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b5e7e07122593b6fa96664307ade09dd691618950e5515ba8f16de1dccfffa83 | def _crop(inname, outname, color):
'\n Crop the image and colour it in a flat colour. The alpha channel is left unchanged.\n :param inname: base name of input file. Input image is {inname}1.png. The 1 is added by latex.\n :param outname: base name of output file. Output image is {outname}.png\n :param color:\n :return:\n '
image = Image.open('{}1.png'.format(inname))
image.load()
image_data = np.asarray(image)
shape = image_data.shape
shape = (shape[0], shape[1], 4)
image_temp = np.zeros(shape, dtype=np.uint8)
for i in range(shape[0]):
for j in range(shape[1]):
image_temp[i][j][3] = (255 - image_data[i][j][0])
image_data = image_temp
image_data_bw = image_data.max(axis=2)
non_empty_columns = np.where((image_data_bw.max(axis=0) > 0))[0]
non_empty_rows = np.where((image_data_bw.max(axis=1) > 0))[0]
cropbox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))
image_data_new = image_data[(cropbox[0]:(cropbox[1] + 1), cropbox[2]:(cropbox[3] + 1), :)]
image_data_colored = np.zeros_like(image_data_new)
color_data = ((color.r * 255), (color.g * 255), (color.b * 255))
for i in range(image_data_new.shape[0]):
for j in range(image_data_new.shape[1]):
image_data_colored[i][j][3] = image_data_new[i][j][3]
image_data_colored[i][j][0] = color_data[0]
image_data_colored[i][j][1] = color_data[1]
image_data_colored[i][j][2] = color_data[2]
new_image = Image.fromarray(image_data_colored)
filename = '{}.png'.format(outname)
new_image.save(filename)
return (filename, ((cropbox[3] - cropbox[2]), (cropbox[1] - cropbox[0]))) | Crop the image and colour it in a flat colour. The alpha channel is left unchanged.
:param inname: base name of input file. Input image is {inname}1.png. The 1 is added by latex.
:param outname: base name of output file. Output image is {outname}.png
:param color:
:return: | generativepy/formulas.py | _crop | sthagen/martinmcbride-generativepy | 0 | python | def _crop(inname, outname, color):
'\n Crop the image and colour it in a flat colour. The alpha channel is left unchanged.\n :param inname: base name of input file. Input image is {inname}1.png. The 1 is added by latex.\n :param outname: base name of output file. Output image is {outname}.png\n :param color:\n :return:\n '
image = Image.open('{}1.png'.format(inname))
image.load()
image_data = np.asarray(image)
shape = image_data.shape
shape = (shape[0], shape[1], 4)
image_temp = np.zeros(shape, dtype=np.uint8)
for i in range(shape[0]):
for j in range(shape[1]):
image_temp[i][j][3] = (255 - image_data[i][j][0])
image_data = image_temp
image_data_bw = image_data.max(axis=2)
non_empty_columns = np.where((image_data_bw.max(axis=0) > 0))[0]
non_empty_rows = np.where((image_data_bw.max(axis=1) > 0))[0]
cropbox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))
image_data_new = image_data[(cropbox[0]:(cropbox[1] + 1), cropbox[2]:(cropbox[3] + 1), :)]
image_data_colored = np.zeros_like(image_data_new)
color_data = ((color.r * 255), (color.g * 255), (color.b * 255))
for i in range(image_data_new.shape[0]):
for j in range(image_data_new.shape[1]):
image_data_colored[i][j][3] = image_data_new[i][j][3]
image_data_colored[i][j][0] = color_data[0]
image_data_colored[i][j][1] = color_data[1]
image_data_colored[i][j][2] = color_data[2]
new_image = Image.fromarray(image_data_colored)
filename = '{}.png'.format(outname)
new_image.save(filename)
return (filename, ((cropbox[3] - cropbox[2]), (cropbox[1] - cropbox[0]))) | def _crop(inname, outname, color):
'\n Crop the image and colour it in a flat colour. The alpha channel is left unchanged.\n :param inname: base name of input file. Input image is {inname}1.png. The 1 is added by latex.\n :param outname: base name of output file. Output image is {outname}.png\n :param color:\n :return:\n '
image = Image.open('{}1.png'.format(inname))
image.load()
image_data = np.asarray(image)
shape = image_data.shape
shape = (shape[0], shape[1], 4)
image_temp = np.zeros(shape, dtype=np.uint8)
for i in range(shape[0]):
for j in range(shape[1]):
image_temp[i][j][3] = (255 - image_data[i][j][0])
image_data = image_temp
image_data_bw = image_data.max(axis=2)
non_empty_columns = np.where((image_data_bw.max(axis=0) > 0))[0]
non_empty_rows = np.where((image_data_bw.max(axis=1) > 0))[0]
cropbox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))
image_data_new = image_data[(cropbox[0]:(cropbox[1] + 1), cropbox[2]:(cropbox[3] + 1), :)]
image_data_colored = np.zeros_like(image_data_new)
color_data = ((color.r * 255), (color.g * 255), (color.b * 255))
for i in range(image_data_new.shape[0]):
for j in range(image_data_new.shape[1]):
image_data_colored[i][j][3] = image_data_new[i][j][3]
image_data_colored[i][j][0] = color_data[0]
image_data_colored[i][j][1] = color_data[1]
image_data_colored[i][j][2] = color_data[2]
new_image = Image.fromarray(image_data_colored)
filename = '{}.png'.format(outname)
new_image.save(filename)
return (filename, ((cropbox[3] - cropbox[2]), (cropbox[1] - cropbox[0])))<|docstring|>Crop the image and colour it in a flat colour. The alpha channel is left unchanged.
:param inname: base name of input file. Input image is {inname}1.png. The 1 is added by latex.
:param outname: base name of output file. Output image is {outname}.png
:param color:
:return:<|endoftext|> |
ac41ec54f3d8116288088ea4ea8e288e6b0cf2ce6880a35424b64ce27afa1402 | def _remove_ignore_errors(filename):
"\n Remove a file but ignore errors. We shouldn;t fail just because a temp file didn't get deleted.\n :param filename:\n :return: None\n "
try:
os.remove(filename)
except Exception:
pass | Remove a file but ignore errors. We shouldn;t fail just because a temp file didn't get deleted.
:param filename:
:return: None | generativepy/formulas.py | _remove_ignore_errors | sthagen/martinmcbride-generativepy | 0 | python | def _remove_ignore_errors(filename):
"\n Remove a file but ignore errors. We shouldn;t fail just because a temp file didn't get deleted.\n :param filename:\n :return: None\n "
try:
os.remove(filename)
except Exception:
pass | def _remove_ignore_errors(filename):
"\n Remove a file but ignore errors. We shouldn;t fail just because a temp file didn't get deleted.\n :param filename:\n :return: None\n "
try:
os.remove(filename)
except Exception:
pass<|docstring|>Remove a file but ignore errors. We shouldn;t fail just because a temp file didn't get deleted.
:param filename:
:return: None<|endoftext|> |
4855c1cc2ed6f44e0e81920fafe16843af3ff5ca85d165e16aa2f038d7661c1d | def rasterise_formula(name, formula, color, dpi=600):
'\n Convert a latex formula into a PNG image. The PNG image will be tightly cropped, with a transparent background and\n text in the selected colour.\n :param name: Name of the output images. String with no extension, eg "myformula". The final output will be stored\n using this name, in the current working folder, so if you are creating multiple formulae give each one a unique name\n :param formula: The forumal, as a latex string.\n :param color: Color object defining the required colour of the output.\n :param dpi: The resolution, in dpi. This indirectly controls the size of the image,\n :return: A tuple containing the filename of teh result (with a png extension) and the (width, height) of the image\n in pixels.\n '
unique_name = '{}-{}'.format(name, random.randint(100000, 999999))
tex = '\n'.join([tex1, formula, tex2])
tex_fn = '{}.tex'.format(unique_name)
with open(tex_fn, 'w') as tex_file:
tex_file.write(tex)
process = subprocess.Popen('latex {}.tex'.format(unique_name), shell=True, stdout=subprocess.PIPE)
process.wait()
process = subprocess.Popen('dvipng -T tight -D {} {}.dvi'.format(dpi, unique_name), shell=True, stdout=subprocess.PIPE)
process.wait()
(filename, size) = _crop(unique_name, name, color)
_remove_ignore_errors('{}.aux'.format(unique_name))
_remove_ignore_errors('{}.log'.format(unique_name))
_remove_ignore_errors('{}.tex'.format(unique_name))
_remove_ignore_errors('{}.dvi'.format(unique_name))
_remove_ignore_errors('{}1.png'.format(unique_name))
return (filename, size) | Convert a latex formula into a PNG image. The PNG image will be tightly cropped, with a transparent background and
text in the selected colour.
:param name: Name of the output images. String with no extension, eg "myformula". The final output will be stored
using this name, in the current working folder, so if you are creating multiple formulae give each one a unique name
:param formula: The forumal, as a latex string.
:param color: Color object defining the required colour of the output.
:param dpi: The resolution, in dpi. This indirectly controls the size of the image,
:return: A tuple containing the filename of teh result (with a png extension) and the (width, height) of the image
in pixels. | generativepy/formulas.py | rasterise_formula | sthagen/martinmcbride-generativepy | 0 | python | def rasterise_formula(name, formula, color, dpi=600):
'\n Convert a latex formula into a PNG image. The PNG image will be tightly cropped, with a transparent background and\n text in the selected colour.\n :param name: Name of the output images. String with no extension, eg "myformula". The final output will be stored\n using this name, in the current working folder, so if you are creating multiple formulae give each one a unique name\n :param formula: The forumal, as a latex string.\n :param color: Color object defining the required colour of the output.\n :param dpi: The resolution, in dpi. This indirectly controls the size of the image,\n :return: A tuple containing the filename of teh result (with a png extension) and the (width, height) of the image\n in pixels.\n '
unique_name = '{}-{}'.format(name, random.randint(100000, 999999))
tex = '\n'.join([tex1, formula, tex2])
tex_fn = '{}.tex'.format(unique_name)
with open(tex_fn, 'w') as tex_file:
tex_file.write(tex)
process = subprocess.Popen('latex {}.tex'.format(unique_name), shell=True, stdout=subprocess.PIPE)
process.wait()
process = subprocess.Popen('dvipng -T tight -D {} {}.dvi'.format(dpi, unique_name), shell=True, stdout=subprocess.PIPE)
process.wait()
(filename, size) = _crop(unique_name, name, color)
_remove_ignore_errors('{}.aux'.format(unique_name))
_remove_ignore_errors('{}.log'.format(unique_name))
_remove_ignore_errors('{}.tex'.format(unique_name))
_remove_ignore_errors('{}.dvi'.format(unique_name))
_remove_ignore_errors('{}1.png'.format(unique_name))
return (filename, size) | def rasterise_formula(name, formula, color, dpi=600):
'\n Convert a latex formula into a PNG image. The PNG image will be tightly cropped, with a transparent background and\n text in the selected colour.\n :param name: Name of the output images. String with no extension, eg "myformula". The final output will be stored\n using this name, in the current working folder, so if you are creating multiple formulae give each one a unique name\n :param formula: The forumal, as a latex string.\n :param color: Color object defining the required colour of the output.\n :param dpi: The resolution, in dpi. This indirectly controls the size of the image,\n :return: A tuple containing the filename of teh result (with a png extension) and the (width, height) of the image\n in pixels.\n '
unique_name = '{}-{}'.format(name, random.randint(100000, 999999))
tex = '\n'.join([tex1, formula, tex2])
tex_fn = '{}.tex'.format(unique_name)
with open(tex_fn, 'w') as tex_file:
tex_file.write(tex)
process = subprocess.Popen('latex {}.tex'.format(unique_name), shell=True, stdout=subprocess.PIPE)
process.wait()
process = subprocess.Popen('dvipng -T tight -D {} {}.dvi'.format(dpi, unique_name), shell=True, stdout=subprocess.PIPE)
process.wait()
(filename, size) = _crop(unique_name, name, color)
_remove_ignore_errors('{}.aux'.format(unique_name))
_remove_ignore_errors('{}.log'.format(unique_name))
_remove_ignore_errors('{}.tex'.format(unique_name))
_remove_ignore_errors('{}.dvi'.format(unique_name))
_remove_ignore_errors('{}1.png'.format(unique_name))
return (filename, size)<|docstring|>Convert a latex formula into a PNG image. The PNG image will be tightly cropped, with a transparent background and
text in the selected colour.
:param name: Name of the output images. String with no extension, eg "myformula". The final output will be stored
using this name, in the current working folder, so if you are creating multiple formulae give each one a unique name
:param formula: The forumal, as a latex string.
:param color: Color object defining the required colour of the output.
:param dpi: The resolution, in dpi. This indirectly controls the size of the image,
:return: A tuple containing the filename of teh result (with a png extension) and the (width, height) of the image
in pixels.<|endoftext|> |
80e8e516ac9d2e6922f9d15498998e0745a761c501e7448d3f1830ec89c42859 | def export_model():
'Exports the model'
trained_checkpoint_prefix = 'linear_regression'
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
sess.run(tf.global_variables_initializer())
loader = tf.train.import_meta_graph((trained_checkpoint_prefix + '.meta'))
loader.restore(sess, trained_checkpoint_prefix)
graph = tf.get_default_graph()
inputs = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('X:0'))
outputs = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('y_model:0'))
signature = signature_def_utils.build_signature_def(inputs={'X': inputs}, outputs={'y_model': outputs}, method_name=signature_constants.PREDICT_METHOD_NAME)
signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature}
builder = tf.saved_model.builder.SavedModelBuilder('./my_model')
builder.add_meta_graph_and_variables(sess, signature_def_map=signature_map, tags=[tf.saved_model.tag_constants.SERVING])
builder.save() | Exports the model | Chapter12/01-chapter-content/tensorflow/linear_regression_tensorflow/tensorflow_save_and_load_using_model_builder.py | export_model | ProgrammerWaterworth/Mastering-OpenCV-4-with-Python | 2 | python | def export_model():
trained_checkpoint_prefix = 'linear_regression'
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
sess.run(tf.global_variables_initializer())
loader = tf.train.import_meta_graph((trained_checkpoint_prefix + '.meta'))
loader.restore(sess, trained_checkpoint_prefix)
graph = tf.get_default_graph()
inputs = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('X:0'))
outputs = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('y_model:0'))
signature = signature_def_utils.build_signature_def(inputs={'X': inputs}, outputs={'y_model': outputs}, method_name=signature_constants.PREDICT_METHOD_NAME)
signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature}
builder = tf.saved_model.builder.SavedModelBuilder('./my_model')
builder.add_meta_graph_and_variables(sess, signature_def_map=signature_map, tags=[tf.saved_model.tag_constants.SERVING])
builder.save() | def export_model():
trained_checkpoint_prefix = 'linear_regression'
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
sess.run(tf.global_variables_initializer())
loader = tf.train.import_meta_graph((trained_checkpoint_prefix + '.meta'))
loader.restore(sess, trained_checkpoint_prefix)
graph = tf.get_default_graph()
inputs = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('X:0'))
outputs = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('y_model:0'))
signature = signature_def_utils.build_signature_def(inputs={'X': inputs}, outputs={'y_model': outputs}, method_name=signature_constants.PREDICT_METHOD_NAME)
signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature}
builder = tf.saved_model.builder.SavedModelBuilder('./my_model')
builder.add_meta_graph_and_variables(sess, signature_def_map=signature_map, tags=[tf.saved_model.tag_constants.SERVING])
builder.save()<|docstring|>Exports the model<|endoftext|> |
167afe5d948d22c647ae9c15bd36c521cc41c237b3098f94481870407a4fdaa0 | def Default(value: _TDefaultType) -> _TDefaultType:
"\n You shouldn't use this function directly.\n\n It's used internally to recognize when a default value has been overwritten, even\n if the overriden default value was truthy.\n "
return _DefaultPlaceholder(value) | You shouldn't use this function directly.
It's used internally to recognize when a default value has been overwritten, even
if the overriden default value was truthy. | sqlmodel/default.py | Default | strickvl/sqlmodel | 5,490 | python | def Default(value: _TDefaultType) -> _TDefaultType:
"\n You shouldn't use this function directly.\n\n It's used internally to recognize when a default value has been overwritten, even\n if the overriden default value was truthy.\n "
return _DefaultPlaceholder(value) | def Default(value: _TDefaultType) -> _TDefaultType:
"\n You shouldn't use this function directly.\n\n It's used internally to recognize when a default value has been overwritten, even\n if the overriden default value was truthy.\n "
return _DefaultPlaceholder(value)<|docstring|>You shouldn't use this function directly.
It's used internally to recognize when a default value has been overwritten, even
if the overriden default value was truthy.<|endoftext|> |
78e92c7265fb801c2a051d5e7003ccfbd93129a4c744d42d8a80e95dbe628dd4 | def __init__(self, *, host: str='compute.googleapis.com', credentials: ga_credentials.Credentials=None, credentials_file: str=None, scopes: Sequence[str]=None, client_cert_source_for_mtls: Callable[([], Tuple[(bytes, bytes)])]=None, quota_project_id: Optional[str]=None, client_info: gapic_v1.client_info.ClientInfo=DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool]=False, url_scheme: str='https') -> None:
'Instantiate the transport.\n\n Args:\n host (Optional[str]):\n The hostname to connect to.\n credentials (Optional[google.auth.credentials.Credentials]): The\n authorization credentials to attach to requests. These\n credentials identify the application to the service; if none\n are specified, the client will attempt to ascertain the\n credentials from the environment.\n\n credentials_file (Optional[str]): A file with credentials that can\n be loaded with :func:`google.auth.load_credentials_from_file`.\n This argument is ignored if ``channel`` is provided.\n scopes (Optional(Sequence[str])): A list of scopes. This argument is\n ignored if ``channel`` is provided.\n client_cert_source_for_mtls (Callable[[], Tuple[bytes, bytes]]): Client\n certificate to configure mutual TLS HTTP channel. It is ignored\n if ``channel`` is provided.\n quota_project_id (Optional[str]): An optional project to use for billing\n and quota.\n client_info (google.api_core.gapic_v1.client_info.ClientInfo):\n The client info used to send a user-agent string along with\n API requests. If ``None``, then default info will be used.\n Generally, you only need to set this if you are developing\n your own client library.\n always_use_jwt_access (Optional[bool]): Whether self signed JWT should\n be used for service account credentials.\n url_scheme: the protocol scheme for the API endpoint. Normally\n "https", but for testing or local servers,\n "http" can be specified.\n '
super().__init__(host=host, credentials=credentials, client_info=client_info, always_use_jwt_access=always_use_jwt_access)
self._session = AuthorizedSession(self._credentials, default_host=self.DEFAULT_HOST)
if client_cert_source_for_mtls:
self._session.configure_mtls_channel(client_cert_source_for_mtls)
self._prep_wrapped_messages(client_info) | Instantiate the transport.
Args:
host (Optional[str]):
The hostname to connect to.
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
credentials_file (Optional[str]): A file with credentials that can
be loaded with :func:`google.auth.load_credentials_from_file`.
This argument is ignored if ``channel`` is provided.
scopes (Optional(Sequence[str])): A list of scopes. This argument is
ignored if ``channel`` is provided.
client_cert_source_for_mtls (Callable[[], Tuple[bytes, bytes]]): Client
certificate to configure mutual TLS HTTP channel. It is ignored
if ``channel`` is provided.
quota_project_id (Optional[str]): An optional project to use for billing
and quota.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you are developing
your own client library.
always_use_jwt_access (Optional[bool]): Whether self signed JWT should
be used for service account credentials.
url_scheme: the protocol scheme for the API endpoint. Normally
"https", but for testing or local servers,
"http" can be specified. | google/cloud/compute_v1/services/region_commitments/transports/rest.py | __init__ | LaudateCorpus1/python-compute | 0 | python | def __init__(self, *, host: str='compute.googleapis.com', credentials: ga_credentials.Credentials=None, credentials_file: str=None, scopes: Sequence[str]=None, client_cert_source_for_mtls: Callable[([], Tuple[(bytes, bytes)])]=None, quota_project_id: Optional[str]=None, client_info: gapic_v1.client_info.ClientInfo=DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool]=False, url_scheme: str='https') -> None:
'Instantiate the transport.\n\n Args:\n host (Optional[str]):\n The hostname to connect to.\n credentials (Optional[google.auth.credentials.Credentials]): The\n authorization credentials to attach to requests. These\n credentials identify the application to the service; if none\n are specified, the client will attempt to ascertain the\n credentials from the environment.\n\n credentials_file (Optional[str]): A file with credentials that can\n be loaded with :func:`google.auth.load_credentials_from_file`.\n This argument is ignored if ``channel`` is provided.\n scopes (Optional(Sequence[str])): A list of scopes. This argument is\n ignored if ``channel`` is provided.\n client_cert_source_for_mtls (Callable[[], Tuple[bytes, bytes]]): Client\n certificate to configure mutual TLS HTTP channel. It is ignored\n if ``channel`` is provided.\n quota_project_id (Optional[str]): An optional project to use for billing\n and quota.\n client_info (google.api_core.gapic_v1.client_info.ClientInfo):\n The client info used to send a user-agent string along with\n API requests. If ``None``, then default info will be used.\n Generally, you only need to set this if you are developing\n your own client library.\n always_use_jwt_access (Optional[bool]): Whether self signed JWT should\n be used for service account credentials.\n url_scheme: the protocol scheme for the API endpoint. Normally\n "https", but for testing or local servers,\n "http" can be specified.\n '
super().__init__(host=host, credentials=credentials, client_info=client_info, always_use_jwt_access=always_use_jwt_access)
self._session = AuthorizedSession(self._credentials, default_host=self.DEFAULT_HOST)
if client_cert_source_for_mtls:
self._session.configure_mtls_channel(client_cert_source_for_mtls)
self._prep_wrapped_messages(client_info) | def __init__(self, *, host: str='compute.googleapis.com', credentials: ga_credentials.Credentials=None, credentials_file: str=None, scopes: Sequence[str]=None, client_cert_source_for_mtls: Callable[([], Tuple[(bytes, bytes)])]=None, quota_project_id: Optional[str]=None, client_info: gapic_v1.client_info.ClientInfo=DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool]=False, url_scheme: str='https') -> None:
'Instantiate the transport.\n\n Args:\n host (Optional[str]):\n The hostname to connect to.\n credentials (Optional[google.auth.credentials.Credentials]): The\n authorization credentials to attach to requests. These\n credentials identify the application to the service; if none\n are specified, the client will attempt to ascertain the\n credentials from the environment.\n\n credentials_file (Optional[str]): A file with credentials that can\n be loaded with :func:`google.auth.load_credentials_from_file`.\n This argument is ignored if ``channel`` is provided.\n scopes (Optional(Sequence[str])): A list of scopes. This argument is\n ignored if ``channel`` is provided.\n client_cert_source_for_mtls (Callable[[], Tuple[bytes, bytes]]): Client\n certificate to configure mutual TLS HTTP channel. It is ignored\n if ``channel`` is provided.\n quota_project_id (Optional[str]): An optional project to use for billing\n and quota.\n client_info (google.api_core.gapic_v1.client_info.ClientInfo):\n The client info used to send a user-agent string along with\n API requests. If ``None``, then default info will be used.\n Generally, you only need to set this if you are developing\n your own client library.\n always_use_jwt_access (Optional[bool]): Whether self signed JWT should\n be used for service account credentials.\n url_scheme: the protocol scheme for the API endpoint. Normally\n "https", but for testing or local servers,\n "http" can be specified.\n '
super().__init__(host=host, credentials=credentials, client_info=client_info, always_use_jwt_access=always_use_jwt_access)
self._session = AuthorizedSession(self._credentials, default_host=self.DEFAULT_HOST)
if client_cert_source_for_mtls:
self._session.configure_mtls_channel(client_cert_source_for_mtls)
self._prep_wrapped_messages(client_info)<|docstring|>Instantiate the transport.
Args:
host (Optional[str]):
The hostname to connect to.
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
credentials_file (Optional[str]): A file with credentials that can
be loaded with :func:`google.auth.load_credentials_from_file`.
This argument is ignored if ``channel`` is provided.
scopes (Optional(Sequence[str])): A list of scopes. This argument is
ignored if ``channel`` is provided.
client_cert_source_for_mtls (Callable[[], Tuple[bytes, bytes]]): Client
certificate to configure mutual TLS HTTP channel. It is ignored
if ``channel`` is provided.
quota_project_id (Optional[str]): An optional project to use for billing
and quota.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you are developing
your own client library.
always_use_jwt_access (Optional[bool]): Whether self signed JWT should
be used for service account credentials.
url_scheme: the protocol scheme for the API endpoint. Normally
"https", but for testing or local servers,
"http" can be specified.<|endoftext|> |
cb32e08094e510b8f83280cfddbf61066c6f954004cf64d1bc84e6ca6a5ed169 | def __call__(self, request: compute.AggregatedListRegionCommitmentsRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.CommitmentAggregatedList:
'Call the aggregated list method over HTTP.\n\n Args:\n request (~.compute.AggregatedListRegionCommitmentsRequest):\n The request object. A request message for\n RegionCommitments.AggregatedList. See\n the method description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.CommitmentAggregatedList:\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/aggregated/commitments'}]
request_kwargs = compute.AggregatedListRegionCommitmentsRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.AggregatedListRegionCommitmentsRequest.to_json(compute.AggregatedListRegionCommitmentsRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.CommitmentAggregatedList.from_json(response.content, ignore_unknown_fields=True) | Call the aggregated list method over HTTP.
Args:
request (~.compute.AggregatedListRegionCommitmentsRequest):
The request object. A request message for
RegionCommitments.AggregatedList. See
the method description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.CommitmentAggregatedList: | google/cloud/compute_v1/services/region_commitments/transports/rest.py | __call__ | LaudateCorpus1/python-compute | 0 | python | def __call__(self, request: compute.AggregatedListRegionCommitmentsRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.CommitmentAggregatedList:
'Call the aggregated list method over HTTP.\n\n Args:\n request (~.compute.AggregatedListRegionCommitmentsRequest):\n The request object. A request message for\n RegionCommitments.AggregatedList. See\n the method description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.CommitmentAggregatedList:\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/aggregated/commitments'}]
request_kwargs = compute.AggregatedListRegionCommitmentsRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.AggregatedListRegionCommitmentsRequest.to_json(compute.AggregatedListRegionCommitmentsRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.CommitmentAggregatedList.from_json(response.content, ignore_unknown_fields=True) | def __call__(self, request: compute.AggregatedListRegionCommitmentsRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.CommitmentAggregatedList:
'Call the aggregated list method over HTTP.\n\n Args:\n request (~.compute.AggregatedListRegionCommitmentsRequest):\n The request object. A request message for\n RegionCommitments.AggregatedList. See\n the method description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.CommitmentAggregatedList:\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/aggregated/commitments'}]
request_kwargs = compute.AggregatedListRegionCommitmentsRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.AggregatedListRegionCommitmentsRequest.to_json(compute.AggregatedListRegionCommitmentsRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.CommitmentAggregatedList.from_json(response.content, ignore_unknown_fields=True)<|docstring|>Call the aggregated list method over HTTP.
Args:
request (~.compute.AggregatedListRegionCommitmentsRequest):
The request object. A request message for
RegionCommitments.AggregatedList. See
the method description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.CommitmentAggregatedList:<|endoftext|> |
08913fe9f5edfa6a8db11e72ce4bfc9ecca3f248d8e29920cfaf389a544908bd | def __call__(self, request: compute.GetRegionCommitmentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.Commitment:
'Call the get method over HTTP.\n\n Args:\n request (~.compute.GetRegionCommitmentRequest):\n The request object. A request message for\n RegionCommitments.Get. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.Commitment:\n Represents a regional Commitment\n resource. Creating a commitment resource\n means that you are purchasing a\n committed use contract with an explicit\n start and end time. You can create\n commitments based on vCPUs and memory\n usage and receive discounted rates. For\n full details, read Signing Up for\n Committed Use Discounts.\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments/{commitment}'}]
request_kwargs = compute.GetRegionCommitmentRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.GetRegionCommitmentRequest.to_json(compute.GetRegionCommitmentRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.Commitment.from_json(response.content, ignore_unknown_fields=True) | Call the get method over HTTP.
Args:
request (~.compute.GetRegionCommitmentRequest):
The request object. A request message for
RegionCommitments.Get. See the method
description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.Commitment:
Represents a regional Commitment
resource. Creating a commitment resource
means that you are purchasing a
committed use contract with an explicit
start and end time. You can create
commitments based on vCPUs and memory
usage and receive discounted rates. For
full details, read Signing Up for
Committed Use Discounts. | google/cloud/compute_v1/services/region_commitments/transports/rest.py | __call__ | LaudateCorpus1/python-compute | 0 | python | def __call__(self, request: compute.GetRegionCommitmentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.Commitment:
'Call the get method over HTTP.\n\n Args:\n request (~.compute.GetRegionCommitmentRequest):\n The request object. A request message for\n RegionCommitments.Get. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.Commitment:\n Represents a regional Commitment\n resource. Creating a commitment resource\n means that you are purchasing a\n committed use contract with an explicit\n start and end time. You can create\n commitments based on vCPUs and memory\n usage and receive discounted rates. For\n full details, read Signing Up for\n Committed Use Discounts.\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments/{commitment}'}]
request_kwargs = compute.GetRegionCommitmentRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.GetRegionCommitmentRequest.to_json(compute.GetRegionCommitmentRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.Commitment.from_json(response.content, ignore_unknown_fields=True) | def __call__(self, request: compute.GetRegionCommitmentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.Commitment:
'Call the get method over HTTP.\n\n Args:\n request (~.compute.GetRegionCommitmentRequest):\n The request object. A request message for\n RegionCommitments.Get. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.Commitment:\n Represents a regional Commitment\n resource. Creating a commitment resource\n means that you are purchasing a\n committed use contract with an explicit\n start and end time. You can create\n commitments based on vCPUs and memory\n usage and receive discounted rates. For\n full details, read Signing Up for\n Committed Use Discounts.\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments/{commitment}'}]
request_kwargs = compute.GetRegionCommitmentRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.GetRegionCommitmentRequest.to_json(compute.GetRegionCommitmentRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.Commitment.from_json(response.content, ignore_unknown_fields=True)<|docstring|>Call the get method over HTTP.
Args:
request (~.compute.GetRegionCommitmentRequest):
The request object. A request message for
RegionCommitments.Get. See the method
description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.Commitment:
Represents a regional Commitment
resource. Creating a commitment resource
means that you are purchasing a
committed use contract with an explicit
start and end time. You can create
commitments based on vCPUs and memory
usage and receive discounted rates. For
full details, read Signing Up for
Committed Use Discounts.<|endoftext|> |
6b928ae2d07010fb58ac355413d872bd9eddad1e9e8f9827a99c68762b5bc551 | def __call__(self, request: compute.InsertRegionCommitmentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.Operation:
'Call the insert method over HTTP.\n\n Args:\n request (~.compute.InsertRegionCommitmentRequest):\n The request object. A request message for\n RegionCommitments.Insert. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.Operation:\n Represents an Operation resource. Google Compute Engine\n has three Operation resources: \\*\n `Global </compute/docs/reference/rest/v1/globalOperations>`__\n \\*\n `Regional </compute/docs/reference/rest/v1/regionOperations>`__\n \\*\n `Zonal </compute/docs/reference/rest/v1/zoneOperations>`__\n You can use an operation resource to manage asynchronous\n API requests. For more information, read Handling API\n responses. Operations can be global, regional or zonal.\n - For global operations, use the ``globalOperations``\n resource. - For regional operations, use the\n ``regionOperations`` resource. - For zonal operations,\n use the ``zonalOperations`` resource. For more\n information, read Global, Regional, and Zonal Resources.\n\n '
http_options = [{'method': 'post', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments', 'body': 'commitment_resource'}]
request_kwargs = compute.InsertRegionCommitmentRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
body = compute.Commitment.to_json(compute.Commitment(transcoded_request['body']), including_default_value_fields=False, use_integers_for_enums=False)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.InsertRegionCommitmentRequest.to_json(compute.InsertRegionCommitmentRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params), data=body)
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.Operation.from_json(response.content, ignore_unknown_fields=True) | Call the insert method over HTTP.
Args:
request (~.compute.InsertRegionCommitmentRequest):
The request object. A request message for
RegionCommitments.Insert. See the method
description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.Operation:
Represents an Operation resource. Google Compute Engine
has three Operation resources: \*
`Global </compute/docs/reference/rest/v1/globalOperations>`__
\*
`Regional </compute/docs/reference/rest/v1/regionOperations>`__
\*
`Zonal </compute/docs/reference/rest/v1/zoneOperations>`__
You can use an operation resource to manage asynchronous
API requests. For more information, read Handling API
responses. Operations can be global, regional or zonal.
- For global operations, use the ``globalOperations``
resource. - For regional operations, use the
``regionOperations`` resource. - For zonal operations,
use the ``zonalOperations`` resource. For more
information, read Global, Regional, and Zonal Resources. | google/cloud/compute_v1/services/region_commitments/transports/rest.py | __call__ | LaudateCorpus1/python-compute | 0 | python | def __call__(self, request: compute.InsertRegionCommitmentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.Operation:
'Call the insert method over HTTP.\n\n Args:\n request (~.compute.InsertRegionCommitmentRequest):\n The request object. A request message for\n RegionCommitments.Insert. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.Operation:\n Represents an Operation resource. Google Compute Engine\n has three Operation resources: \\*\n `Global </compute/docs/reference/rest/v1/globalOperations>`__\n \\*\n `Regional </compute/docs/reference/rest/v1/regionOperations>`__\n \\*\n `Zonal </compute/docs/reference/rest/v1/zoneOperations>`__\n You can use an operation resource to manage asynchronous\n API requests. For more information, read Handling API\n responses. Operations can be global, regional or zonal.\n - For global operations, use the ``globalOperations``\n resource. - For regional operations, use the\n ``regionOperations`` resource. - For zonal operations,\n use the ``zonalOperations`` resource. For more\n information, read Global, Regional, and Zonal Resources.\n\n '
http_options = [{'method': 'post', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments', 'body': 'commitment_resource'}]
request_kwargs = compute.InsertRegionCommitmentRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
body = compute.Commitment.to_json(compute.Commitment(transcoded_request['body']), including_default_value_fields=False, use_integers_for_enums=False)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.InsertRegionCommitmentRequest.to_json(compute.InsertRegionCommitmentRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params), data=body)
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.Operation.from_json(response.content, ignore_unknown_fields=True) | def __call__(self, request: compute.InsertRegionCommitmentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.Operation:
'Call the insert method over HTTP.\n\n Args:\n request (~.compute.InsertRegionCommitmentRequest):\n The request object. A request message for\n RegionCommitments.Insert. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.Operation:\n Represents an Operation resource. Google Compute Engine\n has three Operation resources: \\*\n `Global </compute/docs/reference/rest/v1/globalOperations>`__\n \\*\n `Regional </compute/docs/reference/rest/v1/regionOperations>`__\n \\*\n `Zonal </compute/docs/reference/rest/v1/zoneOperations>`__\n You can use an operation resource to manage asynchronous\n API requests. For more information, read Handling API\n responses. Operations can be global, regional or zonal.\n - For global operations, use the ``globalOperations``\n resource. - For regional operations, use the\n ``regionOperations`` resource. - For zonal operations,\n use the ``zonalOperations`` resource. For more\n information, read Global, Regional, and Zonal Resources.\n\n '
http_options = [{'method': 'post', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments', 'body': 'commitment_resource'}]
request_kwargs = compute.InsertRegionCommitmentRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
body = compute.Commitment.to_json(compute.Commitment(transcoded_request['body']), including_default_value_fields=False, use_integers_for_enums=False)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.InsertRegionCommitmentRequest.to_json(compute.InsertRegionCommitmentRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params), data=body)
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.Operation.from_json(response.content, ignore_unknown_fields=True)<|docstring|>Call the insert method over HTTP.
Args:
request (~.compute.InsertRegionCommitmentRequest):
The request object. A request message for
RegionCommitments.Insert. See the method
description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.Operation:
Represents an Operation resource. Google Compute Engine
has three Operation resources: \*
`Global </compute/docs/reference/rest/v1/globalOperations>`__
\*
`Regional </compute/docs/reference/rest/v1/regionOperations>`__
\*
`Zonal </compute/docs/reference/rest/v1/zoneOperations>`__
You can use an operation resource to manage asynchronous
API requests. For more information, read Handling API
responses. Operations can be global, regional or zonal.
- For global operations, use the ``globalOperations``
resource. - For regional operations, use the
``regionOperations`` resource. - For zonal operations,
use the ``zonalOperations`` resource. For more
information, read Global, Regional, and Zonal Resources.<|endoftext|> |
384b70f0890d75055cff8255a5bffe134aacf97d35443df4784d073961905956 | def __call__(self, request: compute.ListRegionCommitmentsRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.CommitmentList:
'Call the list method over HTTP.\n\n Args:\n request (~.compute.ListRegionCommitmentsRequest):\n The request object. A request message for\n RegionCommitments.List. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.CommitmentList:\n Contains a list of Commitment\n resources.\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments'}]
request_kwargs = compute.ListRegionCommitmentsRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.ListRegionCommitmentsRequest.to_json(compute.ListRegionCommitmentsRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.CommitmentList.from_json(response.content, ignore_unknown_fields=True) | Call the list method over HTTP.
Args:
request (~.compute.ListRegionCommitmentsRequest):
The request object. A request message for
RegionCommitments.List. See the method
description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.CommitmentList:
Contains a list of Commitment
resources. | google/cloud/compute_v1/services/region_commitments/transports/rest.py | __call__ | LaudateCorpus1/python-compute | 0 | python | def __call__(self, request: compute.ListRegionCommitmentsRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.CommitmentList:
'Call the list method over HTTP.\n\n Args:\n request (~.compute.ListRegionCommitmentsRequest):\n The request object. A request message for\n RegionCommitments.List. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.CommitmentList:\n Contains a list of Commitment\n resources.\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments'}]
request_kwargs = compute.ListRegionCommitmentsRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.ListRegionCommitmentsRequest.to_json(compute.ListRegionCommitmentsRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.CommitmentList.from_json(response.content, ignore_unknown_fields=True) | def __call__(self, request: compute.ListRegionCommitmentsRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: float=None, metadata: Sequence[Tuple[(str, str)]]=()) -> compute.CommitmentList:
'Call the list method over HTTP.\n\n Args:\n request (~.compute.ListRegionCommitmentsRequest):\n The request object. A request message for\n RegionCommitments.List. See the method\n description for details.\n\n retry (google.api_core.retry.Retry): Designation of what errors, if any,\n should be retried.\n timeout (float): The timeout for this request.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n\n Returns:\n ~.compute.CommitmentList:\n Contains a list of Commitment\n resources.\n\n '
http_options = [{'method': 'get', 'uri': '/compute/v1/projects/{project}/regions/{region}/commitments'}]
request_kwargs = compute.ListRegionCommitmentsRequest.to_dict(request)
transcoded_request = path_template.transcode(http_options, **request_kwargs)
uri = transcoded_request['uri']
method = transcoded_request['method']
query_params = json.loads(compute.ListRegionCommitmentsRequest.to_json(compute.ListRegionCommitmentsRequest(transcoded_request['query_params']), including_default_value_fields=False, use_integers_for_enums=False))
query_params.update(self._get_unset_required_fields(query_params))
headers = dict(metadata)
headers['Content-Type'] = 'application/json'
response = getattr(self._session, method)('https://{host}{uri}'.format(host=self._host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params))
if (response.status_code >= 400):
raise core_exceptions.from_http_response(response)
return compute.CommitmentList.from_json(response.content, ignore_unknown_fields=True)<|docstring|>Call the list method over HTTP.
Args:
request (~.compute.ListRegionCommitmentsRequest):
The request object. A request message for
RegionCommitments.List. See the method
description for details.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.compute.CommitmentList:
Contains a list of Commitment
resources.<|endoftext|> |
4f5857e111db227bac06443a57ac15211bee235a59be6030a1237993ad22dc6a | def __init__(self, W3=0.003, H2=0.003, Zs=36, init_dict=None):
'Constructor of the class. Can be use in two ways :\n - __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values\n for Matrix, None will initialise the property with an empty Matrix\n for pyleecan type, None will call the default constructor\n - __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys\n\n ndarray or list can be given for Vector and Matrix\n object or dict can be given for pyleecan Object'
if (init_dict is not None):
check_init_dict(init_dict, ['W3', 'H2', 'Zs'])
if ('W3' in list(init_dict.keys())):
W3 = init_dict['W3']
if ('H2' in list(init_dict.keys())):
H2 = init_dict['H2']
if ('Zs' in list(init_dict.keys())):
Zs = init_dict['Zs']
self.W3 = W3
self.H2 = H2
super(SlotW24, self).__init__(Zs=Zs) | Constructor of the class. Can be use in two ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for Matrix, None will initialise the property with an empty Matrix
for pyleecan type, None will call the default constructor
- __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys
ndarray or list can be given for Vector and Matrix
object or dict can be given for pyleecan Object | Classes/SlotW24.py | __init__ | Superomeg4/pyleecan | 0 | python | def __init__(self, W3=0.003, H2=0.003, Zs=36, init_dict=None):
'Constructor of the class. Can be use in two ways :\n - __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values\n for Matrix, None will initialise the property with an empty Matrix\n for pyleecan type, None will call the default constructor\n - __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys\n\n ndarray or list can be given for Vector and Matrix\n object or dict can be given for pyleecan Object'
if (init_dict is not None):
check_init_dict(init_dict, ['W3', 'H2', 'Zs'])
if ('W3' in list(init_dict.keys())):
W3 = init_dict['W3']
if ('H2' in list(init_dict.keys())):
H2 = init_dict['H2']
if ('Zs' in list(init_dict.keys())):
Zs = init_dict['Zs']
self.W3 = W3
self.H2 = H2
super(SlotW24, self).__init__(Zs=Zs) | def __init__(self, W3=0.003, H2=0.003, Zs=36, init_dict=None):
'Constructor of the class. Can be use in two ways :\n - __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values\n for Matrix, None will initialise the property with an empty Matrix\n for pyleecan type, None will call the default constructor\n - __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys\n\n ndarray or list can be given for Vector and Matrix\n object or dict can be given for pyleecan Object'
if (init_dict is not None):
check_init_dict(init_dict, ['W3', 'H2', 'Zs'])
if ('W3' in list(init_dict.keys())):
W3 = init_dict['W3']
if ('H2' in list(init_dict.keys())):
H2 = init_dict['H2']
if ('Zs' in list(init_dict.keys())):
Zs = init_dict['Zs']
self.W3 = W3
self.H2 = H2
super(SlotW24, self).__init__(Zs=Zs)<|docstring|>Constructor of the class. Can be use in two ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for Matrix, None will initialise the property with an empty Matrix
for pyleecan type, None will call the default constructor
- __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys
ndarray or list can be given for Vector and Matrix
object or dict can be given for pyleecan Object<|endoftext|> |
a3b0a7082993d47c043d10502823a761823c8d990c5f1c34a52ec6fa06156248 | def __str__(self):
'Convert this objet in a readeable string (for print)'
SlotW24_str = ''
SlotW24_str += (super(SlotW24, self).__str__() + linesep)
SlotW24_str += (('W3 = ' + str(self.W3)) + linesep)
SlotW24_str += ('H2 = ' + str(self.H2))
return SlotW24_str | Convert this objet in a readeable string (for print) | Classes/SlotW24.py | __str__ | Superomeg4/pyleecan | 0 | python | def __str__(self):
SlotW24_str =
SlotW24_str += (super(SlotW24, self).__str__() + linesep)
SlotW24_str += (('W3 = ' + str(self.W3)) + linesep)
SlotW24_str += ('H2 = ' + str(self.H2))
return SlotW24_str | def __str__(self):
SlotW24_str =
SlotW24_str += (super(SlotW24, self).__str__() + linesep)
SlotW24_str += (('W3 = ' + str(self.W3)) + linesep)
SlotW24_str += ('H2 = ' + str(self.H2))
return SlotW24_str<|docstring|>Convert this objet in a readeable string (for print)<|endoftext|> |
5d6049173519bd4f579b8d222eefe4e83aab6131bffebff2577d821c76838d76 | def __eq__(self, other):
'Compare two objects (skip parent)'
if (type(other) != type(self)):
return False
if (not super(SlotW24, self).__eq__(other)):
return False
if (other.W3 != self.W3):
return False
if (other.H2 != self.H2):
return False
return True | Compare two objects (skip parent) | Classes/SlotW24.py | __eq__ | Superomeg4/pyleecan | 0 | python | def __eq__(self, other):
if (type(other) != type(self)):
return False
if (not super(SlotW24, self).__eq__(other)):
return False
if (other.W3 != self.W3):
return False
if (other.H2 != self.H2):
return False
return True | def __eq__(self, other):
if (type(other) != type(self)):
return False
if (not super(SlotW24, self).__eq__(other)):
return False
if (other.W3 != self.W3):
return False
if (other.H2 != self.H2):
return False
return True<|docstring|>Compare two objects (skip parent)<|endoftext|> |
a3a84312826d203ccb175ae53e3ac047657c6ca093bdc4a4c3df2e47cd8d3e76 | def as_dict(self):
'Convert this objet in a json seriable dict (can be use in __init__)\n '
SlotW24_dict = super(SlotW24, self).as_dict()
SlotW24_dict['W3'] = self.W3
SlotW24_dict['H2'] = self.H2
SlotW24_dict['__class__'] = 'SlotW24'
return SlotW24_dict | Convert this objet in a json seriable dict (can be use in __init__) | Classes/SlotW24.py | as_dict | Superomeg4/pyleecan | 0 | python | def as_dict(self):
'\n '
SlotW24_dict = super(SlotW24, self).as_dict()
SlotW24_dict['W3'] = self.W3
SlotW24_dict['H2'] = self.H2
SlotW24_dict['__class__'] = 'SlotW24'
return SlotW24_dict | def as_dict(self):
'\n '
SlotW24_dict = super(SlotW24, self).as_dict()
SlotW24_dict['W3'] = self.W3
SlotW24_dict['H2'] = self.H2
SlotW24_dict['__class__'] = 'SlotW24'
return SlotW24_dict<|docstring|>Convert this objet in a json seriable dict (can be use in __init__)<|endoftext|> |
63ca1f80e8c3fae78e12a57e74bbb47d462678abddd28e823e40c77079d040d5 | def _set_None(self):
'Set all the properties to None (except pyleecan object)'
self.W3 = None
self.H2 = None
super(SlotW24, self)._set_None() | Set all the properties to None (except pyleecan object) | Classes/SlotW24.py | _set_None | Superomeg4/pyleecan | 0 | python | def _set_None(self):
self.W3 = None
self.H2 = None
super(SlotW24, self)._set_None() | def _set_None(self):
self.W3 = None
self.H2 = None
super(SlotW24, self)._set_None()<|docstring|>Set all the properties to None (except pyleecan object)<|endoftext|> |
db0c5c7ddabe7b22defd211bd4745c62c7d9d73ea2de24847b390acf80ec10c8 | def _get_W3(self):
'getter of W3'
return self._W3 | getter of W3 | Classes/SlotW24.py | _get_W3 | Superomeg4/pyleecan | 0 | python | def _get_W3(self):
return self._W3 | def _get_W3(self):
return self._W3<|docstring|>getter of W3<|endoftext|> |
79d5927136e0d2b72442510bb9b888d900483e8fd43dcb357ea75fdf9776efdd | def _set_W3(self, value):
'setter of W3'
check_var('W3', value, 'float', Vmin=0)
self._W3 = value | setter of W3 | Classes/SlotW24.py | _set_W3 | Superomeg4/pyleecan | 0 | python | def _set_W3(self, value):
check_var('W3', value, 'float', Vmin=0)
self._W3 = value | def _set_W3(self, value):
check_var('W3', value, 'float', Vmin=0)
self._W3 = value<|docstring|>setter of W3<|endoftext|> |
4456531bfd45b3d2d5de2ff45ac780cdc30fa16f31b405d7791a922380a33bda | def _get_H2(self):
'getter of H2'
return self._H2 | getter of H2 | Classes/SlotW24.py | _get_H2 | Superomeg4/pyleecan | 0 | python | def _get_H2(self):
return self._H2 | def _get_H2(self):
return self._H2<|docstring|>getter of H2<|endoftext|> |
76bcd51e088a21cae6a9cb34a9240aceed84295946f028b70abace9304ee6fa1 | def _set_H2(self, value):
'setter of H2'
check_var('H2', value, 'float', Vmin=0)
self._H2 = value | setter of H2 | Classes/SlotW24.py | _set_H2 | Superomeg4/pyleecan | 0 | python | def _set_H2(self, value):
check_var('H2', value, 'float', Vmin=0)
self._H2 = value | def _set_H2(self, value):
check_var('H2', value, 'float', Vmin=0)
self._H2 = value<|docstring|>setter of H2<|endoftext|> |
d4bb2bfbc03277fba32a700c3582b96dbe2d91e41ccfc8f09657764d652e9dfa | def get_coordinates(gnd_id):
' takes a GeoNames-ID and returns a dict with lat, lng '
g = Graph()
WSG84 = Namespace('http://www.w3.org/2003/01/geo/wgs84_pos#')
try:
parsed = g.parse('https://www.geonames.org/{}/about.rdf'.format(gnd_id))
except Exception as e:
print(e)
return None
if parsed:
lat = [x for x in parsed.subject_objects(WSG84.lat)][0][1].value
lng = [x for x in parsed.subject_objects(WSG84.long)][0][1].value
return {'lat': lat, 'lng': lng}
else:
return None | takes a GeoNames-ID and returns a dict with lat, lng | entities/utils.py | get_coordinates | reading-in-the-alps/vfbr | 0 | python | def get_coordinates(gnd_id):
' '
g = Graph()
WSG84 = Namespace('http://www.w3.org/2003/01/geo/wgs84_pos#')
try:
parsed = g.parse('https://www.geonames.org/{}/about.rdf'.format(gnd_id))
except Exception as e:
print(e)
return None
if parsed:
lat = [x for x in parsed.subject_objects(WSG84.lat)][0][1].value
lng = [x for x in parsed.subject_objects(WSG84.long)][0][1].value
return {'lat': lat, 'lng': lng}
else:
return None | def get_coordinates(gnd_id):
' '
g = Graph()
WSG84 = Namespace('http://www.w3.org/2003/01/geo/wgs84_pos#')
try:
parsed = g.parse('https://www.geonames.org/{}/about.rdf'.format(gnd_id))
except Exception as e:
print(e)
return None
if parsed:
lat = [x for x in parsed.subject_objects(WSG84.lat)][0][1].value
lng = [x for x in parsed.subject_objects(WSG84.long)][0][1].value
return {'lat': lat, 'lng': lng}
else:
return None<|docstring|>takes a GeoNames-ID and returns a dict with lat, lng<|endoftext|> |
be49493c67d047790713dcb22dd0bdca11e7e4a0127c1c5604a71c24ffb484fe | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
'Set up the Bravia TV platform.'
host = config[CONF_HOST]
bravia_config_file_path = hass.config.path(BRAVIA_CONFIG_FILE)
bravia_config = (await hass.async_add_executor_job(load_json, bravia_config_file_path))
if (not bravia_config):
_LOGGER.error('Configuration import failed, there is no bravia.conf file in the configuration folder')
return
while bravia_config:
(host_ip, host_config) = bravia_config.popitem()
if (host_ip == host):
pin = host_config[CONF_PIN]
hass.async_create_task(hass.config_entries.flow.async_init(DOMAIN, context={'source': SOURCE_IMPORT}, data={CONF_HOST: host, CONF_PIN: pin}))
return | Set up the Bravia TV platform. | homeassistant/components/braviatv/media_player.py | async_setup_platform | maurerle/core | 5 | python | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
host = config[CONF_HOST]
bravia_config_file_path = hass.config.path(BRAVIA_CONFIG_FILE)
bravia_config = (await hass.async_add_executor_job(load_json, bravia_config_file_path))
if (not bravia_config):
_LOGGER.error('Configuration import failed, there is no bravia.conf file in the configuration folder')
return
while bravia_config:
(host_ip, host_config) = bravia_config.popitem()
if (host_ip == host):
pin = host_config[CONF_PIN]
hass.async_create_task(hass.config_entries.flow.async_init(DOMAIN, context={'source': SOURCE_IMPORT}, data={CONF_HOST: host, CONF_PIN: pin}))
return | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
host = config[CONF_HOST]
bravia_config_file_path = hass.config.path(BRAVIA_CONFIG_FILE)
bravia_config = (await hass.async_add_executor_job(load_json, bravia_config_file_path))
if (not bravia_config):
_LOGGER.error('Configuration import failed, there is no bravia.conf file in the configuration folder')
return
while bravia_config:
(host_ip, host_config) = bravia_config.popitem()
if (host_ip == host):
pin = host_config[CONF_PIN]
hass.async_create_task(hass.config_entries.flow.async_init(DOMAIN, context={'source': SOURCE_IMPORT}, data={CONF_HOST: host, CONF_PIN: pin}))
return<|docstring|>Set up the Bravia TV platform.<|endoftext|> |
157515fdaf1e3a98f01d1167599d1133d3395308911797a4ea577ee41ed912a3 | async def async_setup_entry(hass, config_entry, async_add_entities):
'Set up Bravia TV Media Player from a config_entry.'
coordinator = hass.data[DOMAIN][config_entry.entry_id][BRAVIA_COORDINATOR]
unique_id = config_entry.unique_id
device_info = {'identifiers': {(DOMAIN, unique_id)}, 'name': DEFAULT_NAME, 'manufacturer': ATTR_MANUFACTURER, 'model': config_entry.title}
async_add_entities([BraviaTVMediaPlayer(coordinator, DEFAULT_NAME, unique_id, device_info)]) | Set up Bravia TV Media Player from a config_entry. | homeassistant/components/braviatv/media_player.py | async_setup_entry | maurerle/core | 5 | python | async def async_setup_entry(hass, config_entry, async_add_entities):
coordinator = hass.data[DOMAIN][config_entry.entry_id][BRAVIA_COORDINATOR]
unique_id = config_entry.unique_id
device_info = {'identifiers': {(DOMAIN, unique_id)}, 'name': DEFAULT_NAME, 'manufacturer': ATTR_MANUFACTURER, 'model': config_entry.title}
async_add_entities([BraviaTVMediaPlayer(coordinator, DEFAULT_NAME, unique_id, device_info)]) | async def async_setup_entry(hass, config_entry, async_add_entities):
coordinator = hass.data[DOMAIN][config_entry.entry_id][BRAVIA_COORDINATOR]
unique_id = config_entry.unique_id
device_info = {'identifiers': {(DOMAIN, unique_id)}, 'name': DEFAULT_NAME, 'manufacturer': ATTR_MANUFACTURER, 'model': config_entry.title}
async_add_entities([BraviaTVMediaPlayer(coordinator, DEFAULT_NAME, unique_id, device_info)])<|docstring|>Set up Bravia TV Media Player from a config_entry.<|endoftext|> |
81bd22d92d779836399da9b0aa58b30fd6918354ec63dee00598526a395b5220 | def __init__(self, coordinator, name, unique_id, device_info):
'Initialize the entity.'
self._name = name
self._unique_id = unique_id
self._device_info = device_info
super().__init__(coordinator) | Initialize the entity. | homeassistant/components/braviatv/media_player.py | __init__ | maurerle/core | 5 | python | def __init__(self, coordinator, name, unique_id, device_info):
self._name = name
self._unique_id = unique_id
self._device_info = device_info
super().__init__(coordinator) | def __init__(self, coordinator, name, unique_id, device_info):
self._name = name
self._unique_id = unique_id
self._device_info = device_info
super().__init__(coordinator)<|docstring|>Initialize the entity.<|endoftext|> |
959514ff3ad36bfa3d9e493ec80719a74614a7e62c186c5799451855e3d3f810 | @property
def name(self):
'Return the name of the device.'
return self._name | Return the name of the device. | homeassistant/components/braviatv/media_player.py | name | maurerle/core | 5 | python | @property
def name(self):
return self._name | @property
def name(self):
return self._name<|docstring|>Return the name of the device.<|endoftext|> |
bbc24990453d9ce2e5567ef31f40b20a343650e2fe4e97cfc322928b08150336 | @property
def unique_id(self):
'Return a unique_id for this entity.'
return self._unique_id | Return a unique_id for this entity. | homeassistant/components/braviatv/media_player.py | unique_id | maurerle/core | 5 | python | @property
def unique_id(self):
return self._unique_id | @property
def unique_id(self):
return self._unique_id<|docstring|>Return a unique_id for this entity.<|endoftext|> |
5ed6caad8ee1bdd3d6694fbffe8bbec4befb02e267da8cf31285b977b31c8f2b | @property
def device_info(self):
'Return the device info.'
return self._device_info | Return the device info. | homeassistant/components/braviatv/media_player.py | device_info | maurerle/core | 5 | python | @property
def device_info(self):
return self._device_info | @property
def device_info(self):
return self._device_info<|docstring|>Return the device info.<|endoftext|> |
cf65832884c6cbdd856af47c748a86a6d95084629097db8e799b80d25fe461da | @property
def state(self):
'Return the state of the device.'
if self.coordinator.is_on:
return (STATE_PLAYING if self.coordinator.playing else STATE_PAUSED)
return STATE_OFF | Return the state of the device. | homeassistant/components/braviatv/media_player.py | state | maurerle/core | 5 | python | @property
def state(self):
if self.coordinator.is_on:
return (STATE_PLAYING if self.coordinator.playing else STATE_PAUSED)
return STATE_OFF | @property
def state(self):
if self.coordinator.is_on:
return (STATE_PLAYING if self.coordinator.playing else STATE_PAUSED)
return STATE_OFF<|docstring|>Return the state of the device.<|endoftext|> |
e83d969892985171cf3d5a379d5618fc5f6f15198b6ceedf8d7490209e1bacf8 | @property
def source(self):
'Return the current input source.'
return self.coordinator.source | Return the current input source. | homeassistant/components/braviatv/media_player.py | source | maurerle/core | 5 | python | @property
def source(self):
return self.coordinator.source | @property
def source(self):
return self.coordinator.source<|docstring|>Return the current input source.<|endoftext|> |
f9fc13a045390847e0aab4e6a18dceb8a9994ee5fcfdd4133a70879e6c45a4a0 | @property
def source_list(self):
'List of available input sources.'
return self.coordinator.source_list | List of available input sources. | homeassistant/components/braviatv/media_player.py | source_list | maurerle/core | 5 | python | @property
def source_list(self):
return self.coordinator.source_list | @property
def source_list(self):
return self.coordinator.source_list<|docstring|>List of available input sources.<|endoftext|> |
2880e5abd50aa4a57d98bcd2433f2ac830985827a8908186313941604523ef71 | @property
def volume_level(self):
'Volume level of the media player (0..1).'
if (self.coordinator.volume is not None):
return (self.coordinator.volume / 100)
return None | Volume level of the media player (0..1). | homeassistant/components/braviatv/media_player.py | volume_level | maurerle/core | 5 | python | @property
def volume_level(self):
if (self.coordinator.volume is not None):
return (self.coordinator.volume / 100)
return None | @property
def volume_level(self):
if (self.coordinator.volume is not None):
return (self.coordinator.volume / 100)
return None<|docstring|>Volume level of the media player (0..1).<|endoftext|> |
2f81d6a2bf069b560ac9a96e7897109fa5e6ed57c76f4df1f1d135028d527936 | @property
def is_volume_muted(self):
'Boolean if volume is currently muted.'
return self.coordinator.muted | Boolean if volume is currently muted. | homeassistant/components/braviatv/media_player.py | is_volume_muted | maurerle/core | 5 | python | @property
def is_volume_muted(self):
return self.coordinator.muted | @property
def is_volume_muted(self):
return self.coordinator.muted<|docstring|>Boolean if volume is currently muted.<|endoftext|> |
776d9e3fd3bf351a39ad4b072775a03f19fe1ecbd4db909d7a7bff57dd8697a1 | @property
def media_title(self):
'Title of current playing media.'
return_value = None
if (self.coordinator.channel_name is not None):
return_value = self.coordinator.channel_name
if (self.coordinator.program_name is not None):
return_value = f'{return_value}: {self.coordinator.program_name}'
return return_value | Title of current playing media. | homeassistant/components/braviatv/media_player.py | media_title | maurerle/core | 5 | python | @property
def media_title(self):
return_value = None
if (self.coordinator.channel_name is not None):
return_value = self.coordinator.channel_name
if (self.coordinator.program_name is not None):
return_value = f'{return_value}: {self.coordinator.program_name}'
return return_value | @property
def media_title(self):
return_value = None
if (self.coordinator.channel_name is not None):
return_value = self.coordinator.channel_name
if (self.coordinator.program_name is not None):
return_value = f'{return_value}: {self.coordinator.program_name}'
return return_value<|docstring|>Title of current playing media.<|endoftext|> |
a56a86b5fdebeff1653c602fa58c14d7c60bccf6f789aeeeb81a07b63ea214cb | @property
def media_content_id(self):
'Content ID of current playing media.'
return self.coordinator.channel_name | Content ID of current playing media. | homeassistant/components/braviatv/media_player.py | media_content_id | maurerle/core | 5 | python | @property
def media_content_id(self):
return self.coordinator.channel_name | @property
def media_content_id(self):
return self.coordinator.channel_name<|docstring|>Content ID of current playing media.<|endoftext|> |
402c48c8e7cca4069207a32e23c24054da6f2dc85e08e29f1e04b78a8c760823 | @property
def media_duration(self):
'Duration of current playing media in seconds.'
return self.coordinator.duration | Duration of current playing media in seconds. | homeassistant/components/braviatv/media_player.py | media_duration | maurerle/core | 5 | python | @property
def media_duration(self):
return self.coordinator.duration | @property
def media_duration(self):
return self.coordinator.duration<|docstring|>Duration of current playing media in seconds.<|endoftext|> |
301150404221e179cd2edc5b67708c1d89c133a6f4964f703fa492e13f643c8d | async def async_turn_on(self):
'Turn the device on.'
(await self.coordinator.async_turn_on()) | Turn the device on. | homeassistant/components/braviatv/media_player.py | async_turn_on | maurerle/core | 5 | python | async def async_turn_on(self):
(await self.coordinator.async_turn_on()) | async def async_turn_on(self):
(await self.coordinator.async_turn_on())<|docstring|>Turn the device on.<|endoftext|> |
4fc4317e914e5d234923a341ba06a291693bf9f7d46e616e01cc19a018d8763a | async def async_turn_off(self):
'Turn the device off.'
(await self.coordinator.async_turn_off()) | Turn the device off. | homeassistant/components/braviatv/media_player.py | async_turn_off | maurerle/core | 5 | python | async def async_turn_off(self):
(await self.coordinator.async_turn_off()) | async def async_turn_off(self):
(await self.coordinator.async_turn_off())<|docstring|>Turn the device off.<|endoftext|> |
ce1c3ac24c3379e1f5faa0f27038eafd1946f9420f0990ac62eebc0f444c49c8 | async def async_set_volume_level(self, volume):
'Set volume level, range 0..1.'
(await self.coordinator.async_set_volume_level(volume)) | Set volume level, range 0..1. | homeassistant/components/braviatv/media_player.py | async_set_volume_level | maurerle/core | 5 | python | async def async_set_volume_level(self, volume):
(await self.coordinator.async_set_volume_level(volume)) | async def async_set_volume_level(self, volume):
(await self.coordinator.async_set_volume_level(volume))<|docstring|>Set volume level, range 0..1.<|endoftext|> |
c65deebb8fa426880a0026df4eb8b413ee97a72b3f58bd419832ee3b99fede36 | async def async_volume_up(self):
'Send volume up command.'
(await self.coordinator.async_volume_up()) | Send volume up command. | homeassistant/components/braviatv/media_player.py | async_volume_up | maurerle/core | 5 | python | async def async_volume_up(self):
(await self.coordinator.async_volume_up()) | async def async_volume_up(self):
(await self.coordinator.async_volume_up())<|docstring|>Send volume up command.<|endoftext|> |
ccf8981de54cd3ad92771d226f33a74e4f12910ea87efd967c1bb18664ab44a8 | async def async_volume_down(self):
'Send volume down command.'
(await self.coordinator.async_volume_down()) | Send volume down command. | homeassistant/components/braviatv/media_player.py | async_volume_down | maurerle/core | 5 | python | async def async_volume_down(self):
(await self.coordinator.async_volume_down()) | async def async_volume_down(self):
(await self.coordinator.async_volume_down())<|docstring|>Send volume down command.<|endoftext|> |
d74a05663c9c5d996b246e2bfd323b7e8b055e20408976f2fd89e44f51cee922 | async def async_mute_volume(self, mute):
'Send mute command.'
(await self.coordinator.async_volume_mute(mute)) | Send mute command. | homeassistant/components/braviatv/media_player.py | async_mute_volume | maurerle/core | 5 | python | async def async_mute_volume(self, mute):
(await self.coordinator.async_volume_mute(mute)) | async def async_mute_volume(self, mute):
(await self.coordinator.async_volume_mute(mute))<|docstring|>Send mute command.<|endoftext|> |
1825d7c7aa309ffb7efa78cc7aa1da89a2abcb80fc98f7bc4fb9cea7e7f88c5b | async def async_select_source(self, source):
'Set the input source.'
(await self.coordinator.async_select_source(source)) | Set the input source. | homeassistant/components/braviatv/media_player.py | async_select_source | maurerle/core | 5 | python | async def async_select_source(self, source):
(await self.coordinator.async_select_source(source)) | async def async_select_source(self, source):
(await self.coordinator.async_select_source(source))<|docstring|>Set the input source.<|endoftext|> |
a866d75b87a267cc1cf1d507d273cf8f2811f1de0462b40863a055285a755374 | async def async_media_play(self):
'Send play command.'
(await self.coordinator.async_media_play()) | Send play command. | homeassistant/components/braviatv/media_player.py | async_media_play | maurerle/core | 5 | python | async def async_media_play(self):
(await self.coordinator.async_media_play()) | async def async_media_play(self):
(await self.coordinator.async_media_play())<|docstring|>Send play command.<|endoftext|> |
8625d2b8486fc7604ac1ddb5c668323b4fa96c6d708f64be7654edcf8c4059cc | async def async_media_pause(self):
'Send pause command.'
(await self.coordinator.async_media_pause()) | Send pause command. | homeassistant/components/braviatv/media_player.py | async_media_pause | maurerle/core | 5 | python | async def async_media_pause(self):
(await self.coordinator.async_media_pause()) | async def async_media_pause(self):
(await self.coordinator.async_media_pause())<|docstring|>Send pause command.<|endoftext|> |
808fdaf28431bb72c987e99607e02d7491a6c9ce2e314236cb344898b6fe93b4 | async def async_media_stop(self):
'Send media stop command to media player.'
(await self.coordinator.async_media_stop()) | Send media stop command to media player. | homeassistant/components/braviatv/media_player.py | async_media_stop | maurerle/core | 5 | python | async def async_media_stop(self):
(await self.coordinator.async_media_stop()) | async def async_media_stop(self):
(await self.coordinator.async_media_stop())<|docstring|>Send media stop command to media player.<|endoftext|> |
c2542d36e1fff3e1bc88042f2f1ce895ad529905fb57a763c48e5c0d40c46036 | async def async_media_next_track(self):
'Send next track command.'
(await self.coordinator.async_media_next_track()) | Send next track command. | homeassistant/components/braviatv/media_player.py | async_media_next_track | maurerle/core | 5 | python | async def async_media_next_track(self):
(await self.coordinator.async_media_next_track()) | async def async_media_next_track(self):
(await self.coordinator.async_media_next_track())<|docstring|>Send next track command.<|endoftext|> |
c9357745e2b61f2fd6d2ff2f97521f891faa07bc45611248dc019d330606f3d1 | async def async_media_previous_track(self):
'Send previous track command.'
(await self.coordinator.async_media_previous_track()) | Send previous track command. | homeassistant/components/braviatv/media_player.py | async_media_previous_track | maurerle/core | 5 | python | async def async_media_previous_track(self):
(await self.coordinator.async_media_previous_track()) | async def async_media_previous_track(self):
(await self.coordinator.async_media_previous_track())<|docstring|>Send previous track command.<|endoftext|> |
efe051ff99120a9965df08edfd39456a3d704ced1dd127d6820017a223cfee1c | def __call__(self, targets, logits, seq_length=None):
'\n\t\tCompute the loss\n\n\t\tCreates the operation to compute the crossentropy multi loss\n\n\t\tArgs:\n\t\t\ttargets: a dictionary of [batch_size x ... x ...] tensor containing\n\t\t\t\tthe targets\n\t\t\tlogits: a dictionary of [batch_size x ... x ...] tensors containing the logits\n\n\t\tReturns:\n\t\t\tloss: a scalar value containing the loss\n\t\t\tnorm: a scalar value indicating how to normalize the loss\n\t\t'
if (('av_anchors_time_flag' in self.lossconf) and (self.lossconf['av_anchors_time_flag'] in ['true', 'True'])):
av_anchors_time_flag = True
else:
av_anchors_time_flag = False
if (('resh_logits' in self.lossconf) and (self.lossconf['resh_logits'] in ['true', 'True'])):
resh_logits = True
else:
resh_logits = False
if (('allow_permutation' not in self.lossconf) or (self.lossconf['allow_permutation'] == 'True')):
allow_permutation = True
else:
allow_permutation = False
spkids = targets['spkids']
logits = logits['spkest']
if av_anchors_time_flag:
logits = tf.reduce_mean(logits, 1)
if resh_logits:
nrS = spkids.get_shape()[1]
logits = tf.reshape(logits, [self.batch_size, nrS, (- 1)])
(loss, norm) = ops.crossentropy_multi_loss(spkids, logits, self.batch_size, allow_permutation=allow_permutation)
return (loss, norm) | Compute the loss
Creates the operation to compute the crossentropy multi loss
Args:
targets: a dictionary of [batch_size x ... x ...] tensor containing
the targets
logits: a dictionary of [batch_size x ... x ...] tensors containing the logits
Returns:
loss: a scalar value containing the loss
norm: a scalar value indicating how to normalize the loss | nabu/neuralnetworks/loss_computers/crossentropy_multi_loss.py | __call__ | Darleen2019/Nabu-MSSS | 18 | python | def __call__(self, targets, logits, seq_length=None):
'\n\t\tCompute the loss\n\n\t\tCreates the operation to compute the crossentropy multi loss\n\n\t\tArgs:\n\t\t\ttargets: a dictionary of [batch_size x ... x ...] tensor containing\n\t\t\t\tthe targets\n\t\t\tlogits: a dictionary of [batch_size x ... x ...] tensors containing the logits\n\n\t\tReturns:\n\t\t\tloss: a scalar value containing the loss\n\t\t\tnorm: a scalar value indicating how to normalize the loss\n\t\t'
if (('av_anchors_time_flag' in self.lossconf) and (self.lossconf['av_anchors_time_flag'] in ['true', 'True'])):
av_anchors_time_flag = True
else:
av_anchors_time_flag = False
if (('resh_logits' in self.lossconf) and (self.lossconf['resh_logits'] in ['true', 'True'])):
resh_logits = True
else:
resh_logits = False
if (('allow_permutation' not in self.lossconf) or (self.lossconf['allow_permutation'] == 'True')):
allow_permutation = True
else:
allow_permutation = False
spkids = targets['spkids']
logits = logits['spkest']
if av_anchors_time_flag:
logits = tf.reduce_mean(logits, 1)
if resh_logits:
nrS = spkids.get_shape()[1]
logits = tf.reshape(logits, [self.batch_size, nrS, (- 1)])
(loss, norm) = ops.crossentropy_multi_loss(spkids, logits, self.batch_size, allow_permutation=allow_permutation)
return (loss, norm) | def __call__(self, targets, logits, seq_length=None):
'\n\t\tCompute the loss\n\n\t\tCreates the operation to compute the crossentropy multi loss\n\n\t\tArgs:\n\t\t\ttargets: a dictionary of [batch_size x ... x ...] tensor containing\n\t\t\t\tthe targets\n\t\t\tlogits: a dictionary of [batch_size x ... x ...] tensors containing the logits\n\n\t\tReturns:\n\t\t\tloss: a scalar value containing the loss\n\t\t\tnorm: a scalar value indicating how to normalize the loss\n\t\t'
if (('av_anchors_time_flag' in self.lossconf) and (self.lossconf['av_anchors_time_flag'] in ['true', 'True'])):
av_anchors_time_flag = True
else:
av_anchors_time_flag = False
if (('resh_logits' in self.lossconf) and (self.lossconf['resh_logits'] in ['true', 'True'])):
resh_logits = True
else:
resh_logits = False
if (('allow_permutation' not in self.lossconf) or (self.lossconf['allow_permutation'] == 'True')):
allow_permutation = True
else:
allow_permutation = False
spkids = targets['spkids']
logits = logits['spkest']
if av_anchors_time_flag:
logits = tf.reduce_mean(logits, 1)
if resh_logits:
nrS = spkids.get_shape()[1]
logits = tf.reshape(logits, [self.batch_size, nrS, (- 1)])
(loss, norm) = ops.crossentropy_multi_loss(spkids, logits, self.batch_size, allow_permutation=allow_permutation)
return (loss, norm)<|docstring|>Compute the loss
Creates the operation to compute the crossentropy multi loss
Args:
targets: a dictionary of [batch_size x ... x ...] tensor containing
the targets
logits: a dictionary of [batch_size x ... x ...] tensors containing the logits
Returns:
loss: a scalar value containing the loss
norm: a scalar value indicating how to normalize the loss<|endoftext|> |
91fcbd304bb1d28ff6db07a5e82beaad22c5e6557724fc0efacefcfe1d4c2a37 | def _get_file(genome_dir: str, fname: str, warn_missing: Optional[bool]=True):
'\n Returns the filepath to a single (gzipped) file in the genome_dir with matching ext.\n '
fpath = os.path.join(genome_dir, fname)
if os.path.exists(fpath):
return fpath
if os.path.exists(f'{fpath}.gz'):
return f'{fpath}.gz'
if warn_missing:
logger.warning(f"Could not find '{fname}(.gz)' in directory {genome_dir}. Methods using this file won't work!")
return | Returns the filepath to a single (gzipped) file in the genome_dir with matching ext. | genomepy/annotation/__init__.py | _get_file | vanheeringen-lab/genomepy | 146 | python | def _get_file(genome_dir: str, fname: str, warn_missing: Optional[bool]=True):
'\n \n '
fpath = os.path.join(genome_dir, fname)
if os.path.exists(fpath):
return fpath
if os.path.exists(f'{fpath}.gz'):
return f'{fpath}.gz'
if warn_missing:
logger.warning(f"Could not find '{fname}(.gz)' in directory {genome_dir}. Methods using this file won't work!")
return | def _get_file(genome_dir: str, fname: str, warn_missing: Optional[bool]=True):
'\n \n '
fpath = os.path.join(genome_dir, fname)
if os.path.exists(fpath):
return fpath
if os.path.exists(f'{fpath}.gz'):
return f'{fpath}.gz'
if warn_missing:
logger.warning(f"Could not find '{fname}(.gz)' in directory {genome_dir}. Methods using this file won't work!")
return<|docstring|>Returns the filepath to a single (gzipped) file in the genome_dir with matching ext.<|endoftext|> |
ab592063a5436ead4f504a8c9ef410efa9b8fb3e251c8e6ae42367b23a00a96d | def filter_regex(df: pd.DataFrame, regex: str, invert_match: Optional[bool]=False, column: Union[(str, int)]=0) -> pd.DataFrame:
'\n Filter a pandas dataframe by a column (default: 1st, contig name).\n\n Parameters\n ----------\n df: pd.Dataframe\n annotation to filter (a pandas dataframe)\n regex : str\n regex string to match\n invert_match : bool, optional\n keep contigs NOT matching the regex string\n column: str or int, optional\n column name or number to filter (default: 1st, contig name)\n\n Returns\n -------\n pd.DataFrame\n filtered dataframe\n '
if (column not in df.columns):
if isinstance(column, int):
column = df.columns[column]
else:
raise ValueError(f"Column '{column}' not found in annotation columns {list(df.columns)}")
pattern = re.compile(regex)
filter_func = df[column].map((lambda x: (bool(pattern.match(x)) is not invert_match)))
return df[filter_func] | Filter a pandas dataframe by a column (default: 1st, contig name).
Parameters
----------
df: pd.Dataframe
annotation to filter (a pandas dataframe)
regex : str
regex string to match
invert_match : bool, optional
keep contigs NOT matching the regex string
column: str or int, optional
column name or number to filter (default: 1st, contig name)
Returns
-------
pd.DataFrame
filtered dataframe | genomepy/annotation/__init__.py | filter_regex | vanheeringen-lab/genomepy | 146 | python | def filter_regex(df: pd.DataFrame, regex: str, invert_match: Optional[bool]=False, column: Union[(str, int)]=0) -> pd.DataFrame:
'\n Filter a pandas dataframe by a column (default: 1st, contig name).\n\n Parameters\n ----------\n df: pd.Dataframe\n annotation to filter (a pandas dataframe)\n regex : str\n regex string to match\n invert_match : bool, optional\n keep contigs NOT matching the regex string\n column: str or int, optional\n column name or number to filter (default: 1st, contig name)\n\n Returns\n -------\n pd.DataFrame\n filtered dataframe\n '
if (column not in df.columns):
if isinstance(column, int):
column = df.columns[column]
else:
raise ValueError(f"Column '{column}' not found in annotation columns {list(df.columns)}")
pattern = re.compile(regex)
filter_func = df[column].map((lambda x: (bool(pattern.match(x)) is not invert_match)))
return df[filter_func] | def filter_regex(df: pd.DataFrame, regex: str, invert_match: Optional[bool]=False, column: Union[(str, int)]=0) -> pd.DataFrame:
'\n Filter a pandas dataframe by a column (default: 1st, contig name).\n\n Parameters\n ----------\n df: pd.Dataframe\n annotation to filter (a pandas dataframe)\n regex : str\n regex string to match\n invert_match : bool, optional\n keep contigs NOT matching the regex string\n column: str or int, optional\n column name or number to filter (default: 1st, contig name)\n\n Returns\n -------\n pd.DataFrame\n filtered dataframe\n '
if (column not in df.columns):
if isinstance(column, int):
column = df.columns[column]
else:
raise ValueError(f"Column '{column}' not found in annotation columns {list(df.columns)}")
pattern = re.compile(regex)
filter_func = df[column].map((lambda x: (bool(pattern.match(x)) is not invert_match)))
return df[filter_func]<|docstring|>Filter a pandas dataframe by a column (default: 1st, contig name).
Parameters
----------
df: pd.Dataframe
annotation to filter (a pandas dataframe)
regex : str
regex string to match
invert_match : bool, optional
keep contigs NOT matching the regex string
column: str or int, optional
column name or number to filter (default: 1st, contig name)
Returns
-------
pd.DataFrame
filtered dataframe<|endoftext|> |
a952ce81102168fa95d229cc615702b2ac4dd08b62952ffce016c8383326202a | def genes(self, annot: str='bed') -> list:
'\n Retrieve gene names from an annotation.\n\n For BED files, names are taken from the \'name\' columns.\n\n For GTF files, names are taken from the \'gene_name\' field\n in the attribute column, if available.\n\n Parameters\n ----------\n annot : str, optional\n Annotation file type: \'bed\' or \'gtf\' (default: "bed")\n\n Returns\n -------\n list\n gene names\n '
if (annot.lower() == 'bed'):
return list(set(self.bed.name))
return list(set(self.named_gtf.index)) | Retrieve gene names from an annotation.
For BED files, names are taken from the 'name' columns.
For GTF files, names are taken from the 'gene_name' field
in the attribute column, if available.
Parameters
----------
annot : str, optional
Annotation file type: 'bed' or 'gtf' (default: "bed")
Returns
-------
list
gene names | genomepy/annotation/__init__.py | genes | vanheeringen-lab/genomepy | 146 | python | def genes(self, annot: str='bed') -> list:
'\n Retrieve gene names from an annotation.\n\n For BED files, names are taken from the \'name\' columns.\n\n For GTF files, names are taken from the \'gene_name\' field\n in the attribute column, if available.\n\n Parameters\n ----------\n annot : str, optional\n Annotation file type: \'bed\' or \'gtf\' (default: "bed")\n\n Returns\n -------\n list\n gene names\n '
if (annot.lower() == 'bed'):
return list(set(self.bed.name))
return list(set(self.named_gtf.index)) | def genes(self, annot: str='bed') -> list:
'\n Retrieve gene names from an annotation.\n\n For BED files, names are taken from the \'name\' columns.\n\n For GTF files, names are taken from the \'gene_name\' field\n in the attribute column, if available.\n\n Parameters\n ----------\n annot : str, optional\n Annotation file type: \'bed\' or \'gtf\' (default: "bed")\n\n Returns\n -------\n list\n gene names\n '
if (annot.lower() == 'bed'):
return list(set(self.bed.name))
return list(set(self.named_gtf.index))<|docstring|>Retrieve gene names from an annotation.
For BED files, names are taken from the 'name' columns.
For GTF files, names are taken from the 'gene_name' field
in the attribute column, if available.
Parameters
----------
annot : str, optional
Annotation file type: 'bed' or 'gtf' (default: "bed")
Returns
-------
list
gene names<|endoftext|> |
81f2fc048cf633ac056e21640e320167248a10bb2170d3593997c9aeb916ec38 | def gene_coords(self, genes: Iterable[str], annot: str='bed') -> pd.DataFrame:
'\n Retrieve gene locations.\n\n Parameters\n ----------\n genes : Iterable\n List of gene names as found in the given annotation file type\n annot : str, optional\n Annotation file type: \'bed\' or \'gtf\' (default: "bed")\n\n Returns\n -------\n pandas.DataFrame\n gene annotation\n '
gene_list = list(genes)
if (annot.lower() == 'bed'):
df = self.bed.set_index('name')
gene_info = df[['chrom', 'start', 'end', 'strand']]
else:
df = self.named_gtf
df = df.groupby(['gene_name', 'seqname', 'strand']).agg({'start': np.min, 'end': np.max}).reset_index(level=['seqname', 'strand'])
gene_info = df[['seqname', 'start', 'end', 'strand']]
gene_info = gene_info.reindex(gene_list).dropna()
pct = int(((100 * len(set(gene_info.index))) / len(gene_list)))
if (pct < 90):
logger.warning(((f'Only {pct}% of genes was found. ' if pct else 'No genes found. ') + 'A list of all gene names can be found with `Annotation.genes()`'))
if (annot.lower() == 'bed'):
return gene_info.reset_index()[['chrom', 'start', 'end', 'name', 'strand']]
else:
return gene_info.reset_index()[['seqname', 'start', 'end', 'gene_name', 'strand']] | Retrieve gene locations.
Parameters
----------
genes : Iterable
List of gene names as found in the given annotation file type
annot : str, optional
Annotation file type: 'bed' or 'gtf' (default: "bed")
Returns
-------
pandas.DataFrame
gene annotation | genomepy/annotation/__init__.py | gene_coords | vanheeringen-lab/genomepy | 146 | python | def gene_coords(self, genes: Iterable[str], annot: str='bed') -> pd.DataFrame:
'\n Retrieve gene locations.\n\n Parameters\n ----------\n genes : Iterable\n List of gene names as found in the given annotation file type\n annot : str, optional\n Annotation file type: \'bed\' or \'gtf\' (default: "bed")\n\n Returns\n -------\n pandas.DataFrame\n gene annotation\n '
gene_list = list(genes)
if (annot.lower() == 'bed'):
df = self.bed.set_index('name')
gene_info = df[['chrom', 'start', 'end', 'strand']]
else:
df = self.named_gtf
df = df.groupby(['gene_name', 'seqname', 'strand']).agg({'start': np.min, 'end': np.max}).reset_index(level=['seqname', 'strand'])
gene_info = df[['seqname', 'start', 'end', 'strand']]
gene_info = gene_info.reindex(gene_list).dropna()
pct = int(((100 * len(set(gene_info.index))) / len(gene_list)))
if (pct < 90):
logger.warning(((f'Only {pct}% of genes was found. ' if pct else 'No genes found. ') + 'A list of all gene names can be found with `Annotation.genes()`'))
if (annot.lower() == 'bed'):
return gene_info.reset_index()[['chrom', 'start', 'end', 'name', 'strand']]
else:
return gene_info.reset_index()[['seqname', 'start', 'end', 'gene_name', 'strand']] | def gene_coords(self, genes: Iterable[str], annot: str='bed') -> pd.DataFrame:
'\n Retrieve gene locations.\n\n Parameters\n ----------\n genes : Iterable\n List of gene names as found in the given annotation file type\n annot : str, optional\n Annotation file type: \'bed\' or \'gtf\' (default: "bed")\n\n Returns\n -------\n pandas.DataFrame\n gene annotation\n '
gene_list = list(genes)
if (annot.lower() == 'bed'):
df = self.bed.set_index('name')
gene_info = df[['chrom', 'start', 'end', 'strand']]
else:
df = self.named_gtf
df = df.groupby(['gene_name', 'seqname', 'strand']).agg({'start': np.min, 'end': np.max}).reset_index(level=['seqname', 'strand'])
gene_info = df[['seqname', 'start', 'end', 'strand']]
gene_info = gene_info.reindex(gene_list).dropna()
pct = int(((100 * len(set(gene_info.index))) / len(gene_list)))
if (pct < 90):
logger.warning(((f'Only {pct}% of genes was found. ' if pct else 'No genes found. ') + 'A list of all gene names can be found with `Annotation.genes()`'))
if (annot.lower() == 'bed'):
return gene_info.reset_index()[['chrom', 'start', 'end', 'name', 'strand']]
else:
return gene_info.reset_index()[['seqname', 'start', 'end', 'gene_name', 'strand']]<|docstring|>Retrieve gene locations.
Parameters
----------
genes : Iterable
List of gene names as found in the given annotation file type
annot : str, optional
Annotation file type: 'bed' or 'gtf' (default: "bed")
Returns
-------
pandas.DataFrame
gene annotation<|endoftext|> |
0df2594904702dcf0b86237c6be22580f93a80950a218ea7aae5750ab848a617 | def map_locations(self, annot: Union[(str, pd.DataFrame)], to: str, drop=True) -> Union[(None, pd.DataFrame)]:
'\n Map chromosome mapping from one assembly to another.\n\n Uses the NCBI assembly reports to find contigs.\n Drops missing contigs.\n\n Parameters\n ----------\n annot : str or pd.Dataframe\n annotation to map: "bed", "gtf" or a pandas dataframe.\n to: str\n target provider (UCSC, Ensembl or NCBI)\n drop: bool, optional\n if True, replace the chromosome column.\n If False, add a 2nd chromosome column.\n\n Returns\n -------\n pandas.DataFrame\n chromosome mapping.\n '
genomes_dir = os.path.dirname(self.genome_dir)
mapping = map_locations(self.genome, to, genomes_dir)
if (mapping is None):
return
df = _parse_annot(self, annot)
index_name = df.index.name
if (not (set(([index_name] + df.columns.to_list())) & {'chrom', 'seqname'})):
raise ValueError("Location mapping requires a column named 'chrom' or 'seqname'.")
is_indexed = (df.index.to_list() != list(range(df.shape[0])))
if is_indexed:
df = df.reset_index(level=index_name)
index_col = ('chrom' if ('chrom' in df.columns) else 'seqname')
df = df.set_index(index_col)
df = mapping.join(df, how='inner')
df = df.reset_index(drop=drop)
df.columns = ([index_col] + df.columns.to_list()[1:])
if is_indexed:
df = df.set_index((index_name if index_name else 'index'))
return df | Map chromosome mapping from one assembly to another.
Uses the NCBI assembly reports to find contigs.
Drops missing contigs.
Parameters
----------
annot : str or pd.Dataframe
annotation to map: "bed", "gtf" or a pandas dataframe.
to: str
target provider (UCSC, Ensembl or NCBI)
drop: bool, optional
if True, replace the chromosome column.
If False, add a 2nd chromosome column.
Returns
-------
pandas.DataFrame
chromosome mapping. | genomepy/annotation/__init__.py | map_locations | vanheeringen-lab/genomepy | 146 | python | def map_locations(self, annot: Union[(str, pd.DataFrame)], to: str, drop=True) -> Union[(None, pd.DataFrame)]:
'\n Map chromosome mapping from one assembly to another.\n\n Uses the NCBI assembly reports to find contigs.\n Drops missing contigs.\n\n Parameters\n ----------\n annot : str or pd.Dataframe\n annotation to map: "bed", "gtf" or a pandas dataframe.\n to: str\n target provider (UCSC, Ensembl or NCBI)\n drop: bool, optional\n if True, replace the chromosome column.\n If False, add a 2nd chromosome column.\n\n Returns\n -------\n pandas.DataFrame\n chromosome mapping.\n '
genomes_dir = os.path.dirname(self.genome_dir)
mapping = map_locations(self.genome, to, genomes_dir)
if (mapping is None):
return
df = _parse_annot(self, annot)
index_name = df.index.name
if (not (set(([index_name] + df.columns.to_list())) & {'chrom', 'seqname'})):
raise ValueError("Location mapping requires a column named 'chrom' or 'seqname'.")
is_indexed = (df.index.to_list() != list(range(df.shape[0])))
if is_indexed:
df = df.reset_index(level=index_name)
index_col = ('chrom' if ('chrom' in df.columns) else 'seqname')
df = df.set_index(index_col)
df = mapping.join(df, how='inner')
df = df.reset_index(drop=drop)
df.columns = ([index_col] + df.columns.to_list()[1:])
if is_indexed:
df = df.set_index((index_name if index_name else 'index'))
return df | def map_locations(self, annot: Union[(str, pd.DataFrame)], to: str, drop=True) -> Union[(None, pd.DataFrame)]:
'\n Map chromosome mapping from one assembly to another.\n\n Uses the NCBI assembly reports to find contigs.\n Drops missing contigs.\n\n Parameters\n ----------\n annot : str or pd.Dataframe\n annotation to map: "bed", "gtf" or a pandas dataframe.\n to: str\n target provider (UCSC, Ensembl or NCBI)\n drop: bool, optional\n if True, replace the chromosome column.\n If False, add a 2nd chromosome column.\n\n Returns\n -------\n pandas.DataFrame\n chromosome mapping.\n '
genomes_dir = os.path.dirname(self.genome_dir)
mapping = map_locations(self.genome, to, genomes_dir)
if (mapping is None):
return
df = _parse_annot(self, annot)
index_name = df.index.name
if (not (set(([index_name] + df.columns.to_list())) & {'chrom', 'seqname'})):
raise ValueError("Location mapping requires a column named 'chrom' or 'seqname'.")
is_indexed = (df.index.to_list() != list(range(df.shape[0])))
if is_indexed:
df = df.reset_index(level=index_name)
index_col = ('chrom' if ('chrom' in df.columns) else 'seqname')
df = df.set_index(index_col)
df = mapping.join(df, how='inner')
df = df.reset_index(drop=drop)
df.columns = ([index_col] + df.columns.to_list()[1:])
if is_indexed:
df = df.set_index((index_name if index_name else 'index'))
return df<|docstring|>Map chromosome mapping from one assembly to another.
Uses the NCBI assembly reports to find contigs.
Drops missing contigs.
Parameters
----------
annot : str or pd.Dataframe
annotation to map: "bed", "gtf" or a pandas dataframe.
to: str
target provider (UCSC, Ensembl or NCBI)
drop: bool, optional
if True, replace the chromosome column.
If False, add a 2nd chromosome column.
Returns
-------
pandas.DataFrame
chromosome mapping.<|endoftext|> |
2dc52e74de8b214a6a899c8b52118e9e26d8705e5fd698a8fe110adc1234464a | def filter_regex(self, annot: Union[(str, pd.DataFrame)], regex: Optional[str]='.*', invert_match: Optional[bool]=False, column: Union[(str, int)]=0) -> pd.DataFrame:
'\n Filter a dataframe by any column using regex.\n\n Parameters\n ----------\n annot : str or pd.Dataframe\n annotation to filter: "bed", "gtf" or a pandas dataframe\n regex : str\n regex string to match\n invert_match : bool, optional\n keep contigs NOT matching the regex string\n column: str or int, optional\n column name or number to filter (default: 1st, contig name)\n\n Returns\n -------\n pd.DataFrame\n filtered dataframe\n '
df = _parse_annot(self, annot)
return filter_regex(df, regex, invert_match, column) | Filter a dataframe by any column using regex.
Parameters
----------
annot : str or pd.Dataframe
annotation to filter: "bed", "gtf" or a pandas dataframe
regex : str
regex string to match
invert_match : bool, optional
keep contigs NOT matching the regex string
column: str or int, optional
column name or number to filter (default: 1st, contig name)
Returns
-------
pd.DataFrame
filtered dataframe | genomepy/annotation/__init__.py | filter_regex | vanheeringen-lab/genomepy | 146 | python | def filter_regex(self, annot: Union[(str, pd.DataFrame)], regex: Optional[str]='.*', invert_match: Optional[bool]=False, column: Union[(str, int)]=0) -> pd.DataFrame:
'\n Filter a dataframe by any column using regex.\n\n Parameters\n ----------\n annot : str or pd.Dataframe\n annotation to filter: "bed", "gtf" or a pandas dataframe\n regex : str\n regex string to match\n invert_match : bool, optional\n keep contigs NOT matching the regex string\n column: str or int, optional\n column name or number to filter (default: 1st, contig name)\n\n Returns\n -------\n pd.DataFrame\n filtered dataframe\n '
df = _parse_annot(self, annot)
return filter_regex(df, regex, invert_match, column) | def filter_regex(self, annot: Union[(str, pd.DataFrame)], regex: Optional[str]='.*', invert_match: Optional[bool]=False, column: Union[(str, int)]=0) -> pd.DataFrame:
'\n Filter a dataframe by any column using regex.\n\n Parameters\n ----------\n annot : str or pd.Dataframe\n annotation to filter: "bed", "gtf" or a pandas dataframe\n regex : str\n regex string to match\n invert_match : bool, optional\n keep contigs NOT matching the regex string\n column: str or int, optional\n column name or number to filter (default: 1st, contig name)\n\n Returns\n -------\n pd.DataFrame\n filtered dataframe\n '
df = _parse_annot(self, annot)
return filter_regex(df, regex, invert_match, column)<|docstring|>Filter a dataframe by any column using regex.
Parameters
----------
annot : str or pd.Dataframe
annotation to filter: "bed", "gtf" or a pandas dataframe
regex : str
regex string to match
invert_match : bool, optional
keep contigs NOT matching the regex string
column: str or int, optional
column name or number to filter (default: 1st, contig name)
Returns
-------
pd.DataFrame
filtered dataframe<|endoftext|> |
7e42c26584652ab6ada74d9670203cc31cc7db498aa7bca2820f68937fdefd91 | def query_blastn(infile: str, query: str) -> list:
'\n Checks a BLASTn file for matching the provided query string\n :param infile: Path to BLASTn file\n :param query: String containing query keywords\n :return: List of hits\n '
query = query.lower()
query = query.split(' ')
hits = []
with open(infile, 'r') as infile:
for line in infile:
line_flag = True
for keyword in query:
if (keyword not in line.lower()):
line_flag = False
if line_flag:
hits.append(line.strip())
return hits | Checks a BLASTn file for matching the provided query string
:param infile: Path to BLASTn file
:param query: String containing query keywords
:return: List of hits | BLASTn_Extract/blastn_extract.py | query_blastn | bfssi-forest-dussault/BLASTn_Extract | 1 | python | def query_blastn(infile: str, query: str) -> list:
'\n Checks a BLASTn file for matching the provided query string\n :param infile: Path to BLASTn file\n :param query: String containing query keywords\n :return: List of hits\n '
query = query.lower()
query = query.split(' ')
hits = []
with open(infile, 'r') as infile:
for line in infile:
line_flag = True
for keyword in query:
if (keyword not in line.lower()):
line_flag = False
if line_flag:
hits.append(line.strip())
return hits | def query_blastn(infile: str, query: str) -> list:
'\n Checks a BLASTn file for matching the provided query string\n :param infile: Path to BLASTn file\n :param query: String containing query keywords\n :return: List of hits\n '
query = query.lower()
query = query.split(' ')
hits = []
with open(infile, 'r') as infile:
for line in infile:
line_flag = True
for keyword in query:
if (keyword not in line.lower()):
line_flag = False
if line_flag:
hits.append(line.strip())
return hits<|docstring|>Checks a BLASTn file for matching the provided query string
:param infile: Path to BLASTn file
:param query: String containing query keywords
:return: List of hits<|endoftext|> |
895a27a5a67f57574732309aeb65709a2402874fd93687d0ad53b2a67f45f977 | def extract_contigs(contigs: str, node_dict: dict, outfile: str, delimiter: str) -> str:
'\n Searches through a FASTA file and extracts only contigs that are in the provided node list\n :param contigs: FASTA file\n :param node_dict: Dictionary generated with extract_nodes()\n :param outfile: String path to output file\n :param delimiter: String delimiter to split BLASTn file on (defaults to tab)\n :return: String path to output FASTA\n '
outfile_ = open(outfile, 'w')
write_flag = False
with open(os.path.abspath(contigs), 'r') as infile:
for line in infile:
if line.startswith('>'):
for (node, hit) in node_dict.items():
if (node in line):
outfile_.write((((('>' + node.rsplit('_', 2)[0]) + ' ') + ''.join(hit.split(delimiter)[1:3])) + '\n'))
write_flag = True
break
else:
write_flag = False
elif write_flag:
outfile_.write(line)
outfile_.close()
return outfile | Searches through a FASTA file and extracts only contigs that are in the provided node list
:param contigs: FASTA file
:param node_dict: Dictionary generated with extract_nodes()
:param outfile: String path to output file
:param delimiter: String delimiter to split BLASTn file on (defaults to tab)
:return: String path to output FASTA | BLASTn_Extract/blastn_extract.py | extract_contigs | bfssi-forest-dussault/BLASTn_Extract | 1 | python | def extract_contigs(contigs: str, node_dict: dict, outfile: str, delimiter: str) -> str:
'\n Searches through a FASTA file and extracts only contigs that are in the provided node list\n :param contigs: FASTA file\n :param node_dict: Dictionary generated with extract_nodes()\n :param outfile: String path to output file\n :param delimiter: String delimiter to split BLASTn file on (defaults to tab)\n :return: String path to output FASTA\n '
outfile_ = open(outfile, 'w')
write_flag = False
with open(os.path.abspath(contigs), 'r') as infile:
for line in infile:
if line.startswith('>'):
for (node, hit) in node_dict.items():
if (node in line):
outfile_.write((((('>' + node.rsplit('_', 2)[0]) + ' ') + .join(hit.split(delimiter)[1:3])) + '\n'))
write_flag = True
break
else:
write_flag = False
elif write_flag:
outfile_.write(line)
outfile_.close()
return outfile | def extract_contigs(contigs: str, node_dict: dict, outfile: str, delimiter: str) -> str:
'\n Searches through a FASTA file and extracts only contigs that are in the provided node list\n :param contigs: FASTA file\n :param node_dict: Dictionary generated with extract_nodes()\n :param outfile: String path to output file\n :param delimiter: String delimiter to split BLASTn file on (defaults to tab)\n :return: String path to output FASTA\n '
outfile_ = open(outfile, 'w')
write_flag = False
with open(os.path.abspath(contigs), 'r') as infile:
for line in infile:
if line.startswith('>'):
for (node, hit) in node_dict.items():
if (node in line):
outfile_.write((((('>' + node.rsplit('_', 2)[0]) + ' ') + .join(hit.split(delimiter)[1:3])) + '\n'))
write_flag = True
break
else:
write_flag = False
elif write_flag:
outfile_.write(line)
outfile_.close()
return outfile<|docstring|>Searches through a FASTA file and extracts only contigs that are in the provided node list
:param contigs: FASTA file
:param node_dict: Dictionary generated with extract_nodes()
:param outfile: String path to output file
:param delimiter: String delimiter to split BLASTn file on (defaults to tab)
:return: String path to output FASTA<|endoftext|> |
26d29c710004f5d9e045f6346608666c1d0aa463474b7dd22a8bc3f17f1eef55 | def setup():
'Setups logging.'
FORMAT = u'%(levelname)-8s [%(asctime)s] %(message)s ### %(filename)s[LINE:%(lineno)d]'
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler())
logger.addHandler(logging.FileHandler(f'logs/{ctime()}.log'))
coloredlogs.install(level='INFO', fmt=FORMAT, logger=logger)
logging.getLogger('discord').setLevel(logging.WARNING) | Setups logging. | core/logs.py | setup | A-Zalt/flasher-rewrite | 0 | python | def setup():
FORMAT = u'%(levelname)-8s [%(asctime)s] %(message)s ### %(filename)s[LINE:%(lineno)d]'
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler())
logger.addHandler(logging.FileHandler(f'logs/{ctime()}.log'))
coloredlogs.install(level='INFO', fmt=FORMAT, logger=logger)
logging.getLogger('discord').setLevel(logging.WARNING) | def setup():
FORMAT = u'%(levelname)-8s [%(asctime)s] %(message)s ### %(filename)s[LINE:%(lineno)d]'
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler())
logger.addHandler(logging.FileHandler(f'logs/{ctime()}.log'))
coloredlogs.install(level='INFO', fmt=FORMAT, logger=logger)
logging.getLogger('discord').setLevel(logging.WARNING)<|docstring|>Setups logging.<|endoftext|> |
dd661e8daf26472b15e39006a1ca4339d93e14ae762efc8b7248827f8a37efd4 | def max_index(X):
'Return the index of the maximum in a numpy array.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, n_features)\n The input array.\n\n Returns\n -------\n i : int\n The row index of the maximum.\n j : int\n The column index of the maximum.\n\n Raises\n ------\n ValueError\n If the input is not a numpy error or\n if the shape is not 2D.\n '
if ((X is None) or (type(X) is not np.ndarray)):
raise ValueError('The input array X is None or not a numpy array.')
if (len(X.shape) != 2):
raise ValueError('The shape is not 2D.')
s = X.shape[0]
result = np.argmax(X)
i = (result // s)
j = (result % s)
return (i, j) | Return the index of the maximum in a numpy array.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The input array.
Returns
-------
i : int
The row index of the maximum.
j : int
The column index of the maximum.
Raises
------
ValueError
If the input is not a numpy error or
if the shape is not 2D. | numpy_questions.py | max_index | OlivierBn/datacamp-assignment1 | 0 | python | def max_index(X):
'Return the index of the maximum in a numpy array.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, n_features)\n The input array.\n\n Returns\n -------\n i : int\n The row index of the maximum.\n j : int\n The column index of the maximum.\n\n Raises\n ------\n ValueError\n If the input is not a numpy error or\n if the shape is not 2D.\n '
if ((X is None) or (type(X) is not np.ndarray)):
raise ValueError('The input array X is None or not a numpy array.')
if (len(X.shape) != 2):
raise ValueError('The shape is not 2D.')
s = X.shape[0]
result = np.argmax(X)
i = (result // s)
j = (result % s)
return (i, j) | def max_index(X):
'Return the index of the maximum in a numpy array.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, n_features)\n The input array.\n\n Returns\n -------\n i : int\n The row index of the maximum.\n j : int\n The column index of the maximum.\n\n Raises\n ------\n ValueError\n If the input is not a numpy error or\n if the shape is not 2D.\n '
if ((X is None) or (type(X) is not np.ndarray)):
raise ValueError('The input array X is None or not a numpy array.')
if (len(X.shape) != 2):
raise ValueError('The shape is not 2D.')
s = X.shape[0]
result = np.argmax(X)
i = (result // s)
j = (result % s)
return (i, j)<|docstring|>Return the index of the maximum in a numpy array.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The input array.
Returns
-------
i : int
The row index of the maximum.
j : int
The column index of the maximum.
Raises
------
ValueError
If the input is not a numpy error or
if the shape is not 2D.<|endoftext|> |
3c89573b067da9ccff22291dca03066ed9a44c07c4b24ff51169613409adaa1c | def wallis_product(n_terms):
'Implement the Wallis product to compute an approximation of pi.\n\n See:\n https://en.wikipedia.org/wiki/Wallis_product\n\n '
x = 2
for n in range(1, (n_terms + 1)):
x *= ((4 * (n ** 2)) / ((4 * (n ** 2)) - 1))
return x | Implement the Wallis product to compute an approximation of pi.
See:
https://en.wikipedia.org/wiki/Wallis_product | numpy_questions.py | wallis_product | OlivierBn/datacamp-assignment1 | 0 | python | def wallis_product(n_terms):
'Implement the Wallis product to compute an approximation of pi.\n\n See:\n https://en.wikipedia.org/wiki/Wallis_product\n\n '
x = 2
for n in range(1, (n_terms + 1)):
x *= ((4 * (n ** 2)) / ((4 * (n ** 2)) - 1))
return x | def wallis_product(n_terms):
'Implement the Wallis product to compute an approximation of pi.\n\n See:\n https://en.wikipedia.org/wiki/Wallis_product\n\n '
x = 2
for n in range(1, (n_terms + 1)):
x *= ((4 * (n ** 2)) / ((4 * (n ** 2)) - 1))
return x<|docstring|>Implement the Wallis product to compute an approximation of pi.
See:
https://en.wikipedia.org/wiki/Wallis_product<|endoftext|> |
571e3f5108e89a4f49a33de2f67867489524b7fdb06c0e3739aa940b2ea78924 | def __init__(self, **kwargs):
"\n :param data: input DataFrame include ['user_id', 'item_id', 'timestamp']\n "
pass | :param data: input DataFrame include ['user_id', 'item_id', 'timestamp'] | build/lib/DMRecall/baseclass/recall.py | __init__ | busesese/DMRecall | 9 | python | def __init__(self, **kwargs):
"\n \n "
pass | def __init__(self, **kwargs):
"\n \n "
pass<|docstring|>:param data: input DataFrame include ['user_id', 'item_id', 'timestamp']<|endoftext|> |
7ef5ac1ea4ed36331310ad878407979f060ee20c8c54b3a2ea708fda48a2f6b2 | def processData(self):
'\n process data before train to get the right data format\n :return:\n '
pass | process data before train to get the right data format
:return: | build/lib/DMRecall/baseclass/recall.py | processData | busesese/DMRecall | 9 | python | def processData(self):
'\n process data before train to get the right data format\n :return:\n '
pass | def processData(self):
'\n process data before train to get the right data format\n :return:\n '
pass<|docstring|>process data before train to get the right data format
:return:<|endoftext|> |
d4f2e2a2a19bb7f82e44e78146b6c12829e8cb96ec2fc3057b1612f69c456bd1 | def train(self):
'\n train the data\n :return:\n '
pass | train the data
:return: | build/lib/DMRecall/baseclass/recall.py | train | busesese/DMRecall | 9 | python | def train(self):
'\n train the data\n :return:\n '
pass | def train(self):
'\n train the data\n :return:\n '
pass<|docstring|>train the data
:return:<|endoftext|> |
0ddac82ec73767cb03c6e6bb592eff8ff2da8207840da18e6abcd30eb24bdf5d | def predict(self, items, k=10):
'\n predict result for a given user\n :param user: str, user id\n :param items: list, user recent behavior item list\n :param k: predict top k result\n :return: dict\n '
result = dict()
if isinstance(items, list):
for item in items:
if (item in self.item_similarity):
for (i, val) in self.item_similarity[item].items():
if (i not in items):
if (i not in result):
result[i] = val
else:
result[i] += val
return [i for (i, val) in sorted(result.items(), key=(lambda x: x[1]), reverse=True)[:k]]
else:
raise TypeError('Input parameter type is not list') | predict result for a given user
:param user: str, user id
:param items: list, user recent behavior item list
:param k: predict top k result
:return: dict | build/lib/DMRecall/baseclass/recall.py | predict | busesese/DMRecall | 9 | python | def predict(self, items, k=10):
'\n predict result for a given user\n :param user: str, user id\n :param items: list, user recent behavior item list\n :param k: predict top k result\n :return: dict\n '
result = dict()
if isinstance(items, list):
for item in items:
if (item in self.item_similarity):
for (i, val) in self.item_similarity[item].items():
if (i not in items):
if (i not in result):
result[i] = val
else:
result[i] += val
return [i for (i, val) in sorted(result.items(), key=(lambda x: x[1]), reverse=True)[:k]]
else:
raise TypeError('Input parameter type is not list') | def predict(self, items, k=10):
'\n predict result for a given user\n :param user: str, user id\n :param items: list, user recent behavior item list\n :param k: predict top k result\n :return: dict\n '
result = dict()
if isinstance(items, list):
for item in items:
if (item in self.item_similarity):
for (i, val) in self.item_similarity[item].items():
if (i not in items):
if (i not in result):
result[i] = val
else:
result[i] += val
return [i for (i, val) in sorted(result.items(), key=(lambda x: x[1]), reverse=True)[:k]]
else:
raise TypeError('Input parameter type is not list')<|docstring|>predict result for a given user
:param user: str, user id
:param items: list, user recent behavior item list
:param k: predict top k result
:return: dict<|endoftext|> |
f0c0bc2266e0a4e4e9696b5104f20b7e1e8462c68fd94d18b25ab12f5920e7fc | def recommend(self, data_pre, k=10):
'\n recommend result for given users\n :param data_pre: dict, include user id and user recent behavior item\n :param k: int predict top k result\n :return: dict, key = user and value = user result\n '
result = dict()
for (uid, item_list) in data_pre.items():
pred = self.predict(item_list, k)
result[uid] = pred
return result | recommend result for given users
:param data_pre: dict, include user id and user recent behavior item
:param k: int predict top k result
:return: dict, key = user and value = user result | build/lib/DMRecall/baseclass/recall.py | recommend | busesese/DMRecall | 9 | python | def recommend(self, data_pre, k=10):
'\n recommend result for given users\n :param data_pre: dict, include user id and user recent behavior item\n :param k: int predict top k result\n :return: dict, key = user and value = user result\n '
result = dict()
for (uid, item_list) in data_pre.items():
pred = self.predict(item_list, k)
result[uid] = pred
return result | def recommend(self, data_pre, k=10):
'\n recommend result for given users\n :param data_pre: dict, include user id and user recent behavior item\n :param k: int predict top k result\n :return: dict, key = user and value = user result\n '
result = dict()
for (uid, item_list) in data_pre.items():
pred = self.predict(item_list, k)
result[uid] = pred
return result<|docstring|>recommend result for given users
:param data_pre: dict, include user id and user recent behavior item
:param k: int predict top k result
:return: dict, key = user and value = user result<|endoftext|> |
659b8033daefb899d9076573225049ee9465d07bb0309b8a01d915eb0f519f41 | def AddPageWithDefaultRunNavigate(self, page_url):
' Add a simple page with url equals to page_url that contains only default\n RunNavigateSteps.\n '
self.AddUserStory(page_module.Page(page_url, self, self.base_dir)) | Add a simple page with url equals to page_url that contains only default
RunNavigateSteps. | tools/telemetry/telemetry/page/page_set.py | AddPageWithDefaultRunNavigate | sunjc53yy/chromium | 0 | python | def AddPageWithDefaultRunNavigate(self, page_url):
' Add a simple page with url equals to page_url that contains only default\n RunNavigateSteps.\n '
self.AddUserStory(page_module.Page(page_url, self, self.base_dir)) | def AddPageWithDefaultRunNavigate(self, page_url):
' Add a simple page with url equals to page_url that contains only default\n RunNavigateSteps.\n '
self.AddUserStory(page_module.Page(page_url, self, self.base_dir))<|docstring|>Add a simple page with url equals to page_url that contains only default
RunNavigateSteps.<|endoftext|> |
be25ce8a4f59095f42de122d1d8a3a894a9c4fd906313ea126990e0ae5372d1c | @property
def wpr_archive_info(self):
"Lazily constructs wpr_archive_info if it's not set and returns it."
if (self.archive_data_file and (not self._wpr_archive_info)):
self._wpr_archive_info = page_set_archive_info.PageSetArchiveInfo.FromFile(os.path.join(self.base_dir, self.archive_data_file))
return self._wpr_archive_info | Lazily constructs wpr_archive_info if it's not set and returns it. | tools/telemetry/telemetry/page/page_set.py | wpr_archive_info | sunjc53yy/chromium | 0 | python | @property
def wpr_archive_info(self):
if (self.archive_data_file and (not self._wpr_archive_info)):
self._wpr_archive_info = page_set_archive_info.PageSetArchiveInfo.FromFile(os.path.join(self.base_dir, self.archive_data_file))
return self._wpr_archive_info | @property
def wpr_archive_info(self):
if (self.archive_data_file and (not self._wpr_archive_info)):
self._wpr_archive_info = page_set_archive_info.PageSetArchiveInfo.FromFile(os.path.join(self.base_dir, self.archive_data_file))
return self._wpr_archive_info<|docstring|>Lazily constructs wpr_archive_info if it's not set and returns it.<|endoftext|> |
457944cd6b4f3ecc07207fe7dc64aa93d7e1a2a08ff0faa77142d92be8cf2cb4 | def ReorderPageSet(self, results_file):
'Reorders this page set based on the results of a past run.'
page_set_dict = {}
for page in self.user_stories:
page_set_dict[page.url] = page
user_stories = []
with open(results_file, 'rb') as csv_file:
csv_reader = csv.reader(csv_file)
csv_header = csv_reader.next()
if ('url' not in csv_header):
raise Exception('Unusable results_file.')
url_index = csv_header.index('url')
for csv_row in csv_reader:
if (csv_row[url_index] in page_set_dict):
self.AddPage(page_set_dict[csv_row[url_index]])
else:
raise Exception('Unusable results_file.')
return user_stories | Reorders this page set based on the results of a past run. | tools/telemetry/telemetry/page/page_set.py | ReorderPageSet | sunjc53yy/chromium | 0 | python | def ReorderPageSet(self, results_file):
page_set_dict = {}
for page in self.user_stories:
page_set_dict[page.url] = page
user_stories = []
with open(results_file, 'rb') as csv_file:
csv_reader = csv.reader(csv_file)
csv_header = csv_reader.next()
if ('url' not in csv_header):
raise Exception('Unusable results_file.')
url_index = csv_header.index('url')
for csv_row in csv_reader:
if (csv_row[url_index] in page_set_dict):
self.AddPage(page_set_dict[csv_row[url_index]])
else:
raise Exception('Unusable results_file.')
return user_stories | def ReorderPageSet(self, results_file):
page_set_dict = {}
for page in self.user_stories:
page_set_dict[page.url] = page
user_stories = []
with open(results_file, 'rb') as csv_file:
csv_reader = csv.reader(csv_file)
csv_header = csv_reader.next()
if ('url' not in csv_header):
raise Exception('Unusable results_file.')
url_index = csv_header.index('url')
for csv_row in csv_reader:
if (csv_row[url_index] in page_set_dict):
self.AddPage(page_set_dict[csv_row[url_index]])
else:
raise Exception('Unusable results_file.')
return user_stories<|docstring|>Reorders this page set based on the results of a past run.<|endoftext|> |
2549015a0af542a8c7381c950b13af3ab634148415deb675f1f77f1969001ccb | @conan_command(group='Consumer', formatters={'cli': output_search_cli, 'json': output_search_json})
def search(conan_api, parser, *args, **kwargs):
'\n Searches for package recipes whose name contain <query> in a remote or in the local cache\n '
parser.add_argument('query', help="Search query to find package recipe reference, e.g., 'boost', 'lib*'")
exclusive_args = parser.add_mutually_exclusive_group()
exclusive_args.add_argument('-r', '--remote', default=None, action=Extender, help='Remote to search. Accepts wildcards. To search in all remotes use *')
exclusive_args.add_argument('-c', '--cache', action='store_true', help='Search in the local cache')
args = parser.parse_args(*args)
remotes = (args.remote or [])
info = conan_api.search_recipes(args.query, remote_patterns=remotes, local_cache=args.cache)
return info | Searches for package recipes whose name contain <query> in a remote or in the local cache | conans/cli/commands/search.py | search | photex/conan | 6,205 | python | @conan_command(group='Consumer', formatters={'cli': output_search_cli, 'json': output_search_json})
def search(conan_api, parser, *args, **kwargs):
'\n \n '
parser.add_argument('query', help="Search query to find package recipe reference, e.g., 'boost', 'lib*'")
exclusive_args = parser.add_mutually_exclusive_group()
exclusive_args.add_argument('-r', '--remote', default=None, action=Extender, help='Remote to search. Accepts wildcards. To search in all remotes use *')
exclusive_args.add_argument('-c', '--cache', action='store_true', help='Search in the local cache')
args = parser.parse_args(*args)
remotes = (args.remote or [])
info = conan_api.search_recipes(args.query, remote_patterns=remotes, local_cache=args.cache)
return info | @conan_command(group='Consumer', formatters={'cli': output_search_cli, 'json': output_search_json})
def search(conan_api, parser, *args, **kwargs):
'\n \n '
parser.add_argument('query', help="Search query to find package recipe reference, e.g., 'boost', 'lib*'")
exclusive_args = parser.add_mutually_exclusive_group()
exclusive_args.add_argument('-r', '--remote', default=None, action=Extender, help='Remote to search. Accepts wildcards. To search in all remotes use *')
exclusive_args.add_argument('-c', '--cache', action='store_true', help='Search in the local cache')
args = parser.parse_args(*args)
remotes = (args.remote or [])
info = conan_api.search_recipes(args.query, remote_patterns=remotes, local_cache=args.cache)
return info<|docstring|>Searches for package recipes whose name contain <query> in a remote or in the local cache<|endoftext|> |
261ab643d4c25cd6cc0c08ae766d6711b8f3bc59d6ee6a8dbddbab2abe414aef | def create_sun(self):
' \n create a yellow sun, with coordinates (0,0,0) \n '
sun_source = vtkSphereSource()
sun_source.SetRadius(0.2)
sun_source.SetThetaResolution(20)
sun_source.SetPhiResolution(20)
sun_mapper = vtkPolyDataMapper()
self.planets['sun'] = Planet('sun', sun_source, sun_mapper)
self.planets['sun'].actor.GetProperty().SetColor(1, 1, 0)
self.renderer.AddActor(self.planets['sun'].actor) | create a yellow sun, with coordinates (0,0,0) | sandbox/src1/TCSE3-3rd-examples/src/py/examples/canvas/vtkplanet.py | create_sun | sniemi/SamPy | 5 | python | def create_sun(self):
' \n \n '
sun_source = vtkSphereSource()
sun_source.SetRadius(0.2)
sun_source.SetThetaResolution(20)
sun_source.SetPhiResolution(20)
sun_mapper = vtkPolyDataMapper()
self.planets['sun'] = Planet('sun', sun_source, sun_mapper)
self.planets['sun'].actor.GetProperty().SetColor(1, 1, 0)
self.renderer.AddActor(self.planets['sun'].actor) | def create_sun(self):
' \n \n '
sun_source = vtkSphereSource()
sun_source.SetRadius(0.2)
sun_source.SetThetaResolution(20)
sun_source.SetPhiResolution(20)
sun_mapper = vtkPolyDataMapper()
self.planets['sun'] = Planet('sun', sun_source, sun_mapper)
self.planets['sun'].actor.GetProperty().SetColor(1, 1, 0)
self.renderer.AddActor(self.planets['sun'].actor)<|docstring|>create a yellow sun, with coordinates (0,0,0)<|endoftext|> |
ae6c3a2d7abad33b741a193a1815e84bbd823c5314887f01e66b580f70756056 | def create_planet(self, name, x, y, z):
' \n create a planet (with disk) at specified coordinates \n '
if ((self.__planet_source == None) and (self.__planet_mapper == None)):
self.__planet_source = vtkSphereSource()
self.__planet_source.SetRadius(0.1)
self.__planet_source.SetThetaResolution(20)
self.__planet_source.SetPhiResolution(20)
self.__planet_mapper = vtkPolyDataMapper()
self.planets[name] = Planet(name, self.__planet_source, self.__planet_mapper, x, y, z, with_disk=1)
self.renderer.AddActor(self.planets[name].actor)
self.renderer.AddActor(self.planets[name].disk_actor) | create a planet (with disk) at specified coordinates | sandbox/src1/TCSE3-3rd-examples/src/py/examples/canvas/vtkplanet.py | create_planet | sniemi/SamPy | 5 | python | def create_planet(self, name, x, y, z):
' \n \n '
if ((self.__planet_source == None) and (self.__planet_mapper == None)):
self.__planet_source = vtkSphereSource()
self.__planet_source.SetRadius(0.1)
self.__planet_source.SetThetaResolution(20)
self.__planet_source.SetPhiResolution(20)
self.__planet_mapper = vtkPolyDataMapper()
self.planets[name] = Planet(name, self.__planet_source, self.__planet_mapper, x, y, z, with_disk=1)
self.renderer.AddActor(self.planets[name].actor)
self.renderer.AddActor(self.planets[name].disk_actor) | def create_planet(self, name, x, y, z):
' \n \n '
if ((self.__planet_source == None) and (self.__planet_mapper == None)):
self.__planet_source = vtkSphereSource()
self.__planet_source.SetRadius(0.1)
self.__planet_source.SetThetaResolution(20)
self.__planet_source.SetPhiResolution(20)
self.__planet_mapper = vtkPolyDataMapper()
self.planets[name] = Planet(name, self.__planet_source, self.__planet_mapper, x, y, z, with_disk=1)
self.renderer.AddActor(self.planets[name].actor)
self.renderer.AddActor(self.planets[name].disk_actor)<|docstring|>create a planet (with disk) at specified coordinates<|endoftext|> |
1e8d9c9c16c7d696167fcec3e73c443cec3b84330983d707180b3e0a2ef48659 | def generate_random_password(length=9, letters=True, digits=True, punctuation=True, ignored_chars=''):
'\n Generates a cryptographically secure random password\n :param length: Length of password\n :param letters: True to use letters in password\n :param digits: True to use digits in password\n :param punctuation: True to use punctuation in password\n :param ignored_chars: str containing all the characters that should be ignored during generation\n :return: A dictionary containing the password and entropy\n '
if (not (letters or digits or punctuation)):
raise ParameterError('At least one set of characters must be selected for a password to be generated')
char_pool = ''
if letters:
char_pool += string.ascii_letters
if digits:
char_pool += string.digits
if punctuation:
char_pool += string.punctuation
char_list = [char for char in char_pool if (char not in ignored_chars)]
result = ''
for _ in range(length):
result += secrets.choice(char_list)
return {'password': result, 'entropy': __calc_entropy_password(result, len(char_list))} | Generates a cryptographically secure random password
:param length: Length of password
:param letters: True to use letters in password
:param digits: True to use digits in password
:param punctuation: True to use punctuation in password
:param ignored_chars: str containing all the characters that should be ignored during generation
:return: A dictionary containing the password and entropy | generators.py | generate_random_password | Tauag/SPass | 2 | python | def generate_random_password(length=9, letters=True, digits=True, punctuation=True, ignored_chars=):
'\n Generates a cryptographically secure random password\n :param length: Length of password\n :param letters: True to use letters in password\n :param digits: True to use digits in password\n :param punctuation: True to use punctuation in password\n :param ignored_chars: str containing all the characters that should be ignored during generation\n :return: A dictionary containing the password and entropy\n '
if (not (letters or digits or punctuation)):
raise ParameterError('At least one set of characters must be selected for a password to be generated')
char_pool =
if letters:
char_pool += string.ascii_letters
if digits:
char_pool += string.digits
if punctuation:
char_pool += string.punctuation
char_list = [char for char in char_pool if (char not in ignored_chars)]
result =
for _ in range(length):
result += secrets.choice(char_list)
return {'password': result, 'entropy': __calc_entropy_password(result, len(char_list))} | def generate_random_password(length=9, letters=True, digits=True, punctuation=True, ignored_chars=):
'\n Generates a cryptographically secure random password\n :param length: Length of password\n :param letters: True to use letters in password\n :param digits: True to use digits in password\n :param punctuation: True to use punctuation in password\n :param ignored_chars: str containing all the characters that should be ignored during generation\n :return: A dictionary containing the password and entropy\n '
if (not (letters or digits or punctuation)):
raise ParameterError('At least one set of characters must be selected for a password to be generated')
char_pool =
if letters:
char_pool += string.ascii_letters
if digits:
char_pool += string.digits
if punctuation:
char_pool += string.punctuation
char_list = [char for char in char_pool if (char not in ignored_chars)]
result =
for _ in range(length):
result += secrets.choice(char_list)
return {'password': result, 'entropy': __calc_entropy_password(result, len(char_list))}<|docstring|>Generates a cryptographically secure random password
:param length: Length of password
:param letters: True to use letters in password
:param digits: True to use digits in password
:param punctuation: True to use punctuation in password
:param ignored_chars: str containing all the characters that should be ignored during generation
:return: A dictionary containing the password and entropy<|endoftext|> |
5a4a6c66790c5ca96b9c0e1c7ba5edf2120d41d699c19ff63af2d9febea62d09 | def generate_passphrase(word_count=5, pad_length=0, digits=True, punctuation=True, ignored_symbols=''):
'\n Generates a passphrase with the specified amount of padding\n :param word_count: Number of words in passphrase\n :param pad_length: The number of padding characters\n :param digits: True to use digits in padding\n :param punctuation: True to use punctuation in padding\n :param ignored_symbols: str containing all the symbols to ignore during padding generation\n :return: A dictionary containing the passphrase, entropy and the +- deviation of the entropy\n '
if (word_count < 2):
raise ParameterError('You need at least two words to make a passphrase')
with open('object/word_map.pickle', 'rb') as words:
word_bank = pickle.load(words)
(placements, pad_bank_size) = ({}, 0)
if (pad_length > 0):
if (not (digits or punctuation)):
raise ParameterError('At least one set of characters must be selected for the padding')
(placements, pad_bank_size) = __scatter_padding(word_count, pad_length, digits, punctuation, ignored_symbols)
(result, words_used, coin) = ('', [], [0, 1])
for i in range(word_count):
if (i in placements):
result += ''.join((sym for sym in placements[i]))
word = secrets.choice(word_bank)
if (secrets.choice(coin) == 0):
word = (word[0].upper() + word[1:])
result += word
words_used.append(word)
if (word_count in placements):
result += ''.join((sym for sym in placements[word_count]))
(entropy, deviation) = __calc_entropy_passphrase(word_count, len(word_bank), pad_length, pad_bank_size)
return {'password': result, 'entropy': entropy, 'deviation': deviation} | Generates a passphrase with the specified amount of padding
:param word_count: Number of words in passphrase
:param pad_length: The number of padding characters
:param digits: True to use digits in padding
:param punctuation: True to use punctuation in padding
:param ignored_symbols: str containing all the symbols to ignore during padding generation
:return: A dictionary containing the passphrase, entropy and the +- deviation of the entropy | generators.py | generate_passphrase | Tauag/SPass | 2 | python | def generate_passphrase(word_count=5, pad_length=0, digits=True, punctuation=True, ignored_symbols=):
'\n Generates a passphrase with the specified amount of padding\n :param word_count: Number of words in passphrase\n :param pad_length: The number of padding characters\n :param digits: True to use digits in padding\n :param punctuation: True to use punctuation in padding\n :param ignored_symbols: str containing all the symbols to ignore during padding generation\n :return: A dictionary containing the passphrase, entropy and the +- deviation of the entropy\n '
if (word_count < 2):
raise ParameterError('You need at least two words to make a passphrase')
with open('object/word_map.pickle', 'rb') as words:
word_bank = pickle.load(words)
(placements, pad_bank_size) = ({}, 0)
if (pad_length > 0):
if (not (digits or punctuation)):
raise ParameterError('At least one set of characters must be selected for the padding')
(placements, pad_bank_size) = __scatter_padding(word_count, pad_length, digits, punctuation, ignored_symbols)
(result, words_used, coin) = (, [], [0, 1])
for i in range(word_count):
if (i in placements):
result += .join((sym for sym in placements[i]))
word = secrets.choice(word_bank)
if (secrets.choice(coin) == 0):
word = (word[0].upper() + word[1:])
result += word
words_used.append(word)
if (word_count in placements):
result += .join((sym for sym in placements[word_count]))
(entropy, deviation) = __calc_entropy_passphrase(word_count, len(word_bank), pad_length, pad_bank_size)
return {'password': result, 'entropy': entropy, 'deviation': deviation} | def generate_passphrase(word_count=5, pad_length=0, digits=True, punctuation=True, ignored_symbols=):
'\n Generates a passphrase with the specified amount of padding\n :param word_count: Number of words in passphrase\n :param pad_length: The number of padding characters\n :param digits: True to use digits in padding\n :param punctuation: True to use punctuation in padding\n :param ignored_symbols: str containing all the symbols to ignore during padding generation\n :return: A dictionary containing the passphrase, entropy and the +- deviation of the entropy\n '
if (word_count < 2):
raise ParameterError('You need at least two words to make a passphrase')
with open('object/word_map.pickle', 'rb') as words:
word_bank = pickle.load(words)
(placements, pad_bank_size) = ({}, 0)
if (pad_length > 0):
if (not (digits or punctuation)):
raise ParameterError('At least one set of characters must be selected for the padding')
(placements, pad_bank_size) = __scatter_padding(word_count, pad_length, digits, punctuation, ignored_symbols)
(result, words_used, coin) = (, [], [0, 1])
for i in range(word_count):
if (i in placements):
result += .join((sym for sym in placements[i]))
word = secrets.choice(word_bank)
if (secrets.choice(coin) == 0):
word = (word[0].upper() + word[1:])
result += word
words_used.append(word)
if (word_count in placements):
result += .join((sym for sym in placements[word_count]))
(entropy, deviation) = __calc_entropy_passphrase(word_count, len(word_bank), pad_length, pad_bank_size)
return {'password': result, 'entropy': entropy, 'deviation': deviation}<|docstring|>Generates a passphrase with the specified amount of padding
:param word_count: Number of words in passphrase
:param pad_length: The number of padding characters
:param digits: True to use digits in padding
:param punctuation: True to use punctuation in padding
:param ignored_symbols: str containing all the symbols to ignore during padding generation
:return: A dictionary containing the passphrase, entropy and the +- deviation of the entropy<|endoftext|> |
b5cc2412841b586fad0e2db9a40d2593a2cf131cdd4564299f347c095f86c0d9 | def __scatter_padding(word_count, pad_length, digits, punctuation, ignored_symbols):
'\n Randomly decides where to add padding and which characters to use\n :param word_count: Number of words in passphrase\n :param pad_length: Number of characters to use for padding\n :param digits: True to use digits in padding\n :param punctuation: True to use punctuation in padding\n :param ignored_symbols: str containing all characters to ignore during padding generation\n :return: A tuple containing the padding placements and the size of the character pool used to pad\n '
char_pool = ''
if digits:
char_pool += string.digits
if punctuation:
char_pool += string.punctuation
char_list = [char for char in char_pool if (char not in ignored_symbols)]
indexes = [index for index in range((word_count + 1))]
placements = {}
for _ in range(pad_length):
idx = secrets.choice(indexes)
if (idx not in placements):
placements.update({idx: [secrets.choice(char_list)]})
else:
placements[idx].append(secrets.choice(char_list))
return (placements, len(char_list)) | Randomly decides where to add padding and which characters to use
:param word_count: Number of words in passphrase
:param pad_length: Number of characters to use for padding
:param digits: True to use digits in padding
:param punctuation: True to use punctuation in padding
:param ignored_symbols: str containing all characters to ignore during padding generation
:return: A tuple containing the padding placements and the size of the character pool used to pad | generators.py | __scatter_padding | Tauag/SPass | 2 | python | def __scatter_padding(word_count, pad_length, digits, punctuation, ignored_symbols):
'\n Randomly decides where to add padding and which characters to use\n :param word_count: Number of words in passphrase\n :param pad_length: Number of characters to use for padding\n :param digits: True to use digits in padding\n :param punctuation: True to use punctuation in padding\n :param ignored_symbols: str containing all characters to ignore during padding generation\n :return: A tuple containing the padding placements and the size of the character pool used to pad\n '
char_pool =
if digits:
char_pool += string.digits
if punctuation:
char_pool += string.punctuation
char_list = [char for char in char_pool if (char not in ignored_symbols)]
indexes = [index for index in range((word_count + 1))]
placements = {}
for _ in range(pad_length):
idx = secrets.choice(indexes)
if (idx not in placements):
placements.update({idx: [secrets.choice(char_list)]})
else:
placements[idx].append(secrets.choice(char_list))
return (placements, len(char_list)) | def __scatter_padding(word_count, pad_length, digits, punctuation, ignored_symbols):
'\n Randomly decides where to add padding and which characters to use\n :param word_count: Number of words in passphrase\n :param pad_length: Number of characters to use for padding\n :param digits: True to use digits in padding\n :param punctuation: True to use punctuation in padding\n :param ignored_symbols: str containing all characters to ignore during padding generation\n :return: A tuple containing the padding placements and the size of the character pool used to pad\n '
char_pool =
if digits:
char_pool += string.digits
if punctuation:
char_pool += string.punctuation
char_list = [char for char in char_pool if (char not in ignored_symbols)]
indexes = [index for index in range((word_count + 1))]
placements = {}
for _ in range(pad_length):
idx = secrets.choice(indexes)
if (idx not in placements):
placements.update({idx: [secrets.choice(char_list)]})
else:
placements[idx].append(secrets.choice(char_list))
return (placements, len(char_list))<|docstring|>Randomly decides where to add padding and which characters to use
:param word_count: Number of words in passphrase
:param pad_length: Number of characters to use for padding
:param digits: True to use digits in padding
:param punctuation: True to use punctuation in padding
:param ignored_symbols: str containing all characters to ignore during padding generation
:return: A tuple containing the padding placements and the size of the character pool used to pad<|endoftext|> |
63282f342ceb9115663092c246dd8d46658d2c6adbfb3f55905d9f41a16950d6 | def __calc_entropy_password(password, pool_size):
'\n Calculates the entropy of a random password\n :param password: The password\n :param pool_size: The size of the character pool used to generate password\n :return: Entropy\n '
if ((not password) or (not pool_size)):
return 0
inner = math.pow(pool_size, len(password))
return math.log(inner, 2) | Calculates the entropy of a random password
:param password: The password
:param pool_size: The size of the character pool used to generate password
:return: Entropy | generators.py | __calc_entropy_password | Tauag/SPass | 2 | python | def __calc_entropy_password(password, pool_size):
'\n Calculates the entropy of a random password\n :param password: The password\n :param pool_size: The size of the character pool used to generate password\n :return: Entropy\n '
if ((not password) or (not pool_size)):
return 0
inner = math.pow(pool_size, len(password))
return math.log(inner, 2) | def __calc_entropy_password(password, pool_size):
'\n Calculates the entropy of a random password\n :param password: The password\n :param pool_size: The size of the character pool used to generate password\n :return: Entropy\n '
if ((not password) or (not pool_size)):
return 0
inner = math.pow(pool_size, len(password))
return math.log(inner, 2)<|docstring|>Calculates the entropy of a random password
:param password: The password
:param pool_size: The size of the character pool used to generate password
:return: Entropy<|endoftext|> |
a86f9ced928be27f3221768c2003f9c34a30976e12009eea0eb71ebfc90765ed | def __calc_entropy_passphrase(word_count, word_bank_size, pad_length, pad_bank_size):
'\n Approximates the minimum entropy of the passphrase with its possible deviation\n :param word_count: Number of words in passphrase\n :param word_bank_size: Total number of words in the word bank\n :param pad_length: Number of characters used in padding\n :param pad_bank_size: The size of the character pool used to generate padding\n :return: A tuple containing the minimum entropy and deviation\n '
inner = math.pow((word_bank_size * 2), word_count)
entropy = math.log(inner, 2)
inner = math.pow(pad_bank_size, pad_length)
deviation = math.log(inner, 2)
return (entropy, deviation) | Approximates the minimum entropy of the passphrase with its possible deviation
:param word_count: Number of words in passphrase
:param word_bank_size: Total number of words in the word bank
:param pad_length: Number of characters used in padding
:param pad_bank_size: The size of the character pool used to generate padding
:return: A tuple containing the minimum entropy and deviation | generators.py | __calc_entropy_passphrase | Tauag/SPass | 2 | python | def __calc_entropy_passphrase(word_count, word_bank_size, pad_length, pad_bank_size):
'\n Approximates the minimum entropy of the passphrase with its possible deviation\n :param word_count: Number of words in passphrase\n :param word_bank_size: Total number of words in the word bank\n :param pad_length: Number of characters used in padding\n :param pad_bank_size: The size of the character pool used to generate padding\n :return: A tuple containing the minimum entropy and deviation\n '
inner = math.pow((word_bank_size * 2), word_count)
entropy = math.log(inner, 2)
inner = math.pow(pad_bank_size, pad_length)
deviation = math.log(inner, 2)
return (entropy, deviation) | def __calc_entropy_passphrase(word_count, word_bank_size, pad_length, pad_bank_size):
'\n Approximates the minimum entropy of the passphrase with its possible deviation\n :param word_count: Number of words in passphrase\n :param word_bank_size: Total number of words in the word bank\n :param pad_length: Number of characters used in padding\n :param pad_bank_size: The size of the character pool used to generate padding\n :return: A tuple containing the minimum entropy and deviation\n '
inner = math.pow((word_bank_size * 2), word_count)
entropy = math.log(inner, 2)
inner = math.pow(pad_bank_size, pad_length)
deviation = math.log(inner, 2)
return (entropy, deviation)<|docstring|>Approximates the minimum entropy of the passphrase with its possible deviation
:param word_count: Number of words in passphrase
:param word_bank_size: Total number of words in the word bank
:param pad_length: Number of characters used in padding
:param pad_bank_size: The size of the character pool used to generate padding
:return: A tuple containing the minimum entropy and deviation<|endoftext|> |
f4b36ac5573e0af23bc86d4f97f4a29dc98cc8c1f2ed98a49db7a21d0bbfd33e | def __call__(self, fun, mean, var, *args, **kwargs):
"\n Compute the Gaussian Expectation of a function f:\n\n X ~ N(mean, var)\n E[f(X)] = ∫f(x, *args, **kwargs)p(x)dx\n\n Using the formula:\n E[f(X)] = sum_{i=1}^{N_quad_points} f(x_i) * w_i\n\n where x_i, w_i must be provided by the inheriting class through self._build_X_W.\n The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].\n\n :param fun: Callable or Iterable of Callables that operates elementwise, with\n signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:\n X shape: [N_quad_points, b1, b2, ..., bX, d],\n usually [N_quad_points, N, d]\n f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],\n usually [N_quad_points, N, 1] or [N_quad_points, N, d]\n In most cases, f should only operate over the last dimension of X\n :param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],\n representing the mean of a d-Variate Gaussian distribution\n :param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],\n representing the variance of a d-Variate Gaussian distribution\n :param *args: Passed to fun\n :param **kargs: Passed to fun\n :return: Array/Tensor with shape [b1, b2, ...., bX, d'],\n usually [N, d] or [N, 1]\n "
(X, W) = self._build_X_W(mean, var)
if isinstance(fun, Iterable):
return [tf.reduce_sum((f(X, *args, **kwargs) * W), axis=0) for f in fun]
return tf.reduce_sum((fun(X, *args, **kwargs) * W), axis=0) | Compute the Gaussian Expectation of a function f:
X ~ N(mean, var)
E[f(X)] = ∫f(x, *args, **kwargs)p(x)dx
Using the formula:
E[f(X)] = sum_{i=1}^{N_quad_points} f(x_i) * w_i
where x_i, w_i must be provided by the inheriting class through self._build_X_W.
The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].
:param fun: Callable or Iterable of Callables that operates elementwise, with
signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:
X shape: [N_quad_points, b1, b2, ..., bX, d],
usually [N_quad_points, N, d]
f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],
usually [N_quad_points, N, 1] or [N_quad_points, N, d]
In most cases, f should only operate over the last dimension of X
:param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],
representing the mean of a d-Variate Gaussian distribution
:param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],
representing the variance of a d-Variate Gaussian distribution
:param *args: Passed to fun
:param **kargs: Passed to fun
:return: Array/Tensor with shape [b1, b2, ...., bX, d'],
usually [N, d] or [N, 1] | gpflow/quadrature/base.py | __call__ | HarrySpearing/GPflow | 1,724 | python | def __call__(self, fun, mean, var, *args, **kwargs):
"\n Compute the Gaussian Expectation of a function f:\n\n X ~ N(mean, var)\n E[f(X)] = ∫f(x, *args, **kwargs)p(x)dx\n\n Using the formula:\n E[f(X)] = sum_{i=1}^{N_quad_points} f(x_i) * w_i\n\n where x_i, w_i must be provided by the inheriting class through self._build_X_W.\n The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].\n\n :param fun: Callable or Iterable of Callables that operates elementwise, with\n signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:\n X shape: [N_quad_points, b1, b2, ..., bX, d],\n usually [N_quad_points, N, d]\n f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],\n usually [N_quad_points, N, 1] or [N_quad_points, N, d]\n In most cases, f should only operate over the last dimension of X\n :param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],\n representing the mean of a d-Variate Gaussian distribution\n :param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],\n representing the variance of a d-Variate Gaussian distribution\n :param *args: Passed to fun\n :param **kargs: Passed to fun\n :return: Array/Tensor with shape [b1, b2, ...., bX, d'],\n usually [N, d] or [N, 1]\n "
(X, W) = self._build_X_W(mean, var)
if isinstance(fun, Iterable):
return [tf.reduce_sum((f(X, *args, **kwargs) * W), axis=0) for f in fun]
return tf.reduce_sum((fun(X, *args, **kwargs) * W), axis=0) | def __call__(self, fun, mean, var, *args, **kwargs):
"\n Compute the Gaussian Expectation of a function f:\n\n X ~ N(mean, var)\n E[f(X)] = ∫f(x, *args, **kwargs)p(x)dx\n\n Using the formula:\n E[f(X)] = sum_{i=1}^{N_quad_points} f(x_i) * w_i\n\n where x_i, w_i must be provided by the inheriting class through self._build_X_W.\n The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].\n\n :param fun: Callable or Iterable of Callables that operates elementwise, with\n signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:\n X shape: [N_quad_points, b1, b2, ..., bX, d],\n usually [N_quad_points, N, d]\n f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],\n usually [N_quad_points, N, 1] or [N_quad_points, N, d]\n In most cases, f should only operate over the last dimension of X\n :param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],\n representing the mean of a d-Variate Gaussian distribution\n :param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],\n representing the variance of a d-Variate Gaussian distribution\n :param *args: Passed to fun\n :param **kargs: Passed to fun\n :return: Array/Tensor with shape [b1, b2, ...., bX, d'],\n usually [N, d] or [N, 1]\n "
(X, W) = self._build_X_W(mean, var)
if isinstance(fun, Iterable):
return [tf.reduce_sum((f(X, *args, **kwargs) * W), axis=0) for f in fun]
return tf.reduce_sum((fun(X, *args, **kwargs) * W), axis=0)<|docstring|>Compute the Gaussian Expectation of a function f:
X ~ N(mean, var)
E[f(X)] = ∫f(x, *args, **kwargs)p(x)dx
Using the formula:
E[f(X)] = sum_{i=1}^{N_quad_points} f(x_i) * w_i
where x_i, w_i must be provided by the inheriting class through self._build_X_W.
The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].
:param fun: Callable or Iterable of Callables that operates elementwise, with
signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:
X shape: [N_quad_points, b1, b2, ..., bX, d],
usually [N_quad_points, N, d]
f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],
usually [N_quad_points, N, 1] or [N_quad_points, N, d]
In most cases, f should only operate over the last dimension of X
:param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],
representing the mean of a d-Variate Gaussian distribution
:param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],
representing the variance of a d-Variate Gaussian distribution
:param *args: Passed to fun
:param **kargs: Passed to fun
:return: Array/Tensor with shape [b1, b2, ...., bX, d'],
usually [N, d] or [N, 1]<|endoftext|> |
8a48fa580430b7c8f4573df8e42afa506db2853fa8d5ad05db332f784ee62f5f | def logspace(self, fun: Union[(Callable, Iterable[Callable])], mean, var, *args, **kwargs):
"\n Compute the Gaussian log-Expectation of a the exponential of a function f:\n\n X ~ N(mean, var)\n log E[exp[f(X)]] = log ∫exp[f(x, *args, **kwargs)]p(x)dx\n\n Using the formula:\n log E[exp[f(X)]] = log sum_{i=1}^{N_quad_points} exp[f(x_i) + log w_i]\n\n where x_i, w_i must be provided by the inheriting class through self._build_X_W.\n The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].\n\n :param fun: Callable or Iterable of Callables that operates elementwise, with\n signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:\n X shape: [N_quad_points, b1, b2, ..., bX, d],\n usually [N_quad_points, N, d]\n f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],\n usually [N_quad_points, N, 1] or [N_quad_points, N, d]\n In most cases, f should only operate over the last dimension of X\n :param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],\n representing the mean of a d-Variate Gaussian distribution\n :param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],\n representing the variance of a d-Variate Gaussian distribution\n :param *args: Passed to fun\n :param **kargs: Passed to fun\n :return: Array/Tensor with shape [b1, b2, ...., bX, d'],\n usually [N, d] or [N, 1]\n "
(X, W) = self._build_X_W(mean, var)
logW = tf.math.log(W)
if isinstance(fun, Iterable):
return [tf.reduce_logsumexp((f(X, *args, **kwargs) + logW), axis=0) for f in fun]
return tf.reduce_logsumexp((fun(X, *args, **kwargs) + logW), axis=0) | Compute the Gaussian log-Expectation of a the exponential of a function f:
X ~ N(mean, var)
log E[exp[f(X)]] = log ∫exp[f(x, *args, **kwargs)]p(x)dx
Using the formula:
log E[exp[f(X)]] = log sum_{i=1}^{N_quad_points} exp[f(x_i) + log w_i]
where x_i, w_i must be provided by the inheriting class through self._build_X_W.
The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].
:param fun: Callable or Iterable of Callables that operates elementwise, with
signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:
X shape: [N_quad_points, b1, b2, ..., bX, d],
usually [N_quad_points, N, d]
f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],
usually [N_quad_points, N, 1] or [N_quad_points, N, d]
In most cases, f should only operate over the last dimension of X
:param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],
representing the mean of a d-Variate Gaussian distribution
:param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],
representing the variance of a d-Variate Gaussian distribution
:param *args: Passed to fun
:param **kargs: Passed to fun
:return: Array/Tensor with shape [b1, b2, ...., bX, d'],
usually [N, d] or [N, 1] | gpflow/quadrature/base.py | logspace | HarrySpearing/GPflow | 1,724 | python | def logspace(self, fun: Union[(Callable, Iterable[Callable])], mean, var, *args, **kwargs):
"\n Compute the Gaussian log-Expectation of a the exponential of a function f:\n\n X ~ N(mean, var)\n log E[exp[f(X)]] = log ∫exp[f(x, *args, **kwargs)]p(x)dx\n\n Using the formula:\n log E[exp[f(X)]] = log sum_{i=1}^{N_quad_points} exp[f(x_i) + log w_i]\n\n where x_i, w_i must be provided by the inheriting class through self._build_X_W.\n The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].\n\n :param fun: Callable or Iterable of Callables that operates elementwise, with\n signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:\n X shape: [N_quad_points, b1, b2, ..., bX, d],\n usually [N_quad_points, N, d]\n f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],\n usually [N_quad_points, N, 1] or [N_quad_points, N, d]\n In most cases, f should only operate over the last dimension of X\n :param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],\n representing the mean of a d-Variate Gaussian distribution\n :param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],\n representing the variance of a d-Variate Gaussian distribution\n :param *args: Passed to fun\n :param **kargs: Passed to fun\n :return: Array/Tensor with shape [b1, b2, ...., bX, d'],\n usually [N, d] or [N, 1]\n "
(X, W) = self._build_X_W(mean, var)
logW = tf.math.log(W)
if isinstance(fun, Iterable):
return [tf.reduce_logsumexp((f(X, *args, **kwargs) + logW), axis=0) for f in fun]
return tf.reduce_logsumexp((fun(X, *args, **kwargs) + logW), axis=0) | def logspace(self, fun: Union[(Callable, Iterable[Callable])], mean, var, *args, **kwargs):
"\n Compute the Gaussian log-Expectation of a the exponential of a function f:\n\n X ~ N(mean, var)\n log E[exp[f(X)]] = log ∫exp[f(x, *args, **kwargs)]p(x)dx\n\n Using the formula:\n log E[exp[f(X)]] = log sum_{i=1}^{N_quad_points} exp[f(x_i) + log w_i]\n\n where x_i, w_i must be provided by the inheriting class through self._build_X_W.\n The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].\n\n :param fun: Callable or Iterable of Callables that operates elementwise, with\n signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:\n X shape: [N_quad_points, b1, b2, ..., bX, d],\n usually [N_quad_points, N, d]\n f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],\n usually [N_quad_points, N, 1] or [N_quad_points, N, d]\n In most cases, f should only operate over the last dimension of X\n :param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],\n representing the mean of a d-Variate Gaussian distribution\n :param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],\n representing the variance of a d-Variate Gaussian distribution\n :param *args: Passed to fun\n :param **kargs: Passed to fun\n :return: Array/Tensor with shape [b1, b2, ...., bX, d'],\n usually [N, d] or [N, 1]\n "
(X, W) = self._build_X_W(mean, var)
logW = tf.math.log(W)
if isinstance(fun, Iterable):
return [tf.reduce_logsumexp((f(X, *args, **kwargs) + logW), axis=0) for f in fun]
return tf.reduce_logsumexp((fun(X, *args, **kwargs) + logW), axis=0)<|docstring|>Compute the Gaussian log-Expectation of a the exponential of a function f:
X ~ N(mean, var)
log E[exp[f(X)]] = log ∫exp[f(x, *args, **kwargs)]p(x)dx
Using the formula:
log E[exp[f(X)]] = log sum_{i=1}^{N_quad_points} exp[f(x_i) + log w_i]
where x_i, w_i must be provided by the inheriting class through self._build_X_W.
The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].
:param fun: Callable or Iterable of Callables that operates elementwise, with
signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:
X shape: [N_quad_points, b1, b2, ..., bX, d],
usually [N_quad_points, N, d]
f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],
usually [N_quad_points, N, 1] or [N_quad_points, N, d]
In most cases, f should only operate over the last dimension of X
:param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],
representing the mean of a d-Variate Gaussian distribution
:param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],
representing the variance of a d-Variate Gaussian distribution
:param *args: Passed to fun
:param **kargs: Passed to fun
:return: Array/Tensor with shape [b1, b2, ...., bX, d'],
usually [N, d] or [N, 1]<|endoftext|> |
e84ea1f81a084b99126a46c9a39cdc67820d7fd3df9a629073e9cb2e6c1977e7 | @pytest.mark.skipif((not product_details.last_update), reason="We don't want to download product_details on travis")
def test_spotcheck():
"Check a couple product-details files to make sure they're available."
languages = product_details.languages
assert (languages['el']['English'] == 'Greek')
assert (languages['el']['native'] == u'Ελληνικά')
assert (product_details.firefox_history_major_releases['1.0'] == '2004-11-09') | Check a couple product-details files to make sure they're available. | src/olympia/amo/tests/test_amo_utils.py | test_spotcheck | fdintino/nginxconf-2018-mozilla-addons-server | 10 | python | @pytest.mark.skipif((not product_details.last_update), reason="We don't want to download product_details on travis")
def test_spotcheck():
languages = product_details.languages
assert (languages['el']['English'] == 'Greek')
assert (languages['el']['native'] == u'Ελληνικά')
assert (product_details.firefox_history_major_releases['1.0'] == '2004-11-09') | @pytest.mark.skipif((not product_details.last_update), reason="We don't want to download product_details on travis")
def test_spotcheck():
languages = product_details.languages
assert (languages['el']['English'] == 'Greek')
assert (languages['el']['native'] == u'Ελληνικά')
assert (product_details.firefox_history_major_releases['1.0'] == '2004-11-09')<|docstring|>Check a couple product-details files to make sure they're available.<|endoftext|> |
88a508e67775ac38c93433301f11b04426aaeb3739e6488802c787d06efb1489 | def __args_default__(self) -> dict:
'Defines a Dictionary of Default Values for Keyword Arguments (or Attributes)'
return {'unitGroup': 'metric', 'contentType': 'csv', 'aggregateHours': 24} | Defines a Dictionary of Default Values for Keyword Arguments (or Attributes) | VisualCrossing/_api.py | __args_default__ | ayushsingh-07/VisualCrossing | 3 | python | def __args_default__(self) -> dict:
return {'unitGroup': 'metric', 'contentType': 'csv', 'aggregateHours': 24} | def __args_default__(self) -> dict:
return {'unitGroup': 'metric', 'contentType': 'csv', 'aggregateHours': 24}<|docstring|>Defines a Dictionary of Default Values for Keyword Arguments (or Attributes)<|endoftext|> |
d9d5c39656ddd95ba4b52b3f6aea33fed1928c9a33f56aa8b0d016652f77e26d | def __get_args_default__(self, args: str):
'Get the Default Value associated with a Keyword Argument'
return self.__args_default__().get(args, None) | Get the Default Value associated with a Keyword Argument | VisualCrossing/_api.py | __get_args_default__ | ayushsingh-07/VisualCrossing | 3 | python | def __get_args_default__(self, args: str):
return self.__args_default__().get(args, None) | def __get_args_default__(self, args: str):
return self.__args_default__().get(args, None)<|docstring|>Get the Default Value associated with a Keyword Argument<|endoftext|> |
2e1505d691970fd02e80255a0822d13213ddca98d93c8326037255f2592bbbab | @property
def __optional_args__(self):
'Get List of all the Optional Keyword Arguments Accepted by the API'
return self.__args_default__().keys() | Get List of all the Optional Keyword Arguments Accepted by the API | VisualCrossing/_api.py | __optional_args__ | ayushsingh-07/VisualCrossing | 3 | python | @property
def __optional_args__(self):
return self.__args_default__().keys() | @property
def __optional_args__(self):
return self.__args_default__().keys()<|docstring|>Get List of all the Optional Keyword Arguments Accepted by the API<|endoftext|> |
cf85fa0e70b2168eed2dc283f13198e4affc5b1a8dc9033f91f225365012a07e | def generate_config(self, defaultSettings: bool=True, fileName: str='config.json', overwrite: bool=False, keepBackup: bool=True, **kwargs) -> bool:
'Generate configuration file at `__homepath__` when executed\n\n The configuration file can be generated with default settings as defined at\n :func:`__args_default__` else, user is requested to pass all necessary settings\n in a correct format (as required by API) to the function, setting `key` as the\n attribute name, and `value` as the desired value. Users are also advised not to\n save the `API_KEY` in the configuration file (for security purpose), and to use\n :func:`_generate_key` to save the key file in an encrypted format.\n\n :param defaultSettings: Should you wish to save the configuration file with\n the default settings. If set to `False` then user is\n requested to pass all necessary attributes (`key`) and\n their values. Defaults to `True`.\n\n :param fileName: Output file name (with extension - `json`). Defaults to\n `config.json`.\n\n :param overwrite: Overwrite existing configuration file, if exists (same filename).\n Defaults to `False`.\n\n :param keepBackup: If same file name exists, then setting the parameter to `True` will\n create a backup of the file with the following format\n `<original-name>.<UUID>.json` where `UUID` is a randomly generated\n 7-charecters long name. Defaults to `True`.\n\n Accepts n-Keyword Arguments, which are all default settings that can be used to initialize\n the API.\n '
outfile = join(__homepath__, fileName)
if defaultSettings:
attrs = self.__args_default__()
else:
attrs = kwargs
attrs = {'__header__': {'program': __name__, 'version': __version__, 'homepath': __homepath__}, 'platform': {'platform': platform.platform(), 'architecture': platform.machine(), 'version': platform.version(), 'system': platform.system(), 'processor': platform.processor(), 'uname': platform.uname()}, 'attributes': attrs, 'timestamp': ctime()}
def write_json(kv: dict, file: str):
with open(file, 'w') as f:
json.dump(kv, f, sort_keys=False, indent=4, default=str)
if Path(outfile).is_file():
warnings.warn(f'{outfile} already exists.', FileExists)
if keepBackup:
try:
(name, extension) = fileName.split('.')
except ValueError as err:
name = fileName.split('.')[0]
extension = 'json'
warnings.warn(f'{fileName} is not of proper type. Setting name as: {name}', ImproperFileName)
new_file = '.'.join([name, str(uuid4())[:7], extension])
print(f'Old configuration file is available at {new_file}')
try:
copy(outfile, join(__homepath__, new_file))
except TypeError:
copy(str(outfile), str(join(__homepath__, new_file)))
else:
warnings.warn(f'{outfile} will be overwritten without a backup.', FileExists)
if overwrite:
write_json(attrs, outfile)
else:
raise ValueError(f'{outfile} already exists, and `overwrite` is set to `False`')
else:
write_json(attrs, outfile)
return True | Generate configuration file at `__homepath__` when executed
The configuration file can be generated with default settings as defined at
:func:`__args_default__` else, user is requested to pass all necessary settings
in a correct format (as required by API) to the function, setting `key` as the
attribute name, and `value` as the desired value. Users are also advised not to
save the `API_KEY` in the configuration file (for security purpose), and to use
:func:`_generate_key` to save the key file in an encrypted format.
:param defaultSettings: Should you wish to save the configuration file with
the default settings. If set to `False` then user is
requested to pass all necessary attributes (`key`) and
their values. Defaults to `True`.
:param fileName: Output file name (with extension - `json`). Defaults to
`config.json`.
:param overwrite: Overwrite existing configuration file, if exists (same filename).
Defaults to `False`.
:param keepBackup: If same file name exists, then setting the parameter to `True` will
create a backup of the file with the following format
`<original-name>.<UUID>.json` where `UUID` is a randomly generated
7-charecters long name. Defaults to `True`.
Accepts n-Keyword Arguments, which are all default settings that can be used to initialize
the API. | VisualCrossing/_api.py | generate_config | ayushsingh-07/VisualCrossing | 3 | python | def generate_config(self, defaultSettings: bool=True, fileName: str='config.json', overwrite: bool=False, keepBackup: bool=True, **kwargs) -> bool:
'Generate configuration file at `__homepath__` when executed\n\n The configuration file can be generated with default settings as defined at\n :func:`__args_default__` else, user is requested to pass all necessary settings\n in a correct format (as required by API) to the function, setting `key` as the\n attribute name, and `value` as the desired value. Users are also advised not to\n save the `API_KEY` in the configuration file (for security purpose), and to use\n :func:`_generate_key` to save the key file in an encrypted format.\n\n :param defaultSettings: Should you wish to save the configuration file with\n the default settings. If set to `False` then user is\n requested to pass all necessary attributes (`key`) and\n their values. Defaults to `True`.\n\n :param fileName: Output file name (with extension - `json`). Defaults to\n `config.json`.\n\n :param overwrite: Overwrite existing configuration file, if exists (same filename).\n Defaults to `False`.\n\n :param keepBackup: If same file name exists, then setting the parameter to `True` will\n create a backup of the file with the following format\n `<original-name>.<UUID>.json` where `UUID` is a randomly generated\n 7-charecters long name. Defaults to `True`.\n\n Accepts n-Keyword Arguments, which are all default settings that can be used to initialize\n the API.\n '
outfile = join(__homepath__, fileName)
if defaultSettings:
attrs = self.__args_default__()
else:
attrs = kwargs
attrs = {'__header__': {'program': __name__, 'version': __version__, 'homepath': __homepath__}, 'platform': {'platform': platform.platform(), 'architecture': platform.machine(), 'version': platform.version(), 'system': platform.system(), 'processor': platform.processor(), 'uname': platform.uname()}, 'attributes': attrs, 'timestamp': ctime()}
def write_json(kv: dict, file: str):
with open(file, 'w') as f:
json.dump(kv, f, sort_keys=False, indent=4, default=str)
if Path(outfile).is_file():
warnings.warn(f'{outfile} already exists.', FileExists)
if keepBackup:
try:
(name, extension) = fileName.split('.')
except ValueError as err:
name = fileName.split('.')[0]
extension = 'json'
warnings.warn(f'{fileName} is not of proper type. Setting name as: {name}', ImproperFileName)
new_file = '.'.join([name, str(uuid4())[:7], extension])
print(f'Old configuration file is available at {new_file}')
try:
copy(outfile, join(__homepath__, new_file))
except TypeError:
copy(str(outfile), str(join(__homepath__, new_file)))
else:
warnings.warn(f'{outfile} will be overwritten without a backup.', FileExists)
if overwrite:
write_json(attrs, outfile)
else:
raise ValueError(f'{outfile} already exists, and `overwrite` is set to `False`')
else:
write_json(attrs, outfile)
return True | def generate_config(self, defaultSettings: bool=True, fileName: str='config.json', overwrite: bool=False, keepBackup: bool=True, **kwargs) -> bool:
'Generate configuration file at `__homepath__` when executed\n\n The configuration file can be generated with default settings as defined at\n :func:`__args_default__` else, user is requested to pass all necessary settings\n in a correct format (as required by API) to the function, setting `key` as the\n attribute name, and `value` as the desired value. Users are also advised not to\n save the `API_KEY` in the configuration file (for security purpose), and to use\n :func:`_generate_key` to save the key file in an encrypted format.\n\n :param defaultSettings: Should you wish to save the configuration file with\n the default settings. If set to `False` then user is\n requested to pass all necessary attributes (`key`) and\n their values. Defaults to `True`.\n\n :param fileName: Output file name (with extension - `json`). Defaults to\n `config.json`.\n\n :param overwrite: Overwrite existing configuration file, if exists (same filename).\n Defaults to `False`.\n\n :param keepBackup: If same file name exists, then setting the parameter to `True` will\n create a backup of the file with the following format\n `<original-name>.<UUID>.json` where `UUID` is a randomly generated\n 7-charecters long name. Defaults to `True`.\n\n Accepts n-Keyword Arguments, which are all default settings that can be used to initialize\n the API.\n '
outfile = join(__homepath__, fileName)
if defaultSettings:
attrs = self.__args_default__()
else:
attrs = kwargs
attrs = {'__header__': {'program': __name__, 'version': __version__, 'homepath': __homepath__}, 'platform': {'platform': platform.platform(), 'architecture': platform.machine(), 'version': platform.version(), 'system': platform.system(), 'processor': platform.processor(), 'uname': platform.uname()}, 'attributes': attrs, 'timestamp': ctime()}
def write_json(kv: dict, file: str):
with open(file, 'w') as f:
json.dump(kv, f, sort_keys=False, indent=4, default=str)
if Path(outfile).is_file():
warnings.warn(f'{outfile} already exists.', FileExists)
if keepBackup:
try:
(name, extension) = fileName.split('.')
except ValueError as err:
name = fileName.split('.')[0]
extension = 'json'
warnings.warn(f'{fileName} is not of proper type. Setting name as: {name}', ImproperFileName)
new_file = '.'.join([name, str(uuid4())[:7], extension])
print(f'Old configuration file is available at {new_file}')
try:
copy(outfile, join(__homepath__, new_file))
except TypeError:
copy(str(outfile), str(join(__homepath__, new_file)))
else:
warnings.warn(f'{outfile} will be overwritten without a backup.', FileExists)
if overwrite:
write_json(attrs, outfile)
else:
raise ValueError(f'{outfile} already exists, and `overwrite` is set to `False`')
else:
write_json(attrs, outfile)
return True<|docstring|>Generate configuration file at `__homepath__` when executed
The configuration file can be generated with default settings as defined at
:func:`__args_default__` else, user is requested to pass all necessary settings
in a correct format (as required by API) to the function, setting `key` as the
attribute name, and `value` as the desired value. Users are also advised not to
save the `API_KEY` in the configuration file (for security purpose), and to use
:func:`_generate_key` to save the key file in an encrypted format.
:param defaultSettings: Should you wish to save the configuration file with
the default settings. If set to `False` then user is
requested to pass all necessary attributes (`key`) and
their values. Defaults to `True`.
:param fileName: Output file name (with extension - `json`). Defaults to
`config.json`.
:param overwrite: Overwrite existing configuration file, if exists (same filename).
Defaults to `False`.
:param keepBackup: If same file name exists, then setting the parameter to `True` will
create a backup of the file with the following format
`<original-name>.<UUID>.json` where `UUID` is a randomly generated
7-charecters long name. Defaults to `True`.
Accepts n-Keyword Arguments, which are all default settings that can be used to initialize
the API.<|endoftext|> |
34f23798ae001ffb7e170a119322d6ecd9f4762b1b3a80b818b65d45b275c6f5 | def CreatePrivateKey() -> List[int]:
'Create private key.'
lib.CreatePrivateKey.restype = BuffBytes
return toBytes(lib.CreatePrivateKey()) | Create private key. | wrappers/python/lirisi/library.py | CreatePrivateKey | noot/lirisi | 0 | python | def CreatePrivateKey() -> List[int]:
lib.CreatePrivateKey.restype = BuffBytes
return toBytes(lib.CreatePrivateKey()) | def CreatePrivateKey() -> List[int]:
lib.CreatePrivateKey.restype = BuffBytes
return toBytes(lib.CreatePrivateKey())<|docstring|>Create private key.<|endoftext|> |
9d5c5cd4b3909f22975e3397679bfde1bd42fdcb5c1f483db7627df5f59199f9 | def ExtractPublicKey(privateKey: List[int]) -> List[int]:
'Extract public key form private key.'
lib.ExtractPublicKey.argtypes = [GoSlice]
lib.ExtractPublicKey.restype = BuffBytes
return toBytes(lib.ExtractPublicKey(goSlice(privateKey))) | Extract public key form private key. | wrappers/python/lirisi/library.py | ExtractPublicKey | noot/lirisi | 0 | python | def ExtractPublicKey(privateKey: List[int]) -> List[int]:
lib.ExtractPublicKey.argtypes = [GoSlice]
lib.ExtractPublicKey.restype = BuffBytes
return toBytes(lib.ExtractPublicKey(goSlice(privateKey))) | def ExtractPublicKey(privateKey: List[int]) -> List[int]:
lib.ExtractPublicKey.argtypes = [GoSlice]
lib.ExtractPublicKey.restype = BuffBytes
return toBytes(lib.ExtractPublicKey(goSlice(privateKey)))<|docstring|>Extract public key form private key.<|endoftext|> |
9482cf931b7a72b8396c10ad59fdca59720bc2c6bdbca23f637b204e06dd184a | def CreateRingOfPublicKeys(size: int) -> bytes:
'Create ring of public keys.'
lib.CreateRingOfPublicKeys.argtypes = [ctypes.c_longlong]
lib.CreateRingOfPublicKeys.restype = BuffBytes
return toBytes(lib.CreateRingOfPublicKeys(size)) | Create ring of public keys. | wrappers/python/lirisi/library.py | CreateRingOfPublicKeys | noot/lirisi | 0 | python | def CreateRingOfPublicKeys(size: int) -> bytes:
lib.CreateRingOfPublicKeys.argtypes = [ctypes.c_longlong]
lib.CreateRingOfPublicKeys.restype = BuffBytes
return toBytes(lib.CreateRingOfPublicKeys(size)) | def CreateRingOfPublicKeys(size: int) -> bytes:
lib.CreateRingOfPublicKeys.argtypes = [ctypes.c_longlong]
lib.CreateRingOfPublicKeys.restype = BuffBytes
return toBytes(lib.CreateRingOfPublicKeys(size))<|docstring|>Create ring of public keys.<|endoftext|> |
bc56fd96b07a37ee58f1e92169d2eb38b47fad110f14e748bc99ee8500e850bb | def CreateSignature(message: List[int], pubKeysRing: List[int], privateKey: List[int]) -> List[int]:
'Extract public key form private key.'
lib.CreateSignature.argtypes = [GoSlice, GoSlice, GoSlice]
lib.CreateSignature.restype = BuffBytes
return toBytes(lib.CreateSignature(goSlice(message), goSlice(pubKeysRing), goSlice(privateKey))) | Extract public key form private key. | wrappers/python/lirisi/library.py | CreateSignature | noot/lirisi | 0 | python | def CreateSignature(message: List[int], pubKeysRing: List[int], privateKey: List[int]) -> List[int]:
lib.CreateSignature.argtypes = [GoSlice, GoSlice, GoSlice]
lib.CreateSignature.restype = BuffBytes
return toBytes(lib.CreateSignature(goSlice(message), goSlice(pubKeysRing), goSlice(privateKey))) | def CreateSignature(message: List[int], pubKeysRing: List[int], privateKey: List[int]) -> List[int]:
lib.CreateSignature.argtypes = [GoSlice, GoSlice, GoSlice]
lib.CreateSignature.restype = BuffBytes
return toBytes(lib.CreateSignature(goSlice(message), goSlice(pubKeysRing), goSlice(privateKey)))<|docstring|>Extract public key form private key.<|endoftext|> |
04cab579ac82bc3df7183d759209698bad3950eb4c4b8c7a1a79f9256b3983fd | def VerifySignature(message: List[int], pubKeysRing: List[int], signature: List[int]) -> bool:
'Verify signature.'
lib.VerifySignature.argtypes = [GoSlice, GoSlice, GoSlice]
return bool(lib.VerifySignature(goSlice(message), goSlice(pubKeysRing), goSlice(signature))) | Verify signature. | wrappers/python/lirisi/library.py | VerifySignature | noot/lirisi | 0 | python | def VerifySignature(message: List[int], pubKeysRing: List[int], signature: List[int]) -> bool:
lib.VerifySignature.argtypes = [GoSlice, GoSlice, GoSlice]
return bool(lib.VerifySignature(goSlice(message), goSlice(pubKeysRing), goSlice(signature))) | def VerifySignature(message: List[int], pubKeysRing: List[int], signature: List[int]) -> bool:
lib.VerifySignature.argtypes = [GoSlice, GoSlice, GoSlice]
return bool(lib.VerifySignature(goSlice(message), goSlice(pubKeysRing), goSlice(signature)))<|docstring|>Verify signature.<|endoftext|> |
61b773f575c157076bd4d906e15f75b1cfcabab221bd0bc3da7ffa9f58e12cf3 | def SignToPEM(sign: List[int]) -> List[int]:
'Signature to PEM.'
lib.SignToPEM.argtypes = [GoSlice]
lib.SignToPEM.restype = BuffBytes
return toBytes(lib.SignToPEM(goSlice(sign))) | Signature to PEM. | wrappers/python/lirisi/library.py | SignToPEM | noot/lirisi | 0 | python | def SignToPEM(sign: List[int]) -> List[int]:
lib.SignToPEM.argtypes = [GoSlice]
lib.SignToPEM.restype = BuffBytes
return toBytes(lib.SignToPEM(goSlice(sign))) | def SignToPEM(sign: List[int]) -> List[int]:
lib.SignToPEM.argtypes = [GoSlice]
lib.SignToPEM.restype = BuffBytes
return toBytes(lib.SignToPEM(goSlice(sign)))<|docstring|>Signature to PEM.<|endoftext|> |
766d6f5b73275a7344af3dcf12e74ebb5dd6f28ce7f57f365f64fd5d7b22b19e | def PEMtoSign(sign: List[int]) -> List[int]:
'PEM to signature.'
lib.PEMtoSign.argtypes = [GoSlice]
lib.PEMtoSign.restype = BuffBytes
return toBytes(lib.PEMtoSign(goSlice(sign))) | PEM to signature. | wrappers/python/lirisi/library.py | PEMtoSign | noot/lirisi | 0 | python | def PEMtoSign(sign: List[int]) -> List[int]:
lib.PEMtoSign.argtypes = [GoSlice]
lib.PEMtoSign.restype = BuffBytes
return toBytes(lib.PEMtoSign(goSlice(sign))) | def PEMtoSign(sign: List[int]) -> List[int]:
lib.PEMtoSign.argtypes = [GoSlice]
lib.PEMtoSign.restype = BuffBytes
return toBytes(lib.PEMtoSign(goSlice(sign)))<|docstring|>PEM to signature.<|endoftext|> |
ff857af3e0e3470dac4574472ec33d70a61b1648d538a86f706c030a5db62871 | def GetPubKeyBytesSize() -> int:
'Get size of bytes serializted public key.'
return lib.GetPubKeyBytesSize() | Get size of bytes serializted public key. | wrappers/python/lirisi/library.py | GetPubKeyBytesSize | noot/lirisi | 0 | python | def GetPubKeyBytesSize() -> int:
return lib.GetPubKeyBytesSize() | def GetPubKeyBytesSize() -> int:
return lib.GetPubKeyBytesSize()<|docstring|>Get size of bytes serializted public key.<|endoftext|> |
334139f55cc49861d5d0d883b2c49d7f10a012797524705649ba937c0264c00a | def GetKeyImage(sign: List[int]) -> List[int]:
'Get KeyImage from signature.'
lib.GetKeyImage.argtypes = [GoSlice]
lib.GetKeyImage.restype = BuffBytes
return toBytes(lib.GetKeyImage(goSlice(sign))) | Get KeyImage from signature. | wrappers/python/lirisi/library.py | GetKeyImage | noot/lirisi | 0 | python | def GetKeyImage(sign: List[int]) -> List[int]:
lib.GetKeyImage.argtypes = [GoSlice]
lib.GetKeyImage.restype = BuffBytes
return toBytes(lib.GetKeyImage(goSlice(sign))) | def GetKeyImage(sign: List[int]) -> List[int]:
lib.GetKeyImage.argtypes = [GoSlice]
lib.GetKeyImage.restype = BuffBytes
return toBytes(lib.GetKeyImage(goSlice(sign)))<|docstring|>Get KeyImage from signature.<|endoftext|> |
f4c3942d4b90cb91930a3837e43291688207b76fa132e4e7716131fef370e44d | def goSlice(values: List[int]) -> GoSlice:
'Make GoClice instance with aaray of bytes.'
length = len(values)
return GoSlice((ctypes.c_ubyte * length)(*values), length, length) | Make GoClice instance with aaray of bytes. | wrappers/python/lirisi/library.py | goSlice | noot/lirisi | 0 | python | def goSlice(values: List[int]) -> GoSlice:
length = len(values)
return GoSlice((ctypes.c_ubyte * length)(*values), length, length) | def goSlice(values: List[int]) -> GoSlice:
length = len(values)
return GoSlice((ctypes.c_ubyte * length)(*values), length, length)<|docstring|>Make GoClice instance with aaray of bytes.<|endoftext|> |
3fe6e0e0541faa78c7f5e0f2b32dca4976a3495256da940eeadfbd3d873ab1ed | def toBytes(buff: BuffBytes) -> List[int]:
'Create array of bytes from buffer.'
size = int.from_bytes(buff.data[:8], byteorder='little')
return buff.data[:(size + 8)][8:] | Create array of bytes from buffer. | wrappers/python/lirisi/library.py | toBytes | noot/lirisi | 0 | python | def toBytes(buff: BuffBytes) -> List[int]:
size = int.from_bytes(buff.data[:8], byteorder='little')
return buff.data[:(size + 8)][8:] | def toBytes(buff: BuffBytes) -> List[int]:
size = int.from_bytes(buff.data[:8], byteorder='little')
return buff.data[:(size + 8)][8:]<|docstring|>Create array of bytes from buffer.<|endoftext|> |
3ccea5a23e4a55d0287902ac4cb9e6816934c6fc61a5e05d2cdb224e72f5fd9b | def ToHex(array: List[int]) -> bytes:
'Convert array of bytes to hex string.'
return ''.join(['{:>02x}'.format(n) for n in array]).encode() | Convert array of bytes to hex string. | wrappers/python/lirisi/library.py | ToHex | noot/lirisi | 0 | python | def ToHex(array: List[int]) -> bytes:
return .join(['{:>02x}'.format(n) for n in array]).encode() | def ToHex(array: List[int]) -> bytes:
return .join(['{:>02x}'.format(n) for n in array]).encode()<|docstring|>Convert array of bytes to hex string.<|endoftext|> |
b5c489c5f18280694658e16d34f47a6ba3072bfac22c500779654ba94e85fcfa | def ToBase64(array: List[int]) -> bytes:
'Convert array of bytes to base64 string.'
return base64.standard_b64encode(bytes(array)) | Convert array of bytes to base64 string. | wrappers/python/lirisi/library.py | ToBase64 | noot/lirisi | 0 | python | def ToBase64(array: List[int]) -> bytes:
return base64.standard_b64encode(bytes(array)) | def ToBase64(array: List[int]) -> bytes:
return base64.standard_b64encode(bytes(array))<|docstring|>Convert array of bytes to base64 string.<|endoftext|> |
bbe5a6e42128459842608f88e94270182dca0b8a773f87f65b788251bd547468 | def create_glove_embedding_init(idx2word, glove_file):
'\n Bilinear Attention Networks\n Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang\n https://github.com/jnhwkim/ban-vqa\n '
word2emb = {}
with open(glove_file, 'r', encoding='utf-8') as f:
entries = f.readlines()
emb_dim = (len(entries[0].split(' ')) - 1)
print(('embedding dim is %d' % emb_dim))
weights = np.zeros((len(idx2word), emb_dim), dtype=np.float32)
for entry in entries:
vals = entry.split(' ')
word = vals[0]
vals = list(map(float, vals[1:]))
word2emb[word] = np.array(vals)
for idx in range(1, len(idx2word)):
word = ind2word.get(str(idx))
if (word not in word2emb):
continue
weights[(idx - 1)] = word2emb[word]
return weights | Bilinear Attention Networks
Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang
https://github.com/jnhwkim/ban-vqa | utils/utils.py | create_glove_embedding_init | gicheonkang/DAN-VisDial | 35 | python | def create_glove_embedding_init(idx2word, glove_file):
'\n Bilinear Attention Networks\n Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang\n https://github.com/jnhwkim/ban-vqa\n '
word2emb = {}
with open(glove_file, 'r', encoding='utf-8') as f:
entries = f.readlines()
emb_dim = (len(entries[0].split(' ')) - 1)
print(('embedding dim is %d' % emb_dim))
weights = np.zeros((len(idx2word), emb_dim), dtype=np.float32)
for entry in entries:
vals = entry.split(' ')
word = vals[0]
vals = list(map(float, vals[1:]))
word2emb[word] = np.array(vals)
for idx in range(1, len(idx2word)):
word = ind2word.get(str(idx))
if (word not in word2emb):
continue
weights[(idx - 1)] = word2emb[word]
return weights | def create_glove_embedding_init(idx2word, glove_file):
'\n Bilinear Attention Networks\n Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang\n https://github.com/jnhwkim/ban-vqa\n '
word2emb = {}
with open(glove_file, 'r', encoding='utf-8') as f:
entries = f.readlines()
emb_dim = (len(entries[0].split(' ')) - 1)
print(('embedding dim is %d' % emb_dim))
weights = np.zeros((len(idx2word), emb_dim), dtype=np.float32)
for entry in entries:
vals = entry.split(' ')
word = vals[0]
vals = list(map(float, vals[1:]))
word2emb[word] = np.array(vals)
for idx in range(1, len(idx2word)):
word = ind2word.get(str(idx))
if (word not in word2emb):
continue
weights[(idx - 1)] = word2emb[word]
return weights<|docstring|>Bilinear Attention Networks
Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang
https://github.com/jnhwkim/ban-vqa<|endoftext|> |
7cb6268a95ffeb1411c888bc1f548925a928be4eb9416299e7b7816d9e89a111 | @mock.patch('datapackage_pipelines.lib.dump.dumper_base.ingest')
@mock.patch('datapackage_pipelines.lib.dump.dumper_base.spew')
def mock_dump_test(processor, ingest_tuple, mock_spew, mock_ingest):
'Helper function returns the `spew` for a given processor with a given\n `ingest` tuple.'
mock_ingest.return_value = ingest_tuple
file_path = processor
module_name = '__main__'
spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return mock_spew.call_args | Helper function returns the `spew` for a given processor with a given
`ingest` tuple. | tests/test_dump_to_ckan.py | mock_dump_test | OriHoch/datapackage-pipelines-ckan | 0 | python | @mock.patch('datapackage_pipelines.lib.dump.dumper_base.ingest')
@mock.patch('datapackage_pipelines.lib.dump.dumper_base.spew')
def mock_dump_test(processor, ingest_tuple, mock_spew, mock_ingest):
'Helper function returns the `spew` for a given processor with a given\n `ingest` tuple.'
mock_ingest.return_value = ingest_tuple
file_path = processor
module_name = '__main__'
spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return mock_spew.call_args | @mock.patch('datapackage_pipelines.lib.dump.dumper_base.ingest')
@mock.patch('datapackage_pipelines.lib.dump.dumper_base.spew')
def mock_dump_test(processor, ingest_tuple, mock_spew, mock_ingest):
'Helper function returns the `spew` for a given processor with a given\n `ingest` tuple.'
mock_ingest.return_value = ingest_tuple
file_path = processor
module_name = '__main__'
spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return mock_spew.call_args<|docstring|>Helper function returns the `spew` for a given processor with a given
`ingest` tuple.<|endoftext|> |
b20cd1370460e073819867bd1aa7260f51d4d69c14256fa93cc6de63a21a7ab6 | @requests_mock.mock()
def test_dump_to_ckan_package_create_error(self, mock_request):
'Create failed due to existing package, no overwrite so raise\n exception'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
mock_request.post(package_create_url, json={'success': False, 'error': {'__type': 'Validation Error', 'name': ['That URL is already in use.']}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': []}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key'}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
with self.assertRaises(Exception):
list(spew_res_iter)
requests = mock_request.request_history
assert (len(requests) == 1)
assert (requests[0].url == package_create_url) | Create failed due to existing package, no overwrite so raise
exception | tests/test_dump_to_ckan.py | test_dump_to_ckan_package_create_error | OriHoch/datapackage-pipelines-ckan | 0 | python | @requests_mock.mock()
def test_dump_to_ckan_package_create_error(self, mock_request):
'Create failed due to existing package, no overwrite so raise\n exception'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
mock_request.post(package_create_url, json={'success': False, 'error': {'__type': 'Validation Error', 'name': ['That URL is already in use.']}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': []}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key'}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
with self.assertRaises(Exception):
list(spew_res_iter)
requests = mock_request.request_history
assert (len(requests) == 1)
assert (requests[0].url == package_create_url) | @requests_mock.mock()
def test_dump_to_ckan_package_create_error(self, mock_request):
'Create failed due to existing package, no overwrite so raise\n exception'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
mock_request.post(package_create_url, json={'success': False, 'error': {'__type': 'Validation Error', 'name': ['That URL is already in use.']}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': []}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key'}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
with self.assertRaises(Exception):
list(spew_res_iter)
requests = mock_request.request_history
assert (len(requests) == 1)
assert (requests[0].url == package_create_url)<|docstring|>Create failed due to existing package, no overwrite so raise
exception<|endoftext|> |
573fd7663a38669d305c36011a5e7e77bf6e0b7fb490f79acbbfc636fd15b139 | @requests_mock.mock()
def test_dump_to_ckan_package_create_error_overwrite(self, mock_request):
'Create failed due to existing package, overwrite so update existing\n package.'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
package_update_url = '{}package_update'.format(base_url)
mock_request.post(package_create_url, json={'success': False, 'error': {'__type': 'Validation Error', 'name': ['That URL is already in use.']}})
mock_request.post(package_update_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': []}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
assert (list(spew_res_iter) == [])
requests = mock_request.request_history
assert (len(requests) == 2)
assert (requests[0].url == package_create_url)
assert (requests[1].url == package_update_url) | Create failed due to existing package, overwrite so update existing
package. | tests/test_dump_to_ckan.py | test_dump_to_ckan_package_create_error_overwrite | OriHoch/datapackage-pipelines-ckan | 0 | python | @requests_mock.mock()
def test_dump_to_ckan_package_create_error_overwrite(self, mock_request):
'Create failed due to existing package, overwrite so update existing\n package.'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
package_update_url = '{}package_update'.format(base_url)
mock_request.post(package_create_url, json={'success': False, 'error': {'__type': 'Validation Error', 'name': ['That URL is already in use.']}})
mock_request.post(package_update_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': []}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
assert (list(spew_res_iter) == [])
requests = mock_request.request_history
assert (len(requests) == 2)
assert (requests[0].url == package_create_url)
assert (requests[1].url == package_update_url) | @requests_mock.mock()
def test_dump_to_ckan_package_create_error_overwrite(self, mock_request):
'Create failed due to existing package, overwrite so update existing\n package.'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
package_update_url = '{}package_update'.format(base_url)
mock_request.post(package_create_url, json={'success': False, 'error': {'__type': 'Validation Error', 'name': ['That URL is already in use.']}})
mock_request.post(package_update_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': []}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
assert (list(spew_res_iter) == [])
requests = mock_request.request_history
assert (len(requests) == 2)
assert (requests[0].url == package_create_url)
assert (requests[1].url == package_update_url)<|docstring|>Create failed due to existing package, overwrite so update existing
package.<|endoftext|> |
063eb53499df3284b2659b1aeca0b91dc748fd72b13e6b851a164e96c0978d1f | @requests_mock.mock()
def test_dump_to_ckan_package_create_resources(self, mock_request):
'Create package with non-streaming resources.'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
resource_create_url = '{}resource_create'.format(base_url)
mock_request.post(package_create_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
mock_request.post(resource_create_url, json={'success': True, 'result': {'id': 'ckan-resource-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': [{'dpp:streamedFrom': 'https://example.com/file.csv', 'name': 'resource_not_streamed', 'path': '.', 'format': 'csv'}, {'dpp:streamedFrom': 'https://example.com/file_02.csv', 'name': 'resource_not_streamed_02', 'path': '.'}]}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
assert (list(spew_res_iter) == [])
requests = mock_request.request_history
assert (len(requests) == 3)
assert (requests[0].url == package_create_url)
assert (requests[1].url == resource_create_url)
assert (requests[2].url == resource_create_url) | Create package with non-streaming resources. | tests/test_dump_to_ckan.py | test_dump_to_ckan_package_create_resources | OriHoch/datapackage-pipelines-ckan | 0 | python | @requests_mock.mock()
def test_dump_to_ckan_package_create_resources(self, mock_request):
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
resource_create_url = '{}resource_create'.format(base_url)
mock_request.post(package_create_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
mock_request.post(resource_create_url, json={'success': True, 'result': {'id': 'ckan-resource-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': [{'dpp:streamedFrom': 'https://example.com/file.csv', 'name': 'resource_not_streamed', 'path': '.', 'format': 'csv'}, {'dpp:streamedFrom': 'https://example.com/file_02.csv', 'name': 'resource_not_streamed_02', 'path': '.'}]}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
assert (list(spew_res_iter) == [])
requests = mock_request.request_history
assert (len(requests) == 3)
assert (requests[0].url == package_create_url)
assert (requests[1].url == resource_create_url)
assert (requests[2].url == resource_create_url) | @requests_mock.mock()
def test_dump_to_ckan_package_create_resources(self, mock_request):
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
resource_create_url = '{}resource_create'.format(base_url)
mock_request.post(package_create_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
mock_request.post(resource_create_url, json={'success': True, 'result': {'id': 'ckan-resource-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': [{'dpp:streamedFrom': 'https://example.com/file.csv', 'name': 'resource_not_streamed', 'path': '.', 'format': 'csv'}, {'dpp:streamedFrom': 'https://example.com/file_02.csv', 'name': 'resource_not_streamed_02', 'path': '.'}]}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, []))
spew_res_iter = spew_args[1]
assert (list(spew_res_iter) == [])
requests = mock_request.request_history
assert (len(requests) == 3)
assert (requests[0].url == package_create_url)
assert (requests[1].url == resource_create_url)
assert (requests[2].url == resource_create_url)<|docstring|>Create package with non-streaming resources.<|endoftext|> |
0fdbc62f4eb3fef30676e3c901e6cb9d4f1644c0de2aed8aaf4795574bd74f79 | @requests_mock.mock()
def test_dump_to_ckan_package_create_streaming_resource(self, mock_request):
'Create package with streaming resource.'
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
resource_create_url = '{}resource_create'.format(base_url)
mock_request.post(package_create_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
mock_request.post(resource_create_url, json={'success': True, 'result': {'id': 'ckan-resource-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': [{'dpp:streamedFrom': 'https://example.com/file.csv', 'dpp:streaming': True, 'name': 'resource_streamed.csv', 'path': 'data/file.csv', 'schema': {'fields': [{'name': 'first', 'type': 'string'}, {'name': 'last', 'type': 'string'}]}}, {'dpp:streamedFrom': 'https://example.com/file_02.csv', 'name': 'resource_not_streamed.csv', 'path': '.'}]}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True, 'force-format': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
json_file = {'first': 'Fred', 'last': 'Smith'}
json_file = json.dumps(json_file)
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, iter([ResourceIterator(io.StringIO(json_file), datapackage['resources'][0], {'schema': {'fields': []}})])))
spew_res_iter = spew_args[1]
for r in spew_res_iter:
list(r)
requests = mock_request.request_history
assert (len(requests) == 3)
assert (requests[0].url == package_create_url)
assert (requests[1].url == resource_create_url)
assert (requests[2].url == resource_create_url) | Create package with streaming resource. | tests/test_dump_to_ckan.py | test_dump_to_ckan_package_create_streaming_resource | OriHoch/datapackage-pipelines-ckan | 0 | python | @requests_mock.mock()
def test_dump_to_ckan_package_create_streaming_resource(self, mock_request):
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
resource_create_url = '{}resource_create'.format(base_url)
mock_request.post(package_create_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
mock_request.post(resource_create_url, json={'success': True, 'result': {'id': 'ckan-resource-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': [{'dpp:streamedFrom': 'https://example.com/file.csv', 'dpp:streaming': True, 'name': 'resource_streamed.csv', 'path': 'data/file.csv', 'schema': {'fields': [{'name': 'first', 'type': 'string'}, {'name': 'last', 'type': 'string'}]}}, {'dpp:streamedFrom': 'https://example.com/file_02.csv', 'name': 'resource_not_streamed.csv', 'path': '.'}]}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True, 'force-format': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
json_file = {'first': 'Fred', 'last': 'Smith'}
json_file = json.dumps(json_file)
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, iter([ResourceIterator(io.StringIO(json_file), datapackage['resources'][0], {'schema': {'fields': []}})])))
spew_res_iter = spew_args[1]
for r in spew_res_iter:
list(r)
requests = mock_request.request_history
assert (len(requests) == 3)
assert (requests[0].url == package_create_url)
assert (requests[1].url == resource_create_url)
assert (requests[2].url == resource_create_url) | @requests_mock.mock()
def test_dump_to_ckan_package_create_streaming_resource(self, mock_request):
base_url = 'https://demo.ckan.org/api/3/action/'
package_create_url = '{}package_create'.format(base_url)
resource_create_url = '{}resource_create'.format(base_url)
mock_request.post(package_create_url, json={'success': True, 'result': {'id': 'ckan-package-id'}})
mock_request.post(resource_create_url, json={'success': True, 'result': {'id': 'ckan-resource-id'}})
datapackage = {'name': 'my-datapackage', 'project': 'my-project', 'resources': [{'dpp:streamedFrom': 'https://example.com/file.csv', 'dpp:streaming': True, 'name': 'resource_streamed.csv', 'path': 'data/file.csv', 'schema': {'fields': [{'name': 'first', 'type': 'string'}, {'name': 'last', 'type': 'string'}]}}, {'dpp:streamedFrom': 'https://example.com/file_02.csv', 'name': 'resource_not_streamed.csv', 'path': '.'}]}
params = {'ckan-host': 'https://demo.ckan.org', 'ckan-api-key': 'my-api-key', 'overwrite_existing': True, 'force-format': True}
processor_dir = os.path.dirname(datapackage_pipelines_ckan.processors.__file__)
processor_path = os.path.join(processor_dir, 'dump/to_ckan.py')
json_file = {'first': 'Fred', 'last': 'Smith'}
json_file = json.dumps(json_file)
(spew_args, _) = mock_dump_test(processor_path, (params, datapackage, iter([ResourceIterator(io.StringIO(json_file), datapackage['resources'][0], {'schema': {'fields': []}})])))
spew_res_iter = spew_args[1]
for r in spew_res_iter:
list(r)
requests = mock_request.request_history
assert (len(requests) == 3)
assert (requests[0].url == package_create_url)
assert (requests[1].url == resource_create_url)
assert (requests[2].url == resource_create_url)<|docstring|>Create package with streaming resource.<|endoftext|> |
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