 'max_features': None, 'min_samples_leaf': 5, 'min_samples_split': 2, # 'n_estimators': 64, 'n_estimators': 256, } # Hack to only use missing values if we have the right sklearn if 'missing_values' in ut.get_func_kwargs( sklearn.ensemble.RandomForestClassifier.__init__ ): est_kw1['missing_values'] = np.nan est_kw2 = { 'random_state': 3915904814, 'verbose': 0, 'n_jobs': -1, } elif est_type in {'SVC', 'SVM'}: est_kw1 = dict(kernel='linear') est_kw2 = {} elif est_type in {'Logit', 'LogisticRegression'}: est_kw1 = {} est_kw2 = {} elif est_type in {'MLP'}: est_kw1 = dict( activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(10, 10), learning_rate='constant', learning_rate_init=0.001, max_iter=200, momentum=0.9, nesterovs_momentum=True, power_t=0.5, random_state=3915904814, shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1, warm_start=False, ) est_kw2 = dict(verbose=False) else: raise KeyError('Unknown Estimator') return est_kw1, est_kw2 def _get_estimator(pblm, clf_key): """ Returns sklearn classifier """ tup = clf_key.split('-') wrap_type = None if len(tup) == 1 else tup[1] est_type = tup[0] multiclass_wrapper = { None: ut.identity, 'OVR': sklearn.multiclass.OneVsRestClassifier, 'OVO': sklearn.multiclass.OneVsOneClassifier, }[wrap_type] est_class = { 'RF': sklearn.ensemble.RandomForestClassifier, 'SVC': sklearn.svm.SVC, 'Logit': sklearn.linear_model.LogisticRegression, 'MLP': sklearn.neural_network.MLPClassifier, }[est_type] est_kw1, est_kw2 = pblm._estimator_params(est_type) est_params = ut.merge_dicts(est_kw1, est_kw2) # steps = [] # steps.append((est_type, est_class(**est_params))) # if wrap_type is not None: # steps.append((wrap_type, multiclass_wrapper)) if est_type == 'MLP': def clf_partial(): pipe = sklearn.pipeline.Pipeline( [ ('inputer', sklearn.impute.SimpleImputer(strategy='mean')), # ('scale', sklearn.preprocessing.StandardScaler), ('est', est_class(**est_params)), ] ) return multiclass_wrapper(pipe) elif est_type == 'Logit': def clf_partial(): pipe = sklearn.pipeline.Pipeline( [ ('inputer', sklearn.impute.SimpleImputer(strategy='mean')), ('est', est_class(**est_params)), ] ) return multiclass_wrapper(pipe) else: def clf_partial(): return multiclass_wrapper(est_class(**est_params)) return clf_partial def _train_deploy_clf(pblm, task_key, data_key, clf_key): X_df = pblm.samples.X_dict[data_key] labels = pblm.samples.subtasks[task_key] assert np.all(labels.encoded_df.index == X_df.index) clf_partial = pblm._get_estimator(clf_key) logger.info( 'Training deployment {} classifier on {} for {}'.format( clf_key, data_key, task_key ) ) clf = clf_partial() index = X_df.index X = X_df.loc[index].values y = labels.encoded_df.loc[index].values.ravel() clf.fit(X, y) return clf def _optimize_rf_hyperparams(pblm, data_key=None, task_key=None): """ helper script I've only run interactively Example: >>> # DISABLE_DOCTEST >>> from wbia.algo.verif.vsone import * # NOQA >>> pblm = OneVsOneProblem.from_empty('PZ_PB_RF_TRAIN') #>>> pblm = OneVsOneProblem.from_empty('GZ_Master1') >>> pblm.load_samples() >>> pblm.load_features() >>> pblm.build_feature_subsets() >>> data_key=None >>> task_key=None """ from sklearn.model_selection import RandomizedSearchCV # NOQA from sklearn.model_selection import GridSearchCV # NOQA from sklearn.ensemble import RandomForestClassifier from wbia.algo.verif import sklearn_utils if data_key is None: data_key = pblm.default_data_key if task_key is None: task_key = pblm.primary_task_key # Load data X = pblm.samples.X_dict[data_key].values y = pblm.samples.subtasks[task_key].y_enc groups = pblm.samples.group_ids # Define estimator and parameter search space grid = { 'bootstrap': [True, False], 'class_weight': [None, 'balanced'], 'criterion': ['entropy', 'gini'], # 'max_features': ['sqrt', 'log2'], 'max_features': ['sqrt'], 'min_samples_leaf': list(range(2, 11)), 'min_samples_split': list(range(2, 11)), 'n_estimators': [8, 64, 128, 256, 512, 1024], } est = RandomForestClassifier(missing_values=np.nan) if False: # debug params = ut.util_dict.all_dict_combinations(grid)[0] est.set_params(verbose=10, n_jobs=1, **params) est.fit(X=X, y=y) cv = sklearn_utils.StratifiedGroupKFold(n_splits=3) if True: n_iter = 25 SearchCV = ut.partial(RandomizedSearchCV, n_iter=n_iter) else: n_iter = ut.prod(map(len, grid.values())) SearchCV = GridSearchCV search = SearchCV(est, grid, cv=cv, verbose=10) n_cpus = ut.num_cpus() thresh = n_cpus * 1.5 n_jobs_est = 1 n_jobs_ser = min(n_cpus, n_iter) if n_iter < thresh: n_jobs_est = int(max(1, thresh / n_iter)) est.set_params(n_jobs=n_jobs_est) search.set_params(n_jobs=n_jobs_ser) search.fit(X=X, y=y, groups=groups) res = search.cv_results_.copy() alias = ut.odict( [ ('rank_test_score', 'rank'), ('mean_test_score', 'μ-test'), ('std_test_score', 'σ-test'), ('mean_train_score', 'μ-train'), ('std_train_score', 'σ-train'), ('mean_fit_time', 'fit_time'), ('params', 'params'), ] ) res = ut.dict_subset(res, alias.keys()) cvresult_df = pd.DataFrame(res).rename(columns=alias) cvresult_df = cvresult_df.sort_values('rank').reset_index(drop=True) params = pd.DataFrame.from_dict(cvresult_df['params'].values.tolist()) logger.info('Varied params:') logger.info(ut.repr4(ut.map_vals(set, params.to_dict('list')))) logger.info('Ranked Params') logger.info(params) logger.info('Ranked scores on development set:') logger.info(cvresult_df) logger.info('Best parameters set found on hyperparam set:') logger.info('best_params_ = %s' % (ut.repr4(search.best_params_),)) logger.info('Fastest params') cvresult_df.loc[cvresult_df['fit_time'].idxmin()]['params'] def _dev_calib(pblm): """ interactive script only """ from sklearn.ensemble import RandomForestClassifier from sklearn.calibration import CalibratedClassifierCV from sklearn.calibration import calibration_curve from sklearn.metrics import log_loss, brier_score_loss # Load data data_key = pblm.default_data_key task_key = pblm.primary_task_key X = pblm.samples.X_dict[data_key].values y = pblm.samples.subtasks[task_key].y_enc groups = pblm.samples.group_ids # Split into test/train/valid cv = sklearn_utils.StratifiedGroupKFold(n_splits=2) test_idx, train_idx = next(cv.split(X, y, groups)) # valid_idx = train_idx[0::2] # train_idx = train_idx[1::2] # train_valid_idx = np.hstack([train_idx, valid_idx]) # Train Uncalibrated RF est_kw = pblm._estimator_params('RF')[0] uncal_clf = RandomForestClassifier(**est_kw) uncal_clf.fit(X[train_idx], y[train_idx]) uncal_probs = uncal_clf.predict_proba(X[test_idx]).T[1] uncal_score = log_loss(y[test_idx] == 1, uncal_probs) uncal_brier = brier_score_loss(y[test_idx] == 1, uncal_probs) # Train Calibrated RF method = 'isotonic' if len(test_idx) > 2000 else 'sigmoid' precal_clf = RandomForestClassifier(**est_kw) # cv = sklearn_utils.StratifiedGroupKFold(n_splits=3) cal_clf = CalibratedClassifierCV(precal_clf, cv=2, method=method) cal_clf.fit(X[train_idx], y[train_idx]) cal_probs = cal_clf.predict_proba(X[test_idx]).T[1] cal_score = log_loss(y[test_idx] == 1, cal_probs) cal_brier = brier_score_loss(y[test_idx] == 1, cal_probs) logger.info('cal_brier = %r' % (cal_brier,)) logger.info('uncal_brier = %r' % (uncal_brier,)) logger.info('uncal_score = %r' % (uncal_score,)) logger.info('cal_score = %r' % (cal_score,)) import wbia.plottool as pt ut.qtensure() pt.figure() ax = pt.gca() y_test = y[test_idx] == 1 fraction_of_positives, mean_predicted_value = calibration_curve( y_test, uncal_probs, n_bins=10 ) ax.plot([0, 1], [0, 1], 'k:', label='Perfectly calibrated') ax.plot( mean_predicted_value, fraction_of_positives, 's-', label='%s (%1.3f)' % ('uncal-RF', uncal_brier), ) fraction_of_positives, mean_predicted_value = calibration_curve( y_test, cal_probs, n_bins=10 ) ax.plot( mean_predicted_value, fraction_of_positives, 's-', label='%s (%1.3f)' % ('cal-RF', cal_brier), ) pt.legend() @ut.reloadable_class class ClfResult(ut.NiceRepr): r""" Handles evaluation statistics for a multiclass classifier trained on a specific dataset with specific labels. """ # Attributes that identify the task and data the classifier is evaluated on _key_attrs = ['task_key', 'data_key', 'class_names'] # Attributes about results and labels of individual samples _datafame_attrs = ['probs_df', 'probhats_df', 'target_bin_df', 'target_enc_df'] def __init__(res): pass def __nice__(res): return '{}, {}, {}'.format(res.task_key, res.data_key, len(res.index)) @property def index(res): return res.probs_df.index @classmethod def make_single(ClfResult, clf, X_df, test_idx, labels, data_key, feat_dims=None): """ Make a result for a single cross validiation subset """ X_df_test = X_df.iloc[test_idx] if feat_dims is not None: X_df_test = X_df_test[feat_dims] index = X_df_test.index # clf_probs = clf.predict_proba(X_df_test) # index = pd.Series(test_idx, name='test_idx') # Ensure shape corresponds with all classes def align_cols(arr, arr_cols, target_cols): import utool as ut alignx = ut.list_alignment(arr_cols, target_cols, missing=True) aligned_arrT = ut.none_take(arr.T, alignx) aligned_arrT = ut.replace_nones(aligned_arrT, np.zeros(len(arr))) aligned_arr = np.vstack(aligned_arrT).T return aligned_arr res = ClfResult() res.task_key = labels.task_name res.data_key = data_key res.class_names = ut.lmap(str, labels.class_names) res.feat_dims = feat_dims res.probs_df = sklearn_utils.predict_proba_df(clf, X_df_test, res.class_names) res.target_bin_df = labels.indicator_df.iloc[test_idx] res.target_enc_df = labels.encoded_df.iloc[test_idx] if hasattr(clf, 'estimators_') and labels.n_classes > 2: # The n-th estimator in the OVR classifier predicts the prob of the # n-th class (as label 1). probs_hat = np.hstack( [est.predict_proba(X_df_test)[:, 1:2] for est in clf.estimators_] ) res.probhats_df = pd.DataFrame( align_cols(probs_hat, clf.classes_, labels.classes_), index=index, columns=res.class_names, ) # In the OVR-case, ideally things will sum to 1, but when they # don't normalization happens. An Z-value of more than 1 means # overconfidence, and under 0 means underconfidence. res.confidence_ratio = res.probhats_df.sum(axis=1) else: res.probhats_df = None return res def compress(res, flags): res2 = ClfResult() res2.task_key = res.task_key res2.data_key = res.data_key res2.class_names = res.class_names res2.probs_df = res.probs_df[flags] res2.target_bin_df = res.target_bin_df[flags] res2.target_enc_df = res.target_enc_df[flags] if res.probhats_df is None: res2.probhats_df = None else: res2.probhats_df = res.probhats_df[flags] # res2.confidence_ratio = res.confidence_ratio[flags] return res2 @classmethod def combine_results(ClfResult, res_list, labels=None): """ Combine results from cross validation runs into a single result representing the performance of the entire dataset """ # Ensure that res_lists are not overlapping for r1, r2 in ut.combinations(res_list, 2): assert ( len(r1.index.intersection(r2.index)) == 0 ), 'ClfResult dataframes must be disjoint' # sanity check for r in res_list: assert np.all(r.index == r.probs_df.index) assert np.all(r.index == r.target_bin_df.index) assert np.all(r.index == r.target_enc_df.index) # Combine them with pandas res = ClfResult() res0 = res_list[0] # Transfer single attributes (which should all be the same) for attr in ClfResult._key_attrs: val = getattr(res0, attr) setattr(res, attr, val) assert all( [getattr(r, attr) == val for r in res_list] ), 'ClfResult with different key attributes are incompatible' # Combine dataframe properties (which should all have disjoint indices) for attr in ClfResult._datafame_attrs: if getattr(res0, attr) is not None: combo_attr = pd.concat([getattr(r, attr) for r in res_list]) setattr(res, attr, combo_attr) else: setattr(res, attr, None) for attr in ClfResult._datafame_attrs: val = getattr(res, attr) if val is not None: assert np.all(res.index == val.index), 'index got weird' return res def hardness_analysis(res, samples, infr=None, method='argmax'): """ samples = pblm.samples # TODO MWE with sklearn data # ClfResult.make_single(ClfResult, clf, X_df, test_idx, labels, # data_key, feat_dims=None): import sklearn.datasets iris = sklearn.datasets.load_iris() # TODO: make this setup simpler pblm = ClfProblem() task_key, clf_key, data_key = 'iris', 'RF', 'learn(all)' X_df = pd.DataFrame(iris.data, columns=iris.feature_names) samples = MultiTaskSamples(X_df.index) samples.apply_indicators({'iris': {name: iris.target == idx for idx, name in enumerate(iris.target_names)}}) samples.X_dict = {'learn(all)': X_df} pblm.samples = samples pblm.xval_kw['type'] = 'StratifiedKFold' clf_list, res_list = pblm._train_evaluation_clf( task_key, data_key, clf_key) labels = pblm.samples.subtasks[task_key] res = ClfResult.combine_results(res_list, labels) res.get_thresholds('mcc', 'maximize') predict_method = 'argmax' """ meta = {} easiness = ut.ziptake(res.probs_df.values, res.target_enc_df.values) # pred = sklearn_utils.predict_from_probs(res.probs_df, predict_method) if method == 'max-mcc': method = res.get_thresholds('mcc', 'maximize') pred = sklearn_utils.predict_from_probs(res.probs_df, method, force=True) meta['easiness'] = np.array(easiness).ravel() meta['hardness'] = 1 - meta['easiness'] meta['aid1'] = res.probs_df.index.get_level_values(0) meta['aid2'] = res.probs_df.index.get_level_values(1) # meta['aid1'] = samples.aid_pairs.T[0].take(res.probs_df.index.values) # meta['aid2'] = samples.aid_pairs.T[1].take(res.probs_df.index.values) # meta['pred'] = res.probs_df.values.argmax(axis=1) meta['pred'] = pred.values meta['real'] = res.target_enc_df.values.ravel() meta['failed'] = meta['pred'] != meta['real'] meta = pd.DataFrame(meta) meta = meta.set_index(['aid1', 'aid2'], drop=False) if infr is not None: ibs = infr.ibs edges = list(meta.index.tolist()) conf_dict = infr.get_edge_attrs( 'confidence', edges, on_missing='filter', default=ibs.const.CONFIDENCE.CODE.UNKNOWN, ) conf_df = pd.DataFrame.from_dict(conf_dict, orient='index') conf_df = conf_df[0].map(ibs.const.CONFIDENCE.CODE_TO_INT) meta = meta.assign(real_conf=conf_df) meta['real_conf'] = np.nan_to_num(meta['real_conf']).astype(np.int) meta = meta.sort_values('hardness', ascending=False) res.meta = meta return res.meta def missing_classes(res): # Find classes that were never predicted unique_predictions = np.unique(res.probs_df.values.argmax(axis=1)) n_classes = len(res.class_names) missing_classes = ut.index_complement(unique_predictions, n_classes) return missing_classes def augment_if_needed(res): """ Adds in dummy values for missing classes """ missing_classes = res.missing_classes() n_classes = len(res.class_names) y_test_enc_aug = res.target_enc_df.values y_test_bin_aug = res.target_bin_df.values clf_probs_aug = res.probs_df.values sample_weight = np.ones(len(y_test_enc_aug)) n_missing = len(missing_classes) if res.probhats_df is not None: clf_probhats_aug = res.probhats_df.values else: clf_probhats_aug = None # Check if augmentation is necessary if n_missing > 0: missing_bin = np.zeros((n_missing, n_classes)) missing_bin[(np.arange(n_missing), missing_classes)] = 1.0 missing_enc = np.array(missing_classes)[:, None] y_test_enc_aug = np.vstack([y_test_enc_aug, missing_enc]) y_test_bin_aug = np.vstack([y_test_bin_aug, missing_bin]) clf_probs_aug = np.vstack([clf_probs_aug, missing_bin]) # make sample weights where dummies have no weight sample_weight = np.hstack([sample_weight, np.full(n_missing, 0)]) if res.probhats_df is not None: clf_probhats_aug = np.vstack([clf_probhats_aug, missing_bin]) res.clf_probs = clf_probs_aug res.clf_probhats = clf_probhats_aug res.y_test_enc = y_test_enc_aug res.y_test_bin = y_test_bin_aug res.sample_weight = sample_weight def extended_clf_report(res, verbose=True): res.augment_if_needed() pred_enc = res.clf_probs.argmax(axis=1) y_pred = pred_enc y_true = res.y_test_enc sample_weight = res.sample_weight target_names = res.class_names report = sklearn_utils.classification_report2( y_true, y_pred, target_names=target_names, sample_weight=sample_weight, verbose=verbose, ) return report def print_report(res): res.augment_if_needed() pred_enc = res.clf_probs.argmax(axis=1) res.extended_clf_report() report = sklearn.metrics.classification_report( y_true=res.y_test_enc, y_pred=pred_enc, target_names=res.class_names, sample_weight=res.sample_weight, ) logger.info('Precision/Recall Report:') logger.info(report) def get_thresholds(res, metric='mcc', value='maximize'): """ get_metric = 'thresholds' at_metric = metric = 'mcc' at_value = value = 'maximize' a = [] b = [] for x in np.linspace(0, 1, 1000): a += [cfms.get_metric_at_metric('thresholds', 'fpr', x, subindex=True)] b += [cfms.get_thresh_at_metric('fpr', x)] a = np.array(a) b = np.array(b) d = (a - b) logger.info((d.min(), d.max())) """ threshes = {} for class_name in res.class_names: cfms = res.confusions(class_name) thresh = cfms.get_metric_at_metric('thresh', metric, value) threshes[class_name] = thresh return threshes @profile def get_pos_threshes( res, metric='fpr', value=1e-4, maximize=False, warmup=200, priors=None, min_thresh=0.5, ): """ Finds a threshold that achieves the desired `value` for the desired metric, while maximizing or minimizing the threshold. For positive classification you want to minimize the threshold. Priors can be passed in to augment probabilities depending on support. By default a class prior is 1 for threshold minimization and 0 for maximization. """ pos_threshes = {} if priors is None: priors = {name: float(not maximize) for name in res.class_names} for class_name in res.class_names: cfms = res.confusions(class_name) learned_thresh = cfms.get_metric_at_metric('thresh', metric, value) # learned_thresh = cfms.get_thresh_at_metric( # metric, value, maximize=maximize) prior_thresh = priors[class_name] n_support = cfms.n_pos if warmup is not None: """ python -m wbia.plottool.draw_func2 plot_func --show --range=0,1 \ --func="lambda x: np.maximum(0, (x - .6) / (1 - .6))" """ # If n_support < warmup: then interpolate to learned thresh nmax = warmup if isinstance(warmup, int) else warmup[class_name] # alpha varies from 0 to 1 alpha = min(nmax, n_support) / nmax # transform alpha through nonlinear function (similar to ReLU) p = 0.6 # transition point alpha = max(0, (alpha - p) / (1 - p)) thresh = prior_thresh * (1 - alpha) + learned_thresh * (alpha) else: thresh = learned_thresh pos_threshes[class_name] = max(min_thresh, thresh) return pos_threshes def report_thresholds(res, warmup=200): # import vtool as vt ut.cprint('Threshold Report', 'yellow') y_test_bin = res.target_bin_df.values # y_test_enc = y_test_bin.argmax(axis=1) # clf_probs = res.probs_df.values # The maximum allowed false positive rate # We expect that we will make 1 error every 1,000 decisions # thresh_df['foo'] = [1, 2, 3] # thresh_df['foo'][res.class_names[k]] = 1 # for k in [2, 0, 1]: choice_mv = ut.odict( [ ('@fpr=.01', ('fpr', 0.01)), ('@fpr=.001', ('fpr', 0.001)), ('@fpr=.0001', ('fpr', 1e-4)), ('@fpr=.0000', ('fpr', 0)), ('@max(mcc)', ('mcc', 'max')), # (class_name + '@max(acc)', ('acc', 'max')), # (class_name + '@max(mk)', ('mk', 'max')), # (class_name + '@max(bm)', ('bm', 'max')), ] ) for k in range(y_test_bin.shape[1]): thresh_dict = ut.odict() class_name = res.class_names[k] cfms = res.confusions(class_name) # probs, labels = clf_probs.T[k], y_test_bin.T[k] # cfms = vt.ConfusionMetrics().fit(probs, labels) for k, mv in choice_mv.items(): metric, value = mv idx = cfms.get_index_at_metric(metric, value) key = class_name + k thresh_dict[key] = ut.odict() for metric in ['thresh', 'fpr', 'tpr', 'tpa', 'bm', 'mk', 'mcc']: thresh_dict[key][metric] = cfms.get_metric_at_index(metric, idx) thresh_df = pd.DataFrame.from_dict(thresh_dict, orient='index') thresh_df = thresh_df.loc[list(thresh_dict.keys())] if cfms.n_pos > 0 and cfms.n_neg > 0: logger.info('Raw 1vR {} Thresholds'.format(class_name)) logger.info(ut.indent(thresh_df.to_string(float_format='{:.4f}'.format))) # chosen_type = class_name + '@fpr=0' # pos_threshes[class_name] = thresh_df.loc[chosen_type]['thresh'] for choice_k, choice_mv in iter(choice_mv.items()): metric, value = choice_mv pos_threshes = res.get_pos_threshes(metric, value, warmup=warmup) logger.info('Choosing threshold based on %s' % (choice_k,)) res.report_auto_thresholds(pos_threshes) def report_auto_thresholds(res, threshes, verbose=True): report_lines = [] print_ = report_lines.append print_( 'Chosen thresholds = %s' % (ut.repr2(threshes, nl=1, precision=4, align=True),) ) res.augment_if_needed() target_names = res.class_names sample_weight = res.sample_weight y_true = res.y_test_enc.ravel() y_pred, can_autodecide = sklearn_utils.predict_from_probs( res.clf_probs, threshes, res.class_names, force=False, multi=False, return_flags=True, ) can_autodecide[res.sample_weight == 0] = False auto_pred = y_pred[can_autodecide].astype(np.int) auto_true = y_true[can_autodecide].ravel() auto_probs = res.clf_probs[can_autodecide] total_cases = int(sample_weight.sum()) print_('Will autodecide for %r/%r cases' % (can_autodecide.sum(), (total_cases))) def frac_str(a, b): return '{:}/{:} = {:.2f}%'.format(int(a), int(b), a / b) y_test_bin = res.target_bin_df.values supported_class_idxs = [k for k, y in enumerate(y_test_bin.T) if y.sum() > 0] print_(' * Auto-Decide Per-Class Summary') for k in supported_class_idxs: # Look at fail/succs in threshold name = res.class_names[k] # number of times this class appears overall n_total_k = (y_test_bin.T[k]).sum() # get the cases where this class was predicted auto_true_k = auto_true == k auto_pred_k = auto_pred == k # number of cases auto predicted n_pred_k = auto_pred_k.sum() # number of times auto was right n_tp = (auto_true_k & auto_pred_k).sum() # number of times auto was wrong n_fp = (~auto_true_k & auto_pred_k).sum() fail_str = frac_str(n_fp, n_pred_k) pass_str = frac_str(n_tp, n_total_k) fmtstr = '\n'.join( [ '{name}:', ' {n_total_k} samples existed, and did {n_pred_k} auto predictions', ' got {pass_str} right', ' made {fail_str} errors', ] ) print_(ut.indent(fmtstr.format(**locals()))) report = sklearn_utils.classification_report2( y_true, y_pred, target_names=target_names, sample_weight=can_autodecide.astype(np.float), verbose=False, ) print_(' * Auto-Decide Confusion') print_(ut.indent(str(report['confusion']))) print_(' * Auto-Decide Metrics') print_(ut.indent(str(report['metrics']))) if 'mcc' in report: print_(ut.indent(str(report['mcc']))) try: auto_truth_bin = res.y_test_bin[can_autodecide] for k in supported_class_idxs: auto_truth_k = auto_truth_bin.T[k] auto_probs_k = auto_probs.T[k] if auto_probs_k.sum(): auc = sklearn.metrics.roc_auc_score(auto_truth_k, auto_probs_k) print_( ' * Auto AUC(Macro): {:.4f} for class={}'.format( auc, res.class_names[k] ) ) except ValueError: pass report = '\n'.join(report_lines) if verbose: logger.info(report) return report def confusions(res, class_name): import vtool as vt y_test_bin = res.target_bin_df.values clf_probs = res.probs_df.values k = res.class_names.index(class_name) probs, labels = clf_probs.T[k], y_test_bin.T[k] confusions = vt.ConfusionMetrics().fit(probs, labels) return confusions def ishow_roc(res): import vtool as vt import wbia.plottool as pt ut.qtensure() y_test_bin = res.target_bin_df.values # The maximum allowed false positive rate # We expect that we will make 1 error every 1,000 decisions # thresh_df['foo'] = [1, 2, 3] # thresh_df['foo'][res.class_names[k]] = 1 # for k in [2, 0, 1]: for k in range(y_test_bin.shape[1]): if y_test_bin.shape[1] == 2 and k == 0: # only show one in the binary case continue class_name = res.class_names[k] confusions = res.confusions(class_name) ROCInteraction = vt.interact_roc_factory( confusions, show_operating_point=True ) fnum = pt.ensure_fnum(k) # ROCInteraction.static_plot(fnum, None, name=class_name) inter = ROCInteraction(fnum=fnum, pnum=None, name=class_name) inter.start() # if False: # X = probs # y = labels # encoder = vt.ScoreNormalizer() # encoder.fit(probs, labels) # learn_thresh = encoder.learn_threshold2() # encoder.inverse_normalize(learn_thresh) # encoder.visualize(fnum=k) pass def show_roc(res, class_name, **kwargs): import vtool as vt labels = res.target_bin_df[class_name].values probs = res.probs_df[class_name].values confusions = vt.ConfusionMetrics().fit(probs, labels) confusions.draw_roc_curve(**kwargs) def roc_scores_ovr_hat(res): res.augment_if_needed() for k in range(len(res.class_names)): class_k_truth = res.y_test_bin.T[k] class_k_probs = res.probhats_df.values.T[k] auc = sklearn.metrics.roc_auc_score(class_k_truth, class_k_probs) yield auc def roc_scores_ovr(res): res.augment_if_needed() for k in range(res.y_test_bin.shape[1]): class_k_truth = res.y_test_bin.T[k] class_k_probs = res.clf_probs.T[k] auc = sklearn.metrics.roc_auc_score(class_k_truth, class_k_probs) yield auc def confusions_ovr(res): # one_vs_rest confusions import vtool as vt res.augment_if_needed() for k in range(res.y_test_bin.shape[1]): class_k_truth = res.y_test_bin.T[k] class_k_probs = res.clf_probs.T[k] cfms = vt.ConfusionMetrics().fit(class_k_probs, class_k_truth) # auc = sklearn.metrics.roc_auc_score(class_k_truth, class_k_probs) yield res.class_names[k], cfms def roc_score(res): res.augment_if_needed() auc_learn = sklearn.metrics.roc_auc_score(res.y_test_bin, res.clf_probs) return auc_learn @ut.reloadable_class class MultiTaskSamples(ut.NiceRepr): """ Handles samples (i.e. feature-label pairs) with a combination of non-mutually exclusive subclassification labels CommandLine: python -m wbia.algo.verif.clf_helpers MultiTaskSamples Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> samples = MultiTaskSamples([0, 1, 2, 3]) >>> tasks_to_indicators = ut.odict([ >>> ('task1', ut.odict([ >>> ('state1', [0, 0, 0, 1]), >>> ('state2', [0, 0, 1, 0]), >>> ('state3', [1, 1, 0, 0]), >>> ])), >>> ('task2', ut.odict([ >>> ('state4', [0, 0, 0, 1]), >>> ('state5', [1, 1, 1, 0]), >>> ])) >>> ]) >>> samples.apply_indicators(tasks_to_indicators) """ def __init__(samples, index): samples.index = index samples.subtasks = ut.odict() # def set_simple_scores(samples, simple_scores): # if simple_scores is not None: # edges = ut.emap(tuple, samples.aid_pairs.tolist()) # assert (edges == simple_scores.index.tolist()) # samples.simple_scores = simple_scores # def set_feats(samples, X_dict): # if X_dict is not None: # edges = ut.emap(tuple, samples.aid_pairs.tolist()) # for X in X_dict.values(): # assert np.all(edges == X.index.tolist()) # samples.X_dict = X_dict def supported_tasks(samples): for task_key, labels in samples.subtasks.items(): labels = samples.subtasks[task_key] if labels.has_support(): yield task_key def apply_indicators(samples, tasks_to_indicators): """ Adds labels for a specific task Args: tasks_to_indicators (dict): takes the form: { `my_task_name1' { 'class1': [list of bools indicating class membership] ... 'classN': [list of bools indicating class membership] } ... `my_task_nameN': ... } """ n_samples = None samples.n_tasks = len(tasks_to_indicators) for task_name, indicator in tasks_to_indicators.items(): labels = MultiClassLabels.from_indicators( indicator, task_name=task_name, index=samples.index ) samples.subtasks[task_name] = labels if n_samples is None: n_samples = labels.n_samples elif n_samples != labels.n_samples: raise ValueError('numer of samples is different') samples.n_samples = n_samples def apply_encoded_labels(samples, y_enc, class_names, task_name): """ Adds labels for a specific task. Alternative to `apply_indicators` Args: y_enc (list): integer label indicating the class for each sample class_names (list): list of strings indicating the class-domain task_name (str): key for denoting this specific task """ # convert to indicator structure and use that tasks_to_indicators = ut.odict( [ ( task_name, ut.odict( [ (name, np.array(y_enc) == i) for i, name in enumerate(class_names) ] ), ) ] ) samples.apply_indicators(tasks_to_indicators) # @ut.memoize def encoded_2d(samples): encoded_2d = pd.concat([v.encoded_df for k, v in samples.items()], axis=1) return encoded_2d def class_name_basis(samples): """corresponds with indexes returned from encoded1d""" class_name_basis = [ t[::-1] for t in ut.product(*[v.class_names for k, v in samples.items()][::-1]) ] # class_name_basis = [(b, a) for a, b in ut.product(*[ # v.class_names for k, v in samples.items()][::-1])] return class_name_basis def class_idx_basis_2d(samples): """2d-index version of class_name_basis""" class_idx_basis_2d = [ (b, a) for a, b in ut.product( *[range(v.n_classes) for k, v in samples.items()][::-1] ) ] return class_idx_basis_2d def class_idx_basis_1d(samples): """1d-index version of class_name_basis""" n_states = np.prod([v.n_classes for k, v in samples.items()]) class_idx_basis_1d = np.arange(n_states, dtype=np.int) return class_idx_basis_1d # @ut.memoize def encoded_1d(samples): """Returns a unique label for each combination of samples""" # from sklearn.preprocessing import MultiLabelBinarizer encoded_2d = samples.encoded_2d() class_space = [v.n_classes for k, v in samples.items()] offsets = np.array([1] + np.cumprod(class_space).tolist()[:-1])[None, :] encoded_1d = (offsets * encoded_2d).sum(axis=1) # e = MultiLabelBinarizer() # bin_coeff = e.fit_transform(encoded_2d) # bin_basis = (2 ** np.arange(bin_coeff.shape[1]))[None, :] # # encoded_1d = (bin_coeff * bin_basis).sum(axis=1) # encoded_1d = (bin_coeff * bin_basis[::-1]).sum(axis=1) # # vt.unique_rows(sklearn.preprocessing.MultiLabelBinarizer().fit_transform(encoded_2d)) # [v.encoded_df.values for k, v in samples.items()] # encoded_df_1d = pd.concat([v.encoded_df for k, v in samples.items()], axis=1) return encoded_1d def __nice__(samples): return 'nS=%r, nT=%r' % (len(samples), samples.n_tasks) def __getitem__(samples, task_key): return samples.subtasks[task_key] def __len__(samples): return samples.n_samples def print_info(samples): for task_name, labels in samples.items(): labels.print_info() logger.info('hist(all) = %s' % (ut.repr4(samples.make_histogram()))) logger.info('len(all) = %s' % (len(samples))) def make_histogram(samples): """label histogram""" class_name_basis = samples.class_name_basis() class_idx_basis_1d = samples.class_idx_basis_1d() # logger.info('class_idx_basis_1d = %r' % (class_idx_basis_1d,)) # logger.info(samples.encoded_1d()) multi_task_idx_hist = ut.dict_hist( samples.encoded_1d().values, labels=class_idx_basis_1d ) multi_task_hist = ut.map_keys(lambda k: class_name_basis[k], multi_task_idx_hist) return multi_task_hist def items(samples): for task_name, labels in samples.subtasks.items(): yield task_name, labels # def take(samples, idxs): # mask = ut.index_to_boolmask(idxs, len(samples)) # return samples.compress(mask) @property def group_ids(samples): return None def stratified_kfold_indices(samples, **xval_kw): """ TODO: check xval label frequency """ from sklearn import model_selection X = np.empty((len(samples), 0)) y = samples.encoded_1d().values groups = samples.group_ids type_ = xval_kw.pop('type', 'StratifiedGroupKFold') if type_ == 'StratifiedGroupKFold': assert groups is not None # FIXME: The StratifiedGroupKFold could be implemented better. splitter = sklearn_utils.StratifiedGroupKFold(**xval_kw) skf_list = list(splitter.split(X=X, y=y, groups=groups)) elif type_ == 'StratifiedKFold': splitter = model_selection.StratifiedKFold(**xval_kw) skf_list = list(splitter.split(X=X, y=y)) return skf_list def subsplit_indices(samples, subset_idx, **xval_kw): """split an existing set""" from sklearn import model_selection X = np.empty((len(subset_idx), 0)) y = samples.encoded_1d().values[subset_idx] groups = samples.group_ids[subset_idx] xval_kw_ = xval_kw.copy() if 'n_splits' not in xval_kw_: xval_kw_['n_splits'] = 3 type_ = xval_kw_.pop('type', 'StratifiedGroupKFold') if type_ == 'StratifiedGroupKFold': assert groups is not None # FIXME: The StratifiedGroupKFold could be implemented better. splitter = sklearn_utils.StratifiedGroupKFold(**xval_kw_) rel_skf_list = list(splitter.split(X=X, y=y, groups=groups)) elif type_ == 'StratifiedKFold': splitter = model_selection.StratifiedKFold(**xval_kw_) rel_skf_list = list(splitter.split(X=X, y=y)) # map back into original coords skf_list = [ (subset_idx[rel_idx1], subset_idx[rel_idx2]) for rel_idx1, rel_idx2 in rel_skf_list ] for idx1, idx2 in skf_list: assert len(np.intersect1d(subset_idx, idx1)) == len(idx1) assert len(np.intersect1d(subset_idx, idx2)) == len(idx2) # assert return skf_list @ut.reloadable_class class MultiClassLabels(ut.NiceRepr): """ Used by samples to encode a single set of mutually exclusive labels. These can either be binary or multiclass. import pandas as pd pd.options.display.max_rows = 10 # pd.options.display.max_rows = 20 pd.options.display.max_columns = 40 pd.options.display.width = 160 """ def __init__(labels): # Helper Info labels.task_name = None labels.n_samples = None labels.n_classes = None labels.class_names = None labels.classes_ = None # Core data labels.indicator_df = None labels.encoded_df = None labels.default_class = None def has_support(labels): return len(labels.make_histogram()) > 1 def lookup_class_idx(labels, class_name): return ut.dzip(labels.class_names, labels.classes_)[class_name] @classmethod def from_indicators(MultiClassLabels, indicator, index=None, task_name=None): labels = MultiClassLabels() n_samples = len(next(iter(indicator.values()))) # if index is None: # index = pd.Series(np.arange(n_samples), name='index') indicator_df = pd.DataFrame(indicator, index=index) assert np.all( indicator_df.sum(axis=1).values ), 'states in the same task must be mutually exclusive' labels.indicator_df = indicator_df labels.class_names = indicator_df.columns.values labels.encoded_df = pd.DataFrame( indicator_df.values.argmax(axis=1), columns=[task_name], index=index ) labels.task_name = task_name labels.n_samples = n_samples labels.n_classes = len(labels.class_names) if labels.n_classes == 1: labels.n_classes = 2 # 1 column means binary case labels.classes_ = np.arange(labels.n_classes) labels.default_class_name = labels.class_names[1] return labels @property def target_type(labels): return sklearn.utils.multiclass.type_of_target(labels.y_enc) def one_vs_rest_task_names(labels): return [ labels.task_name + '(' + labels.class_names[k] + '-v-rest)' for k in range(labels.n_classes) ] def gen_one_vs_rest_labels(labels): """ Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> indicator = ut.odict([ >>> ('state1', [0, 0, 0, 1]), >>> ('state2', [0, 0, 1, 0]), >>> ('state3', [1, 1, 0, 0]), >>> ]) >>> labels = MultiClassLabels.from_indicators(indicator, task_name='task1') >>> sublabels = list(labels.gen_one_vs_rest_labels()) >>> sublabel = sublabels[0] """ if labels.target_type == 'binary': yield labels return task_names_1vR = labels.one_vs_rest_task_names() for k in range(labels.n_classes): class_name = labels.class_names[k] task_name = task_names_1vR[k] index = labels.indicator_df.index indicator_df = pd.DataFrame() indicator_df['not-' + class_name] = 1 - labels.indicator_df[class_name] indicator_df[class_name] = labels.indicator_df[class_name] indicator_df.index = index # indicator = labels.encoded_df == k # indicator.rename(columns={indicator.columns[0]: class_name}, inplace=True) n_samples = len(indicator_df) sublabel = MultiClassLabels() sublabel.indicator_df = indicator_df sublabel.class_names = indicator_df.columns.values # if len(indicator_df.columns) == 1: # sublabel.encoded_df = pd.DataFrame( # indicator_df.values.T[0], # columns=[task_name] # ) # else: sublabel.encoded_df = pd.DataFrame( indicator_df.values.argmax(axis=1), columns=[task_name], index=index ) sublabel.task_name = task_name sublabel.n_samples = n_samples sublabel.n_classes = len(sublabel.class_names) # if sublabel.n_classes == 1: # sublabel.n_classes = 2 # 1 column means binary case sublabel.classes_ = np.arange(sublabel.n_classes) # sublabel = MultiClassLabels.from_indicators(indicator, # task_name=subname, index=samples.index) yield sublabel @property def y_bin(labels): return labels.indicator_df.values @property def y_enc(labels): return labels.encoded_df.values.ravel() def __nice__(labels): parts = [] if labels.task_name is not None: parts.append(labels.task_name) parts.append('nD=%r' % (labels.n_samples)) parts.append('nC=%r' % (labels.n_classes)) return ' '.join(parts) def __len__(labels): return labels.n_samples def make_histogram(labels): class_idx_hist = ut.dict_hist(labels.y_enc) class_hist = ut.map_keys(lambda idx: labels.class_names[idx], class_idx_hist) return class_hist def print_info(labels): logger.info( 'hist(%s) = %s' % (labels.task_name, ut.repr4(labels.make_histogram())) ) logger.info('len(%s) = %s' % (labels.task_name, len(labels))) class IrisProblem(ClfProblem): """ Simple demo using the abstract clf problem to work on the iris dataset. Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.verif.clf_helpers import * # NOQA >>> pblm = IrisProblem() >>> pblm.setup() >>> pblm.samples """ def setup(pblm): import sklearn.datasets iris = sklearn.datasets.load_iris() pblm.primary_task_key = 'iris' pblm.default_data_key = 'learn(all)' pblm.default_clf_key = 'RF' X_df = pd.DataFrame(iris.data, columns=iris.feature_names) samples = MultiTaskSamples(X_df.index) samples.apply_indicators( { 'iris': { name: iris.target == idx for idx, name in enumerate(iris.target_names) } } ) samples.X_dict = {'learn(all)': X_df} pblm.samples = samples pblm.xval_kw['type'] = 'StratifiedKFold'

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src/RNN_utils.py

SergioEG/ScalableAVITM

3

152536

<reponame>SergioEG/ScalableAVITM<filename>src/RNN_utils.py """ Grouping all parsers specifications here @authors: <NAME> (<EMAIL>) AND <NAME> (<EMAIL>) """ import argparse import pickle from pandas import read_csv import numpy as np from sklearn.feature_extraction.text import CountVectorizer import logging logging.getLogger().setLevel(logging.INFO) def parse_args(): desc = 'Variational Inference for Topic Models based on Dense Layers' parser = argparse.ArgumentParser(description=desc) parser.add_argument('--batch_size', type=int, default=200, help='Size of the batches') parser.add_argument('--epochs',type=int,default=10, help='Number of epochs of the simulations') parser.add_argument('--train', type=int,default=1, help='Training model flag') parser.add_argument('--verbose', type=int,default=1, help='Verbose option flag') parser.add_argument('--save_it', type=int,default=1000, help='Save variables every save_it iterations') parser.add_argument('--restore', type=int,default=0, help='To restore session, to keep training or evaluation') parser.add_argument('--save_file', type=str, default='abcnews', help='Save file name (will be extended with other conf. parameters)') parser.add_argument('--corpus', type=str, default='abcnews', help='Corpus. Instructions to read each corpus are given in utils.py') parser.add_argument('--ntopics', type=int, default=10, help='Number of topics') parser.add_argument('--display', type=int, default=1, help='If verbose, print results every display epochs') parser.add_argument('--nlayers', type=int, default=2, help='Number of dense layers for the inference network') parser.add_argument('--hidden_list','--list', nargs='+', help='List of hidden units per layer',default=100) #If only one number, same number of units perlayer! parser.add_argument('--rate', type=float,default=0.005, help='Learning rate') parser.add_argument('--vocabulary_size', type=int,default=10000, help='Vocabulary size') parser.add_argument('--alpha', type=float,default=1.0, help='Dirich hiper.') return parser.parse_args() def get_corpus(corpus_name,nwords): bow = None dictionary = None logging.info(f"Getting corpus and embeds of {corpus_name}") if(corpus_name == 'FECYT'): documents = list(pickle.load(open( "data/df_proyectosFECYT.pkl", "rb" ) )['LEMAS_UC3M']) vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=nwords, stop_words='english') bow = vectorizer.fit_transform(documents) dictionary = vectorizer.get_feature_names() embeds = np.load("data/embeds_FECYT_256_LSTM.npy") if(corpus_name == 'abcnews'): data = read_csv('Data/abcnews-date-text.csv', error_bad_lines=False) documents = data[['headline_text']].values.reshape(-1).tolist() vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=nwords, stop_words='english') bow = vectorizer.fit_transform(documents) dictionary = vectorizer.get_feature_names() embeds = np.load("data/embeds_abcnews_512.npy") logging.info(f"Corpus and embeds obtained") return documents, bow,dictionary, embeds def display_topics(lda_matrix, dictionary, no_top_words): for topic_idx, topic in enumerate(lda_matrix): print ("Topic %d:" % (topic_idx)) print (" ".join([dictionary[i] for i in topic.argsort()[:-no_top_words - 1:-1]])) def next_batch(idx_vector,index_batch,batch_size): return idx_vector[index_batch*batch_size:(index_batch*batch_size+batch_size)]

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1678, 363, 11261, 29918, 13140, 29892, 11261, 297, 26985, 29898, 430, 29874, 29918, 5344, 1125, 13, 4706, 1596, 4852, 7031, 293, 1273, 29881, 6160, 1273, 313, 13010, 29918, 13140, 876, 13, 4706, 1596, 4852, 11393, 7122, 4197, 27126, 29961, 29875, 29962, 363, 474, 297, 11261, 29889, 5085, 441, 580, 7503, 29899, 1217, 29918, 3332, 29918, 9303, 448, 29871, 29896, 13018, 29896, 5262, 876, 13, 13, 13, 1753, 2446, 29918, 16175, 29898, 13140, 29918, 8111, 29892, 2248, 29918, 16175, 29892, 16175, 29918, 2311, 1125, 13, 1678, 736, 22645, 29918, 8111, 29961, 2248, 29918, 16175, 29930, 16175, 29918, 2311, 5919, 2248, 29918, 16175, 29930, 16175, 29918, 2311, 29974, 16175, 29918, 2311, 4638, 13, 268, 2 ]

migrations/versions/5ae213c6353_.py

eugeneandrienko/PyArtistsGallery

0

193247

"""empty message Revision ID: 5ae213c6353 Revises: 3c73f5517a2 Create Date: 2015-07-01 16:08:50.298141 """ # revision identifiers, used by Alembic. revision = '<KEY>' down_revision = '3c73f5517a2' from alembic import op import sqlalchemy as sa def upgrade(): op.drop_index('ix_pictures_path_to_image') op.drop_index('ix_pictures_path_to_thumbnail') op.drop_index('ix_pictures_name') op.drop_index('ix_pictures_description') op.drop_index('ix_pictures_upload_date') op.drop_index('ix_pictures_size') with op.batch_alter_table( 'pictures', reflect_args=[sa.Column( 'description', sa.VARCHAR())] ) as batch_op: batch_op.alter_column('description', existing_type=sa.VARCHAR(), nullable=True) op.create_index( op.f('ix_pictures_path_to_image'), 'pictures', ['path_to_image'], unique=False) op.create_index( op.f('ix_pictures_path_to_thumbnail'), 'pictures', ['path_to_thumbnail'], unique=False) op.create_index(op.f('ix_pictures_name'), 'pictures', ['name'], unique=False) op.create_index(op.f('ix_pictures_description'), 'pictures', ['description'], unique=False) op.create_index( op.f('ix_pictures_upload_date'), 'pictures', ['upload_date'], unique=False) op.create_index(op.f('ix_pictures_size'), 'pictures', ['size'], unique=False) def downgrade(): op.drop_index('ix_pictures_path_to_image') op.drop_index('ix_pictures_path_to_thumbnail') op.drop_index('ix_pictures_name') op.drop_index('ix_pictures_description') op.drop_index('ix_pictures_upload_date') op.drop_index('ix_pictures_size') with op.batch_alter_table( 'pictures', reflect_args=[sa.Column( 'description', sa.VARCHAR())] ) as batch_op: batch_op.alter_column('description', existing_type=sa.VARCHAR(), nullable=False)

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zink/templatetags/markdownify.py

jw/zink

2

195206

<reponame>jw/zink import logging from django import template import mistune from pygments import highlight from pygments.formatters import html from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.util import ClassNotFound logger = logging.getLogger(__name__) register = template.Library() class HighlightRenderer(mistune.Renderer): def block_code(self, code, lang): if not lang: first_line, _, code = code.partition('\n') name = first_line.replace(":", "") try: lexer = get_lexer_by_name(name, stripall=True) formatter = html.HtmlFormatter() return highlight(code, lexer, formatter) except ClassNotFound as e: return f'<pre><em>{e}</em><code>{mistune.escape(code)}</code></pre>' lexer = get_lexer_by_name(lang, stripall=True) formatter = html.HtmlFormatter() return highlight(code, lexer, formatter) @register.filter def markdown(value): markdown = mistune.Markdown(renderer=HighlightRenderer()) return markdown(value)

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build/lib/tnetwork/DCD/analytics/dynamic_partition.py

Yquetzal/tnetwork

4

87368

import tnetwork as tn import sklearn import sklearn.metrics import scipy import statistics import networkx as nx from tnetwork.DCD.analytics.onmi import onmi import numpy as np __all__ = ["longitudinal_similarity", "consecutive_sn_similarity", "similarity_at_each_step", "entropy_by_node","nb_node_change","quality_at_each_step","SM_N","SM_P","SM_L"] def longitudinal_similarity(dynamicCommunityReference:tn.DynCommunitiesSN, dynamicCommunityObserved:tn.DynCommunitiesSN, score=None,convert_coms_sklearn_format=True): """ Longitudinal similarity The longitudinal similarity between two dynamic clusters is computed by considering each couple (node,time) as an element belong to a cluster, a cluster containing therefore nodes in differnt times It takes into account the fact that the reference might by incomplete by removing from the partition to evaluate all (node,time) not present in the reference. :param dynamicCommunityReference: the dynamic partition used as reference (ground truth) :param dynamicCommunityObserved: the dynamic partition to evaluate (result of an algorithm) :param score: community comparison score, by default the adjsted NMI. (sklearn) :param convert_coms_sklearn_format: if the score expect in input clusters represented as in sklearn, True. if False, score will receive in input lists of sets of nodes :return: score """ if score==None: score=lambda x,y : sklearn.metrics.adjusted_mutual_info_score(x,y,average_method="arithmetic") affilReference=[] affilToEvaluate=[] if convert_coms_sklearn_format: comsToEvaluate = dynamicCommunityObserved.snapshot_affiliations() #for each step for t,affils in dynamicCommunityReference.snapshot_affiliations().items(): #for each node for n,comId in affils.items(): affilReference.append(str(list(comId)[0])) if n in comsToEvaluate[t]: affilToEvaluate.append(str(list(comsToEvaluate[t][n])[0])) else: print("node not in partition to evaluate: ",str(n)," ",str(t)) affilToEvaluate.append("-1") else: affilReference={} affilToEvaluate={} for t,coms in dynamicCommunityReference.snapshot_communities().items(): all_nodes = set() for id,nodes in coms.items(): node_sn = {(n,t) for n in nodes} all_nodes.update(node_sn) affilReference.setdefault(id,set()).update(node_sn) for id,nodes in dynamicCommunityObserved.snapshot_communities(t).items(): node_sn = {(n,t) for n in nodes} affilToEvaluate.setdefault(id,set()).update(node_sn & all_nodes) affilReference = list(affilReference.values()) affilToEvaluate = list(affilToEvaluate.values()) return score(affilReference,affilToEvaluate) def consecutive_sn_similarity(dynamicCommunity:tn.DynCommunitiesSN,score=None): """ Similarity between partitions in consecutive snapshots. Compute the average of a similarity score between all pair of successive partitions :param dynamicCommunity: the dynamic partition to evaluate :param score: the score to use for computing the similarity between each pair of snapshots. default: Overlapping NMI :return: pair (list of scores, list of partition sizes (avg both partitions)) """ if score==None: score=onmi #We use onmi because the number of labels can be different scores=[] sizes=[] #for each step com_snapshots = list(dynamicCommunity.snapshot_communities().values()) #print(com_snapshots) for i in range(len(com_snapshots)-1): partition_before = list(com_snapshots[i].values()) partition_after = list(com_snapshots[i+1].values()) elts_before = sum([len(x) for x in partition_before]) elts_after = sum([len(x) for x in partition_after]) scores.append(score(partition_before,partition_after)) sizes.append((elts_after+elts_before)/2) return scores,sizes def similarity_at_each_step(dynamicCommunityReference:tn.DynCommunitiesSN, dynamicCommunityObserved:tn.DynCommunitiesSN, score=None): """ Compute similarity at each step It takes into account the fact that the reference might by incomplete. (remove from the observations all nodes/time not present in the reference) :param dynamicCommunityReference: the dynamic partition to use as reference :param dynamicCommunityObserved: the dynamic partition to evaluate :param score: score to use, default adjusted NMI :return: pair (list of scores, list of sizes) """ if score==None: score=sklearn.metrics.adjusted_mutual_info_score scores=[] sizes=[] comsToEvaluate = dynamicCommunityObserved.snapshot_affiliations() #for each step for t,affils in dynamicCommunityReference.snapshot_affiliations().items(): affilReference = [] affilToEvaluate = [] #for each node for n,comId in affils.items(): affilReference.append(list(comId)[0]) if n in comsToEvaluate[t]: affilToEvaluate.append(list(comsToEvaluate[t][n])[0]) else: affilToEvaluate.append("-1") scores.append(score(affilReference,affilToEvaluate)) sizes.append(len(affilReference)) return scores,sizes def quality_at_each_step(dynamicCommunities:tn.DynCommunitiesSN,dynamicGraph:tn.DynGraphSN, score=None): """ Compute a community quality at each step :param dynamicCommunities: dynamic communities as SN :param score: score to use, default: Modularity :return: pair(scores, sizes) """ if score==None: score=nx.algorithms.community.modularity scores=[] sizes=[] #for each step for t,affils in dynamicCommunities.snapshot_communities().items(): g = dynamicGraph.snapshots(t) partition = list(affils.values()) try: sc = score(g,partition) scores.append(sc) except: #print("problem to compute with partition: ",partition," nodes",g.nodes()) scores.append(None) sizes.append(len(g.nodes)) return scores,sizes def nb_node_change(dyn_com:tn.DynCommunitiesSN): """ Compute the total number of node changes Measure of smoothness at the level of nodes, adapated to evaluate glitches :param dyn_com: The dynamic community :return: total number of node changes """ coms_by_nodes={} for t,coms in dyn_com.snapshot_communities().items(): #print(t,coms) for com,nodes in coms.items(): #print(n,com) for n in nodes: coms_by_nodes.setdefault(n,[com]) if coms_by_nodes[n][-1]!=com: coms_by_nodes[n].append(com) nb_changes = 0 for n in coms_by_nodes: #print(n,coms_by_nodes[n]) nb_changes+=len(coms_by_nodes[n])-1 return nb_changes # def entropy(dyn_com,sn_duration=1): # """ # Compute the entropy. # # Consider each community label as a data value. The probability of observing this data value is the frequency of a random node to belong to the corresponding community. # # Interpretation: The less communities, the lower the score. The less homogeneous the community sizes, the lower the score. # # This score does not take into account the order of the community changes. # # Be careful, convert SN graph into IG. # # # :param dyn_com: dynamic community to evaluate, can be SN or IG # :param sn_duration: if graph is SN, used to # :return: # """ # dc2 = dyn_com # fractions = [] # if isinstance(dc2,tn.DynCommunitiesSN): # dc2 = dc2.to_DynCommunitiesIG(sn_duration=sn_duration) # for com,nodes in dc2.communities().items(): # this_com_cumulated = 0 # for n,times in nodes.items(): # this_com_cumulated += times.duration() # fractions.append(this_com_cumulated) # sum_durations = sum(fractions) # fractions = [x/sum_durations for x in fractions] # # return scipy.stats.entropy(fractions) def entropy_by_node(dyn_com,sn_duration=1,fast_on_sn=False): """ Compute the entropy by node. For each node, compute the shannon entropy of its labels. (always same label=min entropy, every step a new label=max entropy) return the average value for all nodes :param dyn_com: dynamic community to evaluate, can be SN or IG :param sn_duration: if graph is SN, used to discretize :return: """ dc2 = dyn_com if not fast_on_sn: if isinstance(dc2,tn.DynCommunitiesSN): if sn_duration==1: dc2 = dc2._to_DynCommunitiesIG_fast() else: dc2 = dc2.to_DynCommunitiesIG(sn_duration=sn_duration) all_entropies = [] for n,coms in dc2.affiliations().items(): fractions = [] for com,times in coms.items(): fractions.append(times.duration()) sum_durations = sum(fractions) fractions = [x/sum_durations for x in fractions] ent_this_node = scipy.stats.entropy(fractions) all_entropies.append(ent_this_node) return statistics.mean(all_entropies) def SM_N(dyn_com): """ Smoothness for nodes Inverse of the number of node changes :param dyn_com: dynamic partition :return: SM-N score """ return 1/nb_node_change(dyn_com) def SM_P(dyn_com): """ Smoothness for partitions Averge of the NMI between successive snapshots :param dyn_com: dynamic partition :return: SM-P score """ consecutive_NMIs = consecutive_sn_similarity(dyn_com) return np.average(consecutive_NMIs[0], weights=consecutive_NMIs[1]) def SM_L(dyn_com,sn_duration=1): """ Smoothness for labels Inverse of the entropy by node :param dyn_com: dyanamic partition :param sn_duration: used to indicate the duration of snapshots if provided graph is a snapshot graph :return: SM-L score """ return 1/entropy_by_node(dyn_com,sn_duration=sn_duration)

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core/homography.py

Kaktusava/Vis-MVSNet

131

153493

import time import os import torch from utils.utils import NanError def depth2class(depth, depth_start, depth_interval, depth_num, inv=False): if not inv: return (depth - depth_start) / (depth_interval + 1e-9) else: depth_end = depth_start + (depth_num-1) * depth_interval inv_interv = (1/(depth_start+1e-9) - 1/(depth_end+1e-9)) / (depth_num-1+1e-9) return (1/(depth+1e-9) - 1/(depth_end+1e-9)) / (inv_interv + 1e-9) def class2depth(class_, depth_start, depth_interval, depth_num, inv=False): if not inv: return depth_start + class_ * depth_interval else: depth_end = depth_start + (depth_num-1) * depth_interval inv_interv = (1/(depth_start+1e-9) - 1/(depth_end+1e-9)) / (depth_num-1+1e-9) return 1/( 1/(depth_end+1e-9) + class_ * inv_interv + 1e-9) def get_homographies(left_cam, right_cam, depth_num, depth_start, depth_interval, inv=False): # n244 n244 1 n111/n1hw n111/n1hw with torch.no_grad(): n, _, sh, sw = depth_start.size() n, _, ih, iw = depth_interval.size() d = depth_num # cameras (K, R, t) R_left = left_cam[:, 0, :3, :3] # n33 R_right = right_cam[:, 0, :3, :3] # n33 t_left = left_cam[:, 0, :3, 3:4] # n31 t_right = right_cam[:, 0, :3, 3:4] # n31 K_left = left_cam[:, 1, :3, :3] # n33 K_right = right_cam[:, 1, :3, :3] # n33 # depth nd1111/ndhw11 if not inv: depth = depth_start + depth_interval * torch.arange(0, depth_num, dtype=left_cam.dtype, device=left_cam.device).view(1,d,1,1) else: depth_end = depth_start + (depth_num-1) * depth_interval inv_interv = (1/(depth_start+1e-9) - 1/(depth_end+1e-9)) / (depth_num-1+1e-9) depth = 1/( 1/(depth_end+1e-9) + inv_interv * torch.arange(0, depth_num, dtype=left_cam.dtype, device=left_cam.device).view(1,d,1,1) ) depth = depth.unsqueeze(-1).unsqueeze(-1) # preparation K_left_inv = K_left.float().inverse().to(left_cam.dtype) R_left_trans = R_left.transpose(-2, -1) R_right_trans = R_right.transpose(-2, -1) fronto_direction = R_left[:, 2:3, :3] # n13 c_left = -R_left_trans @ t_left c_right = -R_right_trans @ t_right # n31 c_relative = c_right - c_left # compute temp_vec = (c_relative @ fronto_direction).view(n,1,1,1,3,3) # n11133 middle_mat0 = torch.eye(3, dtype=left_cam.dtype, device=left_cam.device).view(1,1,1,1,3,3) - temp_vec / (depth + 1e-9) # ndhw33 middle_mat1 = (R_left_trans @ K_left_inv).view(n,1,1,1,3,3) # n11133 middle_mat2 = (middle_mat0 @ middle_mat1) # ndhw33 homographies = K_right.view(n,1,1,1,3,3) @ R_right.view(n,1,1,1,3,3) @ middle_mat2 # ndhw33 if (homographies!=homographies).any(): raise NanError return homographies def get_pixel_grids(height, width): x_coord = (torch.arange(width, dtype=torch.float32).cuda() + 0.5).repeat(height, 1) y_coord = (torch.arange(height, dtype=torch.float32).cuda() + 0.5).repeat(width, 1).t() ones = torch.ones_like(x_coord) indices_grid = torch.stack([x_coord, y_coord, ones], dim=-1).unsqueeze(-1) # hw31 return indices_grid def interpolate(image, coord): # nchw, nhw2 => nchw with torch.no_grad(): warped_coord = coord.clone() warped_coord[..., 0] /= (warped_coord.size()[2]) warped_coord[..., 1] /= (warped_coord.size()[1]) warped_coord = (warped_coord * 2 - 1).clamp(-1.1, 1.1) warped = torch.nn.functional.grid_sample(image, warped_coord, mode='bilinear', padding_mode='zeros', align_corners=False) if (warped != warped).any(): raise NanError return warped def homography_warping(input, H): # nchw, n33/nhw33 -> nchw if len(H.size()) == 3: H = H.view(-1, 1, 1, 3, 3) with torch.no_grad(): pixel_grids = get_pixel_grids(*input.size()[-2:]).unsqueeze(0) # 1hw31 warped_homo_coord = (H @ pixel_grids).squeeze(-1) # nhw3 warped_coord = warped_homo_coord[..., :2] / (warped_homo_coord[..., 2:3] + 1e-9) # nhw2 warped = interpolate(input, warped_coord) return warped # nchw

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calixis/plan/submodels/weighted.py

kyleady/SectorsOfInequity

0

72591

from django.db import models from django.forms.models import model_to_dict class Weighted_Value(models.Model): class Meta: abstract = True def get_weights_as_str(self): weights = [] has_conditional_weights = False for weight in self.weights.all(): if not weight.required_flags: weights.append(str(weight)) else: has_conditional_weights = True weights_as_str = "+".join(weights) if has_conditional_weights: weights_as_str += "*" return weights_as_str def __repr__(self): return json.dumps(model_to_dict( self, fields=[field.name for field in self._meta.fields] )) def __str__(self): return "({weights}) {value_name}".format(weights=self.get_weights_as_str(), value_name=self.value.name) weights = models.ManyToManyField('Roll', related_name='+') order = models.ManyToManyField('Roll', related_name='+') value = None class Weighted_Inspiration(Weighted_Value): value = models.ForeignKey('Inspiration', on_delete=models.CASCADE) class Weighted_Region(Weighted_Value): value = models.ForeignKey('Config_Region', on_delete=models.CASCADE) class Weighted_Type(Weighted_Value): value = models.ForeignKey('Config_Name', on_delete=models.CASCADE) class Weighted_Table(Weighted_Value): value = models.ForeignKey('Inspiration_Table', on_delete=models.CASCADE)

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tests/test_github.py

xhusar2/repocribro

5

136567

<filename>tests/test_github.py import pytest REPOSITORY = 'MarekSuchanek/pyplayground' NOT_REPOSITORY = 'MarekSuchanek/pyplaygroundzzzz7' def test_login_bad(github_api): assert not github_api.login('random_code') def test_get_user_self(github_api): user = github_api.get('/user') assert user.is_ok assert 'login' in user.data def test_get_webhooks_bad(github_api): res = github_api.webhooks_get(NOT_REPOSITORY) assert not res.is_ok def test_get_webhook_bad(github_api): res = github_api.webhook_get(REPOSITORY, 666) assert not res.is_ok def test_get_webhook_create_check(github_api): res = github_api.webhook_create( REPOSITORY, 'http://localhost:5000/test' ) assert res is not None webhook_id = res['id'] res = github_api.webhooks_get(REPOSITORY) assert len(res.data) >= 1 found = False for webhook in res.data: if webhook['id'] == webhook_id: found = True break assert found res = github_api.webhook_get(REPOSITORY, webhook_id) assert res.is_ok assert res.data['id'] == webhook_id # Delivery on localhost must fail assert not github_api.webhook_tests(REPOSITORY, webhook_id) res = github_api.webhook_delete(REPOSITORY, webhook_id) assert res def test_get_webhook_delete_check(github_api): res = github_api.webhook_create( REPOSITORY, 'http://localhost:5000/test' ) assert res is not None webhook_id = res['id'] res = github_api.webhook_delete(REPOSITORY, webhook_id) assert res res = github_api.webhook_get(REPOSITORY, webhook_id) assert not res.is_ok def compute_signature(payload, secret): import hmac import hashlib h = hmac.new( secret.encode('utf-8'), payload.encode('utf-8'), hashlib.sha1 ) return h.hexdigest() @pytest.mark.parametrize('payload', [ 'aasas', '["a","b","c"]', '{"nope":"nope"}' ]) def test_get_webhook_verify(github_api, payload): sig = compute_signature(payload, github_api.webhooks_secret) assert github_api.webhook_verify_signature( payload.encode('utf-8'), sig ) @pytest.mark.parametrize('payload', [ 'aasas', '["a","b","c"]', '{"nope":"nope"}' ]) def test_get_webhook_unverify(github_api, payload): sig = compute_signature(payload, github_api.webhooks_secret) + 'xxx' assert not github_api.webhook_verify_signature( payload.encode('utf-8'), sig )

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qsBack/app/urls.py

joest67/qsProj

0

58903

from django.conf.urls import patterns, url from qs import views urlpatterns = patterns('', url(r'^$', views.index, name='index'), url(r'^sendqs/$', views.sendqs, name='sendqs'), url(r'^show/$', views.showall, name='showall'), )

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6.0_prs_random_forest.py.py

psohn/Forest_Fires_Regression

2

53061

<reponame>psohn/Forest_Fires_Regression ### because how can you not use random forest regression on a forest fire ### regression? this is lazily coded and uncommented. purely for entertainment ### and completion import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import r2_score, mean_squared_error train_X = pd.read_csv('data/features_train.csv', index_col = 0) train_y = pd.read_csv('data/target_train.csv', index_col = 0).astype(int).values.ravel() test_X = pd.read_csv('data/features_test.csv', index_col = 0) test_y = pd.read_csv('data/target_test.csv', index_col = 0).astype(int).values.ravel() model = RandomForestClassifier(random_state = 0, max_depth = 5).fit(X = train_X, y = train_y) pred_train = model.predict(train_X) pred_test = model.predict(test_X) r2_train = r2_score(train_y, pred_train) r2_test = r2_score(test_y, pred_test) mse_train = mean_squared_error(train_y, pred_train, squared = False) mse_test = mean_squared_error(test_y, pred_test, squared = False) print(r2_train, mse_train) print(r2_test, mse_test) ### r2_train = -0.1386 ### mse_train = 0.5807 ### r2_test = -0.1518 ### mse_test = 0.6487

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ch02-安装OpenCV/最简单-使用pip安装opencv-python和opencv-contrib-python/test_video.py

makelove/OpenCV-Python-Tutorial

2,875

47463

# -*- coding: utf-8 -*- # @Time : 2017/8/2 10:46 # @Author : play4fun # @File : test_video.py # @Software: PyCharm """ test_video.py: """ import numpy as np import cv2 from matplotlib import pyplot as plt cap = cv2.VideoCapture('../../data/vtest.avi')#不支持读取视频 # cap = cv2.VideoCapture('output.avi') # cap = cv2.VideoCapture('Minions_banana.mp4') # 帧率 fps = cap.get(cv2.CAP_PROP_FPS) # 25.0 print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps)) # 总共有多少帧 num_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) print('共有', num_frames, '帧') # frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) frame_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) print('高：', frame_height, '宽：', frame_width) FRAME_NOW = cap.get(cv2.CAP_PROP_POS_FRAMES) # 第0帧 print('当前帧数', FRAME_NOW) # 当前帧数 0.0 # 读取指定帧,对视频文件才有效，对摄像头无效？？ # frame_no = 121 # cap.set(1, frame_no) # Where frame_no is the frame you want ret, frame = cap.read() # Read the frame print(ret, frame) # cv2.imshow('frame_no'+str(frame_no), frame) FRAME_NOW = cap.get(cv2.CAP_PROP_POS_FRAMES) print('当前帧数', FRAME_NOW) # 当前帧数 122.0 if frame is not None:#出错 plt.imshow(frame) # plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) plt.show()

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tools/train.py

RocketFlash/SiameseNet

24

1611133

import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(ROOT_DIR) import numpy as np from embedding_net.models import EmbeddingNet, TripletNet, SiameseNet from tensorflow.keras.callbacks import TensorBoard, LearningRateScheduler from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint from embedding_net.datagenerators import ENDataLoader, SimpleDataGenerator, TripletsDataGenerator, SimpleTripletsDataGenerator, SiameseDataGenerator from embedding_net.utils import parse_params, plot_grapths from embedding_net.backbones import pretrain_backbone_softmax from embedding_net.losses_and_accuracies import contrastive_loss, triplet_loss, accuracy import argparse from tensorflow import keras from tensorflow.keras.utils import multi_gpu_model import tensorflow as tf def parse_args(): parser = argparse.ArgumentParser(description='Train a classificator') parser.add_argument('config', help='model config file path') parser.add_argument('--resume_from', help='the checkpoint file to resume from') args = parser.parse_args() return args def create_save_folders(params): work_dir_path = os.path.join(params['work_dir'], params['project_name']) weights_save_path = os.path.join(work_dir_path, 'weights/') weights_pretrained_save_path = os.path.join(work_dir_path, 'pretraining_model/weights/') encodings_save_path = os.path.join(work_dir_path, 'encodings/') plots_save_path = os.path.join(work_dir_path, 'plots/') tensorboard_save_path = os.path.join(work_dir_path, 'tf_log/') tensorboard_pretrained_save_path = os.path.join(work_dir_path, 'pretraining_model/tf_log/') weights_save_file_path = os.path.join(weights_save_path, 'epoch_{epoch:03d}' + '.hdf5') os.makedirs(work_dir_path , exist_ok=True) os.makedirs(weights_save_path, exist_ok=True) os.makedirs(weights_pretrained_save_path, exist_ok=True) os.makedirs(encodings_save_path, exist_ok=True) os.makedirs(plots_save_path, exist_ok=True) os.makedirs(tensorboard_pretrained_save_path, exist_ok=True) return tensorboard_save_path, weights_save_file_path, plots_save_path def main(): print('LOAD PARAMETERS') args = parse_args() cfg_params = parse_params(args.config) params_train = cfg_params['train'] params_model = cfg_params['model'] params_dataloader = cfg_params['dataloader'] params_generator = cfg_params['generator'] tensorboard_save_path, weights_save_file_path, plots_save_path = create_save_folders(cfg_params['general']) work_dir_path = os.path.join(cfg_params['general']['work_dir'], cfg_params['general']['project_name']) weights_save_path = os.path.join(work_dir_path, 'weights/') initial_lr = params_train['learning_rate'] decay_factor = params_train['decay_factor'] step_size = params_train['step_size'] if params_dataloader['validate']: callback_monitor = 'val_loss' else: callback_monitor = 'loss' print('LOADING COMPLETED') callbacks = [ LearningRateScheduler(lambda x: initial_lr * decay_factor ** np.floor(x/step_size)), ReduceLROnPlateau(monitor=callback_monitor, factor=0.1, patience=4, verbose=1), EarlyStopping(monitor=callback_monitor, patience=10, verbose=1), ModelCheckpoint(filepath=weights_save_file_path, monitor=callback_monitor, save_best_only=True, verbose=1) ] print('CREATE DATALOADER') data_loader = ENDataLoader(**params_dataloader) print('DATALOADER CREATED!') if cfg_params['general']['tensorboard_callback']: callbacks.append(TensorBoard(log_dir=tensorboard_save_path)) if cfg_params['general']['wandb_callback']: import wandb from wandb.keras import WandbCallback wandb.init() callbacks.append(WandbCallback(data_type="image", labels=data_loader.class_names)) val_generator = None print('CREATE MODEL AND DATA GENETATORS') if params_model['mode'] == 'siamese': model = SiameseNet(cfg_params, training=True) train_generator = SiameseDataGenerator(class_files_paths=data_loader.train_data, class_names=data_loader.class_names, **params_generator) if data_loader.validate: val_generator = SiameseDataGenerator(class_files_paths=data_loader.val_data, class_names=data_loader.class_names, val_gen = True, **params_generator) losses = {'output_siamese' : contrastive_loss} metric = {'output_siamese' : accuracy} else: if cfg_params['general']['gpu_ids']: print('Multiple gpu mode') gpu_ids = cfg_params['general']['gpu_ids'] os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids print(f'Using gpu ids: {gpu_ids}') gpu_ids_list = gpu_ids.split(',') n_gpu = len(gpu_ids_list) else: os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = '0' n_gpu = 1 print('Use single gpu mode') model = TripletNet(cfg_params, training=True) if n_gpu>1: strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model.base_model = multi_gpu_model(model.base_model, gpus=n_gpu) # model.base_model = tf.keras.utils.multi_gpu_model(model.base_model, gpus=n_gpu) train_generator = TripletsDataGenerator(embedding_model=model.base_model, class_files_paths=data_loader.train_data, class_names=data_loader.class_names, **params_generator) if data_loader.validate: val_generator = SimpleTripletsDataGenerator(data_loader.val_data, data_loader.class_names, **params_generator) losses = triplet_loss(params_generator['margin']) metric = ['accuracy'] print('DONE') if args.resume_from is not None: model.load_model(args.resume_from) print('COMPILE MODEL') model.model.compile(loss=losses, optimizer=params_train['optimizer'], metrics=metric) if 'softmax' in cfg_params: params_softmax = cfg_params['softmax'] params_save_paths = cfg_params['general'] pretrain_backbone_softmax(model.backbone_model, data_loader, params_softmax, params_save_paths) history = model.model.fit_generator(train_generator, validation_data=val_generator, epochs=params_train['n_epochs'], callbacks=callbacks, verbose=1, use_multiprocessing=False) if params_train['plot_history']: plot_grapths(history, plots_save_path) if __name__ == '__main__': main()

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osprey/open.py

cjbassi/claw

14

107087

import subprocess import sys config_dir_path = None history_file_path = None log_file_path = None def open(path): if sys.platform == 'darwin': subprocess.Popen(['open', path]) elif sys.platform == 'win32': subprocess.Popen(['start', '', path]) else: subprocess.Popen(['xdg-open', path]) def open_config_dir(): global config_dir_path open(config_dir_path) def open_history_file(): global history_file_path open(history_file_path) def open_log_file(): global log_file_path open(log_file_path)

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django_render/url_patterns_maker/url_patterns_maker.py

jasonmaoverlord/django_render

76

81123

<gh_stars>10-100 #!/usr/bin/env python # -*- coding: utf-8 -*- # Last modified: <NAME> (<EMAIL>) """urlpatterns_maker This urlpatterns_maker is used to auto load url configurations loosely distributed in the ``views`` package. Usage: 1. got views like this #################################### my_app/views/default.py ##################################################### from django_render.annotations import * @url(ur'^login/auth$', method=M.POST) @post(auth_type=int, auth_id=str, auth_token=str, jpush_registration_id=str) def login_auth(request, auth_type, auth_id, auth_token, jpush_registration_id): pass return True, {'token': login_token, 'user_id': user_id} #################################### my_app/views/profile.py ##################################################### ... #################################### my_app/views/friend.py ###################################################### ... #################################### my_app/views/feed.py ######################################################## ... 2. config urlpatterns as follow #################################### my_app/views/__init__.py #################################################### from django_render.url_patterns_maker import urlpatterns_maker urlpatterns = urlpatterns_maker(default='^', profile='^my/') 3. equivalent to urlpatterns = patterns('', url(ur'^', include('chooper_api.views.default')), url(ur'^my/', include('chooper_api.views.profile')), url(ur'^friend/', include('chooper_api.views.friend')), url(ur'^feed/', include('chooper_api.views.feed')), ) note that unspecified views(friend.py and feed.py here) got their file name(without extension, and surrounded by '^' and '/') as the url regex """ import inspect import os from django.conf.urls import patterns, include, url revision = '0.1' def urlpatterns_maker(**kwargs): path_init = inspect.getouterframes(inspect.currentframe())[1][1] path_views, file_name_init = os.path.split(path_init) path_app, folder_name_views = os.path.split(path_views) app_name = os.path.split(path_app)[1] files = [f.split('.')[0] for f in os.listdir(path_views) if not f.startswith('_') and (f.endswith('.py') or os.path.isdir(os.path.join(path_views, f))) and file_name_init != f] path_list = path_init.split('/') views_index = path_list.index('views') prefix = '.'.join(path_list[views_index-1:-1]) # print(path_init) # print(file_name_init) # print(path_views) # print(path_app) # print(folder_name_views) # print(app_name) # print(files) urlpatterns = [] for file_name in files: if file_name in kwargs: urlpatterns.append( url(r'{0}'.format(kwargs[file_name]), include('{0}.{1}'.format(prefix, file_name))) ) else: urlpatterns.append( url(r'^{0}/'.format(file_name), include('{0}.{1}'.format(prefix, file_name))) ) for urlpattern in urlpatterns: urlpattern.regex # url pattern check, thanks to django return urlpatterns

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mlops/data_validation/__init__.py

colingwuyu/mlops

1

58361

<reponame>colingwuyu/mlops from mlops.data_validation.view import view_report

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DML/datasets_ids.py

yuichikano/Fashion-Image-Retrieval-System

0

1613209

# Copyright 2019 <NAME> and <NAME> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== ################# LIBRARIES ############################### import warnings warnings.filterwarnings("ignore") import numpy as np, os, sys, pandas as pd, csv, copy import torch, torch.nn as nn, matplotlib.pyplot as plt, random from torch.utils.data import Dataset from PIL import Image from torchvision import transforms from tqdm import tqdm import pretrainedmodels.utils as utils import auxiliaries as aux """============================================================================""" ################ FUNCTION TO RETURN ALL DATALOADERS NECESSARY #################### def give_dataloaders(dataset, opt): """ Args: dataset: string, name of dataset for which the dataloaders should be returned. opt: argparse.Namespace, contains all training-specific parameters. Returns: dataloaders: dict of dataloaders for training, testing and evaluation on training. """ #Dataset selection if opt.dataset=='cub200': datasets = give_CUB200_datasets(opt) elif opt.dataset=='cars196': datasets = give_CARS196_datasets(opt) elif opt.dataset=='online_products': datasets = give_OnlineProducts_datasets(opt) elif opt.dataset=='in-shop': datasets = give_InShop_datasets(opt) elif opt.dataset=='vehicle_id': datasets = give_VehicleID_datasets(opt) else: raise Exception('No Dataset >{}< available!'.format(dataset)) #Move datasets to dataloaders. dataloaders = {} for key,dataset in datasets.items(): is_val = dataset.is_validation dataloaders[key] = torch.utils.data.DataLoader(dataset, batch_size=opt.bs, num_workers=opt.kernels, shuffle=not is_val, pin_memory=True, drop_last=not is_val) return dataloaders """============================================================================""" ################# FUNCTIONS TO RETURN TRAIN/VAL PYTORCH DATASETS FOR CUB200, CARS196, STANFORD ONLINE PRODUCTS, IN-SHOP CLOTHES, PKU VEHICLE-ID #################################### def give_CUB200_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the CUB-200-2011 dataset. For Metric Learning, the dataset classes are sorted by name, and the first half used for training while the last half is used for testing. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ image_sourcepath = opt.source_path+'/images' #Find available data classes. image_classes = sorted([x for x in os.listdir(image_sourcepath) if '._' not in x], key=lambda x: int(x.split('.')[0])) #Make a index-to-labelname conversion dict. conversion = {int(x.split('.')[0]):x.split('.')[-1] for x in image_classes} #Generate a list of tuples (class_label, image_path) image_list = {int(key.split('.')[0]):sorted([image_sourcepath+'/'+key+'/'+x for x in os.listdir(image_sourcepath+'/'+key) if '._' not in x]) for key in image_classes} image_list = [[(key,img_path) for img_path in image_list[key]] for key in image_list.keys()] image_list = [x for y in image_list for x in y] #Image-dict of shape {class_idx:[list of paths to images belong to this class] ...} image_dict = {} for key, img_path in image_list: key = key-1 if not key in image_dict.keys(): image_dict[key] = [] image_dict[key].append(img_path) keys = sorted(list(image_dict.keys())) #Following "Deep Metric Learning via Lifted Structured Feature Embedding", we use the first half of classes for training. train,test = keys[:len(keys)//2], keys[len(keys)//2:] train_image_dict, val_image_dict = {key:image_dict[key] for key in train},{key:image_dict[key] for key in test} train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) val_dataset = BaseTripletDataset(val_image_dict, opt, is_validation=True) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) train_dataset.conversion = conversion val_dataset.conversion = conversion eval_dataset.conversion = conversion return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset} def give_CARS196_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the CARS196 dataset. For Metric Learning, the dataset classes are sorted by name, and the first half used for training while the last half is used for testing. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ image_sourcepath = opt.source_path+'/images' #Find available data classes. image_classes = sorted([x for x in os.listdir(image_sourcepath)]) #Make a index-to-labelname conversion dict. conversion = {i:x for i,x in enumerate(image_classes)} #Generate a list of tuples (class_label, image_path) image_list = {i:sorted([image_sourcepath+'/'+key+'/'+x for x in os.listdir(image_sourcepath+'/'+key)]) for i,key in enumerate(image_classes)} image_list = [[(key,img_path) for img_path in image_list[key]] for key in image_list.keys()] image_list = [x for y in image_list for x in y] #Image-dict of shape {class_idx:[list of paths to images belong to this class] ...} image_dict = {} for key, img_path in image_list: key = key # key = key-1 if not key in image_dict.keys(): image_dict[key] = [] image_dict[key].append(img_path) keys = sorted(list(image_dict.keys())) #Following "Deep Metric Learning via Lifted Structured Feature Embedding", we use the first half of classes for training. train,test = keys[:len(keys)//2], keys[len(keys)//2:] train_image_dict, val_image_dict = {key:image_dict[key] for key in train},{key:image_dict[key] for key in test} train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) val_dataset = BaseTripletDataset(val_image_dict, opt, is_validation=True) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) train_dataset.conversion = conversion val_dataset.conversion = conversion eval_dataset.conversion = conversion return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset} def give_OnlineProducts_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the Online-Products dataset. For Metric Learning, training and test sets are provided by given text-files, Ebay_train.txt & Ebay_test.txt. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ image_sourcepath = opt.source_path+'/images' #Load text-files containing classes and imagepaths. training_files = pd.read_table(opt.source_path+'/Info_Files/Ebay_train.txt', header=0, delimiter=' ') test_files = pd.read_table(opt.source_path+'/Info_Files/Ebay_test.txt', header=0, delimiter=' ') #Generate Conversion dict. conversion = {} for class_id, path in zip(training_files['class_id'],training_files['path']): conversion[class_id] = path.split('/')[0] for class_id, path in zip(test_files['class_id'],test_files['path']): conversion[class_id] = path.split('/')[0] #Generate image_dicts of shape {class_idx:[list of paths to images belong to this class] ...} train_image_dict, val_image_dict = {},{} for key, img_path in zip(training_files['class_id'],training_files['path']): key = key-1 if not key in train_image_dict.keys(): train_image_dict[key] = [] train_image_dict[key].append(image_sourcepath+'/'+img_path) for key, img_path in zip(test_files['class_id'],test_files['path']): key = key-1 if not key in val_image_dict.keys(): val_image_dict[key] = [] val_image_dict[key].append(image_sourcepath+'/'+img_path) ### Uncomment this if super-labels should be used to generate resp.datasets # super_conversion = {} # for super_class_id, path in zip(training_files['super_class_id'],training_files['path']): # conversion[super_class_id] = path.split('/')[0] # for key, img_path in zip(training_files['super_class_id'],training_files['path']): # key = key-1 # if not key in super_train_image_dict.keys(): # super_train_image_dict[key] = [] # super_train_image_dict[key].append(image_sourcepath+'/'+img_path) # super_train_dataset = BaseTripletDataset(super_train_image_dict, opt, is_validation=True) # super_train_dataset.conversion = super_conversion train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) val_dataset = BaseTripletDataset(val_image_dict, opt, is_validation=True) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) train_dataset.conversion = conversion val_dataset.conversion = conversion eval_dataset.conversion = conversion return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset} # return {'training':train_dataset, 'testing':val_dataset, 'evaluation':eval_dataset, 'super_evaluation':super_train_dataset} def give_InShop_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the In-Shop Clothes dataset. For Metric Learning, training and test sets are provided by one text file, list_eval_partition.txt. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing (by query and gallery separation) and evaluation. """ #Load train-test-partition text file. data_info = np.array(pd.read_table(opt.source_path+'/Eval/list_eval_partition.txt', header=1, delim_whitespace=True))[1:,:] #Separate into training dataset and query/gallery dataset for testing. train, query, gallery = data_info[data_info[:,2]=='train'][:,:2], data_info[data_info[:,2]=='query'][:,:2], data_info[data_info[:,2]=='gallery'][:,:2] #Generate conversions(id verson) # use_train_image_num = 10000 # use_val_image_num = int(use_train_image_num/3) # np.random.seed(0) # train_idx = np.random.choice(len(train), size=use_train_image_num, replace = False) # train = train[train_idx] # query_idx = np.random.choice(len(query), size=use_val_image_num, replace = False) # query = query[query_idx] # gallery_idx = np.random.choice(len(gallery), size=use_val_image_num, replace = False) # gallery = gallery[gallery_idx] #Generate conversions lab_conv = {x:i for i,x in enumerate(np.unique(np.array([int(x.split('_')[-1]) for x in train[:,1]])))} train[:,1] = np.array([lab_conv[int(x.split('_')[-1])] for x in train[:,1]]) lab_conv = {x:i for i,x in enumerate(np.unique(np.array([int(x.split('_')[-1]) for x in np.concatenate([query[:,1], gallery[:,1]])])))} query[:,1] = np.array([lab_conv[int(x.split('_')[-1])] for x in query[:,1]]) gallery[:,1] = np.array([lab_conv[int(x.split('_')[-1])] for x in gallery[:,1]]) #Generate Image-Dicts for training, query and gallery of shape {class_idx:[list of paths to images belong to this class] ...} train_image_dict = {} for img_path, key in train: if not key in train_image_dict.keys(): train_image_dict[key] = [] train_image_dict[key].append(opt.source_path+'/'+img_path) query_image_dict = {} for img_path, key in query: if not key in query_image_dict.keys(): query_image_dict[key] = [] query_image_dict[key].append(opt.source_path+'/'+img_path) gallery_image_dict = {} for img_path, key in gallery: if not key in gallery_image_dict.keys(): gallery_image_dict[key] = [] gallery_image_dict[key].append(opt.source_path+'/'+img_path) ### Uncomment this if super-labels should be used to generate resp.datasets # super_train_image_dict, counter, super_assign = {},0,{} # for img_path, _ in train: # key = '_'.join(img_path.split('/')[1:3]) # if key not in super_assign.keys(): # super_assign[key] = counter # counter += 1 # key = super_assign[key] # # if not key in super_train_image_dict.keys(): # super_train_image_dict[key] = [] # super_train_image_dict[key].append(opt.source_path+'/'+img_path) # super_train_dataset = BaseTripletDataset(super_train_image_dict, opt, is_validation=True) train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) query_dataset = BaseTripletDataset(query_image_dict, opt, is_validation=True) gallery_dataset = BaseTripletDataset(gallery_image_dict, opt, is_validation=True) return {'training':train_dataset, 'testing_query':query_dataset, 'evaluation':eval_dataset, 'testing_gallery':gallery_dataset} # return {'training':train_dataset, 'testing_query':query_dataset, 'evaluation':eval_dataset, 'testing_gallery':gallery_dataset, 'super_evaluation':super_train_dataset} def give_VehicleID_datasets(opt): """ This function generates a training, testing and evaluation dataloader for Metric Learning on the PKU Vehicle dataset. For Metric Learning, training and (multiple) test sets are provided by separate text files, train_list and test_list_<n_classes_2_test>.txt. So no random shuffling of classes. Args: opt: argparse.Namespace, contains all traininig-specific parameters. Returns: dict of PyTorch datasets for training, testing and evaluation. """ #Load respective text-files train = np.array(pd.read_table(opt.source_path+'/train_test_split/train_list.txt', header=None, delim_whitespace=True)) small_test = np.array(pd.read_table(opt.source_path+'/train_test_split/test_list_800.txt', header=None, delim_whitespace=True)) medium_test = np.array(pd.read_table(opt.source_path+'/train_test_split/test_list_1600.txt', header=None, delim_whitespace=True)) big_test = np.array(pd.read_table(opt.source_path+'/train_test_split/test_list_2400.txt', header=None, delim_whitespace=True)) #Generate conversions lab_conv = {x:i for i,x in enumerate(np.unique(train[:,1]))} train[:,1] = np.array([lab_conv[x] for x in train[:,1]]) lab_conv = {x:i for i,x in enumerate(np.unique(np.concatenate([small_test[:,1], medium_test[:,1], big_test[:,1]])))} small_test[:,1] = np.array([lab_conv[x] for x in small_test[:,1]]) medium_test[:,1] = np.array([lab_conv[x] for x in medium_test[:,1]]) big_test[:,1] = np.array([lab_conv[x] for x in big_test[:,1]]) #Generate Image-Dicts for training and different testings of shape {class_idx:[list of paths to images belong to this class] ...} train_image_dict = {} for img_path, key in train: if not key in train_image_dict.keys(): train_image_dict[key] = [] train_image_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) small_test_dict = {} for img_path, key in small_test: if not key in small_test_dict.keys(): small_test_dict[key] = [] small_test_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) medium_test_dict = {} for img_path, key in medium_test: if not key in medium_test_dict.keys(): medium_test_dict[key] = [] medium_test_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) big_test_dict = {} for img_path, key in big_test: if not key in big_test_dict.keys(): big_test_dict[key] = [] big_test_dict[key].append(opt.source_path+'/image/{:07d}.jpg'.format(img_path)) train_dataset = BaseTripletDataset(train_image_dict, opt, samples_per_class=opt.samples_per_class) eval_dataset = BaseTripletDataset(train_image_dict, opt, is_validation=True) val_small_dataset = BaseTripletDataset(small_test_dict, opt, is_validation=True) val_medium_dataset = BaseTripletDataset(medium_test_dict, opt, is_validation=True) val_big_dataset = BaseTripletDataset(big_test_dict, opt, is_validation=True) return {'training':train_dataset, 'testing_set1':val_small_dataset, 'testing_set2':val_medium_dataset, \ 'testing_set3':val_big_dataset, 'evaluation':eval_dataset} ################## BASIC PYTORCH DATASET USED FOR ALL DATASETS ################################## class BaseTripletDataset(Dataset): """ Dataset class to provide (augmented) correctly prepared training samples corresponding to standard DML literature. This includes normalizing to ImageNet-standards, and Random & Resized cropping of shapes 224 for ResNet50 and 227 for GoogLeNet during Training. During validation, only resizing to 256 or center cropping to 224/227 is performed. """ def __init__(self, image_dict, opt, samples_per_class=8, is_validation=False): """ Dataset Init-Function. Args: image_dict: dict, Dictionary of shape {class_idx:[list of paths to images belong to this class] ...} providing all the training paths and classes. opt: argparse.Namespace, contains all training-specific parameters. samples_per_class: Number of samples to draw from one class before moving to the next when filling the batch. is_validation: If is true, dataset properties for validation/testing are used instead of ones for training. Returns: Nothing! """ #Define length of dataset self.n_files = np.sum([len(image_dict[key]) for key in image_dict.keys()]) self.is_validation = is_validation self.pars = opt self.image_dict = image_dict self.avail_classes = sorted(list(self.image_dict.keys())) #Convert image dictionary from classname:content to class_idx:content, because the initial indices are not necessarily from 0 - <n_classes>. self.image_dict = {i:self.image_dict[key] for i,key in enumerate(self.avail_classes)} self.avail_classes = sorted(list(self.image_dict.keys())) #Init. properties that are used when filling up batches. if not self.is_validation: self.samples_per_class = samples_per_class #Select current class to sample images from up to <samples_per_class> self.current_class = np.random.randint(len(self.avail_classes)) self.classes_visited = [self.current_class, self.current_class] self.n_samples_drawn = 0 #Data augmentation/processing methods. normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) transf_list = [] if not self.is_validation: transf_list.extend([transforms.RandomResizedCrop(size=224) if opt.arch=='resnet50' else transforms.RandomResizedCrop(size=227), transforms.RandomHorizontalFlip(0.5)]) else: transf_list.extend([transforms.Resize(256), transforms.CenterCrop(224) if opt.arch=='resnet50' else transforms.CenterCrop(227)]) transf_list.extend([transforms.ToTensor(), normalize]) self.transform = transforms.Compose(transf_list) #Convert Image-Dict to list of (image_path, image_class). Allows for easier direct sampling. self.image_list = [[(x,key) for x in self.image_dict[key]] for key in self.image_dict.keys()] self.image_list = [x for y in self.image_list for x in y] #Flag that denotes if dataset is called for the first time. self.is_init = True def ensure_3dim(self, img): """ Function that ensures that the input img is three-dimensional. Args: img: PIL.Image, image which is to be checked for three-dimensionality (i.e. if some images are black-and-white in an otherwise coloured dataset). Returns: Checked PIL.Image img. """ if len(img.size)==2: img = img.convert('RGB') return img def __getitem__(self, idx): """ Args: idx: Sample idx for training sample Returns: tuple of form (sample_class, torch.Tensor() of input image) """ if self.is_init: self.current_class = self.avail_classes[idx%len(self.avail_classes)] self.is_init = False if not self.is_validation: if self.samples_per_class==1: return self.image_list[idx][-1], self.transform(self.ensure_3dim(Image.open(self.image_list[idx][0]))) if self.n_samples_drawn==self.samples_per_class: #Once enough samples per class have been drawn, we choose another class to draw samples from. #Note that we ensure with self.classes_visited that no class is chosen if it had been chosen #previously or one before that. counter = copy.deepcopy(self.avail_classes) for prev_class in self.classes_visited: if prev_class in counter: counter.remove(prev_class) self.current_class = counter[idx%len(counter)] self.classes_visited = self.classes_visited[1:]+[self.current_class] self.n_samples_drawn = 0 class_sample_idx = idx%len(self.image_dict[self.current_class]) self.n_samples_drawn += 1 out_img = self.transform(self.ensure_3dim(Image.open(self.image_dict[self.current_class][class_sample_idx]))) return self.current_class,out_img else: return self.image_list[idx][-1], self.transform(self.ensure_3dim(Image.open(self.image_list[idx][0]))) def __len__(self): return self.n_files

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tests/component_tests/test_session.py

lsawade/radical.pilot

0

121768

# pylint: disable=protected-access, unused-argument __copyright__ = 'Copyright 2020, The RADICAL-Cybertools Team' __license__ = 'MIT' import glob import os import shutil from unittest import TestCase, mock import radical.pilot as rp TEST_CASES_PATH = '%s/test_cases' % os.path.dirname(__file__) # ------------------------------------------------------------------------------ # class TestSession(TestCase): # -------------------------------------------------------------------------- # @classmethod @mock.patch.object(rp.Session, '_initialize_primary', return_value=None) def setUpClass(cls, *args, **kwargs): cls._session = rp.Session() # -------------------------------------------------------------------------- # @classmethod def tearDownClass(cls): for d in glob.glob('./rp.session.*'): if os.path.isdir(d): try: shutil.rmtree(d) except OSError as e: print('[ERROR] %s - %s' % (e.filename, e.strerror)) # -------------------------------------------------------------------------- # def test_list_resources(self): listed_resources = self._session.list_resources() self.assertIsInstance(listed_resources, list) self.assertIn('local.localhost', listed_resources) # -------------------------------------------------------------------------- # def test_get_resource_config(self): rcfg_label = 'xsede.comet_ssh' # schemas are ["ssh", "gsissh"] rcfg = self._session.get_resource_config(rcfg_label) self.assertEqual(rcfg.job_manager_endpoint, rcfg[rcfg.schemas[0]].job_manager_endpoint) new_schema = 'gsissh' rcfg = self._session.get_resource_config(rcfg_label, schema=new_schema) self.assertEqual(rcfg.job_manager_endpoint, rcfg[new_schema].job_manager_endpoint) # check exceptions with self.assertRaises(RuntimeError): self._session.get_resource_config(resource='wrong_domain.host') with self.assertRaises(RuntimeError): self._session.get_resource_config(resource='local.wrong_host') with self.assertRaises(RuntimeError): self._session.get_resource_config( resource='local.localhost', schema='wrong_schema') # ------------------------------------------------------------------------------ # if __name__ == '__main__': tc = TestSession() tc.test_list_resources() tc.test_get_resource_config() # ------------------------------------------------------------------------------

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js_search/cms_apps.py

compoundpartners/js-search

0

133077

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.utils.translation import ugettext_lazy as _ from cms.app_base import CMSApp from cms.apphook_pool import apphook_pool from . import DEFAULT_APP_NAMESPACE class SearchApp(CMSApp): name = _('Search') app_name = DEFAULT_APP_NAMESPACE urls = ['js_search.urls'] def get_urls(self, *args, **kwargs): return self.urls apphook_pool.register(SearchApp)

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model/gif2png.py

THCloud9/naruto

20

119220

<reponame>THCloud9/naruto<filename>model/gif2png.py #-*- coding: UTF-8 -*- import os from PIL import Image def analyseImage(path): ''''' Pre-process pass over the image to determine the mode (full or additive). Necessary as assessing single frames isn't reliable. Need to know the mode before processing all frames. ''' im = Image.open(path) results = { 'size': im.size, 'mode': 'full', } try: while True: if im.tile: tile = im.tile[0] update_region = tile[1] update_region_dimensions = update_region[2:] if update_region_dimensions != im.size: results['mode'] = 'partial' break im.seek(im.tell() + 1) except EOFError: pass return results def processImage(path): ''''' 传入GIF图的完整路径名字,自动在该位置创建png文件夹来保存所有图片 ''' mode = analyseImage(path)['mode'] im = Image.open(path) i = 0 p = im.getpalette() last_frame = im.convert('RGBA') try: while True: print ("saving %s (%s) frame %d, %s %s" % (path, mode, i, im.size, im.tile) ) ''''' If the GIF uses local colour tables, each frame will have its own palette. If not, we need to apply the global palette to the new frame. ''' if not im.getpalette(): im.putpalette(p) new_frame = Image.new('RGBA', im.size) ''''' Is this file a "partial"-mode GIF where frames update a region of a different size to the entire image? If so, we need to construct the new frame by pasting it on top of the preceding frames. ''' if mode == 'partial': new_frame.paste(last_frame) new_frame.paste(im, (0,0), im.convert('RGBA')) fp = "/".join(path.split('/')[:-1]) + '/png' make_path(fp) fp = "/".join(path.split('/')[:-1]) + '/png/' + 'action' new_frame.save('%s-%02d.png' % (fp , i), 'PNG') i += 1 last_frame = new_frame im.seek(im.tell() + 1) except EOFError: pass def make_path(p): if not os.path.exists(p): # 判断文件夹是否存在 os.makedirs(p) # 创建文件夹 def main(): processImage('image/shuidun/shuidun.gif') if __name__ == "__main__": main()

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ambry/dbexceptions.py

kball/ambry

1

1605968

"""Common exception objects Copyright (c) 2013 Clarinova. This file is licensed under the terms of the Revised BSD License, included in this distribution as LICENSE.txt """ import textwrap class BundleError(Exception): def __init__(self, message, *args, **kwargs): # Call the base class constructor with the parameters it needs #Exception.__init__(self,textwrap.fill(message, 120), *args, **kwargs) Exception.__init__(self, message, *args, **kwargs) class BadRequest(BundleError): '''The functioncall or request was malformed or incorrect''' class ProcessError(BundleError): '''Error in the configuration files''' class ObjectStateError(BundleError): '''Object not put into appropriate state before uses''' class ConfigurationError(BundleError): '''Error in the configuration files''' class ResultCountError(BundleError): '''Got too many or too few results''' class FilesystemError(BundleError): '''Missing file, etc. ''' class NotFoundError(BundleError): '''Failed to find resource''' class DependencyError(Exception): """Required bundle dependencies not satisfied""" class NoLock(BundleError): '''Error in the configuration files''' class Locked(BundleError): '''Error in the configuration files''' class QueryError(BundleError): """Error while executing a query""" class ConflictError(BundleError): """Conflict with existing resource""" class SyncError(BundleError): """Could not sync a resource""" class FatalError(BundleError): """A Fatal Bundle Error, generated in testing instead of a system exit. """ class DatabaseError(BundleError): """A general database error """ class DatabaseMissingError(DatabaseError): """A general database error """

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test_file.py

lulivi/gh-actions-tests

0

66498

<reponame>lulivi/gh-actions-tests import pathlib from os import environ import path # type: ignore def sample_function(text: str) -> None: print(f"This is a test function") print(f"The argument value is {text}") def main() -> None: """Multiples errors.""" print("Sample file with bad formatting!!!") environ["SAMPLE_VAR"] = "test" This_isAList: list = ["Sample object", "bad", "Formatted", "Ups" ] sample_function( "12345" ) if __name__ == "__main__": main()

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build_dataset.py

KrishnaGarg/named-entity-recognition

1

95575

<gh_stars>1-10 import csv import os import sys def load_dataset(path_csv): """Load dataset from csv file""" with open(path_csv, encoding="windows-1252") as f: csv_file = csv.reader(f, delimiter=',') dataset = []; words = []; tags = [] # Each line of csv corresponds to one word for idx, row in enumerate(csv_file): if idx == 0: continue # first row has only labels sentence, word, pos, tag = row # Non-empty sentence marks beginning of new sentence if len(sentence) != 0: if len(words) > 0: assert len(words) == len(tags) dataset.append((words, tags)) words, tags = [], [] # try: word, tag = str(word), str(tag) words.append(word) tags.append(tag) return dataset def save_dataset(dataset, save_dir): """Create splits from dataset""" print("Saving in {}...".format(save_dir)) if not os.path.exists(save_dir): os.makedirs(save_dir) # Export the dataset with open(os.path.join(save_dir, 'sentences.txt'), 'w') as file_sentences: with open(os.path.join(save_dir, 'labels.txt'), 'w') as file_labels: for words, tags in dataset: file_sentences.write("{}\n".format(" ".join(words))) file_labels.write("{}\n".format(" ".join(tags))) print("- done.") if __name__ == "__main__": dataset_path = 'data/ner_dataset.csv' msg = '{} file not found. Make sure you have downloaded the right dataset'.format(dataset_path) assert os.path.isfile(dataset_path), msg # Load the dataset print("Loading dataset...") dataset = load_dataset(dataset_path) print("- done.") # Split the dataset into train, val and test train_dataset = dataset[:int(0.7*len(dataset))] val_dataset = dataset[int(0.7*len(dataset)) : int(0.85*len(dataset))] test_dataset = dataset[int(0.85*len(dataset)):] save_dataset(train_dataset, 'data/train') save_dataset(val_dataset, 'data/val') save_dataset(test_dataset, 'data/test')

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sdk/python/pulumi_azure/automation/connection.py

suresh198526/pulumi-azure

0

165971

<gh_stars>0 # coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from .. import _utilities, _tables __all__ = ['Connection'] class Connection(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, automation_account_name: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[str]] = None, values: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, __props__=None, __name__=None, __opts__=None): """ Manages an Automation Connection. ## Import Automation Connection can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:automation/connection:Connection example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Automation/automationAccounts/account1/connections/conn1 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] automation_account_name: The name of the automation account in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] description: A description for this Connection. :param pulumi.Input[str] name: Specifies the name of the Connection. Changing this forces a new resource to be created. :param pulumi.Input[str] resource_group_name: The name of the resource group in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] type: The type of the Connection - can be either builtin type such as `Azure`, `AzureClassicCertificate`, and `AzureServicePrincipal`, or a user defined types. Changing this forces a new resource to be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] values: A mapping of key value pairs passed to the connection. Different `type` needs different parameters in the `values`. Builtin types have required field values as below: """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() if automation_account_name is None: raise TypeError("Missing required property 'automation_account_name'") __props__['automation_account_name'] = automation_account_name __props__['description'] = description __props__['name'] = name if resource_group_name is None: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name if type is None: raise TypeError("Missing required property 'type'") __props__['type'] = type if values is None: raise TypeError("Missing required property 'values'") __props__['values'] = values super(Connection, __self__).__init__( 'azure:automation/connection:Connection', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, automation_account_name: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[str]] = None, values: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None) -> 'Connection': """ Get an existing Connection resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] automation_account_name: The name of the automation account in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] description: A description for this Connection. :param pulumi.Input[str] name: Specifies the name of the Connection. Changing this forces a new resource to be created. :param pulumi.Input[str] resource_group_name: The name of the resource group in which the Connection is created. Changing this forces a new resource to be created. :param pulumi.Input[str] type: The type of the Connection - can be either builtin type such as `Azure`, `AzureClassicCertificate`, and `AzureServicePrincipal`, or a user defined types. Changing this forces a new resource to be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] values: A mapping of key value pairs passed to the connection. Different `type` needs different parameters in the `values`. Builtin types have required field values as below: """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["automation_account_name"] = automation_account_name __props__["description"] = description __props__["name"] = name __props__["resource_group_name"] = resource_group_name __props__["type"] = type __props__["values"] = values return Connection(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="automationAccountName") def automation_account_name(self) -> pulumi.Output[str]: """ The name of the automation account in which the Connection is created. Changing this forces a new resource to be created. """ return pulumi.get(self, "automation_account_name") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ A description for this Connection. """ return pulumi.get(self, "description") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Specifies the name of the Connection. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Output[str]: """ The name of the resource group in which the Connection is created. Changing this forces a new resource to be created. """ return pulumi.get(self, "resource_group_name") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The type of the Connection - can be either builtin type such as `Azure`, `AzureClassicCertificate`, and `AzureServicePrincipal`, or a user defined types. Changing this forces a new resource to be created. """ return pulumi.get(self, "type") @property @pulumi.getter def values(self) -> pulumi.Output[Mapping[str, str]]: """ A mapping of key value pairs passed to the connection. 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start.py

AndersonOyama/TCC

0

121507

import re from spellchecker import SpellChecker def countSpellError(file): spell = SpellChecker(language='pt') lines = [] with open(file, 'r') as f: lines = [word.replace('.', '').replace(',','') for line in f for word in line.split()] f.close() count = 0 misspelled = spell.unknown(lines) for word in misspelled: # spell.correction(word) # print(word) # print(word, spell.correction(word)) count += 1 return misspelled, count

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test-projects/django_1_0/urls.py

TheLitvinoff/django-async

13

182659

from django.conf.urls.defaults import * from django.contrib import admin admin.autodiscover() urlpatterns = patterns('', # Enable Slumber (r'^slumber/', include('slumber.urls')), # Uncomment the next line to enable the admin: (r'^admin/(.*)', admin.site.root), )

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dockerimp.py

N4SJAMK/dockerimp

0

195181

#!/usr/bin/env python DOCUMENTATION = ''' --- module: dockerimp short_description: improved docker container management module description: - Manage docker containers with ansible options: name: description: - Set the name of the container required: false default: null aliases: ["id"] state: description: - Set the state of the container required: true default: null choices: [ "present", "running", "stopped", "absent", "restarted", "image_present", "image_latest", "image_absent" ] aliases: [] image: description: - Set the image for the container required: false default: null aliases: [] env: description: - Set the container environment variables required: false default: null aliases: [] volumes: description: - Mount voulumes for the container required: false default: null aliases: [] ports: description: - Map ports for container required: false default: null aliases: [] command: description: - Set the command for container required: false default: null aliases: [] expose: description: - Expose ports required: false default: null aliases: [] links: description: - Link containers required: false default: null aliases: [] client_url: description: - Client base url required: false default: "unix://var/run/docker.sock" aliases: [] insecure_registry: description: - Trust insecure registrys required: false default: false aliases: [] ''' import sys import copy try: import docker.client except ImportError as e: print("Failed to import module {0}".format(e)) sys.exit(1) class ContainerManagerException(Exception): pass class ContainerManager(): def __init__(self, module): self.module = module self.client = docker.Client(base_url = module.params.get('client_url')) self.changed = False self.check_mode = module.check_mode self.changes_made = [] self.params = self.fix_parameters() def fix_parameters(self): params = copy.deepcopy(self.module.params) if params.get('volumes'): try: if type(params['volumes']) is str: volumes = params['volumes'].split(",") elif type(params['volumes']) is list: volumes = params['volumes'] else: raise ContainerManagerException({'Invalid argument': params['volumes']}) mount_points = [x.split(":")[1] for x in volumes] binds = {} for i in volumes: j = i.split(":") len_j = len(j) if len_j != 2 and len_j != 3: raise ContainerManagerException({'Invalid argument': params['volumes']}) ro = False if len_j == 3: if j[2] == "ro": ro = True elif j[2] == "rw": ro = False else: raise ContainerManagerException({'Invalid argument': params['volumes']}) binds[j[0]] = {'bind': j[1], 'ro': ro} params['binds'] = binds params['volumes'] = mount_points except IndexError as e: raise ContainerManagerException({'Invalid argument': params['volumes']}) if params.get('image'): # add 'latest' tag to the image name if no tag is already provided image = params['image'] image_split = image.split("/")[-1].split(":") if len(image_split) == 1: params['image'] = "{0}:latest".format(params['image']) if params.get('ports'): try: if type(params['ports']) is str: port_params = params['ports'].split(",") elif type(params['ports']) is list: port_params = params['ports'] else: raise ContainerManagerException({'Invalid argument': params['ports']}) ports = [] for i in port_params: values = i.split(":") len_values = len(values) if len_values != 2 and len_values != 3: raise ContainerManagerException({'Invalid argument': params['ports']}) port_and_prot = values[-1].split("/") len_port_and_prot = len(port_and_prot) if len_port_and_prot > 2: raise ContainerManagerException({'Invalid argument': params['ports']}) p = (port_and_prot[0], port_and_prot[1]) if len_port_and_prot == 2 else port_and_prot[0] ports.append(p) port_bindings = {} for i in port_params: values = i.split(":") len_values = len(values) if len_values == 2: host_port = values[0] prot_and_port = values[1] bind_ip = None elif len_values == 3: host_port = values[1] prot_and_port = values[2] bind_ip = values[0] else: raise ContainerManagerException({'Invalid argument': params['ports']}) prot_and_port = prot_and_port.split("/") len_prot_and_port = len(prot_and_port) if len_prot_and_port == 2: key = "{0}/{1}".format(prot_and_port[0], prot_and_port[1]) elif len_prot_and_port == 1: key = prot_and_port[0] else: raise ContainerManagerException({'Invalid argument': params['ports']}) if bind_ip: val = (bind_ip, host_port) if host_port else (bind_ip,) else: val = host_port or None port_bindings[key] = val params['ports'] = ports params['port_bindings'] = port_bindings except IndexError as e: raise ContainerManagerException({'Invalid argument': params['ports'], 'error': e}) if params.get('env'): if type(params['env']) is str: envs = params['env'].split(",") elif type(params['env']) is list: envs = params['env'] elif type(params['env']) is dict: envs = ["{0}={1}".format(x, params['env'][x]) for x in params['env']] else: raise ContainerManagerException({'Invalid argument': params['env']}) # Add special ANSIBLE_MANAGED_ENVS variable so we can track which # variables are managed by ansible envs.append("ANSIBLE_MANAGED_ENVS={0}".format(":".join([x.split("=")[0] for x in envs]))) params['environment'] = envs return params def ensure_present(self): required_params = ("name", "image") self.check_required_parameters(required_params) container = self.find_container(self.params['name']) self.__ensure_present(container) def ensure_running(self): required_params = ("name", "image") self.check_required_parameters(required_params) container = self.find_container(self.params['name']) container = self.__ensure_present(container) if not container['State']['Running']: container = self.start_container(container) def ensure_running_latest(self): required_params = ("name", "image") self.check_required_parameters(required_params) container = self.find_container(self.params['name']) image = self.find_image(self.params['image']) if not container: container = self.__ensure_present(container) elif not self.is_running_latest_image(container, image): self.remove_container(container) container = self.__ensure_present() elif not self.ensure_same(container): self.ensure_absent() container = self.__ensure_present() if not container['State']['Running']: self.start_container(container) def ensure_stopped(self): required_params = ("name",) self.check_required_parameters(required_params) container = self.find_container(self.params['name']) if not container: raise ContainerManagerException("Container not found") if container['State']['Running']: self.stop_container(container) def ensure_absent(self): required_params = ("name",) self.check_required_parameters(required_params) container = self.find_container(self.params['name']) self.remove_container(container) def restart(self): required_params = ("name",) self.check_required_parameters(required_params) container = self.find_container(self.params.get('name')) if not container: raise ContainerManagerException("Container not found") if not container['State']['Running']: raise ContainerManagerException("Container not running") self.restart_container(container) def ensure_image_present(self): required_params = ("image",) self.check_required_parameters(required_params) self.__ensure_image_present(self.params['image']) def ensure_image_latest(self): required_params = ("image",) self.check_required_parameters(required_params) self.__ensure_image_latest(self.params['image']) def ensure_image_absent(self): required_params = ("image",) self.check_required_parameters(required_params) name = self.params['image'] if self.find_image(name): self.client.remove_image(name) def __ensure_present(self, container = None): if not container: self.__ensure_image_present(self.params['image']) container = self.create_container() elif not self.ensure_same(container): self.ensure_absent() container = self.__ensure_present() return container def __ensure_image_present(self, name): image = self.find_image(name) if not image: self.pull_image(name) def __ensure_image_latest(self, name): self.pull_image(name) return self.find_image(name) def check_required_parameters(self, required): for i in required: if not self.params.get(i): state = self.params['state'] error_msg = "{0} required for {1} satate".format(i, state) raise ContainerManagerException(error_msg) def find_container(self, name): containers = self.client.containers(all = True) c = [x for x in containers if ((x['Names'] or [""])[0] == "/{0}".format(name)) or (len(name) > 9 and x['Id'].startswith(name))] if len(c) > 1: error_msg = "Found more than one container with name or id" raise ContainerManagerException({'Unexpected error': error_msg}) if c: container = self.get_info(c[0]) return container return None def find_image(self, name): # client.images method does not throw an error if image is not found, it just # returns an empty array. client.inspect_image throws an error if image is not # found. Propably cleaner to do this way than to catch an error. image_name = name.split(":") # image name may contain port, so rejoin everything exept last item which is tag images = self.client.images(name = ":".join(image_name[:-1])) image_len = len(images) if image_len == 0: return None else: for i in images: if name in i['RepoTags']: return self.client.inspect_image(name) else: return None def is_running_latest_image(self, container, image): if not image: return False if image['Id'] == container['Image']: return True else: return False def get_info(self, container): return self.client.inspect_container(container) def get_image_info(self, image): return self.client.inspect_image(image) def pull_image(self, name): insecure_registry = self.params['insecure_registry'] old = self.find_image(name) self.client.pull(name, insecure_registry = insecure_registry) new = self.find_image(name) if not new: error_msg = "Cannot find {0}".format(name) raise ContainerManagerException({'Image not found': error_msg}) elif new['Id'] != (old or {}).get('Id'): self.write_log('PULLED', new) def create_container(self): params = self.params key_filter = ( 'image', 'command', 'hostname', 'user', 'detach', 'stdin_open', 'tty', 'mem_limit', 'ports', 'environment', 'dns', 'volumes', 'volumes_from', 'network_disabled', 'name', 'entrypoint', 'cpu_shares', 'working_dir', 'memswap_limit' ) filtered = { x: params[x] for x in key_filter if x in params } c = self.client.create_container(**filtered) container = self.get_info(c) self.write_log('CREATED', container) return container def start_container(self, container): params = self.params key_filter = ( 'binds', 'port_bindings', 'lxc_conf', 'publish_all_ports', 'links', 'privileged', 'dns', 'dns_search', 'volumes_from', 'network_mode', 'restart_policy', 'cap_add', 'cap_drop' ) filtered = { x: params[x] for x in key_filter if x in params } self.client.start(container, **filtered) container = self.get_info(container) self.write_log('STARTED', container) return container def stop_container(self, container): self.client.stop(container) container = self.get_info(container) self.write_log('STOPPED', container) return container def remove_container(self, container): if container['State']['Running']: container = self.stop_container(container) self.client.remove_container(container) c = self.find_container(container['Id']) if c: raise ContainerManagerException("Could not remove the container") self.write_log('REMOVED', container) def restart_container(self, container): self.client.restart(container) container = self.get_info(container) self.write_log('RESTARTED', container) def ensure_same(self, container): params = self.params require_restart = False # Ensure running the right image if container['Config']['Image'] != params['image']: require_restart = True # Ensure running latest image if the parameter is provided same = True if params.get('latest_image'): self.client.pull(params['image']) if not self.running_latest_image(container, params['image']): same = False require_restart = True # Ensure environment vars are up to date for i in container['Config']['Env']: if "ANSIBLE_MANAGED_ENVS" in i: ansible_managed_envs = i.split("=")[1].split(":") # Add the magic ANSIBLE_MANAGED_ENVS key value here # so that the two lists are easily comparable with # set() below ansible_managed_envs.append("ANSIBLE_MANAGED_ENVS") has_ansible_managed_envs = True break else: has_ansible_managed_envs = False has_env_params = params.get('environment') != None if has_env_params or has_ansible_managed_envs: if has_env_params and has_ansible_managed_envs: env_params = params['environment'] # Check same variables are set if set(ansible_managed_envs) != set([x.split("=")[0] for x in env_params]): require_restart = True # Check that the values are right else: for env in env_params: if env not in container['Config']['Env']: require_restart = True break else: require_restart = True # Ensure volume mountings are right container_binds = container['HostConfig']['Binds'] bind_params = params.get('binds') if container_binds or bind_params: if container_binds and bind_params: _bind_params = [ ":".join([ x, bind_params[x]['bind'], "ro" if bind_params[x]['ro'] else "rw" ]) for x in bind_params ] if set(_bind_params) != set(container_binds): require_restart = True else: require_restart = True # Ensure command is right if params.get('command'): if params['command'] != container['Command']: require_restart = True return require_restart != True def generate_message(self): if not self.has_changes(): msg = "Up to date. No changes made" else: msg = self.changes_made return msg def write_log(self, action, info): key_filter = ( 'Name', 'Id', 'Image', ) filtered = { x: info[x] for x in key_filter if x in info } self.changes_made.append({action: filtered}) def has_changes(self): if self.changes_made: return True return False def main(): arguments = { 'state': { 'required': True, 'choices': [ "present", "running", "running_latest", "stopped", "absent", "restarted", "image_present", "image_latest", ] }, 'name': { 'default': None, 'aliases': ["id"] }, 'image': { 'default': None }, 'env': { 'default': None }, 'volumes': { 'default': None }, 'ports': { 'default': None }, 'command': { 'default': None }, 'expose': { 'default': None }, 'links': { 'default': None }, 'insecure_registry': { 'default': False, 'choises': BOOLEANS }, } #module = AnsibleModule(argument_spec = arguments, supports_check_mode = True) module = AnsibleModule(argument_spec = arguments) try: manager = ContainerManager(module) state = module.params.get('state') if state == "present": manager.ensure_present() elif state == "running": manager.ensure_running() elif state == "running_latest": manager.ensure_running_latest() elif state == "stopped": manager.ensure_stopped() elif state == "absent": manager.ensure_absent() elif state == "restarted": manager.restart() elif state == "image_present": manager.ensure_image_present() elif state == "image_latest": manager.ensure_image_latest() elif state == "image_absent": manager.ensure_image_absent() module.exit_json(changed = manager.has_changes(), msg = manager.generate_message()) except ContainerManagerException as e: module.fail_json(msg = str(e)) except docker.errors.APIError as e: module.fail_json(msg = str(e)) except docker.errors.DockerException as e: module.fail_json(msg = str(e)) from ansible.module_utils.basic import * main()

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test/sql/test_insert.py

smarkets/sqlalchemy

1

1607613

<filename>test/sql/test_insert.py<gh_stars>1-10 #! coding:utf-8 from sqlalchemy import Column, Integer, MetaData, String, Table,\ bindparam, exc, func, insert, select from sqlalchemy.dialects import mysql, postgresql from sqlalchemy.engine import default from sqlalchemy.testing import AssertsCompiledSQL,\ assert_raises_message, fixtures class _InsertTestBase(object): @classmethod def define_tables(cls, metadata): Table('mytable', metadata, Column('myid', Integer), Column('name', String(30)), Column('description', String(30))) Table('myothertable', metadata, Column('otherid', Integer), Column('othername', String(30))) class InsertTest(_InsertTestBase, fixtures.TablesTest, AssertsCompiledSQL): __dialect__ = 'default' def test_generic_insert_bind_params_all_columns(self): table1 = self.tables.mytable self.assert_compile(insert(table1), 'INSERT INTO mytable (myid, name, description) ' 'VALUES (:myid, :name, :description)') def test_insert_with_values_dict(self): table1 = self.tables.mytable checkparams = { 'myid': 3, 'name': 'jack' } self.assert_compile(insert(table1, dict(myid=3, name='jack')), 'INSERT INTO mytable (myid, name) VALUES (:myid, :name)', checkparams=checkparams) def test_insert_with_values_tuple(self): table1 = self.tables.mytable checkparams = { 'myid': 3, 'name': 'jack', 'description': 'mydescription' } self.assert_compile(insert(table1, (3, 'jack', 'mydescription')), 'INSERT INTO mytable (myid, name, description) ' 'VALUES (:myid, :name, :description)', checkparams=checkparams) def test_insert_with_values_func(self): table1 = self.tables.mytable self.assert_compile(insert(table1, values=dict(myid=func.lala())), 'INSERT INTO mytable (myid) VALUES (lala())') def test_insert_with_user_supplied_bind_params(self): table1 = self.tables.mytable values = { table1.c.myid: bindparam('userid'), table1.c.name: bindparam('username') } self.assert_compile(insert(table1, values), 'INSERT INTO mytable (myid, name) VALUES (:userid, :username)') def test_insert_values(self): table1 = self.tables.mytable values1 = {table1.c.myid: bindparam('userid')} values2 = {table1.c.name: bindparam('username')} self.assert_compile(insert(table1, values=values1).values(values2), 'INSERT INTO mytable (myid, name) VALUES (:userid, :username)') def test_prefix_with(self): table1 = self.tables.mytable stmt = table1.insert().\ prefix_with('A', 'B', dialect='mysql').\ prefix_with('C', 'D') self.assert_compile(stmt, 'INSERT C D INTO mytable (myid, name, description) ' 'VALUES (:myid, :name, :description)') self.assert_compile(stmt, 'INSERT A B C D INTO mytable (myid, name, description) ' 'VALUES (%s, %s, %s)', dialect=mysql.dialect()) def test_inline_default(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('foo', Integer, default=func.foobar())) self.assert_compile(table.insert(values={}, inline=True), 'INSERT INTO sometable (foo) VALUES (foobar())') self.assert_compile(table.insert(inline=True), 'INSERT INTO sometable (foo) VALUES (foobar())', params={}) def test_insert_returning_not_in_default(self): table1 = self.tables.mytable stmt = table1.insert().returning(table1.c.myid) assert_raises_message( exc.CompileError, "RETURNING is not supported by this dialect's statement compiler.", stmt.compile, dialect=default.DefaultDialect() ) def test_insert_from_select_select(self): table1 = self.tables.mytable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().\ from_select(("otherid", "othername"), sel) self.assert_compile( ins, "INSERT INTO myothertable (otherid, othername) " "SELECT mytable.myid, mytable.name FROM mytable " "WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) def test_insert_from_select_select_alt_ordering(self): table1 = self.tables.mytable sel = select([table1.c.name, table1.c.myid]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().\ from_select(("othername", "otherid"), sel) self.assert_compile( ins, "INSERT INTO myothertable (othername, otherid) " "SELECT mytable.name, mytable.myid FROM mytable " "WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) def test_insert_from_select_select_no_defaults(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('foo', Integer, default=func.foobar())) table1 = self.tables.mytable sel = select([table1.c.myid]).where(table1.c.name == 'foo') ins = table.insert().\ from_select(["id"], sel) self.assert_compile( ins, "INSERT INTO sometable (id) SELECT mytable.myid " "FROM mytable WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) def test_insert_mix_select_values_exception(self): table1 = self.tables.mytable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().\ from_select(("otherid", "othername"), sel) assert_raises_message( exc.InvalidRequestError, "This construct already inserts from a SELECT", ins.values, othername="5" ) def test_insert_mix_values_select_exception(self): table1 = self.tables.mytable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = self.tables.myothertable.insert().values(othername="5") assert_raises_message( exc.InvalidRequestError, "This construct already inserts value expressions", ins.from_select, ("otherid", "othername"), sel ) def test_insert_from_select_table(self): table1 = self.tables.mytable ins = self.tables.myothertable.insert().\ from_select(("otherid", "othername"), table1) # note we aren't checking the number of columns right now self.assert_compile( ins, "INSERT INTO myothertable (otherid, othername) " "SELECT mytable.myid, mytable.name, mytable.description " "FROM mytable", checkparams={} ) def test_insert_from_select_union(self): mytable = self.tables.mytable name = 'name' description = 'desc' sel = select( [name, mytable.c.description], ).union( select([name, description]) ) ins = mytable.insert().\ from_select( [mytable.c.name, mytable.c.description], sel) self.assert_compile( ins, "INSERT INTO mytable (name, description) " "SELECT name, mytable.description FROM mytable " "UNION SELECT name, desc" ) def test_insert_from_select_col_values(self): table1 = self.tables.mytable table2 = self.tables.myothertable sel = select([table1.c.myid, table1.c.name]).where(table1.c.name == 'foo') ins = table2.insert().\ from_select((table2.c.otherid, table2.c.othername), sel) self.assert_compile( ins, "INSERT INTO myothertable (otherid, othername) " "SELECT mytable.myid, mytable.name FROM mytable " "WHERE mytable.name = :name_1", checkparams={"name_1": "foo"} ) class EmptyTest(_InsertTestBase, fixtures.TablesTest, AssertsCompiledSQL): __dialect__ = 'default' def test_empty_insert_default(self): table1 = self.tables.mytable stmt = table1.insert().values({}) # hide from 2to3 self.assert_compile(stmt, 'INSERT INTO mytable () VALUES ()') def test_supports_empty_insert_true(self): table1 = self.tables.mytable dialect = default.DefaultDialect() dialect.supports_empty_insert = dialect.supports_default_values = True stmt = table1.insert().values({}) # hide from 2to3 self.assert_compile(stmt, 'INSERT INTO mytable DEFAULT VALUES', dialect=dialect) def test_supports_empty_insert_false(self): table1 = self.tables.mytable dialect = default.DefaultDialect() dialect.supports_empty_insert = dialect.supports_default_values = False stmt = table1.insert().values({}) # hide from 2to3 assert_raises_message(exc.CompileError, "The 'default' dialect with current database version " "settings does not support empty inserts.", stmt.compile, dialect=dialect) def _test_insert_with_empty_collection_values(self, collection): table1 = self.tables.mytable ins = table1.insert().values(collection) self.assert_compile(ins, 'INSERT INTO mytable () VALUES ()', checkparams={}) # empty dict populates on next values call self.assert_compile(ins.values(myid=3), 'INSERT INTO mytable (myid) VALUES (:myid)', checkparams={'myid': 3}) def test_insert_with_empty_list_values(self): self._test_insert_with_empty_collection_values([]) def test_insert_with_empty_dict_values(self): self._test_insert_with_empty_collection_values({}) def test_insert_with_empty_tuple_values(self): self._test_insert_with_empty_collection_values(()) class MultirowTest(_InsertTestBase, fixtures.TablesTest, AssertsCompiledSQL): __dialect__ = 'default' def test_not_supported(self): table1 = self.tables.mytable dialect = default.DefaultDialect() stmt = table1.insert().values([{'myid': 1}, {'myid': 2}]) assert_raises_message( exc.CompileError, "The 'default' dialect with current database version settings " "does not support in-place multirow inserts.", stmt.compile, dialect=dialect) def test_named(self): table1 = self.tables.mytable values = [ {'myid': 1, 'name': 'a', 'description': 'b'}, {'myid': 2, 'name': 'c', 'description': 'd'}, {'myid': 3, 'name': 'e', 'description': 'f'} ] checkparams = { 'myid_0': 1, 'myid_1': 2, 'myid_2': 3, 'name_0': 'a', 'name_1': 'c', 'name_2': 'e', 'description_0': 'b', 'description_1': 'd', 'description_2': 'f', } dialect = default.DefaultDialect() dialect.supports_multivalues_insert = True self.assert_compile(table1.insert().values(values), 'INSERT INTO mytable (myid, name, description) VALUES ' '(:myid_0, :name_0, :description_0), ' '(:myid_1, :name_1, :description_1), ' '(:myid_2, :name_2, :description_2)', checkparams=checkparams, dialect=dialect) def test_positional(self): table1 = self.tables.mytable values = [ {'myid': 1, 'name': 'a', 'description': 'b'}, {'myid': 2, 'name': 'c', 'description': 'd'}, {'myid': 3, 'name': 'e', 'description': 'f'} ] checkpositional = (1, 'a', 'b', 2, 'c', 'd', 3, 'e', 'f') dialect = default.DefaultDialect() dialect.supports_multivalues_insert = True dialect.paramstyle = 'format' dialect.positional = True self.assert_compile(table1.insert().values(values), 'INSERT INTO mytable (myid, name, description) VALUES ' '(%s, %s, %s), (%s, %s, %s), (%s, %s, %s)', checkpositional=checkpositional, dialect=dialect) def test_inline_default(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('data', String), Column('foo', Integer, default=func.foobar())) values = [ {'id': 1, 'data': 'data1'}, {'id': 2, 'data': 'data2', 'foo': 'plainfoo'}, {'id': 3, 'data': 'data3'}, ] checkparams = { 'id_0': 1, 'id_1': 2, 'id_2': 3, 'data_0': 'data1', 'data_1': 'data2', 'data_2': 'data3', 'foo_1': 'plainfoo', } self.assert_compile(table.insert().values(values), 'INSERT INTO sometable (id, data, foo) VALUES ' '(%(id_0)s, %(data_0)s, foobar()), ' '(%(id_1)s, %(data_1)s, %(foo_1)s), ' '(%(id_2)s, %(data_2)s, foobar())', checkparams=checkparams, dialect=postgresql.dialect()) def test_server_default(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('data', String), Column('foo', Integer, server_default=func.foobar())) values = [ {'id': 1, 'data': 'data1'}, {'id': 2, 'data': 'data2', 'foo': 'plainfoo'}, {'id': 3, 'data': 'data3'}, ] checkparams = { 'id_0': 1, 'id_1': 2, 'id_2': 3, 'data_0': 'data1', 'data_1': 'data2', 'data_2': 'data3', } self.assert_compile(table.insert().values(values), 'INSERT INTO sometable (id, data) VALUES ' '(%(id_0)s, %(data_0)s), ' '(%(id_1)s, %(data_1)s), ' '(%(id_2)s, %(data_2)s)', checkparams=checkparams, dialect=postgresql.dialect()) def test_server_default_absent_value(self): metadata = MetaData() table = Table('sometable', metadata, Column('id', Integer, primary_key=True), Column('data', String), Column('foo', Integer, server_default=func.foobar())) values = [ {'id': 1, 'data': 'data1', 'foo': 'plainfoo'}, {'id': 2, 'data': 'data2'}, {'id': 3, 'data': 'data3', 'foo': 'otherfoo'}, ] checkparams = { 'id_0': 1, 'id_1': 2, 'id_2': 3, 'data_0': 'data1', 'data_1': 'data2', 'data_2': 'data3', 'foo_0': 'plainfoo', 'foo_2': 'otherfoo', } # note the effect here is that the first set of params # takes effect for the rest of them, when one is absent self.assert_compile(table.insert().values(values), 'INSERT INTO sometable (id, data, foo) VALUES ' '(%(id_0)s, %(data_0)s, %(foo_0)s), ' '(%(id_1)s, %(data_1)s, %(foo_0)s), ' '(%(id_2)s, %(data_2)s, %(foo_2)s)', checkparams=checkparams, dialect=postgresql.dialect())

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shadow/apis/item.py

f1uzz/shadow

1

16634

from functools import lru_cache from typing import Optional import requests from .patches import Patches class Item: """ Manipulation of static item data """ ITEM_URL = f"http://ddragon.leagueoflegends.com/cdn/{Patches.get_current_patch()}/data/en_US/item.json" items = requests.get(ITEM_URL).json() @classmethod @lru_cache() def id_for_name(cls, name: str) -> Optional[str]: """ Finds the id for an item given its name Returns the id, None if not found name - full name of item """ for item_id, item in cls.items["data"].items(): if item["name"].casefold() == name.casefold(): return item_id @classmethod @lru_cache() def name_for_id(cls, item_id: str) -> Optional[str]: """ Finds the name for an item given its id Returns the name, None if not found item_id - id of item """ for found_item_id, item in cls.items["data"].items(): if found_item_id == item_id: return item["name"]

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data/data_utils.py

hjonnala/deeplab2

0

91394

<filename>data/data_utils.py # coding=utf-8 # Copyright 2022 The Deeplab2 Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Contains common utility functions and classes for building dataset.""" import collections import io import numpy as np from PIL import Image from PIL import ImageOps import tensorflow as tf from deeplab2 import common _PANOPTIC_LABEL_FORMAT = 'raw' def read_image(image_data): """Decodes image from in-memory data. Args: image_data: Bytes data representing encoded image. Returns: Decoded PIL.Image object. """ image = Image.open(io.BytesIO(image_data)) try: image = ImageOps.exif_transpose(image) except TypeError: # capture and ignore this bug: # https://github.com/python-pillow/Pillow/issues/3973 pass return image def get_image_dims(image_data, check_is_rgb=False): """Decodes image and return its height and width. Args: image_data: Bytes data representing encoded image. check_is_rgb: Whether to check encoded image is RGB. Returns: Decoded image size as a tuple of (height, width) Raises: ValueError: If check_is_rgb is set and input image has other format. """ image = read_image(image_data) if check_is_rgb and image.mode != 'RGB': raise ValueError('Expects RGB image data, gets mode: %s' % image.mode) width, height = image.size return height, width def _int64_list_feature(values): """Returns a TF-Feature of int64_list. Args: values: A scalar or an iterable of integer values. Returns: A TF-Feature. """ if not isinstance(values, collections.Iterable): values = [values] return tf.train.Feature(int64_list=tf.train.Int64List(value=values)) def _bytes_list_feature(values): """Returns a TF-Feature of bytes. Args: values: A string. Returns: A TF-Feature. """ if isinstance(values, str): values = values.encode() return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values])) def create_features(image_data, image_format, filename, label_data=None, label_format=None): """Creates image/segmentation features. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg', 'jpg', or 'png'. filename: String, image filename. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. Returns: A dictionary of feature name to tf.train.Feature maaping. """ if image_format not in ('jpeg', 'png', 'jpg'): raise ValueError('Unsupported image format: %s' % image_format) # Check color mode, and convert grey image to rgb image. image = read_image(image_data) if image.mode != 'RGB': image = image.convert('RGB') image_data = io.BytesIO() image.save(image_data, format=image_format) image_data = image_data.getvalue() height, width = get_image_dims(image_data, check_is_rgb=True) feature_dict = { common.KEY_ENCODED_IMAGE: _bytes_list_feature(image_data), common.KEY_IMAGE_FILENAME: _bytes_list_feature(filename), common.KEY_IMAGE_FORMAT: _bytes_list_feature(image_format), common.KEY_IMAGE_HEIGHT: _int64_list_feature(height), common.KEY_IMAGE_WIDTH: _int64_list_feature(width), common.KEY_IMAGE_CHANNELS: _int64_list_feature(3), } if label_data is None: return feature_dict if label_format == 'png': label_height, label_width = get_image_dims(label_data) if (label_height, label_width) != (height, width): raise ValueError('Image (%s) and label (%s) shape mismatch' % ((height, width), (label_height, label_width))) elif label_format == 'raw': # Raw label encodes int32 array. expected_label_size = height * width * np.dtype(np.int32).itemsize if len(label_data) != expected_label_size: raise ValueError('Expects raw label data length %d, gets %d' % (expected_label_size, len(label_data))) else: raise ValueError('Unsupported label format: %s' % label_format) feature_dict.update({ common.KEY_ENCODED_LABEL: _bytes_list_feature(label_data), common.KEY_LABEL_FORMAT: _bytes_list_feature(label_format) }) return feature_dict def create_tfexample(image_data, image_format, filename, label_data=None, label_format=None): """Converts one image/segmentation pair to TF example. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg' or 'png'. filename: String, image filename. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. Returns: TF example proto. """ feature_dict = create_features(image_data, image_format, filename, label_data, label_format) return tf.train.Example(features=tf.train.Features(feature=feature_dict)) def create_video_tfexample(image_data, image_format, filename, sequence_id, image_id, label_data=None, label_format=None, prev_image_data=None, prev_label_data=None): """Converts one video frame/panoptic segmentation pair to TF example. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg' or 'png'. filename: String, image filename. sequence_id: ID of the video sequence as a string. image_id: ID of the image as a string. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. prev_image_data: An optional string or byte stream of encoded previous image data. prev_label_data: An optional string or byte stream of (potentially) encoded previous label data. Returns: TF example proto. """ feature_dict = create_features(image_data, image_format, filename, label_data, label_format) feature_dict.update({ common.KEY_SEQUENCE_ID: _bytes_list_feature(sequence_id), common.KEY_FRAME_ID: _bytes_list_feature(image_id) }) if prev_image_data is not None: feature_dict[common.KEY_ENCODED_PREV_IMAGE] = _bytes_list_feature( prev_image_data) if prev_label_data is not None: feature_dict[common.KEY_ENCODED_PREV_LABEL] = _bytes_list_feature( prev_label_data) return tf.train.Example(features=tf.train.Features(feature=feature_dict)) def create_video_and_depth_tfexample(image_data, image_format, filename, sequence_id, image_id, label_data=None, label_format=None, next_image_data=None, next_label_data=None, depth_data=None, depth_format=None): """Converts an image/segmentation pair and depth of first frame to TF example. The image pair contains the current frame and the next frame with the current frame including depth label. Args: image_data: String or byte stream of encoded image data. image_format: String, image data format, should be either 'jpeg' or 'png'. filename: String, image filename. sequence_id: ID of the video sequence as a string. image_id: ID of the image as a string. label_data: String or byte stream of (potentially) encoded label data. If None, we skip to write it to tf.train.Example. label_format: String, label data format, should be either 'png' or 'raw'. If None, we skip to write it to tf.train.Example. next_image_data: An optional string or byte stream of encoded next image data. next_label_data: An optional string or byte stream of (potentially) encoded next label data. depth_data: An optional string or byte sream of encoded depth data. depth_format: String, depth data format, should be either 'png' or 'raw'. Returns: TF example proto. """ feature_dict = create_features(image_data, image_format, filename, label_data, label_format) feature_dict.update({ common.KEY_SEQUENCE_ID: _bytes_list_feature(sequence_id), common.KEY_FRAME_ID: _bytes_list_feature(image_id) }) if next_image_data is not None: feature_dict[common.KEY_ENCODED_NEXT_IMAGE] = _bytes_list_feature( next_image_data) if next_label_data is not None: feature_dict[common.KEY_ENCODED_NEXT_LABEL] = _bytes_list_feature( next_label_data) if depth_data is not None: feature_dict[common.KEY_ENCODED_DEPTH] = _bytes_list_feature( depth_data) feature_dict[common.KEY_DEPTH_FORMAT] = _bytes_list_feature( depth_format) return tf.train.Example(features=tf.train.Features(feature=feature_dict)) class SegmentationDecoder(object): """Basic parser to decode serialized tf.Example.""" def __init__(self, is_panoptic_dataset=True, is_video_dataset=False, is_depth_dataset=False, use_two_frames=False, use_next_frame=False, decode_groundtruth_label=True): self._is_panoptic_dataset = is_panoptic_dataset self._is_video_dataset = is_video_dataset self._is_depth_dataset = is_depth_dataset self._use_two_frames = use_two_frames self._use_next_frame = use_next_frame self._decode_groundtruth_label = decode_groundtruth_label string_feature = tf.io.FixedLenFeature((), tf.string) int_feature = tf.io.FixedLenFeature((), tf.int64) self._keys_to_features = { common.KEY_ENCODED_IMAGE: string_feature, common.KEY_IMAGE_FILENAME: string_feature, common.KEY_IMAGE_FORMAT: string_feature, common.KEY_IMAGE_HEIGHT: int_feature, common.KEY_IMAGE_WIDTH: int_feature, common.KEY_IMAGE_CHANNELS: int_feature, } if decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_LABEL] = string_feature if self._is_video_dataset: self._keys_to_features[common.KEY_SEQUENCE_ID] = string_feature self._keys_to_features[common.KEY_FRAME_ID] = string_feature # Two-frame specific processing. if self._use_two_frames: self._keys_to_features[common.KEY_ENCODED_PREV_IMAGE] = string_feature if decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_PREV_LABEL] = string_feature # Next-frame specific processing. if self._use_next_frame: self._keys_to_features[common.KEY_ENCODED_NEXT_IMAGE] = string_feature if decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_NEXT_LABEL] = string_feature # Depth specific processing. if self._is_depth_dataset and decode_groundtruth_label: self._keys_to_features[common.KEY_ENCODED_DEPTH] = string_feature def _decode_image(self, parsed_tensors, key): """Decodes image udner key from parsed tensors.""" image = tf.io.decode_image( parsed_tensors[key], channels=3, dtype=tf.dtypes.uint8, expand_animations=False) image.set_shape([None, None, 3]) return image def _decode_label(self, parsed_tensors, label_key): """Decodes segmentation label under label_key from parsed tensors.""" if self._is_panoptic_dataset: flattened_label = tf.io.decode_raw( parsed_tensors[label_key], out_type=tf.int32) label_shape = tf.stack([ parsed_tensors[common.KEY_IMAGE_HEIGHT], parsed_tensors[common.KEY_IMAGE_WIDTH], 1 ]) label = tf.reshape(flattened_label, label_shape) return label label = tf.io.decode_image(parsed_tensors[label_key], channels=1) label.set_shape([None, None, 1]) return label def __call__(self, serialized_example): parsed_tensors = tf.io.parse_single_example( serialized_example, features=self._keys_to_features) return_dict = { 'image': self._decode_image(parsed_tensors, common.KEY_ENCODED_IMAGE), 'image_name': parsed_tensors[common.KEY_IMAGE_FILENAME], 'height': tf.cast(parsed_tensors[common.KEY_IMAGE_HEIGHT], dtype=tf.int32), 'width': tf.cast(parsed_tensors[common.KEY_IMAGE_WIDTH], dtype=tf.int32), } return_dict['label'] = None if self._decode_groundtruth_label: return_dict['label'] = self._decode_label(parsed_tensors, common.KEY_ENCODED_LABEL) if self._is_video_dataset: return_dict['sequence'] = parsed_tensors[common.KEY_SEQUENCE_ID] if self._use_two_frames: return_dict['prev_image'] = self._decode_image( parsed_tensors, common.KEY_ENCODED_PREV_IMAGE) if self._decode_groundtruth_label: return_dict['prev_label'] = self._decode_label( parsed_tensors, common.KEY_ENCODED_PREV_LABEL) if self._use_next_frame: return_dict['next_image'] = self._decode_image( parsed_tensors, common.KEY_ENCODED_NEXT_IMAGE) if self._decode_groundtruth_label: return_dict['next_label'] = self._decode_label( parsed_tensors, common.KEY_ENCODED_NEXT_LABEL) if self._is_depth_dataset and self._decode_groundtruth_label: return_dict['depth'] = self._decode_label( parsed_tensors, common.KEY_ENCODED_DEPTH) return return_dict

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29873, 575, 943, 29892, 3619, 29889, 10818, 29918, 1430, 16524, 29928, 29918, 2287, 29925, 4690, 29897, 13, 1678, 736, 736, 29918, 8977, 13, 2 ]

project/graddetmf.py

felixzheng02/pypkpd

9

185599

<gh_stars>1-10 """ ## Function translated automatically using 'matlab.to.r()' ## Author: <NAME>, <NAME> """ import numpy as np from project.cell import cell from project.size import size from project.zeros import zeros from project.mftot import mftot from project.mf_all import mf_all from project.get_fim_size import get_fim_size from project.update_designinlist import update_designinlist def graddetmf(model_switch,aX,groupsize,ni,xt,x,a,bpop,d,sigma,docc,poped_db,lndet=False,gradxt=False): n = get_fim_size(poped_db) m = size(ni)[0] if (gradxt == False): gdmf = np.ones(m*size(a)[1]).reshape(m, size(a)[1]) else: gdmf = np.ones(m*size(xt)[1]).reshape(m, size(xt)[1]) iParallelN = (poped_db["settings"]["parallel"]["bParallelSG"] == 1) + 1 #1 if no parallel, 2 if parallel if iParallelN == 2: designsin = cell(1,0) it = 1 for p in range(0, iParallelN): if p == 2: #Execute parallel designs designout = designsin raise Exception("Parallel execution not yet implemented in PopED for R") #designout = execute_parallel(designsin,poped_db) designout = designsin if iParallelN == 1: returnArgs = mftot(model_switch,groupsize,ni,xt,x,a,bpop,d,sigma,docc,poped_db) mft = returnArgs[0] poped_db = returnArgs[1] else: if p == 1: designsin = update_designinlist(designsin,groupsize,ni,xt,x,a,-1,0) else: mft = designout["it"]["FIM"] it = it + 1 if iParallelN == 1 or p == 2: if all(size(poped_db["settings"]["prior_fim"]) == size(mft)): mft = mft + poped_db["settings"]["prior_fim"] imft = np.linalg.inv(mft) if np.isinf(imft[1,1]): imft = zeros(size(mft)) for i in range(0, m): if groupsize[i] == 0: gdmf[i, 1:ni[i]] = zeros(1, ni(i)) else: if x.size != 0: x_i = np.transpose(x[i,:]) else: x_i = zeros(0,1) if a.size != 0: a_i = np.transpose(a[i,:]) else: a_i = zeros(0,1) if iParallelN == 1: returnArgs = mf_all(np.transpose(model_switch[i,1:ni[i]]), np.transpose(xt[i,1:ni[i]]),x_i,a_i,bpop,d,sigma,docc,poped_db) mf_tmp = returnArgs[0] poped_db = returnArgs[1] else: if p == 1: designsin = update_designinlist(designsin, 1, ni, xt, x, a, -1, i) else: mf_tmp = designout["it"]["FIM"] it = it + 1 mfb = groupsize[i]*mf_tmp a0 = a xt0 = xt if gradxt is False: nCtl = size(poped_db["design"]["a"])[1] else: nCtl = ni[i] for ct1 in range(0, nCtl): if aX[i,ct1] != 0: if gradxt == False: a = a0 a[i,ct1] = a[i,ct1] + poped_db["settings"]["hgd"] else: xt = xt0 xt[i,ct1] = xt[i,ct1] + poped_db["settings"]["hgd"] if iParallelN == 1: returnArgs = mf_all(np.transpose(model_switch[i,1:ni[i]]), np.transpose(xt[i,1:ni[i]]), x_i, np.transpose(a[i,:]), bpop, d, sigma, docc, poped_db) mf_tmp = returnArgs[0] poped_db = returnArgs[1] else: if p == 1: designsin = update_designinlist(designsin, 1, ni, xt, x, a, -1, i) else: mf_tmp = designout["it"]["FIM"] it = it + 1 if iParallelN == 1 or p == 2: mf_plus = groupsize[i]*mf_tmp ir = (mf_plus-mfb)/poped_db["settings"]["hgd"] s = 0 #Calc the tr(A^-1 * dA/dX) for some X for ct2 in range(0, n): s = s + np.matmul(imft[ct2,:], ir[:,ct2]) if s == 0: #The model doesn't depend on design variable (a or t), e.g. PD is only dependent on time and not dose, fix the a-gradient to a small value or PD with Placebo dose, fix the xt-gradient to a small value. s = 1e-12 gdmf[i, ct1] = s if lndet == False: ret = gdmf*np.linalg.det(mft) else: ret = gdmf return {"ret": ret, "poped_db": poped_db}

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main.py

Meat0Project/ChatBot

4

1682

''' Made by - <NAME> Purpose - Python mini project Date - 18 october 2020 ''' from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer form termcolor import cprint import time chatbot = ChatBot('Bot') trainer = ChatterBotCorpusTrainer(chatbot) trainer.train('chatterbot.corpus.english') cprint("#" * 50, "magenta") cprint((f"A Chatot ").center(50), "yellow") cprint("#" * 50, "magenta") print('You can exit by type exit\n') while True: query = input(">> ") if 'exit' in query: exit() else: print(chatbot.get_response(query))

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188.py

RafaelHuang87/Leet-Code-Practice

0

138961

<filename>188.py class Solution: def maxProfit(self, k: int, prices: List[int]) -> int: n, res = len(prices), 0 if n < 2: return 0 if k > n // 2: for i in range(1, n): if prices[i] > prices[i - 1]: res += prices[i] - prices[i - 1] return res hold, sold = [float('-inf')] * (k + 1), [0] * (k + 1) for price in prices: for j in range(1, k + 1): hold[j] = max(hold[j], sold[j - 1] - price) sold[j] = max(sold[j], hold[j] + price) return sold[k]

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run_create_ec2_keypair.py

HardBoiledSmith/johanna

64

119448

<reponame>HardBoiledSmith/johanna<filename>run_create_ec2_keypair.py #!/usr/bin/env python3 import os import subprocess from multiprocessing import Process env = dict(os.environ) def read_file(file_path): f = open(file_path) lines = list() for ll in f.readlines(): lines.append(ll) f.close() return lines # noinspection PyShadowingNames def run(cmd, file_path_name=None, cwd=None): def _f(): if not file_path_name: _p = subprocess.Popen(cmd, cwd=cwd, env=env) _p.communicate() if _p.returncode != 0: raise Exception() else: with open(file_path_name, 'a') as f: _p = subprocess.Popen(cmd, stdout=f, cwd=cwd, env=env) _p.communicate() if _p.returncode != 0: raise Exception() pp = Process(target=_f) pp.start() pp.join() if pp.exitcode != 0: raise Exception() def run_create_ec2_keypair(key_name): cmd = ['rm', '-f'] cmd += [f'{key_name}.pem'] cmd += [f'{key_name}.pub'] run(cmd) cmd = ['openssl', 'genrsa'] cmd += ['-out', f'{key_name}.pem'] cmd += ['2048'] run(cmd) print('Private key file:', f'{key_name}.pem', '\n') run(['chmod', '400', f'{key_name}.pem']) cmd = ['openssl', 'rsa'] cmd += ['-in', f'{key_name}.pem'] cmd += ['-pubout'] run(cmd, file_path_name=f'{key_name}.pub') print('Public key file:', f'{key_name}.pub', '\n') print('AWS_KEY_PAIR_NAME:', key_name, '\n') pub_key = read_file(f'{key_name}.pub') pub_key = pub_key[1:-1] pp_list = list() for pk in pub_key: pk = pk.strip() pp_list.append(pk) pub_key = ''.join(pp_list) print('AWS_KEY_PAIR_MATERIAL:', pub_key, '\n') run(['chmod', '400', f'{key_name}.pub']) cmd = ['ssh-keygen', '-y'] cmd += ['-f', f'{key_name}.pem'] result, error = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE).communicate() # noinspection PyUnresolvedReferences result = result.decode('utf-8') print('OpenSSH public key:', result.strip(), '\n') cmd = ['openssl', 'pkey'] cmd += ['-in', f'{key_name}.pem'] cmd += ['-pubout'] cmd += ['-outform', 'DER'] proc = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE) cmd = ['openssl', 'md5', '-c'] result, _ = subprocess.Popen(cmd, env=env, stdin=proc.stdout, stdout=subprocess.PIPE).communicate() # noinspection PyUnresolvedReferences result = result.decode('utf-8') print('OpenSSH finger print:', result) return pub_key ################################################################################ # # start # ################################################################################ if __name__ == "__main__": from run_common import parse_args _, args = parse_args() if len(args) != 2: print('usage:', args[0], '<key-name>') raise Exception() run_create_ec2_keypair(args[1])

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exercises/es/test_04_10.py

mariacamilagl/spacy-course

0

167693

def test(): assert len(TRAINING_DATA) == 4, "Los datos de entrenamiento no concuerdan – esperaba 4 ejemplos." assert all( len(entry) == 2 and isinstance(entry[1], dict) for entry in TRAINING_DATA ), "Formato incorrecto de los datos de entrenamiento. Esperaba una lista de tuples dónde el segundo elemento es un dict." assert all( entry[1].get("entities") for entry in TRAINING_DATA ), "Todas las anotaciones en los datos de entrenamiento deberían incluir entidades." assert TRAINING_DATA[0][1]["entities"] == [ (10, 19, "GPE") ], "Vuelve a revisar las entidades en el primer ejemplo." assert TRAINING_DATA[1][1]["entities"] == [ (17, 22, "GPE") ], "Vuelve a revisar las entidades en el segundo ejemplo." assert TRAINING_DATA[2][1]["entities"] == [ (15, 20, "GPE"), (24, 32, "GPE"), ], "Vuelve a revisar las entidades en el tercer ejemplo." assert TRAINING_DATA[3][1]["entities"] == [ (0, 6, "GPE") ], "Vuelve a revisar las entidades en el cuarto ejemplo." __msg__.good( "¡Muy buen trabajo! Una vez el modelo logra buenos resultados detectando entidades GPE " "en los comentarios de los viajeros, podrías añadir un componente basado " "en reglas para determinar si la entidad es un destino turístico en " "este contexto. Por ejemplo, puedes resolver los tipos de entidades en relación con un knowledge base o " "buscarlas en un wiki de viajes." )

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python_fundamentals/lists_basics_exs/football_cards.py

deboradyankova/python_education

0

1605434

<gh_stars>0 team_a_players = 11 team_b_players = 11 cards_list = set(input().split()) is_terminated = False for player in cards_list: if player.startswith('A'): team_a_players -= 1 else: team_b_players -= 1 if team_a_players < 7 or team_b_players < 7: is_terminated = True break print(f'Team A - {team_a_players}; Team B - {team_b_players}') if is_terminated: print('Game was terminated')

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modules/image/image_processing/prnet/utils/rotate_vertices.py

AK391/PaddleHub

1

132611

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np # import scipy.io as def frontalize(vertices): canonical_vertices = np.load('Data/uv-data/canonical_vertices.npy') vertices_homo = np.hstack((vertices, np.ones([vertices.shape[0], 1]))) #n x 4 P = np.linalg.lstsq(vertices_homo, canonical_vertices)[0].T # Affine matrix. 3 x 4 front_vertices = vertices_homo.dot(P.T) return front_vertices

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src/ethogram.py

dstern/SongExplorer

5

1602902

<reponame>dstern/SongExplorer #!/usr/bin/python3 # apply per-class thresholds to discretize probabilities # ethogram.py <logdir> <model> <thresholds-file> <tf-file> <wav-tic-rate> # e.g. # ethogram.py `pwd`/trained-classifier 1k 50 `pwd`/groundtruth-data/round1/20161207T102314_ch1_p1.tf 5000 import sys import os import re import numpy as np import csv from sys import argv from scipy.io import wavfile from itertools import cycle sys.path.append(os.path.dirname(os.path.realpath(__file__))) from lib import * _,logdir,model,thresholds_file,tf_file,audio_tic_rate = argv print('logdir: '+logdir) print('model: '+model) print('thresholds_file: '+thresholds_file) print('tf_file: '+tf_file) print('audio_tic_rate: '+audio_tic_rate) audio_tic_rate=float(audio_tic_rate) tfpath, tfname = os.path.split(tf_file) tfname_noext = os.path.splitext(tfname)[0] if os.path.isfile(os.path.join(tfpath,tfname_noext+'.wav')): wavname = tfname_noext+'.wav' elif os.path.isfile(os.path.join(tfpath,tfname_noext+'.WAV')): wavname = tfname_noext+'.WAV' else: print('cannot find corresponding WAV file') exit() precision_recall_ratios, thresholds = read_thresholds(logdir, model, thresholds_file) labels = [x[0] for x in thresholds] thresholds = np.array([x[1:] for x in thresholds], dtype=np.float64) sample_rate_probabilities, half_stride_sec, probability_matrix = \ read_probabilities(os.path.join(tfpath, tfname_noext), labels) for ithreshold in range(len(precision_recall_ratios)): features, start_tics, stop_tics = discretize_probabilites(probability_matrix, thresholds[:,[ithreshold]], labels, sample_rate_probabilities, half_stride_sec, audio_tic_rate) filename = os.path.join(tfpath, tfname_noext+'-predicted-'+precision_recall_ratios[ithreshold]+'pr.csv') isort = np.argsort(start_tics) with open(filename,'w') as fid: csvwriter = csv.writer(fid) csvwriter.writerows(zip(cycle([wavname]), \ start_tics[isort], stop_tics[isort], \ cycle(['predicted']), features[isort]))

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tests/test_script.py

sharad28/Tensile_-_compressive_strength

1

1611631

import unittest from main import app import os class TestToPerform(unittest.TestCase): def setUp(self): self.app = app.test_client() def tearDown(self): pass def test_page(self): response = self.app.get('/', follow_redirects=True) print(response) self.assertEqual(response.status_code, 200) if __name__ == '__main__': unittest.main()

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ad2/aula_09/leitura_com_close.py

renzon/fundamentos-de-programacao

6

138559

<gh_stars>1-10 arquivo = open('exemplo.txt', 'r', encoding='utf8') for linha in arquivo: print(linha.strip()) arquivo.close()

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bin/analyze_path.py

sjtuzyk/chandra-acis-analysis

3

104108

<reponame>sjtuzyk/chandra-acis-analysis #!/usr/bin/env python3 # # Copyright (c) 2017-2018 <NAME> <<EMAIL>> # MIT license # """ Extract the object name and observation ID from the directory path. The base directory of the object data has the format: <name>_oi<obsid> """ import os import argparse from _context import acispy from acispy.utils import get_name_from_path, get_obsid_from_path def main(): parser = argparse.ArgumentParser( description="Extract object name and ObsID from data directory") parser.add_argument("-b", "--brief", dest="brief", action="store_true", help="Be brief") parser.add_argument("-n", "--name", dest="name", action="store_true", help="Only get object name") parser.add_argument("-i", "--obsid", dest="obsid", action="store_true", help="Only get observation ID") parser.add_argument("path", nargs="?", default=os.getcwd(), help="Path to the data directory " + "(default: current working directory)") args = parser.parse_args() path = os.path.abspath(args.path) b_get_name = False if args.obsid else True b_get_obsid = False if args.name else True if b_get_name: if not args.brief: print("Name:", end=" ") print(get_name_from_path(path)) if b_get_obsid: if not args.brief: print("ObsID:", end=" ") print(get_obsid_from_path(path)) if __name__ == "__main__": main()

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Collection_Resources/Email_Integration.py

data4d/Data_Collect

0

199085

<reponame>data4d/Data_Collect # Email Integration for Notifications import sys import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart ## Import Passwords - Importing Passwords from a file stored elsewhere on my computer # sys.path.append('C:/Users/GTayl/Desktop/Finance Modeling') # from passwords import Gmail as pwd def send_mail(fromaddr,toaddr,password,subject,text): fromaddr = fromaddr toaddr = toaddr msg = MIMEMultipart() msg['From'] = fromaddr msg['To'] = toaddr msg['Subject'] = subject body = text msg.attach(MIMEText(body, 'plain')) server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login(fromaddr, password) text = msg.as_string() server.sendmail(fromaddr, toaddr, text) server.quit()

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scripts/git2types/git2_type_rebase.py

git24j/git24j

25

125347

import re from .git2_types import Git2Type from .git2_type_common import ( Git2TypeConstObject, Git2TypeOutObject, PAT1_STR, PAT2_STR, PAT3_STR, ) class Git2TypeConstRebaseOptions(Git2TypeConstObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_options)" + PAT2_STR) class Git2TypeOutRebaseOptions(Git2TypeOutObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_options)" + PAT3_STR) class Git2TypeConstRebase(Git2TypeConstObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase)" + PAT2_STR) class Git2TypeOutRebase(Git2TypeOutObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase)" + PAT3_STR) class Git2TypeConstRebaseOperation(Git2TypeConstObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_operation)" + PAT2_STR) class Git2TypeOutRebaseOperation(Git2TypeOutObject): PAT = re.compile(PAT1_STR + "(?P<obj_name>rebase_operation)" + PAT3_STR)

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app.py

Andrew-Ritchie/Web-Based-Dashboard-for-Nanoindentation-Experiments

1

1602932

import dash import dash_bootstrap_components as dbc from flask_caching import Cache app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP]) app.config.suppress_callback_exceptions = True cache = Cache(app.server, config={ 'CACHE_TYPE': 'filesystem', 'CACHE_DIR': 'cache-directory' }) server = app.server

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PytoTests/test_pyto.py

snazari/Pyto

701

67576

<gh_stars>100-1000 """ Tests for Pyto before submitting to the App Store. """ import unittest import runpy import sys import os class TestPyto(unittest.TestCase): def test_lib(self): from Lib import (apps, location, mainthread, motion, multipeer, music, notification_center, notifications, pasteboard, photos, pyto_core, pyto_ui, remote_notifications, sharing, sound, speech, userkeys, xcallback) def test_modules(self): runpy.run_module("download_all") def test_pip(self): import pip pip.main(["install", "sympy"]) import sympy pip.main(["uninstall", "sympy"]) def test_command_runner(self): expected_result = "usage: pip.py [-h] [--verbose] [-6]\n sub-command ...\npip.py: error: argument sub-command: invalid choice: '—-help' (choose from 'list', 'install', 'download', 'search', 'versions', 'uninstall', 'update')" out_path = os.path.join(os.path.expanduser("~/tmp"), "out.txt") out = open(out_path, "w+") _out = sys.stdout _err = sys.stderr sys.stdout = out sys.stderr = out sys.argv = ["pyto", "pip", "—-help"] runpy.run_module("command_runner") sys.stdout = _out sys.stderr = _err out.close() out = open(out_path, "r") res = out.read() out.close() res = res.replace(" ", "") res = res.replace("\n", "") res = res.replace("\t", "") expected_result = expected_result.replace(" ", "") expected_result = expected_result.replace("\n", "") expected_result = expected_result.replace("\t", "") self.assertEqual(res, expected_result) if __name__ == '__main__': try: unittest.main() except SystemExit: pass

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CodingInterviews/python/48_add.py

YorkFish/git_study

0

62080

#!/usr/bin/env python3 # coding:utf-8 class Solution: def Add(self, num1, num2): """ num1 ^ num2: 不计进位的相加 (num1 & num2) << 1: 进位循环至进位为零 ~(num1 ^ 0xffffffff): 模仿 C，当 num1 为负数时，把它从无符号数转为有符号数 C 的 int 的高位是符号位，Python 没有 """ while num2 != 0: num1, num2 =\ (num1 ^ num2) & 0xffffffff, ((num1 & num2) << 1) & 0xffffffff return num1 if num1 <= 0x7fffffff else ~(num1 ^ 0xffffffff) if __name__ == "__main__": num1 = 66 num2 = 66 s = Solution() ans = s.Add(num1, num2) print(ans)

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Server/website/serializers.py

HarshShah1997/Rotary-Website

0

121020

""" Author: harsh """ from rest_framework.serializers import ModelSerializer from website.models import Event, Announcement, BoardMember, Photo class EventSerializer(ModelSerializer): class Meta: model = Event fields = '__all__' class AnnouncementSerializer(ModelSerializer): class Meta: model = Announcement fields = '__all__' class BoardMemberSerializer(ModelSerializer): class Meta: model = BoardMember fields = '__all__' class PhotoSerializer(ModelSerializer): class Meta: model = Photo fields = '__all__'

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linkedSPLs/LinkedSPLs-update/mappings/ChEBI-DrugBank-bio2rdf-mapping/scripts/backups/parseDBIdAndUNIIsByInChikey.py

kqingcan/bio2rdf-scripts

76

137787

<gh_stars>10-100 ''' Created 03/20/2014 @authors: <NAME> @summary: parse inchikey and drugbank_id from drugbank.xml then parse inchikey, FDA Preferred Term and UNII from UNIIs records match the results from drugbank and results of parse UNIIs records output terms: FDA Preferred Term, UNII, Drugbank URI output file: PT-UNIIs-Drugbank-03202014.txt ''' from lxml import etree from lxml.etree import XMLParser, parse import os, sys DRUGBANK_XML = "drugbank.xml" UNIIS_RECORDS = "UNIIs 27Jun2014 Records.txt" NS = "{http://www.drugbank.ca}" DRUGBANK_BIO2RDF = "http://bio2rdf.org/drugbank:" DRUGBANK_CA = "http://www.drugbank.ca/drugs/" dict_ickey_dbid = {} ''' data structure of drugbank.xml </drug><drug type="small molecule" created="2005-06-13 07:24:05 -0600" updated="2013-09-16 17:11:29 -0600" version="4.0"> <drugbank-id>DB00641</drugbank-id> <name>Simvastatin</name> <calculated-properties> <property> <kind>InChIKey</kind> <value>InChIKey=RYMZZMVNJRMUDD-HGQWONQESA-N</value> <source>ChemAxon</source> </property> ''' #get mappings of inchikey and drugbank id def parseDbIdAndInChiKey(root): for childDrug in root.iter(tag=NS + "drug"): subId = childDrug.find(NS + "drugbank-id") if subId == None: continue else: drugbankid = subId.text drugbankName = unicode(childDrug.find(NS + "name").text) for subProp in childDrug.iter(NS + "property"): subKind = subProp.find(NS + "kind") if subKind == None: continue elif subKind.text == "InChIKey": subValue = subProp.find(NS + "value") if subValue == None: continue else: #print drugbankid + '\t' + subValue.text[9:] ikey = subValue.text[9:] dict_ickey_dbid [ikey] = (drugbankid,drugbankName) p = XMLParser(huge_tree=True) tree = parse(DRUGBANK_XML,parser=p) root = tree.getroot() parseDbIdAndInChiKey(root) #print dict_ickey_dbid #read mapping file that contains UNII PT INCHIKEY for line in open(UNIIS_RECORDS,'r').readlines(): row = line.split('\t') inchikey = row[4] if len(inchikey) == 0: continue #print "mapping inchikey:" + inchikey if dict_ickey_dbid.has_key(inchikey): drugbankid = dict_ickey_dbid[inchikey][0] drugbankName = dict_ickey_dbid[inchikey][1] output = row[1] +'\t'+ row[0] +'\t'+ drugbankName +'\t'+ DRUGBANK_CA + drugbankid +'\t'+ DRUGBANK_BIO2RDF + drugbankid print output.encode('utf-8').strip()

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groclient/constants.py

eric-gro/api-client

18

6915

"""Constants about the Gro ontology that can be imported and re-used anywhere.""" REGION_LEVELS = { 'world': 1, 'continent': 2, 'country': 3, 'province': 4, # Equivalent to state in the United States 'district': 5, # Equivalent to county in the United States 'city': 6, 'market': 7, 'other': 8, 'coordinate': 9 } ENTITY_TYPES_PLURAL = ['metrics', 'items', 'regions', 'frequencies', 'sources', 'units'] DATA_SERIES_UNIQUE_TYPES_ID = [ 'metric_id', 'item_id', 'region_id', 'partner_region_id', 'frequency_id', 'source_id' ] ENTITY_KEY_TO_TYPE = { 'metric_id': 'metrics', 'item_id': 'items', 'region_id': 'regions', 'partner_region_id': 'regions', 'source_id': 'sources', 'frequency_id': 'frequencies', 'unit_id': 'units' } DATA_POINTS_UNIQUE_COLS = DATA_SERIES_UNIQUE_TYPES_ID + [ 'reporting_date', 'start_date', 'end_date' ]

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processing/router.py

uncharted-distil/distil-auto-ml

2

49246

<filename>processing/router.py #!/usr/bin/env python """ distil/router.py """ import logging import sys from copy import deepcopy from typing import Sequence, Tuple from d3m.metadata import problem as _problem logger = logging.getLogger(__name__) SMALL_DATASET_THRESH = 2000 # -- # Detect semantic problem type def get_resource_types(dataset_doc: dict) -> Sequence[str]: return sorted([dr["resType"] for dr in dataset_doc["dataResources"]]) def is_tabular(dataset_doc: dict, problem_desc: dict) -> bool: resource_types = get_resource_types(dataset_doc) task_keywords = problem_desc["problem"]["task_keywords"] if ( len( set(task_keywords) & set( [_problem.TaskKeyword.REGRESSION, _problem.TaskKeyword.CLASSIFICATION] ) ) == 0 ): return False elif ( len( set(task_keywords) & set( [ _problem.TaskKeyword.SEMISUPERVISED, _problem.TaskKeyword.REMOTE_SENSING, ] ) ) != 0 ): return False elif resource_types == ["table"]: return True elif ( set(resource_types) == {"table"} and len(resource_types) > 2 and not is_question_answering(dataset_doc) ): logger.warn( "is_tabular: found more than two tables, which we can't handle. falling back to single table" ) return True else: return False def is_edgelist(dataset_doc: dict, problem_desc: dict) -> bool: resource_types = get_resource_types(dataset_doc) task_keywords = problem_desc["problem"]["task_keywords"] if ( len( set(task_keywords) & set( [ _problem.TaskKeyword.VERTEX_CLASSIFICATION, _problem.TaskKeyword.VERTEX_NOMINATION, _problem.TaskKeyword.LINK_PREDICTION, ] ) ) == 0 ): return False elif "edgeList" in resource_types: return True else: return False def is_multitable(dataset_doc: dict) -> bool: return ["table", "table"] == get_resource_types(dataset_doc) def is_timeseries_classification(dataset_doc: dict, problem: dict) -> bool: timeseries_resource = ["table", "timeseries"] == get_resource_types(dataset_doc) classification_task = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) return timeseries_resource and classification_task def is_timeseries_forecasting(problem: dict) -> bool: return _problem.TaskKeyword.FORECASTING in problem["problem"]["task_keywords"] def is_question_answering(dataset_doc: dict) -> bool: res_paths = sorted([r["resPath"] for r in dataset_doc["dataResources"]]) return res_paths == [ "tables/learningData.csv", "tables/questions.csv", "tables/sentences.csv", "tables/vocabulary.csv", ] def is_audio(dataset_doc: dict) -> bool: return "audio" in get_resource_types(dataset_doc) def is_image(dataset_doc: dict, problem: dict) -> bool: classification = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) regression = _problem.TaskKeyword.REGRESSION in problem["problem"]["task_keywords"] remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( "image" in get_resource_types(dataset_doc) and (classification or regression) and not remote_sensing ) def is_remote_sensing_pretrained(dataset_doc: dict, problem: dict) -> bool: classification = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) regression = _problem.TaskKeyword.REGRESSION in problem["problem"]["task_keywords"] remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( "image" not in get_resource_types(dataset_doc) and (classification or regression) and remote_sensing and _problem.TaskKeyword.SEMISUPERVISED not in problem["problem"]["task_keywords"] ) def is_remote_sensing(dataset_doc: dict, problem: dict) -> bool: classification = ( _problem.TaskKeyword.CLASSIFICATION in problem["problem"]["task_keywords"] ) regression = _problem.TaskKeyword.REGRESSION in problem["problem"]["task_keywords"] remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( "image" in get_resource_types(dataset_doc) and (classification or regression) and remote_sensing and _problem.TaskKeyword.SEMISUPERVISED not in problem["problem"]["task_keywords"] ) def is_object_detection(problem: dict) -> bool: return _problem.TaskKeyword.OBJECT_DETECTION in problem["problem"]["task_keywords"] def is_graph_matching(problem: dict) -> bool: return _problem.TaskKeyword.GRAPH_MATCHING in problem["problem"]["task_keywords"] def is_community_detection(problem: dict) -> bool: return ( _problem.TaskKeyword.COMMUNITY_DETECTION in problem["problem"]["task_keywords"] ) def is_clustering(problem: dict) -> bool: return _problem.TaskKeyword.CLUSTERING in problem["problem"]["task_keywords"] def is_vertex_classification(problem: dict) -> bool: return ( _problem.TaskKeyword.VERTEX_CLASSIFICATION in problem["problem"]["task_keywords"] ) def is_vertex_nomination(problem: dict) -> bool: return _problem.TaskKeyword.VERTEX_NOMINATION in problem["problem"]["task_keywords"] def is_collaborative_filtering(problem: dict) -> bool: return ( _problem.TaskKeyword.COLLABORATIVE_FILTERING in problem["problem"]["task_keywords"] ) def is_link_prediction(problem: dict) -> bool: return _problem.TaskKeyword.LINK_PREDICTION in problem["problem"]["task_keywords"] def is_text(dataset_doc: dict) -> bool: return ["table", "text"] == get_resource_types(dataset_doc) def is_semisupervised_tabular(problem: dict) -> bool: remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( not remote_sensing and _problem.TaskKeyword.SEMISUPERVISED in problem["problem"]["task_keywords"] ) def is_semisupervised_remote_sensing_pretrained(problem: dict) -> bool: remote_sensing = ( _problem.TaskKeyword.REMOTE_SENSING in problem["problem"]["task_keywords"] ) return ( remote_sensing and _problem.TaskKeyword.SEMISUPERVISED in problem["problem"]["task_keywords"] ) # -- # Routing def get_routing_info(dataset_doc: dict, problem: dict, metric: str) -> Tuple[str, dict]: # Shouldn't evaluate these in serial -- should do in parallel, then check for # conflicts if is_tabular(dataset_doc, problem): return "table", {} elif is_multitable(dataset_doc): resources = deepcopy(dataset_doc["dataResources"]) learning_resource = [ r for r in resources if "learningData.csv" in r["resPath"] ][0] resources.remove(learning_resource) assert len(resources) == 1 other_resource = resources[0] is_table = other_resource["resType"] == "table" is_collection = other_resource.get("isCollection", False) if is_table and is_collection: # return 'multitable', {} return "table", {"multi": True} else: print( ( "get_routing_info: is_multitable, but other_resource is not a collection\n" " falling back to table (eg. ignoring resources)" ), file=sys.stderr, ) return "table", {} elif is_question_answering(dataset_doc): return "question_answering", {} elif is_text(dataset_doc): return "text", {} elif is_image(dataset_doc, problem): return "image", {} elif is_semisupervised_remote_sensing_pretrained(problem): return "semisupervised_remote_sensing_pretrained", {} elif is_remote_sensing_pretrained(dataset_doc, problem): return "remote_sensing_pretrained", {} elif is_remote_sensing(dataset_doc, problem): return "remote_sensing", {} elif is_object_detection(problem): return "object_detection", {} elif is_clustering(problem): resources = dataset_doc["dataResources"] assert len(resources) == 1 learning_resource = resources[0] # !! Not sure if I'm supposed to be looking at this n_clusters = problem["inputs"][0]["targets"][0]["clusters_number"] all_float = set( [ r["colType"] for r in learning_resource["columns"] if ("suggestedTarget" not in r["role"]) and ("d3mIndex" != r["colName"]) ] ) == {"real"} return "clustering", { "n_clusters": n_clusters, "all_float": all_float, } elif is_timeseries_classification(dataset_doc, problem): return ( "timeseries_classification", { # "metrics": ['euclidean', 'cityblock', 'dtw'], # "diffusion": True, # "forest": True, # "ensemble_size": 3, }, ) elif is_timeseries_forecasting(problem): return "timeseries_forecasting", {} elif is_audio(dataset_doc): return "audio", {} elif is_graph_matching(problem): return "graph_matching", {} elif is_vertex_nomination(problem): return "vertex_nomination", {} elif is_vertex_classification(problem): return "vertex_nomination", {"is_edgelist": is_edgelist(dataset_doc, problem)} elif is_collaborative_filtering(problem): return "collaborative_filtering", {} elif is_link_prediction(problem): return "link_prediction", {"is_edgelist": is_edgelist(dataset_doc, problem)} elif is_community_detection(problem): # TODO what should subtype be? assert ( _problem.TaskKeyword.COMMUNITY_DETECTION in problem["problem"]["task_keywords"] ) return "community_detection", { "overlapping": False, } elif is_semisupervised_tabular(problem): # return 'table', {'semi': True} return "semisupervised_tabular", {} else: raise Exception("!! router failed on problem")

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src/resource_resolver/core/ResourceProxy.py

Moyoka22/resource-resolver

0

102860

<reponame>Moyoka22/resource-resolver<gh_stars>0 import logging import shutil from io import StringIO, TextIOBase from pathlib import Path import tempfile from typing import IO, Literal, Union, cast from .errors import ResourceResolverError from .managers import ManagerRegistry class ResourceProxy: GET_AS_FORMATS = ['str', 'buffer', 'file_handle'] def __init__(self, location: Union[str, IO[str], Path], read_only=False, **kwargs): self._location = location self._read_only = read_only Manager = ManagerRegistry.get_manager(location) if not Manager: location = repr(location) raise ResourceResolverError.UnsupportedProtocol(location) self._manager = Manager(location, **kwargs) def put(self, data: Union[str, IO[str]]) -> None: if self.is_read_only: raise ResourceResolverError.ReadOnly(self._location) if not (isinstance(data, str) or isinstance(data, TextIOBase)): raise ResourceResolverError.UnsupportedWriteType(data) if isinstance(data, str): data = cast(str, data) data = self._produce_stream_from_data(data) data = cast(IO[str], data) self._manager.put(data) def get(self, as_a: str) -> Union[str, IO[str], StringIO]: supported_formats = self.GET_AS_FORMATS if as_a not in supported_formats: raise ResourceResolverError.\ UnsupportedGetAsFormat(as_a, supported_formats) if as_a == 'str': return self._manager.get().read() elif as_a == 'buffer': buffer = StringIO() data = self._manager.get() shutil.copyfileobj(data, buffer) buffer.seek(0, 0) return buffer else: as_a = cast(Literal['file_handle'], as_a) return self._manager.get() @property def is_read_only(self) -> bool: return self._read_only def _produce_stream_from_data(self, data: str) -> IO[str]: try: buffer: IO[str] = StringIO(data) buffer.seek(0, 0) except MemoryError: logging.warning('Failed to allocation memory for string. ' f'String has length {len(data)}.') fp = tempfile.TemporaryFile(mode="w+") fp.write(data) fp.seek(0, 0) return fp else: return buffer

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orange3/Orange/tests/test_domain.py

rgschmitz1/BioDepot-workflow-builder

54

63788

# Test methods with long descriptive names can omit docstrings # pylint: disable=missing-docstring import warnings from time import time from numbers import Real from itertools import starmap, chain import unittest import pickle import numpy as np from numpy.testing import assert_array_equal from Orange.data import ( ContinuousVariable, DiscreteVariable, StringVariable, TimeVariable, Variable, Domain, Table, DomainConversion, ) from Orange.data.domain import filter_visible from Orange.preprocess import Continuize, Impute from Orange.tests.base import create_pickling_tests from Orange.util import OrangeDeprecationWarning def create_domain(*ss): Variable._clear_all_caches() vars = dict( age=ContinuousVariable(name="AGE"), gender=DiscreteVariable(name="Gender", values=["M", "F"]), incomeA=ContinuousVariable(name="incomeA"), income=ContinuousVariable(name="income"), education=DiscreteVariable(name="education", values=["GS", "HS", "C"]), ssn=StringVariable(name="SSN"), race=DiscreteVariable( name="race", values=["White", "Hypsanic", "African", "Other"] ), arrival=TimeVariable("arrival"), ) def map_vars(s): return [vars[x] for x in s] return Domain(*[map_vars(s) for s in ss]) PickleDomain = create_pickling_tests( "PickleDomain", ("empty_domain", lambda: create_domain([])), ("with_continuous_variable", lambda: create_domain(["age"])), ("with_discrete_variable", lambda: create_domain(["gender"])), ("with_mixed_variables", lambda: create_domain(["age", "gender"])), ("with_continuous_class", lambda: create_domain(["age", "gender"], ["incomeA"])), ("with_discrete_class", lambda: create_domain(["age", "gender"], ["education"])), ( "with_multiple_classes", lambda: create_domain(["age", "gender"], ["incomeA", "education"]), ), ("with_metas", lambda: create_domain(["age", "gender"], [], ["ssn"])), ( "with_class_and_metas", lambda: create_domain(["age", "gender"], ["incomeA", "education"], ["ssn"]), ), ) age, gender, incomeA, income, education, ssn, race, arrival = create_domain( [], [], ["age", "gender", "incomeA", "income", "education", "ssn", "race", "arrival"], ).metas class TestDomainInit(unittest.TestCase): def test_init_class(self): attributes = (age, gender, income) d = Domain(attributes, race) self.assertEqual(d.variables, attributes + (race,)) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, race) self.assertEqual(d.class_vars, (race,)) self.assertEqual(d.metas, ()) def test_init_class_list(self): attributes = (age, gender, income) d = Domain(attributes, [race]) self.assertEqual(d.variables, attributes + (race,)) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, race) self.assertEqual(d.class_vars, (race,)) self.assertEqual(d.metas, ()) def test_init_no_class(self): attributes = (age, gender, income) d = Domain(attributes) self.assertEqual(d.variables, attributes) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, None) self.assertEqual(d.class_vars, ()) self.assertEqual(d.metas, ()) def test_init_no_class_false(self): attributes = (age, gender, income) d = Domain(attributes, None) self.assertEqual(d.variables, attributes) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, None) self.assertEqual(d.class_vars, ()) self.assertEqual(d.metas, ()) def test_init_multi_class(self): attributes = (age, gender, income) d = Domain(attributes, (education, race)) self.assertEqual(d.variables, attributes + (education, race)) self.assertEqual(d.attributes, attributes) self.assertIsNone(d.class_var) self.assertEqual(d.class_vars, (education, race)) self.assertEqual(d.metas, ()) def test_init_source(self): attributes = (age, gender, income) d = Domain(attributes, (education, race)) d2 = Domain(["Gender", 0, income], source=d) self.assertEqual(d2.variables, (gender, age, income)) def test_init_source_class(self): attributes = (age, gender, income) d = Domain(attributes, (education, race)) d2 = Domain(["Gender", 0], "income", source=d) self.assertEqual(d2.variables, (gender, age, income)) def test_init_metas(self): attributes = (age, gender, income) metas = (ssn, race) d = Domain(attributes, race, metas=metas) self.assertEqual(d.variables, attributes + (race,)) self.assertEqual(d.attributes, attributes) self.assertEqual(d.class_var, race) self.assertEqual(d.class_vars, (race,)) self.assertEqual(d.metas, metas) def test_from_numpy_names(self): for n_cols, name in [ (5, "Feature {}"), (99, "Feature {:02}"), (100, "Feature {:03}"), ]: d = Domain.from_numpy(np.zeros((1, n_cols))) self.assertTrue(d.anonymous) self.assertEqual( [var.name for var in d.attributes], [name.format(i) for i in range(1, n_cols + 1)], ) d = Domain.from_numpy(np.zeros((1, 1))) self.assertTrue(d.anonymous) self.assertEqual(d.attributes[0].name, "Feature") d = Domain.from_numpy(np.zeros((1, 3)), np.zeros((1, 1)), np.zeros((1, 100))) self.assertTrue(d.anonymous) self.assertEqual( [var.name for var in d.attributes], ["Feature {}".format(i) for i in range(1, 4)], ) self.assertEqual(d.class_var.name, "Target") self.assertEqual( [var.name for var in d.metas], ["Meta {:03}".format(i) for i in range(1, 101)], ) def test_from_numpy_dimensions(self): for dimension in [[5], [5, 1]]: d = Domain.from_numpy(np.zeros((1, 1)), np.zeros(dimension)) self.assertTrue(d.anonymous) self.assertEqual(len(d.class_vars), 1) self.assertRaises(ValueError, Domain.from_numpy, np.zeros(2)) self.assertRaises(ValueError, Domain.from_numpy, np.zeros((2, 2, 2))) self.assertRaises( ValueError, Domain.from_numpy, np.zeros((2, 2)), np.zeros((2, 2, 2)) ) def test_from_numpy_values(self): for aran_min, aran_max, vartype in [ (1, 3, ContinuousVariable), (0, 2, DiscreteVariable), (18, 23, ContinuousVariable), ]: n_rows, n_cols, = aran_max - aran_min, 1 d = Domain.from_numpy( np.zeros((1, 1)), np.arange(aran_min, aran_max).reshape(n_rows, n_cols) ) self.assertTrue(d.anonymous) self.assertIsInstance(d.class_var, vartype) if isinstance(vartype, DiscreteVariable): self.assertEqual( d.class_var.values, ["v{}".format(i) for i in range(1, 3)] ) def test_wrong_vartypes(self): attributes = (age, gender, income) for args in ((attributes, ssn), (attributes + (ssn,)), ((ssn,) + attributes)): with self.assertRaises(TypeError): Domain(*args) def test_wrong_vartypes_w_source(self): d = Domain((age, gender), metas=(ssn,)) with self.assertRaises(TypeError): Domain(-1, source=d) def test_wrong_types(self): with self.assertRaises(TypeError): Domain((age, [])) with self.assertRaises(TypeError): Domain((age, "income")) with self.assertRaises(TypeError): Domain(([], age)) with self.assertRaises(TypeError): Domain(("income", age)) with self.assertRaises(TypeError): Domain((age,), self) with self.assertRaises(TypeError): Domain((age,), metas=("income",)) def test_get_item(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx, var in [ (age, age), ("AGE", age), (0, age), (income, income), ("income", income), (2, income), (ssn, ssn), ("SSN", ssn), (-1, ssn), (-2, race), ]: self.assertEqual(d[idx], var) def test_index(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx, var in [ (age, 0), ("AGE", 0), (0, 0), (np.int_(0), 0), (income, 2), ("income", 2), (2, 2), (np.int_(2), 2), (ssn, -1), ("SSN", -1), (-1, -1), (np.int_(-1), -1), (-2, -2), (np.int_(-2), -2), ]: self.assertEqual(d.index(idx), var) def test_get_item_slices(self): d = Domain((age, gender, income, race), metas=(ssn, race)) self.assertEqual(d[:2], (age, gender)) self.assertEqual(d[1:3], (gender, income)) self.assertEqual(d[2:], (income, race)) def test_get_item_error(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx in (3, -3, incomeA, "no_such_thing"): with self.assertRaises(KeyError): _ = d[idx] with self.assertRaises(TypeError): _ = d[[2]] def test_index_error(self): d = Domain((age, gender, income), metas=(ssn, race)) for idx in (3, np.int(3), -3, np.int(-3), incomeA, "no_such_thing"): with self.assertRaises(ValueError): d.index(idx) with self.assertRaises(TypeError): d.index([2]) def test_contains(self): d = Domain((age, gender, income), metas=(ssn,)) for var in [ "AGE", age, 0, np.int_(0), "income", income, 2, np.int_(2), "SSN", ssn, -1, np.int_(-1), ]: self.assertIn(var, d) for var in ["no_such_thing", race, 3, np.int_(3), -2, np.int_(-2)]: self.assertNotIn(var, d) with self.assertRaises(TypeError): {} in d with self.assertRaises(TypeError): [] in d def test_iter(self): with warnings.catch_warnings(record=True): warnings.simplefilter("error") d = Domain((age, gender, income), metas=(ssn,)) with self.assertRaises(OrangeDeprecationWarning): list(d) warnings.simplefilter("ignore") self.assertEqual([var for var in d], [age, gender, income]) d = Domain((age,), metas=(ssn,)) self.assertEqual([var for var in d], [age]) d = Domain((), metas=(ssn,)) self.assertEqual([var for var in d], []) def test_str(self): cases = ( (((),), "[]"), (((age,),), "[AGE]"), (((), age), "[ | AGE]"), (((gender,), age), "[Gender | AGE]"), (((gender, income), None), "[Gender, income]"), (((gender, income), age), "[Gender, income | AGE]"), (((gender,), (age, income)), "[Gender | AGE, income]"), (((gender,), (age, income), (ssn,)), "[Gender | AGE, income] {SSN}"), ( ((gender,), (age, income), (ssn, race)), "[Gender | AGE, income] {SSN, race}", ), (((), (), (ssn, race)), "[] {SSN, race}"), ) for args, printout in cases: self.assertEqual(str(Domain(*args)), printout) def test_has_discrete(self): self.assertFalse(Domain([]).has_discrete_attributes()) self.assertFalse(Domain([], [age]).has_discrete_attributes()) self.assertFalse(Domain([], race).has_discrete_attributes()) self.assertFalse(Domain([age], None).has_discrete_attributes()) self.assertTrue(Domain([race], None).has_discrete_attributes()) self.assertTrue(Domain([age, race], None).has_discrete_attributes()) self.assertTrue(Domain([race, age], None).has_discrete_attributes()) self.assertFalse(Domain([], [age]).has_discrete_attributes(True)) self.assertTrue(Domain([], [race]).has_discrete_attributes(True)) self.assertFalse(Domain([age], None).has_discrete_attributes(True)) self.assertTrue(Domain([race], None).has_discrete_attributes(True)) self.assertTrue(Domain([age], race).has_discrete_attributes(True)) self.assertTrue(Domain([race], age).has_discrete_attributes(True)) self.assertTrue(Domain([], [race, age]).has_discrete_attributes(True)) d = Domain([], None, [gender]) self.assertTrue(d.has_discrete_attributes(False, True)) d = Domain([], None, [age]) self.assertFalse(d.has_discrete_attributes(False, True)) d = Domain([], [age], [gender]) self.assertTrue(d.has_discrete_attributes(True, True)) d = Domain([], [incomeA], [age]) self.assertFalse(d.has_discrete_attributes(True, True)) def test_has_continuous(self): self.assertFalse(Domain([]).has_continuous_attributes()) self.assertFalse(Domain([], [age]).has_continuous_attributes()) self.assertFalse(Domain([], [race]).has_continuous_attributes()) self.assertTrue(Domain([age], None).has_continuous_attributes()) self.assertFalse(Domain([race], None).has_continuous_attributes()) self.assertTrue(Domain([age, race], None).has_continuous_attributes()) self.assertTrue(Domain([race, age], None).has_continuous_attributes()) self.assertTrue(Domain([], [age]).has_continuous_attributes(True)) self.assertFalse(Domain([], [race]).has_continuous_attributes(True)) self.assertTrue(Domain([age], None).has_continuous_attributes(True)) self.assertFalse(Domain([race], None).has_continuous_attributes(True)) self.assertTrue(Domain([age], race).has_continuous_attributes(True)) self.assertTrue(Domain([race], age).has_continuous_attributes(True)) self.assertTrue(Domain([], [race, age]).has_continuous_attributes(True)) d = Domain([], None, [age]) self.assertTrue(d.has_continuous_attributes(False, True)) d = Domain([], None, [gender]) self.assertFalse(d.has_continuous_attributes(False, True)) d = Domain([], [gender], [age]) self.assertTrue(d.has_continuous_attributes(True, True)) d = Domain([], [race], [gender]) self.assertFalse(d.has_continuous_attributes(True, True)) def test_has_time(self): self.assertFalse(Domain([]).has_time_attributes()) self.assertFalse(Domain([], [age]).has_time_attributes()) self.assertFalse(Domain([], [race]).has_time_attributes()) self.assertFalse(Domain([], [arrival]).has_time_attributes()) self.assertFalse(Domain([], [], [arrival]).has_time_attributes()) self.assertTrue(Domain([arrival], []).has_time_attributes()) self.assertTrue(Domain([], [arrival]).has_time_attributes(include_class=True)) self.assertTrue( Domain([], [], [arrival]).has_time_attributes(include_metas=True) ) self.assertFalse(Domain([arrival], []).has_time_class) self.assertTrue(Domain([], [arrival]).has_time_class) self.assertFalse(Domain([], [], [arrival]).has_time_class) def test_get_conversion(self): compute_value = lambda: 42 new_income = income.copy(compute_value=compute_value) d = Domain((age, gender, income), metas=(ssn, race)) e = Domain((gender, race), None, metas=(age, gender, ssn)) f = Domain((gender,), (race, income), metas=(age, income, ssn)) g = Domain((), metas=(age, gender, ssn)) h = Domain((gender,), (race, new_income), metas=(age, new_income, ssn)) for conver, domain, attr, class_vars, metas in ( (d, e, [1, -2], [], [0, 1, -1]), (d, f, [1], [-2, 2], [0, 2, -1]), (f, g, [], [], [-1, 0, -3]), (g, h, [-2], [None, compute_value], [-1, compute_value, -3]), ): to_domain = domain.get_conversion(conver) self.assertIs(to_domain.source, conver) self.assertEqual(to_domain.attributes, attr) self.assertEqual(to_domain.class_vars, class_vars) self.assertEqual(to_domain.metas, metas) def test_conversion(self): domain = Domain([age, income], [race], [gender, education, ssn]) x, y, metas = domain.convert([42, 13, "White"]) assert_array_equal(x, np.array([42, 13])) assert_array_equal(y, np.array([0])) metas_exp = [gender.Unknown, education.Unknown, ssn.Unknown] def equal(a, b): if ( isinstance(a, Real) and isinstance(b, Real) and np.isnan(a) and np.isnan(b) ): return True else: return a == b self.assertTrue(all(starmap(equal, zip(metas, metas_exp)))) x, y, metas = domain.convert([42, 13, "White", "M", "HS", "1234567"]) assert_array_equal(x, np.array([42, 13])) assert_array_equal(y, np.array([0])) assert_array_equal(metas, np.array([0, 1, "1234567"], dtype=object)) def test_conversion_size(self): domain = Domain([age, gender, income], [race]) self.assertRaises(ValueError, domain.convert, [0] * 3) self.assertRaises(ValueError, domain.convert, [0] * 5) domain = Domain([age, income], [race], [gender, education, ssn]) self.assertRaises(ValueError, domain.convert, [0] * 2) self.assertRaises(ValueError, domain.convert, [0] * 4) self.assertRaises(ValueError, domain.convert, [0] * 7) domain.convert([0] * 3) domain.convert([0] * 6) def test_preprocessor_chaining(self): domain = Domain( [DiscreteVariable("a", values="01"), DiscreteVariable("b", values="01")], DiscreteVariable("y", values="01"), ) table = Table(domain, [[0, 1], [1, np.NaN]], [0, 1]) pre1 = Continuize()(Impute()(table)) pre2 = Table(pre1.domain, table) np.testing.assert_almost_equal(pre1.X, pre2.X) def test_unpickling_recreates_known_domains(self): Variable._clear_all_caches() domain = Domain([]) unpickled_domain = pickle.loads(pickle.dumps(domain)) self.assertTrue(hasattr(unpickled_domain, "_known_domains")) def test_different_domains_with_same_attributes_are_equal(self): domain1 = Domain([]) domain2 = Domain([]) self.assertEqual(domain1, domain2) var1 = ContinuousVariable("var1") domain1.attributes = (var1,) self.assertNotEqual(domain1, domain2) domain2.attributes = (var1,) self.assertEqual(domain1, domain2) domain1.class_vars = (var1,) self.assertNotEqual(domain1, domain2) domain2.class_vars = (var1,) self.assertEqual(domain1, domain2) domain1._metas = (var1,) self.assertNotEqual(domain1, domain2) domain2._metas = (var1,) self.assertEqual(domain1, domain2) def test_domain_conversion_is_fast_enough(self): attrs = [ContinuousVariable("f%i" % i) for i in range(10000)] class_vars = [ContinuousVariable("c%i" % i) for i in range(10)] metas = [ContinuousVariable("m%i" % i) for i in range(10)] source = Domain(attrs, class_vars, metas) start = time() cases = ( ( (attrs[:1000], class_vars, metas), list(range(1000)), list(range(10000, 10010)), list(range(-1, -11, -1)), ), ( (metas, attrs[:1000], class_vars), list(range(-1, -11, -1)), list(range(1000)), list(range(10000, 10010)), ), ( (class_vars, metas, attrs[:1000]), list(range(10000, 10010)), list(range(-1, -11, -1)), list(range(1000)), ), ) for domain_args, attributes, class_vars, metas in cases: c1 = DomainConversion(source, Domain(*domain_args)) self.assertEqual(c1.attributes, attributes) self.assertEqual(c1.class_vars, class_vars) self.assertEqual(c1.metas, metas) self.assertLessEqual(time() - start, 1) def test_copy(self): age.number_of_decimals = 5 attributes = (age, gender, income) domain = Domain(attributes, [race], [ssn]) new_domain = domain.copy() new_domain[age].number_of_decimals = 10 self.assertEqual(domain[age].number_of_decimals, 5) self.assertEqual(new_domain[age].number_of_decimals, 10) def test_domain_conversion_sparsity(self): destination = Domain( attributes=[ ContinuousVariable(name="a"), ContinuousVariable(name="b"), ContinuousVariable(name="c"), ], class_vars=[DiscreteVariable("d", values=["e"])], metas=[StringVariable("f")], ) # all dense source = Domain(attributes=[]) conversion = DomainConversion(source, destination) self.assertFalse(conversion.sparse_X) self.assertFalse(conversion.sparse_Y) self.assertFalse(conversion.sparse_metas) # set destination attributes as sparse for a in destination.attributes: a.sparse = True source = Domain(attributes=[]) conversion = DomainConversion(source, destination) self.assertTrue(conversion.sparse_X) self.assertFalse(conversion.sparse_Y) self.assertFalse(conversion.sparse_metas) # set all destination variable as sparse for a in chain(destination.variables, destination.metas): a.sparse = True source = Domain(attributes=[]) conversion = DomainConversion(source, destination) self.assertTrue(conversion.sparse_X) self.assertTrue(conversion.sparse_Y) self.assertFalse(conversion.sparse_metas) class TestDomainFilter(unittest.TestCase): def setUp(self): self.iris = Table("iris") def test_filter_visible(self): n_feats = len(self.iris.domain.attributes) self.iris.domain.attributes[0].attributes.update({"hidden": True}) filtered = list(filter_visible(self.iris.domain.attributes)) self.assertNotIn(self.iris.domain.attributes[0], filtered) self.assertEqual(len(filtered), n_feats - 1) if __name__ == "__main__": unittest.main()

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utils/save_result.py

AnnLIU15/SegCovid

0

146313

<reponame>AnnLIU15/SegCovid import os import cv2 import numpy as np def torch2imgs(output): ''' 函数为将torch数据转为2维图片保存 ''' shapeOFoutput = output.shape if len(shapeOFoutput) == 3 and shapeOFoutput[0] > 1: # 将one-hot结果选取最大值 imgs_array = output.argmax(dim=0).clone().detach().cpu().numpy() elif len(shapeOFoutput) == 3 and shapeOFoutput[0] == 1: # 去掉无用维度 imgs_array = output.clone().detach().cpu().numpy() elif len(shapeOFoutput) == 4: # batchsize~=1，递归保存(反正不考虑效率) imgs_array = np.zeros( shape=(shapeOFoutput[0], shapeOFoutput[2], shapeOFoutput[3])) for idx in range(shapeOFoutput[0]): imgs_array[idx] = torch2imgs(output[idx]) else: assert False, '2D图片的输出维度应该是4.维度不匹配,目前维度'+str(len(shapeOFoutput)) return imgs_array def saveImage(imgs_array, name_of_imgs, save_dir='./output/segResult/', npy_type=False): ''' 使用OPENCV保存图片 ''' # 转为ndarray get_numpy = torch2imgs(imgs_array).astype(np.uint8) shapeofimg = get_numpy.shape if not os.path.exists(save_dir): os.mkdir(save_dir) # 保存 if len(shapeofimg) == 3: for idx in range(shapeofimg[0]): if isinstance(name_of_imgs[idx], tuple): writer_name = name_of_imgs[idx][0] else: writer_name = name_of_imgs[idx] if npy_type: writer_name = writer_name+'.png' cv2.imwrite(save_dir+'/'+writer_name, get_numpy[idx], [ int(cv2.IMWRITE_PNG_COMPRESSION), 0]) else: if isinstance(name_of_imgs, tuple): name_of_imgs = name_of_imgs[0] if npy_type: writer_name = name_of_imgs+'.png' cv2.imwrite(save_dir+'/'+writer_name, get_numpy, [int(cv2.IMWRITE_PNG_COMPRESSION), 0]) if __name__ == '__main__': # import torch # imgs=torch.randint(0,256,(512,512)) # cv2.imwrite('./output/lena.png',imgs.numpy(), [int(cv2.IMWRITE_PNG_COMPRESSION), 0])#第三个参数表示的是压缩级别。默认为3. # cv2.imwrite('./output/1lena.png',imgs.numpy(), [int(cv2.IMWRITE_PNG_COMPRESSION), 9])#第三个参数表示的是压缩级别。默认为3.取最小就好 imgs1 = cv2.imread('./output/lena.png', cv2.IMREAD_GRAYSCALE) imgs = cv2.imread('./output/1lena.png', cv2.IMREAD_GRAYSCALE) print(imgs1 == imgs, (imgs1-imgs).sum())

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test/dpt_tests/dpt_float_test.py

kistlin/xknx

1

130004

"""Unit test for KNX 2 and 4 byte float objects.""" import math import struct from unittest.mock import patch import pytest from xknx.dpt import ( DPT2ByteFloat, DPT4ByteFloat, DPTElectricCurrent, DPTElectricPotential, DPTEnthalpy, DPTFrequency, DPTHumidity, DPTLux, DPTPartsPerMillion, DPTPhaseAngleDeg, DPTPower, DPTTemperature, DPTVoltage, ) from xknx.exceptions import ConversionError class TestDPTFloat: """Test class for KNX 2 & 4 byte/octet float object.""" # #################################################################### # DPT2ByteFloat # def test_value_from_documentation(self): """Test parsing and streaming of DPT2ByteFloat -30.00. Example from the internet[tm].""" assert DPT2ByteFloat.to_knx(-30.00) == (0x8A, 0x24) assert DPT2ByteFloat.from_knx((0x8A, 0x24)) == -30.00 def test_value_taken_from_live_thermostat(self): """Test parsing and streaming of DPT2ByteFloat 19.96.""" assert DPT2ByteFloat.to_knx(16.96) == (0x06, 0xA0) assert DPT2ByteFloat.from_knx((0x06, 0xA0)) == 16.96 def test_zero_value(self): """Test parsing and streaming of DPT2ByteFloat zero value.""" assert DPT2ByteFloat.to_knx(0.00) == (0x00, 0x00) assert DPT2ByteFloat.from_knx((0x00, 0x00)) == 0.00 def test_room_temperature(self): """Test parsing and streaming of DPT2ByteFloat 21.00. Room temperature.""" assert DPT2ByteFloat.to_knx(21.00) == (0x0C, 0x1A) assert DPT2ByteFloat.from_knx((0x0C, 0x1A)) == 21.00 def test_high_temperature(self): """Test parsing and streaming of DPT2ByteFloat 500.00, 499.84, 500.16. Testing rounding issues.""" assert DPT2ByteFloat.to_knx(500.00) == (0x2E, 0x1A) assert round(abs(DPT2ByteFloat.from_knx((0x2E, 0x1A)) - 499.84), 7) == 0 assert round(abs(DPT2ByteFloat.from_knx((0x2E, 0x1B)) - 500.16), 7) == 0 assert DPT2ByteFloat.to_knx(499.84) == (0x2E, 0x1A) assert DPT2ByteFloat.to_knx(500.16) == (0x2E, 0x1B) def test_minor_negative_temperature(self): """Test parsing and streaming of DPT2ByteFloat -10.00. Testing negative values.""" assert DPT2ByteFloat.to_knx(-10.00) == (0x84, 0x18) assert DPT2ByteFloat.from_knx((0x84, 0x18)) == -10.00 def test_very_cold_temperature(self): """ Test parsing and streaming of DPT2ByteFloat -1000.00,-999.68, -1000.32. Testing rounding issues of negative values. """ assert DPT2ByteFloat.to_knx(-1000.00) == (0xB1, 0xE6) assert DPT2ByteFloat.from_knx((0xB1, 0xE6)) == -999.68 assert DPT2ByteFloat.from_knx((0xB1, 0xE5)) == -1000.32 assert DPT2ByteFloat.to_knx(-999.68) == (0xB1, 0xE6) assert DPT2ByteFloat.to_knx(-1000.32) == (0xB1, 0xE5) def test_max(self): """Test parsing and streaming of DPT2ByteFloat with maximum value.""" assert DPT2ByteFloat.to_knx(DPT2ByteFloat.value_max) == (0x7F, 0xFF) assert DPT2ByteFloat.from_knx((0x7F, 0xFF)) == DPT2ByteFloat.value_max def test_min(self): """Test parsing and streaming of DPT2ByteFloat with minimum value.""" assert DPT2ByteFloat.to_knx(DPT2ByteFloat.value_min) == (0xF8, 0x00) assert DPT2ByteFloat.from_knx((0xF8, 0x00)) == DPT2ByteFloat.value_min def test_close_to_max(self): """Test parsing and streaming of DPT2ByteFloat with maximum value -1.""" assert DPT2ByteFloat.to_knx(670433.28) == (0x7F, 0xFE) assert DPT2ByteFloat.from_knx((0x7F, 0xFE)) == 670433.28 def test_close_to_min(self): """Test parsing and streaming of DPT2ByteFloat with minimum value +1.""" assert DPT2ByteFloat.to_knx(-670760.96) == (0xF8, 0x01) assert DPT2ByteFloat.from_knx((0xF8, 0x01)) == -670760.96 def test_to_knx_min_exceeded(self): """Test parsing of DPT2ByteFloat with wrong value (underflow).""" with pytest.raises(ConversionError): DPT2ByteFloat.to_knx(DPT2ByteFloat.value_min - 1) def test_to_knx_max_exceeded(self): """Test parsing of DPT2ByteFloat with wrong value (overflow).""" with pytest.raises(ConversionError): DPT2ByteFloat.to_knx(DPT2ByteFloat.value_max + 1) def test_to_knx_wrong_parameter(self): """Test parsing of DPT2ByteFloat with wrong value (string).""" with pytest.raises(ConversionError): DPT2ByteFloat.to_knx("fnord") def test_from_knx_wrong_parameter(self): """Test parsing of DPT2ByteFloat with wrong value (wrong number of bytes).""" with pytest.raises(ConversionError): DPT2ByteFloat.from_knx((0xF8, 0x01, 0x23)) def test_from_knx_wrong_parameter2(self): """Test parsing of DPT2ByteFloat with wrong value (second parameter is a string).""" with pytest.raises(ConversionError): DPT2ByteFloat.from_knx((0xF8, "0x23")) # # DPTTemperature # def test_temperature_settings(self): """Test attributes of DPTTemperature.""" assert DPTTemperature.value_min == -273 assert DPTTemperature.value_max == 670760 assert DPTTemperature.unit == "°C" assert DPTTemperature.resolution == 0.01 def test_temperature_assert_min_exceeded(self): """Testing parsing of DPTTemperature with wrong value.""" with pytest.raises(ConversionError): DPTTemperature.to_knx(-274) def test_temperature_assert_min_exceeded_from_knx(self): """Testing parsing of DPTTemperature with wrong value.""" with pytest.raises(ConversionError): DPTTemperature.from_knx((0xB1, 0xE6)) # -1000 # # DPTLux # def test_lux_settings(self): """Test attributes of DPTLux.""" assert DPTLux.value_min == 0 assert DPTLux.value_max == 670760 assert DPTLux.unit == "lx" assert DPTLux.resolution == 0.01 def test_lux_assert_min_exceeded(self): """Test parsing of DPTLux with wrong value.""" with pytest.raises(ConversionError): DPTLux.to_knx(-1) # # DPTHumidity # def test_humidity_settings(self): """Test attributes of DPTHumidity.""" assert DPTHumidity.value_min == 0 assert DPTHumidity.value_max == 670760 assert DPTHumidity.unit == "%" assert DPTHumidity.resolution == 0.01 def test_humidity_assert_min_exceeded(self): """Test parsing of DPTHumidity with wrong value.""" with pytest.raises(ConversionError): DPTHumidity.to_knx(-1) # # DPTEnthalpy # def test_enthalpy_settings(self): """Test attributes of DPTEnthalpy.""" assert DPTEnthalpy.unit == "H" # # DPTPartsPerMillion # def test_partspermillion_settings(self): """Test attributes of DPTPartsPerMillion.""" assert DPTPartsPerMillion.unit == "ppm" # # DPTVoltage # def test_voltage_settings(self): """Test attributes of DPTVoltage.""" assert DPTVoltage.unit == "mV" # #################################################################### # DPT4ByteFloat # def test_4byte_float_values_from_power_meter(self): """Test parsing DPT4ByteFloat value from power meter.""" assert DPT4ByteFloat.from_knx((0x43, 0xC6, 0x80, 00)) == 397 assert DPT4ByteFloat.to_knx(397) == (0x43, 0xC6, 0x80, 00) assert DPT4ByteFloat.from_knx((0x42, 0x38, 0x00, 00)) == 46 assert DPT4ByteFloat.to_knx(46) == (0x42, 0x38, 0x00, 00) def test_14_033(self): """Test parsing DPTFrequency unit.""" assert DPTFrequency.unit == "Hz" def test_14_055(self): """Test DPTPhaseAngleDeg object.""" assert DPT4ByteFloat.from_knx((0x42, 0xEF, 0x00, 0x00)) == 119.5 assert DPT4ByteFloat.to_knx(119.5) == (0x42, 0xEF, 0x00, 0x00) assert DPTPhaseAngleDeg.unit == "°" def test_14_057(self): """Test DPT4ByteFloat object.""" assert DPT4ByteFloat.from_knx((0x3F, 0x71, 0xEB, 0x86)) == 0.9450001 assert DPT4ByteFloat.to_knx(0.945000052452) == (0x3F, 0x71, 0xEB, 0x86) assert DPT4ByteFloat.unit == "" def test_4byte_float_values_from_voltage_meter(self): """Test parsing DPT4ByteFloat from voltage meter.""" assert DPT4ByteFloat.from_knx((0x43, 0x65, 0xE3, 0xD7)) == 229.89 assert DPT4ByteFloat.to_knx(229.89) == (0x43, 0x65, 0xE3, 0xD7) def test_4byte_float_zero_value(self): """Test parsing and streaming of DPT4ByteFloat zero value.""" assert DPT4ByteFloat.from_knx((0x00, 0x00, 0x00, 0x00)) == 0.00 assert DPT4ByteFloat.to_knx(0.00) == (0x00, 0x00, 0x00, 0x00) def test_4byte_float_special_value(self): """Test parsing and streaming of DPT4ByteFloat special value.""" assert math.isnan(DPT4ByteFloat.from_knx((0x7F, 0xC0, 0x00, 0x00))) assert DPT4ByteFloat.to_knx(float("nan")) == (0x7F, 0xC0, 0x00, 0x00) assert math.isinf(DPT4ByteFloat.from_knx((0x7F, 0x80, 0x00, 0x00))) assert DPT4ByteFloat.to_knx(float("inf")) == (0x7F, 0x80, 0x00, 0x00) assert DPT4ByteFloat.from_knx((0xFF, 0x80, 0x00, 0x00)) == float("-inf") assert DPT4ByteFloat.to_knx(float("-inf")) == (0xFF, 0x80, 0x00, 0x00) assert DPT4ByteFloat.from_knx((0x80, 0x00, 0x00, 0x00)) == float("-0") assert DPT4ByteFloat.to_knx(float("-0")) == (0x80, 0x00, 0x00, 0x00) def test_4byte_float_to_knx_wrong_parameter(self): """Test parsing of DPT4ByteFloat with wrong value (string).""" with pytest.raises(ConversionError): DPT4ByteFloat.to_knx("fnord") def test_4byte_float_from_knx_wrong_parameter(self): """Test parsing of DPT4ByteFloat with wrong value (wrong number of bytes).""" with pytest.raises(ConversionError): DPT4ByteFloat.from_knx((0xF8, 0x01, 0x23)) def test_4byte_float_from_knx_wrong_parameter2(self): """Test parsing of DPT4ByteFloat with wrong value (second parameter is a string).""" with pytest.raises(ConversionError): DPT4ByteFloat.from_knx((0xF8, "0x23", 0x00, 0x00)) def test_4byte_flaot_from_knx_unpack_error(self): """Test DPT4ByteFloat parsing with unpack error.""" with patch("struct.unpack") as unpack_mock: unpack_mock.side_effect = struct.error() with pytest.raises(ConversionError): DPT4ByteFloat.from_knx((0x01, 0x23, 0x02, 0x02)) # # DPTElectricCurrent # def test_electric_current_settings(self): """Test attributes of DPTElectricCurrent.""" assert DPTElectricCurrent.unit == "A" # # DPTElectricPotential # def test_electric_potential_settings(self): """Test attributes of DPTElectricPotential.""" assert DPTElectricPotential.unit == "V" # # DPTPower # def test_power_settings(self): """Test attributes of DPTPower.""" assert DPTPower.unit == "W"

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liftaway/low_level.py

elj/liftaway

0

167872

#!/usr/bin/env python # -*- coding: utf-8 -*- """Liftaway Low-Level (GPIO and PCA) module.""" import random import busio import RPi.GPIO as GPIO from adafruit_pca9685 import PCA9685 from board import SCL, SDA from liftaway.constants import control_outputs i2c_bus = busio.I2C(SCL, SDA) pca = PCA9685(i2c_bus) def init(): """Initialize LEDs.""" pca.frequency = 60 # Turn all lights off all_lights_off() # Init Overhead/Ceiling Light green = pca.channels[13] red = pca.channels[14] blue = pca.channels[15] # TODO: make this changable red.duty_cycle = 0xFFFF green.duty_cycle = 0x4000 blue.duty_cycle = 0x0000 def gpio_output(gpio: int, high: bool = True): """Set a GPIO Output High or Low.""" if high: GPIO.output(gpio, GPIO.HIGH) else: GPIO.output(gpio, GPIO.LOW) def cancel_call_led(on: bool = True): """Turn on/off the cancel call LED.""" gpio_output(control_outputs.get("cancel_call"), on) def direction_led(on: bool = True): """Turn on/off the direction lights; pick a rando direction...""" if on: if random.choice((True, False)): # noqa gpio_output(control_outputs.get("direction_up"), on) else: gpio_output(control_outputs.get("direction_dn"), on) else: # on == False -> Off gpio_output(control_outputs.get("direction_up"), on) gpio_output(control_outputs.get("direction_dn"), on) def door_close_led(on: bool = True): """Turn on/off the door close LED.""" gpio_output(control_outputs.get("door_close"), on) def door_open_led(on: bool = True): """Turn on/off the door open LED.""" gpio_output(control_outputs.get("door_open"), on) def floor_button_led(floor, on: bool = True): """Turn on/off floor LED.""" if on: pca.channels[floor].duty_cycle = 0xFFFF else: pca.channels[floor].duty_cycle = 0 def all_lights_off(): """Turn all Lights/LEDS off.""" for i in range(0, 15): pca.channels[i].duty_cycle = 0 for i in control_outputs.values(): gpio_output(i, False)

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examples/retrieve/owners_retrieve.py

jhurd-tc/threatconnect-python

0

127972

# -*- coding: utf-8 -*- """ standard """ import ConfigParser from random import randint import sys """ custom """ from threatconnect import ThreatConnect from threatconnect.Config.FilterOperator import FilterSetOperator # configuration file config_file = "tc.conf" # retrieve configuration file config = ConfigParser.RawConfigParser() config.read(config_file) try: api_access_id = config.get('threatconnect', 'api_access_id') api_secret_key = config.get('threatconnect', 'api_secret_key') api_default_org = config.get('threatconnect', 'api_default_org') api_base_url = config.get('threatconnect', 'api_base_url') api_result_limit = int(config.get('threatconnect', 'api_result_limit')) except ConfigParser.NoOptionError: print('Could not retrieve configuration file.') sys.exit(1) tc = ThreatConnect(api_access_id, api_secret_key, api_default_org, api_base_url) tc.set_api_result_limit(api_result_limit) tc.report_enable() """ Get Owners """ enable_example1 = False enable_example2 = False enable_example3 = False # shared method to display results from examples below def show_data(result_obj): """ """ for obj in result_obj: print('\n{0!s:_^80}'.format(obj.name)) print('{0!s:<20}{1!s:<50}'.format('ID', obj.id)) print('{0!s:<20}{1!s:<50}'.format('Type', obj.type)) # # api_uris # if len(obj.request_uris) > 0: print('\n{0!s:-^40}'.format(' Request URIs ')) for request_uri in obj.request_uris: print('{0!s:<20}{1!s:<50}'.format('URI', request_uri)) # # matched filters # if len(obj.matched_filters) > 0: print('\n{0!s:-^40}'.format(' API Matched Filters ')) for api_filter in obj.matched_filters: print('{0!s:<20}{1!s:<50}'.format('Filter', api_filter)) # # print report # print(tc.report.stats) def main(): """ """ # set threat connect log (tcl) level tc.set_tcl_file('log/tc.log', 'debug') tc.set_tcl_console_level('critical') if enable_example1: """ This is a basic example that pull all owners. """ # optionally set the max results the api should return in one request tc.set_api_result_limit(500) # get owner object owners = tc.owners() # retrieve owners try: owners.retrieve() except RuntimeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # show owner data show_data(owners) if enable_example2: """ This example retrieves all owners that a particular indicator appears. """ # get owner object owners = tc.owners() # filter results try: filter1 = owners.add_filter() filter1.add_indicator('10.20.30.40') filter1.add_pf_name('Example Community') filter1.add_pf_type('Community') # Organization, Community, Source except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # retrieve owners try: owners.retrieve() except RuntimeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # show owner data show_data(owners) """ Method: get_owners() -> This method can be used to get a object containing owners filtered by indicator. """ if enable_example3: # get owner object owners = tc.owners() # filter results try: filter1 = owners.add_filter() filter1.add_indicator('10.20.30.40') except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) try: filter2 = owners.add_filter() filter2.add_filter_operator(FilterSetOperator.AND) filter2.add_indicator('<EMAIL>') except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # retrieve owners try: owners.retrieve() except AttributeError as e: print('Error: {0!s}'.format(e)) sys.exit(1) # show owner data show_data(owners) if __name__ == "__main__": main()

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anime_downloader/__version__.py

cmjesz/anime-downloader

1

110593

__version__ = '4.0.0.r1'

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drogher/package/fedex.py

thisisnotmyuserid/drogher

13

14932

<filename>drogher/package/fedex.py import itertools from .base import Package class FedEx(Package): shipper = 'FedEx' class FedExExpress(FedEx): barcode_pattern = r'^\d{34}$' @property def tracking_number(self): return self.barcode[20:22].lstrip('0') + self.barcode[22:] @property def valid_checksum(self): chars, check_digit = self.tracking_number[:-1], self.tracking_number[-1] total = 0 for digit, char in zip(itertools.cycle([1, 3, 7]), reversed(chars)): total += int(char) * digit return total % 11 % 10 == int(check_digit) class FedExGround96(FedEx): barcode_pattern = r'^96\d{20}$' @property def tracking_number(self): return self.barcode[7:] @property def valid_checksum(self): chars, check_digit = self.tracking_number[:-1], self.tracking_number[-1] odd = even = 0 for i, char in enumerate(reversed(chars)): if i & 0x1: odd += int(char) else: even += int(char) check = ((even * 3) + odd) % 10 if check != 0: check = 10 - check return check == int(check_digit)

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tests/eth_detectors.py

Srinivas11789/manticore

0

57757

<filename>tests/eth_detectors.py """ File name is purposefully not test_* to run this test separately. """ import inspect import shutil import struct import tempfile import unittest import os from manticore.core.smtlib import operators from eth_general import make_mock_evm_state from manticore.ethereum import ManticoreEVM, DetectInvalid, DetectIntegerOverflow, Detector, NoAliveStates, ABI, \ EthereumError, DetectReentrancySimple, DetectReentrancyAdvanced, DetectUnusedRetVal, DetectSelfdestruct, LoopDepthLimiter, DetectDelegatecall, \ DetectEnvInstruction, DetectExternalCallAndLeak, DetectEnvInstruction import shutil THIS_DIR = os.path.dirname(os.path.abspath(__file__)) # FIXME(mark): Remove these two lines when logging works for ManticoreEVM from manticore.utils.log import init_logging, set_verbosity init_logging() set_verbosity(0) class EthDetectorTest(unittest.TestCase): """ Subclasses must assign this class variable to the class for the detector """ DETECTOR_CLASS = None def setUp(self): self.mevm = ManticoreEVM() self.mevm.verbosity(0) self.worksp = self.mevm.workspace def tearDown(self): self.mevm = None shutil.rmtree(self.worksp) def _test(self, name, should_find): """ Tests DetectInvalid over the consensys benchmark suit """ mevm = self.mevm filename = os.path.join(THIS_DIR, 'binaries', 'detectors', '{}.sol'.format(name)) self.mevm.register_detector(self.DETECTOR_CLASS()) mevm.multi_tx_analysis(filename, contract_name='DetectThis', args=(mevm.make_symbolic_value(),)) expected_findings = set(((c, d) for b, c, d in should_find)) actual_findings = set(((c, d) for a, b, c, d in mevm.global_findings)) self.assertEqual(expected_findings, actual_findings) class EthRetVal(EthDetectorTest): """ Detect when a return value of a low level transaction instruction is ignored """ DETECTOR_CLASS = DetectUnusedRetVal def test_retval_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_retval_not_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(337, 'Returned value at CALL instruction is not used', False)}) def test_retval_crazy(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_retval_lunatic(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) class EthSelfdestruct(EthDetectorTest): DETECTOR_CLASS = DetectSelfdestruct def test_selfdestruct_true_pos(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(307, 'Reachable SELFDESTRUCT', False)}) def test_selfdestruct_true_pos1(self): self.mevm.register_plugin(LoopDepthLimiter(2)) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(307, 'Reachable SELFDESTRUCT', False)}) def test_selfdestruct_true_neg(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_selfdestruct_true_neg1(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) class EthExternalCallAndLeak(EthDetectorTest): DETECTOR_CLASS = DetectExternalCallAndLeak def test_etherleak_true_neg(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_etherleak_true_neg1(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_etherleak_true_neg2(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False)}) def test_etherleak_true_neg3(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False)}) def test_etherleak_true_pos_argument(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable ether leak to sender via argument", False)}) def test_etherleak_true_pos_argument1(self): self.mevm.register_plugin(LoopDepthLimiter(5)) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable ether leak to sender via argument", False)}) def test_etherleak_true_pos_argument2(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable ether leak to user controlled address via argument", False)}) def test_etherleak_true_pos_msgsender(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False), (0x1c5, "Reachable ether leak to sender", False)}) def test_etherleak_true_pos_msgsender1(self): self.mevm.register_plugin(LoopDepthLimiter(5)) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(0x1c5, "Reachable external call to sender", False), (0x1c5, "Reachable ether leak to sender", False)}) class EthIntegerOverflow(unittest.TestCase): def setUp(self): self.io = DetectIntegerOverflow() self.state = make_mock_evm_state() def test_mul_no_overflow(self): """ Regression test added for issue 714, where we were using the ADD ovf check for MUL """ arguments = [1 << 248, self.state.new_symbolic_value(256)] self.state.constrain(operators.ULT(arguments[1], 256)) cond = self.io._unsigned_mul_overflow(self.state, *arguments) check = self.state.can_be_true(cond) self.assertFalse(check) def test_mul_overflow0(self): arguments = [1 << 249, self.state.new_symbolic_value(256)] self.state.constrain(operators.ULT(arguments[1], 256)) cond = self.io._unsigned_mul_overflow(self.state, *arguments) check = self.state.can_be_true(cond) self.assertTrue(check) def test_mul_overflow1(self): arguments = [1 << 255, self.state.new_symbolic_value(256)] cond = self.io._unsigned_mul_overflow(self.state, *arguments) check = self.state.can_be_true(cond) self.assertTrue(check) class DetectEnvInstruction(EthDetectorTest): DETECTOR_CLASS = DetectEnvInstruction def test_predictable_not_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(174, 'Warning ORIGIN instruction used', False), (157, 'Warning DIFFICULTY instruction used', False), (129, 'Warning TIMESTAMP instruction used', False), (165, 'Warning NUMBER instruction used', False), (132, 'Warning COINBASE instruction used', False), (167, 'Warning BLOCKHASH instruction used', False), (160, 'Warning NUMBER instruction used', False), (199, 'Warning GASPRICE instruction used', False), (202, 'Warning GASLIMIT instruction used', False)}) class EthDelegatecall(EthDetectorTest): """ Test the detection of funny delegatecalls """ DETECTOR_CLASS = DetectDelegatecall def test_delegatecall_ok(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_delegatecall_ok1(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_delegatecall_ok2(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) @unittest.skip("Too slow for this modern times") def test_delegatecall_ok3(self): name = inspect.currentframe().f_code.co_name[5:] self._test(name, set()) def test_delegatecall_not_ok(self): self.mevm.register_plugin(LoopDepthLimiter()) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(179, 'Delegatecall to user controlled function', False), (179, 'Delegatecall to user controlled address', False)}) @unittest.skip("Too slow for this modern times") def test_delegatecall_not_ok1(self): self.mevm.register_plugin(LoopDepthLimiter(loop_count_threshold=500)) name = inspect.currentframe().f_code.co_name[5:] self._test(name, {(179, 'Delegatecall to user controlled function', False)})

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dataactcore/migrations/versions/20b5109967bf_.py

brianherman/data-act-broker-backend

1

138424

"""empty message Revision ID: 20b5109967bf Revises: 17ec44522729, <PASSWORD> Create Date: 2017-04-03 11:25:27.381702 """ # revision identifiers, used by Alembic. revision = '<KEY>' down_revision = ('17ec44522729', '<PASSWORD>') branch_labels = None depends_on = None from alembic import op import sqlalchemy as sa def upgrade(engine_name): globals()["upgrade_%s" % engine_name]() def downgrade(engine_name): globals()["downgrade_%s" % engine_name]() def upgrade_data_broker(): pass def downgrade_data_broker(): pass

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python/edna/src/edna/emit/StdoutEmit.py

prernaagarwal/cs6235Project

3

75778

from edna.emit import BaseEmit from sys import stdout class StdoutEmit(BaseEmit): """An Emitter that writes to Stdout""" def write(self): """Writes the message to the standard output. Ideally, the message should be a string. This is useful only for debugging.""" # NOT an efficient method, but really, who uses this for a real Job? for buffer_idx in range(self.emit_buffer_index+1): print(self.emit_buffer[buffer_idx], file=stdout)

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tests/creditcrawler_test.py

Mivinci/cqupt-piper

3

16606

import requests from bs4 import BeautifulSoup from prettytable import PrettyTable # html = requests.get( # 'http://jwzx.cqu.pt/student/xkxfTj.php', # cookies={'PHPSESSID': 'o2r2fpddrj892dp1ntqddcp2hv'}).text # soup = BeautifulSoup(html, 'html.parser') # for tr in soup.find('table', {'id': 'AxfTjTable'}).findAll('tr')[1:]: # tds = tr.findAll('td') # print(tds[1:5]) table = PrettyTable(['aaa', 'bbb']) print(table)

[ 1, 1053, 7274, 13, 3166, 24512, 29946, 1053, 25685, 29903, 1132, 13, 3166, 5051, 2371, 1053, 4721, 4349, 3562, 13, 13, 29937, 3472, 353, 7274, 29889, 657, 29898, 13, 29937, 268, 525, 1124, 597, 29926, 29893, 29920, 29916, 29889, 29883, 339, 29889, 415, 29914, 18945, 29914, 29916, 29895, 24660, 29911, 29926, 29889, 1961, 742, 13, 29937, 268, 21046, 3790, 29915, 17130, 1660, 1799, 1367, 2396, 525, 29877, 29906, 29878, 29906, 18091, 1289, 29878, 29926, 29947, 29929, 29906, 6099, 29896, 593, 29939, 1289, 6814, 29906, 29882, 29894, 29915, 7690, 726, 13, 13, 29937, 22300, 353, 25685, 29903, 1132, 29898, 1420, 29892, 525, 1420, 29889, 16680, 1495, 13, 13, 29937, 363, 534, 297, 22300, 29889, 2886, 877, 2371, 742, 11117, 333, 2396, 525, 29909, 24660, 29911, 29926, 3562, 29915, 7690, 2886, 3596, 877, 509, 29861, 29896, 29901, 5387, 13, 29937, 268, 260, 6289, 353, 534, 29889, 2886, 3596, 877, 1594, 1495, 13, 29937, 268, 1596, 29898, 1594, 29879, 29961, 29896, 29901, 29945, 2314, 13, 13, 13, 2371, 353, 4721, 4349, 3562, 18959, 7340, 29874, 742, 525, 1327, 29890, 11287, 13, 13, 2158, 29898, 2371, 29897, 2 ]

antlr/SevenBillionHumansParser.py

behrmann/7billionhumans

45

28907

from antlr4 import * from antlr4.error.ErrorListener import ErrorListener from antlr.SBHasmLexer import SBHasmLexer from antlr.SBHasmListener import SBHasmListener from antlr.SBHasmParser import SBHasmParser class MyErrorListener(ErrorListener): def __init__(self): super(MyErrorListener, self).__init__() def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e): raise Exception("SyntaxError in {},{} msg={}".format(line, column, msg)) def reportAmbiguity(self, recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs): raise Exception("reportAmbiguity") def reportAttemptingFullContext(self, recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs): raise Exception("reportAttemptingFullContext") def reportContextSensitivity(self, recognizer, dfa, startIndex, stopIndex, prediction, configs): raise Exception("reportContextSensitivity") class SBHCodeSizeListener(SBHasmListener): def __init__(self): self.cmd_cnt = 0 def enterCmd(self, ctx): self.cmd_cnt += 1 def enterSonst(self, ctx): self.cmd_cnt += 1 class Pickup: def __init__(self, item): self.item = item def __str__(self): return "Pickup" class Mem: def __init__(self, slot): self.slot = slot def __str__(self): return self.slot class Dir: def __init__(self, direction): self.dir = direction class SevenBillionHumansParser: def __init__(self, filepath=None, source=None): if source: self.parse(InputStream(source)) elif filepath: self.parse(FileStream(filepath)) def parse(self, source_stream): lexer = SBHasmLexer(source_stream) stream = CommonTokenStream(lexer) parser = SBHasmParser(stream) # parser._listeners = [ MyErrorListener() ] tree = parser.asm() printer = SBHCodeSizeListener() walker = ParseTreeWalker() walker.walk(printer, tree) self.cmd_size = printer.cmd_cnt if __name__ == '__main__': s = SevenBillionHumansParser("../solutions/55 - Data Flowers/size-10_speed-23.asm")

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analyzer/codechecker_analyzer/analyzers/clangsa/ctu_autodetection.py

ryankurte/codechecker

1,601

97498

# ------------------------------------------------------------------------- # # Part of the CodeChecker project, under the Apache License v2.0 with # LLVM Exceptions. See LICENSE for license information. # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception # # ------------------------------------------------------------------------- """ Clang Static Analyzer related functions. """ import subprocess from codechecker_common.logger import get_logger from codechecker_analyzer import host_check from codechecker_analyzer.analyzers.clangsa import clang_options, version LOG = get_logger('analyzer.clangsa') CTU_ON_DEMAND_OPTION_NAME = 'ctu-invocation-list' def invoke_binary_checked(binary_path, args=None, environ=None): """ Invoke the binary with the specified args, and return the output if the command finished running with zero exit code. Return False otherwise. Possible usage can be used to check the existence binaries. :param binary_path: The path to the executable to invoke :param args: The arguments of the invocation :type binary_path: str :type args: list :rtype str """ args = args or [] invocation = [binary_path] invocation.extend(args) try: output = subprocess.check_output( invocation, env=environ, encoding="utf-8", errors="ignore") except (subprocess.CalledProcessError, OSError) as e: LOG.debug('Command invocation failed because of non-zero exit code!' 'Details: %s', str(e)) return False return output class CTUAutodetection: """ CTUAutodetection is responsible for providing the availability information of CTU feature, the the relevant mapping tool path and the mapping file name. """ def __init__(self, analyzer_binary, environ): self.__analyzer_binary = analyzer_binary self.environ = environ self.__analyzer_version_info = None if self.__analyzer_binary is None: LOG.debug( 'Trying to detect CTU capability, but analyzer binary is not ' 'set!') return None analyzer_version = invoke_binary_checked( self.__analyzer_binary, ['--version'], self.environ) if analyzer_version is False: LOG.debug('Failed to invoke command to get Clang version!') return None version_parser = version.ClangVersionInfoParser(self.__analyzer_binary) version_info = version_parser.parse(analyzer_version) if not version_info: LOG.debug('Failed to parse Clang version information!') return None self.__analyzer_version_info = version_info @property def analyzer_version_info(self): """ Returns the relevant parameters of the analyzer by parsing the output of the analyzer binary when called with version flag. """ if not self.__analyzer_version_info: return False return self.__analyzer_version_info @property def major_version(self): """ Returns the major version of the analyzer, which is used for CTU analysis. """ return self.analyzer_version_info.major_version @property def installed_dir(self): """ Returns the installed directory of the analyzer, which is used for CTU analysis. """ return self.analyzer_version_info.installed_dir @property def mapping_tool_path(self): """Return the path to the mapping tool.""" tool_path, _ = clang_options.ctu_mapping(self.analyzer_version_info) if tool_path: return tool_path return False @property def display_progress(self): """ Return analyzer args if it is capable to display ctu progress. Returns None if the analyzer can not display ctu progress. The ctu display progress arguments depend on the clang analyzer version. """ if not self.analyzer_version_info: return None ctu_display_progress_args = ['-Xclang', '-analyzer-config', '-Xclang', 'display-ctu-progress=true'] ok = host_check.has_analyzer_config_option( self.__analyzer_binary, "display-ctu-progress", self.environ) if not ok: return None return ctu_display_progress_args @property def mapping_file_name(self): """ Returns the installed directory of the analyzer, which is used for CTU analysis. """ _, mapping_file_name = \ clang_options.ctu_mapping(self.analyzer_version_info) if mapping_file_name: return mapping_file_name return False @property def is_ctu_capable(self): """ Detects if the current clang is CTU compatible. Tries to autodetect the correct one based on clang version. """ tool_path = self.mapping_tool_path if not tool_path: return False return invoke_binary_checked(tool_path, ['-version'], self.environ) \ is not False @property def is_on_demand_ctu_available(self): """ Detects if the current Clang supports on-demand parsing of ASTs for CTU analysis. """ analyzer_options = invoke_binary_checked( self.__analyzer_binary, ['-cc1', '-analyzer-config-help'], self.environ) if analyzer_options is False: return False return CTU_ON_DEMAND_OPTION_NAME in analyzer_options

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stsc_autograd.py

NathanHowell/STSC

27

162068

from autograd import numpy as npy from functools import reduce from scipy.optimize import minimize from autograd import grad def generate_Givens_rotation(i, j, theta, size): g = npy.eye(size) c = npy.cos(theta) s = npy.sin(theta) g[i, i] = 0 g[j, j] = 0 g[j, i] = 0 g[i, j] = 0 ii_mat = npy.zeros_like(g) ii_mat[i, i] = 1 jj_mat = npy.zeros_like(g) jj_mat[j, j] = 1 ji_mat = npy.zeros_like(g) ji_mat[j, i] = 1 ij_mat = npy.zeros_like(g) ij_mat[i, j] = 1 return g + c * ii_mat + c * jj_mat + s * ji_mat - s * ij_mat def generate_U_list(ij_list, theta_list, size): return [generate_Givens_rotation(ij[0], ij[1], theta, size) for ij, theta in zip(ij_list, theta_list)] def get_rotation_matrix(X, C): ij_list = [(i, j) for i in range(C) for j in range(C) if i < j] def cost(theta_list): U_list = generate_U_list(ij_list, theta_list, C) R = reduce(npy.dot, U_list, npy.eye(C)) Z = X.dot(R) M = npy.max(Z, axis=1, keepdims=True) return npy.sum((Z / M) ** 2) theta_list_init = npy.array([0.0] * int(C * (C - 1) / 2)) opt = minimize(cost, x0=theta_list_init, method='CG', jac=grad(cost), options={'disp': False}) return opt.fun, reduce(npy.dot, generate_U_list(ij_list, opt.x, C), npy.eye(C))

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grpc_api_client/grpc/api.py

djtaylor/python-grpc-api-client

0

184513

<reponame>djtaylor/python-grpc-api-client import json from importlib import import_module from google.protobuf.json_format import MessageToJson class gRPC_API_Response(object): """ Class object for handling responses from Dex gRPC server. """ def __init__(self, response, output_handler, fields): self.json = json.loads(MessageToJson(response)) self.protobuf = output_handler(**self._get_field_values(response, fields)) def _get_field_values(self, response, fields): """ Return an object of field/key values from a request object post-request. """ response_fields = {} for field in fields: if hasattr(response, field[0]): response_fields[field[0]] = getattr(response, field[0]) return response_fields class gRPC_API_Method_IO_Map(object): """ Map the i/o type (from the descriptor) and the *pb2.py file """ def __init__(self, io_type, io_handler): self.type = io_type self.handler = io_handler class gRPC_API_Path(object): """ Class object for storing API path attributes and objects. """ def __init__(self, method, settings): """ Initialize and store the stub object for later use. """ self.name = method['name'] # Client settings self._settings = settings # Input and output objects self.input = gRPC_API_Method_IO_Map(method['input_type'], method['input_handler']) self.output = gRPC_API_Method_IO_Map(method['output_type'], method['output_handler']) # Stub instance self.stub = self._settings.bindings.stub_object def input_fields(self): """ Return an iterator for available input fields. """ return self.input.type.fields_by_name.items() def __call__(self, **kwargs): """ Call the underlying stub method for this API path. """ response = getattr(self.stub, self.name)(self.input.handler(**kwargs)) return gRPC_API_Response(response, self.output.handler, self.input_fields()) class gRPC_API_Collection(object): """ Class object for storing API path objects. """ def __init__(self, settings): """ :param settings: The generated settings object :type settings: gRPC_API_Client_Settings """ self._settings = settings self._methods = [] def __iter__(self): """ Return a dictionary of gRPC methods. """ for method_name in self._methods: yield(method_name, getattr(self, method_name)) def add(self, method): """ Add a new gRPC method. :param service: The generated gRPC service method object :type name: str """ self._methods.append(method['name']) # Store the API method setattr(self, method['name'], gRPC_API_Path(method, self._settings))

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data_scout/transformations/math_custom.py

janthiemen/data_scout

0

5718

<filename>data_scout/transformations/math_custom.py from __future__ import division from .transformation import Transformation from pyparsing import (Literal, CaselessLiteral, Word, Combine, Group, Optional, ZeroOrMore, Forward, nums, alphas, oneOf) import math import re import operator __author__ = '<NAME>' __version__ = '$Revision: 0.0 $' __date__ = '$Date: 2009-03-20 $' __source__ = '''http://pyparsing.wikispaces.com/file/view/fourFn.py http://pyparsing.wikispaces.com/message/view/home/15549426 ''' __note__ = ''' All I've done is rewrap Paul McGuire's fourFn.py as a class, so I can use it more easily in other places. ''' class Custom(Transformation): """ Most of this code comes from the fourFn.py pyparsing example """ title = "Custom equation" key = "Math equation" fields = { "equation": {"name": "Equation", "type": "string", "help": "The equation to evaluate. Column values should be entered as {COLUMN NAME}", "required": True, "input": "text", "default": ""}, "output": {"name": "Output column", "type": "string", "input": "text", "required": True, "help": "The name of the (newly created) column that contains the results", "default": ""}, } def __init__(self, arguments: dict, sample_size: int, example: dict = None): """ Initialize the transformation with the given parameters. expop :: '^' multop :: '*' | '/' addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI | E | real | fn '(' expr ')' | '(' expr ')' factor :: atom [ expop factor ]* term :: factor [ multop factor ]* expr :: term [ addop term ]* Arguments: arguments {dict} -- The arguments """ super().__init__(arguments, sample_size, example) self.equation = arguments["equation"] self.output = arguments["output"] point = Literal(".") e = CaselessLiteral("E") fnumber = Combine(Word("+-" + nums, nums) + Optional(point + Optional(Word(nums))) + Optional(e + Word("+-" + nums, nums))) ident = Word(alphas, alphas + nums + "_$") plus = Literal("+") minus = Literal("-") mult = Literal("*") div = Literal("/") mod = Literal("%") lpar = Literal("(").suppress() rpar = Literal(")").suppress() addop = plus | minus multop = mult | div | mod expop = Literal("^") pi = CaselessLiteral("PI") expr = Forward() atom = ((Optional(oneOf("- +")) + (ident + lpar + expr + rpar | pi | e | fnumber).setParseAction(self.push_first)) | Optional(oneOf("- +")) + Group(lpar + expr + rpar) ).setParseAction(self.push_u_minus) # by defining exponentiation as "atom [ ^ factor ]..." instead of # "atom [ ^ atom ]...", we get right-to-left exponents, instead of left-to-right # that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor << atom + \ ZeroOrMore((expop + factor).setParseAction(self.push_first)) term = factor + \ ZeroOrMore((multop + factor).setParseAction(self.push_first)) expr << term + \ ZeroOrMore((addop + term).setParseAction(self.push_first)) # addop_term = ( addop + term ).setParseAction( self.push_first ) # general_term = term + ZeroOrMore( addop_term ) | OneOrMore( addop_term) # expr << general_term self.bnf = expr # map operator symbols to corresponding arithmetic operations epsilon = 1e-12 self.opn = {"+": operator.add, "-": operator.sub, "*": operator.mul, "%": operator.mod, "/": operator.truediv, "^": operator.pow} self.expr_stack = None self.fn = {"sin": math.sin, "sinh": math.sinh, "cos": math.cos, "cosh": math.cosh, "tan": math.tan, "tanh": math.tanh, "exp": math.exp, "sqrt": math.sqrt, "radians": math.radians, "degrees": math.degrees, "sign": lambda x: 0 if x == 0 else x / abs(x), "log": math.log10, "ln": math.log, "abs": abs, "trunc": lambda a: int(a), "round": round, "floor": math.floor, "ceil": math.ceil, "sgn": lambda a: abs(a) > epsilon and cmp(a, 0) or 0} def push_first(self, strg, loc, toks): self.expr_stack.append(toks[0]) def push_u_minus(self, strg, loc, toks): if toks and toks[0] == '-': self.expr_stack.append('unary -') def evaluate_stack(self, s): op = s.pop() if op == 'unary -': return -self.evaluate_stack(s) if op in "+-*/^%": op2 = self.evaluate_stack(s) op1 = self.evaluate_stack(s) return self.opn[op](op1, op2) elif op == "PI": return math.pi # 3.1415926535 elif op == "E": return math.e # 2.718281828 elif op in self.fn: return self.fn[op](self.evaluate_stack(s)) elif op[0].isalpha(): return 0 else: return float(op) def eval(self, num_string, parse_all=True): self.expr_stack = [] results = self.bnf.parseString(num_string, parse_all) val = self.evaluate_stack(self.expr_stack[:]) return val def __call__(self, row, index: int): """This class is called on each row. Arguments: row {dict} -- The complete row Returns: dict -- The row, including the extra output column """ row[self.output] = self.eval(re.sub(r'{(\w+)}', lambda x: str(row.get(x.group(1), 0)), self.equation)) return row, index

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web-server/webserver/urls.py

ApLight/groenlandicus

0

25625

<reponame>ApLight/groenlandicus<gh_stars>0 """webserver URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.11/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url from django.contrib import admin from account import views as account_views from quiz import views as quiz_views from entry import views as entry_views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^login/', account_views.login, name="login"), url(r'^feed/', account_views.feed_seolgi, name="feed"), url(r'^quizzes/', quiz_views.get_quizzes, name="get_quizzes"), url(r'^index/', entry_views.get_index, name="get_index"), ] quiz_views.update_quizzes() # When the project starts, execute "update_quizzes".

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day-11/package_demo/package1/pathprint.py

JohnLockwood/100-days-of-python

0

151207

<filename>day-11/package_demo/package1/pathprint.py """Demo module to show python search path""" import sys import pprint def print_path(): """Pretty print the system path""" print("Printing the search path:") pprint.pprint(sys.path)

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invoicer/_units/forms.py

mtik00/invoicer

0

44231

<filename>invoicer/_units/forms.py from flask_wtf import FlaskForm from wtforms import StringField, FloatField from wtforms.validators import DataRequired class UnitForm(FlaskForm): description = StringField(u'Description', validators=[DataRequired()]) unit_price = FloatField(u'Unit Price', validators=[DataRequired()]) units = StringField(u'Units (e.g. hr, day, etc)')

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tests/test_transform_compile_releases_from_records.py

matiasSanabria/kingfisher-process

1

132254

import datetime import os import sqlalchemy as sa from ocdskingfisherprocess.store import Store from ocdskingfisherprocess.transform import TRANSFORM_TYPE_COMPILE_RELEASES from ocdskingfisherprocess.transform.compile_releases import CompileReleasesTransform from tests.base import BaseDataBaseTest class TestTransformCompileReleasesFromRecords(BaseDataBaseTest): def _setup_collections_and_data_run_transform(self, filename, load_a_second_time=False): # Make source collection source_collection_id = self.database.get_or_create_collection_id("test", datetime.datetime.now(), False) source_collection = self.database.get_collection(source_collection_id) # Load some data store = Store(self.config, self.database) store.set_collection(source_collection) json_filename = os.path.join(os.path.dirname( os.path.realpath(__file__)), 'fixtures', filename ) store.store_file_from_local("test.json", "http://example.com", "record_package", "utf-8", json_filename) if load_a_second_time: store.store_file_from_local("test2.json", "http://example.com", "record_package", "utf-8", json_filename) # Make destination collection destination_collection_id = self.database.get_or_create_collection_id( source_collection.source_id, source_collection.data_version, source_collection.sample, transform_from_collection_id=source_collection_id, transform_type=TRANSFORM_TYPE_COMPILE_RELEASES) destination_collection = self.database.get_collection(destination_collection_id) # transform! Nothing should happen because source is not finished transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 0 == result.rowcount # Mark source collection as finished self.database.mark_collection_store_done(source_collection_id) # transform! This should do the work. transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() return source_collection_id, source_collection, destination_collection_id, destination_collection def test_compiled_release(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check it's the compiled release in the record table # Because releases are linked, the only way to get data is to take the compiled release compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table])\ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings == [ 'This already had a compiledRelease in the record! It was passed through this transform unchanged.', ] # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 0 # transform again! This should be fine transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # destination collection should be closed destination_collection = self.database.get_collection(destination_collection_id) assert destination_collection.store_end_at is not None def test_no_compiled_release_linked_records_so_cant_do_anything(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_linked_releases_not_compiled.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 0 == result.rowcount # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 1 assert 'OCID ocds-213czf-000-00001 could not be compiled because at least one release in the releases ' +\ 'array is a linked release or there are no releases with dates, ' +\ 'and the record has neither a compileRelease nor a release with a tag of "compiled".' == \ notes[0].note def test_transform_compiles(self): # This data files has full releases and nothing else, so the transform should compile itself using ocdsmerge source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_releases_not_compiled.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check we've compiled something here. # The only way it could get data here is if it compiled it itself. compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table])\ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings is None # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 0 # transform again! This should be fine transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # destination collection should be closed destination_collection = self.database.get_collection(destination_collection_id) assert destination_collection.store_end_at is not None def test_two_records_same_ocid(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform( 'sample_1_1_record_releases_not_compiled.json', load_a_second_time=True ) # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check it's the compiled release in the record table # Because releases are linked, the only way to get data is to take the compiled release compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table]) \ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings == [ 'There are multiple records for this OCID! The record to pass through was selected arbitrarily.', ] # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 0 # transform again! This should be fine transform = CompileReleasesTransform(self.config, self.database, destination_collection) transform.process() # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # destination collection should be closed destination_collection = self.database.get_collection(destination_collection_id) assert destination_collection.store_end_at is not None def test_no_dates(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_releases_not_compiled_no_dates.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 0 == result.rowcount # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 1 assert 'OCID ocds-213czf-000-00001 could not be compiled ' +\ 'because at least one release in the releases array is a ' +\ 'linked release or there are no releases with dates, ' +\ 'and the record has neither a compileRelease nor a release with a tag of "compiled".' == \ notes[0].note def test_some_dates(self): source_collection_id, source_collection, destination_collection_id, destination_collection = \ self._setup_collections_and_data_run_transform('sample_1_1_record_releases_not_compiled_some_dates.json') # check with self.database.get_engine().begin() as connection: s = sa.sql.select([self.database.compiled_release_table]) result = connection.execute(s) assert 1 == result.rowcount # Check a couple of fields just to sanity check it's the compiled release in the record table # Because releases are linked, the only way to get data is to take the compiled release compiled_release = result.fetchone() data = self.database.get_data(compiled_release['data_id']) assert 'ocds-213czf-000-00001-2011-01-10T09:30:00Z' == data.get('id') assert '2011-01-10T09:30:00Z' == data.get('date') assert 'ocds-213czf-000-00001' == data.get('ocid') # Check warnings s = sa.sql.select([self.database.collection_file_item_table]) \ .where(self.database.collection_file_item_table.c.id == compiled_release['collection_file_item_id']) result_file_item = connection.execute(s) assert 1 == result_file_item.rowcount collection_file_item = result_file_item.fetchone() assert collection_file_item.warnings == [ 'This OCID had some releases without a date element. We have compiled all other releases.'] # Check collection notes notes = self.database.get_all_notes_in_collection(destination_collection_id) assert len(notes) == 0

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grAdapt/sampling/equidistributed/Mitchell.py

mkduong-ai/grAdapt

25

24506

# Python Standard Libraries import warnings # grAdapt # from .base import Equidistributed from .MaximalMinDistance import MaximalMinDistance class Mitchell(MaximalMinDistance): """ [Mitchell et al., 1991], Spectrally optimal sampling for distribution ray tracing """ def __init__(self, m=3): """ Parameters ---------- m : integer number of candidates = m * n """ warnings.warn('Mitchell\' best candidate has a time complexity of O(n^3) ' 'and memory issues when dealing with higher sample numbers. ' 'Use MaximalMinDistance instead which is an improved version ' 'with linear time complexity.', ResourceWarning) super().__init__(n_candidates=m, window_size=0) self.candidates_set = False def sample(self, bounds, n, x_history=None): """Samples low discrepancy/equidistributed sequences according to Mitchell. Method has to handle with new bounds and n. Parameters ---------- bounds : list of tuples or list of grAdapt.space.datatype.base Each tuple in the list defines the bounds for the corresponding variable Example: [(1, 2), (2, 3), (-1, 4)...] n : int number of points to be sampled x_history : array-like (2d) History points. Consider those to prevent sampling in dense regions. Returns ------- array-like (n, len(bounds)) Returns a 2D array. dim is the dimension of a single point Each row corresponds to a single point. Each column corresponds to a dimension. """ if self.candidates_set is False: self.n_candidates = self.n_candidates * n self.candidates_set = True return super().sample(bounds, n, x_history)

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cubedash/_product.py

vconrado/datacube-explorer

0

3561

<reponame>vconrado/datacube-explorer import logging from datetime import timedelta from flask import Blueprint, Response, abort, redirect, url_for from cubedash import _model, _utils, _utils as utils _LOG = logging.getLogger(__name__) bp = Blueprint("product", __name__) @bp.route("/about.csv") def legacy_about_csv(): return redirect(".storage_csv") @bp.route("/audit/storage.csv") def storage_csv(): """Get the product storage table as a CSV""" product_locations = _model.STORE.products_location_samples_all() return utils.as_csv( filename_prefix="product-information", headers=( "name", "count", "locations", "license", "definition", "summary_time", "metadata_type", ), rows=( ( product.name, summary.dataset_count, [ location.common_prefix for location in (product_locations.get(product.name) or []) ], _utils.product_license(product), url_for("product.raw_product_doc", name=product.name, _external=True), summary.last_refresh_time, product.metadata_type.name, ) for product, summary in _model.get_products_with_summaries() ), ) @bp.route("/products.txt") def product_list_text(): # This is useful for bash scripts when we want to loop products :) return Response( "\n".join(t.name for t in _model.STORE.all_dataset_types()), content_type="text/plain", ) @bp.route("/metadata-types.txt") def metadata_type_list_text(): # This is useful for bash scripts when we want to loop them :) return Response( "\n".join(t.name for t in _model.STORE.all_metadata_types()), content_type="text/plain", ) @bp.route("/audit/storage") def storage_page(): product_locations = _model.STORE.products_location_samples_all() return utils.render( "storage.html", product_summary_and_location=[ (product, summary, (product_locations.get(product.name) or [])) for product, summary in _model.get_products_with_summaries() ], ) @bp.route("/product") def product_redirect(): """ If people remove the name from a "/product/<name>" url, take them somewhere useful """ return redirect(url_for(".products_page")) @bp.route("/products") def products_page(): return utils.render( "products.html", ) @bp.route("/metadata-types") def metadata_types_page(): return utils.render( "metadata-types.html", ) @bp.route("/product/<name>.odc-product.yaml") def legacy_raw_product_doc(name): return redirect(url_for(".raw_product_doc", name=name)) @bp.route("/products/<name>.odc-product.yaml") def raw_product_doc(name): product = _model.STORE.index.products.get_by_name(name) if not product: abort(404, f"Unknown product {name!r}") ordered_metadata = utils.prepare_document_formatting( product.definition, "Product", include_source_url=True ) return utils.as_yaml(ordered_metadata) @bp.route("/metadata-type/<name>") def legacy_metadata_type_page(name): return redirect(url_for(".metadata_type_page", name=name)) @bp.route("/metadata-types/<name>") def metadata_type_page(name): metadata_type = _model.STORE.index.metadata_types.get_by_name(name) if not metadata_type: abort(404, f"Unknown metadata type {name!r}") ordered_metadata = utils.prepare_document_formatting(metadata_type.definition) products_using_it = sorted( ( p for p in _model.STORE.index.products.get_all() if p.metadata_type.name == name ), key=lambda p: p.name, ) return utils.render( "metadata-type.html", metadata_type=metadata_type, metadata_doc=ordered_metadata, products_using_it=products_using_it, ) @bp.route("/metadata-type/<name>.odc-type.yaml") def legacy_metadata_type_doc(name): return redirect(url_for(".raw_metadata_type_doc", name=name)) @bp.route("/metadata-types/<name>.odc-type.yaml") def raw_metadata_type_doc(name): metadata_type = _model.STORE.index.metadata_types.get_by_name(name) if not metadata_type: abort(404, f"Unknown metadata type {name!r}") ordered_metadata = utils.prepare_document_formatting( metadata_type.definition, "Metadata Type", include_source_url=True ) return utils.as_yaml(ordered_metadata) @bp.route("/products.odc-product.yaml") def raw_all_products_doc(): resp = utils.as_yaml( *( utils.prepare_document_formatting( product.definition, f"Product {product.name}", include_source_url=url_for( ".raw_product_doc", name=product.name, _external=True ), ) for product in _model.STORE.all_dataset_types() ) ) # Add Explorer ID to the download filename if they have one. utils.suggest_download_filename( resp, prefix="products", suffix=".odc-product.yaml", ) return resp @bp.route("/metadata-types.odc-type.yaml") def raw_all_metadata_types_doc(): resp = utils.as_yaml( *( utils.prepare_document_formatting( type_.definition, f"Metadata Type {type_.name}", include_source_url=url_for( ".raw_metadata_type_doc", name=type_.name, _external=True ), ) for type_ in _model.STORE.all_metadata_types() ), ) # Add Explorer ID to the download filename if they have one. utils.suggest_download_filename( resp, prefix="metadata-types", suffix=".odc-type.yaml", ) return resp def _iso8601_duration(tdelta: timedelta): """ Format a timedelta as an iso8601 duration >>> _iso8601_duration(timedelta(seconds=0)) 'PT0S' >>> _iso8601_duration(timedelta(seconds=1)) 'PT1S' >>> _iso8601_duration(timedelta(seconds=23423)) 'PT6H30M23S' >>> _iso8601_duration(timedelta(seconds=4564564556)) 'P52830DT14H35M56S' """ all_secs = tdelta.total_seconds() secs = int(all_secs % 60) h_m_s = ( int(all_secs // 3600 % 24), int(all_secs // 60 % 60), secs if secs % 1 != 0 else int(secs), ) parts = ["P"] days = int(all_secs // 86400) if days: parts.append(f"{days}D") if any(h_m_s): parts.append("T") if all_secs: for val, name in zip(h_m_s, ["H", "M", "S"]): if val: parts.append(f"{val}{name}") else: parts.append("T0S") return "".join(parts)

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model.py

melorian94/minerl_imitation_learning

18

166230

import torch from torch import nn from torch.nn import functional as F import math class Network(nn.Module): def __init__(self, num_actions, image_channels, vec_size, cnn_module, hidden_size=256, dueling=True, double_channels=False): super().__init__() self.num_actions = num_actions self.dueling = dueling self.cnn = cnn_module(image_channels) self.conv_output_size = self.cnn.output_size self.fc_im = nn.Linear(self.conv_output_size, hidden_size) if not double_channels: vec_channel_size = 128 else: vec_channel_size = 256 self.fc_vec = nn.Linear(vec_size, vec_channel_size) self.fc_h_a = nn.Linear(hidden_size + vec_channel_size, hidden_size) self.fc_a = nn.Linear(hidden_size, num_actions) if self.dueling: self.fc_h_v = nn.Linear(hidden_size + vec_channel_size, hidden_size) self.fc_v = nn.Linear(hidden_size, 1) def forward(self, x, vec): x = self.cnn(x) x = x.view(-1, self.conv_output_size) x = self.fc_im(x) vec = self.fc_vec(vec) x = F.relu(torch.cat((x, vec), 1)) output = self.fc_a(F.relu(self.fc_h_a(x))) if self.dueling: v = self.fc_v(F.relu(self.fc_h_v(x))) output = v + output - output.mean(1, keepdim=True) return output class AtariCNN(nn.Module): def __init__(self, input_channels): super().__init__() self.conv_layers = nn.Sequential(nn.Conv2d(input_channels, 32, 8, stride=4, padding=0), nn.ReLU(), nn.Conv2d(32, 64, 4, stride=2, padding=0), nn.ReLU(), nn.Conv2d(64, 64, 3, stride=1, padding=0), nn.ReLU()) self.output_size = 64 * 4 * 4 def forward(self, x): return self.conv_layers(x) class ImpalaResNetCNN(nn.Module): class _ImpalaResidual(nn.Module): def __init__(self, depth): super().__init__() self.conv1 = nn.Conv2d(depth, depth, 3, padding=1) self.conv2 = nn.Conv2d(depth, depth, 3, padding=1) def forward(self, x): out = F.relu(x) out = self.conv1(out) out = F.relu(out) out = self.conv2(out) return out + x def __init__(self, input_channels): super().__init__() depth_in = input_channels layers = [] for depth_out in [32, 64, 64]: layers.extend([ nn.Conv2d(depth_in, depth_out, 3, padding=1), nn.MaxPool2d(3, stride=2, padding=1), self._ImpalaResidual(depth_out), self._ImpalaResidual(depth_out), ]) depth_in = depth_out self.conv_layers = nn.Sequential(*layers, nn.ReLU()) self.output_size = math.ceil(64 / 8) ** 2 * depth_in def forward(self, x): return self.conv_layers(x) class FixupResNetCNN(nn.Module): """source: https://github.com/unixpickle/obs-tower2/blob/master/obs_tower2/model.py""" class _FixupResidual(nn.Module): def __init__(self, depth, num_residual): super().__init__() self.conv1 = nn.Conv2d(depth, depth, 3, padding=1, bias=False) self.conv2 = nn.Conv2d(depth, depth, 3, padding=1, bias=False) for p in self.conv1.parameters(): p.data.mul_(1 / math.sqrt(num_residual)) for p in self.conv2.parameters(): p.data.zero_() self.bias1 = nn.Parameter(torch.zeros([depth, 1, 1])) self.bias2 = nn.Parameter(torch.zeros([depth, 1, 1])) self.bias3 = nn.Parameter(torch.zeros([depth, 1, 1])) self.bias4 = nn.Parameter(torch.zeros([depth, 1, 1])) self.scale = nn.Parameter(torch.ones([depth, 1, 1])) def forward(self, x): x = F.relu(x) out = x + self.bias1 out = self.conv1(out) out = out + self.bias2 out = F.relu(out) out = out + self.bias3 out = self.conv2(out) out = out * self.scale out = out + self.bias4 return out + x def __init__(self, input_channels, double_channels=False): super().__init__() depth_in = input_channels layers = [] if not double_channels: channel_sizes = [32, 64, 64] else: channel_sizes = [64, 128, 128] for depth_out in channel_sizes: layers.extend([ nn.Conv2d(depth_in, depth_out, 3, padding=1), nn.MaxPool2d(3, stride=2, padding=1), self._FixupResidual(depth_out, 8), self._FixupResidual(depth_out, 8), ]) depth_in = depth_out layers.extend([ self._FixupResidual(depth_in, 8), self._FixupResidual(depth_in, 8), ]) self.conv_layers = nn.Sequential(*layers, nn.ReLU()) self.output_size = math.ceil(64 / 8) ** 2 * depth_in def forward(self, x): return self.conv_layers(x)

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pythonAlgorithm/linklist/reverse_linklist.py

Sky-zzt/lintcodePractice

1

154446

<reponame>Sky-zzt/lintcodePractice class Node: def __init__(self, val=0): self.value = val self.next = None class LinkList: c = 10 def __init__(self, *args): self.val = (*args,) self.head = self.__constructLinklist() #self.printLinklist(self.head) # self.pre = self.revserlinklist() # self.printLinklist(self.pre) def printLinklist(self, head): while head is not None: print(head.val) head = head.next def __constructLinklist(self): dummyNode = Node() head = Node(self.val[0]) dummyNode.next = head for i in range(1, len(self.val)): next = Node(self.val[i]) head.next = next head = next return dummyNode.next def revserlinklist(self): pre = None while self.head is not None: next = self.head.next self.head.next = pre pre = self.head self.head = next return pre def revserFromM2N(self, m, n): #if m<0 or n<0 or n>self.linklength():return None head2=self.head head3=self.head head2.next.next.next=None self.printLinklist(self.head) def linklength(self): length = 0 while self.head is not None: self.head = self.head.next length += 1 return length a = LinkList(1, 2, 3, 4, 5) a.revserFromM2N(2,3)

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one_fm/grd/utils.py

ONE-F-M/One-FM

16

90158

# -*- coding: utf-8 -*- # encoding: utf-8 from __future__ import unicode_literals import frappe from frappe import _ from frappe.utils import today, add_days, get_url, date_diff, getdate from frappe.model.document import Document from frappe.utils import cstr, cint, get_fullname from frappe.utils import today, add_days, get_url from datetime import date from frappe.model.mapper import get_mapped_doc from dateutil.relativedelta import relativedelta from one_fm.api.notification import create_notification_log def todo_after_insert(doc, method): doctypes = ['Work Permit', 'Medical Insurance'] if doc.reference_type in doctypes and doc.reference_name and doc.owner and 'GRD Operator' in frappe.get_roles(doc.owner): if not frappe.db.get_value(doc.reference_type, doc.reference_name, 'grd_operator'): frappe.db.set_value(doc.reference_type, doc.reference_name, 'grd_operator', doc.owner) def sendmail_reminder_to_book_appointment_for_pifss(): #before 1 week of the new month today = date.today() first_day = today.replace(day=1) + relativedelta(months=1) if date_diff(first_day,today) == 7: operator = frappe.db.get_single_value('GRD Settings', 'default_grd_operator_pifss') supervisor = frappe.db.get_single_value('GRD Settings', 'default_grd_supervisor') if ('@' in operator): email_name = operator.split('@')[0] content = "<h4>Dear "+ email_name +",</h4><p>This month will end soon. Please make sure to book an apointment now for collecting PIFSS documents.</p>" content = content frappe.sendmail(recipients=[operator], sender=supervisor, subject="Book Apointment For PIFSS", content=content) def sendmail_reminder_to_collect_pifss_documents(): #before 1 day of the new month today = date.today() first_day = today.replace(day=1) + relativedelta(months=1) if date_diff(first_day,today) == 1: operator = frappe.db.get_single_value('GRD Settings', 'default_grd_operator') supervisor = frappe.db.get_single_value('GRD Settings', 'default_grd_supervisor') if ('@' in operator): email_name = operator.split('@')[0] content = "<h4>Dear "+ email_name +",</h4><p> This email is reminder for you to collect PIFSS documents.</p>" content = content frappe.sendmail(recipients=[operator], sender=supervisor, subject="Collect PIFSS Documents", content=content) @frappe.whitelist() def mappe_to_work_permit_cancellation(source_name, target_doc=None): pifss_103_record = frappe.get_doc('PIFSS Form 103',source_name) print(pifss_103_record.employee) doc = get_mapped_doc("PIFSS Form 103", source_name, { "PIFSS Form 103": { "doctype": "Work Permit", "field_map": { "attach_end_of_service_from_pifss_website":"end_of_service_screenshot", "date_of_acceptance":"date_of_application", "work_permit_type":"work_permit_type", "employee":"employee" } } }, target_doc) return doc @frappe.whitelist() def mappe_to_work_permit_registration(source_name, target_doc=None): pifss_103_record = frappe.get_doc('PIFSS Form 103',source_name) print(pifss_103_record.employee) doc = get_mapped_doc("PIFSS Form 103", source_name, { "PIFSS Form 103": { "doctype": "Work Permit", "field_map": { "attach_registration_from_pifss_website":"registration_from_pifss_website", "date_of_acceptance":"date_of_application", "work_permit_type":"work_permit_type", "employee":"employee" } } }, target_doc) return doc @frappe.whitelist() def mappe_to_mgrp(source_name, target_doc=None): work_permit_record = frappe.get_doc('Work Permit',source_name) print(work_permit_record.employee) doc = get_mapped_doc("Work Permit", source_name, { "Work Permit": { "doctype": "MGRP", "field_map": { "work_permit_type":"status", "employee":"employee", "first_name":"first_name", "civil_id":"civil_id", "last_name":"last_name", "employee_id":"employee_id", "end_of_service_date":"end_of_service_date", } } }, target_doc) return doc

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lastpass/blob.py

guilt/lastpass-python

1

130575

<gh_stars>1-10 # coding: utf-8 class Blob(object): def __init__(self, bytes_, key_iteration_count): self.bytes = bytes_ self.key_iteration_count = key_iteration_count def encryption_key(self, username, password): from . import fetcher return fetcher.make_key(username, password, self.key_iteration_count) def __eq__(self, other): return self.bytes == other.bytes and self.key_iteration_count == other.key_iteration_count

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tf_trace_publisher/src/tf_trace_publisher/text_publisher.py

ipa-lth/rviz_utils

2

1603343

<filename>tf_trace_publisher/src/tf_trace_publisher/text_publisher.py #!/usr/bin/env python import rospy from visualization_msgs.msg import Marker # Give ourselves the ability to run a dynamic reconfigure server. # from dynamic_reconfigure.server import Server as DynamicReconfigureServer # from ipa325_itasc_commons.cfg import textMarkerPublisher_paramsConfig as ConfigType class TextPublisher(): def __init__(self): # initalize textMarker = Marker() textMarker.header.frame_id = "/textFrame" textMarker.id = rospy.get_param('~id', 1) textMarker.type= Marker.TEXT_VIEW_FACING textMarker.action = Marker.MODIFY # textMarker.ns = tf_child # textMarker.scale.x = rospy.get_param('~scale_X', 0.01) # textMarker.scale.y = rospy.get_param('~scale_Y', 0.01) textMarker.scale.z = rospy.get_param('~scale_Z', 0.1) textMarker.text = 'x_' textMarker.color.r = rospy.get_param('~color_Red', 0.0) textMarker.color.g = rospy.get_param('~color_Green', 1.0) textMarker.color.b = rospy.get_param('~color_Blue', 0.0) textMarker.color.a = rospy.get_param('~color_Alpha', 1.0) rate = rospy.Rate(rospy.get_param('~rate', 5)) pub_markerLine = rospy.Publisher('visualization_marker', Marker) cnt = 0 maxCnt = 10 self.text = 'x_' while not rospy.is_shutdown(): if cnt < maxCnt: textMarker.action = Marker.MODIFY self.text += str(cnt) cnt += 1 else: self.text = 'x_' textMarker.action = Marker.DELETE cnt = 0 textMarker.text = self.text pub_markerLine.publish(textMarker) rate.sleep() if __name__ == '__main__': rospy.init_node('tf_textMarker_Publisher', anonymous=True) try: node = TextPublisher() except rospy.ROSInterruptException: pass

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progue/ai/actions/misc.py

BradyKieffer/progue

0

177051

""" Misc / Weird actions can go here """ import random from action import Action from progue.utils.actor_constants import ATTRIBUTE_IDLE, ATTR_VALUE, MAX_TURNS, CURRENT_TURN, RANGE class ActionIdle(Action): """ Used for lazy monsters """ def __init__(self, actor): Action.__init__(self, actor=actor, update_func=self.idle) self.actor.attributes[ATTRIBUTE_IDLE][ATTR_VALUE] = True rng = self.actor.attributes[ATTRIBUTE_IDLE][RANGE] self.percent_idle = max(min(rng[1], random.random()), rng[0]) self.curr_turn = self.actor.attributes[ATTRIBUTE_IDLE][CURRENT_TURN] self.max_turns = self.actor.attributes[ATTRIBUTE_IDLE][ MAX_TURNS] + random.randrange(-5, 5) def idle(self): if (self.curr_turn > self.max_turns) or (random.random() > self.percent_idle): self.complete = True self.actor.attributes[ATTRIBUTE_IDLE][ATTR_VALUE] = False self.curr_turn += 1

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tsdf/utils.py

changgyhub/semantic-tsdf

9

139659

<reponame>changgyhub/semantic-tsdf ''' TSDF utility functions. ''' import numpy as np # Get corners of 3D camera view frustum of depth image. def get_view_frustum(depth_im, cam_intr, cam_pose): im_h = depth_im.shape[0] im_w = depth_im.shape[1] max_depth = np.max(depth_im) view_frust_pts = np.array([(np.array([0, 0, 0, im_w, im_w]) - cam_intr[0, 2]) * np.array([0, max_depth, max_depth, max_depth, max_depth]) / cam_intr[0, 0], (np.array([0, 0, im_h, 0, im_h]) - cam_intr[1, 2]) * np.array([0, max_depth, max_depth, max_depth, max_depth]) / cam_intr[1, 1], np.array([0, max_depth, max_depth, max_depth, max_depth])]) view_frust_pts = np.dot(cam_pose[:3, :3], view_frust_pts) + np.tile(cam_pose[:3, 3].reshape(3, 1), (1, view_frust_pts.shape[1])) # from camera to world coordinates return view_frust_pts # Save 3D mesh to a polygon .ply file. def meshwrite(filename, verts, faces, norms, colors): # Write header. ply_file = open(filename,'w') ply_file.write("ply\n") ply_file.write("format ascii 1.0\n") ply_file.write("element vertex {:d}\n".format(verts.shape[0])) ply_file.write("property float x\n") ply_file.write("property float y\n") ply_file.write("property float z\n") ply_file.write("property float nx\n") ply_file.write("property float ny\n") ply_file.write("property float nz\n") ply_file.write("property uchar red\n") ply_file.write("property uchar green\n") ply_file.write("property uchar blue\n") ply_file.write("element face {:d}\n".format(faces.shape[0])) ply_file.write("property list uchar int vertex_index\n") ply_file.write("end_header\n") # Write vertex list. for i in range(verts.shape[0]): ply_file.write("{:f} {:f} {:f} {:f} {:f} {:f} {:d} {:d} {:d}\n".format( verts[i, 0], verts[i, 1], verts[i, 2], norms[i, 0], norms[i, 1], norms[i, 2], colors[i, 0], colors[i, 1], colors[i, 2])) # Write face list. for i in range(faces.shape[0]): ply_file.write("3 {:d} {:d} {:d}\n".format(faces[i, 0], faces[i, 1], faces[i, 2])) ply_file.close()

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11955, 29961, 29875, 29892, 29871, 29896, 1402, 11955, 29961, 29875, 29892, 29871, 29906, 12622, 13, 268, 13, 1678, 396, 14350, 3700, 1051, 29889, 13, 1678, 363, 474, 297, 3464, 29898, 8726, 29889, 12181, 29961, 29900, 29962, 1125, 13, 4706, 282, 368, 29918, 1445, 29889, 3539, 703, 29941, 12365, 29881, 29913, 12365, 29881, 29913, 12365, 29881, 1012, 29876, 1642, 4830, 29898, 8726, 29961, 29875, 29892, 29871, 29900, 1402, 17240, 29961, 29875, 29892, 29871, 29896, 1402, 17240, 29961, 29875, 29892, 29871, 29906, 12622, 13, 13, 1678, 282, 368, 29918, 1445, 29889, 5358, 580, 2 ]

PresentMonAnalysis.py

CarHol/PresentMonAnalysis

0

148635

<filename>PresentMonAnalysis.py import argparse import numpy as np import time import ntpath import style import ArrayUtils from bokeh.plotting import figure, output_file, show from bokeh.models import Span, Label, Tabs, Panel from bokeh.models.widgets import Div from bokeh.layouts import column, row, widgetbox from PresentMonResult import PresentMonResult, LoadFromCsv # Debug? debug = False # FUNCTIONS def CreateHistogram(data, width, histTitle, xLabel, yLabel, indicateMu=True): """ Generates a Bokeh histogram from a numpy array of data. :param data: Any numpy array of ints or floats :param width: Target rendering width :param histTitle: Chart title :param xLabel: X-axis label :param yLabel: Y-axis label :param indicateMu: Whether or not to draw a vertical line indicating the data mean :return: A Bokeh quad-graph with range from mu +/- (4*sigma). """ # Get data from result: # indicateMu = True, indicateSigma=0 # TODO: Add automatic fps conversion in PresentMonResult class # TODO: Add parallellization for large datasets mu = np.mean(data) sigma = np.std(data) # Create plot (hard coded settings for now) p = figure(title=histTitle, x_axis_label=xLabel, y_axis_label=yLabel) p.x_range.end = mu + 4 * sigma p.x_range.start = mu - 4 * sigma p.width = width p.toolbar.logo = None hist, edges = np.histogram(data, density=True, bins=min(2000, data.size)) p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color=style.histogramStyle['fill_color'], line_color=style.histogramStyle['line_color']) # Add markers for mean, stdev if indicateMu: muLine = Span(location=mu, dimension='height', line_color='red', line_width=1) p.renderers.extend([muLine]) return p def CreateTabbedHistogram(dataSet, width): """ Creates a panel containing two histograms, one with frametimes and one with framerate :param dataSet: A numpy array of frametimes in ms :param width: Target rendering width :return: A Bokeh panel with one tab for each histogram """ # Create the charts frequencyData = 1000. / np.array(dataSet) timeHistogram = CreateHistogram(dataSet, width, histTitle="Frametimes", xLabel="Frame time (ms)", yLabel="Relative frequency") frequencyHistogram = CreateHistogram(frequencyData, width, histTitle="Framerate", xLabel="Framerate (fps)", yLabel="Relative frequency") # Create panels timeTab = Panel(child=timeHistogram, title="Frametimes") frequencyTab = Panel(child=frequencyHistogram, title="FPS") # Create tabs return Tabs(tabs=[timeTab, frequencyTab]) def CreateLineDiagram(frameData, timeData, width, chartTitle, xLabel, yLabel): """ Creates a line diagram over time from a numpy array :param frameData: A numpy array containing framerate or frametimes :param timeData: A numpy array containing timestamps :param width: Target rendering width :param chartTitle: The title of the chart :param xLabel: X-axis label :param yLabel: Y-axis label :return: A Bokeh line diagram with frameData plotted against timeData """ # Generate figure p = figure(title=chartTitle, x_axis_label=xLabel, y_axis_label=yLabel) p.width = width p.toolbar.logo = None # Add line p.line(timeData, frameData, line_width=0.5) return p def CreateTabbedLineDiagram(dataSet, width): """ Generates a panel with two tabs, one for framerate and one for frametimes :param dataSet: A PresentMonResult object :param width: Target rendering width of the panel :return: A Bokeh panel containing two tabs """ # Create charts frequencyData = 1000. / dataSet.msBetweenPresents timeChart = CreateLineDiagram(dataSet.msBetweenPresents, dataSet.timeStamps, width, "Framtimes", "Runtime (s)", "Frame time (ms)") frequencyChart = CreateLineDiagram(frequencyData, dataSet.timeStamps, width, "Framerate", "Runtime (s)", "Framerate (fps)") # Create panels timeTab = Panel(child=timeChart, title="Frametimes") frequencyTab = Panel(child=frequencyChart, title="Framerate") return Tabs(tabs=[timeTab, frequencyTab]) def GenerateTextStatistics(data, width): """ Generates a Bokeh Div containing statistics in text form. :param data: A numpy array of frametimes in ms. :param width: Target rendering width. :return: A Bokeh Div based on the input data. """ # Get components: basicStatistics = GenerateBasicStatisticsText(data) frameToFrameStatistics = GenerateFrameToFrameStatistics(data) thresholdStatistics = GenerateThresholdStatistics(data, [60, 90, 120, 144]) # Generate graphic text = "<div style=\"padding-left:10px\">" + \ basicStatistics + frameToFrameStatistics + thresholdStatistics + \ "</div>" div = Div(text=text, width=width) return div # Text block generation: def GenerateBasicStatisticsText(data): """ Generates a basic statistics section in HTML format. :param data: A PresentMonResult object. :return: Basic statistics as an HTML string (header + paragraphs). """ nFrames = data.timeStamps.size # Mean, meadian and standard deviation times meanFrametime = np.mean(data.msBetweenPresents) medianFrameTime = np.median(data.msBetweenPresents) stdDevFrameTime = np.std(data.msBetweenPresents) # Corresponding framerates: meanFramerate = 1000. / meanFrametime medianFramerate = 1000. / medianFrameTime stdDevFramerate = 1000. / stdDevFrameTime # Generate and return text return "<h3>Basic statistics</h3>" + \ "<p>Number of processed frames: " + str(nFrames) + \ "<p>Median: " + "{:10.3f}".format(medianFramerate) + " fps (" + "{:10.3f}".format(medianFrameTime) + " ms)" + \ "<p>Mean: " + "{:10.3f}".format(meanFramerate) + " fps (" + "{:10.3f}".format(meanFrametime) + " ms)" + \ "<p>Standard deviation: " + "{:10.3f}".format(stdDevFramerate) + " fps (" + "{:10.3f}".format( stdDevFrameTime) + " ms)" def GenerateFrameToFrameStatistics(data): """ Generates frame-to-frame statistics in HTML format. :param data: A PresentMonResult object. :return: Frame-to-frame statistics as an HTML string (header + paragraphs). """ # Calulate average frame-to-frame difference magnitude diffsMs = ArrayUtils.getArrDiffs(data.msBetweenPresents) maxDiffAmp = np.max(diffsMs) minDiffAmp = np.min(diffsMs) avgDiffAmp = np.mean(diffsMs) # Generate and return text return "<h3>Frame-to-frame statistics</h3>" + \ "<p>Average magnitude of difference: " + "{:10.3f}".format(avgDiffAmp) + " ms</p>" + \ "<p>Maximum magnitude of difference: " + "{:10.3f}".format(maxDiffAmp) + " ms</p>" + \ "<p>Minimum magnitude of difference: " + "{:10.3f}".format(minDiffAmp) + " ms</p>" def GenerateThresholdStatistics(data, thresholdFramerates): """ Generates threshold statistics in HTML data. :param data: A PresentMonResult object. :param thresholdFramerates: A list of framerates to use as thresholds. :return: Threshold statistics as an HTML string (header + paragraphs). """ totalTimeSeconds = data.timeStamps[-1] thresholds = [] for frameRate in thresholdFramerates: thresholds.append((frameRate, [], 0.0)) # Thresholds (time spent above x fps) # (threshold fps, list of frames, fraction of total time spent) for frame in data.msBetweenPresents: for index in range(0, len(thresholds)): # for threshold in thresholds: if (1000. / frame) > thresholds[index][0]: thresholds[index][1].append(frame) thresholds[index] = ( thresholds[index][0], thresholds[index][1], thresholds[index][2] + (frame / 1000) / totalTimeSeconds) # Piece together the values and return text = "<h3>Framerate threshold statistics:</h3>" for threshold in thresholds: text = text + "<p>Fraction of time spent above " + str(threshold[0]) + " fps: " + "{:10.3f}".format( threshold[2] * 100) + "%</p>" return text # SETTINGS plotWidth = 1068 # Parse arguments parser = argparse.ArgumentParser() parser.add_argument("-debug", help="Enable debug mode", action="store_true") parser.add_argument("-file", help="Path to a PresentMon generated CSV-file") startTime = time.time() args = parser.parse_args() fileSource = "TestData/csgo-2.csv" if args.file: fileSource = args.file elif args.debug: debug = True else: parser.error('Either file or debug mode most be selected') # Load data data = LoadFromCsv(fileSource) # Set output file output_file("Analysis.html") # Generate header fileName = ntpath.basename(fileSource) header = Div(text="<div class=headerDiv><h1>Analysis: {}</h1></div>".format(fileName), width=plotWidth, style=style.headerStyle) # Generate plots histogram = CreateTabbedHistogram(data.msBetweenPresents, plotWidth) # Relative frequency histograms frameToFrameHistogram = CreateHistogram(ArrayUtils.getArrDiffs(data.msBetweenPresents), plotWidth, histTitle="Frame-to-frame deviations", xLabel="Frame time difference (ms)", yLabel="Relative frequency") lineDiagram = CreateTabbedLineDiagram(data, plotWidth) # Framerate/frametimes as line diagram textStatistics = GenerateTextStatistics(data, plotWidth) # Statistics in text form endTime = time.time() # For debug diagnostics if debug: print("Script runtime: " + str(endTime - startTime)) # Output and show show( column( header, row( column( Div(text="<div><h3>Performance distribution</h3></div>", width=plotWidth, style=style.subheaderStyle), histogram, Div(text="<div><h3>Performance timeline</h3></div>", width=plotWidth, style=style.subheaderStyle), lineDiagram, Div(text="<div><h3>Frame-to-frame deviations</h3></div>", width=plotWidth, style=style.subheaderStyle), frameToFrameHistogram ), textStatistics ) ) )

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components/studio/projects/migrations/0019_mlflow.py

aitmlouk/stackn

25

1618034

# Generated by Django 3.1.6 on 2021-03-18 21:32 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('apps', '0049_auto_20210318_1401'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('projects', '0018_auto_20210308_2132'), ] operations = [ migrations.CreateModel( name='MLFlow', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=512)), ('mlflow_url', models.CharField(max_length=512)), ('s3_host', models.CharField(max_length=512)), ('access_key', models.CharField(max_length=512)), ('secret_key', models.CharField(max_length=512)), ('region', models.CharField(blank=True, default='', max_length=512)), ('ba_username', models.CharField(blank=True, default='', max_length=100)), ('ba_password', models.CharField(blank=True, default='', max_length=100)), ('updated_on', models.DateTimeField(auto_now=True)), ('created_on', models.DateTimeField(auto_now_add=True)), ('app', models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='mlflowobj', to='apps.appinstance')), ('owner', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)), ('project', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='mlflow_project', to='projects.project')), ], ), ]

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