body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def test_get_filepaths_by_extensions(self):
'Test get_filepaths_by_extensions only returns filepaths in\n directory with given extensions.\n '
filepaths = []
build.ensure_directory_exists(MOCK_ASSETS_DEV_DIR)
extensions = ('.json', '.svg')
self.assertEqual(len(filepaths), 0)
filepa... | -3,231,396,549,521,197,600 | Test get_filepaths_by_extensions only returns filepaths in
directory with given extensions. | scripts/build_test.py | test_get_filepaths_by_extensions | muarachmann/oppia | python | def test_get_filepaths_by_extensions(self):
'Test get_filepaths_by_extensions only returns filepaths in\n directory with given extensions.\n '
filepaths = []
build.ensure_directory_exists(MOCK_ASSETS_DEV_DIR)
extensions = ('.json', '.svg')
self.assertEqual(len(filepaths), 0)
filepa... |
def test_get_file_hashes(self):
'Test get_file_hashes gets hashes of all files in directory,\n excluding file with extensions in FILE_EXTENSIONS_TO_IGNORE.\n '
with self.swap(build, 'FILE_EXTENSIONS_TO_IGNORE', ('.html',)):
file_hashes = dict()
self.assertEqual(len(file_hashes), 0)... | -5,967,998,651,860,690,000 | Test get_file_hashes gets hashes of all files in directory,
excluding file with extensions in FILE_EXTENSIONS_TO_IGNORE. | scripts/build_test.py | test_get_file_hashes | muarachmann/oppia | python | def test_get_file_hashes(self):
'Test get_file_hashes gets hashes of all files in directory,\n excluding file with extensions in FILE_EXTENSIONS_TO_IGNORE.\n '
with self.swap(build, 'FILE_EXTENSIONS_TO_IGNORE', ('.html',)):
file_hashes = dict()
self.assertEqual(len(file_hashes), 0)... |
def test_filter_hashes(self):
'Test filter_hashes filters the provided hash correctly.'
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('*',)):
hashes = {'path/to/file.js': '123456', 'path/file.min.js': '123456'}
filtered_hashes = build.filter_hashes(hashes)
self.assertEqual(fil... | -8,685,788,288,741,124,000 | Test filter_hashes filters the provided hash correctly. | scripts/build_test.py | test_filter_hashes | muarachmann/oppia | python | def test_filter_hashes(self):
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('*',)):
hashes = {'path/to/file.js': '123456', 'path/file.min.js': '123456'}
filtered_hashes = build.filter_hashes(hashes)
self.assertEqual(filtered_hashes['/path/to/file.js'], hashes['path/to/file.js... |
def test_get_hashes_json_file_contents(self):
'Test get_hashes_json_file_contents parses provided hash dict\n correctly to JSON format.\n '
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('*',)):
hashes = {'path/file.js': '123456'}
self.assertEqual(build.get_hashes_json_fi... | 6,738,413,142,785,860,000 | Test get_hashes_json_file_contents parses provided hash dict
correctly to JSON format. | scripts/build_test.py | test_get_hashes_json_file_contents | muarachmann/oppia | python | def test_get_hashes_json_file_contents(self):
'Test get_hashes_json_file_contents parses provided hash dict\n correctly to JSON format.\n '
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('*',)):
hashes = {'path/file.js': '123456'}
self.assertEqual(build.get_hashes_json_fi... |
def test_execute_tasks(self):
'Test _execute_tasks joins all threads after executing all tasks.'
build_tasks = collections.deque()
TASK_COUNT = 2
count = TASK_COUNT
while count:
task = threading.Thread(target=build._minify, args=(INVALID_INPUT_FILEPATH, INVALID_OUTPUT_FILEPATH))
buil... | -1,951,065,411,219,942,100 | Test _execute_tasks joins all threads after executing all tasks. | scripts/build_test.py | test_execute_tasks | muarachmann/oppia | python | def test_execute_tasks(self):
build_tasks = collections.deque()
TASK_COUNT = 2
count = TASK_COUNT
while count:
task = threading.Thread(target=build._minify, args=(INVALID_INPUT_FILEPATH, INVALID_OUTPUT_FILEPATH))
build_tasks.append(task)
count -= 1
self.assertEqual(threa... |
def test_generate_build_tasks_to_build_all_files_in_directory(self):
'Test generate_build_tasks_to_build_all_files_in_directory queues up\n the same number of build tasks as the number of files in the source\n directory.\n '
asset_hashes = build.get_file_hashes(MOCK_ASSETS_DEV_DIR)
task... | -4,802,834,169,793,278,000 | Test generate_build_tasks_to_build_all_files_in_directory queues up
the same number of build tasks as the number of files in the source
directory. | scripts/build_test.py | test_generate_build_tasks_to_build_all_files_in_directory | muarachmann/oppia | python | def test_generate_build_tasks_to_build_all_files_in_directory(self):
'Test generate_build_tasks_to_build_all_files_in_directory queues up\n the same number of build tasks as the number of files in the source\n directory.\n '
asset_hashes = build.get_file_hashes(MOCK_ASSETS_DEV_DIR)
task... |
def test_generate_build_tasks_to_build_files_from_filepaths(self):
'Test generate_build_tasks_to_build_files_from_filepaths queues up a\n corresponding number of build tasks to the number of file changes.\n '
new_filename = 'manifest.json'
recently_changed_filenames = [os.path.join(MOCK_ASSETS... | -7,522,509,054,264,482,000 | Test generate_build_tasks_to_build_files_from_filepaths queues up a
corresponding number of build tasks to the number of file changes. | scripts/build_test.py | test_generate_build_tasks_to_build_files_from_filepaths | muarachmann/oppia | python | def test_generate_build_tasks_to_build_files_from_filepaths(self):
'Test generate_build_tasks_to_build_files_from_filepaths queues up a\n corresponding number of build tasks to the number of file changes.\n '
new_filename = 'manifest.json'
recently_changed_filenames = [os.path.join(MOCK_ASSETS... |
def test_generate_build_tasks_to_build_directory(self):
'Test generate_build_tasks_to_build_directory queues up a\n corresponding number of build tasks according to the given scenario.\n '
EXTENSIONS_DIRNAMES_TO_DIRPATHS = {'dev_dir': MOCK_EXTENSIONS_DEV_DIR, 'compiled_js_dir': MOCK_EXTENSIONS_COM... | -4,306,466,468,645,775,000 | Test generate_build_tasks_to_build_directory queues up a
corresponding number of build tasks according to the given scenario. | scripts/build_test.py | test_generate_build_tasks_to_build_directory | muarachmann/oppia | python | def test_generate_build_tasks_to_build_directory(self):
'Test generate_build_tasks_to_build_directory queues up a\n corresponding number of build tasks according to the given scenario.\n '
EXTENSIONS_DIRNAMES_TO_DIRPATHS = {'dev_dir': MOCK_EXTENSIONS_DEV_DIR, 'compiled_js_dir': MOCK_EXTENSIONS_COM... |
def test_get_recently_changed_filenames(self):
'Test get_recently_changed_filenames detects file recently added.'
build.ensure_directory_exists(EMPTY_DIR)
assets_hashes = build.get_file_hashes(MOCK_ASSETS_DEV_DIR)
recently_changed_filenames = []
self.assertEqual(len(recently_changed_filenames), 0)
... | -888,671,311,690,525,000 | Test get_recently_changed_filenames detects file recently added. | scripts/build_test.py | test_get_recently_changed_filenames | muarachmann/oppia | python | def test_get_recently_changed_filenames(self):
build.ensure_directory_exists(EMPTY_DIR)
assets_hashes = build.get_file_hashes(MOCK_ASSETS_DEV_DIR)
recently_changed_filenames = []
self.assertEqual(len(recently_changed_filenames), 0)
recently_changed_filenames = build.get_recently_changed_filenam... |
def test_generate_delete_tasks_to_remove_deleted_files(self):
'Test generate_delete_tasks_to_remove_deleted_files queues up the\n same number of deletion task as the number of deleted files.\n '
delete_tasks = collections.deque()
file_hashes = dict()
self.assertEqual(len(delete_tasks), 0)
... | -5,156,963,052,401,631,000 | Test generate_delete_tasks_to_remove_deleted_files queues up the
same number of deletion task as the number of deleted files. | scripts/build_test.py | test_generate_delete_tasks_to_remove_deleted_files | muarachmann/oppia | python | def test_generate_delete_tasks_to_remove_deleted_files(self):
'Test generate_delete_tasks_to_remove_deleted_files queues up the\n same number of deletion task as the number of deleted files.\n '
delete_tasks = collections.deque()
file_hashes = dict()
self.assertEqual(len(delete_tasks), 0)
... |
def test_compiled_js_dir_validation(self):
'Test that build.COMPILED_JS_DIR is validated correctly with\n outDir in build.TSCONFIG_FILEPATH.\n '
build.require_compiled_js_dir_to_be_valid()
out_dir = ''
with open(build.TSCONFIG_FILEPATH) as f:
config_data = json.load(f)
out_... | 3,159,340,658,849,626,600 | Test that build.COMPILED_JS_DIR is validated correctly with
outDir in build.TSCONFIG_FILEPATH. | scripts/build_test.py | test_compiled_js_dir_validation | muarachmann/oppia | python | def test_compiled_js_dir_validation(self):
'Test that build.COMPILED_JS_DIR is validated correctly with\n outDir in build.TSCONFIG_FILEPATH.\n '
build.require_compiled_js_dir_to_be_valid()
out_dir =
with open(build.TSCONFIG_FILEPATH) as f:
config_data = json.load(f)
out_di... |
def test_compiled_js_dir_is_deleted_before_compilation(self):
'Test that compiled_js_dir is deleted before a fresh compilation.'
def mock_check_call(unused_cmd):
pass
def mock_require_compiled_js_dir_to_be_valid():
pass
with self.swap(build, 'COMPILED_JS_DIR', MOCK_COMPILED_JS_DIR), se... | -8,542,689,104,886,041,000 | Test that compiled_js_dir is deleted before a fresh compilation. | scripts/build_test.py | test_compiled_js_dir_is_deleted_before_compilation | muarachmann/oppia | python | def test_compiled_js_dir_is_deleted_before_compilation(self):
def mock_check_call(unused_cmd):
pass
def mock_require_compiled_js_dir_to_be_valid():
pass
with self.swap(build, 'COMPILED_JS_DIR', MOCK_COMPILED_JS_DIR), self.swap(build, 'require_compiled_js_dir_to_be_valid', mock_require... |
def test_compiled_js_dir_is_deleted_before_watch_mode_compilation(self):
'Test that compiled_js_dir is deleted before a fresh watch mode\n compilation.\n '
def mock_call(unused_cmd, shell, stdout):
pass
def mock_popen(unused_cmd, stdout):
pass
def mock_require_compiled_j... | -4,450,239,991,488,878,000 | Test that compiled_js_dir is deleted before a fresh watch mode
compilation. | scripts/build_test.py | test_compiled_js_dir_is_deleted_before_watch_mode_compilation | muarachmann/oppia | python | def test_compiled_js_dir_is_deleted_before_watch_mode_compilation(self):
'Test that compiled_js_dir is deleted before a fresh watch mode\n compilation.\n '
def mock_call(unused_cmd, shell, stdout):
pass
def mock_popen(unused_cmd, stdout):
pass
def mock_require_compiled_j... |
def _mock_safe_delete_file(unused_filepath):
'Mocks build.safe_delete_file().'
pass | -2,236,168,809,398,343,000 | Mocks build.safe_delete_file(). | scripts/build_test.py | _mock_safe_delete_file | muarachmann/oppia | python | def _mock_safe_delete_file(unused_filepath):
pass |
@pytest.mark.usefixtures('os', 'instance')
def test_existing_hosted_zone(hosted_zone_factory, pcluster_config_reader, clusters_factory, vpc_stack, cfn_stacks_factory, key_name, scheduler, region, instance):
'Test hosted_zone_id is provided in the config file.'
num_computes = 2
(hosted_zone_id, domain_name) ... | -1,448,538,545,670,695,400 | Test hosted_zone_id is provided in the config file. | tests/integration-tests/tests/dns/test_dns.py | test_existing_hosted_zone | Chen188/aws-parallelcluster | python | @pytest.mark.usefixtures('os', 'instance')
def test_existing_hosted_zone(hosted_zone_factory, pcluster_config_reader, clusters_factory, vpc_stack, cfn_stacks_factory, key_name, scheduler, region, instance):
num_computes = 2
(hosted_zone_id, domain_name) = hosted_zone_factory()
cluster_config = pcluster... |
@pytest.fixture(scope='class')
def hosted_zone_factory(vpc_stack, cfn_stacks_factory, request, region):
'Create a hosted zone stack.'
hosted_zone_stack_name = generate_stack_name('integ-tests-hosted-zone', request.config.getoption('stackname_suffix'))
domain_name = (hosted_zone_stack_name + '.com')
def... | -8,856,291,509,646,637,000 | Create a hosted zone stack. | tests/integration-tests/tests/dns/test_dns.py | hosted_zone_factory | Chen188/aws-parallelcluster | python | @pytest.fixture(scope='class')
def hosted_zone_factory(vpc_stack, cfn_stacks_factory, request, region):
hosted_zone_stack_name = generate_stack_name('integ-tests-hosted-zone', request.config.getoption('stackname_suffix'))
domain_name = (hosted_zone_stack_name + '.com')
def create_hosted_zone():
... |
def build_run_config():
'Return RunConfig for TPU estimator.'
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
eval_steps = (FLAGS.num_eval_images // FLAGS.eval_batch_size)
iterations_per_loop = (eval_steps if (FLAGS.mode ==... | 4,576,793,555,163,632,600 | Return RunConfig for TPU estimator. | models/official/amoeba_net/amoeba_net.py | build_run_config | boristown/tpu | python | def build_run_config():
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
eval_steps = (FLAGS.num_eval_images // FLAGS.eval_batch_size)
iterations_per_loop = (eval_steps if (FLAGS.mode == 'eval') else FLAGS.iterations_per_lo... |
def build_image_serving_input_receiver_fn(shape, dtype=tf.float32):
'Returns a input_receiver_fn for raw images during serving.'
def _preprocess_image(encoded_image):
'Preprocess a single raw image.'
image = tf.image.decode_image(encoded_image, channels=shape[(- 1)])
image.set_shape(sha... | 3,808,841,042,814,280,700 | Returns a input_receiver_fn for raw images during serving. | models/official/amoeba_net/amoeba_net.py | build_image_serving_input_receiver_fn | boristown/tpu | python | def build_image_serving_input_receiver_fn(shape, dtype=tf.float32):
def _preprocess_image(encoded_image):
'Preprocess a single raw image.'
image = tf.image.decode_image(encoded_image, channels=shape[(- 1)])
image.set_shape(shape)
return tf.cast(image, dtype)
def serving_in... |
def _encode_image(image_array, fmt='PNG'):
'encodes an (numpy) image array to string.\n\n Args:\n image_array: (numpy) image array\n fmt: image format to use\n\n Returns:\n encoded image string\n '
pil_image = Image.fromarray(image_array)
image_io = io.BytesIO()
pil_image.save(image_io, form... | -2,579,279,950,762,076,700 | encodes an (numpy) image array to string.
Args:
image_array: (numpy) image array
fmt: image format to use
Returns:
encoded image string | models/official/amoeba_net/amoeba_net.py | _encode_image | boristown/tpu | python | def _encode_image(image_array, fmt='PNG'):
'encodes an (numpy) image array to string.\n\n Args:\n image_array: (numpy) image array\n fmt: image format to use\n\n Returns:\n encoded image string\n '
pil_image = Image.fromarray(image_array)
image_io = io.BytesIO()
pil_image.save(image_io, form... |
def write_warmup_requests(savedmodel_dir, model_name, image_size, batch_sizes=None, num_requests=8):
'Writes warmup requests for inference into a tfrecord file.\n\n Args:\n savedmodel_dir: string, the file to the exported model folder.\n model_name: string, a model name used inside the model server.\n ima... | 2,654,014,410,891,139,600 | Writes warmup requests for inference into a tfrecord file.
Args:
savedmodel_dir: string, the file to the exported model folder.
model_name: string, a model name used inside the model server.
image_size: int, size of image, assuming image height and width.
batch_sizes: list, a list of batch sizes to create diff... | models/official/amoeba_net/amoeba_net.py | write_warmup_requests | boristown/tpu | python | def write_warmup_requests(savedmodel_dir, model_name, image_size, batch_sizes=None, num_requests=8):
'Writes warmup requests for inference into a tfrecord file.\n\n Args:\n savedmodel_dir: string, the file to the exported model folder.\n model_name: string, a model name used inside the model server.\n ima... |
def override_with_flags(hparams):
'Overrides parameters with flag values.'
override_flag_names = ['aux_scaling', 'train_batch_size', 'batch_norm_decay', 'batch_norm_epsilon', 'dense_dropout_keep_prob', 'drop_connect_keep_prob', 'drop_connect_version', 'eval_batch_size', 'gradient_clipping_by_global_norm', 'lr',... | 4,258,256,473,116,058,600 | Overrides parameters with flag values. | models/official/amoeba_net/amoeba_net.py | override_with_flags | boristown/tpu | python | def override_with_flags(hparams):
override_flag_names = ['aux_scaling', 'train_batch_size', 'batch_norm_decay', 'batch_norm_epsilon', 'dense_dropout_keep_prob', 'drop_connect_keep_prob', 'drop_connect_version', 'eval_batch_size', 'gradient_clipping_by_global_norm', 'lr', 'lr_decay_method', 'lr_decay_value', 'l... |
def build_hparams():
'Build tf.Hparams for training Amoeba Net.'
hparams = model_lib.build_hparams(FLAGS.cell_name)
override_with_flags(hparams)
return hparams | 7,598,903,149,163,873,000 | Build tf.Hparams for training Amoeba Net. | models/official/amoeba_net/amoeba_net.py | build_hparams | boristown/tpu | python | def build_hparams():
hparams = model_lib.build_hparams(FLAGS.cell_name)
override_with_flags(hparams)
return hparams |
def _preprocess_image(encoded_image):
'Preprocess a single raw image.'
image = tf.image.decode_image(encoded_image, channels=shape[(- 1)])
image.set_shape(shape)
return tf.cast(image, dtype) | -2,410,232,163,323,720,000 | Preprocess a single raw image. | models/official/amoeba_net/amoeba_net.py | _preprocess_image | boristown/tpu | python | def _preprocess_image(encoded_image):
image = tf.image.decode_image(encoded_image, channels=shape[(- 1)])
image.set_shape(shape)
return tf.cast(image, dtype) |
def add_port(component: Component, **kwargs) -> Component:
'Return Component with a new port.'
component.add_port(**kwargs)
return component | -5,908,829,619,112,604,000 | Return Component with a new port. | gdsfactory/functions.py | add_port | jorgepadilla19/gdsfactory | python | def add_port(component: Component, **kwargs) -> Component:
component.add_port(**kwargs)
return component |
@cell
def add_text(component: ComponentOrFactory, text: str='', text_offset: Float2=(0, 0), text_anchor: Anchor='cc', text_factory: ComponentFactory=text_rectangular_multi_layer) -> Component:
'Return component inside a new component with text geometry.\n\n Args:\n component:\n text: text string.\n... | 6,078,697,613,539,204,000 | Return component inside a new component with text geometry.
Args:
component:
text: text string.
text_offset: relative to component anchor. Defaults to center (cc).
text_anchor: relative to component (ce cw nc ne nw sc se sw center cc).
text_factory: function to add text labels. | gdsfactory/functions.py | add_text | jorgepadilla19/gdsfactory | python | @cell
def add_text(component: ComponentOrFactory, text: str=, text_offset: Float2=(0, 0), text_anchor: Anchor='cc', text_factory: ComponentFactory=text_rectangular_multi_layer) -> Component:
'Return component inside a new component with text geometry.\n\n Args:\n component:\n text: text string.\n ... |
def add_texts(components: List[ComponentOrFactory], prefix: str='', index0: int=0, **kwargs) -> List[Component]:
'Return a list of Component with text labels.\n\n Args:\n components: list of components\n prefix: Optional prefix for the labels\n index0: defaults to 0 (0, for first component, ... | 2,259,754,371,796,914,400 | Return a list of Component with text labels.
Args:
components: list of components
prefix: Optional prefix for the labels
index0: defaults to 0 (0, for first component, 1 for second ...)
keyword Args:
text_offset: relative to component size info anchor. Defaults to center.
text_anchor: relative to ... | gdsfactory/functions.py | add_texts | jorgepadilla19/gdsfactory | python | def add_texts(components: List[ComponentOrFactory], prefix: str=, index0: int=0, **kwargs) -> List[Component]:
'Return a list of Component with text labels.\n\n Args:\n components: list of components\n prefix: Optional prefix for the labels\n index0: defaults to 0 (0, for first component, 1 ... |
@cell
def rotate(component: ComponentOrFactory, angle: float=90) -> Component:
'Return rotated component inside a new component.\n\n Most times you just need to place a reference and rotate it.\n This rotate function just encapsulates the rotated reference into a new component.\n\n Args:\n component... | 3,448,322,324,605,236,700 | Return rotated component inside a new component.
Most times you just need to place a reference and rotate it.
This rotate function just encapsulates the rotated reference into a new component.
Args:
component:
angle: in degrees | gdsfactory/functions.py | rotate | jorgepadilla19/gdsfactory | python | @cell
def rotate(component: ComponentOrFactory, angle: float=90) -> Component:
'Return rotated component inside a new component.\n\n Most times you just need to place a reference and rotate it.\n This rotate function just encapsulates the rotated reference into a new component.\n\n Args:\n component... |
@cell
def mirror(component: Component, p1: Float2=(0, 1), p2: Float2=(0, 0)) -> Component:
'Return new Component with a mirrored reference.\n\n Args:\n p1: first point to define mirror axis\n p2: second point to define mirror axis\n '
component_new = Component()
component_new.component =... | 2,300,571,083,734,599,700 | Return new Component with a mirrored reference.
Args:
p1: first point to define mirror axis
p2: second point to define mirror axis | gdsfactory/functions.py | mirror | jorgepadilla19/gdsfactory | python | @cell
def mirror(component: Component, p1: Float2=(0, 1), p2: Float2=(0, 0)) -> Component:
'Return new Component with a mirrored reference.\n\n Args:\n p1: first point to define mirror axis\n p2: second point to define mirror axis\n '
component_new = Component()
component_new.component =... |
@cell
def move(component: Component, origin=(0, 0), destination=None, axis: Optional[Axis]=None) -> Component:
'Return new Component with a moved reference to the original component.\n\n Args:\n origin: of component\n destination:\n axis: x or y axis\n '
component_new = Component()
... | -3,906,964,808,911,511,000 | Return new Component with a moved reference to the original component.
Args:
origin: of component
destination:
axis: x or y axis | gdsfactory/functions.py | move | jorgepadilla19/gdsfactory | python | @cell
def move(component: Component, origin=(0, 0), destination=None, axis: Optional[Axis]=None) -> Component:
'Return new Component with a moved reference to the original component.\n\n Args:\n origin: of component\n destination:\n axis: x or y axis\n '
component_new = Component()
... |
def move_port_to_zero(component: Component, port_name: str='o1'):
'Return a container that contains a reference to the original component.\n where the new component has port_name in (0, 0)\n '
if (port_name not in component.ports):
raise ValueError(f'port_name = {port_name!r} not in {list(componen... | 3,064,900,530,110,951,000 | Return a container that contains a reference to the original component.
where the new component has port_name in (0, 0) | gdsfactory/functions.py | move_port_to_zero | jorgepadilla19/gdsfactory | python | def move_port_to_zero(component: Component, port_name: str='o1'):
'Return a container that contains a reference to the original component.\n where the new component has port_name in (0, 0)\n '
if (port_name not in component.ports):
raise ValueError(f'port_name = {port_name!r} not in {list(componen... |
def update_info(component: Component, **kwargs) -> Component:
'Return Component with updated info.'
component.info.update(**kwargs)
return component | 2,849,792,957,458,223,000 | Return Component with updated info. | gdsfactory/functions.py | update_info | jorgepadilla19/gdsfactory | python | def update_info(component: Component, **kwargs) -> Component:
component.info.update(**kwargs)
return component |
@validate_arguments
def add_settings_label(component: Component, layer_label: Layer=(66, 0), settings: Optional[Strs]=None) -> Component:
'Add a settings label to a component.\n\n Args:\n component:\n layer_label:\n settings: tuple or list of settings. if None, adds all changed settings\n\n ... | -811,722,326,502,638,200 | Add a settings label to a component.
Args:
component:
layer_label:
settings: tuple or list of settings. if None, adds all changed settings | gdsfactory/functions.py | add_settings_label | jorgepadilla19/gdsfactory | python | @validate_arguments
def add_settings_label(component: Component, layer_label: Layer=(66, 0), settings: Optional[Strs]=None) -> Component:
'Add a settings label to a component.\n\n Args:\n component:\n layer_label:\n settings: tuple or list of settings. if None, adds all changed settings\n\n ... |
def _summarize_str(st):
'Aux function'
return (st[:56][::(- 1)].split(',', 1)[(- 1)][::(- 1)] + ', ...') | 30,060,154,108,599,572 | Aux function | mne/fiff/meas_info.py | _summarize_str | Anevar/mne-python | python | def _summarize_str(st):
return (st[:56][::(- 1)].split(',', 1)[(- 1)][::(- 1)] + ', ...') |
def read_fiducials(fname):
'Read fiducials from a fiff file\n\n Returns\n -------\n pts : list of dicts\n List of digitizer points (each point in a dict).\n coord_frame : int\n The coordinate frame of the points (one of\n mne.fiff.FIFF.FIFFV_COORD_...)\n '
(fid, tree, _) = fi... | -6,872,709,896,282,553,000 | Read fiducials from a fiff file
Returns
-------
pts : list of dicts
List of digitizer points (each point in a dict).
coord_frame : int
The coordinate frame of the points (one of
mne.fiff.FIFF.FIFFV_COORD_...) | mne/fiff/meas_info.py | read_fiducials | Anevar/mne-python | python | def read_fiducials(fname):
'Read fiducials from a fiff file\n\n Returns\n -------\n pts : list of dicts\n List of digitizer points (each point in a dict).\n coord_frame : int\n The coordinate frame of the points (one of\n mne.fiff.FIFF.FIFFV_COORD_...)\n '
(fid, tree, _) = fi... |
def write_fiducials(fname, pts, coord_frame=0):
"Write fiducials to a fiff file\n\n Parameters\n ----------\n fname : str\n Destination file name.\n pts : iterator of dict\n Iterator through digitizer points. Each point is a dictionary with\n the keys 'kind', 'ident' and 'r'.\n c... | -5,395,714,530,013,654,000 | Write fiducials to a fiff file
Parameters
----------
fname : str
Destination file name.
pts : iterator of dict
Iterator through digitizer points. Each point is a dictionary with
the keys 'kind', 'ident' and 'r'.
coord_frame : int
The coordinate frame of the points (one of
mne.fiff.FIFF.FIFFV_COORD_... | mne/fiff/meas_info.py | write_fiducials | Anevar/mne-python | python | def write_fiducials(fname, pts, coord_frame=0):
"Write fiducials to a fiff file\n\n Parameters\n ----------\n fname : str\n Destination file name.\n pts : iterator of dict\n Iterator through digitizer points. Each point is a dictionary with\n the keys 'kind', 'ident' and 'r'.\n c... |
@verbose
def read_info(fname, verbose=None):
'Read measurement info from a file\n\n Parameters\n ----------\n fname : str\n File name.\n verbose : bool, str, int, or None\n If not None, override default verbose level (see mne.verbose).\n\n Returns\n -------\n info : instance of mn... | 8,250,280,954,245,872,000 | Read measurement info from a file
Parameters
----------
fname : str
File name.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
info : instance of mne.fiff.meas_info.Info
Info on dataset. | mne/fiff/meas_info.py | read_info | Anevar/mne-python | python | @verbose
def read_info(fname, verbose=None):
'Read measurement info from a file\n\n Parameters\n ----------\n fname : str\n File name.\n verbose : bool, str, int, or None\n If not None, override default verbose level (see mne.verbose).\n\n Returns\n -------\n info : instance of mn... |
@verbose
def read_meas_info(fid, tree, verbose=None):
'Read the measurement info\n\n Parameters\n ----------\n fid : file\n Open file descriptor.\n tree : tree\n FIF tree structure.\n verbose : bool, str, int, or None\n If not None, override default verbose level (see mne.verbose... | -1,168,243,709,760,774,000 | Read the measurement info
Parameters
----------
fid : file
Open file descriptor.
tree : tree
FIF tree structure.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
info : instance of mne.fiff.meas_info.Info
Info on dataset.
meas : dict
N... | mne/fiff/meas_info.py | read_meas_info | Anevar/mne-python | python | @verbose
def read_meas_info(fid, tree, verbose=None):
'Read the measurement info\n\n Parameters\n ----------\n fid : file\n Open file descriptor.\n tree : tree\n FIF tree structure.\n verbose : bool, str, int, or None\n If not None, override default verbose level (see mne.verbose... |
def read_extra_meas_info(fid, tree, info):
'Read extra blocks from fid'
blocks = [FIFF.FIFFB_EVENTS, FIFF.FIFFB_HPI_RESULT, FIFF.FIFFB_HPI_MEAS, FIFF.FIFFB_PROCESSING_HISTORY]
info['orig_blocks'] = blocks
fid_str = BytesIO()
fid_str = start_file(fid_str)
start_block(fid_str, FIFF.FIFFB_MEAS_INFO... | -7,852,157,372,996,325,000 | Read extra blocks from fid | mne/fiff/meas_info.py | read_extra_meas_info | Anevar/mne-python | python | def read_extra_meas_info(fid, tree, info):
blocks = [FIFF.FIFFB_EVENTS, FIFF.FIFFB_HPI_RESULT, FIFF.FIFFB_HPI_MEAS, FIFF.FIFFB_PROCESSING_HISTORY]
info['orig_blocks'] = blocks
fid_str = BytesIO()
fid_str = start_file(fid_str)
start_block(fid_str, FIFF.FIFFB_MEAS_INFO)
for block in blocks:
... |
def write_extra_meas_info(fid, info):
'Write otherwise left out blocks of data'
if (('orig_blocks' in info) and (info['orig_blocks'] is not None)):
blocks = info['orig_blocks']
(fid_str, tree, _) = fiff_open(info['orig_fid_str'])
for block in blocks:
nodes = dir_tree_find(tre... | 1,894,005,886,610,068,200 | Write otherwise left out blocks of data | mne/fiff/meas_info.py | write_extra_meas_info | Anevar/mne-python | python | def write_extra_meas_info(fid, info):
if (('orig_blocks' in info) and (info['orig_blocks'] is not None)):
blocks = info['orig_blocks']
(fid_str, tree, _) = fiff_open(info['orig_fid_str'])
for block in blocks:
nodes = dir_tree_find(tree, block)
copy_tree(fid_str, ... |
def write_meas_info(fid, info, data_type=None, reset_range=True):
"Write measurement info into a file id (from a fif file)\n\n Parameters\n ----------\n fid : file\n Open file descriptor\n info : instance of mne.fiff.meas_info.Info\n The measurement info structure\n data_type : int\n ... | -3,615,014,654,560,701,400 | Write measurement info into a file id (from a fif file)
Parameters
----------
fid : file
Open file descriptor
info : instance of mne.fiff.meas_info.Info
The measurement info structure
data_type : int
The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT),
5 (FIFFT_DOUBLE), or 16 (mne.fiff.FIF... | mne/fiff/meas_info.py | write_meas_info | Anevar/mne-python | python | def write_meas_info(fid, info, data_type=None, reset_range=True):
"Write measurement info into a file id (from a fif file)\n\n Parameters\n ----------\n fid : file\n Open file descriptor\n info : instance of mne.fiff.meas_info.Info\n The measurement info structure\n data_type : int\n ... |
def write_info(fname, info, data_type=None, reset_range=True):
"Write measurement info in fif file.\n\n Parameters\n ----------\n fname : str\n The name of the file. Should end by -info.fif.\n info : instance of mne.fiff.meas_info.Info\n The measurement info structure\n data_type : int\... | -2,834,309,715,596,339,700 | Write measurement info in fif file.
Parameters
----------
fname : str
The name of the file. Should end by -info.fif.
info : instance of mne.fiff.meas_info.Info
The measurement info structure
data_type : int
The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT),
5 (FIFFT_DOUBLE), or 16 (mne.f... | mne/fiff/meas_info.py | write_info | Anevar/mne-python | python | def write_info(fname, info, data_type=None, reset_range=True):
"Write measurement info in fif file.\n\n Parameters\n ----------\n fname : str\n The name of the file. Should end by -info.fif.\n info : instance of mne.fiff.meas_info.Info\n The measurement info structure\n data_type : int\... |
def __repr__(self):
'Summarize info instead of printing all'
strs = ['<Info | %s non-empty fields']
non_empty = 0
for (k, v) in self.items():
if (k in ['bads', 'ch_names']):
entr = (', '.join((b for (ii, b) in enumerate(v) if (ii < 10))) if v else '0 items')
if (len(entr)... | -5,143,204,878,215,623,000 | Summarize info instead of printing all | mne/fiff/meas_info.py | __repr__ | Anevar/mne-python | python | def __repr__(self):
strs = ['<Info | %s non-empty fields']
non_empty = 0
for (k, v) in self.items():
if (k in ['bads', 'ch_names']):
entr = (', '.join((b for (ii, b) in enumerate(v) if (ii < 10))) if v else '0 items')
if (len(entr) >= 56):
entr = _summari... |
def generate_code(root_path, gen_dict=None):
'Generate pyleecan Classes code according to doc in root_path\n\n Parameters\n ----------\n root_path : str\n Path to the main folder of Pyleecan\n gen_dict : dict\n Generation dictionnary (contains all the csv data)\n Returns\n -------\n ... | -3,105,398,278,533,187,000 | Generate pyleecan Classes code according to doc in root_path
Parameters
----------
root_path : str
Path to the main folder of Pyleecan
gen_dict : dict
Generation dictionnary (contains all the csv data)
Returns
-------
None | pyleecan/Generator/run_generate_classes.py | generate_code | IrakozeFD/pyleecan | python | def generate_code(root_path, gen_dict=None):
'Generate pyleecan Classes code according to doc in root_path\n\n Parameters\n ----------\n root_path : str\n Path to the main folder of Pyleecan\n gen_dict : dict\n Generation dictionnary (contains all the csv data)\n Returns\n -------\n ... |
@property
def action_space(self):
'See class definition.'
max_decel = self.env_params.additional_params['max_decel']
max_accel = self.env_params.additional_params['max_accel']
lb = ([1, (- 0.2)] * self.num_rl)
ub = ([2, 0.2] * self.num_rl)
return Box(np.array(lb), np.array(ub), dtype=np.float32) | 54,758,527,748,066,650 | See class definition. | traci_pedestrian_crossing/movexy_ped.py | action_space | KarlRong/Safe-RL-for-Driving | python | @property
def action_space(self):
max_decel = self.env_params.additional_params['max_decel']
max_accel = self.env_params.additional_params['max_accel']
lb = ([1, (- 0.2)] * self.num_rl)
ub = ([2, 0.2] * self.num_rl)
return Box(np.array(lb), np.array(ub), dtype=np.float32) |
@property
def observation_space(self):
'See class definition.'
return Box(low=(- 1000), high=3000, shape=((((4 * self.num_rl) * self.num_lanes) + (2 * self.num_rl)),), dtype=np.float32) | 5,053,630,444,488,890,000 | See class definition. | traci_pedestrian_crossing/movexy_ped.py | observation_space | KarlRong/Safe-RL-for-Driving | python | @property
def observation_space(self):
return Box(low=(- 1000), high=3000, shape=((((4 * self.num_rl) * self.num_lanes) + (2 * self.num_rl)),), dtype=np.float32) |
def compute_reward(self, rl_actions, **kwargs):
'See class definition.'
reward = 0
rl_velocity = np.array(self.k.vehicle.get_speed(self.rl_veh))
target_vel = self.env_params.additional_params['target_velocity']
max_cost = np.array(([target_vel] * self.num_rl))
max_cost = np.linalg.norm(max_cost)... | 589,851,366,946,258,200 | See class definition. | traci_pedestrian_crossing/movexy_ped.py | compute_reward | KarlRong/Safe-RL-for-Driving | python | def compute_reward(self, rl_actions, **kwargs):
reward = 0
rl_velocity = np.array(self.k.vehicle.get_speed(self.rl_veh))
target_vel = self.env_params.additional_params['target_velocity']
max_cost = np.array(([target_vel] * self.num_rl))
max_cost = np.linalg.norm(max_cost)
cost = (rl_velocit... |
def _apply_rl_actions(self, actions):
'See class definition.'
acceleration = actions[::2]
direction = actions[1::2]
for (i, veh_id) in enumerate(self.rl_veh):
if (self.time_counter <= (self.env_params.additional_params['lane_change_duration'] + self.k.vehicle.get_last_lc(veh_id))):
d... | 3,311,372,121,974,978,600 | See class definition. | traci_pedestrian_crossing/movexy_ped.py | _apply_rl_actions | KarlRong/Safe-RL-for-Driving | python | def _apply_rl_actions(self, actions):
acceleration = actions[::2]
direction = actions[1::2]
for (i, veh_id) in enumerate(self.rl_veh):
if (self.time_counter <= (self.env_params.additional_params['lane_change_duration'] + self.k.vehicle.get_last_lc(veh_id))):
direction[i] = 0
... |
def get_state(self):
'See class definition.'
obs = [0 for _ in range((((4 * self.num_rl) * self.num_lanes) + (2 * self.num_rl)))]
self.visible = []
self.update_veh_id()
speeds = []
for (i, rl_id) in enumerate(self.rl_veh):
x = self.k.vehicle.get_x_by_id(rl_id)
if (x == (- 1001)):... | -5,605,300,636,699,024,000 | See class definition. | traci_pedestrian_crossing/movexy_ped.py | get_state | KarlRong/Safe-RL-for-Driving | python | def get_state(self):
obs = [0 for _ in range((((4 * self.num_rl) * self.num_lanes) + (2 * self.num_rl)))]
self.visible = []
self.update_veh_id()
speeds = []
for (i, rl_id) in enumerate(self.rl_veh):
x = self.k.vehicle.get_x_by_id(rl_id)
if (x == (- 1001)):
continue
... |
def checkWaitingPersons(self):
'check whether a person has requested to cross the street'
for edge in self.WALKINGAREAS:
peds = self.k.kernel_api.edge.getLastStepPersonIDs(edge)
for ped in peds:
if ((self.k.kernel_api.person.getWaitingTime(ped) == 1) and (self.k.kernel_api.person.get... | 3,743,804,071,605,469,000 | check whether a person has requested to cross the street | traci_pedestrian_crossing/movexy_ped.py | checkWaitingPersons | KarlRong/Safe-RL-for-Driving | python | def checkWaitingPersons(self):
for edge in self.WALKINGAREAS:
peds = self.k.kernel_api.edge.getLastStepPersonIDs(edge)
for ped in peds:
if ((self.k.kernel_api.person.getWaitingTime(ped) == 1) and (self.k.kernel_api.person.getNextEdge(ped) in self.CROSSINGS)):
numWait... |
def step(self, rl_actions):
"Advance the environment by one step.\n\n Assigns actions to autonomous and human-driven agents (i.e. vehicles,\n traffic lights, etc...). Actions that are not assigned are left to the\n control of the simulator. The actions are then used to advance the\n simu... | 2,799,618,293,451,251,000 | Advance the environment by one step.
Assigns actions to autonomous and human-driven agents (i.e. vehicles,
traffic lights, etc...). Actions that are not assigned are left to the
control of the simulator. The actions are then used to advance the
simulator by the number of time steps requested per environment step.
Res... | traci_pedestrian_crossing/movexy_ped.py | step | KarlRong/Safe-RL-for-Driving | python | def step(self, rl_actions):
"Advance the environment by one step.\n\n Assigns actions to autonomous and human-driven agents (i.e. vehicles,\n traffic lights, etc...). Actions that are not assigned are left to the\n control of the simulator. The actions are then used to advance the\n simu... |
def reset(self):
'See parent class.\n\n This also includes updating the initial absolute position and previous\n position.\n '
self.rl_queue.clear()
self.rl_veh.clear()
obs = super().reset()
print('reset')
for veh_id in self.k.vehicle.get_ids():
self.absolute_positio... | -2,498,678,424,320,711,000 | See parent class.
This also includes updating the initial absolute position and previous
position. | traci_pedestrian_crossing/movexy_ped.py | reset | KarlRong/Safe-RL-for-Driving | python | def reset(self):
'See parent class.\n\n This also includes updating the initial absolute position and previous\n position.\n '
self.rl_queue.clear()
self.rl_veh.clear()
obs = super().reset()
print('reset')
for veh_id in self.k.vehicle.get_ids():
self.absolute_positio... |
def loss_fn(outputs, labels):
'\n Compute the cross entropy loss given outputs and labels.\n\n Args:\n outputs: (Variable) dimension batch_size x 6 - output of the model\n labels: (Variable) dimension batch_size, where each element is a value in [0, 1, 2, 3, 4, 5]\n\n Returns:\n loss (... | -8,691,466,486,941,953,000 | Compute the cross entropy loss given outputs and labels.
Args:
outputs: (Variable) dimension batch_size x 6 - output of the model
labels: (Variable) dimension batch_size, where each element is a value in [0, 1, 2, 3, 4, 5]
Returns:
loss (Variable): cross entropy loss for all images in the batch
Note: you... | model/studentB.py | loss_fn | eungbean/knowledge-distillation-cifar10 | python | def loss_fn(outputs, labels):
'\n Compute the cross entropy loss given outputs and labels.\n\n Args:\n outputs: (Variable) dimension batch_size x 6 - output of the model\n labels: (Variable) dimension batch_size, where each element is a value in [0, 1, 2, 3, 4, 5]\n\n Returns:\n loss (... |
def loss_fn_kd(outputs, labels, teacher_outputs, params):
'\n Compute the knowledge-distillation (KD) loss given outputs, labels.\n "Hyperparameters": temperature and alpha\n\n NOTE: the KL Divergence for PyTorch comparing the softmaxs of teacher\n and student expects the input tensor to be log probabil... | 3,821,292,463,632,088,000 | Compute the knowledge-distillation (KD) loss given outputs, labels.
"Hyperparameters": temperature and alpha
NOTE: the KL Divergence for PyTorch comparing the softmaxs of teacher
and student expects the input tensor to be log probabilities! See Issue #2 | model/studentB.py | loss_fn_kd | eungbean/knowledge-distillation-cifar10 | python | def loss_fn_kd(outputs, labels, teacher_outputs, params):
'\n Compute the knowledge-distillation (KD) loss given outputs, labels.\n "Hyperparameters": temperature and alpha\n\n NOTE: the KL Divergence for PyTorch comparing the softmaxs of teacher\n and student expects the input tensor to be log probabil... |
def accuracy(outputs, labels):
'\n Compute the accuracy, given the outputs and labels for all images.\n\n Args:\n outputs: (np.ndarray) output of the model\n labels: (np.ndarray) [0, 1, ..., num_classes-1]\n\n Returns: (float) accuracy in [0,1]\n '
outputs = np.argmax(outputs, axis=1)
... | -2,892,165,881,102,442,500 | Compute the accuracy, given the outputs and labels for all images.
Args:
outputs: (np.ndarray) output of the model
labels: (np.ndarray) [0, 1, ..., num_classes-1]
Returns: (float) accuracy in [0,1] | model/studentB.py | accuracy | eungbean/knowledge-distillation-cifar10 | python | def accuracy(outputs, labels):
'\n Compute the accuracy, given the outputs and labels for all images.\n\n Args:\n outputs: (np.ndarray) output of the model\n labels: (np.ndarray) [0, 1, ..., num_classes-1]\n\n Returns: (float) accuracy in [0,1]\n '
outputs = np.argmax(outputs, axis=1)
... |
def __init__(self, params):
'\n We define an convolutional network that predicts the sign from an image. The components\n required are:\n\n Args:\n params: (Params) contains num_channels\n '
super(studentB, self).__init__()
self.num_channels = params.num_channels
s... | 7,160,409,673,777,569,000 | We define an convolutional network that predicts the sign from an image. The components
required are:
Args:
params: (Params) contains num_channels | model/studentB.py | __init__ | eungbean/knowledge-distillation-cifar10 | python | def __init__(self, params):
'\n We define an convolutional network that predicts the sign from an image. The components\n required are:\n\n Args:\n params: (Params) contains num_channels\n '
super(studentB, self).__init__()
self.num_channels = params.num_channels
s... |
def forward(self, s):
'\n This function defines how we use the components of our network to operate on an input batch.\n\n Args:\n s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .\n\n Returns:\n out: (Variable) dimension batch_size x 6 with... | -3,429,025,557,422,772,700 | This function defines how we use the components of our network to operate on an input batch.
Args:
s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .
Returns:
out: (Variable) dimension batch_size x 6 with the log probabilities for the labels of each image.
Note: the dimensions ... | model/studentB.py | forward | eungbean/knowledge-distillation-cifar10 | python | def forward(self, s):
'\n This function defines how we use the components of our network to operate on an input batch.\n\n Args:\n s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .\n\n Returns:\n out: (Variable) dimension batch_size x 6 with... |
def create_or_get_cache_dir(self, module=''):
'create (if not exists) or return cache dir path for module'
cache_dir = '{}/{}'.format(self.__cache_dir, module)
if (not os.path.exists(cache_dir)):
os.makedirs(cache_dir)
return cache_dir | -3,946,185,517,127,907,300 | create (if not exists) or return cache dir path for module | ods/ods.py | create_or_get_cache_dir | open-datastudio/ods | python | def create_or_get_cache_dir(self, module=):
cache_dir = '{}/{}'.format(self.__cache_dir, module)
if (not os.path.exists(cache_dir)):
os.makedirs(cache_dir)
return cache_dir |
def main():
'Run the simulation that infers an embedding for three groups.'
n_stimuli = 30
n_dim = 4
n_group = 3
n_restart = 1
epochs = 1000
n_trial = 2000
batch_size = 128
model_true = ground_truth(n_stimuli, n_dim, n_group)
generator = psiz.trials.RandomRank(n_stimuli, n_refere... | -4,177,223,168,496,596,500 | Run the simulation that infers an embedding for three groups. | examples/rank/mle_3g.py | main | rgerkin/psiz | python | def main():
n_stimuli = 30
n_dim = 4
n_group = 3
n_restart = 1
epochs = 1000
n_trial = 2000
batch_size = 128
model_true = ground_truth(n_stimuli, n_dim, n_group)
generator = psiz.trials.RandomRank(n_stimuli, n_reference=8, n_select=2)
docket = generator.generate(n_trial)
... |
def ground_truth(n_stimuli, n_dim, n_group):
'Return a ground truth embedding.'
stimuli = tf.keras.layers.Embedding((n_stimuli + 1), n_dim, mask_zero=True, embeddings_initializer=tf.keras.initializers.RandomNormal(stddev=0.17))
shared_similarity = psiz.keras.layers.ExponentialSimilarity(trainable=False, bet... | 3,894,005,208,590,680,600 | Return a ground truth embedding. | examples/rank/mle_3g.py | ground_truth | rgerkin/psiz | python | def ground_truth(n_stimuli, n_dim, n_group):
stimuli = tf.keras.layers.Embedding((n_stimuli + 1), n_dim, mask_zero=True, embeddings_initializer=tf.keras.initializers.RandomNormal(stddev=0.17))
shared_similarity = psiz.keras.layers.ExponentialSimilarity(trainable=False, beta_initializer=tf.keras.initializer... |
def build_model(n_stimuli, n_dim, n_group):
'Build model.\n\n Arguments:\n n_stimuli: Integer indicating the number of stimuli in the\n embedding.\n n_dim: Integer indicating the dimensionality of the embedding.\n\n Returns:\n model: A TensorFlow Keras model.\n\n '
stimu... | 3,748,000,712,402,987,500 | Build model.
Arguments:
n_stimuli: Integer indicating the number of stimuli in the
embedding.
n_dim: Integer indicating the dimensionality of the embedding.
Returns:
model: A TensorFlow Keras model. | examples/rank/mle_3g.py | build_model | rgerkin/psiz | python | def build_model(n_stimuli, n_dim, n_group):
'Build model.\n\n Arguments:\n n_stimuli: Integer indicating the number of stimuli in the\n embedding.\n n_dim: Integer indicating the dimensionality of the embedding.\n\n Returns:\n model: A TensorFlow Keras model.\n\n '
stimu... |
def build_kernel(similarity, n_dim):
'Build kernel for single group.'
mink = psiz.keras.layers.Minkowski(rho_trainable=False, rho_initializer=tf.keras.initializers.Constant(2.0), w_constraint=psiz.keras.constraints.NonNegNorm(scale=n_dim, p=1.0))
kernel = psiz.keras.layers.DistanceBased(distance=mink, simil... | -5,725,182,606,263,217,000 | Build kernel for single group. | examples/rank/mle_3g.py | build_kernel | rgerkin/psiz | python | def build_kernel(similarity, n_dim):
mink = psiz.keras.layers.Minkowski(rho_trainable=False, rho_initializer=tf.keras.initializers.Constant(2.0), w_constraint=psiz.keras.constraints.NonNegNorm(scale=n_dim, p=1.0))
kernel = psiz.keras.layers.DistanceBased(distance=mink, similarity=similarity)
return ker... |
def __repr__(self):
'Return a string representation of the device.'
return '<WeMo LightSwitch "{name}">'.format(name=self.name) | -6,814,544,005,257,611,000 | Return a string representation of the device. | pywemo/ouimeaux_device/lightswitch.py | __repr__ | GarlicToum/pywemo | python | def __repr__(self):
return '<WeMo LightSwitch "{name}">'.format(name=self.name) |
@property
def device_type(self):
'Return what kind of WeMo this device is.'
return 'LightSwitch' | 1,603,105,175,854,432,300 | Return what kind of WeMo this device is. | pywemo/ouimeaux_device/lightswitch.py | device_type | GarlicToum/pywemo | python | @property
def device_type(self):
return 'LightSwitch' |
def send_single_ans(self, ID, name: str):
'\n Send a single message to specific id with a specific name.\n\n :params ID: User quiz id.\n :type ID: int\n :params name: Name you want on the message.\n :type name: str\n '
self.data = {'userFullName': name, 'userQuizId': 1}... | -2,713,328,840,263,826,000 | Send a single message to specific id with a specific name.
:params ID: User quiz id.
:type ID: int
:params name: Name you want on the message.
:type name: str | buddymojoAPI/BuddyMojoAPI.py | send_single_ans | jasonjustin/BuddymojoAPI | python | def send_single_ans(self, ID, name: str):
'\n Send a single message to specific id with a specific name.\n\n :params ID: User quiz id.\n :type ID: int\n :params name: Name you want on the message.\n :type name: str\n '
self.data = {'userFullName': name, 'userQuizId': 1}... |
def send_range_ans(self, start, end, name: str):
'\n Send messages to a range of users id.\n\n :params start: The start user id.\n :type start: int\n :params end: The end user id.\n :type end: int\n :params name: The name you want.\n :type name: str\n '
fo... | -2,403,878,059,931,526,700 | Send messages to a range of users id.
:params start: The start user id.
:type start: int
:params end: The end user id.
:type end: int
:params name: The name you want.
:type name: str | buddymojoAPI/BuddyMojoAPI.py | send_range_ans | jasonjustin/BuddymojoAPI | python | def send_range_ans(self, start, end, name: str):
'\n Send messages to a range of users id.\n\n :params start: The start user id.\n :type start: int\n :params end: The end user id.\n :type end: int\n :params name: The name you want.\n :type name: str\n '
fo... |
def get_userQuizId(self, encUserQuizId):
'\n Returns a user id string of the encUserQuizId.\n '
try:
req = requests.request('GET', str((match + encUserQuizId)))
data = json.loads(req.text)
print(data)
except:
return 'User not found' | -5,446,436,008,461,802,000 | Returns a user id string of the encUserQuizId. | buddymojoAPI/BuddyMojoAPI.py | get_userQuizId | jasonjustin/BuddymojoAPI | python | def get_userQuizId(self, encUserQuizId):
'\n \n '
try:
req = requests.request('GET', str((match + encUserQuizId)))
data = json.loads(req.text)
print(data)
except:
return 'User not found' |
def get_link(self, ID):
'\n Returns a url string of the id.\n\n :params ID: The id to get the url from.\n :type ID: int\n :returns: A url string.\n :rtype: String\n '
self.payloadf.update(userQuizId=ID)
try:
req = requests.request('GET', self.url, params=sel... | 8,604,263,190,504,289,000 | Returns a url string of the id.
:params ID: The id to get the url from.
:type ID: int
:returns: A url string.
:rtype: String | buddymojoAPI/BuddyMojoAPI.py | get_link | jasonjustin/BuddymojoAPI | python | def get_link(self, ID):
'\n Returns a url string of the id.\n\n :params ID: The id to get the url from.\n :type ID: int\n :returns: A url string.\n :rtype: String\n '
self.payloadf.update(userQuizId=ID)
try:
req = requests.request('GET', self.url, params=sel... |
def _detect_thread_group(self, executor):
'\n Detect preferred thread group\n :param executor:\n :return:\n '
tg = self.TG
if (not self.force_ctg):
return tg
msg = 'Thread group detection: %s, regular ThreadGroup will be used'
if (not self.load.duration):
... | -4,644,660,773,016,732,000 | Detect preferred thread group
:param executor:
:return: | bzt/jmx/tools.py | _detect_thread_group | greyfenrir/taurus | python | def _detect_thread_group(self, executor):
'\n Detect preferred thread group\n :param executor:\n :return:\n '
tg = self.TG
if (not self.force_ctg):
return tg
msg = 'Thread group detection: %s, regular ThreadGroup will be used'
if (not self.load.duration):
... |
def _divide_concurrency(self, concurrency_list):
'\n calculate target concurrency for every thread group\n '
total_old_concurrency = sum(concurrency_list)
for (idx, concurrency) in enumerate(concurrency_list):
if (total_old_concurrency and (concurrency_list[idx] != 0)):
par... | 209,768,109,835,262,200 | calculate target concurrency for every thread group | bzt/jmx/tools.py | _divide_concurrency | greyfenrir/taurus | python | def _divide_concurrency(self, concurrency_list):
'\n \n '
total_old_concurrency = sum(concurrency_list)
for (idx, concurrency) in enumerate(concurrency_list):
if (total_old_concurrency and (concurrency_list[idx] != 0)):
part_of_load = (((1.0 * self.load.concurrency) * concu... |
def _add_shaper(self, jmx):
'\n Add shaper\n :param jmx: JMX\n :return:\n '
if (not self.load.duration):
self.log.warning("You must set 'ramp-up' and/or 'hold-for' when using 'throughput' option")
return
etree_shaper = jmx.get_rps_shaper()
if self.load.ramp_up... | 5,178,974,408,345,509,000 | Add shaper
:param jmx: JMX
:return: | bzt/jmx/tools.py | _add_shaper | greyfenrir/taurus | python | def _add_shaper(self, jmx):
'\n Add shaper\n :param jmx: JMX\n :return:\n '
if (not self.load.duration):
self.log.warning("You must set 'ramp-up' and/or 'hold-for' when using 'throughput' option")
return
etree_shaper = jmx.get_rps_shaper()
if self.load.ramp_up... |
def __init__(self, executor, original=None):
'\n :type executor: ScenarioExecutor\n :type original: JMX\n '
super(JMeterScenarioBuilder, self).__init__(original)
self.executor = executor
self.scenario = executor.get_scenario()
self.engine = executor.engine
self.system_props ... | 11,199,671,135,209,920 | :type executor: ScenarioExecutor
:type original: JMX | bzt/jmx/tools.py | __init__ | greyfenrir/taurus | python | def __init__(self, executor, original=None):
'\n :type executor: ScenarioExecutor\n :type original: JMX\n '
super(JMeterScenarioBuilder, self).__init__(original)
self.executor = executor
self.scenario = executor.get_scenario()
self.engine = executor.engine
self.system_props ... |
@staticmethod
def __add_jsr_elements(children, req, get_from_config=True):
'\n :type children: etree.Element\n :type req: Request\n '
jsrs = []
if get_from_config:
jsrs = req.config.get('jsr223', [])
else:
jsrs = req.get('jsr223', [])
if (not isinstance(jsrs, lis... | -3,542,814,545,030,569,500 | :type children: etree.Element
:type req: Request | bzt/jmx/tools.py | __add_jsr_elements | greyfenrir/taurus | python | @staticmethod
def __add_jsr_elements(children, req, get_from_config=True):
'\n :type children: etree.Element\n :type req: Request\n '
jsrs = []
if get_from_config:
jsrs = req.config.get('jsr223', [])
else:
jsrs = req.get('jsr223', [])
if (not isinstance(jsrs, lis... |
def compile_request(self, request):
'\n\n :type request: HierarchicHTTPRequest\n :return:\n '
sampler = children = None
protocol_name = request.priority_option('protocol', default=self.default_protocol)
if (protocol_name in self.protocol_handlers):
protocol = self.protocol_h... | -1,291,728,201,988,147,500 | :type request: HierarchicHTTPRequest
:return: | bzt/jmx/tools.py | compile_request | greyfenrir/taurus | python | def compile_request(self, request):
'\n\n :type request: HierarchicHTTPRequest\n :return:\n '
sampler = children = None
protocol_name = request.priority_option('protocol', default=self.default_protocol)
if (protocol_name in self.protocol_handlers):
protocol = self.protocol_h... |
def compile_foreach_block(self, block):
'\n :type block: ForEachBlock\n '
elements = []
controller = JMX._get_foreach_controller(block.input_var, block.loop_var)
children = etree.Element('hashTree')
for compiled in self.compile_requests(block.requests):
for element in compiled:... | 3,921,619,715,577,166,300 | :type block: ForEachBlock | bzt/jmx/tools.py | compile_foreach_block | greyfenrir/taurus | python | def compile_foreach_block(self, block):
'\n \n '
elements = []
controller = JMX._get_foreach_controller(block.input_var, block.loop_var)
children = etree.Element('hashTree')
for compiled in self.compile_requests(block.requests):
for element in compiled:
children.app... |
def compile_action_block(self, block):
'\n :type block: ActionBlock\n :return:\n '
actions = {'stop': 0, 'pause': 1, 'stop-now': 2, 'continue': 3}
targets = {'current-thread': 0, 'all-threads': 2}
action = actions[block.action]
target = targets[block.target]
duration = 0
... | 7,389,238,544,759,741,000 | :type block: ActionBlock
:return: | bzt/jmx/tools.py | compile_action_block | greyfenrir/taurus | python | def compile_action_block(self, block):
'\n :type block: ActionBlock\n :return:\n '
actions = {'stop': 0, 'pause': 1, 'stop-now': 2, 'continue': 3}
targets = {'current-thread': 0, 'all-threads': 2}
action = actions[block.action]
target = targets[block.target]
duration = 0
... |
def __generate(self):
'\n Generate the test plan\n '
thread_group = JMX.get_thread_group(testname=self.executor.label)
thread_group_ht = etree.Element('hashTree', type='tg')
self.request_compiler = RequestCompiler(self)
for element in self.compile_scenario(self.scenario):
threa... | -7,969,458,648,921,240,000 | Generate the test plan | bzt/jmx/tools.py | __generate | greyfenrir/taurus | python | def __generate(self):
'\n \n '
thread_group = JMX.get_thread_group(testname=self.executor.label)
thread_group_ht = etree.Element('hashTree', type='tg')
self.request_compiler = RequestCompiler(self)
for element in self.compile_scenario(self.scenario):
thread_group_ht.append(elem... |
def save(self, filename):
'\n Generate test plan and save\n\n :type filename: str\n '
self.__generate()
super(JMeterScenarioBuilder, self).save(filename) | 861,738,620,378,334,000 | Generate test plan and save
:type filename: str | bzt/jmx/tools.py | save | greyfenrir/taurus | python | def save(self, filename):
'\n Generate test plan and save\n\n :type filename: str\n '
self.__generate()
super(JMeterScenarioBuilder, self).save(filename) |
@staticmethod
def __gen_authorization(scenario):
'\n Generates HTTP Authorization Manager\n\n '
elements = []
authorizations = scenario.get('authorization')
if authorizations:
clear_flag = False
if isinstance(authorizations, dict):
if (('clear' in authorizations... | 4,335,678,651,450,887,000 | Generates HTTP Authorization Manager | bzt/jmx/tools.py | __gen_authorization | greyfenrir/taurus | python | @staticmethod
def __gen_authorization(scenario):
'\n \n\n '
elements = []
authorizations = scenario.get('authorization')
if authorizations:
clear_flag = False
if isinstance(authorizations, dict):
if (('clear' in authorizations) or ('list' in authorizations)):
... |
def __init__(self, label=None, display_order=None, local_vars_configuration=None):
'PropertyGroupUpdate - a model defined in OpenAPI'
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._label = None
... | 5,236,590,290,660,018,000 | PropertyGroupUpdate - a model defined in OpenAPI | hubspot/crm/properties/models/property_group_update.py | __init__ | cclauss/hubspot-api-python | python | def __init__(self, label=None, display_order=None, local_vars_configuration=None):
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._label = None
self._display_order = None
self.discriminator ... |
@property
def label(self):
'Gets the label of this PropertyGroupUpdate. # noqa: E501\n\n A human-readable label that will be shown in HubSpot. # noqa: E501\n\n :return: The label of this PropertyGroupUpdate. # noqa: E501\n :rtype: str\n '
return self._label | 2,917,488,915,512,171,500 | Gets the label of this PropertyGroupUpdate. # noqa: E501
A human-readable label that will be shown in HubSpot. # noqa: E501
:return: The label of this PropertyGroupUpdate. # noqa: E501
:rtype: str | hubspot/crm/properties/models/property_group_update.py | label | cclauss/hubspot-api-python | python | @property
def label(self):
'Gets the label of this PropertyGroupUpdate. # noqa: E501\n\n A human-readable label that will be shown in HubSpot. # noqa: E501\n\n :return: The label of this PropertyGroupUpdate. # noqa: E501\n :rtype: str\n '
return self._label |
@label.setter
def label(self, label):
'Sets the label of this PropertyGroupUpdate.\n\n A human-readable label that will be shown in HubSpot. # noqa: E501\n\n :param label: The label of this PropertyGroupUpdate. # noqa: E501\n :type: str\n '
self._label = label | 3,503,763,217,207,940,000 | Sets the label of this PropertyGroupUpdate.
A human-readable label that will be shown in HubSpot. # noqa: E501
:param label: The label of this PropertyGroupUpdate. # noqa: E501
:type: str | hubspot/crm/properties/models/property_group_update.py | label | cclauss/hubspot-api-python | python | @label.setter
def label(self, label):
'Sets the label of this PropertyGroupUpdate.\n\n A human-readable label that will be shown in HubSpot. # noqa: E501\n\n :param label: The label of this PropertyGroupUpdate. # noqa: E501\n :type: str\n '
self._label = label |
@property
def display_order(self):
'Gets the display_order of this PropertyGroupUpdate. # noqa: E501\n\n Property groups are displayed in order starting with the lowest positive integer value. Values of -1 will cause the property group to be displayed after any positive values. # noqa: E501\n\n :ret... | 5,386,896,482,861,787,000 | Gets the display_order of this PropertyGroupUpdate. # noqa: E501
Property groups are displayed in order starting with the lowest positive integer value. Values of -1 will cause the property group to be displayed after any positive values. # noqa: E501
:return: The display_order of this PropertyGroupUpdate. # noqa:... | hubspot/crm/properties/models/property_group_update.py | display_order | cclauss/hubspot-api-python | python | @property
def display_order(self):
'Gets the display_order of this PropertyGroupUpdate. # noqa: E501\n\n Property groups are displayed in order starting with the lowest positive integer value. Values of -1 will cause the property group to be displayed after any positive values. # noqa: E501\n\n :ret... |
@display_order.setter
def display_order(self, display_order):
'Sets the display_order of this PropertyGroupUpdate.\n\n Property groups are displayed in order starting with the lowest positive integer value. Values of -1 will cause the property group to be displayed after any positive values. # noqa: E501\n\... | -5,371,300,951,071,094,000 | Sets the display_order of this PropertyGroupUpdate.
Property groups are displayed in order starting with the lowest positive integer value. Values of -1 will cause the property group to be displayed after any positive values. # noqa: E501
:param display_order: The display_order of this PropertyGroupUpdate. # noqa: ... | hubspot/crm/properties/models/property_group_update.py | display_order | cclauss/hubspot-api-python | python | @display_order.setter
def display_order(self, display_order):
'Sets the display_order of this PropertyGroupUpdate.\n\n Property groups are displayed in order starting with the lowest positive integer value. Values of -1 will cause the property group to be displayed after any positive values. # noqa: E501\n\... |
def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
e... | 8,442,519,487,048,767,000 | Returns the model properties as a dict | hubspot/crm/properties/models/property_group_update.py | to_dict | cclauss/hubspot-api-python | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
... |
def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | 5,849,158,643,760,736,000 | Returns the string representation of the model | hubspot/crm/properties/models/property_group_update.py | to_str | cclauss/hubspot-api-python | python | def to_str(self):
return pprint.pformat(self.to_dict()) |
def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | -8,960,031,694,814,905,000 | For `print` and `pprint` | hubspot/crm/properties/models/property_group_update.py | __repr__ | cclauss/hubspot-api-python | python | def __repr__(self):
return self.to_str() |
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, PropertyGroupUpdate)):
return False
return (self.to_dict() == other.to_dict()) | -2,793,007,724,244,214,000 | Returns true if both objects are equal | hubspot/crm/properties/models/property_group_update.py | __eq__ | cclauss/hubspot-api-python | python | def __eq__(self, other):
if (not isinstance(other, PropertyGroupUpdate)):
return False
return (self.to_dict() == other.to_dict()) |
def __ne__(self, other):
'Returns true if both objects are not equal'
if (not isinstance(other, PropertyGroupUpdate)):
return True
return (self.to_dict() != other.to_dict()) | -8,805,428,320,412,282,000 | Returns true if both objects are not equal | hubspot/crm/properties/models/property_group_update.py | __ne__ | cclauss/hubspot-api-python | python | def __ne__(self, other):
if (not isinstance(other, PropertyGroupUpdate)):
return True
return (self.to_dict() != other.to_dict()) |
@property
def hexagonal_edges(self):
'Gets the three half-edges on the hexagonal boundary incident to a black node and point in ccw direction.'
first = self.half_edge
res = [first]
second = first.opposite.next.opposite.next
res.append(second)
third = second.opposite.next.opposite.next
res.ap... | 6,849,385,400,819,632,000 | Gets the three half-edges on the hexagonal boundary incident to a black node and point in ccw direction. | planar_graph_sampler/combinatorial_classes/dissection.py | hexagonal_edges | petrovp/networkx-related | python | @property
def hexagonal_edges(self):
first = self.half_edge
res = [first]
second = first.opposite.next.opposite.next
res.append(second)
third = second.opposite.next.opposite.next
res.append(third)
for he in res:
assert (he.is_hexagonal and (he.color is 'black'))
return res |
def root_at_random_hexagonal_edge(self):
'Selects a random hexagonal half-edge and makes it the root.'
self._half_edge = rnd.choice(self.hexagonal_edges) | 8,306,759,444,568,594,000 | Selects a random hexagonal half-edge and makes it the root. | planar_graph_sampler/combinatorial_classes/dissection.py | root_at_random_hexagonal_edge | petrovp/networkx-related | python | def root_at_random_hexagonal_edge(self):
self._half_edge = rnd.choice(self.hexagonal_edges) |
@property
def is_admissible_slow(self):
'Checks if there is a path of length 3 with an inner edge from the root to the opposite outer vertex.'
start_node = self.half_edge
assert (start_node.color is 'black')
end_node = self.half_edge.opposite.next.opposite.next.opposite
assert (end_node.color is 'wh... | 5,505,801,646,970,087,000 | Checks if there is a path of length 3 with an inner edge from the root to the opposite outer vertex. | planar_graph_sampler/combinatorial_classes/dissection.py | is_admissible_slow | petrovp/networkx-related | python | @property
def is_admissible_slow(self):
start_node = self.half_edge
assert (start_node.color is 'black')
end_node = self.half_edge.opposite.next.opposite.next.opposite
assert (end_node.color is 'white')
start_node = start_node.node_nr
end_node = end_node.node_nr
g = self.to_networkx_gra... |
@property
def is_admissible(self):
'Checks if there is a path of length 3 with an inner edge from the root to the opposite outer vertex.'
start_node = self.half_edge
assert (start_node.color is 'black')
end_node = self.half_edge.opposite.next.opposite.next.opposite
assert (end_node.color is 'white')... | 4,637,412,663,304,804,000 | Checks if there is a path of length 3 with an inner edge from the root to the opposite outer vertex. | planar_graph_sampler/combinatorial_classes/dissection.py | is_admissible | petrovp/networkx-related | python | @property
def is_admissible(self):
start_node = self.half_edge
assert (start_node.color is 'black')
end_node = self.half_edge.opposite.next.opposite.next.opposite
assert (end_node.color is 'white')
queue = deque(list())
queue.append((self.half_edge, 0, False, set()))
while (len(queue) !... |
@property
def u_size(self):
'The u-size is the number of inner faces.'
return ((self.number_of_half_edges - 6) / 4) | 7,695,950,379,170,463,000 | The u-size is the number of inner faces. | planar_graph_sampler/combinatorial_classes/dissection.py | u_size | petrovp/networkx-related | python | @property
def u_size(self):
return ((self.number_of_half_edges - 6) / 4) |
@property
def l_size(self):
'The l-size is the number of black inner vertices.'
node_dict = self.half_edge.node_dict()
black_vertices = len([node_nr for node_nr in node_dict if (node_dict[node_nr][0].color is 'black')])
return (black_vertices - 3) | -6,482,723,490,751,050,000 | The l-size is the number of black inner vertices. | planar_graph_sampler/combinatorial_classes/dissection.py | l_size | petrovp/networkx-related | python | @property
def l_size(self):
node_dict = self.half_edge.node_dict()
black_vertices = len([node_nr for node_nr in node_dict if (node_dict[node_nr][0].color is 'black')])
return (black_vertices - 3) |
def to_networkx_graph(self, include_unpaired=None):
'Converts to networkx graph, encodes hexagonal nodes with colors.'
from planar_graph_sampler.combinatorial_classes.half_edge_graph import color_scale
nodes = self.half_edge.node_dict()
G = super(IrreducibleDissection, self).to_networkx_graph(include_un... | -7,118,483,803,622,384,000 | Converts to networkx graph, encodes hexagonal nodes with colors. | planar_graph_sampler/combinatorial_classes/dissection.py | to_networkx_graph | petrovp/networkx-related | python | def to_networkx_graph(self, include_unpaired=None):
from planar_graph_sampler.combinatorial_classes.half_edge_graph import color_scale
nodes = self.half_edge.node_dict()
G = super(IrreducibleDissection, self).to_networkx_graph(include_unpaired=False)
for v in G:
if any([he.is_hexagonal for ... |
def read_json(json_file: str, debug=False) -> List[Dict]:
'\n reads the json files, and formats the description that\n is associated with each of the json dictionaries that are read in.\n\n :param json_file: json file to parse from\n :param debug: if set to true, will print the json dictionaries as\n ... | -2,822,950,693,851,630,000 | reads the json files, and formats the description that
is associated with each of the json dictionaries that are read in.
:param json_file: json file to parse from
:param debug: if set to true, will print the json dictionaries as
they are read in
:return: list of all of the json dictionaries | app.py | read_json | Jim-Shaddix/Personal-Website | python | def read_json(json_file: str, debug=False) -> List[Dict]:
'\n reads the json files, and formats the description that\n is associated with each of the json dictionaries that are read in.\n\n :param json_file: json file to parse from\n :param debug: if set to true, will print the json dictionaries as\n ... |
def translate_batch(self, batch, fast=False):
"\n Translate a batch of sentences.\n\n Mostly a wrapper around :obj:`Beam`.\n\n Args:\n batch (:obj:`Batch`): a batch from a dataset object\n data (:obj:`Dataset`): the dataset object\n fast (bool): enables fast beam s... | -2,044,400,624,652,274,400 | Translate a batch of sentences.
Mostly a wrapper around :obj:`Beam`.
Args:
batch (:obj:`Batch`): a batch from a dataset object
data (:obj:`Dataset`): the dataset object
fast (bool): enables fast beam search (may not support all features)
Todo:
Shouldn't need the original dataset. | src/models/predictor.py | translate_batch | SebastianVeile/PreSumm | python | def translate_batch(self, batch, fast=False):
"\n Translate a batch of sentences.\n\n Mostly a wrapper around :obj:`Beam`.\n\n Args:\n batch (:obj:`Batch`): a batch from a dataset object\n data (:obj:`Dataset`): the dataset object\n fast (bool): enables fast beam s... |
def log(self, sent_number):
'\n Log translation.\n '
output = '\nSENT {}: {}\n'.format(sent_number, self.src_raw)
best_pred = self.pred_sents[0]
best_score = self.pred_scores[0]
pred_sent = ' '.join(best_pred)
output += 'PRED {}: {}\n'.format(sent_number, pred_sent)
output += '... | 6,652,500,622,530,272,000 | Log translation. | src/models/predictor.py | log | SebastianVeile/PreSumm | python | def log(self, sent_number):
'\n \n '
output = '\nSENT {}: {}\n'.format(sent_number, self.src_raw)
best_pred = self.pred_sents[0]
best_score = self.pred_scores[0]
pred_sent = ' '.join(best_pred)
output += 'PRED {}: {}\n'.format(sent_number, pred_sent)
output += 'PRED SCORE: {:.4... |
def test_get_scalars_with_actual_inf_and_nan(self):
'Test for get_scalars() call that involve inf and nan in user data.'
mock_api_client = mock.Mock()
def stream_experiment_data(request, **kwargs):
self.assertEqual(request.experiment_id, '789')
self.assertEqual(kwargs['metadata'], grpc_util... | 2,623,809,314,212,357,600 | Test for get_scalars() call that involve inf and nan in user data. | tensorboard/data/experimental/experiment_from_dev_test.py | test_get_scalars_with_actual_inf_and_nan | AseiSugiyama/tensorboard | python | def test_get_scalars_with_actual_inf_and_nan(self):
mock_api_client = mock.Mock()
def stream_experiment_data(request, **kwargs):
self.assertEqual(request.experiment_id, '789')
self.assertEqual(kwargs['metadata'], grpc_util.version_metadata())
response = export_service_pb2.StreamExp... |
def class_from_module_path(module_path: Text, lookup_path: Optional[Text]=None) -> Type:
'Given the module name and path of a class, tries to retrieve the class.\n\n The loaded class can be used to instantiate new objects.\n\n Args:\n module_path: either an absolute path to a Python class,\n ... | -4,786,117,763,749,435,000 | Given the module name and path of a class, tries to retrieve the class.
The loaded class can be used to instantiate new objects.
Args:
module_path: either an absolute path to a Python class,
or the name of the class in the local / global scope.
lookup_path: a path where to load the class from... | rasa/shared/utils/common.py | class_from_module_path | GCES-2021-1/rasa | python | def class_from_module_path(module_path: Text, lookup_path: Optional[Text]=None) -> Type:
'Given the module name and path of a class, tries to retrieve the class.\n\n The loaded class can be used to instantiate new objects.\n\n Args:\n module_path: either an absolute path to a Python class,\n ... |
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